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6th International Conference on Intelligent Communication, Control and Devices, ICICCD 2024

作者： Kulkarni, Radhika V. Shilimkar, Avanish Bhegade, Sakshi Dadmal, Jidnyasa Department of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9789819783281

In order to provide real-time health monitoring in the healthcare industry, the article presents a novel integration of augmented reality (AR) and the Internet of Things (IoT). Our goal is to build a unified system that combines IoT and AR technologies to produce an interesting health monitoring service. Our study reveals the combined potential of these technologies, which have been explored separately in previous research. We close the gap in the literature by utilising AR for vital indicator monitoring and demonstration, which allows for accurate real-time patient monitoring. Our system architecture incorporates Blynk Cloud for data storage, Unity 3D for complex augmented reality health data visualisation, MQTT protocol for secure data sharing, and sensors connected to ESP32s. Our method, which improves medical education and technology integration, lays the groundwork for a revolutionary change in healthcare by utilising ARKit, ARCore, Vuforia, and OpenCV. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Enhancing Crop Health Monitoring: A ResNet50 Approach to Automated Plant Disease Severity Prediction

Enhancing Crop Health Monitoring: A ResNet50 Approach to Aut...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Gunavathi, H.S. Priya, Ishani Sree, Kothakota Bindhu Kushwaha, Aparna Bangalore Institute of Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798331529833

Plant diseases significantly impact agricultural productivity, making it essential to accurately assess not only the presence of disease but also its severity. This paper presents a deep learning-based framework using ResNet50 to predict plant disease severity across multiple levels (Healthy, Mild, Moderate, and Severe). The system is designed to process plant leaf images, perform augmentation to improve robustness and classify disease severity with high accuracy. The proposed method leverages labelled and pseudo- labelled datasets to enhance the model's performance through data augmentation and training on unlabelled images. The results demonstrate the model's effectiveness in distinguishing between varying levels of disease progression, providing valuable insights for early intervention and optimized crop management strategies. This platform is crucial for automating plant disease severity monitoring, and aiding decision-making processes for timely interventions in agriculture. © 2025 IEEE.

关键词： agriculture data augmentation deep learning image classification plant disease ResNet50 severity prediction

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An Efficient DDoS Attacks Detection and Mitigation Approach in SDN-IoT Network 6

An Efficient DDoS Attacks Detection and Mitigation Approach ...

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6th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2024

作者： Singh, Chandrapal Jain, Ankit Kumar Department of Computer Engineering National Institute of Technology Kurukshetra India

The Internet of Things (IoT) has revolutionized modern life by introducing advanced solutions and enhancing processing power. However, managing IoT networks can be challenging due to the multitude of devices and communication protocols involved. Software-Defined Networking (SDN) offers a potential solution by simplifying network management, providing network abstraction, facilitating network development, and potentially addressing the complexities of IoT networks. Despite the numerous benefits, it is important to note that IoT systems still face significant security concerns. This study uses entropy as a metric on the SDN controller in an IoT network to assess the homogeneity of network traffic. And on the SDN controller network log data is present so to filter network traffic, the technique additionally incorporates machine learning (ML) classifiers. We tested the suggested methodology's effectiveness using real-world network traffic information. SDN-IoT networks and ML are used to improve the process of detection and mitigation. The proposed method has a detection rate between 98% and 100%, demonstrating its exceptional performance. Compared to existing methods, our approach not only detects Distributed Denial of Service (DDoS) attacks with remarkable accuracy, but also enables early detection while maintaining a remarkable false-positive rate. This combination of high precision and early detection enables us to effectively identify and mitigate DDoS attacks, thereby enhancing security and minimizing the potential harm caused by malicious activities. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Internet of things

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Comprehensive insights into healthcare IoT: the role of machine learning and deep learning approaches

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Multimedia Tools and Applications 2025年 1-42页

作者： Kumar, Atul Dewang, Rupesh Kumar Computer Science and Engineering Department Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj211004 India

