19c96fd517d854497e8-26cd 20220214071548116 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 01 30 2021 9 5 Associate Professor, Department of Management Science(Statistics), Nepal Commerce Campus, Tribhuwan University, Nepal. Arun Kumar Chaudhary Vijay Kumar Department of Mathematics and Statistics, DDU Gorakhpur University, Gorakhpur, India. A novel distribution using Poisson-Generating family of distribution with parent distribution as shifted Gompertz distribution called Poisson shifted Gompertz distribution with relevant properties has been introduced. The estimation of unknown parameters is carried out via established methods including Maximum likelihood estimation (MLE). R software is applied for computational purposes. The application of the proposed model has been illustrated considering a real set of data and investigated the goodness-of-fit attained by the Poisson shifted Gompertz model through different graphical methods and test statistics where better fit was observed for the set of real data. 01 30 2021 202 208 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5265.019521 https://www.ijrte.org/portfolio-item/E5265019521/ Department of Computer Science, Desh Bhagat University, Mandi Gobindgarh, India. Ujwala W. Wasekar R. K. Bathla Department of Computer Science, Desh Bhagat University, Mandi Gobindgarh, India. The disorder of Diabetic Retinopathy (DR), a complication of Diabetes that may lead to blindness if not treated at an early stage, is diagnosed by evaluating the retina images of eye. However, the manual grading of images for identifying the seriousness of DR disease requires many resources and it also takes a lot of time. Automated systems give accurate results along with saving time. Ophthalmologists may find it useful in reducing their workload. Proposed work presents the method to correctly identify the lesions and classify DR images efficiently. Blood leaking out of veins form features such as exudates, microaneurysms and haemorrhages, on retina. Image processing techniques assist in DR detection. Median filtering is used on gray scale converted image to reduce noise. The features of the pre-processed images are extracted by textural feature analysis. Optic disc (OD) segmentation methodology is implemented for the removal of OD. Blood vessels are extracted using haar wavelet filters. KNN classifier is applied for classifying retinal image into diseased or healthy .The proposed algorithm is executed in MATLAB software and analyze results with regard to certain parameters such as accuracy, sensitivity, and specificity. The outcomes prove the superiority of the new method with sensitivity of 92.6%, specificity of 87.56% and accuracy of 95% on Diaretdb1 database. 01 30 2021 209 215 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5267.019521 https://www.ijrte.org/portfolio-item/E5267019521/ Assitant Professor Jouf University (Saudia arabia )& Neelain University (Sudan ). Alameen Abdalrahman The main objective of this research is to use AES 256 GCM encryption and decryption of a web application system database called Accounting Information System (AIS) for achieving more privacy and security in a cloud environment. A cloud environment provides many services such as software, platform, and infrastructure. AIS can use the cloud to store data to achieve accounting with more performance, efficiency, convenience, and cost reduction. On the other hand, cloud environment is not secure because data is kept away from the organization. This paper focuses on how we deal with secure sensitive data such as accounting data AIS web application at web level encryption by using AES 256 GCM encryption to store data as encrypted data at cloud in a secure manner? Accounting Information System (AIS) has very sensitive data and its need to be more secure and safe specially in cloud because it’s not saved at local servers but at another cloud service provider. The storage of encryption and decryption keys are stored in locations and devices different from those in which the database is stored in the cloud for ensuring more safety. 