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

作者： Surendran, R. Anandaraj, B. Deepa, P. Mageshwari, M. Balamurugan, K.S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Madnapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer science and engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India Department of Electronics and communication engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

An Electrocardiogram (ECG) contributes significantly to early diagnosis and classification of heart diseases, arrhythmia which means irregular heart rate. Regrettably, the process became difficult due to asymmetric and minimal amplitude from recordings of ECG and cannot do the extraction from multiple signals. By using machine learning technology, it can classify and extract multiple samples, but cannot extract the data features from complex data structures. To overcome the limitations, previously introduced metaheuristic optimization (MHO) to improve the classification and extraction. Proposed a method which supports and K-Nearest Neighbour (KNN) Gaussian Mixture Model (GMM) algorithms for determine the abnormalities, predicting cardiac events, and improve diagnostic accuracy. As a result, this can help us to get results more quickly and accurately with sensitivity 99.7% and accuracy 99.2%, for helping healthcare professionals in diagnosing arrythmias, myocardial infarction and any other heart conditions. © 2025 IEEE.

关键词： Adversarial machine learning

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Black Hole Attack Detection Using Dynamic Threshold Values and Residual Energy in VANETs

Black Hole Attack Detection Using Dynamic Threshold Values a...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Shalom Priscilla, I. Senthil Kumar, C. Buvaneswari, B. Shiva Kumar, M. Thiagarajar College of Engineering Department of Computer Science and Engineering Madurai India SRM Madurai College for Engineering and Technology Department of Electronics and Communication Engineering Madurai India

ISBN: (纸本)9798331512965

In black hole attack, a hostile vehicle or node within the VANET pretends to have the optimal route to the destination, convincing other nodes to route their data packets through it as they head towards the destination, trailing behind the malicious node, receives the data, it either discards the packets completely or forwards them selectively, causing disruptions in communication. To overcome this dilemma the research discusses a novel approach for categorizing nodes in AODV (Adhoc On demand vector Routing Protocol) networks according to their residual energy (RE) and forward rate (FR). With the goal to identify nodes that are considered energy-constrained, the proposed approach defines a residual energy threshold (RET) and includes dynamic threshold values (MinDTV and MaxDTV) for the forward rate. Three categories are applied to nodes: 1. (Trusted), 2. (Intermediate), and 3. (Untrusted or Energy-Constrained). This methodology results in a more trustworthy and energy-efficient network by dynamically updating node selection based on present forwarding behavior and levels of energy. The proposed approach boosts the overall strength, longevity as well, and integrity of RPL networks through the elimination of energy-depleted and unstable nodes;hence, it is especially suitable for applications in scenarios with scarce resources and malicious activity. © 2025 IEEE.

关键词： Black holes

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SAMFNet: Scene-aware sampling and multi-stage fusion for multimodal 3D object detection

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Alexandria engineering Journal 2025年 126卷 90-104页

作者： Wang, Baotong Xia, Chenxing Gao, Xiuju Ge, Bin Li, Kuan-Ching Fang, Xianjin Zhang, Yan Yang, Yuan College of Computer Science and Engineering Anhui University of Science and Technology Huainan232001 China College of Electrical and Information Engineering Anhui University of Science and Technology Huainan232001 China Department of Computer Science and Information Engineering Providence University Taichung43301 China The School of Electronics and Information Engineering Anhui University Hefei230039 China School of Mathematics and Big Data Anhui University of Science and Technology Huainan232001 China

Recently, multimodal 3D object detection (M3OD) that fuses the complementary information from LiDAR data and RGB images has gained significant attention. However, the inherent structural differences between point clouds and images pose fusion challenges, significantly hindering the exploration of correlations within multimodal data. To address this issue, this paper introduces an enhanced multimodal 3D object detection framework (SAMFNet), which leverages virtual point clouds generated from depth completion. Specifically, we design a scene-aware sampling module (SASM) that employs tailored sampling strategies for different bins based on the density distribution of point clouds. This effectively alleviates the detection bias problem while ensuring the key information of virtual points, significantly reducing the computational cost. In addition, we introduce a multi-stage feature fusion module (MSFFM) that embeds point-level and regional-adaptive feature fusion strategies to generate more informative multimodal features by fusing features with different granularities. To further improve the accuracy of model detection, we also introduce a confidence prediction branch unit (CPBU), which improves the detection accuracy by predicting the confidence of feature classification in the intermediate stage. Extensive experiments on the challenging KITTI dataset demonstrate the validity of our model. © 2025 The Authors

