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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Thiyagarajan, Gomathi Vijayalakshmi, V. Department of Computer Applications CMR Institute of Technology Bengaluru India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798331527549

AI and virtual assistants are transforming higher education by using digital tools to enhance teaching and learning in ways that go beyond traditional methods. These digital tools are not merely supplementary aids but are increasingly gaining popularity in educationalsetting offering innovative approaches that transcend conventional methods. In this research we introduce ByteBuddy a virtual assistant which assists instructors on Java ***'s framework is designed in a way to integrate easily into educational context. This study reveals the architecture of ByteBuddy, the process model that captured the behavior of students in the learning environment and the observation of its application in java instructions. By applying this study, we provide profound insights on how the effective use of ByteBuddy supplements the teaching practices and measure learning outcomes. © 2025 IEEE.

关键词： Java programming language

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Fuzzy Decision-Based Clustering for Efficient Data Aggregation in Mobile UWSNs

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computers, Materials & Continua 2025年第4期83卷 259-279页

作者： Aadil Mushtaq Pandith Manni Kumar Naveen Kumar Nitin Goyal Sachin Ahuja Yonis Gulzar Rashi Rastogi Rupesh Gupta Department of Computer Science and Engineering Lovely Professional UniversityJalandhar144402PunjabIndia Department of Computer Science and Engineering Chandigarh UniversityMohali140413PunjabIndia Salesforce Inc. DallasTX 75201USA Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendragarh123031HaryanaIndia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Application Sir Chhotu Ram Institute of Engineering and TechnologyCh.Charan Singh UniversityMeerut250001Uttar PradeshIndia Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpura140401PunjabIndia

Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the ***,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater ***,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole ***,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network *** address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile *** proposed method has four main phases:clustering,CH selection,data aggregation,and *** CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy *** the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving *** adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects *** results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.

关键词： Clustering data aggregation data collection fuzzy model monitoring UWSN

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Image Analysis for Historical Knowledge Discovery and Preservation

Image Analysis for Historical Knowledge Discovery and Preser...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Sakthivel, K. Ashwin, J. Poongodi, K. Oviya, S. K.S. Rangasamy College of Technology Department of Computer Science and Business Systems Namakkal India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521394

Digitized archival collections offer rich sources for cultural heritage research. Current systems, though, fail to effectively integrate heterogeneous data forms like images, textual descriptions, and metadata. This study offers a sophisticated framework that blends image recognition, natural language processing (NLP), and knowledge graph methods to facilitate access, enable discovery, and reveal concealed historical patterns. The system comprises several modules such as a data input module, preprocessing pipeline, image recognition, NLP analysis, knowledge graph generation, semantic search engine, and an interactive user interface. Testing the system on a large historical archive dataset proved high accuracy, efficiency, and usability for all modules. Major takeaways are 93.4% accuracy in object detection for images, 94.8% accuracy for named entity recognition, and 95.5% search accuracy in semantic search feature. © 2025 IEEE.

关键词： Usability engineering

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Thermal-Aware Communication

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IEEE Transactions on Information Theory 2025年第6期71卷 4145-4155页

作者： Chee, Yeow Meng Etzion, Tuvi Immink, Kees A. Schouhamer Nguyen, Tuan Thanh Vu, Van Khu Weber, Jos H. Yaakobi, Eitan Singapore University of Technology and Design Singapore Technion — Israel Institute of Technology Department of Computer Science Haifa3200003 Israel National University of Singapore Department of Industrial Systems Engineering and Management Singapore Turing Machines Inc Rotterdam3016 DK Netherlands Singapore University of Technology and Design Science Mathematics and Technology Cluster 487372 Singapore Delft University of Technology Department of Applied Mathematics Delft2628 CD Netherlands

Temperature control is of utmost importance in transmission systems. In this paper, a binary channel model is considered in which the transmission of a one causes a temperature increase while communicating a zero causes a temperature drop. By putting constraints on the input sequences, it is guaranteed that the channel temperature will not exceed a certain pre-determined maximum. In the asymptotic regime, the capacity of such a channel is studied. For the non-asymptotic regime, fixed-length codes are presented, with the property that codewords can be freely cascaded without violating the temperature constraint. Optimization of the code size is investigated and codewords are enumerated using generating functions. © 1963-2012 IEEE.

