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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
May 2022 | Vol. 100 No.10

Title:	INTERNET OF THINGS (IOT) APPROACH TO COMBATING ECONOMIC AND ENVIRONMENTAL ISSUES
Author:	SEUN EBIESUWA, OHWO ONOME BLAISE, ADESINA ADIO, RICHMOND U. KANU, ADEGBENJO ADERONKE
Abstract:	Global warming, pollution and costs of power production are the major environmental and economic issues faced by the planet today. Many research works have been done and are ongoing to reduce the adverse effects that may arise due to these issues. With these issues plaguing much of the planet, the rapid proliferation of the Internet of Things (IoT), efficient, economical, and sustainable solutions are within reach. There have been various researches carried out in energy generation, agriculture, automotive, and so on. Also, there is a growing awareness among organizations that going green—taking measures to reduce carbon footprint, conserve resources, and protect the environment— have enormous advantages and benefits, as it is also good business. This paper aims at providing an insight into Internet of Things (IoT) and its applications in combating global warming, by conducting a literature review to highlight various ways via which IoT is being applied, and discussing the various benefits, whether economical or environmental, that comes with adopting IoT; as well as challenges hindering IoT adoption. It concludes by looking at the exciting potential of IoT to deliver solutions that are both economically and environmentally smart.
Keywords:	Internet of Things, Global Warming, Internet, Climate Change, Pollution
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	ENHANCING A RELIABLE TRADITIONAL CLOTHES PATTERN RETRIEVAL: CNN MODEL AND DISTANCE METRICS
Author:	ABDUL HARIS RANGKUTI, VARYL HASBI ATHALA, EVAWATY TANUAR, JOHAN MULIADI KERTA
Abstract:	This study describes the traditional clothing patterns retrieval very diverse forms and textures. With so many variations in each clothes pattern, an appropriate and supported CNN model is needed as well as the right distance matrix method to support retrieval performance. This research was conducted on 76 types of traditional cloth patterns originating from 22 regions in Indonesia. In conducting an experiment to retrieve this clothes pattern, 3 CNN models were used, namely EfficientnetB7, InceptionV3 and VGG 19 and 2 distance matrix methods, namely Euclidean and Manhattan. Based on the experiment, the average with 2 measurement distances is 86.2%. The CNN model has the highest accuracy on EfficientNetB7 with an average of 92%. Inception V3 with an average accuracy of 85%, and the next VGG 19 has an average of 81%. Basically, some batik cloth patterns have an accuracy of up to 100% retrieval using 3 CNN models, but some patterns have an accuracy below 50%, so this is part of the continuation of research on traditional cloth patterns should convey the importance of your research in a concise and logical manner.
Keywords:	CNN, EfficientnetB7, Inception V3, VGG 19, Manhattan, Euclidean
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	ENTERPRISE ARCHITECTURE OF AN APPLICATION OF MOBILE REFERRAL MARKETING IN CONSUMER FINANCE COMPANY WITH TOGAF FRAMEWORK
Author:	HANIFAH AHDIANI, NILO LEGOWO
Abstract:	This study was conducted on a mobile referral marketing application owned by a consumer finance company. The purpose of this research is to design an Enterprise Architecture Information System model for the company which can be applied to mobile referral marketing applications and produce an IT roadmap recommendation that can be used as a reference in developing the Enterprise Architecture Information System for mobile referral marketing applications. The data is taken from the interview process of the Department Head of Enterprise and Digital Solution as a resource person, analyzing the condition of the current system by using TOGAF framework from the Preliminary Analysis, Architecture Vision, Business Architecture, Information System Architecture, Technology Architecture. The design results obtained are in the form of data integration, adding online-chat features, implementing the Google Map API, reducing data redundancy, and implementing docker. It can be concluded that the design made can reduce existing problems and improve the mobile referral marketing business process so that it will provide additional value and produce recommendations for the consumer finance company as a reference in the development of Enterprise Architecture Information Systems for mobile referral marketing applications.
Keywords:	Enterprise Architecture, Information Systems, Mobile Referral Marketing, TOGAF ADM, Consumer Finance Company.
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	AN ENHANCED ACTIVE CONTOUR FOR IMAGE SEGMENTATION WITH STRONG NOISE
Author:	OMAR GOUASNOUANE, SOUMAYA BOUJENA, KARIMA KABLI
Abstract:	Image segmentation is a useful and important technique in image processing, its purpose is to simplify and/or change the representation of an image into components that are more meaningful and easier to analyze. Nevertheless, even after the evolution of segmentation methods, they unfortunately lead to over-segmentation or to under-segmentation when applied to noisy images. Among different techniques for the segmentation of pixels of interest from an image we focus on segmentation by active contour models. Those models have been widely successfully used, in recent years, for different applications. However, for noisy images it is necessary to perform a denoising preprocessing step for a satisfactory segmentation. In this work we propose a segmentation model wich is a combination between a nonlinear diffusion model for image denoising and classical active contour models which use mean curvature motion techniques. For this we take, in the edge indicator function, the gradient of the image to be segmented which has been denoised by a nonlinear diffusion instead a Gaussian filter. Finally, we will present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable.
