Jafari Navimipour, Nima
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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
50 results
Scholarly Output Search Results
Now showing 1 - 10 of 50
Article Citation - WoS: 4Citation - Scopus: 8Quantum-based serial-parallel multiplier circuit using an efficient nano-scale serial adder(Soc Microelectronics, Electron Components Materials-midem, 2024) Jafari Navimipour, Nima; Jiang, Shuai; Seyedi, Saeid; Navimipour, Nima Jafari; Computer EngineeringQuantum dot cellular automata (QCA) is one of the newest nanotechnologies. The conventional complementary metal oxide semiconductor (CMOS) technology was superbly replaced by QCA technology. This method uses logic states to identify the positions of individual electrons rather than defining voltage levels. A wide range of optimization factors, including reduced power consumption, quick transitions, and an extraordinarily dense structure, are covered by QCA technology. On the other hand, the serialparallel multiplier (SPM) circuit is an important circuit by itself, and it is also very important in the design of larger circuits. This paper defines an optimized circuit of SPM circuit using QCA. It can integrate serial and parallel processing benefits altogether to increase efficiency and decrease computation time. Thus, all these mentioned advantages make this multiplier framework a crucial element in numerous applications, including complex arithmetic computations and signal processing. This research presents a new QCAbased SPM circuit to optimize the multiplier circuit's performance and enhance the overall design. The proposed framework is an amalgamation of highly performance architecture with efficient path planning. Other than that, the proposed QCA-based SPM circuit is based on the majority gate and 1-bit serial adder (BSA). BCA circuit has 34 cells and a 0.04 mu m2 area and uses 0.5 clock cycles. The outcomes showed the suggested QCA-based SPM circuit occupies a mere 0.28 mu m 2 area, requires 222 QCA cells, and demonstrates a latency of 1.25 clock cycles. This work contributes to the existing literature on QCA technology, also emphasizing its capabilities in advancing VLSI circuit layout via optimized performance.Article Citation - WoS: 13Citation - Scopus: 14A New Nano-Design of 16-Bit Carry Look-Ahead Adder Based on Quantum Technology(Iop Publishing Ltd, 2023) Ahmadpour, Seyed-Sajad; Jafari Navimipour, Nima; Navimipour, Nima Jafari; Computer EngineeringThere is a requirement and a desire to develop reliable and energy-efficient circuit designs that adapt to the expanding field of low-power circuit engineering in the VLSI domain based on nanotechnology. The quantum-dot cellular automata (QCA) technology possesses the potential to supplant the conventional, complementary metal-oxide-semiconductor (CMOS) technology in low-power nano-scale applications due to its diminutive cell dimensions, dependable circuitry architecture, and robust structural integrity. On the other hand, the carry look-ahead adder (CLA) is one of the vital circuits in digital processing utilized in diverse digital applications. In addition, for the design of this essential circuit, the occupied area and the delay play the primary role because using a simple formulation can reduce the occupied area, energy consumption, and the number of gates count. In the previous structures, high delay and use of traditional technology (like CMOS) caused an increase in the number of gate counts and occupied areas. Using QCA technology, simple quantum cells, and a low delay, all the previous shortcomings can be resolved to reduce the number of gate counts and low occupied area in the CLA circuit. This paper proposes a new method that helps the propagation characteristics generate suitable signals to reduce the number of gate counts based on adders in QCA technology. Several new blocks are used to design fast binary adders. Finally, an optimal four and 16-bit CLA circuit will be proposed based on the adder circuit. Furthermore, the execution and experimentation of outcomes are carried out utilizing QCADesigner-2.0.3. The simulation-based comparison of values justified the proposed design's accuracy and efficiency. The simulation results demonstrate that the proposed circuit has a low area and quantum cell.Article Citation - WoS: 2Citation - Scopus: 3An Energy-Aware Resource Management Strategy Based on Spark and YARN in Heterogeneous Environments(Ieee-inst Electrical Electronics Engineers inc, 2024) Jafari Navimipour, Nima; Navimipour, Nima Jafari; Computer EngineeringApache Spark is a popular framework for processing big data. Running Spark on Hadoop YARN allows it to schedule Spark workloads alongside other data-processing frameworks on Hadoop. When an application is deployed in a YARN cluster, its resources are given without considering energy efficiency. Furthermore, there is no way to enforce any user-specified deadline constraints. To address these issues, we propose a new deadline-aware resource management system and a scheduling algorithm to minimize the total energy consumption in Spark on YARN for heterogeneous clusters. First, a deadline-aware energy-efficient model for the considered problem is proposed. Then, using a locality-aware method, executors are assigned to applications. This algorithm sorts the nodes based on the performance per watt (PPW) metric, the number of application data blocks on nodes, and the rack locality. It also offers three ways to choose executors from different machines: greedy, random, and Pareto-based. Finally, the proposed heuristic task scheduler schedules tasks on executors to minimize total energy and tardiness. We evaluated the performance of the suggested algorithm regarding energy efficiency and satisfying the Service Level Agreement (SLA). The results showed that the method outperforms the popular algorithms regarding energy consumption and meeting deadlines.