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Development and Investigation of Vision System for a Small-Sized Mobile Humanoid Robot in a Smart Environment

International Journal of Crowd science 2025年第1期9卷 29-43页

作者： Abu-Jassar, Amer Tahseen Attar, Hani Amer, Ayman Lyashenko, Vyacheslav Yevsieiev, Vladyslav Solyman, Ahmed Department of Computer Science College of Information Technology Amman Arab University Amman11937 Jordan Faculty of Engineering Zarqa University Zarqa2000 Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv61166 Ukraine Department of Electrical and Electronics Engineering Faculty of Engineering and Architecture Nişantaşı University Istanbul34398 Turkey

The conducted research aims to develop a computer vision system for a small-sized mobile humanoid robot. The decentralization of the servomotor control and the computer vision systems is investigated based on the hardware solution point of view, moreover, the required software level to achieve an efficient matched design is obtained. A computer vision system using the upgraded tiny-You Only Look Once (YOLO) network model is developed to allow recognizing and identifying objects and making decisions on interacting with them, which is recommended for crowd environment. During the research, a concept of a computer vision system was developed, which describes the interaction between the main elements, on the basis of which hardware modules were selected to implement the task. A structure of information interaction between hardware modules is proposed, and a connection scheme is developed, on the basis of which a model of a computer vision system is assembled for research, with the required algorithmic and software for solving the problem. To ensure the high speed of the computer vision system based on the ESP32-CAM module, the neural network was improved by replacing the Visual Geometry Group 16 (VGG-16) network as the base network for extracting the functions of the Single Shot Detector (SSD) network model with the tiny-YOLO lightweight network model, which made it possible to preserve the multidimensional structure of the network model feature graph, resulting in increasing the detection accuracy, while significantly reducing the amount of calculations generated by the network operation, thereby significantly increasing the detection speed, due to a limited set of objects. Finally, a number of experiments were carried out, both in static and dynamic environments, which showed a high accuracy of identifications. © The author(s) 2025.

关键词： Anthropomorphic robots

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Classification of Sleep-Related Disorders Using Machine Learning Techniques 25

Classification of Sleep-Related Disorders Using Machine Lear...

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25th International Arab Conference on information technology, ACIT 2024

作者： Nasir, Ola Alazaidah, Raed Al-Safarini, Maram Y. Alamleh, Amneh Zaid, Ali Abu Mansur, Hasan Elrashidi, Ali Alzoubi, Haneen Bani-Bakr, Alaa Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan American University of Madaba Faculty of Business Department of Accounting Madaba Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Private University Faculty of It Applied Science Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Several people suffer from irregular sleep patterns, a problem that negatively affects their daily lives. It presents a multitude of challenges, including daytime distress, disturbances in sleep-wake patterns, feelings of unease, and a variety of other complications. Diabetes, obesity, and psychological disorders are among the diseases that may be responsible for this sleep disorder. Therefore, the primary aim of this research is to employ the robust capabilities of machine learning in forecasting sleep disorders. This research classifies three diseases: diabetes, obesity, and psychological disorders, using thirty-two different classifiers belonging to five learning strategies. The research compares and evaluates their predictive performance on four datasets. The comparative analysis includes four evaluation metrics: Accuracy, Precision, ROC Area, and MCC. The research results indicate that the best classifier is SGD, followed by LMT, while NBM is the weakest classifier. In addition, the research results revealed that the best strategy is Functions, followed by Rules, with Bayes being the least effective strategy. While conducting the experiment and analyzing the data, it is observed that the best feature selection method that yielded the most favorable outcome is the CorrelationAttributeEval. Furthermore, it has been found that the classification algorithm known as SGD achieved the highest Accuracy and Precision in categorizing the data. This indicates that the combination of CorrelationAttributeEval as the feature selection approach and SGD as the classification algorithm proved to be the most successful and efficient strategy for this research © 2024 IEEE.

