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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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A Machine Learning Approach for Predicting Microsatellite Instability using RNA-seq

A Machine Learning Approach for Predicting Microsatellite In...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Simões, Miguel Pereira, Tania Silva, Francisco MacHado, José Carlos Oliveira, Hélder P. University of Porto Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Feup - Faculty of Engineering Porto Portugal Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Porto Portugal University of Porto Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Fcup - Faculty of Science Porto Portugal University of Porto Ipatimup - Institute of Molecular Pathology and Immunology i3S - Instituto de Investigação e Inovação em Saúde Fmup - Faculty of Medicine Porto Portugal

ISBN: (纸本)9798350337488

Microsatellite Instability (MSI) is an important biomarker in cancer patients, showing a defective DNA mismatch repair system. Its detection allows the use of immunotherapy to treat cancer, an approach that is revolutionizing cancer treatment. MSI is especially relevant for three types of cancer: Colon Adenocarcinoma (COAD), Stomach Adenocarcinoma (STAD), and Uterus corpus endometrial cancer (UCEC). In this work, learning algorithms were employed to predict MSI using RNA-seq data from The Cancer Genome Atlas (TCGA) database, with a focus on the selection of the most informative genomic features. The Multi-Layer Perceptron (MLP) obtained the best score (AUC = 98.44%), showing that it is possible to exploit information from RNA-seq data to find relevant relationships with the instability levels of microsatellites (MS). The accurate prediction of MSI with transcription data from cancer patients will help with the correct determination of MSI status and adequate prescription of immunotherapy, creating more precise and personalized patient care. At the genetic level, the study revealed a high expression of genes related to cell regulation functions, and a low expression of genes responsible for Mismatch Repair functions, in patients with high instability. © 2023 IEEE.

关键词： Artificial Neural Networks Cancer Immunotherapy Microsatellite Instability Prediction RNA-seq Data

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Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning

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Big Data Mining and Analytics 2024年第2期7卷 294-314页

作者： Shahriar Kaisar Md Mamunur Rashid Abdullahi Chowdhury Sakib Shahriar Shafin Joarder Kamruzzaman Abebe Diro Department of Information Systems and Business Analytics RMIT UniversityMelbourne 3000Australia School of Engineering and Technology Central Queensland UniversityRockhampton 4700Australia Faculty of Engineering Computer and Mathematical ScienceUniversity of AdelaideAdelaide 5005Australia Centre for Smart Analytics Federation University AustraliaBallarat 3350Australia School of Accounting Information Systems and Supply ChainRMIT UniversityMelbourne 3000Australia

Telemarketing is a well-established marketing approach to offering products and services to prospective *** effectiveness of such an approach,however,is highly dependent on the selection of the appropriate consumer base,as reaching uninterested customers will induce annoyance and consume costly enterprise resources in vain while missing interested *** introduction of business intelligence and machine learning models can positively influence the decision-making process by predicting the potential customer base,and the existing literature in this direction shows promising ***,the selection of influential features and the construction of effective learning models for improved performance remain a ***,from the modelling perspective,the class imbalance nature of the training data,where samples with unsuccessful outcomes highly outnumber successful ones,further compounds the problem by creating biased and inaccurate ***,customer preferences are likely to change over time due to various reasons,and/or a fresh group of customers may be targeted for a new product or service,necessitating model retraining which is not addressed at all in existing works.A major challenge in model retraining is maintaining a balance between stability(retaining older knowledge)and plasticity(being receptive to new information).To address the above issues,this paper proposes an ensemble machine learning model with feature selection and oversampling techniques to identify potential customers more accurately.A novel online learning method is proposed for model retraining when new samples are available over *** newly introduced method equips the proposed approach to deal with dynamic data,leading to improved readiness of the proposed model for practical adoption,and is a highly useful addition to the *** experiments with real-world data show that the proposed approach achieves excellent results in all cases(e.g.,98.6

关键词： machine learning online learning oversampling telemarketing imbalanced dataset ensemble model

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On Understanding Sports-HAR: Hierarchical, Mobile, Multi-Sensor based Classification of Table-Tennis Strokes 1

On Understanding Sports-HAR: Hierarchical, Mobile, Multi-Sen...

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1st International Conference of Intelligent Methods, systems and Applications, IMSA 2023

作者： Ezzeldin, Mustafa Ghoneim, Amr S. Abdelhamid, Laila Atia, Ayman Helwan University Faculty of Computers AI Software Engineering Cairo Egypt Helwan University Faculty of Computers and AI Computer Science Cairo Egypt Helwan University Faculty of Computers and AI Information Systems Cairo Egypt Helwan Univeristy HCI-LAB Faculty of Computers and AI Computer Science Cairo Egypt Faculty of Computer Science Giza Egypt

ISBN: (纸本)9798350335569

Table Tennis is a renowned competitive and recreational sport. An Olympic sport since 1988, table tennis in-cludes several movements, shots (i.e., strokes), and positions. Consequently, many factors can affect the strokes' accuracy and strength. This paper aims twofold: to classify the type of shot and to enhance the performance of the classification by improving the recognition accuracy and reducing the computational time. To track the movement of the body parts and extract table tennis stroke information, sensors (both Accelerometers and Gyroscopes) were utilized. The extracted data are processed and passed to a P-Dollar classifier to recognize six different table tennis strokes. This work utilizes two classification approaches to evaluate the performance, both linear and hierarchical. The presented results demonstrate that the hierarchical classification yields a better accuracy of 88%, achieved in 4 seconds, while traditional linear classification achieved 83% within 6 seconds. © 2023 IEEE.

