Latest Articles from JUCS - Journal of Universal Computer Science Latest 4 Articles from JUCS - Journal of Universal Computer Science https://lib.jucs.org/ Sat, 30 Mar 2024 03:00:24 +0200 Pensoft FeedCreator https://lib.jucs.org/i/logo.jpg Latest Articles from JUCS - Journal of Universal Computer Science https://lib.jucs.org/ Cost-Effective Scheduling in Fog Computing: An Environment Based on Modified PROMETHEE Technique https://lib.jucs.org/article/90429/ JUCS - Journal of Universal Computer Science 29(4): 397-416

DOI: 10.3897/jucs.90429

Authors: Shefali Varshney, Rajinder Sandhu, P. K. Gupta

Abstract: With the rising use of Internet of Things (IoT)-enabled devices, there is a significant increase in the use of smart applications that provide their response in real time. This rising demand imposes many issues such as scheduling, cost, overloading of servers, etc. To overcome these, a cost-effective scheduling technique has been proposed for the allocation of smart applications. The aim of this paper is to provide better profit by the Fog environment and minimize the cost of smart applications from the user end. The proposed framework has been evaluated with the help of a test bed containing four analysis phases and is compared on the basis of five metrics- average allocation time, average profit by the Fog environment, average cost of smart applications, resource utilization and number of applications run within given latency. The proposed framework performs better under all the provided metrics.

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Research Article Fri, 28 Apr 2023 12:00:06 +0300
A Late Acceptance Hyper-Heuristic Approach for the Optimization Problem of Distributing Pilgrims over Mina Tents https://lib.jucs.org/article/72900/ JUCS - Journal of Universal Computer Science 28(4): 396-413

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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Research Article Thu, 28 Apr 2022 10:00:00 +0300
Forecasting Air Travel Demand for Selected Destinations Using Machine Learning Methods https://lib.jucs.org/article/68185/ JUCS - Journal of Universal Computer Science 27(6): 564-581

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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Research Article Mon, 28 Jun 2021 10:00:00 +0300
Multi-scaled Spatial Analytics on Discovering Latent Social Events for Smart Urban Services https://lib.jucs.org/article/23075/ JUCS - Journal of Universal Computer Science 24(3): 322-337

DOI: 10.3217/jucs-024-03-0322

Authors: O-Joun Lee, Yunhu Kim, Hoang Nguyen, Jai Jung

Abstract: The goal of this paper is to discover latent social events from social media for sensitively understanding social opinions that appeared within a city. The latent social event indicates a regional and inconspicuous social event which is mostly buried under macroscopic trends or issues. To detect the latent social event, we propose three methods: i) discovering areas-ofinterest (AOIs), ii) allocating social texts to the AOIs, and iii) detecting social events in each AOI. The AOIs can be composed by grouping social texts which are topically and spatially homogeneous. To make the AOIs dynamic and incremental, we use windows for allocating a social text to an adequate AOI. Lastly, the latent social events are detected from the AOI on the basis of keywords and temporal distribution of the social texts. Although, in this study, we limited the proposed method into analyzing social media, it could be extended to detecting events among agents/things/sensors.

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Research Article Wed, 28 Mar 2018 00:00:00 +0300