01 30 2021 216 221 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5269.019521 https://www.ijrte.org/portfolio-item/E5269019521/ Department of Electrical Power, The Public Authority for Applied Education and Training, Higher Institute of Energy, Kuwait. Khaled S. AlRasheed Siti Fauziah Toha Department of Mechatronics, Faculty of Engineering, International Islamic University Malaysia. Hazleen Anuar Department of Manufacturing and Materials Engineering, Faculty of Engineering, International Islamic University Malaysia, Malaysia. Yose Fachmi Buys Department of Mechanical Engineering, University of Malaya, Malaysia. In this paper a Maximum Power point (MPP) tracking system is developed using dual-axis DC motor feedback tracking control system. An efficient and accurate DC motor system is used to increase the system efficiency and reduces the solar cell system coast. The suggested automated DC motor control system based on the photovoltaic ( PV ) modules operated with the μ-microcontroller. This servo system will track the sun rays in order to get MPP during the day using direct radiation. A photometric cell is used to sensor the direct sun radiation and to feed a signal to the μ microcontroller and then select the DC motor mechanism to deliver optimum energy. The proposed system is demonstrated through simulation results. Finally, using the proposed system based on microcontroller, the system will be more efficient, minimum cost, and maximum power transfer is obtained. 01 30 2021 222 228 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5272.019521 https://www.ijrte.org/portfolio-item/E5272019521/ Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, Puttaparthi, India. Rohan Yashraj Gupta Satya Sai Mudigonda Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, Puttaparthi, India. Pallav Kumar Baruah Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, Puttaparthi, India. A data-driven Fraud detection model for insurance business can be seen as a two-phase method. Phase I is data-preprocessing of a given dataset, in which, handling class imbalance is a major challenge. Phase II is that of classification using Machine Learning models. It is important to comprehend if there is any influence of the technique used in Phase I on the efficiency of the model used for Phase II. A natural query that intrigues one is whether there is a golden combination of a technique in Phase I and a specific model in Phase II for assured best performance of a Fraud Detection Model.In this work, we study a few techniques for handling data imbalance issue namely, SMOTE, MWMOTE, ADASYN and TGAN in combination with various classifier models like Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), LightGBM, XGBoost and Gradient Boosting Machines (GBM). The study is conducted on a dataset for motor vehicle insurance fraud detection.We present a comparison of various combinations of data imbalance technique and classifier models. It is observed that the combination of TGAN in Phase I and GBM in Phase II gives the best performance. This combination performs best in terms of important metrics such as false positive rate, precision and specificity. We obtained the lowest false positive rate of 0.0011 and precision of 0.9988 which minimizes the most critical risk for the insurance company of falsely classifying a non-fraud claim as a fraud. Finally, the specificity of 0.9989 indicates that the model was also very good at predicting the non-fraudulent claim. 01 30 2021 236 244 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5277.019521 https://www.ijrte.org/portfolio-item/E5277019521/ Research Scholar, Mechanical Engineering Department, National Institute of Technology, Jalandhar (Punjab), India. Surender Kumar Dr. R.S. Bharj Associate Professor, Mechanical Engineering Department, National Institute of Technology, Jalandhar (Punjab), India. Most refrigerating systems are driven by an internal combustion engine that increased the conventional vehicle’s oil consumption and tailpipe emissions. The solar-assisted refrigerating electric vehicle (SAREV) system powered by a hybrid energy mode has been designed. The hybrid energy (solar + grid) was stored in the battery bank to complete this vehicle’s necessary functions. The PV panels are prominently incorporated into this vehicle rooftop to charge the battery bank. In this study, the integrated system was driven by a hybrid energy mode that reducing the wastage and deterioration during temporary storage and transportation in different areas. The performance of the integrated system was tested under different operating conditions. The effect of load variation on maximum speed and travelling distance of vehicle was analyzed. The battery bank charging and discharge performance were studied with and without solar energy. The refrigerator was consuming 116 Wh energy per day to maintain a -12 oC lower temperature on the no-load condition at the higher thermostat position. The refrigerator was run continuously for 4-6 days on battery bank energy and 7-10 days on the full load condition of hybrid energy. The vehicle was travelling at a maximum of 23 km/h speed on full load condition. The vehicle needed torque 14-16 N-m at the initial phase for each load condition. Torque demand was decreasing with the increasing speed of the vehicle. The full-charged battery bank’s initial voltage was 51.04 V, and the cut-off voltage was 46.51 V. The vehicle was covering a distance of 62.4 km with the battery bank alone at full load condition. It was travelling 68.3 km distance with hybrid energy mode. The vehicle’s integrated system was the best in maintaining battery performance, power contribution capability, and drive range enhancement. 