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

作者： Devrani, Rekha Premkumar, R. Kumar, Shyam Vivekananda Global University Department of Computer Science & Engineering Jaipur India School of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India Arka Jain University Department of Computer Science & It Jharkhand Jamshedpur India

ISBN: (纸本)9798350370249

Federated getting to know (FL) is a gadget studying generation which allows a user to train a single model across more than one dispensed system, with none of the person systems having to transfer the statistics sets, nor the version, to a vital device for education. It allows disbursed schooling of an unmarried model throughout more than one disbursed net of things (IoT) gadgets or users. It makes it feasible to improve the accuracy and scalability of our Sensor-Powered inexperienced IoT applications. FL can consequently be instrumental in taking inexperienced IoT answers towards the character sources of environmental records, like air firstclass sensors, as opposed to amassing facts *** permits us to leverage powerful gadget learning models to enhance the accuracy and scalability of our Sensor-Powered green IoT systems. By using federated models throughout education, we will grow the accuracy of our predictions and decrease the amount of effort and time we have to acquire and store sensor facts centrally. Moreover, FL lets us improve the version overall performance of green IoT packages by dispensing schooling and predictions to a disbursed community of sensor-based totally IoT devices. We also can, in addition, reduce the reliance on central systems with the aid of transferring the middle of our Sensor-Powered inexperienced IoT packages from the cloud to allotted nodes. © 2024 IEEE.

关键词： Generation Predictions Scalability Sensor Statistics Throughout Unmarried

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Land Cover Segmentation for Agricultural Monitoring Using Synthetic Aperture Radar (SAR) Imagery with Convolutional Neural Networks and Attention-Based Transformer Models

Land Cover Segmentation for Agricultural Monitoring Using Sy...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Prakash, E.P. Sathiya, K.G. Manoranjitham, S. Sekar, G. Munaf, S. Santhia, R.K. Dr. N.G.P. Institute of Technology Department of Computer Science and Engineering Coimbatore641048 India Arunai Engineering College Department of Electronics and Communication Engineering Tiruvannamalai606604 India Agni college of Technology Department of Information Technology Chennai600130 India Vsb College Of Engineering Department of Electronics and Communication Engineering Technical Campus Coimbatore642109 India Sri Ramakrishna Institute of Technology Department of Electronics and Communication Engineering Coimbatore641010 India Pondicherry University Department of Computer Science and Engineering 605014 India

ISBN: (纸本)9798331509828

Automated classification of land cover types is one of the most crucial aspects within many remote sensing applications, including the monitoring of agricultural land and territories. This work proposes a novel approach for division of land covers using a hybrid of CNNs and Attention-based Transformer networks. CNNs are adopted to extract the spatial features from different levels of the SAR image, while Transformer Model Arch applies self-attention functions to explore the long-range dependency and contextual relation of the entire image. The model learning process is performed end-to-end from multi-temporal SAR data to differentiate different types of land covers, including crops, vegetation, water, and urban surfaces. In this case, we apply the proposed approach to different agricultural areas to show that it outperforms traditional methods, such as thresholding and other machine learning approaches. The results highlight the viability of this mixed form to solve the noise and changeable nature of SAR imagery, in terms of enhancing the use of the agricultural monitoring application. © 2025 IEEE.

关键词： Convolutional neural networks

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MULTI-FREQUENCY POLSAR IMAGE CLASSIFICATION BASED ON CROSS ATTENTION VIT

MULTI-FREQUENCY POLSAR IMAGE CLASSIFICATION BASED ON CROSS A...

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IET International Radar Conference 2023, IRC 2023

作者： Wang, Hongmiao Ren, Dawei Yin, Junjun Yang, Jian Departments of Electronic Engineering Tsinghua University Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781839539954

In the land cover classification of multi-frequency Polarimetric SAR (PolSAR) images, the classification performance highly depends on the effectiveness of feature fusion. Currently, linear fusion methods are widely employed in multi-frequency PolSAR land cover classification. However, linear fusion methods do not introduce extra interactions between the features from different bands, thus failing to fully exploit the advantages of fused features. To address this issue, a feature fusion method based on cross-attention is proposed. The proposed method is based on the Vision Transformer architecture. We enhance the model's capability to extract multi-frequency fusion features by introducing additional cross-frequency feature interactions within the self-attention module. Experimental results demonstrate that using the proposed cross-attention module significantly improves the model's performance in multi-frequency PolSAR image classification without introducing extra parameters or computational costs. © The Institution of engineering & Technology 2023.

