Peramalan kualiti udara menggunakan kaedah pembelajaran mendalam rangkaian perlingkaran temporal (TCN)

Kajian ini bertujuan untuk membina model kualiti udara untuk meramalkan kepekatan bahan pencemar udara di Malaysia. Kaedah peramalan yang dipilih dalam kajian ini adalah suatu teknik pembelajaran mendalam iaitu Rangkaian Perlingkaran Temporal (TCN). Set data yang digunakan adalah siri masa zarahan terampai bersaiz diameter lebih kecil atau sama dengan 10 mikrometer (PM10) yang diperoleh daripada Jabatan Alam Sekitar Malaysia dari 5 Julai 2017 hingga 31 Januari 2019. Data daripada lima stesen pemantauan kualiti udara di Semenanjung Malaysia dipilih untuk kajian ini. Bagi tujuan perbandingan, kaedah rangkaian memori jangka pendek panjang (LSTM) juga digunakan dalam kajian ini yang mana ketepatan antara kedua-dua model dibandingkan. Secara amnya, nilai model ramalan daripada kedua-dua model adalah menghampiri data asal. Walau bagaimanapun, model yang dibina dengan kaedah TCN adalah lebih baik berbanding model LSTM dari segi ketepatan nilai ramalan. Kajian ini menunjukkan bahawa TCN merupakan teknik yang sesuai digunakan dalam peramalan data siri masa bagi kualiti udara di Semenanjung Malaysia

Neurocomputing fundamental climate analysis

Rainfall is a natural phenomenon that needs to be studied more deeply and interesting to be analyzed. It involves numbers of human activities such as aviation, agriculture, fisheries, and also disaster risk reduction. Moreover, the characteristics of rainfall data follows seasonality, fluctuation, not normally distributed and it makes traditional time series challenging to use. Therefore, neurocomputing model can be used as an alternative to extraction information from rainfall data and give high performance also accuracy. In this paper, we give short preview about SST Anomalies in Manado, Northern Sulawesi and at the same time comparing the performance of rainfall forecasting by using three types of neurocomputing methods such as Generalized Regression Neural Network (GRNN), Feed forward Neural Network (FFNN), and Localized Multi Kernel Support Vector Regression (LMKSVR). In a nutshell, all of neurocomputing methods give highly accurate forecasting as well as reach low MAPE FFNN 1.65%, GRNN 2.65% and LMKSVR 0.28%, respectively

Simulasi trafik di beberapa persimpangan utama di bandar raya Melaka

Kesesakan lalu lintas pada waktu puncak adalah satu daripada isu yang sering berlaku di Malaysia. Hal ini boleh menyebabkan pembaziran masa, wang serta memberi tekanan emosi kepada pemandu. Sistem kawalan lampu isyarat yang kurang berkesan adalah satu daripada faktor yang menyumbang kepada masalah ini. Walaupun kebanyakan sistem lampu isyarat telah menggunakan teknologi sensor bagi meningkatkan kecekapan, namun masih berlaku kesesakan lalu lintas pada waktu puncak. Kajian ini membuat simulasi terhadap beberapa persimpangan utama di bandar raya Melaka pada waktu puncak bagi menganalisis kesesakan trafik yang berlaku di persimpangan tersebut. Seterusnya, beberapa model susunan sistem lampu isyarat digunakan untuk melihat keberkesanan dalam mengurangkan purata masa menunggu dan jumlah kenderaan yang menunggu di setiap lorong. Suatu model alternatif yang telah dilaksanakan di beberapa buah negara luar turut dimodelkan ke dalam sistem simulasi tersebut. Hasil simulasi menunjukkan purata masa menunggu dan jumlah kenderaan yang menunggu di setiap lorong di persimpangan tersebut dapat dikurangkan dengan menukar urutan dan fasa pada sistem lampu isyarat

