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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Maji, Anushka Sinha, Sagnik Banerjee, Subhabrata Das, Amit Kumar Gangopadhyay, Malay Rateria, Roshan Institute of Engineering & Management Department of Computer Science and Engineering Kolkata India Institute of Engineering & Management University of Engineering and Management Department of Electronics & Communication Engineering Kolkata India Institute of Engineering & Management University of Engineering and Management Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350377972

Recently, Artificial Intelligence (AI) algorithms have been used to detect the potholes. In this article, a machine learning algorithm has been designed to solve the problem efficiently. A software model has also been created for drivers, enabling them to detect and recognize potholes in advance through a mobile application. This mobile app utilizes various sensors to detect potholes and provides timely alerts to the driver. Moreover, the system is capable of responding to newly detected potholes and alerts the driver about them. The proactive notification system improves road safety by ensuring drivers are informed about upcoming road hazards. © 2024 IEEE.

关键词： Mobile applications

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A Strategic Framework for the Development of a Microtext AI Chatbot for Tourism in Indian Cities 8

A Strategic Framework for the Development of a Microtext AI ...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Sultan, Adnan A. Mukherjee, Unmesh Mondal, Anupam Institute of Engineering & Management University of Engineering & Management Department of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798331520762

Tourism plays a crucial role in India's economy, with its diverse cultural, historical, and geographical richness attracting millions of domes-tic and international visitors annually. To cater to the needs of these travelers and enhance their overall experience, there is a growing need for inno-vative technological solutions. This paper presents a comprehensive literature survey, analyzing existing research, trends, and practical implementations of microtext normalization as well as the application of chatbots in the context of tourism. Furthermore, it proposes a strategic framework for the development and deployment of an effective microtext AI chatbot tailored to the requirements of tourism in India. The proposed design leverages modern machine learning and natural language processing (NLP) strategies for providing personalized, context-aware assistance to tourists in order to enhance their overall experience while exploring the various cities of India. © 2025 IEEE.

关键词： Tourism

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Generating Images from Text Using GANs vs Transformers 8

Generating Images from Text Using GANs vs Transformers

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kumar, Jatin Tanvi, Tanisha Ghosh, Aitijya Mondal, Ritankar Das, Soma Institute of Engineering and Management University of Engineering and Management Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798331520762

This paper compares Generative Adversarial Networks (GANs) and transformer-based models for text-to-image generation, examining each approach's ability to convert textual descriptions into realistic images. GANs, leveraging a generator-discriminator framework, have made strides with models like DF-GAN [1] and StackGAN [2], which improve image fidelity through multi-stage generation and attention. However, GANs face challenges with complex datasets. Transformer-based models, initially used in natural language processing, offer robust handling of dependencies, enhancing the capture of image features from text. Emerging hybrid models combine GAN and transformer strengths for improved stability and image quality. This study aims to identify optimal techniques by analyzing their architectures, training, and output quality, contributing to advancements in multi-modal generative modeling. © 2025 IEEE.

关键词： Chatbots

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A Review: How Employed Word Embeddings in Paraphrase Detection? 8

A Review: How Employed Word Embeddings in Paraphrase Detecti...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Debnath, Binoy Mondal, Anupam Institute of Engineering and Management University of Engineering & Management Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798331520762

A journey through the evolution of word embeddings from their humble beginnings to the techniques of the highest standard in paraphrase detection explain the advantages of using such specific models like Word2Vec and GloVe, which have revolutionized how machines understand human language. Along the way, challenges encountered by these methods such as dealing with bias and handling contextual nuances and adapting pre-trained embeddings to specific domains or tasks. Often requiring significant fine-tuning or domain-specific training. There is a need for more efficient methods to adapt general-purpose embeddings to specialized domains without losing their broad applicability. Exploring meta-learning approaches or developing more sophisticated fine-tuning techniques could enhance domain adaptation capabilities. Temporal dynamics in word embeddings of most current embedding models provide static representations that do not account for how word meanings and relationships evolve over time. There is limited research on creating dynamic word embeddings that can capture temporal shifts in language use and meaning. This paper describes potential future developments in the field, where context-aware embeddings and multi-modal representations promise to further bridge the gap between humans and machines. This comprehensive review serves as both a map and a compass for researchers navigating the complex terrain of word embeddings in paraphrase detection under NLP. © 2025 IEEE.

