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Applied Set-Valued Analysis and Optimization 2024年第3期6卷 309-317页

作者： Trinh, Ngoc Hai Tran, Trung Duc Faculty of Mathematics and Informatics Hanoi University of Science and Technology Hanoi Viet Nam College of engineering and computer science VinUniversity Hanoi Viet Nam

In this paper, we propose two self-adaptive extragradient-like algorithms for solving pseudomonotone variational inequalities. We consider two cases: the mapping is Lipschitz continuous (with unknown modulus) and is not Lipschitz continuous. The step size in our algorithms can either increase or decrease at each step. This feature makes our algorithms more efficient. We also give some numerical examples to compare the performance of our algorithm with the existing one. ©2024 Applied Set-Valued Analysis and Optimization.

关键词： Variational techniques

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Semi-supervised learning for on-street parking violation prediction using graph convolutional networks

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Neural Computing and Applications 2024年第31期36卷 19643-19652页

作者： Karantaglis, Nikolaos Passalis, Nikolaos Tefas, Anastasios Group AIIA Lab Department of Informatics Faculty of Science Aristotle University of Thessaloniki Thessaloniki Greece Department of Chemical Engineering Faculty of Engineering Aristotle University of Thessaloniki Thessaloniki Greece

Controlled parking systems in cities provide designated parking zones and allow citizens to easily find parking spaces increasing comfort and potentially reducing traffic and pollution. However, illegally occupied parking spaces can negatively affect the accuracy of these systems, diminishing their benefits. This work presents a semi-supervised deep learning approach for fine-grained parking violation rate prediction using graph neural networks, which can capture both the spatial and temporal dynamics of parking systems and driver behavior, providing a valuable tool for controlled parking systems. The proposed method addresses several challenges faced in this task, including the need to appropriately construct temporal graph-based training datasets and design a model that can handle missing values. Additionally, the method includes a novel semi-supervised data augmentation and smoothing technique to mitigate the effect of the small amount of annotated data and/or missing data in the training dataset. The proposed method was evaluated using a large-scale dataset from Thessaloniki’s on-street public parking system and found to significantly improve the accuracy of parking violation prediction compared to state-of-the-art approaches. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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A Recommendation System for Double Track Program Skill Selection Using the Analytic Hierarchy Process (AHP) 10

A Recommendation System for Double Track Program Skill Selec...

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10th IEEE Information technology International Seminar, ITIS 2024

作者： Bughowi, Mohamad Kholid Sunaryono, Dwi Baskoro, Fajar Faculty of Intelligent Electrical and Informatics Technology Department of Informatics Engineering Surabaya Indonesia

ISBN: (纸本)9798331521295

The Double Track Program prepares high school students for careers or entrepreneurship through skills training, aiming to reduce post-graduation unemployment in East Java. It offers seven skill areas: multimedia, culinary arts, fashion design, beauty treatment, electrical engineering, electrical technology, and light vehicle engineering. Students choose one skill area for a year. However, mismatched expectations can lead to wasted time, poor outcomes, and frustration. To address this, a web-based recommendation system was developed to suggest skill areas based on criteria such as job availability, salary, interests, facilities, and entrepreneurial opportunities. The system employs the Analytic Hierarchy Process (AHP) method to support decision-making. It is implemented as a website using TypeScript, Go, the *** framework, and PostgreSQL for data storage. Testing with 77 eleventh-grade students from Dharma Wanita Surabaya High School showed that the top three recommendations achieved an average accuracy of 77.3036%, while the top five reached 77.5510%. Overall, the system demonstrated an average accuracy of 78.4521%, proving effective in guiding students toward skill areas that align with their goals. © 2024 IEEE.

关键词： Decision making

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Optimization Regenerative Braking in Electric Vehicles Using Q-Learning for Improving Decision-Making in Smart Cities

Decision Making: Applications in Management and Engineering

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Decision Making: Applications in Management and engineering 2025年第1期8卷 182-216页

作者： Suanpang, Pannee Jamjuntr, Pitchaya Department of Information Technology Faculty of Science & Technology Suan Dusit University Bangkok Thailand Department of Electrical Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand

