Multi-horizon ternary time series forecasting

Time series forecasting techniques have been widely applied in domains such as weather forecasting, electric power demand forecasting, earthquake forecasting, and financial market forecasting. Because of the fact that these time series are affected by a multitude of interrelating macroscopic and microscopic variables, the underlying models that generate these time series are nonlinear and extremely complex. Therefore, it is computationally infeasible to develop full-scale models with the present computing technology. Therefore, researchers have resorted to smaller-scale models that require frequent recalibration. Despite advances in forecasting technology over the past few decades, there have not been algorithms that can consistently produce accurate forecasts with statistical significance. This is mainly because state-of-the-art forecasting algorithms essentially perform single-horizon forecasts and produce continuous numbers as outputs. This paper proposes a novel multi-horizon ternary forecasting algorithm that forecasts whether a time series is heading for an uptrend or downtrend, or going sideways. The proposed system utilizes a cascade of support vector machines, each of which is trained to forecast a specific horizon. Individual forecasts of these support vector machines are combined to form an extrapolated time series. A higher level forecasting system then forward-runs the extrapolated time series and then forecasts the future trend of the input time series in accordance with some volatility measure. Experiments have been carried out on some datasets. Over these datasets, this system achieves accuracy rates well above the baseline accuracy rate, implying statistical significance. The experimental results demonstrate the efficacy of our framework

Utilizing hierarchical extreme learning machine based reinforcement learning for object sorting

Automatic and intelligent object sorting is an important task that can sort different objects without human intervention, using the robot arm to carry each object from one location to another. These objects vary in colours, shapes, sizes and orientations. Many applications, such as fruit and vegetable grading, flower grading, and biopsy image grading depend on sorting for a structural arrangement. Traditional machine learning methods, with extracting handcrafted features, are used for this task. Sometimes, these features are not discriminative because of the environmental factors, such as light change. In this study, Hierarchical Extreme Learning Machine (HELM) is utilized as an unsupervised feature learning to learn the object observation directly, and HELM was found to be robust against external change. Reinforcement learning (RL) is used to find the optimal sorting policy that maps each object image to the object’s location. The reason for utilizing RL is lack of output labels in this automatic task. The learning is done sequentially in many episodes. At each episode, the accuracy of sorting is increased to reach the maximum level at the end of learning. The experimental results demonstrated that the proposed HELM-RL sorting can provide the same accuracy as the labelled supervised HELM method after many episodes

Vehicle classification system using viola Jones and multi-layer perceptron

The automatic vehicle classification system has emerged as an important field of study in image processing and machine vision technologies' implementation because of its variety of applications. Despite many alternative solutions for the classification issue, the vision-based approaches remain the dominant solutions due to their ability to provide a larger number of parameters than other approaches. To date, several approaches with various methods have been implemented to classify vehicles. The fully automatic classification systems constitute a huge barrier for unmanned applications and advanced technologies. This project presents software for a vision-based vehicle classifier using multiple Viola-Jones detectors, moment invariants features, and a multi-layer perceptron neural network to distinguish between different classes. The results obtained in this project show the system’s ability to detect and locate vehicles perfectly in real time via live camera input

Feature map size selection for fMRI classification on end-to-end deep convolutional neural networks

The emergence of convolutional neural networks (CNN) in various fields has also paved numerous ways for advancement in the field of medical imaging. This paper focuses on functional magnetic resonance imaging (fMRI) in the field of neuroimaging. It has high temporal resolution and robust to control or non-control subjects. CNN analysis on structural magnetic resonance imaging (MRI) and fMRI datasets is compared to rule out one of the grey areas in building CNNs for medical imaging analysis. This study focuses on the feature map size selection on fMRI datasets with CNNs where the selected sizes are evaluated for their performances. Although few outstanding studies on fMRI have been published, the availability of diverse previous studies on MRI previous works impulses us to study to learn the pattern of feature map sizes for CNN configuration. Six configurations are analyzed with prominent public fMRI dataset, names as Human Connectome Project (HCP). This dataset is widely used for any type of fMRI classification. With three set of data divisions, the accuracy values for validation set of fMRI classification are assessed and discussed. Despite the fact that only one slice of every 118 subjects' temporal brain images is used in the study, the validation of classification for three training-excluded subjects known as validation set, has proven the need for feature map size selection. This paper emphasizes the indispensable step of selecting the feature map sizes when designing CNN for fMRI classification. In addition, we provide proofs that validation set should consist of distinct subjects for definite evaluation of any model performance

