DOI: 10.3897/jucs.137699

Authors: Wolfram Luther, Ashot Harutyunyan

Abstract: This article presents an extensive literature review on the importance of fairness in society, science, the world of work and leisure, with a focus on healthcare. Depending on the application area, fairness criteria and metrics play a major role in evaluation, classification, and allocation. Different approaches to a general definition of algorithmic fairness for individuals or groups are considered, and their measures from the perspective of the concerned sciences and requirements for the decision-making processes are also formulated. There are many reasons for the lack of fairness: inadequate data quality or low model performance, differences in understanding, competing standards, inappropriate measures in selection, classification and decision-making, lack of accuracy or performance of algorithms paired with insufficient communication, interaction or collaboration of stakeholders. The requirements are illustrated using the example of medical risk prediction tools, e.g., the individual and familial risk for the occurrence of pathogenic variants in BRCA1 (BReast CAncer 1) or BRCA2 genes with impact on early breast cancer (BC) and ovarian cancer (OC) disease, and the 5-year risk that an individual with ocular hypertension will develop Primary Open Angle Glaucoma (POAG), the leading global cause of irreversible blindness.

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DOI: 10.3897/jucs.143527

Authors: Nguyen Thi Hoang Phuong, Phan Minh Nhat, Nguyen Van Hieu

Abstract: Near-infrared (NIR) spectroscopy has emerged as a valuable analytical technique for assessing the composition and quality of various materials. This study proposes NirMACNet, a novel convolutional neural network (CNN) architecture that incorporates a residual-based multi-scale kernel mechanism for enhanced prediction of compositional attributes. The model is evaluated on two distinct NIR spectral datasets, milk and soil, to demonstrate its generalization capability across domains. By leveraging multiscale kernel operations, NirMACNet effectively captures diverse spectral patterns, while its deep architecture facilitates comprehensive feature extraction. To mitigate performance degradation commonly associated with deeper networks, residual learning is employed. Experimental results indicate that NirMACNet consistently outperforms state-of-the-art methods in terms of prediction accuracy. Future work will involve expanding the diversity of training datasets and investigating alternative architectural enhancements to further improve model robustness and applicability.

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DOI: 10.3897/jucs.145223

Authors: Yongbin Li, Xinyue Yang, Linhu Hui, Enlin Fu, Stephanie Chua

Abstract: False-positive reduction is a critical step in the automatic lung nodule detection system, playing a significant role in the early detection and diagnosis of lung cancer. However, accurately distinguishing between nodules and non-nodules remains a challenge due to their high morphological similarity. To effectively reduce the false positives (FPs) in automated nodule detection using computed tomography (CT), we propose an attention-guided multi-scale cross-fusion three-dimensional (3D) convolutional neural network (AMCF-CNN). The proposed network adopts an innovative multi-scale cross-fusion strategy to integrate nodule features across different scales and incorporates a SimAM-Res module to adaptively enhance key features, thereby improving overall feature representation. To further enhance global contextual awareness, a Global Modeling Module (GMM) based on self-attention mechanism is introduced, enabling complementary fusion of local structural details and global semantic context, thereby enhancing structural discrimination in complex imaging backgrounds. We conducted experiments on the LUNA16 dataset consisting of 888 chest CT cases, and evaluated model performance using 10-fold cross-validation. Experimental results demonstrate that the proposed model achieves sensitivities of 0.983 and 0.984 at 4 and 8 FPs/scan, respectively, with a balanced accuracy of 0.983 and a competitive performance metric (CPM) score of 0.936. Compared to existing methods, AMCF-CNN achieves competitive performance in false-positive reduction, offering a more accurate and robust solution for lung nodule detection and demonstrating strong potential for real-world clinical applications.

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DOI: 10.3897/jucs.164692

Authors: Sergio Peñafiel, Esteban Ramírez, Nelson Baloian, Isabel Saffie, Paulo Luz, Inti Paredes

Abstract: Breast cancer is a significant global health problem, and HER2-positive breast cancer accounts for a substantial proportion of cases. The combination of Trastuzumab and Pertuzumab monoclonal antibodies with chemotherapy has demonstrated effectiveness in achieving pathologic complete response (pCR) among HER2-positive breast cancer patients. This study aims to develop an interpretable machine learning model to predict pCR in patients undergoing this neoadjuvant treatment. Previous studies have explored predictors of pCR and utilized statistical techniques, but no prior research has applied machine learning to this specific treatment. This work proposes a rule-based interpretable method based on Dempster-Shafer theory. The model is trained using a dataset of 390 patients, with 57% achieving pCR. The performance of the model is compared with other classification algorithms, demonstrating its moderate but promising results. This work highlights the importance of combining accuracy and interpretability in healthcare applications, providing insights into the factors influencing treatment response in HER2-positive breast cancer patients.

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DOI: 10.3897/jucs.130186

Authors: Yang Zhang, Ziwen Wei, Zhihua Liu, Xiaolong Wu, Junchao Qian

Abstract: Purpose: Incorrect patient positioning during radiotherapy can significantly impact treatment efficacy and pose potential risks. This study aims to develop a model that can rapidly and effectively monitor the patient’s postures during radiotherapy sessions using real-time video. Methods: The neural network utilized in this research employed a two-stream architecture, consisting of spatial and temporal streams. For the spatial stream, RGB frames from the videos were directly used as input. In the temporal stream, representative frames were extracted from the video to construct stacked grayscale 3-channel images (SG3I) frames. This approach enabled capturing motion information through a large-scale dataset pre-trained 2D convolutional neural network (CNN), eliminating the need for computationally expensive optical flow calculations. Additionally, an improved lightweight network architecture was employed. The model was trained and tested using volunteer videos collected from a radiotherapy center in a hospital. Results: The results demonstrated that the proposed model outperforms existing methods in terms of detection accuracy while achieving higher efficiency in frame generation. Conclusion: In this study, we introduced a cost-effective and highly accurate method for recognizing patient’s postures during radiotherapy. This approach could be readily deployed in any radiotherapy facility, ensuring treatment precision and patient safety.

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DOI: 10.3897/jucs.129624

Authors: Thuy Phuong Khuat, Trang Van, Hoang Thien Van

Abstract: Deep learning methods are increasingly used in automated plant species classification systems to support biodiversity conservation and ecological monitoring, particularly for medicinal plants. This study presents a novel approach to plant leaf recognition by integrating the Vision Transformer (ViT) model with the OSSGabor filter, termed the OGViT method. The OSSGabor filter is a leaf feature extraction technique that combines the responses of Gabor filters in 16 directions and optimizes their parameters using the Structural Similarity Index Measure (SSIM). These features capture intricate details such as leaf veins, texture, and frequency variations, which are essential for enabling ViT to fully leverage deep learning for leaf recognition. Experimental results on four public datasets—Swedish Leaf, Flavia, Folio, and UCI Leaf—demonstrate that the OGViT method outperforms state-of-the-art approaches, achieving accuracy scores of 100%, 100%, 100%, and 98.88%, respectively, with a 20% testing set and an 80% training set. This performance highlights the effectiveness of the proposed method for plant classification, offering a robust tool with potential applications in agriculture and biodiversity conservation.

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DOI: 10.3897/jucs.127703

Authors: Mahir Shadid, Mushfiqus Salehin Afnan, Rashed Mustafa, M. Jamshed Alam Patwary

Abstract: Early detection of anxiety disorders in a non-intrusive manner is crucial, as these conditions can profoundly impact an individual’s health and daily functioning. Traditional approaches relying solely on unimodal data often fall short, potentially introducing bias and inaccuracies. TI-Fusion is a novel late multimodal fusion technique that integrates text and image data for a unified reliable outcome, overcoming limitations in existing methods. The primary advantage of TI-Fusion is its non-intrusive nature, ensuring patient comfort by avoiding invasive methods while still delivering robust diagnostic capabilities. The study utilizes six advanced machine learning algorithms (Gaussian Naive Bayes, XGB Classifier, K-Neighbors, SVM, Decision-Tree, and RandomForest) for data classification, pattern recognition, and predictive accuracy. Concurrently, image data from the KDEF and CK+ datasets was processed through a Convolutional Neural Network (CNN) enhanced with a Real Gabor filter, which is particularly adept at capturing textures, edges, and complex visual patterns necessary for precise image analysis and recognition. By employing a late multimodal fusion approach, TI-Fusion integrates the outcomes of models trained on distinct data modalities, yielding a more comprehensive and accurate prediction than unimodal methods. This technique not only surpasses existing multimodal approaches but also achieves a commendable final accuracy rate of 92.38%, demonstrating its effectiveness in enhancing the early detection of anxiety disorders.

