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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Giri, Rakesh Kumar Mahajan, Vibhor Narula, Shaweta Selvi, S. Dhabliya, Dharmesh Putra, Riyan Sisiawan Sunrise University Department of Computer Science & Engineering College Alwar301028 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Nutan College of Engineering and Research Department of Electronics and Telecommunication Pune410507 India Faculty of Management Studies Cms Business School India Vishwakarma Institute of Technology Department of Information Technology Maharashtra Pune India College Universitas Nahdlatul Ulama Surabaya Department of Management Surabaya60222 Indonesia

ISBN: (纸本)9798350370249

This paper presents a method to improve coverage efficiency in Wi-Fi communication systems by leveraging self-sustaining and adaptive communication systems. The approach seeks to exploit machine getting-to-know and related optimization techniques to enable network nodes to autonomously adjust electricity stages, pick out adjacent nodes, regulate transmission stages, and other parameters to acquire progressed insurance and higher record rates. Self-reliance getting to know an algorithm primarily based on reinforcement gaining knowledge of is used to pattern the surroundings, optimizes variables, and permit gadget adaptation even as minimizing human intervention. The paper discusses the structure and an algorithm evolved and explores the blessings of self-sufficient and adaptive communication systems over conventional non-autonomous systems. © 2024 IEEE.

关键词： Adjacent Minimizing Primarily Stages Transmission

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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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A Deep Learning Framework with Learning without Forgetting for Intelligent Surveillance in IoT-enabled Home Environments in Smart Cities

Recent Advances in Computer Science and Communications

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Recent Advances in computer Science and Communications 2025年

作者： Dalal, Surjeet Dahiya, Neeraj Verma, Amit Faujdar, Neetu Rathee, Sarita Jaglan, Vivek Rani, Uma Le, Dac-Nhuong Department of Computer Science & Engineering Amity University Haryana Gurugram India Department of Computer Science & Engineering SRM University Sonipat Haryana Delhi-NCR India Department of Computer Science and Engineering Maharaja Agrasen University Baddi India Department of Computer Science & Applications GLA University UP Mathura India Department of Electronics and Communication Engineering School of Engineering and Technology JECRC University Rajasthan Jaipur303905 India Department of Computer Science & Engineering Amity University M.P. Gwalior India Department of Computer Science & Engineering K. R. Mangalam University Haryana Gurugram India Dean of Faculty of Information Technology Haiphong University Viet Nam

Background: Internet of Things (IoT) technology in smart urban homes has revolutionised sophisticated monitoring. This progress uses interconnected devices and systems to improve security, resource management, and resident safety. Smart cities use technology to improve efficiency, sustainability, and quality. Internet of Things-enabled intelligent monitoring technologies are key to this goal. Objectives: Intelligent monitoring in IoT-enabled homes in smart cities improves security, convenience, and quality of life from advanced technologies. Using live monitoring and risk identification tools to quickly discover and resolve security breaches and suspicious activity to protect citizens. Intelligent devices allow homeowners to remotely control lighting, security locks, and surveillance cameras. Using advanced technologies to regulate heating, cooling, and lighting based on occupancy and usage. Method: This study introduces a deep learning architecture that uses LwF (Learning without Forgetting) to keep patterns while absorbing new data. The authors use IoT devices to collect and analyse data in real-time for monitoring and surveillance. They use sophisticated data pre-processing to handle IoT devices' massive data. The authors train the deep learning model with historical and real-time data and cross-validation to ensure resilience. Result: The proposed model has been validated on two different Robloflow datasets of 7382 images. The proposed model gains an accuracy level of 98.27%. The proposed Yolo-LwF model outperforms both the original Yolo and LwF models in terms of detection speed and adaptive learning. Conclusion: By raising the bar for intelligent monitoring solutions in smart cities, the suggested system is ideal for real-time, adaptive surveillance in IoT-enabled households. By embracing adaptability and knowledge retention, authors envision heightened security and safety levels in urban settings. © 2024 Bentham Science Publishers.

关键词： Smart homes

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Multi-Channel Hypergraph Convolution Group Recommendation with Member information Enhancement 25

Multi-Channel Hypergraph Convolution Group Recommendation wi...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Chen, Tianhao Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach. © 2023 IEEE.

