Jafari Navimipour, Nima
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Name Variants
Jafari Navimipour,Nima
JAFARI NAVIMIPOUR, Nima
N. Jafari Navimipour
Jafari Navimipour, Nima
Jafari Navimipour,N.
J.,Nima
JAFARI NAVIMIPOUR, NIMA
Jafari Navimipour, N.
Nima Jafari Navimipour
Nima JAFARI NAVIMIPOUR
Jafari Navimipour, NIMA
Jafari Navimipour N.
NIMA JAFARI NAVIMIPOUR
J., Nima
Nima, Jafari Navimipour
Navimipour, Nima Jafari
Navimipour, N.J.
Navimpour, Nima Jafari
Navımıpour, Nıma Jafarı
JAFARI NAVIMIPOUR, Nima
N. Jafari Navimipour
Jafari Navimipour, Nima
Jafari Navimipour,N.
J.,Nima
JAFARI NAVIMIPOUR, NIMA
Jafari Navimipour, N.
Nima Jafari Navimipour
Nima JAFARI NAVIMIPOUR
Jafari Navimipour, NIMA
Jafari Navimipour N.
NIMA JAFARI NAVIMIPOUR
J., Nima
Nima, Jafari Navimipour
Navimipour, Nima Jafari
Navimipour, N.J.
Navimpour, Nima Jafari
Navımıpour, Nıma Jafarı
Job Title
Doç. Dr.
Email Address
nima.navimipour@khas.edu.tr
Main Affiliation
Computer Engineering
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Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals Report Points
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Scholarly Output
98
Articles
79
Citation Count
32
Supervised Theses
1
35 results
Scholarly Output Search Results
Now showing 1 - 10 of 35
Review Citation - WoS: 41Citation - Scopus: 51Resilient and Dependability Management in Distributed Environments: a Systematic and Comprehensive Literature Review(Springer, 2023) Amiri, Zahra; Jafari Navimipour, Nima; Heidari, Arash; Navimipour, Nima Jafari; Unal, Mehmet; Computer EngineeringWith the galloping progress of the Internet of Things (IoT) and related technologies in multiple facets of science, distribution environments, namely cloud, edge, fog, Internet of Drones (IoD), and Internet of Vehicles (IoV), carry special attention due to their providing a resilient infrastructure in which users can be sure of a secure connection among smart devices in the network. By considering particular parameters which overshadow the resiliency in distributed environments, we found several gaps in the investigated review papers that did not comprehensively touch on significantly related topics as we did. So, based on the resilient and dependable management approaches, we put forward a beneficial evaluation in this regard. As a novel taxonomy of distributed environments, we presented a well-organized classification of distributed systems. At the terminal stage, we selected 37 papers in the research process. We classified our categories into seven divisions and separately investigated each one their main ideas, advantages, challenges, and strategies, checking whether they involved security issues or not, simulation environments, datasets, and their environments to draw a cohesive taxonomy of reliable methods in terms of qualitative in distributed computing environments. This well-performed comparison enables us to evaluate all papers comprehensively and analyze their advantages and drawbacks. The SLR review indicated that security, latency, and fault tolerance are the most frequent parameters utilized in studied papers that show they play pivotal roles in the resiliency management of distributed environments. Most of the articles reviewed were published in 2020 and 2021. Besides, we proposed several future works based on existing deficiencies that can be considered for further studies.Article Citation - WoS: 62Citation - Scopus: 88A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systems(Elsevier, 2023) Jafari Navimipour, Nima; Javaheri, Danial; Toumaj, Shiva; Navimipour, Nima Jafari; Rezaei, Mahsa; Unal, Mehmet; Computer EngineeringWith an estimated five million fatal cases each year, lung cancer is one of the significant causes of death worldwide. Lung diseases can be diagnosed with a Computed Tomography (CT) scan. The scarcity and trustworthiness of human eyes is the fundamental issue in diagnosing lung cancer patients. The main goal of this study is to detect malignant lung nodules in a CT scan of the lungs and categorize lung cancer according to severity. In this work, cutting-edge Deep Learning (DL) algorithms were used to detect the location of cancerous nodules. Also, the real-life issue is sharing data with hospitals around the world while bearing in mind the organizations' privacy issues. Besides, the main problems for training a global DL model are creating a collaborative model and maintaining privacy. This study presented an approach that takes a modest amount of data from multiple hospitals and uses blockchain-based Federated Learning (FL) to train a global DL model. The data were authenticated using blockchain technology, and FL trained the model internationally while maintaining