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2nd International Conference on Data science and Network Engineering, ICDSNE 2024

作者： Bandopadhyay, Saikat Dutta, Jhalak Das, Smita Datta, Soumyajit Ghosh, Debaditya Dey, Rohit Kumar Nandigrami, Jeet Department of Artificial Intelligence and Machine Learning Netaji Subhash Engineering College Kolkata India Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Computer Science and Engineering National Institute of Technology Agartala India Department of Artificial Intelligence and Machine Learning Heritage Institute of Technology Kolkata India

ISBN: (纸本)9789819783359

Blindness or visual impairment ranks among the top ten disabilities affecting both men and women, impacting over 35 million individuals of all ages in India. Access to visual information is crucial for enhancing the independence and safety of blind and visually impaired individuals. There exists a compelling imperative to develop assistive technologies that can significantly improve their quality of life. This paper presents SightAssist, a multilingual software solution designed to address the challenges faced by the visually impaired community in India. SightAssist utilizes a camera-based approach to serve as a digital prosthetic eye and virtual assistant, capable of currency detection, facial recognition, and providing various functionalities to assist with daily activities. Initially, a custom-labeled dataset has been prepared using a pre-trained YOLO model. Gradually, dataset pre-processing, normalization, and semantic segmentation are employed to optimize model performance. Additionally, voice-based accessibility features have been integrated using the Web Speech API, enabling users to interact with the application through voice commands. The experimental results show the successful development of SightAssist and its potential to significantly improve the lives of visually impaired people. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Assistive technology

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A Survey on Graph Neural Networks and its Applications in Various Domains

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SN computer science 2025年第1期6卷 1-12页

作者： Murgod, Tejaswini R. Reddy, P. Srihith Gaddam, Shamitha Sundaram, S. Meenakshi Anitha, C. Department of Artificial Intelligence & Machine Learning BNM Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering NITTE Meenakshi Institute of Technology Karnataka Bengaluru India

Graph Neural Networks (GNNs) are neural models that use message transmission between graph nodes to represent the dependency of graphs. Variants of Graph Neural Networks (GNNs), such as graph recurrent networks (GRN), graph attention networks (GAT), and graph convolutional networks (GCN), have shown remarkable results on a variety of deep learning tasks in recent years. In this study, we offer a generic design pipeline for GNN models, go over the variations of each part, classify the applications in an organized manner, and suggest four outstanding research issues. Dealing with graph data, which provides extensive connection information among pieces, is necessary for many learning tasks. A model that learns from graph inputs is required for modelling physics systems, learning molecular fingerprints, predicting protein interfaces, and identifying illnesses. Reasoning on extracted structures (such as the dependency trees of sentences and the scene graphs of photos) is an important research issue that also requires graph reasoning models in other domains, such as learning from non-structural data like texts and images. Graph Neural Networks (GNNs) are primarily designed for dealing with graph-structured data, where relationships between entities are modeled as edges in a graph. While GNNs are not traditionally applied to image classification problems, researchers have explored ways to leverage graph-based structures to enhance the performance of Convolutional Neural Networks (CNNs) in certain scenario. GNN have been increasingly applied to Natural Language Processing (NLP) tasks, leveraging their ability to model structured data and capture relationships between elements in a graph. GNN are also applied for traffic related problems particularly in modeling and optimizing traffic flow, analyzing transportation networks, and addressing congestion issues. GNN can be used for traffic flow prediction, dynamic routing & navigation, Anomaly detection, public transport network

关键词： computer vision Graph neural networks Intrusion detection Natural language processing Neural networks Traffic control

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Background initialization in video data using singular value decomposition and robust principal component analysis

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International Journal of computers and Applications 2023年第9期45卷 600-609页

作者： Gowda, Vishruth B. Gopalakrishna, M.T. Megha, J. Mohankumar, Shilpa Department of Information Science and Engineering SJB Institute of Technology Bengaluru India Department of Artificial Intelligence and Machine Learning SJB Institute of Technology Bengaluru India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Bengaluru India

Background initialization is used in video processing applications to extract a scene without the foreground scene. In recent times, the issue of background initialization has gained researchers’ attention in different fields such as video surveillance, video segmentation, computational photography, and so on. The initialization of the background is affected due to different complex dissimilarities such as shadow, intermittent movement, illumination, camera jitter, and clutter. To overcome the aforementioned issues, this paper proposes a decomposition using the combination of the Singular Value Decomposition (SVD) and Robust Principal Component Analysis (RPCA) for Singular Spectrum Analysis (SSA) to perform an effective background initialization. The incorporation of RPCA in SVD is used to overcome the issues related to non-Gaussian noise and it also uses an effective structural knowledge of the video input i.e. sparse and low rank matrix which improves the Peak-Signal-to-Noise-Ratio (PSNR) of the background image. The SBI dataset was used to analyze the performances of SSA-SVDRPCA. The SSA-SVDRPCA is analyzed using MultiScale Structural Similarity Index (MSSSIM), Average gray-level error (AGE), Percentage of clustered error pixels (pCEPS), Percentage of error pixels (pEPs), and PSNR. The existing approaches such as Background Initialization Singular Spectrum Analysis (BISSA) and Quaternion-based Dynamic Mode Decomposition (Q-DMD) are used to compare with the SSA-SVDRPCA method. The PSNR of the SSA-SVDRPCA for Board class is 30.39 dB which is higher than the BISSA and Q-DMD. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Singular value decomposition

