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The disparities in access to public assistances between rural and urban areas are analyzed in this study, corresponding to the intensity in which the constructs are related to expected quality, perceived quality and perceived value as influential factors in citizen satisfaction and loyalty. In Guayaquil, a survey is carried out at the household level, 428 valid questionnaires are obtained in the rural area of Tenguel and 521 Valid questionnaires in the urban area of Tarqui, applyingto the American Customer Satisfaction Index (ACSI). The research used a Structural Equation model (SEM) to evaluate the hypotheses raised, if they observe significant differences in citizen perception between inhabitants of urban and rural areas about the quality of community services, this as a determining cause in the level of citizen satisfaction and loyalty to choose their municipal authorities. The multigroup analysis allowed to identify inequalities in the observation of the quality of municipal or local communityassistances between rural and urban areas; the findings are considered to local public administrators for the design of public policy aimed at improving levels of citizen satisfaction and loyalty.Sentiment analysis is a way to automatically understand and process text data to figure out how someone feels about an opinion sentence. If there are too many reviews, it will take a lot of time and they will start to be biased. Sentiment classification tries to solve this problem by putting user reviews into groups based on whether they are positive, negative, or neutral. The dataset comes from Drone Emprit Academic. It is made up of tweets with the words "online learning method" in them, with as many as 4887 data crawled from them. Information Gain and adaboost on the C4.5 (FS+C4.5) method are used in the feature selection method. We use feature options to get rid of bias and improve accuracy. The results of the experiments will be compared to other algorithms like C4.5 and random forest. Based on the results, the accuracy of the two standard decision tree models (C4.5 and random forest) went up from 48.21% and 50.35% to 94.47 %. The value of how accurate it was went up by 44 percent. The FS+C4.5 model, on the other hand, has an RMSE of 0.204 and a correlation of 0.944. So, adding the feature selection technique to the sentiment analysis of bold learning education can make the C4.5 algorithm even more accurate.

The attendance or attendance system is a recording or provision of information on a person's presence which indicates that the person has provided information about his presence and absence, then it is stated in the form of an attendance report. Along with the development of technology, an attendance recording has also developed, where we used to use paper attendance to fingerprint attendance. In educational institutions such as campuses, attendance has an important role in the process of developing a student. Until now, there are still many campuses that still use conventional attendance to record the presence of students in class, which allows for human error or fraud from both lecturers and students. Therefore, a solution is possible to suppress errors that may occur, namely by creating a website-based attendance or attendance system where the QR Code is used as an auxiliary medium in recording the presence or absence of a student at the STMIK AKBA Makassar Campus.

This study aimed to evaluate the effectiveness of a DSS utilizing the WP method in improving the decision-making process and overall performance of a manufacturing company. The study collected data over a six-month period and found that the use of the DSS resulted in a 5% reduction in production costs, a 40% increase in market demand, and a 40% increase in profit. Additionally, user satisfaction level with the DSS improved by 33%. Regression analysis was conducted to determine the relationship between the use of the DSS and the performance of the company, the results showed that there was a statistically significant relationship between the use of the DSS and the reduction in production costs, increase in market demand, and increase in profit. Additionally, the analysis showed that the satisfaction level of the users of the system also significantly improved. The results of this study provide evidence that the use of a DSS with the WP method can be an effective tool for improving the decision-making process and overall performance of a manufacturing company. The study suggests that the DSS can be an effective tool for improving the performance of the company. However, it's important to note that the results of this study are specific to the manufacturing company, and may not generalize to other types of companies or industries.

