Intelligent Prediction System Research Articles

Machine Learning and Natural Language Processing for Prediction of Human Factors in Aviation Incident Reports

In the aviation sector, human factors are the primary cause of safety incidents. Intelligent prediction systems, which are capable of evaluating human state and managing risk, have been developed over the years to identify and prevent human factors. However, the lack of large useful labelled data has often been a drawback to the development of these systems. This study presents a methodology to identify and classify human factor categories from aviation incident reports. For feature extraction, a text pre-processing and Natural Language Processing (NLP) pipeline is developed. For data modelling, semi-supervised Label Spreading (LS) and supervised Support Vector Machine (SVM) techniques are considered. Random search and Bayesian optimization methods are applied for hyper-parameter analysis and the improvement of model performance, as measured by the Micro F1 score. The best predictive models achieved a Micro F1 score of 0.900, 0.779, and 0.875, for each level of the taxonomic framework, respectively. The results of the proposed method indicate that favourable predicting performances can be achieved for the classification of human factors based on text data. Notwithstanding, a larger data set would be recommended in future research.

Assessment of predicting hourly global solar radiation in Jordan based on Rules, Trees, Meta, Lazy and Function prediction methods

Nowadays, predicting solar radiation is widely increased to maximize the efficiency of solar systems globally. Meteorological data from metrological stations is used to implement the intelligent prediction systems. Unfortunately, uncertainty in the used solar variables and the selected prediction models would increase the difficulties in using intelligent models to predict solar radiation. Several studies perfectly estimated solar radiation using only time and date variables. The main objective of this study is to review different prediction methods in predicting the solar radiation of Jordan. To achieve this target, five main methods including Rules, Trees, Meta, Lazy and Function Methods are selected, and then the most important and used algorithms in each method are selected to build a prediction model. The study shows that M5Rule, Random forest, Random committee, Instance Based Learning with Parameter K and multi-layer perceptron are the best algorithms in Rules, Trees, Meta, Lazy, and Function Methods respectively. Random forest algorithm performed better than other algorithms in predicting global solar radiation. The results of the analysis found that the accuracy of prediction depends on the used category, training algorithm and variables combinations.

Using Machine Learning to Predict Outcomes of Accident Cases in Moroccan Courts

Intelligent prediction systems help a lot in decision-making. Various studies have dealt with the application of these systems in the field of justice. In Morocco, the courts make many judgments every day. These judgments, once processed, can be very useful for judges, lawyers, and citizens. In this study, we focus on accident cases. We collected a dataset that comprises 514 legal judgments from the Errachidia Court in Morocco. Next, this dataset has been processed in order to extract important characteristics that can further control the outcomes of such a type of case. We believe that the compiled dataset is a valuable resource for decision-making. Later, different classification algorithms, such as linear regression, vector support machines, and decision trees, will be applied to the compiled dataset and general conclusions on the performances will be drawn.

О СОЗДАНИИ НЕЙРОСЕТЕВОЙ ГИБРИДНОЙ СИСТЕМЫ ДЛЯ ПРОФЕССИОНАЛЬНОГО ОРИЕНТИРОВАНИЯ СТУДЕНТОВ

The article describes an intelligent predictive system that allows evaluating the student's capabilities in various areas of analytical activity. When designing the prognostic apparatus of this system, a hybrid intelligent approach is taken, combining the advantages of existing methods. Its components are the neural network model and a group method of data handling. In addition, the most popular professions in the labor market are identified; professional skill maps are developed on the basis of the requirements. The training sample of the system is supplemented with images generated through the Monte Carlo method. Applying data on student success in selected key disciplines, as well as other available information, the system offers a numerical equivalent of potential for these professions. In addition to the recommendations, a student has the opportunity to timely and consciously adjust the educational focus of his educational process; this has a positive effect on the graduates' competitiveness in a higher educational establishment.

Intelligent assessment and prediction system for somatic fitness and healthcare using machine learning

This study proposes a health assessment and predictive assistance system for intelligent health monitoring. Through machine learning, the tool features a customized set of quantitative measurements and web analysis systems for physical and mental fitness. The system replaces the manpower and time requirements of the past necessary to conduct interviews and keep paper records, allowing users to observe and analyze physical and mental fitness status through the webpage. To achieve this, ECG, EEG, and EMAS are used to follow physiological, psychological, and meridian energy states. ASP.NET software is used as a development tool for the system cloud page, which constructs, documents, evaluates, and predicts functions for the smart health assistance system. The measurement data is entered and recorded in the cloud database. The data is used to construct an assessment and prediction of the user’s state of mind and body through machine learning methods, as well as the individual’s physical and mental fitness.