关键词： Sleep research

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An Efficient Intrusion Detection Framework for Industrial Internet of Things Security

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computer Systems science & engineering 2023年第7期46卷 819-834页

作者： Samah Alshathri Ayman El-Sayed Walid El-Shafai Ezz El-Din Hemdan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Recently,the Internet of Things(IoT)has been used in various applications such as manufacturing,transportation,agriculture,and healthcare that can enhance efficiency and productivity via an intelligent management console *** the increased use of Industrial IoT(IIoT)applications,the risk of brutal cyber-attacks also *** leads researchers worldwide to work on developing effective Intrusion Detection Systems(IDS)for IoT infrastructure against any malicious ***,this paper provides effective IDS to detect and classify unpredicted and unpredictable severe attacks in contradiction to the IoT infrastructure.A comprehensive evaluation examined on a new available benchmark TON_IoT dataset is *** data-driven IoT/IIoT dataset incorporates a label feature indicating classes of normal and attack-targeting IoT/IIoT ***,this data involves IoT/IIoT services-based telemetry data that involves operating systems logs and IoT-based traffic networks collected from a realistic medium-scale IoT *** is to classify and recognize the intrusion activity and provide the intrusion detection objectives in IoT environments in an efficient ***,several machine learning algorithms such as Logistic Regression(LR),Linear Discriminant Analysis(LDA),K-Nearest Neighbors(KNN),Gaussian Naive Bayes(NB),Classification and Regression Tree(CART),Random Forest(RF),and AdaBoost(AB)are used for the detection intent on thirteen different intrusion *** performance metrics like accuracy,precision,recall,and F1-score are used to estimate the proposed *** experimental results show that the CART surpasses the other algorithms with the highest accuracy values like 0.97,1.00,0.99,0.99,1.00,1.00,and 1.00 for effectively detecting the intrusion activities on the IoT/IIoT infrastructure on most of the employed *** addition,the proposed work accomplishes high performance compared to other recent rela

关键词： Attacks intrusion detection machine learning deep learning industrial IoT TON_IoT dataset

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SkinMarkNet: an automated approach for prediction of monkeyPox using image data augmentation with deep ensemble learning models

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Multimedia Tools and Applications 2025年第18期84卷 20177-20193页

作者： Akram, Aqsa Jamjoom, Arwa A. Innab, Nisreen Almujally, Nouf Abdullah Umer, Muhammad Alsubai, Shtwai Fimiani, Gianluca Bahawalpur Pakistan Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Riyadh Diriyah13713 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy

Monkeypox, a rare but potentially fatal viral disease, poses a significant public health challenge due to its potential for outbreaks and complications. Detecting monkeypox lesions early and accurately is vital to effectively manage and control the disease. This study introduces a novel method for classifying monkeypox lesions, employing data augmentation methods and a framework based on an ensemble of three transfer learning models called "SkinMarkNet". The dataset used in this research consists of skin lesion images collected from the Kaggle data repository, encompassing diverse demographics and lesion characteristics. This research uses image data augmentation techniques to tackle the scarcity of annotated data. This augmentation enriches the training dataset, thereby improving the model’s ability to perform effectively. Moreover, the novelty of this research work lies in the usage of three popular transfer learning models(Inception, Xception, and ResNet) for feature extraction and ensemble learning. The SkinMarkNet achieves promising results showing an accuracy of 90.615% for monkeypox lesion classification, outperforming traditional machine learning and deep learning methods utilized in recent research works. In addition, thorough comparative analysis is done with machine learning models and contemporary approaches to validate the efficacy of the proposed method. Overall, the findings underscore the potential of leveraging advanced deep learning architectures and data augmentation strategies for improving monkeypox lesion classification, thereby facilitating early diagnosis and intervention in clinical settings and public health surveillance efforts. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

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computers, Materials & Continua 2024年第1期78卷 31-63页

作者： Sunil Kumar Dilip Kumar Ramraj Dangi Gaurav Choudhary Nicola Dragoni Ilsun You Department of Computer Science and Engineering National Institute of Technology JamshedpurJamshedpurIndia School of Computing Science and Engineering VIT University BhopalBhopal466114India Center for Industrial Software The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkSonderborgOdenseDenmark Department of Applied Mathematics and Computer Science Technical University of DenmarkLyngby2800Denmark Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoulSouth Korea

The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different *** settings are probed for data,which is then transferred via linked *** are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny *** it comes to deploying IoT,everyone agrees that security is the biggest *** is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square *** research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted *** lightweight cryptography,the information sent between these gadgets may be *** order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance *** also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.