关键词： Image fusion

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A Smart Irrigation System for Coconut Farming using IoT 3

A Smart Irrigation System for Coconut Farming using IoT

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3rd International Conference on electronics and Renewable Systems, ICEARS 2025

作者： Russia, S. Mohanapriya, B. Gowrishankar, V. Parimala Devi, M. Pradeep, K.K. Brindha, P. Velalar College of Engineering and Technology Department of Computer Science and Engineering India Velalar College of Engineering and Technology Department of Electronics and Communication Engineering Erode India Erode Sengunthar Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Coconut farming is an essential agricultural practice that contributes significantly to the global economy by providing valuable products such as coconut water, oil, and meat. However, the management of coconut plantations faces numerous challenges, including labor shortages, pest control, and the demand for precise irrigation and fertilization. This paper proposes an integrated smart farming system that leverages advanced sensor technologies and automation to address these challenges. The system employs precision irrigation and fertigation methods, facilitated by a centralized motor controller and solenoid valves, to deliver water and nutrients efficiently to the base of each coconut tree. A pH sensor monitors soil acidity or alkalinity, ensuring optimal nutrient availability and promoting sustainable farming practices by reducing chemical inputs. Additional features include a tree motion sensor and a tilt sensor for monitoring tree stability under adverse weather conditions. Soil moisture sensors, temperature, humidity, and light intensity sensors provide real-time data to enhance decision-making. Farmers can remotely monitor and control the system via a Node-RED dashboard, receiving alerts through platforms like Telegram. By integrating these technologies, the proposed system aims to optimize resource utilization, enhance crop yield, and reduce the environmental impact of coconut farming. © 2025 IEEE.

关键词： Soil moisture

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Exploring Deep Learning and Word Embedding Techniques for Sentiment Analysis on Diverse Textual Data 3rd

Exploring Deep Learning and Word Embedding Techniques for Se...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Lavanya, B.N. Rathnam, K. V. Anitha Kiran, K. Appaji, Abhishek Shenoy, P. Deepa Venugopal, K.R. Department of Computer Science and Engineering University of Visvesvaraya College of Engineering BangaloreUniversity Bengaluru India Department of Medical Electronics BMS College of Engineering Bangalore India

ISBN: (纸本)9789819743582

The rapid expansion of data poses a significant challenge for analyzing sentiments. The importance of user-generated reviews highlights the need to carefully curate and evaluate text data to extract opinions. This research delves into the world of learning for sentiment analysis exploring models such as Bi-LSTM, CNN, and GRU along with various word embeddings like N-grams, Keras embedding, BERT, Roberta, CT-Bert, and Elmo. It effectively categorizes sentiments and detects sarcasm in Twitter and IMDB data making it a vital tool for applying learning to real-world sentiment analysis tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sentiment analysis

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Bit flipping-based error correcting output code construction for adversarial robustness of neural networks

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ICT Express 2025年第2期11卷 348-353页

作者： Jang, Wooram Hwang, Woojin Jin, Kezhong Park, Hosung Department of Intelligent Electronics and Computer Engineering Chonnam National University Gwangju South Korea Department of Computer Engineering Chonnam National University Gwangju South Korea College of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China

In this paper, we propose a method for constructing error-correcting output codes (ECOCs) based on a codeword bit flipping algorithm to enhance adversarial robustness of neural networks. In the previous work in Verma and Swami (2019), ECOCs are applied to deep neural networks (DNNs) based on the analogy between channel noise and adversarial examples to achieve state-of-the-art adversarial robustness. To improve adversarial robustness, it was proposed in Wan et al. (2022) to optimize the Hamming distance between codewords and employ codeword assignment algorithms. Our study achieves approximately a 8% accuracy improvement on MNIST and CIFAR-10 under adversarial attacks compared to the method proposed in Wan et al. (2022). © 2025 The Authors

关键词： Adversarial robustness Deep neural networks Error correcting output code Hadamard matrix

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Comparative Analysis of Machine Learning Algorithm;for Conversion Predictors of Clinically Isolated Syndrome (CIS) to Multiple Sclerosis (MS) 4