Keywords:	Image Segmentation, Level Set Methods, Nonlinear Diffusion, Nonlinear Diffusion, Active Contour Models.
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	HIGHER EDUCATION MANAGEMENT TO DIGITAL ENTREPRENEURIAL UNIVERSITY
Author:	TIPPAWAN MEEPUNG, PRACHYANUN NILSOOK, PANITA WANNAPIROON
Abstract:	Transform universities with digital technology drives changes in both operations. The procedures in accordance with the planned goal or long-term university development plan are in accordance with state policy guidelines. According to the national strategic plan, national economic and social development plan, long-term higher education plan, national development plan. The purpose of this research the content to propose the structural equation model for high performance digital entrepreneurial university. The research instituted the hypothesized digital transformation, entrepreneurial university, digital organization, enterprise architecture and high-performance organization. The research was conducted in both quantitative and qualitative survey and interview were conducted with 300 representatives were selected by cluster sampling working in the higher education institutions. The results of research the analysis of structural equation model found that the evaluation was consistent with the empirical data. The conclusions are as follows: (Chi-square=90.267 df. =75) (CMIN/DF = 1.204) (GFI =. 974) and (RMSEA =. 026). The results showed that all factors had a direct effect on the significant statistics of 0.001
Keywords:	Structural Equation Modeling; Digital Transformation; Entrepreneurial University, Enterprise Architecture; High Performance Organization
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	HYBRIDIZED SPAMBOT DETECTION SYSTEM USING SOURCE-CONTENT CLASSIFICATION
Author:	ISMAIL ADELABU ADEGBOLA, OLAIYA FOLORUNSHO, TAOFEEKAT TOSIN SALAU-IBRAHIM, RASHEED GBENGA JIMOH
Abstract:	A web robot is an automated program that requests web resources independently from a web server. The growth of bad and good web robot traffic on web 2.0 over the years is on the high side compared to humans. A malicious web robot has been used to spam activities in web 2.0. It, however, poses serious challenges to website owners and can cause Distributed Denial of Service (DDoS) attacks. Previous web robot detection techniques emphasized pre-processing access-log files for identifying web robot session. A few approaches considered the semantic analysis of the content requested within a session as a source of web robot detection. Furthermore, very little effort has been made in combining the strength of behavioural features and semantic features in web robot detection. Therefore, this paper aimed at developing hybridized spambot detection system using source–content classification. This paper revealed that 7568 unique sessions were identified with 6993 humans, 558 spambots and 17 non-spambots; session text coherence (STC), session word relatedness (SWR) and session topic coherence (ST) were functionally expressed as STC=sw/(nm), SWR=k/(nm), and ST=c/(n*m) respectively, where n represents the number of relevant topics, m number of top words in each topic, sw sum of weights, c count of unique topics and k count of unique word. The hybridized features performed effectively with an accuracy of 93.67% compared to the individual features.
Keywords:	Source, Content, Spambot, Detection, Web 2.0.
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	DEVELOPING A DECISION -MAKING FOR OUTSOURCING APPLYING FUZZY ANALYTICAL HIERARCHY PROCESS (FAHP)
Author:	SOUMIA TABIT , AZIZ SOULHI
Abstract:	Outsourcing is nowadays a widespread practice in national and international companies since it allows them to better use time, energy, manpower, technology, capital, resources, etc. Thus, the appropriate selection of the outsourcer plays an important role in establishing the company's position in the market and contributes to its success. To facilitate the entrepreneur's reasoning for the choice of the best provider, to increase the efficiency of decision making in an uncertain environment, given the inherent uncertainty and imprecision of human decision making as well as future market and firm behaviors, we have developed a hybrid multi-attribute, multi-actor decision support model (FMAADM) to address the problem at hand. For this objective, we have combined the AHP concept with fuzzy logic reasoning. For the validation of this proposed model, an experimental study was conducted to prioritize (03) services of outsourcing related to maintenance and industrial installation for the case of a manufacturing company of plastic products. The proposed model meets the objective sought and thus is retained for the selection of the best provider in a certain/uncertain context of multi-attribute and multi-actor.
Keywords:	Outsourcer, Decision making, FMAAD, AHP, Fuzzy set theory, FAHP...
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	AN IMPROVED EVOLUTIONARY ALGORITHM TO OPTIMIZE TRAFFIC FLOW AT SIGNALIZED INTERSECTIONS
Author:	SIDINA BOUDAAKAT, MOHAMED AMINE BASMASSI, AHMED REBBANI, OMAR BOUATTANE, JIHANE ALAMI CHENTOUFI, LAMIA BENAMEUR
Abstract:	Traffic congestion remains a hard phenomenon problem to solve. Due to the difficulty and high cost of solutions, many studies are trying to find the most inexpensive solutions. However, with the exponential growth of technology, new opportunities and challenges are opening up to reduce congestion problems. This paper focuses on the minimization of the waiting vehicles at an isolated four arms intersection. The proposed model is divided into two main stages. First, modeling the intersection using a fuzzy system by generating a generalized fuzzy graph based on the intersection situation. Second, an improved greedy genetic algorithm solution is applied to determine the traffic cycle length to reach the maximum performance at the intersection. The results of the experiments used in the simulation of the intersection modeling generalized fuzzy graph and the novel greedy genetic algorithm (IMGFG-NGGA) performed very well compared with traditional fixed-time traffic signal under different traffic demands.
