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Detection of Criminal Activities/Criminal Through CCTV by Live Footage Analysis

Detection of Criminal Activities/Criminal Through CCTV by Li...

引用

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

作者： Kumar, Rahul Rajpurohit, Divyajeet Singh Hanief, Mushraf Sharma, Shilpi Choudhury, Tanupriya Sar, Ayan Amity School of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Noida India Graphic Era Deemed to Be University Cse Dept Uttarakhand Dehradun248002 India University of Petroleum and Energy Studies School of Computer Science Uttarakhand Dehradun248007 India

ISBN: (纸本)9798350373783

The integration of deep learning techniques, particularly the You Only Look Once (YOLO) object detection model, with Closed-Circuit Television (CCTV) systems has significantly transformed the landscape of crime detection and prevention. This research paper provides a comprehensive analysis of YOLO-based CCTV crime detection, elucidating the innovative strategies, challenges, and potential implications for enhancing public safety. The YOLO framework is well-known for its real-time object detection capabilities, which enable it to swiftly and accurately identify multiple objects within a single image frame. In the context of CCTV surveillance, YOLO has proven invaluable in the identification and tracking of criminal activities. The paper explores how YOLO's object detection architecture is adapted to the unique challenges of CCTV crime detection, including variable lighting conditions, occlusions, and diverse camera angles. One of the fundamental contributions of this research is the development of a YOLO-based CCTV crime detection system. This system combines YOLO's speed and accuracy with a sophisticated backend for video analysis and real-time alerting. The system not only detects suspicious activities but also classifies them into different categories, such as theft, vandalism, or violent behavior, thus facilitating immediate responses from law enforcement agencies. Moreover, the paper delves into the application of deep learning in data pre-processing, noise reduction, and the handling of extensive video feeds. Techniques such as transfer learning and data augmentation are explored to enhance the model's generalization and adaptability to different surveillance environments. The scalability of the system is also discussed, allowing it to be deployed in urban centers, commercial spaces, and residential areas. While the advantages of YOLO-based CCTV crime detection are evident, this research also highlights potential challenges, including privacy concerns, ethical

关键词： Closed circuit television systems

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Review of Software-Defined Network-Enabled Security 1st

Review of Software-Defined Network-Enabled Security

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1st International Conference on Computational Intelligence for engineering and Management Applications, CIEMA 2022

作者： Gupta, Neelam Tanwar, Sarvesh Badotra, Sumit Amity Institute of Information Technology Amity University Noida Uttar Pradesh Noida India School of Computer Science and Engineering Bennett University UP Noida India

ISBN: (纸本)9789811984921

In order to exploit any of the system's security holes attackers will always target software-defined networking (SDN) security. As a game-changer in modern internetworking technologies, SDN appears to be ubiquitous these days, and it has been widely accepted by the scientific community, organizations, and industry. These days, enterprise networks are increasingly using it because of its flexibility in network management and lower operational expenses. No flexibility is available in a dynamic network environment while using traditional networks. Software-defined networking is recognized as it’s thought of as an effective way to manage the entire network and it's also thought to simplify the complex network design into something more manageable. Research on security challenges and concerns in software-defined networks has been conducted up to this point, as described in this paper. SDN creates substantial security risks and increases the complexity of the system as its approach replaces traditional networking in order to provide remote and centralized control features. This article presents methodologies and techniques used for the security enhancements of SDN, as well as the security issues that are explored, as well as a categorization of the research literature over the last ten years. The results present outline the information on the most active research areas, security technologies and methodologies, pros and cons in security in SDN. A detailed review like this will immensely benefit the research community in designing more consistent and effective security solutions for SDN networks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Software defined networking

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Analyse and Evaluate Quixbugs with Open AI Codex and Powering Next Generation Application 6

Analyse and Evaluate Quixbugs with Open AI Codex and Powerin...

引用

6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Tyagi, Deepanshu Tanwar, Sarvesh Mittal, Neetu Badotra, Sumit Amity Institute of Information Technology Amity University Uttar Pradesh Noida India School of Computer Science and Engineering Bennett University Uttar Pradesh Noida India

ISBN: (纸本)9798350304480

The paper investigates the ordinary language age limits of massive language models, with applications to the development of two types of learning tools commonly found in programming courses. We construct programming workouts and code explanations, reviewing these abstractly and statistically, using the Open AI Codex as the massive language model. Our findings indicate that the majority of the content so given is both unique and sensible, and that it is occasionally ready to use without any guarantees. While rehearsing, we discovered that it is astonishingly simple to affect both the programming ideas and the practical subjects they include simply by offering expressions of commitment to the model. Findings indicate massively generative computer intelligence models have fundamental value. © 2023 IEEE.

