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Early Identification and Visualization of Parkinsonian Gaits and their Stages Using convolution neural networks and Finite Element Techniques

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Instrumentation 2020年第3期7卷 33-42页

作者： Musthaq AHAMED P.D.S.H.GUNAWARDANE Nimali T.MEDAGEDARA Department of Mechanical Engineering Faculty of Engineering TechnologyThe Open University of Sri lanka Department of Mechanical Engineering The University of British ColumbiaVancouverCanada

Parkinson’s Disease(PD)is a neurodegenerative disease which shows a deficiency in dopaminehormone in the *** is a common irreversible impairment among elderly *** this disease in its preliminary stage is important to improve the efficacy of the treatment *** gait is one of the key indications of early symptoms of ***,the present paper introduces a novel approach to identify pa rkinsonian gait using raw vertical spatiotemporal ground reaction force.A convolution neural network(CNN)is implemented to identify the features in the parkinsonian gaits and their progressive ***,the var iations of the gait pressures were visually recreated using ANSYS finite element software *** CNN model has shown a 97%accuracy of recognizing parkinsonian gait and their different stages,and ANSYS model is implemented to visualize the pressure variation of the foot during a bottom-up approach.

关键词： convolution neural networks Vertical Ground Reaction Force Parkinsonian Gait Finite Element Analysis

Semantic segmentation of agricultural images:A survey

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Information Processing in Agriculture 2024年第2期11卷 172-186页

作者： Zifei Luo Wenzhu Yang Yunfeng Yuan Ruru Gou Xiaonan Li School of Cyber Security and Computer Hebei UniversityHebei 071002China Hebei Machine Vision Engineering Research Center BaodingHebei 071002China College of Education Hebei UniversityHebei 071002China

As an important research topic in recent years,semantic segmentation has been widely applied to image understanding problems in various *** the successful application of deep learning methods in machine vision,the superior performance has been transferred to agricultural image processing by combining them with traditional *** segmentation methods have revolutionized the development of agricultural automation and are commonly used for crop cover and type analysis,pest and disease identification,*** frst give a review of the recent advances in traditional and deep learning methods for semantic segmentation of agricultural images according to different segmentation *** we introduce the traditional methods that can effectively utilize the original image information and the powerful performance of deep learningbased ***,we outline their applications in agricultural image *** our literature,we identify the challenges in agricultural image segmentation and summarize the innovative developments that address these *** robustness of the existing segmentation methods for processing complex images still needs to be improved urgently,and their generalization abilities are also *** particular,the limited number of labeled samples is a roadblock to new developed deep learning methods for their training and *** this,segmentation methods that augment the dataset or incorporate multimodal information enable deep learning methods to further improve the segmentation *** review provides a reference for the application of image semantic segmentation in the field of agricultural informatization.

关键词： Semantic segmentation Agricultural images Deep learning convolution neural networks

A Low-Power 12-Bit SAR ADC for Analog convolutional Kernel of Mixed-Signal CNN Accelerator

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Computers, Materials & Continua 2023年第5期75卷 4357-4375页

作者： Jungyeon Lee Malik Summair Asghar HyungWon Kim Department of Electronics College of Electrical and Computer EngineeringChungbuk National UniversityCheongju28644Korea Department of Electrical and Computer Engineering COMSATS University IslamabadAbbottabad CampusUniversity RoadTobe CampAbbottabad22044Pakistan

As deep learning techniques such as convolutional neural networks(CNNs)are widely adopted,the complexity of CNNs is rapidly increasing due to the growing demand for CNN accelerator system-on-chip(SoC).Although conventional CNN accelerators can reduce the computational time of learning and inference tasks,they tend to occupy large chip areas due to many multiply-and-accumulate(MAC)operators when implemented in complex digital circuits,incurring excessive power *** overcome these drawbacks,this work implements an analog convolutional filter consisting of an analog multiply-and-accumulate arithmetic circuit along with an analog-to-digital converter(ADC).This paper introduces the architecture of an analog convolutional kernel comprised of low-power ultra-small circuits for neural network accelerator *** is an essential component of the analog convolutional kernel used to convert the analog convolutional result to digital values to be stored in *** work presents the implementation of a highly low-power and area-efficient 12-bit Successive Approximation Register(SAR)*** most other SAR-ADCs with differential structure;the proposed ADC employs a single-ended capacitor array to support the preceding single-ended max-pooling circuit along with minimal power *** SARADCimplementation also introduces a unique circuit that reduces kick-back noise to increase *** was implemented in a test chip using a 55 nm CMOS *** demonstrates that the proposed ADC reduces Kick-back noise by 40%and consequently improves the ADC’s resolution by about 10%while providing a near rail-to-rail dynamic rangewith significantly lower power consumption than conventional *** ADC test chip shows a chip size of 4600μm^(2)with a power consumption of 6.6μW while providing an signal-to-noise-and-distortion ratio(SNDR)of 68.45 dB,corresponding to an effective number of bits(ENOB)of 11.07 bits.

