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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Pavani, Bhupathi Vishva Mahitha, Desham Prabhakar, Priyanka Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

This paper explores sentiment analysis in legal judgments using machine learning techniques. Six machine learning models, including Naïve Bayes, K Nearest Neighbors, Logistic Regression, Support Vector Machine, Random Forest, and Decision Tree, are used along with three data embeddings namely T5, RoBerta, and LegalBert. The study aims to evaluate the effectiveness of these models in sentiment analysis tasks tailored to legal documents. Each model is trained and evaluated based on the unique characteristics of legal texts. Hyperparameter tuning has also been performed for all the models. The focus is on achieving high accuracy, F1 score and understanding the interpretability of the models' predictions. The highest accuracy achieved was by Random Forest of 67.5%. The study aims to provide insights into the applicability of various machine learning algorithms and data embeddings for sentiment analysis in the legal domain. © 2024 IEEE.

关键词： Sentiment analysis

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Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

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Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

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Sapota Quality: Bruise Detection and Shelf Life Prediction 5

Sapota Quality: Bruise Detection and Shelf Life Prediction

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Raviteja, Mellacheruvu Reddy, Vanguru Venkata Varun Kumar Aqibuddin, Shaik Soppa, Mithil Teja Sarath, S. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350379945

The shelf life and consumer appeal of sapota, a tropical fruit, are influenced by fluctuations in its freshness and flavor over time. Apart from classifying bruises, this study looks into the application Using thermal imaging to estimate the shelf life and identify maturity of sapota. This research study intends to predict the shelf life of the sapota, maturity of the sapota and bruises detection for this we collected dataset using fluke thermal camera of 60 sapota fruits, each day for 5 days in order to capture the life cycle of sapota from unripe to ripen state. The weights are monitored for further analysis. Further, a seperate dataset is collected for bruise detection by intentionally damaging the fruit for bruise detection part. The dataset consists of ripen and unripen images together with the number of days that have passed since the photo was taken, along with weights on particular day. For these tasks, various convolutional neural networks CNN ResNet, VGG16 and MobileNet are used. In terms of ripeness classification, shelf life prediction and sapota bruise detection, the efficiencies of the models are assessed and accuracy we got is 93 percent for the best model. These findings suggest that the thermal imaging in combination with deep learning techniques for non-destructive quality Evaluation of sapota fruit may assist in reducing food waste. © 2024 IEEE.

关键词： Shelf life

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Decentralized User Wallet: Transforming Digital Banking with Blockchain 8

Decentralized User Wallet: Transforming Digital Banking with...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Asmitha, M. Kavitha, C.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

Digital payment applications are now a crucial aspect of our everyday routines, completely changing how to handle money transactions. The use of blockchain technology is becoming more common in online payment systems, providing various advantages such as decentralization and trust, enhanced security, reduced costs, global accessibility, smart contracts that add to the importance of these applications in everyday living. These apps are accessible anytime and anywhere, offering users the flexibility to manage their finances at their convenience, regardless of banking hours. Blockchain data is stored in a series of blocks that are connected in order to apply guaranteed and secure records. This solution also involves the linking of each block represented as a hash, which is a digital signature of the block as well as the block that precedes it. Solving the analytical problem, it is feasible to notice that hashing, guaranteeing the formation of permanent fingerprints for each block, plays a very important role in blockchain. SHA-256 hashing algorithm is widely adopted in many blockchain applications due to the nature of the security in collision measurements. Hashing and blockchain designs help the enterprises to create reliable solutions. Since the web interface is applied to allow full access to the blockchain technology, the healthcare, supply chain management, and finance industries may now build confident systems. © 2024 IEEE.

关键词： Decentralized finance

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Deep Learning for Automated Air Quality Classification Using Image Data: A Comparative Study of GoogleNet and MobileNet 8

Deep Learning for Automated Air Quality Classification Using...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Thalapilly, Maanav Agasti, Ketone Kisor, G. Anjali, T. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350367904

