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International Conference on Computing and Networking Technology (ICCNT)

作者： M S Arunkumar R Gopinath M Chandru R Suguna S Deepa V Omprasath Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu Department of Computer Science and Engineering K. S. Rangasamy College of Technology Tiruchgode Department of Computer Science VET-Institute of Arts and Science (Co-Education) College Erode Department of Computer science and Engineering Bannari Amman Institute of Technology Sathyamangalam Department of Computer Technology - PG Kongu Engineering College Perundurai Erode Tamilnadu M.Sc (Software Systems) Kongu Engineering College Perundurai Erode Tamilnadu

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In the increasingly competitive outlook of ecommerce, personalized recommendation systems have emerged as pivotal tools to enhance user experience and drive sales. This paper proposes a novel Fashion Recommendation System (FRS) leveraging deep learning techniques to offer personalized fashion suggestions to users. The system utilizes a combination of convolutional neural networks (CNNs) for image feature extraction. Through a comprehensive analysis of user interactions and preferences, the FRS generates tailored recommendations that align with individual style preferences and current trends. The work proposes a novel approach that utilizes the ResNet50 algorithm to analyze images of fashion items, extracting meaningful features that capture the essence of style, color, and texture. By training the ResNet50 model on vast datasets of fashion images, thereby enhancing its ability to make accurate recommendations. Through the integration of user preferences and historical data, this system can generate personalized fashion recommendations tailored to each user’s unique style and preferences, thus enhancing user satisfaction and engagement. By harnessing the power of deep learning and the ResNet50 algorithm, fashion retailers can offer personalized shopping experiences, increase customer retention, and drive sales.

关键词： Deep learning Training Image color analysis Feature extraction Market research User experience Electronic commerce Convolutional neural networks Recommender systems Residual neural networks

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Machine Learning-based Multi-path Reliable and Energy-efficient Routing Protocol for Underwater Wireless Sensor Networks

Machine Learning-based Multi-path Reliable and Energy-effici...

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Frontiers of Information Technology (FIT)

作者： Zahid Ullah Khan Muhammad Aman Wazir Ur Rahman Faran Khan Tooba Jamil Rifhat Hashim College of Underwater Acoustic Engineering Harbin Engineering University Harbin China Department of Software Engineering University of Science and Technology Bannu Pakistan Department of Computer Science Barani Institute of Information and Technology Rawal Pindi Pakistan

The research on applications for Underwater Wireless Sensor Networks (UWSNs) including the investigation of submerged resources, gathering oceanographic data, conducting operational surveillance, and prevention from natural disasters is rapidly increasing. Traditional wireless sensor networks (TWSNs) are distinct from UWSNs. Routing protocols of TWSNs are also distinct from UWSNs routing protocols in terms of energy efficiency. Most of routing protocols have been developed to extract data from the ocean floor to the surface of waters. There are many challenges for indigenous designing of an efficient routing protocol for UWSNs, such as, design of reliable path for forwarding communicating data packets, managing the movement of nodes, configuration of sensor nodes, removing void communicating nodes and increasing the power efficiency of the system. This research focuses on designing a novel Machine learning-based multi-path reliable and energy-efficient routing protocol (M2RE2RP) for UWSNs. It will select an efficient path among the existing communication paths used by sensor nodes, which will ultimately increase the lifespan of the network. The simulation experiment results dictate that the proposed routing model M2RE2RP achieves better performance in terms of network throughput, end-to-end latency, total energy consumption and network lifetime.

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An ACO-based Approach to Improve C-means Clustering Algorithm

An ACO-based Approach to Improve C-means Clustering Algorith...

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International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce

作者： Wenliang Huang Jin Gou Huifeng Wu College of Computer Science and Technology University of Zhejiang Hangzhou China College of Information Science and Engineering Huaqiao University Quanzhou China Institute of Software and Intelligent Technology HangZhou Dianzi University Huzhou China

This paper presents an improved C-means clustering algorithm based on ACO. The proposed method use pheromone to evaluate individual colony's iterative result. In contrast with the existing C-means clustering algorithm, method in the paper need not appoint the number and pre-centers of clusters beforehand and it updates pheromone according to the transfer process of data points among different temporary clusters so as to avoid the local optima and reduce the iterative times to find actual cluster centers. We test its convergence performance with CRM data sets from China Unicom Corp. The experimental results show feasibility of design rationale.

