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2nd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Revathi, K.P. Mahesh, Indusha Makitha Sree, K. Deepthi, Nadiminti Easwari Engineering College Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350327533

This research explores the application of AI-driven techniques for automated anomaly detection in ultrasound scans during pregnancy. Leveraging a substantial dataset of labeled ultrasound images, this study delves into robust data collection, preprocessing methodologies, meticulous model selection, and comprehensive model training techniques. The combination of U-Net for image segmentation and ResNet for image classification was selectively chosen, followed by an in-depth analysis of their adaptability to medical imaging tasks. The study concludes by proposing a hybrid approach using both segmentation and classification for more accurate anomaly detection in ultrasound images. This study delves into a comprehensive survey of AI methodologies applied to fetal ultrasonography during the second trimester. By leveraging curated datasets and innovative AI techniques, this research navigates the evolving landscape of automated anomaly detection, aiming to enhance diagnostic capabilities and bridge accessibility gaps in prenatal care. © 2024 IEEE.

关键词： Anomaly Detection Techniques artificial intelligence Automated Anomaly Detection Image Classification Image Segmentation Model Training Pregnancy Residual Neural Networkt U-Net Ultrasound Analysis

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Genetic Algorithm Powered Bivariate Function Encryption for Robust data Security 4

Genetic Algorithm Powered Bivariate Function Encryption for ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Hariharan, N.R. Ritheshwar, R.Sai Suseendran, R. Meena, R. Kalaiselvi, T. Easwari Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331523923

This paper introduces a new encryption technique which bases its operations on advanced bivariate mathematical functions and uses a genetic algorithm to optimize these along with random key generation techniques. This, in turn, allows for the enhancement of cryptographic complexity and better security features with fortified protection against any possible attacks. The encryption mechanism integrates random keys along with the evolved bivariate functions, and RSA protocol is employed for securing the channel of exchanging keys, ensuring confidentiality at each stage of the transmission process. The integration of these methodologies has created a two-layer security framework that combines the efficiency of symmetric encryption with the robustness of RSA. Advanced symmetric encryption combined with RSA significantly increases the security measures provided by defending against brute force attacks, cryptanalysis, and intercept threats. Through the combination of symmetric encryption and an asymmetric key exchange protocol, this strategy fully addresses the disadvantages of the typical cryptographic system. It forms an effective solution in meeting the contemporary security demands for digital communication environments. © 2025 IEEE.

关键词： Encryption algorithms

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Yoga Posture Analysis and Feedback System Using Mediapipe

Yoga Posture Analysis and Feedback System Using Mediapipe

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2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Snigdha, D. Tejasree, S. Rithvik, N. Shanmuagasundaram, Hariharan Motikar, Siddharth Reddy, S. Srinivas Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India

ISBN: (纸本)9798331509828

This project integrates Mediapipe with Python to implement a yoga posture analysis system for evaluating and providing efficacious feedback on human poses. The system establishes ideal joint landmarks using reference images, which are compared with uploaded analysis images over multiple simulated epochs. Angles of key joints, such as the elbow, shoulder, hip and knee are calculated and annotated onto the analyzed images for visual feedback. Deviations of analyzed images are identified and corrective suggestions are provided. Comprehensive graphs of accuracy and loss for each epoch are displayed to visualize the trends in accuracy and pose improvement. Accuracy reflects how well the model predicts the correct output, while loss indicates areas for improvement. In addition, for easy reference, all epoch data are tabulated to summarize overall performance, and a final model accuracy percentage is computed. The project also gives visual presentations of accuracy and loss over each epoch. The system serves as an effective tool for posture correction, making it suitable for fitness applications, physiotherapy, and ergonomic evaluations, with its detailed visual and numerical feedback. © 2025 IEEE.

关键词： Joints (anatomy)

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Knowledge based Organizational Personal Assistant 2

Knowledge based Organizational Personal Assistant

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Mohan, Vasanthakumar Paramesh, Anandh Sivaguru Panimalar Engineering College Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350328202

In the modern era, most activities are being digitized. One of the main objectives of AI is to enable human machine interaction. The dialogue systems technology has been utilized by numerous organizations to create a variety of types of personal assistants depending on their applications and domains. Here we introduce the development of a versatile Knowledge Based Personal Assistant for College that leverages advanced technologies, including natural language processing, voice recognition, and text to speech synthesis, to address the diverse and evolving needs of the college community. This AI driven system is designed to facilitate a seamless and efficient user experience, allowing users to interact with the assistant through both voice and text inputs. Alongside core functionalities encompassing academic schedule management, emergency contact assistance, email integration, location based services, and academic inquiries, the assistant provides responses in both voice and text formats. Technical aspects include the integration of state of the art NLP algorithms for query understanding and context aware responses. Users can easily switch between input mode and receive responses, accordingly, offering a flexible and inclusive user experience. The assistant adaptability and expandability are central to its design, featuring modular architecture that facilitates the integration of additional features and services. © 2024 IEEE.

