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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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Towards Autonomous Driving System Using Behavioral Cloning Approach

Towards Autonomous Driving System Using Behavioral Cloning A...

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Global Conference for Advancement in Technology (GCAT)

作者： Sakilam Abhiman B Natarajan M. Umapathy Kamaljit Kaur Bhagwat Sudhanushu Dubey Saurabh Rana Department of Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai Department of AIML CMR Institute of Technology Hyderabad Department of Mathematics Chandigarh university Punjab Department of Mathematics Chandigarh university Department of Mathematics(SCSET) Bennett University

The automotive industry has been focused on developing autonomous driving technology, which has been a subject of research for many years. Recent advancements in Convolutional Neural Networks (CNN) have shown remarkable performance in computer vision tasks, including autonomous driving systems. Behavioral cloning is a widely adopted technique in autonomous vehicle systems, that involves training a model to replicate the driving behavior of human drivers. This paper presents research on implementing behavioral cloning using a CNN architecture for an autonomous vehicle system in the Udacity self-driving car simulator environment. Our approach employs a custom-designed CNN Model to learn driving behavior from a dataset of images captured from the simulator, and we have applied various data augmentation techniques to the dataset to enhance the model’s performance. This model has a total of 27 layers, including convolutional layers, normalization layers, activation layers, dropout layers, flatten layers, and dense layers. We evaluate the model’s performance on a metric, called mean square loss error and demonstrate that our approach outperforms previous works in this domain. The results of this research underscore the effectiveness of using behavioral cloning and data augmentation techniques for autonomous vehicle systems.

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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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Adam Optimization of Burger’s Equation Using Physics-Informed Neural Networks

Adam Optimization of Burger’s Equation Using Physics-Inform...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Soumyendra. Singh Dharminder Chaudhary B. Yogiraj Ram Narayan Prajapathi Saurabh Rana Department of Mathematics Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of mathematics Chandigarh University Chandigarh India Department of Mathematics (SCSET) Bennett University Greater Noida India

In this paper, physics informed neural networks are used for numerical approximation of partial differential equations. The data which is used in the process is generated by Latin Hypercube sampling which has been discussed. Adam optimization technique has been implemented to minimize the loss for the discussed partial differential equation. The above proposed methodology has been applied for Burger’s equation and the obtained results have been discussed in section 5. Loss function graphs have also been provided to showcase the efficiency of the proposed methodologies.

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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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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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Optimized breast cancer diagnosis using self-adaptive quantum metaheuristic feature selection

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Scientific reports 2025年第1期15卷 19900页

作者： Alok Kumar Shukla Shubhra Dwivedi Deepak Singh Sunil Kumar Singh Diwakar Tripathi Ram Kishan Dewangan Thapar Institute of Engineering and Technology Patiala Punjab India. National Institute of Technology Raipur India. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India. sunil.singh@manipal.edu. National Institute of Technology Jamshedpur India. SCSET Bennett University Greater Noida India.

Breast cancer is a leading cause of mortality among women and is increasing rapidly around the world. For early diagnosis of breast cancer, precise classification, and finding the best subset for cancer identification, evolutionary-based feature selection methods play a vital role in effective treatment. Previous studies have shown that existing evolutionary methods are complicated in correctly differentiating BC disease subtypes with high consistency, which seriously affects the performance of classification methods. To prevent diagnostic errors with hostile implications for patient health, in this study, we develop a new evolutionary method called SeQTLBOGA that incorporates the learner quantization before the search capability of the feature space to prevent premature falls into the local optima. In the SeQTLBOGA algorithm, quantum theory and a self-adaptive mechanism are employed to update the Teaching Learning-based Optimization (TLBO) rule to enhance convergence search capabilities. Most importantly, a self-adaptive genetic algorithm (GA) is also incorporated into TLBO to tradeoff between exploration and exploitation to handle slow convergence and exploitation competence, and simultaneously optimizing parameters of support vector machines (SVM) and the best features subset is our primary objective. Comparative results based on optimal computing time and performance are also offered to empirically analyze the traditional algorithms. Therefore, this paper aims to evaluate the most recent quantum-inspired metaheuristic algorithms in WBCD and WDBC databases, emphasizing their advantages and disadvantages.

关键词： Breast Cancer Feature selection Genetic algorithm Quantum Support vector machine Teaching Learning-based optimization

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Stop the Hate, Spread the Hope: An Ensemble Model for Hope Speech Detection in English and Dravidian Languages

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ACM Transactions on Asian and Low-Resource Language Information Processing 1000年

作者： Deepawali Sharma Vedika Gupta Vivek Kumar Singh Bharathi Raja Chakravarthi Department of Computer Science Banaras Hindu University Faculty of Science Varanasi India School of Computer Science Engineering and Technology (SCSET) Bennett University Noida India Jindal Global Business School OP Jindal Global University Sonipat India Department of Computer Science University of Delhi New Delhi India School of Computer Science National University of Ireland Galway Galway Ireland Unit for Linguistic Data Insight SFI Research Centre for Data Analytics Data Science Institute National University of Ireland Galway Galway Ireland

The rise of social media has led to vast amounts of user-generated content, with emotions ranging from joy to anger. Negative comments often target individuals, communities, or brands, prompting successful efforts to detect harmful speech such as hate speech, cyberbullying, and abuse. Recently, another type of speech referred to as ‘Hope Speech’ has gained attention from the research community. Hope speech consists of positive affirmations or words of reassurance, encouragement, consolation or motivation offered to the affected individual/ community during the lean periods of life. However, there has been relatively less research focused on the detection of hope speech, more particularly in low-resource languages. This paper, therefore, attempts to develop an ensemble model for detecting hope speech in some low-resource languages. Data for four different languages, namely English, Kannada, Malayalam and Tamil are obtained and experimented with different deep learning-based models. An ensemble model is proposed to combine the advantages of the better performing models. Experimental results demonstrate the superior performance of the proposed Ensemble (LSTM, mBERT, XLM-RoBERTa) model compared to individual models based on data from all four languages (weighted average F1-score for English is 0.93; for Kannada is 0.74; for Malayalam is 0.82; and for Tamil is 0.60). Thus, the proposed ensemble model proves to be a suitable approach for hope speech detection in the given low resource languages.

关键词： CNN Deep Learning GloVe embedding Hate Speech Hope Speech Low Resource Language LSTM mBERT XLM-RoBERTa

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Sustainable Inventory Management 1

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丛书名： Inventory Optimization

1000年

作者： Nita H. Shah Mandeep Mittal

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