Comparative Analysis of Machine Learning Algorithm;for Conve...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Mendanha, Serena Gudibandi, Keerthi Reddy Hajarathaiah, Koduru Gudibandi, Bharat Reddy Enduri, Murali Krishna School of Computer Science and Engineering VIT-AP University Amaravati India School of Electronics Engineering VIT-AP University Amaravati India Srm University Department of Computer Science and Engineering AP India

ISBN: (纸本)9798331523923

The central nervous system is impacted by multiple sclerosis (MS), a chronic neurological condition that causes significant cognitive and physical deficits. Better disease management and prompt intervention depend on early and precise detection. Effective diagnosis of MS is hampered by a number of factors, such as small datasets, significant clinical presentation variability, difficult feature selection, model generalization problems, and integration of multimodal data such as magnetic resonance imaging and genetic markers. Several machine learning (ML) models are assessed in this study in order to predict the development of clinically isolated syndrome (CIS) to multiple sclerosis. Using clinical and magnetic resonance data from patients with CIS at risk of developing MS, we evaluate the effectiveness of support vector machines (SVM), K nearest neighbor (KNN), decision trees (DT), random forests (RF), logistic regression (LR), Gaussian naive Bayes (Gaussian NB) and XGBoost (XG). Evaluation is performed using performance criteria including F1 score, recall, precision, and precision. According to our research, Random Forest has the best prediction accuracy, which makes it a potentially useful tool for helping doctors diagnose and treat MS patients early. Notwithstanding these developments, issues like model interpretability and data scarcity still exist. In order to improve diagnosis precision, future research will concentrate on enhancing these models by integrating deep learning methods, genetic markers,, and more advanced imaging modalities. © 2025 IEEE.

关键词： Magnetic resonance imaging

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A Tolerant and Energy Optimization Approach for Internet of Things to Enhance the QoS Using Adaptive Blended Marine Predators Algorithm

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computers, Materials & Continua 2025年第5期83卷 2449-2479页

作者： Vijaya Krishna Akula Tan Kuan Tak Pravin Ramdas Kshirsagar Shrikant Vijayrao Sonekar Gopichand Ginnela Department of Information Technology G.Narayanamma Institute of Technology and Science for WomenHyderabad500104India Engineering Cluster Singapore Institute of Technology10 Dover DriveSingapore138683Singapore Department of Electronics and Telecommunication Engineering J.D.College of Engineering&ManagementNagpur441501India Department of Computer Science and Engineering J.D.College of Engineering&ManagementNagpur441501India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014TamilnaduIndia

The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of *** paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and *** results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT *** are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient *** algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node *** study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.

关键词： Internet of things trust energy marine predators algorithm(MPA) differential evolution(DE) nodes throughput lifetime

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Scheduling Bi-Objective Lot-Streaming Hybrid Flow Shops with Consistent Sublots via an Enhanced Artificial Bee Colony Algorithm

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Complex System Modeling and Simulation 2025年第1期5卷 46-67页

作者： Lu, Benxue Gao, Kaizhou Duan, Peiyong Slowik, Adam School of Computer Science Liaocheng University Liaocheng252000 China Macau Institute of Systems Engineering Macau University of Science and Technology Macau999078 China School of Information and Automation Engineering Qilu University of Technology Jinan250353 China Koszalin University of Technology Department of Electronics and Computer Science Koszalin75-453 Poland

This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots (Bi-HFSP_CS). The objectives are to minimize the makespan and total energy consumption. First, the Bi-HFSP_CS is formalized, followed by the establishment of a mathematical model. Second, enhanced version of the artificial bee colony (ABC) algorithms is proposed for tackling the Bi-HFSP_CS. Then, fourteen local search operators are employed to search for better solutions. Two different O-learning tactics are developed to embed into the ABC algorithm to guide the selection of operators throughout the iteration process. Finally, the proposed tactics are assessed for their efficacy through a comparison of the ABC algorithm, its three variants, and three effective algorithms in resolving 95 instances of 35 different problems. The experimental results and analysis showcase that the enhanced ABC algorithm combined with O-learning (QABC1) demonstrates as the top performer for solving concerned problems. This study introduces a novel approach to solve the Bi-HFSP_CS and illustrates its efficacy and superior competitive strength, offering beneficial perspectives for exploration and research in relevant domains. © 2021 TUP.

关键词： Energy utilization

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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

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computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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