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Journal receives papers in continuous flow and we will consider articles from a wide range of Information Technology disciplines encompassing the most basic research to the most innovative technologies. Please submit your papers electronically to our submission system at http://jatit.org/submit_paper.php in an MSWord, Pdf or compatible format so that they may be evaluated for publication in the upcoming issue. This journal uses a blinded review process; please remember to include all your personal identifiable information in the manuscript before submitting it for review, we will edit the necessary information at our side. Submissions to JATIT should be full research / review papers (properly indicated below main title).

Journal of Theoretical and Applied Information Technology
August 2022 | Vol. 100 No.15

Title:	ECC IMAGE ENCRYPTION USING SYSTEM GENERATOR
Author:	SARA CHILLALI, LAHCEN OUGHDIR
Abstract:	This work includes research findings on encryption, decryption, and various image processing operations using programmable operations, on Matlab, Simulink, and the Xilinx system generator. The main question is how to ensure the security of images stored in real time on embedded systems which depends on some precise operations on these systems. To answer this question, we will build new and original methods using cryptography on an efficient elliptical curve. The concept of software and hardware co-simulation for our encryption method, presents an efficient architecture of various imaging, encryption, decryption and key generation algorithms using such an elliptical curve, thus using Simulink Xilinx, we build Simulink Xilinx models for implementing multiple hardware operations on various Xilinx FPGAs, for all kinds of color and grayscale images with the minimum number of generator blocks possible.
Keywords:	Xilinx System Generator, FPGA, ECC, Encryption, Decryption.
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	LIGHTWEIGHT CHAOTIC BLOCK CIPHER FOR IOT APPLICATIONS
Author:	MOUSA FARAJALLAH
Abstract:	The Internet of Things is interrelated computing devices; each one has a unique identifier, one of the main advances of it the possibility of transfer data over the network without the need of human assistance. However, transfer data over the network susceptible to different types of attacks. The required encryption time of the classical encryption, techniques are not suitable to secure IoT data. This introduces the need for lightweight encryption algorithms in order to provide the required security level and decreasing the encryption time. In this paper a lightweight of one encryption round algorithm is proposed based on the Skew Tent Map. This map is used to produce the required confusion as well as diffusion effects to decrease the required encryption time to be used for real-time IoT applications. The obtained security analysis results confirm the high security level of the proposed algorithm. Moreover, the required encryption time comparing to the presented IoT encryption algorithm is less time. Encryption time and standard security analysis of the proposed cryptosystem confirms that this proposal is suitable for securing real-time applications.
Keywords:	IoT security, Skew Tent Map, Confusion, Diffusion, Image Encryption
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	COMPUTATIONAL COMPLEXITY OF RSA AND ELGAMAL CRYPTOGRAPHIC ALGORITHMS ON VIDEO DATA
Author:	ADENIYI ABIDEMI EMMANUEL, ADEBIYI OLUBUNMI MARION, OKEYINKA ADEREMI E., OLUDAYO O. OLUGBARA
Abstract:	The study on the complexity of an algorithms have great impact on the whole fields of computer science, data security and data communication. The more efficient algorithms are the better the data security, communication and sharing of information on various platform. Data security is very important especially in an environment of unprotected data transmission network. There are various techniques of data transfer which leaves the users with the questions of how such data is being secure; cryptographic algorithms provides solution to the security of data transmission whereby ensuring integrity, confidentiality and authentication of any form of data. However, there are still challenges of which cryptographic algorithms is suitable in terms of computation speed and memory usage. Therefore, this study is concerned with the complexity of RSA and ElGamal cryptographic algorithms in terms of time and space usage while encrypting and decrypting video data in order to establish which of the algorithms is more efficient. C-sharp programming language was used to implement the RSA and ElGamal cryptographic algorithms and the experimental result showed that RSA cryptographic algorithm performed better in terms of time complexity while Elgamal cryptographic algorithm is memory efficient.
Keywords:	Cryptographic algorithm, Complexity, Video data, Data security, Data communication.
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	DETECTION OF SQL INJECTION ATTACKS USING MACHINE LEARNING IN CLOUD COMPUTING PLATFORM
Author:	JAMILAH M ALKHATHAMI, SABAH M. ALZAHRANI
Abstract:	The cloud computing holds a massive quantity of private and secret data and information, It uses the internet to communicate with another party so it contain a number of unreliable strings that can be demonstrated to be loopholes, therefore protecting data stored on the cloud is often a serious risk. one of the most serious security dangers to cloud are SQL Injection Attacks (SQLIA), It sends a susceptible query to affect server systems, allowing attackers to gain illegal access to databases, resulting in identity theft and security breaches. In this research we have studied and investigated the recent published approaches to SQL injection attacks detection, and we have presented SQL injection detection tool based on machine learning, The model classifies SQL injection queries into two categories: attack and legitimate. The model is training with four machine learning algorithms We have employed K-Nearest Neighbors (KNN), Multinomial Naive Bayes (MNB), Decision Tree (DT), and Support Vector Machine (SVM), after performing data preprocessing and feature extraction, we compared the values obtained by each model to determine the best model in SQL injection detection. The result show SVM produced the best results with an accuracy of 99.42%. after that The decision tree algorithm obtained results with an accuracy of 99.4%. Then MNB algorithm produced a 97.09%. However KNN produced the worst results, with an accuracy of 92.45 %. Therefore we have found that our models produce near-perfect results.
Keywords:	Cloud Computing, SQL Injection Attacks, SQL Queries, Detection, Machine Learning Algorithm.
