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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
November 2022 | Vol. 100 No.22

Title:	LONG SHORT-TERM MEMORY WITH GATED RECURRENT UNIT BASED ON HYPERPARAMETER SETTINGS AND HYBRIDIZATION FOR REFERENCE EVAPOTRANSPIRATION RATE PREDICTION
Author:	MUHAMMAD ZAHID BIN HILMI, TONI ANWAR, DAYANG ROHAYA BINTI AWANG RAMBLI
Abstract:	The evapotranspiration rate can be used to estimate water loss. However, there are 31 equations available to be chosen, and randomly choosing the equation might not project the actual results. This is very crucial because, without the equation, we cannot proceed with the parameter selection. These findings can justify the parameter chosen for the prediction model development. Long Short-Term Memory (LSTM) is known for its ability to retain memory better than Recurrent Neural Network (RNN). This is due to LSTM architecture, where the memory cell is available to store memory for long-term dependency. RNN suffers from a vanishing gradient that can affect the prediction, whether in accuracy, precision, etc. LSTM was developed specifically to address the issue of RNN. Even though LSTM is better overall, it can be further enhanced. The proposed method is to adjust the Hyperparameter Settings and combine them with Hybridization. Our findings indicate that the prediction accuracy improved significantly. The hybrid model chosen was Gated Recurrent Unit (GRU), combined with LSTM and Hyperparameter Settings, resulting in the best and highest prediction accuracy compared to the LSTM Vanilla and LSTM with Hyperparameter Settings. LSTM Hyperparameter Settings and Hybridization dominate the top three scores. The scoring stretched until 11th place before the LSTM Hyperparameter Settings score came in. The top three scores were for Case 99, Case 36, and Case 90 with 0.0626, 0.06446, 0.06606 MAE, 0.00667, 0.00706, 0.00759 MSE, 0.0817, 0.084, 0.0871 RMSE and 0.99261, 0.99219, 0.9916 R², respectively. As for the LSTM Hyperparameter Settings score, 0.0712 MAE, 0.00861 MSE, 0.09278 RMSE, and 0.99047 R².
Keywords:	Hyperparameter, Hybridization, Deep learning, LSTM, Evapotranspiration
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	HYBRID CNN-LSTM MODEL: RAINFALL ANALYSIS AND PREDICTION FOR KARNATAKA REGION
Author:	SUBIA SALMA, ASHWITHA A
Abstract:	Purpose Rainfall prediction is necessary for harvesting crops throughout the year, agriculture yields crops based on the farmer’s ability to work in a specific field for particular crop fertilization. This idea was not alone necessary to predict the crop’s yield. Seed firms regularly screen how efficiently plant varieties grow in a particular setup. Thirdly to predict agricultural produce is critical to solving emerging concerns for food security in the phase of global climatical changes. Accurate prediction of forecasts assists farmers so they can take more economical and cost-management decisions. They also enhance the prevention of famine. This results in protecting farmers’ efficiency and productivity to reduce the risks associated with environmental gain. Design/Methodology/Approach- This paper proposes a Hybrid machine learning model for efficient prediction of rainfall, this makes a solution that is used for encoding them to create solutions abruptly. A major part of this work here is focused on generating a solution for fitness to meet the highest accuracy. This algorithm works efficiently for the given input data. This algorithm tries to meet the necessary requirements until an optimal analysis is carried out to contribute to maximum accuracy for rainfall prediction. Findings- The performance of our proposed Hybrid Algorithm is compared with existing algorithms such as linear regression, logistic regression, and KNN. The proposed Hybrid Algorithm convolutional neural networks with LSTM (Long short-term memory model) with First-order optimization Algorithm which works along the gradient-descent algorithm based on different metrics like Accuracy, Sensitivity, Specificity, F-score, and found maximum performance. Originality/value- This paper proposes a Hybrid algorithm CNN_LSTM along with this a first-order optimization algorithm that functions based on the gradient descent method. The results found using the proposed algorithm are plots. A comparative analysis is carried out using various results obtained to achieve high performance to solve different constraints.
Keywords:	Crop Fertilization, Convolutional Neural Network (CNN), Long Short-Term Memory Model (LSTM), First-Order Optimization Algorithm.
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	SOCIAL MEDIA CONTRIBUTION AS A POLITICAL COMMUNICATION STRATEGY TOOL IN FORMING THE POLITICAL IMAGE OF PRESIDENTIAL CANDIDATES IN THE 2019 PILPRES IN INDONESIA
Author:	PRAYOGO PUJO HARYONO, IRMAWAN RAHYADI, LA MANI3, YESSY MARGARETHA SIAGIAN, ELSYAFF ALFATH MELENIA
Abstract:	The utilization of the use of communication media at this time has grown so rapidly along with advances in communication technology that is faced with many choices to be able to convey/access information. The use of these media can be used for very high mobility of advertising creativity and information with all the attributes and supporting instruments. The purpose of this study is to examine the role of social media as a political communication strategy to shape the image of politicians. This research is descriptive qualitative, data collection techniques through interviews with politicians in the Jakarta area and documentation including reference books, journals, the internet, and other sources related to this research. The data analysis technique in this study used 3 stages, namely data reduction, data presentation, and conclusion drawing. The results of this study on the role of social media can be used to attract attention to build public trust. The way of political communication through social media can be packaged interestingly and creatively so that it influences the public to vote for it. In addition, the role of social media can be used as a mapping of public responses to political images. In the formation of a political image, politicians try to create social stability and meet public demands and strive to create and maintain political actions that can generate a satisfactory image, so that public opinion support can be obtained from the people as political communication audiences.
