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Helmet Detection System Using Yolo V2

2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Arun, M.D.R. Jaitly, Vanita Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350382693

The risk of head injuries. The methodology The increasing concern for safety in a variety of work and leisure environments mandates the production of robust and efficient helmet detection systems. This study offers a fresh method for helmet detection, utilizing the (You Only Look Once version 2) YOLOv2 algorithm. The proposed system leverages the capabilities of YOLOv2 for real-time object detection, providing a proactive solution to enforce helmet-wearing regulations and mitigate impacts that encompass gathering of data, pre-processing, designing of the model architecture, training, and assessment. Results from experiments show the YOLO has achieved the greatest mAP success rate of 96.72%. How well the suggested approach works in precisely detecting helmets in diverse environments and scenarios. Furthermore, discussions on implementation considerations, challenges, and future research directions are provided, highlighting the potential impact of the helmet detection system on enhancing safety across industries and activities. © 2024 IEEE.

关键词： Deep learning

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A Survey on the Detection of Diseases in Plants Using the computer Vision-Based Model 1

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2nd International Conference on Data Analytics and Learning, DAL 2022

作者： Pujeri, Sowbhagya Takappa Somashekara, M.T. Department of Computer Science and Applications Bangalore University Bengaluru560056 India

ISBN: (数字)9789819963461

ISBN: (纸本)9789819963454

The Agriculture sector acts as the backbone of our country. It has not just remained as simply to feed the increasing population. Rapid growth of the population day by day has resulted in a high demand of agricultural yields. Agriculture sector is struggling to cater to the needs of ever-growing demand of the population. India’s economy greatly depends on the productivity of the agricultural yields. Due to irregular rainfall, drought or different climatic conditions, most of the time agricultural crops suffer from a variety of plant diseases. These diseases affect not only the production quantity but also the quality of the crop yield. This results in catastrophic or chronic losses, due to which Indian farmers suffer to recover from bank loans and finally attempt suicide. To avoid such situations, a means has to be discovered to appropriately identify the diseases affecting the plants in the early stage. Diagnosing diseases incorrectly might lead to improper chemical sprayed on the plantations, which results in ill impacts on the environment, wastage of costs with a significant loss in economy. The present diagnosis of disease is based on human vision, and it is a time-consuming process and expensive. Making use of a computer vision-based model to detect the diseases affecting the plants would help in increasing the accuracy and efficiency. Huge variances in the disease symptoms, genetic variations and conditions of light on the plants, all decrease the accuracy. Both the productivity and quality of the crop yield can be increased by detecting the diseases affecting the crops at an early stage so as to reduce the loss incurred at a later stage. This paper contains a survey of the advanced techniques used to detect different diseases affecting the plants. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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COMPARATIVE ANALYSIS ON CARDIAC AND DIABETIC DISEASE PREDICTION USING MACHINE LEARNING TECHNIQUES 6

COMPARATIVE ANALYSIS ON CARDIAC AND DIABETIC DISEASE PREDICT...

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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Sheetal Deora, Sukhvinder Singh Department of Computer Science and Applications Maharishi Dayanand University Rohtak India

ISBN: (纸本)9798350374629

Diabetes and cardiac conditions are essentially distinguished by elevated blood glucose levels and cardiovascular disorders, respectively. A diverse array of complex illnesses, including but not limited to heart attack, renal failure, and stroke, are associated with it. Diabetes has a global prevalence of over 422 million people in 2014, making it the most common metabolic disease on the planet. The primary objective of this study is to evaluate and compare the performance of Support Vector Machines (SVM), Decision Trees, Artificial Neural Networks (ANN), and k-nearest Neighbors (KNN) in classifying diabetes and cardiac patients using a specific dataset. The present study serves as a roadmap for future research in predictive analytics for cardiac and diabetes ailments by analyzing current literature and identifying research gaps. The primary objective is to create the way for the generation of robust, accessible, and clinically feasible ML models that can significantly contribute to the early identification and management of these common health conditions, thus enhancing the outcomes of patients as well as decreasing the burden on healthcare systems around the world. Models based on supervised learning algorithms have been discussed along with other interesting findings that are important in developing overall understanding of model for disease prediction algorithms. This study carefully assesses the selected papers and finds gaps in the current literature that will be helpful for researchers to develop and use in clinical domains, especially on heart and diabetic disease data sets. This study will also help medical professionals to identify cardiac and diabetic risks in advance, enabling them to take preventive actions. ©2024 IEEE.

关键词： Neural networks

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A Critical Analysis on Early Stage of Lungs and Colon Cancer Detection Using CNN Algorithm 1

A Critical Analysis on Early Stage of Lungs and Colon Cancer...

