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1st International Conference on Intelligent Systems and computational Networks, ICISCN 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Vinston Raja, R. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

The investigation aims to evaluate the performance of machine learning techniques, particularly the XGBoost regression method, for stock price prediction with the help of technical indicators. The research targets the adjusted closing price to reduce the autocorrelation problem in time series. The Five Days and Ten Days Exponential Moving Averages (EMA-5 and EMA-10) are involved in refining feature selection. The performance of the model was evaluated using different error metrics, namely, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2). The results indicate that the XGBoost model, combined with EMA-5 and EMA-10, achieves high predictive performance, obtaining an r2 value of 0.98. It implies that incorporating short-term EMAs into advanced machine learning techniques drastically improves stock price forecasting. © 2025 IEEE.

关键词： Time series

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A Deep Learning Framework with Attention Mechanism for the Classification of Mild Cognitive Impairment using Brain MRI

A Deep Learning Framework with Attention Mechanism for the C...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Illakiya, T. Vidhya, B. Chelladurai, S. Prince Hariprasad, S. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Chennai India

ISBN: (纸本)9798331521394

Mild Cognitive Impairment (MCI) refers to a neurological disorder marked by observable changes in cognitive decline. MCI signifies an intermediate phase linking normal aging and Alzheimer's Disease (AD). It is typically categorized into two types: Early MCI (EMCI) and Late MCI (LMCI), representing varying levels of cognitive impairment. An effective classification between EMCI and LMCI is vital, as it allows timely interventions that can potentially slow the progression of AD, improving patient outcomes and quality of life. Identifying the subtle features and patterns in EMCI and LMCI is essential for early diagnosis and effective intervention. This work proposes a novel Deep Learning (DL) network integrating EfficientNet-B0, a Global Context Network (GCN), and Neighborhood Attention blocks. The EfficientNet-B0 acts as the foundation for feature extraction in the proposed model. It uses its efficient architecture and pre-trained capabilities to capture meaningful patterns. To further enhance feature representation, a Global Context Network (GCN) is employed to capture long-range dependencies and global semantic information. Additionally, a Neighborhood Attention Module is used to refine local features by focusing on important regions in the MRI scans. The proposed framework balances global semantic understanding and localized feature extraction, resulting in improved classification performance. With systematic training and evaluation, the model achieves an accuracy of 95.3%, outperforming existing approaches and providing a better solution for neuroimaging-based MCI subtype classification. © 2025 IEEE.

关键词： Convolutional neural networks

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Detecting Leaf Diseases Using Centralized CNN'S with Efficient R-CNN and Mask R-CNN

Detecting Leaf Diseases Using Centralized CNN'S with Efficie...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Pongiannan, R.K. Banerjee, Samudra Poornima, P.U. Brindha, R. Richard Pravin, A. Franklin, J. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur India

ISBN: (纸本)9798331510022

Plant diseases have emerged as a significant concern, as they can drastically reduce both the quality and quantity of agricultural products. The research aims to identify plant leaf infections early on, enabling timely treatment to prevent the spread of diseases to other plants. The objective is to determine the severity of leaf diseases, allowing for targeted treatment based on the level of severity. Additionally, the severity of leaf diseases can help predict crop losses. This work is to improve the accuracy of leaf detection and measurement of morphological traits by Region Convolutional Neural Network (RCNN), Network namely state of the Mask region art. Experimental results verify that our automated approach effectively segments leaves from different plant families. Measured leaves of comparable size have an average relative error of about 4.6 percent for leaf length and approximately 5.7 percent for leaf width when compared to manual measurements by expert ecologists and botanists. © 2024 IEEE.

关键词： Plant diseases

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AI-Powered Diagnostic Tool for Retinal Disease Detection Using Convolutional Neural Networks 5

AI-Powered Diagnostic Tool for Retinal Disease Detection Usi...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Moovendran, Manikandan Kumar, K. K. Dhanush Sashanth, J.M. Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai603203 India Chennai603203 India

ISBN: (纸本)9798350389609

Accurate and timely detection of retinal disease helps prevent severe vision impairment, although traditional diagnostics take plenty of time and are demanding to perform. This led to the design of the advanced diagnostic tool, in which AI and Convolutional Neural Networks (CNNs) will enhance the identification of retinal disorders. This system will analyze a retina image to detect among others, diabetic retinopathy and age-related macular degeneration, among others. Advanced deep learning algorithms enable the tool to rapidly identify the subtle patterns and anomalies in retinal scans, which even an astute human analyst might not identify, providing insightful and diagnostic support to healthcare experts. This AI-based approach leads to the acceleration of the diagnostic workflow and the minimization of errors, which elevates overall diagnostic accuracy and allows earlier intervention. This technology will contribute to making high-quality retinal care more accessible and efficient, finally arriving at the benefit of a patient at an earlier time of early detection and treatment. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning Video Streaming Web Application with AI Botsusing NLP

Deep Learning Video Streaming Web Application with AI Botsus...

