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1st IEEE International Conference on Electronics, computing, Communication and Control technology, ICECCC 2024

作者： Agarwal, Umansh Gupta, Rishika Saranya, G. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur Chennai603203 India

ISBN: (纸本)9798350371802

Today it is more crucial than ever to remove language barriers in the linked world of today. This study compares and contrasts on-demand live translation services with an emphasis on effectiveness and accuracy. It presents a workable approach to smooth multilingual communication, with a focus on Indian languages. A state-of-the-art on-call speech translation system built on a sentence-level Neural Machine Translation (NMT) model is revealed in this work. The NMT approach translates individual sentences, as opposed to previous algorithms that work with full documents, guaranteeing accuracy and context awareness. NMT, BERT, and XL Net are also compared, emphasizing their unique qualities and areas of use. To facilitate easy communication in chosen languages, this model supports Hindi, Tamil, Telugu, Malayalam, Urdu, Bengali, and English. This helps address the linguistic variety of India. Its versatility and efficacy are a result of intensive training on a wide range of language pairings, highlighting the contribution of technology to intercultural communication. The study emphasizes how the NMT-based on-call voice translation system improves commu-nication accessibility in our multicultural, globalized society by bridging linguistic gaps, particularly in Indian languages. In addition, a unique application of multithreading has been employed to lower latency by concurrently executing the transcription and translation operations. Research shows that when multithreading is used, the time required for a translation decreases to 93.82 seconds from an initial 137 seconds. This helps to some extent with the latency issue © 2024 IEEE.

关键词： Neural machine translation

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Data Augmentation for Cognitive Behavioral Therapy: Leveraging ERNIE Language Models using Artificial Intelligence 3

Data Augmentation for Cognitive Behavioral Therapy: Leveragi...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sambana, Bosubabu Archana, Kondreddygari Reddy, Suram Indhra Sena Basha, Shaik Meethaigar Jameer Karishma, Shaik School of Computing Mohan Babu University Tirupati India Department of Information Technology Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331527549

Cognitive Behavioral Therapy (CBT) is a proven approach for addressing the irrational thought patterns associated with mental health disorders, but its effectiveness relies on accurately identifying cognitive pathways to provide targeted treatment. In today's digital age, individuals often express negative emotions on social media, where they may reveal cognitive distortions, and in severe cases, exhibit suicidal tendencies. However, there is a significant gap in methodologies designed to analyze these cognitive pathways, which could be critical for psychotherapists aiming to deliver timely and effective interventions in online environments. Cognitive Behavioral Therapy (CBT) framework leveraging acceptance, commitment and data augmentation to categorize and address both textual and visual content as positive or negative. Specifically, the system employs BERT, RoBERTa for Sentiment Analysis and T5, PEGASUS for Text Summarization, mT5 for Text Translation in Multiple Languages focusing on detecting negative emotions and cognitive distortions within social media data. While existing models are primarily designed to identify negative thoughts, the proposed system goes beyond this by predicting additional negative side effects and other potential mental health disorders likes Phobias, Eating Disorders. This enhancement allows for a more comprehensive understanding and intervention strategy, offering psychotherapists a powerful tool for early detection and treatment of various psychological issues. © 2025 IEEE.

关键词： Acceptance Cognitive Behavioral Therapy (CBT) Commitment Therapy Data Augmentation Phobias Sentiment Analysis Text Classification

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Enhancing Efficiency in 6G Radio Resource Allocation Using Machine Learning Algorithms 5th

Enhancing Efficiency in 6G Radio Resource Allocation Using M...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Kamalraj, R. Nautiyal, Kshitij Taranum, Ayesha Savita Karnataka Bangalore India Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Using machine gaining knowledge of algorithms with 6G radio resource allocation can assist to enhance network performance by using automating the selection making technique on a cease-to-stop foundation. This paper proposes a more suitable implementation of 6G radio aid allocation the usage of device getting to know algorithms to expand a framework for optimized useful resource utilization. The consequences of the experiments conducted in a real-world testbed validate the effectiveness of the proposed structure in achieving the desired goals consisting of improved community performance and decreased strength consumption. Moreover, the proposed improved implementation might be relevant in a huge range of other resource allocation eventualities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Resource allocation

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Improving Quality of Service Using a Innovative Energy Effective Reinforcement Based Path Finding in Mobile Ad Hoc Network 2

Improving Quality of Service Using a Innovative Energy Effec...