This paper presents the application of the ELECTRE III multi-criteria decision making method for human resource management. The case study conducted involves the evaluation of different recruitment and retention strategies using multiple criteria such as cost, time to fill a position, quality of candidates, retention rate, diversity and inclusion, employee satisfaction, and compliance. The study demonstrates the step-by-step process of how to conduct an ELECTRE III analysis, including the identification of criteria and alternatives, the calculation of concordance and discordance indices, and the determination of final rankings using the global outranking relation. The results of this analysis can be used by organizations to make informed decisions about recruitment and retention strategies that best align with their goals and objectives. The study highlights the importance of data quality and the need for sensitivity analysis to check the robustness of the results. Additionally, it is suggested that future research could be conducted on how to effectively communicate the results of the analysis to stakeholders and decision-makers, and on comparing the results of this analysis with other methods.

The purpose of this research is to analyze mapping results in the form of clusters formed using clustering method measures. This is done to determine the connections that the existing clusters create. Some of the measurements used are mixed measurements, Bregman differences, and number measurements (Mixed Euclidean Distance, Generalized Divergence, Squared Euclidean Distance, Mahalanobis Distance, and Euclidean Distance). Distance measurement shall be applied on number with primary school facilities in Indonesia. The Davies Bouldin Index (DBI) is different from the cluster number test (k = 2-10) for each Distance Measure. The average DBI value in the type of measure (mixed measure) and numerical measurement (Mixed Euclidean Distance) is 0.54. The average DBI value in the type of measure (Bregman divergences) and numeric measurements (generalized IDivergence) is 0.66. The average DBI value is 0.77 for the measurement type (Bregman divergences) and numerical measurement (Squared Euclidean Distance). From the results, the measurement of distance with mixed measurement and the mixed Euclidean distance with the cluster number (k = 2), namely 0.269, have the best DBI value.

The fuzzy logic Mamdani model is a fuzzy reasoning system that can be used to calculate values based on various criteria for evaluating teacher performance. In this study, three fuzzy sets were used to determine the level of success of teachers in teaching through online media in one of the vocational schools in North Sumatra: the teacher set, the value set, and the success rate set. The teacher set is divided into three criteria: bad, sufficient, and good teachers. The set of values is also divided into three categories: bad, sufficient, and good. Furthermore, the set of success rates is divided into three categories: low, medium, and high success rates. The goal of this study is to use the mamdani fuzzy logic method to assess the level of success in teaching teachers using online media. According to the study's findings, the mamdani method could be used in this situation.

KNN categorization is simple and successful in healthcare. In this research's example case study, the KNN algorithm classified the new record as "Abnormal." The classification method began with choosing K, then calculating the Euclidean distance between the new record and the training set, finding the K nearest neighbors, then classifying the new record based on those K neighbors. The findings show that the KNN algorithm is effective in healthcare and highlight several shortcomings that should be addressed in future study. Weighting variables, choosing the best K value, and handling non-uniform data are these restrictions. The findings show the KNN algorithm's medical potential.

Malaria is an infectious disease throughout the world where the disease is transmitted by infected female Anopheles mosquitoes. Malaria has some symptoms that are almost like COVID-19. Malaria has several other symptoms, characterized by chills, anemia, cold sweats, nausea and vomiting, and a sudden drop in blood pressure. Identification of the type of malaria begins with preprocessing, feature extraction, and classification for identification. Image improvement is part of the preprocessing stage to improve image quality so that the malaria parasite object in the image can be seen clearly. This study tries to improve the algorithm with hybrid dark and contrast stretching. Performance evaluation of malaria parasite image improvement using Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR). The results obtained with the improvement of dark hybrids and contrast stretching can improve the image quality of malaria parasite objects with MSE value = 0.0095 and PSNR value = 22.8404, compared with dark stretching, contrast stretching, histogram equalization.

