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Computing and information technology (ICCIT-1441), 2020 International Conference on

作者： Anitha Thavasimuthu P. Jesu Jayarain Narmatha C. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Department of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Faculty of Computers and Information Technology University of Tabuk Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

In recent times, deep-learning (DL) methods are implemented to classify neuroimaging data associated with Alzheimer's disease (AZD), and notable advancements have been achieved. Traditional Machine Learning (ML) has been shown to be helpful in aiding the diagnosis of AZD, however only a limited number of research have explored the capabilities of DL and deep transfer learning (DTL) in this intricate process. In this research, a novel DTL model called Cross attention multi scale Vision Transformer (CrossViT) is proposed to classify the AZD using MRI images. For this classification, a set of MRI images of AZD is collected from the Kaggle website. The collected dataset has 6400 images of MRI related to AZD and its various stages. The MRI images are preprocessed and divided into training and testing sets as 80% and 20%. After preprocessing, the segmentation is performed using the UNet architecture. The segmented images are fed to the proposed CrossViT model for AZD classification. The classification is performed as a binary classification, as it classified the results as either the AZD is present or not. The performances of the research model are assessed based on accuracy, specificity, sensitivity, f1-score, and precision. The research model has attained 99.63% accuracy, 99.51% sensitivity, 99.69% precision, 99.35% specificity, and 99.50% f1-score in training. The model attained 99.21% accuracy, 98.94% sensitivity, 99.25% precision, 99.10% specificity, and 99.04% f1-score in testing. The test results were compared with the current models for validation. The research model has outperformed all the compared model in comparison.

关键词： Training Image segmentation computer vision Sensitivity Accuracy Magnetic resonance imaging Computational modeling Transformers Alzheimer's disease Testing

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Plant Leaf Disease Identification Based on Machine and Deep Learning Methods

Plant Leaf Disease Identification Based on Machine and Deep ...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： P. Kalaiarasi R. Ramalakshmi V. Kannan A. Bhuvanesh Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Agriculture Kalasalingam Academy of Research and Education Krishnankoil India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The substantial effects of plant diseases on agriculture, which result in losses in production of crops, financial losses, and food insecurity, highlight the necessity for plant leaf disease detection and categorization. In order to effectively control illnesses, stop them from spreading to other plants, and develop tailored treatment plans, early and precise disease identification is essential. Various strategies are used for plant leaf disease diagnosis, such as manual symptom assessment, computer image processing, and visual examination by skilled professionals. Traditional methods rely on human expertise to visually identify disease symptoms based on characteristic patterns, discolorations, or lesions on plant leaves. These techniques, however, are laborintensive, time-consuming, and sensitive to bias and mistakes. The development of automated and computer-based techniques for the identification of plant leaf disease through the use of ML (machine learning) algorithms and digital imaging technology has garnered increasing attention in recent years. There are still a number of issues with the DL (deep learning) and ML algorithms. In order to identify research gaps and address them in the future while developing a unique framework, this study presents an examination of ML and DL models.

关键词： Deep learning Visualization Plant diseases Machine learning algorithms Microorganisms Plants (biology) Production Manuals Medical diagnosis Diseases

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Expectation maximization—vector approximate message passing based generalized linear model for channel estimation in intelligent reflecting surface-assisted millimeter multi-user multiple-input multiple-output systems

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PeerJ computer science 2025年 11卷 e2582-e2582页

作者： Ali, Shoukath K. Philip, Sajan P. Khan, Arfat Ahmad Moses, Leeban Cengiz, Korhan Akleylek, Sedat Ivković, Nikola Department of Electronics and Communication Engineering Presidency University Karnataka Bengaluru India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamil Nadu Erode India Department of Computer Science College of Computing Khon Kaen University Khon Kaen Thailand Department of Electrical-Electronics Engineering Istinye University Istanbul Turkey Institute of Computer Science University of Tartu Tartu Estonia Faculty of Organization and Informatics University of Zagreb Pavlinska Varaždin Croatia

Channel estimation poses a main challenge in intelligent reflecting surface (IRS)assisted millimeter wave (mmWave) multi-user multiple-input multiple-output (MIMO) systems due to the substantial number of antennas at the base station (BS) and the passive reflective elements within the IRS lacking sufficient signal processing capabilities. This article addresses this challenge by proposing a channel estimation technique for IRS-assisted mmWave MIMO systems. The problem of channel estimation is normally taken as a compressed sensing (CS) problem, typically addressed through algorithms such as Orthogonal Matching Pursuit (OMP), Generalized Approximate Message Passing (GAMP), and Vector Approximate Message Passing with Expectation-Maximization (EM-VAMP). EM-VAMP demonstrates better performance only when a Gaussian mixture (GM) distribution is chosen as the prior for the sparse channel, especially at high signal-to-noise ratios (SNRs). To address this, the article introduces the application of generalized linear models (GLMs), extensions of standard linear models, providing increased flexibility in modeling data that deviates from Gaussian distribution. Numerical results unveil that the proposed Its EM-VAMP-GLM is much more robust to the existing OMP, GAMP and EM-LAMP algorithms. Copyright 2025 K et al. Distributed under Creative Commons CC-BY 4.0

