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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： R. Dhanalakshmi N. Vijayaraghavan Akhil Kumar B. Sahaya Beni Prathiba Center for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology - Chennai Chennai India Department of Mathematics KCG College of Technology Chennai India SCSET Bennett University Greater Noida India Center for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

In the ever-evolving landscape of cyber security, the prevalence of phishing attacks poses a formidable threat to information security systems worldwide, compromising data integrity and eroding user trust. This paper introduces an innovative solution harnessing artificial intelligence to identify and analyze phishing domains, particularly focusing on newly registered domains from public databases like WHOIS. This approach is crucial as traditional security measures often prove insufficient against the sophistication of phishing tactics. Our proposed AI-driven system employs an analysis strategy. It utilizes machine learning to conduct an in-depth backend code and content analysis. This involves comparing suspect and legitimate sites for structural and syntactical disparities, utilizing deep learning models trained on extensive datasets of known phishing and authentic domains. These methodologies offer a nuanced evaluation, crucial for distinguishing sophisticated phishing attempts from genuine domains. Additionally, the system excels at identifying malicious links within these domains, enhancing its overall protective capabilities. The efficiency of our solution lies in its capacity for real-time, accurate threat detection. We introduce a probabilistic scoring mechanism that assesses the likelihood of a domain being a phishing site, facilitating informed decision-making for cyber security personnel. Furthermore, our system is designed for seamless integration into existing cyber security frameworks, providing versatility and ease of use.

关键词： Deep learning Content management Codes Phishing Focusing Probabilistic logic Threat assessment Real-time systems computer crime Long short term memory

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Harnessing Machine Learning for Enhanced Agricultural Efficiency through Advanced Data Analysis and IoT Integration

Harnessing Machine Learning for Enhanced Agricultural Effici...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Khushboo Devi Naween Kumar Sachikant Dash Yajnaseni Dash Sasmita Padhy SCSET Bennett University Greater Noida Uttar Pradesh India Computer Science and Engineering GIET University Gunupur Odisha India School of Artificial Intelligence Bennett University Greater Noida Uttar Pradesh India School of Computing Science and Engineering VIT Bhopal University Bhopal Madhya Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The agricultural sector is poised to benefit greatly from the improved data processing and decision-making capabilities enabled by machine learning, which is significantly impacting economies worldwide. To alleviate the urgent worldwide food shortage, this study investigates the potential of incorporating machine learning algorithms to maximize harvest yields while decreasing food waste. The paper analyses the present state of these algorithms in farming, finds important problems and possibilities, and finds out how changing data labels affects the accuracy of the algorithms. The results show that farmers may increase crop yields and decrease waste by making more educated decisions about planting, watering, and harvesting using big data from farms and data collected in real time from the Internet of Things sensors. The comparative analysis of five classification algorithms for agricultural data highlighted the Naive Bayes Classifier with the highest accuracy ($\mathbf{9 9. 5 3 \%}$), while Random Forest showed balanced performance across all categories ($\mathbf{9 8. 8 1 \%}$). Each algorithm demonstrated strengths depending on the specific prediction task. These findings indicate that there is potential for improvement in terms of production rates, cost savings, and farm resilience. Future farmers may also be able to use this study’s findings to improve crop production efficiency, spot diseases earlier, and reduce food shortages.

关键词： Machine learning algorithms Accuracy Decision making Production Prediction algorithms Real-time systems Classification algorithms Sensors Internet of Things Random forests

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Comparative Analysis of Machine Learning Algorithms in Parkinson Disease Diagnosis

Comparative Analysis of Machine Learning Algorithms in Parki...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Deepak Garg Nishka Mehlawat Yogesh Pal Akansha Singh Vivek Mehta SCSET Bennett University (Times of India Group) Greater Noida Uttar Pradesh India School of Computer Science Engineering and Technology Bennett University (Times of India Group) Greater Noida Uttar Pradesh India

