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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Matkovic, Jelena University of Mostar Faculty of Mechanical Engineering Computing and Electrical Engineering Mostar Bosnia and Herzegovina

ISBN: (纸本)9798331515799

The structure of business processes can vary significantly, encompassing complex behaviors that can become intricate and challenging to track or validate. This paper examines the constructs provided by the Web Services Business Process Execution Language standard that can be utilized to design possibly complex business process executions. It then identifies categories of potentially unpredictable behaviors, such as unintended parallelization or unwanted bottlenecks. The identified behaviors and proposed techniques for their early detection can serve as the basis for employing different further verification techniques. Furthermore, these behaviors can also be used as a reference for identifying similar behaviors in orchestrations of modern microservices, given the conceptual similarities in integrating such microservices into business processes. © 2025 IEEE.

关键词： Administrative data processing

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AI-Progressions in Automated Facial Paralysis Evaluation: A Comprehensive Survey of Traditional to Deep Learning Approaches 4

AI-Progressions in Automated Facial Paralysis Evaluation: A ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kiruthiga, S. Anbuananth, C. Dhilipkumar, V. Annamalai University Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798331523923

Deep learning mechanisms allow computers to solve complex real-time problems with complex neural networks, it employs a vital role in health sector, for assisting the medical practitioners with quick and accurate decision making. Various studies on this research area proves that deep learning approaches are more effective in detecting facial paralysis, a common visible symptom of acute stroke. The sealing sign of stroke is facial weakness which destroys the newline muscle movements in the face on one or both sides of the nerves in the brain stream. Accurate and timely detection is most important and need of the golden hour, to patients effectively. Human assessment and traditional diagnostic methods have demonstrated poor inter rated reliability, demanding improved diagnostic tools. By analysing facial asymmetry and other relevant features like Face feature analysis, extracting characteristics such as eye shape, movement, and wrinkles from static images to detect facial weakness is done using various deep learning techniques. This work encompass the real-time challenges such as curating datasets, ethical concerns and data privacy of patients. The objective of this paper is to review the potentiality of various traditional to deep learning algorithms in automising the facial paralysis detection for accurate stroke diagnosis in patients. AI models can provide objective assessments that enhance the reliability of stroke detection. © 2025 IEEE.

关键词： Convolutional neural networks

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Synergizing Underwater Robotics and AI: A Tethered ROV and YOLOv7 Approach to Giant Clam Detection 2nd

Synergizing Underwater Robotics and AI: A Tethered ROV and Y...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Badhan, Al Mominul Haque Hanif, Mariam Faudzi, Ahmad Athif Mohd Hafidz, Muhamad Hazwan Abdul Ruslan, Mohammad Izzuddin Bin Adam, Muhammad Amirul Amri Khamis, Nurulaqilla Faculty of Electrical Engineering Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru81310 Malaysia Faculty of Computing Universiti Teknologi Malaysia Johor Bahru81310 Malaysia Centre for Artificial Intelligence and Robotics Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia A2TECH Sdn. Bhd Johor Bahru81310 Malaysia

ISBN: (纸本)9789819639489

Integrating a tethered Remotely Operated Vehicle (ROV) with the YOLOv7 object recognition algorithm offers a novel approach to marine monitoring, particularly for tracking giant clam populations in the reefs of Tioman Island, Malaysia. The ROV, designed for enhanced underwater exploration, is equipped with advanced imaging and sonar technologies, resilient tethered power management and communication systems. The control and navigation system are built on the pixhawk flight controller, supported by ArduPilot firmware and a customized QGroundControl interface, ensuring real-time operations. The primary objective of this research is to develop an algorithm capable of accurately recognizing and counting giant clams through underwater images and videos. The study compares the performance of the YOLOv7 algorithm with the manual counting. The findings indicate that the YOLOv7 algorithm demonstrates average accuracy of the model of 79.65%. The integration of YOLOv7 enables identification and quantification of giant clam populations enhancing data collection for research and conservation efforts providing practical solutions for environmental monitoring and the sustainable management of marine resources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Firmware

