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A Comparative Analysis on Various Machine Learning Methods for GAN Based Video Anomaly Detection 7th

A Comparative Analysis on Various Machine Learning Methods f...

引用

7th IFIP TC 12 International Conference on Computational Intelligence in data science, ICCIDS 2024

作者： Chiranjeevi, Rahul Shyam, C. Hirthik Kaushiik, K. Kumar, P. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9783031699818

In recent years, surveillance has undergone tremendous change and developed into an essential instrument for maintaining security and keeping an eye on sensitive areas. This essay investigates the idea of what defines surveillance. It explores the crucial topic of anomaly detection, which is a vital component of contemporary surveillance systems. The expanding importance of the use of deep learning methods is highlighted in this paper’s discussion of current developments in surveillance technology. Through the processing of massive volumes of data, deep learning has transformed surveillance by allowing more precise and effective anomaly detection. Examining several kinds of deep learning techniques, their special qualities and uses are highlighted. This study concludes with an in-depth analysis of the monitoring, highlighting the role of deep learning in improving anomaly detection. It is an invaluable tool for researchers, professionals and decision makers interested in the development of surveillance technology and its use in different contexts. © IFIP International Federation for Information Processing 2024.

关键词： Contrastive Learning

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Patient Health Diagnostic and Fitness Recommendation Application 1

Patient Health Diagnostic and Fitness Recommendation Applica...

引用

1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Nidhin, R. Syamraj, B.S. Philip, Diya Mariam Abraham, Joel P. Nair, Rohith R. John, Shalin Jeena Providence College of Engineering Department of Computer Science and Engineering Chengannur India

ISBN: (纸本)9798350307795

With the busy lifestyle and lack of time, people are looking for a way for the proactive management of their well-being. A health monitoring app helps users obtain insights about their health and take necessary steps to improve their health. The app uses advanced machine learning techniques like Bayesian Ridge Regression and Lasso regression and Ridge classifier for prediction of health factors such as blood pressure, cholesterol and blood sugar respectively. The evaluation of the app produced favorable results concerning both user satisfaction and model accuracy. The accuracy and usability of prediction models are quite satisfactory, with each model trained using the best algorithm for the specified prediction task achieving the best results. Users can actively manage and enhance their well-being through the system's tailored food recommendations, which is possible using rule-based filtering. This app makes it easier to closely examine one's own health patterns and obtain useful suggestions for leading healthy lives. © 2024 IEEE.

关键词： Forecasting

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SDN Defense: Detection and mitigation of DDoS attack via IoT Network 24

SDN Defense: Detection and mitigation of DDoS attack via IoT...

引用

25th International Conference on Distributed Computing and Networking, ICDCN 2024

作者： Kumar, R. Shalinie, M. Stanly, Hamil Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai India

ISBN: (纸本)9798400716737

Internet-of-things is one of the prominent communication technologies in the 21st century. We can connect everyday objects, like baby monitors, thermostats, e-health, etc., Connecting IoT with Software-defined networking is the best approach to security provisioning during network communication. Because the enormous features of SDN are programmable and centralized management, attackers can create multiple security vulnerabilities in SDN that redeem the distributed denial-of-service attack. This security breach causes bandwidth depletion and server resource impoverishment and perplexes benign users. This study proposes and builds a DDoS attack detection and mitigation defense system for SDN to address this issue. Two different defense modules are deployed in the SDN controller: suspicious identification, and mitigation of the malicious traffic flow. The first module of the SDN defense system used for detecting malignant traffic from DDoS attacks which works under a CNN-ELM is an integrated deep learning approach that combines a convolutional neural network with an extreme learning machine. The suspicious flows are identified, whether benign or malignant, by using a hybrid CCN-ELM model, and this process improves the accuracy of the attack detection. The second module of the mitigation strategy identifies the attacker's location by using IP traceback and removes that malicious traffic by transmitting the flow rule from the controller. These two SDN defense modules are evaluated by simulation processes. Finally, the experimental results of the SDN defense system, precisely detect the DDoS attack with an accuracy of 99.85% and efficiently mitigate the malicious traffic flow in real-time. © 2024 ACM.