关键词： IoT a sensor device lightweight cryptography block cipher smart card security and privacy

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Identification of Diabetic Related Eye Diseases Using Deep Learning 5

Identification of Diabetic Related Eye Diseases Using Deep L...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Wijesinghe, K.H. Dilshan, U.K.T. Dilshan, K.B.G.L. Tharupathi, M.A.U. Kasthuriarachchi, Sanvitha Rajapaksha, Samantha Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

Visual disability seems to be increasing and widespread worldwide. Current diagnostics require manual expertise for diagnosis. The advancement of Artificial Intelligence research for medical applications has been in focus in recent years. Fundus images and oct images are valuable sources of information to help ophthalmologists diagnose vision disorders or eye diseases. Early detection can improve the chances of recovery and prevent blindness. The application of an intelligent computer-based approach to classifying different eye disorders is extremely beneficial for both diagnosis and prevention. The proposed expert system for diagnosing diabetic macular edema, diabetic retinopathy, cataracts, and glaucoma diseases is discussed in this research study. It is based on deep learning image processing techniques. For the detection of eye diseases, software used retinal and OTC images. This study presents Yolo, UNET, customized CNN, and Auto ML model-based system for detecting eye diseases. For the tasks of disease identification and grading, the suggested methods each obtained more than 87% accuracy. The primary objective of this study is to achieve automated identification of eye disease images. This is accomplished through the implementation of a deep learning model alongside advanced image processing techniques, enabling the automatic classification of eyes into either a healthy or diseased category. © 2023 IEEE.

关键词： Diagnosis

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Personalized and Interactive Demented Care and Learning Mate with a Virtual System Using Emotion Recognition 5

Personalized and Interactive Demented Care and Learning Mate...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Kaushan, Nipuna Fernando, Devshan Gayashan, Chamod Udayakantha, Sachitha De Silva, Hansi Perera, Sanduni Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This study investigates the development and application of a personalized, interactive virtual system to enhance dementia care and protect people with dementia. A significant challenge in providing care for individuals with dementia is the propensity to wander and become lost, which can lead to anxiety and distress for both the patient and the carers as well as safety concerns. The system combines cutting-edge emotion recognition technology and healthcare IOT technology to effectively identify and provide real-time solutions in response to users' emotional states. This initiative combines dementia care, educational support, and patient security to establish a comprehensive strategy for maintaining well-being and managing the disease in dementia patients. This project has experimentally designed a security system to enhance the safety of dementia patients, a Health Checker system aimed at maintaining their health stability, and a virtual assistant that offers guidance by identifying real-time emotions and providing activities to improve cognitive function and promote relaxation Given a vast amount of patient data, the AI model has been able to identify 98% of patients' illnesses early. security software successfully detected patients with 99% accuracy using GPS technology and machine learning algorithms. © 2023 IEEE.

关键词： Emotion Recognition

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Abnormal Events Detection in Surveillance Environments using Machine Learning

Abnormal Events Detection in Surveillance Environments using...

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2024 International Conference on science, technology, engineering and Management, ICSTEM 2024

作者： Priya, D. Divya Theam, Lim Jit Vinisha, J. Krishnan, Kesavan Ayyasamy, Ramesh Kumar Subramaniam, Suthashini Department of Computer Science Engineering MLR Institue of Technology Hyderabad India Kampar Perak Malaysia Department of Information Technology St.Joseph's Institute of Technology Tamilnadu India Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798350376913

The ability of intelligent CCTV systems to detect unusual events, detect human behaviour and recognize objects is essential. This system enables the detection of anomalous events in the environment, anomalous human behaviour and the level of alertness in the environment. The system uses machine learning and machine vision capabilities to detect and identify specific anomalies in CCTV video feeds. This system often uses frames through frame processing, and supervised learning is often used as a training method. However, unsupervised and semi-supervised learning replace supervised learning in the system learning process due to the large variety of anomalies and the impossibility of predicting and preparing for all of them. This system can decrease or do away with the amount of labour-intensive human work needed to manually spot anomalies in the live CCTV feed and provide alerts. The initial monitoring of unusual events and low-quality archiving of typical scenarios also increases the efficiency of the system’s storage. In addition, this system provides an extension for developing a distributed anomaly classification system so that only abnormal events are directed to different unique systems for categorization. Regular recordings, on the other hand, will be archived with low quality. Furthermore, this approach offers a distributed anomaly classification system development path. © 2024 IEEE.