Keywords:	Social Media, Political Communication, and Politician Image
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	AN IMPROVED SMART INDOOR PLANT IRRIGATION SYSTEM BASED ON IIOT AND BLYNK APPLICATION
Author:	SAIF ALI MOUSA
Abstract:	Increasing technological innovations have been applied to a variety of daily life activities, including agriculture. Plant care is improved by the use of modern technological applications, such as a systematic irrigation approach, which helps to prolong plant life. Various studies have been presented to address the challenges of indoor plant care. However, there are currently no ideal solutions for caring for and prolonging the life of plants. Numerous problems have been reported relevant to the subject of indoor plant care, one of which is the intense thirst for plant soil, which causes a variety of problems, including cutting the roots of plants, especially when the soil is covered with clay, reducing the plant's capacity to conduct the basic processes required to preserve its life. Finally, if the thirst continues, the plant withers and dies. Furthermore, overwatering suffocates the roots, halts respiration, and eventually kills the plants. Unwanted soil fungus also thrives in constantly wet conditions. As a result, the goal of this study is to design and create an automated irrigation system based on the Industrial Internet of Things (IIoT) that can irrigate plants. The suggested system is divided into three stages. First, test the soil sensor's operation. Second, use the AskSensor IoT platform to monitor soil moisture. Third, send irrigation method information via Blynk apps. The findings of this paper demonstrated that the proposed system is superior to other related systems in several ways.
Keywords:	Industrial Internet Of Things, Smart Irrigation System, Asksensor, Blynk Apps, Indoor Plant Care.
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	FOG COMPUTING AND SIMILAR DISTRIBUTED COMPUTING PARADIGMS: A REVIEW
Author:	RUCHIKA, Dr. RAJENDER SINGH CHHILLAR
Abstract:	Nowadays, Users are increasingly adopting Internet of Things-based gadgets, resulting in significant data creation. All IoT devices, whether they are appliances, sensors, actuators, or other gadgets, continuously generate data that is processed in the cloud. The ever-increasing volume of data can cause a variety of cloud-based bottlenecks, such as latency and bandwidth concerns. Cloud being a central computing unit suffer from bandwidth issues due an expanding number of IoT (Internet of Things) devices. Fog computing is a novel paradigm to address these challenges. It provides the storage, networking and computation facility near the data sources and helps in spanning the aperture between cloud and the end devices. This paper provides an overview of cloud computing and fog computing paradigms, some basic differences between them, technologies similar to them and a basic idea about different applications and tools used for fog assisted cloud architecture. In the end of this paper, by finding research gaps from the literature of Fog assisted cloud architecture, we will address some ongoing research issues and challenges in this field.
Keywords:	Cloud Computing, Internet of Things, Edge computing, Fog Computing, Research Challenges
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	OPTIMAL DEEP FEATURE MODEL SELECTION WITH WEIGHTED MULTI-SIMILARITY-BASED CONTENT-BASED IMAGE RETRIEVAL FRAMEWORK
Author:	G.KRISHNA RAJU, P.PADMANABHAM, A.GOVARDHAN
Abstract:	The scheme of Content-Based Image Retrieval (CBIR) is developed for the process of retrieving of images on the basis of the visual elements defining the image content. Even though, it has certain complications of huge calculation problems such as scoring of the image similarity and feature extraction. The conventional feature extraction models only aim toward the high or low-level features and utilize few manmade features to minimize these gaps. It is required to establish a framework for feature extraction to minimize the gap without utilizing the manmade features from joining low and high-level features. The deep learning is considered as a very powerful tool for feature indication that can represent high and low-level information entirely. In this paper, new optimal deep learning model-based feature extractions for CBIR model are performed for retrieving the accurate images. In the training phase, diverse images are gathered from the standard database, which is used into Optimal Deep Feature Selection Model (ODFSM)by the deep learning models with VGG16, VGG19, Inception, Xception, Resnet50, Resnet101, Resnet 152, and Densenet for extracting the features. Here, the optimization in deep learning models takes place with the help of hybrid optimization algorithm known as Probability-based Coyote-Forest Optimization Algorithm (P-CFOA) for getting the efficient features. In the testing phase, the query images are given into optimal deep feature selection model for extracting the features of the query image. Then, the weighted multi-similarity is performed between the extracted features of database images and query image, in which the weights are optimized using the same P-CFOA. The database images with less weighted multi-similarity when correlated with extracted features of query image are retrieved finally. The simulation analyses are made to demonstrate the better efficiency of image retrieval through the optimal deep feature extractors.
Keywords:	Content Based Image Retrieval,Multi-Similarity Function, VGG16, VGG19, Inception, Xception, Resnet, Densenet, Coyote-Forest Optimization, Optimal Deep feature Selection.
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	THE ROLES OF IT SKILL AND GRIT IN ACADEMIC ACHIEVEMENTS MEDIATED BY ONLINE LEARNING AND COLLABORATIVE LEARNING
Author:	GAT, EDI ABDURACHMAN, DEZIE LEONARDA WARGANEGARA, WIBOWO KOSASIH
Abstract:	It is essential for a university to recognize factors which influence learners' academic achievement. The aim of this inquiry to analyze the influence of technology information skills to online learning, the indirect influence of technology information skills to learns' online learning academic achievment, the indirect influence of technology infmation skills to learns' academic achievment through collabortive learning, the influence of collabortive learning through learns' academic achievment, influence of grit to collabortive learning, grit effect to learns' academic achievment and grit effect to online learning. The data collection method for the quantitative calculation is conducted through one snapshot cross-sectional survey or only conducted 1 (one) time at a certain time. The survey begins in April 2020 until June 2020. Proportional stratified random sampling method is used for sampel collecting, and 394 samples are situated proportionally. This study sums up that 3 hypothesis do not influence either direct or indirect, while 5 others hypothesis proven to be effective influence. Two things as the research results, namely theorities and managerial implications. Though the data source are from STMIK through Indonesia, this inquiry result can be implemented to other University in Indonesia.
Keywords:	IT Skill, Grit, Academic Achievements, Online Learning, Collaborative Learning.