the organization's anonymity. First, we presented a data normalization approach that addresses the variability of data obtained from various institutions using various CT scanners. Furthermore, using a CapsNets method, we classified lung cancer patients in local mode. Finally, we devised a way to train a global model cooperatively utilizing blockchain technology and FL while maintaining anonymity. We also gathered data from real-life lung cancer patients for testing purposes. The suggested method was trained and tested on the Cancer Imaging Archive (CIA) dataset, Kaggle Data Science Bowl (KDSB), LUNA 16, and the local dataset. Finally, we performed extensive experiments with Python and its well-known libraries, such as Scikit-Learn and TensorFlow, to evaluate the suggested method. The findings showed that the method effectively detects lung cancer patients. The technique delivered 99.69 % accuracy with the smallest possible categorization error.Article Citation - WoS: 27Citation - Scopus: 28Implementation of a Product-Recommender System in an Iot-Based Smart Shopping Using Fuzzy Logic and Apriori Algorithm(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Yan, Shu-Rong; Jafari Navimipour, Nima; Pirooznia, Sina; Heidari, Arash; Navimipour, Nima Jafari; Unal, Mehmet; Computer EngineeringThe Internet of Things (IoT) has recently become important in accelerating various functions, from manufacturing and business to healthcare and retail. A recommender system can handle the problem of information and data buildup in IoT-based smart commerce systems. These technologies are designed to determine users' preferences and filter out irrelevant information. Identifying items and services that customers might be interested in and then convincing them to buy is one of the essential parts of effective IoT-based smart shopping systems. Due to the relevance of product-recommender systems from both the consumer and shop perspectives, this article presents a new IoT-based smart product-recommender system based on an apriori algorithm and fuzzy logic. The suggested technique employs association rules to display the interdependencies and linkages among many data objects. The most common use of association rule discovery is shopping cart analysis. Customers' buying habits and behavior are studied based on the numerous goods they place in their shopping carts. As a result, the association rules are generated using a fuzzy system. The apriori algorithm then selects the product based on the provided fuzzy association rules. The results revealed that the suggested technique had achieved acceptable results in terms of mean absolute error, root-mean-square error, precision, recall, diversity, novelty, and catalog coverage when compared to cutting-edge methods. Finally, themethod helps increase recommender systems' diversity in IoT-based smart shopping.Book Part Citation - WoS: 0Citation - Scopus: 0Machine/Deep Learning Techniques for Multimedia Security(inst Engineering Tech-iet, 2023) Jafari Navimipour, Nima; Navimipour, Nima Jafari; Azad, Poupak; Computer EngineeringMultimedia security based on Machine Learning (ML)/ Deep Learning (DL) is a field of study that focuses on using ML/DL techniques to protect multimedia data such as images, videos, and audio from unauthorized access, manipulation, or theft. Developing and implementing algorithms and systems that use ML/DL techniques to detect and prevent security breaches in multimedia data is the main subject of this field. These systems use techniques like watermarking, encryption, and digital signature verification to protect multimedia data. The advantages of using ML/DL in multimedia security include improved accuracy, scalability, and automation. ML/DL algorithms can improve the accuracy of detecting security threats and help identify multimedia data vulnerabilities. Additionally, ML models can be scaled up to handle large amounts of multimedia data, making them helpful in protecting big datasets. Finally, ML/DL algorithms can automate the process of multimedia security, making it easier and more efficient to protect multimedia data. The disadvantages of using ML/DL in multimedia security include data availability, complexity, and black box models. ML and DL algorithms require large amounts of data to train the models, which can sometimes be challenging. Developing and implementing ML algorithms can also be complex, requiring specialized skills and knowledge. Finally, ML/DL models are often black box models, which means it can be difficult to understand how they make their decisions. This can be a challenge when explaining the decisions to stakeholders or auditors. Overall, multimedia security based on ML/DL is a promising