01 30 2021 305 315 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5278.019521 https://www.ijrte.org/portfolio-item/E5278019521/ M.E student from Madhav Institute of Technology and Science, Gwalior, Madhya Pradesh, India. Akanksha Gupta Dr. Ravindra Pratap Narwaria Assistant Professor in MITS, Gwalior, Madhya Pradesh, India. In this digital world, everything including documents, notes is kept in digital form. The requirement of converting these digital documents into processed information is in demand. This process is called as Handwritten digit recognition (HDR). The digital scan document is processed and classified to identify the hand written words into digital text so that it can be used to keep it in the documents format means in computerized font so that everybody can read it properly. In this paper, it is discussed that classifiers like KNN, SVM, CNN are used for HDR. These classifiers are trained with some predefined dataset and then used to process any digital scan document into computer document format. The scanned document is passed through four different stages for recognition where image is pre-processed, segmented and then recognized by classifier. MNIST dataset is used for training purpose. Complete CNN classifier is discussed in this paper. It is found that CNN is very accurate for HDR but still there is a scope to improve the performance in terms of accuracy, complexity and timing. 01 30 2021 245 247 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5287.019521 https://www.ijrte.org/portfolio-item/E5287019521/ Lecturer, Universiti Tun Hussein Onn, Malaysia. Dahlia Fernandez Aini Aman Lecturer, Universiti Tun Hussein Onn, Malaysia. There are various organizations that have automated the technology used in accounting and financial services to increase productivity and optimize operating costs. Among the automation technology transformations used in accounting and financial services is Robotic Process Automation (RPA). However, not all technological transformations are positive because they may cause fear among employees due to changes in the work process. Hence, the aim of this study is to understand the influence of RPA towards employee acceptance in the finance and accounting unit. This study uses an in-depth case study approach in one of the largest oil and gas company in the world. The result of the study showed that RPA technology has significant influences on employee acceptance. The results are discussed according to five elements which are threatening job opportunities, changes in the scope of work, adaptation to technology, career advancement, work-life balance, and job satisfaction. The results showed that employees must adapt with the changes due to the new technology implementation and embrace it positively because at the end of the day, new automation will always appear. Furthermore, the changes that take place must be openly accepted in order to maintain the reputation of their profession as well as the achievements of the organization. 01 30 2021 295 299 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5289.019521 https://www.ijrte.org/portfolio-item/E5289019521/ Department of Chemistry, Govt. RR (PG) College, Alwar, India. Mrs. Rekha Sharma Dr. R. N. Yadav Department of Chemistry, Govt. RR (PG) College, Alwar, India. Drug repositioning is a compelling technique to find new signs for existing medications. Despite the fact that few exploration have attempted to improve the precision of repositioning by joining information from more than one assets and various levels, it is as yet appealing to additionally review how to effectively abuse significant information for drug repositioning. As contrasted and the customary medication improvement from particle to item, drug repositioning is additional time and worth effective, quickening drug revelation technique. Medication repositioning methods might be ordered as both sicknesses based or drug-based. In this study at, propose an effective strategy, by means of utilizing Adverse Drug Reactions (ADRs) in light of the fact that the middle of the road, a heterogeneous wellbeing network containing drugs, infections, proteins and ADRs is constructed. The repositioning procedure dependent on ADR is equipped for profiling drugs related phenotypic information and can accordingly aid the resulting drugs utilize the disclosure of new recuperating. 