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1st International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems, iTech SECOM 2023

作者： Ramesh, K. Bhavadharani, C. Narmatha, V.S. Samraj, Andrews Nandhakumar, R. Department of Computer Science and Applications Namakkal India Pg and Research Department of Computer Science and Applications Namakkal India Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798350371185

One of the most dangerous problems that people experience globally is lung and colon tumor that has spread rapidly to become a normal medical challenging problem. It is essential to achieve an accurate and preliminary detection in order to minimize the hike of death. The complexity of the task entirely relies on the histopathologists experience. The analysis of the histopathology images of lung and colon cancer is crucial because early and accurate diagnosis of cancer histology is actually needed and since the treatment of cancer is aimed on the kind of histology, genetic profile and stage of the illness. Histologists may even endanger the patient's life. In recent times popularity of deep learning increase and recognized in the diagnosis and treatment of medical imaging. In order to identify lung and colon cancer using histopathological image and more effective augmentation techniques, this research involves studying and modifying the current CNN model. In the past few years, CNN has reported amazing progress in the fields of CT, MRI, and ultrasound. A novel technique that combines the advantages of two imaging modalities, 3D CT scan and histopathological image processing - to produce results that are more accurate and dependable. They are trained on the LC25000 dataset. CNN are the most preferred approach of deep learning algorithm for the advanced detection of lung and colon cancer. The level of accuracy offered by CNN in this regard may help to reduce the frequency and improve evaluation and quality © 2023 IEEE.

关键词： computerized tomography

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URL Phishing attack Detection using Machine Learning Algorithms

URL Phishing attack Detection using Machine Learning Algorit...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Karthick Kumar, M. Sivakumar, N. Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350373783

The pervasive integration of the Internet into our daily lives has made it an essential aspect;however, it has also created opportunities for malicious activities, such as Phishing. Perpetrators employ deceptive strategies, utilizing social engineering and mockup URLs, to surreptitiously obtain sensitive information such as account IDs, usernames, and passwords from individuals and organizations. Despite numerous proposed methods for detecting phishing websites, perpetrators adapt continuously, evading conventional detection techniques. Machine Learning emerges as a robust approach in identifying these malicious activities due to the shared characteristics inherent in most Phishing attacks. This paper conducts a comparative analysis of various machine learning methods utilized in predicting phishing websites, emphasizing the efficacy of these techniques in addressing cybercrime. © 2024 IEEE.

关键词： Phishing

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Synergistic Solutions for Cloud Cybersecurity and Financial Operations using AI, Blockchain and Quantum Computing

Synergistic Solutions for Cloud Cybersecurity and Financial ...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Boopalan, Sunil SRM Institute of Science and Technology Department of Computer Applications Chennai India

ISBN: (纸本)9798331542344

This research paper further builds on previous research by proposing a combination of AI and blockchain in the context of integration of quantum computing to improve the cybersecurity of cloud and financial operations, especially for India's UPI and digital rupee platforms. This is especially the case when digital transactions are on the rise and their security against rampant cyber threats and overall soundness of operations must be guaranteed. The following is a novel framework that embellishes AI, blockchain, and quantum computing to meet these challenges outlined in this paper. Machine learning and pattern recognition will be implemented to scope out and prevent security threats, while blockchain is to ensure a secure and open system of transactions;quantum computing will help to provide extraordinary features for data security and data encryption. The research also seeks to examine the current implementation approaches, advantages, and disbursement issues of this integration and subsequently makes recommendations that can help policymakers and practitioners make improvements for the improvement of trust and security in the implementation of financial solutions on the cloud. © 2024 IEEE.

关键词： Cybersecurity

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Efficient loss updated XGBoost with deep emended genetic algorithm for detecting online fraudulent transactions

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Multimedia Tools and applications 2024年第37期83卷 84471-84494页

作者： Lingeswari, R. Brindha, S. Research Scholar Department of Computer Applications St.Peter’s Institute of Higher Education & ampResearch Tamilnadu Chennai India Department of Computer Science and Applications St.Peter’s Institute of Higher Education & ampResearch Tamilnadu Chennai India