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	ARTIFICIAL INTELLIGENCE-BASED PROCESS AUTOMATION IN E PROCUREMENT: A SYSTEMATIC LITERATURE REVIEW
Author:	HAYAT EL ASRI , LAILA BENHLIMA
Abstract:	Electronic Procurement (e-procurement) via a series of complex bids or competitive tendering processes, involving compliance with terms, requirements and conditions and purchasing of goods, services, or works from external suppliers, necessitates a systemic approach streamlining every step and keeping track of itemized technical specifications and procedures. Additionally, this systematic approach involves the electronic processing of data while harnessing the power of Artificial Intelligence (AI) in the form of smart computer algorithms to efficiently solve issues related to the processing of large amounts of procurement data. This paper, which provides a systematic literature review on the use of AI in e-procurement, aims at synthesizing, analyzing, and discussing how AI has been used up till now in different e-procurement processes, like bidding and negotiation, and the extent to which it helped in automating the procedure(s).
Keywords:	E-Procurement, E-Tendering, E-Bidding, Artificial Intelligence, Process Automation.
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	FPGA IMPLEMENTATION OF OPTIMIZED FIR FILTER FOR ECG DENOISING USING PERES REVERSIBLE LOGIC GATE
Author:	JAYASHREE S, SHANTHA PREETHA S, Dr.SAKTHIVEL SM, Dr. Umadevi.S, Dr. Srivatsan.K
Abstract:	In recent days, the Finite Impulse Response (FIR) filter plays a crucial role in denoising Electro Cardiogram (ECG) signals in an effective way. In this paper, an efficient Vedic multiplier-based 8-tap FIR filter architecture with a Carry Save Adder using Peres reversible logic gate is proposed for ECG signal denoising with low power, area and delay. The low power consumption is achieved by the incorporation of Vedic multiplier and Carry Save Adder architectures, whereas the delay is reduced by the incorporation of reversible logic gate-based realization of filter architecture and the Urdhva Tiryagbhyam Sutra based Vedic multiplication process. Further in this work, the FIR filter is first designed using the MATLAB filter design & analysis (FDA) tool, then with the corresponding filter coefficients, the filter is implemented in FPGA using Verilog Hardware description language (HDL). The implemented FIR filter architecture for ECG signal denoising performs the noise reduction with a better Mean Square Error (MSE) of 0.41 and a Signal Noise Ratio (SNR) of 22.36 dB which is better than the other reported filter's performance in the literature. Also, the proposed FIR filter FPGA implementation using the Xilinx tool in the Virtex6 xc6vcx75t family consumes 41 LUTs & 46 slices, 21 no. of. flip flops with a delay of 5.19ns and 0.081 mW power. Thus, the proposed Architecture results, in a 30% reduction in power, 20% reduction in area and more than 15% improvement in delay than the reported Filter structures in the literature.
Keywords:	FIR Filter, Reversible Logic Gate, Peres Gate, 8-Tap, ECG Signal Denoising, Low Power, Reversible Gate, Peres Gate, Vedic Multiplier, Carry Save Adder.
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	IMPLEMENTING MULTI-THREADED AUTONOMOUS ANOMALY DETECTION (MAAD) IN HEALTH TRACKING DEVICES
Author:	MUHAMMAD YUNUS BIN IQBAL BASHEER, AZLIZA MOHD ALI, NURZEATUL HAMIMAH ABDUL HAMID, MUHAMMAD AZIZI MOHD ARIFFIN, ROZIANAWATY OSMAN, SHARIFALILLAH NORDIN
Abstract:	Nowadays, people commonly wear health tracking devices or smartwatches as regular trackers for their health. There are many brands available in the market that are offered different functionalities. The device produces real-time health data, which should be monitored autonomously. The data from these devices is the number of calories burned, steps taken, heart rate, sleeping pattern and exercises. Based on daily activities, these devices produce a vast amount of data that could be used for health monitoring purposes. Analyzing data patterns can also provide insights into detecting health anomaly data. This paper presents a multithreaded autonomous anomaly detection algorithm (MAAD) for use in health tracking devices. The data analysis will be done in incoming streaming data, which continuously changes dynamically in the pipeline. Hence, we propose an autonomous approach using MAAD to detect anomalies from incoming smartwatch data. Firstly, we conduct data pre-processing using data gathered from several wearable devices. Once the data were ready, we sent the data via the chosen internet of things (IoT) pipeline. The data is then received by MAAD, which can handle two different processes or threads simultaneously. The first thread runs incoming data, and the other performs anomaly detection. The result shows that MAAD performs better in detecting health tracking data anomalies. This algorithm is also faster than AAD and streaming TEDA when presented with streaming data. In the future, the algorithm can be applied to any brand of smartwatch or IoT device that can supply data continuously.
Keywords:	Autonomous, Anomaly, Health Tracking Device, IoT, MAAD.
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	MOBILE BANKING RESISTANCE: EVIDENCE FROM INDONESIA
Author:	KHADIK WINARTO RIDWAN , SFENRIANTO
Abstract:	This research aims to examine the influence of functional barriers consisting of usage barrier, value barrier and risk barrier and psychological barrier consisting of tradition barrier and image barrier and the influence of demographic factors on Mobile Banking service resistance in Indonesia. Because it is very rare for research on mobile banking resistance to be carried out, with research conducted in Indonesia, which is the top five countries with a population in the world with 270 million people, this is expected to help provide an up-to-date picture of bank’s customers resistance to mobile banking services. The study was conducted in Jakarta on 241 successfully collected respondents who were customers of the biggest prive bank in Indonesia. The research method uses Partial Least Sequares (PLS) with SmartPLS application. The results showed that risk barrier was a single factor that affects Mobile Banking service resistance, while usage barrier, value barrier, tradition barrier and image barrier have no effect on Mobile Banking service resistance. The conclusion is that the results of this research provide an illustration that the community no longer experiences usage barriers, value barriers, tradition barriers and image barriers but still feels that risk factors are the main barriers.
Keywords:	Mobile Banking Services, Innovation Resistance, Functional Barrier, Psychological Barrier, Demographic Factor.
