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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Teotia, Satwik Prajapati, Rahul Maurya, Deepak Kumar Gupta, Prahlad Yadav, Jay Singh Department of Computer Science AKTU University ABES Engineering College Ghaziabad India

ISBN: (纸本)9781032911571

The modern-day educational landscape demands progressive answers to enhance challenge control practices amongst university students. In reaction to this want, the principal project, "Pro Tech" introduces an internet-primarily based Project Management System tailor-made to empower students in effectively overseeing their instructional endea-vours. Leveraging the MERN (MongoDB, Express. Js, React, ***) generation stack, Pro Tech offers an intuitive platform for venture initiation, collaboration, and monitoring. This machine now not handiest benefits college students through fostering teamwork and enhancing challenge management abilities however also affords corporations with a streamlined portal for project evaluation and potential collaboration. This paper offers a complete overview of Pro Tech, delving into its technology stack, goals, execution plan, feasibility examine, methodology, and predicted effects. The synthesis of those factors contributes to the creation of a strong Project Management System poised to elevate the instructional mission experience for students and facilitate seamless collaboration between educational establishments and businesses. © 2025 the Author(s).

关键词： Project management

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Campus Ease: Elevating Student Placements and Career Success

Campus Ease: Elevating Student Placements and Career Success

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Mahendru, Mansi Nigam, Shruti Bajpai, Rishabh Ahmad, Zubain ABES Engineering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350378092

Campus Ease, a student-centric mobile application, is designed to enhance the college experience by addressing various needs throughout the academic journey. Its primary focus is on providing timely career notifications from the college's Career and Professional Development department (CCPD), ensuring that students stay informed about job and internship opportunities. The app further streamlines the application process by sending targeted push notifications to students with pending job registrations, promoting timely actions and facilitating efficient communication with CCPD. One of Campus Ease's key features is its Resource Hub, which empowers students with company-specific resources like past- year papers and mock tests. This resource hub aids in effective preparation for placement opportunities, providing students with valuable tools to excel in their career pursuits. Additionally, the app offers a unique dimension through Alumni Insights, presenting firsthand knowledge from successful alumni interviews categorized by respective companies. This feature enables students to gain valuable insights into career paths, learning from those who have navigated similar journeys. With its multifaceted approach, Campus Ease strives to be a comprehensive platform supporting students in their academic and professional endeavors. © 2024 IEEE.

关键词： Students

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Exploring Deep Learning Approaches for Epileptic Seizure Classification 1

Exploring Deep Learning Approaches for Epileptic Seizure Cla...

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

作者： Khandelwal, Shubh Vaish, Shivanshi Singh, Shivanchha ABES Engineering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350353648

A seizure is an uncontrollable electrical disruption in the brain that modifies movement, sensations, behaviour, and consciousness. Individuals with epilepsy are likely to suffer recurrent seizures. Seizures based on EEG data have been classified using deep learning and machine learning techniques to aid in epilepsy identification. In this work, LSTM, Bi-Directional LSTM, Stacked LSTM, and GRU are checked to obtain the most suitable classification model. The model is then utilized to identify the class of the testing data. Also, the accuracy, precision, and sensitivity of the model are determined in Two-Class Classification, Three-Class Classification and Five-Class Classification for obtaining reliable results. © 2024 IEEE.

关键词： Deep learning

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A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

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computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai Jehad Ali Department of Computer Applications Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India Department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection *** model utilises Recurrent Neural Networks,specifically exploiting LSTM *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

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Analytical Study of Pre-trained Transformers in Natural Language Generation

Analytical Study of Pre-trained Transformers in Natural Lang...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Goyal, Varun Gaur, Mukul Rawat, Anuj Garg, Shivam Jain, Sapna ABES Engineering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350378092

The paper presents an overview of how conversational agents have evolved over time from rule-based to complex models of NLP to address issues in creating an agent that understands the user's intentions and responds in a natural way. How these different applications of machine learning, deep neural networks, and reinforcement learning helped to maintain the contexts, understand the languages better, generate responses with ethical considerations are discussed hereafter for improving the user experience. This paper contributes valuable insights into the field of AI chatbot development by fusing existing technologies with future opportunities and manages to come out with a pathway for innovations further down the track of conversational AI. © 2024 IEEE.

