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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Sajid, Muhammad Hassan, Ali Khan, Dilshad Ahmed Abbas Gilani, Sayed Tanveer Khan, Shoab Ahmed Bakhshi, Asim Dilawar CEME National University of Sciences and Technology Department of Computer and Software Engineering Islamabad Pakistan Rawalpindi Pakistan MCS National University of Sciences and Technology Department of Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

Coronary artery disease (CAD) is one of the most common causes of sudden cardiac arrest and is responsible for a high percentage of global mortality. Early diagnosis of CAD in patients with chest pain is challenging. Coronary angiography, an invasive procedure that is not widely available in developing countries, confirms the diagnosis of CAD. It is imperative to establish noninvasive biomarkers for the early diagnosis of CAD in patients with angina. Motivated by two recent medical diagnostic studies that used novel molecular (microRNA) and chemical (atherosclerosis-inflammatory) biomarkers for early detection of CAD, the proposed study explores the hidden potential of novel biomarkers using machine learning (ML). The study combines the biomarkers of two medical studies and presents a NUMS-NIHD dataset. From the complete dataset, the best feature set is shortlisted after an extensive evaluative search in the biomarker feature space. Ten ML classifiers are applied to the selected biomarker inputs, including clinical, chemical, and molecular cardiac biomarkers. To select optimal biomarker-classifier combinations, experiments use K-fold cross-validation. The results of K-fold cross-validation are validated against a hold-out set of data. The results are then compared to current state-of-the-art benchmark studies. The detection strategy achieved an accuracy of 90.18%, a sensitivity of 92.33 % and a specificity of 100.00%. The proposed study validated previous medical diagnostic studies and significantly improved the results. These ML biomarkers-classifier combinations can be used to exclude healthy individuals non-invasively before angiography. © 2024 IEEE.

关键词： Diseases

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High Performance Automatic ROI Extraction from Hand Images with Convolutional Neural Networks 8

High Performance Automatic ROI Extraction from Hand Images w...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Kosmaz Sunnetci, Betul Bingol, Ozkan Gedikli, Eyup Ekinci, Murat Gumushane University Department of Mathematical Engineering Gümüşhane Turkey Gumushane University Department of Software Engineering Gümüşhane Turkey Karadeniz Technical University Department of Software Engineering Trabzon Turkey Karadeniz Technical University Department of Computer Engineering Trabzon Turkey

ISBN: (纸本)9798331531492

Automatic ROI extraction from hand images is a critical step in palmprint recognition. Traditional methods involve steps such as identifying the region of the hand, finding the valleys between the fingers and marking the palm area. Some of these processes are performed manually and some are performed with image processing techniques. However, these methods are time-consuming, vulnerable to human error, and computationally redundant. In this study, a deep learning-based convolutional neural network is developed for high-performance automatic ROI extraction from hand images. The proposed method eliminates the hand segmentation process and provides a solution where the palm region can be automatically selected directly. This reduces processing time, minimizes human error and improves overall accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Preprocessing Method for Color-Adaptive Pix2Pix Generative Neural Network Data

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Journal of Institute of Control, Robotics and Systems 2023年第6期29卷 521-526页

作者： Lee, Hayoung Son, Bong-Ki Lee, Jaeho Department of IT Media Engineering Duksung Women’s University Korea Republic of Department of Computer Science and Engineering Seowon University Korea Republic of Department of Software Duksung Women’s University Korea Republic of

Adversarial generative neural networks (GANs) have received increased research interest in recent years, and they have been applied in various fields, such as image generation, transformation, and restoration. Pix2Pix, a popular GAN model, trains data in which input and output data are paired. However, because this method is based on sketches, it cannot consider colors once the training is completed. Not only to apply colors requires coloring on edges of all training data, which requires high effort to build data, but also it is difficult to automatically generate edges. In this paper, we proposed a method that considers colors and performs the automatic generation of edges required for Pix2Pix. The proposed method extracts edge data by using the canny edge detection algorithm and constructs color edge and color background data by using the extracted edge data. The applicability of the proposed method was verified by conducting experiments in the fashion field. © ICROS 2023.

