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1st Workshop on Bangla Language Processing, BLP 2023

作者： Seth, Pratinav Goel, Rashi Mathur, Komal Vemulapalli, Swetha Dept. of Data Science & Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of Computer Science & Engineering Dept. of Information and Communication Technology

ISBN: (纸本)9798891760585

This paper describes our approach to submissions made at Shared Task 2 at BLP Workshop - Sentiment Analysis of Bangla Social Media Posts(Hasan et al., 2023a;Islam et al., 2021;Hasan et al., 2023b). Sentiment Analysis is an action research area in the digital age. With the rapid and constant growth of online social media sites and services and the increasing amount of textual data, the application of automatic Sentiment Analysis is on the rise. However, most of the research in this domain is based on the English language. Despite being the world’s sixth most widely spoken language, little work has been done in Bangla. This task aims to promote work on Bangla Sentiment Analysis while identifying the polarity of social media content by determining whether the sentiment expressed in the text is Positive, Negative, or Neutral. Our approach consists of experimenting and finetuning various multilingual and pre-trained BERT-based models on our downstream tasks and using a Majority Voting and Weighted ensemble model that outperforms individual baseline model scores. Our system scored 0.711 for the multiclass classification task and scored 10th place among the participants on the leaderboard for the shared task. Our code is available at https://***/ptnv-s/RSM-NLP-BLP-Task2. © 2023 Association for Computational Linguistics.

关键词： Sentiment analysis

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Performance Analysis of Classifiers in Predicting Car Insurance Claim 8

Performance Analysis of Classifiers in Predicting Car Insura...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Bhoji, Spoorthi Pandey, Trilok Nath Dash, Bibhuti Bhusan Satpathy, Rabinarayan De, Utpal Chandra Patra, Sudhansu Shekhar Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India Sri Sri University Faculty of Emerging Technologies Cuttack India

ISBN: (纸本)9798350386578

Insurance claims pose a significant and costly challenge for insurance companies. Managing insurance claims presents a fundamental issue for insurance firms. Insurers continually face the challenge of mitigating the escalation of claim losses due to fraudulent activities and the increasing volume of claim data within their operations. Consequently, accurately assessing the status of insured claims during the review process becomes increasingly complex. In order to make the car insurance claim prediction more accurate and feasible a comparison study on the performance of various machine learning and deep learning algorithms is presented in this paper. In this research study, the primary research contribution is that various machine learning and deep learning algorithms like logistic regression, decision trees, random forest, catboost, multilayer perceptron etc. have been analyzed and performance has been evaluated based on the accuracy, precision, recall, F1-score, ROC AUC and confusion matrices. This study shows that some algorithms outperform the others and the Random Forest (RF) algorithm has resulted with the highest accuracy of 98.0%. © 2024 IEEE.

关键词： Logistic regression

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GPU Side-Channel Attack Classification for Targeted Secure Shader Mitigation

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SN computer science 2024年第8期5卷 1-23页

作者： Lungu, Nelson Patra, Sudhansu Shekhar Mishra, Manoj Ranjan Dash, Bibhuti Bhusan Sasmal, Gopal Charan Pattnayak, Parthasarathi Singh, Satyendr Gourisaria, Mahendra Kumar Department of Electrical and Electronics Engineering University of Zambia Lusaka Zambia School of Computer Applications Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India MCA Department Trident Academy of Creative Technology Bhubaneswar India Computer Science and Engineering Department BML Munjal University Gurugram India School of Computer Engineering Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India