关键词： Network embeddings

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Advancements in Deep Learning for Smart Surveillance: A Survey on Crime and Anomaly Detection 8

Advancements in Deep Learning for Smart Surveillance: A Surv...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Pal, Amrita Ghosh, Promita Roy, Shubhasri Das, Soma Mukherjee, Shreejita Institute of Engineering and Management University of Engineering and Management Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798331520762

The increasing prevalence of advanced surveillance technologies has spurred significant research in automated anomaly detection systems using deep learning. This survey synthesizes recent developments across various distinct studies addressing diverse security challenges. These include theft detection using Convolutional Neural Networks (CNN) with object tracking, robbery identification using convolutional LSTM models trained on the UNI-Crime dataset, anomaly detection leveraging weakly supervised learning on UCF-Crime data, and weapon recognition using YOLOv3. Several other approaches demonstrate significant accuracy and efficiency improvements, achieving accuracies. Despite varying datasets and architectures, the integration of CNNs, YOLO, and LSTM models reflects a shared emphasis on spatial-temporal feature extraction for real-time applications. This paper provides a comparative analysis of these methodologies, highlighting the role of dataset quality, transfer learning, and computational efficiency in advancing smart surveillance systems to mitigate crime and enhance public safety. © 2025 IEEE.

关键词： Convolutional neural networks

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Evaluating the Efficacy of Text Summarization Models: A Comparison of NLP Algorithms 8

Evaluating the Efficacy of Text Summarization Models: A Comp...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Chakraborti, Rounak Banerjee, Romit Das, Soma Institute of Engineering and Management University of Engineering and Management Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798331520762

Text Summarization is a critical tool nowadays. People do not have enough time to read big articles which take a lot of time. A short summary is no doubt an enthralling option for this case. As this demand increases numerous algorithms and methods have come up which can serve this purpose. This survey paper encompasses the different algorithms and methods used by different researchers which includes methods like TF-IDF and LSA alongside neural networks like Seq2Seq, Pegasus, and Transformer-based models like BART and T5. We analyze these on metrics like ROUGE scores. This survey paper will provide a valuable resource for researchers in the future who want to study text summarization in the news domain. © 2025 IEEE.

关键词： BART Latent Semantic Analysis (LSA) News Summarization Pegasus ROUGE Score Seq2Seq T5 Text Summarization TF-IDF Transformer Models

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Multi-class Classification Approach for Smart Intrusion Attack Detection Using AI and ML Classifiers 8th

Multi-class Classification Approach for Smart Intrusion Atta...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Rathod, Dushyantsinh B. Patel, Yesha Jethava, Archana Gohel, Namrata Suthar, Dhruvi Prajapati, Ramesh Rathod, Ghanshyam Computer Engineering Department Ahmedabad Institute of Technology Gujarat Ahmedabad India Department of CSE Faculty of Engineering and Technology GLS University Gujarat Ahmedabad India Computer Engineering Department Shree Swaminarayan Institute of Technology Gujarat Gandhinagar India Department of Computer Application Parul Institute of Computer Application Baroda India

ISBN: (纸本)9789819766772

Due to increased network traffic and security concerns, intrusion detection system (IDS) research has received a lot of attention in computer science. Advanced intrusion detection systems have arbitrary incursion categories and demand a high processing power usage. Notwithstanding the fact that there is a wealth of literature on IDS issues, our objective is to provide a more thorough review. We offer the taxonomy to differentiate between modern IDSs based on the extensive survey and intricate organization. In addition, the architecture we provided in the content aids the reader in grasping the whole idea of IDSs with ease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network intrusion

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Nonlinearity in Large-Cycle Cellular Automata

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Complex Systems 2025年第1期34卷 129-160页

