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7th IET Smart Cities Symposium, SCS 2023

作者： Pradhan, Aditya Ghosh, Amartya Mondal, Rintu Saxena, Kumar Abhi Dutta, P.K. Mitra, Anirban Dept. of Computer Science and Engineering Amity School of Engineering and Technology Kolkata Kolkata India Dept. of Electronics and Communication Engineering Amity School of Engineering and Technology Kolkata Kolkata India

ISBN: (纸本)9781839539831

The rapid growth of e-commerce has revolutionized the way businesses operate and interact with customers. E- commerce has revolutionized the way businesses operate, making it easier than ever to reach customers and sell products. However, with this convenience comes a challenge: how can we promote sustainable products and encourage consumers to make eco- friendly choices? The primary objective of this project is to pro- mote sustainability by utilizing artificial intelligence techniques in recommending eco-friendly alternatives. Our goal is to provide users with recommendations on sustainable products based on their shopping history, behavior patterns, and demographic information. The system continuously learns from user interactions, enabling it to provide increasingly accurate and personalized recommendations over time. © The Institution of engineering & technology 2023.

关键词： Electronic commerce

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Deep Learning Model for Weather Prediction 1

Deep Learning Model for Weather Prediction

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

作者： Singh, Shubhanshi Tyagi, Neha Amity School of Engineering and Technology Dept. Computer Science and Engineering Noida India

ISBN: (纸本)9798350353648

Weather forecasting predicts atmospheric conditions at a particular time and place. It is an important task with a wide range of applications, including agriculture, transportation, and disaster prevention. Traditional weather forecasting methods rely on physical models of the atmosphere, which can be complex and computationally expensive. In recent years, deep learning techniques are effective for weather forecasting. Deep learning models can learn complex patterns from data, which can be used to predict future weather conditions accurately. This project aims to create a deep-learning model for forecasting weather. A huge dataset of historical meteorological data, including radar, satellite, and surface measurements, will be used to train the model. Numerous meteorological factors, such as temperature, precipitation, and wind speed, will be predicted by the model. We will compare the deep learning model's performance with conventional weather forecasting techniques. Creating a deep learning model that is more accurate and effective than current approaches is the project's main objective. The initiative may result in improved weather forecasts that are prompt and accurate, better agricultural, transportation, and disaster prevention decision-making, and less money lost because of weather-related incidents. Possible obstacles for the project are gathering and purifying a sizable dataset of meteorological data, using this data to train a deep learning model, and then comparing the model's output with more conventional techniques. © 2024 IEEE.

关键词： Wind forecasting

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Live Audio Recognition and Text Conversion System 2

Live Audio Recognition and Text Conversion System

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

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

ISBN: (纸本)9798331530389

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. © 2025 IEEE.

关键词： Hidden Markov models

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Lip Reading Into Text Using Deep Learning 14

Lip Reading Into Text Using Deep Learning

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Manglani, Krishna Bahuguna, Utkarsh Gupta, Subhash Chand Amity School of Engineering and Technology Dept. of Computer Science Uttar Pradesh India

ISBN: (纸本)9798350344837

An essential part of computer vision, lip reading, has grown significantly and is now used in autonomous driving, public safety, and hearing-impaired communication. This work provides an in-dept. analysis of both conventional and deep learning-based lip reading systems, examining their development over time, difficulties encountered, and critical role in human communication. The suggested LipNet model, which leverages the complementary abilities of Long Short-Term Memory networks (LSTMs) for temporal modelling and Convolutional Neural Networks (CNNs) for spatial feature extraction, is a central focus of this research. When LipNet is tested using the GRID Corpus dataset, it outperforms baseline models in terms of performance. Saliency maps and viseme analysis highlight LipNet's ability to capture phonologically relevant areas in lip motions, providing insight into the network's attention processes. Apart from examining the importance of datasets such as OuluVs2and GRID Corpus, this study presents GhostNet, a lightweight model that tackles the requirement for effective network designs. The testing results highlight LipNet's competitive Character Error Rate (CER) and Word Error Rate (WER) in comparison to baseline models, proving that it is the best method for real-world use. The research ends with a summary of potential future developments in lip reading technology, such as modal integration, realtime applications, personalisation, ethical issues, and the goal of creating a communication environment that is more inclusive. This study highlights the effectiveness of LipNet and paves the way for future developments in the ever-evolving area of lip reading by examining a variety of methodologies. © 2024 IEEE.