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	SOCIAL SKI DRIVER BASED EFFICIENT PARKING DYNAMICS AND COMPUTATION USING DEEP REINFORCEMENT LEARNING IN VEHICULAR CLOUD
Author:	SHIVANAND C. HIREMATH, JAYASHREE D. MALLAPUR
Abstract:	Modern urbanization is experiencing an increased number of autonomous vehicles day by day leading to traffic congestion, parking problems, and overcrowding at the parking lots are one of the major concerns that the smart cities are facing nowadays and due to floating prices of inflammable gases recently there is a drastic escalation of electric motor vehicles (EMV) on the roads needs Parking with charging facility. This research explores and resolves the issues of dynamic provisioning of parking spaces for EMV with or without charging facility in three phases, firstly using cluster-based searching for vacant parking slots, secondly using deep reinforcement learning (DRL) based user parking request processing using cloud server and distributed fog nodes with the assistance of Vehicular Adhoc networks (VANET) model and finally using evolutionary optimization based social ski driven routing (SSD) algorithm is used to efficiently route the EMV to the vacant parking lots. The fitness function is newly devised considering multi-objective parameters such as traffic density,battery power As well as distance parameter and shortest routing decision are made, Hence by using proposed cluster-based parking system using deep reinforcement learning-based Social Ski driven (DRL-SSD) routing protocol achieves allocation of parking slots with a high probability of success rate of 95% with minimal traffic density 7.5 per lane, minimal delay of 7.55min and minimal fitness value of 15.88.
Keywords:	VANET; EMV; DRL-SSD;RL-SSD;DRL-PSO
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	DIGITAL MODELING OF DECISION-MAKING RISKS IN NATURAL AND MAN-CAUSED CRITICAL SITUATIONS
Author:	YESMAGAMBETOVA MARZHAN, TEN TATYANA, OSPANOVA TLEUGAISHA, BELGINOVA SAULE, ALIBEKKYZY KARLYGASH
Abstract:	The purpose of the work is to create mathematical and methodological support for a dynamic digital information-analytical system for operational and situational assessment and forecasting of flood threats in the activities of administrative authorities in emergency conditions. The East Kazakhstan region was chosen as the region under study. A high category highway was chosen as a critical flood object. Operational monitoring satellite information arrives at the analysis center with a frequency of 12 hours, forming an uncontrolled time window. To solve this problem, a simulation model has been developed for predicting the dynamics of flooding in the area and identifying the critical vector of flooding in a 12-hour time interval with a pixel accuracy of 200 meters and a given time lag. A criterion for measuring the danger for a controlled object - a motor road - from the intensity and dynamics of flooding in the mathematical form of the roadway stability coefficient has been selected. It is proposed to evaluate the level and dynamics of danger by reducing the coefficient of roadway stability as a function of the distance to the predicted flood boundary. The stability coefficient depends on the hydrodynamic pressure, which is largely functionally determined by the volume of water in large nearby natural and artificial reservoirs. Long-term changes in the volume of water in natural reservoirs have been studied and empirical models for forecasting volumes for monthly and annual periods have been constructed. A mathematical model for assessing the risk and reliability of decision-making under the conditions of parametric uncertainty of control agents of the road structure stability control system has been developed.
Keywords:	Process, Model, Probability, Flood, Decision Making, Simulation, Distribution Law, Hydrodynamics, Geosystems.
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	ETHERNET LINK NETWORK DESIGN USING AUTO FAILOVER AND LOAD BALANCING TECHNOLOGY IN THROUGHPUT OPTIMIZATION
Author:	MUHAMMAD RIFQI NUR HADI, EMIL R. KABURUAN
Abstract:	The era of information technology is currently developing rapidly, so people need a stable internet connection and able to support data and information exchange activities quickly. To support these needs, the network must be supported with backup links to minimize downtime and also so that high availability networks can be maintained. When there are many requests from network users, network devices will be burdened because they have to do a lot of service processes for requests from these users. this causes the connection to be slow and the connection to be lost if the network device cannot serve all the requests. The solution is to divide the traffic load that comes to network devices. The Load Balancing method can be used in dividing the traffic load that enters the network through several available network links so that it is not centered on one ISP (Internet Service Provider). The purpose of this study is to increase the throughput value so that traffic can run optimally. So that it can maintain network stability with the Load Balancing method and reduce the occurrence of downtime due to one ISP (Internet Service Provider) experiencing network problems by applying the Autofailover technology method. The results showed an increase in the throughput value after using the load balancing method, The measurement of the throughput value based on the TIPHON index has an average value of “4 (excellent)”. By maximizing the throughput value, it is expected to increase the upload speed and download speed of an ethernet link network. Furthermore, by applying the Autofailover method as a backup link when one connection is problematic or experiencing downtime, the backup link will automatically run to support all network traffic.
Keywords:	Network , Load Balancing, Internet Service Provider, Failover, Throughput
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	ESTIMATES OF THE EFFICIENCY OF FRACTAL CONTROL IN ACTIVE SYSTEMS
Author:	N.T. RUSTAMOV, M.ZH. ZHASUZAKOVA2, B.A.SERIMBETOV, S.K.SERIKBAYEVA, A.ZH.TANIRBERGENOV, A.S.ABILDAEVA, T.T.OSPANOVA, A.B.KASSEKEYEVA
Abstract:	In this paper, for the first time, the problem associated with the self-organization of an Active system is considered. It is shown that such self-organization is closely related to the fractality of the Active system itself. These properties of the Active system ensure its adaptation to the conditions of the chaos of the external environment. The organization in the work is defined as a self-developing Active system Ac. At the same time, the concept is put forward that self-development is the cause of self-organization of an Active system. Citing the fractal properties of chaos, it is pointed out that it is necessary, first of all, to adapt the fractal properties Ac to the fractal of chaos. Based on the fractality of the organization's potential , it is proved that the self -development of Ac depends on the "point" of the planning procedure. It is determined that this procedure itself depends on the human fActor, i.e. from the Active elements of the Ae system. Determining the role of administrative control Ato the self-development coefficient ρ of the Active system is introduced. The frActal form of administrative control А to playing the role of a "synovial membrane", providing synergy self -development A s .
Keywords:	Chaos, Self-Development, Self-Organization, Active System Fractals, Control Action, "Synovial Membrane", Synergetics.
