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Öğe Çalışanların Liderlik, Mobbing ve İş Tutumu Arasındaki İlişkilerinin İncelenmesi: Nahçivan Öğretmenler Enstitüsü(Alanya Alaaddin Keykubat Üniversitesi, 2021) Tunay, Mustafa; Kamilov, RuslanYapılan bu çalışmada liderlik, mobbing (yıldırma) ve iş tutumu gibi kavramlar üzerinde durularak ve geçmişten günümüze süregelen liderlik teorilerine, mobbing algılarına detaylı olarak yer verilmiştir. Liderlik tarzlarından hangilerinin mobbing oluşumuna zemin hazırladığını, bunun çalışanların iş tutumu performansları üzerinde nasıl etkiler oluşturabileceği ve çalıştığı kurumlardaki çalışma süresinin liderlik tarzları ile mobbing algısının arasındaki ilişkiyi ölçerek, bunun iş tutumu üzerinde nasıl etki yaratabileceği gibi hususlar, yapılan bu çalışmada belirtilmiştir. Çalışmada araştırma birimi olarak, Nahçivan Özerk Cumhuriyeti!nde bulunan Nahçivan Öğretmenler Enstitüsü (NÖE) seçilmiştir. Nahçivan Öğretmenler Enstitüsü bünyesinde yer alan yöneticiler (rektör, rektör yardımcısı, dekan ve bölüm başkanı) ve çalışanları (öğretim görevlisi/üyesi) arasındaki iletişim şekli ve davranış tarzlarını belirlemek amacıyla SPPS 22 istatiksel analiz programından yararlanılmıştır. Çalışanların demografik özelliklerinin yanı sıra, çalıştıkları birimlerinden de bilgi edinerek liderlik tarzlarının ve mobbing algısının; çalışanların iş tutumu, iş performansı üzerine etkilerinin önemi vurgulanmıştır. Araştırmanın sonucunda, çalışanların mobbing davranışı ve liderlik özellikleri ile iş tutumu arasında anlamlı bir ilişki olduğu görülmüştür.Öğe Detection of Cyber Attacks Targeting Autonomous Vehicles Using Machine Learning(Springer Science and Business Media Deutschland GmbH, 2024) Onur, Furkan; Barışkan, Mehmet Ali; Gönen, Serkan; Kubat, Cemallettin; Tunay, Mustafa; Yılmaz, Ercan NurcanThe advent of Industry 4.0, characterized by the integration of digital technology into mechanical and electronic sectors, has led to the development of autonomous vehicles as a notable innovation. Despite their advanced driver assistance systems, these vehicles present potential security vulnerabilities, rendering them susceptible to cyberattacks. To address this, the study emphasized investigating these attack methodologies, underlining the need for robust safeguarding strategies for autonomous vehicles. Existing preventive or detection mechanisms encompass intrusion detection systems for Controller Area Networks and Vehicle-to-Vehicle communication, coupled with AI-driven attack identification. The critical role of artificial intelligence, specifically machine learning and deep learning subdomains, was emphasized, given their ability to dissect vehicular communications for attack detection. In this study, a mini autonomous vehicle served as the test environment, where the network was initially scanned, followed by the execution of Man-in-the-Middle, Deauthentication, DDoS, and Replay attacks. Network traffic was logged across all stages, enabling a comprehensive analysis of the attack impacts. Utilizing these recorded network packets, an AI system was trained to develop an attack detection mechanism. The resultant AI model was tested by transmitting new network packets, and its detection efficiency was subsequently evaluated. The study confirmed successful identification of the attacks, signifying the effectiveness of the AI-based model. Though the focus remained on autonomous vehicles, the study proposes that the derived methodology can be extended to other IoT systems, adhering to the steps delineated herein. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Öğe Enhancing Najran’s sustainable smart city development in the face of urbanization challenges in Saudi- Arabia(TAYLOR & FRANCIS LTD, 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND, 2024) Alotaibi, Badr Saad; Elnaklah, Rana; Agboola, Oluwagbemiga Paul; Abuhussain, Mohammed Awad; Tunay, Mustafa; Dodo, Yakubu Aminu; Maghrabi, Ammar; Alyami, ManaThis research offers valuable perspectives on the nexus between urbanization and the smart environment within the Najran city, Saudi Arabian context. Najran City, with its diverse districts and evolving urban landscape, is at the forefront of adopting advanced technologies and participatory governance models to create a resilient, environmentally conscious, and sustainable urban environment, likewise positioning itself as a model of smart urban development but is also fostering an innovation-driven community equipped to address the difficulties and exploit the prospects given by the 21st century. The study systematically analyzes the multifaceted factors of smart city variables in Saudi Arabia and their direct impact on urban sustainability through extensive quantitative investigations. This study reveals the positive correlations between smart city attributes, on the attainment of urban sustainability. In order words, the smart city’s attributes such as IoT (Internet of Things) devices, smart infrastructure, and data-driven solutions are linked to the creation of a resilient urban environment in the face of