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AI Techniques for Anomaly Detection in Video Surveillance Using Deep Learning Method 3

AI Techniques for Anomaly Detection in Video Surveillance Us...

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Jain, Swati Choudhary, Naveen College of Technology and Engineering Department of Computer Science and Engineering Udaipur India

ISBN: (纸本)9798350372120

Researchers are becoming more interested in the topic of anomaly detection in video surveillance, which has made substantial advancements. In many domains, including industrial safety, surveillance, and healthcare monitoring, video anomaly detection is essential. This work suggests a novel approach for applying deep learning algorithms to identify abnormalities in films. The complexity and variety of real-world data sometimes pose challenges for traditional approaches, which results in a lack of accuracy and resilience. Deep learning methods, on the other hand, have demonstrated incredible promise in identifying spatiotemporal patterns in video data. This research investigates and identifies outliers in video surveillance by examining different kinds of video *** application scenario, which depends on the types of data and anomalous types of behavior with different circumstances, is the emphasis of the various methodologies. Large datasets are used to train the Convolutional Neural Network architecture, which enhances its capacity to learn general visual representations. Our work gives futuristic performance in terms of efficiency and accuracy as demonstrated by the experimental findings. © 2024 IEEE.

关键词： Recurrent neural networks

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A Survey on Marine Debris Detection and Location Using Machine Learning Technologies 2

A Survey on Marine Debris Detection and Location Using Machi...

引用

2nd International Conference on computer, Communication and Control, IC4 2024

作者： Abins, A Arockia Darwin Wilfred, S.M. Ashwin Ram, K. Hari Prasad, D. Easwari Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387933

The large amounts of debris present in our ocean water poses a major threat to the flora and fauna of our marine ecosystem. The continuous increase in dumping of solid waste, and the very slow rate of degradation of most items, are together leading to a gradual increase in marine litter found in the ocean. While a large number of human-based campaigns have been proposed to tackle this issue, the sheer amount of litter present in the ocean results in their efforts to be insufficient. But the increasing development in the field of computer vision combined with the help of Autonomous Underwater Vehicle (AUV), presents itself to be a better solution to reduce the human complexities and automate the tedious task of decontaminating our ocean. This paper contains a literature review of the different Machine learning models using various neural architectures currently used to detect and locate various debris present in marine water (i.e., plastic, metals, glass, styrofoam, cigarette butts etc.). © 2024 IEEE.

关键词： computer vision

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Real-Time Eye State Detection Using Deep Learning for Blink and Dry Eye Diagnosis using Smart Phone

Real-Time Eye State Detection Using Deep Learning for Blink ...

引用

2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Vijayalakshmi, R. Kayalvizhi, S. Dharaniya, R. Easwari Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

Inspection of blinks and real-time recognition of eye states is crucial for diagnosing ophthalmologic symptoms: dry eye, drowsiness, fatigue, etc. This work presents a new eye state recognition method using a VGG16-based model with deep learning. The model is trained on 1234 training photos and 218 test images with equal numbers of closed and open in a balanced dataset to detect eye conditions accurately. To increase the robustness of the model, data augmentation techniques like random rotation, zooming, and horizontal flip are applied to training data. The model learns to fine-tune after transfer learning on the pre-trained VGG16 and several dense layers make improvements on feature extraction. Experimental results show that the proposed model has achieved an excellent accuracy of 98% for both closed and open-eye classification. Similarly, we can attain over 97% precision, recall, and F1 for both classes. The high throughput of the system to obtain blink patterns shows how important it is as a powerful tool for real-time eye monitoring, such as dry eye detection and sleep alertness systems installation. This work demonstrates the effectiveness of deep learning models in medical and safety-critical domains, showcasing superior generalization abilities across various test environments. © 2024 IEEE.

关键词： Convolutional neural networks

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Poultry Pathogen Detection: Using Deep Learning for Coccidiosis Identification 5

Poultry Pathogen Detection: Using Deep Learning for Coccidio...