关键词： Internet of things

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Attention Contours: VR in ADHD Assessment 10

Attention Contours: VR in ADHD Assessment

引用

10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Rajarajeswari, P. Jothikumar, Karthikeyan Gomathi, V. National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (纸本)9798350353068

Virtual reality (VR) technology offers potential opportunities for the assessment and intervention of Attention-Deficit Hyperactivity Disorder (ADHD). This research article presents a study evaluating the effectiveness and usability of a VR-based attention assessment tool designed to measure attention profiles across diverse populations in diverse settings. The study aims to evaluate the effectiveness and usability of the tool in healthcare settings through user trials involving participants with varying levels of VR experience. The user experiences, such as comfort levels, the incidence of motion sickness, ease of controls, immersion, sense of presence, and overall satisfaction, are evaluated in terms of VR features. Results indicated positive feedback from participants on the developed tool. These findings highlight the potential of the tool as a valuable tool for attention assessment in healthcare settings. This study accentuates the need for VR technology in healthcare to enhance attention disorder assessment and management, opening avenues for future research and implementation. © 2024 IEEE.

关键词： Virtual reality

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Smart fish monitoring system using IoT

Smart fish monitoring system using IoT

引用

International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Divya, T. Ramyadevi, R. Vijaya Chamundeeswari, V. Department of Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9781032669663

Smart fish monitoring system using IoT used to maintain a fresh water in the aquarium for aqua life environment. It features to monitor the freshness of the water for healthier aqua life environment. This proposed system works as a fish feeding system which can be controlled by a portable mobile phone in its operation even when not in close vicinity. This system works based on IoT and makes sure that the observation of the aquarium can be done via an application on the mobile phone or via the dashboard provided by Arduino IoT Cloud in the laptop. System uses various calibrated sensors to monitor the water level, temperature readings of the tank or small ponds. Apart from that air pump, water heater and fish feeding process can be turned on or off based on the input from the user from the application. For this proposed design distance, is never an issue. This helps you to feed your fishes with just the press of a button on your phone. It also gives you access to turn off and on the air pump in the aquarium as per your wish. Apart from this, the temperature conditions in your fish tank are constantly monitored and if the temperature gets anywhere below the certain range, it ensures that the water heater is automatically turned on and regulated as needed. Apart from this it has a water level monitor which helps notify you when the water level has reduced below a certain point. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Water pollution

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Deep Learning in Medical Image Analysis: A Survey

Deep Learning in Medical Image Analysis: A Survey

引用

2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Agneya, D.A. Shekar, Mogaveera Sahil Bharadwaj, Anagha Vineeth, Nandhini Neelima, M. Lakshmi Department of Computer Science Engineering BMS College of Engineering Bengaluru India

ISBN: (纸本)9798331505134

Deep learning has emerged as one of the main study areas in recent years because of its wide range of applications. One of the major applications of deep learning is image processing, which, when integrated with the data received from medical imaging, has the ability to improve the diagnostic process by reducing analysis time and providing accurate results. The field of deep learning can provide scope for affordable diagnosis, proper medication assistance, and the tracking of disease progression. This survey article presents an overview of the numerous deep learning models that have been suggested for medical image analysis, highlighting the various designs of convolutional neural networks, datasets, and a brief description of the neural network layers that are utilized to produce extremely accurate findings. Since image processing is classified as supervised learning because of domain specificity, the application of a model proposed for the diagnosis of one disorder may not be applicable for the diagnosis of another. The high progress of research in this field can lead to an accurate, unsupervised image processing technology soon. This paper contrasts the accuracies of multiple algorithms and provides a brief overview of current research and development on this topic, including data pertaining to various CNN designs and layers. © 2024 IEEE.