The purpose of this research is to analyze the C4.5 and Random Forest algorithms for classification. The two methods were compared to see which one in the classification process was more accurate. The case is the success of university students at one of the private universities. Data is obtained from the https://osf.io/jk2ac data set. The attributes used were gender, student, average evaluation (NEM), reading session, school origin, and presence as input and success as a result (label). The process of analysis uses Rapid Miner software with the same test parameters (k-folds = 2, 3, 4, 5) with the same type of sample (stratified sample, linear sample, shuffled sampling). The first result shows that the sample type test k-fold (stratified sampling) achieved an average accuracy of 55.76 percent (C4,5) and 5618 percent (Random Forest). The second result showed that the kfold (linear sampling) sample test achieved an average precision of 58.06 percent (C4.5) and 6506 percent. (Random Forest).The third result shows that the k-fold test with the sampling type has averaged 58.68 per cent (C4,5) and 60,76 per cent (shuffled sampling) precision (Random Forest). From the three test results, in the case of student success at a private university, the Random Forest method is better than C4.5.

DC motor is an electrical motor widely used for industrial applications, mostly to support production processes. It is known for its flexibility and operational-friendly characteristics. However, the speed of the DC motor needs to be controlled to have desired speed performance or transient response, especially when it is loaded. This paper aims to design a DC motor model and its speed controller. First, the state space representation of a DC motor was modeled. Then, the controllability and observability were analyzed. The transfer function was made based on the model after the model was ensured to be fully controllable and observable. Therefore, a fuzzy logic controller is employed as its speed controller. Fuzzy logic controller provides the best system performance among other algorithms; the overshoot was successfully eliminated, rise time was improved, and the steady-state error was minimized. The proposed control algorithm showed that the speed controller of the DC motor, which was designed based on the fuzzy logic controller, could quickly control the speed of the DC motor. The detail of resulted system performance was 2.427 seconds of rising time, 11 seconds of settling time, and only required 12 seconds to reach the steady state. These results were proved faster and better than the system performance of PI and PID controllers.

COVID-19 (coronavirus disease 2019) is a new type of disease caused by a coronavirus, namely SARS-CoV-2, also known as the Corona virus. The Corona virus outbreak is causing concern all over the world, including in Indonesia. Many people became paranoid as a result of the virus's widespread spread, which was followed by reports of a number of deaths among victims. The spread of the Corona virus in Indonesia has had a negative impact on all aspects of life. Because of the density of settlements and the vastness of Indonesia's territory, this virus became out of control and spread quickly. The lack of socialization in dealing with this virus has an impact on the community's understanding of the importance of following health protocols. The goal of this research is to use data mining techniques to determine the pattern of symptoms caused by the Corona virus. The Association method is used in data mining. APRIORI is a popular association method that employs a high frequency pattern. This method is expected to be used to determine how likely it is that one case has the first symptom in addition to other symptoms. The parameters Support and Confidence support whether or not the association rules are required. After obtaining the results of the multiplication of Support and Confidence, the rule with the highest multiplication result will be discovered. The rule with the highest result is used as a rule to be applied in the next case, namely "If you have cough symptoms, they will be accompanied by fever symptoms with 90% support and 90% confidence".

Motorcycles are a highly efficient and effective means of transport. Currently, the increasing number of motorcycle products allows consumers to buy the correct motorcycle and according to their wishes, requirements, and abilities. The objective is to design and use the Multi-Attribute Utility Theory (MAUT) method to choose a motorcycle product. The MAUT method has advantages, including simple calculations, leads to the ultimate value used to classify vehicles and determines the weight value of the criteria that leads to vehicle selection according to the buyer's wishes and needs. The data sources are based on the data required, namely observations and interviews with the public as motorcycle users and motorcycle dealers in the North Sumatra Region as a sample. Price, quality, design, after sales, fuel consumption and popularity are the criteria for evaluation. While Honda, Yamaha and Suzuki for motorcycles moped; sport; and matic are the alternatives used in research. The ranking results are obtained with the final value on alternative 1 = 0.8500; alternative 2 = 0.7500; and alternative 3 = 0.2250, so alternative 1 (Honda (New Vario 125 Esp CBS-ISS) is the alternative chosen as a recommendation in the selection of two-wheeled vehicles based on needs. The results of this study demonstrate that MAUT can be used as a recommendation for the public in choosing motorcycle products based on needs.

