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the Mavericks@LT-EDI: Detection of Signs of Depression from Social Media Text Quotes using Naive Bayes Approach 3

The Mavericks@LT-EDI: Detection of Signs of Depression from ...

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3rd Workshop on Language technology for Equality, Diversity and Inclusion, LTEDI 2023

作者： Sathvika, V.S. Vaishnavi, S. Angel Deborah, S. Rajalakshmi, S. Mirnalinee, T.T. Department of Computer Science and Engineering Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai603110 India

ISBN: (纸本)9789544520847

Social media platforms have revolutionized the landscape of communication, providing individuals with an outlet to express their thoughts, emotions, and experiences openly. this paper focuses on the development of a model to determine whether individuals exhibit signs of depression based on their social media texts. With the aim of optimizing performance and accuracy, a Naive Bayes approach was chosen for the detection task. the Naive Bayes algorithm, a probabilistic classifier, was applied to extract features and classify the texts. the model leveraged linguistic patterns, sentiment analysis, and other relevant features to capture indicators of depression within the texts. Preprocessing techniques, including tokenization, stemming, and stop-word removal, were employed to enhance the quality of the input data. the performance of the Naive Bayes model was evaluated using standard metrics such as accuracy, precision, recall, and *** acheived a macro-avergaed F1 score of 0.263. © 2023 LTEDI 2023 - 3rd Workshop on Language technology for Equality, Diversity and Inclusion, associated with the 14th international conference on Recent advances in Natural Language Processing, RANLP 2023 - Proceedings. All rights reserved.

关键词： Sentiment analysis

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SANBAR@LT-EDI-2023:Automatic Speech Recognition: vulnerable old-aged and transgender people in Tamil 3

SANBAR@LT-EDI-2023:Automatic Speech Recognition: vulnerable ...

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3rd Workshop on Language technology for Equality, Diversity and Inclusion, LTEDI 2023

作者： Saranya, S. Bharathi, B. Computer Science and Engineering Department Sri Siva Subramaniya Nadar College of Engineering Kalavakkam603110 India

ISBN: (纸本)9789544520847

An Automatic Speech Recognition systems for Tamil are designed to convert spoken language or speech signals into written Tamil text. Seniors go to banks, clinics and authoritative workplaces to address their regular necessities. A lot of older people are not aware of the use of the facilities available in public places or office. they need a person to help them. Likewise, transgender people are deprived of primary education because of social stigma, so speaking is the only way to help them meet their needs. In order to build speech enabled systems, spontaneous speech data is collected from seniors and transgender people who are deprived of using these facilities for their own benefit. the proposed system is developed with pretraind models are IIT Madras transformer ASR model and akashsivanandan/wav2vec2-large-xls-r-300m-tamil model. Both pretrained models are used to evaluate the test speech utterances, and obtained the WER as 37.7144% and 40.55% respectively. © 2023 LTEDI 2023 - 3rd Workshop on Language technology for Equality, Diversity and Inclusion, associated with the 14th international conference on Recent advances in Natural Language Processing, RANLP 2023 - Proceedings. All rights reserved.

关键词： Character recognition

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TechSSN4@LT-EDI-RANLP2023: Depression Sign Detection in Social Media Postings using DistilBERT Model 3

TechSSN4@LT-EDI-RANLP2023: Depression Sign Detection in Soci...

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3rd Workshop on Language technology for Equality, Diversity and Inclusion, LTEDI 2023

作者： thannickal, Krupa Elizabeth Sanmati, P. Rajalakshmi, S. Angel Deborah, S. Department of Computer Science and Engineering Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai603110 India

ISBN: (纸本)9789544520847

As world population increases, more people are living to the age when depression or Major Depressive Disorder (MDD) commonly occurs. Consequently, the number of those who suffer from such disorders is rising. there is a pressing need for faster and reliable diagnosis methods. this paper proposes a method to analyse text input from social media posts of subjects to determine the severity class of depression. We have used the DistilBERT transformer to process these texts and classify the posts across three severity labels-’not depression’,’moderate’ and’severe’. the results showed the macro F1-score of 0.437 when the model was trained for 5 epochs with a comparative performance across the labels. the team acquired 6th rank while the top team scored macro F1-score as 0.470. We hope that this system will support further research into the early identification of depression in individuals to promote effective medical research and related treatments. © 2023 LTEDI 2023 - 3rd Workshop on Language technology for Equality, Diversity and Inclusion, associated with the 14th international conference on Recent advances in Natural Language Processing, RANLP 2023 - Proceedings. All rights reserved.

关键词： Social networking (online)

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Development of an Arabic Sign Language Recognition System Utilizing Deep Convolutional Neural Network 5

Development of an Arabic Sign Language Recognition System Ut...