关键词： Color

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Tracking daily paths in home contexts with RSSI fingerprinting based on UWB through deep learning models

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Multimedia Tools and Applications 2024年 1-25页

作者： Polo-Rodríguez, A. Valera, J.C. Peral, J. Gil, D. Medina-Quero, J. Department of Computer Engineering Automation and Robotics University of Granada Granada18071 Spain Alicante03690 Spain Department of Software and Computing Systems University of Alicante Alicante03690 Spain

The field of human activity recognition has evolved significantly, driven largely by advancements in Internet of Things (IoT) device technology, particularly in personal devices. This study investigates the use of ultra-wideband (UWB) technology for tracking inhabitant paths in home environments using deep learning models. UWB technology estimates user locations via time-of-flight and time-difference-of-arrival methods, which are significantly affected by the presence of walls and obstacles in real environments, reducing their precision. To address these challenges, we propose a fingerprinting-based approach utilizing received signal strength indicator (RSSI) data collected from inhabitants in two flats (60 m2 and 100 m2) while performing daily activities. We compare the performance of convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN+LSTM models, as well as the use of Bluetooth technology. Additionally, we evaluate the impact of the type and duration of the temporal window (future, past, or a combination of both). Our results demonstrate a mean absolute error close to 50 cm, highlighting the superiority of the hybrid model in providing accurate location estimates, thus facilitating its application in daily human activity recognition in residential settings. © The Author(s) 2024.

关键词： Ultra-wideband (UWB)

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Accurate threat hunting in industrial internet of things edge devices

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Digital Communications and Networks 2023年第5期9卷 1123-1130页

作者： Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software Engineering University of CalgaryAlbertaCanada Department of Mathematics and Computer Science Brandon UniversityBrandonCanada College of Computing and Software Engineering Kennesaw State UniversityGAUSA Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102Lebanon

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

关键词： IIoT Threat hunting Edge devices Multi-class anomalies Ensemble methods

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Detection of Man-in-the-Middle Attack Through Artificial Intelligence Algorithm 12th

Detection of Man-in-the-Middle Attack Through Artificial Int...

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12th International Symposium on Intelligent Manufacturing and Service Systems, IMSS 2023

作者： Taştan, Ahmet Nail Gönen, Serkan Barışkan, Mehmet Ali Kubat, Cemallettin Kaplan, Derya Yıltaş Pashaei, Elham Computer Engineering Department Istanbul Gelisim University İstanbul Turkey Software Engineering Department Istanbul Gelisim University İstanbul Turkey Aeronautical Engineering Department Istanbul Gelisim University İstanbul Turkey Computer Engineering Department Istanbul University-Cerrahpaşa İstanbul Turkey

ISBN: (纸本)9789819960613

The amalgamation of information technologies and progressive wireless communication systems has profoundly impacted various facets of everyday life, encompassing communication mediums, occupational procedures, and living standards. This evolution, combined with enhanced wireless communication quality, has culminated in an exponential rise in interconnected devices, including domestic appliances, thereby birthing the Internet of Things (IoT) era. This proliferation, facilitated by cloud computing enabling remote device control, concurrently intensifies cybersecurity threats. Traditional Information and Communication Technology (ICT) architectures, characterized by a hub-and-spoke model, are inherently vulnerable to illicit access and Man-in-the-Middle (MITM) intrusions, thereby endangering information confidentiality. Leveraging Artificial Intelligence (AI) can ameliorate this scenario, enhancing threat training and detection capabilities, enabling precise and preemptive attack countermeasures. This research underscores the criticality of addressing the security implications accompanying technological advancements and implementing protective measures. Deploying AI algorithms facilitates efficient passive attack identification and alleviates network device burdens. Specifically, this study scrutinized the ramifications of an MITM attack on the system, emphasizing the detection of this elusive threat using AI. Our findings attest to AI’s efficacy in detecting MITM attacks, promising significant contributions to future cybersecurity research. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cybersecurity