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DOI: 10.3897/jucs.121757

Authors: Phan Minh Nhat, Ngo Le Huy Hien, Dinh Minh Toan, Le Viet Hung, Phan Binh, Phung Thi Anh, Nguyen Thi Hoang Phuong, Nguyen Van Hieu

Abstract: The examination of Near Infrared Reflectance Spectroscopy (NIR) in cattle and poultry fertilizers provides a viable solution for determining optimal fertilizer composition for crop growth while mitigating adverse impacts on soil and groundwater quality. In recent studies, conventional machine learning models combined with spectral analysis have been used to ascertain cattle and poultry fertilizer concentrations. However, these traditional machine learning models encounter challenges in achieving data generalization, resulting in suboptimal prediction accuracy. To address this issue, this study proposes a synthesized machine learning model named EBAR (Error Based Accumulation Regression), which exhibits a commendable coefficient of determination, with an average R2 = 0.865 across 7 chemical substances, surpassing the performance of existing traditional machine learning models. Additionally, a Backward Elimination technique is designed to identify crucial wavelength ranges for monitoring component concentrations. The research outcome is promising and acts as a novel benchmark for later models in determining component concentrations through NIR spectroscopy. Future research gears toward expanding datasets and increasing samples of fertilizers, extending examined wavelength, and improving the model’s efficiency to apply to various types of foods, including seafood, vegetables, and fruits.

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DOI: 10.3897/jucs.119828

Authors: Seyedeh Aridis Ahadi, Kian Jazayeri, Sahand Tebyani

Abstract: The identification of individuals who indicate suicidal behaviors on social media platforms has become more significant in recent years. The utilization of textual data may help in the development of systems aimed at predicting individuals' mental health. This article proposes an integrated framework for the identification of suicidal thoughts in social media through the implementation of a layered classifier model consisting of a convolutional neural network (CNN) and a long short-term memory (LSTM) model. Various combinations of embedding techniques, activation functions, and solver algorithms are applied to the network. The mixture of these techniques forms 82 distinct methodologies employed, followed by comparing the results obtained. A collection of approximately 60,0000 user posts from 2018 to 2020 was compiled from Reddit for the study. It has resulted in the combination of TF-IDF (word embedding), RReLU (activation function), and Adam (solver algorithm) reaching the highest overall performance. The model achieved impressive accuracy, F1 Score, and AUC of 86%, with precision and recall score of 91% and 82% respectively. It was fitted in just 8.69 seconds, demonstrating its time efficiency as well. This approach has great potential for creating a platform in real life to not only reduce the social impacts of suicidality and mental illness, but also increase social access to mental health resources for all individuals.

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DOI: 10.3897/jucs.109933

Authors: Abdulkadir Buldu, Kaplan Kaplan, Melih Kuncan

Abstract: Epilepsy, a neurological disease characterized by recurrent seizures, can be diagnosed using Electroencephalogram (EEG) signals. Traditional diagnostic methods often face limitations, leading to delays and potential misdiagnoses. In response, researchers have been developing low-cost assistive systems to enhance diagnostic accuracy and reduce life-threatening risks for epilepsy patients. In this study, a hybrid approach is proposed to diagnose epilepsy disease. To validate the success of the proposed algorithm, Hauz Khas and Bonn data sets were used. AlexNet, GoogleNet, VGG19, ResNet50, and ResNet101 classifiers were employed in this study along with the Continuous Wavelet Transform (CWT) and Short Time Fourier Transform (STFT). To increase the generalization capability, 10-fold cross-validation method was used in the classification process. Firstly, the preictal and ictal moments in the Hauz Khas dataset was classified with 99.5% success rate by CWT method and Resnet101. Similarly, 99.8% accuracy was achieved in the binary classification of the Bonn dataset using the CWT method with Resnet101. Finally, for the classification with the AB-CD-E group, 99.33% classification success rate was achieved by using the CWT method with the Resnet-101 model. These findings underscore the potential of the proposed assistive system to significantly improve the diagnosis and management of epilepsy, demonstrating high accuracy and reliability across different datasets and classification techniques.

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DOI: 10.3897/jucs.112977

Authors: Mauricio Solar, Pablo Aguirre

Abstract: The idea of using X–rays and Computed Tomography (CT) images as diagnostic method has been explored in several studies. Most of these studies work with slices of CT image in 2D, requiring less computational capacity and less time to process them than 3D. The processing of volumetric data (the complete CT images in 3D) adds an extra dimension of information. However, the magnitude of the data is considerably larger than working with slices in 2D, so extra computational processing is required. In this study a model capable of performing a classification of a 3D input that represents the volume of the CT scan is proposed. The model is able to classify the 3D input between COVID–19 and Non–COVID–19, but reducing the use of resources when performing the classification. The proposed model is the ResNet–50 model with a new dimension of information added, which is a simple autoencoder. This autoencoder is trained on the same dataset, and a vector representation of each exam is generated and used together with the exams to feed the ResNet–50. To validate the proposal, the same proposed model is compared with and without the autoencoder module that provides more information to the proposed model. The proposed model obtains better metrics than the same model without the autoencoder, confirming that extracting relevant features from the dataset helps improve the performance of the model.

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DOI: 10.3897/jucs.112639

Authors: Sivaramasamy Elayaraja, Sunil Yeruva, Vlastimil Stejskal, Satish Nandipati

Abstract: Protozoan parasites cause a wide range of devastating diseases in various kinds of organisms, including humans. It may be lethal if untreated promptly. To detect specific disease-causingorganisms parasites, a wide range of immunological and molecular technologies are now widely available. However, all of this depends on the worker's expertise and are time-consuming, error-prone, and expensive. With the development of technology, compared to traditional biological techniques, convolutional neural networks have reached excellent achievements in image classification, cutting costs while attaining an overall higher accuracy and eliminating human error. Many models include numerous convolutional layers and offer an accuracy between 90 and 95 percent. In this study, 4740 microscopic images of protozoan parasites from six classes with a balanced dataset and an 80–20% split were classified using three convolutional layers with stochastic gradient descent as an optimizer. A 5-fold cross-validation approach is used to evaluate the proposed method. We also examine and evaluate with deep learning models namely VGG16, ResNet50, and InceptionV3. The performance evaluation of the proposed model shows an accuracy of 94% with a precision range (of 0.83-0.99) and a recall range (of 0.76-1.00), respectively. The retrained model was able to recognize and classify all 6 different parasites. Except for class Leishmania, where 24% of images are incorrectly classified as Plasmodium and Trichomonas, the model demonstrates that most cases are correctly identified.

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DOI: 10.3897/jucs.112797

Authors: Lorenz Gillner, Ekaterina Auer

Abstract: In this paper, we describe the requirements for traceable open-source data retrieval in the context of computation of BRCA1/2 mutation probabilities (mutations in two tumor-suppressor genes responsible for hereditary BReast or/and ovarian CAncer). We show how such data can be used to develop a Dempster-Shafer model for computing the probability of BRCA1/2 mutations enhanced by taking into account the actual age of a patient or a family member in an appropriate way even if it is not known exactly. The model is compared with PENN II and BOADICEA (based on undisclosed data), two established platforms for this purpose accessible online, as well as with our own previous models. A proof-of-concept implementation shows that set-based techniques are able to provide better information about mutation probabilities, simultaneously highlighting the necessity for ground truth data of high quality.

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DOI: 10.3897/jucs.94657

Authors: Nguyen Van Hieu, Ngo Le Huy Hien, Luu Van Huy, Nguyen Huy Tuong, Pham Thi Kim Thoa

Abstract: The natural ecosystem incorporates thousands of plant species and distinguishing them is normally manual, complicated, and time-consuming. Since the task requires a large amount of expertise, identifying forest plant species relies on the work of a team of botanical experts. The emergence of Machine Learning, especially Deep Learning, has opened up a new approach to plant classification. However, the application of plant classification based on deep learning models remains limited. This paper proposed a model, named PlantKViT, combining Vision Transformer architecture and the KNN algorithm to identify forest plants. The proposed model provides high efficiency and convenience for adding new plant species. The study was experimented with using Resnet-152, ConvNeXt networks, and the PlantKViT model to classify forest plants. The training and evaluation were implemented on the dataset of DanangForestPlant, containing 10,527 images and 489 species of forest plants. The accuracy of the proposed PlantKViT model reached 93%, significantly improved compared to the ConvNeXt model at 89% and the Resnet-152 model at only 76%. The authors also successfully developed a website and 2 applications called ‘plant id’ and ‘Danangplant’ on the iOS and Android platforms respectively. The PlantKViT model shows the potential in forest plant identification not only in the conducted dataset but also worldwide. Future work should gear toward extending the dataset and enhance the accuracy and performance of forest plant identification.

