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Smart Mood Detection: Real-Time Emotion Analysis for Human-computer Interaction Using Deep Learning

Smart Mood Detection: Real-Time Emotion Analysis for Human-C...

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2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025

作者： Chandrappa, S. Banerjee, Tapabrata Saha, Sohan Zaid, Mohd Aditya Pai, H. Guru Prasad, M.S. Rajesh, I.S. Dept. of Computer Science and Engineering Bengaluru India Bengaluru India MIT Art Design and Technology University Department of CSE Pune India Department of Computer Science and Engineering Dehradun India BMS Institute of Technology and Management Dept. of AI & ML Bengaluru India

ISBN: (纸本)9798331527518

A convolutional neural network (CNN)-based face emotion recognition system that analyzes human emotions in real-time, including happy, sorrow, rage, and surprise, is described. Existing approaches often struggle with accurately recognizing subtle or complex emotions due to limited feature extraction capabilities and the need for large, diverse training datasets. Additionally, they face challenges in adapting to real-world conditions, such as variations in lighting, angles, and occlusions. In order to improve the model's generalizability, the proposed research used a preprocessing pipeline that included gray scale conversion, scaling, normalization, and augmentation techniques including rotation and flipping on a labeled and balanced dataset of facial photos. Dropout and regularization techniques were used to reduce over fitting, and the CNN architecture was meticulously adjusted to maximize speed. High accuracy and resilience across emotion categories are demonstrated by preliminary results, indicating the model's potential for real-world uses in security surveillance, healthcare, and human-computer interaction. © 2025 IEEE.

关键词： Convolutional neural networks

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Unmasking the AI Hand: A Machine Learning Approach to Deciphering Authorship 3

Unmasking the AI Hand: A Machine Learning Approach to Deciph...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Bhandarkar, Abhay Mushtaq Ahmed, D.M. Vishwachetan, D. Mushtaq, Arfaat Kadam, Dhrithi Saxena, Salaj Ramaiah Institute of Technology Dept. of Computer Science and Engg Bangalore54 India Ramaiah Institute of Technology Dept. of Information Science and Engg Bangalore54 India

ISBN: (纸本)9798350381931

In an era of explosive data growth, accurate text analysis tools are paramount. The surge of AI-generated texts presents a pressing challenge: discerning human-written content from AI-generated content. This affects text analysis accuracy, necessitating innovative solutions. We introduce an AI-driven model within the Model-View-Controller (MVC) architecture, categorising text as AI-generated or human-authored. Leveraging supervised learning, the model uses Count Vectorizers and Multinomial Naive Bayes algorithm for training and classification. The proposed model shows a good accuracy of 86.2%. This, with enhanced precision and F1 scores, underscores efficacy in classifying text origins. Beyond its immediate contributions, our model promises to reshape content authenticity verification, from AI-generated social media posts to verifying news sources. The model offers future directions, advancing AI-generated content detection. As AI-generated content evolves, our approach ensures credibility and reliability in text analysis within the MVC framework, addressing the critical need to navigate the AI-generated content landscape. © 2024 IEEE.

关键词： Reliability analysis

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Foreign Exchange Market Analysis based on Novel Grey Wolf based Extreme Learning Machine Predictor 4

Foreign Exchange Market Analysis based on Novel Grey Wolf ba...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Dash, Swaty Sahu, Pradip Kumar Mishra, Debahuti Veer Surendra Sai University of Technology Burla Dept. of Information Technology Odisha Sambalpur India University Dept. of Computer Science & Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350349856

Foreign exchange trading basically bridges a gap between buyer and seller to transact at a set of prices of the currencies to make profit out of it by the traders and investors. In this paper, foreign exchange predictive models are explored based on basic Extreme Learning Machine (ELM) along with optimized version of ELM. Initially, this work tried to obtain the best optimized ELM network based upon the four nature-inspired optimization methodologies such as;particle swarm, ant colony, moth flame and grey wolf optimization (GWO). From this phase, it has been observed that the ELM network optimized with GWO outperforms rest of other experimented optimized ELM networks with respect to error graphs and performance metrics. In order to evaluate the performance, the ELM-GWO currency exchange predictor, the recognition performance metrics such as;EVS, MAE, MSE and R2 Score are computed, the error graphs are obtained and the average computational time taken are compared. © 2024 IEEE.

关键词： Ant colony optimization

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Design of a High Voltage Common Mode Resilient FlexRay Receiver in 180nm/5V CMOS Process 6

Design of a High Voltage Common Mode Resilient FlexRay Recei...

