Pemodelan data indeks komposit Kuala Lumpur menggunakan neurofuzzy

Stock market transaction is one of the most popular investments activities. There are many conventional techniques being used as a tool to predict the behaviour of the stock market, and these include technical and fundamental analysis. Recently, Artificial Intelligence (AI) such as Artificial Neural Networks (ANN), Genetic Algorithms (GA), Fuzzy Logic (FL) and Rough Set (RS) are widely used by the researchers due to their ability to predict the behaviour of the stock market efficiently. In this research, a comprehensive pre-processing data modeling of stock market is developed to acquire granular informations that represent the behaviour of the data that is to be fed to the classifier. The pre-process methodology includes splitting, scaling, normalization, feature selection, and follows by the Ten-Fold Cross Validation method as a benchmark for estimating the predictive accuracy and effectiveness of splitting and selecting the input data. Daily data of Kuala Lumpur Composite Index (KLCI) is captured and analyzed, and it is found that the movements of the Indices are unstable; hence the forecasting process becomes difficult. Therefore, in this study, a Hybrid Neurofuzzy approached using Adaptive Neurofuzzy Inference System (ANFIS) model is suggested to predict the behaviour of the Indices. Furthermore, technical indicator such as moving average, relative strength index, stochastic indicator and price of change are used to analyze the data, and these parameters become input to ANFIS. Root Mean Square Error (RMSE) and Mean Absolute Percentage of Error (MAPE) are chosen to measure the prediction accuracy. In addition, to verify the effectiveness of the ANFIS model, the economic related factors as well as natural disaster are also provided. These factors such as tsunami, human actions, politics and even psychological have influenced on stock movement, thus compliance with the proposed model The results are promising and conforming with the actual price of Composite Index at Bursa Saham Malaysia

Interactivity Recognition Graph Neural Network (IR-GNN) Model for Improving Human–Object Interaction Detection

Human–object interaction (HOI) detection is important for promoting the development of many fields such as human–computer interactions, service robotics, and video security surveillance. A high percentage of human–object pairs with invalid interactions are discovered in the object detection phase of conventional human–object interaction detection algorithms, resulting in inaccurate interaction detection. To recognize invalid human–object interaction pairs, this paper proposes a model structure, the interactivity recognition graph neural network (IR-GNN) model, which can directly infer the probability of human–object interactions from a graph model architecture. The model consists of three modules: The first one is the human posture feature module, which uses key points of the human body to construct relative spatial pose features and further facilitates the discrimination of human–object interactivity through human pose information. Second, a human–object interactivity graph module is proposed. The spatial relationship of human–object distance is used as the initialization weight of edges, and the graph is updated by combining the message passing of attention mechanism so that edges with interacting node pairs obtain higher weights. Thirdly, the classification module is proposed; by finally using a fully connected neural network, the interactivity of human–object pairs is binarily classified. These three modules work in collaboration to enable the effective inference of interactive possibilities. On the datasets HICO-DET and V-COCO, comparative and ablation experiments are carried out. It has been proved that our technology can improve the detection of human–object interactions

Data modeling for Kuala Lumpur composite index with ANFIS

Stock market transaction is one of the most popular investments activities. There are many conventional techniques being used and these include technical and fundamental analysis. Recently, AI such as ANN, GA, FL and RS are widely used by the researchers due to their ability to predict the behavior of the stock market efficiently. In this research, a comprehensive preprocessing data modeling of stock market is developed to acquire granular information that represents the behavior of the data that is to be fed to the classifier. The pre-process methodology includes splitting, scaling, normalization, feature selection, and follows by the TenFold Cross Validation method as a benchmark for estimating the predictive accuracy and effectiveness of splitting and selecting the input data. Daily data of KLCI is captured and analyzed, and it is found that the movements of the Indices are unstable; hence the forecasting process becomes difficult. A Hybrid Neurofuzzy with ANFIS is suggested to predict the behavior of the Indices. Four technical indicators are chosen to analyze the data. To verify the effectiveness of the ANFIS model, two experimental have been carried out and the results show that ANFIS method is competent in forecasting the KLCI fabulously compared to ANN

