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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Bharathi, V. Saranya, A. Shukla, Garima Shekhawat, Deepika Awasthi, Vanshaj Kumar, C.N.S. Vinoth Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Mumbai India Amity University Department of Computer Science and Engineering Mumbai India Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur Chennai India

ISBN: (纸本)9798331533311

Natural and man-made disasters may have severe impacts, but disaster prediction can help lessen such effects. The natural catastrophes that strike India on a regular basis cause immense devastation in terms of human lives and material possessions. Predicting the start and development of disasters in real-time is essential for reducing their effect. For more precise and trustworthy catastrophe predictions, this research presents an AI-powered catastrophe Ensemble B-Classifier network (DE BCN). The DPEC ensemble technique improves forecast accuracy over individual models by integrating various predictive outputs from numerous machine learning models that specialise in distinct kinds of disasters. This is where the data was originally pulled from Kaggle. To finish off the preprocessing, the norma clash filter was used. The next step is to use the optimisation method known as mustard twin swarm to extract characteristics that are connected to the tragedy. With any luck, the calamity Ensemble-B Classifier network will be able to forecast the calamity. Python and the Kaggle database were the environments used to carry out the investigation. According to the results of the simulations, the suggested approach is better than the current standard for predicting the catastrophic state. © 2024 IEEE.

关键词： Prediction models

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A quantum federated learning framework for classical clients

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Science China(Physics,Mechanics & Astronomy) 2024年第5期67卷 1-10页

作者： Yanqi Song Yusen Wu Shengyao Wu Dandan Li Qiaoyan Wen Sujuan Qin Fei Gao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 00876China Department of Physics The University of Western AustraliaPerthWA 6009Australia School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing100876China

Quantum federated learning(QFL)enables collaborative training of a quantum machine learning(QML)model among multiple clients possessing quantum computing capabilities,without the need to share their respective local ***,the limited availability of quantum computing resources poses a challenge for each client to acquire quantum computing *** raises a natural question:Can quantum computing capabilities be deployed on the server instead?In this paper,we propose a QFL framework specifically designed for classical clients,referred to as CC-QFL,in response to this *** each iteration,the collaborative training of the QML model is assisted by the shadow tomography technique,eliminating the need for quantum computing capabilities of ***,the server constructs a classical representation of the QML model and transmits it to the *** clients encode their local data onto observables and use this classical representation to calculate local *** local gradients are then utilized to update the parameters of the QML *** evaluate the effectiveness of our framework through extensive numerical simulations using handwritten digit images from the MNIST *** framework provides valuable insights into QFL,particularly in scenarios where quantum computing resources are scarce.

关键词： client server quantum

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A Comprehensive Analysis on Clustering and Routing Techniques in Wireless Sensor Network

A Comprehensive Analysis on Clustering and Routing Technique...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Samson Paul, K. Prabhu Chakkaravarthy, A. Veeresha, M. Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India Dr.K.V.Subba Reddy Institute of Technology Department of Computer Science and Engineering Kurnool India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360660

Wireless Sensor Networks (WSN) is utilized as front-end communication systems in home automation, environment monitoring, industry, Internet of Things (IoT), etc. The extension of the lifetime of WSNs enhances the utilization of various applications and systems to communicate and interact efficiently. However, cluster heads (CHs) and relay nodes face the difficulties of energy balancing, nodes having limited capability for storage systems, communication, resource management and computational power which further results in corrupted nodes and data becoming vulnerable to improper access. The optimization algorithms namely Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Butterfly Optimization Mechanism (BOM), and Fuzzy Logic (FL)-based techniques are used for reduce the delay in CH selection and routing protocols. This research aims to research the strengths, relationships, and challenges of delay-aware clustering and routing for WSN-based approaches, thereby minimizing the EC and increasing network lifetime. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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An Energy-Efficient Protocol for Internet of Things Based Wireless Sensor Networks

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computers, Materials & Continua 2023年第5期75卷 2397-2412页

作者： Mohammed Mubarak Mustafa Ahmed Abelmonem Khalifa Korhan Cengiz Nikola Ivkovic Department of Information Technology Sri Krishna College of Engineering&TechnologyCoimbatoreIndia Department of Networking and Security College of Information TechnologyUniversity of FujairahFujairahUAE Switching and Traffic Department National Telecommunication InstituteCairoEgypt Department of Computer Engineering Istinye UniversityZeytinburnuIstanbulTurkey Faculty of Organization and Informatics University of ZagrebVarazdinCroatia