Graphics processing units (GPUs) provide massively parallel processing capabilities, enabling accelerated computation across diverse applications. However, their parallel architecture and shared resources also introduce significant security vulnerabilities, especially side-channel attacks that can compromise sensitive data. This article presents a systematic classification of GPU side-channel attack vectors, including power analysis, timing analysis, electromagnetic analysis, and combined attacks. This taxonomy proposes countermeasures for the NVIDIA GeForce RTX 3090 GPU to mitigate these threats. The countermeasures encompass hardware techniques such as isolated execution environments, secure memory partitioning, and hardware random number generators to reduce the attack surface. Software methods, including constant-time algorithms, data-independent execution paths, masking, and oblivious memory access, are also implemented to eliminate timing and cache side channels. Comprehensive experiments demonstrate the effectiveness of the countermeasures, achieving up to 95% reduction in power analysis leakage, 90% in timing analysis, and 92% in electromagnetic analysis with acceptable performance overheads. The secure shader programming framework provides robust mitigation against GPU side-channel attacks while maintaining efficiency and compatibility with the RTX 3090 architecture. This work contributes vital insights and solutions for analysing GPU security vulnerabilities, designing focused countermeasures, and enabling confidential computing leveraging modern GPU capabilities. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Graphics processing units security Hardware countermeasures NVIDIA GeForce RTX 3090 Secure shader Side-channel attacks Software mitigation

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Artificial Intelligence in Cybersecurity: Enhancing Automated Defense Mechanisms to Combat Sophisticated Cyber Threats and Guarantee Digital Resilience 5

Artificial Intelligence in Cybersecurity: Enhancing Automate...

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

作者： Prakash, V.S. Murugesan, P. Poongothai, P. Shivakumar, B.N. Vijayakumar, P. Shriidhar, P.J. Department of Computer Science Bengaluru India Bannari Amman Institute of Technology Department of Computer Technology Sathyamangalam India Cmr Institute of Technology Department of Mathematics Bengaluru India Kongu Engineering College Department of Computer Applications Perundurai Erode India Department of Cse Salem India

ISBN: (纸本)9798350376685

The rapid evolution of cyber threats has necessitated a paradigm shift in cybersecurity approaches, with a growing emphasis on leveraging artificial intelligence (AI) to bolster automated defense mechanisms. This research delves into the integration of AI technologies within cybersecurity frameworks, exploring their potential to enhance digital resilience against sophisticated cyber threats. By examining the current landscape of cyber attacks and the limitations of traditional defense strategies, the study underscores the need for intelligent, adaptive systems capable of learning from vast datasets and responding in real-time to emerging threats. The research focuses on the development and evaluation of AI-driven defense mechanisms, including machine learning algorithms for anomaly detection, predictive analytics for threat forecasting, and natural language processing for cyber threat intelligence. Through a series of controlled experiments and case studies, the effectiveness of these AI-enhanced systems is assessed in terms of their accuracy, speed, and adaptability to new attack vectors. Findings indicate that AI-augmented cybersecurity solutions can significantly improve the detection and mitigation of advanced persistent threats, zero-day exploits, and other complex cyber attacks. Moreover, the research highlights the importance of ethical considerations and the responsible use of AI in cybersecurity to prevent the misuse of powerful AI tools and ensure privacy and fairness. The study concludes with recommendations for the strategic implementation of AI in cybersecurity, emphasizing the need for a holistic approach that combines human expertise with AI capabilities to achieve robust digital resilience. By fostering a collaborative ecosystem between cybersecurity professionals, AI developers, and policymakers, this research aims to contribute to the ongoing effort to safeguard digital infrastructure against the ever-evolving landscape of cyber threats. © 2024 IEE

关键词： Adversarial machine learning

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Instantaneous Interpretation into Sign Language for the Hearing Impaired 1

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3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Information Technology Guru Ghasidas Vishwavidyalaya Bilaspur India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

