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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Charulatha. R.T G. Nirmaladevi Vasumathi G S. Hemajothi K. Suresh Karthikeyan. C Department of ECE Rajalakshmi Institute of Technology Chennai Dept of Computer Science and Engineering Sri Sairam Institute of Technology Chennai Department of ECE S.A. Engineering College Chennai Department of ECE Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of Electrical and Electronics Engineering QIS College of Engineering and Technology Ongole AndhraPradesh Department of ECE R.M.K. Engineering College Kavaraipettai Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Glaucoma is a leading cause of irreversible blindness, requiring early and accurate diagnosis to prevent vision loss. This study proposes a robust methodology for detecting glaucoma using a hybrid approach that integrates deep learning (Autoencoder) with a machine learning classifier (multi-Layer Perceptron). The methodology utilizes image processing techniques, including noise reduction and data augmentation, to enhance the quality of retinal fundus images. Feature extraction was performed using Convolutional Neural Networks (CNNs) and Gabor filters, followed by dimensionality reduction through Principal Component Analysis (PCA) and wavelet decomposition. The model was tested on multiple publicly available datasets, including RIGA, HRF, ACRImA, and Kaggle. The hybrid model achieved an accuracy of 97.1% on the RIGA dataset, 95.6% on the HRF dataset, and 96.3% on the ACRImA dataset. The model outperformed traditional classifiers such as Support Vector machine (SVm) and Random Forest, which achieved accuracies of 92.3% and 94.5% on the RIGA dataset, respectively. The study demonstrates that combining deep learning with machine learning classifiers, along with effective preprocessing, significantly improves glaucoma detection accuracy. Furthermore, explainable AI techniques were employed to enhance model interpretability, ensuring that clinical practitioners can trust and understand the model's decisions.

关键词： Glaucoma Deep learning Support vector machines Accuracy Explainable AI Computational modeling Autoencoders Wavelet analysis Convolutional neural networks Principal component analysis

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Data Identification Optimal Control for Quantized Linear Systems under DoS Attacks

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wang, Xianming Li, Liwei Shen, mouquan Zhao, Xudong Wang, Qing-Guo Zhu, Zheng Hong Nanjing Tech University School of Mechanical and Power Engineering Nanjing211816 China Nanjing Tech University College of Electrical Engineering and Control Science Nanjing211816 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116024 China Beijing Normal University Institute of Ai and Future Networks Zhuhai519087 China BNU-HKBU United International College Guangdong Provincial Key Laboratory Irads Department of Computer Science Zhuhai519087 China York University Department of Mechanical Engineering TorontoONM3J 1P3 Canada

This paper is devoted to data identification optimal control for quantized linear systems under denial of service (DoS) attacks. A least-squares algorithm is provided to identify the unknown system matrices using a data-based representation. An output estimation algorithm utilizing identified matrices is supplied to offset DoS attacks. Consequently, an optimal controller is built on the quantized and attacked data by adaptive dynamic programming. Sufficient conditions are presented to guarantee that the resultant closed-loop system is ultimately uniformly bounded. An example is produced to verify the validity of the proposed approach. © 1965-2011 IEEE.

关键词： Optimal control systems

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Optimistic Boosting Internet Connection Throughput by Incorporating Blockchain Filters into Network Infrastructure

Optimistic Boosting Internet Connection Throughput by Incorp...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： C Ambika Bhuvaneswari Neha Garg B. R. Supreeth Pramod Kumar Patjoshi C. UshaPriya mohit Tiwari Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science Oxford Degree and PG College Sir. M. Visveshwaraiah Layout Ullal Bangalore School of Management Centurion University of Technology and Management BBSR Odisha Department of AIML St. Martins Engineering College Secunderabad Telangana Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi

This study examines how blockchain filters might modify network architecture to boost internet speed. This novel strategy may address the growing need for faster, more reliable internet connections in a future where digital connectedness is crucial. Our work develops a blockchain-based filtering mechanism, evaluates performance, and uses mathematical modelling, simulations, and statistical analysis. Blockchain filters reduce latency, increase throughput, and reduce packet loss. It has major ramifications for network providers and customers. Network providers can improve resource allocation, customer happiness, and income streams, while customers may enjoy quicker connections, lower latency, and better security online. The report also suggests scalability, security, and real-world deployment to maximize this disruptive technology. This discovery enables a brighter, more efficient future for global internet access as the digital world evolves.

