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Computer Modeling in engineering & Sciences 2023年第5期135卷 1345-1369页

作者： Adedoyin A.Hussain Fadi Al-Turjman Computer Engineering Department and Research Centre for AI and IoT Near East UniversityNicosiaTurkey Computer Engineering Department CyprusWest UniversityGazimagusaTurkey Artificial Intelligence Engineering Department and AI and Robotics Institutes Near East UniversityNicosiaTurkey Research Centre for AI and IoT Faculty of EngineeringUniversity of KyreniaKyreniaTurkey

The internet of medical things(IoMT)empowers patients to get adaptable,and virtualized gear over the *** scheduling is the most fundamental problem in the IoMT-cloud since cloud execution commonly relies on ***,a proposition is being made for a distinct scheduling technique to suitably meet these *** manage the scheduling issue,an artificial intelligence(ai)method known as a hybrid genetic algorithm(HGA)is *** proposed ai method will be justified by contrasting it with other traditional optimization and ai scheduling *** CloudSim is utilized to quantify its effect on various parameters like time,resource utilization,cost,and *** proposed ai technique enhanced the viability of task scheduling with a better execution rate of 32.47ms and a reduced time of ***,the experimented outcomes show that the HGA reduces cost as well as time profoundly.

关键词： artificial intelligence IoMT hybrid genetic algorithm cloud

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Advancements and Performance Analysis of Feature Detection and Matching Methods in Visual Odometry

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Journal of Institute of Control, robotics and Systems 2025年第3期31卷 231-236页

作者： Choi, Ji Hun Kang, Chang Ho Kim, Sun Young Department of Artificial Intelligence Sejong University Korea Republic of Department of AI Robotics Sejong University Korea Republic of School of Mechanical Engineering Kunsan National University Korea Republic of

This study presents a comprehensive performance analysis of feature detectors and descriptors in visual odometry (VO) based on four key metrics: absolute trajectory error (ATE), relative pose error (RPE), cumulative drift, and frames per second (FPS). Five algorithm-based methods (SIFT, ROOTSIFT, ORB, KAZE, and AKAZE) and five ai-based methods (SuperPoint, DISK, ALIKED, D2NET, and Xfeat) are evaluated using two matching strategies: Brute-Force and LIGHTGLUE. Performance assessment is conducted on the KITTI dataset, demonstrating that KAZE achieves the best results in ATE and cumulative drift, while ALIKED outperforms in RPE and FPS. These findings offer valuable insights for selecting the optimal feature detection and description techniques in real-world VO applications. © ICROS 2025.

关键词： Deep learning

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Data augmentation and denoising of computed tomography scan images in training deep learning models for rapid COVID-19 detection

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International Journal of Business intelligence and Data Mining 2024年第2期24卷 203-216页

作者： Mubarak, Auwalu Saleh Serte, Sertan Al-Turjman, Fadi Ameen, Zubaida Sa'Id Artificial Intelligence Engineering Department AI and Robotics Institute Near East University Mersin10 Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin10 Turkey Department of Electrical and Electronics Engineering Near East University Mersin10 Turkey

The deadly respiratory disease corona virus-2 (COVID-19) which was declared a pandemic by the World Health Organization (WHO) has resulted in over a million deaths around the world within less than a year. With the rapid spread of the virus, the currently adopted COVID-19 test by the WHO is the reverse transcription polymerase chain reaction (RT-PCR) test, which is expensive, time-consuming and not accessed by underdeveloped countries. Computed tomography (CT) scan images that were used in profiling suspected COVID-19 patients can serve as an alternative to the RT PCR test method. In this study, two different pre-Trained deep learning models ResNet-50 and ResNet-101 were trained to classify positive COVID-19 scan images. The best model which was trained on the augmented CT scan images achieved an accuracy of 98.3%, a sensitivity of 0.984, specificity of 0.983. © 2024 Inderscience Enterprises Ltd.. All rights reserved.

关键词： COVID-19

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A comprehensive survey on security issues in vehicle-to-grid networks

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Journal of Control and Decision 2023年第2期10卷 150-159页

作者： Arun Sekar Rajasekaran Maria Azees Fadi Al-Turjman Department of Electronics and Communication Engineering GMR Institute of TechnologyRajamIndia Artificial Intelligence Engineering Department. Research Center for AI and IoTAI and Robotics InstituteNear East UniversityMersin 10Turkey

Vehicle to grid(V2G)is the most hopeful approach to transfer energy as well as information in the bidirectional way.V2G network is formed by electric vehicles which connect with smart metres for information and energy transfer in a wireless *** though many security preserving schemes developed in V2G networks,they were prone to enormous number of security breaches.A countless deal of works has been done towards it,but security mechanisms in V2G networks are not *** survey work provides a summary about the V2G network characteristics,significance,security services and the security ***,this work offers a summary of some foremost security attacks on various security services such as accessibility,confidentiality,authentication,integrity and non-repudiation and the related countermeasures to make the V2G communications more protected.

