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Enhancing aquaculture water quality forecasting using novel adaptive multi-channel spatial-temporal graph convolutional network

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International Journal of Agricultural and Biological engineering 2025年第1期18卷 279-291页

作者： Tianqi Xiang Xiangyun Guo Junjie Chi Juan Gao Luwei Zhang College of Computer Science Beijing Information Science&Technology UniversityBeijing 102206China College of Management Science and Engineering Beijing Information Science&Technology UniversityBeijing 102206China College of Engineering China Agricultural UniversityBeijing 100083China

In recent years,aquaculture has developed rapidly,especially in coastal and open ocean *** practice,water quality prediction is of critical ***,traditional water quality prediction models face limitations in handling complex spatiotemporal *** address this challenge,a prediction model was proposed for water quality,namely an adaptive multi-channel temporal graph convolutional network(AMTGCN).The AMTGCN integrates adaptive graph construction,multi-channel spatiotemporal graph convolutional network,and fusion layers,and can comprehensively capture the spatial relationships and spatiotemporal patterns in aquaculture water quality *** aquaculture water quality data and the metrics MAE,RMSE,MAPE,and R^(2) were collected to validate the *** results show that the AMTGCN presents an average improvement of 34.01%,34.59%,36.05%,and 17.71%compared to LSTM,respectively;an average improvement of 64.84%,56.78%,64.82%,and 153.16%compared to the STGCN,respectively;an average improvement of 55.25%,48.67%,57.01%,and 209.00%compared to GCN-LSTM,respectively;and an average improvement of 7.05%,5.66%,7.42%,and 2.47%compared to TCN,*** indicates that the AMTGCN,integrating the innovative structure of adaptive graph construction and multi-channel spatiotemporal graph convolutional network,could provide an efficient solution for water quality prediction in aquaculture.

关键词： water quality prediction aquaculture spatial-temporal graph convolutional network multi-channel adaptive graph construction

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Double integral-enhanced Zeroing neural network with linear noise rejection for time-varying matrix inverse

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 197-210页

作者： Bolin Liao Luyang Han Xinwei Cao Shuai Li Jianfeng Li College of Computer Science and Engineering Jishou UniversityJishouChina School of Management Shanghai UniversityShanghaiChina School of Engineering Swansea UniversitySwanseaUK

In engineering fields,time-varying matrix inversion(TVMI)issue is often *** neural network(ZNN)has been extensively employed to resolve the TVMI ***,the original ZNN(OZNN)and the integral-enhanced ZNN(IEZNN)usually fail to deal with the TVMI problem under unbounded noises,such as linear ***,a neural network model that can handle the TVMI under linear noise interference is urgently *** paper develops a double integral-enhanced ZNN(DIEZNN)model based on a novel integral-type design formula with inherent linear-noise ***,its convergence and robustness are verified by deriva-tion *** comparison and verification,the OZNN and the IEZNN models are adopted to resolve the TVMI under multiple identical noise *** experi-ments proved that the DIEZNN model has excellent advantages in solving TVMI problems under linear *** general,the DIEZNN model is an innovative work and is proposed for the first ***,the errors of DIEZNN are always less than 1�10−3 under linear noises,whereas the error norms of OZNN and IEZNN models are not convergent to *** addition,these models are applied to the control of the controllable permanent magnet synchronous motor chaotic system to indicate the superiority of the DIEZNN.

关键词： neural network real-time systems

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Manifold grasshopper optimization based extremely disruptive vision transformer model for automatic heart disease detection in raw ECG signals

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Multimedia Tools and Applications 2024年 1-31页

作者： Golande, Avinash L. Pavankumar, T. Department of Computer Engineering JSPM’s Rajarshi Shahu College of Engineering Pune India Department of Computer Science & amp Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada520002 India