关键词： Gaussian distribution

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An Improved Bearing Fault Investigation Scheme Using 1D CNN with PCA and SVM

An Improved Bearing Fault Investigation Scheme Using 1D CNN ...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Rajeev Kumar R S Anand Muhammad Nameer Akhtar Rintu Khanna Department of Electrical Engineering Punjab Engineering College Deemed-to-be-University Chandigarh India Department of Electrical Engineering Indian Institute of Technology Roorkee India Department of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

This study presents a novel approach for accurate motor bearing fault detection through a sequential process involving one-dimensional convolutional neural network (1D CNN) for feature extraction, Principal Component Analysis (PCA) for dimensionality reduction, and Support Vector Machine (SVM) for classification. The main objective of this approach is to improve the model performance for complex fault combinations by achieving high precision and recall scores. This study has been carried out using online available datasets of bearings faults provided by Bearing Data Center, Case Western Reserve University (CWRU), USA. In this study, bearing inner race, outer race and rolling ball defects are analysed under variable load operations using the proposed 1D-CNN scheme. This research highlights the potential of the proposed model to advance motor condition monitoring systems, offering a reliable and efficient method for fault detection across diverse operational scenarios.

关键词： Support vector machines Dimensionality reduction Condition monitoring Fault detection Computational modeling Motors Feature extraction Convolutional neural networks Principal component analysis Load modeling

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Hybrid Time-Frequency Domain Analysis for Cardiovascular Disease Forecasting Over ECG Data 1st

Hybrid Time-Frequency Domain Analysis for Cardiovascular Dis...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Zaidi, Abdelhamid Byeon, Haewon Keshta, Ismail Soni, Mukesh Kumar, K. Keshav Garg, Ansh Department of Mathematics College of Science Qassim University P.O.Box 6644 Buraydah51452 Saudi Arabia Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Dr. D. Y. Patil School of Science and Technology Tathawade Pune India College Telangana Hyderabad500 104 India Institute of Engineering and Technology Chitkara University Chitkara University Punjab India

ISBN: (纸本)9783031860683

Due to the few data samples available for computer-aided systems, arrhythmia, a common cardiovascular ailment, creates hurdles for accurate identification. This paper presents a unique methodology for feature extraction in arrhythmia classification that uses a hybrid time-frequency domain analysis within a Convolutional Neural Network (CNN) architecture. The suggested method combines temporal information from RR intervals, frequency-domain information from the Hilbert-Huang transformation, and combined time-frequency information from the continuous wavelet transformation. Following that, a CNN is trained with Focal Loss as the designated loss function. The approach has been carefully tested and verified on an arrhythmia database, proving its ability to classify four unique types of electrocardiogram (ECG) data. Empirical results show that the proposed hybrid time-frequency domain feature extraction method outperforms current classification methodologies in terms of accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hilbert-Huang transform

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MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention

arXiv

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arXiv 2025年

作者： Wang, Tianyi Fan, Jianan Zhang, Dingxin Liu, Dongnan Xia, Yong Huang, Heng Cai, Weidong School of Computer Science The University of Sydney SydneyNSW2006 Australia National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China Ningbo Institute of Northwestern Polytechnical University Ningbo315048 China Department of Computer Science University of Maryland College ParkMD20742 United States