01 30 2021 248 253 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5294.019521 https://www.ijrte.org/portfolio-item/E5294019521/ Research Scholar, Dept. of ECE, R V College of Engineering, Bangalore, India. Bhavya Rudraiah Dr. Geetha K S Professor, HOD, Dept. of ECE, R V College of Engineering, Bangalore, India. Multiple object detection and tracking in a cluttered background is most important in vision-based applications. In this paper, the goal is to develop a classifier that detects and tracks multiple objects thereby ensuring robustness and accuracy. Locality Sensitive Histogram feature extraction is used, which adds contributions from all the pixels in an image. These features extracted are trained using decision tree classifier which performs with an accuracy of 97%. Experimental results demonstrate the objects tracked and detected under different scale and pose variations. Evaluation and comparison of the proposed method with various other techniques is performed using performance parameters. Results depict that the proposed technique outperforms with increased accuracy and is the top performer. 01 30 2021 259 262 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5295.019521 https://www.ijrte.org/portfolio-item/E5295019521/ Political Science Faculty, International Relation Dept., Istanbul University, Istanbul, Turkey. Fatemeh Dehghan Khangahi Farzad Kiani Engineering and Architecture Faculty, Computer Engineering Dept., Istanbul Arel University, Istanbul, Turkey. Today, the outbreak of the coronavirus has become a major global crisis and has affected many countries. One of the consequences of the spread of this virus is the creation of social panic and rapid changes in people’s lifestyles, which the social networks are noticing. The impact of social media, which plays an effective role even in people’s lifestyles, is being examined in the Covid-19 Pandemic. The purpose of this paper is to investigate the role of social networks in lifestyle changes in the coronavirus pandemic period. The present research is quantitative in terms of approach and in terms of type and nature, it is a descriptive survey. The data collection tool was assumed to be the Twitter social network. A total of 100.000 cases have been investigated based on the support vector machine (SVM) method and its results have been compared with decision tree and naive Bayes methods. Data processing is done using Python software. The trained model of SVM has a success rate on accuracy as near to 97% and also has 92% in the F1 score. The results show that social networks have about a 30% effect on lifestyle changes and stress during the pandemic periods. In order to form logical and desirable behaviors instead of dramatic behaviors such as fear and social stress in the use of social networks, social agents have their priority in organizing information and knowledge and informing the target community about the constructive and harmful cases of these networks and place different social roles and activities in society. Accordingly, providing the right news and information through trusted and responsible channels and platforms can play an important role in the proper management of society. 01 30 2021 263 267 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5297.019521 https://www.ijrte.org/portfolio-item/E5297019521/ Head of Software Engineering Department, Avenue Mohamed Ben Abdellah Regragui, Rabat, Morocco. Jaouad Maqboul Bouchaib Bounabat Head of Software Engineering Department, Avenue Mohamed Ben Abdellah Regragui, Rabat, Morocco. In this work we have developed a quality approach for the quality assessment of data related to the business process for quality projects, this approach uses the cost of the implementation of quality combined with the impact of quality broken down into the benefit and efficiency of data, shapley value helps us choose the business processes that will collaborate to reduce the cost of improvement, Deep learning helps us calculate the quality values for any dimension based on history of previous improvements. To reach our goal, we used the cost-benefit approach (ACB) and the cost-effective approach (ACE) to extract the impact and cost factors then using a multi-optimization algorithm. -objective we will minimize the cost and maximize the impact for each business process and the deep learning introduced will complement our approach to learn from the previous improvements after validation of the processes which will be chosen as well as the values calculated after improvement. The importance of this research lies in the use of impact factors and the cost of the quality evaluation which represent the basis of any improvement, our approach uses generic multi-objective optimization algorithms which will help choose the minimum value of each business process before the improvement, adding a layer of predicting and estimating the quality value of the data generated by the business process before the improvement even, while the value of shapley has aim to minimize the cost of quality projects during fission and merger of companies and even within a company composed of several services and departments to have the lowest possible total cost to help companies manage the portfolios of quality. 