关键词： convolution neural networks split-capacitor-based digital-toanalog converter(DAC) SAR analog-to-digital converter artificial intelligence system-on-chip analog convolutional kernel

Identification of Weather Phenomena Based on Lightweight convolutional neural networks

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Computers, Materials & Continua 2020年第9期64卷 2043-2055页

作者： Congcong Wang Pengyu Liu Kebin Jia Xiaowei Jia Yaoyao Li Faculty of Information Technology Beijing University of TechnologyBeijing100124China Beijing Laboratory of Advanced Information networks Beijing100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of TechnologyBeijing100124China Department of Computer Science and Engineering University of MinnesotaTwin CitiesUSA

Weather phenomenon recognition plays an important role in the field of ***,weather radars and weathers sensor have been widely used for weather ***,given the high cost in deploying and maintaining the devices,it is difficult to apply them to intensive weather phenomenon ***,advanced machine learning models such as convolutional neural networks(CNNs)have shown a lot of promise in meteorology,but these models also require intensive computation and large memory,which make it difficult to use them in *** practice,lightweight models are often used to solve such ***,lightweight models often result in significant performance *** this end,after taking a deep dive into a large number of lightweight models and summarizing their shortcomings,we propose a novel lightweight CNNs model which is constructed based on new building *** experimental results show that the model proposed in this paper has comparable performance with the mainstream non-lightweight model while also saving 25 times of memory *** memory reduction is even better than that of existing lightweight models.

关键词： Deep learning convolution neural networks lightweight models weather identification

Deep Learning convolutional neural Network for ECG Signal Classification Aggregated Using IoT

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Computer Systems Science & Engineering 2022年第9期42卷 851-866页

作者： S.Karthiga A.M.Abirami Department of Information Technology Thiagarajar College of Engineering625015Tamil NaduIndia

Much attention has been given to the Internet of Things (IoT) by citizens, industries, governments, and universities for applications like smart buildings, environmental monitoring, health care and so on. With IoT, networkconnectivity is facilitated between smart devices from anyplace and ***-based health monitoring systems are gaining popularity and acceptance forcontinuous monitoring and detect health abnormalities from the data *** (ECG) signals are widely used for heart diseases detection.A novel method has been proposed in this work for ECG monitoring using IoTtechniques. In this work, a two-stage approach is employed. In the first stage, arouting protocol based on Dynamic Source Routing (DSR) and Routing byEnergy and Link quality (REL) for IoT healthcare platform is proposed for effi-cient data collection, and in the second stage, classification of ECG for Arrhythmia. Furthermore, this work has evaluated Support Vector Machine (SVM),Artificial neural Network (ANN), and convolution neural networks (CNNs)-based approach for ECG signals classification. Deep-ECG will use a deep CNNto extract critical features and then compare through evaluation of simple and fastdistance functions in order to obtain an efficient classification of heart abnormalities. For the identification of abnormal data, this work has proposed techniquesfor the classification of ECG data, which has been obtained from mobile watchusers. For experimental verification of the proposed methods, the Beth Israel Hospital (MIT/BIH) Arrhythmia and Massachusetts Institute of Technology (MIT)Database was used for evaluation. Results confirm the presented method’s superior performance with regards to the accuracy of classification. The CNN achievedan accuracy of 91.92% and has a higher accuracy of 4.98% for the SVM and2.68% for the ANN.

关键词： Internet of things electrocardiographic signals dynamic source routing routing by energy and link quality convolution neural networks

An Optimized and Hybrid Framework for Image Processing Based Network Intrusion Detection System

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Computers, Materials & Continua 2022年第11期73卷 3921-3949页

作者： Murtaza Ahmed Siddiqi Wooguil Pak Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

The network infrastructure has evolved rapidly due to the everincreasing volume of users and *** massive number of online devices and users has forced the network to transform and facilitate the operational necessities of *** these necessities,network security is of prime *** intrusion detection systems(NIDS)are among the most suitable approaches to detect anomalies and assaults on a ***,keeping up with the network security requirements is quite challenging due to the constant mutation in attack patterns by the *** paper presents an effective and prevalent framework for NIDS by merging image processing with convolution neural networks(CNN).The proposed framework first converts non-image data from network traffic into images and then further enhances those images by using the Gabor *** images are then classified using a CNN *** assess the efficacy of the recommended method,four benchmark datasets i.e.,CSE-CIC-IDS2018,CIC-IDS-2017,ISCX-IDS 2012,and NSL-KDD were *** proposed approach showed higher precision in contrast with the recent work on the mentioned ***,the proposed method is compared with the recent well-known image processing methods for NIDS.