Keywords:	Signalized Intersection, Fuzzy Graph Coloring, Genetic Algorithm, Modeling Intersection, Traffic Signal Control, Traffic management
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	IMPLEMENTATION OF PMBLDC MOTOR DRIVEN ELECTRIC VEHICLE POWERED BY SOLAR
Author:	M.V. RAMESH, RAVI KUMAR MELIMI, T.SRINIVASA RAO, P.MUTHU KUMAR
Abstract:	In the recent years the awareness on the global warming effect leads to the interest in development of electric vehicle technology for all the stakeholders. The technology is growing rapidly. With more concern on our environment Solar PV system being introduced. The aim of the paper is the application of solar PV system to electric vehicle along with its design aspects. The driven system to the electric vehicle is through PMBLDC motor. PMBLDC motor input is taken from battery bank. The battery bank is employed for the storage of the power. The power produced by the PV system is transmitted through MPPT charge controller, battery bank and PMBLDC motor. The designed Electric Vehicle is tested and validated successfully.
Keywords:	Solar PV system; MPPT, Battery bank, PMBLDC motor, Electric Vehicle
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	ENSEMBLE CLASSIFIER FOR BREAST CANCER DETECTION
Author:	MUAWIA. A. ELSADIG
Abstract:	The late discovery of breast cancer is the main reason for the low survival rate, so its early detection can lessen the risk and prevent progression, thus reducing mortality rates. Machine learning classification approaches play a significant role in predicting breast cancer in its early stages, which support the process of taking the appropriate treatment that leads to enhanced survival rates. However, selecting an adequate classifier, dataset and features are the keys to successfully improving prediction accuracy and performance. This paper has introduced an efficient ensemble classification model that is based on a stacking technique with feature selection method. The base classifiers of the proposed ensemble model are SVM, neural network, random forest, gradient boosting and KNN, while logistic regression is used as a meta classifier. An enhanced version of WBCD has been employed. WBCD is one of the most common datasets for breast cancer detection. A feature selection method has been applied and, accordingly, only 23 of the 30 features are selected. Only the features with a high influence on the prediction process are considered. Our proposed model shows a high accuracy rate that reached 98.4%. It outperforms the single classifiers and causes neglected classification errors. The proposed model has also been compared to some similar existing approaches and showed better performance.
Keywords:	Machine Learning, Deep learning, ensemble classification, stacking technique, Breast Cancer Detection.
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	DIVERSIFIED SOFT COMPUTING TECHNIQUES SOLUTION FOR ECONOMIC LOAD DISPATCH PROBLEM
Author:	GUDAVALLI MADHAVI, VEMULAPALLI HARIKA, IMRAN ABDUL, MOHAMMED AZAHARAHMED
Abstract:	The electricity consumption increases day by day, and we cannot imagine our lives without electricity. In today's scenario, the electricity should be available to all the consumers at minimum cost. So Economic Dispatch is a vital optimization problem in power system planning. The objective function of the economic load dispatch problem is highly non-linear. Consequently, standard optimization procedures may diverge or trap in local minima. This paper presents an overview of the economic dispatch problem, its formulation, and a comparison of addressing the issue with diversified soft computing techniques, i.e., differential evolution (D.E.), artificial bee colony algorithm (A.B.C.), particle swarm optimization (PSO), firefly algorithm (F.F.), Invasive weed optimization(I.W.O.), social group optimization(S.G.O.) and shuffled complex evolution(S.C.E.). All the methods are tested on 6-units, 7- units, and ten units test systems.
Keywords:	Economic Load Dispatch (ELD), Cost, Differential Evolution, Artificial Bee Colony Algorithm, Particle Swarm Optimization, Firefly Algorithm, Invasive Weed Optimization, Social Group Optimization, And Shuffled Complex Evolution.
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	AESTHETIC EDUCATION: A CONCEPTUAL FRAMEWORK OF TEACHING MATHEMATICS USING THE OPEN-SOURCE SOFTWARE
Author:	AL.AKTAYEVA, KYMBAT SAGINBAYEVA, AIBEKDAUTOV, ROZAMGUL NIYAZOVA, GULZHAN SEITBEKOVA, ANARA KARYMSAKOVA, ELENA V. ZUBAREVA
Abstract:	In the article one of leading aims of educating mathematics is examined is aesthetic education of student facilities of mathematics. Presentation of aesthetic beauty at her decisions possibility of students is investigated, specifying them on the decision of one problem in several ways that assists the detailed consideration of idea of aesthetic education, through Open-source Software. The technical capabilities and elegant ease of use of systems Open-source Software provides a seamless, integrated and constantly expanding system that covers the breadth and depth of mathematical computing, and is available seamlessly through any web browser along with all modern systems used in the educational process. The article will describe understanding of beauty the decision of a problem; methods of decisions that are accompanied by the use make possible a uniquely flexible and convenient approach to charting and information visualization in a mathematical calculate. Such sort of activity assists aesthetic education, allowing to develop a culture and logical thinking, forming at students a different choice, grace of decision of problems.