The growing prevalence of electric vehicles (EVs) in urban settings underscores the need for advanced decision-making frameworks designed to optimise energy efficiency and improve overall vehicle performance. Regenerative braking, a critical technology in EVs, facilitates energy recovery during deceleration, thereby enhancing efficiency and extending driving range. This study presents an innovative Q-learning-based approach to refine regenerative braking control strategies, aiming to maximise energy recovery, ensure passenger comfort through smooth braking, and maintain safe driving distances. The proposed system leverages real-time feedback on driving patterns, road conditions, and vehicle performance, enabling the Q-learning agent to autonomously adapt its braking strategy for optimal outcomes. By employing Q-learning, the system demonstrates the ability to learn and adjust to dynamic driving environments, progressively enhancing decision-making capabilities. Extensive simulations conducted within a smart city framework revealed substantial improvements in energy efficiency and notable reductions in energy consumption compared to conventional braking systems. The optimisation process incorporated a state space comprising vehicle speed, distance to the preceding vehicle, battery charge level, and road conditions, alongside an action space permitting dynamic braking adjustments. The reward function prioritised maximising energy recovery while minimising jerk and ensuring safety. Simulation outcomes indicated that the Q-learning-based system surpassed traditional control methods, achieving a 15.3% increase in total energy recovered (132.8 kWh), enhanced passenger comfort (jerk reduced to 7.6 m/s3), and a 13% reduction in braking distance. These findings underscore the system's adaptability across varied traffic scenarios. Broader implications include integration into smart city infrastructures, where the adaptive algorithm could enhance real-time traffic management,

关键词： Braking performance

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Advancing emotion recognition via EEG signals using a deep learning approach with ensemble model

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Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 143-154页

作者： Immanuel, Rajeswari Rajesh Sangeetha, S.K.B. Department of Computer Science and Engineering SRM Institute of Science and Technology Faculty of Engineering and Technology Vadapalani Campus Tamil Nadu Chennai India

Human emotions are the mind's responses to external stimuli, and due to their dynamic and unpredictable nature, research in this field has become increasingly important. There is a growing trend in utilizing deep learning and machine learning techniques for emotion recognition through EEG (electroencephalogram) signals. This paper presents an investigation based on a real-time dataset that comprises 15 subjects, consisting of 7 males and 8 females. The EEG signals of these subjects were recorded during exposure to video stimuli. The collected real-time data underwent preprocessing, followed by the extraction of features using various methods tailored for this purpose. The study includes an evaluation of model performance by comparing the accuracy and loss metrics between models applied to both raw and preprocessed data. The paper introduces the EEGEM (Electroencephalogram Ensemble Model), which represents an ensemble model combining LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) to achieve the desired outcomes. The results demonstrate the effectiveness of the EEGEM model, achieving an impressive accuracy rate of 95.56%. This model has proven to surpass the performance of other established machine learning and deep learning techniques in the field of emotion recognition, making it a promising and superior tool for this application. © 2024 - IOS Press. All rights reserved.

关键词： Convolutional neural networks

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Insights into research on blockchain for smart contracts: a bibliometric analysis

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Multimedia Tools and Applications 2024年 1-25页

作者： Singh, Renu Gupta, Ashlesha Mittal, Poonam Faculty of Informatics and Computing J.C. Bose University of Science and Technology YMCA Faridabad India Department of Computer Science & amp Engineering Indraprastha Institute of Information Technology IIIT New Delhi India

Over the past few years, blockchain technology has gained significant attention. This surge in popularity can be attributed to the emergence of cryptocurrencies and the development of smart contracts. Cryptocurrency is a digital currency that eliminates the problem of double spending. Cryptocurrencies like Bitcoin, Ethereum, Litecoin, Stellar, Zcash, Maker, Aave, etc. become popular and are preferred for money transfers. Smart contracts are the next popular technology on the blockchain after cryptocurrency. It can be considered a piece of code that can execute automatically when the predefined conditions are fulfilled. Researchers believe that the potential of blockchain with smart contracts is only in its initial stages and that its true potential has yet to be fully discovered. Hence, an extensive bibliometric analysis is conducted to understand blockchain trends for smart contracts and to give future directions in this field. For this analysis, various steps are followed, starting with formulating the research question, defining the scope of our research, extracting and analyzing data, answering the research question, and finally, drawing a conclusion. This research paper will be fruitful for scholars and researchers, providing an extensive statistical and network analysis of extracted smart contracts publications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Bibliometric analysis Blockchain Crypto contract Scientometrics Scopus Smart contracts VOSViewer

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Bio-Inspired Intelligent Routing in WSN: Integrating Mayfly Optimization and Enhanced Ant Colony Optimization for Energy-Efficient Cluster Formation and Maintenance

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Computer Modeling in engineering & sciences 2024年第10期141卷 127-150页

作者： V.G.Saranya S.Karthik Department of Electronics and Communication Engineering Faculty of Engineering&TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026India

Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless *** sensor nodes gather and store data about the real world around ***,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the *** research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and *** present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base *** performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy *** objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the *** proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).