Conventional healthcare systems have long struggled to address the varied needs of large patient populations, often leading to inefficiencies and less-than-optimal outcomes. Yet, the advent of machine learning (ML) and deep learning (DL) models has heralded a transformative shift toward value-based care, enabling healthcare providers to offer personalized and remarkably effective treatments. Contemporary medical equipment and devices are equipped with internal applications that gather and store extensive patient data, forming a valuable resource for ML-powered predictive *** have conducted a systematic literature review (SLR) which mainly focuses on Theoretical background, challenges, Application framework, Organization of IoT in healthcare and HIoT *** the SLR process some efficient methodologies has been used. In the SLR process we have conducted Quality metadata analysis (QMA) which provides the complete statistical analysis by considering different *** learning algorithms enable the analysis of the extensive data produced by IoT devices, uncovering patterns and irregularities that may escape human detection. Deep learning, leveraging sophisticated neural network models, enhances the accuracy of medical image interpretation, disease prognosis, and forecasting patient outcomes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Diagnosis

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Efficient resource allocation for D2D-enabled social IoT networks:A tripartite and time-scale optimization approach

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Intelligent and Converged Networks 2024年第4期5卷 380-401页

作者： Saurabh Chandra Rajeev Arya Maheshwari Prasad Singh Department of Electronics&Communication Engineering National Institute of Technology PatnaPatna 800005India Department of Computer Science and Engineering National Institute of Technology PatnaPatna 800005India

In the densification of Device-to-Device(D2D)-enabled Social Internet of Things(SIoT)networks,improper allocation of resources can lead to high interference,increased signaling overhead,latency,and disruption of Channel State Information(CSI).In this paper,we formulate the problem of sum throughput maximization as a Mixed Integer Non-Linear Programming(MINLP)*** problem is solved in two stages:a tripartite graph-based resource allocation stage and a time-scale optimization *** proposed approach prioritizes maintaining Quality of Service(QoS)and resource allocation to minimize power consumption while maximizing sum *** results demonstrate the superiority of the proposed algorithm over standard benchmark *** of the proposed algorithm using performance parameters such as sum throughput shows improvements ranging from 17%to 93%.Additionally,the average time to deliver resources to CSI users is minimized by 60.83%through optimal power *** approach ensures QoS requirements are met,reduces system signaling overhead,and significantly increases D2D sum throughput compared to the state-of-the-art *** proposed methodology may be well-suited to address the challenges SIoT applications,such as home automation and higher education systems.

关键词： Device-to-Device(D2D)communication Social Internet of Things(SIoT) resource allocation Quality of Service(QoS) tripartite graph time scale

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Smart City: Practice of Artificial Intelligence, Robotics and Internet of Things

Smart City: Practice of Artificial Intelligence, Robotics an...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Gupta, Suresh Chand Kumar, Deepak Parkash, Ved Bindra, Sandeep Singh Panipat Institute Of Engineering And Technology Department Of Computer Science & Engineering Haryana Panipat India State Institute Of Engineering And Technology Department Of Computer Engineering Haryana Nilokheri India

ISBN: (纸本)9798350306446

The discussion about smart cities under review about for a long while in established researchers, enterprises, and cultural gatherings, and various audits on the point are available. The term "smart city"alludes to new businesses using data and correspondence advancements alongside the capacities and conditions of metropolitan areas. Discussing about smart city thoughts and analyses towards certifiable acknowledgment, while empowering the mentioned attributes, is a momentum contest. The further empowering of broad smart city facilities and their combination need financially and monetarily solid executions. This will enhance the demands on the designing, sending, activity, upkeep, and development of smart cities. © 2024 IEEE.

关键词： Smart city

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Leveraging Network Slicing in SDNs for Handling Application Failures

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第4期51卷 409-421页

作者： Lokesh, Bommareddy Rajagopalan, Narendran School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal609609 India

Unlike traditional networks, Software-defined networks (SDNs) provide an overall view and centralized control of all the devices in the network. SDNs enable the network administrator to implement the network policy by programming applications on top of the SDN controller using generic APIs. One or more controller instances can be deployed to administer the data flow by maintaining unified control of the entire network. The controller is expected to respond quickly to queries from forwarding devices. Presuming quick responses from the controller while enforcing a complex security mechanism is unreasonable. In this paper, the authors propose a unique, adaptive, lightweight, yet efficient technique called ISOLATOR to mitigate the effect of insider attacks and malfunctioning of distributed applications in an SDN-enabled cloud. The proposed security application upon detecting any suspicious activity by a virtual machine, isolates it by removing the interface to its respective shared network and reconnects it through a restricted network operating in a highly selective mode. By subjecting the data traffic to deep packet inspection, the restricted network searches for a match with a known worm pattern. The application is programmed for the OpenDayLight controller and the results show a significant improvement in identifying malicious activities with minimal latency and computational cost. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Controllers