关键词： Convolutional neural networks

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Advanced Machine Learning Techniques for Fake News Detection: A Comprehensive Analysis Using the LIAR Dataset 3rd

Advanced Machine Learning Techniques for Fake News Detectio...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Paul, Subrata Ghosh, Shivnath Mitra, Anirban Bhattacharya, Pronaya Department of CSE-AI Brainware University West Bengal Barasat India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University West Bengal Kolkata India Department of Computer Science and Engineering Amity School of Engineering and Technology and Research and Innovation Cell Amity University West Bengal Kolkata India

ISBN: (纸本)9789819624676

In today’s digital world, fake news presents a serious challenge to social cohesiveness, trust among individuals, and the functioning of democracy. Overcoming this issue demands novel solutions that make efficient use of machine learning (ML) for identifying and battle disinformation. This paper examines the important problem of fake news, including its meaning, its consequences, and the significance of social media in its rapid spread. The study emphasises the complexities of identifying fake news, focusing on sophisticated techniques like AI-generated content and the widespread dissemination of misinformation. In order to tackle such obstacles, the paper uses ML approaches, particularly the LIAR dataset and the XGBoost model, to create a reliable fake news detection system. The outcomes show that these methods have been successful at successfully recognising false information, highlighting ML’s prospective in reducing misinformation. This research contributes to the broader discourse on media literacy and the need for reliable information in the digital age. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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ViT-SENet-Tom: machine learning-based novel hybrid squeeze–excitation network and vision transformer framework for tomato fruits classification

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Neural Computing and Applications 2025年第9期37卷 6583-6600页

作者： Swapno, S M Masfequier Rahman Nobel, S. M. Nuruzzaman Islam, Md Babul Bhattacharya, Pronaya Mattar, Ebrahim A. Department of CSE Bangladesh University of Business and Technology Dhaka1216 Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Department of Computer Science and Engineering Amity School of Engineering and Technology and Research and Innovation Cell Amity University West Bengal Kolkata India Robotics - Cybernetic College of Engineering University of Bahrain Manama Bahrain

Tomatoes are essential fruits in numerous nations for their vast demand. It is very important to maintain the freshness of tomatoes. One of the primary challenges in the recent culinary landscape is accurately identifying healthy tomatoes while effectively eliminating damaged or rejected ones. Existing approaches employ various strategies for categorizing tomato fruit, but they often suffer from inaccuracies, slow detection, and suboptimal performance. Thus, motivated by this gap, in this paper, we propose a novel machine learning (ML) framework, ViT-SENet-Tom, which is a hybrid vision transformer (ViT) model with squeeze and excitation (SENet) block network for fast, accurate, and efficient tomato fruit classification. The framework works on three tomato classes, respectively, the ripe, unripe, and reject. In developing the proposed model, we utilized advanced and newly designed layers and functions. This integration created a more complex and sophisticated neural network, significantly enhancing efficiency and contributing to the model’s novelty. Our chosen dataset was small initially, but we implemented augmentation techniques to increase its size. This approach made our system more reliable, efficient, and effective. The hybrid ViT-SENet framework employs encoders and self-attention networks with squeeze and excitation channel functions to allow precise, robust, fast, and efficient tomato classification. In simulation, the framework achieves a training accuracy of 99.87% and validation accuracy of 93.87%, indicating the precise classification of tomatoes. Besides, this work tests accuracy using fivefold cross-validation. The highest accuracy seen at fold-5 is 99.90%. These testing results demonstrate the efficacy of the proposed framework in real-deployment scenarios. The implementation has the potential to provide enhanced and more sustainable food security and safety in future. © The Author(s) 2025.

关键词： Fruits

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Design and Architectural Implementation of Consortium Blockchain Based Framework for Open Banking Customer Consent and Data Handling

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SN computer science 2024年第2期5卷 271页

作者： Ghosh, Abir Mukhopadhyay, Indraneel Chakraborty, Subhalaxmi University of Engineering and Management West Bengal Kolkata India Amity School of Information Technology Amity University West Bengal Kolkata India Department of Computer Science University of Engineering and Management West Bengal Kolkata India

Presently blockchain-based open banking approaches have gained immense popularity in financial sector. In open banking, customer consent is mandatory by regulation before any customer data can be shared by bank with Third Party Providers (TPP) following strict security protocols. Recent study shows that customer remains unaware about private data leakages where data is shared by bank not adhering agreement between bank and customer. To mitigate such type of problem, an innovative consortium permissioned blockchain-based structure comprising banks, regulators, Third Party Providers, and customers is addressed in this proposed work. The implemented framework manages consent handling and data sharing under the gamut of blockchain security. Security analysis of the framework proves it to be secure from threats and performance evaluation of the framework is satisfactory with more than fifty customer data parameters and ten nodes of blockchain. This blockchain based framework traces data sharing non-compliances and reports it real-time basis to customer. Thus it enhances the transparency in the whole open banking data sharing process and boosts customer confidence to adopt open banking practices. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Bank Blockchain Consent management Customer trust Data violation Open banking Regulator Third party providers

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Optimizing Satellite Constellations for Global Internet Coverage: A Comparative Analysis of Communication Protocols and Orbital Configuration

Optimizing Satellite Constellations for Global Internet Cove...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Banerjee, Pallab Raj, Vivek Kumar, Biresh Thakur, Kanika Mitra, Dipra Kumar, Purushottam Amity University Jharkhand Dept. of Computer Science Engineering Jharkhand Ranchi India Amity University Jharkhand Amity School of Information Technology Jharkhand Ranchi India Galgotia University Dept. of Computer Science Engineering Greater Noida campus India Amity University Dept. of Computer Science Engineering Jharkhand Ranchi India