关键词： Chatbots

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Automatic Number Plate Recognition System using Machine Learning Techniques

Automatic Number Plate Recognition System using Machine Lear...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Dayal, Avnish Porwal, Aryan Gupta, Avinash Srivastava, Aditya Kumar Mahendru, Mansi ABES Engineering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350378092

The system employs video processing technology to detect the image of a vehicle's number plate from a video stream. Subsequently, various algorithms are applied to extract the number from the detected plate. This system can be used at college entrances, cantonment area and other places with strict access restrictions. The system detects the number plate whenever a vehicle passes by, converting it into both images and text. Subsequently, this information is stored in the database for future reference or analysis. This research paper introduces ANPR system which is designed to optimize accuracy, efficiency, and ease of implementation which includes combination of Python programming language, YOLOv8 (You Only Look One-level), and EasyOCR, the proposed system addresses the complexities associated with detection of license plate and recognition in diverse real-world situations. Core of the system lies in the adoption of YOLOv8, an object detection model renowned for its real-time capabilities and high accuracy. EasyOCR, a user-friendly Optical Character Recognition (OCR) library, is seamlessly integrated into the ANPR system. This addition enhances the system's ability to accurately extract alphanumeric information from detected license plates, bridging the gap between detection and recognition phases. Due to this the system ensures efficient noise reduction, image segmentation, and overall improvement in the quality of input images, contributing to the system's reliability. The paper presents an in-depth evaluation of the ANPR system's performance through datasets. Additionally, the research explores potential applications of the ANPR system in real-world scenarios, emphasizing its applicability in traffic management, law enforcement, and security systems. © 2024 IEEE.

关键词： Noise abatement

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Technological Insights into Yoga Posture Recognition: A State-of-the-Art Review

Technological Insights into Yoga Posture Recognition: A Stat...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Tyagi, Rohan Yadav, Ravi Mishra, Devendra Abes Engineering College Department Of Computer Science And Engineering Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350366846

In the contemporary world, the ancient practice of yoga has gained immense popularity for its holistic approach to well-being. However, with the rise of self-guided yoga practice facilitated by digital platforms, practitioners face challenges in achieving precise alignment and correctness in postures. This paper delves into the intersection of traditional yoga and modern technology, presenting a comprehensive analysis of existing literature and innovative methodologies to address these challenges. Through the integration of deep learning techniques such as PoseNet, OpenPose, and LSTM, our research aims to provide real-time feedback and guidance for self-practitioners. We review studies exploring the significance of yoga in modern life, the surge in self-practice, challenges encountered, and the role of technology in enhancing the yoga experience. Additionally, we present a detailed methodology encompassing data collection, preprocessing, augmentation techniques, and the development of a Yoga Pose Recognition and Correction System. Our approach integrates advanced machine learning models with traditional algorithms, offering a multi-faceted framework for accurate pose identification. Technological innovations such as real-time feedback mechanisms and user-centric interfaces further enhance the system's usability and effectiveness. We discuss diverse applications ranging from yoga education to healthcare and propose future research directions to address challenges such as dataset variability and real-time processing. In conclusion, this research contributes to the evolving landscape of technology-enabled wellness applications, paving the way for accessible and personalized yoga guidance in the digital age. © 2024 IEEE.

关键词： Long short-term memory

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Decentralised Autonomous Organisation based Ecosystem Structure for commercial companies and organisations 1

Decentralised Autonomous Organisation based Ecosystem Struct...