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	AN INTEGRATED FUZZY MULTI-CRITERIA DECISION-MAKING METHODS FOR SERVICE SELECTION: A SYSTEMATIC LITERATURE REVIEW AND META-ANALYSIS
Author:	PAUL AAZAGREYIR, PETER APPIAHENE, OBED APPIAH, SAMUEL BOATENG, WILLIAM LESLIE BROWN-ACQUAYE, GODFRED YAW KOI-AKRO FI
Abstract:	Background: Service selection refers to the process of picking services that best fulfill the user's functional and non-functional requirements. It is possible to pick a web service or a cloud service. Researchers examined a large number of service selection assessments that utilized different services based on Quality of Service (QoS) variables utilizing Multi-Criteria Decision Making Methods. Despite its positive outcomes, earlier research has shown that Multi-Criteria Decision Making Methods alone cannot handle the incompleteness, ambiguity, uncertainty, and, most importantly the fuzziness inherent in decision-making processes due to human involvement. To circumvent these constraints, the usage of Fuzzy Multi-Criteria Decision Making Methods is a growing study topic. Objective: While the research community carefully examined the methodologies used by researchers when studying service selection, there is still a noticeable limited knowledge on how Fuzzy Multi-Criteria Decision Making Methods have been adopted for service selection and whether there are points of improvement to allow for a better service selection evaluation. The purpose of this paper is to offer an overview of and examine the use of Fuzzy Multi-Criteria Decision Making Methods in the subject of service selection with a focus on five research questions. Method: A Systematic Literature Review (SLR) on Fuzzy Multi-Criteria Decision Making Methods for service selection is presented in this work. Our research looks at publications published between 2010 and 2021. Our initial database search resulted in 508 publications. Also, a search through another source (i.e. Reference Lists examination) resulted in 15 publications. After a thorough paper selection process using the PRISMA standard, only 60 publications met the final inclusion criteria. We looked at them from five distinct angles: (i) Quality of Service (QoS) factors used, (ii) Service Application Domains, (iii) Fuzzy Multi-Criteria Decision Making Methods employed, (iv) Dataset used, and (v) Sensitivity Analysis Results: According to the results of the research, Response Time, Success Ability, Reliability, Throughput, and Performance have all been carefully studied in the literature. Other choices, the Cloud service option, and Web service selection received 68 percent, 20%, and 12%, respectively. Ten percent of the research employed heterogeneous datasets, whereas the other ninety percent used homogeneous datasets. The most popular integrated Fuzzy Multi-Criteria Decision Making Method used was the Fuzzy AHP+ Fuzzy TOPSIS. Thirty percent of the research performed a sensitivity analysis, whereas seventy percent did not. The findings indicate that more primary studies combining fuzzy MCDM methods are needed. Also, further reviews can consider the types of fuzzy numbers used as well as the membership functions used. Conclusion: Based on our findings, we believe that Fuzzy Multi-Criteria Decision Making Methods for Service Selection still have room for improvement. This study sets the pace for more primary studies utilizing Fuzzy MCDM methods in the subject of service selection.This, by extension will result in the development of intelligent applications to help service users moving forward. Researchers interested in developing more powerful approaches can look at the findings and conceptualize papers that will combine some powerful fuzzy MCDM techniques based on our overview findings. Also, the Type-3 Fuzzy Logic system can be explored with MCDM Method in service selection research moving forward as it has improved capabilities in terms of handling uncertainties than the others.
Keywords:	Fuzzy, Multi-Criteria Decision Making Methods, Service Selection, Fuzzy-TOPSIS.
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	PERFORMANCE OF RIDGE LOGISTIC REGRESSION AND DECISION TREE IN THE BINARY CLASSIFICATION
Author:	MARJI , SAMINGUN HANDOYO
Abstract:	In everyday life, we are always faced with making decisions to choose the right decision between 2 choices of decision candidates. The development of high-performance binary classification models is a challenge for researchers in the modeling field. Deployment both of logistic regression and decision tree model use the dataset having predictor features which are a mixture of categorical and numerical features, both models tend to suffer an overfitting problem. This study has the aim of building a ridge logistic regression and decision tree model on a dataset that has all features of a binary categorical scale. The novelty of this study is to observe the distribution of the two classes in the dataset using the transformation of principal components and linear discriminant projections and also to explore the importance of feature that plays a role in building the decision tree model. The ridge logistic regression model has an accuracy performance of 84% which is better than the decision tree model having an accuracy performance of 81%. There are only 2 features in the dataset dominating around 80% of the feature importance.
Keywords:	Confusion Matrix, Decision Tree, Feature Importance, Machine Learning, Ridge Logistic Regression
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	THE MODERN TRANSFORMATION OF INTERNET COMMUNICATIONS
Author:	ANASTASIIA BESSARAB, TETIANA HYRINA, OLEKSII SYTNYK, NATALIA KODATSKA, OLHA YATCHUK, LIUDMYLA PONOMARENKO
Abstract:	The article considers the evolution of Internet communications with the development of digital technologies. How new information and telecommunication technologies not only change the forms and methods of communication between people but also call into question the survival of "traditional" methods and forms of communication. The specificity of modern communication systems is reflected; the classification of Internet communication is given according to the composition of participants, the time of receipt of the message and the response to it, and according to the nature of the sign means and forms used. The article also provides definitions for various stages of the formation of the Internet and prospects for development from Web 1.0 to Web 3.0. And the socio-psychological problems that arise with the development of Internet communications are considered, as well as the opportunities and limitations created by Internet communications in the future. These problems and limitations play an essential role in the transformation of current Internet communications, which in the future will become utterly decentralized with the transition to virtual reality. The key difference between Web 3.0 Internet communication will not be the principle of human interaction with the Internet but vice versa - the principle that "the Internet communicates with a person". The possibilities and limitations of such a transformation make it possible to single out only the main socio-cultural parameters of Internet communications due to electronic technologies. But they also make it possible to see that we live in an era of the formation of new approaches to communication, education, protection of human health and safety, and the organization of all life activities. Without considering the risks and limitations created by Internet communications, it is impossible to use the personal and social development opportunities they generate effectively.
