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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Vallileka, N. Sundaravadivel, P. Karthikeyan, U. Krishnan, R. Santhana Narayanan, K. Lakshmi Sundararajan, S. Psna College of Engineering and Technology Department of Computer Applications Dindigul India Saveetha Engineering College Department of Artificial Intelligence & Machine Learning Thandalam India Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Kattankulathur Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Francis Xavier Engineering College Department of Electronics and Communication Engineering Tirunelveli India Scad College of Engineering and Technology Department of Mechanical Engineering Cheranmahadevi India

ISBN: (纸本)9798350341485

With the increase in the popularity of the social media sites, there arises lot of adverse effects including the spread of fake news. This research study proposes a novel algorithm, called DeepTweet, which leverages transformer-based models for enhanced fake news detection in Twitter sentiment analysis. The aim is to improve the accuracy and efficiency of identifying fake news in real-time by exploiting the contextual information and semantic representations captured by the transformer-based models. The proposed DeepTweet algorithm utilizes a pre-trained transformer model, such as BERT or GPT, to encode the textual content of tweets and extract rich contextual embeddings. These embeddings capture both local and global dependencies, enabling a more comprehensive understanding of the sentiment expressed in a tweet. Furthermore, a novel attention mechanism tailored for fake news detection is introduced to focus on key textual features that are indicative of potential misinformation. To assess the efficiency of DeepTweet, extensive simulations using a large dataset of labelled tweets containing both genuine and fake news are done. Further, this study compares the performance of DeepTweet with existing algorithms, including LSTM, CNN, and traditional machine learning approaches. Various simulation metric measures such as accuracy, precision, recall, and F1 score are employed to analyze the algorithm's performance. The simulation grades demonstrate that DeepTweet outperforms the prevailing algorithms by means of accuracy and overall detection performance. The proposed attention mechanism proves to be particularly effective in identifying subtle linguistic cues and deceptive patterns associated with fake news. The enhanced contextual embeddings captured by the transformer-based models contribute to improved sentiment analysis and subsequently lead to more accurate fake news detection. © 2023 IEEE.

关键词： Embeddings

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SentinelAI: Next-Gen Accident Detection and Emergency Notification System Integrating Autoencoders and Proximity Analytics 5

SentinelAI: Next-Gen Accident Detection and Emergency Notifi...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Karthikeyan, U. Raj, J. Relin Francis Gopikumar, S. Sangeetha, A. Priyanka, B. Rani, P. Alice School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Civil Engineering Chennai Avadi India Psna College of Engineering and Technology Department of Information Technology Dindigul India J.P College of Engineering Department of Electronics and Communication Engineering Thenkasi India Sarah Tucker College Department of Tamil Tirunelveli India

ISBN: (纸本)9798350379945

SentinelAI introduces an advanced accident detection and emergency notification system, utilizing cutting-edge technologies for superior performance. By employing autoencoder-based anomaly detection algorithms, SentinelAI precisely identifies accidents through G-force data from the ADXL377 accelerometer sensor, ensuring rapid and accurate event recognition. The system incorporates proximity analytics using the Haversine formula to calculate real-time distances to nearby hospitals and police stations, facilitating swift emergency response. This proximity assessment optimizes dispatch and minimizes response times, crucial in critical situations. SentinelAI features a robust communication framework, integrating Wi-Fi and GSM modules for efficient information transmission. Upon detecting an accident, the proposed system immediately notifies the nearest emergency services and a predefined insurance company, providing essential location and event details for prompt action. Combining autoencoder-based anomaly detection, proximity analytics, and rapid notification capabilities, SentinelAI represents a significant advancement in accident detection and emergency response systems. By leveraging next-generation technologies, SentinelAI aims to enhance road safety and save lives globally. © 2024 IEEE.

关键词： Accelerometers

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Application of Bayes Rule for Precise Analysis of Class-Imbalanced Stroke Data

Application of Bayes Rule for Precise Analysis of Class-Imba...

