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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Shwethashree, G.C. Manjula, S. Sri Jayachamarajendra College of Engineering Jss Science and Technology University Department of Computer Science and Engineering Mysore India

ISBN: (纸本)9798350397284

In recent years, there has been an increasing demand for edge computing, which utilizes resources at the edge of the network to improve response times and reduce bandwidth requirements. However, existing methods for managing and securing edge computing resources have failed to meet the Quality of Service (QoS) and Service Level Agreement (SLA) requirements of many applications. This has led to a need for a new approach that can address these requirements while maintaining the security and reliability of the system. This survey presents an overview of a new Security Aware Resource Management Framework (SARMF), focusing on how it addresses the QoS and SLA requirements of edge cloud computing. The study begins by discussing the challenges faced by existing methods, and how the SARMF is designed to overcome these challenges. This study has provided a detailed description of the SARMF, including its architecture, key components, and how it operates. It describes how the SARMF uses machine learning algorithms to analyze data on resource usage and network traffic patterns, and how it adapts to changing circumstances to ensure that resources are allocated effectively and that the system remains secured against new and evolving security threats. This study also discusses about the benefits of the SARMF, including improved accuracy and efficiency of resource allocation, reduced risk of security incidents and disruptions, and the ability to meet the QoS and SLA requirements of different applications and organizations. Overall, this study provides an in-depth analysis of the SARMF, highlighting how it addresses the QoS and SLA requirements of edge cloud computing in a secured and efficient manner. By providing a comprehensive overview of the SARMF, this research study aims to help researchers and practitioners understand how this framework can be used to improve the management and security of computing resources in edge cloud computing environments. © 2023 IEEE.

关键词： Internet of things

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Blockchain-Powered Student Feedback for Enhanced Learning Experiences

Blockchain-Powered Student Feedback for Enhanced Learning Ex...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Naik, Girish R. Naik, Poornima G. KIT's College of Engineering Department of Mechanical Engineering Kolhapur India Csiber School of Computer Science and Applications Kolhapur India

ISBN: (纸本)9798350319019

In the contemporary landscape of education, the invaluable input provided by students through feedback is widely acknowledged as pivotal in elevating the quality of teaching and learning experiences. Yet, conventional feedback systems encounter notable constraints, notably in the realms of data security and transparency. Current research is dedicated to exploring the integration of blockchain technology as a robust and secure framework for collecting student feedback. Blockchain's distinct features, including its assurance of data integrity, transparency mechanisms, and the utilization of smart contracts, hold the promise of significantly refining the feedback process. This research meticulously outlines the academic significance of student feedback systems, defines essential components for effective feedback mechanisms, and elucidates the role of blockchain in constructing such a system. Moreover, it presents a conceptual model design, details the system's architecture, and elucidates the functionalities of smart contracts developed using Solidity. By leveraging blockchain, this innovative approach ensures the security, transparency, and streamlined efficiency of the feedback process, proclaiming unprecedented potential for transformative advancements in educational methodologies. © 2024 IEEE.

关键词： Smart contract

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Application of Machine Learning for Detection and Prediction of Malaria: A Review 3

Application of Machine Learning for Detection and Prediction...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Apetorgbor, Mawuli Wankhede, Vidhi Dakhare, Kajal Verma, Prateek Raut, Yudhishthir Tripathi, Chanchla Department of Science and Technology Maharashtra Wardha India Faculty of Engineering and Technology Maharashtra Wardha India Pimpri Chichwad University Department of Computer Science and Engineering Maval Sate Pune India Yeshwantrao Chavan College of Engineering Department of Computer Science and Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350381931

Mosquitoes carries a parasitic infection that affects millions of peoples globally. The initial and meticulous prediction of outbursts, patient verdict, and treatment enhancement can all considerably diminish the consequences of malaria. Machine learning is a promising method to tackle this problem with its capability to unveil and understand complex patterns from broad and multifaceted dataset. Accuracy of drug dosage and disease management in healthcare industry has proven phenomenal accuracy with use of machine learning algorithm. The current analysis explores the application of machine learning for prediction of malaria cases. This paper showcase the inclusion of numerous data source, comprising environmental factors, medical history and epidemiologic information to demonstrate how machine learning influences this to abundance of data to enhance prediction accuracy. This study also enhances the major advancements in prior malaria detection that have been made feasible and practicable by machine learning driven symptomatic technologies such prompt analytical test and image analysis of blood taints. Concerns like model comprehensibility, data secrecy, and equitable distribution of healthcare assets are also concealed in this review study. © 2024 IEEE.