area of research with many potential benefits. However, it also presents challenges that must be addressed to ensure the security and privacy of multimedia data.Article Citation - WoS: 42Citation - Scopus: 49A hybrid approach for latency and battery lifetime optimization in IoT devices through offloading and CNN learning(Elsevier, 2023) Jafari Navimipour, Nima; Navimipour, Nima Jafari; Jamali, Mohammad Ali Jabraeil; Akbarpour, Shahin; Computer EngineeringOffloading assists in overcoming the resource constraints of specific elements, making it one of the primary technical enablers of the Internet of Things (IoT). IoT devices with low battery capacities can use the edge to offload some of the operations, which can significantly reduce latency and lengthen battery lifetime. Due to their restricted battery capacity, deep learning (DL) techniques are more energy-intensive to utilize in IoT devices. Because many IoT devices lack such modules, numerous research employed energy harvester modules that are not available to IoT devices in real-world circumstances. Using the Markov Decision Process (MDP), we describe the offloading problem in this study. Next, to facilitate partial offloading in IoT devices, we develop a Deep Reinforcement learning (DRL) method that can efficiently learn the policy by adjusting to network dynamics. Convolutional Neural Network (CNN) is then offered and implemented on Mobile Edge Computing (MEC) devices to expedite learning. These two techniques operate together to offer the proper offloading approach throughout the length of the system's operation. Moreover, transfer learning was employed to initialize the Qtable values, which increased the system's effectiveness. The simulation in this article, which employed Cooja and TensorFlow, revealed that the strategy outperformed five benchmarks in terms of latency by 4.1%, IoT device efficiency by 2.9%, energy utilization by 3.6%, and job failure rate by 2.6% on average.Article Citation - WoS: 0Citation - Scopus: 0A New a Flow-Based Approach for Enhancing Botnet Detection Using Convolutional Neural Network and Long Short-Term Memory(Springer London Ltd, 2025) Jafari Navimipour, Nima; Heidari, Arash; Navimipour, Nima Jafari; Computer EngineeringDespite the growing research and development of botnet detection tools, an ever-increasing spread of botnets and their victims is being witnessed. Due to the frequent adaptation of botnets to evolving responses offered by host-based and network-based detection mechanisms, traditional methods are found to lack adequate defense against botnet threats. In this regard, the suggestion is made to employ flow-based detection methods and conduct behavioral analysis of network traffic. To enhance the performance of these approaches, this paper proposes utilizing a hybrid deep learning method that combines convolutional neural network (CNN) and long short-term memory (LSTM) methods. CNN efficiently extracts spatial features from network traffic, such as patterns in flow characteristics, while LSTM captures temporal dependencies critical to detecting sequential patterns in botnet behaviors. Experimental results reveal the effectiveness of the proposed CNN-LSTM method in classifying botnet traffic. In comparison with the results obtained by the leading method on the identical dataset, the proposed approach showcased noteworthy enhancements, including a 0.61% increase in precision, a 0.03% augmentation in accuracy, a 0.42% enhancement in the recall, a 0.51% improvement in the F1-score, and a 0.10% reduction in the false-positive rate. Moreover, the utilization of the CNN-LSTM framework exhibited robust overall performance and notable expeditiousness in the realm of botnet traffic identification. Additionally, we conducted an evaluation concerning the impact of three widely recognized adversarial attacks on the Information Security Centre of Excellence dataset and the Information Security and Object Technology dataset. The findings underscored the proposed method's propensity for delivering a promising performance in the face of these adversarial challenges.Article Citation - WoS: 30Citation - Scopus: 43Comprehensive Survey of Artificial Intelligence Techniques and Strategies for Climate Change Mitigation(Pergamon-elsevier Science Ltd, 2024) Amiri, Zahra; Jafari Navimipour, Nima; Heidari, Arash; Navimipour, Nima Jafari; Computer EngineeringWith the galloping progress of the changing climates all around the world, Machine Learning (ML) approaches have been prevalently studied in many types of research in this area. ML is a robust tool for acquiring perspectives from data. In this paper, we elaborate on climate change mitigation issues and ML approaches leveraged