CSLM: Levenberg Marquardt based Back Propagation Algorithm Optimized with Cuckoo Search

Training an artificial neural network is an optimization task, since it is desired to find optimal weight sets for a neural network during training process. Traditional training algorithms such as back propagation have some drawbacks such as getting stuck in local minima and slow speed of convergence. This study combines the best features of two algorithms; i.e. Levenberg Marquardt back propagation (LMBP) and Cuckoo Search (CS) for improving the convergence speed of artificial neural networks (ANN) training. The proposed CSLM algorithm is trained on XOR and OR datasets. The experimental results show that the proposed CSLM algorithm has better performance than other similar hybrid variants used in this study

Is Halal Certification Process “Green”?

These days, the environmental perspective on operations is becoming more common. In fact, any effort in improving efficiency in the organization is closely related to sustainability of our environment. The Environmental Management System (EMS) certification such as ISO 14001 has been accepted as the world standard. In addition to these ISO standards, there are other certifications such as Halal certification. There is no research that investigates the relationship between Halal Certification process and its effect on our environment. Hence, our main research question is that is Halal Certification process can be considered as environmental friendly? In this paper, we argue that Halal Certification also contributes towards green initiatives. We used EDC-UUM as our case study. EDC-UUM is actively seeking the Halal certification from Malaysian authority agency or JAKIM. In this study, we assessed the perception of the EDC-UUM staff on the issue of going green. The findings and implications are discussed in the paper

WCBP: A new water cycle based back propagation algorithm for data classification

Water Cycle algorithm is a modern nature inspired meta-heuristic algorithm to provide derivative-free solution to optimize complex problems. The back-propagation neural network (BPNN) algorithm performs well on many complex data types but it possess the problem of network stagnancy and local minima. Therefore, this paper proposed the use of WC algorithm in combination with Back-Propagation neural network (BPNN) algorithm to solve the local minima problem in gradient descent trajectory. The performance of the proposed Water Cycle based Back-Propagation (WCBP) algorithm is compared with the conventional BPNN, ABC-BP and ABC-LM algorithms on selected benchmark classification problems from UCI Machine Learning Repository. The simulation results show that the BPNN training process is highly enhanced when combined with WC algorithm

Gunung Tahan Trail: a historical review

There are still a lot of information on the history of Gunung Tahan Trail which remain unknown to the Malaysian public; some were buried with the demise of the elderly living around this mountain. This paper attempts to reveal the history of this famous trail which is located in Taman Negara in relation to the origin of its name, local belief and folklore of the mountain, colonial proposal for the establishment of grand hill station and early attempts to explore the mountain. Most of the data and information for this review were gathered from field notes and expedition reports published in various journals between 1880 and 1940. These information would be useful to Taman Negara Park Management in enriching recreational and nature tourism experiences among users of Gunung Tahan Trail

BAT-BP: A new BAT based back-propagation algorithm for efficient data classification

Training neural networks particularly back propagation algorithm is a complex task of great importance in the field of supervised learning. One of the nature inspired meta-heuristic Bat algorithm is becoming a popular method in solving many complex optimization problems. Thus, this study investigates the use of Bat algorithm along with back-propagation neural network (BPNN) algorithm in-order to gain optimal weights to solve the local minima problem and also to enhance the convergence rate. This study intends to show the superiority (time performance and quality of solution) of the proposed meta-heuristic Bat-BP algorithm over other more standard neural network training algorithms. The performance of the proposed Bat-BP algorithm is then compared with Artificial Bee Colony using BPNN (ABC-BP), Artificial Bee Colony using Levenberg-Marquardt (ABC-LM) and BPNN algorithm. Classification datasets from UCI machine learning repository are used to train the network. The simulation results show that the efficiency of BPNN training process is highly enhanced when combined with BAT algorithm