关键词： Forecasting

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Design of Energy Efficient WSN Using a Noble SMOWA Algorithm

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computers, Materials & Continua 2022年第8期72卷 3585-3600页

作者： Avishek Banerjee Deepak Garg Victor Das Laxminarayan Sahoo Ira Nath Vijayakumar Varadarajan Ketan Kotecha Department of Computer Science and Engineering Bennett UniversityGreater Noida201310India Department of Information Technology Asansol Engineering CollegeAsansol713305India Department of Computer and Information Science Raiganj UniversityRaiganj733134India Department of Computer Science and Engineering JIS College of EngineeringKalyani741235India School of Computer Science and Engineering University of New South WalesSydneyAustralia Symbiosis Centre for Applied Artificial Intelligence Symbiosis International(Deemed University)PuneIndia

In this paper,the establishment of efficientWireless Sensor Network(WSN)networks has been projected to minimize the consumption of energy using a new Self-adaptive Multi-Objective Weighted Approach(SMOWA)algorithm for solving a multi-objective *** Different WSN nodes deployment policies have been proposed and applied in this paper to design an efficientWireless Sensor Network to minimize energy *** that,the cluster head for each cluster has been selected with the help of the duty *** configuring the WSN networks,the SMOWA algorithms have been developed to obtain the minimum energy consumption for the *** minimization,as well as the amount of day-saving,has been calculated for the differentWSNswhich has been configured through different deployment *** major finding of the research paper is to improve the durability of Wireless Sensor Network(i)applying different deployment strategies:(Random,S pattern and nautilus shell pattern),and(ii)using a new Meta-heuristic algorithm(SMOWA Algorithm).In this research,the lifetime of WSN has been increased to a significant *** choose the best result set from all the obtained results set some constraints such as“equivalent distribution”,“number of repetitions”,“maximum amount energy storage by a node”has been set to an allowable range.

关键词： Wireless Sensor Network(WSN) Self-adaptive Multi-Objective Weighted Approach(SMOWA) deployment strategies Meta-heuristicMethods energy minimization duty cycle

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Enhancing Credit Card Fraud Detection through LSTM-Based Sequential Analysis with Early Stopping 2

Enhancing Credit Card Fraud Detection through LSTM-Based Seq...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Jhansi Ida, S. Balasubadra, K. Skandarsini, R.R. Lakshmi Narayanaa, T. RMK Engineering College Dept. of Computer Science and Business Systems Thiruvallur India RMD Engineering College Department of Information Technology Thiruvallur India

ISBN: (纸本)9798350365269

There is still a serious problem with credit card fraud in the world of financial transactions, requiring innovative solutions to effectively detect and halt illicit behavior. This research presents a unique method of detecting credit card fraud by strategically implementing early pausing in a Long Short-Term Memory (LSTM) neural network. Leveraging the sequential dependencies inherent in transaction data, the LSTM model demonstrates remarkable efficacy in discerning fraudulent from legitimate transactions. The early stopping mechanism ensures that the model's training halts at the optimal epoch, preventing over fitting and enhancing generalization. Our thorough analysis highlights the model's versatility by demonstrating a dynamic compromise between recall and accuracy throughout training. Practical implications include enhanced fraud detection accuracy, reduced false alarms, and real-time decision support for financial institutions. The model's adaptability and generalization make it a valuable asset in the ongoing battle against credit card fraud, contributing to a more secure financial environment. Future research directions may explore additional features and alternative architectures to further advance fraud detection capabilities. © 2024 IEEE.

关键词： Long short-term memory

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Prediction of Lung Disease from Respiratory Sounds Using Convolutional Neural Networks 5th

Prediction of Lung Disease from Respiratory Sounds Using Con...

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5th International Conference on Recent Innovations in Computing , ICRIC 2022

作者： Rajadevi, R. Devi, E. M. Roopa Dharani, M.K. Logeswaran, K. Dineshkumar, S. Chandru, G. Mohan Department of Information Technology Kongu Engineering College Perundurai India Department of Computer Science and Engineering Kongu Engineering College Perundurai India

ISBN: (纸本)9789819906000

Recently, the pollution in the world is leading into many diseases which are incurable at current situation, and also the treatments are not in the leading position. These ailments have an impact on the tubes (airways) that carry gases like oxygen into and out of the lungs. They frequently obstruct or tighten airways. Airway ailments include chronic, asthma, obstructive pulmonary disease (COPD), and bronchiectasis. People with airway illnesses frequently describe their sensation as "attempting to exhale through a straw". The main aim is to detect the lung diseases that are affected in humans and that are very vulnerable to the human bodies. Currently, a collective system for identifying and recommending lung problems is not easily available to a large number of people, and it is technically implemented by machine learning algorithms. This research makes use of deep learning techniques to learn from the respiratory audio signal of patients. The unwanted noise is removed by means of preprocessing, and it is converted to overlapping frames. The FFT is used to convert the signals into the spectrogram, and it is used for predicting the lung diseases. The convolution neural network is applied to spectrogram with eight hidden layers to predict the COPD, upper respiratory tract infection (URTI), lower respiratory tract information (LRTI), etc. This model provides 95% of accuracy, and also comparison is done with the help of different optimizers like ADAM, SGD, and RMSPROP. This system might be improved by further changing both the preprocessing techniques and fine tuning the parameters. This system detects the lungs diseases in the earlier stage itself, so that the disease may not be affect the whole body. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Detection of Brain Tumour using CNN in Federated Machine Learning 5