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DOI: 10.3897/jucs.93498

Authors: Muath Sabha, Thaer Thaher, Marwa M. Emam

Abstract: The Coronavirus Disease 2019 (COVID-19) is widespread throughout the world and poses a serious threat to public health and safety. A COVID-19 infection can be recognized using computed tomography (CT) scans. To enhance the categorization, some image segmentation techniques are presented to extract regions of interest from COVID-19 CT images. Multi-level thresholding (MLT) is one of the simplest and most effective image segmentation approaches, especially for grayscale images like CT scan images. Traditional image segmentation methods use histogram approaches; however, these approaches encounter some limitations. Now, swarm intelligence inspired meta-heuristic algorithms have been applied to resolve MLT, deemed an NP-hard optimization task. Despite the advantages of using meta-heuristics to solve global optimization tasks, each approach has its own drawbacks. However, the common flaw for most meta-heuristic algorithms is that they are unable to maintain the diversity of their population during the search, which means they might not always converge to the global optimum. This study proposes a cooperative swarm intelligence-based MLT image segmentation approach that hybridizes the advantages of parallel meta-heuristics and MLT for developing an efficient image segmentation method for COVID-19 CT images. An efficient cooperative model-based meta-heuristic called the CPGH is developed based on three practical algorithms: particle swarm optimization (PSO), grey wolf optimizer (GWO), and Harris hawks optimization (HHO). In the cooperative model, the applied algorithms are executed concurrently, and a number of potential solutions are moved across their populations through a procedure called migration after a set number of generations. The CPGH model can solve the image segmentation problem using MLT image segmentation. The proposed CPGH is evaluated using three objective functions, cross-entropy, Otsu’s, and Tsallis, over the COVID-19 CT images selected from open-sourced datasets. Various evaluation metrics covering peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and universal quality image index (UQI) were employed to quantify the segmentation quality. The overall ranking results of the segmentation quality metrics indicate that the performance of the proposed CPGH is better than conventional PSO, GWO, and HHO algorithms and other state-of-the-art methods for MLT image segmentation. On the tested COVID-19 CT images, the CPGH offered an average PSNR of 24.8062, SSIM of 0.8818, and UQI of 0.9097 using 20 thresholds.

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DOI: 10.3897/jucs.96878

Authors: José L. López Ruiz, Ángeles Verdejo Espinosa, Alicia Montoro Lendínez, Macarena Espinilla Estévez

Abstract: Nowadays, it is becoming increasingly important to understand the multiple configuration factors of BLE anchors in indoor location systems. This task becomes particularly crucial in the context of activity recognition in multi-occupancy smart environments. Knowing the impact of the configuration of BLE anchors in an indoor location system allows us to distinguish the interactions performed by each inhabitant in a smart environment according to their proximity to each sensor. This paper proposes a new methodology, OBLEA, that determines the optimisation of Bluetooth Low Energy (BLE) anchors in indoor location systems, considering multiple BLE variables to increase flexibility and facilitate transferability to other environments. Concretely, we present a model based on a data-driven approach that considers configurations to obtain the best performing configuration with a minimum number of anchors. This methodology includes a flexible framework for the indoor space, the architecture to be deployed, which considers the RSSI value of the BLE anchors, and finally, optimisation and inference for indoor location. As a case study, OBLEA is applied to determine the location of ageing inhabitants in a nursing home in Alcaudete, Jaén (Spain). Results show the extracted knowledge related to the optimisation of BLE anchors involved in the case study.

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DOI: 10.3897/jucs.89412

Authors: Oscar I Caldas, Mauricio Mauledoux, Oscar F Aviles, Carlos Rodriguez Guerrero

Abstract: Dynamically Difficulty Adjustment (DDA) has been widely used to preserve engagement in serious and entertaining games, reach better learning, and enhance user performance. A variety of studies suggests that in DDA, task performance (score) rises until hitting a plateau associated with the skill level. However, the sense of engagement is individual and context-dependent, and the effect of DDA on other engagement indicators for immersive virtual environments is still unclear. This study measured objective indicators of engagement while study subjects played an immersive virtual game with DDA to find evidence of dynamic response, similar to game performance. Participants were demanded to perform repetitive upper-limb motions while recording the following indicators: Response Latency as perceptive engagement (elapsed time after sensory stimulus), Exercise Intensity as motion engagement (hand velocity), and psychophysiological responses as emotional engagement (Heart Rate, Skin Conductance, and Respiratory Rate). In addition, 30 features were extracted from the signals to evaluate their variations between time windows. Results indicate that response latency, vertical hand velocity, and heart rate showed significant changes over time during DDA and grew until hitting a plateau, i.e., at the subject's maximum performance. Moreover, some of the features extracted from the signals showed significant differences between time windows, and having strong correlation with the mean of score: max Latency, min velocity on the Y-axis, and mean heart rate, which suggest a promising application for evaluating changes in engagement between different experimental conditions in VR.

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DOI: 10.3897/jucs.94134

Authors: Loveleen Kumar, Manish Jain

Abstract: Image superresolution (SR) is the process of enlarging and enhancing a low-resolution image. Image superresolution helps in industrial image enhancement, classification, detection, pattern recognition, surveillance, satellite imaging, medical diagnosis, image analytics, etc. It is of utmost importance to keep the features of the low-resolution image intact while enlarging and enhancing it. In this research paper, a framework is proposed that works in three phases and generates superresolution images while keeping low-resolution image features intact and reducing image blurring and artifacts. In the first phase, image enlargement is done, which enlarges the low-resolution image to the 2x/4x scale using two standard algorithms. The second phase enhances the image using an AI-empowered Generative adversarial network (GAN). We have used a GAN with dual generators and named it EffN-GAN (EfficientNet-GAN). Fusion is done in the last phase, wherein the final improved image is generated by fusing the enlarged image and GAN output image. The fusion phase helps in reducing the artifacts. We have used the DIV2K dataset to train the GAN and further tested the results on the images of Set5, Set14, B100, Urban100, Manga109 datasets with ground truth of size 224x224x3. The obtained results were compared with the state-of-the-art superresolution approach based on important image quality parameters, namely, Peak signal-to--to-noise ratio (PSNR), Structural similarity index (SSIM), Visual information fidelity (VIF) image quality parameters. The results show that the proposed framework for generating super-resolution images from 2x/4x resolution downgraded images improves the aforementioned mentioned image quality parameters significantly.

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DOI: 10.3897/jucs.94132

Authors: Vijay Krishna Pallaw, Kamred Udham Singh

Abstract: The network’s convenience has created a copyright dilemma for some multimedia works. Nowadays, every healthcare system relies on digital medical images for diagnosis. These medical images are transmitted through communication channels, so there is a risk of tampering and copyright violation. A digital watermarking system can ensure and guarantee that tampering and copyright violation are prevented. This study presents a nonblind digital watermarking approach to X-ray medical images based on Contourlet transform (C.T.) and Slantlet Transform (SLT). Since the two-dimensional signals are represented flexibly by contourlet transforms, the contour plot can be used efficiently to represent curves and smooth contours. At the same time, the SLT has better time-localization & smoothness properties. The maximum energy of an image is conceived in the LL band if SLT transform are employed. Therefore, the LL band is used to entrench the watermark. The additive quantization method has been used to entrench the watermark. The efficiency of our scheme is assessed by different quality parameters and compared with several existing schemes. The results of the experiment show that the proposed scheme performs better and has the ability to resist several attacks.

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DOI: 10.3897/jucs.94127

Authors: Lalan Kumar, Kamred Udham Singh

Abstract: Watermarking is a valuable technique for verifying medical images obtained through the internet for diagnosis. There is a greater need for security in medical pictures with ever- increasing security risks. This research presented a Finite Ridgelet Transform (FRT)-Hessenberg based watermarking scheme in medical images. The suggested paradigm is divided into two stages. Before watermark insertion, FRT is applied to medical images. The coefficients are combined into blocks of 4 x 4 and each block is decomposed using Hessenberg decomposition. The second column of the Q matrix is used to insert the watermark using the additive quantization technique. The results obtained from our experiment have given good visual quality of the watermarked images. The high PSNR value 53.6121 and NC value 1.0 show that our scheme is performing better. Moreover, the performance of our scheme is robust against several attacks. The consequences of this result imply that the anticipated scheme is effective for medical image watermarking.

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DOI: 10.3897/jucs.77237

Authors: Atsuko Matsumoto, Takeshi Kamita, Yukari Tawaratsumida, Ayako Nakamura, Harumi Fukuchimoto, Yuko Mitamura, Hiroko Suzuki, Tsunetsugu Munakata, Tomoo Inoue

Abstract: In recent years, various organizations, such as companies and governments, have been required to take measures for the mental health of their employees, and the importance of self-care for mental health by employees themselves has been increasing, as well as being supported by administrators, such as doctors and workplace managers. As a means of self-care of mental health that can be implemented by busy professionals during their workdays and daily lives, the Digital-SAT method has been developed to implement the stress-care process of the SAT method, a psychological counseling technique for resolving psychological stress problems, in a self-guided manner using digital media. To realize the Digital-SAT method, two issues need to be addressed: first, to obtain the same emotional stress reduction effect as the SAT method and, second, to ensure the continuous implementation of the Digital-SAT method. Previous studies have shown that applications (apps) using virtual reality are effective in solving the former issue, and an app using a chatbot can be effective in solving the latter. In this research, an intervention study was conducted to verify the effectiveness of combined use of the two apps to encourage continuous use, resulting in increased emotional stress reduction, with the aim of making it feasible in actual work environments. An intervention of four weeks of app use was conducted with 70 nurses working in two hospitals where measures for mental health due to emotional labour and overwork were required. The emotional stress reduction effects of the intervention were evaluated using psychological scales and blood pressure levels, and it was confirmed that combined use of apps was more effective than using them separately to practice the Digital-SAT method in an actual work environment.

