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.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.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.121185

Authors: Emre Önal, Abdullah Bülbül

Abstract: Optimizing game elements through iterative human playtests can be time-consuming and insufficient for games with complex intransitive mechanics. Imbalances in games often require the release of numerous balance patches. We present a computational method for making each game unit equally preferable against a uniform play strategy. We leverage concepts from game theory to model intricate relationships among intransitive entities. Matching units against each other is modeled as a symmetric zero-sum game, where unit selection represents a strategy and the error is quantified using payoff values derived from unit parameters. The algorithm takes the initial unit parameters provided by the game designer and optimizes them with minimal changes using gradient descent. Consequently, the payoff matrix converges to a state where a uniform strategy is a near Nash equilibrium, ensuring that each unit is equally preferable under the optimized condition. We implemented a testing environment based on fictitious play and verified our results on different scenarios. While the majority of game theory research focuses on finding optimal strategies given specific environmental conditions, this paper takes a different perspective within the context of game design. We explore game theoretic concepts to address the goal of designing environments that lead to desired strategy choices.

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DOI: 10.3897/jucs.102428

Authors: Ngo Le Huy Hien, Ah-Lian Kor, Mei Choo Ang, Eric Rondeau, Jean-Philippe Georges

Abstract: The deployment of autonomous vehicles has the potential to significantly lessen the variety of current harmful externalities, (such as accidents, traffic congestion, security, and environmental degradation), making autonomous vehicles an emerging topic of research. In this paper, a literature review of autonomous vehicle development has been conducted with a notable finding that autonomous vehicles will inevitably become an indispensable future greener solution. Subsequently, 5 different deep learning models, YOLOv5s, EfficientNet-B7, Xception, MobilenetV3, and InceptionV4, have been built and analyzed for 2-D object recognition in the navigation system. While testing on the BDD100K dataset, YOLOv5s and EfficientNet-B7 appear to be the two best models. Finally, this study has proposed Hessian, Laplacian, and Hessian-based Ridge Detection filtering techniques to optimize the performance of those 2 models. The results demonstrate that these filters could increase the mean average precision by up to 11.81%, and reduce detection time by up to 43.98% when applied to YOLOv5s and EfficientNet-B7 models. Overall, all the experiment results are promising and could be extended to other domains for semantic understanding of the environment. Additionally, various filtering algorithms for multiple object detection and classification could be applied to other areas. Different recommendations and future work have been clearly defined in this study.

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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.72900

Authors: Mohd Khaled Y. Shambour, Esam A. Khan

Abstract: About three million Muslims are traveling annually to Makkah in Saudi Arabia to perform the rituals of Hajj (i.e. the pilgrimage), the fifth pillar of Islam. It requires the pilgrims to move to several holy sites while performing the Hajj ritual, including Mina, Arafat, and Muzdalifah sites. However, pilgrims spend most of their time in prepared tent-camps at the Mina site during the days of Hajj. Among the challenges that the organizers face in the Hajj is the distribution of pilgrims over the camps of Mina while considering a range of constraints, which is considered a real-world optimization problem. This paper introduces a hyper-heuristic approach to optimize the distribution process of pilgrims over Mina tent-camps in an efficient manner, named the hyper-heuristic Mina tents distribution algorithm (HyMTDA). The proposed algorithm, iteratively, selects one heuristic among four predefined low-level heuristics to produce a new solution; thereafter the late move acceptance strategy is applied as a judgment to accept or reject the new solution. The performed simulations show that the proposed HyMTDA can effectively explore the search space and avoid falling into local minima during the iterations process. Moreover, comparisons show that HyMTDA outperforms other heuristic algorithms in the literature in terms of solution quality and convergence rate.

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DOI: 10.3897/jucs.68185

Authors: Murat Firat, Derya Yiltas-Kaplan, Ruya Samli

Abstract: Over the past decades, air transportation has expanded and big data for transportation era has emerged. Accurate travel demand information is an important issue for the transportation systems, especially for airline industry. So, “optimal seat capacity problem between origin and destination pairs” which is related to the load factor must be solved. In this study, a method for determining optimal seat capacity that can supply the highest load factor for the flight operation between any two countries has been introduced. The machine learning methods of Artificial Neural Network (ANN), Linear Regression (LR), Gradient Boosting (GB), and Random Forest (RF) have been applied and a software has been developed to solve the problem. The data set generated from The World Bank Database, which consists of thousands of features for all countries, has been used and a case study has been done for the period of 2014-2019 with Turkish Airlines. To the best of our knowledge, this is the first time that 1983 features have been used to forecast air travel demand in the literature within a model that covers all countries while previous studies cover only a few countries using far fewer features. Another valuable point of this study is the usage of the last regular data about the air transportation before COVID-19 pandemic. In other words, since many airline companies have experienced a decline in the air travel operation in 2020 due to COVID-19 pandemic, this study covers the most recent period (2014-2019) when flight operation performed on a regular basis. As a result, it has been observed that the developed model has forecasted the passenger load factor by an average error rate of 6.741% with GB, 6.763% with RF, 8.161% with ANN, and 9.619 % with LR.

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DOI: 10.3217/jucs-018-20-2832

Authors: Daewon Kim, Doosung Hwang

Abstract: As the augmented reality technology has become more pervasive and applicable, it is easily seen in our daily lives regardless of fields and scopes. Existing camera vision based augmented reality techniques depend on marker based approaches rather than real world information. The augmented reality technology using marker recognition has limitations in its applicability and provision of proper environment to guarantee user's immersiveness to relevant service application programs. This study aims to implement a smart mobile terminal based augmented reality technology by using a camera built in a terminal device and image and video processing technology without any markers so that users can recognize multimedia objects from real world images and build an augmented reality service, where 3D content connected to objects and relevant information are added to the real world image. Object recognition from a real world image is involved in a process of comparison against preregistered reference information, where operation to measure similarity is reduced for faster running of the application, considering the characteristics of smart mobile devices. Furthermore, the design allows users to interact through touch events on the smart device after 3D content is output onto the terminal screen. Afterward, users can browse object related information on the web. The augmented reality technology appropriate for the smart mobile environment is proposed and tested through several experiments and showed reliable performances in the results.

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DOI: 10.3217/jucs-011-06-1102

Authors: E. Zanaty, Moheb Girgis

Abstract: Surface reconstruction addresses the problem of creating a surface model from a point set digitized from a physical object. After performing the surface fitting on the bases of individual patches (adjacent surfaces), it is necessary to improve the obtained results by connecting the adjacent surfaces according to the desired smoothness. This paper presents a fast method for collecting the adjacent surfaces into complex based on C0 continuity. The method works with the surfaces and their segments. Firstly, an arbitrary surface is selected, and the points with closest distances to that surface are extracted. Then, the points are sorted according to the Euclidean distance to the surface. Finally, the surface and the sorted points are joined together and presented to a refitting technique. This technique includes a procedure to decide if the data is similar to the current surface and for updating the surface parameters for each new point.

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