The purpose of this research is to analyze using artificial neural network techniques in predicting the realization of provincial government spending in Indonesia according to the type of expenditure with Back-Propagation. This research needs to be done because it can be seen from the side of realization of provincial government spending in Indonesia that there can be a surplus and a deficit. Therefore, it is necessary to make predictions as an effort to address this. The data comes from the publication by the Central Statistical Agency of the provincial government of financial statistics (BPS). Financial provincial government data was collected through local government financial surveys from provincial government agencies in Indonesia. The analysis process uses the help of Rapidminer software and is validated with K-Fold values from 2 to 10. The data is divided into training data and testing data. Training data is data from 2016-2018 and testing data is data from 2017-2019. Several architectural models were tested namely '3-2-1; 3-5-1; 3-10-1; 3-5-10-1 'to obtain an accurate prediction by considering the value of Root Mean Square Error (RMSE). The results of the back-propagation analysis state that the 3-5-1 model is the best model with an RMSE value of 0.027 at k-fold = 9 for training data and an RMSE value of 0.035 for testing data. These results confirm that the back-propagation algorithm can be implemented in this case.

Food is the ingredient that enables people to grow, develop, and achieve. For this reason, food quality and types of food must be considered so that they are safe for consumption and managed. Some plant-based foodstuffs are often processed and consumed by the community, even the most needed in food processing. In this case, the research was carried out using data mining with market basket analysis algorithms to obtain very valuable information to decide the inventory of the type of material needed. Market Based Analysis method is used to analyze all data and create patterns for each data. One method of Market Based Analysis in question is the association rule with a priori algorithm. This algorithm produces sales transactions with strong associations between items in the transaction which are used as sales recommendations that help users (owners) get recommendations when users see details of the itemset purchased. From the results of the trials in this study, it was found that the greater the minimum support (minsup) and minimum confidence (minconf), the less time it takes to produce recommendations and the fewer recommendations are given, but the recommendations given come from transactions that often appear.

The purpose of this research is to choose the best method by comparing two classification methods of data mining C4.5 and Naïve Bayes on Educational Data Mining, in which the data used is student graduation data consisting of 79 records. Both methods are tested for validation with 10-ford X Validation and perform a T-Test difference test to produce a table that contains the best method ranking. Different results were obtained for each method. Based on the results of these two methods, it is very influential on the dataset and the value of the area under curve in the Naïve Bayes method is better than the C4.5 method in various datasets. Comparison of the method with the 10-Ford X Validation test and the T-Test difference test is that the Naïve Bayes method is better than C4.5 with an average accuracy value of 73.41% and an under-curve area of 0.664.

The lower Davies Bouldin Index (DBI) is considered the best clustering algorithm based on the criteria that yields a cluster set. The purpose of this research is to optimize the clustering results using DBI. The data sources used are the number of villages that have school facilities and the level of education is obtained from the government website (https://www.bps.go.id). The level of education in question is senior high school and vocational high school. The method used is k-means. The results show that from the number of clusters (k = 2, 3, 4, 5, 6) the optimal DBI for (k = 2) is obtained with a value of 0.168 for Measure Type = Mixed Measures. For the value of k = 2, a mapping of areas with L0 (low) = 31 province and L1 (high) = 3 provinces is obtained. The final centroids obtained for each cluster are L0 (315 and 155) and L1 (1710 and 1259). Based on the results of mapping by optimizing k-means and DBI, more than 90% of the villages still have school facilities, especially at the high school and vocational high school levels.