引用

6th Iranian International Conference on Microelectronics, IICM 2024

作者： Mansoury, Hamid Sadat Saeedi, Saeed Atarodi, Mojtaba Sharif University of Technology Dept. of Electrical Engineering Tehran Iran Tarbiat Modares University Dept. of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331511371

A FlexRay receiver that is capable of operating within [-60V, 60V] common mode range using a common mode cancellation method is presented in this paper. The receiver uses 5V MOSFET transistors and incorporates a digital block to reduce the required number of comparators to 2, potentially leading to lower power consumption and die area. Extensive simulations have been conducted to ensure the receiver's functionality within a temperature range of [-40°C, 150°C], which is particularly crucial for automotive applications. Furthermore, corner and mismatch simulations have been performed to ensure the circuit's process tolerance and suitability for mass production. Additionally, Electromagnetic Immunity simulations have been performed to ensure that the receiver, intended for automotive applications, meets the necessary standards. The layout of the receiver occupies an area of 153.6 × 128.7 μm2.. © 2024 IEEE.

关键词： CMOS integrated circuits

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Real-Time Sign Language to Speech Converter Using OpenCV and MediaPipe 5

Real-Time Sign Language to Speech Converter Using OpenCV and...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Pathak, Anvisha Patil, Niraj Padhy, Prashant Jadhav, Adarsh Rukhande, Smita Gadhikar, Lakshmi Fr. Conceicao Rodrigues Institute of Technology Dept. of Information Technology Navi Mumbai India Fr. Conceicao Rodrigues Institute of Technology Dept. of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798331522100

Communication barriers between deaf individuals and hearing people can lead to several significant problems such as social integration, reduced access to information and reduced opportunities thereby affecting quality of life of deaf individuals. Use of sign language for communication serves as the primary means to address these challenges. However, understanding of the sign language is very difficult for hearing individuals. To address this issue, we present a Real-Time Sign Language to text and Speech Converter to facilitate effective communication between deaf and hearing individuals. This paper presents a novel approach to sign language recognition using OpenCV and MediaPipe. The main aim of the paper is to develop real time computer vision that can translate sign language gestures into text in real-time. We use image processing techniques to segment and analyze sign gestures, OpenCV to record and process these gestures, MediaPipe for hand tracking and gesture prediction and a text-to-speech engine to translate the text into speech for auditory communication. Thus, this study offers a comprehensive sign language to speech converter system that has the potential to significantly improve communication between the deaf community and the hearing people. © 2025 IEEE.

关键词： Gesture recognition

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Information technology Risk Management Model for Remote Control Vehicles 14

Information Technology Risk Management Model for Remote Cont...

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14th International Conference on Information and Knowledge technology, IKT 2023

作者： Naji, Hamid Reza Ayati, Aref Graduate University of Advanced Technology Dept. of Computer Engineering and Information Technology Kerman Iran

ISBN: (纸本)9798350349412

This paper presents a lightweight and quick IT risk management model for Remote Control Vehicles (RCVs). The increasing use of RCVs in many fields and the existence of different threats are the main reasons for having an efficient risk management method in this field. This paper will cover the information technology risks based on a continuous, iterative, and cyclic flow. This structure is adaptable to agile methods and decreases the chance of losing data, failing any equipment or system, and losing the reputation of the enterprise. Our risk management model is based on qualitative measures and can cover most IT-based risks in this area. © 2023 IEEE.

关键词： Drones

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A Convolution Neural Network Based Classifier for Discerning AI Generated Images and Real Images 4

A Convolution Neural Network Based Classifier for Discerning...

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

作者： Kumar, Uppala Eswar Basava Vivekreddy, Chintakuntla Sujatha, Kamepalli Vignan's Foundation for Science Technology & Research Deemed to be University Dept. Information Technology & Computer Applications Guntur India

ISBN: (纸本)9798350354218

The exponential advancement of artificial intelligence (AI) has led to the creation of synthetic images that closely resemble real-world photographs. However, the widespread distribution of AI-generated content poses significant challenges in distinguishing between genuine and synthetic imagery, particularly in cybersecurity, content moderation, and image forensics. Traditional image classification methods struggle with this task due to the striking visual similarities between the two types of images. Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, leveraging their ability to extract complex features from raw pixel data. In this study, we propose an enhanced CNN methodology to differentiate between AI-generated and real images. Specifically, we focus on three popular CNN architectures: ResNet-50, RexNet-150, and RexNet-200. By augmenting these models with advanced architectural enhancements and training strategies, we aim to improve their discriminative capabilities. Our experimental findings reveal that RexNet-150 surpasses ResNet-50 and RexNet-200, achieving superior accuracy in distinguishing between AI-generated and real images. RexNet-150 attained an impressive 97.9% accuracy on the test dataset, outperforming ResNet-50 (93.1%) and RexNet-200 (90.3%). Moreover, RexNet-150 demonstrated enhanced training convergence and speed, indicating its effectiveness in learning discriminative features for image classification tasks. The superior performance of RexNet-150 holds significant implications for online safety, content moderation, and image authenticity detection. These advancements in CNN technology foster trust in digital imagery and enhance confidence in AI applications across diverse domains. © 2024 IEEE.

