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A Novel Generative AI-Powered Approach for Sentiment Analysis: Enhancing Deep learning Models with Contextual Understanding 3

A Novel Generative AI-Powered Approach for Sentiment Analysi...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Naik, Mahendra Shridhar Chaitra, S.N. Shashikala, K.S. New Horizon College of Engineering Department of Electronics & Communication Bengaluru India Gm Institute of Technology Department of Electronics & Communication Davangere India New Horizon College of Engineering Department of Artificial Intelligence & Machine Learning Bengaluru India

ISBN: (纸本)9798331515911

Sentiment analysis, a pivotal aspect of natural language processing (NLP), has seen notable advancements with the advent of deep learning models. Despite these advancements, challenges persist in accurately capturing nuanced emotions and understanding context-dependent sentiments. This paper introduces an innovative method that integrates generative adversarial networks (GANs) into deep learning architectures to enhance sentiment analysis. By leveraging GAN's ability to generate contextually relevant text, the proposed approach enriches the training data, thereby improving the model's ability to discern subtle sentiment variations. Our extensive experiments on benchmark datasets reveal that our method surpasses traditional deep learning models, achieving a 5% improvement in accuracy, a 7% higher F1-score, and demonstrating superior robustness to sarcasm and irony. © 2025 IEEE.

关键词： Generative adversarial networks

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Partial Convolution U-Net for Inpainting Distorted Images 15

Partial Convolution U-Net for Inpainting Distorted Images

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

作者： Rashmi Adyapady, R. Annappa, B. Sagar, Prem Nmam Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Nitte India National Institute of Technology Karnataka Discover Lab Department of Computer Science and Engineering Karnataka Mangalore India

ISBN: (纸本)9798350370249

Image inpainting is a domain in which researchers have shown considerable interest, and when it comes to deep learning techniques, realistic problems become interesting and challenging. In image inpainting, a corrupted facial image with missing holes or significant holes can be restored and compared to the original image to see if it is real or fake. In addition to fixing the texture of the image and getting the image's high-level abstract properties, it may also recover semantic images such as human faces. In the field of image-inpainting models, the Attention model with features learned through semantic approaches and progressive networks has become particularly popular. The proposed model introduces (i) Attention blocks in each decoder layer of U-Net architecture and (ii) a hybrid loss function leveraging both Mean Square Error (MSE) and Mean Absolute Error (MAE). The proposed Attention-based U-Net showed remarkable performance with SSIM and PSNR by 0.1067 and 13.63, respectively, compared to the previous approaches. © 2024 IEEE.

关键词： Distorted Images Image Inpainting Partial Convolution U-Net

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Hybrid Deep learning with TSOA Model for Predicting the Parkinson Disease using Speech Features 2

Hybrid Deep Learning with TSOA Model for Predicting the Park...

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2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Pareek, Piyush Kumar Zanke, Pankaj Prasad, Anoop N. Mohan, B.R. Nitte Meenakshi Institute Of Technology Artificial Intelligence And Machine Learning Bengaluru India Sapiens International Corporation Israel East West Institute Of Technology Department Of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350381665

True early identification of Parkinson's illness (PD) is absolutely fundamental to stopping the ill health course and planning affected role with access to disease-modifying medications. To do this, significant attention should be paid to the premotor stage of PD. Based on premotor characteristics, a unique deep-learning algorithm is provided to swiftly detect whether or not a person has PD. By identifying and assessing indicators that indicate speech attribute and degree of worsening, a voiced numerical biomarker may supplement the laborious traditional physical test. The project attempts to develop a revolutionary end-to-end deep learning model using speech data for Parkinson's disease identification. The current study employs a mixed deep learning model to evaluate and identify PD. The study's objectives were to identify vocal anomalies in PD patients and create an automatic test technique that can distinguish concerning PD patients' voices and those of fit informs while also grading voice quality. his study investigates the effectiveness of utilizing deep learning architectures for speech data analysis in the context of [insert context]. Specifically, we employ a convolutional neural network (CNN) architecture with recurrent layers (e.g., Long Short-Term Memory, LSTM) to extract hierarchical features from raw speech signals. Our methodology involves preprocessing the raw audio data to extract relevant acoustic features, such as mel-frequency cepstral coefficients (MFCCs) and spectrograms. These features are then fed into the CNN-LSTM model, which learns discriminative representations for various speech tasks, including [mention specific tasks, e.g., speech recognition, emotion recognition, etc.].The category accurateness was evaluated using the Italian PVS speaking quantities and the Lithuanian PD voice dataset. With values of 0.92 showing that the results are medically adequate, the findings demonstrated that the suggested model is highly generalizable. © 2024

关键词： Network architecture

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From 5G to 6G: The Role of AI, machine learning, and Deep learning in Wireless Systems 4

From 5G to 6G: The Role of AI, Machine Learning, and Deep Le...