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	PRACTICAL MQTT-SN EDGE GATEWAY INTEGRATION WITH CLOUD SERVER AND END TO END SENSOR DATA TRANSMISSION FOR IOT APPLICATIONS
Author:	M.OBULA REDDY, J.B. SEVENTLINE
Abstract:	In traditional two tier MQTT IoT Network, Sensors and related IoT platform directly connected to the Cloud MQTT Server. In this architecture sending sensor data to the cloud MQTT-Server is not efficient due to frequently sending of data to the cloud server, in the cloud storage server memory related issues will occur due to large volume of sensor data. To reduce the data volume traffic local edge MQTT-SN gateway incorporated into the network to filter out some of the sensor data before sending it to the Cloud MQTT Server. MQTT-SN is MQ Telemetry Transport Interface protocol is used in the sensor devices and local edge gateway due to low power and low message overhead compared to other protocols. In this paper we addressed practical MQTT-SN gateway integration with private secure Cloud MQTT Server, Sensor data transmission to the Internet of Thing (IOT)application through the EDGE MQTT-SN gateway, MQTT Server. MQTT-SN gateway to sensor device, Gateway to MQTT-Server and MQTT-Server to IoT Application Signaling analysis. Real time sensor data transmission to the IoT application through the Edge gateway and MQTT-Server. Finally, MQTT based IoT application received publish message from the MQTT serve, decode the publish msg and stored sensor data in the SQL Lite database for further data analytics.
Keywords:	IOT, MQTT-SN, CLOUD MQTT, WIRESHARK
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	THE INFLUENCE OF IT INVESTMENT, IT INNOVATION, FINANCIAL INNOVATION ON SUSTAINABLE COMPETITIVE ADVANTAGE THROUGH BUSINESS TRANSFORMATION MODERATED BY IT IMPLEMENTATION (AN EMPIRICAL STUDY ON RURAL BANKING)
Author:	GUSTI SYARIFUDIN , HARJANTO PRABOWO , MOHAMMAD HAMSAL , PANTRI HERIYATI
Abstract:	This study has several research objectives, especially to analyze several variables including the influence of the IT Investment variable on the IT Innovation variable, the influence of the IT Innovation variable on the Financial Innovation variable, the influence of the Financial Innovation variable on the Business Transformation variable, the influence of the IT Innovation variable on the Business Transformation variable, the effect of the IT Investment variable on the Business Transformation variable, the influence of the IT Investment variable on the Business Transformation variable is strengthened by IT Implementation and the influence of the Business Transformation variable on the Sustainable Competitive Advantage variable. The quantitative research approach used is supported by analytical techniques using the SEM-AMOS method. In research-based on multivariate analysis, the number of samples specified is at least 10 times the number of variables or 60 samples of RB with capital above IDR 10 billion. Data collection was carried out by means of a survey by sending an online questionnaire in the period May 2020 to July 2020. This study found that most of the variables were proven to have a significant effect, including the IT Investment variable which had a significant effect on IT Innovation, the IT Innovation variable had a significant effect on the financial variable. Innovation, the Financial Innovation variable has a significant effect on Business Transformation, the IT Innovation variable has a significant effect on Business Transformation with a negative value, the IT Investment variable has a significant effect with a negative value on the Business Transformation variable, the IT Implementation variable has a significant effect on Business Transformation, and the Business Transformation variable has an effect significant to the Sustainable Competitive Advantage. This study has limitations on the number of samples used. In addition, this research also contributes theoretically and practically, especially to understanding the phenomenon of information technology in the rural banking business.
Keywords:	IT Investment, IT Innovation, Financial Innovation, Sustainable Competitive Advantage, Business Transformation, IT Implementation, Rural Bank
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	SWINE FLU HOTSPOT PREDICTION IN REGIONS BASED ON DYNAMIC HOTSPOT DETECTION ALGORITHM
Author:	P.NAGARAJ, DR.A.V.KRISHNA PRASAD, Dr.M.VENKATA DASS, KALLEPALLI ROHIT KUMAR
Abstract:	The purpose of this work is to provide a strategy for locating outbreaks of swine flu by making use of geographical data. Following their generation by the Dynamic Boundary Location Algorithm (DBLA), the localization coordinates are then sent on to the suggested KDH approach. Identifying swine flu cases, locating clusters of cases, and pinpointing their geographical locations using geographic information system coordinates are all accomplished with the help of the Dynamic Boundary Localization approach. A severity index is assigned to each illness, and data on its location is also recorded. The technique makes use of a Dynamic Hotspot detection algorithm (DHDA) which gives max number of patients death i.e., hotspot and Gaussian mixture model in conjunction with the Severity index. Data collected globally using Moran's I are what's utilized to calculate the severity index. The Gi instrument is what we use to determine how accurate the predicted hotspots are. This technique makes use of statistical analysis to provide a more accurate estimation of the locations in which swine flu is most common.
Keywords:	Swine Flu, Gaussian Mixture Model, Machine Learning, Global Moron, Gi, Statistical Evaluation
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	MODELLING THE RELATIONSHIP BETWEEN PRIVACY AND LOSS OF DATA UTILITY WHEN ANALYSING THE DATA
Author:	CHOKRI NOUAR, AHMED DRISSI
Abstract:	Big data has become a primary resource for decision-makers. Data analysis makes it possible to extract new data which can be very useful. Meanwhile, the data owner has to protect the privacy of people and establishments. Therefore, private information can neither be all shared nor extractable from any analysis. That is why the owner of the data must negotiate with the explorer according to the objectives of each of the two. In many cases objectives can contradict each other. The challenge is to make a compromise between protecting privacy and retaining the usefulness of the data processed. This article strives to propose a model of anonymization based on the theory of fuzzy logic. In the process of anonymization, we assign, in the first step, a degree of identification and sensitivity to each data, then the qualitative data is encoded before we finally anonymize depending on the type of data. Our model has been proved to be useful and reliable for both the owner and the explorer of the data. Therefore, it helps protect the data and ensures its privacy.
Keywords:	Data analysis security, Privacy, Fuzzy logic, Yager, Anonymization.