Ensemble Machine Learning Approach for Diabetes Prediction

The technological advancements applied in the area of healthcare systems helps to meet the requirement of increasing global population. Due to the infections by the various microorganisms, people around the world are affected with different types of life-threatening diseases. Among the different types of commonly existing diseases, diabetes remains the deadliest disease. Diabetes is a major cause for the change in all physical metabolism, heart attacks, kidney failure, blindness, etc. Computational advancements help to create health care monitoring systems for identifying different deadliest diseases and its symptoms. Advancements in the machine learning algorithms are applied in various applications of the health care systems which automates the working model of health care equipment’s and enhances the accuracy of disease prediction. This work proposes the ensemble machine learning based boosting approaches for developing an intelligent system for diabetes prediction. The data collected from Pima Indians Diabetes (PID) database by national institute of diabetes from 75664 patients is used for model building. The results show that the histogram gradient boosting algorithms manages to produce better performance with minimum root mean square error of 4.35 and maximum r squared error of 89%. Proposed model can be integrated with the handheld biomedical equipment’s for earlier prediction of diabetes.

An intelligent bio-inspired system for detection and prediction of osteoporosis

Osteoporosis, a bone-related disorder that causes bone loss and other medical complications. Osteoporosis diagnosis is a lengthy process and requires a number of procedures. This disease is found to be difficult to detect in remote areas due to lack of high-tech equipment. Vibroacoustic response of bones may recognize the quality of the bone. The signal (Mel-frequency cepstral coefficients) from the reflex hammer which is used in an artificial neural network provides more than 70% accuracy. To avoid the above-mentioned problems and to improve the process of identifying this disease and to improve the efficiency and accuracy of the identification process, we propose this method which helps to determine the chance of this disease in patients by analyzing a classifier using an artificial immune system that classifies the affected and the unaffected using the history of the patient The use of the artificial immune system is 94 per cent effective. The classifier we used therefore defines and predicts the probability of the disease occurring.

An AI-based intelligent system for healthcare analysis using Ridge-Adaline Stochastic Gradient Descent Classifier

Recent technological advancements in information and communication technologies introduced smart ways of handling various aspects of life. Smart devices and applications are now an integral part of our daily life; however, the use of smart devices also introduced various physical and psychological health issues in modern societies. One of the most common health care issues prevalent among almost all age groups is diabetes mellitus. This work aims to propose an artificial intelligence-based intelligent system for earlier prediction of the disease using Ridge-Adaline Stochastic Gradient Descent Classifier (RASGD). The proposed scheme RASGD improves the regularization of the classification model by using weight decay methods, namely least absolute shrinkage and selection operator and ridge regression methods. To minimize the cost function of the classifier, the RASGD adopts an unconstrained optimization model. Further, to increase the convergence speed of the classifier, the Adaline Stochastic Gradient Descent Classifier is integrated with ridge regression. Finally, to validate the effectiveness of the intelligent system, the results of the proposed scheme have been compared with state-of-the-art machine learning algorithms such as support vector machine and logistic regression methods. The RASGD intelligent system attains an accuracy of 92%, which is better than the other selected classifiers.

An Intelligent Prediction System of Students Academic Performance based on Deep Learning and FPSOPCNN

This paper proposes a new method based on text extraction techniques for predicting student outcomes using cognitive computation. Predicting student academic achievement is most helpful in helping educators and learners improve their teaching and learning processes. This shows that these students have different experiences that influence their level of information capture in the classroom as they have the potential to use different lenses for training. This document provides a predictive examination of student academic performance in Tamil Nadu College in India during the academic year 2018 and 2019. First, this work applies statistical examination to gain insights from the data. Then, two datasets were obtained. The first dataset contains variables obtained before the beginning of the school year and the second includes study variables collected two months after the beginning of the semester. Convolution Neural Network and Fuzzy Particle Swarm Optimization Pulse Coupled Neural Network (FPSOPCNN) are designed to predict the end-of-year student performance for each dataset.