关键词： Convolutional neural networks

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Machine Learning Prognosis for Smartphone Dependency 5

Machine Learning Prognosis for Smartphone Dependency

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Julian, Anitha Prathima, S. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Concern over smartphone addiction has grown recently as more and more people show signs of addiction, including compulsive phone use, decreased productivity, and even physical and mental health issues. Consequently, a growing necessity exists for the creation of efficient tools to forecast smartphone addiction and pinpoint individuals susceptible to this behavior. The proposed model is crafted to predict smartphone addiction using data collected from a survey of individuals who use smartphones. The survey encompassed inquiries related to demographics, phone use patterns, and various psychological aspects like anxiety, depression, and stress. a popular and effective machine learning method, to build our model. After pre-processing the data, the model is trained and its performance assessed by its accuracy, to see whether the model could be learned efficiently. Our findings demonstrated how effective the recommended treatment was at preventing smartphone addiction. Key features crucial for predicting addiction included patterns of phone use, such as the frequency of checking notifications, daily hours spent on the phone, and the predominant types of frequently used apps. Other important factors included age, gender, and stress levels. Our designed model has multiple possible uses. The solution finds its usage by healthcare professionals to identify individuals prone to potential risks of developing smartphone addiction and provide appropriate interventions. It could also be used by app developers to design apps that are less addictive and promote healthier phone use habits. © 2024 IEEE.

关键词： addiction healthcare machine learning prediction smart phone

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AI Based Native Food Nutrition Prescribing Chatbot

AI Based Native Food Nutrition Prescribing Chatbot

引用

2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Kumar, P. Ananthajothi, K. Duraimurugan, N. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331541170

Several studies on the advantage of eating mindfully have concentrated on the idea that maintaining a healthy diet can aid people in preventing the onset of chronic illness including cancer, diabetes, and obesity. It is time-consuming to learn the nutritional worth and contents of each item we eat on a regular basis. So, we require a "Intelligent Chat Bot"that uses artificial intelligence to respond to queries about food's nutritional information and other healthier substitutes. It makes use of algorithms and information databases to comprehend user input and produce responses about diet and nutrition. Natural language comprehension techniques form the basis of the chatbot, but voice synthesis and recognition are also crucial for the achievement of the agent's interactions. This Chabot offers the user the nutritional information for the foods we consume every day, together with recommendations for local food options from the closest market, and also safe chat history storage for future users' input. © 2024 IEEE.

关键词： Bot (Internet)

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Multi-Modal Visual Features Perception Technology for Internet of Vehicles (IoV) 6

Multi-Modal Visual Features Perception Technology for Intern...

引用

6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Mahendran, Rakesh Kumar Thiyagarajan, Abirami Angelin Gracia, A. Kumar, P. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350306613

In the realm of the Internet of Vehicles (IoV), this study addresses a fundamental question that causes the emerging trends within the IoV landscape, Previous research in IoV has laid the groundwork but is limited by conventional single-modal methods. This study advances our understanding by incorporating innovative Multi-Modal Visual Features Perception Technology by inputting various images from LiDAR cameras, Radar and processing using ConvolutionalNeural Networks (CNN), alongside single-modal approaches. Through this research, we uncover hidden facets of IoV, promising transformative insights. The implementation of Multi-Modal Visual Features Perception Technology using deep learning, along with single-modal methods, holds the potential to revolutionize vehicular connectivity and shape a more connected and secure future. © 2024 IEEE.

关键词： Advanced neural network techniques Convolutional Neural Networks LiDAR Multimodal Scene perception Self-driving technology sensor fusion

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License Number Plate Recognition(LNPR) 4

License Number Plate Recognition(LNPR)

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4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Garg, Khushi Gautam, Kapil Singh, Karamveer Gaur, Madhvi Abes Engineering College Ghaziabad Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9781665474368

License number plate recognition is a technology which is used to detect the number of vehicles;it can be achieved by the OCR (optical character recognition) and OpenCV which is a computer vision technology. Most of the time the accuracy depends on hardware which is used to collect the data and the environment also like whether, Lighting. LNPR can be used in for traffic system where police can easily identify the vehicle number with the help of LNPR and it can be used in toll-tax areas where road authority can used it to get the tax from the public. It can also be used in the parking areas to get the data which vehicle is present at which time.. ANPR is a fascinating subject for academics because of its unreliability, tough surroundings, and other complications. © 2022 IEEE.

关键词： Optical character recognition

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