In the fast-paced technological era, online financial transactions have gained widespread use as it offers significant merits to customers for easy transfer of money through smart phones. Nevertheless, fraudulent transactions put individual’s money into risk, for which, suitable approaches are required to detect such deceits. Concurrently, with the progress of ML (Machine Learning) approaches, existing works have bidden to identify the fraudulent and normal transactions. However, studies lacked in accordance with accuracy rate and only limited focus has been provided for detection of generalized fraudulent transactions. Considering this, the current study considers IoT fraud dataset and proposes DEGA (Deep Emended Genetic Algorithm) to attain better performance for detecting fraudulent and normal transactions. This model employs a competitive approach, integrating, new crossover and selection methods. This intend to improvise the ability of global search and partition the chromosomes into losers and winners. This ensures high quality parent for selection. Besides, a dynamic-mutation function is also proposed for enhancing the model’s searching ability. Subsequently, the study proposes EL-UXGB (Efficient Loss-Updated eXtreme Gradient Boosting) wherein dual sigmoid loss functions are proposed to resolve the imbalanced label cases. The overall performance of this study is assessed through analysis that confirms its effectiveness in detecting fraudulent transactions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Genetic algorithms

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An Extensive Research of Women Distress Arising from Harassment Circumstances: Leveraging Data Analytics and Machine Learning Methodologies to Systematically Address the Psychological Distress and Apprehension through web app 3

An Extensive Research of Women Distress Arising from Harassm...

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

作者： Sumithra, M. Sowmya, V.J. Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350372120

In a time of notable progress, the persistent concerns of women regarding safety and harassment underscore the imperative to safeguard their well-being in diverse spheres, encompassing homes, schools, colleges, and workplaces. Despite notable advancements, a significant hindrance emerges as women often withhold their voices and actions, primarily driven by privacy apprehensions. This research endeavors to meticulously analyse the multifaceted forms of harassment experienced by women and illuminate the resilient responses they adopt By exploring the complexities of their experiences, our aim is to provide a succinct yet thorough understanding of the challenges faced by women. This exploration not only identifies prevailing issues but also brings to light the proactive measures undertaken by women in combating harassment. Additionally, it recognizes the profound impact on mental health, shedding light on how harassment can detrimentally affect the psychological well-being of women. Through this research, we aspire to amplify the voices of women, heighten awareness, and contribute to the creation of safer environments for women navigating a world where harassment remains a persistent concern, acknowledging the critical intersection of mental health and the experiences of women in such situations. Just technically, machine-learning algorithms were used to inform decisions during the study. © 2024 IEEE.

关键词： Data Analytics

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Cluster-Based Risk Analysis for Big Data Security Framework 7

Cluster-Based Risk Analysis for Big Data Security Framework

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Hariharan, A. Angeline Benita, D. Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372816

Nowadays, ensuring data security has become paramount as organizations leverage vast datasets for insights and decision-making. Traditional security measures struggle to address the unique challenges posed by big data's scale, velocity, and diversity. Despite the emergence of various tools and techniques, a singular solution remains elusive. This study explores the intersection of big data and security, emphasizing the need for layered security technologies and prioritized systems. It proposes a novel security model integrating advanced risk analysis and prioritization techniques, enabling organizations to tailor security measures effectively. By categorizing data based on sensitivity and criticality, this model provides a comprehensive framework for safeguarding data throughout its lifecycle. By embracing innovative methodologies, organizations can mitigate risks and protect their data assets in today's interconnected world. © 2024 IEEE.

关键词： Data consistency

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A Machine Learning-Based Attendance Management System

A Machine Learning-Based Attendance Management System

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Mageshvaran, G. Sowmya, V.J. Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350382693

The primary goal of this project is to update and modernize the current system, as well as boost its efficacy and efficiency, by developing an attendance tracking system for educational institutions based on facial recognition technology. Recording and manually inputting attendance logs in a classroom is not the most efficient method. In the current age, some students may find it interesting or creative to skip classes or arrange for proxies for those who are absent. Manually recording attendance in logbooks becomes a challenging and readily manipulable activity. This technology uses facial recognition to automatically identify students in the classroom and record their attendance. In order to construct this system, real-time facial recognition is used. The reference faces in the dataset are compared with the detected faces, marking the participants' attendance The data is trained using the Fisher Face Recognizer algorithm, and faces are detected using the haar-cascade classifier. SMTP is utilized for email transmission and reception, and the Tkinter graphical user interface is employed for input capture. The technology in question is a facial recognition system. One of a person's natural features that can be used to identify them specifically is their face. Faces are used to trace identification since they are unlikely to deviate or be copied. The project comprises creating facial databases in order to enhance the recognizer algorithm's capabilities. During attendance sessions, a systematic comparison of faces against the established database will be conducted to accurately determine individual identities. Upon recognition, the presence of the individual will be automatically documented, and the pertinent information will be saved in an Excel spreadsheet. The excel document with every person's attendance detail is mailed to the relevant professors at the conclusion of the day. © 2024 IEEE.

关键词： Students

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