The performance of Wireless Sensor Networks(WSNs)is an important fragment of the Internet of Things(IoT),where the current WSNbuilt IoT network’s sensor hubs are enticing due to their critical *** grouping hubs,a clustering convention offers a useful solution for ensuring energy-saving of hubs andHybridMedia Access Control(HMAC)during the course of the ***,current grouping standards suffer from issues with the grouping structure that impacts the exhibition of these conventions *** this investigation,we recommend an Improved Energy-Proficient Algorithm(IEPA)for HMAC throughout the lifetime of the WSN-based *** consecutive segments are *** the covering of adjusted clusters,an ideal number of clusters is determined ***,fair static clusters are shaped,based on an updated calculation for fluffy cluster heads,to reduce and adapt the energy use of the sensor *** heads(CHs)are,ultimately,selected in optimal locations,with the pivot of the cluster heads working among cluster ***,the proposed convention diminishes and balances the energy utilization of hubs by improving the grouping structure,where the IEPAis reasonable for systems that need a long *** assessment results demonstrate that the IEPA performs better than existing conventions.

关键词： Energy consumption improved energy-proficient algorithm internet of things wireless sensor network

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Towards Enhanced Deep CNN for Early and Precise Skin Cancer Diagnosis 1

Towards Enhanced Deep CNN for Early and Precise Skin Cancer ...

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1st IEEE International Conference on networking and Communications, ICNWC 2023

作者： Malaiarasan, S. Ravi, R. Maheswari, D.R. Yesubai Rubavathi, C. Ramnath, M. Hemamalini, V. Francis Xavier Engineering College Department of Computer Science and Engineering Tamil Nadu Tirunelveli India Thamirabharani Engineering College Department of Computer Science and Engineering Tamil Nadu Tirunelveli India School of Computing SRMIST KTR Department of Networking and Communications Tamil Nadu Chennai India

ISBN: (纸本)9798350336009

Most people's first experience with cancer will be with skin cancer, which is also the most prevalent and potentially fatal kind. Determining a skin cancer diagnosis also requires the use of information technologies. This highlights the need of developing and deploying highly effective deep-learning methods for the early and accurate diagnosis and detection of skin cancer. Deep Convolution Neural Network (DCNN) is proposed for automated skin cancer detection in this study. This study's unique contribution is the use of a deep convolution neural network containing 12 nested processing layers to improve the accuracy of skin cancer diagnosis and detection. As a consequence of this study's findings, researchers have determined that deep learning techniques are superior to machine learning for spotting skin cancer. As a consequence, pathologists' precision and competence may be improved by using automated evidence-based detection of skin cancer. To accurately distinguish between benign and malignant skin lesions, we present a deep convolution neural network (DCNN) model in this research that uses a deep learning technique. First, we normalize the input photos and identify characteristics that aid in correct classification, then we apply a filter or Gaussian to eliminate noise and artifacts, and lastly, we supplement the data to increase the number of images, which enhances the accuracy of the classification rate. © 2023 IEEE.

关键词： Image enhancement

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Urban Slum Mapping Using Homogeneous Urban Patches 3

Urban Slum Mapping Using Homogeneous Urban Patches

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Prabhu, R. Priya, M. Sangeetha Dayana, R. Suresh, M. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur Campus Chennai India Saveetha Engineering College Department of Computer Science and Engineering Thandalam Chennai India Jeppiaar Institute of Technology Department of Computer Science and Engineering Sriperumbudur Chennai India R.M.D. Engineering College Kavaraipettai Department of Mathematics Thiruvallur India

ISBN: (纸本)9798350361186

Slums (Informal Settlements) are the most dynamic areas whose geographical locations are inaccurate according to official statistics and maps because of the exponential expansion of urbanization strategies. As a result, a method has to be developed to distinguish between informal settlements and track their spatial patterns. This paper presents an integrated method for detecting slums from satellite images that employs homogeneous urban patches (HUP) and grey level co-occurrence matrix (GLCM)-based image segmentation. In order to extract meaningful spatial features, urban patches are segmented according to their homogeneity and GLCM analysis provides useful texture information about slum areas. The combination of both these GLCM and HUP features allows for the creation of a classification map that accurately identifies and maps slum areas. We utilized WorldView-2 (1.84m) satellite imagery of Madurai to test the effectiveness of proposed HUP approach. The validation process includes rigorous testing against ground truth data, with careful consideration given to the selection of training and testing datasets. The effectiveness of the proposed method is evaluated using metrics including overall accuracy, user accuracy, and producer accuracy Furthermore, the proposed algorithm outperformed existing GLCM-based approaches in terms of accuracy. © 2024 IEEE.

