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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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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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Analysis of Bigmart Outlets sales using Big Data Analytics Method 2

Analysis of Bigmart Outlets sales using Big Data Analytics M...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAaiC 2023

作者： Ramasami, Meaga Varsha Thangaraj, Rajasekaran Kumar, S Manoj Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Centre for IoT and Ai Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665456302

Sales prediction is an important problem for businesses. A sales forecast can guide a company's production, pricing, and promotion strategies, and can help mitigate risks associated with under or overproduction. Traditional methods of sales prediction utilized statistical techniques like regression analysis or time series analysis, but these methods have certain limitations, including the need for large and clean data sets. This research work focuses on exploring the patterns and trends present in a large-scale retail dataset through exploratory data analysis (EDA). The primary objective of this research is to uncover insights that can enhance the understanding of customer behaviour, identify opportunities to improve business processes and inform data-driven decisions. The data consists of daily sales records for 1560 products of ten BigMart Outlets. This study explores several preprocessing steps, such as handling missing values and outliers, scaling and normalization, and feature engineering. The findings also revealed that the top-selling items were bought by a diverse range of customer demographics, indicating that marketing campaigns should target a broader audience. Furthermore, the study identified several unusual transactions that required further investigation. © 2023 IEEE.

关键词： Sales

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A Comparative Study of Deep Learning Models for Guava Leaf Disease Detection 3

A Comparative Study of Deep Learning Models for Guava Leaf D...

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3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Thangaraj, Rajasekaran Mohan, M. Moulik, M. Logeshwari, A. Loganathan, T. Koushika, P. Centre for IoT and AI Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665494007

Plant diseases are regarded as one of the major variables affecting food production and reducing production losses. Early identification and disease diagnosis of fruit crops are essential for the food industry to increase production. The development of the healthy fruit sector is highly dependent on plant diseases. Guava fruit is a good source of fiber, calcium, pectins, and vitamin C. Guava disease has become a major issue for guava production, which has a negative impact on farmers' socioeconomic growth. Infections that harm guava plants and fruits pose the greatest obstacles, although they can be avoided by early identification. Deep learning has recently been widely used to automatically identify a variety of diseases that cannot be seen with the human eye. In this study, convolution neural models are suggested to use leaf images to detect guava disease. The presented models can be used to categorize the main guava leaf diseases, including Canker, Dot, Healthy, Mummification and Rust. The kaggle dataset is used to gather images of guava with the disease Canker, Dot, Healthy, Mummification and Rust. In our work, CNN models like DenseNet121, DenseNet169, InceptionV3, and Xception are used to categorize the diseases that affect guava leaves. The experimental findings indicate that, with an accuracy of 96.12%, the DenseNet169 model outperformed other models. When the model's effectiveness is assessed using performance metrics like precision, recall, and F1 score, the results demonstrate that DenseNet169 gives excellent results. © 2023 IEEE.

关键词： Convolutional neural networks

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Smart Cities Using Artificial Intelligence (ai) and Blockchain in the Internet of Things (iot) Era 1st

Smart Cities Using Artificial Intelligence (AI) and Blockcha...

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1st International Conference on Smart Environment and Green Technologies, ICSEGT 2024

作者： Al-Turjman, Fadi Salama, Ramiz Sadikoglu, Gunay Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Nicosia Mersin Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Mersin Turkey Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Mersin Turkey Near East University Nicosia Mersin Turkey

ISBN: (纸本)9783031815638

Around the world, modern cities are changing dramatically in order to install smart infrastructure, provide smart services to residents, improve accessibility for vulnerable populations, and provide a clean, sustainable, and secure environment. Numerous initiatives that address significant issues with urban transportation, health, water management, energy conservation, housing, safety, and accessibility are centered on the adoption of new technologies. To overcome these obstacles and enhance the resourcefulness, sustainability, and security of cities, it is imperative to embrace new and creative technologies, especially in light of the advancements in communication and sensor technology over the past few decades. This article gives a summary of the metropolitan issues that smart urban communities are now experiencing. It investigates how edge ai and blockchain, two recent technological innovations, might revolutionize urban intelligence. It also examines significant research initiatives in these two crucial areas of today’s smart cities, along with a variety of applications for edge ai and blockchain in smart energy and transportation. It discusses numerous uses of blockchain in the context of secure vehicle-to-vehicle trade and communication, along with a range of methods for processing speed detection, counting, and identification to lessen traffic congestion. The purpose of this review article is to set the stage for next research on applying blockchain technology and edge artificial intelligence to smarter city applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sustainable city

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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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Vision-Based Object Sorting in Dynamic Environments using YOLO for RoboCup ARM Challenge 2023 7

Vision-Based Object Sorting in Dynamic Environments using YO...