Automated detection of cardiovascular diseases based on heartbeats is a difficult and demanding task in signal processing because the routine analysis of the patient’s cardiac arrhythmia is crucial to reducing the mortality rate. Detecting and preventing these deaths requires long-term monitoring and manual examination of electrocardiogram (ECG) signals, which takes a lot of time. This article uses an optimized Vision Transformer technique to effectively detect heart disease. The four key processes are pre-processing input data, feature extraction from pre-processed data, and optimal feature selection and classification to detect heart disease. In the pre-processing phase, single-channel adaptive blind source separation is used for artifact removal and empirical mode decomposition for noise reduction of the ECG signal. After pre-processing, the ECG signal is fed into the Enhanced Pan-Tompkins algorithm (EPTA) and the Hybrid Gabor-Walsh-Hadamard transform (HGWHT) for feature extraction. The extracted feature is selected using a Manifold Grasshopper Optimization algorithm (MGOA). Finally, an Optimized Vision Transformer (OVT) detects heart disease. The experiment is carried out on PTB diagnostic ECG and PTB-XL database, a publicly accessible research datasets. The experiment obtained the following values: accuracy 99.9%, sensitivity 98%, F1 score 99.9%, specificity 90%, processing time 13.254 s, AUC 99.9% and MCC 91% using PTB diagnostic ECG. On the other hand, the proposed method has obtained an accuracy of 99.57%, f1-score of 99.17% and AUC of 99% using PTB-XL dataset. Thus, the overall findings prove that the proposed method outperforms the existing methodology. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blind source separation

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NVS-GAN: Benefit of generative adversarial network on novel view synthesis

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International Journal of Intelligent Networks 2024年第1期5卷 184-195页

作者： Shrisha, H.S. Anupama, V. Department of Computer Science & Engineering St Joseph Engineering College Mangaluru575028 India Visvesvaraya Technological University Belagavi590018 India

The methodology to generate new views for an object from provided input object view is called Novel View Synthesis (NVS). Humans imagine novel views through prior knowledge gathered through their lifetime. NVS-GAN predicts the novel views through computation. Literature survey reveals that there are limited NVS models with low Trainable Parameter Count (TPC) and low model size. Also, a study on the effect of different loss functions on NVS models was lacking. Lowering the TPC indicates less computational steps for the model to predict the output, therefore desirable. Combined with a low model size, the proposed model will become more suitable for deployment in diverse devices having limited resources for computation. Application of right combination of loss functions yield better accuracy. To address these research gaps, NVS-GAN is proposed. NVS-GAN is a Generative Adversarial Network (GAN) approach which yields NVS-Generator which performs NVS. NVS-Generator incorporates identity skip connections, bilinear sampling module, Depthwise Separable Convolution (DSC) as design features and results in low TPC, model size. In addition to discriminator loss, NVS-GAN is trained with different combinations of loss functions i.e. Mean Absolute Error (MAE) loss, Structural Similarity Index Measure (SSIM) loss, Huber loss on chair and car objects of ShapeNet dataset. The performance of NVS-Generator on test set measured in terms of MAE and SSIM is tabulated and analysed. The performance is compared with existing NVS models. The proposed NVS-GAN experiment recorded reduction in NVS-Generator TPC in 37 %–54.6 % range and reduction in model size between 37.2 % and 47.6 % range. NVS-Generator reduced MAE upto 55 % and improved SSIM upto 4 % than existing models. Summarily, NVS-GAN increased model performance and made the model "lightweight". © 2024 The Authors

关键词： Generative adversarial networks

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Conditional Generative Adversarial Network Approach for Autism Prediction

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computer Systems science & engineering 2023年第1期44卷 741-755页

作者： K.Chola Raja S.Kannimuthu Department of Computer Science and Engineering Sri Eshwar College of EngineeringCoimbatore641202TamilnaduIndia Department of Information Technology Karpagam College of EngineeringCoimbatore641032TamilnaduIndia

Autism Spectrum Disorder(ASD)requires a precise diagnosis in order to be managed and ***-invasive neuroimaging methods are disease markers that can be used to help diagnose *** majority of available techniques in the literature use functional magnetic resonance imaging(fMRI)to detect ASD with a small dataset,resulting in high accuracy but low *** supervised machine learning classification algorithms such as support vector machines function well with unstructured and semi structured data such as text,images,and videos,but their performance and robustness are restricted by the size of the accompanying training *** learning on the other hand creates an artificial neural network that can learn and make intelligent judgments on its own by layering *** takes use of plentiful low-cost computing and many approaches are focused with very big datasets that are concerned with creating far larger and more sophisticated neural *** modelling,also known as Generative Adversarial Networks(GANs),is an unsupervised deep learning task that entails automatically discovering and learning regularities or patterns in input data in order for the model to generate or output new examples that could have been drawn from the original *** are an exciting and rapidly changingfield that delivers on the promise of generative models in terms of their ability to generate realistic examples across a range of problem domains,most notably in image-to-image translation tasks and hasn't been explored much for Autism spectrum disorder prediction in the *** this paper,we present a novel conditional generative adversarial network,or cGAN for short,which is a form of GAN that uses a generator model to conditionally generate *** terms of prediction and accuracy,they outperform the standard *** pro-posed model is 74%more accurate than the traditional methods and takes only around 10 min for training even with a huge dat