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Multi-Faceted Reinforcement Learning Frameworks for Dynamic Cluster Head Selection and Energy-Efficient Routing in Wireless Sensor Networks

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SN computer Science 2025年第5期6卷 1-20页

作者： Mouli, S. Sudhamsu Talagondapati, T. Veeraiah Singh, M.P. Department of Computer Science and Engineering Ecole Centrale School of Engineering Mahindra University Hyderabad India Department of Computer Science and Engineering National Institute of Technology Patna India

Wireless Sensor Networks (WSNs) face critical energy efficiency challenges due to resource limitations, especially in extending network lifetime. This paper presents a reinforcement learning-based solution combining Linear Optimization State-Action-Reward-State-Action (LO-SARSA) for energy-aware Cluster Head (CH) selection and a Proportional Optimization Policy Gradient Method (PO-PGM) for optimal routing. CH selection is modeled as an RL problem, where LO-SARSA learns policies based on residual energy and node distance, while PO-PGM uses a neural network to make adaptive routing decisions. Extensive simulations demonstrate that the proposed approach significantly improves energy consumption, network lifetime, and packet delivery ratio, achieving up to a 20% gain in network lifetime over conventional methods. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： CH Selection LO-SARSA Optimal Routing PO-PGM Reinforcement Learning(RL) Wireless Sensor Networks (WSNs)

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Handwritten optical character recognition using TransRNN trained with self improved flower pollination algorithm (SI-FPA)

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Multimedia Tools and Applications 2025年第18期84卷 19947-19969页

作者： Devi, S. Nandhini Fatima, N. Sabiyath Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science & Technology Chennai India

The difficulty of successfully scanning handwritten text arises from variances in style, size, and orientation, which affect handwriting optical character recognition (OCR). This study provides a novel strategy that incorporates innovative techniques for effectively addressing these difficulties. Initially, preprocessing steps involve Gaussian blur to reduce noise and CLAHE to enhance contrast, improving the visibility of text regions. Canny Edge Detection is then utilized for segmentation, followed by text localization using the Efficient and Accurate Scene Text (EAST) algorithm. Next, features are extracted via Inception V3 to capture both low-level and high-level information. From the collected characteristics, Long Short-Term Memory (LSTM) networks are used to extract chronological data. Then, with the help of the Self-Improved Flower Pollination Algorithm (SI-FPA), features are selected using Optimized Principal Component Analysis (PCA). Finally, a combination of Transformer and Recurrent neural networks (RNNs) is used to perform text recognition, maximizing the benefits of each network’s advantages in processing sequential and contextual data. Experiments on MNST dataset with validation using different optimization algorithms show the effectiveness and resilience of the suggested method in different optimization scenarios. The combination of these methods results in a sophisticated OCR system that can recognize handwritten characters with accuracy, even under difficult conditions. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Principal component analysis

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Breast Cancer Classification Using Graph Convolutional Networks and DenseNet121 with Pruning

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Journal of Shanghai Jiaotong University (Science) 2025年 1-18页

作者： Kavitha, S.J. Sridevi, S. Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies Tamil Nadu Chennai600117 India

Breast cancer remains a significant global health burden, particularly among women, necessitating accurate and accessible diagnostic systems for early detection and intervention. Despite advancements in medical technology, challenges persist such as high dimensionality, variability, and complexity. Hence, this study introduces a novel approach that amalgamates DenseNe5t121 architecture within graph convolutional networks (GCNs) for breast cancer classification. The proposed method combines the strengths of a pre-trained DenseNet121 model, pruning techniques, and GCN with a cross-space filter (CSF). DenseNet121 is employed for feature extraction, while pruning enhances efficiency and prevents overfitting. GCNs capture structural and semantic information from the data and the CSF aids in regularization. Furthermore, the incorporation of the CSF enhances model performance by effectively integrating information from both graph topology and node attributes. The proposed approach was trained and evaluated using the BreakHis dataset, a widely recognized resource for breast cancer classification through histopathological images. By leveraging several performance measures consisting of precision, recall, F1-score, AUC, MCC, and accuracy, the proposed approach was compared against existing models to assess its effectiveness. The results indicate that the proposed method, particularly when combined with the CSF, outperforms existing approaches, demonstrating its potential as an advanced tool for accurate and interpretable breast cancer diagnosis. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Diseases

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