关键词： Convolutional neural networks

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DeepStock Forecast: Unveiling Market Movements through Advanced Deep Learning Models 4

DeepStock Forecast: Unveiling Market Movements through Advan...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Das, Smruti Rekha Mishra, Debahuti Lenka, Arundhati Shaw, Kailash Gitam Deemed to Be University Dept. Computer Science and Engineering Andhra Pradesh Visakhapatnam India Siksha 'o' Anusandhan Deemed to Be University Odisha Dept. Computer Science and Engineering Odisha India Dept. of Aiml Symbiosis Institute of Technology Pune India

ISBN: (纸本)9798350349856

This study uses deep neural network (DNN) methodologies, including auto encoder, deep belief network (DBN), and backpropagation neural network (BPNN), to predict stock prices over 15 and 30 days. Utilizing BSE Sensex and NSE Sensex datasets with diverse technical indicators, the research highlights DNN's superior training accuracy, measured by minimal mean squared error (MSE). The study's key contribution is applying DNN to predict stock market volatility. While acknowledging the limitation of relying solely on training error, the study incorporates additional measures like RMSE, MAPE, MAE, and ARV during testing. Notably, DNN's MAPE results fall within a narrow range (0.0221 to 0.0255), indicating minimal error deviation. In a comprehensive evaluation across all datasets, DNN consistently outperforms DBN and BPNN in both training and testing performance. © 2024 IEEE.

关键词： Deep neural networks

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Denial-of-Service Attack Detection and Mitigation Strategy for Synchrophasor Data Flowing in Power Transmission System 4

Denial-of-Service Attack Detection and Mitigation Strategy f...

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4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Nath, Soumya Ranjan Bhalja, Bhavesh R. Sidhu, Tarlochan S. Indian Institute of Technology Dept. of Electrical Engineering Roorkee India Ontario Tech University Dept. of Electrical and Computer Engineering Oshawa Canada

ISBN: (纸本)9798350383997

The coupling of the legacy power infrastructure with complex computer networks and the integration of communication devices has led to the problem of cyberattacks, which in turn may induce partial/full blackouts. In this regard, an approach based on a Decision Tree for the detection and mitigation of Denial-of-Service (DoS) attacks is proposed in this paper. Based on the patterns and effects observed during DoS attacks on the Synchrophasor data flowing from the Phasor Measurement Unit (PMU) to the Phasor Data Concentrator (PDC), various supervised machine learning models are employed, which classify healthy and attack data and detect DoS attacks. Thereafter, a recurrent deep learning network employing Gated Recurrent Unit (GRU) architecture is utilized to reconstruct the lost data, which in turn is helpful in mitigating the impacts of a DoS attack. The suggested algorithms are validated using a real-world scenario of a communication dataflow between multiple PMUs placed at different buses of a power transmission network and a PDC present at a control center, representing a wide-area network, and are incorporated using Real Time Digital Simulator and openPDC. By comparing various metrics of ML classification, the results indicate that the decision tree based model gives better accuracy along with the least detection time compared to other machine learning-based models. The suggested GRU-based mitigation algorithm can reconstruct the DoS-impacted lost data from the available pre-attack phasors. © 2024 IEEE.

关键词： Phasor measurement units

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Federated Learning-based Techniques for Healthcare IoV: Challenges and Future Directions -A Knowledge Review 9

Federated Learning-based Techniques for Healthcare IoV: Chal...

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

作者： Narsimhulu, Pallati Sahay, Rashmi Dept. of Cse Hyderabad India Chaitanya Bharathi Institute of Technology Department of Computer Engineering And Technology Hyderabad500075 India

ISBN: (纸本)9798350394474

Federated Learning (FL) is a cutting-edge method for creating Machine Learning (ML) models using networked data sets. It supports advanced Artificial Intelligence (AI), keeps information neighborhoods, protects personal information, and allows for ownership. FL's features make it a good candidate for IoT networks due to its inherent distributed infrastructure. On the other hand, FL provides several challenges, the most significant of which is training on IoT devices with mostly heterogeneous data sources. The diversity of devices and algorithms in complex Internet of Vehicles (IoV) networks significantly impacts the FL training process and renders traditional FL unsuitable for direct deployment. While numerous recent publications claim to lessen the negative impacts of variety in FL networks, the efficacy of these proposed ways has never been tested and assessed. In this study, we examine the impact of heterogeneity in FL in dept. and provide an overview of the actual challenges imposed by the system and statistical heterogeneity. We researched on cutting-edge algorithms, emphasizing their practical application in IoT networks. We also compared the popular FL algorithms on a dynamic IoV network. Our study shows that existing methods fail to alleviate the issue appropriately, emphasizing the significance of considering system and statistical diversity while developing FL systems. To properly investigate the consequences of the network heterogeneity, we tested 10%, 30%, 50%, 70%, and 90% stragglers. These conditions serve as a training ground, allowing us to see how various degrees of network heterogeneity impact a technique's efficiency. The test-bed results show that the Scafold technique outperforms existing techniques in terms of training time reduced to 18% and accuracy increased to 21% © 2024 IEEE.

关键词： Learning systems

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