01 30 2021 268 274 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5298.019521 https://www.ijrte.org/portfolio-item/E5298019521/ Assistant Professor Department Of Electrical Engineering, AU College of Engineering, Visakhapatnam, A.P, India. K. Padma Yeshitela Shiferaw Maru Currently a Ph.D. Candidate, Andhra University, Visakhapatnam, India. Incremental industrialization and urbanization is the cause of enhanced energy use as it increases the building of new lines and more inductive loads. As a result, the transmission system losses increased, and the magnitudes of voltage profile values deviated from the stated value, resulting in increased cost of active power generation. To mitigate these issues, adequate reactive power compensation in the transmission line and bus systems should be done. Reactive power is regulated by the proper position of the Flexible AC Transmission System (FACTS). Unified Power Flow Controller (UPFC) is a voltage converter system that increases the voltage profile and reduces loss. In this paper, the optimal power flow solution is considered using a FACTS device based on Multi Population Modified Jaya (MPMJ) optimization algorithm. Using the Analytical Hierarchy Process (AHP) system, the optimal position of the UPFC device is determined by considering the most useful objective function provided by priorities and weighting factors. Therefore, on the standard IEEE-57 bus test system, the proposed MPMJ optimization algorithm is implemented with UPFC for optimal fuel cost values of generation, real power loss, voltage deviation and sum of squared voltage stability index. The result obtained by the proposed algorithm is contrasted with the recent literature algorithm. 01 30 2021 316 326 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5301.019521 https://www.ijrte.org/portfolio-item/E5301019521/ School of Housing, Building and Planning, Universiti Sains Malaysia. Omosebi Taiwo O Noor Faisal Abas School of Housing, Building and Planning, Universiti Sains Malaysia. Managing plastic waste is a global challenge that challenges the protection of our ecosystem due to its high rate of generation and its non-biodegradability. PWs must, however, be carefully handled to mitigate the emissions involved with their incineration and dumping into landfills. Plastic waste can be recycled into new usable building materials. In this analysis, shredded PET waste aggregate from a recycling center was heated at 230 0C and used as a binding aggregate incomplete replacement of cement with river sand to produce floor tiles. The properties of the aggregate materials and roof tiles (including their distribution of particle size, silt, clay and dust content, relative density, water absorption, porosity, flexural and compressive strength) were tested on different PET waste: sand mixing ratio, 100%, 90%, 70%, 50%, and 30%. Results revealed that the tiles produced by 30% PET and 70% river sand (3:7) achieved higher density, flexural and compressive strength than the other percentages of the mixture. The compressive strength of the tiles produced with 30 percent PET waste composition was greater than that of cement concrete (at 28 days of curing) for residential buildings. As a result of this low water absorption and eco-friendliness, PET waste can be used for roof tiles at 30 percent PET substitution based on the test results. 01 30 2021 300 304 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5303.019521 https://www.ijrte.org/portfolio-item/E5303019521/ Research Scholar, Visvesvaraya Technical University, Belagavi, , Karnataka, India. Yamanappa. N. Doddamani Ravindra R Malagi Research Scholar, Visvesvaraya Technical University, Belagavi, , Karnataka, India. U C Kapale Research Scholar, Visvesvaraya Technical University, Belagavi, , Karnataka, India. Sugar industry which plans for power usage from Bagasse also needs the load forecasting carried out using the energy audit data. The stochastic nature of the load demand of the sugar industry needs to be forecasted in advance for the assuring uninterrupted power delivery to the industry. The manual energy audit data obtained from the sugar industry for a period of time is obtained and trained on a regression based on Multi Kernel Learning (MKL). The Support Vector Regression (SVR) formulation is applied with the Multi Kernel topology and the performance parameters including the Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) is observed in the implementation. The algorithm is the Multi Kernel Support Vector Regression algorithm using the Python based toolbox. 