Histopathology and transcriptomics are fundamental modalities in cancer diagnostics, encapsulating the morphological and molecular characteristics of the disease. Multi-modal self-supervised learning has demonstrated remarkable potential in learning pathological representations by integrating diverse data sources. Conventional multi-modal integration methods primarily emphasize modality alignment, while paying insufficient attention to retaining the modality-specific intrinsic structures. However, unlike conventional scenarios where multi-modal inputs often share highly overlapping features, histopathology and transcriptomics exhibit pronounced heterogeneity, offering orthogonal yet complementary insights. Histopathology data provides morphological and spatial context, elucidating tissue architecture and cellular topology, whereas transcriptomics data delineates molecular signatures through quantifying gene expression patterns. This inherent disparity introduces a major challenge in aligning these modalities while maintaining modality-specific fidelity. To address these challenges, we present MIRROR, a novel multi-modal representation learning framework designed to foster both modality alignment and retention. MIRROR employs dedicated encoders to extract comprehensive feature representations for each modality, which is further complemented by a modality alignment module to achieve seamless integration between phenotype patterns and molecular profiles. Furthermore, a modality retention module safeguards unique attributes from each modality, while a style clustering module mitigates redundancy and enhances disease-relevant information by modeling and aligning consistent pathological signatures within a clustering space. Extensive evaluations on The Cancer Genome Atlas (TCGA) cohorts for cancer subtyping and survival analysis highlight MIRROR’s superior performance, demonstrating its effectiveness in constructing comprehensive oncological feature representations and be

关键词： Self-supervised learning

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Cognitive Congestion Alleviation Framework in IoT-Enabled WSN for Next-Gen Intelligent Transport Systems via Optimized Capsule Attention Network

Cognitive Congestion Alleviation Framework in IoT-Enabled WS...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： R. Mohandas Sudha Y Narmatha P P.Venkata Prasad N. Manikandan Department of Electronics & Communication Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Presidency University Bangalore Karnataka India Department of Computer Science and Engineering Sri Sairam College of Engineering Bangalore Karnataka India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Department of Business Administration Kalasalingam Academy of Research and Education Kalasalingam Business School Krishnankoil Viruthunagar India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The rapid advancement of Intelligent Transportation Systems (ITS) has heightened the importance of reliable, real-time data transmission in Wireless Sensor Networks (WSNs). However, congestion in data-heavy Internet of Things (IoT)-enabled networks remains a critical challenge, impacting communication efficiency and decision-making in ITS. In high-density IoT-ITS applications, traditional congestion control methods are insufficient to address the complexity and dynamics of data flows. Existing solutions often fail to adapt in real time to varying traffic loads, leading to delays and data loss. There is a strong need for a congestion alleviation framework capable of intelligently prioritizing data while dynamically managing network resources to ensure seamless information exchange in large-scale deployments. This study presents a Cognitive Congestion Alleviation Framework in IoT-Enabled WSN for Next-Gen Intelligent Transport Systems via Optimized Capsule Attention Network (V-CapMiAN-Parr), which combines the Vectorized Adaptive Capsule Neural Network (V-AdCapNet) and Multi-instance Attention Network (MAN), fine-tuned by the Parrot Optimizer (ParrOpt). This integration aims to effectively detect, mitigate, and prevent congestion through an advanced capsule-based attention mechanism with adaptive optimization. The suggested V-CapMiAN-Parr framework demonstrated significant improvements in congestion control, achieving data throughput efficiencies above 99.7%, packet delivery rates above 99.5%, and network reliability reaching 99.2% under high traffic loads. The model's adaptive weighting mechanism ensures real-time responsiveness and reliability, crucial for next-gen ITS applications. The V-CapMiAN-Parr framework effectively addresses congestion issues in IoT-enabled WSNs, providing a robust, scalable solution for ITS applications. This cognitive framework's advanced data prioritization and adaptive optimization capabilities are well-suited for enhancing the performanc

关键词： Wireless communication Wireless sensor networks Adaptive systems Transportation Telecommunication traffic Real-time systems Internet of Things Data communication Optimization Next generation networking

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AI Personalized Mental Health Monitoring System using Machine Learning, and Natural Language Processing

AI Personalized Mental Health Monitoring System using Machin...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： S. Mohamed Jamal Irfan G. Dhivya N. Raghavendran M. C. Babu T. Jasperline R. Saravanakumar Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Artificial Intelligence and Date Science RMK College of Engineering and Technology Chennai Tamil Nadu India Artificial Intelligence and Data Sciences Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Dr. G. U. Pope College of Engineering Tuticorin Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

This study introduces an AI-driven customized mental health monitoring system that uses machine learning (ML) and natural language processing (NLP) to provide ongoing mental health evaluation and assistance. The system uses user-generated textual data, including journal entries, chat logs, and social media postings, to assess emotional states, stress levels, and mental health issues. The system uses sophisticated natural language processing methods to identify linguistic patterns associated with mental health problems such as depression, anxiety, and others. The machine learning algorithms customize monitoring by adjusting to the unique characteristics of each user and use past data to provide customized suggestions and preemptive alerts. The technology aims to enhance human interaction and function as a proactive tool in mental health treatment by providing real-time information. The system extracts emotional indicators from textual input using sentiment analysis methods and pre-trained natural language processing models such as BERT. The results are used with user demographics and history to build machine learning classifiers, such as Random Forest (RF) and Support Vector Machine (SVM), which predict mental health disorders. Supervised learning is used to develop the model, and suggestions are progressively improved using a tailored feedback system. The SVM and RF algorithms demonstrated an 88% and 92%accuracy rate in forecasting mood swings and early warning indicators of mental health disorders when evaluated on a set of anonymized mental health information. The 30% increase in user involvement demonstrated the efficacy of tailored suggestions in proactive mental health treatment.

