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Hybrid IT? Algorithm for Large-Scale Colored Traveling Salesman Problem

Chinese Journal of Electronics 2024年第6期33卷 1337-1345页

作者： Xueshi DONG College of Computer Science and Technology Qingdao University Beijing Key Laboratory of Urban Spatial Information Engineering School of Computer Science Wuhan University

In the fields of intelligent transportation and multi-task cooperation, many practical problems can be modeled by colored traveling salesman problem(CTSP). When solving large-scale CTSP with a scale of more than 1000dimensions, their convergence speed and the quality of their solutions are limited. This paper proposes a new hybrid IT?(HIT?) algorithm, which integrates two new strategies, crossover operator and mutation strategy, into the standard IT?. In the iteration process of HIT?, the feasible solution of CTSP is represented by the double chromosome coding, and the random drift and wave operators are used to explore and develop new unknown regions. In this process, the drift operator is executed by the improved crossover operator, and the wave operator is performed by the optimized mutation strategy. Experiments show that HIT? is superior to the known comparison algorithms in terms of the quality solution.

关键词： Transportation Traveling salesman problems Multitasking Encoding Standards Biological cells Convergence

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Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

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computers, Materials & Continua 2024年第3期78卷 3469-3487页

作者： Sureka Sivavelu Venkatesh Palanisamy School of Computer Science Engineering and Information Systems Vellore Institute of TechnologyVellore632014India

The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two sta

关键词： Software defect prediction feature selection nonparametric statistical Torgerson-Gower scaling technique quadratic censored regressive convolution deep neural network softstep activation function nelder-mead method

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An efficient secure channel establishment through lightweight key distribution in e-health communication systems

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Multimedia Tools and Applications 2025年第11期84卷 9111-9131页

作者： Ebrahimi, Iman Nikooghadam, Morteza Department of Computer Engineering and Information Technology Imam Reza International University Mashhad Iran

E-healthcare has significantly improved healthcare services and overall health by utilizing digital technologies such as the internet, computers, and mobile devices. However, secure communication channels play a critical role in ensuring the safety and confidentiality of patients' information. Consequently, several user authentication and key agreement protocols have been developed to address this issue. Although numerous proposed protocols are robust against security attacks, researchers have reported vulnerabilities. In this study, we evaluate one such protocol and identify vulnerabilities to insider attacks, server impersonation attacks, and replay attacks. To address these vulnerabilities, we propose a two-factor mutual authentication and key agreement scheme that offers a secure, efficient, and effective solution with low computation cost, enhanced security, and superior performance against diverse security attacks. Our proposed technique guarantees the integrity, confidentiality, and availability of patient information while ensuring the authenticity of users during communication. We conducted thorough comparisons focusing on the practical implementation of ARM microcontrollers to assess computation efficiency. The total estimated execution time of our proposed scheme for registration and authentication phases is approximately 975.8 µs, which is the shortest among all related works. This finding illustrates that our proposed scheme is well-suited for applications in e-health, telemedicine, and healthcare systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

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CRAQL: a novel clustering-based resource allocation using the Q-learning in fog environment

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International Journal of Cloud Computing 2024年第3期13卷 243-266页

作者： Ahlawat, Chanchal Krishnamurthi, Rajalakshmi Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India

Fog computing is an emerging paradigm that provides services near the end-user. The tremendous increase in IoT devices and big data leads to complexity in fog resource allocation. Inefficient resource allocation can lead to resource starvation and unable to complete the task assignment within a specific time. Hence, to enhance the efficiency of the fog resources, it is critical to perform proper resource allocation. This work targets to provide the solution to the resource allocation problem with a novel clustering-based resource allocation using the Q-learning (CRAQL) model. For this purpose, the problem is defined as a decision-making problem and formulated as Markov decision process (MDP). Next, to find the optimal resource, an enhanced optimal resource allocation (EORA) algorithm is proposed and detailed study is performed to analyse the impact of various performance parameters. Simulation results show the comparison of the EORA versus conventional Brute force method by varying the performance parameters such as learning rate and number of trials. The experimental results exhibit optimal solutions with significant improvement in learning rate at an average probability of 0.5 within limited epochs. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Reinforcement learning

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2P3FL:A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

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computer Modeling in engineering & Sciences 2024年第8期140卷 2035-2051页

作者： Sandeep Dasari Rajesh Kaluri School of Computer Science Engineering and Information Systems Vellore Institute of TechnologyVellore632014India

The increasing data pool in finance sectors forces machine learning(ML)to step into new *** data has significant financial implications and is *** users data from several organizations for various banking services may result in various intrusions and privacy *** a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global ***,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of *** address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global *** improves the privacy of the local *** analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)***,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and *** experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.