urbanization and sustainability challenges. On the other hand, the study noted that the integration of smart technologies might lead to unintended consequences in urban planning. For example, an overemphasis on technology may overshadow human-centric approaches, which might potentially affect the community well-being and social cohesion. Study’s insights provide policymakers, urban planners, and players with an indepth knowledge of the pathways to harness smart city attributes for sustainable Najran urban development. The implications of this research extend beyond the borders of Saudi Arabia, serving as a valuable reference for regions worldwide grappling with similar urbanization and sustainability challenges in the 21st century. By harnessing the potential of smart cities, Saudi Arabia and other nations can pave the way for greener, more resilient urban futures.Öğe Hybrid Hypercube Optimization Search Algorithm and Multilayer Perceptron Neural Network for Medical Data Classification(HINDAWI LTD, ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON W1T 5HF, ENGLAND, 2022) Tunay, Mustafa; Pashaei, Elnaz; Pashaei, ElhamThe hypercube optimization search (HOS) approach is a new efficient and robust metaheuristic algorithm that simulates the dove's movement in quest of new food sites in nature, utilizing hypercubes to depict the search zones. In medical informatics, the classification of medical data is one of the most challenging tasks because of the uncertainty and nature of healthcare data. This paper proposes the use of the HOS algorithm for training multilayer perceptrons (MLP), one of the most extensively used neural networks (NNs), to enhance its efficacy as a decision support tool for medical data classification. The proposed HOS-MLP model is tested on four significant medical datasets: orthopedic patients, diabetes, coronary heart disease, and breast cancer, to assess HOS's success in training MLP. For verification, the results are compared with eleven different classifiers and eight well-regarded MLP trainer metaheuristic algorithms: particle swarm optimization (PSO), biogeography-based optimizer (BBO), the firefly algorithm (FFA), artificial bee colony (ABC), genetic algorithm (GA), bat algorithm (BAT), monarch butterfly optimizer (MBO), and the flower pollination algorithm (FPA). The experimental results demonstrate that the MLP trained by HOS outperforms the other comparative models regarding mean square error (MSE), classification accuracy, and convergence rate. The findings also reveal that the HOS help the MLP to produce more accurate results than other classification algorithms for the prediction of diseases.Öğe Improved Hypercube Optimisation Search Algorithm for Optimisation of High Dimensional Functions(HINDAWI LTD, ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON W1T 5HF, ENGLAND, 2022) Tunay, Mustafa; Abiyev, RahibThis paper proposes a stochastic search algorithm called improved hypercube optimisation search (HOS+) to find a better solution for optimisation problems. This algorithm is an improvement of the hypercube optimisation algorithm that includes initialization, displacement-shrink and searching area modules. The proposed algorithm has a new random parameters (RP) module that uses two control parameters in order to prevent premature convergence and slow finishing and improve the search accuracy considerable. Many optimisation problems can sometimes cause getting stuck into an interior local optimal solution. HOS+ algorithm that uses a random module can solve this problem and find the global optimal solution. A set of experiments were done in order to test the performance of the algorithm. At first, the performance of the proposed algorithm is tested using low and high dimensional benchmark functions. The simulation results indicated good convergence and much better performance at the lowest of iterations. The HOS+ algorithm is compared with other meta heuristic algorithms using the same benchmark functions on different dimensions. The comparative results indicated the superiority of the HOS+ algorithm in terms of obtaining the best optimal value and accelerating convergence solutions.Öğe Machine learning-based identification of cybersecurity threats affecting autonomous vehicle systems(Pergamon-Elsevier Science Ltd, 2024) Onur, Furkan; Gonen, Serkan; Bariskan, Mehmet Ali; Kubat, Cemallettin; Tunay, Mustafa; Yilmaz, Ercan NurcanWith the advancement of humanity, transportation and trade activities have increased, leading to the development process of basic land vehicles as more than physical power became necessary. Hand tools were developed with the invention of the wheel, followed by animal-powered vehicles, and then steam engine technology. After the advancement of electromechanical technologies, today's modern vehicles have been developed. Those who used these vehicles thought of transferring control from the human to autonomous driving systems to solve their safety and comfort problems. Today, instead of fully autonomous systems targeted for the future, autonomous driving support systems have