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4th IEEE International Conference on Data engineering and Communication systems, ICDECS 2024

作者： Kadyan, Laksh Neerugatti, Vikram Taranum, Ayesha Rani, Geetha Somasekar, J. Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350393354

Machine learning (ML) has developed at a superlative rate, accompanying requests spanning various fields. This research investigates the experience of strength data, exceptionally the request of machine learning (ML) algorithms to a Body Mass Index (BMI) dataset. The basic aim of searching out unwinds the dossier's many linkages and patterns, eventually chief to more thorough information of the variables deciding BMI. The study starts accompanying an initiation to the subject within reach, understood by a thorough study of appropriate work, a complex mechanics division, and an itemized reason of the reached results. However, because of advances in Machine Learning, we immediately have the talent to handle this issue in a more excellent manner. We've built an advance dossier-study system that can think a patient has diabetes, a suggestion of correction, admitting for early mediation. This predicting plan uses dossier analysis methods to extractable intuitions from a big number of diabetes-accompanying facts. Its basic aim is to correctly determine a patient's risk of diabetes. We've working categorization plans to a degree Decision Tree, Artificial Neural Networks (ANN), Naive Bayes, and Support Vector Machine (SVM) algorithms to cultivate the model. These outcomes show the influence of the subsystems in thinking diabetes risk admit a large size of veracity. This predictive finish can create a meaningful dissimilarity in labeling at-risk things early and providing bureaucracy with essential care and counseling before the ailment progresses. In summary, our machine intelligence-located scheme offers a natural still strong solution to call the risk of diabetes in subjects. By controlling the wherewithal of dossier reasoning and categorization algorithms, we can enhance early discovery and deterrent measures for this weighty affliction, eventually reconstructing patient consequences and reducing the burden of BMI-related complications. © 2024 IEEE.

关键词： Decision trees

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A self-predictive diagnosis system of liver failure based on multilayer neural networks

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Multimedia Tools and Applications 2024年第36期83卷 83769-83788页

作者： Dashti, Fatemeh Ghaffari, Ali Seyfollahi, Ali Arasteh, Bahman Department of Engineering Tabriz Electricity Distribution Company Tabriz Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Faculty of Engineering and Natural Science Istinye University Istanbul Turkey

The lack of symptoms in the early stages of liver disease may cause wrong diagnosis of the disease by many doctors and endanger the health of patients. Therefore, earlier and more accurate diagnosis of liver problems is necessary for proper treatment and prevention of serious damage to this vital organ. We attempted to develop an intelligent system to detect liver failure using data mining and artificial neural networks (ANN), this approach considers all factors impacting patient identification and enhances the probability of success in diagnosing liver failure. We employ multilayer perceptron neural networks for diagnosing liver failure via a liver patient dataset (ILDP). The proposed approach using the backpropagation algorithm, improves the diagnosis rate, and predicts liver failure intelligently. The simulation and data analysis outputs revealed that the proposed method has 99.5% accuracy, 99.65% sensitivity, and 99.57% specificity, making it more accurate than Previous related methods. © The Author(s) 2024.

关键词： Multilayer neural networks

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An Electrical Impedance Imaging System Towards Edge Intelligence for Non-Destructive Testing of Concrete

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Tsinghua science and Technology 2024年第3期29卷 883-896页

作者： Abolfazl Roshanpanah Mahdi Abbasi Hani Attar Ayman Amer Mohammad R.Khosravi Ahmed A.Solyman Department of Electrical Engineering Islamic Azad UniversityScience and Research BranceTehran ***Iran Department of Computer Engineering Bu-Ali Sina UniversityHamedan ***Iran Department of Energy Engineering Zarqa UniversityZarqa 13110Jordan Department of Computer Engineering Persian Gulf UniversityBushehr ***Iran Department of Electrical and Electronics Engineering Faculty of Engineering and ArchitectureNişantaşıUniversityIstanbul 34398Turkey

In the construction industry,to prevent accidents,non-destructive tests are necessary and *** impedance tomography is a new technology in non-invasive imaging in which the image of the inner part of conductive bodies is reconstructed by the arrays of external electrodes that are connected on the periphery of the *** equipment is cheap,fast,and edge *** this imaging method,the image of electrical conductivity distribution(or its opposite;electrical impedance)of the internal parts of the target object is *** image reconstruction process is performed by injecting a precise electric current to the peripheral boundaries of the object,measuring the peripheral voltages induced from it and processing the collected *** an electrical impedance tomography system,the voltages measured in the peripheral boundaries have a non-linear equation with the electrical conductivity *** paper presents a cheap Electrical Impedance Tomography(EIT)instrument for detecting impurities in the concrete.A voltage-controlled current source,a micro-controller,a set of multiplexers,a set of electrodes,and a personal computer constitute the structure of the *** conducted tests on concrete with impurities show that the designed EIT system can reveal impurities with a good accuracy in a reasonable time.

