There are many methods or algorithms applicable for detecting electricity theft. However, comparative studies on supervised learning methods for electricity theft detection are still insufficient. In this paper, comparisons based on predictive accuracy, recall, precision, AUC, and F1-score of several supervised learning methods such as decision tree (DT), artificial neural network (ANN), deep artificial neural network (DANN), and AdaBoost are presented and their performances are analyzed. A public dataset from the State Grid Corporation of China (SGCC) was used for this study. The dataset consisted of power consumption in kWh unit. Based on the analysis results, the DANN outperforms compared to other supervised learning classifiers such as ANN, AdaBoost, and DT in recall, F1-Score, and AUC. A future research direction is the experiments can be performed on other supervised learning algorithms with different types of datasets and suitable preprocessing methods can be applied to produce better performance.

Brain magnetic resonance imaging (MRI) classification into normal and abnormal is a critical and challenging task. Owing to that, several medical imaging classification techniques have been devised in which Learning Vector Quantization (LVQ) is amongst the potential. The main goal of this paper is to enhance the performance of LVQ technique in order to gain higher accuracy detection for brain tumor in MRIs. The classical way of selecting the winner code vector in LVQ is to measure the distance between the input vector and the codebook vectors using Euclidean distance function. In order to improve the winner selection technique, round off function is employed along with the Euclidean distance function. Moreover, in competitive learning classifiers, the fitting model is highly dependent on the class distribution. Therefore this paper proposed a multiresampling technique for which better class distribution can be achieved. This multiresampling is executed by using random selection via preclassification. The test data sample used are the brain tumor magnetic resonance images collected from Universiti Kebangsaan Malaysia Medical Center and UCI benchmark data sets. Comparative studies showed that the proposed methods with promising results are LVQ1, Multipass LVQ, Hierarchical LVQ, Multilayer Perceptron, and Radial Basis Function.

The microgrid communication network with proper connectivity among microgrid resources is play important role to maintain a stability and reliability of the microgrid. Application of suitable communication network and protocol and highlighted the best security measurement is one of the elements to achieve those broad objectives.  The communication network and protocol that has been implemented in existing microgrid has different types and objective which is depend on specific application.  To secure the communication network and protocol, many security approaches is proposed.  In this paper, a review of microgrid communication and its security is shown and future direction of communication network and protocol with its security also provided.

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Serial crime recognition is a critical task. Usually, police officer investigates the serial crime behavior based on their heuristics, evidence or prior information from public. Sometimes, the police officer makes inadequate decision when handling the serial crime problems due to lack of preliminary study on relationship between serial crime and amenities. Therefore, this study explores k-means to identify pattern of surroundings area at serial comersial crime scene. In Malaysia, precisely Selangor, Wilayah Persekutuan Kuala Lumpur and Wilayah Persekutuan Putrjaya, a set data of serial crime including index and non-index, and its surroundings area at crime scene are being investigated. Experimental result shows that ‘hot spot’ amenities such as bank, commercial center, restorant, place of worship, resident and school are highly involved with three types of crime namely house breaking at night, day and robbery without firearm. Furthermore, radius distance with 0.2 km and 0.3 km between the crime scene location and its amenities at surroundings area captured from Safe City Monitoring System are also being evaluated and analyzed. Consequently, our finding helps the police to easily observe and prevent criminal behavior by assigning necessary human resource based on their ‘hot spot’ amenities.