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SARCASAM Analysis in Social Networks Using Deep Learning Algorithm

Procedia computer Science 2025年 252卷 510-518页

作者： Priya M Vijaya kumar K Vennila P Prasanna M A Professor Dept. of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam -611002 Lecturer Dept. of Computer Science and Engineering Women’s Engineering College Puducherry- 605008 Asst. Professor Dept. of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam -611002 Asst. Professor Dept. of Computer Science and Engineering K.Ramakrishnan college of Technology Trichy -621112

Twitter is the one of the biggest social media sites, where users may share their thoughts, ideas, and opinions as well as discuss current events and live tweets. In the subject of opinion mining, creating reliable and effective algorithms for sarcasm detection on Twitter is an intriguing task. Sarcasm is the use of positive language to convey depressing emotions while speaking in opposition to one’s own intentions. Sarcasm is frequently employed on social networking and micro blogging platforms, where users can offend others and find it difficult to express their true feelings. The deep learning technique utilised in the current algorithms to identify these sarcastic tweets has the limitation of not being able to predict continuous variables. A novel deep learning algorithm is proposed to identify both positive and negative terms as well as sarcasm in comments. Deep neural networks are used to classify the comments into positive and negative word categories. Customers’ opinions are mined using sentiment analysis to find and extract information from the text. Sarcastically stated statements from social networking sites can be quickly categorised and recognised by using VADER (Valence Aware Dictionary and Sentiment Reasoner).

关键词： Data analytics Sarcasm Lexical dictionary N-gram Deep learning Text mining

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Advanced Medical Question and Answering Chatbot Powered by Llama-2 For Precis Health Answer 2

Advanced Medical Question and Answering Chatbot Powered by L...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Patni, Jagdish Chandra Anup, S.B. Swetha Chitra, K.S. Poornima, B.M. Raviprakash, B.P. Kant Verma, Kamal Dept. of CSE Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering School of Technology GSFC University Gujarat Vadodara India School of Computer Science IILM University Greater Noida India

ISBN: (纸本)9798331530389

LLaMA-2 model, which is known for having the best capabilities in natural language processing at the core of our system. With respect to symptom diagnosis, it ranked among the most accurate in being able to give the chances of as many conditions possible as per the information that it receives from the user. Creating custom responses to unique user queries all the time, which may potentially increase user satisfaction and engagement. We have tested and perfected our chatbot to ensure that the capabilities stood out at a level of accuracy as high as 85 percent-showing outstanding care in the matters of healthcare assistance issues under numerous medical topics. Our work adds advanced features with the improvement techniques to the conventional medical Q and A chatbot. The new deep learning chatbot is not only great for symptom diagnosis but also uses its capabilities to provide context-aware responses based on user health history, lifestyles, and preferences with adjustment of its advice for a more personal healthcare experience. © 2025 IEEE.

关键词： Chatbot LLaMA-2 model medical Question and Answers Natural language processing (NLP) symptom identification

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Hybrid Quantum Approximate Optimization Algorithm (HQAOA) for Efficient Blockchain Transaction Scheduling 3

Hybrid Quantum Approximate Optimization Algorithm (HQAOA) fo...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Paul, Soumyadip Chakraborty, Sarit Government College of Engg. & Ceramic Technology Dept. Of Information Technology WB Kolkata 700010 India Government College of Engineering and Leather Technology Dept. of Computer Science and Engineering WB Kolkata 700106 India

ISBN: (纸本)9798331523893

Quantum technology offers a transformative approach to solving complex computational challenges in decentralized systems, particularly in blockchain transaction scheduling. Efficient transaction scheduling is critical in distributed ledger systems, which often face challenges like dynamic transaction loads. This work proposes a novel approach namely Hybrid Quantum Approximate Optimization Algorithm (HQAOA) which combines Multi-Agent QAOA (MA-QAOA) and Adaptive Layer QAOA (AL-QAOA) to provide a robust solution to such issues. HQAOA leverages a decentralized optimization framework where multiple agents, each representing a node in the network, optimize local transaction scheduling while considering global constraints. Additionally, HQAOA adapts to varying transaction loads by dynamically adjusting the number of quantum layers for each agent, ensuring computational efficiency under different conditions. Such dynamic layer adjustment mechanisms mitigate the adverse effects of quantum noise and ensures optimal performance in scenarios with fluctuating transaction loads. In contrast to traditional QAOA, which struggles with noise and scaling issues, HQAOA provides improved performance by balancing the complexity of the problem with available quantum resources. Experimental results signify the future potential of HQAOA to outperform QAOA, particularly in noisy environments and irregular transaction conditions. This work demonstrates the potential of HQAOA to optimize blockchain transaction scheduling in decentralized systems. © 2025 IEEE.

