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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Sreenivas Prasad, B. Akash Babu, N. Harithikeswar Reddy, T. Vishnu Vardhan Reddy, A. Vekkot, Susmitha Nair, Priyanka C. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India Amrita School of Engineering Department of Electronics & Communication Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350349900

In the current age of information saturation, where headlines serve as primary points of engagement, the pervasive use of sarcasm in news headlines poses a challenge for readers' comprehension, failing to discern sarcasm may lead to misunderstandings, misinformation dissemination, and harm journalistic integrity. This study addresses this critical issue and enhances the credibility of news sources by employing various ensemble learning algorithms and deep learning models with different types of word embedding, the evaluation includes various possible performance metrics and a complete correlation of the outcomes. XGBoost consistently outperforms other models with higher accuracy, precision, recall, and F 1-score in sarcasm detection. Among deep learning models, BiLSTM emerges as the top performer, achieving 0.86 accuracy, capturing nuanced language cues effectively and appears as the optimal model for sarcasm detection. BERT stands out with superior F1 scores, showcasing its exceptional ability in deciphering sarcastic language nuances. © 2024 IEEE.

关键词： Contrastive Learning

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A Cost-Effective Autonomous Beach Cleaning Rover Using Deep Learning 1

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6th International Conference on Information Systems and Management science, ISMS 2023

作者： Girish, S. Harikrishnan, S. Nair, Rahul H. Sreerajan, Midhun Sathyan, Soumya Pandi, V. Ravikumar Valsan, Vipina Suresh, Kavya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9783031664106

ISBN: (纸本)9783031664090

This work aims to transform beach garbage management by developing an autonomous rover that utilizes deep learning and computer vision. The main goal is to enable the rover to traverse coastal environments on its own while using its sophisticated onboard camera to identify various waste products. The rover will identify different sorts of rubbish by employing the YOLO algorithm that uses the Bounding Box Regression technique to improve waste management tactics. The work involves hardware design, neural network training, real-time computer vision integration, and extensive field testing. The hardware components of the rover are carefully engineered through iterative development, guaranteeing smooth mobility and efficient data collection. By incorporating real-time computer vision techniques, the rover will be able to evaluate visual data and make decisions that will help identify waste. In order to verify the project’s effectiveness in tackling beach pollution field testing is carried out to confirm the rover’s accuracy in navigation and waste detection. An autonomous rover that embodies technical innovation for long-term, environmentally sustainable preservation is successfully developed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Integration testing

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Securing Democracy: The Two-Phase Authentication Approach to Electronic Voting 3

Securing Democracy: The Two-Phase Authentication Approach to...

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3rd International Conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Sidharth, P.V. Nagarajan, P.M. Johnson, Dany Geo Kumar, Aadith Anil Namitha, Kalakunnath Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India Amrita School of Engineering Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350375190

The foundation of a democratic society is voting. However, in countries with high population densities, the electoral process is frequently not given enough attention. Concerns have been raised about electronic voting systems' dependability, security, and transparency on a global scale. The proposed framework overcomes these difficulties by providing a two-phase authentication system that combines facial recognition and fingerprint authentication. This work aims to create an intelligent voting system that improves security, accuracy, and accessibility by including these strong authentication techniques. Biometrics enhances the system's security characteristics. The proposed system integrates various components, including an Arduino microcontroller board, a facial recognition module, a biometric fingerprint scanner, a Wi-Fi module, and the Global System for Mobile Communication (GSM) module, to enable voter identification. Furthermore, election authorities are notified with an electronic alert message when an invalid vote is cast. Experimental results confirm the efficacy of our proposed authentication mechanism. © 2024 IEEE.