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DOI: 10.3897/jucs.2020.004

Authors: Qanita Baker, Farah Shatnawi, Saif Rawashdeh, Mohammad Al-Smadi, Yaser Jararweh

Abstract: Opinion mining is an important step towards facilitating information in health data. Several studies have demonstrated the possibility of tracking diseases using public tweets. However, most studies were applied to English language tweets. Influenza is currently one of the world's greatest infectious disease challenges. In this study, a new approach is proposed in order to detect Influenza using machine learning techniques from Arabic tweets in Arab countries. This paper is the first study of epidemic diseases based on Arabic language tweets. In this work, we have collected, labeled, filtered and analyzed the influenza-related tweets written in the Arabic language. Several classifiers were used to measure the quality and the performance of the approach, which are: Naive Bayes, Support Vector Machines, Decision Trees, and K-Nearest Neighbor. The classifiers which achieved the best accuracy results for the three experiments were: Naïve Bayes with 89.06%, and K-Nearest Neighbor with 86.43%, respectively.

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DOI: 10.3217/jucs-025-08-0988

Authors: Valeria Herskovic, Sergio Ochoa, José Pino

Abstract: People-driven mobile collaborative processes are increasingly mediated by technology due to the ubiquity, efficiency and flexibility that modern groupware systems provide their users. However, identifying groupware requirements to be considered in their development is a challenging task, since the processes being supported by them do not have a clear workflow coordinating the activities performed by the participants. Thus, software developers must usually guess these requirements based on their own experience, and so the elicitation process becomes a creative activity instead of an engineering process. Trying to reduce this uncertainty about groupware requirements identification, and thus helping developers improve their capability to predict the suitability of a collaborative system, this paper presents a visual notation to represent user interaction scenarios through models. These models are processed to automatically determine a set of potentially required groupware services. Thus, this proposal reduces the uncertainty about the groupware requirements to be considered in the development of a system supporting a particular people-driven mobile collaborative process. The United States of Americability and usefulness of the visual notation and the method to derive the groupware requirements are illustrated with a running example, and also through its application to a case study. The results are encouraging and consistent, allowing us to augur potential adoption in research and industrial settings.

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DOI: 10.3217/jucs-025-07-0740

Authors: Sonia Estévez, M. Cambronero, Yolanda García-Ruiz, Luis Llana Díaz

Abstract: Parkinson's disease (PD) is the most common age-related neurodegenerative motor disease. People with Parkinson's have different motor symptoms related to movement, the most common of which are tremor, muscle rigidity and slowness of movement. In addition, there are other problems that are unrelated to motor symptoms, such as sleep behavior disorders, personality changes, pain and depression. Numerous apps designed for people with this disease have been developed in recent years. Due to the diversity of symptoms, there are very many different apps. Our goal is to carry out a systematic review of available apps related to PD for the operating systems iOS and Android and to assess their features. In addition, we are interested in the United States of Americability of the apps. A search for the representative terms "Parkinson" and "Parkinson's Disease", together with the descriptors of the symptoms, was conducted in the Google Play and Apple App stores. Next, we screened the PD-related apps. Finally, we assessed the apps with respect to symptoms, users, purpose and features. In addition, a United States of Americability evaluation was carried out.

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DOI: 10.3217/jucs-024-09-1192

Authors: Dingkun Li, Yaning Li, Zhou Ye, Musa Ibrahim, Keun Ryu, Seon Jeong

Abstract: Never before in history is the data growing at such a high volume, variety and velocity. It not only provides multi-sources of information for people to discover useful, important and valuable nuggets of information, but also increases the difficulty in finding such nuggets in almost all fields. Particularly, the field of healthcare is known for its dominical or ontological complexity and variety of clinical data or medical data regarding its variable data standards and data quality and so as the high data dimensionality. In order to effectively use the data at the hand to improve healthcare outcomes and processes, this paper illustrates a model called Risk Factor Detection and Disease Prediction (RFD-DP) model. The model incorporates statistics, data mining and MapReduce techniques on high dimensional clinical data to detect risk factors and generate predicator for a specified disease, hypertension disease. The experimental results indicate that the proposed model outperforms traditional feature selection and classification methods in terms of accuracy, F-score, and AUC. Consequently, the proposed model is promising to be applied to healthcare system.

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DOI: 10.3217/jucs-024-06-0775

Authors: Yenny Villuendas-Rey, Mariana-D. Alanis-Tamez, Carmen-F. Benguría, Cornelio Yáñez-Márquez, Oscar Camacho-Nieto

Abstract: Computational Intelligence techniques in medicine have become an increasing area of research worldwide. Among them, the application and development of new models and algorithms for disease diagnosis and prediction have been an active research topic. The research contribution of the current paper is the proposal of a novel classification model, and its application to the diagnosis of chronic diseases. One of the main characteristics of the new model is that it is designed to deal with imbalanced data. With the purpose of making experimental comparisons to demonstrate the benefits of the proposed model, we tested five classification models, over medical data. The application of the supervised classification algorithms is done over the Knowledge Extraction based on Evolutionary Learning (KEEL) environment, using a distributed optimally balanced stratified 5-fold cross validation scheme. In addition, the experimental results obtained were validated in order to identify significant differences in performance by mean of a non-parametric statistical test (the Friedman test), and a post-hoc test (the Holm test). The hypothesis testing analysis of the experimental results indicates that the proposed model outperforms other supervised classifiers for medical diagnosis.

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DOI: 10.3217/jucs-024-06-0711

Authors: Quan Zou, Chi-Wei Chen, Hao-Chen Chang, Yen-Wei Chu

Abstract: Gelatinases proteases with the ability to cleave the extracellular matrix (ECM). Two types of gelatinases exist: Gelatinase A, also referred to as matrix metalloproteinase-2 (MMP-2), and gelatinase B, also referred to as matrix metalloproteinase-9 (MMP-9). MMP-2 and MMP-9 degrade ECM, which is highly expressed during tumor metastasis. The poor therapeutic effects of inhibitors can be attributed to the high structural homology shared by members of the matrix metalloproteinase family. The highly similar structures of these proteases preclude the specific binding of inhibitor drugs. Moreover, the regulatory pathways of MMP-2 and MMP-9 remain poorly understood. An accurate model for the prediction of substrates and the cleavage sites of gelatinases should be developed to enable screening and exploring the physiological and pathological mechanisms of these enzymes. Prediction is based on various types of information on binary integration, physical-chemical properties, protein stability, solvent accessibility, and protein secondary structure. In this study, the first level of the prediction model was constructed on the basis of intergroup differences and support vector machine. Predictive probability was then taken as the characteristic of the second level of the prediction model, which was constructed using different machine-learning methods. The Mathews correlation coefficients of the MMP-2 and MMP-9 prediction models were 89.4% and 64.4%, respectively. The physical-chemical properties of the active sites of MMP-2 and MMP-4 were selected for analysis. The completion of this prediction system will aid the discovery of regulatory paths and novel applications of MMP-2 and MMP-9, as well as provide references for drug design.

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DOI: 10.3217/jucs-024-06-0665

Authors: Linglong Ye, Tian-Shyug Lee, Robert Chi

Abstract: Along with the worldwide trend of rapidly aging populations, diabetes mellitus and its comprehensive complications have become major public health issues. Considerable evidence suggests patients with diabetes mellitus have a higher risk of breast cancer. However, the relationships between the complications of diabetes mellitus and occurrence of breast cancer have not been well characterized. Despite the higher risk of breast cancer among patients with diabetes mellitus, patients with breast cancer constitute only a relatively small proportion of the diabetes mellitus data, leading to an imbalanced data set. This study proposes a hybrid machine learning scheme to cope with imbalanced data in the analysis of risk factors of breast cancer in patients with diabetes mellitus. The scheme combines the undersampling based on the clustering algorithm, the k-means algorithm, and the extreme gradient boosting algorithm. The results identify that occlusion stroke, diabetes with peripheral circulatory disorders, peripheral angiopathy in diseases classified elsewhere, and other forms of chronic ischemic heart disease are risk factors. This study provides an application of advanced methods in health care and shows the epidemiologic and informatics value of the proposed hybrid machine learning scheme.