关键词： AL-QAOA Blockchain HQAOA MA-QAOA Quantum computing

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Smith Predictor Based Fractional LFC Strategy for Storage Supported Thermal Power System Amid Communication Dead Time and Cyber Threats

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IEEE Transactions on Industry Applications 2025年第3期61卷 4515-4529页

作者： Kumar, Deepak Raja, G. Lloyds Alkhatib, Mohamed Deep, Akash Kumar Muduli, Utkal Ranjan United Arab Emirates University Department of Electrical and Communication Engineering Abu Dhabi Al Ain15551 United Arab Emirates National Institute of Technology Department of Electrical Engineering Patna800005 India Khalifa University APEC Dept. of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates

Communication dead time (CDT) amid integration of renewable energy sources (RESs) and energy storage systems (ESSs) into modern power system networks can result in instabilities, phase lags and sluggish corrective action when dealing with frequency and tie-line power deviations. To compensate CDT, a Smith predictor based Fractional Order Proportional-Integral-Derivative with Integrator (FPIDI) controller is analytically designed for a dual-area thermal power system (TPS) with integrated RES, battery ESS, flywheel ESS and Ultra-capacitors. The FPIDI controller is designed using a modified internal model control (IMC) theory that is based on the frequency-relocated fractional-order model of the TPS. This modified IMC method eliminates the need of phase-lag inducing IMC filter. The performance-robustness tradeoff is enhanced by appropriate selection of the single tuning parameter (frequency-relocation factor (ψ)) by minimizing the integral of time-weighted absolute value of area control errors. The proposed controller is validated both by simulation and hardware-in-loop implementation to show its practical feasibility. © 1972-2012 IEEE.

关键词： Hardware-in-the-loop simulation

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Bone Fracture Classification in X-ray Images: A Deep Learning Approach Leveraging Transfer Learning

Bone Fracture Classification in X-ray Images: A Deep Learnin...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md. Sabbir Ahammed Md. Musfiqur Rahman Mridha Md. Taufiq Khan Md. Jamil Chaudhary Md. Fatin Nibbrash Nakib Sakib Imtiaz Md. Ashikuzzaman Ashik Dept. of Computer Science & Engineering Varendra University Rajshahi Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Bone fracture is a very common medical issue faced by medical practitioners, and fracture detection is quite crucial for treatment. Till now, the most popular diagnosing method for bone fracture is X-ray imaging. However, in many cases, an X-ray image of a fractured bone is prone to human error. Advancements in artificial intelligence (AI) have demonstrated the potential to achieve high accuracy in tasks like X-ray image classification of fractured bone. In this work, we have incorporated a deep learning-based approach to facilitate the diagnosis of bone fracture. We have utilized different pre-trained state-of-the-art (SOTA) convolutional neural networks (CNN), namely, DenseNet, VGG, ResNet, Xception, and EfficientNet as feature extractors, to extract higher-level feature space representations of X-ray images. We have incorporated multiple classifiers, namely Logistic Regression, Random Forest, Linear Regression, XGBoost, and a custom feed-forward network (FFN) to discriminate among the higher-level representations extracted by the aforementioned pre-trained feature extractors. Among the several feature extractor and classifier combinations we have experimented with, DenseNet169 combined with the custom FFN produced the best results, reporting an overall accuracy of 99.48%, Receiver Operating Characteristic Area Under the Curve (ROC-AUC) score of 99.99%, precision of 99.48%, recall of 99.48%, and F1 score of 99.48%. The results demonstrate that combining an appropriate pretrained SOTA CNN model and a classifier can achieve high classification accuracy in the bone-fracture X-ray image classification task. Such a method previews a promising avenue in helping medical practitioners to achieve better diagnostic performance with patient benefit.