MATHEMATICAL METHODS OF INVESTIGATION AND MODELLING FOR INTERACTION PROCESSES OF RELATIVISTIC ELECTRON BEAMS WITH PLASMA

The developed mathematical models permitted to show the sufficient role of the induction effects at development of the Buneman's instability in the plasma, to reveal the influence of the Hall fields on the plasma dynamics and heating of the ions, to construct the theory of ambipolar weak-collision plasma expansion, to describe the electron beam interaction with weak-conducting plasma channel, to formulate the requirements to the parameters of the beam-plasma systems, to explain the results of some experiments. The obtained results have been introduced in the Moscow Radiotechnical Institute (Russian Academy of Sciences) at design of the collective ion accelerator and in the Computer Centre of the Moscow State University at performing researches. Application field: mathematical modelling of plasma, plasma electronics, accelerating engineering, transportation of beams.Available from VNTIC / VNTIC - Scientific & Technical Information Centre of RussiaSIGLERURussian Federatio

Predictive modelling for motor insurance claims using artificial neural networks

The expected claim frequency and the expected claim severity are used in predictive modelling for motor insurance claims. There are two category of claims were considered, namely, third party property damage (TPPD) and own damage (OD). Data sets from the year 2001 to 2003 are used to develop the predictive model. The main issues in modelling the motor insurance claims are related to the nature of insurance data, such as huge information, uncertainty, imprecise and incomplete information; and classical statistical techniques which cannot handle the extreme value in the insurance data. This paper proposes the back propagation neural network (BPNN) model as a tool to model the problem. A detailed explanation of how the BPNN model solves the issues is provided

Intergrating a Minimal Differentiator Expressions approach into CBR for linguistic pattern reuse in crime relation: Proposed method

The relation extraction of crime news can help the monitoring specialists to accelerate the crime investigation. However, constructing patterns or designing templates manually requires domain experts. Also the built patterns do not guarantee complete differentiation among different relation instances. The automatic detection of crime entities and relationship among entities can help the regulatory authorities to accelerate the crime investigation and decision support instead of being reliant on manual process. This study aims to increase the effectiveness of the extraction of crime entities and relationship among entities based on the determination of crime lingusitic pattern using Minimal Differentiator Expressions (MDEs) that represent the cases that will be used by the CBR classifier. The proposed extraction methods can help in compiling a highly accurate and machine-understandable crime knowledge bases which can support the regulatory authorities' investigation. This paper conducted on our proposed MDEs algorithm for linguistic pattern reuse in CBR approaches

Ensemble Based Filter Feature Selection with Harmonize Particle Swarm Optimization and Support Vector Machine for Optimal Cancer Classification

Explosive increase of dataset features may intensify the complexity of medical data analysis in deciding necessary treatment for the patient. In most cases, the accuracy of diagnosis system is vitally impacted by the data dimensionality and classifier parameters. Since these two processes are dependent, conducting them independently could deteriorate the accuracy performance. Filter algorithm is used to eliminate irrelevant features based on ranking. However, independent filter still incapable to consider features dependency and resulting in imbalance selection of significant features which consequently degrade the classification performance. In order to mitigate this problem, ensemble of multi filters algorithm such as Information Gain (IG), Gain Ratio (GR), Chi-squared (CS) and Relief-F (RF) are utilized as it can considers the intercorrelation between features. The proper kernel parameters settings may also influence the classification performance. Hence, a harmonize classification technique using Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is employed to optimize the searching of optimal significant features and kernel parameters synchronously without degrading the accuracy. Therefore, an ensemble filter feature selection with harmonize classification of PSO and SVM (Ensemble-PSO-SVM) are proposed in this research. The effectiveness of the proposed method is examined on standard Breast Cancer and Lymphography datasets. Experimental results showed that the proposed method successfully signify the classifier accuracy performance with optimal significant features compared to other existing methods such as PSO-SVM and classical SVM. Hence, the proposed method can be used as an alternative method for determining the optimal solution in handling high dimensional data