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7th IEEE Conference on Information and Communication Technology, CICT 2023

作者： Sahu, Yash Kumar Mittal, Radhika Patel, Deep Maiti, Chayan Sreekumar, M. Indian Institute of Information Technology Design and Manufacturing Centre for AI IoT and Robotics Department of Mechanical Engineering Kancheepuram Chennai India Indian Institute of Information Technology Design and Manufacturing Centre for AI IoT and Robotics Department of Computer Science Kancheepuram Chennai India

ISBN: (纸本)9798350305173

In the intersection of Artificial Intelligence (ai) and Robotics, autonomous object manipulation has emerged as a pivotal research domain, finding applications in sectors as diverse as manufacturing and healthcare. While significant strides have been made in object identification, trajectory planning, and manipulation tasks, these advancements often fall short in real-world, dynamic settings, revealing a critical research gap in algorithmic adaptability and computational efficiency. This paper addresses the gap by applying YOLOv4, a state-of-the-art object detection ai algorithm, within the context of the RoboCup Autonomous Robot Manipulation (ARM) Challenge - a competitive platform designed to simulate real-world complexities in autonomous systems. The research objectives are twofold: firstly, the performance of YOLOv4 in object detection and classification under dynamically changing conditions is rigorously evaluated, employing varied training strategies in both simulated and real-world scenarios. Secondly, the robustness and adaptability of the algorithm in object manipulation tasks are scrutinized and facilitated by the real-world dynamics inherent to the competition. Empirical results demonstrate the algorithm's high accuracy, efficiency, and robustness in dynamic environments, substantiating its broader applicability in the development of autonomous systems. © 2023 IEEE.

关键词： Object detection

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Social engineering attack types and prevention techniques- A survey 1

Social engineering attack types and prevention techniques- A...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Bhatia, Shruti Yadav, Satya Prakash Near East University Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Nicosia Turkey Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

The term "Cyber Security"is getting more and more popular and important over the last few years. Since computers and most of the devices are connected to the internet, they are likely to be hacked by the hackers. In the past, this issue was not a big problem, because not every device is required to be connected to the internet or the internet was not popular as today. But the case has changed over the years. All the changes in the technology area also changed the cyber-attack models as well. With the development of technology and the change of usage of the internet over the last years cyber-attacks have become more common and popular by the hackers. Hackers have discovered that they have an opportunity to steal or earn money in a short time without having to spend too much effort. These days, the type of cyber-attacks is not the same as the ones in the past. As time passes, the cyber-attack methods are also changing and evolving. Today, hackers are using more advanced and effective cyber-attack methods compared to past years. There are many methods that are impossible to cover all of them in this article. Our main focus will be on social engineering attacks in this article. Social engineering attacks use different approaches to cyber attacking. Unlike trying to explode a victim's social media password etc. using advanced exporting programs, algorithms or techniques, social engineering attacks focus on fooling victims into providing their data to hackers by themselves without using or implementing any password cracking, exploiting techniques etc. We will go over what social media engineering is, type of social engineering methods, what countermeasures can be used to protect from social engineering and more in this article. © 2023 IEEE.

关键词： Crime

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Future Communication Technology Using Huge Millimeter Waves - An Overview 1

Future Communication Technology Using Huge Millimeter Waves ...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Chaudhary, Preeti Banda, Latha Near East University Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Nicosia Turkey Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey Graphic Era Hill University Computer Science and Engineering Dehradun India Abes Engineering College Ghaziabad India

ISBN: (纸本)9798350338027

Frequency selective surface (FSS) was first created by G. Marconi and C. S. Franklin in 1919 and is known as the "Reflector for Wireless Telegraph and Telephone,"making a significant contribution to our way of life. In terms of the 1960s, it is impossible to overlook the success attained with the employment of regularly selective surfaces in military intelligence units. These structures, which were occasionally applied to land, air, and sea platforms, displayed low pass, high pass, band pass, or band stop characteristics for particular frequencies and served as electromagnetic filters. Many accomplishments have been made, particularly with platforms that are invisible to target detection systems. The geometry and arrangement of the unit cell in these structures, which are successfully used in many fields such as wireless communication systems, radars, and microwaves, allows for the reflection or transmission of the incoming electromagnetic wave. This is made possible by the advancement of technology. High-speed internet connectivity is possible using millimeter wave, a frequency spectrum between 30 GHz and 300 GHz. Its significance has grown in the fields of radio astronomy, radar, remote sensing, and wireless communications. © 2023 IEEE.

关键词： Millimeter waves

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