关键词： Recommender systems

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An Embedded System for Eggs Freshness Detection 2nd

An Embedded System for Eggs Freshness Detection

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2nd International Conference on Intelligent Systems and Data Science, ISDS 2024

作者： Pham, Quoc-Hung Nguyen, Thanh-Nhan Vo, Huy-Hoang Nguyen, Duy-Khanh Pham, Tan-Nhat Tran, Nhut-Thanh Faculty of Computer Engineering University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Faculty of Automation Engineering Can Tho University Can Tho94000 Viet Nam

ISBN: (纸本)9789819796120

Traditional methods to quantify egg freshness, including laborious sorting and chemical analysis, have been widely used with significant errors and sample destruction. This study introduces a non-destructive system for detecting egg freshness using near-infrared spectroscopy and machine learning models. The research successfully developed a portable embedded system device using a low-cost multi-spectral sensor and Raspberry Pi 4B microprocessor for non-invasive egg freshness detection. Our non-invasive proposed system for detecting egg quality was created with an affordable price of around 250 US dollar and is a portable device. Some regression methods, such as Multiple Linear Regression and Support Vector Regression, were utilized to predict egg freshness with a coefficient of determination of 0.8. It was also compared with invasive measurements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Near infrared spectroscopy

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Success Factors of Requirement Elicitation in the Field of Software engineering

Success Factors of Requirement Elicitation in the Field of S...

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Advancements in Computing (ICAC), International Conference on

作者： Buddhima Attanayaka Dasuni Nawinna Kalpani Manathunga Pradeep K.W. Abeygunawardhana Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka Department of Computer Systems & Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (纸本)9798350398106

Requirement elicitation (RE) is a cognitively challenging and time-consuming task in software development due to the numerous challenges associated with it including conflicting requirements, unspoken, or assumed requirements, difficulty meeting with relevant stakeholders, stakeholder resistance to change, and not enough time set aside for meetings with all stakeholders. The prime causes of software implementation failure have been identified as inadequacies in the treatment of requirements. Without collecting the quality requirement, cannot achieve the goal of a quality software product. Through identifying the success factors affecting requirement elicitation, the paths to the quality requirements can be identified. The success factors identify through this research are experience, business analyst skills, stakeholder relationship, organizational elicitation process. This study aims to identify the factors affecting requirement elicitation based on the data collected from business analysts and similar positions in the software industry through a survey, interviews, and analyzed data to provide the initial validation for the identified factors. Through the analysis, we identified the main factors affecting successful requirement elicitation with a perfect significance value of less than 0.05 for all factors.

关键词： Resistance Industries Software Stakeholders Task analysis Interviews Business

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Exploring the Benefits of Deep Learning for Data Science Practices

Exploring the Benefits of Deep Learning for Data Science Pra...

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International Conference on Computing and Networking technology (ICCNT)

作者： Mohit Kumar Sharma Ritesh Kumar Deepak Kumar Sinha K.K. Senthilkumar Dharmesh Dhabliya Gunveen Ahluwalia Department of Electrical Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar District Karnataka India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

With the growing occurrence of records technology and analytics, Deep studying has become an effective device within the records technology field. Deep mastering is a subset of artificial Intelligence that uses algorithms to construct complex fashions for huge-scale records analysis. The models created through Deep studying are often extra correct than traditional analytic techniques, as they use a wide style of capabilities and labels to discover records. Deep studying models can remedy many facts and technological know-how problems, from herbal language processing to photo class to regression modeling. Deep mastering models are compelling in conditions in which a high degree of accuracy is needed and where the inputs are complicated and varied. In brief, the blessings of Deep studying for data technological know-how consist of improved accuracy, faster processing, and automated feature extraction.

