Prediction Systems

Nowadays addiction to drugs and alcohol has become a significant threat to the youth of the society as Bangladesh's population. So, being a conscientious member of society, we must go ahead to prevent these young minds from life-threatening addiction. In this paper, we approach a machinelearning-based way to forecast the risk of becoming addicted to drugs using machine-learning algorithms. First, we find some significant factors for addiction by talking to doctors, drug-addicted people, and read relevant articles and write-ups. Then we collect data from both addicted and nonaddicted people. After preprocessing the data set, we apply nine conspicuous machine learning algorithms, namely k-nearest neighbors, logistic regression, SVM, naïve bayes, classification, and regression trees, random forest, multilayer perception, adaptive boosting, and gradient boosting machine on our processed data set and measure the performances of each of these classifiers in terms of some prominent performance metrics. Logistic regression is found outperforming all other classifiers in terms of all metrics used by attaining an accuracy approaching 97.91%. On the contrary, CART shows poor results of an accuracy approaching 59.37% after applying principal component analysis.

The aim of this project is to implement the heart disease prediction application. Many people all over the world like nearly 60% of the world's population are now-a-days suffering from heart disease. Heart Disease Prediction system is an android application which helps the users for the easy prediction, prevention and curing of the disease. An android application gets the disease name as the output based on the data provided by the user or patient. Android application is also used to get the preventive measures like nearby doctor details, hospitals details and location for hospitals. Proposed system consists of an intelligent system which works on the machine learning algorithms like KNN. The data entered by the user is compared with some existing standard datasets to get probability. KNN used to find the probability.

In spite of the insight commonality might have regarding rural follow, reality is that these days agricultural science diligence is correct, precise, data-driven, and vigorous than ever. The emanation of the technologies supported net of Things (IoT) has reformed nearly every trade like sensible town, sensible health, smart grid, smart home, as well as ‘‘smart agriculture or exactitude agriculture”. Applying machine learning victimization the IoT information analytics in agricultural sector can rise new advantages to extend the amount and quality of production from the crop fields to satisfy the increasing food demand. Such world-shaking advancements area unit rocking these rural approaches and generating novel and best possibilities besides variety of limitations. This paper climaxes the facility and capability of computing techniques as well as net of things, wireless detector networks, information analytics and machine learning in agriculture. The paper planned the prediction model of Apple unwellness within the apple orchards of geographical region depression victimization information analytics and Machine learning in IoT system. what is more, an area survey was conducted to grasp from the farmers regarding the trending technologies and their impact in exactitude agriculture. Finally, the paper discusses the challenges faced once incorporating these technologies within the ancient farming approaches.

Data mining has contributed various type of applications domains related to information technology, stock, marketing, healthcare and banking among them. Lung cancer is the most rapidly increase disease which is causing human death worldwide due to respiratory problems; this cancer disease has exceeded the death rate compared to breast cancer. This disease has characterized based on growth of uncontrolled cells. If this disease is not diagnosed at the early stages and cured before the second stage, it will increase the death percentage in human. This tissue will be spread rapidly to other parts of the body like brain, heart, bones, glands and liver. As from early research, there is no such tool for early detection of lung cancer disease in human.

This paper describes the development of nonlinear autoregressive moving average with exogenous input (NARMAX) models in diagnosing dengue infection. The developed system bases its prediction solely on the bioelectrical impedance parameters and physiological data. Three different NARMAX model order selection criteria namely FPE, AIC and Lipschitz have been evaluated and analyzed. This model is divided two approaches which are unregularized approach and regularized approach. The results show that using Lipschitz number with regularized approach yield better accuracy by 88.40% to diagnose the dengue infections disease. Furthermore, this analysis show that the NARMAX model yield better accuracy as compared to autoregressive moving average with exogenous input (ARMAX) model in diagnosis intelligent system based on the input variables namely gender, weight, vomiting, reactance and the day of the fever as recommended by the outcomes of statistical tests with 76.70% accuracy. © ICROS.