Keywords:	Aesthetic Education, Mathematical Education, Software, Computer Programs, Aesthetic Learning Processes.
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	DIGITAL TRANSFORMATION AND INNOVATION IN THE TEACHING OF MATHEMATICS
Author:	AL. AKTAYEVA, A.DAUTOV, N. AUSSILOVA, A. ILYUBAYEV, U. KUSSAINOVA, D. AKTAEVA, ZH.SARSENBAEVA
Abstract:	The article considers the basic components of the methodical system of teaching mathematics, including purposes, content, methods, forms and tutorials, with an innovative component of the use of information and communication technologies in the educational process. In addition, the article describes science in various segments of math teaching, starting with the nature of math to mathematical tasks as an important method of forming a system of basic mathematical knowledge, skills and habits of students. This technique greatly facilitates the assimilation of the material, allows you to clearly show logical transitions and highlight the key points of lectures and practical classes of the educational process when teaching mathematics. This article attempts to discuss innovations and innovative practices in teaching mathematics, within the framework of teaching methods, strategies, and pedagogic resources, within the innovative component of the use of information-and-communication technologies in the educational process.
Keywords:	Teaching methods, Mathematical education, Mathematical skills, ICT.
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	NOVEL MACHINE LEARNING METHODOLOGY IN RESOURCE PROVISIONING FOR FORECASTING OF WORKLOAD IN DISTRIBUTED CLOUD ENVIRONMENT
Author:	C.DASTAGIRAIAH, DR.V.KRISHNA REDDY
Abstract:	These days, countless cloud administrations contain distributed in addition to facilitated via geo-appropriated cloud server farms (Geo-2DCs). Despite various advantages, those Geo-2DCs face critical difficulties, for example, dynamic asset scaling where responsibility determining assumes a pivotal part intending to such a test. Be that as it may, the profoundly powerful and different component of cloud jobs and conditions extensively raises the intricacy of arrangement instruments. AI calculations are likewise utilized by compartment arrangement frameworks for conduct demonstrating and forecast multi-dimensional execution measurements. Such experiences could additionally work on the nature of asset provisioning choices because of the changing responsibilities under complex conditions. This document provides a Novel Improved Fuzzy Fertilization based Clustering with optimized Pollination Flower Calculation (IFFCOPFC) for haze registering. At the prior stage, the asset credits are normalized constantly. Then, the fuzzy grouping with OPF is created for apportioning the assets and the versatility of asset looking has been limited. Finally, the introduced asset provisioning calculation dependent on advanced fuzzy grouping has been contrived. The presentation of the projected IFFCOPFC model has been tried utilizing a bunch of ‘2’ benchmarks Iris and Wine dataset. Our result guaranteed that the IFFCOPFC model has exposed pro-client outcomes over the thought about strategies by contributing the greatest client fulfillment and viable asset provisioning.
Keywords:	Cloud Computing, Clustering, Pollination Flower Calculation, Machine Learning, Provisioning Of Resource Utilization, Work Load Assistance.
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	OPTIMIZED UNCERTAINTY HANDLING FEATURE SELECTION USING MOTH FLAME BASED DBSCAN CLUSTERING FOR DIABETIC RELATED CHRONIC KIDNEY DISEASE
Author:	P. USHA, N. KAVITHA
Abstract:	In this work, to overcome the problem of voluminous dataset handling, improving the relevancy of features to detect the chronic kidney disease related diabetic patients, an uncertainty handling feature selection is constructed. The dataset is initially clustered by DBSCAN, the efficiency of clustering is achieved by two parameters they are epsilon and min-points. These are assigned in a random manner in standard DBSCAN, if the value is not assigned with any knowledge, then the clustering will not produce appropriate result. Hence, this presented work adapted the Moth Flame Optimization to search for optimized parameter values using its flight path behavior It discovers the best fittest value based on the flame position and those are fine tuned. The feature’s linear correlation among the class variable is identified using Pearson’s linear correlation based on it’s the features which doesn’t belong to any cluster are eliminated and based on the correlation score they are ranked. Thus, the significant feature subset is used for chronic kidney disease detection. The uncertainty about the outliers is well handled using the moth flame metaheuristic model by finding the best fittest and worst fittest features. The simulation results also proved that the accuracy obtained by the proposed Moth Flame based DBSCAN along with Pearson Correlation (MFDBSCAN-PC) produced highest accuracy compared to other standard feature selection models in chronic kidney disease detection with reduced error rate.