关键词： Knowledge based systems

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OpenAI-Enhanced Personal Desktop Assistant: A Revolution in Human-Computer Interaction 2

OpenAI-Enhanced Personal Desktop Assistant: A Revolution in ...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Benny, Rijo Muralidharan, Ashwin Subramanian, Malathi Panimalar Engineering College Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350328202

The creation of a natural conversation between humans and machines is the primary objective of artificial intelligence (AI). One of the greatest advancements in AI that has the potential to significantly alter people's way of life are voice assistants. We know that the role of AI based voice assistants is to recognize the input human voice and respond to the given input via integrated voices. A lot of IT based organizations have been using dialogue systems technology to create various types of Virtual Personal Assistants (VPAs) based on their application requirements and areas for increasing interaction between human and a machine, for example, Apple's Siri, Microsoft's Cortana, Amazon Alexa and Google Assistant. By early 2023, Amazon Alexa was used daily by approximately 70% of users, yielding satisfaction levels surpassing 70%. Google Assistant boasted a global user base of 500 million per month, enjoying satisfaction rates over 80%. Apple Siri similarly engaged around 500 million users monthly, maintaining a satisfaction rate of about 78%. Microsoft Cortana had limited usage data but attained satisfaction rates around 60-65%. Roughly 60-70% of users across platforms expressed worries regarding data privacy and security when utilizing these desktop assistants. So, the main objective of our work is to create a personalized desktop assistant embedded with the OpenAI technology and improvement in security. Like the Microsoft's Cortana (which was discontinued by Microsoft), we aim to design a more efficient and personal desktop assistant, enhanced with OpenAI solely for Windows using Python, which can be accessed using any of the Windows Explorer like Windows 7,8,9,10,11. Here we use Python as the major programming language as it has a lot of inbuilt libraries that could make the task even easier. The way of developing a personal desktop assistant without requiring cloud services is discussed in this paper, which will also allow the future extension of such devices. ©

关键词： Human computer interaction

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MusiciAI: A Hybrid Generative Model for Music Therapy using Cross-Modal Transformer and Variational Autoencoder 2

MusiciAI: A Hybrid Generative Model for Music Therapy using ...

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2nd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Venkatachalam, Nirmala Raghuram Karthic Ganesh, V. Hari Vigneshwaran, S. Easwari Engineering College Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350327533

Music has long been recognized for its therapeutic potential, offering emotional and psychological benefits. In the contemporary landscape, the intersection of artificial intelligence and music therapy introduces innovative applications. MusiciAI introduces an advanced generative model designed to harness the healing power of music through a novel hybrid approach. Combining a Cross-Modal Transformer and a Variational Autoencoder, MusiciAI interprets textual prompts and generates diverse, emotionally resonant music. The proposed model is trained on a carefully curated dataset, incorporating language models for precise annotation. MusiciAI integrates user interaction, allowing prompt-based music generation and user adjustments for a personalized therapeutic experience. Through a comprehensive exploration of the hybrid model's architecture, training, evaluation, and user-centric features, and contributes to the evolving landscape of generative AI in music therapy. © 2024 IEEE.

关键词： artificial intelligence Cross-Modal Transformer Emotional Resonance Generative AI Hybrid Modal Language Models Music Therapy Personalized Music User Interaction Variational Autoencoder

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Real-Time Driver Alertness Assessment Using Neural Networks

Real-Time Driver Alertness Assessment Using Neural Networks

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2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Yoga, M. Ramyasri, M.M. Lokitha, R. Jane Karunya, J. Jeeva, N. Kokila, M.M. Kongu Engineering College Department of Artificial Intelligence Tamilnadu India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamilnadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Drivers have profited significantly from developments in computer technology with the introduction of intelligent car systems. However, driver weariness is a key contributing cause to many car accidents. This research suggests an intelligent system for detecting driver tiredness based on vision, employing a convolutional neural network (CNN) to capture latent facial traits rather than relying on hand-engineered features like blink rate, eye closure, and yawning. The driver is classified as either sleepy or not by a softmax layer, which enables the system to alert the driver and avoid collisions. The proposed approach relies on extracting features from driver photos using a CNN and assessing eye states and facial expressions to reliably identify tiredness. The algorithm's excellent accuracy in identifying sleepy states is demonstrated by the results, underscoring its potential to improve road safety and lower the number of accidents caused by fatigued drivers. © 2025 IEEE.

关键词： Convolutional neural networks

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An Innovative Method for Identifying Deepfake Videos Through Ensemble Learning 15

An Innovative Method for Identifying Deepfake Videos Through...