关键词： Gyroscopes

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Water-harvesting polymer coatings for plant leaves

RSC Applied Polymers

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RSC Applied Polymers 2024年第1期3卷 173-180页

作者： Milatz, Roland Reink, Carmen van den Berg, Tomas E. Duvigneau, Joost Vancso, G. Julius Wurm, Frederik R. Sustainable Polymer Chemistry Department of Molecules and Materials MESA+ Institute for Nanotechnology Faculty of Science and Technology University of Twente Enschede Netherlands Department of Integrated Devices and Systems Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede 7522 NB Netherlands

Climate change-induced water scarcity threatens global plant life and agricultural productivity. Here, we present a novel atmospheric water harvesting (AWH) coating designed to alleviate heat and dry stress potentially. This polymer coating utilizes block copolymers carrying catechol-anchoring groups, specifically poly(dopamine methacrylamide) (PDOMA), to adhere to plant leaves. As a hydrophilic block, either poly((oligoethylene glycol) methacrylate) (POEGMA) or the thermoresponsive block poly(N-isopropylacrylamide) (PNIPAM) was used, which can adsorb water from the air during cooler periods in its hydrophilic state. As the temperature increases above the lower critical solution temperature (LCST) of PNIPAM, the polymer transitions to a hydrophobic state, releasing the captured water to the leaf surface. We synthesized PNIPAM-b-PDOMA copolymers via RAFT polymerization and confirmed their composition (IR, ¹H NMR and ¹H DOSY NMR spectroscopy) with a cloud point temperature of 33 ± 1 °C. The coatings were applied to model substrates (SiO₂, polyethylene) and corn leaves. Compared to uncoated controls, coated substrates demonstrated a substantial increase in water uptake from humid air, absorbing up to 50 wt% of the coating's weight. The coating's adherence and thermoresponsive behavior were confirmed on corn leaves through contact angle measurements, showing a shift from hydrophilic (29 ± 3°) below the LCST to hydrophobic (80 ± 2°) above the LCST, closer to the native, hydrophobic leaf (110 ± 10°). Crucially, photosynthesis induction experiments revealed that the coating did not negatively impact the plant's natural photosynthetic processes. This study establishes a promising copolymer platform for developing AWH coatings to support plants in the face of increasing drought conditions. © 2025 RSC.

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Secure and fast mobility handover scheme for IoMT in 5 G and future 6 G networks

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Alexandria engineering Journal 2025年 128卷 127-143页

作者： Islam, Shayla Topazal, S.M. Hasan, Mohammad Kamrul Kolandaisamy, Raenu AL Abbas, Huda Saleh Vo, Nguyen Thakkar, Rahul Khan, Muhammad Attique Gupta, Deepak AlHammadi, Dina Abdulaziz Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Center for Cyber Security Faculty of Information Science and Technology the National University of Malaysia Selangor Bangi43600 Malaysia Department of computer science College of Computer Science and Engineering Taibah University Madina Saudi Arabia Victorian Institute of Technology Melbourne Australia Center of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia Maharaja Agrasen Institute of Technology Delhi India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O.Box 84428 Riyadh11671 Saudi Arabia

The emergence of 5 G and 6 G networks promises significant advancements in the Internet of Medical Things (IoMT) by enabling real-time medical data transmission and continuous connectivity for mobile healthcare devices. However, traditional Handover (HO) mechanisms face latency, security, energy consumption, and scalability challenges when supporting fast-moving and dense IoMT environments. This paper presents a secure and fast mobility handover scheme for IoMT in 5 G and future 6 G networks, incorporating a predictive algorithm, Blockchain-Based (BC-B) decentralized authentication mechanism, and AI-driven anomaly detection. The predictive algorithm anticipates device movement and pre-allocates resources at the target Base Station (BS), reducing HO latency by 20–30 % in 5 G and up to 40 % in 6 G. Meanwhile, blockchain-based authentication ensures tamper-proof and decentralized security, reducing vulnerability to cyberattacks by 90 % compared to traditional methods. Additionally, the energy consumption during HO events is reduced by 20–35 %, dropping from 15 mJ in traditional schemes to 12 mJ in 5 G and further to 10 mJ in 6 G. The scheme also incorporates AI-driven anomaly detection, achieving an accuracy of 96.7 %, ensuring proactive identification and mitigation of potential security threats. The analyzed scheme maintains consistent throughput with less than 5 % reduction and scales effectively for up to 300 devices per cell. The simulation results demonstrate significant improvements across key performance metrics, including latency, security, energy efficiency, and scalability, making this approach highly suitable for critical IoMT applications such as telesurgery, remote patient monitoring, and innovative healthcare environments. These advancements position the analyzed framework as a cornerstone for next-generation (NG) healthcare networks. © 2025 The Authors

关键词： Remote patient monitoring

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A Short Survey on Multimodal Data Fusion in Image Classification 1

A Short Survey on Multimodal Data Fusion in Image Classifica...