关键词： Authentication confidentiality integrity privacy security

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A Framework for Pothole Detection via the ai-Blockchain Integration

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2022年 129卷 398-406页

作者： Mubarak, Auwalu Saleh Ameen, Zubaida Said Tonga, Paul Altrjman, Chadi Al-Turjman, Fadi Artificial Intelligence Engineering Department Research Center for AI and IoT AI and Robotics Institute Near East University Mersin 10 Turkey

Potholes are a major cause of road accidents. In smart cities, developing a system that will help in detecting the potholes in real-time while warning the motorist before reaching that location is very important. Lightweight EfficientDet family models were proposed in the literature to detect potholes in real-time. The work is divided into two parts, first a mobile application was developed using these trained models and it can efficiently detect the potholes. Also, in the second part, a blockchain was proposed to keep a record of the location of the pothole in real-time so that it can be updated on the network. The blockchain serves as a security mechanism so that the potholes coordinates can be altered and become up to date. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Reward-Based Energy-Aware Routing Protocols in Wireless Sensor Networks

Reward-Based Energy-Aware Routing Protocols in Wireless Sens...

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International Conference on Smart Applications and Sustainability in the artificial intelligence of Things, SAS-aioT 2024

作者： Salama, Ramiz Alturjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Cyprus Artificial Intelligence Software and Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Cyprus

ISBN: (纸本)9783031782756

Smart cities, industrial automation, healthcare, and environmental monitoring are just a few of the industries that have seen revolutionary changes as a result of Wireless Sensor Networks’ (WSNs) explosive growth. WSNs are significantly hampered by the limited energy resources of their sensor nodes, which are usually battery-powered and placed in inaccessible locations, despite their wide-ranging applications. As a result, energy efficiency is now a crucial component of these networks’ operation and design. Though these protocols frequently fail to adjust to the dynamic and unpredictable nature of WSNs, traditional energy-aware routing methods have been created to decrease energy consumption by choosing the best paths for data transfer. By allowing sensor nodes to learn from their surroundings and make wise routing decisions, the machine learning field of reinforcement learning (RL) provides a viable answer to these problems. In contrast to conventional techniques, RL-based protocols provide continuous energy optimization by dynamically modifying their routing strategies in response to real-time feedback. This paper investigates how to include RL methods—specifically, Q-learning and SARSA—into WSN energy-aware routing protocols. The suggested method creates a more distributed and scalable solution by enabling each node to independently learn and modify its routing choices. The effectiveness of RL-based routing protocols is compared to traditional methods through comprehensive simulations. The findings show that by slowing down the rate at which energy is being used up by each node, RL not only increases energy efficiency but also lengthens the network's operational life. In particular, Q-learning demonstrates a notable improvement in sustaining data delivery rates and network connectivity under various circumstances. Although a little more computationally demanding, the SARSA algorithm offers greater flexibility in situations where network topology changes often. Th

关键词： Multi agent systems

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Using Gamification to Increase Healthcare Professionals’ Cybersecurity Awareness

Using Gamification to Increase Healthcare Professionals’ Cy...

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International Conference on Smart Applications and Sustainability in the artificial intelligence of Things, SAS-aioT 2024

作者： Salama, Ramiz Alturjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Cyprus Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Cyprus

ISBN: (纸本)9783031782756

Cybersecurity knowledge is essential in today’s increasingly digital healthcare environment to protect sensitive patient data and guarantee the integrity of healthcare systems. But conventional cybersecurity training approaches frequently fail to hold healthcare professionals’ attention, which results in inadequate application and retention of the material. In order to increase cybersecurity awareness among healthcare professionals, this study investigates the use of gamification—a technological technique that incorporates game design aspects into non-gaming contexts—as an innovative method. A mixed-methods research methodology is used in the study to assess the effectiveness of a cybersecurity training program that is gamified. To encourage user involvement and motivation, the program incorporates aspects like leaderboards, interactive scenarios, badges, and points. The study included a total of 100 healthcare professionals from various departments, offering a thorough understanding of the program’s effects across a range of positions and experience levels. Pre- and post-training surveys were used to gather quantitative data on participant changes in knowledge, attitudes, and behaviors pertaining to cybersecurity procedures. Furthermore, semi-structured interviews yielded qualitative insights that further elucidated the participants’ perspectives and experiences with the gamified training method. Comparing gamification to conventional training approaches, the results show that gamification dramatically improves engagement and knowledge retention. The willingness of the participants to acquire and use cybersecurity techniques in their daily lives has reportedly increased. Additionally, the gamified components strengthened the training objectives by fostering a competitive yet cooperative learning environment. The potential of gamification as a game-changing instrument for cybersecurity education in healthcare settings is highlighted by this study. Healthcare busines

