Asian Journal of Research in Computer Science

Nowadays the guts malady is one amongst the foremost causes of death within the world. Thus it s early prediction and diagnosing is vital in medical field, which might facilitate in on time treatment, decreasing health prices and decreasing death caused by it. The treatment values the disease is not cheap by most of the patients and Clinical choices are usually raised supported by doctors‟ intuition and skill instead of on the knowledge-rich information hidden within the stored data. The model  for prediction of heart disease using a classification techniques in data mining reduce medical errors, decreases unwanted exercise variation, enhance patient well-being and improves patient results. The model has been developed to support decision making in heart disease prediction based on data mining techniques. The experiments were performed using the model, based on the three techniques, and their accuracy in prediction noted. The decision tree, naïve Bayes, KNN (K-Nearest Neighbors) and WEKA API (Waikato Environment for Knowledge Analysis-application programming interface) were the various data mining methods that were used. The model predicts the likelihood of getting a heart disease using more input medical attributes. 13 attributes that is: blood pressure, sex, age, cholesterol, blood sugar among other factors such as genetic factors, sedentary behavior, socio-economic status and race has been use to predict the likelihood of patient getting a Heart disease until now. This study research added two more attributes that is: Obesity and Smoking.740 Record sets with medical attributes was obtained from a publicly available database for heart disease from machine learning repository with the help of the datasets, and the patterns significant to the heart attack prediction was extracted and divided into two data sets, one was used for training which consisted of 296 records & another for testing consisted of 444 records, and the fraction of accuracy of every data mining classification that was applied was used as standard for performance measure. The performance was compared by calculating the confusion matrix that assists to find the precision recall and accuracy. High performance and accuracy was provided by the complete system model. Comparison between the proposed techniques and the existing one in the prediction capability was presented. The model system assists clinicians in survival rate prediction of an individual patient and future medication is planned for. Consequently, the families, relatives, and their patients can plan for treatment preferences and plan for their budget consequently.

Diabetic Retinopathy (DR) is a medical condition where the retina is damaged because fluid leaks from blood vessels into the retina. Ophthalmologists recognize diabetic retinopathy based on features, such as blood vessel area, exudes, hemorrhages, microaneurysms and texture.

Aim: The focus of this paper is to evaluate the performance of Decision Tree (DT), Support Vector Machine (SVM) and Probabilistic Neural Network (PNN) Classifiers in Diabetes Retinopathy Detection.

Results: Corresponding results showed SVM has the best classification strength by achieving Recognition Accuracy (RA) of 98.50%, while PNN and DT achieved RA of 97.60% and 89.20% respectively. In terms of False Acceptance Rate (FAR) and False Rejection Rate (FRR), SVM has the least values of 7.21, 8.10 while DT and PNN showed 11.10, 9.30 and 13.21, 10.10 respectively. However, in this paper a Mobile based Diabetes Retinopathy Detection System was developed to make the system available for the masses for early detection of the disease.

Deliberating the importance of rainfall in determining process such as agriculture, flood and water management, these study aim at evaluation of non-linear techniques on seasonal rainfall prediction (SRP). One of the non-linear method widely used is the Artificial Neural Networks (ANN) approach which has the ability of mapping between input and output patterns. The complexity of the atmospheric processes that generate rainfall makes quantitative forecasting of rainfall an extremely, difficult task. The research goal is to train/develop Artificial Neural Network model using backward propagation algorithm to predict seasonal Rainfall. Using some meteorological variables like, sea surface temperature (SST), U-wind at (surface, 700, 850 and 1000), air temperature, specific humidity, ITD and relative humidity. The study adopt  monthly June-October (JJASO) rainfall data and January-May (JFMAM) monthly data of SST, U-wind at (surface, 700, 850 and 1000), air temperature, specific humidity and relative humidity for a period of 31 years (1986-2017) over Ikeja. The proposed ANN model architecture (9-4-1) in training the network using back-propagation algorithm indicated that the statistical performance of the model for predicting 2013 to 2017 (JJASO) rainfall amount indicated as follows; MSE, RMSE, and MAE were 7174, 84.7 and 18.6 respectively with a high statistical coefficient of variation of 94% when the ANN model prediction is validated with the observed rainfall. The result indicated that the propose ANN built network is reliable in prediction of seasonal rainfall amount in Ikeja with a minimal error.

Along with the development of technology in retail, consumers have increased their expectation about experience convenience in retail. Starting with the growth of various platform, the next development is the experience that combined both offline and online service known as Omnichannel. The Omnichannel Service Adoption is explained by Wixom Model shows the relationship of object-based beliefs, channel integration quality, perceived fluency, and internal and external usage experience as moderating effects of perceived fluency. The adoption of Omnichannel is important to deliver a consistency of data and user experience compared to multichannel. The research uses quantitative approach with Structural Equation Model (SEM) PLS for data analytic. The population is referred to Berrybenka, a prominent fashion e-commerce in Jakarta, customers. The result shows that Breadth Channel Choice, Channel Service Transparency, Content Consistency and Process Consistency have a significant and positive influence on perceived fluency. The implication and limitation of the research are also highlighted.

The study acknowledged the Internet of Things (IoT) as it relates to the increasement of agriculture and rural productivity. It’s importance in the area of the irrigation/fertilizer application, weather forecast, internet banking, tracking of farm produce, pests, disease handling, and control were seen. However, Pakistani agriculture has not indicated such realignment and transformation due to some challenges. Rural cities of Pakistan face several identical issues in the domains of agriculture, connectedness, health, transport, water, and education, and many more that can play a necessary part in the development of rural areas, which claims for potentially comparable solutions to be applied towards solving these issues. The purpose of this research is to check out the potential contributions of the IoT technologies towards poverty minimization in these rural areas, in order with the requirements are seen in these societies and with the concern on agriculture. The paper classifies the usage of IoT technology can easily reduce the agricultural needs of these communities or also improve their lifestyle for the domains of crop farming, weather forecasting, rural financing, livestock farming, growth management, market identification, and forest ranging. Accordingly, some recommendations were carried out to oppose these barriers and move Pakistan agriculture to an excellent status of world-class standards.