关键词： Federated learning FedAvg FedProx flower framework privacy preservation financial sectors

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Multi-dimensional information-driven many-objective software remodularization approach

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Frontiers of computer Science 2023年第3期17卷 45-62页

作者： Amarjeet PRAJAPATI Anshu PARASHAR Amit RATHEE Department of Computer Science Engineering&Information Technology Jaypee Istitute of Information TechnologyNoida 201307India Department of Computer Science&Engineering Thapar Institute of Engineering&TechnologyPunjab 147004India Department of Computer Science Government College BarotaHaryana 131301India

Most of the search-based software remodularization(SBSR)approaches designed to address the software remodularization problem(SRP)areutilizing only structural information-based coupling and cohesion quality ***,in practice apart from these quality criteria,there require other aspects of coupling and cohesion quality criteria such as lexical and changed-history in designing the modules of the software ***,consideration of limited aspects of software information in the SBSR may generate a sub-optimal modularization ***,such modularization can be good from the quality metrics perspective but may not be acceptable to the *** produce a remodularization solution acceptable from both quality metrics and developers’perspectives,this paper exploited more dimensions of software information to define the quality criteria as modularization ***,these objectives are simultaneously optimized using a tailored manyobjective artificial bee colony(MaABC)to produce a remodularization *** assess the effectiveness of the proposed approach,we applied it over five software *** obtained remodularization solutions are evaluated with the software quality metrics and developers view of *** demonstrate that the proposed software remodularization is an effective approach for generating good quality modularization solutions.

关键词： software restructuring remodularization multiobjective optimization software coupling and cohesion

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Artificial Intelligence and Natural Language Processing Inspired Chabot Technologies

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Recent Advances in computer Science and Communications 2024年第1期17卷 11-20页

作者： Singh, Deepti Manju Jatain, Aman Netaji Subhash Institute of Technology New Delhi India Department of Computer Science and Engineering & Information Technology Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering Amity University Haryana India

Chatbots use artificial intelligence (AI) and natural language processing (NLP) algorithms to construct a clever system. By copying human connections in the most helpful way possi-ble, chatbots emulate individuals and serve as virtual assistants. They easily interface and respond to customers' requests. In the modern technical environment, these conversation agents or chatbots are considered the next-generation invention. Chatbot has become more popular in the business field right now as it can reduce customer service cost and handle multiple users at a time. There are many techniques used to involve such intelligent experts in daily business. A comprehensive analysis of the methods is needed to determine the viability of the different strategies. This paper tracks the progress of this invention and further clarifies the influence of chatbots on numerous businesses. Besides, a survey of the multiple chatbot methodologies suggested by various researchers is provid-ed. Along with the survey, a chatbot e-commerce customer service is designed to provide an efficient and accurate answer for any query based on the dataset of frequently asked questions. This chatbot can reduce customer service costs and can handle multiple customers at the same time. © 2024 Bentham Science Publishers.

关键词： Machine learning

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Knowledge-aware Recommendation with Attention Mechanism and Contrastive Learning

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第3期52卷 615-624页

作者： Chen, Cuihong Sun, Gang School of Information Engineering Anhui Business And Technology College Hefei China School of Computer and Information Engineering Fuyang Normal University Fuyang China