引用

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

作者： Harshitha, S. Likhitha, K. Varshini, Katta R G Mahure, Sonali Jagadish Yasaswi, Anaparthi New Horizon College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350367171

Poultry farming plays a crucial part in fulfilling the need for animal protein worldwide, but the industry faces significant challenges in controlling infectious diseases that impact bird health and productivity. Protozoan parasites of the genus Eimeria are the source of coccidiosis, which is common and economically significant poultry disease. Traditional methods of coccidiosis diagnosis rely on labor-intensive and time-consuming microscopic examination of fecal samples, leading to delayed detection and response. This study investigates the use of deep learning methods for the quick and precise diagnosis of coccidiosis in poultry. A novel deep learning model trained on a comprehensive dataset of microscopic images of fecal samples obtained from infected birds has been proposed in this study. This study presents an innovative approach to coccidiosi detection using the VGGNet architecture, a deep convolutional neural network renowned for its hierarchical feature learning capabilities. Leveraging this architecture, we explore the potential for automated identification of coccidia-infected samples through microscopic image analysis. This study evaluates the performance of the deep learning model against traditional diagnostic methods and also architectures like YOLOv5, ResNet and demonstrating its ability to significantly reduce the time required for coccidiosis detection without compromising accuracy. We also discuss the model's flexibility in a variety of environments and its potential for early identification, enabling prompt intervention and disease management. This research contributes to the ongoing efforts to modernize poultry disease diagnostics through the utilization of deep learning. © 2024 IEEE.

关键词： Convolutional neural networks

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Indian Sign Language Classification Using Transfer Learning 1

Indian Sign Language Classification Using Transfer Learning

引用

1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Gayathri, S. Madhumitta, P. Usha Nandhini, G. Anuradha, M. Computer Science and Engineering S.A.ENGINEERING COLLEGE Chennai India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353648

In recent years, there has been increased interest in the field of machine learning to develop accurate and efficient Indian Sign Language (ISL) classification models. This interest stems from the urgent need to overcome communication barriers and promote the inclusion of the hearing impaired. Here, ISL classification using transfer learning techniques with both a conventional convolutional neural network (CNN) and the powerful ResNet50 architecture has been used. Transfer learning has become a promising strategy in machine learning to enable models to be ready trained to adapt large datasets with limited labelled data to new tasks. The methodology involves fine-tuning models pre-trained on the ISL dataset using knowledge learned from different visual concepts. By exploiting the learned features of these deep learning architectures, this methodology can effectively capture the complex patterns and nuances of ISL gestures. Experimental results demonstrate the effectiveness of transfer learning in ISL classification tasks. Both custom CNN and ResNet50 architectures achieve significant accuracy, which demonstrates the robustness and generalization of the transmitted data. These high accuracy rates highlight the potential of transfer learning as a valuable tool to solve the unique challenges of ISL classification. In addition, it is presented here as a user-friendly web-based application built with Stream lit to facilitate the smooth use of trained models. This application provides a practical platform for users to interact with the ISL classification system, enabling real-world applications and promoting accessibility for the hard of hearing. © 2024 IEEE.

关键词： Convolutional neural networks

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Retinal Authentication for E-Voting 9

Retinal Authentication for E-Voting

引用

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

作者： Hanne, Lakshmi S. Sundaram, B. Meenakshi Rakshitha, M. Kishore, V. Meenakshi, Arthy New Horizon College of Engineering Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

Elections are crucial in India as they reflect the democratic principles that underpin the country's governance. Since the general elections in 2004, Electronic Voting Machines (EVMs) have been used in India. EVMs are independent gadgets that permit electors to electronically project their votes. They are intended to be secure, sealed, and give a fast and exact counting of votes. The use of electronic voting machines (EVMs) has come under criticism and legal challenge from political parties and civil society activists who have questioned the security and vulnerability of EVMs. Traditional EVMs with Voter Verifiable Paper Audit Trail (VVPAT) are in place, but the idea of electronic voting over the internet (e-voting) is still in the making. The implementation of online voting would face several challenges in terms of security, authentication and ensuring a transparent and free electoral process. The paper focuses on implementing a security methodology in E-voting systems, with a primary focus on retina security mechanisms. It acknowledges the uniqueness of human traits such as iris patterns, eye-retina scans, and fingerprints. Specifically, the paper asserts that retina security stands out as the most sophisticated vulnerability checking mechanism in the realm of security. The use of retina security in e-voting systems is attributed to the provision of improved security features in order to guarantee the integrity and reliability of the entire process. © 2024 IEEE.