Because they are unable to convey their thoughts to others who can hear, deaf persons may have a difficult time controlling their emotions and may find that their routines are often interrupted. This is the primary impetus behind the progression of things. These individuals would be helped by this system, and it would make it possible for them to convey their ideas in a manner that is on par with that of persons who do not have any kind of physical impairment. Recent advances in artificial intelligence have made it possible to design a system that is capable of resolving this problem, which was previously thought to be impossible. The purpose of this research is to develop, for the benefit of the deaf community, a system that can translate speech into text. Because it’s possible that some individuals won’t comprehend only the written word, the speech will also be translated into international sign language (ISL). In this method, persons who are deaf or hard of hearing will have their sign language translated into spoken language. In order to develop the most accurate model possible, we will integrate NLP with a variety of ML and AI techniques. For the purpose of prediction, convolutional neural networks (CNN) will be used since lip movements are continuous and rapid, making them challenging to capture. CNN and attention-based long and short-term memory (LSTM) approaches will function since they are both effective at predicting visual input. In order to get more desirable results, we will be using several techniques for augmenting the data. TensorFlow and Keras are going to be the Python libraries that are employed in order to do the voice-to-text translation. At the moment, there is an abundance of software accessible;however, all of them require the use of a network. This piece of hardware can function very well without being connected to the internet. Using the suggested technique, we were able to attain a 100% accuracy rate in sign language prediction and a 96% a

关键词： Forecasting

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Evasion Techniques in Cybersecurity: An In-Depth Analysis

Evasion Techniques in Cybersecurity: An In-Depth Analysis

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Turukmane, Anil V Khekare, Ganesh Shelke, Nilesh Sakarkar, Gopal Buchade, Siddhant School of Computer Science & Engineering Vellore Institute of Technology-AP Amaravati India Symbiosis Institute of Technology Nagpur Campus Symbiosis International Deemed University Pune India Dr. Vishwanath Karad MIT World Peace University Department of Computer Science and Applications Pune India

ISBN: (纸本)9798331517953

Because of the rising sophistication of cybersecurity threats, it is imperative to have a thorough grasp of the evasion strategies used by malevolent actors. This study offers a detailed analysis of evasion strategies in several cybersecurity sectors, such as intrusion detection systems, network security, and endpoint protection. This paper employs a methodical approach to examine the development of these methods, showcases actual case studies, and suggests approaches for detection and mitigation. The findings emphasize how crucial it is to conduct ongoing research and development to combat new cyber threats. Key discoveries are reviewed, and directions for further research are indicated. © 2024 IEEE.

关键词： Network intrusion

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Segmentation of Retinal Pigment Epithelium Layer in Retinal OCT Images 5

Segmentation of Retinal Pigment Epithelium Layer in Retinal ...

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5th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Sheeba, T.M. Antony Raj, S. Albert Anand, M. SRM Institute of Science and Technology Kattankulathur Faculty of Science and Humanities Department of Computer Applications Chennai India SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350305449

Optical Coherence Tomography (OCT) is a relatively recent procedure for studying eyes that has proven to be quite beneficial. We propose automatic detection of the RPE (Retinal Pigment Epithelium) layer in Retinal OCT images in this work. The bottom layer of the retina in retinal OCT images is known as the RPE layer, which is extracted in this study and may be studied to diagnose retinal disorders. After effectively increasing the quality of the OCT image using the Wiener filter, we extract the Retinal Pigment Epithelium layer using the Threshold approach. The proposed approach is tested using 40 OCT images of the retina. The results demonstrate 95.50 percent accuracy in RPE layer segmentation. © 2023 IEEE.

关键词： Optical tomography

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Automated Detection of Pain Across Varied Intensity Levels Through the Fusion of CNN and Random Forest 3

Automated Detection of Pain Across Varied Intensity Levels T...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Suryavanshi, Ankita Kukreja, Vinay Dogra, Ayush Bhattacherjee, Abhishek Singh Brar, Tejinder Pal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Lovely Professional University Computer Science and Engineering Punjab Phagwara India Cgc Landran Department of Computer Applications Mohali India