Parkinson’s disease is a neurodegenerative condition that gradually impairs both motor function and cognitive abilities. This decline stems from by the deterioration of cells responsible for producing dopamine in the brain. Parkinson’s disease can occur in individuals across diverse age brackets, with a higher incidence in those aged 60 and older. Yet, it should be emphasized that Parkinson’s can also impact individuals at a younger age, often in their 40s or 50s, termed “early-onset” Parkinson’s disease. In rarer cases, Parkinson’s has even been identified in much younger individuals. There are two kinds of symptoms in PD (motor and non-motor symptoms). In motor-symptoms it’s having (tremors, rigidity, bradykinesia, postural instability, freezing), and in non-motor symptoms it’s having (cognitive changes, mood disorders, sleep disorders, automatic dysfunction, sensory issues, fatigue, constipation, sexual dysfunction). However, many feature extraction methods face challenges with low performance accuracy, which is unacceptable when dealing with the detection of a chronic condition of this disease. Diverse approaches within machine learning, encompassing decision trees, logistic regression, SVM, and KNN, were used to determine which individuals with Parkinson’s disease and which ones were healthy controls. It’s interesting to note that baseline models, such as the frequency domain method, performed comparably, indicating that they would be a good choice for PD diagnosis.

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ChatGPT: A Comprehensive Review of a Large Language Model

ChatGPT: A Comprehensive Review of a Large Language Model

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Communication, Security and Artificial Intelligence (ICCSAI), International Conference on

作者： Kuldeep Singh Kaswan Jagjit Singh Dhatterwal Reenu Batra Dileep Kumar Yadav School of Computing Science and Engineering Galgotias University Greater Noida UP India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Guntur AP India Department of Computer Science and Engineering SGT University Gurugram Haryana India SCSET Bennett University Greater Noida UP India

In the evolving landscape of Natural Language Processing (NLP), the emergence of large language models has redefined the boundaries of human-computer interaction. This paper presents an in-depth review of ChatGPT, a pioneering exemplar in this domain. We commence with a thorough discussion on the evolution of NLP techniques and models, culminating in the inception of ChatGPT. The critical examination of pertinent research papers, projects, and benchmarks showcases the progression of large language models in the context of complex language understanding and generation tasks. The primary focus of this paper is to elucidate the intricate methodology underpinning ChatGPT's architecture and technology. We meticulously outline the comprehensive training process encompassing pretraining and fine-tuning phases, shedding light on the nuanced decisions that bolster model performance. The dataset employed for training and validation is delineated, contributing to an informed understanding of the model's capabilities. The user experience and feedback section encapsulates the empirical perspectives of interacting with ChatGPT, elucidating its strengths and limitations. The paper also prognosticates on the challenges and future directions for ChatGPT. The extant limitations are outlined, and plausible avenues for research and development are suggested to propel the model's potential. In conclusion, this review synthesizes the contributions of ChatGPT in the NLP landscape, underscoring its significance in reshaping the frontiers of language-based human-computer interaction. By amalgamating insights from methodology, applications, ethics, and performance, this paper offers a comprehensive compendium of the evolution and impact of ChatGPT in the realm of NLP research and applications.

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Post Quantum Blockchain Assisted Privacy Preserving Protocol for Internet of Medical Things

Post Quantum Blockchain Assisted Privacy Preserving Protocol...

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Dushyant Kumar Yadav Dinesh Yadav Yogesh Pal Dharminder Chaudhary Hemlal Sahu A. S. L. Manasa Dept. of Mathematics Govt. J. Y. Chhattisgarh College Raipur India Dept. of Computer Science & Engineering Shivalik College of Engineering Raipur India Dept. of Mathematics (SCSET) Bennet University Greater Noida India Dept. of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

Internet of Medical Things (IoMT) is an advanced ecosystem made with the help of Internet of Things technology, smart sensors, medical equipment, and connected end users. Internet of Medical Things is very helpful to enhance efficiency, accuracy, and qualities traditional healthcare system. A smart IoMT contains end users, heterogeneous network, and private data is transferred through public channel. Therefore, it becomes hard to achieve both security and privacy in the IoMT system. In this article, we have focused on designing privacy preserving authenticated key mechanism between two parties. We have proposed a blockchain assisted authenticated key exchange mechanism for IoMT systems supporting fog computing paradigm. The proposed privacy preserving authenticated key exchange protocol is based post quantum assumption “ring learning with errors” and physical unclonable function (PUF), respectively. A detailed security analysis ensures that proposed protocol is secure against most of the existing attacks. This protocol is also secure against quantum attacks based on the assumption.

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Quantum Safe Construction of Authentication and Key Exchange Based on Module Lattices

Quantum Safe Construction of Authentication and Key Exchange...