Keywords:	Digital Technologies, Internet Communications, Web 1.0, Web 2.0, Web 3.0)
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	SOLVING FUZZY FRACTIONAL NUCLEAR DECAY EQUATIONS BY REPRODUCING KERNEL HILBERT SPACE METHOD
Author:	MOATH ALI ALSHORMAN, NURNADIAH ZAMRI , MALEK KHALAF ALBZEIRAT
Abstract:	This paper deals with the solutions of fuzzy fractional nuclear decay equations (FNDEs) under Caputo H-differentiability by Reproducing Kernel Hilbert Space Method (RKHSM). The equations were reformulated under the influence of fuzzy logic. The Residual Power Series has been applied in solving differential equations with fuzzy initial conditions. This method is illustrated by solving two examples. The solution method has shown high efficiency and accuracy in the case of comparison with the exact solution and another method.
Keywords:	Nuclear Decay Equations, Reproducing Kernel Hilbert Space Method.
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	EFFECTIVENESS AND PRACTICALITY CAI BASED SIMULATION FOR LEARNING MEDIA OF SHORT CIRCUIT CURRENT
Author:	AGUS JUNAIDI, RAHMANIAR, ADI SASTRA T., ABDUL HAKIM B., MARWAN AFANDI, JANNER SIMARMATA, WANAPRI PANGARIBUAN
Abstract:	The research will examine the manufacture of online practicum module products for the Power System Analysis (PSA) course. The PSA online module contains study material using a Real Problem Oriented (RPO)-based Computer-Assisted Instruction (CAI) model. The selection of research topics is based on 3 main problems. PSA learning is currently not available for PSA practicum modules. PSA courses, currently not equipped with online practicum modules as a basic need for students in learning during the Covid-19 pandemic, need to be realized, and Study materials on PSA have not been applied optimally in solving real problems. The online modules that were compiled were tested with instruments of effectiveness and practicality. Effectiveness test is carried out by applying module in small class training in two classes (experimental class and control class) with a difference in the value of the two classes. while for practicality modeled by testing the concept of aiken-v. To compose measurement simulations, a GUI is designed to study symmetrical and asymmetrical analysis on PSA. The simulation media is prepared to measure effectiveness and practicality. from the research results show that the GUI can solve the problem PSA analysis, and effectiveness and practicality simulation instruments designed to be valid as measuring instruments.
Keywords:	Short Circuit, Graphical User Interface, PSA, Usymmetrical Fault, Symmetrical Fault
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	DEEP LEARNING-ASSISTED EXPLICIT AND IMPLICIT CONTEXT-BASED PERSONALIZED RECOMMENDER SYSTEM
Author:	NIRUPMA SINGH, DR. ABID HUSSAIN
Abstract:	In the digital world, recommending the desired items to the users is still a challenging task with the unprecedented growth of the information on the Web. The recommender system plays a significant role in addressing the information overload problem in the abundance collection of the data. Recently, a context-aware recommender system has become a potential solution for providing personalized service to users. Several conventional recommendation researchers have considered contextual information in addition to the user-item-rating preferences. However, only a few recommendation research works have focused on effectively utilizing the explicit and implicit contextual information in the deep learning-based recommendations. Hence, to improve the recommendation quality, extracting the contextual user-item interaction and modeling the deep learninSg with latent context learning is essential. Thus, this work presents the DEep learning-assisted explicit and implicit COntext-based personalized Recommendation (DECOR) model to suggest the contextually desired as well as the popular items to the users. The proposed DECOR approach incorporates contextual preference extraction and deep learning-based personalized recommendation processes. Initially, it focuses on analyzing the users’ explicit and implicit contexts and the items’ contexts to improve the quality of the personalized recommendation. In essence, the proposed DECOR approach utilizes the explicit context of the rating, time, and location along with the implicit context from the users’ reviews as the context vectors. Moreover, it enriches the individual users’ rating with the items’ popularity and generates the normalized rating score to facilitate the contextual and personalized recommendation. Secondly, the DECOR approach learns the contextual preferences of the users on the items with the assistance of the Long Short-Term Memory (LSTM) deep learning model. Thus, the proposed approach predicts the user preferences on the items with the knowledge of users’ preference score and normalized rating score and several additional contextual factors and suggests the relevant items. Thus, the experimental results show that the DECOR approach significantly outperforms the existing recommendation model on the benchmark test dataset.
Keywords:	Recommender System, Explicit Context, Implicit Context, Deep Learning, Items’ Context, and Personalized Recommendation
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	THE IMPACT OF ENHANCING AWARENESS OF CYBERSECURITY ON UNIVERSITIES STUDENTS: A SURVEY PAPER
Author:	MARAM MOHAMMED, DOAA M. BAMASOUD
Abstract:	The great technological development and digital transformations that the world is witnessing increase the rate of cyber threats and cybercrime, and due to the Covid19 pandemic, education, and commerce have relied on the Internet for the continuation of education and to maintain the economy. Threats arising from the behaviors of the individuals are among the main cyber threats, this appears from the limited awareness of individuals about cyber security and its threats. This survey paper discusses the importance of enhancing cyber security awareness among university students in Saudi Arabia to reduce cyber threats. As cybersecurity awareness is one of the areas of cybersecurity controls that aim to enhance awareness of cybersecurity, its threats, and risks, and build a positive cybersecurity culture. In addition, cybersecurity awareness is an important component of ensuring the protection and privacy of critical information assets. Students' awareness of cybersecurity, its threats, and risks enhances students' references to action when facing cybercrime to protect the information, and technology assets to reach safe cyberspace to achieve the Saudi Arabia Vision of 2030.