关键词： non-destructive testing system concrete electrical impedance tomography

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Behavior Based Group Recommendation from Social Media Dataset by Using Deep Learning and Topic Modeling

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SN computer science 2024年第6期5卷 712页

作者： Mukta, Md. Saddam Hossain Ahmed, Jubaer Raiaan, Mohaimenul Azam Khan Fahad, Nur Mohammad Islam, Muhammad Nazrul Imtiaz, Nafiz Islam, Md. Adnanul Ali, Mohammed Eunus Azam, Sami LUT School of Engineering Sciences Lappeenranta-Lahti University of Technology Department of Computer Science and Engineering United International University Department of Computer Science and Engineering Military Institute of Science and Technology Department of HCC Action Lab Monash University Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Faculty of Science and Technology Charles Darwin University

In this digital era, users frequently share their thoughts, preferences, and ideas through social media, which reflect their Basic Human Values. Basic Human Values (aka values) are the fundamental aspects of human behavior, which define what we consider important, and worth having and pursue them. Existing studies identify the values of individuals from different social network usages such as Facebook and Reddit. However, discovering the similarity (or diversity) of value priorities among the members in a group is important since we can reveal many interesting insights such as finding a set of target customers, identifying the chain of misdeed groups, searching for similar acquaintances in workplaces, etc. In this paper, a graph dataset is compiled using the strongest correlation among the features and then we apply a graph clustering technique to identify a suitable hedonist group (i.e., one dimension of values) for users’ recommendations. Then, we also propose a behavior based (i.e., value) group recommendation technique by analyzing users’ contextual and psychological attributes. Finally, we validate those group members in real life by introducing two hypotheses. In particular, we analyze the tweets of a total of 1140 users collected from Twitter. We obtain a substantial intra-cluster correlation coefficient (ICC) and silhouette clustering coefficient (SCC) scores of 65% and 76%, respectively, among the members in our discovered group. © The Author(s) 2024.

关键词： Graph neural network (GNN) Group recommendation Psychological attributes Social media Topic modeling Twitter Values

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Novel Framework of Segmentation 3D MRI of Brain Tumors

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computers, Materials & Continua 2023年第2期74卷 3489-3502页

作者： Ibrahim Mahmoud El-Henawy Mostafa Elbaz Zainab H.Ali Noha Sakr Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt Department of Computer Science Faculty of Computers and InformaticsKafrelsheikh UniversityKafrelsheikhEgypt Department of Embedded Network Sys.and Tec. Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikhEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Medical image segmentation is a crucial process for computer-aided diagnosis and *** image segmentation refers to portioning the images into small,disjointed parts for simplifying the processes of analysis and *** and speckle noise are different types of noise in magnetic resonance imaging(MRI)that affect the accuracy of the segmentation process ***,image enhancement has a significant role in MRI *** paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free *** proposed techniques consider the values of Peak Signal to Noise Ratio(PSNR)and the level of noise as inputs to the attention-U-Net model for segmentation of the *** framework has been divided into three stages:removing speckle and Rician noise,the segmentation stage,and the feature extraction *** framework presents solutions for each problem at a different stage of the *** the first stage,the framework uses Vibrational Mode Decomposition(VMD)along with Block-matching and 3D filtering(Bm3D)algorithms to remove the ***,the most significant Rician noise-free images are passed to the three different methods:Deep Residual Network(DeRNet),Dilated Convolution Auto-encoder Denoising Network(Di-Conv-AE-Net),andDenoising Generative Adversarial Network(DGAN-Net)for removing the speckle *** Bm3D have achieved PSNR values for levels of noise(0,0.25,0.5,0.75)for reducing the Rician noise by(35.243,32.135,28.214,24.124)and(36.11,31.212,26.215,24.123)*** framework also achieved PSNR values for removing the speckle noise process for each level as follows:(34.146,30.313,28.125,24.001),(33.112,29.103,27.110,24.194),and(32.113,28.017,26.193,23.121)forDeRNet,Di-Conv-AE-Net,and DGAN-Net,*** experiments that have been conducted have proved the efficiency of the proposed framework a

关键词： MRI Rician noise speckle noise segmentation deep learning

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Hybrid Deep Learning Based Attack Detection for Imbalanced Data Classification

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Intelligent Automation & Soft Computing 2023年第1期35卷 297-320页

作者： Rasha Almarshdi Laila Nassef Etimad Fadel Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science Faculty of Computer Science and EngineeringUniversity of HailHailSaudi Arabia