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

作者： Ponmalar, P. Sridevi Singh, Shivam Agrahari, Rahul Kumar SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Chennai603203 India

ISBN: (纸本)9798350319200

In this digital age, online learning has become increasingly prevalent, but learners often struggle with asking questions and getting immediate clarification. To address this issue, this research paper proposes a solution in the form of a web application that uses deep learning, AI bots, and NLP to generate keywords and provide real-time responses to user queries during video playback. The title of the paper is "Deep Learning Video Streaming Web Application with AI Bots using NLP". It highlights the application's key features and technologies used, including deep learning, AI bots, and natural language processing (NLP). The problem addressed in this paper is the difficulty learners face when seeking real-time answers to their questions during online video tutorials. The proposed solution aims to address this issue by providing a platform that allows users to add questions and answers to a database, which can then be accessed during video playback. The solution presented in this paper uses a combination of deep learning, AI bots, and NLP to generate relevant keywords and provide real-time responses to user queries. The paper outlines the methodology used to develop the application and discusses the results obtained from testing the platform. The analysis output shows that the proposed solution can effectively address the issue of real-time question answering during online video tutorials. The application's use of deep learning, AI bots, and NLP allows for a more personalized and interactive learning experience. The merits of this work lie in the potential to revolutionize the online learning experience by enabling learners to ask questions and get immediate answers. Possible applications of this research include the development of similar web applications for other domains, such as customer service or technical support. Additionally, the findings of this study may inspire further research on the use of deep learning, AI bots, and NLP in online learning and other fiel

关键词： AI bots Deep-Learning NLTK (Natural Language Toolkit)

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MC-Nonlocal-PINNs:Handling Nonlocal Operators in PINNs Via Monte Carlo Sampling

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Numerical Mathematics(Theory,Methods and Applications) 2023年第3期16卷 769-791页

作者： Xiaodong Feng Yue Qian Wanfang Shen Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina Shandong Key Laboratory of Blockchain Finance Shandong University of Finance and EconomicsJinan 250014China

We propose Monte Carlo Nonlocal physics-informed neural networks(MC-Nonlocal-PINNs),which are a generalization of MC-fPINNs in *** et al.(*** ***.400(2022),115523)for solving general nonlocal models such as integral equations and nonlocal *** to MC-fPINNs,our MC-Nonlocal-PINNs handle nonlocal operators in a Monte Carlo way,resulting in a very stable approach for high dimensional *** present a variety of test problems,including high dimensional Volterra type integral equations,hypersingular integral equations and nonlocal PDEs,to demonstrate the effectiveness of our approach.

关键词： Nonlocal models PINNs Monte Carlo sampling deep neural networks

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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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Optimally arranged TiO_(2)@MoS_(2) heterostructures with effectively induced built-in electric field for high-performance lithium-sulfur batteries

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Journal of Energy Chemistry 2023年第8期83卷 496-508,I0012页

作者： Jeongyoub Lee Changhoon Choi Jung Been Park Seungho Yu Jinho Ha Hyungsoo Lee Gyumin Jang Young Sun Park Juwon Yun Hayoung Im Subin Moon Soobin Lee Jung-Il Choi Dong-Wan Kim Jooho Moon Department of Materials Science and Engineering Yonsei UniversitySeoul 03722Republic of Korea School of Civil Environmental and Architectural EngineeringKorea UniversitySeoul 02841Republic of Korea Energy Storage Research Center Korea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea School of Mathematics and Computing(Computational Science and Engineering) Yonsei UniversitySeoul 03722Republic of Korea

To overcome the serious technological issues affecting lithium-sulfur(Li-S) batteries,such as sluggish sulfur redox kinetics and the detrimental shuttle effect,heterostructure engineering has been investigated as a strategy to effectively capture soluble lithium polysulfide intermediates and promote their conversion reaction by integrating highly polar metal oxides with catalytically active metals ***,to fully exploit the outstanding properties of heterostructure-based composites,their detailed structure and interfacial contacts should be designed ***,optimally arranged TiO_(2)and MoS_(2)-based heterostructures(TiO_(2)@MoS_(2)) are fabricated on carbon cloth as a multifunctional interlayer to efficiently trap polysulfide intermediates and accelerate their redox *** to the synergistic effects between TiO_(2)and MoS_(2)and the uniform heterointerface distribution that induces the ideally oriented built-in electric field,Li-S batteries with TiO_(2)@MoS_(2)interlayers exhibit high rate capability(601 mA h g^(-1)at 5 C),good cycling stability(capacity-fade rate of 0.067% per cycle over 500 cycles at2 C),and satisfactory areal capacity(5.2 mA h cm^(-2)) under an increased sulfur loading of 5.2 mg cm^(-2).Moreover,by comparing with a MoS_(2)@TiO_(2)interlayer composed of reversely arranged heterostructures,the effect of the built-in electric field’s direction on the electrocatalytic reactions of polysulfide intermediates is thoroughly investigated for the first *** superior electrocatalytic activities of the rationally arranged TiO_(2)@MoS_(2)interlayer demonstrate the importance of optimizing the built-in electric field of heterostructures for producing high-performance Li-S batteries.

关键词： Lithium-sulfur batteries Shuttle effect TiO_(2)-MoS_(2)heterostructure engineering Built-in electric field Multifunctional interlayers

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Convergent and Orthogonality Preserving Schemes for Approximating the Kohn-Sham Orbitals

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Numerical Mathematics(Theory,Methods and Applications) 2023年第1期16卷 1-25页

作者： Xiaoying Dai Liwei Zhang Aihui Zhou LSEC Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China

To obtain convergent numerical approximations without using any orthogonalization operations is of great importance in electronic structure *** this paper,we propose and analyze a class of iteration schemes for the discretized Kohn-Sham Density Functional Theory model,with which the iterative approximations are guaranteed to converge to the Kohn-Sham orbitals without any orthogonalization as long as the initial orbitals are orthogonal and the time step sizes are given *** addition,we present a feasible and efficient approach to get suitable time step sizes and report some numerical experiments to validate our theory.

关键词： Gradient flow based model density functional theory orthogonality preserving scheme convergence temporal discretization

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Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

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Computer Modeling in engineering & sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho School of Computing Sastra Deemed to be UniversityThanjavur613401India Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India Department of AI and Software Engineering Kangwon National UniversitySamcheok25913Republic of Korea Department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical engineering of Southern Medical University(SMU-dataset),and The National institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

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