关键词： Anomaly detection convolution neural networks deep learning image processing intrusion detection network intrusion detection

Improving Date Fruit Classification Using Cycle GAN-Generated Dataset

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Computer Modeling in Engineering & Sciences 2022年第4期131卷 331-348页

作者： Dina M.Ibrahim Nada M.Elshennawy Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computers and Control Engineering Department Faculty of EngineeringTanta UniversityTanta37133Egypt

Dates are an important part of human *** are high in essential nutrients and provide a number of health *** fruits are also known to protect against a number of diseases,including cancer and heart *** fruits have several sizes,colors,tastes,and *** are a lot of challenges facing the date *** of the most significant challenges is the classification and sorting of *** there is no public dataset for date fruits,which is a major limitation in order to improve the performance of convolutional neural networks(CNN)models and avoid the overfitting *** this paper,an augmented date fruits dataset was developed using Deep convolutional Generative Adversarial networks(DCGAN)and CycleGAN approach to augment our collected date fruit *** augmentation is required to address the issue of a restricted number of images in our datasets,as well as to establish a balanced *** are three types of dates in our proposed dataset:Sukkari,Ajwa,and *** dataset augmentation,we train our created dataset using ResNet152V2 and CNN models to assess the classification process for our three categories in the *** train these two models,we start with the original ***,the models were trained using the DCGAN-generated dataset,followed by the CycleGAN-generated *** resulting results demonstrated that when using the ResNet152V2model,the CycleGAN-generated dataset had the highest classification performance with 96.8%accuracy,followed by the CNN model with 94.3%accuracy.

关键词： Dates fruits data augmentation DCGAN CycleGAN,deep learning convolution neural networks

Liver Tumor Prediction with Advanced Attention Mechanisms Integrated into a Depth-Based Variant Search Algorithm

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Computers, Materials & Continua 2023年第10期77卷 1209-1226页

作者： P.Kalaiselvi S.Anusuya Department of Computer Science and Engineering Saveetha School of EngineeringChennaiTamil Nadu602117India Information Technology Saveetha School of EngineeringChennaiTamil Nadu602117India

In recent days,Deep Learning(DL)techniques have become an emerging transformation in the field of machine learning,artificial intelligence,computer vision,and so ***,researchers and industries have been highly endorsed in the medical field,predicting and controlling diverse diseases at specific *** tumor prediction is a vital chore in analyzing and treating liver *** paper proposes a novel approach for predicting liver tumors using convolutional neural networks(CNN)and a depth-based variant search algorithm with advanced attention mechanisms(CNN-DS-AM).The proposed work aims to improve accuracy and robustness in diagnosing and treating liver *** anticipated model is assessed on a Computed Tomography(CT)scan dataset containing both benign and malignant liver *** proposed approach achieved high accuracy in predicting liver tumors,outperforming other state-of-the-art ***,advanced attention mechanisms were incorporated into the CNN model to enable the identification and highlighting of regions of the CT scans most relevant to predicting liver *** results suggest that incorporating attention mechanisms and a depth-based variant search algorithm into the CNN model is a promising approach for improving the accuracy and robustness of liver tumor *** can assist radiologists in their diagnosis and treatment *** proposed system achieved a high accuracy of 95.5%in predicting liver tumors,outperforming other state-of-the-art methods.

关键词： Deep learning convolution neural networks liver tumors CT scans attention mechanism classifier

An Enhanced Deep Learning Method for Skin Cancer Detection and Classification

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Computers, Materials & Continua 2022年第10期73卷 1109-1123页

作者： Mohamed W.Abo El-Soud Tarek Gaber Mohamed Tahoun Abdullah Alourani Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt School of Science Engineeringand EnvironmentUniversity of SalfordUK

The prevalence of melanoma skin cancer has increased in recent *** greatest risk from melanoma is its ability to broadly spread throughout the body by means of lymphatic vessels and ***,the early diagnosis of melanoma is a key factor in improving the prognosis of the *** learning makes it possible to design and develop intelligent systems that can be used in detecting and classifying skin lesions from visible-light *** systems can provide early and accurate diagnoses of melanoma and other types of skin *** paper proposes a new method which can be used for both skin lesion segmentation and classification *** solution makes use of convolutional neural networks(CNN)with the architecture two-dimensional(Conv2D)using three phases:feature extraction,classification and *** proposed method is mainly designed for skin cancer detection and *** the public dataset International Skin Imaging Collaboration(ISIC),the impact of the proposed segmentation method on the performance of the classification accuracy was *** obtained results showed that the proposed skin cancer detection and classification method had a good performance with an accuracy of 94%,sensitivity of 92%and specificity of 96%.Also comparing with the related work using the same dataset,i.e.,ISIC,showed a better performance of the proposed method.

关键词： convolution neural networks activation function separable convolution 2D batch normalization max pooling classification