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Augmenting Sentiments into Chat-GPT Using FacialEmotion Recognition 10

Augmenting Sentiments into Chat-GPT Using FacialEmotion Reco...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Iyer, Aditya A. Vojjala, Saipranav Andrew, J. Manipal Institute of Technology Dept. of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350384369

This research initiative addresses the task of enhancing Chat Generative Pre-trained Transformer's (ChatGPT's) conversational capabilities by integrating the comprehension and response to user emotions conveyed through facial expressions. The central challenge lies in refining the AI system's proficiency to tailor responses according to users' detected emotional states. In this pursuit, our study adopts a comprehensive approach, aiming to seamlessly incorporate emotional intelligence into AI-driven interactions. To achieve this objective, the methodology involves integrating real-time sentiment analysis based on facial expressions into the ChatGPT framework. This is carried out through the utilization of a deep convolutional neural network (DCNN) architecture, designed to recognize and interpret various emotions exhibited in facial expressions. The primary goal is to enable ChatGPT to dynamically adjust its responses, fostering a more empathetic and contextually relevant interaction with users. In terms of evaluation metrics for facial expression recog- nition, our assessment employs a confusion matrix to quantify the model's performance across different emotional categories. Additionally, a heuristic approach is implemented, wherein the sum total probability of each detected emotion is calculated over the duration the user enters the prompt. These evaluation methodologies aim to provide a comprehensive understanding of the model's accuracy and effectiveness in discerning and responding to user emotions. Overall, this research contributes to the ongoing endeavor of imbuing AI systems with emotional intelligence, paving the way for more nuanced and human-like interactions. © 2024 IEEE.

关键词： Chatbots

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SQL Injection Detection using Machine Learning and Convolutional Neural Networks 5

SQL Injection Detection using Machine Learning and Convoluti...

引用

5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Misquitta, Jason Asha, S. Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665474672

SQL Injection Attacks are a concern to databasedriven online applications because they can compromise the security and integrity of information in databases. It gives hackers the potential to read, delete, modify or copy data. Exploiting the flaws in the validation of user inputs provided through login pages is the simplest technique to conduct this attack. Each login page a user accesses can help to reveal more about who they are. Feedbacks sent by the server while running a SQL command can provide details on weaknesses in the database server's validation procedure. This research paper analyses code that focuses on detecting SQL injections. Different supervised Machine Learning algorithms are used to evaluate the model. A CNN model is also created for real-time identification of whether an input is a potential attack or not. © 2023 IEEE.

关键词： Personal computing

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Comprehensive Methodologies for Breast Cancer Classification: Leveraging XAI LIME, SHAP Bagging Boosting, and Diverse Single Classifiers 4

Comprehensive Methodologies for Breast Cancer Classification...

引用

4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Avlani, Dhruvi T. Abhijna, M.B. Sai Disha, G.S. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Information Science and Engineering Karnataka Bangalore India Global Academy of Technology Dept. of Artificial Intelligence and & Data Science Karnataka Bangalore India

ISBN: (纸本)9798350354218

Breast cancer is one of the most common causes for deaths owed to cancer. It is very crucial and necessary to detect cancer at early phases. There are various Machine Learning techniques and computational techniques available for the aim of diagnosis of breast cancer data. This paper offers a Machine Learning model to perform automated diagnosis for the classification of breast cancer. This method employs single classifier model and bagging boosting for feature selection. Also, four algorithms SVM, Decision Tree, KNN and Logistic Regression which gives the highest accuracy of 88%. The system was trailed on BCGENES dataset. The performance of the system is measured on the basis of accuracy and precision. The incorporation of XAI methods, such as SHAP values and LIME, provides a nuanced understanding of feature importance and individual predictions, elevating model interpretability. The amalgamation of machine learning classifiers, hyperparameter tuning, and XAI techniques contributes to advanced screening tools, fostering trust among clinicians and researchers. © 2024 IEEE.

关键词： Logistic regression

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Navigating Breakthroughs in Pediatric Brain Stroke Detection: A Comprehensive Survey

Navigating Breakthroughs in Pediatric Brain Stroke Detection...

