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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Patel, Vandan Patel, Kirtan Goel, Parth Shah, Milind Computer Science & Engineering Department India Computer Engineering Department India

ISBN: (纸本)9798350385649

Artificial intelligence (AI) has the potential to revolutionize the field of gastrointestinal disease diagnosis by enabling the development of accurate and efficient automated systems. This study comprehensively investigates endoscopic image classification for gastrointestinal diseases using deep learning models. Gastrointestinal diseases are a significant healthcare concern worldwide, necessitating accurate diagnostic tools. The proposed architecture relies on the convolutional layers of pre-trained CNN networks (DenseNet121, EfficientNetB5, Xception, and ResNet50) for feature extraction with a newly designed fully connected layer and output layer to facilitate reliable intestinal disease classification. The primary goal is to improve clinical decision support to diagnose gastrointestinal diseases. The paper demonstrates the efficiency of these models in precisely categorizing gastrointestinal diseases through extensive evaluation using the KVASIR Dataset, which includes 4000 different types of intestinal images. The experimental results show that EfficientNetB5 achieved the highest testing accuracy of 92.58% and outperformed all networks trained using the proposed framework. © 2024 IEEE.

关键词： Convolutional neural networks

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ENHANCING THE QUALITY OF RECYCLED AGGREGATE BY SURFACE TREATMENT TECHNIQUES FOR SUSTAINABLE CONSTRUCTION

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Indian Concrete Journal 2024年第2期98卷 17-35页

作者： Vengadesh Marshall Raman, J. Ramasamy, V. Kandasamy, S. Department of Civil Engineering at Mailam Engineering College Tamil Nadu Mailam India Department of Civil Engineering at Adhiparasakthi Engineering College Tamil Nadu Melmaruvathur India Department of Civil Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India

Due to the shortage of natural coarse aggregate, demolition of old concrete deposits are incorporated into structural concrete after various surface treatments. The presence of adherent mortar on the exterior of the recycled aggregate tends to degrade the properties of the recycled coarse aggregate. To eradicate the old mortar on the recycled aggregate, there are several types of treatment. In this current study, we discuss the different treatment methods, namely mechanical scrubbing, sulphuric acid, hydrochloric acid, cement coating and silica fume coating treatments. Each techniques is uniquely analyzed and the results are also analyzed for each and every methods. Treating recycled aggregate with all treatment methods improves its physical, mechanical and durability properties compared to untreated concrete made from recycled coarse aggregate. All techniques show their best results from their treatment. Some of the techniques are environmentally friendly and cost-effective for the treatment method. Fresh concrete properties are enhanced by 20.79 % after using abrasive treated recycled aggregates and its most effective treatments for enhancing fresh properties. The compressive strength of recycled aggregate concrete reduced significantly by 23.96 % after abrasive treatment it’s enhanced by 17.85 % at 28 days when compared to controlled concrete. © 2024, Associated Cement Companies Ltd.. All rights reserved.

关键词： Silica fume

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Prediction of central deflection and slenderness limit for lateral stability of simply supported concrete beam using machine learning techniques

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Asian Journal of civil engineering 2024年第7期25卷 5443-5466页

作者： Mustafa, Rashid Ahmad, Md Talib Kumar, Akash Kumar, Sonu Sah, Navin Kumar Kumar, Abhishek Department of Civil Engineering Katihar Engineering College Katihar Bihar Katihar 854109 India Department of Computer Science and Engineering Katihar Engineering College Katihar Bihar Katihar 854109 India

In the field of mechanics, large deflection of simply supported beam (SSB) carrying a load intensity (w) is well-known topic that has been thoroughly studied by many researchers. Numerous methods have been used, such as the analytical precise solution and the finite element approach. A small number of researchers computed central deflection of SSB having uniformly distributed load and also checked the slenderness limit for lateral stability (L_s) of SSB using numerous machine learning (ML) techniques. This study compares the suitability and flexibility of the extreme gradient boosting (XGBoost), support vector regression (SVR) and polynomial regression (PR) model in the reliability investigation of SSB. It also provides an ML-based prediction method for δ and checks the slenderness limit for L_s of SSB. These three ML models apply to 400 datasets and predict the δ and as well as checks the slenderness limit for L_s of SSB by taking account five major input parameters such as beam width (b), beam depth (h), beam length (L), uniformly distributed load (w) and characteristics compressive strength of concrete (f_ck). Numerous performance indicators, including coefficient of determination (R²), variance account factor (VAF), a-20 index, root mean square error (RMSE), mean absolute error (MAE) and mean absolute deviation (MAD) are used to assess the efficacy of the well-established ML models. PR model achieved the best performance according to the performance metrics. This was attributed to its maximum R² = 0.999 and 1.000 and the lowest RMSE = 0.003 and 0 during the training phase, as well as R² = 0.994 and 1 and RMSE = 0.017 and 0 during the testing phase, while predicting central deflection (δ) and slenderness limit (Ls) of SSB respectively. The reliability index (β) was calculated using the first-order second moment (FOSM) method for all models. Rank analysis, reliability analysis, regression cur