关键词： Anomaly detection

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Streamlining plant disease diagnosis with convolutional neural networks and edge devices

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Neural Computing and Applications 2024年第29期36卷 18445-18477页

作者： Ahamed, Md. Faysal Salam, Abdus Nahiduzzaman, Md. Abdullah-Al-Wadud, M. Islam, S. M. Riazul Department of Computer Science and Engineering Rajshahi University of Engineering & ampTechnology Rajshahi6204 Bangladesh Department of Electrical and Computer Engineering Rajshahi University of Engineering & ampTechnology Rajshahi6204 Bangladesh Department of Electrical Engineering Qatar University Doha Qatar Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Natural and Computing Sciences University of Aberdeen AberdeenAB24 3FX United Kingdom

In addressing labor-intensive process of manual plant disease detection, this article introduces an innovative solution—the lightweight parallel depthwise separable convolutional neural network (PDSCNN) coupled with a hybrid ridge regression extreme learning machine classifier. This combined approach automates plant disease classification, significantly expediting the learning process and reducing computational complexity. The PDSCNN model excels in feature capture, boasting a mere 9 layers and 0.053 million parameters, resulting in exceptional computational efficiency. On top of that, the classification performances of traditional ELM increased by introducing a hybrid ridge ELM model. Its accelerated operations, reduced memory usage, and optimized parallel processing render it lightweight and ideal for edge devices. The proposed framework exhibits remarkable efficacy, showcasing a precision of 95.76%, a recall of 89.29%, an F1-score of 91.25%, an accuracy of 94.22%, and an AUC of 99.72%. Moreover, training and testing times clock in at just 0.2314 and 0.0259 s, respectively. A comparative analysis against state-of-the-art transfer learning methods underscores its real-time analytical prowess. To demonstrate real-world applicability, we successfully validated our model on the Jetson Nano hardware, making it suitable for resource-constrained settings. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Computational efficiency

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A Hypergraph Matching-Based Subchannel Allocation for Multi-Platoon's Communications

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IEEE Access 2023年 11卷 139345-139365页

作者： Huang, Chung-Ming Lam, Dan-Nguyen Dao, Duy-Tuan National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan The University of Danang-University of Science and Technology Faculty of Electronics and Telecommunications Engineering Da Nang50000 Viet Nam

In a platoon communication scenario, the Platoon Leader (PL) vehicle interacts with the gNodeB (gNB) to obtain some assigned radio resources and then the obtained radio resources are allocated to the PL vehicle and its Platoon Member (PM) vehicles. The high transmission delay in the platoon network affects the quality of vehicles' connectivity. Therefore, the issues of improving radio resource's spectral efficiency and stability of the vehicle platooning with limited radio resources need to be tackled. This work proposes the 2-STage Resource Allocation (2-STRA) method for multi-platoon communication to reduce the transmission latency of platoon vehicles, i.e., it can improve the data transmission rates of platoon vehicles for the conditions of various speeds and various numbers of subchannels. In the $1^{st}$ stage, a greedy algorithm that jointly considers subchannel allocation and power control for PL vehicles' resource allocation is devised. In the $2^{nd}$ stage, the resource allocation for PM vehicles, which is based on the subchannels that are not allocated to PL vehicles, is resolved. A tripartite hypergraph, in which a tripartite hyperedge links:I 1) cluster of PM vehicles that use the same subchannel;2) individual cellular user;and 3) subchannel, is adopted to devise the PM vehicles' resource allocation algorithm. The simulation results show that the proposed 2-STRA method can have better performance in terms of spectral efficiency and the sum of data transmission rate comparing with the other methods. © 2013 IEEE.

关键词： Quality of service

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