关键词： Deep learning Accuracy Transfer learning Feature extraction Bones Convolutional neural networks X-ray imaging Medical diagnostic imaging Random forests Image classification

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A Deep Learning Approach for Classifying Retinal Diseases and Diabetic Retinopathy Stages to Enhance Early Detection in Diagnosis

A Deep Learning Approach for Classifying Retinal Diseases an...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Shifa Tasmiah Tisha Sharmistha Chanda Tista Md Nazmul Hoq Dept. of Computer Science and Engineering Chittagong University of Engineering & Technology Chattagram Bangladesh Dept. of Computer Science and Engineering Concordia University Montreal Canada

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Diabetic Retinopathy (DR) is a common consequence of diabetes mellitus, resulting in retinal lesions that can adversely affect vision. If undetected and mistreated, diabetic retinopathy can ultimately result in blindness. This study concentrates on creating an enhanced model for the precise classification of retinal illnesses, particularly Diabetic Retinopathy. The suggested model increases the precision and effectiveness of disease classification by utilising deep learning methods and attention mechanisms. The study analyses the performance of many pre-trained models, such as ResNet152, EfficientNetB7, and MobileNetV3Large, using a varied dataset of retinal pictures. The ResNet152 model, enhanced with the Spatial Attention Module, achieved the best results in retinal disease classification, with an accuracy of 87.65(%), precision of 89.88(%), recall of 90.69(%), and F1-score of 88.58(%). This performance surpasses other models and demonstrating the effectiveness of attention modules in improving classification accuracy and robustness. The study also highlights the competitive performance of the suggested model by contrasting it with earlier studies in the field. All things considered, this research advances the classification of retinal diseases and may improve patient treatment and ophthalmology diagnostic precision.

关键词： Deep learning Diabetic retinopathy Accuracy Transfer learning Retina Software Robustness Medical diagnostic imaging Diseases Residual neural networks

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Investigation of the Coupling Characteristics of Wheel Polygon and Rail Corrugation in High-Speed Railways 3

Investigation of the Coupling Characteristics of Wheel Polyg...

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3rd International Conference on Rail Transportation, ICRT 2024

作者： Gao, Wanlin Yin, Yue Bu, Han Wang, Shiqi Cui, Xiaolu Dept. of Computer Science and Technology Chongqing Jiaotong Univ. Chongqing China Dept. of Rail Transit Chongqing Vocational College of Transportation Research State Univ. Chongqing China Dept. of Mechanical and Electrical Engineering and Vehicle Engineering Chongqing Jiaotong Univ. Chongqing China

ISBN: (数字)9780784485941

ISBN: (纸本)9780784485941

Wheel polygon and rail corrugation are common types of periodic wear in the wheel-rail system. This research investigates their interaction in high-speed railways using friction coupling vibration theory. A finite element model of the wheel-rail system is developed to study friction coupling vibrations in straight sections of long slopes. The study identifies the causes of 22nd-order wheel polygonization and rail corrugation with a 130 mm wavelength and creates wear models for both. The effects of their interaction on friction coupling vibrations are analyzed, with an emphasis on frequency-dependent coupling characteristics. Results indicate that saturated wheel-rail creep forces in long slopes easily trigger friction coupling vibrations, which are worsened by both wheel polygon and rail corrugation, with wheel polygon having a more pronounced impact. These findings offer insights for improving wheel-rail system design and maintenance in high-speed railways. Future research should aim to develop advanced techniques to mitigate the negative effects of wheel polygon and rail corrugation on system performance and safety. © ASCE.

关键词： Railroads

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Advancing Fake News Detection: From Traditional Machine Learning to Transformer-based Approaches 3

Advancing Fake News Detection: From Traditional Machine Lear...