关键词： Biometrics

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A Hand Gesture-Operated System for Rehabilitation Using an End-to-End Detection Framework

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 698-708页

作者： Dutta, H Pallab Jyoti Bhuyan, M.K. Neog, Debanga Raj Macdorman, Karl F. Laskar, R.H. Department of Electronics and Electrical Engineering Indian Institute of Technology Assam Guwahati781039 India Department of Electronics and Electrical Engineering and Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Assam Guwahati781039 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute ofTechnology Assam Guwahati781039 India School of Informatics and Computing Indiana University IN46202 United States Department of Electronics and Communication Engineering National Institute of Technology Assam Silchar788010 India

In this article, we propose a hand gesture-operated system as an AI application to relieve discomfort and restore function in hand and arm movements caused by injuries and nerve and muscle complications. The system trains patients with hand exercises, such as performing hand gestures accurately, traversing within specified bounds, and operating a hand gesture calculator. However, the system requires accurate hand gesture detection, which is impeded by background clutter and variations in illumination and in the hand's size and angle. To address this, we developed a robust hand detection module that uses a single-stage transformer deep network. The transformer network encodes global information and uses bipartite matching to reduce the frequency of spurious detections. It drives a regression head and a classification head to localize the hand gesture in a bounding box and assign it a class label. Hand keypoints are also detected to support drawing, path traversal, and calculator use. The approach is evaluated on two benchmark datasets: 1) OUHANDS and 2) NUS. The method yields 89.6% accuracy for OUHANDS and 100% for NUS. These results indicate that precise hand detection can support a robust system for rehabilitation through hand exercises. Our experiments confirm that the users' hand function progressively improved. © 2023 IEEE.

关键词： Deep neural networks

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Optimised Design of a Current Mirror in 150 nm GaAs Technology 5

Optimised Design of a Current Mirror in 150 nm GaAs Technolo...

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5th International Conference on computing, Networking, Telecommunications and engineering sciences Applications, CoNTESA 2024

作者： Qian, Lua Ying Kang, Chia Chao Ding, Tan Jian Bhuiyan, Mohammad Arif Sobhan Minhad, Khairun Nisa Miraz, Mahdi H. Electrical and Electronics Engineering Xiamen University Malaysia Selangor Malaysia School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia

ISBN: (纸本)9798331534530

The Current Mirror (CM) is a basic building block commonly used in analogue and mixed-signal integrated circuits. Its significance lies in its ability to replicate and precisely regulate the current, making it crucial in various applications such as amplifiers, filters and data converters. Recently, there has been a growing need for smaller and more energy-efficient Radio Frequency (RF) devices due to the advancements in wireless communication, the Internet of Things (IoT) and portable electronics. This research aims to propose an improved and optimised CM design focusing on compactness and energy-efficient operation. Through a comprehensive methodology involving transistor sizing, biasing techniques, load resistance selection, frequency response stabilisation and noise analysis, the proposed high swing CM design achieves a gain of at least 6.005 dB, a reduced power consumption of 91.17 mW, a wide bandwidth of 22.60 kHz and improved linearity as well as accuracy through precise current matching and minimised mismatch. This optimised CM design will further boost the realisation of compact and lower power RF devices, contributing to the advancement of analogue circuit design techniques and enhancing system performance, accuracy and reliability. © 2024 IEEE.

关键词： Gallium arsenide

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A Review on Environmental Sustainability Using Machine Learning Applications 5

A Review on Environmental Sustainability Using Machine Learn...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Nirmala, E. Yadav, Manish Naveen, Palanichamy Maragatharajan, M. Suresh, M. Bhargava, Pranjal School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Kothri Kalan India School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Madhya Pradesh Kothri Kalan India KPR Institute of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331518097