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1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024

作者： Rasool, Juveria Gupta, Shruti Abes Engineering College Department of Computer Science and Engineering Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350392111

The advent of blockchain technology has precipitated a monumental transformation in the financial landscape, ushering in a paradigm characterized by transparency, accuracy, and immutability. This research paper delves into the imperative of enhancing organizational decision-making accountability by actively engaging users as stakeholders in a public consensus framework, thereby fostering a more equitable resource distribution. The vehicle for this transformative endeavor is the application of decentralized autonomous organizations (DAOs), which represent autonomous, immutable code entities decentralized in nature. Unlike conventional structures, DAOs operate beyond the control of a singular entity or individual, deriving governance from a network of participants with vested interests in the organization. At the core of this research lies the introduction of a versatile DAO smart contract algorithm, seamlessly implemented within a decentralized application (dApp). This innovation constitutes a software solution adaptable for any organization seeking to infuse decentralization and transparency into its operational framework. The integration of decentralization not only introduces an additional layer of incentivization for both stakeholders and organizations but also fosters a trustless environment. The paper presents a pioneering approach to organizational governance, offering a blueprint for entities aspiring to embrace the principles of decentralization, transparency, and accountability in their ecosystems. © 2024 IEEE.

关键词： Decentralized finance

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Towards Intelligent Attendance Monitoring for Scalable Organization with Hybrid Model Using Deep Learning

Towards Intelligent Attendance Monitoring for Scalable Organ...

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International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Srivastava, Akhilesh Kumar Mishra, Chandrahas Mishra, Anurag Srivastava, Atul Department of Computer Science and Engineering ABES Engineering College Ghaziabad India Department of Computer Science ABES Engineering College Ghaziabad India Department of Computer Science KIET Group of Institutions Ghaziabad India Amity School of Engineering and Technology AUUP Lucknow India

ISBN: (纸本)9789819706402

Face recognition technology using AI has seen paradigm shift in the evolving world. Automatic attendance monitoring using real-time face identification is a solution to handle attendance in any small/large as well as scalable organization. Traditional methods in the organization involve calling names or signing sheets with individuals, which is a very time-consuming process and provides insurance. This is also subjected to manual errors. Automation of attendance recording and monitoring through face recognition is a process of identifying the face for taking attendance by using the image of the human face as biometric parameter captured through a surveillance camera in the premise. This article presents an effective way of attendance monitoring by making use of deep learning technology and compares its results with the state-of-the-art approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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Q-Net Compressor: Deep Learning Model Compression with Quantization 15

Q-Net Compressor: Deep Learning Model Compression with Quant...

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

作者： Bansal, Yash Garg, Sejal Saroha, Kartik Singh, Manu Department of Computer Science ABES Engineering College UP Ghaziabad India

ISBN: (纸本)9798331300579

The rapid advancement of deep learning has brought about remarkable achievements in various fields, particularly by utilizing Deep Neural Networks. It offers a high rate of accuracy, extensive parameters, and intensive computational requirements that often characterize these networks. Due to high memory consumption and energy usage, this results in significant challenges when deploying DNNs on hardware-constrained devices. Researchers have extensively explored various compression techniques (Al-Qurabat 2022) to address these challenges, quantifying the merging as a promising solution. However, the extensive use of quantization in previous works necessitates a comprehensive survey that provides a complete study, analysis, and comparison of various quantization approaches. This paper introduces the "Q-Net Compressor," a novel solution for installing substantial neural (Tao, Hou, et al. 2022) network training on constrained resources edge devices. Utilizing adaptive precision levels to maintain model functionality while reducing memory and computation demands. The Q-Net Compressor, implemented with TensorFlow and PyTorch, offers flexibility across diverse deep-learning architectures, striking a balance between significant model size reduction and the preservation of predictive power. This innovative approach contributes to the evolving deep learning model compression field with quantization, bridging the gap between computational demands and resource constraints. We hope to address this gap in this survey study by providing an integrative report that investigates the ideas and methods of quantization, (Hu and Wen 2021) with a particular emphasis on its use in image classification problems. © Grenze Scientific Society, 2024.

关键词： Contrastive Learning

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