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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Singaravelan, M. Saleem Raja, A. Thangam, S. School of Computing Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai India College of Computing and information Sciences University of Technology and Applied Sciences Shinas Oman Amrita school of computing Department of computer science and engineering Bengaluru India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542573

RL stands for reinforcement learning, is a component of the broader field of machine learning. It provides a framework its features help to select an efficient action using previously learned information of interaction and its environment. Mobile nodes are grouped to form a network for communicating themselves termed autonomous network with mobile nodes (MANET). This is a dynamic and independent network requiring no administrative control. The mobile nodes in the network are to be part or apart at any time that causes the topology changes. The frequent topology changes make it hard to determine the destination route. The route discovery in the dynamic topology that consumes more node energy and, minimize the network lifetime. The energy usage and network lifetime are a vital part of the MANET. This paper proposes a new global routing protocol using a reinforcement learning algorithm called modified energy and signal strength Q-learning ad hoc on-demand distance vector algorithm (ESQAODV). This process conducts a result comparison to a local routing algorithm known as AODVSARSA. The proposed algorithm proved a better result with reference to average distribution ratio compared to the other, and complete delay metrics © 2024 IEEE.

关键词： Adversarial machine learning

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Efficient Bit-Plane Based Medical Image Cryptosystem Using Novel and Robust Sine-Cosine Chaotic Map

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Computers, Materials & Continua 2025年第4期83卷 917-933页

作者： Zeric Tabekoueng Njitacke Louai A.Maghrabi Musheer Ahmad Turki Althaqafi Department of Electrical and Electronic Engineering College of Technology(COT)University of BueaBueaP.O.Box 63Cameroon Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah22246Saudi Arabia Department of Computer Engineering Jamia Millia IslamiaNew Delhi110025India Department of Computer Science School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah22246Saudi Arabia

This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine *** map demonstrates remarkable chaotic dynamics over a wide range of *** employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map,which allows us to select optimal parameter configurations for the encryption *** findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors,an essential characteristic for effective *** encryption technique is based on bit-plane decomposition,wherein a plain image is divided into distinct bit *** planes are organized into two matrices:one containing the most significant bit planes and the other housing the least significant *** subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance *** auxiliary matrix is then generated,comprising the combined bit planes that yield the final encrypted *** results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical *** a result,image quality is evaluated using the Structural Similarity Index(SSIM),yielding values close to zero for encrypted images and approaching one for decrypted ***,the entropy values of the encrypted images are near 8,with a Number of Pixel Change Rate(NPCR)and Unified Average Change Intensity(UACI)exceeding 99.50%and 33%,***,quantitative assessments of occlusion attacks,along with comparisons to leading algorithms,validate the integrity and efficacy of our medical image encryption approach.

关键词： Image cryptosystem robust chaos sine-cosine map nonlinear analysis tools medical images

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VitalLink: NodeMCU IoT Solution for Vital Signs Surveillance 7

VitalLink: NodeMCU IoT Solution for Vital Signs Surveillance

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Ramitha Rajeswari, J. Chamanthi, Reddy Mary Posonia, A. Albert Mayan, J. Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India Sathyabama Institute of Science And Technology School of Computing Chennai India

ISBN: (纸本)9798350359299

This research study proposes the development of comprehensive vital sign surveillance designed for real-time patient assessment. The system integrates multiple sensors, including the LM35 temperature sensor, Heartbeat Rate (HBR) and Blood Oxygen (SpO2) Sensor, DHT11 humidity and temperature sensor, OLED display, and gyroscope sensor, to provide a holistic approach to vital signs. The LM35 sensor is employed for accurate temperature measurement, providing crucial data for monitoring the patient's thermal status. This information is essential for detecting fever or abnormal temperature variations, aiding in the early identification of potential health issues. The HBR and SpO2 sensor plays a vital role in cardiovascular vital signs. The system can assess the patient's cardiac and respiratory well-being by measuring the heart rate and blood oxygen levels. Abnormalities in these parameters can trigger timely alerts, enabling swift medical intervention. The DHT11 sensor enhances the system's capabilities by monitoring ambient temperature and humidity. This additional environmental data contributes to a more comprehensive understanding of the patient's surroundings, ensuring that external factors are considered in the health assessment process. Integrating an OLED display serves as an intuitive user interface, providing real-time feedback on the measured parameters. This ensures that patients and healthcare providers can easily interpret and respond to the health data presented by the system. The display can showcase temperature, humidity, heart rate, blood oxygen levels, and other relevant information in a user-friendly format. Furthermore, the gyroscope sensor adds a layer of sophistication to vital sign surveillance by enabling the assessment of body movement and posture. This feature is particularly beneficial for patients with mobility issues or those prone to falls. The gyroscope data can be analyzed to detect sudden movements or abnormal postures, triggering alerts