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ANALYSIS OF IN-PLACE mATRIX ROTATION OF SQUARE mATRIX FOR INFORmATION SECURITY APPLICATIONS

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Procedia computer science 2022年 215卷 131-139页

作者： S. Kiran A. Ashok Kumar R. Pradeep Kumar Reddy D. Sandeep Reddy Dept. of Computer Science and Engineering YSR Engineering College of YVU Proddatur-516360 India Dept. of Physics YSR Engineering College of YVU Proddatur 516360 India M. Tech Student ISSE NIT Jamshedpur Jharkhand 831014

The concept of matrices was developed to represent complex data into understandable and formattable data. There is a huge number of applications in which matrices are used like classification and data size reduction used to store it. matrices are widely used in most cryptographic algorithms. Every image can be represented as a matrix where each pixel value is stored as an element of a matrix. The rotation of the matrix defines that a matrix is rotated in the XY-plane to a certain angle concerning the x-axis about the origin. A rotation matrix is a transformation matrix in linear algebra that is used to make a rotation in Euclidean space. The rotation of the matrix includes transformation such that it requires additional memory for a new transformed value. This paper describes the in-place matrix rotation of square matrix using 45° rotation of information. In-place algorithm alters the input without requiring additional memory. A little amount of additional memory could be needed for the implementation of an in-place algorithm. However, the amount of memory required should be consistent and not dependent on the size of the input. With the help of In-place matrix Rotation (ImR) of the complementary angle, the ImR of 45° is proved. In ImR, the elements change their positions, but the elements remain the same in the matrix. Using ImR is the matrix dimensions are changed with the center fixed to a particular position just by rotating the matrix not transforming the position.

关键词： matrix Rotation Transformation In place matrix rotation complementary angle Image Security

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Correction: materials laboratories of the future for alloys, amorphous, and composite materials (mRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

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mRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit meng, Y. Shirley minor, Andrew m. Zhang, minghao Zaluzec, Nestor J. Chan, maria K.Y. Seidler, Gerald mcComb, David W. Agar, Joshua mukherjee, Partha P. melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. mcCue, Ian D. Voyles, Paul m. Robertson, Ian Li, Ling Chi, miaofang Destino, Joel F. Devaraj, Arun marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. momeni, Kasra misra, Amit Abdolrahim, Niaz medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, melike mehta, Apurva Graham-Brady, Lori maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig m. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct minghao Zhang’s affiliation from "Pritzker School of molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of molecular engineering, The University of Chicago, Chicago, USA;Argonne National Laboratory, Lemont, USA." Argonne National Laboratory was left off the original publication. © The Author(s) 2025.

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An Effective Framework for Detecting Epileptic Seizures using CNN and Encrypted EEG Signals

An Effective Framework for Detecting Epileptic Seizures usin...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： G Pradeep Saroj Bala N P Satheesh m mahalakshmi K Balasamy S Suganyadevi Department of M.Tech. Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore Department of MCA Ajay Kumar Garg Engineering College Ghaziabad Department of AI & DS Bannari Amman Institute of Technology Sathyamangalam Dept of Artificial Intelligence & Data Science Saveetha Engineering College Chennai Department of ECE KPR Institute of Engineering and Technology Coimbatore

Electroencephalogram (EEG) signals may be used to autonomously diagnose epilepsy, eliminating the requirement for a medical professional’s involvement in the process. A good classification performance is really necessary if you do not want to pass up any possible discoveries. In this piece of research, the authors offer a technique for the automated identification of epilepsy using EEG waves. In order to extract features, the raw EEG signals were first put through a discrete Fourier transform, also known as a DFT, as well as a discrete wavelet transform (DWT). Therefore, researchers have been exploring different methods to improve the accuracy of EEG signal analysis. One such method proposed in this study is the Wavelet Transform based Fourier-Bessel series expansion (WT - FBSE) method. This method utilizes the WT - FBSE spectrum to segment multiple frame-size time-segmented EEG signals using the scale-space boundary detection method. The WT - FBSE technique decomposes the EEG signals into narrow sub-band signals, which are then used to extract various features such as log-energy-entropy (LEnt), line-length (LL), and norm-entropy (NEnt) from different frequency ranges. In order to choose the features that are most important, the relief-F feature ranking approach is applied. This helps to limit the amount of computing work that is required by the models. The study evaluates two time-segmentation approaches and four different frame sizes to analyse the EEG signals and achieves better performance when compared to the existing systems.