Detection of Brain Tumour using CNN in Federated Machine Lea...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Bhadauriya, Siddharth Merothiya, Tanishk Yadav, Shiv Chandra Chandraprabha, M. Galgotias College Of Engineering And Technology Computer Science And Engineering Greater Noida India Galgotias College Of Engineering And Technology Greater Noida India

ISBN: (纸本)9798350330861

Brain tumours are a deadly illness that kills many people. The earlier a brain tumour is detected, the better the treatment, and the longer the patient lives. Artificial intelligence (AI), which has recently emerged as a promising strategy for the early identification of cancers, is a crucial subject of research in the diagnosis of brain tumours. The implementation of brain tumour makes advantage of federated machine learning. It's a method for building AI models without allowing anybody else to view or access your data, acting as a conduit for making data accessible to new AI applications. Federated learning, which is comparable to a group presentation or report, allows for remote data sharing by several persons in order to cooperatively and iteratively train a single deep learning model. Each participant downloads the model, which is typically a foundation model that has already been trained, from a cloud datacenter. CNN is the algorithm in use. Convolutional neural networks are a specific class of multi-layered neural networks. It evaluates data that is laid out in a grid-like pattern and then draws out important traits. The significant advantage of CNN use is that it requires less photo preprocessing. The suggested technique yields rapid and exact tumour site identification in addition to accurate tumour detection in the brain. This process has allowed us to attain 96% accuracy. © 2023 IEEE.

关键词： Diseases

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Pandemic Analysis and Prediction of COVID-19 Using Gaussian Doubling Times

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computers, Materials & Continua 2022年第7期72卷 833-849页

作者： Saleh Albahli Farman Hassan Ali Javed Aun Irtaza Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of Computer Science and Engineering Oakland UniversityMIUSA Department of Computer and Electrical Engineering University of MichiganDearbornUSA

COVID-19 has become a pandemic,with cases all over the world,with widespread disruption in some countries,such as Italy,US,India,South Korea,and *** and reliable detection of COVID-19 is mandatory to control the spread of ***,prediction of COVID-19 spread in near future is also crucial to better plan for the disease *** this purpose,we proposed a robust framework for the analysis,prediction,and detection of *** make reliable estimates on key pandemic parameters and make predictions on the point of inflection and possible washout time for various countries around the *** estimates,analysis and predictions are based on the data gathered fromJohns Hopkins Center during the time span of April 21 to June 27,*** use the normal distribution for simple and quick predictions of the coronavirus pandemic model and estimate the parameters of Gaussian curves using the least square parameter curve fitting for several countries in different *** predictions rely on the possible outcomes of Gaussian time evolution with the central limit theorem of statistics the predictions to be well *** parameters of Gaussian distribution,i.e.,maximumtime and width,are determined through a statisticalχ^(2)-fit for the purpose of doubling times after April 21,*** COVID-19 detection,we proposed a novel method based on the Histogram of Oriented Gradients(HOG)and CNN in multi-class classification scenario i.e.,Normal,COVID-19,viral pneumonia *** results show the effectiveness of our framework for reliable prediction and detection of COVID-19.

关键词： Prediction coronavirus time-series prediction gaussian mathematical model pandemic spreading

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Visual Analysis of Time Series Data for Multi-Agent Systems Driven by Large Language Models 24

Visual Analysis of Time Series Data for Multi-Agent Systems ...

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3rd International Conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Xu, Chao Zhang, Qi Li, Baiyan Wang, Anmin Bao, Jingsong College of Computer Science and Technology Donghua University Shanghai China College of Mechanical Engineering Donghua University Shanghai China

ISBN: (纸本)9798400710834

The application of digitalization in manufacturing involves using sensors to collect and transmit large amounts of data in real-time. These complex, timestamped sequence data require effective analytical support to drive the generation of analysis summaries. Visual analysis helps researchers identify hidden patterns by intuitively displaying complex data, but its implementation relies on specialized knowledge and a deep understanding of data analysis techniques. Moreover, the analysis process often requires users to recall and process large amounts of information, which increases the complexity of the analysis. Therefore, there is an increasing demand for intelligent visual analysis technology. Large language models, with their powerful reasoning, summarization, and code generation capabilities, provide the possibility of visual analysis for general users. We propose a large language model-based multi-agent framework that integrates domain knowledge to achieve an automated data analysis workflow, from data acquisition to analysis summarization, helping users extract valuable analytical results from complex data and improving both efficiency and experience. © 2024 Copyright held by the owner/author(s).

关键词： Interactive Data Analysis

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