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Deep learning-Based Object Detection System for Crowd Monitoring in High-Security Areas 2

Deep Learning-Based Object Detection System for Crowd Monito...

引用

2nd IEEE international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Pandey, Anurag Kandpal, Hitesh Rawat, Shubham Singh Vaidya, Himadri Rawat, Ruchira Kumar, Bura Vijay Computer Science and Engineering Graphic Era Hill University Dehradun India Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350306415

In order to maintain crowd control and assure safety, contemporary security systems require effective monitoring tools for sensitive and high-traffic locations. In particular, headcount estimate in sensitive areas like high-security buildings, crowded areas is the subject of this research's novel using deep learning algorithms for real-time object identification in surveillance settings. The main goal is to create a reliable system that can count the number of people in a certain area, recognize their presence, and sound an alarm when the number of people in the crowd rises over pre-established levels. YOLO (You Only Look Once), RetinaNet, SSD (Single Shot MultiBox Detector), and RCNN (Region-based Convolutional Neural Networks) are four of the cutting-edge deep learning models that are applied in this study of object recognition. In order to identify the best method for precise and effective headcount estimation, the study comprises a comparison review of various models. And of all the models examined, YOLO consistently showed the greatest results for precision, recall, and average precision, making it the most efficient model for this specific project. © 2024 IEEE.

关键词： Deep learning algorithms Object detection RCNN RetinaNet SSD YOLO

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Mitigating Malvertising Threats: An Exploration of Machine learning Classification Algorithms for Effective Detection 2

Mitigating Malvertising Threats: An Exploration of Machine L...

引用

2nd international conference on Advancements in Smart, Secure and Intelligent computing, ASSIC 2024

作者： Kumari, Ankita Sharma, Ishu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350370188

In today's fast-paced digital world, malicious advertising, also known as Malvertising, is increasingly prevalent, endangering people's online privacy and safety. This is so because a Malvertising attack may seem to be many different things, including fraud, the distribution of malware, and data theft. Data security can be improved with the integration of machine learning and deep learning techniques. In order to protect consumers from Malvertising attacks, machine learning models can detect malicious web connections, domain reputation, and covert encoding or masking techniques. These models are effective at keeping you secure because they incorporate lessons from the past and adapt to new attack strategies. This research paper use machine learning-based classification techniques to reduce the harmful advertisements. The paper examines how well three well-known techniques may identify and manage these hazards in network environments that are Support Vector Machines, Naive Bayes, and Random Forest. This research article demonstrates how machine learning techniques might aid in the prevention of hazardous advertisements. We demonstrate via in-depth research and analysis how these methods might make it simpler to identify harmful advertising and take action to prevent them. © 2024 IEEE.

关键词： Network security

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Unveiling the Pathways to Entrepreneurial Mindset: Exploring Aspirations, Challenges, and Socio-Cultural Dynamics Among North Indian engineering Students 13

Unveiling the Pathways to Entrepreneurial Mindset: Exploring...

引用

13th IEEE international conference on teaching, Assessment and learning for engineering, TALE 2024

作者： Kumar, Vikas Monga, Divya Rucha Joshi, Dr. Plaksha University Department of Engineering Mohali India Plaksha University Office of Research Mohali India Plaksha University Innovation Lab and Grand Challenge Studio Mohali India

ISBN: (纸本)9798350376234

This study explores the entrepreneurial mindset of North Indian engineering students, emphasizing their aspirations, challenges, and the socio-cultural dynamics influencing their entrepreneurial journeys. Conducted in two phases, this research includes a 2023 survey of undergraduates from Northern Uttar Pradesh and a 2024 field survey with postgraduate students at the Indian School of Business (ISB), Hyderabad. Key findings highlight significant gender differences in risk-taking behavior, parental support, and career aspirations, revealing that female students demonstrate strong perseverance and organizational skills but face societal and familial constraints. The study underscores the critical role of educational environments and targeted interventions in promoting an inclusive entrepreneurial ecosystem. Recommendations are provided for educators and policymakers to better support and nurture the entrepreneurial potential of engineering students in India. This research contributes valuable insights into how socio-cultural factors and educational settings shape the evolving entrepreneurial landscape among Indian students. © 2024 IEEE.

关键词： Students

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Smart Oral Health Monitor 3

Smart Oral Health Monitor

引用

3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Kamkar, Spandana Anilkumar Sanjay, Srushti Vaishnavi, P.S. Karthik, S. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331515911

Oral health is vital to overall well-being but is often overlooked due to inefficient monitoring tools and delayed diagnosis. This project presents a smart handheld device featuring a miniaturized camera for detecting oral issues like plaque, cavities, teeth discoloration and gum diseases in real-time. Using image processing and deep learning algorithms, the device provides precise feedback and actionable insights. Paired with a mobile application offering detailed analysis and recommendations, it empowers users to adopt proactive oral hygiene practices to prevent teeth disorder. © 2025 IEEE.