关键词： Enhanced ant colony optimization mayfly optimization algorithm wireless sensor networks cluster head base station(BS)

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Offshore Software Maintenance Outsourcing Process Model Validation:A Case Study Approach

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Computers, Materials & Continua 2023年第3期74卷 5035-5048页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Adel Sulaiman Muhammad Akram Ahmad Salman Khan Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan

The successful execution and management of Offshore Software Maintenance Outsourcing(OSMO)can be very beneficial for OSMO vendors and the OSMO *** a lot of research on software outsourcing is going on,most of the existing literature on offshore outsourcing deals with the outsourcing of software development *** frameworks have been developed focusing on guiding software systemmanagers concerning offshore software ***,none of these studies delivered comprehensive guidelines for managing the whole process of *** is a considerable lack of research working on managing OSMO from a vendor’s ***,to find the best practices for managing an OSMO process,it is necessary to further investigate such complex and multifaceted phenomena from the vendor’s *** study validated the preliminary OSMO process model via a case study research *** results showed that the OSMO process model is applicable in an industrial setting with few *** industrial data collected during the case study enabled this paper to extend the preliminary OSMO process *** refined version of the OSMO processmodel has four major phases including(i)Project Assessment,(ii)SLA(iii)Execution,and(iv)Risk.

关键词： Offshore outsourcing process model model validation vendor challenges case study

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VoteDroid: a new ensemble voting classifier for malware detection based on fine-tuned deep learning models

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Multimedia Tools and Applications 2025年第12期84卷 10923-10944页

作者： Bakır, Halit Faculty of Engineering and Natural Sciences Department of Computer Engineering Sivas University of Science and Technology Sivas Turkey

In this work, VoteDroid a novel fine-tuned deep learning models-based ensemble voting classifier has been proposed for detecting malicious behavior in Android applications. To this end, we proposed adopting the random search optimization algorithm for deciding the structure of the models used as voter classifiers in the ensemble classifier. We specified the potential components that can be used in each model and left the random search algorithm taking a decision about the structure of the model including the number of each component that should be used and its location in the structure. This optimization method has been used to build three different deep learning models namely CNN-ANN, pure CNN, and pure ANN. After selecting the best structure for each DL model, the selected three models have been trained and tested using the constructed image dataset. Afterward, we suggested hybridizing the fine-tuned three deep-learning models to form one ensemble voting classifier with two different working modes namely MMR (Malware Minority Rule) and LMR (Label Majority Rule). To our knowledge, this is the first time that an ensemble classifier has been fine-tuned and hybridized in this way for malware detection. The results showed that the proposed models were promising, where the classification accuracy exceeded 97% in all experiments. © The Author(s) 2024.

关键词： Android malware

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A new CNN-based watermarking method for color medical images in a fusion domain

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Neural Computing and Applications 2025年 1-19页

作者： Hosny, Khalid M. Abdel-Aziz, Mostafa M. Lashin, Nabil A. Hamza, Hanaa M. Department of Information Technology Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Department of Information Technology Faculty of Information Technology and Computer Science Sinai University Arish Campus Al-Arish45518 Egypt

The transmission of medical images via medical agencies raises security concerns, necessitating increased security measures to ensure integrity and security. However, many watermarking algorithms overlook equipoise;the relation between robustness, invisibility, and payload capacity results in a less satisfactory performance. To bridge this gap, we propose a new blind watermarking method for securing medical images based on a convolutional neural network in a fusion domain. First, we transmute the host color image using a phase-only transform (PHOT) to detect the surface pattern. We feed the detected surface pattern into a pre-trained VGG19 model to impeccably extract the stable feature vector. We encrypt the extracted image features using a hybrid Chirikov map to enhance their robustness and reliability. The synergy between the pre-trained VGG19 model and PHOT in an integrated domain effectively captures additional native and localized image features. A hybrid Fibonacci Q-matrix method scrambles the binary watermark to enhance security. Integrating double encryption into the proposed method markedly enhances its resistance against diverse countermeasures. Experimental outcomes indicate that the proposed scheme performs efficiently in robustness and invisibility. The obtained PSNR value was up to 60.15 dB, which is optimal for human perception. The embedded watermark can be retrieved without any warping. The retrieved watermark seems to be authentic, exhibiting ideal BER and NC values. In almost all attack circumstances, the BER values approached zero while the NC values got closer. The proposed method demonstrates notable enhancements in robustness and invisibility compared to prior methods. © The Author(s) 2025.

关键词： Watermarking

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