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DOI: 10.3217/jucs-024-05-0634

Authors: Simon Vrhovec, Blaž Markelj

Abstract: Critical infrastructure is a high value target in the real world and cyberspace. A failure to protect the critical infrastructure in the cyberspace could lead to serious financial and material losses and violate the effective functioning of a country. In this paper, we will focus on healthcare as an important part of the critical infrastructure. An important part of the healthcare infrastructure are hospitals. Hospital personnel is increasingly using mobile devices in their everyday work to improve patient care. Hospitals may however fail to adequately address the use of mobile devices and adapt their information security policies in time. Hospital personnel may use both their personal and work mobile devices for everyday work. Sometimes they do it without adhering to an adequate hospital information security policy. The objective of this paper is to study the relation between the use of mobile devices, adhering to hospital information security policy and perceived consequences of data breaches. An exploratory survey (N = 95) has been conducted in a Slovenian hospital. Respondents were asked about the use of their personal and work mobile devices for accessing medical data, adhering to the hospital information security policy, and the perceived consequences of data breaches for themselves, the hospital and the patients. The results show that perceived personal consequences are negatively correlated with personal and work mobile device use for work. Also, adhering to information security policy is positively correlated with perceived data breach consequences for both the patients and the hospital.

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DOI: 10.3217/jucs-022-10-1418

Authors: Yasuhito Noguchi, Tomoo Inoue

Abstract: Asynchronous exchange of video messaging is a way to achieve time-shifted communication for the people who have difficulties to enjoy daily family communication in real time, because of time-zone differences or life-rhythm differences. However face-to-face communication and video messaging communication is significantly different. Since mealtime is the most common opportunity for daily family communication, it has been proposed to synchronize the video message with the viewer by changing its playback speed in dining situations to improve video messaging communication. This paper studies the influence of the synchronization method by means of Wizard of Oz (WoZ), and by means of the implemented prototype system. In the synchronization method, the dining progress of the video person is matched with that of the viewer by real-time meal weight detection. The lab study found that the synchronization via WoZ increased speech frequency, decreased the duration of switching pauses, and led to a higher ratio of eating actions immediately after verbal responses of the user. This indicated that a more active commitment of the user was observed. The prototype system with finer control of the video than WoZ also achieved comparable result in terms of questionnaire scores, indicating the feasibility of a videoconferencing system with such a function.

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DOI: 10.3217/jucs-022-08-1123

Authors: Guilherme Melo e Maranhão, Renato Bulcão-Neto

Abstract: A great challenge in context-aware computing is dealing with the heterogeneity and volume of sensors data. A problem regarding that scenario is to notify context-aware applications, which have distinct interests of context events in terms of volume, semantic and complexity, in an efficient and relevant manner. Aiming to solve this problem, this research focuses on a new approach for filtering semantic context towards supporting context dissemination. This mechanism is to be aligned with the reasoning capabilities of a context-aware solution and also be maintainable and extensible to efficiently support changes in an ontological model. A performance evaluation is carried out in a simulated scenario of vital signs monitoring in Intensive Care Units and wards. Hermes Interpreter's behaviour is analysed when dealing with filters of different complexities and also an increasing number of subscribers per vital sign. Results demonstrate the high cost of the semantic filtering mechanism in comparison with pure context reasoning activities.

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DOI: 10.3217/jucs-020-12-1629

Authors: Carmen Panadero, Valentín de la Cruz Barquero, Carlos Delgado-Kloos, David Núñez

Abstract: Psychology has widely probed the relationship between body, mind and emotions, these findings have been traditionally applied to physical learning but its penetration into the academic environment was still slower than expected. Virtual worlds, augmented reality and gamification applied to learning experiences, have once again highlighted the correlation between the emotional state of the student and his learning outcomes. There have been many studies around the concept of flow proposed by Csíkszentmihályi in 1988, what factors influence their extent and how to promote it. Although the proposed model is widely accepted by the scientific community there are some studies showing discrepancies between theoretical models and experimental results. The scientific community demands more studies on how to measure flow and how to analyse the factors behind these discrepancies. This paper presents a study with 20 students between 21 and 36 years using a wheelchair simulator to reach awareness about the difficulties that people with disabilities face daily. Experience confirms the discrepancies between emotions calculated from the model and expressed directly by students. Two of the main findings of this study are: (1) the influence of gender on emotions and (2) some of the factors that moderate the theoretical measures to fit empirical values are related to the four defining traits of a game proposed by McGonigal (challenging goals, clear rules, real time feedback and voluntary participation).

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DOI: 10.3217/jucs-020-09-1327

Authors: João Lopes, Rodrigo Souza, Cláudio Geyer, Cristiano Costa, Jorge Luis Victória Barbosa, Ana Pernas, Adenauer Yamin

Abstract: The development of applications that adapt to the environment and remain running even when the user is moving or switching device, remains an open research challenge. In this article we present a view of the EXEHDA middleware and a new service created for dynamic adaptation. EXEHDA is service-oriented, adaptive and was conceived to support the execution of ubiquitous applications. The main concept in the proposed design for the middleware and for the application is context awareness expressed in an adaptive behavior. The middleware manages and implements the follow-me semantics for ubiquitous applications. This is also a key to provide functionality adapted to the constraints and unpredictability of the large-scale environment. To achieve this objective, EXEHDA employs various strategies in its services to allow the adaptation to the current context, such as on-demand adaptive service loading, and dynamic discovery and configuration. In that sense, EXEHDA provides services for distributed adaptive execution, context recognition, ubiquitous storage and access, and anonymous and asynchronous communications. To evaluate the new service proposed for dynamic adaptation we developed a case study, implementing an application in medical area. Analyzing the results we can see that the users found the application easy to use and usefulness for health workers at a hospital. This work is sponsored by RNP, FINEP and CNPq - Brazilian Foundations.

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DOI: 10.3217/jucs-019-15-2292

Authors: Javier Fernández, Juan Augusto, Giuseppe Trombino, Ralf Seepold, Natividad Madrid

Abstract: Traders are required to work in the financial market with highly complex information and to perform efficiently under high levels of psychological pressure. Multiple disciplines, from programs with artificial intelligence to complex mathematical functions, are used to help traders in their effort to maximize profits. However, an essential problem not yet considered in this rapidly evolving environment is that traders are not supported to adequately manage how stress influences their decisions. This paper takes into consideration the negative influences of stress on individuals and proposes a system designed to support traders by providing them with information that can reduce the likelihood of poor decision-making. The system has been designed considering both technical and physiological aspects to make information available in a suitable way. Biometric sensors are used to collect data associated with stress, a software platform then analyses this information and displays it to the trader. The resulting system is capable of making individual traders, as well as teams of traders, self-aware of their levels of stress. The system has been tested in real environments and the results provide evidence that self-aware traders benefit from the system by reducing risky decision-making.

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DOI: 10.3217/jucs-019-15-2207

Authors: Juan Frausto-Solís, Mishael Sánchez-Pérez, Ernesto Liñan-García, Juan Sánchez-Hernández, Manoj Ramachandran

Abstract: In this paper, an improved Simulated Annealing algorithm for Protein Folding Problem (PFP) is presented. This algorithm called Cluster Perturbation Simulated Annealing (CPSA) is based on a brand new scheme to generate new solutions using a cluster perturbation. The algorithm is divided into two phases: Cluster Perturbation Phase and the Reheat Phase. The first phase obtains a good solution in a small amount of time, and it is applied at very high temperatures. The second phase starts with a threshold temperature and reheats the system for a better exploration. CPSA reduces the execution time of the Simulated Annealing Algorithm without sacrificing quality to find a native structure in PFP in Ab-Initio approaches.

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DOI: 10.3217/jucs-019-09-1295

Authors: Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge Reyes-Ortiz

Abstract: In this paper we propose a novel energy efficient approach for the recog-nition of human activities using smartphones as wearable sensing devices, targeting assisted living applications such as remote patient activity monitoring for the disabledand the elderly. The method exploits fixed-point arithmetic to propose a modified multiclass Support Vector Machine (SVM) learning algorithm, allowing to better pre-serve the smartphone battery lifetime with respect to the conventional floating-point based formulation while maintaining comparable system accuracy levels. Experimentsshow comparative results between this approach and the traditional SVM in terms of recognition performance and battery consumption, highlighting the advantages of theproposed method.

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DOI: 10.3217/jucs-019-09-1275

Authors: Maria-Aydee Sanchez-Santana, Jean-Baptiste Aupet, Marie-Laure Betbeder, Jean-Christophe Lapayre, Antonio Camarena-Ibarrola

Abstract: In this paper, we present a new telediagnosis environment for the detection of cardiovascular problems.This tool, called VACODIS (VAscular COllaborative teleDIagnosiS), allows practitioners to semi-automatically identify and quantify a patient's potential cardiovascular complications. The system generates first-time automatic detection of cardiovascular abnormalities using Doppler ultrasound images.The system then provides remote collaborative sharing of this information among different doctors to allow distance telediagnostics. With this new system, different actors in the field of medicine (nurses, practitioners, etc.) will be able to contribute to a more reliable diagnosis in the cardiovascular domain.

