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AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation 42

AOCC-FL: Federated Learning with Aligned Overlapping via Cal...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Wang, Haozhao Xu, Wenchao Fan, Yunfeng Li, Ruixuan Zhou, Pan Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China The Hong Kong Polytechnic University Department of Computing Hong Kong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350334142

Federated Learning enables collaboratively model training among a number of distributed devices with the coordination of a centralized server, where each device alternatively performs local gradient computation and communication to the server. FL suffers from significant performance degradation due to the excessive communication delay between the server and devices, especially when the network bandwidth of these devices is limited, which is common in edge environments. Existing methods overlap the gradient computation and communication to hide the communication latency to accelerate the FL training. However, the overlapping can also lead to an inevitable gap between the local model in each device and the global model in the server that seriously restricts the convergence rate of learning process. To address this problem, we propose a new overlapping method for FL, AOCC-FL, which aligns the local model with the global model via calibrated compensation such that the communication delay can be hidden without deteriorating the convergence performance. Theoretically, we prove that AOCC-FL admits the same convergence rate as the non-overlapping method. On both simulated and testbed experiments, we show that AOCC-FL achieves a comparable convergence rate relative to the non-overlapping method while outperforming the state-of-the-art overlapping methods. © 2023 IEEE.

关键词： Learning systems

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A Hybrid Approach to Intrusion Detection in Wireless Sensor Networks using Deep Learning

A Hybrid Approach to Intrusion Detection in Wireless Sensor ...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Lakshmi, B. Jain, Upma Venkatesh, J. Rao, K. B. V. Brahma Lakshmi, B. Gulati, Jivantika Deemed to Be University School of Computing Sastra Tiruchirappalli India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India Madanapalle Institute of Technology and Science Department of Computer Science and Technology Madanapalle India Department of Law Pune India

ISBN: (纸本)9798350375442

There is a vast array of fields making use of WSN, including healthcare, transportation, combat, surveillance, environment, and more. Their limited energy resources, communication capabilities, and processing power define them. Because of their design and placement in dangerous, unsupervised areas, they are susceptible to assaults. The network's overall security is being enhanced with the introduction of new techniques. The three mainstays of WSN security - intrusion prevention, intrusion detection, and mitigation - are usually implemented together. The Intrusion Detection System (IDS), the second line of defense in a wireless sensor network, receives the algorithms and schemes needed to detect intrusions at various levels from this study. Hierarchical sensor networks, which are the building blocks of this study's sensor network design, include clustering and a tree-like structure for communication with the base station. At each architectural level - the node, the cluster head, and the base station - a hybrid intrusion detection system is deployed. The data modification assault was detected with the use of temporal correlations of sensor signals in the node level intrusion detection system. To identify the discrepancy between the detected value and the threshold value, data anomaly is calculated, updated, and compared. Accuracy and precision are considered while deciding on a threshold value. Handling data with care. The cluster-level intrusion detection system uses the Radial Basis function to identify malicious patterns, malicious flooding assaults, and selfish misbehavior. In order to minimize the number of iterations required for RBF training, the optimization techniques of Self Organizing Map and Particle Swarm Optimization are used. The perfect RBF is taught to identify sinkhole attacks, anomalous behavior, selective forwarding, and self-serving misconduct in base station intrusion detection systems. In order to coordinate the three IDS and identify the assaul

关键词： Sensor nodes

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Enhancing Healthcare Data Security in Cloud computing Systems through Advanced Homomorphic Encryption Techniques Integrated with Cuckoo Search Optimization Algorithm

Enhancing Healthcare Data Security in Cloud Computing System...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Sudha, I. Ramesh, P.S. Ponmalar, A. Jagannathan, J. Pandiarajan, Rajkumar Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India R.M.K. Engineering College Tamilnadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India Kalasalingam Academy of Research and Education School of Computing Department of Information and Technology Krishnankoil Virudhunagar India