Keywords:	Chronic kidney disease, Density based Spatial Clustering along with Noise, Feature selection, Moth Flame, Metaheuristic model, Pearson Correlation
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	ROBUST FEATURE SELECTION WITH CHICKEN SWARM INTELLIGENCE IMPROVED MULTILAYER PERCEPTRON FOR EARLY DETECTION OF MENTAL ILLNESS DISORDER
Author:	S. SARANYA , N. KAVITHA
Abstract:	Mental illness is a kind of health condition highly impacts a person’s emotions, mind and their behavior. When mental disorder is not diagnosed at its earlier condition it results in depression, anxiety, schizophrenia, autism etc., are extremely widespread today. Recently many machine learning methods have been developed to help experts such as psychologists’ and psychiatrists’ for making decision regarding mental health of patient based on historical data of the patients. However conventional machine learning methods require optimal performance of feature engineering to obtained better performance and reduce computation complexity. This paper focuses on developing improved multilayer perceptron its objective is to handle over fitting, fine-tuning parameter values by integrating optimized feature selection and adopting mimetic meta heuristic model. LASSO (Least Absolute Shrinkage and Selection Operator) model-based feature selection is used for discovering best feature subset because it controls over fitting and prediction error with the help of ridge regression. In improved MLP instead of using trial and error based random parameter value assignment it inherits the Intelligence of Chicken Swam behavior. The best fittest value is assigned to the parameters of MLP (Multi-Layer Perceptron) to improve the accuracy rate of mental illness disorder detection. The simulation results are conducted on OSMI 2019 dataset and the outcome proved that CSI-MLP accomplished highest rate of accuracy in early detection of mental illness disorder compared to other conventional models.
Keywords:	Chicken Swarm Intelligence, LASSO Regression, Mental Illness Disorder, Multilayer Perceptron, Optimization Ridge Regression.
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	EARLY COVID-19 SPREAD PREDICTION USING SAMMON PROJECTIVE AND PERCEPTRON BOOSTING
Author:	KALAISELVI, VIJAYABHANU
Abstract:	Text mining is an Artificial Intelligence (AI) technology with Natural Language Processing (NLP) that converts unstructured text and databases into meaningful information. The prediction of disease at earlier stage is an essential and demanding task. Numerous existing methods were introduced for performing efficient disease prediction at an early stage. However, with the inception of new strains the accuracy and time with which the prediction was made was not found to be satisfactory. In order to address these issues, Nonlinear Sammon Projective and Perceptron Boosting (NSP-PB) method for early COVID-19 prediction is introduced. First, a Nonlinear Sammon Projective Pattern Selection is applied to the input patient files to select relevant patterns. Next, Emphasis Perceptron Boosting Classification is applied to the selected relevant pattern to categorize the COVID disease patient files. Here, Emphasis Boost Classifier merges weak learner result to form strong classifier output. This in turn helps to improve COVID disease prediction with higher accuracy and minimal time consumption. Experimental evaluation is carried out for factors such as prediction accuracy, prediction time and error rate with respect to number of patient files. The experimental result reveal that the proposed NSP-PB performs better with a 9% improvement in prediction accuracy, 39% reduction of error rate, and28% faster prediction time for Covid 19 spread prediction compared to existing works.
Keywords:	Artificial Intelligence, Natural Language Processing, World Health Organization, Nonlinear Sammon, Emphasis Perceptron, Boosting Classification.
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	IN-DEPTH REVIEW ON MACHINE LEARNING MODELS FOR LONG-TERM FLOOD FORECASTING
Author:	NAZLI MOHD KHAIRUDIN, NORWATI MUSTAPHA, TEH NORANIS MOHD ARIS, MASLINA ZOLKEPLI
Abstract:	Flood is a natural disaster that can cause damage in human life, infrastructure, and socioeconomics. Forecasting the flood is essential to provide sustainable flood risk management for the people. Long-term flood forecasting is very important to provide early knowledge and information for decision maker in minimizing the impact of flood. Early warning can also be disseminated to the potential flood victim and area while proper action can be triggered such as mitigation and evacuation process. The development of long-term flood forecasting model has growing recently with the adoption of machine learning models. It has spark interest among researchers to explore the ability of machine learning characteristics in providing accurate forecasting. Nevertheless, the machine learning models has shown uncertainty and instability in their forecast. The goal of this paper is to provide an understanding and in-depth review of machine learning models in long-term flood forecasting. It includes investigating machine learning models used for long-term flood forecasting and performing comparative assessment in the type of parameters, pre-processing methods and performance measurements used by the models. This review indicates that machine learning models has widely been used involving single and hybrid models for long-term flood forecasting. Various parameters or flood variables have been used as the predictors. The performance of the forecast has been found to be improved through the hybridization of the model. Evaluation of the machine learning models can be done through various performance measurement that prove the models can provide acceptable forecast. The outcome of this study will help future researchers by providing insights of the current progress in the use of machine learning in long-term flood forecasting.
Keywords:	Flood Forecasting, Machine Learning Models, Hybrid Model, Literature Review, Hydrological Forecasting
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	A SURVEY ON SECURITY THREATS IN THE INTERNET OF MEDICAL THINGS (IoMT)
Author:	WAEL TOGHUJ, NIDAL TURAB
Abstract:	Recent developments in the Internet of Things (IoT) have led to the development of the Internet of Medical Things (IoMT). Data collection, analysis, and transmission are key elements of IoMT tools that are revolutionizing healthcare delivery. Nevertheless, researchers and industry practitioners have several challenges with IoMT, particularly in the area of data security. Security breaches in the healthcare industry have downsides such as patient's personal information breach, possibly of death incidence. As a such, IoMT requires high standards of security. In this paper, we provide a review of IoMT security challenges and their possible mitigations. By surveying multiple scientific research papers, we aimed to guide researches on latest trends in medical device security provide mitigation of threats against various IoMT devices. In addition, the current weaknesses of various e-Health domains and demonstrates the results of recent works to overcome these obstacles is also explored.