关键词： Autism classification attributes imaging adversarial fMRI functional graph neural networks

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Analyzing Body Language and Facial Expressions Using Machine Learning Techniques

Analyzing Body Language and Facial Expressions Using Machine...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Patel, Harvendra Kumar Rai, Vijendra Singh, Hare Ram Kumar, Rakesh Iimt College of Engineering Greator Department of Computer Science and Engineering Noida India Iec College of Engineering and Technology Greator Department of Computer Science and Engineering Noida India Gniot Noida India

ISBN: (纸本)9798331508685

Body Language decoding is an important aspect of understanding human emotions, intentions, and personality by interpreting non-verbal cu es. It helps to get insights into human behavior, mindset, and psychology of the person. AI Body Talk is a software system that is capable of detecting and interpreting the body language of a person in real-time inference. It can recognize the sign language along with face, hand, and pose estimation with immediate feedback. It empowers the minority group of society who use sign language to communicate in society equipped with the Mediapipe to train custom models with its pre-built solutions to detect face landmarks, hand-tracking, and pose estimation. The results exemplify the system's potential for various applications like monitoring audience distraction, inspecting interviewee personality, understanding cultural sensitivity, diagnosing patients' health, and more. © 2025 IEEE.

关键词： Face recognition

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MediServe:An IoT-Enhanced Deep Learning Framework for Personalized Medication Management for Elderly Care

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computers, Materials & Continua 2025年第4期83卷 935-976页

作者： Smita Kapse Ganesh Yenurkar Vincent Omollo Nyangaresi Gunjan Balpande Shravani Kale Manthan Jadhav Sahil Lawankar Vikrant Jaunjale Department of Computer Technology Yeshwantrao Chavan College of EngineeringWanadongariNagpur441110MaharashtraIndia Computer Science&Engineering Jaramogi Oginga Odinga University of Science&TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennai602105TamilnaduIndia

In today’s fast-paced world,many elderly individuals struggle to adhere to their medication schedules,especially those with memory-related conditions like Alzheimer’s disease,leading to serious health risks,hospital-izations,and increased healthcare *** reminder systems often fail due to a lack of personalization and real-time *** address this critical challenge,we introduce MediServe,an advanced IoT-enabled medication management system that seamlessly integrates deep learning techniques to provide a personalized,secure,and adaptive *** features a smart medication box equipped with biometric authentication,such as fingerprint recognition,ensuring authorized access to prescribed medication while preventing misuse.A user-friendly mobile application complements the system,offering real-time notifications,adherence tracking,and emergency alerts for caregivers and healthcare *** system employs predictive deep learning models,achieving an impressive classification accuracy of 98%,to analyze user behavior,detect anomalies in medication adherence,and optimize scheduling based on an individual’s habits and health ***,MediServe enhances accessibility by employing natural language processing(NLP)models for voice-activated interactions and text-to-speech capabilities,making it especially beneficial for visually impaired users and those with cognitive ***-based data analytics and wireless connectivity facilitate remote monitoring,ensuring that caregivers receive instant alerts in case of missed doses or medication ***,machine learning-based clustering and anomaly detection refine medication reminders by adapting to users’changing health *** combining IoT,deep learning,and advanced security protocols,MediServe delivers a comprehensive,intelligent,and inclusive solution for medication *** innovative approach not only improves the quality of life for elderly

关键词： MediServe medication health risks smart medication box

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Comparative analysis of twelve transfer learning models for the prediction and crack detection in concrete dams,based on borehole images

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Frontiers of Structural and Civil engineering 2024年第10期18卷 1507-1523页