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	DESIGN AND PERFORMANCE OF DUAL AXIS SOLAR TRACKER BASED ON LIGHT SENSORS TO MAXIMIZE THE PHOTOVOLTAIC ENERGY OUTPUT
Author:	ASNIL, KRISMADINATA1, IRMA HUSNAINI1, ERITA ASTRID
Abstract:	The effectiveness of solar energy absorption has become an issue in photovoltaic (PV) performance. In addition to environmental considerations such as cloud shadows, the ever-changing position of the sun is a determining factor in maximizing the output of electrical energy from PV systems. This research proposes a design of a dual axis solar tracker to increase energy production and discusses its function to track the sun's location in the effective way. To verify the effectiveness of the proposed system, a test is conducted by comparing the system’s performance with a system that does not employ a solar position tracking system know as a fixed system.The test is carried out under several conditions, including on a sunny day, a cloudy day, and a cloudy day with intermittent rain. According to the test results, the solar position tracking system with dual axes is more efficient at generating electrical energy in PV systems. The electrical energy generated is 18.56% higher than that of a fixed system
Keywords:	Dual Axis Solar Tracker, Photovoltaic, Energy Production, Solar Tracker
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	SRABDE: SECURE ROUTING ALGORITHM BASED ON DIFFERENTIAL EVOLUTION FOR MOBILE ADHOC NETWORKS
Author:	K.VIMALA, DR. D. MARUTHANAYAGAM
Abstract:	Mobile ad-hoc network is an infrastructure-less spontaneously formed wireless environment that is deployed without any centralized unit. In this type of network, all nodes are assumed to be trustworthy but in the real scenario, a few can be malicious and, therefore, secure and reliable route for data transmission is always a matter of question. A new protocol called Secure Routing Algorithm Based on Differential Evolution (SRABDE) is proposed for Mobile Adhoc networks with effective fitness function. Differential Evolution (DE) is an evolutionary computational method inspired by the biological processes of evolution and mutation used to minimize routing overload in terms of battery power, node reliability and stability. The objective of this algorithm is to find the most suitable path for data transmission taking into account three influencing factors: reliability, battery power and node stability, then to resolve the malicious performing nodes such as black hole and gray hole attacks. The component indices for the effective fitness function involved are trust factor, residual battery power and node stability for the purpose of selecting the most promising routes through the proposed SRABDE algorithm. The trust is developed to predict the nature of the node, whether malicious or not; while the stability of the nodes ensures longer stable routes.The simulation is carried out in various scenarios to evaluate the performance of the differential evolutionary secure routing algorithm with packet delivery ratio, average end-to-end delay, routing overhead, energy consumption, detection accuracy, and throughput. Results are compared with three existing algorithms Intruder Free Route Discovery (IFRD), Trust Path Ant Colony Optimization (TPACO), and DE algorithm-based ad hoc on-demand multi-path distance vector (DE_AOMDV).
Keywords:	MANET, Routing Protocol, Differential Evolution, Trust, Node, Path, Security, Attacks,
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	THE RESISTANCE OF SOCIAL MEDIA ACTIVISTS IN THE STRUGGLE FOR FREEDOM OF EXPRESSION ON INSTAGRAM TOWARDS ISRAELS OCCUPATION IN PALESTINE
Author:	NADYA SISTHA SUPRIYANTO, MAUREN INTAN PRATIWI, FITHROTUL KAMILAH, Z. HIDAYAT
Abstract:	The ongoing occupation and injustice carried out by Israel, which has been experienced by the Palestinians for 74 years, has resulted in numerous resistance efforts. Aware of this, the sense of solidarity of individuals and activists across the globe has increased. This study aims to analyze the opinions, experiences, and efforts of social media activists, vocal individuals about the Palestinian cause on Instagram, and an expert on Middle East regarding freedom of expression and human rights. Moreover, this study uses a qualitative approach with a phenomenological approach along with primary data (in-depth interview) and secondary data. Given the large number individuals from the United States of America, Canada, Jordan, Indonesia, Palestine, United Arab Emirates, and Germany. The findings of this research contribute to the understanding of the Palestinian cause through the perspective of communication sciences, specifically on social media in terms of their resistance efforts or struggle on Instagram. This study found that, in spite of Instagram being able to facilitate a platform to voice out about Palestine, activists and individuals are being curtailed and censored. Based on these findings, further research can be continued to analyze other aspects of the Palestinian struggle, such as the efforts of the authorities through diplomatic aspects.
Keywords:	Social Media Activists, Resistance, Freedom of Expression, Human Rights, Instagram.
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	IMPLEMENTING CONTINUOUS DELIVERY IN A FINTECH COMPANY: A CASE STUDY
Author:	LEWIS, RIYANTO JAYADI
Abstract:	Various experts promote Continuous Delivery (CD) due to this principle's benefits. Even so, implementing CD is difficult to do correctly. This study evaluates how a company adopts and implements the CD principle, the challenges during implementation, and its impact on a company represented as PT XYZ. PT XYZ is a tech company that has adopted CD for several months as an essential principle of the company’s development. We conducted a case study on PT XYZ through observation, interviews, and documentation review, which was then analyzed using thematic analysis and descriptive statistical analysis based on data collected by the company. The results showed that PT XYZ implemented several points of CD technical capability well. However, PT XYZ faced several challenges when transforming its development process using the CD principle: no clear CD implementation framework defined by practitioners, the lack of measurement, and the need to balance the application of CD capabilities. Nevertheless, the application of the CD principle shows a positive impact on PT XYZ psychologically for the IT team and affects the organization's performance.
Keywords:	Continuous Delivery, Continuous Integration, Deployment, Pipeline, Software, Development, DevOps
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	FACIAL EMOTION RECOGNITION USINGHYBRID PSO AND GA OPTIMIZED CONVOLUTIONAL NEURAL NETWORK
Author:	KALAIARASI.P, ESTHER RANI.P
Abstract:	An emerging and interesting research field which focuses on the enhancement of developing an automatic emotion recognition system is Facial Expression Recognition. This technology creates a major impact on various applications such as safety, healthcare, gaming and education. Researchers in this field are working to develop methods that can extract and interpret facial expressions. Deep learning plays a vital role in the field of computer vision technology and Convolutional Neural Network (CNN) is the most significant part in the deep learning. There are few drawbacks in the training algorithm, Stochastic Gradient Descent (SGD) used in CNN. This proposed work overcomes the drawbacks of SGD by introducing an alternative algorithm based on Genetic Algorithm and Particle Swarm Optimization to improve the performance of CNN. This paper presents the novel PSO-GA algorithm and addresses the benefits of PSO-GA algorithm which challenges and outperforms other existing methods and achieves robust and state-of-the art results in various challenging datasets. This hybrid PSO-GA algorithm makes the CNN to identify the facial expressions and emotions very effectively when compared to other methodology.