Keywords:	Security, E-Health, IoMT, WBAN, IMD, EHR, EMR.
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	A NEAR OPTIMAL MULTICAST SCHEME FOR MOBILE AD-HOC NETWORKS – AN IMPLEMENTATION PERSPECTIVE
Author:	MR. P ANIL KUMAR, Prof. CH KAVITHA , Prof. M BABURAO , Dr. CH SURESHBABU
Abstract:	An ad-hoc mobile network is a collection of mobile nodes that are dynamically and arbitrarily located in such a manner that the interconnections between nodes are capable of changing on a continual basis. The primary goal of such an ad-hoc network routing protocol is correct and efficient route establishment between a pair of nodes so that messages may be delivered in a timely manner.Multicasting is to send single copy of a packet to all of those of clients that requested it, and not to send multiple copies of a packet over the same portion of the network, nor to send packets to clients who don’t want it.The Adhoc Multicast Routing Protocol (AMRoute) presents a novel approach for robust IP Multicast in mobile ad-hoc networks by exploiting user-multicast trees and dynamic logical cores. It creates a bi-directional, shared tree for data distribution using only group senders and receivers as tree nodes. Unicast tunnels are used as tree links to connect neighbors on the User-multicast tree. Thus AMRoute does not need to be supported by network nodes that are not interested/capable of multicast, and group State Cost is incurred only by group senders and receivers. Also, the use of tunnels as tree links implies that tree structure does not need to change even in case of a dynamic network topology, which reduces the signaling traffic and packet loss. Thus AMRoute does not need to track network dynamics; the underlying Unicast protocol is solely responsible for this function. AMRoute does not require a specific Unicast routing protocol; therefore, it can operate seamlessly over separate domains with different Unicast protocols. We have tried to overcome the transient loops in the mesh creation. Also we have implemented the Dynamic core migration technique by using a timer which periodically changes the current core node, so that the efficiency of the protocol can be improved.
Keywords:	Adhoc, Multicast, AMRoute, Multicast Tree
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	IMPROVING SECURITY IN A VIRTUAL LOCAL AREA NETWORK
Author:	OSAHON OKORO, EMMANUEL AZOM EDIM, OFEM AJAH OFEM, EYO ESSIEN, IWARA OFEM OBONO, BUKIE PAUL TAWO
Abstract:	Network services running in a native-VLAN infrastructure are vulnerable to security threats as a result of IP information exposure when connected with unmanageable switches. The aim of this study is to create VLANs with bounded network packets in order to reduce the security threats. The cisco hierarchical network design model was used to create separate VLANs, segment the network using IEEE 802.1q (dot1q) encapsulated sub-interfaces and assign ethernet ports (FastEthernet) to respective VLANs. Network packets and other data were captured and analyzed. The study found that on a flat-scale network infrastructure, broadcast packets destined for other vlans created in the switched network were visible to any terminal/node on the network infrastructure thereby exposing the classful IP information. In this study, system attack surface was reduced thereby protecting the VLAN-network. The study found that the 802.1q protocol added overhead to the vlan packets, reduced network efficiency and increased the network loss
Keywords:	IEEE 802.1q protocol, Network Performance, Network Security, VLAN Segmentation, Internet of Things(IoT)
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	DETERMINATION OF PROJECT VARIABLES USING FUZZY DECISION TREE FOR EFFORT ESTIMATIONS
Author:	DR G LALITHA KUMARI MRS. Y SUREKHA DR M SITARAM MR. N RAMESH BABU DR K KOTESWARA RAO
Abstract:	The success of a project depends on accurate effort estimations, managers are always under pressure to prepare accurate effort estimations, in COCOMO model to estimate the effort it requires project parameters. Identification of the type of the project and choosing the project parameters are very important aspect, accurate project parameters generation and estimations are coming from mature organizations others owing to lack of history databases. Estimations are based on lines of code (size of the project), functionality of the project. If the project estimations are based on size of the project the Constructive Cost Model plays vital role. This work explains an expert system that integrates conventional and Soft Computing techniques for dealing uncertainty i.e. virtual nodes generated in the decision paths at leaf node level and we will create an additional node, the main objective of the paper is how to generate suitable values for these nodes. For this purpose we propose the method to generate project parameters using fuzzy logic. In this work solid line indicates the project already done; dotted line indicates the project with uncertainties. After the project parameters are generated using Fuzzy Logic then effort estimations can be prepared.
Keywords:	Fuzzy Systems, Estimations, Virtual node, Expert systems.