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Enhancement of Infant Health Assessment: Predicting Body Mass Index (BMI) from Real-Time Facial Images Using machine learning Techniques 23th

Enhancement of Infant Health Assessment: Predicting Body Mas...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Krishnaveni, M. Subashini, P. Janani, R. Jeeva, N. Department of Computer Science Centre for Machine Learning and Intelligence Avinashilingam Institute for Home Science and Higher Education for Women Coimbatore India Centre for Machine Learning and Intelligence Avinashilingam Institute for Home Science and Higher Education for Women Coimbatore India Department of Computer Science Avinashilingam Institute for Home Science and Higher Education for Women Coimbatore India

ISBN: (纸本)9783031648120

Body Mass Index in infants is a valuable indicator for assessing their growth and nutritional state, helping to identify any potential issues in the early stages. It establishes whether infants are underweight, within a healthy weight range, or overweight, which can specify their overall health. The capability to accurately determine an infant’s BMI can provide significant insight into their general health and helps parents to adopt smart feeding and healthcare decisions. In today’s world, where parents often have limited time and wealth, supporting the proper growth of their infant is vital. The fundamental objective of this research is to propose a user-friendly mobile application which is constructed for predicting infant BMI. This proposed application provides a simple and easy way for parents to track and understand their infant’s nutritionary condition. The proposed application requires only a facial image of an infant, eliminating the necessity for addition parameters such as height, weight. For the prediction of infant BMI, regression algorithms, which includes Linear, Polynomial, Random Forest, and Kernal Ridge, have been employed. The performance of these models has been assessed using several evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R2) score, and the results clearly show their effectiveness in this significant task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： machine learning

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Kontho: An AI-Driven Smart Glove for Enhanced Sign Language Communication 2nd

Kontho: An AI-Driven Smart Glove for Enhanced Sign Language...

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Dasgupta, Dyuti Datta, Soumyajit Pramanik, Sagnik Dey, Rohit Kumar Nandigrami, Jeet Ghosh, Debaditya Department of Artificial Intelligence and Machine Learning Heritage Institute of Technology Kolkata India Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India

ISBN: (纸本)9783031845420

In India, over 63 million individuals are affected by hearing loss, yet less than 350 certified sign language interpreters are available, thus leading to communication barriers and significant social and economic exclusion. This paper introduces Kontho, an ergonomic, AI-driven, multi-sensor glove integrated with a mobile app for real-time sign language communication via American Sign Language (ASL). Utilizing flex sensors, accelerometers, gyroscopes and EMG sensors, Kontho captures gestures and muscle activity which is processed through a combination of ensemble models and Large Language Models (LLMs) to enhance communication by predicting and completing incomplete gestures. Evaluated on a dataset of 23 ASL gestures at over 3867 data points with an accuracy of 99.61%, Kontho offers a scalable solution to foster social inclusion and address the economic isolation of hearing and speech-impaired individuals, enabling their active participation in the society. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Signaling

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Identifying Spatio-Temporal Drivers of Extreme Events 38

Identifying Spatio-Temporal Drivers of Extreme Events

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hakam, Mohamad Eddin, Shams Gall, Juergen Institute of Computer Science University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence United States

The spatio-temporal relations of impacts of extreme events and their drivers in climate data are not fully understood and there is a need of machine learning approaches to identify such spatio-temporal relations from data. The task, however, is very challenging since there are time delays between extremes and their drivers, and the spatial response of such drivers is inhomogeneous. In this work, we propose a first approach and benchmarks to tackle this challenge. Our approach is trained end-to-end to predict spatio-temporally extremes and spatio-temporally drivers in the physical input variables jointly. By enforcing the network to predict extremes from spatio-temporal binary masks of identified drivers, the network successfully identifies drivers that are correlated with extremes. We evaluate our approach on three newly created synthetic benchmarks, where two of them are based on remote sensing or reanalysis climate data, and on two real-world reanalysis datasets. The source code and datasets are publicly available at the project page https://***/IDE. © 2024 Neural information processing systems foundation. All rights reserved.