关键词： Application Codex GPT-3 Intelligence Java Open AI Security

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Survey Paper: Comparative Study of Machine Learning Techniques and its Recent Applications 2

Survey Paper: Comparative Study of Machine Learning Techniqu...

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2nd International Conference on Innovative Practices in technology and Management, ICIPTM 2022

作者： Shivahare, Basu Dev Suman, Shashikant Challapalli, Sai Sri Nandan Kaushik, Prakarsh Gupta, Amar Deep Bibhu, Vimal Amity School Of Engineering Technology Department Of Computer Science Greater Noida India

ISBN: (纸本)9781665466431

The main objective of human evolution has always been to look for ways to mold the nature to satisfy our needs. A key milestone in this regard is the invention of a machine-called the computer that can complete a task given to it in fraction of time taken by an average human. While that sounds great, the only drawback is that the decision must still be taken by a man who is bound by limitations of the human body. The run to reap the complete benefits has given rise to what is called the Artificial Intelligence. Machine learning is a part of AI, which deals with imparting knowledge to the computer through various related examples. Throughout the years, various machine learning algorithms have been developed each with their own merits and demerits. This paper is a consolidated effort to bring together different ML algorithms like linear regression, KNN (k-nearest neighbours) etc. This research paper discusses the most recent developments in these areas of study and tries to define the best applications for each of those based on previous researches. © 2022 IEEE.

关键词： Deep learning

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Advanced Deep Learning Approaches for Real-Time Anomaly Detection in IoT Environments 7

Advanced Deep Learning Approaches for Real-Time Anomaly Dete...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Goyal, Himanshu Rai Husain, Saif O. Dixit, Krishna Kant Boob, Nandini Shirish Reddy, B. Ravali Kumar, Jaysheel Sharma, Smita Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Electrical Engineering Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Bangalore India Amity University Amity School of Engineering & Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350067

The proliferation of Internet of Things (IoT) systems has led to the generation of vast amounts of data, increasing the need for effective anomaly detection mechanisms to ensure system reliability and security. Traditional methods often fall short due to the high dimensionality and dynamic nature of IoT data. This paper presents a deep learning-based approach for real-time anomaly detection in IoT systems, leveraging the power of neural networks to identify patterns and anomalies within complex datasets. By employing deep learning algorithms such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, this framework can efficiently detect abnormal behaviors in real-time, offering a significant improvement over conventional methods. The proposed system is trained on labeled datasets collected from IoT devices, allowing it to distinguish between normal and anomalous activity. Furthermore, the model adapts to new data, enhancing its detection capabilities over time. Experimental results demonstrate that this approach achieves high accuracy and low false-positive rates, making it suitable for deployment in real-world IoT environments. The framework's scalability and adaptability highlight its potential for enhancing the security and reliability of IoT networks, particularly in critical applications such as smart cities, healthcare, and industrial IoT. © 2024 IEEE.

关键词： Convolutional neural networks

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Live Audio Recognition and Text Conversion System

Live Audio Recognition and Text Conversion System

引用

Computational Intelligence, Communication technology and Networking (CICTN), International Conference on

作者： Pratyaksha Sharma Sumita Gupta Boddu Asmitha Bhavya Khushi Sharma Saru Dhir Sapna Gambhir Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University UP Department of Information Technology Amity School of Engineering & Technology Amity University UP Dept. of Computer Science College of Engineering and Computing George Mason University USA

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

With the advancement of technologies, different methods are currently being used for converting spoken language into text. These systems offer a hands-free alternative to traditional input methods, especially for individuals with physical disabilities or those requiring efficient documentation of spoken content. The goal of this study is to review recent advancements in this field and highlight gaps in current research, with a focus on accuracy, language adaptability, ease of use, and the integration of various technologies. We also suggest future directions for enhancing and developing this technology further.