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

作者： Gote, Pradnyawant M. Kumar, Praveen Verma, Prateek Yesankar, Prajyot Pawar, Adesh Saratkar, Saniya Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

The swift progression of wireless communication technologies-specifically from 5G to 6G is an approach that could be the most significant revolutionary leap towards changing connectivity and data transmission forever. Additionally, algorithms of machine learning (ML) and deep learning (DL) in 5G and 6G can offer promising advancements toward predictive maintenance, dynamic spectrum management, and enhanced security. The review of the potentially transformative nature of these technologies underlines the need for future directions of research efforts called for in circumventing challenges towards making full capability exploitation of 6G possible. © 2025 IEEE.

关键词： Adversarial machine learning

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Analyzing the Feasibility of Bert Model for Toxicity Analysis of Text 6th

Analyzing the Feasibility of Bert Model for Toxicity Analysi...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Chakraverty, Yuvraj Kaintura, Aman Kumar, Bharat Khanna, Ashish Sharma, Moolchand Pareek, Piyush Kumar Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology New Delhi India Department of Artificial Intelligence and Machine Learning and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9789819940707

Online comments can often be toxic, offensive, and harmful to individuals and communities. In recent years, there has been a growing need to automatically identify and mitigate these toxic comments. For this problem, NLP models are often used to identify such toxicity and harshness but each model has its own efficiency and performance limitations. In this paper, we propose the use of the bidirectional encoder representations from transformers (BERT) algorithm for toxicity classification of online comments. BERT is a state-of-the-art natural language processing model developed by Google in 2018 that has shown strong results on a variety of tasks. In this paper, we used the BERT algorithm for toxicity classification and evaluated its performance on a real world dataset and performed comparative analysis with conventional NLP models, logistic regression (TF-IDF) over which BERT showed an improvement of 6.9% in accuracy, 26.1% in f1-score, 21.5% in ROC score;logistic regression (BOW) over which BERT showed an improvement of 9.1% in accuracy, 70.6% in f1-score, 39.8% in ROC score;multinomialNB (BOW) over which BERT showed an improvement of 9.2% in accuracy, 25.9% in f1-score, 10.6% in ROC score. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Natural language processing systems

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Secure Electronic Health Record Storage in the Cloud Based on Multiple Fragmentation and Reconstruction Using Blockchain with Cryptographic Techniques 4

Secure Electronic Health Record Storage in the Cloud Based o...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Mercy, W. Annabel, L. Sherly Puspha Kcg College of Technology Anna University Dept. of Cse Chennai India St. Joseph's College of Engineering Dept. of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331529635

Data privacy is an important factor in cloud data storage. Still there are some security issues such as hacking that may take place with respect to data. In the existing techniques the time and cost consumption are more in computing and data storage. Data privacy is a challenging one against hacking and to avoid hacking data can be fragmented over multiple locations when storing and reconstructed while accessing the data. In the existing implementation single field query is used to access the Electronic Health Records (EHR). It will take more time to fetch the data and the fetched data also will be huge with some unwanted data. This paper proposes multi field skyline conditional query to access the accurate EHR data for minimizing the accessing time to improve the performance by implementing B+-Tree indexing and skyline query. Cloud data storage privacy also will be improved with Cryptography and Blockchain techniques. Secure Hashing Algorithm (SHA 512), Consensus algorithm and Smart Contracts are used for data integrity and fault tolerance mechanism. The possible goals include effective data storage and access, reducing security breach, storage overhead, avoiding hacking, enhancing data privacy. © 2024 IEEE.

关键词： B+ - Tree Blockchain Cloud Storage Consensus algorithm Data Fragmentation Data Reconstruction Electronic Health Records Secure Hashing Algorithm Skyline Query Smart Contracts

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Optimizing Medical Image Classification: Leveraging Advanced Segmentation Models for Enhanced Object Detection in Real-World Scenarios

Optimizing Medical Image Classification: Leveraging Advanced...