A predictive system integrating intrinsic mode functions, artificial neural networks, and genetic algorithms for forecasting S&P500 intra‐day data

SummaryThere is an abundant literature on the design of intelligent systems to forecast stock market indices. In general, the existing stock market price forecasting approaches can achieve good results. The goal of our study is to develop an effective intelligent predictive system to improve the forecasting accuracy. Therefore, our proposed predictive system integrates adaptive filtering, artificial neural networks (ANNs), and evolutionary optimization. Specifically, it is based on the empirical mode decomposition (EMD), which is a useful adaptive signal‐processing technique, and ANNs, which are powerful adaptive intelligent systems suitable for noisy data learning and prediction, such as stock market intra‐day data. Our system hybridizes intrinsic mode functions (IMFs) obtained from EMD and ANNs optimized by genetic algorithms (GAs) for the analysis and forecasting of S&P500 intra‐day price data. For comparison purposes, the performance of the EMD‐GA‐ANN presented is compared with that of a GA‐ANN trained with a wavelet transform's (WT's) resulting approximation and details coefficients, and a GA‐general regression neural network (GRNN) trained with price historical data. The mean absolute deviation, mean absolute error, and root‐mean‐squared errors show evidence of the superiority of EMD‐GA‐ANN over WT‐GA‐ANN and GA‐GRNN. In addition, it outperformed existing predictive systems tested on the same data set. Furthermore, our hybrid predictive system is relatively easy to implement and not highly time‐consuming to run. Furthermore, it was found that the Daubechies wavelet showed quite a higher prediction accuracy than the Haar wavelet. Moreover, prediction errors decrease with the level of decomposition.

COMPARISON OF DT& GBDT ALGORITHMS FOR PREDICTIVE MODELING OF CURRENCY EXCHANGE RATES

Recently, many uses of artificial intelligence have appeared in the commercial field. Artificial intelligence allows computers to analyze very large amounts of information and data, reach logical conclusions on many important topics, and make difficult decisions, this will help consumers and businesses make better decisions to improve their lives, and it will also help startups and small companies achieve great long-term success. Currency exchange rates are important matters for both governments, companies, banks and consumers. The decision tree is one of the most widely artificial intelligence tools used in data mining. With the development of this field the decision tree and Gradient boosting decision tree are used to predicate through constructed intelligent predictive system based on it. These algorithms have been used in many stock market forecasting systems based on global market data. The Iraqi dinar exchange rates for the US dollar are affected in local markets, depending on the exchange rate of the Central Bank of Iraq and the features of that auction. The proposed system is used to predict the dollar exchange rates in the Iraq markets Depending on the daily auction data of the Central Bank of Iraq (CBI). The decision tree and Gradient boosting decision tree was trained and testing using dataset of three-year issued by the CBI and compare the performance of both algorithms and find the correlation between the data. (Runtime, accuracy and correlation) criteria are adopted to select the best methods. In system, the characteristic of artificial intelligence have been integrated with the characteristic of data mining to solve problems facing organization to use available data for decision making and multi-source data linking, to provide a unified and integrated view of organization data.

Investigated iterative convergences of neural network for prediction turning tool wear

This study investigates the iterative convergences of neural network for prediction turning tool wear. For the smart manufacturing, the intelligent prediction systems have been gradually developing for processing of CNC machine tools. Recently, many artificial intelligent algorithms of machine learning have been widely applied for forecasting and decision making in intelligent manufacturing. In general, the cutting tool wear in manufacturing of CNC machine tool plays a major role for a high quality and an efficient operation, but it is very difficult to diagnose and prognoses the tool wear for tool life due to many cutting parameters. Therefore, the study investigates the iterative gradient convergences of backpropagation neural network (BNN) algorithm for prediction tool life with analytics of its convergence and stability. The estimative methods of iterative convergences include stochastic gradient descent (SGD), momentum, adaptive gradient (Adagrad), adaptive delta (Adadelta), and adaptive moment (ADAM) algorithms. In BNN prediction model, the data inputs are the cutting speed, feed rate, and total material removal volume and data output is tool wear measured from the microscope. Results showed that the tool wear curves at different cutting conditions can be predicted and trained using BNN model for intelligent manufacturing. In addition, the convergence of ADAM gradient for the tool wear in all cases is the best prediction for the BNN model. However, it is worth to notice that the momentum gradient is faster training speed to converge to a constant error at fewer iteration numbers.