关键词： Clustering algorithms Iterative algorithms Ant colony optimization Partitioning algorithms software algorithms Educational institutions Iterative methods Computational modeling Computational intelligence Cities and towns

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Empirical studies of evolving systems

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Empirical software engineering 1999年第4期4卷 370-380页

作者： Bennett, K. Burd, E. Kemerer, C. Lehman, M.M. Lee, M. Madachy, R. Mair, C. Sjoberg, D. Slaughter, S. Department of Computer Science University of Durham United Kingdom Katz Graduate School of Business University of Pittsburgh United States Department of Computing Imperial College United Kingdom Department of Informatics and Simulation RMCS Cranfield University United Kingdom Litton Guidance and Control Systems USC Center for Software Engineering United States Design Engineering and Computing University of Bournemouth United Kingdom Department of Informatics University of Oslo Norway Graduate School of Industrial Administrations Carnegie Melon University United States

This paper describes the results of the working group investigating the issues of empirical studies for evolving systems. The groups found that there were many issues that were central to successful evolution and this concluded that this is a very important area within software engineering. Finally nine main areas were selected for consideration. For each of these areas the central issues were identified as well as success factors. In some cases success stories were also described and the critical factors accounting for the success analyzed. In some cases it was later found that a number of areas were so tightly coupled that it was important to discuss them together.

关键词： software engineering

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Privacy Reference Monitor - A computer Model for Law Compliant Privacy Protection

Privacy Reference Monitor - A Computer Model for Law Complia...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Fei Xu K.P. Chow Jingsha He Xu Wu College of Computer Science and Technology Beijing University of Technology Beijing China Department of Computer Science University of Hong Kong Hong Kong China School of Software Engineering Beijing University of Technology Beijing China

The Internet and computers did not invent or even cause privacy issues. The issues existed long before the creation of computers and Internet. The existence of the Internet, computers and large data storage make it possible to collect, process and transmit large volumes of data, including personal data. In this paper, we shall study the privacy from following two different views, namely legal framework and computer security model, and attempt to identify the difference between them. Because of the difference, we further argue that the current computer security model is not sufficient to support the privacy requirements in the legal framework. We propose a computer model ¿privacy reference monitor¿ to handle those unsupported requirements. The design of the privacy reference monitor is privacy policy neutral with a small number of functions. With minimal functionalities, we believe that it is possible to implement a verifiable privacy reference monitor.

关键词： computerized monitoring Protection Law Internet Data privacy computer security computer science Legal factors Pervasive computing Educational institutions

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An Ensemble Machine Learning Technique for Stroke Prognosis

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computer Systems science & engineering 2023年第10期47卷 413-429页

作者： Mesfer Al Duhayyim Sidra Abbas Abdullah Al Hejaili Natalia Kryvinska Ahmad Almadhor Uzma Ghulam Mohammad Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz University Al-Kharj16273Saudi Arabia Department of Computer Science COMSATS UniversityIslamabad53000Pakistan Computer Science Department Faculty of Computers&Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Information Systems Department Faculty of ManagementComenius University in BratislavaOdbojárovBratislava440Slovakia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan

Stroke is a life-threatening disease usually due to blockage of blood or insufficient blood flow to the *** has a tremendous impact on every aspect of life since it is the leading global factor of disability and *** can range from minor to severe(extensive).Thus,early stroke assessment and treatment can enhance survival *** prediction is extremely time and resource *** prediction methods such as Modern Information and Communication Technologies(ICTs),particularly those inMachine Learning(ML)area,are crucial for the early diagnosis and prognosis of ***,this research proposed an ensemble voting model based on three Machine Learning(ML)algorithms:Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Light Gradient Boosting Machine(LGBM).We apply data preprocessing to manage the outliers and useless instances in the ***,to address the problem of imbalanced data,we enhance the minority class’s representation using the Synthetic Minority Over-Sampling Technique(SMOTE),allowing it to engage in the learning process *** reveal that the suggested model outperforms existing studies and other classifiers with 0.96%accuracy,0.97%precision,0.97%recall,and 0.96%*** experiment demonstrates that the proposed ensemble voting model outperforms state-of-the-art and other traditional approaches.

关键词： Stroke prediction machine learning ensemble model data analysis Synthetic Minority Over-Sampling

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Determine word sense based on semantic and syntax information

International Journal of Database Theory and Application

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International Journal of Database Theory and Application 2016年第2期9卷 17-22页

作者： Zhang, Chun-Xiang Sun, Lu-Rong Gao, Xue-Yao School of Software Harbin University of Science and Technology Harbin China College of Information and Communication Engineering Harbin Engineering University Harbin China School of Computer Science and Technology Harbin University of Science and Technology Harbin China

Word sense disambiguation (WSD) plays an important role in natural language processing fields. Semantic category is semantic knowledge and part-of-speech is syntax knowledge. In this paper, word window is opened to get semantic category and part-of-speech of left and right adjacent words around an ambiguous word. A new approach of determining true meanings of ambiguous words based on support vector machine (SVM) is given. The training corpus in SemEval-2007: Task#5 is applied to optimize SVM and the optimized SVM is tested. Experimental results show that the performance of the proposed method is improved.