Keywords:	Facial, Emotion Recognition, PSO, GA, NN
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	AUTOMATED HATE SPEECH CLASSIFICATION USING EMOTION ANALYSIS IN SOCIAL MEDIA USER GENERATED TEXTS
Author:	AIGERIM TOKTAROVA, ZHALGASBEK IZTAEV, PERNEKUL KOZHABEKOVA, NABAT SUIEUOVA, ROBERT OWINO OPONDO, MUKHTAR KERIMBEKOV, ZHANNA ZHUNISBEKOVA
Abstract:	As a result of the ease with which any viewpoint may be posted on social networking sites, online hate speech has become more widespread in recent years. This trend is mostly attributable to the fast growth of mobile computers and the Internet. Studies that were done in the past demonstrate that being exposed to hate speech online has substantial implications in real life for historically disadvantaged populations. As a result, there has been a lot of interest in research on the automatic identification of hate speech. Nevertheless, there has not been a lot of research done on how social networking sites might help identify communities that are prone to hate crimes. It is possible for hate speech to have an effect on any demographic group; however, certain groups are more susceptible to the effects of hate speech than others. For example, it is difficult for racial or ethnic groups whose languages have limited computing resources to automatically gather and evaluate online generated texts. This is to say nothing of the difficulty of automatically detecting hate speech on social networking sites. In this article, we present a method for the identification of hate speech posted on social networking sites, applying artificial intelligence methods in text processing and natural language processing. In order to detect, hate speech on social media, firstly, we collect data by using different keywords. Secondly, we apply machine learning algorithms to classify texts into several categories. Thirdly, we evaluate the proposed approaches and assess the result of hate speech detection problem in training and test sets.
Keywords:	Artificial Intelligence, Social Networks, Hate Speech, Classification, Detection.
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	EVALUATION METHOD OF THE PHYSICAL COMPATIBILITY OF EQUIPMENT IN A HYBRID INFORMATION TRANSMISSION NETWORK
Author:	PAVLO ANAKHOV, VIKTORIIA ZHEBKA, ALINA TUSHYCH, VLADISLAV KRAVCHENKO, NAZARII BLAZHENNYI, PAVLO SKLADANNYI, VOLODYMYR SOKOLOV
Abstract:	The article considers the physical compatibility of equipment (PCE) in a hybrid network of information transmissions. The use of information technology is justified in the scheme of the protection measures applied against disturbances. A method of equipment compatibility research has been developed to describe communication channels in network nodes unambiguously. A correlation matrix of the signals of different physical nature, which are used for information transmission via media, has been developed. The corresponding examples have been given. The dependence of the hybrid network parameter values on the physical nature of the signal and the transmission medium has been revealed. It has been shown that the distribution of the telecommunication network resources into channels with signals of different physical nature in different media increases the physical compatibility of the equipment. A method for assessing the PCE in a hybrid information transmission network is proposed. The method proposed in the paper is based on the distribution of resources of a heterogeneous telecommunication network into channels with signals of different physical natures in different transmission environments, which contributes to increasing the compatibility of communication channels. A device has been developed to implement multi-channel information transmission in a hybrid network, allowing optimizing recommendations for increasing the PCE in a hybrid information transmission network.
Keywords:	Interference, Physical Compatibility, Spurious Emission, Parasitic Channel Weakening
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	PERFORMANCE ANALYSIS ON THE RELIABILITY ATTRIBUTES OF NHPP SOFTWARE RELIABILITY MODEL APPLYING EXPONENTIAL AND INVERSE-EXPONENTIAL LIFETIME DISTRIBUTION
Author:	TAE JIN YANG
Abstract:	In this study, after applying the Exponential-exponential and Inverse-exponential distributions to the NHPP software reliability model, the reliability performance of the applied model was newly compared and evaluated with the Exponential-basic model. For this study, the failure time data collected during software system operation was used, and the parameter estimation was solved by utilizing the maximum likelihood estimation (MLE) method. As a result, first, in the analysis of the performance pattern using the mean value function, the Exponential-exponential model with the smallest error in predicting the true value showed efficient performance. Second, in the evaluation of the intensity function, the failure occurring rate of the Exponential-exponential model showed the smallest value at the initial stage and continued to decrease with the lapse of the failure time, so it was evaluated as an efficient model. Third, as a result of analyzing the future reliability performance by applying the mission time, the Exponential-exponential model showed stable high performance, but the Inverse-exponential and the Exponential-basic model showed inefficiency in which the performance continued to decrease. In conclusion, it was found that the Exponential-exponential model has the best performance among the proposed models. Through this study, the reliability performance of distributions with exponential-type attributes were newly identified, and basic design data that could be utilized in the development process could be presented to software operators.
Keywords:	Exponential-basic, Exponential Distribution, Exponential-exponential, Inverse-exponential, NHPP Model, Reliability Performance.
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	AN AUTOMATED TESTING TOOL BASED ON GRAPHICAL USER INTERFACE WITH EXPLORATORY BEHAVIOURAL ANALYSIS
Author:	MS.K.JAGANESHWARI , DR.S.DJODILATCHOUMY
Abstract:	Web-based applications have complex mechanisms, and it is challenging to perform any test. Computerization examination applies automation tools to decrease individual interference and repeatable assignments. In this article, we have designed and implemented an automation testing framework for web applications. The Selenium WebDriver tool was used to execute this innovative automated testing model. With this structure, testers can match the widgets with pre-trained dataset by taking screenshots of the web pages and by clicking automatically using PyAutoGUI. The screenshot property of the framework is useful to creators for evaluating their design of the web page. We introduced a novel methodology for widget detection, widget classification and testing coverage using machine learning and image processing concept. In this approach, we can give the URL as input, so, the GUI widget images can be quickly captured from the server and do not require large storage repositories to be used as training samples. Widgets of GUI images are detected from the pre-trained datasets by applying BLOB text detection. After identifying the widgets, they are classified into domain-specific categories, for example, labels, buttons, input boxes, check boxes, and links). Finally, it is evaluated to achieve a mean GUI component categorization of superior precision. Previous works have mostly used Java GUIs, Python GUIs, and Visual Basic GUIs to distinguish the types of widgets, but in our work, we observed the types of widgets using Image Processing. From this automated testing implementation on web applications, we have achieved 98.4%. of test coverage accuracy.
