By ap ply ing a non-parametric Malm quist Pro duc tiv ity In dex to a sam ple of all post-merger Ma lay sian banks over 2001-2003, this pa per at tempts to inves ti gate to what ex tent the in clu sion of OBS items in the out put defi ni tion of banks af fect the es ti mated to tal fac tor pro duc tiv ity change in dexes. It is found that the in clu sion of OBS items re sults in an in crease in es ti mated produc tiv ity lev els for all banks un der study. How ever, the im pact seems to be the larg est on tech no logi cal change rather than ef fi ciency change.

Internet of Things (IoT) is an emerging system that incorporates many technologies from different areas. In this paper, we present the implementation of IoT in an agriculture industry, particularly in monitoring an automated fertigation system. The monitoring system comprises a web-based system , an automatic fertigation system and a communication network. The main focus of this paper is on the web-based system where the data from the SQLite database is used in the web-GUI to display parameters such as the status of water level, the flow condition of valves and pipes as well as the overall operation of automated fertigation system. The paper also described on how farmers can access the website, set fertigation schedule and determine fertilizer's formulation. Different from others, this system is equipped with emergency mode to stop the fertigation system which can be controlled directly from the website. Our method uses a microprocessor to handle the databases, web-GUI and control communications between the fertigation system and the web-based system. This system will ease farmers in managing their automated fertigation system virtually using their mobile devices.

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Water supplied to residential areas is prone to contaminants due to pipe residues and silt, and therefore resulted in cloudiness, unfavorable taste, and odor in water. Turbidity, a measure of water cloudiness, is one of the important factors for assessing water quality. This paper proposes a low-cost turbidity system based on a light detection unit to measure the cloudiness in water. The automated system uses Intel Galileo 2 as the microprocessor and a server for a web-based monitoring system. The turbidity detection unit consists of a Light Dependent Resistor (LDR) and a Light Emitting Diode (LED) inside a polyvinyl chloride (PVC) pipe. Turbidity readings were recorded for two different positionings; 90° and 180° between the detector (LDR) and the incident light (LED). Once the turbidity level reached a threshold level, the system will trigger the filtration process to clean the water. The voltage output captured from the designed system versus total suspended solid (TSS) in sample water is graphed and analyzed in two different conditions; in total darkness and in the present of ambient light. This paper also discusses and compares the results from the above-mentioned conditions when the system is submerged in still and flowing water. It was found that the trends of the plotted graph decline when the total suspended solid increased for both 90° and 180° detector turbidimeter in all conditions which imitate the trends of a commercial turbidimeter. By taking the consideration of the above findings, the design can be recommended for a low-cost real-time web-based monitoring system of the water quality in an IOT environment.

According to World Health Organization (WHO) report an estimated 17.9 million lives are being lost each year due to cardiovascular diseases (CVDs) and is the top contributor to the death causes. 80% of the cardiovascular cases include heart attacks and strokes. This work is an effort to accurately predict the common heart diseases such as arrhythmia (ARR) and congestive heart failure (CHF) along with the normal sinus rhythm (NSR) based on the integrated model developed using continuous wavelet transform (CWT) and deep neural networks. The proposed method used in this research analyses the time-frequency features of an electrocardiogram (ECG) signal by first converting the 1D ECG signals to the 2D Scalogram images and subsequently the 2D images are being used as an input to the 2D deep neural network model-AlexNet. The reason behind converting the ECG signals to 2D images is that it is easier to extract deep features from images rather than from the raw data for training purposes in AlexNet. The dataset used for this research was obtained from Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) arrhythmia database, MIT-BIH normal sinus rhythm database and Beth Israel Deaconess Medical Center (BIDMC) congestive heart failure database. In this work, we have identified the best fit parameters for the AlexNet model that could successfully predict the common heart diseases with an accuracy of 98.7%. This work is also being compared with the recent research done in the field of ECG Classification for detection of heart conditions and proves to be an effective technique for the classification.

Lung cancer is the most common cancer worldwide and the third most common cancer in Malaysia. Due to its high prevalence worldwide and in Malaysia, it is an utmost importance to have the disease detected at an early stage which would result in a higher chance of cure and possibly better survival. The current methods used for lung cancer screening might not be simple, inexpensive and safe and not readily accessible in outpatient clinics. In this paper, we present the classification of normal and crackles sounds acquired from 20 healthy and 23 lung cancer patients, respectively using Artificial Neural Network. Firstly, the sounds signals were decomposed into seven different frequency bands using Discrete Wavelet Transform (DWT) based on two different mother wavelets namely Daubechies 7 (db7) and Haar. Secondly, mean, standard deviation and maximum PSD of the detail coefficients for five frequency bands (D3, D4, D5, D6, and D7) were calculated as features. Fifteen features were used as input to the ANN classifier. The results of classification show that db7 based performed better than Haar with perfect 100% sensitivity, specificity and accuracy for testing and validation stages when using 15 nodes at the hidden layer. While for Haar, only testing stage shows the perfect 100% for sensitivity, specificity, and accuracy when using 10 nodes at the hidden layer.

In today's life with the influence and vast usage of Internet of Things (IoT), a surveillance system become an essential needs (no longer a luxury facility) to home residents, buildings and other important premises. This project deals with web-based and mobile app surveillance system using Raspberry Pi and its supporting components i.e., Pi Camera, PIR motion sensor, Ultrasonic sensor, web-based mobile application . This proposed solution can be implemented over the internet using any computer and mobile devices from anywhere and anytime. The use of

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