Statistical data are indispensable for macro-economic planning activities such as the Gross Regional Domestic Product (GRDP) where data can determine the economic development strategies and policies that have been adopted and can be continued in the future. This study draws on quantitative data sources from the Regional Statistical Agency of Jakarta for the period 2017-2019, the subject of the Gross Regional Domestic Product based on current business prices. The aim of this research is to test and predict the level of accuracy of GRDP at current prices based on business fields using the Linear Regression method supported by Rapid Miner software. The results show that the validated Linear Regression algorithm with K-Fold values from 2 to 10 with the sampling type linear sampling and shuffled sampling can be used and implemented with the smallest Root Mean Square Error value of IDR 9,977,431 at k = 10 for the sampling.

The photovoltaic (PV) for irrigation system is an emerging technology to harness the solar energy. The performance of the PV modules depends on the incident solar radiation, geographical location, and the surface temperature of the modules. The performance of the PV system needs to be monitored by manually or embedded controllers. The commercially available technologies for monitoring the system are costlier and need to be optimized. The Arduino controller is used to monitor the performance of the photovoltaic (PV) system in Coimbatore (11.016° N, 76.9558° E), Tamilnadu, India. The PV surface temperature is monitored and controlled by flowing the water above the module by setting the mean ambient temperature as a reference temperature 34 °C when the system exceeds the reference temperature. PV surface temperature is reduced up to 16°C thus improved the electrical efficiency by 17% compare to the reference module. The Arduino controller control the relay to switch on the motor to control the mass flow rate of the water at 0.0028kg/s. The various parameters are measured such as voltage, current, and solar radiation of the location and analyzed. The estimated cost of monitoring system and various sensor is 10$ which cost comparatively 50% lower than the other PV monitoring controllers. This method can be employed in the medium and large-scale irrigation system.

In Polarimetric SAR (PolSAR) and synthetic aperture radar (SAR) requires efficient filtering approach for effective processing of captured quad-polarized images. Due to polarization speckles arises in the captured PolSAR images which causes complexity in collection of data from PolSAR images. This limitation has been overcome through application of effective despeckling technique for efficient processing of PolSAR images. Several techniques have been evolved over past three decades to reduce those speckle in image captured by PolSAR, and recent research studies illustrated a efficient trend for filtering local single-point to non-local patch-based or global collaborative filtering. In this paper, applied contourlet technique for effective despeckling of PolSAR images. This contourlet transformation technique perform thresholding and transformation for efficient despeckling of captured images. Contourlet transformation technique is applied in PolSAR images with speckle and performance metrices are observed. Contourlet technique is comparatively examined with conventional transformation technique which involved in despeckling of images. Analysis of results exhibited that DWT (Discrete Wavelet Transform) approach exhibits similar performance related to contourlet transformation technique. Through analysis of results it is observed that computation time also significantly reduced in contourlet transformation rather than conventional technique. Further mean and window sizing also significantly maintained in contourlet technique for various noise variance.

This is Pre-Print article will be published in IOP Journal of Physcis Conference Series

This paper has been published in International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET) Volume 3 Issue 1 year of 2017

Making a decision is a crucial step in a management, the decision will have a significant impact on the policies to be taken by the top management, SMART (Simple Multi-Attribute Rating Technique) is the method used for making a decision with the completion of many criteria with results pretty good, this study provides an overview of how the methods SMART (Simple Multi-Attribute Rating Technique) works with many criteria.

This Paper has been presenting in NICTE (1st Nommensen International Conference on Technology and Engineering 2017 (1st NICTE 2017)) Medan in 11-12 July 2017, this paper already submited to IOP According the Chairman Board NICTE and waiting to be published by IOP

The purpose of this study was to evaluate the back-propagation model by optimizing the parameters for the prediction of broiler chicken populations by provinces in Indonesia. Parameter optimization is changing the learning rate (lr) of the backpropagation prediction model. Data sourced from the Directorate General of Animal Husbandry and Animal Health processed by the Central Statistics Agency (BPS). Data is the population of Broiler Chickens from 2017 to 2019 (34 records). The analysis process uses the help of RapidMiner software. Data is divided into 2 parts, namely training data (2017-2018) and testing data (2018-2019). The backpropagation model used is 1-2-1; 1-25-1 and 1-45-1 with a learning rate (0.1; 0.01; 0.001; 0.2; 0.02; 0.002; 0.3; 0.03; 0.003). From the three models tested, the 1-45-1 model (lr = 0.3) is the best model with Root Mean Squared Error = 0.028 in the training data. With this model, the prediction results obtained with an accuracy value of 91% and Root Mean Squared Error = 0.00555 in the testing data.