关键词： Channel capacity

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Energy and QoS-aware virtual machine placement approach for IaaS cloud datacenter

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Neural Computing and Applications 2025年第4期37卷 2211-2237页

作者： Elsedimy, E.I. Herajy, Mostafa Abohashish, Sara M. M. Department of Information Technology Management Faculty of Management Technology and Information Systems Port Said University Port Said Egypt Department of Mathematics and Computer Science Faculty of Science Port Said University Port Said Egypt Cyber Secuirty Department Faculty of Science Al-Esraa University College Baghdad Iraq

Virtualization technology enables cloud providers to abstract, hide, and manage the underlying physical resources of cloud data centers in a flexible and scalable manner. It allows placing multiple independent virtual machines (VMs) on a single server in order to improve resource utilization and energy efficiency. However, determining the optimal VM placement is crucial as it directly impacts load balancing, energy consumption, and performance degradation within the data center. Furthermore, deciding on VM placement based on a single factor is usually insufficient to improve data center performance because many factors must be considered, and ignoring them may be too expensive. This paper improves a new multi-objective VM placement (MVMP) algorithm using a quantum particle swarm optimization (QPSO) technique. We call it QPSO-MOVMP, and its objective is to find the Pareto optimal solution for the VM placement problem by balancing different goals. This algorithm generates Pareto optimal solutions that save power by minimizing the number of running physical machines, avoid performance degradation by maintaining service level agreement (SLA), and improve load balancing by keeping server loads at optimal utilization. The experimental results show that QPSO-MOVMP had superior performance in terms of power consumption and performance degradation compared to three other multi-objective algorithms and three conventional single-objective algorithms. Simulation results show that the proposed QPSO-MOVMP achieves a consumption of 2.4 × 104 watts in power. Furthermore, it outperformed the others, achieving a minimum of 12% SLA breaches while experiencing a significant surge in requests from VMs. Moreover, the proposed model generated Pareto solutions that had a better distribution than those derived from a comparative method. © The Author(s) 2025.

关键词： Particle swarm optimization (PSO)

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Remote Patient Healthcare Monitoring and Pose Recognition over Video Sensors

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Traitement du Signal 2025年第2期42卷 995-1005页

作者： Alabdullah, Bayan Tayyab, Muhammad Alhasson, Haifa F. Alshammari, Naif S. Jalal, Ahmad Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Department of Computer Science Air University Islamabad44000 Pakistan Department of Information Technology College of Computer Qassim University Buraydah52571 Saudi Arabia Department of Computer Sciences College of Computer and Information Sciences Majmaah University Majmaah11952 Saudi Arabia Department of Computer Science and Engineering College of Informatics Korea University Seoul02841 Korea Republic of

Considering the fact of increasing population and as a result, the number of patients is constantly increasing the delivery of medical services must be prompt and of good quality. There is no question that any perfect healthcare system should track the given activities, behaviours, schedules, and even general health of the patients. The focus of this study is in utilizing ML and DL in tracing patients and diagnosing ailments. Mounted cameras from different orientations and placements are intended for health monitoring and event surveillance for possible medical issues in the future. Classical ML and more advanced DL methods are applied for analyzing physiological and behavioral patterns to identify health conditions and stress the importance of high accuracy and real-time performance for practical purposes. Initial outcomes show that patient outcomes are enhanced by getting timely medical advice while decreasing hospital-based burdens on staff. Two challenging datasets, ETRI-Activity3D and NTU-RGB-D, are pre-processed to remove unimportant data and reduce computational load. A strong multi-feature description system can extract gradient values, and the process is followed by feature matching and feature optimization. Then, in the last Maximum Entropy Markov Model (MEMM) classifier is used, achieving an accuracy of 97.2% for ETRI-Activity3D, and 98.3% for the NTU-RGB-D dataset. ©2025 The authors.

关键词： Remote patient monitoring

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A Comprehensive Survey on Digital Twin: Focusing on Security Threats and Requirements

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IEEE Access 2025年 13卷 73362-73390页

作者： Mun, Hyeran Han, Kyusuk Damiani, Ernesto Yeun, Hyun Ku Kim, Tae-Yeon Martino, Luigi Yeun, Chan Yeob Khalifa University Center for Cyber-Physical Systems Department of Computer Science United Arab Emirates in UAE United Arab Emirates Higher Colleges of Technology Mathematics at the School of Engineering and Technology United Arab Emirates Khalifa University Department of Civil and Environmental Engineering United Arab Emirates