关键词： Central deflection PR Slenderness limit SVR XGBoost

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Probabilistic analysis of simply supported concrete beam using machine learning techniques: a comparative study

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Asian Journal of civil engineering 2024年第5期25卷 3915-3928页

作者： Mustafa, Rashid Ahmad, Md Talib Department of Civil Engineering Katihar Engineering College Katihar Bihar Katihar 854109 India Department of Computer Science and Engineering Katihar Engineering College Katihar Bihar Katihar 854109 India

Large deflection of a simply supported beam (SSB) carrying central point load is a well-known area in mechanics that has been researched extensively by numerous scholars. Various techniques, including the finite element approach and analytical exact solutions, have been implemented. Few researchers also used machine learning (ML) technique to predict deflection of SSB. This study offers ML based prediction method for deflection of SSB carrying point load and compares the applicability and adaptability of ridge regression model, artificial neural network and Gaussian process regression (GPR) in the reliability investigation of SSB. The central point load (W), beam length (L), beam width (B), beam depth (D), and concrete’s modulus of elasticity (E) are the five key input parameters that these three machine learning models take into consideration when applying them to 200 datasets in order to predict the beam deflection (∆) at the centre of the beam. The effectiveness of the well-established ML models is evaluated using a variety of performance metrics, such as coefficient of determination (R²), variance account factor, A-10 index, root mean square error (RMSE), mean absolute error and mean absolute deviation. Based on performance parameters, the findings indicate that, out of the three suggested machine learning models, GPR had the best predictive performance. This was attributed to its maximum R² = 1.000 and the lowest RMSE = 1.157E-06 during the training phase, as well as R² = 0.999 and RMSE = 0.006 during the testing phase. Rank analysis, reliability analysis, regression plot, William’s plot, and error matrix plot are further tools used to assess the performance of the model. First-order second moment (FOSM) approach is used to determine the reliability index (β) of the model and compared with the actual value. A sensitivity analysis is also carried out to determine the impact of each input parameter on the result. © The Author(s),

关键词： ANN Central deflection FOSM GPR RRM William’s plot

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Silver Lining in the Fake News Cloud: Can Large Language Models Help Detect Misinformation?

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 14-24页

作者： Kumar, Raghvendra Goddu, Bhargav Saha, Sriparna Jatowt, Adam Indian Institute of Technology Department of Computer Science and Engineering Patna801106 India University of Innsbruck Department of Computer Science Innsbruck6020 Austria

In the times of advanced generative artificial intelligence, distinguishing truth from fallacy and deception has become a critical societal challenge. This research attempts to analyze the capabilities of large language models (LLMs) for detecting misinformation. Our study employs a versatile approach, covering multiple LLMs with few- and zero-shot prompting. These models are rigorously evaluated across various fake news and rumor detection datasets. Introducing a novel dimension, we additionally incorporate sentiment and emotion annotations to understand the emotional influence on misinformation detection using LLMs. Moreover, to extend our inquiry, we employ ChatGPT to intentionally distort authentic news as well as human-written fake news, utilizing zero-shot and iterative prompts. This deliberate corruption allows for a detailed examination of various parameters such as abstractness, concreteness, and named entity density, providing insights into differentiating between unaltered news, human-written fake news, and its LLM-corrupted counterpart. Our findings aspire to furnish a refined framework for discerning authentic news, human-generated misinformation, and LLM-induced distortions. This multifaceted approach, utilizing various prompt techniques, contributes to a comprehensive understanding of the subtle variations shaping misinformation sources. © 2024 IEEE.

关键词： Iterative methods

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Efficacy of image based deep learning CNN models for bolt loosening detection in steel structures

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Asian Journal of civil engineering 2025年第6期26卷 2409-2417页

作者： Alapati, Mallika Ramesh Chandra, G. Abboju, Shivani Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India

Bolted connections are integral components in steel structures and often are vulnerable to loosening due to cyclic loading and fatigue. Detecting bolt loosening in early stages is critical to prevent sudden catastrophic failures. Recent advancements in image capturing and processing, combined with machine learning techniques have significantly improved the accuracy of detecting bolt loosening. Deep learning techniques, in specific those based on image analysis, have proven to be effective tool for damage detection in structural members. In this study, initially captured dataset of 120 images from a laboratory model is augmented to 576 images using the Roboflow platform. Investigations in this work are tied to one image data set and the metrics are analysed for how well a model performs. Usually, it depends on each use case as to which model would be best suited. In the present work, dataset featured two states of bolt loosening: tight and loose. Convolution Neural Network architectures AlexNet and ResNet are applied individually to the augmented dataset to predict the bolt loosening states as binary classification problem. The results demonstrated an accuracy of 87.931%, indicating that the AlexNet model effectively distinguished between loose and tight conditions. Whereas the ResNet algorithm exhibited relatively better accuracy of 90.8%. Further, an integrated hybrid model (CNN model with LSTM) is also applied on the same image data to further enhance the efficiency of the deep learning frame work. The performance of the trained CNN algorithms was further validated with image responses from various real time scenarios of various lighting conditions and found to be fairly good. This study highlights the potential of using deep learning techniques, specifically AlexNet, ResNet and CNN with LSTM for accurately detecting bolt loosening, thereby contributing to the safety and reliability of steel structures. © The Author(s), under exclusive licence to Springer Nature S