关键词： Electronic health record

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Algorithmic Innovations in Machine learning for Drug Discovery and Pharmaceutical Innovations 2

Algorithmic Innovations in Machine Learning for Drug Discove...

引用

2nd IEEE international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Sangeetha, M. Harin Fernandez, F. Mary Ganesh Ramachandran, A. Saravanan, S.K. Bhanumathi, M. University of Technology and Applied Sciences Department of Computer Engineering Nizwa Oman Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science Chennai India Saveetha School of Engineering Simats BigData and Network Security Department of Computers Science and Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

Algorithmic advancements in machine learning have transformed drug discovery and pharmaceutical innovation. This study examines how machine learning might speed medication development and pharmaceutical innovation. Data from genomes, chemistry, and clinical trials is collected to start the research. Careful data cleansing, normalization, and feature engineering prepare the data for model building. Random Forest, SVM, NN, Decision Tree, and Logistic Regression are tested for prediction. ROC AUC results show that Random Forest is the most accurate model at 86%. Ensemble approaches have drug discovery potential. Beyond accuracy, models are assessed for precision, false positive, and false negative rates. Drug discovery relies on precision to measure positive predictions. The Random Forest model makes the most accurate positive predictions at 0.87. However, false positives and negatives also matter in drug discovery. The Decision Tree model contains the falsest positives (35), underlining the need to reduce them to save resources. The Neural Network approach reduces false negatives to 12, ensuring good medication candidates are not overlooked. The results show that different machine learning models have trade-offs, underscoring the necessity for a personalized strategy depending on objectives and restrictions. Algorithmic breakthroughs in pharmaceutical innovation reduce medication development time by 40% and cost by 25%, according to the study. © 2024 IEEE.

关键词： Drug discovery

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AI-Powered Approaches to Unmasking Fake News: A Simple Comparison of ML and DL Techniques

AI-Powered Approaches to Unmasking Fake News: A Simple Compa...

引用

2024 IEEE international conference on computing, Applications and Systems, COMPAS 2024

作者： Bormon, Sajib Prova, Jebin Ahmed Ahmad, Md Hasan Rimon Sohag, Sohanur Rahman Shishir, Hasanur Rahman Daffodil International University Department of Computer Science and Engineering Ashulia Dhaka Bangladesh Noakhali Science and Technology University Department of Software Engineering Noakhali Bangladesh

ISBN: (纸本)9798331529765

False news spreads quickly due to the extensive distribution of incorrect or misleading information across digital channels, which is a global problem. This bias undermines the credibility of information, promotes the spread of misleading information. Utilizing machine learning and deep learning models this study examines the detection of inaccurate information. Natural Language Processing methods are used for data preprocessing on the text datasets. Five machine learning models are used in this study: Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, and Passive Aggressive Classifier. After preprocessing, the accuracy values of these models are 95%, 99.7%, 99.3%, 98.9%, and 99.5%, respectively. The study demonstrates that the decision tree model has superior performance, with the LSTM algorithm following closely after. The PAC and LSTM model have also demonstrated commendable results of 99.5% accuracy. The accuracies offer valuable information regarding the constraints of each model, aiding researchers in selecting suitable ways for identifying counterfeit news. The findings assist individuals in making informed decisions on the establishment of precise and effective ways for identifying false information, which is essential to maintaining the integrity of information distribution. © 2024 IEEE.

关键词： Adversarial machine learning

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DeepFoliage: A Unified Approach for Plant Disease Detection using Deep learning Techniques 3

DeepFoliage: A Unified Approach for Plant Disease Detection ...

引用

3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Kumar, Kalyan Vardhini, Mounika Brindasri, S. Hindustan Institute of Technology and Science Chennai Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

Plant diseases pose a serious danger to crop productivity and quality, making the agricultural sector one of the main obstacles to maintaining food security. The word 'deep foliage' was coined to combine the terms 'deep' (referring to deep learning) and 'foliage', which refers to leaves or other plant life. In particular, illnesses that impact plant leaves are the main focus of this work, which aims to show how deep learning techniques can be used for plant disease analysis and diagnosis. The proposed method combines two algorithms that were trained on a varied dataset of plant pictures, including different crops and disease presentations, to create a hybrid deep learning technique. The proposed method combines two algorithms that were trained on a varied dataset of plant pictures, including different crops and disease presentations, to create a hybrid deep learning technique. With the ability to recognize and classify possible diseases in plant foliage with accuracy, the deep learning model picks up complex patterns and features. 'Deep Foliage' enables prompt intervention to lessen the impact of illnesses on agricultural productivity by offering rapid and precise diagnosis. By lowering the reliance on antiquated, labor-intensive disease detection techniques, the project seeks to promote sustainable agricultural practices. © 2024 IEEE.