Internet of Things(IoT)is the most widespread and fastest growing technology *** to the increasing of IoT devices connected to the Internet,the IoT is the most technology under security *** IoT devices are not designed with security because they are resource constrained ***,having an accurate IoT security system to detect security attacks is *** Detection systems(IDSs)using machine learning and deep learning techniques can detect security attacks *** paper develops an IDS architecture based on Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)deep learning *** implement our model on the UNSW-NB15 dataset which is a new network intrusion dataset that cate-gorizes the network traffic into normal and attacks *** this work,interpolation data preprocessing is used to compute the missing ***,the imbalanced data problem is solved using a synthetic data generation *** experiments have been implemented to compare the performance results of the proposed model(CNN+LSTM)with a basic model(CNN only)using both balanced and imbalanced ***,with some state-of-the-art machine learning classifiers(Decision Tree(DT)and Random Forest(RF))using both balanced and imbalanced *** results proved the impact of the balancing *** proposed hybrid model with the balance technique can classify the traffic into normal class and attack class with reasonable accuracy(92.10%)compared with the basic CNN model(89.90%)and the machine learning(DT 88.57%and RF 90.85%)***,comparing the proposed model results with the most related works shows that the proposed model gives good results compared with the related works that used the balance techniques.

关键词： IoT IDS deep learning machine learning CNN LSTM

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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Parameter Tuning in Automatic Detection Algorithm for Detecting Artificial Brain Shift 24

Parameter Tuning in Automatic Detection Algorithm for Detect...

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6th International Conference on Intelligent Medicine and Image Processing, IMIP 2024

作者： Watanabe, Kaoru Noborio, Hiroshi Koeda, Masanao Department of Computer Science Osaka Electro-Communication University Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Japan

ISBN: (纸本)9798400710032

In neurosurgery to remove brain tumors, DICOM data, a medical imaging standard, is generated preoperatively using CT and MRI. This data is used for surgical planning. However, brain deformation problems, known as brain shift, occur during surgery, resulting in discrepancies from the preoperative surgical plan. Deviations from the surgical plan due to brain shift are a life-threatening problem as they reduce the success rate of surgery. Brain shifts have not yet been elucidated, and DICOM data acquired from MRIs and CTs for surgical planning remain stored and archived in hospital information centers. Here, it is planned to extract the physical parameters of the brain model from a large amount of brain deformation data using pre- and post-operative brain DICOM pairs, which are stored in large quantities in hospitals and discarded after a particular time. This study investigates the optimization of parameters in the brain shift detection algorithm. © 2024 ACM.

关键词： Digital Imaging and Communications in Medicine (DICOM)

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Mixed-decomposed convolutional network:A lightweight yet efficient convolutional neural network for ocular disease recognition

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 319-332页

作者： Xiaoqing Zhang Xiao Wu Zunjie Xiao Lingxi Hu Zhongxi Qiu Qingyang Sun Risa Higashita Jiang Liu Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhenChina Tomey Corporation NagoyaJapan Guangdong Provincial Key Laboratory of Brain‐inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhenChina Singapore Eye Research Institute SingaporeSingapore

Eye health has become a global health concern and attracted broad *** the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and ***,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model *** alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular *** MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer *** conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,University of California San Diego,and CIFAR-100 *** results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and ***,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset.

关键词： artificial intelligence deep learning deep neural networks image analysis image classification medical applications medical image processing

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AutYOLO-ATT: an attention-based YOLOv8 algorithm for early autism diagnosis through facial expression recognition

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Neural Computing and Applications 2024年第27期36卷 17199-17219页

作者： Hosney, Reham Talaat, Fatma M. El-Gendy, Eman M. Saafan, Mahmoud M. Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Nile Higher Institute for Engineering and Technology Mansoura Egypt

Autism Spectrum Disorder (ASD) is a developmental condition resulting from abnormalities in brain structure and function, which can manifest as communication and social interaction difficulties. Conventional methods for diagnosing ASD may not be effective in the early stages of the disorder. Hence, early diagnosis is crucial to improving the patient's overall health and well-being. One alternative and effective method for early autism diagnosis is facial expression recognition since autistic children typically exhibit distinct facial expressions that can aid in distinguishing them from other children. This paper provides a deep convolutional neural network (DCNN)-based real-time emotion recognition system for autistic kids. The proposed system is designed to identify six facial emotions, including surprise, delight, sadness, fear, joy, and natural, and to assist medical professionals and families in recognizing facial expressions in autistic children for early diagnosis and intervention. In this study, an attention-based YOLOv8 (AutYOLO-ATT) algorithm for facial expression recognition is proposed, which enhances the YOLOv8 model's performance by integrating an attention mechanism. The proposed method (AutYOLO-ATT) outperforms all other classifiers in all metrics, achieving a precision of 93.97%, recall of 97.5%, F1-score of 92.99%, and accuracy of 97.2%. These results highlight the potential of the proposed method for real-world applications, particularly in fields where high accuracy is essential. © The Author(s) 2024.

关键词： Diagnosis

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