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Global Conference for Advancement in Technology (GCAT)

作者： Sayan Banerjee M Nikhilesh Ankit Sharma A G Sreedevi Saurabh Rana Dharminder Chaudhary Dept. of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Dept. of Mathematics Chitkara University Solan India Dept. of Mathematics (SCSET) Bennett University Noida India

An authenticated mobile key exchange is necessary for post quantum secure communication over a public channel. It allows two entities to create a secure and unique session key for every communication session. We feel a need to design two party efficient and secure authenticated key agreement. We have also observed all the protocols given above are based on learning with error assumption over a finite ring. The ”Module Learning with Errors” assumption is the more appropriate choice among ”Learning with Errors” and ”Ring Learning with Errors” for designing practical password-authenticated key exchange protocols. With the help of ”Module Learning with Errors” assumption, and by lowering the number of ring polynomials, several implementations of the protocols can be realized. Therefore, this paper contains quantum safe, efficient, and practical ”Module Learning with Errors” assumption based authenticated key agreement protocol. The protocol’s security analysis is presented within the framework of the random oracle model.

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Improving the Transmission Power of UAVs with Intelligent Reflecting Surfaces in V2X

Improving the Transmission Power of UAVs with Intelligent Re...

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IEEE Conference on Vehicular Technology (VTC)

作者： Shivam Chaudhary Rajat Chaudhary Ishan Budhiraja Aditya Bhardwaj Anushka Nehra Sheshikala Martha SCSET Bennett University Greater Noida U.P. India Department of CSE Thapar Institute of Engineering and Technology Patiala Punjab India School of Computer Science and Artificial Intelligence SR University Warangal India

Unmanned aerial vehicles (UAVs), which can help with high-speed communications and provide better coverage, are an important component of next-generation wireless networks. Because of its high mobility and aerial nature, it is suitable for a wide range of mobile wireless communications-based applications. However, low data rates with limited transmission power constitute a significant difficulty in wireless communication that lowers network performance. To overcome this issue, integrating a UAV with a relay device capable of delivering high data speeds while utilising minimum transmission power is a promising approach. In this research, we presented an edge-cutting framework called UAV-IRS, in which an Intelligent reflective surface (IRS) supports unmanned aerial vehicles (UAVs) that traverse areas with low signal strength. Furthermore, we discussed the applications, challenges and research directions of UAV-IRS in vehicle-to-everything (V2X) communication. We considered a case study of UAV-IRS in V2X communication. The performance evaluation demonstrates how the viable data rate and minimum transmission power decrease with distance as the number of IRS elements increases.

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Advancements in non-alcoholic fatty liver disease (NAFLD) detection: A machine learning approach

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AIP Conference Proceedings 2025年第1期3253卷

作者： Som Chauhan Aditya Bhardwaj Tarun Jain Ankur Pandey Department of Computer Science and Engineering Manipal University Jaipur India School of Computer Science Engineering and Technology (SCSET) Bennett University Greater Noida India

The growth of NAFLD has become an issue in public health because it can advance to more dangerous illnesses like cirrhosis and NASH. Detecting NAFLD on is vital which is why this project wants to explore the application of machine learning algorithms, for early diagnosis. Currently conventional diagnostic methods heavily rely on costly procedures such, as liver biopsies, which are not practical or sustainable considering the widespread prevalence of NAFLD. Currently traditional diagnostic methods heavily rely on costly procedures such, as liver biopsies, which are not practical or sustainable given the widespread occurrence of NAFLD. Harnessing the power of machine learning, with its potential to efficiently process huge volumes of data, predictive models can be methodically designed to reliably diagnose the existence of NAFLD based simply on clinical information. These advanced models have the ability to disclose detailed patterns and biomarkers closely connected with NAFLD by learning from varied patient cohorts. Consequently, this method delivers a non-invasive diagnostic solution with unparalleled levels of sensitivity and specificity. The study at hand exploited a big dataset comprising of a staggering 2024 observations and examinations obtained from 17549 meticulously conducted questionnaires. Out of the countless qualities accessible, a meticulous selection process led to the analysis of six specific attributes. These variables underwent different modifications, and the entire model development process occurred within the powerful framework of Google Collab. The results acquired were nothing short of remarkable; the suggested model displayed an unsurpassed accuracy rate of a breathtaking 100% in predicting the occurrence of NAFLD. The incorporation of machine learning approaches into the area of NAFLD detection marks a significant milestone in current medicine. This innovative development highlights the vital necessity of early detection and gives a realistic

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M- VMT-PC: Metaverse-Enabled Virtual Medical Training and Patient Care Framework Underlying 5G

M- VMT-PC: Metaverse-Enabled Virtual Medical Training and Pa...