关键词： Support vector machines

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Aircraft Ground Service Scheduling Problems and Partheno-Genetic Algorithm With Hybrid Heuristic Rule

Aircraft Ground Service Scheduling Problems and Partheno-Gen...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Fei Tang Shuan Liu Xinyu Dong Baoxia Cui College of Software Shenyang University of technology Shenyang China College of Information Science & Engineering Northeastern University Shenyang China Computer Science Department Stony Brook University Stony Brook United States

Airport ground service is a key step to keep flight service running properly. Ground service optimization scheduling can be described as multi-flight multi-service optimization problem with different time window constraints. Ground service crew assignment and flight service sequence optimization will have direct impact on flight delays cost and flight service tasks cost. To reduce the cost impacted by ground service, we propose a partheno-Genetic algorithm with hybrid heuristic rule to implement the coordinating tasks scheduling plan among multiple kinds of service. Aim at minimizing the summation of flight delay cost and ground service traveling cost, we built a concurrent and chronological multi-flight multi-service problem optimization model. Result shows that, proposed model and algorithm is effective.

关键词： Task analysis Delays Optimization Job shop scheduling Heuristic algorithms Airports

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Machine Learning Approach for Malware Detection by Using APKs

Machine Learning Approach for Malware Detection by Using APK...

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2017 2nd International Conference on computer, Network Security and Communication engineering(CNSCE2017)

作者： Rubata RIASAT Muntaha SAKEENA Abdul Hannan SADIQ Chong WANG Chang-you ZHANG Yong-ji WANG State key laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences College of Electrical and Mechanical Engineering National University of Science and Technology Comsats Institute of Technology Laboratory of parallel Software and Computational Science Institute of SoftwareChinese Academy of Sciences

An android mobile phone is one of the most anticipated smart phone operating systems on the market. The open source Android platform not only allow developer to take full advantage of the mobile system, but also raises significant issues related to malicious applications(Apps). Although understanding the android malware using dynamic analysis can provide a compressive view and it is still subjected to high cost in environment development and manual effort in investigation. In this study our proposed approach provides a static and dynamic analyst paradigm for detecting android malware. The mechanism considers the static information including permissions, deployment of components, intent message passing and API calls for characterizing the android application behavior. In order to recognize different intentions of android malware, different kind of clustering and classification can be applied to enhance the malware detection capability. Our approach extracts the information from the each APKs manifest file, and regards component(Activity, Receiver, Services) as entry points drilling down for tracing API calls related to permissions. Next it applies detection methods based on matching(Signature matching) and learning(SVM and random forest) algorithm to classify the applications as benign or malicious. The experimental results show that the accuracy of our approach is better than one of the well-known tool, Androguard, also it is efficient since its take half of the time than Androguard to predict 600 applications as benign or malicious. The open source Android platform allows developer to take full advantage of the mobile.

关键词： Machine learning Malware detection Android Reverse engineering APKs

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Semi-Asynchronous Federated Learning with Trajectory Prediction for Vehicular Edge Computing

Semi-Asynchronous Federated Learning with Trajectory Predict...

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International Workshop on Quality of Service

作者： Yuxuan Deng Xiuhua Li Chuan Sun Qilin Fan Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China School of Computer Science and Engineering Nanyang Technological University Singapore TKLAN College of Intelligence & Computing Tianjin University Tianjin China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

Federated learning, as a distributed machine learning paradigm, offers promising solutions for vehicular edge computing (VEC) networks. However, federated learning in VEC with classification tasks still faces two key challenges: i) Delayed data labeling hampers supervised training; ii) Dynamic vehicle behavior complicates training scheduling and model uploads to edge servers. In this paper, we propose a semi-asynchronous federated learning algorithm for VEC. Specifically, it utilizes knowledge distillation to generate soft labels from raw data for supervised training, and estimates model training and uploading time through trajectory prediction. We further logically group vehicles based on the characteristics of their dynamic behavior. We then employ synchronous aggregation within groups and asynchronous aggregation between groups to optimize model performance while reducing latency. Finally, we conduct separate comparative experiments for all components, demonstrating that each component possesses unique advantages. Experiment results show that the proposed algorithm outperforms existing schemes in terms of accuracy and latency. The code is available at: https://***/dyxcode/Semi-Asynchronous-Federated-Learning.

关键词： Training Federated learning Heuristic algorithms Predictive models Prediction algorithms Data models Trajectory

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