Nima Jafari Navimipour

Jafari Navimipour, Nima

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

Job Title

Doç. Dr.

Email Address

nima.navimipour@khas.edu.tr

Main Affiliation

Computer Engineering

Full item page

Scholarly Output

86

Articles

69

Citation Count

32

Supervised Theses

1 Scholarly Output Search Results

Now showing 1 - 10 of 86

Citation - WoS: 13
Citation - Scopus: 15
Everything You Wanted To Know About Chatgpt: Components, Capabilities, Applications, and Opportunities
(John Wiley & Sons Ltd, 2024) Heidari, Arash; Jafari Navimipour, Nima; Navimipour, Nima Jafari; Zeadally, Sherali; Chamola, Vinay
Conversational Artificial Intelligence (AI) and Natural Language Processing have advanced significantly with the creation of a Generative Pre-trained Transformer (ChatGPT) by OpenAI. ChatGPT uses deep learning techniques like transformer architecture and self-attention mechanisms to replicate human speech and provide coherent and appropriate replies to the situation. The model mainly depends on the patterns discovered in the training data, which might result in incorrect or illogical conclusions. In the context of open-domain chats, we investigate the components, capabilities constraints, and potential applications of ChatGPT along with future opportunities. We begin by describing the components of ChatGPT followed by a definition of chatbots. We present a new taxonomy to classify them. Our taxonomy includes rule-based chatbots, retrieval-based chatbots, generative chatbots, and hybrid chatbots. Next, we describe the capabilities and constraints of ChatGPT. Finally, we present potential applications of ChatGPT and future research opportunities. The results showed that ChatGPT, a transformer-based chatbot model, utilizes encoders to produce coherent responses.
Citation - WoS: 4
Citation - Scopus: 6
Nano-Design of Ultra-Efficient Reversible Block Based on Quantum-Dot Cellular Automata
(Zhejiang Univ Press, 2023) Ahmadpour, Seyed Sajad; Jafari Navimipour, Nima; Navimipour, Nima Jafari; Mosleh, Mohammad; Yalcin, Senay
Reversible logic has recently gained significant interest due to its inherent ability to reduce energy dissipation, which is the primary need for low-power digital circuits. One of the newest areas of relevant study is reversible logic, which has applications in many areas, including nanotechnology, DNA computing, quantum computing, fault tolerance, and low-power complementary metal-oxide-semiconductor (CMOS). An electrical circuit is classified as reversible if it has an equal number of inputs and outputs, and a one-to-one relationship. A reversible circuit is conservative if the EXOR of the inputs and the EXOR of the outputs are equivalent. In addition, quantum-dot cellular automata (QCA) is one of the state-of-the-art approaches that can be used as an alternative to traditional technologies. Hence, we propose an efficient conservative gate with low power demand and high speed in this paper. First, we present a reversible gate called ANG (Ahmadpour Navimipour Gate). Then, two non-resistant QCA ANG and reversible fault-tolerant ANG structures are implemented in QCA technology. The suggested reversible gate is realized through the Miller algorithm. Subsequently, reversible fault-tolerant ANG is implemented by the 2DW clocking scheme. Furthermore, the power consumption of the suggested ANG is assessed under different energy ranges (0.5Ek, 1.0Ek, and 1.5Ek). Simulations of the structures and analysis of their power consumption are performed using QCADesigner 2.0.03 and QCAPro software. The proposed gate shows great improvements compared to recent designs.
Citation - WoS: 38
Citation - Scopus: 41
Resilient 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
With 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.
Citation - WoS: 49
Citation - Scopus: 64
A 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
With 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.
Citation - WoS: 3
Citation - Scopus: 5
Quantum-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
Quantum 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.
Citation - WoS: 10
Citation - Scopus: 9
A 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
There 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.
Citation - WoS: 27
Citation - Scopus: 34
A Comprehensive and Systematic Literature Review on the Big Data Management Techniques in the Internet of Things
(Springer, 2023) NaghibnAff, Arezou; Jafari Navimipour, Nima; Navimipour, Nima Jafari; Hosseinzadeh, Mehdi; Sharifi, Arash