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5th IEEE international conference on advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Kale, Meenal R. Kaur, Chamandeep Kumar, Indrajeet Mahdi, Haider Sharif Uprikar, Vijay Sankari, V. Yeshwantrao Chavan College of Engineering Department of Humanities Nagpur India Jazan University Department of Computer Science & Information Technology Jizan Saudi Arabia Department of Cse Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun India Al-Farahidi University Department of Biomedical Engineering Baghdad Iraq Datta Meghe Institute of Management Studies Nagpur India K. Ramakrishnan College of Engineering Department of Ai&ds Trichy India

ISBN: (纸本)9798350356236

the ability to recognize sign language movements, especially Arabic Sign Language (ArSL), is necessary to increase the conversation accessibility for the those who are hard of hearing or deaf. However, there are a number of drawbacks to the current ASLR techniques, such as their dependency on manually created features, lack of datasets, and inefficiencies in real-time applications. this study suggests a novel method that utilize Deep Convolutional Neural Network (DCNN) model designed especially for ASLR applications to overcome these difficulties. the suggested DCNN overcomes the drawbacks of conventional handmade feature-based techniques by automatically extracting discriminative features from unprocessed sign language images using hierarchical feature learning. Preprocessing techniques such as normalization and scaling are applied to improve the 7,857 completely labeled images in the RGB Arabic Alphabet Sign Language (AASL) the data set, which is provided for ASLR model evaluation and training. the results of the study demonstrate how successful the recommended DCNN model is, as evidenced by its astounding 99% accuracy on the AASL dataset. Comparison with similar approaches like VGG-16, ResNet-18, and other Efficient Net based architecture, shows the accuracy to which the proposed method can classify Arabic sign language alphabets. the findings presented evidence for how the chosen architecture for DCNNs can significantly enhance ASLR improvement, open up opportunities for improving communication interaction with Deaf and hard-of-hearing people and their integration. In doing the proposed work, we use Python. © 2025 IEEE.

关键词： Convolutional neural networks

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Anomaly Detection from Web Log Data Using Machine Learning Model

Anomaly Detection from Web Log Data Using Machine Learning M...

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international conference on computer Applications In Electrical Engineering-Recent advances (CERA)

作者： Amit Kumar Mishra Piyush Bagla Ravi Sharma Neeraj Kumar Pandey Neha Tripathi Dept. of Computer Science & Engineering Graphic Era Hill University Dehradun India Department of Computer Science & Engineering National Institute of Technology Jalandhar India Department of Computer Science & Engineering Graphic Era (Deemed to be University) Dehradun India

the information in the logs produced by the servers, devices, and applications can be utilized to assess the system’s health. It’s crucial to manually review logs, for instance, during upgrades, to verify whether the update and data movement went smoothly. Manual testing is insufficiently trustworthy, and manual log examination takes much time and effort. In this paper, we propose to search log files for anomalous sequences using the machine learning methods K-Means and DBSCAN. the two data representation approaches examined in this study were feature vector representation and IDF representation. the effectiveness of the deployed machine learning algorithms was examined using evaluation measures like F1 score, recall, and precision. the study found considerable differences in the algorithms’ capacities to spot anomalies, with some algorithms being better at seeing various types of abnormal arrangements than their overall prevalence. By using the study’s findings, the user might be able to spot strange arrangements after manually sifting through the log file.

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Unsupervised Ambient Vibration-Based Feature Extraction for Structural Damage Detection 7th

Unsupervised Ambient Vibration-Based Feature Extraction for ...

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Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

作者： Singh, Ashuvendra Kaloni, Smita National Institute of Technology Uttarakhand Srinagar India

ISBN: (纸本)9783031379390

Structural damage detection (SDD) is one of the major components of the Structural health monitoring (SHM) which essentially indicates the sustainability of industries, buildings, and bridges. Manual field surveys have traditionally been used to evaluate gaps. However, this approach is typically unreliable, time-consuming, and dangerous to surveyors. To overcome these challenges, researchers have been developing automated structural damage methods which include data collection, processing, structural damage identification and diagnosis. Deep learning (DL), a subcategory of ML approaches based on multiple neural networks, has advanced rapidly in recent years. Unlike standard ML approaches, DL does not necessitate a preset feature-based stage and, by providing additional data, may train more broad and robust models. As a consequence of the success of ML approaches, several specific goals in mind in ML-based fracture detection have been undertaken. this study aims to provide a solution using artificial intelligence-based damage detection research in order to guide the SHM. the proposed model achieved 98% accuracy with better testing results on unseen datasets. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Structural health monitoring

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Advancing IAM in the Finance Sector by Integrating Zero Trust and Blockchain technology 20th

Advancing IAM in the Finance Sector by Integrating Zero Trus...

