ZENHACHI- Modern and safe stingless bee nest for housing area

ZENHACHI is the new trend for stingless bee nest product in Malaysia. The name of the product itself represent the safety of the structure in Japanese which means (safety bee). The design produced modern and simple concept which inspired by the basic structure of Japanese lunch box (Bento). Hexagon shape of the storage represent the beehive itself while full structure indicate the shape of modern flowerpot which is very suitable to be place at housing area. Instead of giving weight at the top shield for safety, it also can be replaced and flowerpot itself as the farmer can put any suitable plant on top of it. The storage will not easily open by the human or other animals to make the bee worker feels safe during foraging process. The farmer also can be less concern on colony surrounding area because of food source provided for the colony to making pollen and honey

Case Study: Using Data Mining to Predict Student Performance Based on Demographic Attributes

This study predicts student performance at Universiti Pertahanan Nasional Malaysia (UPNM) based on their socio-demographic profile; it also determines how a prediction algorithm can be used to classify the student data for the most significant demographic attributes. The analytical pattern in academic results per batch has been identified using demographic attributes and the student's grades to improve short-term and long-term learning and teaching plans. Understanding the likely outcome of the education process based on predictions can help UPNM lecturers enhance the achievements of the subsequent batch of students by modifying the factors contributing to the prior success. This study identifies and predicts student performance using data mining and classification techniques such as decision trees, neural networks, and k-nearest neighbors. This frequently adopted method comprises data selection and preparation, cleansing, incorporating previous knowledge datasets, and interpreting precise solutions. This study presents the simplified output from each data mining method to facilitate a better understanding of the result and determine the best data mining method. The results show that the critical attributes influencing student performance are gender, age, and student status. The Neural Networks method has the lowest Root of the Mean of the Square of Errors (RMSE) for accuracy measurement. In contrast, the decision tree method has the highest RMSE, which indicates that the decision tree method has a lower performance accuracy. Moreover, the correlation coefficient for the k-nearest neighbor has been recorded as less than one

Prototyping Digital Tongue Diagnosis System on Raspberry Pi

Tongue inspection is a complementary diagnosis method that widely used in Traditional Chinese Medicine (TCM) by inspecting the tongue body constitution to decide the physiological and pathological functions of the human body. Since tongue manifestation is done by practitioner’s observation using naked eye, many limitations can affect the diagnosis result including environment condition and experiences of the practitioner. Lately, tongue diagnosis has been widely studied in order to solve these limitations via digital system. However, most of recent digital system are bulky and not equipped with intelligent diagnosis system that can finally predict the health status of the patient. In this research, digital tongue diagnosis system that uses intelligent diagnosis consisted of image segmentation analysis, tongue coating recognition analysis, and tongue color classification has been embedded on Raspberry Pi. Tongue segmentation implements Hue, Saturation and Value (HSV) color space with Brightness Conformable Multiplier (BCM) for adaptive brightness filtering to recognized tongue body accurately while eliminating perioral area.  Tongue Coating Recognition uses threshold method to detect tongue coating and eliminate the unwanted features including shadow. Tongue color classification uses hybrid method consisted of k-means clustering and Support Vector Machine (SVM) to classify between red, light red and deep red tongue and further gave diagnosis based on color. This experiment concluded that it is feasible to embed the algorithm on Raspberry Pi to promote system portability while attaining similar accuracy for future telemedicine

Quantification of tongue colour using machine learning in Kampo medicine

AbstractIntroductionThe evaluation of tongue colour has been an important approach to examine human health in Kampo medicine (traditional Japanese medicine) because the change in tongue colour may suggest physical or mental disorders. Several tongue colour quantification methods have been published to objectify clinical information among East Asian countries. However, reliable tongue colour analysis results among Japanese test persons are limited because of a lack of quantitative evaluation of tongue colour. We aimed to use advances in digital imaging processing to quantify and verify clinical data tongue colour diagnosis by characterising differences intongue features.MethodsThe DS01-B tongue colour information acquisition system was used to extract tongue images of 1080 Japanese test subjects. Evaluation of tongue colour, body and coating was performed by 10 experienced Kampo medicine physicians. The acquired images were classified into five tongue body colour categories and six tongue coating colour categories based on evaluations from 10 physicians with extensive Kampo medicine experience. K-means clustering algorithm was applied as a machine learning (the study of pattern recognition by computational learning) method to the acquired images to quantify tongue body and coating colour information.ResultsTongue body (n=550) and tongue coating (n=516) colour samples were classified and analysed. Clusters consisting of five tongue body colour categories and six tongue coating colour categories were experimentally described in the CIELAB colour space. Statistical differences were evident among the clinically primary tongue colours.ConclusionsClinically important tongue colour differences in Kampo medicine can be visualised by applying machine learning to tongue images taken under stable conditions. This has implications for developing globally unified, reliable tongue colour diagnostic criteria which could be used to explore the relevance between clinical status and tongue colour

Dynamic QoS: Automatically Modifying QoS Queue's Maximum Bandwidth Rate-Limit of Network Devices for Network Improvement