Fuzzy-Based Path Analysis

Video surveillance can be a very powerful tool in the fight against crime, by accurately monitoring human activities. Nevertheless, most surveillance systems today provide only a passive form of site monitoring. Extensive video records may be kept to help find the instigator of criminal activities after the crime has been committed but preventive measures usually require human involvement. In addition to this, there is a need for a large amounts of data storage to keep up to several terabytes of video streams that may be needed for later analysis

Vision-based estimation of altitude from aerial images

One of the wide engineering fields is aircraft technologies and one of the most common needs for Airplane or UAV is estimating the altitude, which is some time difficult to estimate due to weather fluctuations and instability of the main parameters like pressure and speed. However, a combination of different sensors has been used to estimate altitude to guarantee an accurate reading and it is the method used these days. To overcome this problem is to use more capable technology such as machine vision based system to estimate the altitude, as advantages light weight, intelligence and accuracy, cheaper than commercial sensors as well as, computationally inexpensive. In this paper, we propose a vision-based system that can perform altitude estimation from aerial images. The satisfactory experimental results demonstrate the effectiveness of the proposed system

Model-based viewpoint invariant human activity recognition from uncalibrated monocular video sequence

There is growing interest in human activity recognition systems, motivated by their numerous promising applications in many domains. Despite much progress, most researchers have narrowed the problem towards fixed camera viewpoint owing to inherent difficulty to train their systems across all possible viewpoints. Fixed viewpoint systems are impractical in real scenarios. Therefore, we attempt to relax the fixed viewpoint assumption and present a novel and simple framework to recognize and classify human activities from uncalibrated monocular video source from any viewpoint. The proposed framework comprises two stages: 3D human pose estimation and human activity recognition. In the pose estimation stage, we estimate 3D human pose by a simple search-based and tracking-based technique. In the activity recognition stage, we use Nearest Neighbor, with Dynamic Time Warping as a distance measure, to classify multivariate time series which emanate from streams of pose vectors from multiple video frames. We have performed some experiments to evaluate the accuracy of the two stages separately. The encouraging experimental results demonstrate the effectiveness of our framework

Vision-based verification of authentic JAKIM halal logo

In Malaysia, the authenticity of halal logo has been a great concern to its Muslim community due to the existence of different types of halal logo in the market. Due to this situation, in this paper, a detection system was developed to classify the authentic Jabatan Kemajuan Islam Malaysia (JAKIM) halal logo from the unauthentic ones. All the distinct features from the authentic logo were used in the implementation part of this project with the intention of producing a reliable detection system. The methods that are chosen to be used are SURF, SIFT, GIST, and k-means. These methods can be said is reliable and practical as the resulted accuracy which was 86.6667 was quite high

Introduction to intelligent video surveillance system

Most surveillance systems today provide only a passive form of site monitoring. Extensive video records may be kept to help find the instigator of criminal activities after the crime has been committed but preventive measures usually require human involvement. In addition to this, there is a need for large amounts of data storage to keep up to several terabytes of video streams that may be needed for later analysis. For any sense of real-time monitoring, guards often need to be employed to watch video feeds for hours on end to recognize suspicious, dangerous or potentially harmful situations. In multi-camera scene monitoring systems, this becomes quite infeasible as there can be up to 20 to 50 cameras on average in a large complex such as an airport or Megamall. However, monitoring and storage space are not the only concerns. Even if these costs can be borne, there is the additional problem of reviewing this vast amount of video data after a crime or incident has occurre

Fuzzy set theory

Fuzzy set theory was introduced in 1965 by Dr. Lotfi Zadeh to represent/manipulate data and information possessing nonstatistical uncertainties. He has developed considerably since its inception. Although initially the new and revolutionary ideas were viewed controversially in the realm of mathematics, they were more welcomed in several fields of engineering, especially control engineering, as they provided a good interface with real world applications of mathematics. The main advantage of fuzzy mathematics is that practical situations can be represented in more than one condition and mathematically rigorous calculations can be performed without the need to restrict the problem in only one representation. One automatic consequence of this is that fuzzy mathematics allows for much more realistic models of real-world situations - a need keenly felt in intelligent video surveillanc