作者： Umer Sadiq KHAN Muhammad ISHFAQUE Saif Ur Rehman KHAN Fang Xu Lerui CHEN Yi LEI School of Computer and Information Science Hubei Engineering UniversityXiaogan 432000China Institute for AI Industrial Technology Research Hubei Engineering UniversityXiaogan 432000China College of Water Conservancy and Hydropower Engineering Hohai UniversityNanjing 210098China School of Computer Science and Engineering Central South UniversityChangsha 410083China College of Aviation Zhongyuan University of TechnologyZhengzhou 451191China School of Civil Engineering Central South UniversityChangsha 410083China

Disaster-resilient dams require accurate crack detection,but machine learning methods cannot capture dam structural reaction temporal patterns and *** research uses deep learning,convolutional neural networks,and transfer learning to improve dam crack *** deep-learning models are trained on 192 crack *** research aims to provide up-to-date detecting techniques to solve dam crack *** finding shows that the EfficientNetB0 model performed better than others in classifying borehole concrete crack surface tiles and normal(undamaged)surface tiles with 91%*** study’s pre-trained designs help to identify and to determine the specific locations of cracks.

关键词： concrete dam borehole closed-circuit television deep learning models crack detection water resources management management

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Advancing Android Malware Detection with BioSentinel Neural Network using Hybrid Deep Learning Techniques 18

Advancing Android Malware Detection with BioSentinel Neural ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Rani, D. Sandhya Gnaneshwar, K. Sampurnima Pattem, K. Sekhar, Soma Krishna, G. Bala Kakarla, Shirisha Cvr College of Engineering Department of Computer Science Engineering Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engineering-Cyber Security Hyderabad India Vardhaman College of Engineering Department of Computer Science Engineering Hyderabad India

ISBN: (纸本)9789380544519

The paper introduces the BioSentinel Neural Network (BSNN), a novel hybrid deep learning model designed to enhance malware detection, particularly focusing on zero-day threats. The BSNN model integrates diverse neural network architectures, leveraging Graph Neural Networks (GNNs) for feature extraction and Transformers for sequential data analysis. It demonstrates significant improvements in accuracy (93.16%), precision (90.89%), recall (88.19%), and F1-score (90.45%) over traditional methods. The model exhibits a high detection rate of 88% for zero-day malware from 1000 samples tested and shows computational efficiency with an average inference time of 30 milliseconds. This research addresses critical gaps in current malware detection approaches, highlighting the potential of hybrid deep learning techniques in the cybersecurity domain. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Cybersecurity Deep Learning Hybrid Neural Networks Malware Detection Real-Time Analysis Zero-Day Threats

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Cognitive agent based fault tolerance in ubiquitous networks: a machine learning approach

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International Journal of Information Technology (Singapore) 2024年第4期16卷 2363-2377页

作者： Bhajantri, Lokesh B. Ayyannavar, Vasudha V. Department of Information Science and Engineering Basaveshwar Engineering College Karnataka Bagalkote India Department of Computer Science and Engineering Basaveshwar Engineering College Bagalkote VTU Karnataka Belagavi India

Ubiquitous Networks play an essential role in accessing ubiquitous computing services at anytime, anywhere, and anyplace through computing nodes of heterogeneous networks. Nowadays, ubiquitous network faces various issues related to fault management or tolerance in a real world environment. In this type of network, fault tolerance is one of the vital issues due to dynamic and uncertain character of ubiquitous networks with various constraints of nodes and networks to maximize network lifetime, availability, and reliability of the networks. In this paper, it is proposed that a cognitive agent based fault tolerance system using reinforcement learning algorithm to provide efficient ubiquitous services to the users over the networks. The main objective of the proposed work is to emphasize the analysis, identification, and recovery of various faults under resource constraints of ubiquitous networks in terms of node, memory, load, and network level faults (link level fault and packet error) using a cognitive agent based machine learning algorithm. Also, the proposed work performs better fault tolerance than conventional methods (without using cognitive agents) by considering fault detection rate, fault recovery rate, fault recovery time, packet delivery ratio, agent computation overhead, and energy consumption for performing the desired set of tasks and services ubiquitously in the ubiquitous computing environment. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Cognitive agents Heterogeneous network systems Reinforcement learning Ubiquitous networks Ubiquitous service systems

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