01 30 2021 275 278 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E5304.019521 https://www.ijrte.org/portfolio-item/E5304019521/ Dean, School of Information Technology & Engineering Coordinator, Doctor in Information Technology Program St. Paul University Philippines. Dr. Marifel Grace Capili-Kummer Maria Leodevina C. Batugal Professor in the Graduate School and in the School of Techer Education, and a Quality Assurance Officer in St. Paul University Philippines. The present study focuses on gathering a real-time data on the employability of graduates. The web-based Dynamic Alumni Monitoring with Decision Support System is developed and linked to the institution’s website to gather alumni information. To realize the objective of this study, the agile method research design process is utilized. The agile methodology is a project management technique in software development process. The system has the capacity to monitor the graduates. It provides alumni verifications and confirmation after the pre-registration. The system has a platform in maintaining alumni data and notifications to periodically update the graduates’ profiles anytime and anywhere. The system has the capacity to make updates concerning alumni activities of the University. These are sent through their registered email addresses. Likewise, the system generates important reports needed by the school and its administrators. 01 30 2021 279 284 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F5308.019521 https://www.ijrte.org/portfolio-item/F5308039621/ Research Scholar, Jain ( Deemed to be University), Bengaluru, Karnataka, India. B Mallikarjuna K N Ravi Prof and Head, Dept of EEE, Sapthagiri College of Engineering, Bengaluru, India. V Muralidhara Professor, Dept of EEE, BNM Institute of Technology, Bengaluru, Karnataka, India. N Vasudev Additional Director (Retd), CPRI, Bengaluru, Karnataka, India. In high voltage transmission porcelain materials are important one. To mount the transmission line on a transmission tower we need an insulation material. Many literatures deal about the silicon and rubber-based insulators. In this paper the porcelain is modelled as FEM model using the PDE tool and electric potential distribution is analyzed. the PDE tool come in handy to draw the shape of the insulator. In this paper the straight shed and alternate shed insulators are analyzed with the MATLAB PDE tool and results are analyzed. then using some random water droplets in the insulator, the impact is observed. 01 30 2021 285 288 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F5318.019521 https://www.ijrte.org/portfolio-item/F5318039621/ Associate Professor, Department of Civil Engineering, JSS Academy of Technical Education, Bangalore, India. Dr. Sanjay Shekar N C Dr. Hemalatha H N Assistant Professor, Department of Civil Engineering, JSS Academy of Technical Education, Bangalore, India. Understanding vegetation characteristics is essential for watershed modeling, like in the prediction of streamflow and evapotranspiration (AET) estimation. So, the present study was taken to analyze the Land use/Land cover characteristics in a Sub-humid tropical river basin which is originating in the forested part of Western Ghats mountain ranges using the Moderate Resolution Imaging Spectroradiometer (MODIS) and Bhuvan satellite data as inputs for the algorithm. All the fourteen LU/LC characteristics present in the Hemavathi basin (5427 km2) were analyzed in the basin using satellite data which is located in Karnataka, India. Land Surface Reflectance (LSR) and Land Surface Temperature (LST) were the two data products used as input to map the pixel-wise variations in albedo, the fraction of vegetation (FV) and Land Surface Temperature (LST). It was found from the rainfall data that the year 2019 experienced higher rainfall than the average and 2012 very low rainfall than the normal. Parameters considered in this study Albedo, LST and FV are susceptible to wetness and temperature conditions. Variations in albedo and LST were similar in that both values in the summer of 2019 and 2012 are high than winter due to the high temperature and less wetness from all the LU/LC classes. Similarly, FV variations show opposite trends that values in the summer of 2019 and 2012 are low than in winter, which is due to the high temperature and less wetness. The results and discussions show that significant realistic variations in albedo, LST and FV with respect to all LU/LC classes. All the LU/LC classes characteristics in this study show significant variations with respect to wetness and temperature conditions, so the methodology proposed in this study can be used in regional monitoring of LU/LC classes in a convenient and cost-effective manner. 01 30 2021 289 294 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.F5322.019521 https://www.ijrte.org/portfolio-item/F5322039621/