关键词： Diseases

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AI and Digital Twins Transforming Healthcare IoT 14

AI and Digital Twins Transforming Healthcare IoT

引用

14th international conference on Cloud computing, Data Science and engineering, Confluence 2024

作者： Sharma, Vikas Sharma, Kapil Kumar, Akshi Delhi Technological University Department of Information Technology Delhi India Goldsmiths University of London Department of Computing United Kingdom

ISBN: (纸本)9798350344837

In this age of digital and smart healthcare, cutting-edge technologies are being used to improve operations, patient well-being, life expectancy, and healthcare costs. Digital Twins (DT) have the potential to significantly change these new technologies. DTs could revolutionise digital healthcare delivery with extraordinary creativity. A digital representation of a physical asset that is always its digital twin due to real-time data processing. This paper proposes and builds a DT-based intelligent healthcare system that is aware of its environment. This approach is a great advance for digital healthcare and could improve service delivery. Our most notable contribution is a machine learning-based electrocardiogram (ECG) classifier model for cardiac diagnostics and early problem detection. Our cardiac models predict some situations with exceptional accuracy when applied to different ways. These findings highlight the potential for Digital Twins in healthcare to create intelligent, comprehensive, and scalable Health-Systems that improve patient-physician communication. Our ECG classifier also sets a precedent for using Artificial Intelligence (AI) and Machine learning (ML) to continually monitor wide range of human body data and identify outliers. ECG data processing has improved significantly using neural network-based algorithms over classic machine learning methods. In conclusion, our work integrates digital twins with cutting-edge AI and machine learning to revolutionise healthcare. Future healthcare will be predictive and improve lives. © 2024 IEEE.

关键词： Internet of things

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A Brief View at Augmented Reality as a Tool for Education 2nd

A Brief View at Augmented Reality as a Tool for Education

引用

2nd EAI international conference on Automation and Control in Theory and Practice, EAI ARTEP 2024

作者： Hrehová, Stella Matisková, Darina Židek, Kamil Department of Industrial Engineering and Informatics of Manufacturing Technologies with the seat in Prešov Technical University of Košice Prešov Slovakia

ISBN: (纸本)9783031592379

With the rapid development of information technologies, especially in the field of hardware, new possibilities for the development of various applications are opening up. We encounter mobile applications in almost every area of our lives. However, this trend is putting a lot of pressure on education. Traditional teaching methods are losing their effectiveness more and more clearly, and educators must therefore look for new alternative methods in order to pass on the necessary knowledge to students in innovative forms. One of the rapidly developing fields is also the field of augmented reality, which brings a breakthrough to the traditional education system. Augmented reality allows users to connect in real-time virtual elements in real contexts. This characteristic element provides the prerequisites for the use of this technology as a learning support and enables the deployment of a constructive educational environment. Applications using this technology have been successfully deployed in many fields of education such as technology, engineering, mathematics, medicine, and many other areas to show practical and creative forms of teaching. The presented paper will describe the main attributes of augmented reality technology and its implementation possibilities in the field of education. Advantages and risks that affect its use in engineering education will be described. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Federated learning

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An Efficient Attendance Management with Deep learning

An Efficient Attendance Management with Deep Learning

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2024 IEEE international conference on computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Shivanna Hema, H.P. Janardhana, D.R. Srinivas, P.M. Sahyadri College of Engineering and Management Department of Computer Science and Engineering Mangaluru India D S Dinakar Polytechnic College Department of Electronics and Communication Engineering Shivamogga India Nitte Meenakshi Institute of Technology Department of Computer Scince and Engineering Bengaluru India

ISBN: (纸本)9798331517892

Accurate attendance recording is critical in educational institutions to ensure students' and teachers' consistency and discipline. Traditional manual attendance systems are frequently inefficient and prone to errors, such as proxy attendance and missed entries, which may impact the accuracy of attendance records. To address these challenges, an automatic attendance system that uses camera-based facial recognition technology is proposed. The latest technology records accurate attendance by taking visuals of students as soon as the class starts and comparing them to an already-existing database. Face recognition happens through human detection. The concept used here is deep learning. For students, this automated solution streamlines attendance management by ensuring that records are accurate and up to date without the need for manual entry. The system efficiently keeps track of the presence of teachers, notifying the Head of Department (HOD) right away in the case of an excused absence, therefore preventing disruptions during scheduled lessons. This technology significantly increases the reliability and efficiency of attendance tracking by automating the process. Enhancing record accuracy and accountability leads to a more regulated and effective learning environment that benefits teachers and students alike. This technological method offers greater precision and oversight, leading to improved institutional administration and educational outcomes. © 2024 IEEE.

关键词： Students

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