Fariza Hanum Nasaruddin

Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges

Voluminous amounts of data have been produced, since the past decade as the miniaturization of Internet of things (IoT) devices increases. However, such data are not useful without analytic power. Numerous big data, IoT, and analytics solutions have enabled people to obtain valuable insight into large data generated by IoT devices. However, these solutions are still in their infancy, and the domain lacks a comprehensive survey. This paper investigates the state-of-the-art research efforts directed toward big IoT data analytics. The relationship between big data analytics and IoT is explained. Moreover, this paper adds value by proposing a new architecture for big IoT data analytics. Furthermore, big IoT data analytic types, methods, and technologies for big data mining are discussed. Numerous notable use cases are also presented. Several opportunities brought by data analytics in IoT paradigm are then discussed. Finally, open research challenges, such as privacy, big data mining, visualization, and integration, are presented as future research directions.

Automated Bone Age Assessment: Motivation, Taxonomies, and Challenges

Bone age assessment (BAA) of unknown people is one of the most important topics in clinical procedure for evaluation of biological maturity of children. BAA is performed usually by comparing an X-ray of left hand wrist with an atlas of known sample bones. Recently, BAA has gained remarkable ground from academia and medicine. Manual methods of BAA are time-consuming and prone to observer variability. This is a motivation for developing automated methods of BAA. However, there is considerable research on the automated assessment, much of which are still in the experimental stage. This survey provides taxonomy of automated BAA approaches and discusses the challenges. Finally, we present suggestions for future research.

E-construction waste exchange in Malaysia: A preliminary study

As a developing country, Malaysia is no exception in facing construction waste management issues. Since demand of new construction project increases every year, construction waste dump at landfill has also increased at an alarming rate. In this preliminary study, current construction waste situation and construction waste exchange system are studied. Representative from Institute for Environmental and Development, LESTARI and Construction Industry Development Board of Malaysia (CIDB) were interviewed, and a survey has was carried out to understand contractors and developers view of construction waste management. As a result, e-conWasteExchange model has been developed and targeted users are contractors, developers, local council and Construction Industry Development Board of Malaysia (CIDB).

A Quantitative Study for Developing a Computerized System for Bone Age Assessment in University of Malaya Medical Center

A quantitative study was conducted to direct the design and development of a computerized system for bone age assessment (BAA) in University of Malaya Medical Center (UMMC). Bone age assessment is a clinical procedure performed in pediatric radiology for evaluation the stage of skeletal maturation. It is usually performed by comparing an x-ray of a child’s left hand with a standard of known samples. The current methods utilized in clinical environment to estimate bone age are time consuming and prone to observer variability. This is motivation for developing a computerized method for BAA. A primary analysis shows the current method used by UMMC radiologists for bone age assessment, their feedbacks, problems encountered and their opinions about new approach for BAA. Our study also extracts user requirements for designing and developing a computerized method for BAA.

Automatic method for bone age assessment based on combined method

Bone age assessment (BAA) is a method of evaluating the level of skeletal maturation in children. Generally, it is applied manually by comparing an X-ray of a left hand-wrist with a standard samples as atlas in the clinical procedure. The manual methods are prone to variability of observation, time consuming and limited to objective decisions. These are big motivations for automatic method for bone age assessment. This study aims to develop an automated method for BAA based on combined method. This method tries to overcome the problems of conducting BAA in manual methods. A survey questionnaire is used as the part of research methodology to identify the BAA method used by radiologists and to extract the factors that effect on determination of bone age in the Faculty of Medicine, University of Malaya Medical Centre (UMMC).

Real-time big data processing for anomaly detection: A Survey

Abstract The advent of connected devices and omnipresence of Internet have paved way for intruders to attack networks, which leads to cyber-attack, financial loss, information theft in healthcare, and cyber war. Hence, network security analytics has become an important area of concern and has gained intensive attention among researchers, off late, specifically in the domain of anomaly detection in network, which is considered crucial for network security. However, preliminary investigations have revealed that the existing approaches to detect anomalies in network are not effective enough, particularly to detect them in real time. The reason for the inefficacy of current approaches is mainly due the amassment of massive volumes of data though the connected devices. Therefore, it is crucial to propose a framework that effectively handles real time big data processing and detect anomalies in networks. In this regard, this paper attempts to address the issue of detecting anomalies in real time. Respectively, this paper has surveyed the state-of-the-art real-time big data processing technologies related to anomaly detection and the vital characteristics of associated machine learning algorithms. This paper begins with the explanation of essential contexts and taxonomy of real-time big data processing, anomalous detection, and machine learning algorithms, followed by the review of big data processing technologies. Finally, the identified research challenges of real-time big data processing in anomaly detection are discussed.

Fog learning for cultivating critical thinking in information seeking process

Fog computing has been emerged as a promising paradigm with the different applications from industry to academic area. In this paper, we proposed a new concept, namely fog learning with the aim of addressing the critical thinking as a demanding issue in academic area. Due to the distributed architecture of our fog learning model, it can be used anywhere, anytime, and with the minimum latency in comparison with the existing mobile and cloud learning models to foster the critical thinking. To test the effectiveness of the proposed method, we applied the Software Usability Measurement Inventory (SUMI) as well as the acceptance test. We firstly showed that more than 74% of participants suffered from lack of critical thinking. Moreover, the participants indicated that they use several critical thinking skills during information seeking process, which shows there are relationships between critical thinking skills and the information seeking processes. According to the relationships between critical thinking skills and the information seeking processes, we designed a prototype for cultivating of critical thinking on the basis of fog learning. The results of the prototype evaluation emphasized on its positive role to foster critical thinking.