The Architecture of Intelligent Career Prediction System based on the Cognitive Technology for Producing Graduates to the Digital Manpower

This research is a documentary research aimed at designing the architecture of the intelligent career prediction system based on the cognitive technology for producing graduates to the digital manpower. The research methods were divided into three phases: Phase 1, Composition Synthesis of Intelligent Career Prediction System. Phase 2, Intelligent Career Prediction System Architecture Designing based on the Cognitive Technology for producing graduates to the digital manpower. Phase 3, an assessment of the suitability of the architecture of the intelligent career prediction system based on the cognitive technology for producing graduates to the digital manpower. The architecture of the intelligent career prediction system by using the cognitive technology can be divided into three parts: 1) People involved in the architecture of the intelligent career prediction system consisting of five groups of related persons: students, staff, teachers, digital Enterprises, system administrator. 2) The architecture of the intelligent career prediction system consisting of four components: 1) User management, 2. Prediction Data Management, 3) Prediction Management system, 4) Prediction Display system, and cloud computing, an assessment of the suitability the architecture of the intelligent career prediction system based on the cognitive technology for producing graduates to the digital manpower by nine experts in the intelligent career prediction system and cognitive technology. The statistics used in the research are Mean and standard deviation. The evaluation results showed that the developed architecture was the most suitable, with the combined mean of 4.54, and the standard deviation was 0.49.

An Intelligent Prediction System for Educational Data Mining Based on Ensemble and Filtering approaches

The ensemble approach such as boosting is based on heuristic system to develop prediction paradigms. The ensemble learning techniques are typically more accurate than individual classifiers to generate predictions. For these grounds, primarily in this study several ensemble techniques have been discussed to get a comprehensive knowledge of key methods. Among various ensemble approaches, researchers have practiced boosting mechanism to predict the performance of students. As application of ensemble methods is contemplated to be significant phenomenon in classification and prediction procedures, therefore the researchers exploited boosting technique to develop an accurate prediction pedagogical model, in view of the pronounced nature and novelty of the proposed method in educational data mining. The base classifiers including random tree, j48, knn and naïve bayes have been evaluated on 10- fold cross validation system. Moreover, filtering procedures such as oversampling (SMOTE) and under-sampling (Spread subsampling), have been exploited to further inspect any significant change in results among meta and base classifiers. Both ensemble and filtering approaches have demonstrated substantial improvement in predicting the performance of students than the application of conventional classifiers. Furthermore, based on the improvement in results two novel prediction models have been propounded after conducting performance analysis on each approach.

Developing an Intelligent System for Prediction of Optimal Dose of Warfarin in Iranian Adult Patients with Artificial Heart Valve

Introduction: Artificial intelligence (AI) research within medicine is growing rapidly. AI is poised to transform medical practice. AI has been studied in several areas of healthcare and medical practice, including diagnosing, treating and caring of patients. Warfarin is one of the most commonly prescribed oral anticoagulant. Among all anticoagulants, warfarin has long been listed among the top ten drugs causing adverse drug events. Due to narrow therapeutic range and significant side effects, warfarin dosage determination becomes a challenging task in clinical practice. The purpose of this study was to determine exact dose of warfarin needed for patients with artificial heart valve using artificial neural networks (ANN).Development: To achieved the best model, some multi-layer perceptron ANNs were constructed with different structures. The dataset used included 846 patients who had been referred to the PT clinic in Tehran heart center in the second six months of the year 2013. Finally, the best structure of ANN for warfarin dose was investigated and used for prediction system developments. In this paper the implementation of ANNs and proposed system in MatLab environment are described.Application: The effectiveness of ANNs were evaluated in terms of classification performance using 10fold cross-validation procedure and the results showed that the best model is a network that has 7 neurons in its hidden layer with an average absolute error of 0.1, disturbance rate of 0.33 and regression of 0.87. Conclusion: The achieved results reveal that ANN-based system is a suitable tool for warfarin dose prediction in Iranian patients with an artificial heartvalve. However, no system can be guaranteed to achieve 100% accuracy, but using such methods can reduce medical errors and thereby improve health care and patient safety.

An Intelligent Diagnosis System for Prediction of Heart Disease Risk based on Feature Selection and Ensemble Classification Techniques

An Intelligent Diagnosis System for Prediction of Heart Disease Risk based on Feature Selection and Ensemble Classification Techniques

Heart Disease Prediction System Using Decision Tree and Naive Bayes Algorithm.