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20th international conference on Mobile Web and Intelligent Information Systems, MobiWIS 2024

作者： Daah, Clement Qureshi, Amna Awan, Irfan Adalat, Omar Konur, Savas School of Computer Science Artificial Intelligence and Electronics Faculty of Engineering and Digital Technologies University of Bradford BradfordBD7 1DP United Kingdom

ISBN: (纸本)9783031680045

Safeguarding confidential data and information from increasingly complex cyberattacks is critical in the financial industry. this paper presents a novel framework that combines blockchain technology and the Zero Trust model to strengthen Identity and Access Management (IAM) systems, addressing prevalent security and privacy concerns. Traditional IAM systems often fail to counter advanced cyberattacks and insider threats effectively;this research advances towards a more resilient IAM system. Employing a mixed-methods approach, the study combines qualitative evaluations of security principles and threat modelling with quantitative analysis through the deployment and testing of a banking application. this application demonstrates the practical application of blockchain-enhanced IAM within a Zero Trust architecture, focusing on authentication, authorization, and transactional integrity. Security and performance tests validate the framework’s effectiveness in preventing unauthorised access and data breaches while ensuring scalability and user privacy. Blockchain technology enhances the security layers and introduces a decentralised trust mechanism, enriching the IAM ecosystem within financial services. Our study proposes a framework that significantly improves IAM systems’ security and reliability in the financial sector, as evidenced by successful deployment and testing outcomes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decentralized finance

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Deep Learning Techniques for a Comparative Study of Crop Disease Detection 5th

Deep Learning Techniques for a Comparative Study of Crop Dis...

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5th international conference on advances in Information Communication technology and Computing, AICTC 2024

作者： Soumya Prasad, S. Sampath Kumar, L. Mallem, Sai Nirupam Gutta, Hemanth Ahmed, Rafeeq Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vijayawada India Computer Science and Engineering Government Engineering College West Champaran Bettiah India

ISBN: (纸本)9789819761050

Agriculture industry can be significantly affected by pests. Deep learning techniques are very useful for detecting various diseases in crops. A dataset consisting of 1530 images was divided into the training set, testing set, and validation set. It consists of labels such as "Healthy", "Powdery," and "Rusty." these labels reflect the conditions of a plant and are used to train the model. the trained models using Deep Convolutional Neural Networks successfully find the relevant crop diseases with a 95% accuracy rate. the early detection of diseases is useful to curb the diseases in the budding stage to avoid the rampant onset of plant diseases which prove fatal to the agricultural field. By implementing this strategy, crop loss can be reduced significantly. In this paper, a detailed explanation of various CNN architectures such as Xception, VGG16, Resnet50, DenseNet, efficientNet, and InceptionV3 is outlined. Each model is trained with training data to predict a plant’s disease. the model’s performance is highly dependent on extracting features from the data. the result analysis and visualizations of obtained results are presented in the analysis section of the paper. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Amalgamation of Cloud computing with big data 7

Amalgamation of Cloud computing with big data

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7th international conference on Parallel, Distributed and Grid Computing, PDGC 2022

作者： Gupta, Vanshit Verma, Vansh Kaur, Supreet Kaur, Inderpreet Chitkara University Chitkara University Institute of Engineering and Technology Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9781665454018

As technology advances, massive amounts of data in many different formats are produced everywhere. the industries that have fueled the growth of data, including retail, media, finance, healthcare, and education, have produced a tremendously large and complicated collection of data that is known as "big data."On the other hand, a virtual service called cloud computing is utilised for processing, data storage, and data mining in order to maximise flexibility and cut costs. It is assisting society in addressing upcoming issues, including large data management, cyber security, and quality control. Additionally, cutting-edge technologies like distributed ledger technology, artificial intelligence, and many other capabilities are becoming accessible as services thanks to cloud computing. the two most important techniques in the field of information technology. these two IT efforts have the potential to transform every aspect of the company and have an impact on how we analyse data. It's become popular in research to use the cloud environment for large data analysis. this work attempts to comprehend the Cloud Computing technology in Big Data so as to obtain efficient and quick results. Furthermore, the paper sheds light on the challenges faced in the domain and concludes with the future scope of these technologies. © 2022 IEEE.

关键词： Big data

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Use that Pinky on the ‘A’ Key: A Finger-Key Identification Module for a Touch Typing Trainer 4th

Use that Pinky on the ‘A’ Key: A Finger-Key Identificati...

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4th international conference on advances in Computational science and Engineering, ICACSE 2023

作者： Valles, Oscar Vian L. Gom-os, Dhong Fhel K. Center for Research in Intelligent Systems Department of Computer Science University of the Philippines Cebu Cebu6000 Philippines

ISBN: (纸本)9789819729760

Typing tests list out words for the user to repeat. these tests measure metrics that quantify the user’s ability to type. However, conventional typing metrics do not measure correct finger placement. this aspect of typing may affect the user’s health. It is also a crucial aspect of keyboard typing education. As such, a method to identify which finger is used to press which key is beneficial. this paper introduces a new technique to achieve this by developing a finger-key identification module that utilizes computer vision algorithms to detect the keys in a keyboard, and a ready-made machine learning solution to track fingers while typing. this module was successful in finger-key identification with an accuracy of 99.58% in a dataset of 942 keypresses. It also had an average finger-key identification time of 0.083 s, which allows it to perform real-time finger-key identification for typing speeds up to 143.040 words per minute, well beyond the median typing speed of the population. Two metrics for correct finger placement were defined and were implemented in a proof-of-concept trainer: (1) finger placement accuracy and (2) historical finger placement accuracy. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Palmprint recognition

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