引用

2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Deepak, N.R. Paital, Santosh Kumar Verma, Saritha Choeyang, Tenzin Singh, Udit Kumar Bhavya Hkbk College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

Brain strokes, characterized by sudden interruptions in cerebral blood flow, pose a significant health concern, especially in children, where detection is intrinsically challenging due to limitations in existing methodologies. Recent attempts leveraging Machine Learning with Deep Learning techniques have grappled with accuracy and efficiency shortcomings in addressing this critical issue. In response, our innovative model integrates the Extreme Learning Machine (ELM), Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs) algorithms. Rigorously evaluated using a comprehensive dataset, our ELM + CNN + GAN model demonstrates unparalleled accuracy, precision, and recall rates, surpassing benchmarks set by conventional approaches like the Random Forest algorithm and the AlexNet + SVM hybrid model. Notably, our model excels in diagnostic speed, offering a promising avenue for swift and efficient early detection of pediatric brain strokes. The strategic incorporation of ELM, recognized for its efficiency in single layer neural networks, along with the analytical power of CNN for image analysis and GAN for synthetic data generation, establishes a robust and comprehensive model. By outperforming existing methods, our approach signifies a substantial leap forward in addressing this critical health concern, revolutionizing stroke detection with enhanced accuracy and speed. © 2024 IEEE.

关键词： Generative adversarial networks

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ConfigMaster: An Interactive Solution for System Management 8

ConfigMaster: An Interactive Solution for System Management

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Ganapathi, Ananya Sivakumar, Parikshith Divya, K.V. Elango, Aditya Gupta, Hardik Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331505462

The 'ConfigMaster: An interactive solution for system management' is a simple utility written using Bash scripting. It is designed for Unix-like systems. The tool lets users easily configure system settings and do administrative tasks without having to remember complex commands. The tool minimizes user's work by automating tasks. The tool can be used even by users with very little technical knowledge, as it has a simple menu-based interface to manage things like network settings, software packages, and wireless connections directly from the terminal. By automating common tasks, the tool makes system management more efficient and accessible. By automating common tasks, the tool makes system management more efficient and accessible. It's very useful to simplify system configuration on Unix-like systems using scripting languages like Bash. 'ConfigMaster' aims to offer a smooth user experience with the incorporation of core management principles from an operating system, such as disk scheduling, process synchronization, and memory management etc. Being flexible itself, this tool will bring further improvements to make effective responses to emerging user needs. The framework being flexible, it can be expanded to add new features to configure under different scenarios. By integrating essentials operating system operations, 'ConfigMaster' enables users to efficiently perform system configurations along with promoting ease of use. © 2024 IEEE.

关键词： Wi-Fi

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TexAVi: Generating Stereoscopic VR Video Clips from Text Descriptions

TexAVi: Generating Stereoscopic VR Video Clips from Text Des...

引用

2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Jayaraman, Shruti Bhavya, R. Srihari, Vriksha Mary Anita Rajam, V. College of Engineering Dept. of Computer Science and Engineering Guindy Chennai India

ISBN: (纸本)9798350376876

While generative models such as text-to-image, large language models and text-to-video have seen significant progress, the extension to text-to-virtual-reality remains largely unexplored, due to a deficit in training data and the complexity of achieving realistic dept. and motion in virtual environments. This paper proposes an approach to coalesce existing generative systems to form a stereoscopic virtual reality video from text. Carried out in three main stages, we start with a base text-to-image model that captures context from an input text. We then employ Stable Diffusion on the rudimentary image produced, to generate frames with enhanced realism and overall quality. These frames are processed with dept. estimation algorithms to create left-eye and right-eye views, which are stitched side-by-side to create an immersive viewing experience. Such systems would be highly beneficial in virtual reality production, since filming and scene building often require extensive hours of work and post-production effort. We utilize image evaluation techniques, specifically Fréchet Inception Distance and CLIP Score, to assess the visual quality of frames produced for the video. These quantitative measures establish the proficiency of the proposed method. Our work highlights the exciting possibilities of using natural language-driven graphics in fields like virtual reality simulations. © 2024 IEEE.

关键词： Virtual environments

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Automated characterization of coal image samples using pretrained deep convolutional neural networks and transfer learning: A comparative Study 3

Automated characterization of coal image samples using pretr...

引用

3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Samanta, Kalyan Firdaus, Shama Mohapatra, Subrajeet Bit Mesra Dept. of Computer Science & Engg Jharkhand Ranchi India

ISBN: (纸本)9798331528201

Quality of coal are usually determined by manually examining samples under an optical microscope, and then conducting complex chemical tests for further grading. However, such methods are expensive, time-consuming, and dependent on the competency and expertise of the concerned petrologist. Preliminary quality assessment of coal can be performed with the help of image analysis and artificial intelligence-based techniques identifying the presence of coal maceral constituents. The usage of image processing-based methods for intelligent coal gradation is simpler, faster, and less expensive, and it eliminates the subjectivity of petrologists. However, the performance of traditional image processing-based coal gradation methods, on the other hand, is dependent on the accuracy of segmentation and feature extraction methods. Deep learning models, which do not require image segmentation or manual feature engineering, are an alternative solution to the above classification problem. Pretrained deep neural networks eliminates the time-consuming process of determining the best combination of layer architecture, which would be required by standard Convolutional Neural Network models. As a result, the proposed research presents an automated coal gradation system that utilizes pretrained deep convolutional neural networks to classify coal microscopic images. Six different types of pretrained neural networks viz. VGG19, VGG16, GoogLeNet, AlexNet, ResNet50, ResNet18 are simulated over 1700 Australian coal optical microscopic images of three grades. For the considered coal data set, all of the pretrained models achieve classification accuracy of over 80%. © 2024 IEEE.