Knowledge graph (KG) is important in recommendation algorithms. For the past few years, graph neural networks (GNNs) models applied to knowledge-aware recommendation (KGR) have been a current research hotspot. However, GNN-based KGR models have some problems, eg. data sparsity, insufficient supervision indication, unbalanced information utilization, and insufficient knowledge extraction. This paper presents a knowledge-aware recommendation algorithm that integrates an attention mechanism with contrastive learning, thereby make the most of the rich entity information available in the KG. Through contrastive learning, the algorithm aims to derive more discriminative expressions for users and items, and the attention mechanism helps to identify key relationships between them. This dual way improves the accuracy and efficiency of recommendations. Firstly, by contrasting hierarchical structures within the KG, non-local graphs are constructed for users and items, facilitating the extraction of additional KG facts in KGR. Secondly, Intra-graph-level interactive contrastive learning is enforced within both non-local graphs to evaluate the levels of collaborative filtering alongside the KG part, so as to obtain more consistent information utilization. In addition, inter-graph-level interactive contrastive learning is implemented between non-local graphs to fully and effectively extract non-local KG indication. Finally, the attention mechanism is integrated to determine the consequence of node neighbors and adaptively propagate the embeddings from the neighbors of nodes. The proposed knowledge-aware recommendation algorithm (KGAC) in this paper, along with other recommendation algorithms, has been subjected to extensive experimentation across three benchmark data sets. The results of these tests confirm that KGAC outperforms other algorithms in terms of recommendation performance. © (2025), (International Association of Engineers). All rights reserved.

关键词： Contrastive Learning

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AMCB: enhancing the authentication process with blockchain integrated with PUF and machine learning

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Multimedia Tools and Applications 2025年第8期84卷 4809-4835页

作者： Al-Ghuraybi, Hind A. AlZain, Mohammed A. Soh, Ben Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Department of Computer Science and Computer Engineering La Trobe University Bundoora Australia

The rapid advancement of technology has given rise to medical cyber-physical systems (MCPS), a subset of cyber-physical systems (CPS) specifically tailored for patient care and healthcare providers. MCPS generate substantial volumes of big data, posing challenges in terms of processing, storage, and safeguarding against unauthorized access. This paper introduces the Authenticated Medical Cyber-Physical Blockchain (AMCB) model within MCPS, leveraging Blockchain technology, machine learning, and physically unclonable functions (PUF) to fortify the authentication process, particularly for accessing electronic health records (EHR) stored on cloud servers. The model also involves analyzing data from authenticated devices to ensure its integrity. Experiments on the AMCB model evaluate its efficiency across four layers. In the physical layer, PUF keys are extracted using a built Static Random-Access Memory (SRAM) circuit and stored in a simulated Blockchain, yielding a Hamming Distance standard deviation of 6.60%. The inspection layer analyzes data from authenticated devices using machine learning, with the Random Forest achieving the highest accuracy at 99.21%. In the application layer, centered on user authentication, the average transaction time is 36.15 MS. Overall, the AMCB model requires an average of 49.82 ms to execute operations across all layers. These findings underscore the effectiveness of the proposed model in securing EHR in the dynamic landscape of MCPS. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

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A Fast Algorithm for Computing the Deficiency Number of a Mahjong Hand

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Chinese Journal of Electronics 2024年第6期33卷 1383-1398页

作者： Xueqing YAN Yongming LI Sanjiang LI School of Computer Science Shaanxi Normal University School of Mathematics and Statistics Shaanxi Normal University Centre for Quantum Software and Information Faculty of Engineering and Information TechnologyUniversity of Technology Sydney

The tile-based multiplayer game Mahjong is widely played in Asia and has also become increasingly popular worldwide. Face-to-face or online, each player begins with a hand of 13 tiles and players draw and discard tiles in turn until they complete a winning hand. An important notion in Mahjong is the deficiency number(*** number in Japanese Mahjong) of a hand, which estimates how many tile changes are necessary to complete the hand into a winning hand. The deficiency number plays an essential role in major decision-making tasks such as selecting a tile to discard. This paper proposes a fast algorithm for computing the deficiency number of a Mahjong hand. Compared with the baseline algorithm, the new algorithm is usually 100 times faster and, more importantly,respects the agent's knowledge about available tiles. The algorithm can be used as a basic procedure in all Mahjong variants by both rule-based and machine learning-based Mahjong AI.

关键词： Machine learning algorithms Knowledge based systems Decision making Asia Games

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