The heterogeneous data traffic of today's network is a huge challenge to existing best-effort network technology, particularly in the context of large Ethernet, which handles hundreds to thousands of users. The existing conventional best-effort network technology is no longer efficient to handle the diversity of traffic types in the network and requires network management equipment such as Quality of Service (QOS). Usually, QOS is implemented on the gateway router. However, for better network performance and management, to guarantee high priority for sensitive traffic like video conferencing, Voice over Internet Protocol (VoIP), and streaming media within an internal network, it is nice to have QoS implemented on each router in the LAN network, starting from the access router to the gateway router. This paper is to demonstrate the effectiveness of the proposed dynamic QoS that has been developed and deployed in the LAN, purposely to provide adequate bandwidth for sensitive traffic when the network utilization is high and congested, by automatically modifying the QoS Queue's Maximum Bandwidth Rate-Limit of the best-effort traffic queue of the related router. The performance of the proposed developed dynamic QoS was evaluated via a comparison study before and after the dynamic QoS was presented in the network simulation environment that was built using Mininet. Results from the testing show that the developed dynamic QoS can improve the network's performance by automatically giving the appropriate bandwidth for sensitive traffic on the fly while needed/on demand

Relationship analysis of formal and experiential learning in military survival skills using text mining

Tacit knowledge tends to be an invaluable knowledge repository in lifelong learning. Critical domains such as military learning cannot solely rely on traditional manuals and methods due to the unforeseen and uncertain scenarios brought about by advances in modern warfare methods and technology. There is essential knowledge hidden in experiential learning that is very difficult to formalise but critical to be incorporated and taught. The current work aims to extract the relationships between lesson learnt from experiences and current basic military survival skills training among a group of officer cadets using the framework of document and keyword relationship analysis (FDKRA). Finally, the relationships are presented using various visualisation techniques, including word cloud, network graph and bubble graph. The research reveals the existence of important knowledge, not contained otherwise in formal documentation, and thus, highlighting the need to examine and generate a tacit knowledge corpus especially in critical domains

Evaluation of scheme selection and parameter effects in the reconfigurable transmitting power in Wireless Network-on-Chip

Wireless Network-on-Chip (WiNoC) introduces long-range and high bandwidth radio frequency (RF) interconnects that can possibly reduce the multi-hop communication of the planar metal interconnects in conventional NoC platforms. In WiNoC, RF transceivers account for a significant power consumption, particularly its transmitter, out of its total communication energy. Current WiNoC architectures employ constant maximum transmitting power for communicating radio hubs regardless of physical location of the receiver. Recently, two closed loop reconfigurable power schemes that dynamically calibrate the transmitting power level needed for communication between the hubs based on bit error rate (BER) have been proposed. In this paper, these schemes are compared in terms of latency, power and area overheads. Both schemes achieve significant energy savings with limited performance degradation and insignificant impact on throughput. Only a small fraction of both area and power overheads are introduced (about 0.1%). The schemes are general and can be applied on any WiNoC architecture

Children with disabilities in Malaysia and their educational rights

Education is essential to everyone, including children with disabilities, since it makes them better citizens and ensures a bright future. The United Nations has recognised the right to education through a number of declarations and treaties. However, in Malaysia, even though children with disabilities are promised to be given educational rights on an equal basis as other normal children, it is still in the grey area due to the non-existence of specific Act relating to special education for children with disabilities. Not to mention, children with disabilities are considered as burden to their families and always being neglected due to their disabilities. These problems can be reduced if their educational rights are protected effectively. Hence, a legal framework of special education for children with disabilities is needed to protect their educational rights. This research adopts a doctrinal or library-based research methodology by examining legal statutes for example the Federal Constitution of Malaysia, the Persons with Disabilities Act 2008, the Education Act 1996 and its regulations, textbooks and journal articles relating to special education for children with disabilities in Malaysia. The purpose is to investigate whether the laws that we have in Malaysia are sufficient to protect the educational rights of children with disabilities. It is found that the educational rights of children with disabilities in Malaysia are not fully protected due to uncertainty in laws, lack of human capital and lack of facilities in the Special Education sector. The current laws that are relating to special education for children with disabilities in Malaysia are too general and lack of punishment provisions which allow someone to violates the laws. Hence, children with disabilities cannot enjoy their educational rights on an equal basis as other normal children. This paper will be beneficial to the Malaysian government, specifically the Ministry of Education and the Ministry of Women, Family and Community Development, in enhancing the protection of the educational rights of children with disabilities in Malaysia. Besides, the children with disabilities themselves, their parents, and society will benefit from this research through the recommendations given to protect children with disabilities' educational rights effectively. It is proposed for future research to compare the law and practice relating to special education in other jurisdictions to see the differences in the implementation in the Special Education sector

Performance comparison of machine learning classifiers on aircraft databases

The aim of this research is to analyse the performance of six different classifiers, which are κ-Nearest Neighbours (kNN), Naive Bayes, Random Tree, J48 Decision Tree, Random Forest Tree and Sequential Minimal Optimisation (SMO), using aircraft databases and optimize their cost parameter for better accuracy. The six algorithms are implemented to classify aircraft type and its country of origin using a Waikato Environment for Knowledge Analysis (WEKA) workbench. Additionally, we report our parameter optimisation results for SMO by varying the cost parameters to obtain the optimum result. It is observed that in both classifications, SMO with linear kernel obtained the best performance as compared to the other classifiers in terms of classification accuracy, which is 100%