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2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Viswapriya, S.E. Rajeswari, D. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur Chennai India

ISBN: (纸本)9798350319200

The interpretability of stroke risk prediction is still lagging and it can be solved by using deep analysisofdata with the application of the Bayes rule. The prediction model has to be produced with accuracy, clarity, and comprehensibility without ambiguity, especially in machine learning and data analytics. The class imbalance problem is a common issue affecting the raw data, which means the imbalanced categorization of outcome feature value. In many fields, such as fraud detection, network intrusion detection, and medical diagnostics, this class imbalance issue is present where negative class samples outnumber positive class samples. If some datasets have class imbalance and imperfect issues, there will be no proper classification pattern. There are many resampling methods to address this class imbalance problem. However, these resampling methods can make an even distribution of classes by generating or omitting some samples, but theycannot handle imperfect data. The probabilistic analysis can lead to the identification of issues that exist in the dataset and provide direction to medical experts on dealing with the condition to avoid *** strength of applying the Bayes rule will provide a meaningful interpretation of stroke data. This paper investigates the Baye rule's significance in stroke data analysis. © 2023 IEEE.

关键词： Bayes rule Class imbalance probabilistic analysis Resampling Stroke data

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Early Retinal Degeneration Detection with Deep Faster CNNs 2

Early Retinal Degeneration Detection with Deep Faster CNNs

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Priyadharshini, K. Balraj, Sindhusaranya Kumar, J.V. Rama Vidhate, Deepak A. Thilakavathy, P. Vanathi, P. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering Sona College of Technonolgy Salem India SRKR Engineering College Andhra Pradesh Bhimavaram India Department of Information Technology Dr. Vithalrao Vikhe Patil College of Engineering Maharashtra Ahmednagar India Vels Institute of Science Technology and Advanced Studies Chennai India Department of Information Technology Adhiyamaan College of Engineering Hosur India

ISBN: (纸本)9798350383867

The issue of retinogenesis probably constitutes a major public health threat which gives the reason to carry out its early detection for intervention purposes to be efficient. Throughout this investigation, we suggest a new technique which uses the faster CNNs for early detection of retina degradation. We set up a diverse data collection where OCT(optical coherence tomography), fundus photography and angiography modality were included . This collection covered the various stages of degeneration. The dataset was prepared for instance with resizing, normalizing, and data augmentation were used to improve share quality and diversity. Deeper and faster networks that were developed and trained using the backpropagation technique performed well and further optimized using Adam, with high accuracy of 94 in the test dataset. 7%. The strength of our results can be seen through evaluation metrics in detail like confusion matrices and classification reports. The model can distinguish various cases of retinal degeneration accurately which proves to have high precision and recall for mild and normal degeneration. Interpolation analysis of the model using Grad-CAM and occlusion sensitivity, giving the clue about the model's decision making process, setting a precedent for clinical interpretation. The enhanced cross-validated model on an independent dataset also arrays the model in a representational where it will be able to generalize in a diverse population and in different imaging conditions. The systematic approach which proved to be more accurate and resilient than the other techniques was the one which was compared to the mentioned ones. In conclusion, our study shows the possibility for application of the Deep Faster CNNs in identifying the early signs of retinal degeneration;the tool therefore becomes an important tool for enhancing patient outcomes and prevention of vision loss in the world at large. ©2024 IEEE.

关键词： Ophthalmology

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ACOVMD: Automatic COVID-19 misinformation detection in Twitter using self-trained semi-supervised hybrid deep learning model

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International Social Science Journal 2024年第252期74卷 713-730页

作者： Birunda, S. Selva Devi, R. Kanniga Muthukannan, M. Babu, M. Mahesh Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamil Nadu Srivilliputhur India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu Kelambakkam India Department of Civil Engineering KCG College of Technology Tamil Nadu Chennai India