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Photoplethysmography Bio-Signal Extraction for Classifying Diabetes Mellitus Diseases Using Pretrained Deep Learning Networks

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SN Computer Science 2025年第2期6卷 1-14页

作者： Tarmizi, Syaidatus Syahira Ahmad Suriani, Nor Surayahani Mohd, Mohd Norzali Bin Hj Bagchi, Susama Debnath, Sanjoy Kumar Shah, Syed Mehr Ali Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor Parit Raja Malaysia Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India College of Computing & Information Sciences University of Technology & Applied Sciences Musandem Oman

This study investigates the association between the presence of blood glucose and blood pressure variations to determine an individual’s health. It classified diabetes mellitus by using a publicly available PPG-BP dataset of 72 subjects. Increasing diabetes trends due to undiagnosed disease were overcome through continuous finger cuff-based blood pressure measurements via photoplethysmography to extract diabetes related features including the baseline anthropometrical characteristics and signal morphology. The variations in blood pressure obtained from single channel PPG are processed to extract signal information and the results which are the STFT transformed version of the signal as the input data to investigate the meaningful patterns of the original raw, filtered and their derivatives between healthy and diabetic individuals. This can be followed by the state of the art of pretrained deep learning networks such as DenseNet121, DenseNet169, EfficientNetB0, EfficientNetB1, InceptionResNetV2, ResNet50, ResNet101 and Xception which were discussed in the comparative analysis that can be effectively used for classification on the respective sets of signals. The result indicates that InceptionResNetV2 and Xception networks outperform the rest of the pretrained deep learning used in the study with the highest recorded accuracy, precision and recall values which making them the most effective networks for classification problems. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blood glucose Blood pressure Diabetes mellitus Photoplethysmography

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Deep Reinforcement Learning-Based Framework for Enhancing Cybersecurity

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第3期19卷 170-190页

作者： Al-Nawashi, Malek M. Al-Hazaimeh, Obaida M. Tahat, Nedal M. Gharaibeh, Nasr Abu-Ain, Waleed A. Abu-Ain, Tarik Al-Huson University College Al-Balqa Applied University Irbid Jordan Faculty of Information Technology Department of Information Security and Cybersecurity Philadelphia University Amman Jordan Faculty of Science The Hashemite University Zarqa Jordan College of Science and Computer Engineering Taibah University Yanbu Saudi Arabia College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The detection of cyberattacks has been increasingly emphasized in recent years, focusing on both infrastructure and people. Conventional security measures such as intrusion detection, firewalls, and encryption are insufficient in protecting cyber systems against growing and changing threats. In order to address this problem, scholars have explored reinforcement learning (i.e., RL) as a potential solution for intricate cybersecurity decision-making difficulties. Nevertheless, the use of RL faces several obstacles, including dynamic attack scenarios, insufficient training data, and the challenge of replicating real-world complexities. This study presents a novel framework that uses deep reinforcement learning (i.e., DRL) to simulate harmful cyberattacks and improve cybersecurity. This study presents an agent-based framework that is capable of ongoing learning and adaptation in a dynamic network security environment. The agent determines the optimal course of action by considering the current state of the network and the rewards it receives for its decisions. The CIC-IDS-2018 database, constructed using Python 3.7 programming, was used. The conducted studies yielded outstanding results, with a detection accuracy of 98.82% achieved for the CIC-IDS-2018 database in cyber-attack classification. © 2025 by the authors of this article. Published under CC-BY.