关键词： Polarimeters

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Internet of Things (IoT) based Watercraft for Environmental Surveillance and Maintenance 5

Internet of Things (IoT) based Watercraft for Environmental ...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Sushmitha, S. Jebaraj, Bharath Singh Vivekrabinson, K. Azarudeen, K. Priyadharshini, S. Gnanasekaran, T. Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu India Kalasalingam Academy of Research and Educatuion Department of Computer Science and Engineeing Sriviliputhur India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India R.M.K. Engineering College Department of Electronics and Communication Engineering Thiruvallur India R.M.K. Engineering College Department of Electronics and Instrumentation Engineering Thiruvallur India

ISBN: (纸本)9798350367171

Water is the topmost priority for all human and industry needs in the universe. This proposed work creates awareness about the conservation of water bodies. Here, a garbage collector is designed to clean the lake and river automatically. The client can supervise the boat from any remote place. The developed model has a live camera that uses image processing technology for detecting the garbage, and the distance between the boat and the garbage is calculated, and the cleaning of plastic and other spills will happen. The values of pH and temperature are continuously monitored and the results are displayed on the web page using IoT. The proposed prototype has been experimented with in a small swimming pool. The proposed watercraft model is made up of heavy-duty material. The movement of the model is controlled using wireless Bluetooth communication from a smartphone application. The exact values of pH and temperature are displayed on the smartphone via an application. The mobile application has switches and can be used to control the boat. © 2024 IEEE.

关键词： Boats

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Effectiveness of Almost Sub Frame Approach as Inter Cell Interference Management in LTE-A Macro - Pico Scenario 9

Effectiveness of Almost Sub Frame Approach as Inter Cell Int...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Jember, Kehali A. Abose, Tadele A. Kassahun, Ermias Madhav, T. Venu Adama Science And Technology University Department Electronics And Communication Engineering Adama Ethiopia Mettu University Department Electrical And Computer Engineering Ethiopia Gondar University Department Electrical And Computer Engineering Gondar Ethiopia

ISBN: (纸本)9798350394474

Heterogeneous networks are promising solutions for enhancing network performance of LTE-A mobile networks by deploying small cells within the area of the serving macro cells. The goal of deploying such networks is to offload data from the congested macro cells towards the small cells. However, cross tier interference is a serious issue, which is introduced due to the transmitted power difference between Macro cell node and small cell node. Hence, cell age users are mainly affected by high power interference originated from Macro node. The paper develops enhanced inter-cell interference coordination using Almost Blank Sub-Frame (ABS) to improve user throughput. This can be achieved by stopping data transmission on in certain sub frame of macro cell while Pico users are scheduled at the same frame. computer simulation results depict that the performance of Pico cell users is highly degraded, especially for cell edge users. The ABS technique improves cell edge user throughput of Pico cell by 95% and center cell user throughput by 75.25%. © 2024 IEEE.

关键词： Heterogeneous networks

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A Hyb-WGWO and Deep EMC–based Intelligent E-healthcare monitoring model for patient condition diagnosis in internet of things connected applications

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Research on Biomedical engineering 2023年第1期39卷 37-49页

作者： Mojjada, Ramesh Kumar Ashok Kumar, K. Tamizhselvan, C. Melingi, Sunil Babu Surender, R. RNTBCI Chennai Tamil Nadu Chengalpattu603 002 India Department of Electronics and Communication Engineering Matrusri Engineering College Hyderabad Telangana Saidabad500 059 India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Nagar Thandalam Tamil Nadu Chennai602 105 India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Ramapuram campus Tamil Nadu Chennai500 089 India Department of Computer Science and Engineering Dhanekula Institute of Engineering and Technology Krishna District Andhra Pradesh Vijayawada521 139 India

