<span>The imbalanced data problems in data mining are common nowadays, which occur due to skewed nature of data. These problems impact the classification process negatively in machine learning process. In such problems, classes have different ratios of specimens in which a large number of specimens belong to one class and the other class has fewer specimens that is usually an essential class, but unfortunately misclassified by many classifiers. So far, significant research is performed to address the imbalanced data problems by implementing different techniques and approaches. In this research, a comprehensive survey is performed to identify the challenges of handling imbalanced class problems during classification process using machine learning algorithms. We discuss the issues of classifiers which endorse bias for majority class and ignore the minority class. Furthermore, the viable solutions and potential future directions are provided to handle the problems<em>.</em></span>

Chronic Kidney Disease (CKD) is one of the leading cause of death contributed by other illnesses such as diabetes, hypertension, lupus, anemia or weak bones that lead to bone fractures. Early prediction of CKD is important in order to contain the disesase. However, instead of predicting the severity of CKD, the objective of this paper is to predict the diagnosis of CKD based on the symptoms or attributes observed in a particular case, whether the stage is acute or chronic. To achieve this, a classification model is proposed to label stage of severity for kidney diseases patients. The experiments then investigated the performance of the proposed classification model based on eight supervised classification algorithms, which are ZeroR, Rule Induction, Support Vector Machine, Naïve Bayes, Decision Tree, Decision Stump, k-Nearest Neighbour, and Classification via Regression. The performance of the all classifiers is evaluated based on accuracy, precision, and recall. The results showed that the regression classifier perform best in the kidney diagnostic procedure.

No abstract

No abstract

Information Management and PSM Evaluation System is a system developed to replace the existing system at the Faculty of Computing. The existing system at the Faculty of Computing is a manual system in which all the evaluation process still utilises paper forms. PSM is divided into two phases; PSM1 and PSM2 and each phase has a different form for evaluation. This process is seen to be less systematic and imposes much time on the evaluator, coordinator and supervisor who are also lecturers. Information Management and PSM Evaluation System is designed to automate information management and evaluation of PSM to keep the information in the database. The scope of these systems focuses on admin, supervisor, evaluator and coordinator bound to PSM1 and PSM2. Some of the functions that can be operated on the system are evaluation, updating PSM students' information and generating reports. The chosen methodology is an Evolutionary Prototype which needs are taken care of the system during modifications. Requirements established during the interview is employed to form a common structure with the essential basic functions of the system. Therefore, Information Management and PSM Evaluation System was developed to automate the manual system to increase efficiency. The system was developed using ASP.net technology and Microsoft Visual Studio 2010 and has been successfully completed within the specified time.

In this paper, an improved method of multi-objective optimization for biochemical system production is presented and discussed in detail. The optimization process of biochemical system production become hard and difficult when involved a large biochemical system that contains many components. In addition, the multi-objective problem also needs to be considered. Due to that, this study proposed and improved a method that comprises with Newton method, differential evolution algorithm (DE) and competitive co-evolutionary algorithm(ComCA). The aim of the proposed method is to maximize the production and simultaneously minimize the total amount of chemical concentrations involves. The operation of the proposed method starts with Newton method by dealing with biochemical system production as a nonlinear equations system. Then DE and ComCA are used to represent the variables in nonlinear equation system and tune the variables in order to find the best solution. The used of DE is to maximize the production while ComCA is to minimize the total amount of chemical concentrations involves. The effectiveness of the proposed method is evaluated using two benchmark biochemical systems, and the experimental results show that the proposed method performs well compared to other works.

Word population of old age people is growing very fast. This is a leading threat to current health care systems which trends to lesser the health facilities in proportional to the population. As reported by US Bureau of Census, the number of aged population is expected to be twice in 2025 from 380 (in 1990) [1]. The aged people mostly fall to different chronic health issues which require steady healthcare. These people needs to stick with hospitals, if not, they may experience the life risk [2]. Researchers affirm that most diseases can be overcome if they could caught earlier [3]. So, there an intense requirement of an intelligent health system which serve the purpose of timely diagnosis of the diseases'. To cope with these issues, scientists and technologists introduced Body Sensor Networks (BSNs) for health care systems [4]. BSN system can be deployed within a hospital or at the patient's home. These networks are made up of minimized bio-medical sensors (BMS) and capable of gathering information such as blood pressure, temperate, ECG from the human body and sending the information to the medical server[5]. Fig. 1 illustrate the types of BMS which can be used in BSN. Typically, medical server is placed at the healthcare facility which stores and analyze the information received form these BMS'. The server then generates alerts to the concerned health care experts and immediate caretakers if the found any abnormality. The system architecture of BSN is illustrated in Fig. 2. The BSN directly related to human body is known as intra-WBAN in which BMS are implanted under the human skin or wearable over the human skin or on clothes. These BMS' are controlled by Body Coordinator (BC) which collects and forward data to the outside BSN. BC has high computational, storage and power resources as compared to BMSs[6].

Microarray technology has become one of the elementary tools for researchers to study the genome of organisms. As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analysis, cancerous classification is an emerging important trend. Significant directed random walk is proposed as one of the cancerous classification approach which have higher sensitivity of risk gene prediction and higher accuracy of cancer classification. In this paper, the methodology and material used for the experiment are presented. Tuning parameter selection method and weight as parameter are applied in proposed approach. Gene expression dataset is used as the input datasets while pathway dataset is used to build a directed graph, as reference datasets, to complete the bias process in random walk approach. In addition, we demonstrate that our approach can improve sensitive predictions with higher accuracy and biological meaningful classification result. Comparison result takes place between significant directed random walk and directed random walk to show the improvement in term of sensitivity of prediction and accuracy of cancer classification.