The following study contributes to the improvement of environmental monitoring by providing an approach for the estimation of air quality in real-time based on deep learning models GoogleNet and MobileNet. With either compromised realtime processing capability or spatial resolution in conventional approaches, these two models can serve as practical and scalable alternatives. Empirical results demonstrate that both GoogleNet and MobileNet can classify AQI levels effectively and transfer learning significantly improves the effectiveness and generalization of comparison for tiny datasets. Class labels include 'AQI-Good' to 'AQI-Hazardous/Severe' Pollution information collected at sites in cities of India and Nepal along with location, time, pollutant concentration. A detailed data collection enhances the capability of models to generate accurate predictions over a range of scenarios and furthers our knowledge of temporal and spatial variability of air quality. Evaluation criteria like recall, accuracy, precision, and F1score portray the stability and reliability of the method. These measures assure their steady performance in a variety of contexts and thus guaranteeing their applicability in real life. Hence, GoogleNet and MobileNet are a few significant developments in the real-time air quality monitoring process, which provides information on would be crucial in dealing with the problems associated with air pollution. © 2024 IEEE.

关键词： Data quality

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Blockchain-Enabled Mitigation Strategies for Distributed Denial of Service Attacks in IoT Sensor Networks:An Experimental Approach

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computers, Materials & Continua 2024年第12期81卷 3679-3705页

作者： Kithmini Godewatte Arachchige Mohsin Murtaza Chi-Tsun Cheng Bader M.Albahlal Cheng-Chi Lee Department of Engineering Technologies School of ScienceComputing and Engineering TechnologiesSwinburne UniversityMelbourne3122Australia School of Engineering Science Technology Engineering and Mathematics CollegeRMIT UniversityMelbourneVIC 3001Australia Department of Information Technology College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Library and Information Science Fu Jen Catholic UniversityNew Taipei City24205TaiwanChina Department of Computer Science and Information Engineering Asia UniversityTaichung City41354TaiwanChina

Information security has emerged as a crucial consideration over the past decade due to escalating cyber security threats,with Internet of Things(IoT)security gaining particular attention due to its role in data communication across various ***,IoT devices,typically low-powered,are susceptible to cyber ***,blockchain has emerged as a robust solution to secure these devices due to its decentralised ***,the fusion of blockchain and IoT technologies is challenging due to performance bottlenecks,network scalability limitations,and blockchain-specific security ***,on the other hand,is a recently emerged information security solution that has great potential to secure low-powered IoT *** study aims to identify blockchain-specific vulnerabilities through changes in network behaviour,addressing a significant research gap and aiming to mitigate future cybersecurity *** blockchain and IoT technologies presents challenges,including performance bottlenecks,network scalability issues,and unique security *** paper analyses potential security weaknesses in blockchain and their impact on network *** developed a real IoT test system utilising three prevalent blockchain applications to conduct *** results indicate that Distributed Denial of Service(DDoS)attacks on low-powered,blockchain-enabled IoT sensor networks cause measurable anomalies in network and device performance,specifically:(1)an average increase in CPU core usage to 34.32%,(2)a reduction in hash rates by up to 66%,(3)an increase in batch timeout by up to 14.28%,and(4)an increase in block latency by up to 11.1%.These findings suggest potential strategies to counter future DDoS attacks on IoT networks.

关键词： Blockchain cyber intrusions DDoS IoT low powered sensors vulnerabilities wireless network

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A Personalized Visual Communication App for Children with Autism 3

A Personalized Visual Communication App for Children with Au...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Karki, Ankit Namitha, Kalakunnath Singh, Kunal Agrawal, Ronak Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350364569

Autism Spectrum Disorder (ASD) is a condition that impacts interaction, communication and behavior usually showing signs, around the age of two. This paper presents a novel mobile application developed using Flutter to help children with ASD improve cognitive and communication skills through personalized, AI-powered learning modules, interactive quizzes, and visual aids. The proposed app aims to empower individuals with autism, facilitate learning and social interactions and equip parents with tools for monitoring progress. = The application includes communication features, tailored educational modules and progress monitoring capabilities. It leverages Flutter for platform functionality ensuring accessibility on both Android and iOS devices. The user friendly interface, symbols, for communication and secure parent dashboard enhance usability. Initial assessments indicate increased user engagement improved communication skills and greater independence. With a technology that offers interactive forms of communication, personalized learning modules, and dynamic assessments to match a student's progress with their performance. This app is available for Android and iOS devices through Flutter, a technology that enables it to work in both platforms. The app is easily accessible by users due to its user-friendly layout, visual communication symbols, and secure parent dashboard. Preliminary evaluations indicate increased user participation, enhanced communication, and higher self-reliance. Compared to others apps, this one adopts a more broad approach, emphasizing communication, education, and life skills. © 2024 IEEE.