to solve these issues and aid in the improvement and function of sustainable energy systems. ML has been employed in multiple applications and many scopes of climate subjects such as ecosystems, agriculture, buildings and cities, industry, and transportation. So, a Systematic Literature Review (SLR) is applied to explore and evaluate findings from related research. In this paper, we propose a novel taxonomy of Deep Learning (DL) method applications for climate change mitigation, a comprehensive analysis that has not been conducted before. We evaluated these methods based on critical parameters such as accuracy, scalability, and interpretability and quantitatively compared their results. This analysis provides new insights into the effectiveness and reliability of DL methods in addressing climate change challenges. We classified climate change ML methods into six key customizable groups: ecosystems, industry, buildings and cities, transportation, agriculture, and hybrid applications. Afterward, state-of-the-art research on ML mechanisms and applications for climate change mitigation issues has been highlighted. In addition, many problems and issues related to ML implementation for climate change have been mapped, which are predicted to stimulate more researchers to manage the future disastrous effects of climate change. Based on the findings, most of the papers utilized Python as the most common simulation environment 38.5 % of the time. In addition, most of the methods were analyzed and evaluated in terms of some parameters, namely accuracy, latency, adaptability, and scalability, respectively. Lastly, classification is the most frequent ML task within climate change mitigation, accounting for 40 % of the total. Furthermore, Convolutional Neural Networks (CNNs) are the most widely utilized approach for a variety of applications.Article Citation - WoS: 61Citation - Scopus: 60A Novel Blockchain-Based Deepfake Detection Method Using Federated and Deep Learning Models(Springer, 2024) Dağ, Hasan; Jafari Navimipour, Nima; Dag, Hasan; Talebi, Samira; Unal, Mehmet; Computer Engineering; Management Information SystemsIn recent years, the proliferation of deep learning (DL) techniques has given rise to a significant challenge in the form of deepfake videos, posing a grave threat to the authenticity of media content. With the rapid advancement of DL technology, the creation of convincingly realistic deepfake videos has become increasingly prevalent, raising serious concerns about the potential misuse of such content. Deepfakes have the potential to undermine trust in visual media, with implications for fields as diverse as journalism, entertainment, and security. This study presents an innovative solution by harnessing blockchain-based federated learning (FL) to address this issue, focusing on preserving data source anonymity. The approach combines the strengths of SegCaps and convolutional neural network (CNN) methods for improved image feature extraction, followed by capsule network (CN) training to enhance generalization. A novel data normalization technique is introduced to tackle data heterogeneity stemming from diverse global data sources. Moreover, transfer learning (TL) and preprocessing methods are deployed to elevate DL performance. These efforts culminate in collaborative global model training zfacilitated by blockchain and FL while maintaining the utmost confidentiality of data sources. The effectiveness of our methodology is rigorously tested and validated through extensive experiments. These experiments reveal a substantial improvement in accuracy, with an impressive average increase of 6.6% compared to six benchmark models. Furthermore, our approach demonstrates a 5.1% enhancement in the area under the curve (AUC) metric, underscoring its ability to outperform existing detection methods. These results substantiate the effectiveness of our proposed solution in countering the proliferation of deepfake content. In conclusion, our innovative approach represents a promising avenue for advancing deepfake detection. By leveraging existing data resources and the power of FL and blockchain technology, we address a critical need for media authenticity and security. As the threat of deepfake videos continues to grow, our comprehensive solution provides an effective means to protect the integrity and trustworthiness of visual media, with far-reaching implications for both industry and society. This work stands as a significant step toward countering the deepfake menace and preserving the authenticity of visual content in a rapidly evolving digital landscape.Review Citation - WoS: 102Citation - Scopus: 126Machine Learning Applications in Internet-Of Systematic