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Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks with Artificial Hummingbird Algorithm for Social media Content Classification and Community Detection

Stereoscopic Gated Graph Scalable Quantum Capsule Convolutio...

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machine Learning and Autonomous Systems (ICmLAS), International Conference on

作者： A. Elumalai S. Rukmani Devi D. Rosy Salomi Victoria A. Jagadeeswaran Aparajita mukherjee K. Vinoth Department of Mathematics R.M.K. Engineering College Chennai Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Information Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Annapoorana Engineering College Salem Tamil Nadu India Department of Computer Science and Engineering Institute of Engineering and Management(IEM) University of Engineering and Management Kolkata West Bengal Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Social media Content Classification and Community Detection (SmCCCD) classify content and identify communities through deep learning, and NLP. Traditional models are weak in scalability and interpretability. To solve all these problems, this paper introduces a Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks with Artificial Hummingbird Algorithm (SGGSQCCN2Nets+AHA) for social content classification and community detection. Data inputs are accessed from the Twitter dataset and are initially preprocessed by the Natural Language (NL) method. After preprocessing, content classification and community detection are done using the Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks (SGGSQCCN2Nets) and optimization is then performed with the Artificial Hummingbird Algorithm (AHA). The performance of the new SGGSQCCN2Nets+AHA model is tested on a Twitter dataset with a high accuracy rate of 99.9% and specificity of 99.8%. The proposed model is developed by utilizing Python programming language. The outcome of the proposed SGGSQCCN2Nets+AHA model enhances the performance of content classification and improves user community detection accuracy. It is effective in addressing scalability issues, obtaining complex features, and offering more interpretability. The model is also resilient against noisy data dynamically adapts to evolving social media trends and outperforms traditional deep learning techniques in real-time analysis.

关键词： Adaptation models Accuracy Social networking (online) Stereo image processing Scalability Heuristic algorithms Blogs Logic gates Classification algorithms Convolutional neural networks

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The Frontier of Geospatial AI Deep Learning Applications in Data mining and Spatial Analysis

The Frontier of Geospatial AI Deep Learning Applications in ...

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Ajit Kumar mishra Vikash Verma R. manivannan N. Krishna Kumar mudunuru Srividya Sanskriti Sharma Department of Electrical & Electronics Engineering IES College of Technology Bhopal M.P India Dept of Artificial Intelligence & Machine Learning GGITS Jabalpur M.P India School of Computing Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai TN India Dept.of CSE Guru Nanak Institutions Technical Campus Ibrahimpatam Hyderabad Telangana India Dept. of CSE MLR Institute of Technology Hyderabad Telangana India Dept of Computer Science & Business System GGITS Jabalpur M.P India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

modern artificial intelligence uses geographical data mining and regional analysis to analyze large datasets. Deep learning is a new geographic AI method. Example: Geographic Deep mining Network (GDmN). The Spatial Feature Extractor (SFE), Temporal Geospatial Attention mechanism (TGAm), and Geospatial Data Fusion module (GDFm) comprise this technique. We examined six popular approaches and found GDmN performed best. It worked successfully for several reasons. GDmN always outperforms its competitors in memory, F1 score, IoU, training time, and inference time. This is always true. These findings suggest that GDmN might boost geospatial AI’s data mining and location analysis abilities. After all the effort, we have developed a solid basis for location intelligence, making it useful in various subject areas. Because it’s versatile, mapping specialists may use GDmN for better environmental monitoring or city development. Deep learning helps GDmN make better space choices and uncover insights in large, challenging data sets that no one knew about. GDmN’s cutting-edge approaches make space and time research and prediction simpler. This wide perspective offers a more full and all-encompassing picture of today’s challenges than previous techniques. This makes it impossible to overstate how significant this work is to location intelligence. Its unmatched research and insights usher in a new era.