been developed. Although these systems aim to increase the safety and comfort of passengers, they can become an easy target for malicious people due to network technologies and remote connection features. The most effective method of protection from these attackers is to conduct vulnerability analysis against newly emerging threats for the systems we use and to rectify identified vulnerabilities. In this research paper, the weaknesses of wireless communication towards remote connection usage of the mini electric autonomous vehicle were investigated, which we developed and produced its mechanics, electronics, and software. In this context, a test environment was created, and the problems and threats in autonomous driving technology were revealed through attacks (Deauth Attack, DoS, DDoS and MitM) made on the test environment. Following the exposed vulnerabilities, studies were conducted for the detection of these attacks using Artificial Intelligence. In the study, different algorithms were used to detect the attacks, and random forest algorithm successfully detected 96.1% of attacks. The main contribution to the field of cybersecurity in autonomous vehicles by providing effective solutions for threat identification and defense.Öğe A New Approach Method of Crossover Process Based On Genetic Algorithm Using High Dimensional Benchmark Functions(2021) Tunay, MustafaThe design of the improved genetic algorithm (GA+) is based on a meta-heuristic search for optimization problems. In this paper, the crossover process in the original genetic algorithm is improved. The improvement of the crossover process is renewed by applying two conditions. One of them is keeping the last genes (constant) for each population; the second one is about rotating genes according to the defined range of points between each two selected populations. The improved genetic algorithm (GA+) has the possibility of accelerating local convergence. Therefore, it gets a chance to search for better values globally using these conditions. All processes in the improved genetic algorithm have been represented in this paper. The performance of the proposed algorithm is evaluated using 7 benchmark functions (test functions) on different dimensions. Ackley function, Rastrigin function and Holzman function are multi-modal minimization functions; Schwefel 2.22 function, Sphere function, Sum Squares function and Rosenbrock function are uni-modal minimization functions. These functions are evaluated by considering cases that are minimized by having a set of dimensions as 30, 60, and 90. Additionally, the performance of the GA+ is compared with the performance of comparative optimization algorithms (meta-heuristics). The comparative results have shown the performance of the GA+ that performs much better than others for optimization functions.Öğe A New Approach Model of e-Visual Career Application in Distance Education(IEEE, 2020) Tunay, MustafaA new approach for ranking visual career counselling based on distance measures is explained. In this paper, the traditional method of career counselling will be carried out by means of distance consulting services to make career counselling. Especially, professional education in the capacity limitation and professional working demand for education has led to a proliferation of virtual applications. Hence, e-Visual Career Application has been developed as a web-based program for clients who have taken professional counseling efficiently. With this application, it will help clients in the process of discovering and making decisions to their interests, abilities, personalities, skills and values. This paper relies on the implications of introducing online visual career consulting in the login sessions and how it affects the people (supervisor and client), the processes (consulting), and the organizations involved.Öğe A New Design of Metaheuristic Search Called Improved Monkey Algorithm Based on Random Perturbation for Optimization Problems(HINDAWI LTD, ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON W1T 5HF, ENGLAND, 2021) Tunay, MustafaThe aim of this paper is to present a design of a metaheuristic search called improved monkey algorithm (MA+) that provides a suitable solution for the optimization problems. The proposed algorithm has been renewed by a new method using random perturbation (RP) into two control parameters (p1 and p2) to solve a wide variety of optimization problems. A novel RP is defined to improve the control parameters and is constructed off the proposed algorithm. The main advantage of the control parameters is that they more generally prevented the proposed algorithm from getting stuck in optimal solutions. Many optimization problems at the maximum allowable number of iterations can sometimes lead to an inferior local optimum. However, the search strategy in the proposed algorithm has proven to have a successful global optimal solution, convergence optimal solution, and much better performance on many optimization problems for the lowest number of iterations against the original monkey algorithm. All details in