关键词： Support vector machines Machine learning algorithms Accuracy Social networking (online) Supervised learning Mental health Natural language processing Monitoring Random forests Classification tree analysis

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PrimeK-Net: Multi-scale Spectral Learning via Group Prime-Kernel Convolutional Neural Networks for Single Channel Speech Enhancement

PrimeK-Net: Multi-scale Spectral Learning via Group Prime-Ke...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zizhen Lin Junyu Wang Ruili Li Fei Shen Xi Xuan School of Electronic Information Sichuan University Chengdu China College of Intelligence and Computing Tianjin University Tianjin China School of Medicine Tohoku University Sendai Japan School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Department of Linguistics and Translation City University of Hong Kong Hong Kong SAR China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Single-channel speech enhancement is a challenging ill-posed problem focused on estimating clean speech from degraded signals. Existing studies have demonstrated the competitive performance of combining convolutional neural networks (CNNs) with Transformers in speech enhancement tasks. However, existing frameworks have not sufficiently addressed computational efficiency and have overlooked the natural multi-scale distribution of the spectrum. Additionally, the potential of CNNs in speech enhancement has yet to be fully realized. To address these issues, this study proposes a Deep Separable Dilated Dense Block (DSDDB) and a Group Prime Kernel Feedforward Channel Attention (GPFCA) module. Specifically, the DSDDB introduces higher parameter and computational efficiency to the Encoder/Decoder of existing frameworks. The GPFCA module replaces the position of the Conformer, extracting deep temporal and frequency features of the spectrum with linear complexity. The GPFCA leverages the proposed Group Prime Kernel Feed-forward Network (GPFN) to integrate multi-granularity long-range, medium-range, and short-range receptive fields, while utilizing the properties of prime numbers to avoid periodic overlap effects. Experimental results demonstrate that PrimeK-Net, proposed in this study, achieves state-of-the-art (SOTA) performance on the VoiceBank+Demand dataset, reaching a PESQ score of 3.61 with only 1.41M parameters.

关键词： Time-frequency analysis Computational modeling Speech enhancement Signal processing Feature extraction Transformers Computational efficiency Complexity theory Convolutional neural networks Kernel

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Smart Sampling and Residual Bottleneck Dense Network for Reliable and Energy-Efficient Routing in WSNs

Smart Sampling and Residual Bottleneck Dense Network for Rel...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Asma Anjum Dattathreya Salman Arafath Mohammed Dhaval Rabadiya K. B. Venkata Brahma Rao Ramya Maranan Department of Computer Science and Engineering HKBK College of Engineering Bengaluru Karnataka Department of Electronics and Communication Engineering Alva’s Institute of Engineering and Technology Moodbidri Karnataka Department of Electrical Engineering Computer Engineering Section College of Engineering King Khalid University Abha Asir Region Saudi Arabia Faculty of Management Studies Parul University Dist. Vadodara Gujarat Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The fundamental requirement for WSNs made up of power-efficient and inexpensive sensor nodes depends on short-distance multi-hop communication. The potential of WSNs expands through new digital electronics and wireless communications technology despite the ongoing critical challenge related to energy efficiency. The main energy issues in WSNs stem from the excessively high control signal overhead between base stations and sensor nodes together with packet losses that require retransmissions to reduce energy efficiency. The proposed routing technique solves energy inefficiencies by employing RBDN-HMOA as an energy-efficient routing method. The proposed approach lowers system power consumption through its algorithm which picks distance and energy residual-based optimum forwarding nodes and adapts sampling rates automatically. The algorithm adopts Human Memory Optimization Algorithm (HMOA) to optimize network parameters. Research data shows that experimental results demonstrate both power efficiency and enhanced data transfer rate. The network demonstrates scalability because both throughputs reach 1100 kbps and energy usage reduces to 0.08 J when using 10 nodes. The RBDN-HMOA creates a practical method which extends network duration in practical deployments by diminishing WSN energy utilization without compromising data integrity.

关键词： Wireless sensor networks Energy consumption Fluctuations Heuristic algorithms Throughput Routing Energy efficiency Real-time systems Delays Optimization

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