ISBN: (纸本)9798350388602

The studies in this research are focused on the mix of homomorphic encryption mechanisms and the Cuckoo Search Optimization Algorithm to promote the security of the healthcare data in the cloud computing system. I have observed that cloud solutions are now at the center of the contemporary health care systems, and security of patient's data remains a top priority. Earlier used encryption techniques, though secure if implemented, have problems in the way that data is to be processed first it has to be decrypted and then made ready for use - it is in this phase that data can be easily hacked into. Homomorphic encryption provides solution to this issue since it enables one to perform computations on encrypted data while maintaining its confidentiality. However, it takes a lot of computation which presents a lot of problems even in terms of simple performance. The Cuckoo Search Algorithm, a new nature-inspired metaheuristic, is incorporated into the encryption process to provide further optimization, whilst preserving the security benefits of the algorithm. The use of the proposed methodology takes place employing Python and its deployment at the cloud platform with elaborated dataset containing anonymized records extracted from electronic health records. The outcome proves that there are great enhancements in encryption and decryption time, computational complexity, and capacity, and thereby making the system appropriate for real-world healthcare solutions. This research offers a strong base for protection of healthcare data in cloud and is useful for the enhancement of adaptive, comprehensive and secure cloud computing systems. © 2024 IEEE.

关键词： Electronic health record

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Revolutionizing Lion Conservation with EfficientNet B7: Real-Time Detection and Health Classification

Revolutionizing Lion Conservation with EfficientNet B7: Real...

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Automation, computing and Renewable Systems (ICACRS), International Conference on

作者： Sampath Kumar Koradaa Syam Virat Gupta Potnuru C. Ashok Kumar C. Jothi Kumar Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

This research introduces a cutting-edge framework leveraging advanced AI and IoT technologies to revolutionize lion conservation. The primary focus is on real-time lion detection, individual identification using unique facial features, gender classification, and health condition assessment, including malnutrition, injuries, and illnesses. By integrating GPS modules and IoT sensors, the system facilitates noninvasive and ethical monitoring of lions and their habitats. The study employs YOLO (You Only Look Once) for real-time detection of lions in video feeds and EfficientNet B7 for multitask classification, enabling precise identification of individual lions and their health status. DeepSORT is utilized for tracking lion movements over time, ensuring continuous observation and territorial mapping. These models are deployed on edge devices, such as drones and camera traps, to deliver instantaneous results while minimizing latency. The proposed system offers numerous advantages, including improved conservation efforts through real-time alerts and actionable insights. Its ability to monitor health conditions and movement patterns enables proactive interventions to safeguard lion populations. Moreover, the integration of IoT sensors ensures comprehensive environmental monitoring, supporting a holistic understanding of the ecosystem. By employing cloud platforms for data analytics and storage, the system provides accessible dashboards and detailed reports, empowering conservationists and wildlife authorities with valuable insights. This framework not only enhances the efficiency of lion conservation but also ensures ethical practices, setting a benchmark for integrating AI and IoT in wildlife preservation. The research underscores the potential of technology to address critical ecological challenges and promote sustainable conservation efforts.

关键词： Biometrics YOLO Accuracy Biological system modeling Wildlife Habitats Real-time systems Sensors Environmental management Injuries

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Optimization Study of Renal CT Image Classification Using Convolution Neural Networks 14

Optimization Study of Renal CT Image Classification Using Co...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Afnaan, K. Nagaraja, K.V. Singh, Tripty Bhanu Prakash, K.N. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mathematics Bengaluru India A∗STAR Clinical Data Analytics and Radiomics Singapore

ISBN: (纸本)9798350335095

Computer tomography (CT), also known as computed tomography, is a medical imaging method that generates detailed and precise horizontal or axial images of targeted regions of the body for diagnostic purposes. In this particular study, the focus was on performing image classification of CT scan images of the Kidney. This organ plays an essential role in detoxification, fluid balance, and maintaining electrolyte levels in the human body. The dataset used for the study contained 12,446 images into four labeled classes - Cyst, Tumor, Stone, and Normal. Convolution Neural Network (CNN) model is built for binary (Normal and Tumor) and multi-image Classification to classify the Renal CT images into four classes. CNN uses ReLU as an activation function for classifying these images. The Model is evaluated and compared based on Accuracy and Loss performance metrics by compiling the CNN model with different deep learning TensorFlow Keras optimizers for Version 2.11.0 for Binary Image Classification at 10,20, and 50 epochs. The CNN model is assembled with Adam Optimizer and Follows the Regularized Leader (FTRL) Optimizer. The Testing and training Accuracy and Losses achieved are evaluated with a learning rate of 0.001 and 0.0001. Adam Optimizer outperforms to be the best Optimizer for Binary and Multi-Image Classification. © 2023 IEEE.