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

作者： Abhinand, I.V. Ajal Krishna, K.P. Krishna, Ajay Jayadev, J. Reji Rahmath, K. Divya, T. Dept. of Computer Science and Engineering Sreepathy Institute of Management and Technology Vavannur A. P. J. Abdul Kalam Technological University kerala Thiruvananthapuram India

ISBN: (纸本)9798331527549

Fake news on online platforms is spreading at a rapid rate, posing great threats to social, political, and economic stability in linguistically diverse regions like Kerala, India. This paper reviews comprehensively the techniques of fake news detection from traditional machine learning methods to transformer-based models. Special attention is given to the detection of fake news in regional languages like Malayalam, characterized by complex morphology and syntax. The study investigates applications of ensemble-based learning as well as state-of-the-art transformer models, BERT, and XLNet, focusing on their ability to capture nuanced linguistic features and contextual relationships. Evaluation results show how significantly better the transformer-based models fare than traditional methods with respect to accuracy and robustness. This review also captures current limitations - such as resource constraints for regional languages - as well as future opportunities in developing scalable, multilingual fake news detection systems. © 2025 IEEE.

关键词： Adversarial machine learning

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Breast Cancer Prediction Using Machine Learning 5

Breast Cancer Prediction Using Machine Learning

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

作者： Dhondiyal, Shiv Ashish Luthra, Pooja Sharma, Bhupender Rajni, Rajni Uniyal, Ishita Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Chandigarh University Department of Computer Science & Engineering Punjab Mohali India Coer University Department of Computer Science and Application Uttarakhand Roorkee India Guru Jambheshwar University of Science and Technology Department Of Computer Science & Engineering Haryana Hisar India Graphic Era Hill University Department of Electrical Engineering India

ISBN: (纸本)9798331522100

The number of newly diagnosed cases of breast cancer exceeds 2.3 million annually worldwide. There is a severe lack of valid prognostic and predictive indicators for the clinical treatment of breast cancer patients at the present time. In order to decrease the mortality rate and increase the survival time of breast cancer patients, early detection of this deadly disease is crucial. Improving breast cancer prognosis relies on the analysis and processing of mammography data, the gold standard for screening and early detection. In order to detect breast cancer in mammograms, the Fuzzy C-means algorithm is employed for image segmentation. Next, we train it to identify the characteristics of the segmented regions, and then we use an effective classifier to categorise the trained pictures into their respective mammography classes. This research used the Breast Cancer Wisconsin Diagnostic dataset and two ML algorithms: K-Nearest Neighbours (KNN) and Random Forest. After getting the results, we compared and evaluated the performance of these two classifiers. Potentially useful tools for both early diagnosis and treatment planning include breast cancer prediction models that account for a wide range of risk factors. Effective disease management involves the collection, storage, and administration of many data sets, as well as intelligent systems based on several aspects for the prediction of breast cancer. © 2025 IEEE.

关键词： Image segmentation

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Federated Learning in Healthcare: A Privacy-Preserving Approach to Predictive Analytics

Federated Learning in Healthcare: A Privacy-Preserving Appro...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Reddy, M. Jithender Kotiyal, Arnav Lakhanpal, Sorabh Badhoutiya, Arti Mounika, T. Jayanthi, Muthuswamy Vasavi College of Engineering Dept. of Computer Science and Engineering Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Punjab Phagwara India GLA University Department of Electrical Engineering Mathura India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798331512965

technology such as machine learning in healthcare has pushed privacy concerns to the point that the handling of sensitive patient data has become paramount. In the context of FL, predictive analytics without data sharing between institutions can be performed through the process. Next, we explore the application of FL in healthcare in order to maintain data privacy while obtaining robust predictive models. FL utilizes aggregated decentralized data from multiple healthcare providers to reduce the chance of data breaches and continue to be compliant with rigorous regulatory frameworks, like HIPAA and GDPR, while doing so. In this work, we evaluate the model performance of FL models in predicting different types of health outcomes in comparison with traditional centralized models. We show that FL can achieve similar predictive accuracy while maintaining data privacy, which makes the choice of FL as a viable alternative to privacy sensitive healthcare environments. Second, applying FL is very challenging: there are communication overhead, data heterogeneity, and security risks. This study also considers ways to minimize these challenges. The results demonstrate the promise of Federated Learning as an innovative paradigm for applying predictive analytics in healthcare, indicative of a robust shift toward more secure, efficient and privacy preserving healthcare systems. © 2025 IEEE.

关键词： Federated learning

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