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	A LIGHTWEIGHT AUTHENTICATION PROTOCOL FOR HEALTHCARE CLOUD COMPUTING
Author:	ZHANG XIAOWEI, AZIZOL BIN HJ ABDULLAH, MOHD TAUFIK ABDULLAH, ABDULLAH BIN MUHAMMED
Abstract:	The obstacle of realizing a widely applied healthcare cloud, which take personal healthcare records (PHRs) as core data, is the safety and privacy of PHRs have not been ensured when it was sharing by treatment members. Fortunately, a secure multi-owner sharing data (MONA) model which based on cloud computing solves secure sharing problem perfectly. However, the research and simulation of MONA have confirmed that the client user in MONA model bears heavy workload, which making it difficult to practically apply in health environment in which various resource restricted portable devices has being widely used by healthcare workers. Therefore, we modified the structure of MONA moderately and a protocol named password authentication key exchange based on verification element for lightweight client (LC-VE-PAKE) was proposed, which can transfer client-side workload securely to reduce its storage and computing cost to realized lightweight client-side. Experimental results show that the optimized model applying with LC-VE-PAKE protocol is a good solution for implementing healthcare cloud computing.
Keywords:	Personal Healthcare Records, Healthcare Cloud, Sharing Data Files, Lightweight Client-side, Password Authentication Key Exchange, Security
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	DESIGNING ASSESSMENT TOOLS FOR QUALITY ASSURANCE IN EDUCATION
Author:	A.A.AKBAYEVA, A.M.MUBARAKOV, B.K.ABDURAIMOVA, A .S.OMARBEKOVA, K.Zh.SADVAKASSOVA, A.Z.ALIMAGAMBETOVA, IGOR KOCHEGAROV
Abstract:	The issue of effective measurement of the learning process and students skills at the modern stage is increasingly being studied. It creates criteria by which the quality of higher education is assessed. Moreover, spreading of the newest world trends in education, which accompanied by dualization, digitalization and decentralization, etc. shows the need to expand the range of techniques and approaches to analyze the quality assurance and enhance the productivity of higher education in the provision of educational services, improving its plan and manage the process to ensure the efficiency of the training of future specialists. New approaches and tools are especially important today, given the increased demand for the training of highly qualified specialists, who are competitive in European labor markets. In this regard, this article is aimed at defining the main features of education quality assessment processes and diagnosis them; identifying the advantages of studying in an accredited university for students; describing the main sources of implementation of state accreditation processes; considering criteria and principles of using tools to assess the quality and efficiency of educational process; studying the level of education in universities. For defining methods of analysis, deduction and induction, comparison of different approaches and methods of education evaluation that will help to define the main directions and tools in monitoring research of activity quality are used. The paper presents comparative characteristics of methods for assessing the educational process quality; focus on qualitative differences in monitoring systems data; proposes an algorithm to introduce an assessment of the quality of education in higher education institutions; essences and contents of designing tools for the studied question; diagnoses university level, ratings and types of state control; justifies the objectives of the training organization; defines the directions for further scientific research on the development of using a variety of tools in the field of education quality evaluation. The materials of this article are of practical and theoretical value to students, future teachers, scientists, teachers, public administration that will be useful quality education on the basis of management decisions to improve the tools for measuring the quality of education and to improve educational programs and textbooks.
Keywords:	Assessment Of Education Quality, Expertise, Organization Of Educational Process, Monitoring, Accreditation, State Control.
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	EMPIRICAL INVESTIGATIONS TO OBJECT DETECTION IN VIDEO USING RESNET-AN IMPLEMENTATION METHOD
Author:	Y SUREKHA, DR K KOTESWARA RAO, DR G LALITHA KUMARI, N RAMESH BABU, Y. SAROJA
Abstract:	Real-time object detection and tracking is a vast, vibrant yet inconclusive and complex area of computer vision. Due to its increased utilization in surveillance, tracking system used in security and many others applications have propelled researchers to continuously devise more efficient and competitive algorithms. However, problems emerge in implementing object detection and tracking in real-time; such as tracking under dynamic environment, expensive computation to fit the real-time performance, or multi-camera multi-objects tracking make this task strenuously difficult. Though, many methods and techniques have been developed, but in this literature review we have discussed some famous and basic methods of object detection and tracking. In the end we have also given their general applications and results.
Keywords:	Image-Processing, Deep Learning, Object Detection, Object Recognition
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	LOCATION-BASED SERVICES USING WEB-GIS BY AN ANDROID PLATFORM TO IMPROVE STUDENTS’ NAVIGATION DURING COVID-19
Author:	M.E. ElAlAMI, IBRAHIM A. ElSHAFEI, HANAN E. ABDELKADER
Abstract:	Educational institutions seek to find optimal ways to provide educational services with the need for alternative solutions due to the requirements of the Covid-19 pandemic. The current study proposed a system that aims to identify the most critical new technologies built on Web-GIS for data analysis and associated information retrieval. It presents an algorithm to analyze the spatial information frequented by the user on the campus and determine the services that target the user based on predetermined spatial information. Provide a system based on integrating location-based services (LBS) using Web-GIS through the Android platform to help campus attendees take full advantage of services information granted to them in their whereabouts. The system employs the rule extraction algorithm to give a recommendations list (using extracted rules with confidence=100% and support= 0.7 to achieve high accuracy for the most points of interest (POI) based on the user's preferences. The proposed system evaluates the given recommendation and the application usage to produce satisfactory results. The average overall F-measure and accuracies are 94.8 % and 94.2, respectively.