Efficient Task Scheduling Approach in Edge-Cloud Continuum based on Flower Pollination and Improved Shuffled Frog Leaping Algorithm

The rise of edge-cloud continuum computing is a result of the growing significance of edge computing, which has become a complementary or substitute option for traditional cloud services. The convergence of networking and computers presents a notable challenge due to their distinct historical development. Task scheduling is a major challenge in the context of edge-cloud continuum computing. The selection of the execution location of tasks, is crucial in meeting the quality-of-service (QoS) requirements of applications. An efficient scheduling strategy for distributing workloads among virtual machines in the edge-cloud continuum data center is mandatory to ensure the fulfilment of QoS requirements for both customer and service provider. Existing research used metaheuristic algorithm to solve tak scheduling problem, however, must of the existing metaheuristics used suffers from falling into local mina due to their inefficiency to avoid unfeasible region in the solution search space. Therefore, there is a dire need for an efficient metaheuristic algorithm for task scheduling.  This study proposed an FPA-ISFLA task scheduling model using hybrid flower pollination and improved shuffled frog leaping algorithms. The simulation results indicate that the FPA-ISFLA algorithm is superior to the PSO algorithm in terms of makespan time, resource utilization, and execution cost reduction, especially with an increasing number of tasks

Exploring Important Factors in Predicting Heart Disease Based on Ensemble- Extra Feature Selection Approach

Heart disease is a significant and impactful health condition that ranks as the leading cause of death in many countries. In order to aid physicians in diagnosing cardiovascular diseases, clinical datasets are available for reference. However, with the rise of big data and medical datasets, it has become increasingly challenging for medical practitioners to accurately predict heart disease due to the abundance of unrelated and redundant features that hinder computational complexity and accuracy. As such, this study aims to identify the most discriminative features within high-dimensional datasets while minimizing complexity and improving accuracy through an Extra Tree feature selection based technique. The work study assesses the efficacy of several classification algorithms on four reputable datasets, using both the full features set and the reduced features subset selected through the proposed method. The results show that the feature selection technique achieves outstanding classification accuracy, precision, and recall, with an impressive 97% accuracy when used with the Extra Tree classifier algorithm. The research reveals the promising potential of the feature selection method for improving classifier accuracy by focusing on the most informative features and simultaneously decreasing computational burden

Facial Emotion Images Recognition Based On Binarized Genetic Algorithm-Random Forest

Most recognition system of human facial emotions are assessed solely on accuracy, even if other performance criteria are also thought to be important in the evaluation process such as sensitivity, precision, F-measure, and G-mean. Moreover, the most common problem that must be resolved in face emotion recognition systems is the feature extraction methods, which is comparable to traditional manual feature extraction methods. This traditional method is not able to extract features efficiently. In other words, there are redundant amount of features which are considered not significant, which affect the classification performance. In this work, a new system to recognize human facial emotions from images is proposed. The HOG (Histograms of Oriented Gradients) is utilized to extract from the images. In addition, the Binarized Genetic Algorithm (BGA) is utilized as a features selection in order to select the most effective features of HOG. Random Forest (RF) functions as a classifier to categories facial emotions in people according to the image samples. The facial human examples of photos that have been extracted from the Yale Face dataset, where it contains the eleven human facial expressions are as follows; normal, left light, no glasses, joyful, centre light, sad, sleepy, wink and surprised. The proposed system performance is evaluated relates to accuracy, sensitivity (i.e., recall), precision, F-measure (i.e., F1-score), and G-mean. The highest accuracy for the proposed BGA-RF method is up to 96.03%. Besides, the proposed BGA-RF has performed more accurately than its counterparts. In light of the experimental findings, the suggested BGA-RF technique has proved its effectiveness in the human facial emotions identification utilizing images