Anomalous behaviour detection using heterogeneous data

Anomaly detection is one of the most important methods to process and find abnormal data, as this method can distinguish between normal and abnormal behaviour. Anomaly detection has been applied in many areas such as the medical sector, fraud detection in finance, fault detection in machines, intrusion detection in networks, surveillance systems for security, as well as forensic investigations. Abnormal behaviour can give information or answer questions when an investigator is performing an investigation. Anomaly detection is one way to simplify big data by focusing on data that have been grouped or clustered by the anomaly detection method. Forensic data usually consists of heterogeneous data which have several data forms or types such as qualitative or quantitative, structured or unstructured, and primary or secondary. For example, when a crime takes place, the evidence can be in the form of various types of data. The combination of all the data types can produce rich information insights. Nowadays, data has become ‘big’ because it is generated every second of every day and processing has become time-consuming and tedious. Therefore, in this study, a new method to detect abnormal behaviour is proposed using heterogeneous data and combining the data using data fusion technique. Vast challenge data and image data are applied to demonstrate the heterogeneous data. The first contribution in this study is applying the heterogeneous data to detect an anomaly. The recently introduced anomaly detection technique which is known as Empirical Data Analytics (EDA) is applied to detect the abnormal behaviour based on the data sets. Standardised eccentricity (a newly introduced within EDA measure offering a new simplified form of the well-known Chebyshev Inequality) can be applied to any data distribution. Then, the second contribution is applying image data. The image data is processed using pre-trained deep learning network, and classification is done using a support vector machine (SVM). After that, the last contribution is combining anomaly result from heterogeneous data and image recognition using new data fusion technique. There are five types of data with three different modalities and different dimensionalities. The data cannot be simply combined and integrated. Therefore, the new data fusion technique first analyses the abnormality in each data type separately and determines the degree of suspicious between 0 and 1 and sums up all the degrees of suspicion data afterwards. This method is not intended to be a fully automatic system that resolves investigations, which would likely be unacceptable in any case. The aim is rather to simplify the role of the humans so that they can focus on a small number of cases to be looked in more detail. The proposed approach does simplify the processing of such huge amounts of data. Later, this method can assist human experts in their investigations and making final decisions

Detecting anomalous behaviour using heterogeneous data

In this paper, we propose a method to detect anomalous behaviour using heterogenous data. This method detects anomalies based on the recently introduced approach known as Recursive Density Estimation (RDE) and the so called eccentricity. This method does not require prior assumptions to be made on the type of the data distribution. A simplified form of the well-known Chebyshev condition (inequality) is used for the standardised eccentricity and it applies to any type of distribution. This method is applied to three datasets which include credit card, loyalty card and GPS data. Experimental results show that the proposed method may simplify the complex real cases of forensic investigation which require processing huge amount of heterogeneous data to find anomalies. The proposed method can simplify the tedious job of processing the data and assist the human expert in making important decisions. In our future research, more data will be applied such as natural language (e.g. email, Twitter, SMS) and images

Gender and Age Classification of Human Faces for Automatic Detection of Anomalous Human Behaviour

In this paper, we introduce an approach to classify gender and age from images of human faces which is an essential part of our method for autonomous detection of anomalous human behaviour. Human behaviour is often uncertain, and sometimes it is affected by emotion or environment. Automatic detection can help to recognise human behaviour which later can assist in investigating suspicious events. Central to our proposed approach is the recently introduced transfer learning. It was used on the basis of deep learning and successfully applied to image classification area. This paper is a continuous study from previous research on heterogeneous data in which we use images as supporting evidence. We present a method for image classification based on a pre-trained deep model for feature extraction and representation followed by a Support Vector Machine classifier. Because very few data sets with labels of gender and age exist of face images, we build one dataset named GAFace and applied our proposed method to this dataset achieving excellent results and robustness (gender classification: 90.33% and age classification: 80.17% accuracy) approaching human performance

SPORTS ITEM DETECTION USING MOBILENETV2 WITH SINGLE SHOT DETECTOR

Object detection can be applied in various situations and systems. In this research, object detection was specifically applied to build a lending system for tracking the details of sports items in a students' dormitory. The current sports item lending system with a manually recording of data was time consuming, and are more prone to human errors. This inspires the researcher to build a real-time object detection web-based sports item lending system. The system was trained using Single Shot Detector (SSD) with Mobilenet V2 technique to detect the sports item in the warehouse. A total of 960 self-collected sports item image data was applied in four different experiments with the same batch-size configuration and learning rate value of 0.02. From the experiments, several models with different number of training iterations and training data were built to find the best model to be implemented in the sports item lending system. The best model was obtained from the second experiment with a high accuracy of 0.93 mean average precision (mAP), a confidence of 97%, and a total loss of 0.28. For future work, it is recommended to increase the volume of training data, include other variations of objects in order to further improve the results, and apply other object detection techniques for comparison purposes