ISBN: (纸本)9798350342338

An important part of preserving well-being is addressing pain, a complex human experience. In the medical field, accurate pain assessment is still difficult, especially when verbal communication is restricted. In response, by utilizing state-of-the-art technology, this research offers a strong and automated approach to pain identification. Feature extraction is done using Convolutional Neural Networks (CNNs), face expression analysis is done using deep learning techniques, and classification is done using Random Forest. When image preprocessing techniques are incorporated, data consistency and quality are improved, leading to consistent outcomes for a range of pain intensities. The system's ability to discriminate between various pain intensities, ranging from minor discomfort to excruciating anguish, is demonstrated by a carefully selected dataset. The accuracy and generalization abilities of the model are demonstrated by performance metrics such as precision, recall, and F1-Score. This study adds to the growing body of research on applications of computer vision in healthcare as well as the area of pain assessment. Promising developments in patient care, early intervention, and the comprehension of pain manifestations in many clinical contexts can be attributed to the computerized pain detection system. © 2023 IEEE.

关键词： CNN emotion detection image processing medical technology pain detection random forest

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A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach to Trainable Freebie Optimization 3

A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Vinitha, A. Jeevitha, S. Kumar, N.B. Mahesh Sathish Kumar, L. Saravanan, D. Karpagam Academy of Higher Education Department of Computer Applications Tamilnadu Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil Tamilnadu Virudhunagar India Hindusthan Institute of Technology Computer Science and Engineering Tamil Nadu Coimbatore India Madhya Pradesh Sehore India

ISBN: (纸本)9798331509675

Significant progress has been made in real-time object detection, with YOLOv7 emerging as a state-of-the-art model that achieves the highest levels of accuracy and speed. This study combines Bag-of-Freebies (BoF) strategies with YOLOv7 to improve performance without incurring extra computational expenses. Our tests show that adding BoF preserves real-time inference capabilities while increasing mAP@0.5 by 3.5%. The accuracy and FPS of YOLOv7 with BoF are superior to those of the current YOLO-based models (YOLOv4, YOLOv5, YOLOv6). According to the results, YOLOv7 with BoF sets a new standard for real-time object detection by achieving a mAP@0.5 of 55.3% and 83 FPS on the COCO dataset. © 2025 IEEE.

关键词： Contrastive Learning

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Enhanced Audio-Visual Speech Enhancement via Attention-based Sparse Graph Convolutional Neural Networks with Fire Hawk Optimization 3

Enhanced Audio-Visual Speech Enhancement via Attention-based...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Hebri, Dheeraj Joon, Rakesh Kumar Mohan, M. Ramapraba, P.S. Dhulipudi, Venkata Satish Narasimharao, Jonnadula Department of Master of Computer Applications Srinivas Institute of Technology Karnataka Mangalore574143 India Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Haryana 124104 India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Chennai641407 India Department of Electrical and Electronics Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Electronics and Communication Engineering BVC Institute of Technology and Science Andhra Pradesh 533221 India Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad501401 India

ISBN: (纸本)9798331532420

Audio-Visual Speech Enhancement (AVSE) has the vision to enhance speech quality and requite intelligibility by using both audio and video inputs, which is quite useful in noisy conditions. This approach acknowledges the fact that modalities such as lip movement may be equally informative of the auditory signals as the actual sound in cases where audio signals are distorted. In this research, an innovation approach to the AVSE is presented, which involves several state-of-the-art techniques. The data preprocessing step uses Z-score Min-Max Normalization to normalize the input data to help enhance the feature extraction process. Differentiable adaptive short-time Fourier transform (DA-STFT) is employed as the method of audio feature extraction that provides key temporal information. In the feature extraction process, a local directional structural pattern (LDSP) is utilized for the extraction of visual features since it examines the moving patterns on the face effectively. The fusion of data is done using a quadruplet depth-wise separable fusion convolutional neural network (QDSFCNN), which enhances the amalgamation of the audio and visual cues. Classification is done using an attention-based sparse graph convolutional neural network, known as ASGCNN, since the focus is on the relevant stipulations. Finally, to achieve the highest performance level, the fire hawk optimizer (FHO) is used for tuning parameters in the presented model. This combined approach presents advanced developments in AVSE, which provide improved speech quality in difficult conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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