Keywords:	Cyber Security Awareness, Cyber Threats, Enhance Cyber Security, Cyber Security, Higher Education Students
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	INTELLIGENT SYSTEM FOR DRIVER SUPPORT USING TWO CLASSIFIERS FOR SIMULATION
Author:	OLGA SHVETS, BAUYRZHAN SMAKANOV, LEVENTE KOVACS, GYORGY GYOROK
Abstract:	The methodology of the study is based on the formulation and formalization of goals and objectives, the development of models, algorithmic methods, and experimental evaluation through experiments, testing, approbation and analysis of the results. In the work were used the methods of developing information systems to support the processes of collecting information, analyzing, designing and implementing such systems, the theory of algorithms for the effective formulation of subtasks and assessing the complexity of algorithms, the theory of machine learning for analyzing the behavior of drivers, generating recommendations for the driver to take measures to prevent the onset of an emergency and improve driving skills, as well as methods for developing software for the implementation of an intelligent accident prevention system. The goal of the work is to research and develop methods and algorithms for the operational control of the driver's condition, as well as the creation of a software video tracking system based on the developed algorithms. The main objectives of the work: 1) analysis of existing technologies, devices and active safety systems for the driver of a vehicle, focused on early warning and prevention of traffic accidents, as well as existing methods and algorithms for intelligent analysis of video surveillance data; 2) modeling using two classifiers and algorithms’ development for improving driver real time safety; 3) development of an automated video tracking system using the proposed methods.
Keywords:	Driver safety, Image Processing, Automation, Transportation, Monitoring
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	A NOVEL ALGORITHM BASED ON THE NODE TIGHTNESS DEGREE FOR COMMUNITY DETECTION IN LARGE GRAPHS
Author:	WAFAA OUBAALLA, LAILA BENHLIMA
Abstract:	Community detection is one of the most important research topics in the complex network area. The identification of community structure in large graphs analyze the information unrevealed in the exterior data relationships, explore the internal structure and the function of networks and improve their efficacity. A lot of approaches and methods have been proposed to identify communities based on network structure. However, the majority of them focus on topologies of nodes but ignore the relevance of interactions between them. In this paper, we propose a novel algorithm especially focused on identifying the initial communities then expanding them by using a new node tightness degree based on the edge clustering coefficient and the shared neighbour’s similarity of nodes. The proposed approach is evaluated based on different small and large datasets corresponding to different contexts. The experiments prove good results in terms of modularity and computation time while using the new node tightness degree
Keywords:	Graph, Node, Edge, Community, Modularity
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	ANALYSIS OF MOTOR IMAGERY EEG SIGNAL CLASSIFICATION BASED ON AMPLITUDE-BASED PEAK DETECTION METHOD AND PISARENKO HARMONIC DECOMPOSITION
Author:	JAY SARRAF, VAIBHAW, PRASANT KUMAR PATTNAIK
Abstract:	This paper presents a critical study on EEG Motor Imagery feature extraction techniques using Pisarenko Harmonic Decomposition and Peak Detection algorithm. It was found that the proposed peak detection technique for feature extractions with the KNN classifier was efficient with 95.67% accuracy as compared to 84.56% achieved using feature extraction using Pisarenko’s method. Further, feature extraction using the peak detection method with the random forest and gradient boosting with the accuracy of 91.58 and 92.68% is suggested over KNN as the computation time is very high when required to compute the distance of each query instance in KNN.
Keywords:	Motor Imagery, BCI, EEG, Classification, Signal processing, Brain Mapping, Classification, Machine Learning
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	ASSESSING THE IOT BUSINESS MODEL CASE STUDY: PT. XYZ
Author:	CHOIRUL ULFA KUSUMOHADI, TUGA MAURITSIUS
Abstract:	The purpose of writing is to analyze the business model of the IoT technology service provider at the XYZ company. XYZ company is diversifying its business in the IoT technology business. There is a problem with XYZ company's ability to run a new business outside the current business environment. For this reason, the data was taken from observations and interviews with company employees. Five Force's and SWOT Analysis is used to analyze the company's business strength, and the analysis of the financial performance of the business is used Return of Investment (ROI), Payback Period, and Net Present Value (NPV). The results of business analysis by designing a new business model from three scenarios: the optimistic scenario with an ROI of 22.2%, moderate 10.7%, and pessimistic 8.7%. The NPV shows a positive number for business projections for the next five years. It can be concluded that the IoT service provider business model designed by XYZ company can positively contribute to the company's financial performance and sustainability.
Keywords:	IoT, Business Model Canvas, Lean Canvas, Porter’s 5 Forces, SWOT
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	COMPARATIVE ANALYSIS OF THE PERFORMANCE OF GENERATING CRYPTOGRAPHIC CIPHERS ON THE CPU AND FPGA
Author:	BEKBOLAT MEDETOV, TANSAULE SERIKOV, ARAY TOLEGENOVA, ZHEXEBAY DAUREN
Abstract:	With the rapid development of technologies for the use of artificial neural networks (ANN) to solve various complex and poorly algorithmized problems, recently there have been more and more ideas to use ANN to decrypt data encrypted by cryptographic protocols such as AES. As a rule, when solving a particular problem, ANNs require large amounts of data for their training. For example, in the task of data decryption, it is necessary to have a sufficiently large array of data that make up a pair: encrypted and original information. In the case of considering block data encryption using protocols of the AES family, the sizes of input and output data for ANN can be 128 bits. Thus, to implement an ANN capable of decoding data, first of all, it is necessary to develop a generator of training data, which is an array of size Nx128, where N is the amount of data. In this paper, the possibilities of implementing a training data generator for ANN using a CPU (central processing unit) and an FPGA (field-programmable logic integrated circuit) are considered. A comparative analysis of their performance in solving this problem is also carried out. Our experimental calculations show that the FPGA based oscillator has better performance than the CPU based oscillator. Moreover, the financial costs of building an FPGA generator turn out to be noticeably lower than the financial costs required to build a computer with a CPU. Thus, in this paper, we conclude that the implementation of the training data generator on the FPGA is more profitable both in terms of performance and cost. The main result of the research is the experimental confirmation of the ability of AES-type encryption algorithms to be executed in parallel at a very high level. This statement is made on the basis of the implementation of encryption functions in the Verilog language for their execution on the FPGA, which perfectly supports parallel computing. We also inform you that we have developed a library written in the Verilog language, where all the functions for data encryption according to the AES protocols are implemented. And finally, this library was used to develop a high-speed encrypted data stream generator using AES encryption algorithms. In the future, this generator is planned to be used to generate a super-large amount of training data needed to train ANNs used to decrypt or search for vulnerabilities in encryption algorithms.