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Communications, Computing, Cybersecurity, and Informatics (CCCI), International Conference on

作者： Aparna Kumari Mohammad Obaidat Shikhar Panchal Nikhil Raval Prasun Kumar Dileep Kumar Yadav Department of CSE Nirma University Ahmedabad India King Abdullah II School of Information Technology The University of Jordan Amman University of Science and Technology Beijing China Department of Computational Intelligence School of Computing SRM University SRM Nagar Kattankulathur TN India School of Engineering The Amity University Noida UP India Department of Computer Science and Engineering Nirma University Ahmedabad India Department of Biotechnology SSET Sharda University Greater Noida India Department of Computer Science and Engineering SCSET Bennett University Greater Noida

ISBN: (数字)9798350349832

ISBN: (纸本)9798350349849

As technology advances, merging virtual environments, blockchain, and 5G networks opens new possibilities, especially in medical training. This paper introduces a framework that combines these technologies to create an innovative medical training system. In this paper, we proposes Metaverse-enabled Virtual Medical Training and Patient Care framework, i.e., M- VMT-PC. In the proposed framework, metaverse comprises a dig-ital space where users can interact in 3D, offers exciting opportunities for medical training. It allows for realistic and interactive simulations, where trainees can practice medical scenarios and procedures in a virtual setting. More, the M- VMT-PC uses blockchain technology within this framework to ensure that the data is securely stored, enhancing its reliability. Next, 5G technology is used to make this framework effective. With ultra-reliable, low-latency communication (uRLLC) and high-speed data transfer (eMBB), 5G supports real-time interactions and smoothly handles large volumes of data. This technology ensures the training environment is responsive and can handle extensive data needs, essential for ongoing updates and improvements. Our proposed framework M- VMT-PC addresses the current challenges in medical training by providing a scalable, secure, and engaging learning environment. It improves the medical professionals training and sets the stage for future advancements in medical education and patient care.

关键词： Training Three-dimensional displays 5G mobile communication Metaverse Merging Ultra reliable low latency communication Real-time systems Blockchains Telecommunication computing Reliability

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Enhanced Deep Learning Algorithm for Skin Cancer Detection

Enhanced Deep Learning Algorithm for Skin Cancer Detection

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Preetam Suman Sasmita Padhy Amrit Suman Masood H Siddiqui Naween Kumar Yajnaseni Dash School of Computing Science and Engineering VIT Bhopal University Bhopal Madhya Pradesh India Computer Science and Engineering Sharda School of Engineering & Technology Sharda University Noida Uttar Pradesh India Department of Statistics University of Lucknow Lucknow Uttar Pradesh India SCSET Bennett University Greater Noida Uttar Pradesh India School of Artificial Intelligence Bennett University Greater Noida Uttar Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Skin cancer is a highly severe kind of cancer that poses an important threat to worldwide health. Nevertheless, there is a significant possibility of decreasing the fatality rate if skin cancer is identified in its first phase. Traditionally, the identification of skin cancer has predominantly depended on the visual examination conducted by dermatologists. This approach, although frequently used, frequently lacks accuracy. Deep learning-based technologies have been implemented for improving the diagnosis precision. The primary objective of these sophisticated approaches is to assist physicians in achieving more accurate and timely identification of skin malignancies. A cloud-based implementation of a model-driven architecture leverages deep learning algorithms as its fundamental component. This method builds prediction models that have enhanced accuracy in detecting skin cancer. The article provides a detailed explanation of the process involved in creating these models and showcases their practical use in categorizing images of dermal cells. The deep learning models developed in this study are assessed using established datasets. Impressively, they attained a metric called area under the curve (AUC) of 99.77%, which signifies outstanding performance. Practitioners can utilize this model-driven architecture to quickly create their deep-learning models, enabling more precise forecasts of skin cancer. This technological breakthrough signifies a substantial progression in medical diagnostics, providing optimism for enhanced patient results utilizing early and accurate identification.

关键词： Deep learning Analytical models Visualization Accuracy Computational modeling Predictive models Skin Data models Lesions Skin cancer

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