Waste disposal of expended lithium-ion batteries enables recovery, recycling and reduction of greenhouse gas emissions. Complete discharge of electrochemical devices, especially release through electrode material and current collector (copper/aluminum foils). The release among electrode material particles, is the essential requirement for ensuring the energy yield of polymer electrolytes. In this work, existing technologies which used release electrode materials with spent lithium-ion batteries. Treating the underlying effects of lithium-ion battery oxidation is especially complex of the indirect pathways identified. Aging mechanisms are also divided into three degradation modes (DMs) for simplicity: loss of conductivity, a deficit of active material, and decline of lithium production. It’s launched the lithium-ion secondary battery structure and assemblies outlined all forms of recycling processes from spent lithium-ion secondary batteries and provided some examples of common mixed recycling processes. Also, the problems and prospects of their recycling technology is discussed.

Wind energy is a created due the uneven heating of the earth surface and Coriolis acceleration. Wind energy source is capable of continuously and sustainably producing energy from renewable sources (RES). However, energy generation from the wind power plant has number of issues, such as initial investment costs, wind power plant stationary properties and difficulty in identifying wind power zones. Three deep learning algorithms are utilized in the study for predict short-term wind power generation from wind speed data. We suggested a system that would effectively predict wind power values of wind power by utilizing machine learning algorithms. The machine algorithms adopted for this study is Long Short-Term Memory (LSTM), Gated Reference Unit (GRU) and Recurrent Neural Network (RNN). The models proposed are applied in six times to the projection of wind farm output. The error analysis to balance performance and other approaches is carried out. More concerns are also discussed on short-term wind energy forecasts accuracy improvement. Furthermore, the results have shown that machine learning models can be used in locations other than models. This research showed that, if the basic location model is reasonable, machine learning algorithms could be efficiently used before installation of the wind power plants in an unknown geographical area.

The purpose of this study was to analyze the k-medoids method in conducting cluster mapping in the ratio of the number of students and teachers in Indonesia by region, especially at the elementary school level. The data source is secondary obtained from the Ministry of Education and Culture which is processed by the Central Statistics Agency (abbreviated as BPS) in the BPS Catalog: 4301008 concerning the Portrait of Indonesian Education. The analysis process uses the help of Rapid Miner software by using parameters of the Davies Bouldin Index (DBI) and Performance (Classification). By using three cluster labels, namely the high cluster (K1), normal cluster (K2) and poor cluster (K3), it was found that 3 provinces were in the high cluster, 9 provinces were in the normal cluster and 22 provinces were in the fewer clusters. By testing the cluster results (k = 3) through the DBI parameter the value = 0.587 was obtained. This shows that the results of the cluster formed are optimal (the smaller the better). The test results with the parameter Performance (Classification) show the results of classification error = 2.50%. The results of the research can be used as information to determine the ratio of students and teachers because the higher the value of this ratio means that the level of teacher supervision and attention to students is reduced so that the quality of teaching tends to be lower.

Present research study shows the evacuated tubes coupled with solar still to enhance the yield. Basin area of 1-meter square taken for the experiments and 14 evacuated tubes have been used at a lower side of the still. Calcium stones as sensible heat storage material (SHSM) have also been utilized to enhance the yield in present work. Three modes of experiments have been performed namely conventional solar still (CSS), solar still with evacuated tubes (SSET) and solar still with evacuated tubes and calcium stones (SSETCS) during January to June 2019 at Patan District, Gujarat. Groundwater was taken as feed water in present experiments. It was examined that the average yield was enhanced by 113.52 % and 104.68 % by use of SSET and SSETCS. The payback time (PBT) also determined in current work, and it was 237 for SSETCS. At last, it was concluded that the calcium stones are good SHSM to reduce water quantity and accumulate the heat during the daytime.