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DOI: 10.3217/jucs-019-09-1219

Authors: Antonio Jara, David Fernandez, Pablo Lopez, Miguel Zamora, Antonio Skarmeta, Leandro Marin

Abstract: Bluetooth Low Energy (BLE) is extending Bluetooth technology to devices with lower communication requirements and higher constraints in terms of memory capabilities and power autonomy. Thereby, BLE makes feasible the wireless transmission of information from smart objects such as wearable clinical devices, ambient sensors and actuators. These smart objects are starting to be internet-enabled devices, reaching the so denominated Internet of Things (IoT). Our research work is focused on analyze the capabilities of these technologies for continuous data transmission and integration of clinical sensors in home-care and Ambient Assisted Living environments. For this purpose, this work analyses exhaustively the capabilities from BLE and compare this with the capabilities from Bluetooth 2.1. In addition, it has been considered the communications requirements from different clinical devices such as an electrocardiogram (ECG) and a capnograph. It is concluded that the performance from BLE is lower than Bluetooth Classic (Bluetooth 2.1) for continuous communications, since BLE is based on attributes (datapoints) transfer instead of offering a communication session where can be transmitted as much packets as required, without any size constraint. Therefore, it is necessary to perform data compression/aggregation when the amount of data to send is too large to be stored in a BLE attribute. This work also presents how to apply pre-processing techniques that greatly reduces the transmission overload (performs signal compression) in order to allow the continuous transmission of the ECG and capnograph signal through BLE making so feasible the integration of continuous clinical devices via BLE.

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DOI: 10.3217/jucs-019-09-1199

Authors: Jaime Guixeres, Javier Saiz, Mariano Alcañiz, Ausias Cebolla, Patricia Escobar, Rosa Baños, Cristina Botella, Juan Lison, Julio Alvarez, Laura Cantero, Empar Lurbe

Abstract: Virtual Reality (VR) could be an interesting tool to combat obesity and sedentariness in children. During the last years a multidisciplinary research team comprised of engineers, psychologists, physiotherapists and paediatricians have been testing these technologies. Throughout the tests, physiological (cardiovascular and metabolic response with biomedical sensors (smart fabrics TIAS) and psychological responses have been collected. The results presented in this paper reflect two main aspects: 1) the feasibility of the monitoring techniques employed and 2) the validity of virtual reality and exergaming technologies as promoters of physical activity and their potential as tools in clinical intervention programs. In the first study (n=90) children, a commercial platform was tested as support tool to aerobic exercise in a treadmill. Results showed a more physiological effort by obese group and limitations to measure effort perception with Borg scale especially in obese group. In second study (n=126) a new VR platform was developed (VREP) and tested as support of aerobic activities, a difference of first study, all the boys completed both conditions (same Aerobic exercise with/without support VR). 59.5% felt that they had to exert more effort in the traditional condition. Regarding to acceptability in both studies the vast majority of the participants liked the idea of combining physical activity with the VR platform as a form of treatment to increase physical activity. The capacity of VR technology to create controllable, multisensory, interactive 3D stimulus environments within which children's performance can be motivated, recorded, and measured, has been tested in these studies, offering clinical assessment and intervention options which are not possible using traditional methods.

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DOI: 10.3217/jucs-017-07-1109

Authors: Markus Kreuzthaler, Marcus Bloice, Lukas Faulstich, Klaus-Martin Simonic, Andreas Holzinger

Abstract: Patient information in health care systems mostly consists of textual data, and free text in particular makes up a significant amount of it. Information retrieval systems that concentrate on these text types have to deal with the different challenges these medical free texts pose to achieve an acceptable performance. This paper describes the evaluation of four different types of information retrieval strategies: keyword search, search performed by a medical domain expert, a semantic based information retrieval tool, and a purely statistical information retrieval method. The different methods are evaluated and compared with respect to its appliance in medical health care systems.

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DOI: 10.3217/jucs-016-09-1152

Authors: James Decraene, Thomas Hinze

Abstract: All processes of life are controlled by networks of interacting biochemical components. The purpose of modelling these networks is manifold. From a theoretical point of view it allows the exploration of network structures and dynamics, to find emergent properties or to explain the organisation and evolution of networks. From a practical point of view, in silico experiments can be performed that would be very expensive or impossible to achieve in the laboratory, such as hypothesis-testing with regards to knock-out experiments or overexpression, or checking the validity of a proposed molecular mechanism. The literature on modelling biochemical networks is growing rapidly and the motivations behind different modelling techniques are sometimes quite distant from each other. To clarify the current context, we review several of the most popular methods and outline the strengths and weaknesses of deterministic, stochastic, probabilistic, algebraic and agent-based approaches. We then present a comparison table which allows one to identify easily key attributes for each approach such as: the granularity of representation or formulation of temporal and spatial behaviour. We describe how through the use of heterogeneous and bridging tools, it is possible to unify and exploit desirable features found in differing modelling techniques. This paper provides a comprehensive survey of the multidisciplinary area of biochemical networks modelling. By increasing the awareness of multiple complementary modelling approaches, we aim at offering a more comprehensive understanding of biochemical networks.

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DOI: 10.3217/jucs-016-05-0604

Authors: Johannes Aßfalg, Jing Gong, Hans-Peter Kriegel, Alexey Pryakhin, Tiandi Wei, Arthur Zimek

Abstract: When considering the prediction of a structural class for a protein as a classificationproblem, usually a classifier is based on a feature vector x ∊ ℝn, where the features represent certain attributes of the primary sequence or derived properties (e.g., the predicted secondary structure) of a given protein. Since the structure of a protein (i.e., its native conformation) is stable only under specific environmental conditions, it is commonly accepted to assume proteins being evolutionarily adapted to specific subcellular localizations and according to their physicochemical environment. Our statistical evaluation shows a strong correlation between the subcellular localization of proteins and their structural class. The correlation is strong enough to allow fora classification of proteins into their structural class solely based on information regarding the subcellular localization. We conclude that knowledge regarding the subcellular localization ofproteins can be useful as a feature for the structural classification of proteins.

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DOI: 10.3217/jucs-015-16-3081

Authors: Eduardo Gasca, Jesus Favela, Monica Tentori

Abstract: Obesity has become a serious health problem affecting millions of people around the world. Obese people have a higher risk to attain chronic diseases, a reduced quality of life, and higher risk of premature death. Persuasive technology and virtual communities can help address these problems by motivating patients to follow a healthier life style. This paper presents the Persuasive Health Network (pHealthNet), a pervasive virtual community designed to promote a healthy lifestyle in patients with chronic degenerative diseases that participated in a national program for the prevention of diseases, education and self-care. The development and deployment of the system supports the program's goal of persuading patients to change their eating habits and to increase their physical activity. The system was incorporated in the program of two self-support groups whose participants are monitored for a period of three months. The results of an in situ evaluation showed that participants increased their trust in themselves, adopted healthier eating and exercising habits and where more satisfied with the program that those who followed the traditional approach.

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DOI: 10.3217/jucs-015-13-2608

Authors: Vitoantonio Bevilacqua, Lucia Cariello, Marco Giannini, Giuseppe Mastronardi, Vito Santarcangelo, Rocco Scaramuzzi, Antonella Troccoli

Abstract: This paper describes a comparative study between an Artificial Neural Network (ANN) and a geometric technique to detect for biometric applications,the bifurcation points of blood vessels in the retinal fundus. The first step is an image preprocessing phase to extract retina blood vessels. The contrast of the blood vessels from the retinal image background is enhanced in order to extract the blood vessels skeleton. Successively, candidate points of bifurcation are individualized by approximating the skeleton lines in segments. The distinction between bifurcations and vessel bends is carried out through the employment of two methods: geometric (through the study of intersections within the region obtained thresholding the image portion inside a circle centered around the junctions point and the circumference of the same circle) and an ANN. The results obtained are compared and discussed.

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DOI: 10.3217/jucs-015-13-2528

Authors: Yifei Chen, Feng Liu, Bram Vanschoenwinkel, Bernard Manderick

Abstract: This paper focuses on the use of support vector machines on a typical context-dependent classification task, splice site prediction. For this type of problems, it has been shown that a context-based approach should be preferred over a transformation approach because the former approach can easily incorporate statistical measures or directly plug sensitivity information into distance functions. In this paper, we designed three types of context-sensitive kernel functions: polynomial-based, radial basis function-based and negative distance-based kernels. From the experimental results it becomes clear that the radial basis function-based kernel with information gain weighting gets the best accuracies and can always outperform their simple non-sensitive counterparts both in accuracy and in model complexity. And with well designed features and carefully chosen context sizes, our system can predict splice sites with fairly high accuracy, which can achieve the F P 95% rate, 3.94 for donor sites and 5.98 for acceptor sites, an approximate state of the art performance for the moment.