作者： Mukherjee, Sukanya Adak, Sumit Department of Computer Science and Engineering Institute of Engineering and Management Kolkata University of Engineering and Management West Bengal Kolkata700091 India Department of Applied Mathematics and Computer Science DTU Compute Technical University of Denmark Kgs. Lyngby2800 Denmark

The class of maximal-length cellular automata (CAs) has gained significant attention over the last few years due to the fact that it can generate cycles with the longest possible lengths. For every l of the form l = 2n-1 where n ∈ , a cycle of length l can be generated by a cellular automaton (CA) of size ⌊log2 l⌋ + 1;this maximal-length CA is in fact the optimal (minimum-sized) CA to generate a cycle of length l. However, maximal-length CAs are, in general, linear and have some drawbacks. Moreover, it is nontrivial to address this problem for any l ∈ , that is, removing the constraint that l is of the form 2n-1. In this paper we therefore employ nonlinearity in the rules and aim to generate the near-optimal CA for any given cycle length l, meaning that the generated CAs will be of size n = (⌊log2 l⌋ + 1) + d, where the displacement d is as small as possible. We use a generic operator to concatenate a collection of component CAs of sizes n1, n2, …, nk by employing an evolutionary strategy. Here, n = n1 + n2 + ⋯ + nk and l ≥ lcm(1, 2, …, k ), where i (1 ≤ i ≤ k) is the maximum cycle length of a CA of size ni . An influencing factor in the design of component CAs is to achieve the near-optimal CA for a given cycle length. Here we consider nonlinear reversible large-cycle CAs as component CAs. We also measure the effectiveness of nonlinearity in large-cycle generation. © 2025, Complex Systems Publications, Inc. All rights reserved.

关键词： CAs cellular automata evolution large-cycle CA neighborhood dependent nonlinearity reversible CAs

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Breast MRI Registration Using Gorilla Troops Optimization 1st

Breast MRI Registration Using Gorilla Troops Optimization

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1st International Conference on Human-Centric Smart Computing, ICHCSC 2022

作者： Nayak, Somen Mondal, Subhodip Department of Computer Application University of Engineering and Management Rajasthan Jaipur India Department of Computer Science and Engineering University of Engineering and Management Rajasthan Jaipur India

ISBN: (纸本)9789811954023

Metaheuristics assume a significant part in enhancing issues, and most of them are enlivened by the aggregate knowledge of living peculiarities in nature. In excess of 10% of ladies are experiencing bosom disease in their lives in the entire world. Registration of breast magnetic resonance imaging is a method to align pre- and post-contrast images to analysis and classifying of cancer category. In this research, breast magnetic resonance imaging registration using gorilla troops algorithm is proposed. GTO is a metaheuristics-based optimizations algorithms that tested to registering the image of breast magnetic resonance. After that, images are successfully registered using GTO algorithm. The result of GTO-based registration method is compared with registration based on PSO method. The results implicates, in case of registration of breast magnetic resonance images, the GTO-based registration methods beat the PSO-based registration method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Particle swarm optimization (PSO)

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Large Scopus Data Sets and Its Analysis for Decision Making

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Annals of Data Science 2024年第2期11卷 589-618页

作者： Singh, Prem Kumar Department of Computer Science and Engineering Gandhi Institute of Technology and Management-Vishakhapatnam Andhra Pradesh530045 India

Recently several authors paid attention towards Scopus Data analysis for intellectual measurement of institutes or authors. It is well known that the SCOPUS contains more than 34,346 peer reviewed Journals from different subjects with 3 lakh conferences. It is difficult to measure the performance or expertise of any institute or author in the given domain for admission, job, and ranking or other decision making process. The reason is several manipulations started in document and citation count via strategic authors or institute which can be measured via average author publications, number of funding, collaborations and retracted papers. It is happening due to rogue editor or business strategic of educationalist for profit. However these types of misconduct impacts lot to real researcher which forces brain drain. To resolve this issue, the current paper provides a way to measure the intellectual achievement of an institute or author based on several metrics. The proposed method is illustrated using the SCOPUS data sets and it’s metric for critical understanding. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Decision making

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