During the COVID-19 pandemic, online social networks are extensively utilized, more than ever before by 8.4%, resulting in the propagation of false information related to COVID-19. Despite the existence of many fake news detection models;annotation inconsistency, memory consumption, accurate and self-trained efficient algorithms for detecting the emerging COVID-19 misinformation tweets are still challenging. Hence, the main aim of this work is to come up with a self-trained semi-supervised model that accurately and automatically detects the reliability of emerging COVID-19 tweets without delay. In this work, COVID-19 tweet dataset is created in English Language from the period January 2020 to January 2022 as a ground truth database. Then self-trained semi-supervised hybrid deep learning model is proposed to train both supervised and unsupervised components simultaneously using the created dataset. The proposed model is self-trained repeatedly and the model gets updated to predict the reliability of upcoming COVID-19 tweets that differ from training tweets. We performed experiments multiple times by limiting the percentage amount of labelled tweets shown to the model, namely 80%, 50%, 40%, 30%, 20% and 10% labelled tweets, respectively. Experimental results show that the proposed model achieves 80.92% accuracy and 98.15% accuracy in the 10% and 80% label-seen experiments, respectively. This shows a clear rising trend in the performance curve. Therefore, this technique will be useful for effectively classifying voluminous amounts of emerging tweets generated as part of the COVID-19 infodemic. The proposed model may efficiently use a huge amount of unlabelled tweets and enhance the model's generalization performance. © 2023 John Wiley & Sons Ltd.

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Towards characterizing bug fixes through dependency-level changes in Apache Java open source projects

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Science China(Information Sciences) 2022年第7期65卷 102-120页

作者： Di CUI Lingling FAN Sen CHEN Yuanfang CAI Qinghua ZHENG Yang LIU Ting LIU School of Computer Science and Technology Xi'an Jiaotong University College of Cyber Science Nankai University College of Intelligence and Computing Tianjin University Department of Computer Science Drexel University School of Computer Science and Engineering Nanyang Technological University School of Cyber Science and Engineering Xi'an Jiaotong University

The complexity and diversity of bug fixes require developers to understand bug fixes from multiple perspectives in addition to fine-grained code changes. The dependencies among files in a software system are an important dimension to inform software quality. Recent studies have revealed that most bug-prone files are always architecturally connected with dependencies, and as one of the best practices in the industry,changes in dependencies should be avoided or carefully made during bug fixing. Hence, in this paper, we take the first attempt to understand bug fixes from the dependencies perspective, which can complement existing code change perspectives. Based on this new perspective, we conducted a systematic and comprehensive study on bug fixes collected from 157 Apache open source projects, involving 140456 bug reports and182621 bug fixes in total. Our study results show that a relatively high proportion of bug fixes(30%) introduce dependency-level changes when fixing the corresponding 33% bugs. The bugs, whose fixes introduce dependency-level changes, have a strong correlation with high priority, large fixing churn, long fixing time,frequent bug reopening, and bug inducing. More importantly, patched files with dependency-level changes in their fixes, consume much more maintenance costs compared with those without these changes. We further summarized three representative patch patterns to explain the reasons for the increasing costs. Our study unveils useful findings based on qualitative and quantitative analysis and also provides new insights that might benefit existing bug prediction techniques. We release a large set of benchmarks and also implement a prototype tool to automatically detect dependency-level changes from bug fixes, which can warn developers and remind them to design a better fix.

关键词： empirical software engineering software maintenance software evolution software architecture software design

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Gaussian weighting—based random walk segmentation and DCNN method for brain tumor detection and classification

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Multimedia Tools and Applications 2025年第8期84卷 4675-4702页

作者： Rani, K. Vijila Sumathy, G. Shoba, L.K. Sivalakshmi, P. Department of ECE Udaya School of Engineering Tamil Nadu Nagercoil India Department of Computational Intelligence SRM Institute Of Science and Technology Kattankulathur CampusChengalpattu Dt Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai India Department of ECE R.M.K College of Engineering and Technology Tamil Nadu Chennai India