Accidents involving vehicles occur almost every day. Most of the accidents are caused due to exhaustion of driver’s consciousness but this cause goes almost unreported due to the absence of systems to detect the fatigue. Techniques to measure driver fatigue can be invasive or non-invasive. All in all, driver-related information uncover a more grounded relationship to the driver's carefulness level than vehicle-related ones, which experience the ill effects of a bigger affectability to outer parameters, for example, street and traffic conditions. A measure of the amount of grip that the driver applies on the steering wheel is a more direct and non-invasive measure of his fatigue. An alcohol sensor when added to this system can further be used to check if the variation in grip is due to the effect of alcohol. A suitable algorithm is developed to confirm the occurrence of driver fatigue. The GSM and the GPS units attached to the system can be useful in notifying the control room in the event of driver fatigue. This project can be very useful in detecting fatigues in drivers associated with vehicle fleets using IoT.

In the recent one and half decades the service oriented architecture (SoA) is the emerging technology and also inevitable for the web community people. The relevant applications are incorporated together through the web service component that provides bountiful Information to the people and the customers all over the world today. Here orchestration and choreography plays a vital role in SoA. Using the initial and the basic information, it is possible to predict the unforeseen schedule of the scenario through random process methodologies. In that particularly stochastic model is much appreciated for the above said. The recommendations can be obtained through the above method and provides all the necessary information’s to the customers using SoA interfaces. Normally hybrid linked applications were in the form of SoA. In the recent year of 2016 the leading journal established the role of SoA is an inevitable web services component and also acting as a aggregation in distributed systems (Traore, B.B., et al., 2016. Integrating MDA and SOA for improving telemedicine services. Telematics and Informatics, 33(3), pp.733–741). For any cross platform communication and commercial transactions can be possible via SoA modern architecture, which is emphasized in the journal published in the year 2014 (Su, X.Y., et al., 2014. A service oriented tele-health promotion information system with mobile application. Procedia Computer Science, 37, pp.274–281). The integration of Applications which has got some relevancy and homogeneity needs interoperable communications in health care systems. Decision making support can also be possible through the above said scenario which is accomplished through SoA [21]. In the research work 21 relevant papers were analyzed in deep and identified the gap about the methodology is the strenuous job. The real theme of the research work is to provide quicker information’s to the customers without making them in starvations.

It’s a new era technology in the field of medical engineering giving awareness about the various healthcare features. Deep learning is a part of machine learning, it is capable of handling high dimensional data and is efficient in concentrating on the right features. Tumor is an unbelievably complex disease: a multifaceted cell has more than hundred billion cells; each cell acquires mutation exclusively. Detection of tumor particles in experiment is easily done by MRI or CT. Brain tumors can also be detected by MRI, however, deep learning techniques give a better approach to segment the brain tumor images. Deep Learning models are imprecisely encouraged by information handling and communication designs in biological nervous system. Classification plays an significant role in brain tumor detection. Neural network is creating a well-organized rule for classification. To accomplish medical image data, neural network is trained to use the Convolution algorithm. Multilayer perceptron is intended for identification of a image. In this study article, the brain images are categorized into two types: normal and abnormal. This article emphasize the importance of classification and feature selection approach for predicting the brain tumor. This classification is done by machine learning techniques like Artificial Neural Networks, Support Vector Machine and Deep Neural Network. It could be noted that more than one technique can be applied for the segmentation of tumor. The several samples of brain tumor images are classified using deep learning algorithms, convolution neural network and multi-layer perceptron.