关键词： Image classification

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Basketball Win Percentage Prediction using Ensemble-based Machine Learning 6

Basketball Win Percentage Prediction using Ensemble-based Ma...

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6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Sikka, Dhruv Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur603203 India

ISBN: (纸本)9781665482714

Basketball is a prominent team sport played on a rectangular court between two teams of five players each. The goal is to shoot the ball through the defender's hoop, which is high on a backboard at each end of the court, while stopping the other team from shooting through its own hoop. Predicting how sports will turn out is an interesting sports analytics problem because it gives valuable data for running the sports market. In this work, data from NBA (National Basketball Association) basketball teams is analyzed, and a machine learning model is made that uses advanced statistical measures and performance data to evaluate and predict the winning percentage of different teams. Voting Regression is used to put together algorithms like Random Forest, Decision Trees, Linear Regression, Gaussian Regression, and Gradient Boosting into a single unit that can predict a team's win percentage over the course of a season with 93.3% accuracy. © 2022 IEEE.

关键词： Forecasting

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Novel Virtual-Ground Single-Phase Buck-Boost Inverter 11

Novel Virtual-Ground Single-Phase Buck-Boost Inverter

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11th International Conference on Power Electronics - ECCE Asia, ICPE 2023-ECCE Asia

作者： Akbar, Fazal Elgenedy, Mohamed Elkhateb, Ahmad Cha, Honnyong Park, Jung-Wook Queen's University School of Electronics Electrical Engineering and Computer Science Belfast United Kingdom Glasgow Caledonian University School of Computing Engineering and Built Environment United Kingdom Kyungpook National University School of Energy Engineering Daegu Korea Republic of Yonsei University School of Electrical and Electronics Engineering Seoul Korea Republic of

ISBN: (纸本)9788957083505

Due to variations in irradiance, temperature, and shading effect, the terminal voltages of photovoltaic panels are variable and therefore buck-boost inverters are required. This article presents a new buck-boost voltage-source inverter (VSI) topology. The proposed VSI is transformerless and has virtual-ground feature, which in terms mitigate the common-mode voltage issue of transformerless inverters and thus resulting in low leakage current. Detailed circuit analysis is provided based on the proposed modulation strategy in which four switches operate at line frequency and hence high power conversion efficiency can be realized. Furthermore, experimental results are provided with 500 W prototype in support of theoretical analysis and effectiveness of the proposed inverter. © 2023 The Korean Institute of Power Electronics.

关键词： Photovoltaic effects

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Deep Learning Algorithm for Brain Tumor Detection and Classification using MRI Images 1

Deep Learning Algorithm for Brain Tumor Detection and Classi...

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1st IEEE International Conference on Applied Intelligence and Sustainable computing, ICAISC 2023

作者： Harshavardhan, A. Maheswari, N. Uma Prakash, M. Sammeta, Naresh VNR Vignana Jyothi Institute of Engineering and Technology Department of CSE Hyderabad India Panimalar Engineering College Department of Computer Science and Business Systems India Vellore Institute of Technology School of Computing Science and Engineering Vellore India R.M.K College of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350323795