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Student’s Health Exercise Recognition Tool for E-Learning Education

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Intelligent Automation & Soft Computing 2023年第1期35卷 149-161页

作者： Tamara al Shloul Madiha Javeed Munkhjargal Gochoo Suliman AAlsuhibany Yazeed Yasin Ghadi Ahmad Jalal Jeongmin Park Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering United Arab Emirates UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

Due to the recently increased requirements of e-learning systems,multiple educational institutes such as kindergarten have transformed their learning towards virtual *** student health exercise is a difficult task but an important one due to the physical education needs especially in young *** proposed system focuses on the necessary implementation of student health exercise recognition(SHER)using a modified Quaternion-basedfilter for inertial data refining and data fusion as the pre-processing ***,cleansed data has been segmented using an overlapping windowing approach followed by patterns identification in the form of static and kinematic signal ***,these patterns have been utilized to extract cues for both patterned signals,which are further optimized using Fisher’s linear discriminant analysis(FLDA)***,the physical exercise activities have been categorized using extended Kalmanfilter(EKF)-based neural *** system can be implemented in multiple educational establishments including intelligent training systems,virtual mentors,smart simulations,and interactive learning management methods.

关键词： E-learning exercise recognition online physical education student’s healthcare

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Defect Detection Model Using Time Series Data Augmentation and Transformation

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computers, Materials & Continua 2024年第2期78卷 1713-1730页

作者： Gyu-Il Kim Hyun Yoo Han-Jin Cho Kyungyong Chung Department of Computer Science Kyonggi UniversitySuwon-si16227Korea Contents Convergence Software Research Institue Kyonggi UniversitySuwon-si16227Korea Department of Energy IT Engineering Far East UniversityEumsung-gun27601Korea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon-si16227Korea

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much ***,detecting anomalies is very difficult due to data imbalance,temporal dependence,and ***,methodologies for data augmentation and conversion of time series data into images for analysis have been *** paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to *** method of data augmentation is set as the addition of *** involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the *** addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into *** enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the *** anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat *** allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies *** performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to ***,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training *** proposed method can provide an important springboard for research in the field of anomaly detection using time series ***,it helps solve problems such as analyzing complex patterns in data lightweight.

关键词： Defect detection time series deep learning data augmentation data transformation

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Stress Analysis in Social Media in Bengali language using BERT-based approaches

Stress Analysis in Social Media in Bengali language using BE...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Khanom, Afroza Akter, Nargis Maisha, Sabrina Jahan Masum, Abdul Kader M. International Islamic University Chittagong Department of Computer Science and Engineering Chattogram Bangladesh Daffodil International University Department of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Recent researches highlight a rapid increase in mental health issues, signaling a concerning rise in stress-related conditions such as high blood pressure, psoriasis, polycystic ovary syndrome (PCOS), etc., and these should have a sophisticated remedy that plays a big role in stress analysis. To proceed with that the ongoing connection between increased stress levels and social media has gained a lot of attention. This study focused on inspecting a complex language, Bengali by extracting comments from YouTube to analyze stress to provide a meaningful solution. We are aware of no research on stress analysis in the Bengali language, although many studies have been done on the same topic in other languages. Thus by paying closer attention to stress analysis in the Bengali language, our work suggested a BERT-based method that has shown good results on a range of NLP tasks. Our approach included Bangla-BERT-base and hybrid models that combine deep and transfer learning, like BERT-LSTM, BERT-GRU, and BERT-CNN-BiLSTM. Our task performed well with an accuracy of 90.36% in stress analysis utilizing the Bengali dataset. Our experiment also helped to get an understanding that hybrid models had an influence in increasing the performance of the BERT-base model. Though our research has made little contribution to the BNLP field considering its vast aspect, expanding the Bengali dataset and conducting further multi-class stress analysis work can have a remarkable contribution. © 2024 IEEE.

关键词： Blood pressure

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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

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