关键词： Deep learning Adaptation models Analytical models Accuracy Data science Predictive models Data models Real-time systems Speech processing Robots

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Artificial Intelligence Approaches in Healthcare Informatics Toward Advanced Computation and Analysis

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Open Biomedical engineering Journal 2024年 18卷

作者： Priyanka, E.B. Thangavel, S. Mohanasundaram, R. Subramaniam, Shamala Department of Mechatronics Engineering Kongu Engineering College Perundurai India School of Computer Science and Engineering Vellore Institue of Technology Tamil Nadu Vellore India Department of Communication Technology and Networks Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang 43400 Malaysia

Introduction: Automated Machine Learning or AutoML is a set of approaches and processes to make machine learning accessible for non-experts. AutoML can exhibit optimized enhancement of an existing model or suggest the best models for precise datasets. In the field of computerized Artificial Intelligence (AI), medical experts better utilize AI models with available encrypted information science ability. Methods: This paper aims to characterize and summarize the stage-wise design of Automated Machine Learning (AutoML) analysis e-healthcare platform starting from the sensing layer and transmission to the cloud using IoT (Internet of Things). To support the AutoML concept, the Auto Weka2.0 package, which serves as the open-source software platform, holds the predominant priority for experimental analysis to generate statistical reports. Results: To validate the entire framework, a case study on Glaucoma diagnosis using the AutoML concept is carried out, and its identification of best-fit model configuration rates is also presented. The Auto-ML built-in model possesses a higher influence factor to generate population-level statistics from the available individual patient histories. Conclusion: Further, AutoML is integrated with the Closed-loop Healthcare Feature Store (CHFS) to support data analysts with an automated end-to-end ML pipeline to help clinical experts provide better medical examination through automated mode. © 2024 The Author(s). Published by Bentham Open.

关键词： AI models AutoML Closed loop healthcare feature store Data analytics Glaucoma Medical datasets

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Distantly Supervised Relation Extraction Based on Multi-head Self-attention and Gate Mechanism 14th

Distantly Supervised Relation Extraction Based on Multi-head...

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14th International Conference on Genetic and Evolutionary Computing, ICGEC 2021

作者： Xu, Fengzhu Li, Meijing Ryu, Keun Ho College of Information Engineering Shanghai Maritime University Shanghai201306 China Data Science Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh700000 Viet Nam Biomedical Engineering Institute Chiang Mai University Chiang Mai50200 Thailand Department of Computer Science College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of

ISBN: (纸本)9789811684296

In the field of distant supervision relation extraction, PCNN (piecewise convolution neural network) is normally involved to trap local features of sentences, and has achieved good results. However, the existing PCNN-based methods are unable to capture the features of long-distance interdependence in sentences and cannot distinguish the influence of the three segments of PCNN on relation classification, which probably results in missing some vital information. To address the above two issues, we come up with a new model with multi-head self-attention and gate mechanism for distant supervised relation extraction. Firstly, to capture the features of long-distance interdependence in sentences, we employ an internal multi-head self-attention in PCNN model, which can grab information in different representation subspaces. Secondly, to distinguish the influence of the three segments of the piecewise max-pooling output on the relation classification, the gate mechanism is introduced to assign different weights to the three segments and highlight important segments. After a series of experiments, it is proven that the model we presented is better than the previous approaches on each evaluation criterion. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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Smart Drive Safe: Harnessing CNN for Enhanced Traffic Sign Recognition and Voice Alerts

Smart Drive Safe: Harnessing CNN for Enhanced Traffic Sign R...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： D. Divya Priya P. Chinnasamy Ramesh Kumar Ayyasamy Ajmeera Kiran Norazira Binti A Jalil Anbuselvan Sangodiah Department of Computer Science Engineering MLR Institue of Technology Hyderabad India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education School of Computing Srivilliputtur Tamil Nadu India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Perak Malaysia Faculty of Computing and Engineering Quest International University Ipoh Perak Malaysia

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Traffic signs secure their safety on the roads and the protection of others around them; everyone must be aware of traffic signs and other aspects of road safety. Traffic sign detection is a highly relevant computer vision problem that forms the basis of numerous applications in sectors like the automotive industry. Between classes, traffic signs differ widely in terms of colour, shape, and the use of pictograms or text. The project's classification of traffic signs in an image into various categories is based on a deep neural network model. IoT devices that capture traffic signs and notify users of them are used to build a model.

关键词： Industries Shape Image color analysis Speech recognition Cameras Road safety Real-time systems Hardware Internet of Things Protection

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