关键词： Cybersecurity

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Deep Learning Algorithm for Person Re-Identification Based on Dual Network Architecture

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Computers, Materials & Continua 2025年第5期83卷 2889-2905页

作者： Meng Zhu Xingyue Wang Honge Ren Abeer Hakeem Linda Mohaisen College of Information Engineering Harbin UniversityHarbin150086China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin UniversityHarbin150086China College of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China Heilongjiang Forestry Intelligent Equipment Engineering Research Center Northeast Forestry UniversityHarbin150040China Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

Changing a person’s posture and low resolution are the key challenges for person re-identification(ReID)in various deep learning *** this paper,we introduce an innovative architecture using a dual attention network that includes an attentionmodule and a joint measurement module of spatial-temporal *** proposed approach can be classified into two main ***,the spatial attention feature map is formed by aggregating features in the spatial ***,the same operation is carried out on the channel dimension to formchannel attention ***,the receptive field size is adjusted adaptively tomitigate the changing person posture ***,we use a joint measurement method for the spatial-temporal information to fully harness the data,and it can also naturally integrate the information into the visual features of supervised ReID and hence overcome the low resolution *** experimental results indicate that our proposed algorithm markedly improves the accuracy in addressing changing human postures and low-resolution issues compared with contemporary leading *** proposed method shows superior outcomes on widely recognized benchmarks,which are the Market-1501,MSMT17,and DukeMTMC-reID ***,the proposed algorithmattains a Rank-1 accuracy of 97.4% and 94.9% mAP(mean Average Precision)on the Market-1501 ***,it achieves a 94.2% Rank-1 accuracy and 91.8% mAP on the DukeMTMC-reID dataset.

关键词： Person reidentification ReID computer vision self-attention spatial-temporal information

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Enhanced Quality of Learning (QOL) via Secured Recommendation and Sentimental Analysis in Online Classes 2

Enhanced Quality of Learning (QOL) via Secured Recommendatio...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Saravanan, T. Saravanakumar, S. Karthikeyan, V Ganesh Dept. of CSE Bengaluru India Faculty of Engineering &Technology Dept. of CSE Bengaluru India School of Computing SASTRA Deemed University Thanjavur India

ISBN: (纸本)9798331505745

Nowadays online classes have become the main source of students learning. To achieve this, students' interaction is required. Basically, Cloud can provide more learning and interaction among the students and teachers (faculty). If teachers are good students are creating problem, if students are good and teachers are not so. To sort out all these things, complete monitoring is required. In case if a proper recommendation with guidance is provided then it will be easy for student to cope up and will be easy for the teachers to validate the students' progress. The proposed system provides a deep learning Recommender System knowing the capability of the students. Here, the quality of learning is enhanced by predicting the student/teacher's emotion via sentimental analysis. Recommender system is best for this. The proposed system also uses LSTM (Long Short-Term Memory Algorithm) to predict the students' activities and prefer recommendation according to their progress. This system helps student for developing their career and notice the teachers' activities for controlling the stress level of the students. By this way the students' creativity will increase and stress will be reduced. The system further provides more security with proper experimental analysis. © 2025 IEEE.

关键词： Students

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Optimizing Heart-Kidney Interaction for Cancer Detection Through Physiological Process Simulation as a Decision Support System

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IEEE Access 2025年 13卷 79169-79179页

作者： Jaddi, Najmeh Sadat Abdullah, Salwani Othman, Zalinda Yahya, Yazrina Zakree Ahmad Nazri, Mohd Leng Goh, Say Alvankarian, Fatemeh Alvankarian, Jafar Iranian eUniversity Faculty of Computer Engineering Tehran96351-14578 Iran Universiti Kebangsaan Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Bangi43600 Malaysia Universiti Kebangsaan Malaysia Center for Software Technology and Management Faculty of Information Science and Technology Bangi43600 Malaysia Universiti Malaysia Sabah Kampus Antarabangsa Labuan Optimisation and Analytics Research Group Faculty of Computing and Informatics Labuan87000 Malaysia University of Maryland Department of Electrical and Computer Engineering College ParkMD20742 United States RayanMiniFab Tehran University Science and Technology Park Tehran*** Iran

A decision support system (DSS) is a computer-based tool used to improve decision-making capabilities for any organization by analyzing the available data. The heart-kidney (HK) model proposed in this paper, as a DSS, simulates the physiological processes of the heart and kidneys in the human body to support cancer detection. The first part of the HK algorithm is inspired by the functions of the heart, where blood molecules (solutions) are sent to the heart, simulating the processes of oxygenation and the pumping of oxygenated blood to the kidneys as a local search. The solutions generated by the heart are then fed into the kidney-inspired algorithm (KA), which performs filtration, reabsorption, secretion, and excretion. The heart component enhances the KA’s local search capability, guiding the algorithm toward optimal solutions. The HK model is evaluated on eight test functions, with results compared to those of the heart algorithm and KA alone. Additionally, the HK algorithm’s effectiveness as a decision support system is tested on benchmark classification and time series prediction problems, with statistical analysis against existing methods in the literature. Finally, the HK algorithm is applied to cancer detection from microRNA data, demonstrating its potential in optimizing decision-making processes in healthcare. © 2013 IEEE.