Keywords:	Programmable logic integrated circuit, Artificial neural networks, Algorithm, Encryption, Central processing unit, Protocol, Flow, Quality, Field-programmable logic integrated circuit, Pseudo-random number generator.
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	CROSS-PROJECT SOFTWARE DEFECT PREDICTION
Author:	YAHAYA ZAKARIYAU BALA, PATHIAH ABDUL SAMAT, KHAIRONI YATIM SHARIF, NORIDAYU MANSHOR
Abstract:	The feasibility of building a software defect prediction (SDP) model in the absence of previous records has been increased by the introduction of the Cross-Project Defect Prediction (CPDP) method. Although this method overcomes the limitations of SDP in the absence of previous historical records, the predictive performance of the CPDP model is relatively poor due to distribution discrepancy between the source and the target datasets. To overcome this challenge, various studies have been published. This SLR was conducted after analyzing research articles published since 2013 in four digital libraries: Scopus, IEEE, Science Direct, and Google Scholar. In this work, five research questions covering the classification algorithms, dataset, independent variables, performance evaluation metrics used in CPDP studies, and as well as the performance of individual machine learning classification algorithms in predicting software defects across different software projects were addressed accordingly. To respond to outlined questions, 34 most relevant articles were selected after passing through quality assessment criteria. Through this work, it was discovered the majority of the selected studies used machine learning techniques as classification algorithms, and 64% of the studies used the combination of Object-Oriented (OO) and Line of Code (LOC) metrics. All the selected studies used publicly available datasets from NASA, PROMISE, SOFLAB, AEEEM, and Relink. The most commonly used evaluation metrics are F_measure and AUC. Best performing classifiers include Logistic Regression and SVM. Despite various efforts to improve the performance of the CPDP model, the performance is below the applicable level. Thus, there is a need for further study that will improve the performance of the CPDP model.
Keywords:	Software Defect Prediction, Cross-Project, Machine Learning Techniques, Statistical Techniques, Performance Evaluation Measure.
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	ANALYSING TRADING STRATEGIES AND FORECASTING STOCK PRICES USING LSTM
Author:	RINKY DWIVEDI, KOYEL DATTA GUPTA, TRIPTI SHARMA, RISHABH RAIZADA, SARTHAK YADAV, VEEPSA BHATIA
Abstract:	Algorithmic Stock Trading has been legalized in India since 2008, with SEBI (Securities and Exchange Board of India) regulating the norms governing the same. Earlier, regulations restricted third-party algorithms to be used in circulation with APIs (Application Programming Interface), as these were unregulated by registered brokerages. Owing to the COVID19 pandemic, SEBI relaxed these underlying norms, easing up both algorithm usage for an end-user, as well as the number of trade orders that can be placed per second (which went up from 20 to 120). This article aims to analyze optimal trading strategies for various stocks using both classic mathematical techniques and Recurrent Neural Networks (RNN). Stock prices will be forecasted using data analytics implementing indicators like supertrend and VWAP and machine learning models like LSTM. Upon fine-tuning parameters in both approaches, trend directions and triggers will be plotted (in the case of existing indicators and strategies) and predictions with trade triggers (in the case of LSTM). Furthermore, stocks will be categorized by trading techniques - growth, momentum, and value and further the best strategy with the expected profit percentage in each case will be identified.
Keywords:	Stock Trading, LSTM, Indicators, Trading Strategies, Forecasting.
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	INVESTIGATION OF SPECTRA OF MOBILE COMMUNICATION SYSTEMS USING RTL-SDR RADIO RECEIVER
Author:	S.S. KENGESBAYEVA, D.ZH. UTEBAYEVA, L.B. ILIPBAYEVA
Abstract:	Mobile phones are becoming an integral part of our daily lives. In fact, the mobile Internet has become one of the most important components of our business transactions and social life. As a result, the number of mobile users has increased significantly. Mobile signal users are distributed across different countries with different densities. This is due to the fact that the provision of mobile services depends on mobile operators and the pace of technological development of communications in countries. And the rise in popularity of mobile communications has made the electromagnetic spectrum of mobile signals unevenly distributed across frequencies, resulting in spectral inefficiencies. Accordingly, some spectra are used extensively, and some are left unused. The relevance of the proposed work lies in the fulfillment of the prerequisites for solving the problem of inefficient use of this mobile spectrum. The uneven distribution of these mobile spectra causes spectrum shortages. More recently, this problem began to be solved with the help of a special technique, such as Cognitive Radio. Cognitive radio first studies the spectra of mobile signals and then determines the signal density. This work proposed to perform the first stage of Cognitive Radio with spectrum recognition of mobile communication standards such as GSM, UMTS and LTE for Kazakhstan using RTL-SDR radio receiver. Since this work is aimed at studying the technical capabilities of the RTL-SDR radio receiver for mobile signals, it considered the study of the standard of mobile signals, such as GSM, UMTS and LTE. The result of the study shows that the technical capabilities of the RTL-SDR radio receiver are fully supported for the GSM and UMTS standards and partially for the LTE standard as a research platform for use in the Cognitive Radio method.
Keywords:	RTL-SDR, Spectra of Mobile Communication Systems, Mobile Signals, GSM, UMTS and LTE.