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: 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: 6Citation - Scopus: 6A Space-Efficient Universal and Multi-Operative Reversible Gate Design Based on Quantum-Dots(World Scientific Publ Co Pte Ltd, 2023) Seyedi, Saeid; Jafari Navimipour, Nima; Navimipour, Nima Jafari; Computer EngineeringBecause of the high speed, low-power consumption, low latency and possible use at the atomic and molecular levels, Quantum-dot Cellular Automata (QCA) technology is one of the future nanoscale technologies that can replace the present transistor-based technology. For the purpose of creating QCA circuits, reversible logic can be regarded as an appropriate candidate. In this research, a new structure for multi-operative reversible designs is suggested. The Saeid Nima Gate (SNG), proposed in this research study, is a brand-new, incredibly effective, multi-operative, universal reversible gate implemented in QCA nanotechnology employing both majority and inverter gates. Reversible gates, also known as reversible logic gates, are gates that have n inputs and n outputs, which is an equal number of inputs and outputs. The amount of energy lost during computations will be reduced if the numbers of inputs and outputs are identical. The proposed gate is modified and reorganized to optimize further, employing exact QCA cell interaction. All fundamental logic gates are implemented using it to demonstrate the universality of the proposed SNG. Reversible logic has advanced, and as a result, our suggested solution has a lower quantum cost than previously reported systems. The suggested design is simulated using the QCADesigner-E tools.Article Citation - WoS: 2Citation - Scopus: 2A New Quantum-Enhanced Approach To Ai-Driven Medical Imaging System(Springer, 2025) Jafari Navimipour, Nima; Avval, D.B.; Darbandi, M.; Navimipour, N.J.; Ain, N.U.; Kassa, S.; Computer EngineeringMedical Imaging Systems (MIS) play a crucial role in modern medicine by providing accurate diagnostic and treatment capabilities. These systems use various physical processes to create images inside the human body for healthcare professionals to identify and address medical conditions. There is a growing interest in integrating artificial intelligence (AI) in medicine from various sources recently. Presently, with improved algorithms and more significant availability of training data, AI can help or even replace some of the tasks that were being performed by medical professionals. Typically, most MIS performance enhancements are achieved by leveraging transistor-based technologies. However, such implementations showcase certain disadvantages: for instance, slow processing speeds, high power consumption, large physical footprints, and restricted switching frequencies, especially in the GHz range. This could limit the effective performance and efficiency of MIS. Quantum computing, in turn, today appears as an alternative, at least for fully digital circuits in MIS; QCA provides advantages related to higher intrinsic switching speeds (up to terahertz) compared with transistor-based technologies, along with an improved throughput owing to its inherent compatibility with pipelining. QCA also has minimum power consumption and a smaller area of circuitry, which makes it amply suitable for establishing frameworks in circuit design for AI applications. The performance requirement in AI is real-time with minimum energy consumption and minimum cost. The ALU, in this regard, forms the basis for processing and computation units within processor systems. The method presented in this work benefits from the merits of QCA for an ALU design featuring low complexity, high performance, minimum power consumption, maximum speed, and reduced area. This approach has been able to successfully integrate the design of adders and multiplexers with that of basic gates to reduce latency and energy consumption with the aim of improving AI in MIS. The development and simulation of the proposed designs are carefully carried out using QCADesigner 2.0.03 software. A comparison of the different structures proposed shows significant improvements in complexity vs. cell count vs. power consumption compared to earlier designs, hence promising quantum computing for the MIS capability development. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.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: 23Citation - Scopus: 27Toward implementing robust quantum logic circuits using effectual fault-tolerant majority voter gate(Elsevier, 2024) Jafari Navimipour, Nima; Mosleh, Mohammad; Ahmadpour, Seyed-Sajad; Navimipour, Nima Jafari; Shahrbanoonezhad, Alireza; Computer EngineeringQuantum -dot Cellular Automata (QCA) has emerged as a revolutionary technology for nano-scale computing circuits and a promising alternative to conventional transistor-based technologies. However, the susceptibility to defects during circuit synthesis is a pivotal challenge, undermining its potential. This study seeks to introduce an innovative and robust fault-tolerant 3 -input majority voter gate comprising 16 simple cells. The primary objective is to enhance the gate's resilience against two specific defects: one-cell omission and extra-cell deposition. Preliminary assessments indicate that the introduced gate achieves remarkable tolerance rates of 100% for one-cell omission and 89.47% for extra-cell deposition defects. A comprehensive evaluation is used based on the QCADesigner 2.0.3 simulator to validate the gate's performance, supplemented by physical proofs. Furthermore, leveraging the novel gate structure, this paper extends its application to the design of fault-tolerant flip-flops and multiplexer circuits. These building blocks are then employed to construct three distinct fault-tolerant sequential circuits.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.