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De-Styling Bias in #Climatical Tweets: A Neural Language Style Transfer Approach 3

De-Styling Bias in #Climatical Tweets: A Neural Language Sty...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： George, Elizabeth Leah Parthasarathy, Subashini Avinashilingam Institute for Home Science and Higher Education for Women Department of Computer Science Coimbatore India Centre for Machine Learning and Intelligence Avinashilingam Institute for Home Science and Higher Education for Women Department of Computer Science Coimbatore India

ISBN: (纸本)9798331515911

The spread of biased and misleading opinions on social media regarding climate change necessitates robust solutions to counteract misinformation and promote balanced discourse. In this study, we introduce the Semantic Neural Style Transfer Bias (SNLT) Framework, an AI-driven solution designed to identify and mitigate bias in climate-related tweets. Leveraging a curated dataset of climate change tweets collected from Twitter, we employ the all-MiniLM-L6-v2 sentence transformer for feature extraction and BERT-based embeddings to capture semantic nuances. Our methodology integrates semantic clustering models to evaluate clustering performance using silhouette scores, including KMeans, Gaussian KMeans, keyNMF clustering, and an Autoencoder-based model. DistilBERT is further employed to classify tweets as biased or unbiased, while a Named Entity Recognition (NER) model trained on RobERTa transformers identifies word-level bias indicators. The fundamental basis of our framework is a transformer-based style transfer model, such as GPT-2, which adjusts the stylistic tone of biased text while preserving its semantic meaning. This approach employs a style-specific decoder to ensure grammatical accuracy and contextual relevance, producing text with a neutral tone through refined phrasing and word choice. Evaluation results demonstrate a substantial reduction in bias without compromising fluency or naturalness, validated through statistical metrics and human assessments. Additionally, Explainable AI techniques are integrated to enhance the transparency of model decisions, offering insights into bias mitigation processes. Our findings highlight the framework's efficacy in addressing climate misinformation and fostering constructive conversations on social media, underscoring its potential as a responsible AI tool for digital discourse. © 2025 IEEE.

关键词： Tweets

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A Deep Insight on Cricket Video to Text Summarization Using Neural Networks 10

A Deep Insight on Cricket Video to Text Summarization Using ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Gopal, J. Vijay Prasad, K. Sai Kumar, Dhatrika Kamal Prashanth, Maddhe Sai Kumar, Vuppala Praneeth Teja, I. Nikhil Sri Sai Computer Science Engineering Artificial Intelligence and Machine Learning Mlr Institute of Technology Hyderabad India

ISBN: (纸本)9798350384369

In this modern era, the demand for efficient and automated cricket video summarization techniques is rapidly increasing. This paper introduces an innovative and advance neural network system that transforms the way cricket match videos are summarized. Cricket video-to-text summarization system overcomes the limitations of traditional manual summarization approach by utilizing various deep learning techniques to completely automate the summarization process, our system can extract crucial insights from lengthy cricket match footage and convert them into easily readable text formats. The system employs three-pronged approach which involves extraction of visual features using VGG16 Convolutional Neural Network (CNN), scoreboard information is extracted through Optical Character Recognition (OCR) technology, and Text Summarization performed by Long Short-Term Memory (LSTM) network. Our system revolutionizes the way cricket enthusiasts engage with match videos, providing a game-changing experience for fans worldwide. © 2024 IEEE.

关键词： Convolutional neural networks

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Decoding Sentiments: A Comprehensive Exploration of Real-Time Emotion Recognition from Text Through Deep learning 10

Decoding Sentiments: A Comprehensive Exploration of Real-Tim...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sowmya, G. Akhilesh, R. Rohan, Aurangabadkar Rao, Garikapati Kausthub Nayak, Atul Kumar Midhilesh, Koppolu Computer Science Engineering Artificial intelligence and Machine learning Mlr Institute of Technology Hyderabad India

ISBN: (纸本)9798350384369

Emotional state of a person can be found out through various modalities such as using facial expressions, voice of the person, text and so on. Our study is solely based on the emotion extracted through the text. Past research focused on categorizing texts into sentiments like positive, negative, or neutral, our novel approach shifts to predicting specific emotional states like angry, sad or happy. Our study covers the previous researches done in this field which are ranging from deep learning models, natural language processing, and machine learning models. Recurrent neural networks and its variants are mainly studied for recognizing the emotion in text. The integration of emotion detection in the customer feedback enhances organizational insights, enabling proactive measures to elevate customer satisfaction. © 2024 IEEE.

关键词： Contrastive learning

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An Analysis and Detection of Depression from Textual Data using Deep learning Methods 2

An Analysis and Detection of Depression from Textual Data us...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Chaithra, I.V. Sunitha, M.R. Department of Computer Science Engineering Adichunchanagiri Institute of Technology Chikkamagaluru India Department of Artificial Intelligence and Machine Learning Adichunchanagiri Institute of Technology Chikkamagaluru India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

Depression is a prevalent sickness, spreading worldwide with potentially serious implications. Timely recognition of emotional responses plays a pivotal function at present. Mental ill health is highly risky, stirring more than 300 million population. Because of this reason more focus of research is on this area. A statement by American Psychiatric Association "6.715 adults (6.7%) is get affected in any given year. One-sixth of people(16.6%) will experience depression in their life." Studies said that Women are in greater proportion to likely experience depression when compared to men. One among three women will share a significant depressive symptoms in their lifetime. Written text reflects its impact on language usage. The count of afflicted individuals can be lessened, using an advanced machine learning techniques and the availability of test data related to depression. This paper proposes different preprocessing techniques. Feature extraction techniques such as BoW(Bag of Words), Word2Vec, Glove, N-Gram, LIWC and LDA. Classification techniques such as Naive Bayes, SVM, LR, Adaboost and RF. © 2024 IEEE.

关键词： Deep learning

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