Accidents preventive practice for high-rise construction

The demand of high-rise projects continues to grow due to the reducing of usable land area in Klang Valley, Malaysia.The rapidly development of high-rise projects has leaded to the rise of fatalities and accidents.An accident that happened in a construction site can cause serious physical injury.The accidents such as people falling from height and struck by falling object were the most frequent accidents happened in Malaysian construction industry.The continuous growth of high-rise buildings indicates that there is a need of an effective safety and health management. Hence, this research aims to identify the causes of accidents and the ways to prevent accidents that occur at high-rise building construction site.Qualitative method was employed in this research. Interview surveying with safety officers who are involved in highrise building project in Kuala Lumpur were conducted in this research. Accidents were caused by man-made factors, environment factors or machinery factors.The accidents prevention methods were provide sufficient Personal Protective Equipment (PPE), have a good housekeeping, execute safety inspection, provide safety training and execute accidents investigation.In the meanwhile, interviewees have suggested the new prevention methods that were develop a proper site layout planning and de-merit and merit system among subcontractors, suppliers and even employees regarding safety at workplace matters.This research helps in explaining the causes of accidents and identifying area where prevention action should be implemented, so that workers and top management will increase awareness in preventing site accidents

Effect of surfactant and surfactant blends on pseudoternary phase diagram behavior of newly synthesized palm kernel oil esters

Background: The purpose of this study was to select appropriate surfactants or blends of surfactants to study the ternary phase diagram behavior of newly introduced palm kernel oil esters. Methods: Nonionic surfactant blends of Tween® and Tween®/Span® series were screened based on their solubilization capacity with water for palm kernel oil esters. Tween® 80 and five blends of Tween® 80/Span® 80 and Tween® 80/Span® 85 in the hydrophilic-lipophilic balance (HLB) value range of 10.7–14.0 were selected to study the phase diagram behavior of palm kernel oil esters using the water titration method at room temperature. Results: High solubilization capacity was obtained by Tween® 80 compared with other surfactants of Tween® series. High HLB blends of Tween® 80/Span® 85 and Tween® 80/Span® 80 at HLB 13.7 and 13.9, respectively, have better solubilization capacity compared with the lower HLB values of Tween® 80/Span® 80. All the selected blends of surfactants were formed as waterin- oil microemulsions, and other dispersion systems varied in size and geometrical layout in the triangles. The high solubilization capacity and larger areas of the water-in-oil microemulsion systems were due to the structural similarity between the lipophilic tail of Tween® 80 and the oleyl group of the palm kernel oil esters. Conclusion: This study suggests that the phase diagram behavior of palm kernel oil esters, water, and nonionic surfactants is not only affected by the HLB value, but also by the structural similarity between palm kernel oil esters and the surfactant used. The information gathered in this study is useful for researchers and manufacturers interested in using palm kernel oil esters in pharmaceutical and cosmetic preparation. The use of palm kernel oil esters can improve drug delivery and reduce the cost of cosmetics

Performance Comparison of VSI Switches Faults Analysis Using STFT and S transform

Switches fault in power converter has become compelling issues over the years. To reduce cost and maintenance downtime, a good fault detection technique is an essential. In this paper, the performance of STFT and S transform techniques are analysed and compared for voltage source inverter (VSI) switches faults. The signal from phase current is represented in jointly timefrequency representation (TFR) to estimate signal parameters and characteristics. Then, the degree of accuracy for both STFT and S transform are determined by the lowest value of mean absolute percentage error (MAPE). The results demonstrate that S transform gives better accuracy compare to STFT and is suitable for VSI switches faults detection and identification system

A case study of green building in Malaysia: cost saving analysis

The building sector consumes about forty percent of world energy, making energy efficiency in existing buildings an important issue. This study has been undertaken to investigate energy consumption of a building that has been redesigned to incorporate energy efficient features. It was found that the introduction of energy efficient features has helped to achieve savings up to 46% of the total spent on energy particularly based on electricity bills

Influence of yarn parameters on cotton/kenaf blended yarn characteristics

Spinning kenaf fibers into yarns is challenging due to the stiffness and lack of cohesiveness of the fibers. Alkali treatment is known to remove hemicellulose, wax, and breaks down lignin, reducing stiffness of kenaf fiber and improving its spinnability. Kenaf fibers were treated at percentages of 4% and 6% and blended with cotton fibers at blend ratios of 40:60 and 50:50 prior to a ring spinning process to produce a double ply yarn of 70 tex. Yarn were twisted at three sets of twist. The responses were measured in terms of carding waste percentages and yarn strength. The results showed that the optimized yarn structural parameter is kenaf fiber treated at 6% and with a kenaf/cotton 40/60 blending ratio based on its tenacity and minimum carding waste. ANOVA shows that there is a good interaction effect between NaOH and kenaf/cotton ratio, and NaOH concentration and twist