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2024 International Conference on Future Telecommunications and artificial intelligence, IC-FTAI 2024

作者： Elrashidy, Nore Elsabawy, Nourhan Mohamed Elnakeeb, Alaa Ebraheem Hussien, Alaa Menisy, Mai Saeed Elkhateb, Abdelkareem El Sappagh, Shaker Kaferelshikh University Retrieval Faculty of Artificial Intelligence Dept. Machine Learning and Information Kaferelshikh Egypt Intelligence Kaferelshikh University Faculty of Artificial Dept. Biological Artificial Intelligence Kaferelshikh Egypt Galala University Computer Engineering Suez Egypt

ISBN: (纸本)9798331534080

In medical imaging, classifiers typically rely on objects of interest, such as nodules, being centered and fully visible within the frame to effectively learn the underlying relationships necessary for accurate classification. However, in real-world scenarios, such conditions are not always met, leading to challenges in classification accuracy. This paper explores the enhancement of classifier performance by incorporating advanced segmentation models, including SAM, SAM2, and Dinov2, all of which are pretrained for automatic semantic segmentation. We compare the results of integrating these modelsto preprocess and segment medical images, thereby improving the classifier's ability to correctly identify and classify objectsin non-ideal conditions. The study aims to demonstrate how leveraging state-of-the-art segmentation techniques can bridge the gap between idealized assumptions and real-world medical data, ultimately leading to more robust and accurate classificationoutcomes. © 2024 IEEE.

关键词： classification Dinov2 SAM2 SAMl segmentation

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A Review on the Applications of machine learning Algorithms for Mental Stress Detection 2

A Review on the Applications of Machine Learning Algorithms ...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Motghare, Renuka Bhoyar, Shivam Verma, Prateek Tale, Abhay Barhate, Aditya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

Stress, a widespread issue affecting both individual well-being and societal productivity, has attracted significant research interest. Defined as a state of mental or emotional strain, stress arises from various factors such as work, relationships, and health concerns, manifesting physically, emotionally, and behaviorally. This paper provides a review of machine learning (ML) techniques to detect mental stress using physiological signals like ECG, GSR, and EEG. Through a review of existing studies, the paper highlights the progress made in this area and the challenges that remain, particularly concerning data quality, algorithm accuracy, and the practical deployment of stress monitoring devices. The study underscores the potential for real-time monitoring and personalized interventions, especially in daily activities. Future research should focus on enhancing model robustness, integrating multiple data sources, and addressing real-world applicability. This paper emphasizes the importance of early stress identification and personalized treatment to improve mental health outcomes © 2024 IEEE.

关键词： Adversarial machine learning

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Dynamic Channel Allocation Using Reinforcement learning Algorithm for Multiple Input Multiple Output Systems 3

Dynamic Channel Allocation Using Reinforcement Learning Algo...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Bittla, Srinivasa Rao Riadhusin, Raami Divyaraj, G.N. Aravindh, S. Ahila, B. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering chennai India Dhanalakshmi Srinivasan College of Engineering Technology Department of Artificial Intelligence and Data Science Mamallapuram India

ISBN: (纸本)9798331508456

In recent years, Multiple-Input Multiple-Output systems (MIMO) play a crucial role in modern wireless networks by enhancing spectral efficiency and data rates. Traditional static, heuristic-based allocation methods struggled to adapt to real-time variations, handling high-dimensional state spaces, minimizing interference, and ensuring fairness in channel allocation. However, decision-making in dynamic environments requires intelligent and scalable approach that enables real-time decision-making by efficiently learning and adapting to changing network conditions. To overcome these issues. This research proposed Deep Q-Network with Prioritized Experience Replay (DQN-PER) for dynamic channel allocation for MIMO Systems. DQN-PER prioritizes high-value learning experiences and while also improving training efficiency and convergence speed. PER prioritizes important experiences based on Temporal-Difference (TD) error by allowing the model to focus on more informative transitions, which speeds up learning and improves adaptability. Simulation results demonstrate enhanced spectral efficiency, reduced packet loss, and improved Quality of Service (QoS) in dynamic wireless environments. These findings establish DQN-PER as a promising solution for intelligent and adaptive MIMO resource allocation. The proposed method achieved better results in terms of a packet drop rate 0.04 compared to existing method Advantage Actor-Critic (A2C). © 2025 IEEE.

关键词： Reinforcement learning

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Interpreting Hand gestures using Object Detection and Digits Classification

arXiv

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arXiv 2024年

作者： Sangeetha, K. Balaji, V.S. Kamalesh, P. Anirudh Ganapathy, P.S. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Thandalam Chennai602105 India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Thandalam Chennai602105 India

Hand gestures have evolved into a natural and intuitive means of engaging with technology. The objective of this research is to develop a robust system that can accurately recognize and classify hand gestures representing numbers. The proposed approach involves collecting a dataset of hand gesture images, preprocessing and enhancing the images, extracting relevant features, and training a machine learning model. The advancement of computer vision technology and object detection techniques, in conjunction with OpenCV's capability to analyze and comprehend hand gestures, presents a chance to transform the identification of numerical digits and its potential applications. The advancement of computer vision technology and object identification technologies, along with OpenCV's capacity to analyze and interpret hand gestures, has the potential to revolutionize human interaction, boosting people's access to information, education, and employment opportunities. © 2024, CC0.

关键词： Object detection

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