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4406927

Nowadays addiction to drugs and alcohol has become a significant threat to the youth of the society as Bangladesh’s population. So, being a conscientious member of society, we must go ahead to prevent these young minds from life-threatening addiction. In this paper, we approach a machinelearning-based way to forecast the risk of becoming addicted to drugs using machine-learning algorithms. First, we find some significant factors for addiction by talking to doctors, drug-addicted people, and read relevant articles and write-ups. Then we collect data from both addicted and nonaddicted people. After preprocessing the data set, we apply nine conspicuous machine learning algorithms, namely k-nearest neighbors, logistic regression, SVM, naïve bayes, classification, and regression trees, random forest, multilayer perception, adaptive boosting, and gradient boosting machine on our processed data set and measure the performances of each of these classifiers in terms of some prominent perform...

Satellite plays vital role in conveying information
around the world. Satellites  communication
systems  provide various services such as TV
broadcasting, internet, telephone, telemedicine
and GPS. However, these systems are threatened
by solar radiation so called solar flare erupted
from the Sun which is one of the major threats
faced by satellites orbiting around the Earth.
Thus, to keep satellites from being damaged an
intelligent  ANFIS based solar flare prediction
system is proposed in this paper. The system is
trained with publicly available time series data of
flares occurred in the past from 2001-2011. The
results show that the system has the accuracy of
85.47 % and it is capable of detecting the flare 24
hour before its occurrence.

Satellite plays vital role in conveying information around the world. Satellites communication systems provide various services such as TV broadcasting, internet, telephone, telemedicine and GPS. However, these systems are threatened by solar radiation so called solar flare erupted from the Sun which is one of the major threats faced by satellites orbiting around the Earth. Thus, to keep satellites from being damaged an intelligent ANFIS based solar flare prediction system is proposed in this paper. The system is trained with publicly available time series data of flares occurred in the past from 2001-2011. The results show that the system has the accuracy of 85.47 % and it is capable of detecting the flare 24 hour before its occurrence.

Widespread development of system software, the process of learning, and the excellence in profession of teaching are the formidable challenges faced by the learning behavior prediction system. e learning styles of teachers have different kinds of content designs to enhance their learning. In this learning environment, teachers can work together with the students, but the learning materials are designed by the teachers. e cognitive style deals with mental activities such as learning, remembering, thinking, and the usage of language. erefore, being motivated by the problems mentioned above, this paper proposes the concept of adaptive optimization-based neural network (AONN). e learning behavior and browsing behavior features are extracted and incorporated into the input of artificial neural network (ANN). Hence, in this paper, the neural network weights are optimized with the use of grey wolf optimizer (GWO) algorithm. e output operation of e-learning with teaching equipment is chosen...

Widespread development of system software, the process of learning, and the excellence in profession of teaching are the formidable challenges faced by the learning behavior prediction system. e learning styles of teachers have different kinds of content designs to enhance their learning. In this learning environment, teachers can work together with the students, but the learning materials are designed by the teachers. e cognitive style deals with mental activities such as learning, remembering, thinking, and the usage of language. erefore, being motivated by the problems mentioned above, this paper proposes the concept of adaptive optimization-based neural network (AONN). e learning behavior and browsing behavior features are extracted and incorporated into the input of artificial neural network (ANN). Hence, in this paper, the neural network weights are optimized with the use of grey wolf optimizer (GWO) algorithm. e output operation of e-learning with teaching equipment is chosen based on the cognitive style predicted by AONN. In experimental section, the measures of accuracy, sensitivity, specificity, time (sec), and memory (bytes) are carried out. Each of the measure is compared with the proposed AONN and existing fuzzy logic methodologies. Ultimately, the proposed AONN method produces higher accuracy, specificity, and sensitivity results. e results demonstrate that the algorithm proposed in this study can automatically learn network structures competitively, unlike those achieved for neural networks through standard approaches.