The huge information of healthcare data is collected from the healthcare industry which is not "mined" unfortunately to make effective decision making for the identification of hidden information. The end user support system is used as the prediction application for the heart disease and this paper proposes windows through the intelligent prediction system the instance guidance for the heart disease is given to the user. Various symptoms of the heart diseases are fed into the application. The user precedes the processes by checking the specific detail and symptoms of the heart disease. The decision tree (ID3) and navie Bayes techniques in data mining are used to retrieve the details associated with each patient. Based on the accurate result prediction, the performance of the system is analyzed.

Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease

At present times, healthcare systems are updated with advanced capabilities like machine learning (ML), data mining and artificial intelligence to offer human with more intelligent and expert healthcare services. This paper introduces an intelligent prediction and classification system for healthcare, namely Density based Feature Selection (DFS) with Ant Colony based Optimization (D-ACO) algorithm for chronic kidney disease (CKD). The proposed intelligent system eliminates irrelevant or redundant features by DFS in prior to the ACO based classifier construction. The proposed D-ACO framework three phases namely preprocessing, Feature Selection (FS) and classification. Furthermore, the D-ACO algorithm is tested using benchmark CKD dataset and the performance are investigated based on different evaluation factors. Comparing the D-ACO algorithm with existing methods, the presented intelligent system outperformed the other methodologies with a significant improvisation in classification accuracy using fewer features.

Intelligent System for Accurate Prediction and Detection of Alzheimer’s Disease

Image processing is a process of converting an image into digital form and achieve some maneuvers on it, in order to get an enhanced image or to mine some useful information from it. It is a type of signal dispensation in which the input is an image, like video frame or photograph and output, may be image along with its characteristics and features associated with that image. An image is defined as a two-dimensional function F(x,y), where x and y are spatial coordinates, and the amplitude of F at any pair of coordinates (x,y) is called the intensity of that image at that point. When x, y, and amplitude values of F are finite, we call it a digital image. Image processing mainly consists of three basic steps. They are as follows: Initially, the image will be imported by using an optical scanner or by high-digital photography. Then the captured image will be subjected to the analyzation and manipulation process. These process also includes compression of data, enhancement of the image and spotting the patterns that are not visible to human eyes like satellite photography. Finally, the output will be obtained as an alternative image or any other essential feature extraction of the pre-processed image. Image Processing consists of two major types. They are Analog Image Processing and Digital Image Processing. Digital Image Processing is a process in which a digital system is developed for processing a digital image and extracting feature form of results. Digital Image Processing works on the basis of an algorithm. An Intelligent System for Accurate Detection and Prediction of Alzheimer’s Disease mainly uses the k-nearest neighbor algorithm. Alzheimer’s Disease is a type of disease in which the brain cells tend to die away and cause memory loss. In our proposed model we predict the accuracy of the amount of memory loss occurred in an affected brain. This system is mainly developed for helping the doctors and psychologists to obtain a maximum level of accuracy of the patient’s affected brain.

An Intelligent Clinical Decision Support System for Preoperative Prediction of Lymph Node Metastasis in Gastric Cancer

PurposeThe aim of this study was to develop and validate a computational clinical decision support system (DSS) on the basis of CT radiomics features for the prediction of lymph node (LN) metastasis in gastric cancer (GC) using machine learning–based analysis. MethodsClinicopathologic and CT imaging data were retrospectively collected from 490 patients who were diagnosed with GC between January 2002 and December 2016. Radiomics features were extracted from venous-phase CT images. Relevant features were selected, ranked, and modeled using a support vector machine classifier in 326 training and validation data sets. A model test was performed independently in a test set (n = 164). Finally, a head-to-head comparison of the diagnostic performance of the DSS and that of the conventional staging criterion was performed. ResultsTwo hundred ninety-seven of the 490 patients examined had histopathologic evidence of LN metastasis, yielding a 60.6% metastatic rate. The area under the curve for predicting LN+ was 0.824 (95% confidence interval, 0.804-0.847) for the DSS in the training and validation data and 0.764 (95% confidence interval, 0.699-0.833) in the test data. The calibration plots showed good concordance between the predicted and observed probability of LN+ using the DSS approach. The DSS was better able to predict LN metastasis than the conventional staging criterion in the training and validation data (accuracy 76.4% versus 63.5%) and in the test data (accuracy 71.3% versus 63.2%) ConclusionsA DSS based on 13 “worrisome” radiomics features appears to be a promising tool for the preoperative prediction of LN status in patients with GC.