Title
Author
Year
Volume
:
2024
Vol. 1
no. 1
Vol. 2
2023
Vol. 1
no. 1
no. 2
2022
Vol. 1
no. 1
2021
Vol. 1
no. 1
2019
Vol. 1
no. 1
no. 2
2013
Vol. 1
no. 1
Vol. 2
no. 1
2011
Vol. 1
no. 1
Vol. 2
no. 1
2010
Vol. 1
no. 1
no. 2
Vol. 2
Vol. 0
Vol. 1
Volume 1, Number 1, 2023
Quantum Computing
Pages
:
1-5
Reza Darijani
Quantumcomputing is a new development in the field of information processing as itperforms complex calculations and solve mathematical problems and equations.Quantum bit (qubit), a two-state quantum mechanical system, is the basic unitin quantum computing. The nature of superposition principle allows the qubit tobe in superposition of states at same time. Superposition of qubits causesquantum computing to inherent parallelism and thus quantum computers canperform calculations faster and more accurate compared to classical computers. Sincequbits are understood as quantum mechanical systems, this paper reviews theconcepts of quantum computing, then qubits and some operators are explained intwo- and N-dimensions in different spaces, and finally, some quantum gates areexpanded.
Lion Optimization Algorithms (LOAs): A Review on Theory, Variants, and Applications
Pages
:
6-19
Mina Avaz Beigi, Mostafa Ghazizadeh-Ahsaee, Saeed Niksaz, Amin Khaleghi, and Hamidreza Rostaminezhad Shamili
The problem of optimization has always existed in all scientific and industrial fields. This issue has been raised in mathematics and computer science as finding the best answer among different answers. This problem has become an essential area for research in the last decades. Not only analytical methods but also meta-heuristic methods have been developed to solve this problem. The Lion Optimizing Algorithm (LOA) is a meta-heuristic algorithm for optimization problems. The LOA was inspired by various aspects of lions' lives, such as hunting, mating, roaming, defense, and migration. So far, this algorithm has been used in several fields such as classification, clustering, image processing, cloud computing, and task scheduling. The LOA has proved promising results in these areas. Multiple algorithms have flourished to tackle various constraints and challenges of the LOA. In this paper, the LOA, its variants, and applications are reviewed from the beginning until now.
Distributed Intrusion Prevention System Based on Cloud Computing in the Internet of Things
Pages
:
20-26
Mohaddese Mir, and Hamid Reza Naji
Cyber security is a serious issue in cyberspace. Thousands of zero-day attacks are constantlyevolving due to the addition of various protocols on the Internet of Things. On the other hand,using deep learning in various information security fields has been successful. Also using deeplearning to detect an attack in cyberspace is a flexible mechanism for detecting new attacks. Inthis research, we introduced a distributed penetration system for the Internet of Things, whichused Apache Spark and also an expert system for optimal performance. The proposed model iscompared with the suggested architecture of a distributed attack detection scheme using the deeplearning approach for the Internet of Things. Also, the effectiveness of the proposed deep learningagainst the shallow learning algorithms for detecting attacks on the Internet of Things isevaluated. The results indicated that the proposed approach is more optimal in accuracy, learningspeed and memory consumption in compare to another discussed method.
Bank Customers Compliment Analysis by Deep Learning Based Sentiment Analysis and Topic Modelling
Pages
:
27-36
Majid Iranpour Mobarakeh, Ali Sebti, Moslem Mohammadi-jenghera, and Ali Ghanbari Sorkhi
Today, the rapid increase in the volume of data, especially in cyberspace and social media, has made organizations seek to improve organizational processes using data analysis. Considering the intense competitive environment, customer satisfaction is considered the main capital of businesses. Therefore, the collection and analysis of customers' opinions has received much attention from researchers. Due to the fact that a large part of customer opinions is available in text form in social networks and web systems, sentiment analysis or opinion analysis is presented as a solution to evaluate customer opinions, which with text analysis automatically discovers opinions and opinions about an Entity pays. As one of the banking service providers, Mehr-e-Iran Bank receives customer complaints in text form through the online complaint registration system. In this work, the goal is to analyze the textual data of customer complaints of this bank in order to identify the influencing factors in reducing customer satisfaction and improving service quality. For this purpose, a combined method for entity extraction - based on thematic text modeling - and sentiment analysis has been introduced simultaneously. In this work, different methods based on deep learning have been evaluated. Experimental results indicate that Bi-GRU Capsule model has performed much better and has 88% for sentiment analysis and 60% for topic modeling for 57 very close classes.
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