Keywords:	Web-GIS, location-based services, android platform, points of interest (POI), location-based recommender systems
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	LOW-LIGHT FACE DETECTION USING DEEP LEARNING
Author:	RHEIVANT BOSCO THEOFFILUS, OWEN JACKSON DHARMADINATA, GEDE PUTRA KUSUMA
Abstract:	A low-light illumination is always a problem for some face detection models. However, we can solve this problem by applying a low-light image enhancement on the face detection model. This research evaluates four models of low-light image enhancement, which are MirNet, Adaptive Gamma Correction, RetinexNet, and Retinex. These four models are evaluated using LOL dataset by PSNR and SSIM. The evaluation results are applied on RetinaFace model as face detection and tested using Dark Face dataset. The final model on our proposed method is a combination of both Retinex and RetinaFace face detection models. The result of combined model between Retinex and RetinaFace are outperforms other combined methods. The combine method between low-light image enhancement Retinex model and face detection Retinaface model achieved a mean average precision (mAP) of 0.43%. While, without applying low-light image enhancement model on face detection model, RetinaFace only yielded 0.27% on mAP.
Keywords:	Face Detection, Low-light Image Enhancement, Deep Learning, RetinaFace Model, Retinex Model
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	ANOMALY DETECTION IN STREAM DATA PROCESSING IN REAL TIME
Author:	MAXIM DUNAEV, KONSTANTIN ZAYTSEV, ROMAN ELCHENKOV, DENIS SAVITSKY
Abstract:	The purpose of the present work is to study methods for solving problems of anomaly detection and prediction of time series values when processing streaming data in real-time in a network environment and their improvement. To solve this problem the authors propose a Real-Time K-Means modification with preliminary markup. The effectiveness of the modification is confirmed by comparing it with the frequently used Streaming K-Means from the Apache Spark Mllib library. To solve the problem of predicting time series when processing streaming data in real-time, the authors propose a modification of the autoregression model with a given AR order by adding the inheritance function of the previous values of the time series to it. The results of comparative experiments of the proposed Real-Time AR modification with classical AR confirmed the effectiveness of the modification, which is especially evident in the presence of anomalies in the behavior of the time series. The proposed algorithm modifications allow not only parallelizing calculations using the deferred computing paradigm but also configuring the model fleetingly in the Apache Spark ecosystem. To conduct experiments with algorithms, a dataset was built – a data slice from 1,000 measurements of the Apache Kafka server metrics log with one topic, two producers, and one consumer. Anomalous fragments were artificially added to the dataset, differing by a large number of messages per second and/or message size. The values of the original dataset were normalized and shifted to the average value of the training fetch. Moreover, static and highly correlated metrics were eliminated. The results of the application of the developed algorithms in solving the problems of detecting and predicting the values of time series have shown that even the presence of behavior anomalies does not distort predictions significantly.
Keywords:	Logs, Technological Platform, Machine Learning, Deep Learning, Apache Software Foundation.
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	FITNESS PROPAGATION AND SELECTION OF SEED NODE FOR MESSAGE DIFFUSION IN SOCIAL NETWORKS
Author:	D.PUNITHA , Dr.S.PRASANNA DEVI
Abstract:	In the social network, viral marketing, influence maximization is attained out by identifying influenced node in a network and attracting them by allocating allowances to propagate the message to their neighbors. The issue is this viral marketing is whether the message content propagated to users increases the utility of the users. Due to the practical importance of this problem to attain a win-win strategy, the problem has been analyzed in different works, and methodology has been proposed on how to attain Fitness in identifying seed node for message diffusion in social networks.
Keywords:	Viral Marketing, Utility Maximization, Message Propagation.
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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Title:	LOST WON OPPORTUNITY PREDICTION IN SALES PIPELINE B2B CRM USING MACHINE LEARNING
Author:	AGUSTIAN MAULANA, TOGAR ALAM NAPITUPULU
Abstract:	Sales pipeline management is needed by companies to manage opportunities. The B2B sales model business has several stages and a long time before dealing with customers. Even though they have used sales pipeline management, the obstacle faced by the sales team and the company is that it is still difficult to analyze data opportunities that have the potential to be lost or won in the early stages. Undetected potential lost or won opportunities at the outset can lead to a risk that many opportunities are lost and the company cannot estimate the planning of resource requirements if the opportunity won. Optimization of B2B CRM data is one of the processes carried out by companies to analyze potentially lost or won. B2B CRM data can be used as supporting customer portfolio data needed to help the sales team analyze lost or won opportunities. Management of historical sales data and customer portfolios using machine learning can generate predictive data that companies need more quickly. Predictive data is needed to help sales teams and companies analyze probability lost or won opportunities more quickly. The sales pipeline management system in B2B CRM assisted by machine learning with the CRISP-DM methodology can identify probability lost or won opportunities in the early stages. So that the sales team has a better chance of converting a lot of opportunities into won.
Keywords:	Opportunity, Sales Pipeline, B2B CRM, CRISP-DM, Machine Learning
Source:	Journal of Theoretical and Applied Information Technology 31^st May 2022 -- Vol. 100. No. 10 -- 2022
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