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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Mythili, J. Surendhar, T. Suryaprakash, P. Suresh Kumar, K. Faculty of Computer Science and Engineering K S Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode India Computer Science and Engineering K S Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode India

ISBN: (纸本)9798350333602

As the prevalence of diabetes mellitus rises, more families are being impacted. Most diabetics don't know much about their health situation or the risks they face before getting a diagnosis. A unique data mining-based method was suggested for the prediction of type 2 diabetes mellitus (T2DM) in this study. The accuracy and robustness of the prediction model are the primary priorities, ensuring flexibility so that it may be used to other datasets. The model is made up of two parts: the enhanced Logistic Regression with Multilayer Perception (LRMLP) and Naïve Bayes (NB) method with a set of preprocessing techniques. To compare the findings with those of other studies, the Diabetes Dataset for Pima Indians was used with the Environment for Knowledge Analysis toolkit. According to the findings, the model's prediction accuracy is higher than that of earlier research. Additionally, this approach guarantees that the dataset quality is enough. The system was applied to two more diabetes datasets in order to assess its effectiveness further. Both research findings show strong performance. The model is shown to be helpful for managing diabetic health in a realistic manner as a result. For experimental analysis, data from the University of California, Irvine's (UCI) machine learning repository's Pima Indian Diabetes Database (PIDD) is used. These classification algorithms' performance is evaluated using a variety of statistical metrics, including sensitivity (recall), precision, F-score, and accuracy. The measurement of correctly and erroneously classified cases is called accuracy. According to the testing findings, LR-MLP outscored other classification algorithms and provides the maximum accuracy of 96.3%. © 2023 IEEE.

关键词： Forecasting

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Numerical Predictions of Laminar Forced Convection Heat Transfer with and without Buoyancy Effects from an Isothermal Horizontal Flat Plate to Supercritical Nitrogen

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Frontiers in Heat and Mass Transfer 2024年第3期22卷 889-917页

作者： K.S.Rajendra Prasad Sathya Sai T.R.Seetharam Adithya Garimella Department of Mechanical and Industrial Engineering Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipal576104India Sustainable Energy Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft2600 AANetherlands Department of Mechanical Engineering PES UniversityBengaluru560085India

Numerical predictions are made for Laminar Forced convection heat transfer with and without buoyancy effects for Supercritical Nitrogen flowing over an isothermal horizontal flat plate with a heated surface facing *** are performed by varying the value ofΔT from5 to 30 K and P_(∞)/P_(cr)ratio from1.1 to *** of all the thermophysical properties of supercritical Nitrogen is *** wall temperatures are chosen in such a way that two values of Tw are less than T∗(T*is the temperature at which the fluid has a maximum value of Cp for the given pressure),one value equal to T∗and two values greater than T∗.Three different values of U∞are used to obtain Re∞range of 3.6×10_(4)to 4.74×10^(5)for forced convection without buoyancy effects and Gr_(∞)/Re^(2)_(∞)range of 0.011 to 3.107 for the case where buoyancy effects are *** different forms of correlations are proposed based on numerical predictions and are compared with actual numerical *** has been found that in all six forms of correlations,the maximum deviations are found to occur in those cases where the pseudocritical temperature TT∗lies between the wall temperature and bulk fluid temperature.

关键词： Supercritical nitrogen laminar flow numerical methods forced convection heat transfer isothermal horizontal surface

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Traffic Sign Recognition using Convolutional Neural Networks YOLOv3 Algorithm

Traffic Sign Recognition using Convolutional Neural Networks...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Gnanabaskaran, A. Pranav Ram, K.S. Vaishnavi, N. Shabari Prasannaa, D. K S Rangasamy College of Technology Faculty of Computer Science and Engineering Department of Computer Science and Engineering Tiruchengode India K S Rangasamy College of Technology Computer Science and Engineering Department of Computer Science and Engineering Tiruchengode India