关键词： Cybersecurity

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Wi-Fi Feedback-Based Power System in the Heterogenous IoT Era – An Overview

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2022年 129卷 432-442页

作者： Al-Turjman, Fadi Abdelazim, Ahmed Ali Artificial Intelligence Engineering Department Research Center for AI and IoT AI and Robotics Institute Near East University Mersin 10 Nicosia Turkey

Electric power has recently been optimized and witnessed a significant transformation in many aspects to providing sustainable clean, distributed energy for the fast-paced, growing world. Modern technologies such as the internet of things (IoT) technology is at the forefront of this digital revolution. Besides, heterogeneous IoT has enabled and offered a variety of applications in power systems since it has enhanced power efficiency, distribution of energy, especially in rural areas. Furthermore, it has accelerated transmitting and receiving the signals from the main electric power grid to the emergency backup system via a smart WI-FI feedback signal to sustain critical loads such as hospitals, schools, plants, banks, etc., working without interruptible. Also, IoT contributes to increasing the volume of the energy shared. This review paper discusses the impact of IoT technology in evolving electric power and energy systems at an unprecedented pace. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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Position Estimation and Tracking with a Fuzzy-Logic-Based Adaptive Strong Tracking Kalman Filter for Capacitive Touch Panels

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2022年 130卷 30-39页

作者： Almomani, Abdelrahman Al-Turjman, Fadi Electrical and Electronic Engineering Department AI and Robotics Institute Near East University Mersin 10 Nicosia Turkey Artificial Intelligence Engineering Department AI and Robotics Institute Near East University Mersin 10 Nicosia Turkey

A capacitive touch panel (CTP) is one of most important applications that is used in most of the human controlled electronic devices. The sensor detects CTP and decide if there is error or not. The error might be caused by the speed of touching the CTP. However, the low touching speeds cause more errors than the fast ones. The paper of consideration introduces an algorithm with smooth tracking and predict the position. Many methods have been studied to mitigate the errors, including moving average filter (MAF) method, Kalman filter (KF) and a strong tracking Kalman filter (STKF). Generally, STKF succeed to estimate the normal and fast speed of touching the CTP, whereas it fails in estimating the low speed. The paper of consideration proposes a method that integrates fuzzy logic with STKF to decrease the error produced from low speed of finger movement. Fuzzy logic provides a solution for this problem that results in a smooth tracking at various speeds (low, high, normal and nonlinear cases). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Kalman filters

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Smart Robot: artificial Intelligent Cleaning Robot with Wifi Boundary Sensing

Smart Robot: Artificial Intelligent Cleaning Robot with Wifi...

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International Conference on Smart Applications and Sustainability in the artificial intelligence of Things, SAS-aioT 2024

作者： Tannous, Gresmarteneanos Ame, Ibrahim Kian, Nafiseh Yousefi Al-Turjman, Fadi Mlimwa, Cliford Peter Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Research Centre for AI and IoT Nicosia Cyprus Research Centre for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Cyprus Department of Computer Engineering Cyprus International University Nicosia Cyprus

ISBN: (纸本)9783031782756

The environment surrounds our daily lives, whether we want to or not. When we have a clean environment, we can enjoy very healthy lifestyles. However, when we have dirty environments, it can cause a myriad of diseases, health risks, and worse, global pandemics like plagues. This became very apparent during the years of the COVID-19 pandemic when we had to sanitize our hands on an hourly basis after touching suspicious items that might have had human contact. This study entails a way to keep our environment clean by picking up trash and items that are left on the floor by other people. A smart robot that uses artificial intelligence to be able to not only move around and understand its surroundings, but also know if there is an object that is identified as trash and needs to be removed from that area. The smart robot is fitted with a high-definition camera for object detection and can see them from 3–4 m. From the image, it will scan the angle direction of the object, change its course, and follow it. After reaching the said location, it will begin its procedure of picking up and storing it in its trash compartment. The robot can be tuned to only clean a certain area and not cross its boundary by comparing fingerprints of registered Wi-Fi RSSIs. This method is most accurate for indoor boundary setting. For outdoors, a GPS method is available to track and bind the robot within a map circumference. Finally, all these metrics and location services can be viewed on a custom-made mobile application that complements the robot's features. What ties all this together is a cloud-based server that feeds the app data from the robot and stores it for future review. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Wi-Fi

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