关键词： Convolutional neural networks

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Boosting Skin Cancer Detection Accuracy: A Hierarchical Ensemble Approach with Feature Fusion

Boosting Skin Cancer Detection Accuracy: A Hierarchical Ense...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Kolachina, Srinivasa Kranthi Kiran Agada, Ruth Yan, Jie Bowie State University Dept. of Computer Science Bowie United States

ISBN: (纸本)9798350362480

Accurate skin lesion classification is crucial for early detection and treatment of skin cancer. However, achieving high diagnostic accuracy remains challenging due to inter-lesion variability, class imbalance, and the need for robust feature extraction. To address these challenges, we propose a hierarchical ensemble approach that integrates lesion masking, feature extraction from multiple pre-trained models, feature fusion, and class imbalance mitigation. Our methodology significantly improved skin lesion classification accuracy compared to single-model architectures. Balancing class distributions further enhances performance, with the fusion of ResNet50, DenseNet121, and AlexNet models achieving an accuracy of 91% on the HAM10000 dataset. The ensemble models demonstrated varying robustness across datasets, highlighting the importance of dataset-specific tuning for optimal performance. Feature quantity showed a moderate positive correlation with precision, suggesting a potential link between representational capacity and precise predictions. These findings pave the way for robust and accurate skin lesion diagnosis, potentially supporting early skin cancer detection and clinical decision-making. © 2024 IEEE.

关键词： Lung cancer

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An Approach to Automatic Leaf Recognition of Bangladeshi Plants Using Image Processing Techniques and Random Forests 2

An Approach to Automatic Leaf Recognition of Bangladeshi Pla...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Hassan, Reyad Akter, Sameya Rahman, Md Obaidur Faridpur Engineering College Dept. of Computer Science and Engineering Faridpur Bangladesh

ISBN: (纸本)9798331517984

This study proposes a novel approach for identi-fying plant leaves using a combination of handcrafted visual leaf image features(shape, color, texture, and preliminary vein properties), their extraction strategies (based on image processing and statistical measures), and classification methods. To do so, a 5-step approach is presented comprising image acquisition, image pre-processing, feature extraction, classification steps, comparison, and evaluation. The presented approach is tested on publicly available standard leaf image datasets such as Flavia, Swedish, and our self-collected BDLeaf20 dataset comprising 20 Bangladeshi local plant species which include fruit, flower, and medicinal plants bearing a total of 1072 leaf images. The plant species included in the BDleaf20 dataset vary in terms of their rarity and popularity within the region. To investigate the effectiveness of the proposed approach, Random Forests classifier is used achieving accuracy scores of 97.21 %,99.11 %, and 93.17% respectively on the Flavia, Swedish, and BDLeaf20 datasets. © 2024 IEEE.

关键词： Random forests

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Optimizing Human Activity Recognition for Precision in Yoga Practice Analysis 8

Optimizing Human Activity Recognition for Precision in Yoga ...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Salian, Supriya Preethi Salian, K. Rodrigues, Maria Viola Ritesh Muhammed Elham, P.N. Mannan, Mohammed Abdul St. Joseph Engineering College Vamanjoor Dept. of Computer Science and Engineering Mangaluru India Dept. of Information Science and Engineering Udupi India

ISBN: (纸本)9798350350593

The proposed system on enhancing human activity recognition in yoga presents an innovative solution using advanced technologies to accurately and quickly analyze data of yoga poses. Through meticulous data collection and preprocessing, including noise removal, normalization, and handling missing data, the system ensures a clean and standardized dataset. Pose estimation algorithms contribute to accurately mapping the body's posture, extracting essential features like joint angles and body orientation. The features form the basis for a powerful machine learning model, trained on a labelled dataset and employing neural networks, enabling the system to recognize yoga poses with remarkable precision. The study embraces an iterative enhancement process, continuously refining the model based on feedback and performance metrics, ensuring ongoing improvements in accuracy. Ultimately, the proposed system represents a cutting-edge solution for advancing human activity recognition in yoga, providing practitioners with real- time feedback and guidance, fostering improved techniques, and enhancing the overall yoga experience. © 2024 IEEE.

关键词： Mapping

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