关键词： Image segmentation

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Attention-Aided Autoencoder-Based Channel Prediction for Intelligent Reflecting Surface-Assisted Millimeter-Wave Communications

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and networking 2023年第4期7卷 1906-1919页

作者： Chen, Hong-Yunn Wu, Meng-Hsun Yang, Ta-Wei Huang, Chih-Wei Chou, Cheng-Fu National Taiwan University Graduate Institute of Networking and Multimedia Taipei10617 Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan National Central University Department of Communication Engineering Taoyuan320317 Taiwan

Sixth-generation (6G) wireless communication networks will provide larger coverage and capacity with lower energy consumption and hardware costs than 5G. Intelligent reflecting surface (IRS)-aided millimeter-wave massive MIMO OFDM communication is a new technology that intelligently manipulates electromagnetic waves. This has recently attracted much attention given its potential to manage the wireless propagation environment at low hardware costs and with minimal energy usage. However, channel prediction is complicated by the fact that IRS is rarely equipped with power amplifiers, various radio frequency chains, or a significant number of reflecting components. In this paper, we propose a convolutional denoising autoencoder model and investigate a joint attention mechanism for channel prediction. Then, we employ the attention mechanism to identify features of channel subcarrier interference to improve the channel prediction performance. Long-range dependent specificity is captured through the attention mechanism to generate useful features from the input signal. The encoder-decoder design of the autoencoder serves as a dimensionality reduction method that enables the autoencoder to predict the spatial and temporal distribution features of continuous signals by exploiting the extraction of sequence features from the model. Numerical results show that the proposed algorithm significantly improves the performance of IRS-aided millimeter-wave massive MIMO OFDM communication systems compared with previous methods. © 2017 IEEE.

关键词： Orthogonal frequency division multiplexing

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Convolutional Neural Network Model based Deep Learning Approach for Osteoporosis Fracture Detection 1

Convolutional Neural Network Model based Deep Learning Appro...

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1st IEEE International Conference on networking and Communications, ICNWC 2023

作者： Dhanalakshmi, R. Thenmozhi, M. Saxena, Swati Mahalingam, Hemalatha Vellore Institute of Technology School of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India KCG College of Technology Department of Computer Science and Engineering Chennai India King Abdulaziz University Faculty of Engineering Department of Electrical and Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798350336009

Osteoporosis is a bone ailment which takes place because of minimum bone physique, damaging of micro-structure of bone, more over an excessive vulnerability to breakage. The main fitness difficulty throughout the globe is Osteoporosis, particularly in aged people. It may create spinal or hip breakages which can result in morbidity and burden. Hence the diagnosis of Osteoporosis at early stages and forecasting the existence of the fracture is highly essential. However automated analysis and diagnosis of osteoporosis from virtual radiographs could be very difficult as they have little variations. The proposed approach in this work uses high-dimensional textured function representations calculated from radiography pictures to distinguish healthy from osteoporotic issues. CNN helps to identify osteoporosis using structural MRI measurements of bone with high accuracy © 2023 IEEE.

关键词： Bone

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Detecting Anomalies for corn crop disease using Isolation forest 1

Detecting Anomalies for corn crop disease using Isolation fo...

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1st IEEE International Conference on networking and Communications, ICNWC 2023

作者： Sowmiya, K. Thenmozhi, M. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350336009

Today, the most challenging issue for researchers is to continue their research for crop disease using deep learning and image processing. One of the crucial detection methods used in one-class classification to find the outliers in the dataset is anomaly detection. The isolation forest approach is used in this work to discover anomalies using the corn dataset from Kaggleas a benchmark dataset. To find abnormalities in the provided dataset, this algorithm mimics a binary decision tree technique. This algorithm's classification accuracy is 89 percent. For the corn dataset, the experimental findings of the isolation forest show improved outcomes. Other anomaly identification methods will eventually combine anomaly scores and new datasets on leaf disease. © 2023 IEEE.

关键词： Image processing

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Survey on Weeding Tools, Equipment, AI-IoT Robots with Recent Advancements

Survey on Weeding Tools, Equipment, AI-IoT Robots with Recen...

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2023 International Conference on Integration of Computational Intelligent System, ICICIS 2023

作者： Michael, J Justina Thenmozhi, M. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350318784

Agriculture is the backbone of many countries across the globe. Nevertheless, the challenges faced by farmers with an aim to provide quality food are enormous. From among these difficulties, weeding is the most troublesome. Right from the neolithic era till this modern era, there exist numerous solutions for eradicating weeds. Modern technologies support agriculturalists to handle these weeds in overcoming the issues. However, there still exists a gap in satisfying the real needs of a farmer. This paper presents a survey of weeds and their problems, categories of weeds, consequences of herbicide, the importance of weed removal, weeding tools, and equipment, modern AI-weeding robots, algorithms used for weed identification, and mobile applications used for weed detection. This paper also focuses on state-of-the-art techniques for weed identification and removal around the world. Future works in the area of weeds were also discussed before concluding. © 2023 IEEE.

关键词： Agricultural robots

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