Keywords:	Automated Testing, GUI, DNN, Computer Vision, Image processing
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	BANDWIDTH REALLOCATION APPROACH FOR OPTIMIZED DATA SLICES DISTRIBUTION OVER MULTI CLOUD STORAGE
Author:	SAIF SAAD ALNUAIMI, ELANKOVAN A SUNDARARAJAN, AND ABDUL HADI ABD RAHMAN
Abstract:	This paper sheds light on cloud computing and the importance of storing data in multiple clouds. Here it becomes an obvious necessity as when large data is transferred to a single cloud; transmission time becomes excessive. A large data distribution on more than one cloud is better for solving a time-consuming problem. However, some factors affect the transmission process as the speed of data transmission varies from one cloud to another and the size of the Internet data packet provided by the service provider. This paper proposed an optimization method to improve the transmission process and optimally redistribute the data packet to prevent data bottlenecks during transmission. It was evident from the research results that the proposed optimization method for distributing data over several clouds with optimal bandwidth reallocation is 13.58% better when compared with the method of equal distribution of data over several clouds without taking into account the optimal bandwidth reallocation.
Keywords:	Bandwidth Allocation, Transmission Optimization, Multi-Cloud Storage
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	BRAIN TUMOR CLASSIFICATION BASED ON 3D AUTO ENCODING TECHNIQUE
Author:	G V SIVANARAYANA, Dr. K NAVEEN KUMAR
Abstract:	The brain is an essential part of the entire nervous system because it can control and regulate body parts. The International Agency for Research on Cancer (IARC) estimates that 76% of deaths from brain tumors are cancer related. Abnormal cells in the brain cause brain tumors and make life dangerous if untreated at the early stages. A significant portion of the Image is extracted using image processing techniques during tumor image classification. The 3D shape is a complex structure in the 3D space; because of this reason, there would be a minimal of 3D shapes for feature learning. To cope with those challenges, research paper transform 3D shapes into 2D space and use automatic encoders to learn features from 2D images. The proposed 3D based Spatial Auto Encoder (3D-SpAE) approach is automated by learning a state representation directly from camera images and the state-space constructions. Once the autoencoder is trained, the coefficients acquired for reconstructing an image based on prototypes which are utilized as a feature for 3D shape matching and retrieval. The autoencoder can achieve high performance for image retrieval because it can learn feature adaptively from training data. 3D-SpAE classified the brain tumor data into Whole-Tumor, Tumor Core, and Enhancing Tumor. The results showed that the suggested method obtained better values of the accuracy of 99.4% when compared to the existing Nonparametric Localization Enhancement Methods with U-Net, 3 D Context Deep Supervised U-Net, Deep Elman Neural network with adaptive fuzzy clustering, NSGA, Convolutional Neural Network, and Kernel-based SVM.
Keywords:	Brain Tumor, Complex Structure, Classification, Feature Points, 3D Based Spatial Autoencoder
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	FUZZY AGGREGATION BASED DEEP LEARNING SYSTEM FOR ANOMALY IDENTIFICATION
Author:	KALLEPALLI ROHIT KUMAR, DR.NISARG GANDHEWAR
Abstract:	Due to the rapid growth of urbanisation and the rise of industry, there is an increased demand for surveillance system that work in real time. Artificial intelligence-based anomaly identification system only addresses a portion of the difficulties, primarily ignoring the dynamic nature of aberrant or abnormal behaviour across time. Using a training dataset with established normality and known error values are two further drawbacks of anomaly identification systems. A new approach to identify anomaly and its localization of video stream in real time called the Step Incremental Learner (SIL), is presented in this study. As new anomalies and normalities emerge over time, the unsupervised deep learning technique known as SIL uses active learning with fuzzy aggregation to continually update and identify them. Three benchmark datasets are used to show and assess SIL in terms of accuracy, robustness, computing overhead, and contextual indicators. Experiments conducted by our team show that the proposed system is very effective for 24x7 surveillance of video systems.
Keywords:	Deep learning, Anomaly Identification, Localization, CNN, Fuzzy Aggregation, Active
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	SECURITY ENHANCEMENT AND LOSS REDUCTION IN DEREGULATED POWER SYSTEMS WITH A SERIES FACTS DEVICE
Author:	RAGALEELA DALAPATI RAO, PADMANABHA RAJU CHINDA, KUMAR CHERUKUPALLI, SRINIVASA RAO MANTRI
Abstract:	Safety and congestion management of electricity systems is an essential concern in competitive markets. The operation of a transmission system within the operating limits is also the main task performed by system operators. In this research, a novel strategy for preserving the integrity of the system while decentralizing power market activities is described. The interior point method, integrated with evolutionary particle swarm optimization, also known as IPM-EPSO, is utilized in order to solve the optimal power flow problem, which aims to maximize the social benefit and system safety in the event of a contingency that is selected to be the most severe possible for the network. The effectiveness of the approach proposed was demonstrated by modified 14-bus IEEE systems for a specific loading condition, subject to contingency. The results show that under the selected network contingency conditions, the proposed technique IPM-EPSO can effectively improve system security.
Keywords:	Power System Security, Contingency Analysis, Static Security Assessment, Composite Logic Criteria, IPM-EPSO.