关键词： Heart

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Unravelling State-wise Crime Patterns with Ensemble Machine Learning and Data Visualization for Public Safety 2

Unravelling State-wise Crime Patterns with Ensemble Machine ...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Sri Harsha, M.V.R. Akhil, M.H.S. Ronald Doni, A. Albert Mayan, J. Yovan Felix, A. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology School of Computing Chennai India

ISBN: (纸本)9798350349221

This study, detailed below, introduces a new method of handling crimes in particular states, using ensemble machine learning and data visualization techniques in particular, as a way to enhance public safety. Crime detection is a hard task for two concerns, the complexities of the crime and the enormity of the data. By means of random forest and AdaBoost ensemble learning techniques we can model sophisticated problems of the relationship between features such as crime data as a way to match predictions. The other point we stress is data visualization which allows us to comprehend the patterns and connections that have been derived from the ensemble learning process. In addition to the analysis of local crime data, the hybrid approach that we propose involves the inclusion of national crime data to make the identification of hidden crime patterns at state level possible. This move will enable the law enforcement agencies to gain a better understanding of the activities and to effectively respond to the crimes. The proposed methodology represents a, robust and efficient solution to statewise crime detection utilizing the advantages of machine learning and mapping tools. © 2024 IEEE.

关键词： Crime

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Determination and Classification of Diabetes Mellitus Applying Ensemble Machine Learning Classifiers 1

Determination and Classification of Diabetes Mellitus Applyi...

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1st International Conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Patel, Siddharth Hassan, Zayd Iniyan, S. SRM Institute of Science and Technology Department of Computer Science and Engineering Kaatankulathur Tamilnadu Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Kaatankulathur Tamilnadu Chennai India

ISBN: (纸本)9798350361780

This paper addresses the accurate and early prediction of the onset and progression of diabetes, an important aspect in the management of this chronic metabolic disease given a variety of information like the history of the diseased person and his day-to-day routine factors by using state-of-the-art machine learning techniques to obtain predictive dynamic models. The proposed work not only demonstrates the effectiveness of different algorithms but also highlights the importance of feature engineering to refine predictions. This work uses ensemble classifiers and voting classifier algorithms to develop better accuracy for diabetes prediction. Combining the results of multiple base classifiers like KNN, SVM, RFC, and decision tree ensemble methods aims to enhance the predictive ability and chances of improvement in the patient. The treatment adopted is different from patient to patient based on the result of the treatment. In this research, we utilize a combination of ensemble techniques and a voting classifier to construct a comprehensive diabetes prediction model. This model is evaluated using real-world data. Our findings reveal the exceptional effectiveness of ensemble classifiers and the voting classifier, underscoring their potential to achieve precision and the possibility of recovery and probability. These outcomes not only enable early intervention but also offer valuable insights into the care of individual patients. © 2024 IEEE.

关键词： Decision trees

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Comparative Analysis of News Articles Summarization using LLMs

Comparative Analysis of News Articles Summarization using LL...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Archanaa, N. Shivanesh, B. Suwin Kumar, J.D.T. Bharathi Mohan, G. Doss, Srinath Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Botho University Faculty of Engineering and Technology Botswana

ISBN: (纸本)9798350361537

In the rapidly evolving domain of Natural Language Processing (NLP), the efficiency of Large Language Models (LLMs) in generating abstractive text summaries plays a pivotal role in information synthesis. This study advances the understanding of LLM performance by conducting a comprehensive evaluation of seven cutting-edge models on four distinct datasets. The models selected for comparison include Distilbart-cnn-12-6, Led-base-16384, Google's Bigbird, Microsoft's ProphetNet, Facebook's BART, T5 fine-tuned, and Google's PEGASUS. Each model's summarization prowess is rigorously assessed using a battery of metrics: ROUGE, METEOR, BERTScore, Cosine Similarity, and BLEU. The goal is to discern the intricate relationship between dataset characteristics and model efficacy, delivering insights into the inherent advantages and limitations of each model in handling specific data contexts. The results contribute to a refined understanding of LLM applicability, offering empirical evidence to aid in the selection of the most suitable model for varied summarization needs. © 2024 IEEE.

关键词： Natural language processing systems

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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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