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

作者： Keerthika, S. Santhiya, S. Jayadharshini, P. Ruthranayaki, J. Hariarasu, R. Naveenan, M. Kongu engineering College Department of Artificial Intelligence Perundurai India Kongu engineering College Artificial Intelligence and Data Science Perundurai India

ISBN: (纸本)9798350370249

The detection of deepfake videos is essential due to the significant potential harm posed by manipulated media. Various deep learning techniques, including customized convolutional neural networks (CNNs), MobileNet, and DenseNet, have demonstrated promise in this domain. This study introduces a system for detecting deepfake videos by leveraging tooth and mouth movements, which are notably difficult to replicate accurately. The proposed methodology utilizes multiple transfer learning algorithms such as InceptionV3, MobileNet, VGG16, VGG19, Xception, and DenseNet121 to enhance the algorithm's capability to detect and classify deepfake videos based on biological signal attributes derived from jaw and tooth frames. The study specifically targets the identification of deepfake videos by extracting and cropping frames from the input video to emphasize the mouth and nose regions. These preprocessed frames are subsequently fed into a customized CNN trained to classify videos as real or fake, achieving an accuracy of 91.7%. Additionally, pre-trained CNN models DenseNet121 and MobileNetV2 further improve accuracy, attaining 88.4% and 90.9%, respectively. An ensemble approach that combines the predictions of these models results in an overall accuracy of 93.2%. © 2024 IEEE.

关键词： convolutional neural networks deep learning Deepfake detection multi-transfer learning video classification

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LesionAid: vision transformers-based skin lesion generation and classification – A practical review

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Multimedia Tools and Applications 2025年 1-22页

作者： K, Mallikharjuna Rao Krishna, Ghanta Sai Supriya, Kundrapu Sorgile, Meetiksha Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur India Computer Science and Engineering International Institute of Information Technology Naya Raipur India

Skin cancer is one of the most prevalent forms of human cancer. It is recognized mainly visually, beginning with clinical screening and continuing with the dermoscopic examination, histological assessment, and specimen collection. Deep convolutional neural networks (CNNs) perform highly segregated and potentially universal tasks against a classified fine-grained object. This study suggests a novel multi-class prediction framework that uses ViT and ViTGAN to categories skin lesions. To address the class disparity, GANs (Generative Adversarial Networks) based on vision transformers are used. The framework comprises four main phases: ViTGANs, Image processing, and explainable AI. Phase 1 consists of generating synthetic images using ViTGAN to balance all the classes in the dataset. To enhance the amount of the data, the second phase involves using various morphological processes and data augmentation techniques. In phases three and four, a ViT model for edge computing systems that can recognize patterns and classify skin lesions from the user's skin that is visible in the picture is developed. In phase 3, after classifying the lesions into the desired class with ViT, we will use explainable AI (XAI) that leads to more explainable results (using activation maps, etc.) while ensuring high predictive accuracy. The results demonstrate that the model used for generating synthetic images has achieved an FID score of 13.32 and the ViT model has achieved 99.2% as its training accuracy and 97.4% as its validation accuracy. The whole framework is compared with the existing frameworks for skin lesion detection. And explainable AI (XAI) is used in the proposed framework in order to increase model transparency and boost trust among users by illustrating the main factors affecting predictions. This interpretability helps physicians in making more informed decisions by providing clear insights into the model's reasoning. © The Author(s), under exclusive licence to Springer science+Bu

关键词： Generative adversarial networks

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Secure Cloud based Process-Oriented Teaching-Quality Approach in Higher Education with General AI

Secure Cloud based Process-Oriented Teaching-Quality Approac...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Tingare, Bhagyashree Ashok Kale, Pranav Nikam, Sakshi Vikas D Y Patil College of Engineering Akurdi Department of Artificial Intelligence and Data Science Pune India D. Y. Patil College of Engineering Akurdi Artificial Intelligence and Data Science Pune India D Y Patil College of Engineering Akrudi Artificial Intelligence and Data Science India

ISBN: (纸本)9798350390049

The integration of General AI with secure cloud-based technologies offers transformative potential for enhancing teaching quality in higher education. This paper explores a process-oriented approach that combines advanced AI capabilities with robust cloud infrastructure to address contemporary challenges in educational institutions. By focusing on systematic instructional design, data-driven decision-making, and stringent security measures, this framework aims to improve pedagogical practices, streamline administrative processes, and safeguard sensitive information. The study evaluates the impact of various AI technologies on teaching quality and student satisfaction, highlighting significant improvements in personalized learning, automated grading, and predictive analytics. The findings suggest that AI-driven solutions not only enhance educational outcomes but also contribute to greater operational efficiency and data security. This innovative paradigm provides a comprehensive model for modernizing higher education through secure, scalable, and process-oriented approaches. © 2024 IEEE.

关键词： Contrastive Learning

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