ISBN: (纸本)9798350300857

Nowadays, traffic sign recognition is disrupted through various external factors such as chromatic aberration, geographical separation, and brightness of lights. This eventually poses possible safety hazards during navigation in real life. To overcome this issue, a deeplearning based method is proposed in this research using YOLOv3 with DenseNet as the backbone design. This approach uses attention mechanism to accelerate the feature extraction process by minimizing the overhead due to high computations to optimize the latency problem. Secondly, it incorporates location-specific hierarchical network to increase the traffic sign detection performance in challenging circumstances such as magnitude and illumination variations while maintaining adaptability and flexibility. In addition to this Cross Stage Partial Network is fused with DenseNet to enhance the recognition accuracy and effectiveness of the proposed system. The experimental findings on the Chinese Traffic sign dataset, CCTSDB dataset, showed that the YOLOv3-DenseNet model exhibited significant detection efficiency of 97.6% than the other deep learning models considered for performance comparison. It is observed that the proposed model is capable enough to detect the traffic signs in complicated scenarios as well as in places where the traffic signs are placed in smaller dimensions. © 2023 IEEE.

关键词： Traffic signs

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Crime Type Prediction based on Various Occurrence using Parallel LSTM

Crime Type Prediction based on Various Occurrence using Para...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Saradha, M. Nithesh Priyan, T. Shreeram, D.U. Viknesh, S. Faculty of Computer Science and Engineering K S Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode India Computer Science and Engineering K S Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode India

ISBN: (纸本)9798350333602

Crime is a widespread societal issue that has a negative impact on people's standard of living and the nation's prosperity. It's a major consideration for potential residents and tourists alike when deciding whether or not to settle in a given area. As crime rates rise, police departments have a growing need for cutting-edge GIS and data mining tools to enhance crime analytics and strengthen public safety. The suggested method includes preprocessing, feature selection, and evaluating the model's performance. We begin by cleaning up the raw crime statistics. For more predictable signals, this comprises both spatial and temporal regularization. Feature selection is performed using a rough spanning tree. To measure the effectiveness of the model, we employ parallel LSTM. When compared to two established approaches, the new strategy fares quite well. © 2023 IEEE.

关键词： Long short-term memory

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A survey of artificial intelligence models for wireless capsule endoscopy videos for superior automatic diagnosis: problems and solutions

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Multimedia Tools and Applications 2025年 1-35页

作者： El-Gammal, Eman M. El-Shafai, Walid Taha, Taha E. El-Fishawy, Adel S. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Center of Advanced Software and Biomedical Clinical Engineering Consultations Faculty of Engineering Cairo University Giza Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia

Wireless Capsule Endoscopy (WCE) emerged as an innovative and patient-centric approach for non-invasive and painless examination of the gastrointestinal (GI) tract. It serves as a pivotal tool in helping medical practitioners in the early detection of anomalies within the intricate domain of the human GI tract. The automated identification of aberrations assumes paramount significance, contributing not only to temporal efficiency but also to timely diagnosis. Within the realm of academic literature, a plethora of Artificial Intelligence (AI) methodologies have been proposed to effectuate the automatic classification, segmentation, and synthesis of anomalies inherent in WCE images. This scholarly endeavor undertakes a comprehensive and meticulous survey, elucidating the spectrum of anomaly classification, summarization, and detection techniques harnessed for computer-Aided Diagnosis (CAD) in the context of WCE images. The survey begins by delineating the methodologies underpinning WCE image classification and video processing. Subsequently, an exhaustive evaluation of techniques for analyzing WCE images is furnished, accompanied by an in-depth exploration of pertinent surveys, in addition to a judicious assessment of their merits and demerits. Furthermore, this study undertakes a rigorous evaluation of prevailing datasets utilized to benchmark the efficacy of various WCE techniques. In summary, this survey serves as a vanguard, delineating promising avenues for future investigations in the realm of WCE image analysis models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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Spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with Explainable AI

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computers, Materials & Continua 2024年第9期80卷 5029-5045页

作者： Sannasi Chakravarthy Bharanidharan Nagarajan Surbhi Bhatia Khan Vinoth Kumar Venkatesan Mahesh Thyluru Ramakrishna Ahlam AlMusharraf Khursheed Aurungzeb Department of Electronics and Communication Engineering Bannari Amman Institute of TechnologySathyamangalam638402India School of Computer Science Engineering and Information Systems(SCORE) Vellore Institute of TechnologyVellore632014India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India Department of Management College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpura140401India

Breast cancer is a type of cancer responsible for higher mortality rates among *** cruelty of breast cancer always requires a promising approach for its earlier *** light of this,the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast *** addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input ***,the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost(ESA-XGBNet)for binary classification of *** this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM,INbreast,and MIAS *** accuracy of 97.585%(CBISDDSM),98.255%(INbreast),and 98.91%(MIAS)is obtained using the proposed ESA-XGBNet architecture as compared with the existing ***,the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique.