关键词： Deep learning Training Urban areas Transforms Feature extraction Solids Spatial databases Geospatial analysis Data mining Artificial intelligence

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LDCP: A Novel Approach to Predict Fake Reviews in Online Social Network by using Learning based Data Classification Principle

LDCP: A Novel Approach to Predict Fake Reviews in Online Soc...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S.Sudha mercy S. Venkatesan Bharaneedharan m Nayagan. S L. Venkateswarlu Nimmagadda Padmaja Department of Computer Science and Design R.M.K. Engineering College Chennai Department of Electrical and Electronics Engineering QIS College of Engineering and Technology Ongole AndhraPradesh Department of ECE S.A. Engineering College Chennai Department of CSE Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of Electrical and Electronics Engineering QIS College of Engineering and -Technology Ongole Andhra Pradesh Department of ECE School of Engineering Mohan Babu University (Erstwhile SreeVidyanikethan Engineering College) Tirupati

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The proliferation of online reviews has significantly impacted consumer decision-making processes, making the detection of fake reviews crucial for maintaining trust in e-commerce platforms. This study presents a novel approach for detecting fake reviews using machine learning (mL) and deep learning (DL) models, with a focus on achieving high accuracy through advanced feature extraction techniques and data augmentation. Various models, including classical mL algorithms such as Support Vector machine (SVm) and Random Forest, alongside DL models like Convolutional Neural Networks (CNN), Bidirectional Long Short-Term memory (BiLSTm), and Cascaded multi-Layer Perceptron (mLP), were evaluated. Feature extraction methods like TF-IDF, GloVe, and combined approaches were used to enhance performance. The DL model RoBERTa achieved the highest accuracy of 99.5%, while Cascaded mLP reached 99.2%. Classical models like Random Forest and SVm also performed well, achieving accuracies of 97.1 % and 96.8%, respectively. Data augmentation techniques, such as back-translation, further improved the models' ability to handle imbalanced datasets. The results demonstrate that combining DL architectures with advanced feature extraction leads to superior performance in fake review detection.

关键词： Support vector machines Deep learning Accuracy Social networking (online) Feature extraction Data augmentation Data models Convolutional neural networks Fake news Random forests

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Analysis of Text Emotional Tendency and Prediction of Internet Public Opinion Trend Based on Support Vector machine Algorithm

Analysis of Text Emotional Tendency and Prediction of Intern...

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International Conference on Smart technologies For Smart Nation (SmarttechCon)

作者： P. S. Ramesh S. Surendran S. Kerthy Ramalakshmi B Uganya G Sanjiv Kumar Jain Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai Department of Computer Science and Engineering Tagore Engineering College Chennai Tamil Nadu India Department of Information Technology Jeppiaar Institute of Technology Chennai Tamil Nadu India Department of CSE Sankara Polytechnic College Coimbatore Tamil Nadu India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai Electrical Engineering Medi-Caps University Indore (M.P.)

Due to the participation of people in online public opinion, users have their own preferences and ideas, which leads to strong chaos in online public opinion. Currently, mL (machine learning) algorithms ignore the chaotic characteristics of online public opinion, so the established model can't fully and accurately describe the changes of online public opinion, and the prediction accuracy needs to be further improved. In this paper, the SVm (support vector machine) algorithm is used to analyze the sentiment tendency of text and predict the trend of Internet public opinion, and the pre-model of sentiment tendency identification and Internet public opinion is established. On the basis of the traditional Gaussian kernel function, we add parameters to increase the displacement change and amplitude adjustment of the kernel function, so that it can accurately control the performance characteristics of the kernel function. The document's overall emotional tendency can be obtained through this accumulation. The results show that the recognition rate of this method is up to 95.828%, and the prediction error is controlled within the effective range (5.028%). This method overcomes the shortcomings of traditional emotional word weighting methods, such as low performance and inability to adapt to multi-viewpoint topics, and can get better recognition accuracy.

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