The step construction of penalized spline in electrical power load data

Electricity is one of the most pressing needs for human life. Electricity is required not only for lighting but also to carry out activities of daily life related to activities Social and economic community. The problems is currently a limited supply of electricity resulting in an energy crisis. Electrical power is not storable therefore it is a vital need to make a good electricity demand forecast. According to this, we conducted an analysis based on power load. Given a baseline to this research, we applied penalized splines (P-splines) which led to a powerful and applicable smoothing technique. In this paper, we revealed penalized spline degree 1 (linear) with 8 knots is the best model since it has the lowest GCV (Generelized Cross Validation). This model have become a compelling model to predict electric power load evidenced by of Mean Absolute Percentage Error (MAPE=0.013) less than 10%

A proposed simulation optimization model framework for emergency department problems in public hospital

The Emergency Department (ED) is a very complex system with limited resources to support increase in demand.ED services are considered as good quality if they can meet the patient’s expectation.Long waiting times and length of stay is always the main problem faced by the management.The management of ED should give greater emphasis on their capacity of resources in order to increase the quality of services, which conforms to patient satisfaction.This paper is a review of work in progress of a study being conducted in a government hospital in Selangor, Malaysia.This paper proposed a simulation optimization model framework which is used to study ED operations and problems as well as to find an optimal solution to the problems. The integration of simulation and optimization is hoped can assist management in decision making process regarding their resource capacity planning in order to improve current and future ED operations

FIS-PNN: a hybrid computational method for protein-protein interactions prediction using the secondary structure information

The study of protein-protein interactions (PPI) is an active area of research in biology because it mediates most of the biological functions in any organism. This work is inspired by the fact that proteins with similar secondary structures mostly share very similar three-dimensional structures, and consequently, very similar functions. As a result, they must interact with each other. In this study we used our approach, namely FIS-PNN, to predict the interacting proteins in yeast from the information of their secondary structures using hybrid machine learning algorithms. Two main stages of our approach are similarity score computation, and classification. The first stage is further divided into three steps: (1) Multiple-sequence alignment, (2) Secondary structure prediction, and (3) Similarity measurement. In the classification stage, several independent first order Sugeno Fuzzy Inference Systems and probabilistic neural networks are generated to model the behavior of similarity scores of all possible proteins pairs. The final results show that the multiple classifiers have significantly improved the performance of the single classifier. Our method, namely FIS-PNN, successfully predicts PPI with 96% of accuracy, a level that is significantly greater than all other sequence-based prediction methods

Characterisation of essential proteins in proteins interaction networks

The identification of essential proteins is theoretically and practically important as it is essential to understand the minimal surviving requirements for cellular lives, and it is fundamental of drug development. As conducting experimental studies to identify essential proteins are both time and resource consuming, here we present a computational approach in predicting them based on network topology properties from protein-protein interaction networks of Saccharomyces cerevisiae, Escherichia coli and Drosophila melanogaster. The proposed method, namely EP3NN (Essential Proteins Prediction using Probabilistic Neural Network), employed a machine learning algorithm called Probabilistic Neural Network as a classifier to identify essential proteins of the organism of interest. EP3NN uses degree centrality, closeness centrality, local assortativity and local clustering coefficient of each protein in the network for such predictions. Results show that EP3NN managed to successfully predict essential proteins with an average accuracy of 95% for our studied organisms. Results also show that most of the essential proteins are close to other proteins, have assortativity behaviour and form clusters/sub-graph in the network

Modelling of distribution system in a factory warehouse using arena (Pemodelan sistem pengedaran gudang kilang menggunakan arena)

Warehousing represents material storage and physical management processes as well as the methods used by these processes that include all material movement and storage. Distribution system or logistics is a combination of processed functions to manage materials and products from manufacturer to consumer. This research focuses on the study of the warehouse distribution system of a cement factory. The processes involved in this system are modelled using simulation to study the operation in the factory warehouse. Data for building the model were collected through interviews and observation of the whole operation in the warehouse. A computer simulation model is designed, built and run using Arena. Results obtained from the simulation model were analysed to identify the weaknesses of the current system and improvement models were proposed. A total of five improvement simulation models were investigated. The effects of models on customer average waiting times at the various checking points and the model output are analysed. Overall, the best improvement model has succeeded in reducing the average customer waiting time and increased the total customers served in the daily operation of the system very significantly