The Internet of Things (IoT) is a communication paradigm and a collection of heterogeneous interconnected devices. It produces large-scale distributed, and diverse data called big data. Big Data Management (BDM) in IoT is used for knowledge discovery and intelligent decision-making and is one of the most significant research challenges today. There are several mechanisms and technologies for BDM in IoT. This paper aims to study the important mechanisms in this area systematically. This paper studies articles published between 2016 and August 2022. Initially, 751 articles were identified, but a paper selection process reduced the number of articles to 110 significant studies. Four categories to study BDM mechanisms in IoT include BDM processes, BDM architectures/frameworks, quality attributes, and big data analytics types. Also, this paper represents a detailed comparison of the mechanisms in each category. Finally, the development challenges and open issues of BDM in IoT are discussed. As a result, predictive analysis and classification methods are used in many articles. On the other hand, some quality attributes such as confidentiality, accessibility, and sustainability are less considered. Also, none of the articles use key-value databases for data storage. This study can help researchers develop more effective BDM in IoT methods in a complex environment.
Citation - WoS: 2
Citation - Scopus: 2
An 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
Apache 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.
Citation - WoS: 3
Citation - Scopus: 3
A New Energy-Aware Method for Gas Lift Allocation in IoT-Based Industries Using a Chemical Reaction-Based Optimization Algorithm
(Mdpi, 2022) Jafari Navimipour, Nima; Bastak, Mostafa Razoughi; Alizadeh, Seyed Mehdi; Navimipour, Nima Jafari; Yalcin, Senay
The Internet of Things (IoT) has recently developed opportunities for various industries, including the petrochemical industry, that allow for intelligent manufacturing with real-time management and the analysis of the produced big data. In oil production, extracting oil reduces reservoir demand, causing oil supply to fall below the economically viable level. Gas lift is a popular artificial lift system that is both efficient and cost-effective. If gas supplies in the gas lift process are not limited, a sufficient amount of gas may be injected into the reservoir to reach the highest feasible production rate. Because of the limited supply of gas, it is essential to achieve the sustainable utilization of our limited resources and manage the injection rate of the gas into each well in order to enhance oil output while reducing gas injection. This study describes a novel IoT-based chemical reaction optimization (CRO) technique to solve the gas lift allocation issue. The CRO algorithm is inspired by the interaction of molecules with each other and achieving the lowest possible state of free energy from an unstable state. The CRO algorithm has excellent flexibility, enabling various operators to modify solutions and a favorable trade-off between intensification and diversity. A reasonably fast convergence rate serves as a powerful motivator to use as a solution. The extensive simulation and computational study have presented that the proposed method using CRO based on IoT systems significantly improves the overall oil production rate and reduces gas injection, energy consumption and cost compared to traditional algorithms. Therefore, it provides a more efficient system for the petroleum production industry.
Citation - WoS: 3
Citation - Scopus: 3
Blockchain Systems in Embedded Internet of Things: Systematic Literature Review, Challenges Analysis, and Future Direction Suggestions
(Mdpi, 2022) Darbandi, Mehdi; Jafari Navimipour, Nima; Al-Khafaji, Hamza Mohammed Ridha; Nasab, Seyed Hamid Hosseini; AlHamad, Ahmad Qasim Mohammad; Ergashevich, Beknazarov Zafarjon; Navimipour, Nima Jafari
Internet of Things (IoT) environments can extensively use embedded devices. Without the participation of consumers; tiny IoT devices will function and interact with one another, but their operations must be reliable and secure from various threats. The introduction of cutting-edge data analytics methods for linked IoT devices, including blockchain, may lower costs and boost the use of cloud platforms. In a peer-to-peer network such as blockchain, no one has to be trusted because each peer is in charge of their task, and there is no central server. Because blockchain is tamper-proof, it is connected to IoT to increase security. However, the technology is still developing and faces many challenges, such as power consumption and execution time. This article discusses blockchain technology and embedded devices in distant areas where IoT devices may encounter network shortages and possible cyber threats. This study aims to examine existing research while also outlining prospective areas for future work to use blockchains in smart settings. Finally, the efficiency of the blockchain is evaluated through performance parameters, such as latency, throughput, storage, and bandwidth. The obtained results showed that blockchain technology provides security and privacy for the IoT.