关键词： Authentication

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An Empirical Assessment of Botnets and Detection Methods 15

An Empirical Assessment of Botnets and Detection Methods

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Shivanna, D. Pooja, M.R. Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India

ISBN: (纸本)9798331300579

a botnet is a group of devices which are controlled by the Attacker and used for fraud, scam and *** attacker is also known as *** Bots are used to conduct many activities varies from stealing user credentials, sending spam, stealing data, ransom ware, fraudulently clicking on ads or distributed denial-of-service (DDoS) *** traffic made up by bots is increasing day by day, now nearly a quarter of the whole *** them becomes an uphill task as the time *** developers are adopting new technologies at a rapid rate in an effort to avoid bot *** some bot problems are industry-specific, researchers found out that bot problems run across all *** top five industries which are targeted with bad bot traffic are the financial, education, IT & services industries, marketplaces, and *** assessment paper briefs the different botnet detection methods that exist at *** main aim of this paper is to give the insight knowledge of various bots and their implications on the present cyber world and also shedding some light on the current methods using to detect them... © Grenze Scientific Society, 2024.

关键词： Bot (Internet)

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Ocular disease recognition using machine learning

Ocular disease recognition using machine learning

引用

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

作者： Ganesh, K. Kishore, P. Julian, Anitha Department of Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9781032669663

Over a quarter of the global population suffers from ocular diseases and millions of people die every year due to them. It takes a lot of time and effort to diagnose such diseases and so over 2/3rd of the people suffering from them don’t even realize that they have them. In our model we are trying to recognize the diseases without any human interaction. So, to achieve it, we propose a model where we were able to detect and recognize certain ocular diseases using the color of the fundus part of the eye from a patient. In this paper, we train our model using the top 4 pre-trained image classification algorithms on thousands of fundus images of the said diseases and achieved a maximum of 91% accuracy on InceptionV3 overall. The model had great accuracy of over 95% on three classes except for diabetic_retinopathy. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Machine learning

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Machine Learning (ML) based Electricity Price Prediction with Visualization 3

Machine Learning (ML) based Electricity Price Prediction wit...

引用

3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Abisha, D. Kumar, Prasanna D. Christy, Ida J. Evanjalin, Navedha R. Rajesh, P. Haris, Mohamed K. Department of Computer Science and Engineering National Engineering College Kovilpatti India

ISBN: (纸本)9798331532420

Electricity price prediction is crucial for businesses reliant on heavy machinery or computing services, as it aids in cost management and energy optimization. Accurate predictions allow these businesses to forecast annual electricity expenses, enhancing cost efficiency. This project aims to forecast electricity prices using machine learning techniques, specifically the Random Forest regression algorithm. The project utilizes historical data on electricity consumption and other influencing factors, including date and time, holiday indicators, day of the week, week of the year, forecasted and actual wind production, national load, temperature, wind speed, and CO2 intensity. The methodology involves converting string values to numerical values, handling missing data, and normalizing the dataset, identifying key features that significantly impact electricity prices, Splitting the data into training and testing sets, and training the model using the Random Forest regression algorithm and assessing the model's performance with appropriate metrics to ensure accuracy and reliability. The results demonstrate that the Random Forest model effectively predicts electricity prices with high accuracy. Visualizing these predictions in Power BI provides intuitive insights into the factors influencing electricity prices. These findings enable businesses to better anticipate electricity expenses, optimize energy usage, and improve financial planning. The comprehensive approach outlined in this project offers a valuable tool for understanding and forecasting electricity prices, thereby enhancing resource management and cost efficiency. © 2024 IEEE.

关键词： Data Preprocessing Electricity Pricing Energy Cost Optimization Machine Learning Random Forest Regression

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Logistic-Based OVA-CNN Model for Alzheimer’s Disease Detection and Prediction Using MR Images 1

引用

4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： John, Chinchu M. Phalgunan, Prafulla Department of Computer Science and Engineering College of Engineering Kerala Trivandrum India

ISBN: (数字)9789819994427

ISBN: (纸本)9789819994410

Alzheimer’s disease (AD) is a form of dementia that diminishes a patient’s cognition and memory. By predicting AD progression early, physicians can begin medication that may reduce the disease’s progression. AD detection and prediction have been extensively performed using convolutional neural networks (CNNs). Despite this, CNN’s use for AD detection is limited by the number of parameters and the extensive training data required. Consequently, this study proposes a logistically based OVA-CNN model for AD detection and prediction using Magnetic Resonance Images (MRI). OVA-CNN consists of the four binary CNN which classified four classes of AD and the logistic regressor predicts the time conversion of pMCI patient who converts AD. The experimental results on the Alzheimer’s Disease Neuro Imaging Initiative (ADNI) demonstrate that the proposed model achieves a high level of accuracy of 93%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Magnetic resonance

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