关键词： Diseases

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GENSIMO - A Generic Framework for Modelling Social Insurance Systems 25th

GENSIMO - A Generic Framework for Modelling Social Insuranc...

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25th International Workshop on Multi-Agent-Based Simulation, MABS 2024

作者： de Haan, Fjalar J. Thompson, Jason School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Parkville Australia Melbourne Centre for Data Science The University of Melbourne Parkville Australia Department of Psychiatry Faculty of Medicine Dentistry and Health Sciences The University of Melbourne Parkville Australia

ISBN: (纸本)9783031880162

We report on the design, development and prototype testing of gensimo (GENeric Social Insurance MOdeling), a free and open-source software framework for modelling and simulation of social insurance systems. We discuss the conceptual and software design considerations of the framework in general before demonstrating how it works in detail by setting it up to model a particular social insurance scheme. We then present how we used this prototype set up to model the impact of a policy intervention on the workload of the insurer. While gensimo is under active development and we are a while away from serious verification and validation, this prototype testing shows realistic results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Software testing

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Face image authentication scheme based on MTCNN and SLT

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Multimedia Tools and Applications 2025年 1-43页

作者： Thabit, Rasha Al-Askari, Mohanad A. Mohammed, Dunya Zeki Anaam, Elham Abdulwahab Mahmood, Zainab H. Jabbar, Dina Jamal Salih, Zahraa Aqeel Information System Department College of Computer Sciences and Information Technology University of Al-Anbar Al-Anbar Iraq Computer Engineering Department College of Engineering Al-Iraqia University Baghdad Iraq Electronic and Communications Engineering Department College of Engineering Gilgamesh University Baghdad Iraq Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya Malaysia Department of Cybersecurity Engineering College of Information Engineering Al-Nahrain University Baghdad Iraq Electrical Engineering Technical Collage Middle Technical University Baghdad Iraq Department of Business Administration Alamaal University College Baghdad Iraq

DeepFakes and face image manipulation methods have been widely distributed in the last few years and several techniques have been presented to check the authenticity of the face image and detect manipulation if exists. Most of the available manipulation detection techniques have been successfully applied to reveal one type of manipulation under specific conditions, however, many limitations and challenges can be encountered in this field. To overcome some limitations and challenges, this paper presents a new face image authentication (FIA) scheme based on Multi-Task Cascaded Conventional Neural Networks (MTCNN) and watermarking in Slantlet transform (SLT) domain. The proposed FIA scheme has three main algorithms that are face detection and selection, embedding, and extraction algorithms. Different block sizes have been used to divide the image into non-overlapping blocks followed by classifying them into two groups that are blocks from face area (FA) and blocks from the remaining area (RA) of the image. In the embedding algorithms, the authentication information is generated from FA blocks and embedded in the RA blocks. In the extraction algorithms, the embedded information is extracted from RA blocks and compared with the calculated data from FA blocks to reveal manipulations and localize the manipulated blocks if exist. Extensive experiments have been conducted to evaluate the performance of the proposed FIA scheme for different face images. The experimental work included tests for payload, capacity, visual quality, time complexity, and localization of manipulations. The results proved the efficiency of the proposed scheme in detecting and localizing different face image manipulations such as attributes attacks, retouching attacks, expression swap, face swap, and morphing attacks. The proposed scheme overcomes many limitations and it is 100% accurate in localizing the tampered blocks which makes it a better candidate for practical applications. © The Author(s), un

关键词： Authentication

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