Environmental sustainability reduces the destruction of natural resources in the hands of mother earth for next generation. Due to the development of cities and its social and economic factors leads the major role of any such destruction. It affects huge volume of biological system in terms of environmental pollution, change in climate and respective population growth. Henceforth, the general population is severely affected with their quality of life in the present scenario. This research study discusses about various resources that are affected severely due to the environmental factors have stated with the proven evident. Along with this various prediction technology using machine learning procedures are discussed in detail to prevent any such destruction by improving the present style of work. The prediction of any such destruction exploiting resources like land, water, mineral and planetaries creates its impact in the form of climatic changes, as a result of this we are facing drought, earthquakes and landslide. Apart from preventing techniques over plantation of greeneries, it is necessary to implement techniques towards efficient use of resources Monitoring is essential features. All these things are now essentially managed with the help of computer aided technology. Further the effective usage of environmental factors results into safe future for the next generation. This can be achieved by using AI with SUV to increase the prediction accuracy. Further this article is directed towards future direction to prevent and use the natural resources is discussed for the benefit of population around the world. © 2024 IEEE.

关键词： Climate change

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Deep Fake Detection Based on Hybrid Approach Involving Residual Network Deep Learning Architecture

Deep Fake Detection Based on Hybrid Approach Involving Resid...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Shibu, N.V. Muthunagai, R. Prabu, S. Srm Institute of Science and Technology Department of Computing Technologies Chengalpattu India Manakula Vinayagar Institute of Technology Department of Electrical & Electronics Engineering Puducherry India School of Computing Srm Institute of Science and Technologies Department of Computing Technologies Chennai India

ISBN: (纸本)9798331510022

This Deep fake Technology aims at generating synthetic video content or morphed video content which is very realistic to the naked eye. It involves the use of deep learning algorithms in the domain of video processing. In most cases, such artificially generated video contents are very difficult to get differentiated from naturally captured video signals. The existing methodologies of detection involved image tamper detection and digital forensics algorithms. These algorithms are ineffective in detecting videos tampered using deep fake technology. The proposed technique involves training and building of deep learning models that works like a black box to detect the artefacts crucial in the detection of video tampering. This paper presents an overview of the technology advancements in the field of deep fake video detection techniques over recent years. This paper also performs a comparative analysis on the performance of hybrid algorithms involving residual network deep learning architecture in the detection of video signals morphed using deep fake technology. The training datasets are obtained from Face forensics to build the model. The algorithm is augmented with image quality assessment, temporal consistency analysis and facial landmarks analysis to improve the detection algorithm. The performance metrics involve the generation of confusion matrix and generation of accuracy measures. The results provide a promising direction towards the construction of better detection models to safeguard against current and future threats. © 2024 IEEE.

关键词： Contrastive Learning

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Heart Disease Classification using Crow Search Algorithm 5

Heart Disease Classification using Crow Search Algorithm

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5th International Conference on Smart electronics and Communication, ICOSEC 2024

作者： Maragatharajan, M. Naveen, P. Nirmala, E. Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Kothrikalan India Kpr Institute of Engineering and Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798331504403

This work introduces cutting-edge research to predict heart diseases. This application designed for accurate and personalized assessment of cardiovascular health. This work aims to provide an overall understanding of an individual's heart health by integrating these multifaceted data points. This work utilizes a vast dataset to establish correlations and patterns, enabling it to categorize users into specific risk groups or diagnose potential heart diseases. Through continuous learning and adaptation, the work refines its predictive capabilities, ensuring that it remains up-to-date with emerging medical knowledge. The work not only assists in early detection of cardiovascular issues but also aids in personalized risk assessment, guiding users towards lifestyle modifications and preventive measures based on their unique health profiles. This innovative solution holds the potential to revolutionize the field of cardiovascular health assessment, offering a proactive and data-driven approach to identify and manage heart diseases efficiently. This study considered two well-known machine learning models namely Extreme learning machine algorithms (ELM) and Crow search algorithm (CSA). The collected dataset consists of 10000 samples. We have attained 92.5% accuracy with the data set. The incorporation of machine learning models ensures scalability, adaptability, and precision, making it a valuable tool for both healthcare professionals and individuals striving for proactive heart care. © 2024 IEEE.