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DOI: 10.3217/jucs-015-04-0826

Authors: Vitoantonio Bevilacqua, Giuseppe Mastronardi, Filippo Menolascina, Paolo Pannarale, Giuseppe Romanazzi

Abstract: Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When time-course data is available, gene interactions may be modeled by a Bayesian Network (BN). Given a structure, that models the conditional independence between genes, we can tune the parameters in a way that maximize the likelihood of the observed data. The structure that best fit the observed data reflects the real gene network's connections. Well known learning algorithms (greedy search and simulated annealing) devoted to BN structure learning have been used in literature. We enhanced the fundamental step of structure learning by means of a classical evolutionary algorithm, named GA (Genetic algorithm), to evolve a set of candidate BN structures and found the model that best fits data, without prior knowledge of such structure. In the context of genetic algorithms, we proposed various initialization and evolutionary strategies suitable for the task. We tested our choices using simulated data drawn from a gene simulator, which has been used in the literature for benchmarking [Yu et al. (2002)]. We assessed the inferred models against this reference, calculating the performance indicators used for network reconstruction. The performances of the different evolutionary algorithms have been compared against the traditional search algorithms used so far (greedy search and simulated annealing). Finally we individuated as best candidate an evolutionary approach enhanced by Crossover-Two Point and Selection Roulette Wheel for the learning of gene regulatory networks with BN. We show that this approach outperforms classical structure learning methods in elucidating the original model of the simulated dataset. Finally we tested the GA approach on a real dataset where it reach 62% of recovered connections (sensitivity) and 64% of direct connections (precision), outperforming the other algorithms.

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DOI: 10.3217/jucs-015-04-0742

Authors: Sing-Wu Liou, Chia-Ming Wang, Yin-Fu Huang

Abstract: For de novo pattern mining in genomic sequences, the main issues are constructingpattern definition model (PDM) and mining sequence patterns (MSP). The representations of PDMs and the discovery of patterns are functionally dependent; the performances thus dependon the adopted PDMs. The popular PDMs provide only descriptive patterns; they lack multifaceted considerations. Many of existing MSP methods are tied up with the exclusively devisedPDMs, and the specialized and sophisticated models make the mined results hard to be reused. In this research, an integrative pattern mining system is proposed, which consists of a computation-oriented PDM (CO-PDM) and general-purpose MSP (GP-MSP) methods. The CO-PDM defines four computational concerns (CCs) as facets of MSP: expression (E), location (L), range (R)and weight (W), which are integrated into a frame-relayed pattern model (FRPM). The GP-MSP develops a frame-relayed search strategy to resolve the ELR-CCs firstly, with the aids of critical-parameter automating (CPA) procedure; and then the W-CC is determined by hybridizing particle swarm optimization (PSO) and artificial neural network (ANN). The proposed FRPM andGP-MSP had been implemented and applied to 22,448 human introns; from the results, all the well-known patterns were recovered and some new ones were also discovered. Furthermore, theeffectiveness of identified patterns were verified by a two-layered k-nearest neighbor (k-NN) classifier; the average precision and recall are 0.88 and 0.92, respectively. By the case study, theintegrative PDM-MSP system is believed to be effective and reliable; it is optimistic the proposed CO-PDM and GP-MSP are both widely applicable and reusable for mining sequence patterns inthe eukaryotic protein-coding genes.

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DOI: 10.3217/jucs-014-22-3781

Authors: Andreas Holzinger, Regina Geierhofer, Felix Mödritscher, Roland Tatzl

Abstract: Most information in Hospitals is still only available in text format and the amount of this data is immensely increasing. Consequently, text mining is an essential area of medical informatics. With the aid of statistic and linguistic procedures, text mining software attempts to dig out (mine) information from plain text. The aim is to transform data into information. However, for the efficient support of end users, facets of computer science alone are insufficient; the next step consists of making the information both usable and useful. Consequently, aspects of cognitive psychology must be taken into account in order to enable the transformation of information into knowledge of the end users. In this paper we describe the design and development of an application for analyzing expert comments on magnetic resonance images (MRI) diagnoses by applying a text mining method in order to scan them for regional correlations. Consequently, we propose a calculation of significant co-occurrences of diseases and defined regions of the human body, in order to identify possible risks for health.

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DOI: 10.3217/jucs-014-22-3767

Authors: Holger Stenzhorn, Stefan Schulz, Martin Boeker, Barry Smith

Abstract: The desideratum of semantic interoperability has been intensively discussed in medical informatics circles in recent years. Originally, experts assumed that this issue could be sufficiently addressed by insisting simply on the application of shared clinical terminologies or clinical information models. However, the use of the term 'ontology’ has been steadily increasing more recently. We discuss criteria for distinguishing clinical ontologies from clinical terminologies and information models. Then, we briefly present the role clinical ontologies play in two multicentric research projects. Finally, we discuss the interactions between these different kinds of knowledge representation artifacts and the stakeholders involved in developing interoperational real-world clinical applications. We provide ontology engineering examples from two EU-funded projects.

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DOI: 10.3217/jucs-014-04-0526

Authors: Claudia Müller, Benedikt Meuthrath, Anne Baumgraß

Abstract: Increasingly wikis are used to support existing corporate knowledge exchange processes. They are an appropriate software solution to support knowledge processes. However, it is not yet proven whether wikis are an adequate knowledge management tool or not. This paper presents a new approach to analyze existing knowledge exchange processes in wikis based on network analysis. Because of their dynamic characteristics four perspectives on wiki networks are introduced to investigate the interrelationships between people, information, and events in a wiki information space. As an analysis method the Social Network Analysis (SNA) is applied to uncover existing structures and temporal changes. A scenario data set of an analysis conducted with a corporate wiki is presented. The outcomes of this analysis were utilized to improve the existing corporate knowledge processes.

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DOI: 10.3217/jucs-012-09-1345

Authors: Peter Nagy, Istvan Pinter, Mihely Bagany

Abstract:

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DOI: 10.3217/jucs-012-09-1267

Authors: István Koós

Abstract: A mathematical model is proposed to allow expressive representation of endocrine systems by graphic means. The differential equations exactly describing the system can be formulated easily and automatically by the graphic model. Different kinds of software are supposed to solve these equations easily. Chaotic operational range can be found by fitting the parameters of equations. The results can account for some endocrine diseases and would be able to help the therapy.

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DOI: 10.3217/jucs-012-09-1229

Authors: András Hajdu, János Kormos, Zsolt Lencse, Lajos Trón, Miklós Emri

Abstract: In this paper we present the structure and the achieved results of the R&D project IKTA-4, 6/2001 MEDIP - Platform independent software system for medical image processing supported by the Hungarian Ministry of Education. The aim of the project was to develop a software background for our basic and applied research in the field of medical imaging that can be used in clinical routine, as well. Realization was based on the experience of information technology and medical imaging research university teams and a company specialized on software and hardware developing for nuclear medicine. The aims also reflect some former research and development activities of the participants. Thus some of them are well experienced in registration, segmentation and image fusion techniques. These experiences were also considered in the determination of the main purposes. The capabilities of the provided software library were demonstrated through test applications from the fields of orthopedics, oncology and nuclear medicine.

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DOI: 10.3217/jucs-012-09-1196

Authors: Nándor Fodor

Abstract: Models have played an important role in scientific research for a long time. The crop models try to simulate the functioning of the atmosphere-soil-plant system with the help of computers. These models can be effective tools in research, education and solving agricultural and environmental protection related problems. The 4M package includes a crop model and several accessories that help to operate the model. The 4M crop model is a daily-step, deterministic model that simulates the water and nutrient balance of the soil, the soil-plant interactions and the plant development and growth. To mention some examples: (1) The package can be used in education to carry out 'zero-cost' virtual agricultural experiments and to challenge and enhance the system oriented thinking of the students. (2) In research it can be used for designing experiments and estimating the present and future characteristics of the investigated system. (3) In practical applications the package can be used to prepare agro-technological advise (fertilization, irrigation, etc.) for farmers, and to carry out economical analyses on farm level.

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DOI: 10.3217/jucs-012-09-1154

Authors: József Berke, Zsolt Polgar, Zoltán Horváth, Tamás Nagy

Abstract: On the field of potato research and breeding, there are several possibilities for the application of modern digital image processing and data collection/analysing techniques. One of the most obvious methods is the multi/hyper spectral analysis. In our experiments research were done in the visible as well as in the infra, near infra and thermal wavelength. For more advanced analysis we developed a multi/hyper-spectral analysis method (spectral fractal dimension measurement and application). In the following we summarize its basic elements and the developed integrated information system of potato research and breeding.