Purpose: This manuscript proposes an IoT-based E-healthcare monitoring method for constantly monitoring the important signs of patient and diagnoses biological and behavioral changes through the technologies of smart healthcare. Method: Patient health monitoring system is used to deep learning techniques for the performance of patient categorization. Here, Hybrid whale and grey wolf optimization approach (Hyb-WGWO) is proposed to the selection of cluster head with various internet of things (IoTs) devices. These devices are utilized to sense the healthcare data into clusters form. In this, the cluster head is chosen by using the proposed Hyb-WGWO approach. Data in the cloud server are transmitted to the cluster head. Then deep ensemble multitask classification (DEMC) model is proposed for identifying patient disease conditions, such as low, normal, abnormal and critical. Results: The experimental outcomes of the proposed Hyb-WGWO-DEMC approach show 25.98% and 31.86% improved accuracy, 28.08%, 26.87% better specificity than the existing methods, such as Elliptic Decryption Algorithm in Elman Neural Network (EDA-ENN) and Grasshopper Optimization and Particle Swarm Optimization with cryptographic encryption (GOPSO-CE) respectively. Conclusion: IoT-based E-healthcare monitoring method achieves higher accuracy and lower error rate through the technologies of smart healthcare. © 2022, The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering.

关键词： Internet of things

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Monitoring the Heart Patient Status Using Hybrid ML with GSO Models in Cloud Computing 7

Monitoring the Heart Patient Status Using Hybrid ML with GSO...

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7th International Conference on electronics, communication and Aerospace Technology, ICECA 2023

作者： Sudhakar, K. Mahaveerakannan, R. Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350340600

The convergence of the IoT and the Cloud makes our lives better by allowing for frictionless communication between humans and inanimate objects. Projecting analytics in the medical arena may help transform a sensitive healthcare one, and cutting-edge artificial intelligence technologies are increasingly making their way into the healthcare industry. In order to provide early intervention and preventative therapy to people at risk, it is crucial to accurately predict the beginning of diseases. As the use of electronic clinical data spreads, it becomes more important than ever to refine existing prediction models. With larger medical datasets, clinicians have to put in more effort to understand complicated feature connections and diagnose illnesses in advance. Therefore, this research study simplifies the process of classifying heart disease by identifying the most important risk variables from a high-dimensional dataset. We have employed two datasets with a wide range of clinical characteristics for our investigation. To begin, we examined the interplay and dependency between a variety of clinical characteristics and coronary heart disease. Last but not least, we looked into a hybrid machine learning classification model (Support Vector Machine with K-Nearest Neighbour) by testing it with both full and reduced feature subsets. Accuracy, ROC curve, and F1-Score were used to assess the trained classifiers, while Galactic Swarm Optimisation (GSO) was used to choose the appropriate kernel weight of the SVM. When associated to models trained on a complete feature set, the presentation of classification models trained with a slighter feature set enhanced melodramatically with a reduced amount of training period. © 2023 IEEE.

关键词： Internet of things

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Implementation of Secure and Verifiable Access Control Procedures using the NTRU Cryptosystem to Store Big Data in the Cloud Environment

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Recent Patents on engineering 2025年第2期19卷 57-70页

作者： Babu B, Ravindra Saxena, Swati Widjaja, Gunawan Vinodha D, Vedha Omarov, Batyrkhan Ramaiah, Gurumurthy B. Vats, Prashant Adama Ethiopia Department of Computer Science KCG College of Technology Chennai India Universitas Krisnadwipayana Jawa Barat 13077 Indonesia Department of Electronics and Communication Engineering JCT College Of Engineering and Technology Pichanur Coimbatore India International University of Tourism and Hospitality Turkistan Kazakhstan Technical and Vocational Training Institute Addis Ababa Ethiopia Department of Computer Science and Engineering Faculty of Engineering and Technology SGT University Haryana Gurugram India