Review, Recent Deployments, and Open Issues(Assoc Computing Machinery, 2023) Heidari, Arash; Jafari Navimipour, Nima; Navimipour, Nima Jafari; Unal, Mehmet; Zhang, Guodao; Computer EngineeringDeep Learning (DL) and Machine Learning (ML) are effectively utilized in various complicated challenges in healthcare, industry, and academia. The Internet of Drones (IoD) has lately cropped up due to high adjustability to a broad range of unpredictable circumstances. In addition, Unmanned Aerial Vehicles ( UAVs) could be utilized efficiently in a multitude of scenarios, including rescue missions and search, farming, mission-critical services, surveillance systems, and so on, owing to technical and realistic benefits such as low movement, the capacity to lengthen wireless coverage zones, and the ability to attain places unreachable to human beings. In many studies, IoD and UAV are utilized interchangeably. Besides, drones enhance the efficiency aspects of various network topologies, including delay, throughput, interconnectivity, and dependability. Nonetheless, the deployment of drone systems raises various challenges relating to the inherent unpredictability of the wireless medium, the high mobility degrees, and the battery life that could result in rapid topological changes. In this paper, the IoD is originally explained in terms of potential applications and comparative operational scenarios. Then, we classify ML in the IoD-UAV world according to its applications, including resource management, surveillance and monitoring, object detection, power control, energy management, mobility management, and security management. This research aims to supply the readers with a better understanding of (1) the fundamentals of IoD/UAV, (2) the most recent developments and breakthroughs in this field, (3) the benefits and drawbacks of existing methods, and (4) areas that need further investigation and consideration. The results suggest that the Convolutional Neural Networks (CNN) method is the most often employed ML method in publications. According to research, most papers are on resource and mobility management. Most articles have focused on enhancing only one parameter, with the accuracy parameter receiving the most attention. Also, Python is the most commonly used language in papers, accounting for 90% of the time. Also, in 2021, it has the most papers published.Review Citation - WoS: 125Citation - Scopus: 179Applications of Ml/Dl in the Management of Smart Cities and Societies Based on New Trends in Information Technologies: a Systematic Literature Review(Elsevier, 2022) Heidari, Arash; Jafari Navimipour, Nima; Navimipour, Nima Jafari; Unal, Mehmet; Computer EngineeringThe goal of managing smart cities and societies is to maximize the efficient use of finite resources while enhancing the quality of life. To establish a sustainable urban existence, smart cities use some new technologies such as the Internet of Things (IoT), Internet of Drones (IoD), and Internet of Vehicles (IoV). The created data by these technologies are submitted to analytics to obtain new information for increasing the smart societies and cities' efficiency and effectiveness. Also, smart traffic management, smart power, and energy management, city surveillance, smart buildings, and patient healthcare monitoring are the most common applications in smart cities. However, the Artificial intelligence (AI), Machine Learning (ML), and Deep Learning (DL) approach all hold a lot of promise for managing automated activities in smart cities. Therefore, we discuss different research issues and possible research paths in which the aforementioned techniques might help materialize the smart city notion. The goal of this research is to offer a better understanding of (1) the fundamentals of smart city and society management, (2) the most recent developments and breakthroughs in this field, (3) the benefits and drawbacks of existing methods, and (4) areas that require further investigation and consideration. IoT, cloud computing, edge computing, fog computing, IoD, IoV, and hybrid models are the seven key emerging de-velopments in information technology that, in this paper, are considered to categorize the state-of-the-art techniques. The results indicate that the Conventional Neural Network (CNN) and Long Short-Term Memory (LSTM) are the most commonly used ML method in the publications. According to research, the majority of papers are about smart cities' power and energy management. Furthermore, most papers have concentrated on improving only one parameter, where the accuracy parameter obtains the most attention. In addition, Python is the most frequently used language, which was used in 69.8% of the papers.