Cancer is a disease that develops when cells divide abnormally. Cancerous cells can spread to surrounding organ tissues, the bloodstream, and the lymphatic system. This process is known as metastasis, and it shortens the patients' lives. Brain cancer is a type of cancer that develops in the brain and nervous system. Typically, MR (Magnetic Resonance) scan images are used by doctors to assess the condition of tissues that have grown abnormally within the human body. A novel segmentation method with classification is specifically suggested here for assessing the tumor site. To improve the quality of an image, it first uses top hat filtering to perform the morphological process. The brain tumor region is subsequently extracted from the morphologically filtered output using the Gaussian Weighted-Based Random Walk (GWBRW) segmentation technique. Lastly, a classification operation is carried out by the Deep Convolutional Neural Network (DCNN) classifier. Five performance measures are then used to compare the segmented output with the original images. The segmentation-based assessment estimations show that the segmentation achieves higher results, with average values of 22.8160, 0.2133, and 0.9704 for PSNR, Dice, and SSIM, respectively, and lower results, with average values of 19.3997 and 422.0844 for RMSE and MSE. The segmentation results show that the suggested computation enhances the tumor region's characteristics. Eventually, the Deep Convolutional Neural Network classifier successfully classifies 96.1% of the predictions. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image enhancement

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Recent Advances in Fiber-Optic Sensors for the Detection of Inorganic Acidic Gases

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Photonic Sensors 2024年第4期14卷 74-100页

作者： Lingnan KONG Yuanke GONG Xiaoyu ZHONG Yang LIU Bo WAN Quanhua XIE Yuanyuan HE Xiaoling PENG Haixing CHANG Dengjie ZHONG Huiling LIU Nianbing ZHONG Chongqing Key Laboratory of Optical Fiber Sensor and Photoelectric Detection Chongqing Key Laboratory of Modern Photoelectric Detection Technology and InstrumentsChongqing Engineering Research Center of Intelligent Optical Fiber Sensing TechnologyChongqing University of TechnologyChongqing 400054China Meishan San Su Shrine Museum Meishan 620010China School of Chemistry and Chemical Engineering Chongqing University of TechnologyChongqing 400054China College of Computing and Informatics Sungkyunkwan UniversitySeoul 03060South Korea

An acidic gas is an important basic chemical raw material used for synthesizing fertilizers,insecticides,explosives,dyes,and ***,inorganic acidic gases that leak into the air have harmful effects on the human health,infrastructure,and cultural ***,the demand for inorganic acidic gas sensors for air quality monitoring and management has continuously increased,enabling the development of various sensing *** them,fiber-optic sensors are promising for acidic gas detection because of their excellent in-situ measurement,resistance to corrosion,anti-electromagnetic interference,long service life,and smart *** particular,fiber-optic sensors have proven to be very useful for the in-situ detection and distributed monitoring of multiple gas ***,the sensitivity,selectivity,repeatability,and limits of detection of these sensors can be improved to achieve acceptable performance levels for practical *** this review,we introduce fiber-optic sensors based on structured optical fibers and fiber gratings for detecting H_(2)S,SO_(2),NO_(2),CO_(2),and N_(2)*** structures of the sensing regions,gas-sensitive materials,and measurement principles of these sensors are *** sensitivity,selectivity,limit of detection,and response time of the sensors are ***,the future of fiber-optic sensors for the detection of inorganic acidic gases is discussed.

关键词： Fiber-optic sensors structured optical fibers fiber gratings inorganic acidic gases measurement principles sensing performance

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ResMHA-Net:Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

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Computers, Materials & Continua 2024年第10期81卷 885-909页

作者： Novsheena Rasool Javaid Iqbal Bhat Najib Ben Aoun Abdullah Alharthi Niyaz Ahmad Wani Vikram Chopra Muhammad Shahid Anwar Department of Computer Science School of Engineering and TechnologyIslamic University of Science and TechnologyAwantipora192122India Faculty of Computing and Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia Research Groups in Intelligent Machines National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia Department of Electrical Engineering College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of AI and Software Gachon UniversitySeongnam-si13120Republic of Korea

Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma *** study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation ***-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention *** powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range *** doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor *** rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 ***,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse ***,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset *** features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival *** model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing *** ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient ***,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.

关键词： Glioma MRI segmentation multihead attention survival prediction deep learning

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical Data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

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