Background: Due to their complexity and size, deploying ciphertexts for clouds is considered the most useful approach to accessing large data stores. Methods: However, access to a user's access legitimacy and improving a decrypted text on the cloud depending on an improved access policy (AP) specified by the data owner are the key challenges for making large data storage realistic and effective in clouds. The traditional ways either totally remove the problem of AP development or offer renewal to arbiter power, but in real-time, enhancing the AP is essential to maximising security and handling agility. Results: In this patent paper, a safe and verifiable access control program characterised by the NTRU cryptographic system for large storage of data in the clouds is proposed. Primarily, an improved NTRU decryption protocol to deal with the decryption failures of the prime NTRU is established, and in addition, the program is analysed for its security strength and computational performance. When a new AP is specified by the data owner, the cloud server allows the program to improve ciphertext and allows the owner to verify the upgrade to oppose the cloud's fraudulent behaviour. Conclusion: It enables (i) checking the user's legitimacy to access the data owner and qualified users, and (ii) allowing the user to verify the data provided by other users for the recovery of the right user. Strong analysis can prevent and block delinquency from various attacks, namely the collusion attack that could potentially target fraud users. © 2025 Bentham science Publishers.

关键词： Cloud security

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Review On the Techniques Used for Detection of Fabric Defects Using AI 1

Review On the Techniques Used for Detection of Fabric Defect...

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1st IEEE International Conference on Advances in electronics, communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Batra, Isheeta Prasad, S A Hari Arvind, K.S. Faculty of Engineering & Technology Department of Computer Science and Engineering Karnataka India Faculty of Engineering & Technology Department of Electronics and Communication Engineering Karnataka India

ISBN: (纸本)9798350348057

The garment industry is the second-most polluting industry after oil. These mass-produced clothes if rejected are dumped and have an enormous impact on the environment. Therefore, to save the cost post production it is important to identify any sorts of defects pre-production when clothes are in a textile form, so that in case the fabric lacks somewhere in quality it can always be replaced, saving all the time and cost. This detection is possible using techniques like segmentation, or on the basis of texture or by using deep learning algorithms each having its own advantages and disadvantages to efficiently identify the defects in textile fabric. This paper is a comprehensive study of these three techniques and comparison made on basis of brief analysis which say that the deep learning technique is the best of the three for detection of defects keeping in contrast the type of research and developing technologies. © 2023 IEEE.

关键词： Deep learning

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A Quasi-Newton Neural Network Based Efficient Intrusion Detection System for Wireless Sensor Network

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computer Systems science & engineering 2023年第4期45卷 427-443页

作者： A.Gautami J.Shanthini S.Karthik Department of Electronics and Communication Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia Department of Computer Science&Engineering Dr.N.G.P Institute of TechnologyCoimbatoreTamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia

In Wireless Sensor Networks(WSN),attacks mostly aim in limiting or eliminating the capability of the network to do its normal *** this misbehaviour is a demanding *** so far the prevailing research methods show poor ***3 centred efficient Intrusion Detection Systems(IDS)is proposed in WSN to ameliorate the *** proposed system encompasses Data Gathering(DG)in WSN as well as Intrusion Detection(ID)*** DG,the Sensor Nodes(SN)is formed as clusters in the WSN and the Distance-based Fruit Fly Fuzzy c-means(DFFF)algorithm chooses the Cluster Head(CH).Then,the data is amassed by the discovered ***,it is tested with the trained *** IDS encompasses‘3’steps:pre-processing,matrix reduction,and *** pre-processing,the data is organized in a clear ***,attributes are presented on the matrix format and the ELDA(entropybased linear discriminant analysis)lessens the matrix ***,the output as of the matrix reduction is inputted to the QN3 classifier,which classifies the denial-of-services(DoS),Remotes to Local(R2L),Users to Root(U2R),and probes into attacked or Normal *** an experimental estimation,the proposed algorithm’s performance is contrasted with the prevailing *** proposed work attains an enhanced outcome than the prevailing methods.

关键词： Distance fruit fly fuzzy c-means(DFFF) entropy-based linear discriminant analysis(ELDA) Quasi-Newton neural network(QN3) remote to local(R2L) denial of service(DoS) user to root(U2R)

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