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1st International Conference on Global Aeronautical engineering and Satellite Technology, GAST 2024

作者： Datsi, Toufik Aznag, Khalid Benali, Brahim Ait Karbout, Karim El Oirrak, Ahmed Khayya, El Kharrachi Computer Systems Engineering Laboratory Cadi Ayyad University Faculty of Sciences Semlalia Marrakesh Morocco Ensa School-University Chouaib Doukkali Lti Laboratory EL Jadida Morocco Computer Science Engineering Laboratory Cadi Ayyad University Faculty of Sciences Semlalia Marrakesh Morocco

ISBN: (纸本)9798350371598

Advancements in multimodal learning have experienced rapid growth over the past decade, particularly within various domains, with a significant emphasis on developments in computer vision. Multimodal data fusion has become increasingly prominent in the realm of image classification, where the integration of diverse data sources enhances the overall understanding and performance of classification models. This survey delves into the recent strides made in multimodal learning over the past decade, particularly within the field of image classification. Additionally, the paper undertakes a comparative study, critically evaluating the effectiveness and performance of different multimodal fusion approaches. The aim is to provide a comprehensive overview of the current state-of-the-art in multimodal data fusion for image classification and to identify key trends, challenges, and opportunities in this evolving field. © 2024 IEEE.

关键词： Image classification

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A self-stabilizing distributed algorithm for the 1-MIS problem under the distance-3 model

A self-stabilizing distributed algorithm for the 1-MIS probl...

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作者： Kakugawa, Hirotsugu Kamei, Sayaka Shibata, Masahiro Ooshita, Fukuhito Faculty of Advanced Science and Technology Ryukoku University Shiga Otsu Japan Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Higashihiroshima Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Iizuka Japan Faculty of Engineering Fukui University of Technology Fukui Fukui Japan

Fault-tolerance and self-organization are critical properties in modern distributed systems. Self-stabilization is a class of fault-tolerant distributed algorithms which has the ability to recover from any kind and any finite number of transient faults and topology changes. In this article, we propose a self-stabilizing distributed algorithm for the 1-MIS problem under the unfair central daemon assuming the distance-3 model. Here, in the distance-3 model, each process can refer to the values of local variables of processes within three hops. Intuitively speaking, the 1-MIS problem is a variant of the maximal independent set (MIS) problem with improved local optimizations. The time complexity (convergence time) of our algorithm is (Formula presented.) steps and the space complexity is (Formula presented.) bits, where (Formula presented.) is the number of processes. Finally, we extend the notion of 1-MIS to (Formula presented.) -MIS for each nonnegative integer (Formula presented.), and compare the set sizes of (Formula presented.) -MIS ((Formula presented.)) and the maximum independent set. © 2024 John Wiley & Sons Ltd.

关键词： Consensus algorithm

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Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

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computers, Materials & Continua 2023年第2期74卷 3183-3198页

作者： Doaa Sami Khafaga Faten Khalid Karim Abdelaziz A.Abdelhamid El-Sayed M.El-kenawy Hend K.Alkahtani Nima Khodadadi Mohammed Hadwan Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt The Department of Civil and Environmental Engineering Florida International UniversityMiamiFLUSA Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human *** attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote *** attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are *** paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic *** employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA *** proposed voting classifier categorizes the network intrusions robustly and *** assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack *** dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and *** achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

关键词： Voting classifier whale optimization algorithm dipper throated optimization intrusion detection internet-of-things

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Securing Patient Information in Connected Healthcare systems in the Age of Pervasive Data Collection 3

Securing Patient Information in Connected Healthcare Systems...

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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Dimitrievski, Ace Loncar-Turukalo, Tatjana Trajkovik, Vladimir Faculty of Computer Science and Engineering Skopje Macedonia University of Novi Sad Centre of Excellence for Vibro-Acoustic Systems and Signal Processing Faculty of Technical Sciences Serbia

ISBN: (纸本)9798350333732

This paper explores the ethical and security challenges of integrating Internet of Things technologies into health-care systems. We examine real-world ethical dilemmas, including privacy and security of personal and medical data. We also analyze security in connected healthcare systems, focusing on securing data transmission, preventing data exfiltration, and mitigating the risk of de-anonymization of data. We propose a fog-based architecture and data avoidance principle to address and minimize the risks to patient data, intending to navigate the ethical landscape of IoT in healthcare, ensuring patient privacy. © 2023 IEEE.

关键词： Data privacy

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