Anomalous behaviour detection based on heterogeneous data and data fusion

In this paper, we propose a new approach to identify anomalous behaviour based on heterogeneous data and a new data fusion technique. There are four types of data sets applied in this study including credit card, loyalty card, GPS, and image data. The first step of the complete framework in this proposed study is to identify the best features for every data set. Then, the new anomaly detection technique which is recently introduced and known as Empirical Data Analytics (EDA) is applied to detect the abnormal behaviour based on the data sets. Standardised eccentricity (a newly introduced within EDA measure offering a new simplified form of the well-known Chebyshev Inequality) can be applied to any data distribution. Image data is processed using pre-trained deep learning network, and classification is done by using support vector machine (SVM). At the final stage of the proposed method is combining anomaly result and image recognition using new data fusion technique. From the experiment results, this proposed technique may simplify the tedious job in the real complex cases of forensic investigation. The proposed techniques can assist the human expert in processing huge amount of heterogeneous data to detect anomalies. In future research, text data can also be used as a part of heterogeneous data mixture, and the new data fusion technique may be applied to other data sets

Influence of Gamification on Students’ Motivation in using E-Learning Applications Based on the Motivational Design Model

Students’ motivation is an important factor in ensuring the success of e-learning implementation. In order to ensure students is motivated to use e-learning, motivational design has been used during the development process of e-learning applications. The use of gamification in learning context can help to increase student motivation. The ARCS+G model of motivational design is used as a guide for the gamification of learning. This study focuses on the influence of gamification on students’ motivation in using e-learning applications based on the ARCS+G model. Data from the Instructional Materials Motivation Scale used, were gathered and analysed for comparison of two groups (one control and one experimental) in attention, relevance, confidence, and satisfaction categories. Based on analysis of the result, students from the experimental group are more motivated to use e-learning applications compared with the controlled group. This proves that gamification affect students’ motivation when used in e-learning applications

Online evolving fuzzy rule-based prediction model for high frequency trading financial data stream

Analyzing and predicting the high frequency trading (HFT) financial data stream is very challenging due to the fast arrival times and large amount of the data samples. Aiming at solving this problem, an online evolving fuzzy rule-based prediction model is proposed in this paper. Because this prediction model is based on evolving fuzzy rule-based systems and a novel, simpler form of data density, it can autonomously learn from the live data stream, automatically build/remove its rules and recursively update the parameters. This model responds quickly to all unpredictable sudden changes of financial data and re-adjusts itself to follow the new data pattern. Experimental results show the excellent prediction performance of the proposed approach with real financial data stream regardless of quick shifts of data patterns and frequent appearances of abnormal data samples

Multi-strain probiotics (Hexbio) containing MCP BCMC strains improved constipation and gut motility in Parkinson's disease: A randomised controlled trial.

ObjectiveWe determined the effectiveness of a multi-strain probiotic (Hexbio®) containing microbial cell preparation MCP®BCMC® on constipation symptoms and gut motility in PD patients with constipation.MethodsPD patients with constipation (ROME III criteria) were randomized to receive a multi-strain probiotic (Lactobacillus sp and Bifidobacterium sp at 30 X 109 CFU) with fructo-oligosaccaride or placebo (fermented milk) twice daily for 8 weeks. Primary outcomes were changes in the presence of constipation symptoms using 9 items of Garrigues Questionnaire (GQ), which included an item on bowel opening frequency. Secondary outcomes were gut transit time (GTT), quality of life (PDQ39-SI), motor (MDS-UPDRS) and non-motor symptoms (NMSS).ResultsOf 55 recruited, 48 patients completed the study: 22 received probiotic and 26 received placebo. At 8 weeks, there was a significantly higher mean weekly BOF in the probiotic group compared to placebo [SD 4.18 (1.44) vs SD 2.81(1.06); (mean difference 1.37, 95% CI 0.68, 2.07, uncorrected p5 times/week) compared to the placebo group. The GTT in the probiotic group [77.32 (SD55.35) hours] reduced significantly compared to placebo [113.54 (SD 61.54) hours]; mean difference -36.22, 95% CI -68.90, -3.54, uncorrected p = 0.030). The mean change in GTT was 58.04 (SD59.04) hour vs 20.73 (SD60.48) hours respectively (mean difference 37.32, 95% CI 4.00, 70.63, uncorrected p = 0.028). No between-groups differences were observed in the NMSS, PDQ39-SI, MDS-UPDRS II and MDS-UPDRS III scores. Four patients in the probiotics group experienced mild reversible side effects.ConclusionThis study showed that consumption of a multi-strain probiotic (Hexbio®) over 8 weeks improved bowel opening frequency and whole gut transit time in PD patients with constipation