Digital Twin (DT) aims to seamlessly replicate physical objects or processes in virtual environments, garnering attention for supporting diverse intelligent management services such as monitoring, analysis, and control. Integration with technologies like artificial intelligence (AI), big data, edge computing, and the Internet of Things (IoT) has propelled the development of DTs. However, the DT may not only introduce new potential security flaws but also inherit existing vulnerabilities from the technologies they incorporate and interact with due to their integration with diverse technologies. Despite its growing impact, research addressing the security concerns of DTs remains insufficient and still in its early stages. In this article, we provide a comprehensive and in-depth review of the current state of DTs, focusing on their security aspects. We first depict an overview of DTs, including the definition of the DT and various DT applications, and then present the architecture for DTs. Subsequently, we conduct a detailed examination of security attacks and threats across the functional layers of the DT architecture. In contrast to the previous surveys on DTs, we derive security properties and security functional requirements (SFRs) based on the Common Criteria (CC) standard for mitigating these attacks and threats. We also introduce technologies that enable the achievement of SFRs with a future research perspective. By addressing these critical security aspects, our article enhances the security and trustworthiness of DTs, contributing to their safe and reliable deployment in various domains. © 2013 IEEE.

关键词： Cyber attacks

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Specification of Complex Analytics Workflows: A Formal Language Model of Decision Options 20th

Specification of Complex Analytics Workflows: A Formal Lang...

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20th International Conference on the Economics of Grids, Clouds, systems, and Services, GECON 2024

作者： Miri, Pouriya Kochovski, Petar Tuler de Oliveira, Marcela Stankovski, Vlado Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Department of Engineering Systems Delft University of Technology Delft Netherlands

ISBN: (纸本)9783031812255

The specification of experiments expressed as Complex Analytics Workflows is a complex task that involves many decision-making steps with various degrees of complexity. The use of the context, the expert knowledge, and the potential for its sharing and reuse in the context of experiment specification have not been addressed sufficiently until now. Moreover, to make such knowledge instrumental, it should be coupled with specific probabilistic measures, such as particular assurances, ranking, and verification of various options. The paper aims to present a novel semantic model for probabilistic reasoning in any experimentation context coupled with a functional system for knowledge generation, reuse, and sharing. The result of our work can be used within existing experimentation engines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Markov processes

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PDNet: A Novel Approach to Plant Disease Detection using CNN and Keras 6

PDNet: A Novel Approach to Plant Disease Detection using CNN...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Jain, Garima Shukla, Amita Chaudhary, Anamika Sharma, Nidhi Bisht, Sovers Singh Jain, Ankush Department of Computer Science and Buisness Systems Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Delhi India Department of Computer Science and Engineering Netaji Subhas University of Technology Delhi India

ISBN: (纸本)9798331522667

This research addresses the pressing global demand for food by leveraging cutting-edge deep learning techniques for automating plant disease detection. Focusing on tomato and potato leaf diseases, the study utilized the PlantVillage dataset to compare classical machine learning methods, such as Local Binary Patterns (LBP) combined with classifiers like kNN, SVM, and Extreme Learning Machines, against modern Convolutional Neural Network (CNN) architectures. These included MobileNet, DenseNet201, ResNet50, Inception V3, and VGG16/19. A custom-designed CNN model was developed with unique configurations implemented using Keras and TensorFlow. The model was tested for binary, 6-class, and 10-class classifications, achieving remarkable accuracy rates of 99.6%, 98.4%, and 97.2%, respectively. Notably, after 50 training epochs, the model achieved a peak accuracy of 99.78% for plant disease classification. A practical web-based application was also developed, integrating the best-performing model to facilitate real-time leaf disease detection and notify farmers via email. This approach demonstrates a significant advancement in sustainable agriculture by providing an efficient, accurate, and scalable solution for early disease detection, which is critical for improving crop yield and productivity. © 2025 IEEE.

关键词： Convolutional neural networks

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Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

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International Journal of Crowd Science 2025年第1期9卷 64-78页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Jebreen, Issam Nabot, Ahmad Rajab, Mohammed Alauthman, Mohammad Aldweesh, Amjad Aburub, Faisal Alangari, Someah Alzgol, Musab Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan University Headquarter University of Anbar Ramadi31001 Iraq Department of Information Security University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan Computer Information Systems Department Faculty of Information Technology Isra University Amman11622 Jordan

Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings. © The author(s) 2025.

关键词： Prediction models

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