ISBN: (纸本)9798350385199

As a way to provide ubiquitous connectivity, satellite constellations are becoming more and more popular due to the growing need for worldwide internet access. In order to provide dependable and seamless worldwide internet coverage, this study investigates how satellite constellations might be optimized by comparing and contrasting communication protocols and orbital topologies. This ambitious aim involves a number of problems and complications, which are explored in the research along with the trade-offs and design considerations that must be made in order for the implementation to be effective. Two main elements form the framework of the comparative analysis. First, by considering factors like latency, data throughput, and coverage efficiency, it evaluates a range of communication protocols, including traditional geostationary satellites, Low Earth Orbit (LEO) constellations, and Medium Earth Orbit (MEO) satellites. Second, to ascertain the effect of various orbital configurations on worldwide internet coverage, the study considers satellite dispersion, orbital altitudes, and coverage patterns. By means of this study, the research endeavors to provide a valuable contribution to the current discussion on satellite constellations for worldwide internet coverage, therefore facilitating well-informed decisions about the design and implementation of these systems. © 2024 IEEE.

关键词： Geostationary satellites

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Fedchain: Decentralized Federated Learning and Blockchain-Assisted System for Sustainable Irrigation

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1-1页

作者： Bera, Somnath Dey, Tanushree Mukherjee, Anwesha Bhattacharya, Pronaya De, Debashis Maulana Abul Kalam Azad University of Technology West Bengal India Mahishadal Raj College Mahishadal West Bengal India Department of Computer Science and Engineering Amity School of Engineering and Technology and Research and Innovation Cell Amity University Kolkata West Bengal India

The conventional Internet of Things-based soil moisture monitoring system for irrigation decision making suffers from huge network traffic, high latency and energy consumption, and compromise in data security. To overcome these challenges, this paper proposes a blockchain-assisted decentralized federated learning strategy, Fedchain for irrigation decision making based on edge-cloud computing. A peer-to-peer network is formed among the edge servers. The edge servers have their local datasets, which are analyzed locally using Long Short-Term Memory (LSTM) network, and the model parameters are exchanged between the peer nodes. The model is updated accordingly. For data security purposes, blockchain is used. The LSTM model updates are recorded in the InterPlanetary File System by the local user, and a unique Content Identifier is generated for data retrieval, and it is stored in the blockchain as a transaction. A Distributed Hash Table in the file system maps the Content Identifier in the blockchain to the stored data in the file system, ensuring effective retrieval. The results show that Fedchain achieves above ~99% prediction accuracy, and reduces latency and energy consumption by 78% than the edge-cloud framework without federated learning. The use of blockchain reduces the mining cost by ~78% than the competing methods. IEEE

关键词： Blockchain

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Personalized Movie Recommendation Based on User Preferences Using Optimized Sequential Transformer Model

Personalized Movie Recommendation Based on User Preferences ...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Ravikumar, R.N. Jain, Sanjay Sarkar, Manash Amity University Rajasthan Dept. of Computer Science and Engineering Jaipur India Atria Institute of Technology Dept. of Computer Science and Engineering Banglore India

ISBN: (纸本)9798350379716

This study explores the application of a Transformer based model to the 'MovieLens 1M' and 'MovieLens 10M' datasets provided by GroupLens, which includes one million and ten movie ratings. The Transformer model, known for its proficiency in handling sequence-to-sequence tasks, employs multi-head self-attention mechanisms and position-wise feed-forward networks. Our implementation, developed in PyTorch, benefits from its capacity for automatic differentiation and GPU acceleration, which significantly enhances the training efficiency. The model underwent rigorous hyperparameter tuning and optimization to adapt optimally to the characteristics of the dataset. Performance evaluation using the Normalized Discounted Cumulative Gain (NDCG) metric demonstrates that the Transformer model substantially outperforms a conventional popular recommendation system across various top-K rankings. Specifically, for the MovieLens 1M dataset, the Transformer model achieved NDCG scores of 0.0455, 0.0805, 0.0995, and 0.1273 at top 1, 3, 5, and 10, respectively. Comparatively, the popular recommendation system scored 0.0023, 0.0044, 0.0061, and 0.0091 at these same rankings. This trend of superior performance by the Transformer model is also consistent across the MovieLens 10M dataset, further confirming its effectiveness in ranking highly relevant items at the top of the list. Also, to address quality and utility of recommendations, MAP, Precision, Recall, F1 Score, Coverage and Serendipity metrics evaluated. These results underscore the effectiveness of Transformer models in extracting and leveraging complex patterns in sequence data, demonstrating a clear superiority over traditional methods in recommendation systems. This study highlights the potential of deep learning architectures to revolutionize fields reliant on understanding user preferences and sequential data interactions. © 2024 IEEE.

关键词： Recommender systems

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