Brain tumor classification is beneficial for identifying and diagnosing the tumor's specific location. According to the medical imaging system, early diagnosis and categorization of a tumor extend a person's life. Clinical specialists rely heavily on magnetic resonance imaging (MRI) among numerous imaging modalities since it provides contrast information on brain malignancies. The primary purpose of this project is to use a competent automated approach that improves tumour identification accuracy. Several segmentation strategies have been developed throughout the years to achieve and improve the categorization precision of brain tumours. Brain picture segmentation has long been recognised as a difficult and time-consuming aspect of medical image processing. This method for detecting brain tumors Brain pictures are classified using the Full Resolution Convolutional Network (FRCN) classification architecture after pre-processing and segmentation. This study presents a Full Resolution Convolutional Network (FRCN) with Support Vector Machine (SVM) approach for detecting tumors on MRI scans. The procedure is broken down into four steps. In the first phase, the anisotropic filter is utilized to pre-process raw MRI images, followed by segmentation using the Support vector machine (SVM) and skull classification. The singular value decomposition and primary component analysis operations are performed in the third step. Tumors are then detected and classified using the Full Resolution Convolutional Network (FRCN) approach. Simultaneously, the Support Vector Machine (SVM) technique is employed to improve the classification precision of the study model. The experimental results showed an amazing accuracy rate of nearly 100% in detecting both normal and diseased tissues from brain MR images, confirming the efficacy of the suggested technique. © 2023 IEEE.

关键词： Support vector machines

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A Deep Learning Approach for ECG-Based Cardiac Anomaly Detection with Improved Feature Extraction 2

A Deep Learning Approach for ECG-Based Cardiac Anomaly Detec...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Thanjaivadivel, M. Purandhar, N. Bhasker, B. Ganesh, A. Reva University School of Computing and Information Technology Karnataka Bengaluru India Madanapalle Institute of Technology & Science School of Computers Andhra Pradesh Madanapalle India Sri Venkateswara College of Engineering Department of Cse Andhra Pradesh Tirupati India

ISBN: (纸本)9798350388916

Heart disease is a global health concern that requires accurate and timely prediction tools for successful management and treatment. Electrocardiogram (ECG) data analysis helps discover cardiac anomalies by revealing heart function and risk factors. ECG data is complicated and variable, making interpretation and prediction difficult. Researchers have used preprocessing, feature extraction, and machine learning algorithms to enhance heart disease prediction. Ensemble learning can improve predictive performance by combining model predictions. This study attempts to improve cardiac risk assessment and patient outcomes by merging multiple *** goal of this paper is to improve heart disease prediction accuracy by evaluating ECG data using machine learning techniques. To improve cardiac abnormality identification, preprocess ECG images, extract key characteristics, and use ensemble learning. The work investigates signal contour extraction and dimensionality reduction to provide a comprehensive cardiac risk assessment *** study uses a multi-step technique to improve heart disease prediction using ECG data. Preprocessing ECG pictures with signal contour extraction, grayscale conversion, and Gaussian blurring improves data quality and clarity. To find predictive modelling features, feature extraction methods like PCA dimensionality reduction are used. Ensemble learning methods like the Voting Classifier combine predictions from numerous machine learning models to increase predictive performance. Hyperparameter adjustment improves model performance, achieving 98% testing set correctness. This study's methods give a complete foundation for precise and fast cardiac risk assessment. © 2024 IEEE.

关键词： Dimensionality reduction

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Enhancing Skin Disease Classification and Privacy Preservation through Federated Learning-Based Deep Learning 1

Enhancing Skin Disease Classification and Privacy Preservati...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Tiwari, Raj Gaurang Maheshwari, Himani Gautam, Vinay Agarwal, Ambuj Kumar Trivedi, Naresh Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computing Graphic Era Hill University Uttrakhand Dehradun India Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350330915

Human skin is a very sensitive part and is infected with various types of issues. Skin disease is one of them and 70% to 80% of the population is infected with one of the skin diseases. Expensive clinical screening and dermatological analysis are the only solutions to detect skin disease. Legacy classification methods are not appropriate to tackle the issues related to the color, texture, and privacy of the skin disease dataset. Both deep learning(DL) techniques and federated learning(FL)can be better solutions for classification and privacy issues related to skin datasets. The collaboration of both in the medical field can be a significant contribution. In this paper, federated learning-based deep learning was applied to a publicly available dataset consisting of 10 different skin diseases. Various deep learning models were applied, and privacy concerns were preserved with a federated learning approach. Here, the dataset was enhanced with several image augmentation strategies. In this research, several models were used and after observation, it was found that the InceptionNet outperformed and produced a better accuracy rate of 98.89%. In this research, complete experiments were set with varying client values and communication rounds. © 2023 IEEE.

关键词： classification convolution neural network deep learning federated learning healthcare privacy preservation skin disease

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