the improved monkey algorithm have been represented in this study. The performance of the proposed algorithm was first evaluated using 12 benchmark functions on different dimensions. These different dimensions can be classified into three different types: low-dimensional (30), medium-dimensional (60), and high-dimensional (90). In addition, the performance of the proposed algorithm was compared with the performance of several metaheuristic algorithms using these benchmark functions on many different types of dimensions. Experimental results show that the improved monkey algorithm is clearly superior to the original monkey algorithm, as well as to other well-known metaheuristic algorithms, in terms of obtaining the best optimal value and accelerating convergence solution.Öğe A New Intense Stochastic Search Method Based on Hypercube Evaluation for Examination Timetabling Problems(Institute of Electrical and Electronics Engineers Inc., 2020) Tunay, MustafaThis study of article presents a novel optimization search algorithm to solve the examination timetabling problems. These are discrete, multi-objective and combinatorial optimization problem which is generally solved using stochastic search techniques as evolutionary algorithms (EAs) and heuristic technique. In this study, the purposed optimization search algorithm is to give a brief introduction to examination timetabling problems that have been solved using Hypercube Optimization Search (HOS) algorithm which is a new intense stochastic search technique that is based on a hypercube evolution. The proposed method is applied to solve a timetabling problem which is hard to solve for many institutions of higher education and as a result this approach has shown good performance when the proposed method is compared with other performances of existing approaches. © 2020 IEEE.Öğe Optimization Search Using Hypercubes(Institute of Electrical and Electronics Engineers Inc., 2020) Abiyev, Rahib H.; Tunay, MustafaAn optimization search algorithm for multivariate systems is proposed. The proposed optimization search algorithm includes initialization-, displacement-shrink- and searching areas stages. The initialization stage generates initial solutions in the search area that represented by hypercube and then evaluates the function inside this hypercube; the displacement-shrink stage calculates the displacement and updates the parameters of the hypercube; the searching areas stage using certain rules find the next hypercube. The design stages of the proposed hypercube optimization search (HOS) algorithm are presented. The proposed HOS algorithm is tested on specific benchmark functions. The experimental results on different test functions demonstrate that the HOS algorithm has shown to be a promising approach for finding the solutions of a set of optimization problems. © 2020 IEEE.Öğe Urban resilience in the digital age: The influence of Information-Communication Technology for sustainability(Elsevier Sci Ltd, 2023) Agboola, Oluwagbemiga Paul; Tunay, MustafaIn the pursuit of advancing urban sustainability within the unique backdrop of Nigeria's built environment and its environmental challenges, this study presents the significance of information and communication technology (ICT). Undertaking this research holds immense importance in illuminating the possibilities inherent in leveraging ICT to foster urban sustainability. The study's objectives encompass a comprehensive investigation into the multifaceted contributions of ICT to urban sustainability, while also delving into its impact on stakeholder engagement and participation in these sustainability endeavors. Addressing an identified gap, the study sheds light on the critical nexus between stakeholders' active involvement and the resulting impact on urban sustainability. This connection serves as a crucial yet under-explored avenue within the broader discourse on leveraging ICT for sustainable urban development. The study employs structural equation modeling (SEM) to evaluate a proposed model and analyze empirical data. The results of the study highlight the critical role of ICTs in urban sustainability (beta = 0.614, R2 = 0.85), demonstrating its capacity to enhance efficiency; which promotes sustainability practices, and improves the quality of life for urban residents. The findings of this study have significant implications, as they suggest the potential for optimizing the impact of ICT-based urban environments to meet the diverse needs and priorities of society as a whole. By leveraging ICT effectively, countries can create a robust smart environment that contributes to sustainable development and addresses environmental concerns. To leverage the benefits of ICT, however, appropriate attention should be committed to the execution of smart urban sustainability through stakeholder participation and involvement. The implication of the study enables the possibility to optimize the impact of an ICT-based urban environment, thereby creating sustainable and resilient communities that meet the needs and priorities of all members of society.