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	PARKINSONS DISEASE CLASSIFICATION USING ADAPTIVE RANDOM FOREST AND IMPROVED FEATURE SELECTION METHODS
Author:	REPUDI PITCHIAH , DR T. SASI ROOBA, DR K. UMA PAVAN KUMAR
Abstract:	One of the most prevalent neurological illnesses, Parkinson's disease (PD) mainly affects the motor system of the brain's core system. In actuality, PD is marked by speech impairment, tremors, muscular rigidity, and gait inaccuracies. Even though the primary symptoms of Parkinson's disease cannot be clearly distinguished from those of other disorders, a definitive diagnosis of the condition is often dependent on several neurological, psychiatric, and physical studies. As a result, several machine learning-based automatic diagnostic assistance systems have lately been used to aid in the assessment of PD patients. One of the most difficult medical issues at hand today is the automatic diagnosis of early Parkinson's disease using feature data sets. Such datasets contain several characteristics that are either worthless or plagued by issues like noise that hinder learning and add to required computing load. This article suggests a hybrid feature selection algorithm built on an enhanced correlation method with Bootstrap to increase the effectiveness of feature selection to ensure most accurate performance of the classier. By combining the finer elements of filters and wrappers, such an algorithm finds the ideal subset of features by removing a majority of noisy or unrelated information. Select optimal features from overall data features is also specific issue behind implementation of feature extraction In order to overcome such problems, we have proposed an adaptive random forest classifier method that uses ensemble feature selection technique for better information gain (IG), improved correlation (IC) and gain ratio (GR). Also,s it seeks to solve the class imbalance problem by applying bootstrap re-sampling for medical data. According to the evaluations in terms of accuracy, precision, recall, and F-Score, the developed model has been found to be more efficient than conventional methods. The analysis of experimental results indicates better accuracy of the proposed framework (88.3% accuracy) as compared to other techniques.
Keywords:	Parkinsons disease (PD), Multiclass Classification, Feature Selection, Random Forest, and High Dimensional Data.
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	AN ENHANCED OPTIMIZATION MODEL WITH ENSEMBLE AUTOENCODER FOR ZERO-DAY ATTACK DETECTION
Author:	ABHIJIT DAS, PRAMOD, S. PRAVEEN KUMAR
Abstract:	Technology and networks have improved significantly in recent decades, and Internet services are now available in almost every business. It has become increasingly important to develop information security technology to identify the most recent attack. Many signature-based intrusion detection techniques promise great accuracy and low false alert rates. However, they struggle when confronted with emerging threats. This work employs the autoencoder, a type of neural network, to find possible zero-day threats by looking for differences in the data. This research is significant because, unlike previous efforts, it does not rely on the use of explicit labels during training. The premature convergence they caused was a problem for the optimization strategy used in the real world to identify zero-day attacks and other novel threats. This work proposed a unified ensemble autoencoder based on the Dynamic weighted Cuckoo Search Algorithm (DWCSA) to choose the most practical features for detecting anomalies in network traffic. As a result, the suggested Dynamic weighted based Cuckoo Search Algorithm (DWCSA) is modified to enhance performance while maintaining its fundamental structure. The model uses an improved DWCSA to determine which dataset features are the most important. Then an ensemble Autoencoder is used to improve further the optimal features that the enhanced DWCSA learned. Accuracy was enhanced by the proposed model, which overcame the constraint problems that had arisen throughout the feature selection process. The study demonstrates how to practically design and deploy a suitable approach and procedure, allowing even non-experts to identify the zero-day attack. The investigation of the results indicates that the proposed zero-day detection methods have more excellent results for the highest overall accuracy of 99.93% on CICIDS18 data sets. Overall, the results for identifying zero-day threats using the proposed technique are promising.
Keywords:	Intrusion Detection System, Autoencoder, Zero-Day Attack, Machine Learning, Cyber-security.
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	ROLE OF BIG DATA ANALYTICS AND FIFA WORLD CUP 2022 ON THE QATAR’S TOURISM INDUSTRY: SYSTEMATIC REVIEW
Author:	SULAIMAN KHAN, HABIB ULLAH KHAN
Abstract:	Globally, events arrangement and management have become an inspiring component for the destination marketing. Tourism industry is intemperately thriving on information produced specifically through big data. Big data provides advanced and relevant inferences concerning behavior patterns, human activities and emotion analysis of tourists that will empower management and the tourism industry. This research work accomplished by executing a systematic literature review (SLR) to analyze the relevant research work reported recently in the targeted research domain. To perform this SLR work the relevant articles are accumulated from well-reputed online repositories including IEEE Xplore, ScienceDirect, Taylor & Francis, and SpringerLink. These articles were analyzed for their quality based on the research questions and their ability to answer them. This research mainly focusing on using the previous work as an evident to efficiently manage the crowd during the FIFA World Cup 2022 in the State of Qatar. This research work also aims to identify the guidelines and key steps that will laterally assist in boosting the hotel and touring industry in the State of Qatar. Findings of this systematic analysis will open new research directions for the research community to improve the tourism and marketing industry in a specific region.
Keywords:	Systematic Literature Review, Tourism, Big Data, Tourism Industry.
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	FUZZY INFERENCE SYSTEM FOR CLASSIFICATION WITH DIMENSION REDUCTION OF ROUGH SET
Author:	SUGIYARTO SURONO; CHOO WOU ONN; ZANI ANJANI RAFSANJANI; ANNISA EKA HARYATI; GOH KHANG WEN
Abstract:	Data obtained from various measurements will have different characteristics. Different data sets can be classified according to the characteristics of each data. One of the classification methods is the Takagi Sugeno Kang (TSK) fuzzy inference system, where the fuzzy TSK output is a constant, linear or polynomial. However, one of the obstacles in fuzzy TSK is regarding the dimensions of the data. Therefore, we propose a dimension reduction using the rough set in this study. Then, the results of the rough set will be used as input to the fuzzy TSK, and each rule will be optimized using MBGD and SGD. This study compares TSK's accuracy and computational time results using MBGD and SGD. The results indicate that the average time of the MBGD-UR produces the shortest time. In addition, MBGD-UR has a time that tends to be more stable than other methods. Then, the BCA value shows that MBGD-A has the most significant BCA value. Therefore, MBGD-A has the best performance compared to other methods.