关键词： Accuracy Crow search algorithm Data preprocessing Extreme learning machine algorithm Feature selection Heart diseases

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Revolutionizing Face Detection: VisionaryNet's Breakthrough in Unraveling Facial Camplexity 2

Revolutionizing Face Detection: VisionaryNet's Breakthrough ...

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2nd International Conference on Computer, Communication and Control, IC4 2024

作者： Nallarasan, V. Arivazhagan, N. Lakshminarayanan, R. Sharmila, D. Sangolgi, Nagaveni B Anand, M. Srm Institute of Science and Technology School of Computing Department of Networking and Communications Chengalpattu Kattankulathur India Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Chengalpattu Kattankulathur India Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Sharnbasva University Department of Electronics and Communication Engineering Kalaburagi India Srm Institute of Science and Technology Department of Computing Technologies Chengalpattu Kattankulathur India

ISBN: (纸本)9798350387933

The initial step in facial recognition and analysis systems often involves facial detection. Traditional methods like Haar cascades and directed gradient histograms relied on manually designed features, limiting their effectiveness in diverse image environments. However, the emergence of deep learning in computer vision has accelerated the development of face detection frameworks, significantly improving accuracy. Detecting small-scale faces, varying positions, occlusions, blurs, and partially obscured faces in uncontrolled settings remains a persistent challenge in face identification. To address this, we propose a new approach called 'Visionary Net.' This single-stage face detector has undergone refinements, boosting both detection speed and accuracy. We tested Visionary Net on two well-known datasets: Mega Face and CelebA. Notably, on the Mega Face benchmark, our method achieved an impressive AP of 48.5 at a speed of 15.2 FPS using single-scale inference and an exceptional AP of 52.7 with multi-scale inference. Implementing it with the PyTorch framework resulted in a remarkable 97.3% accuracy in successfully detecting faces. Our comprehensive experiments show that Visionary Net surpasses existing models, demonstrating superior detection and recognition capabilities across various evaluation metrics. This innovative algorithm holds significant promise for robust face detection in challenging real-world scenarios. © 2024 IEEE.

关键词： Face recognition

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Speech Recognition Paradigms: A Comparative Evaluation of SpeechBrain, Whisper and Wav2Vec2 Models 9

Speech Recognition Paradigms: A Comparative Evaluation of Sp...

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

作者： Yerramreddy, Dhanvanth Reddy Marasani, Jayasurya Gowtham, Ponnuru Sathwik Venkata Harshit, Guduru Anjali Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Amritapuri India Amrita Vishwa Vidyapeetham School of Engineering Dept. of Electronics & Communication Engineering Amritapuri India

ISBN: (纸本)9798350394474

Speech recognition plays a pivotal role in the realm of natural language processing that deals in converting the language into the written text, providing human-computer interaction and enables us to use it widely for applications starting with voice assistants and delving upto the transcription services. Due to the complexity present in performing the task of speech recognition has led to the development of various models to enhance accuracy and efficiency. Our project mainly delves into three prominent speech recognition models that are Whisper, Wav2Vec2 and Speechbrain each of them representing distinct approaches of transcribing spoken language. The significance of the models used lies in their potential to perform better for realtime applications by improving the accuracy and reliability of speech recognition. To evaluate the best model that performs effectively, an array of metrics are used including levenshtein distance and it's similarity percentage, jaccard similarity along with semantic similarity that provides an additional layer of evaluation describing the model's understanding of contextual meaning in spoken language. Out of the three models, Speech Brain model outperformed all the other models through the calculation of Word Error Rate (WER), Character Error rate (CER) and BLEU score. The results have shown the model's efficiency in converting spoken language(audio files) into precise and contextually relevant text. The results shows that these models contribute to the field of speech recognition highlighting the strengths of each approach and among them considering speechbrain as an ideal solution for accurate and meaningful transcription. © 2024 IEEE.

关键词： Speech recognition

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