关键词： Vision aids

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Implementing Multinomial Naive Bayes for Employee Empowerment: A Transparent Platform for Task Management and Issue Resolution

Implementing Multinomial Naive Bayes for Employee Empowermen...

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

作者： Sudeesh Kumar, V. Mahendira Kumar, S. Kumar, S. Praveen Akyf Thanish, A. Rithani, M. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350379716

Developing a positive motivational culture among employees in today's changing workplace is achieved through effective communication and problem-solving. This abstract introduces an innovative Employee Empowerment Platform designed to enhance transparency and collaboration within the work-force. Using a text classification algorithm, this work presents an employee authorization system that classifies and analyzes problem reports submitted by employees. Using a multi-step approach to data preprocessing, the system extracts HTML tags, square brackets, and special characters from text data collected from problem reports. Count Vectorization and TF-IDF are two mathematical techniques used for transforming preprocessed text into features. At its heart, the platform uses a polynomial naive base classification to identify positive and negative emotions in problem reports and assign them to the appropriate category. In addition, the system uses data visualization techniques to provide a comprehensive understanding of the model's performance. Through accuracy plots and confusion matrices, you can study the prediction skill of the classifier. By establishing a clear process for finding work and facilitating problem classification, our employee empowerment program helps companies identify common concerns about work and fosters an exchange of ideas between employees creating a workplace environment that nurtures individual growth and collective success. The main objective of the platform is to increase enthusiasm and empowerment in the workplace by addressing challenges quickly. © 2024 IEEE.

关键词： Empowerment of personnel

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Detection of Real and Manipulated Videos using the Transfer Learning Approach of Deep-Learning Models 5

Detection of Real and Manipulated Videos using the Transfer ...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Reddy, Bandi Rupendra Rup, Daka Chandra Rohith, Mathi Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Deepfake (manipulated images and videos) identification is critical in multimedia forensics, as it can spread misinformation, threaten privacy, and have legal implications. To address this problem, researchers and practitioners are developing deepfake detection techniques that use deep-learning models and computer vision algorithms to analyse the authenticity of images and videos. This study proposes a novel approach providing robust and scalable deepfake detection by using transfer learning in feature extraction and deep-learning models to train features extracted from the video dataset. The transfer-learning has been experimented with the ResNet, XceptionNet, and VGG16 models for feature extractions. Further, the deep-learning models Long short-term memory (LSTM) and Bidirectional long short-term memory (Bi-LSTM) have been experimented with to train the extracted features. To rigorously test the proposed approach, the data set under the experiment combined the three benchmark datasets - The Deepfake Detection Challenge (DFDC), Celeb-DF, and Face-Forensics++ datasets. The prediction rate achieved was 96 % and above on average by the proposed approach for the datasets under consideration for the experiments. The proposed approach performs better than the state-of-the-art techniques, gaining a higher accuracy of 96% and above. © 2024 IEEE.

关键词： Contrastive Learning

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Sea Surface Temperature Prediction using Sample Convolution and Interaction Network 15

Sea Surface Temperature Prediction using Sample Convolution ...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Gayathri, P. Yalavarthi, Rahul Varma, Jayanth Santosh Gopakumar, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350370249

Predicting oceanic phenomena plays a very important role in ensuring maritime safety, managing coastal areas and preparing them for any disasters like cyclone or tsunami. There are several factors that contribute to these severe weather events among which Sea Surface Temperature(SST) is a crucial one. Warmer oceans can often lead to stronger storms or even frequent heavy downpours, highlighting the importance of accurately forecasting SST. This study explores the performance of a deep learning model called Sample Convolution and Interaction Network (SCINet) in forecasting SST. This model follows a decompose-downsample-convolve-interact architecture that helps in learning complex trends in the data. On comparing SCINet’s performance with well established time-series models like Long-Short Term Memory (LSTM) network, it was able to achieve almost 4% - 20% lower Mean Absolute Error (MAE) than LSTM on various testing points. This highlights SCINet’s potential effectiveness in time-series forecasting, contributing to the ongoing development and revolutionary advancements in deep learning models for time series prediction. ©2024 IEEE.

关键词： Long short-term memory

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