Keywords:	Fuzzy TSK, Dimension reduction, MBGD, SG, Rough set.
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	AN ANALYSIS OF PHISHING CASES USING TEXT MINING
Author:	CHAEYEON JANG1, OOK LEE, CHANGBAE MUN, HYODONG HA
Abstract:	In the modern knowledge and information society, hacking is causing great problems in each area of the industry. Recently, techniques such as distributed denial of service attacks and attacks on management vulnerabilities of cloud servers are gradually evolving. In this study, phishing types were analyzed based on the results of word frequency analysis, clusters were identified, and network analysis was conducted. Through the graph derived from the analysis results, it was possible to identify main keywords, relationships, and trends, and present practical review items for countermeasures against phishing attacks. It also provides a foundation for designing phishing attack prevention measures. By applying this research methodology to the analysis of open source vulnerabilities in the future, it will be possible to have an adaptive defense system for changes in hacking techniques.
Keywords:	Text Mining, Phishing Attack, Cases Study, Semantic Network Analysis
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	THE APPLICATION OF FUZZY LOGIC FOR THE SELECTION OF SUPPLIERS FOR THE PURCHASE OF WIND TURBINES IN MOROCCO
Author:	ILYAS BENTACHFINE, MOUNA EL MKHALET, MOHAMMED ERRITALI
Abstract:	To solve the problem related to the uncertainty and the ambiguity and the imprecision of the decision makers, this paper proposes the application of the multicriteria approach of fuzzy logic, to select the best supplier for the purchase of wind turbines for a company in Morocco. In this study, first we discuss the importance and the state of the art of selecting the best supplier and we present the fuzzy logic approach and its steps. We then go on to do the fuzzification for the three entry criteria and the exit variable retained in our study: Cost, Reliability, Proximity of the operating and maintenance teams, supplier. In addition, we build fuzzy rules and then we do defuzzification: We have simulations that tell us in which case we can choose the best supplier for the purchase of wind turbines in Morocco. After we proceed to expose the advantages of the approach used which is the fuzzy logic for this study and we present its limits. Finally we mention our scientific contribution, and some perspectives that we intend to make.
Keywords:	Fuzzy Logic, Supplier Selection, Cost, Proximity Of The Operating and Maintenance Teams, Reliability.
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	THE LIMITS OF CONTEXT SIMPLIFICATION ON QA SYSTEM RESULTS
Author:	RACHID KARRA, ABDELATIF LASFAR
Abstract:	QA systems play a key role in human activities such as customer support, digital assistance in education, health, and public services. Our work is to use a QA system as a black box and see the effect of different simplification models on its results. The present study explores how far state-of-art simplification models can conserve text content. We measure text complexity with different linguistic metrics and meaning conservation with a BERT-based QA system score. Through evaluations, we measured text complexity and proved that context simplification as a multi-step simplification process gives better results in QA systems than ‘direct’ or ‘whole’ simplification. The proposed method has a better performance compared to automatic simplification. It is beneficial for a QA system with changeable contexts. As a task-oriented feature, choosing the convenient text simplification system should depend on its usefulness and the nature of the problem.
Keywords:	BERT, Linguistic analysis, QA system, Seq2Seq, Text simplification.
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	IMPLEMENTATION OF GAMIFICATION FRAMEWORK ON ONLINE LEARNING OF PROCEDURAL PROGRAMMING
Author:	GALIH DEA PRATAMA , GEDE PUTRA KUSUMA
Abstract:	Procedural Programming is one of many important materials for students of Computer Science and related majors, yet the learning suffers from lots of distractions when implemented by online means. The distractions impact the learning, such as lower motivation and achievement of the students. To improve online learning of the material, Octalysis gamification framework is proposed to improve the motivation and achievement of students while doing online learning of Procedural Programming. The framework puts certain gamification elements in the implementation through the presence of core drives in accordance to the online learning currently used. This research includes experiment to see the significance rate of proposed learning process, which is helped with ANCOVA analysis. The result shows that using gamification framework as learning companion can improve student motivation and achievement, and there is relationship between the motivation and achievement of students after the learning process.
Keywords:	Gamification of Learning, Octalysis Framework, Online Learning, Procedural Programming, ANCOVA Analysis
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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Title:	IMPROVING SVM CLASSIFIER MODEL USING TREE STRUCTURED PARZEN ESTIMATOR OPTIMIZATION FOR CROP PREDICTION
Author:	VASANTHANAGESWARI, P. PRABHU
Abstract:	Economy of India profoundly relies upon farming. Conventional methods of suggestions are still utilized for farming. Farming is the cornerstone of emerging nation like India. For the income, most of their populace relies upon farming. As of now, farming is based on different approximations of fertilization amount and the sort of yield to be developed or planted. Agriculture holds a predominant position in the growth of any country's prosperity. However, there exists a major threat in the crop yield due to unpredictable and uncontrolled climatic changes, traditional farming methods, and poor irrigation services [10]. Machine learning is an arising research field in analysis of crop yield. Yield forecast is a very significant issue in agriculture. Machine learning methods are the better decision for this issue. Different Machine learning methods are utilized and assessed in agribusiness for assessing what's to come year's production of crop. This paper proposes the Improved Support Vector Machine (Tree-structured Parzen Estimator (TPEOSM)) is applied in the agricultural dataset to predict the crop yield with fertilizer amount. The performance metrics such as precision, recall, f1 score, and accuracy is evaluated. The proposed method accuracy is better than the traditional SVM classifier. Thus, we hope it will help the farmers to select the right crop for the cultivation in basis of temperature, rainfall and other weather conditions and amount of fertilizers thus to be used for the particular crop.
Keywords:	Agriculture, Machine Learning, Support Vector Machine, Tree-Structured Parzen Estimator, Supervised Classification.
Source:	Journal of Theoretical and Applied Information Technology 30^th November 2022 -- Vol. 100. No. 22-- 2022
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