关键词： EfficientNet mammograms breast cancer Explainable AI deep-learning transfer learning

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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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Comparative Analysis of Machine Learning Algorithms for Classification of Thai Fake News 9

Comparative Analysis of Machine Learning Algorithms for Clas...

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Siriphanpornchana, Phumchai Kanjanawattana, Sarunya Institute of Engineering Suranaree University of Technology Faculty of Computer Engineering Nakhon Ratchasima30000 Thailand

ISBN: (纸本)9798350311402

Social media is an important source of false news because of its accessibility, usability, and large community. Individuals who trust the fabricated news may be misinformed. Classifying true and false information among data is a challenging task for the ordinary person. Hence, machine learning is the best solution for supporting human judgment. In this study, eleven machine learning algorithms were employed to evaluate the performance of models utilizing a Thai fake news dataset. In addition, we designed experiments to demonstrate how stop words influence performance. This study found that stop words had no effect on the performance of the model while classifying Thai fake news, and that SVM with an RBF kernel provided the best performance. © 2023 IEEE.

关键词： Classification (of information)

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Harnessing Long Short Term Memory Networks for Stock Market Forecasts

Harnessing Long Short Term Memory Networks for Stock Market ...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Faheem, Alishba Arshad, Usama Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering Department of Artificial Intelligence Topi Pakistan

ISBN: (纸本)9798350349719

Stock market trend prediction is a classical re-gression problem with effects on stock exchange. This research is presenting a detailed exploration of how Long Short Term Memory (LSTM) networks may be used for predicting stock market trends. The LSTM model was employed instead of the conventional Recurrent Neural Networks (RNNs), to learn from historical financial data. The study examines the operations of the LSTM model and the optimization of hyper parameters that lead to accurate prediction outcomes from the given dataset. Our model gave encouraging results that underlined the LSTM model's prowess in capturing intricate temporal dependencies in data. The model's result were in a low Root Mean Square Error (RMSE) score and an impressively high accuracy level that were good indicators of the model's adaptability at trend identification. Our study amplifies the need for advanced machine learning algorithms like LSTM that increase prediction accuracy while offering insights into decision-making processes for both investors and financial analysts at the same time. Further, it also hints at the potential of deep learning within the stock market. The paper concludes by discussions on all possible improvements to the existing model, future directions, and how incorporating additional data types can increase our predictive accuracy. This research primarily underscores the significant role LSTM plays in deciphering complex financial data trends accurately. © 2024 IEEE.

关键词： Long short-term memory

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Translating Words to Pixels: An Effective Text-To-Image System Utilizing Upsampling and CLIP Models

Translating Words to Pixels: An Effective Text-To-Image Syst...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Saeed, Nuaima Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering Department of Artificial Intelligence Topi Pakistan

ISBN: (纸本)9798350349719

Generative AI and text-based image synthesis are being pursued with more interest than ever and adopt a growing audience. This work offers a new way of producing images within a broad range of quality levels, deducing the extra value of the increase resolution. The system applies sophisticated technologies such as diffusion and upscaling models that are guided by the pre-Training of the Contrastive Language-Image PreTraining (CLIP). However, the model shines primarily in its ability to generate visually cohesive and artistic images which are a good illustration of each specified textual prompt. Upsampling technique resolved the major translation problems by generating more high resolution and detailed resultant images of base images. Beyond that, the system also thoroughly illustrated its diversity by accurately producing a comprehensive collection of images based on the text input. This work would largely be a demonstration of the suitability of it for different fields like digital art, advertising, and content creation. Moreover, it delimits the chances of producing innovative and unique content. Future development of many other applications of the system will include the improvement of the system handling more complex textual descriptions, the addition of user-interactive generation features, the optimization of system performance and expanding to multimodal content generation. Our results here only represent a small but important step in this exciting direction. By means of this survey we enrich further knowledges on the field and its farther issues. © 2024 IEEE.

关键词： Signal sampling

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