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	OPENING YOUR EXTENDED REALITY EYE FOR EASY OF USE DURING HOSPITAL SURGERY
Author:	H. LIU, P.S. JOSEPHNG , K.Y. PHAN, AND X. GONG
Abstract:	Extended Reality Smart Glasses is a way to use scientific and technological innovation to introduce a visual atmosphere that connects the physical and virtual worlds. This study investigated the value and possibility of XRSG for clinical surgical services. Data collection is conducted through the experience of medical experts. Combining the “Technology Acceptance Model” with the “Theory of Planned Behavior”, a new Extended Reality Technology Behavior Model (XRTBM) is constructed through the combination of human-visible social control and data innovation exploration. To improve the accuracy of the review, a triangular mixed research method was used. From the collected information, the SEM survey was used to reflect the relationship between the factors. Reliable positive results demonstrate that the use of XRSG by clinical specialists helps improve the structure, intuition, standardization, and clarity of clinical images, increasing productivity and reducing method time. The significance of this study is that patients can feel the convenience and availability of XRSG through their behavior, which provides a prerequisite for the implementation of XRSG in the medical process.
Keywords:	Extended Reality, Three-dimensional Image, Visualization, Technology Behaviour Model
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	SOFTWARE QUALITY ASSURANCE PERSPECTIVE ON E-COMMERCE SYSTEM DEVELOPMENT
Author:	YEE HAN CHUNG, RAJERMANI THINAKARAN, MALATHY BATUMALAY, NURUL HALIMATUL ASMAK ISMAIL
Abstract:	COVID-19 has accelerated the growth of E-commerce revenue and market size within a few months, causing the quality of E-commerce systems to be top-notch to gain a competitive edge. This study attempts to maximize feasibility and boost confidence throughout the development of e-commerce systems. This paper presents different types of software development methodologies, including traditional and agile methodologies. This study also presents related works on software development methodologies, comprising Extreme Programming, Scrum, and Kanban, and a particular software quality model, ISO/IEC 25010. Their challenges and advantages are discussed. The sub-characteristics of ISO/IEC 25010 are mapped to the features of e-commerce systems. As a result, Scrum with frequent and effective meetings can minimize technical debt, design failures, stress, miscommunication, ambiguity, improve shared vision, continuous feedback for verification, productivity, team morale, delivery predictability, project visibility, risk reduction, and engineering discipline. The ISO/IEC 25010 contributes to the success of E-commerce systems.
Keywords:	Software Development Methodology, Software Quality Model, Extreme Programming, Kanban, Scrum
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	THE ROLE OF INSTAGRAM-FACEBOOK IN CUSTOMER ENGAGEMENT AND BUILDING BRAND AWARENESS
Author:	NI LUH GEDE ANINDA KUSUMA DEVI, ANGELA AYSHA KAMAL, DAVINA ALYSSA SHAFIRA, SITI GRACIELA AYU DAMAYANTI, TANTY OKTAVIA
Abstract:	As the number of social media users in Indonesia increase, it has become a necessity for everyone to exchange information and go shopping online. This makes brands change the way they interact with their customers and develop strategies and tactics to overcome competitive competition to maintain their customers. This research aims to examine the effect of social media marketing, especially Instagram and Facebook activities on customer engagement, and brand awareness. In addition, it has been aimed to analyze the effect of customer engagement on brand awareness in this research. The population of the research consists of people who actively use social media such as Facebook and Instagram. This study uses a sampling method by collecting questionnaire data through google form with a total sample of 101 respondents. The analytical instruments that are used in the hypothesis are validity tests, and reliability tests using SmartPLS software. The obtained data have been analyzed and as a result of the analysis, social media marketing activities have been found as effective factors on customer engagement and brand awareness, besides, it has been determined that the most obvious effect seen on customer engagement. In addition, it has been found that customer engagement has a significant effect on brand awareness. The results of this study show that Social Media, especially Instagram and Facebook have a positive influence on Customer Engagement and Brand Awareness. Also, Customer Engagement strengthens the effect of social media on Brand Awareness. A brand needs to have a greater plan in the use of social media for brand marketing strategies with innovation in social media. And this study can add practical information for marketing and managers about the use of social media in marketing strategies and this study can be used as a new measurement step for improving operation aspects in social media for brands.
Keywords:	Social Media Marketing Activities, Customer Engagement, Brand Awareness
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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Title:	IMPROVE THE EFFICIENCY FOR EMBEDDING IN LSB METHOD BASED DIGITAL IMAGE WATERMARKING
Author:	ALAA ABDULSALAM ALAROOD
Abstract:	The internet revolution has caused the current dramatic reform of multimedia applications. The progression of the internet has significantly eased the transfer of data/image, making the transfer faster and more accurate. However, such advancement has also facilitated modification and misuse of valuable information, by way of hacking. Digital watermarking has been proposed to protect the copyright of multimedia data. A watermark encompassing a form, image or text is embedded. Hiding information is an effective solution for the protection of copyright and confidentiality to allow a person to send the data in the middle of the cover image to a person without knowing any third party in this transmission, methods of delivering secret messages are very important. This research provides a way to hide data (which is a text file) after is encrypted adoption method (Keyword Mixed Transposition) to produce cipher text is included in Low–High coefficient wavelet transform and get a good quality image and the possibility of recovering fully embedded message and decoded without relying on the original image. Results have applied to the digital images to get inline images to the data with a high correlation coefficient when compared with the original images in addition to that they gave a few differences when calculating measurements (SNR, PSNR, MSE) This study demonstrates the implementation of watermarking technique, both the invisible using (Least Significant Bit) algorithm and visible forms. Further, image watermarking and various security issues are reviewed. Countless attacks were attempted on the watermarked images. Then, their effects on the quality of images were examined. Image Watermarking using Least Significant Bit (LSB) algorithm was applied for message/logo embedment into the image and the Experiments have shown that the proposed embedding process has increased the embedding efficiency as it does not require going through all bits to modify.
Keywords:	Watermarking, Least Significant Bit (LSB), Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).
Source:	Journal of Theoretical and Applied Information Technology 15^th August 2022 -- Vol. 100. No. 15 -- 2022
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