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DOI: 10.3217/jucs-012-06-0581

Authors: Benjamin Blankertz, Guido Dornhege, Steven Lemm, Matthias Krauledat, Gabriel Curio, Klaus-Robert Müller

Abstract: We outline the Berlin Brain-Computer Interface (BBCI), a system which enables us to translate brain signals from movements or movement intentions into control commands. The main contribution of the BBCI, which is a non-invasive EEG-based BCI system, is the use of advanced machine learning techniques that allow to adapt to the specific brain signatures of each user with literally no training. In BBCI a calibration session of about 20min is necessary to provide a data basis from which the individualized brain signatures are inferred. This is very much in contrast to conventional BCI approaches that rely on operand conditioning and need extensive subject training of the order 50-100 hours. Our machine learning concept thus allows to achieve high quality feedback already after the very first session. This work reviews a broad range of investigations and experiments that have been performed within the BBCI project. In addition to these general paradigmatic BCI results, this work provides a condensed outline of the underlying machine learning and signal processing techniques that make the BBCI succeed. In the first experimental paradgm we analyze the predictability of limb movement long before the actual movement takes place using only the movement intention measured from the pre-movement (readiness) EEG potentials. The experiments include both off-line studies and an online feedback paradigm. The limits with respect to the spatial resolution of the somatotopy are explored by contrasting brain patterns of movements of left vs. right hand rsp. foot. In a second conplementary paradigm voluntary modulations of sensorimotor rhythms caused by motor imagery (left hand vs. right hand vs. foot) are translated into a continuous feedback signal. Here we report results of a recent feedback study with 6 healthy subjects with no or very little experience with BCI control: half of the subjects achieved an information transfer rate above 35 bits per minute (bmp). Furthermore one subject used the BBCI to operate a mental typewriter in free spelling mode. The overall spelling speed was 4.5-8 letters per minute including the time needed for the correction errors.

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DOI: 10.3217/jucs-012-01-0099

Authors: Zhenjiang Miao, Baozong Yuan, Mengsun Yu

Abstract: This paper proposes a Human-centered Pervasive Computing System Model (HPC), a Layered Architectural Analysis and Design Method (LAAD) and a Waterfall Prototyping Process Model (WPP). Based on the HPC model and the LAAD method, a pervasive computing based multimodal tele-home healthcare system is designed and partly implemented using the Waterfall Prototyping Process. The design and implementation issues are discussed in more detail. Some testing results are presented.

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DOI: 10.3217/jucs-012-01-0080

Authors: Adel Taweel, Alan Rector, Jeremy Rogers

Abstract: The convergence of need between improved clinical care and post genomics research presents a unique challenge to restructuring information flow so that it benefits both without compromising patient safety or confidentiality. The CLEF project aims to link-up heath care with bioinformatics to build a collaborative research platform that enables a more effective biomedical research. In that, it addresses various barriers and issues, including privacy both by policy and by technical means, towards establishing its eventual system. It makes extensive use of language technology for information extraction and presentation, and its shared repository is based around coherent "chronicles" of patients' histories that go beyond traditional health record structure. It makes use of a collaborative research workbench that encompasses several technologies and uses many tools providing a rich platform for clinical researcher.

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DOI: 10.3217/jucs-012-01-0059

Authors: Anthony Glascock, David Kutzik

Abstract: The lack of appropriate and accurate information on the ability of a frail individual to accomplish specific task oriented activities can place the individual at risk or result in the allocation of costly and unnecessary care. Although there have been previous attempts to use computer technology to obtain this information, they have proved to be costly and complex and therefore not widely used. However, a behavioral monitoring technology, based on smart-home and telemedicine applications, has been developed that obtains more accurate and timely information on the ability of frail individuals to accomplish specific tasks in their own residences than any other existing method. During a twelve month pilot study, this system has been used by care providers to assess the status of their clients, respond to immediate needs and alter overall care plans, thus resulting in better care and greater peace of mind for the individual.

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DOI: 10.3217/jucs-012-01-0030

Authors: Haiying Zhou, Kun Hou, Laurent Gineste, Christophe Vaulx, Jean Ponsonnaille

Abstract: More than 60,000 people die suddenly each year in France due to cardiac arrhythmias. The current techniques used to diagnose cardiac arrhythmias such as HOLTER, R.TEST and telemetry system are partially efficient owing to the limitation of the duration of monitoring. This paper presents a new system dedicated to real-time cardiac arrhythmias tele-assistance and monitoring. This system is generally composed of 4 main configurable elements: wireless ECG sensor, local access unit, remote centre server, and remote surveillance terminal. The main technical challenges of this system include three aspects: a real-time automatic ECG diagnostic algorithm, an embedded real-time multi-task operating system, and a real-time reliable telemedicine communication protocol. This paper gives our solutions to these problems and specifies the technical details. Currently, this system has been evaluated on thirty patients at the CHRU of Gabriel Montpied hospital (Clermont-Ferrand, France) and also been used to test the athletes' cardiac status during the physical exercises. The performance results show that this system meets fully the requirements of real-time cardiac monitoring and diagnosing application and can be used as a long-term cardiac healthcare equipment.

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DOI: 10.3217/jucs-012-01-0006

Authors: Jean Roberts

Abstract: Much development work is ongoing addressing technologies and their application in the health domain, in order to achieve solutions that are non-invasive to every day life and work. As with many previous phases of informatics to support health, currently the developments are in islands and there is considerable untapped potential for synergy. Much research development is happening in other domains and show potential for health reversioning and deployment once proven. This paper explores some of the technological, societal and domain-specific issues surrounding this emerging concept of pervasiveness. It concludes that pervasive support to care is emerging but further work on minimizing risk and marketing the concept to professionals and laypeople is necessary to ensure an effective deployment.

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DOI: 10.3217/jucs-011-11-1792

Authors: Klaus Hinum, Silvia Miksch, Wolfgang Aigner, Susanne Ohmann, Christian Popow, Margit Pohl, Markus Rester

Abstract: Tracking and comparing psychotherapeutic data derived from questionnaires involves a number of highly structured, time-oriented parameters. Descriptive and other statistical methods are only suited for partial analysis. Therefore, we created a novel spring-based interactive Information Visualization method for analysing these data more in-depth. With our method the user is able to find new predictors for a positive or negative course of the therapy due to the combination of various visualization and interaction methods.

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DOI: 10.3217/jucs-007-07-0550

Authors: David Hicks, Klaus Tochtermann

Abstract: The efficient management of knowledge has become imperative for almost all types of organizations. Many approaches exist for dealing with knowledge management at a corporate level. But there is also a need to support knowledge management also at an individual level, a level which takes the specific needs, experiences and skills of knowledge workers into account. While largely unexplored within the field of knowledge management, in the field of digital libraries advanced personalization and customization concepts exist. Within this context, this paper examines these concepts and how they can be exploited to address the challenges which are typical for knowledge management. As the paper will show, many synergies exist, if knowledge management at an individual level is dealt with in combination with personal digital libraries.

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DOI: 10.3217/jucs-006-06-0560

Authors: William Hart, Sorin Istrail

Abstract: Crystal lattices are infinite periodic graphs that occur naturally in a variety of geometries and which are of fundamental importance in polymer science. Discrete models of protein folding use crystal lattices to define the space of protein conformations. Because various crystal lattices provide discretizations of the same physical phenomenon, it is reasonable to expect that there will exist "invariants" across lattices related to fundamental properties of the protein folding process. This paper considers whether performance-guaranteed approximability is such an invariant for HP lattice models. We define a master approximation algorithm that has provable performance guarantees provided that a specific sublattice exists within a given lattice. We describe a broad class of crystal lattices that are approximable, which further suggests that approximability is a general property of HP lattice models. 1 C.S.Calude and G.Stefanescu (eds.). Automata, Logic, and Computability. Special issue dedicated to Professor Sergiu Rudeanu Festschrift.

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DOI: 10.3217/jucs-006-05-0517

Authors: Klaus Tochtermann, Hermann Maurer

Abstract: The objective of this paper is to identify synergy fields and relationships between knowledge management and environmental informatics. From the perspective of knowledge management many sophisticated techniques, concepts, and methodologies developed in the domain of environmental informatics can build the starting point for finding answers to open questions in knowledge management. For example, meta-knowledge management can capitalise on existing results gained in the area of metadata management, which plays a key role in environmental informatics. Up to now, many tools for knowledge processing have been applied in the domain of environmental informatics to help solve environmental problems. New knowledge management tools can improve this situation which in turn contributes directly or indirectly to a significant improvement of the protection of our environment. In order to achieve its objective, the paper introduces knowledge management with a strong focus on information technology. This introduction is followed by a literature survey on knowledge processing in environmental applications. Thereafter, several environmental information systems are analysed in the light of knowledge management. A special emphasis is placed on how geographical information systems can be used for knowledge management. Finally, the paper closes with suggestions of further areas of research in the synergy field of knowledge management and environmental informatics.

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DOI: 10.3217/jucs-002-02-0058

Authors: Svetoslav Markov, Y. Akyildiz

Abstract: This paper is devoted to the software implementation of two mathematical methods which are often used in biological applications: interpolation and curve fitting in the presence of uncertainties in the input data given in the form of intervals. The methods involve model functions linear in their parameters and are formulated by means of simple expressions in terms of interval arithmetic allowing the computation of verified bounds for the interpolating/approximating functions. The methods are demonstrated for certain classes of nonlinear modelling functions finding applications in biology. A case study involving enzyme-catalysed reaction is considered. The numerical results are performed in the computer algebra system Mathematica, which supports interval-arithmetic computations.

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