关键词： AlexNet Bolt loosening Bolted connections Convolution neural network architectures Image based deep learning ResNet architecture

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Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

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computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

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Deep ensemble model for skin cancer classification with improved feature set

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Multimedia Tools and Applications 2025年第10期84卷 7599-7626页

作者： Burada, Sreedhar Manjunathswamy, B.E. Kumar, M. Sunil Department of Computer Science and Engineering Don Bosco Institute of Technology Karnataka Bengaluru560074 India Department of Computer Science and Engineering School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India

Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced models for the accurate classification of skin disease. This paper introduces a novel skin cancer detection model under the stages of preprocessing, feature extraction, classification as well as segmentation. The given input is first preprocessed via Gaussian filtering. Subsequently, the Deep learning model termed as ‘U-net’ is used for the segmentation process. Afterward, the extraction of the feature set including features like Improved Center symmetric Local Binary Pattern (ICS-LBP), Local Gradient Pattern (LGP), Median binary pattern (MBP) is performed. Classification takes place using Deep Ensemble Model based on the extracted features, that includes BiGRU, Deep Maxout, and Improved Deep Convolutional Neural Network (CNN), respectively. Also, as the hyperparameter tuning of the classification model enhances the performance, this work introduces a new Hybrid Atom and Arithmetic Operation Algorithm (HAAOA) algorithm for tuning the optimal weights of the Bi-GRU (Bi-directional Gated Recurrent Unit) and Deep Maxout model. At last, the validation of the proposed work is performed through different performance measures by comparing it with different algorithms. Accordingly, accuracy of the adopted method at 90th learning percentage is 93.56%, which is (~) 82.63%, 85.15%, 84.98%, 83.66% and 83.42% better than AOA, ASO, BWO, BMO and DO, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Experimental investigation and SVM-based prediction of compressive and splitting tensile strength of ceramic waste aggregate concrete

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Journal of King Saud University - engineering sciences 2024年第2期36卷 112-121页

作者： Ray, Sourav Haque, Mohaiminul Rahman, Md. Masnun Sakib, Md. Nazmus Al Rakib, Kazi Department of Civil and Environmental Engineering Shahjalal University of Science and Technology Sylhet Bangladesh

Due to the brittle nature of ceramic, the ceramic and construction industry produces a large volume of waste that imposes a severe environmental threat due to its non-biodegradability. In this study, the suitability of ceramic waste as a replacement of natural coarse and fine aggregate in concrete has been investigated by evaluating engineering properties such as bulk density, water absorption, workability, etc. with respect to different concrete samples made with different mix proportions. Furthermore, a prediction model is introduced to predict compressive and splitting tensile strength using the machine learning tool support vector machine (SVM). A data set containing 108 records either for compressive or tensile strength was used for the training and testing purposes of the SVM model. A total of 9 mix proportions was selected and cast cylinders were cured for 7, 28, and 56 days. Four different kernel functions were used to optimize the results and different accuracy parameters like the value of R2, mean absolute error, mean square error, root mean square error, etc. were compared to find the best kernel function for this study. By primarily evaluating the coefficient of determination (R2), SVM showed an acceptable result with an accuracy of over 90%. Moreover, in terms of other accuracy measurement parameters result indicates that the SVM is an effective tool to predict the compressive and splitting tensile strength of concrete comprised of different proportions of ceramic content. © 2021 The Authors

关键词： Compressive strength

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A High-Speed Compression and Secure Authentication Framework for Data Sharing Cloud-Enabled Big Data Environments

A High-Speed Compression and Secure Authentication Framework...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Abirami, I. Selvi, S. Lalitha, R. Julian, Anitha Rmk Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509828

With rapidly expanding cloud-enabled big data environments, there is an imperative need for efficient data-sharing mechanisms that are multidimensional and balance both speed and security. In this connection, high-speed compression and secure authentication come forth playing a very crucial role. The two significant challenges addressed in this framework are data compression for efficient storage and transmission and robust authentication for secure access and integrity of shared data. This way, the framework compresses data size significantly without losing its quality, leading to efficient, faster data transfers and reduced considerably storage costs on cloud infrastructures. At the same time, secure authentication mechanisms [1] such as cryptography techniques and multi-factor authentication ensure that only authorized users access sensitive data, thus protected from potential unauthorized access or cyber-attacks. Integration of both capabilities speed compression and secure authentication-functionalizes big data environments to their peak performance and security. This framework is highly suitable for high-scale data-oriented industries like healthcare, finance, and IoT, which necessitate high-speed, secure, and reliable data sharing. Ultimately, this approach improves scalability and security in cloud-enabled big data systems by enabling organizations to keep pace with growing needs for highly efficient and secure data sharing in a highly dynamic environment. © 2025 IEEE.

关键词： Data integrity

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