关键词： Accuracy Text recognition Reviews Hidden Markov models Speech recognition Machine learning Documentation Natural language processing Communications technology Computational intelligence

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An Approach for Predicting Admissions in Post-Graduate Programme by Using Machine Learning 1

引用

3rd International Conference on Data, engineering, and Applications, IDEA 2021

作者： Sharma, Shivam Soni, Hemant Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Madhya Pradesh MP Maharajpura Gwalior India

ISBN: (数字)9789811946875

ISBN: (纸本)9789811946868

Machine Learning (ML) has spread its wings over a wide range of fields nowadays. For all data-driven areas, machine learning is a true game-changer. Today's most powerful firms are those that have concentrated their resources and energy on understanding client data. Machine Learning is a branch of Artificial Intelligence that contains a collection of sophisticated algorithms. Machine learning aids in machine training by mapping the link between input/inputs and output. Machine Learning is used in the proposed study to estimate the probability of admission to a master's programme from the perspective of Indian students. This work is presented as a web-based interactive application. This web-application was created using the platforms ‘R’ and ‘Shiny’. The Web-App was created to provide students with a platform to get insight into their chances of acceptance. In this study, Support Vector Regression is utilised to train the model for predicting admission probability. Since, the R-squared Value of Support Vector Regression has been greater than 0.8 for both the training and testing sets. As a result, it's a good algorithm to use in the proposed project. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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A Comprehensive Study of a Conversational AI-Driven Healthcare Bot with Pose Estimation for Precision Fitness Monitoring and Personalized Guidance

A Comprehensive Study of a Conversational AI-Driven Healthca...

引用

2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Sinha, Anurag Kumar, Biresh Kundu, Roumo Sharma, Amit Sharma, Mukesh Raza, Asif ICFAI University ICFAI Tech School Computer Science Department Jharkhand Ranchi India Amity University Department of Computer Engineering and Application Jharkhand India Amity Institute of Information Technology Amity University Jharkhand Jharkhand Ranchi India Lovely Professional University Department of Computer Science Phagwara India Amity University Department of Master of Computer Application Jharkhand Ranchi India

ISBN: (纸本)9798350374100

This research investigates a new healthcare paradigm through the analysis of a conversational AI-powered healthcare bot that has posture estimation capabilities for accurate fitness tracking and customized recommendations. User motions during workout routines are accurately and thoroughly assessed thanks to the incorporation of powerful posture estimation technology. With the help of individualized exercise advice, this thorough study explores the capabilities of the system and shows how it may transform wellness practices. The healthcare bot provides an innovative method of fitness tracking by using conversational AI and accurate posture prediction to customize recommendations based on user requirements. This novel combination promises to enhance wellbeing and provide consumers with a more efficient and customized workout experience. The study emphasizes this point. © 2024 IEEE.

关键词： Health care

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Intelligent Carpooling - Machine Learning Route and Pickup Optimisation

Intelligent Carpooling - Machine Learning Route and Pickup O...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Arunya Goojar Parth Verma Nitin Singh Shubhradeep Debnath Neha Tyagi Computer Science and Engineering Amity School of Engineering and Technology UP India

In order to encourage carpooling between workplace personnel and college students, this research article gives a unique method that makes use of machine getting to know and synthetic intelligence (AI) algorithms. The file contends that although carpooling remains an underused mode of transportation, it has the ability to significantly lessen visitors’ congestion and air pollution in metropolitan areas. The look at shows a brand-new web tool that compares commuter routes for drivers and passengers while taking vicinity, vacation spot, and preference into attention. Users might also chat with each other and prepare excursions at the utility’s user-pleasant platform. The effectiveness of the advised software in encouraging carpooling and decreasing traffic congestion and air pollution is classified within the article. The studies’ conclusions offer insightful information on how carpooling can be used to alleviate urban transportation issues.

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Review the Deep Learning Strategies for Enhancing the Security of Body Area Networks Against Adversarial Attack

Review the Deep Learning Strategies for Enhancing the Securi...

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Advances in Computing Research on science engineering and technology (ACROSET), International Conference on

作者： Jagrati Nagdiya Rajeev Goyal Computer Science and Engineering Amity School of Engineering and Technology Gwalior India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

Thereis an urgent need for strong security measures to protect Body Area Networks (BANs) from malicious threats, as their use in health monitoring and telemedicine continues to grow. To improve BAN security, this research presents a new method that makes use of deep learning approaches. Data breaches, unauthorised access, and other cyberattacks that target the particular weaknesses of BANs are among the many cybersecurity threats that our approach uses sophisticated neural network designs to detect and mitigate. Typical network traffic events and simulated adversarial attacks are both included in the extensive dataset that our deep learning model is trained on. The model can then learn to identify suspicious behaviour by observing complicated patterns and outliers. The design of the device is such that it can run smoothly on very little power while still achieving excellent efficiency.

关键词： Deep learning Training Data privacy Federated learning Network security Data breach Body area networks Data models Data communication Monitoring

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