Bayesian network classification of gastrointestinal bleeding

The source of gastrointestinal bleeding (GIB) remains uncertain in patients presenting without hematemesis. This paper aims at studying the accuracy, specificity and sensitivity of the Naive Bayesian Classifier (NBC) in identifying the source of GIB in the absence of hematemesis. Data of 325 patients admitted via the emergency department (ED) for GIB without hematemesis and who underwent confirmatory testing were analysed. Six attributes related to demography and their presenting signs were chosen. NBC was used to calculate the conditional probability of an individual being assigned to Upper Gastrointestinal bleeding (UGIB) or Lower Gastrointestinal bleeding (LGIB). High classification accuracy (87.3 %), specificity (0.85) and sensitivity (0.88) were achieved. NBC is a useful tool to support the identification of the source of gastrointestinal bleeding in patients without hematemesis

Sensitivity of missing values in classification tree for large sample

Missing values either in predictor or in response variables are a very common problem in statistics and data mining. Cases with missing values are often ignored which results in loss of information and possible bias. The objectives of our research were to investigate the sensitivity of missing data in classification tree model for large sample. Data were obtained from one of the high level educational institutions in Malaysia. Students' background data were randomly eliminated and classification tree was used to predict students degree classification. The results showed that for large sample, the structure of the classification tree was sensitive to missing values especially for sample contains more than ten percent missing values

Statistical Fact of Students’ Background and Academic Achievement in Higher Educational Institution

AbstractThe differences in academic achievement among students are a topic that has drawn interest of many academic researchers and Malaysian society. This paper describes the results of a detailed statistical analysis relating to the degree classification obtained at the end of their studies (first class, second class upper or second class lower) between undergraduate students and their backgrounds. In this study, students’ data from one of the higher educational institution in Malaysia from 1997 to 2006 are used. Visual analysis of categorical data techniques had been applied to reveal the students’ academic achievement pattern from various backgrounds.The findings can assist the institution in determining future policy on student admissions and to provide the necessary plans to improve student achievement

Evaluation of a fuzzy 3D color QR code decoder

This paper is an extension of our previous work on color QR code decoder using fuzzy logic. The input is the color QR codes with four versions which are version 3, 13, 14 and 17. These QR code versions are converted to black and white. Then, the QR codes are detected using an open source library named as Zing. Next, the color QR code is retrieved by mapping the black and white QR code with the color image. This is followed by enhancing the color QR code using fuzzy logic. After that the QR code is split into three QR codes, red, green and blue. Each of the color is decoded to get the original file text file. We made a comparison on the success rate for our decoder with other existing decoder. We take in consideration number of color used, camera resolution, QR code version, and QR code error correction level. The comparison with other research work show that by using fuzzy logic improves the decoding success rate up to 93.33% using the same parameter from other research work

Color QR code recognition utilizing neural network and fuzzy logic techniques

Quick Response (QR) code is popular type of two dimensional barcode. The key feature of QR code is larger storage capacity and high damage resistance compared to the traditional barcodes. Color QR code is the future as it provides much higher encoding capacity, but it also brings tremendous challenges to the decoding because of color interference and illumination. This research paper presents a method for QR code recognition using the Neural Network (NN) and fuzzy logic techniques. We created a framework for image decoding. First, the color QR code is converted to black and white then the QR code is recognized using neural network. Next, the original colors are returned to the QR code. The colors are enhanced using fuzzy logic and then, the enhanced color QR code is split into three barcodes which are red, green and blue. Finally, each QR code is converted to black and white and sent to ZXing library for decoding and obtained the original data. ZXing library has been utilized for decoding and recognition purposes and has produced satisfactory results. This research proof that by, utilizing NN and fuzzy logic techniques has produced better QR code success rates of five percent

Development of mangrove sediment quality index in Matang Mangrove Forest Reserve, Malaysia: a synergetic approach

Sediment is an important part of heavy metal cycling in the coastal ecosystem, acting as a potential sink and source of inorganic and organic contaminants as environmental conditions change. The productivity of mangroves is utterly dependent on sediment enrichment. Moreover, mangrove sediment can trap pollutants discharged by households, industries, and agriculture activities. In this regard, it is essential to assess sediment quality in the presence–absence of heavy metals that are toxic to most living organisms. Thus, the question of how sediment quality is used as an index in the mangrove domain has arisen. Due to the many complex characteristics such as seasonal zones, tidal patterns, flora and fauna, and water, no specific method is used in Malaysia for assessing and monitoring mangrove sediment quality. Thus, the current study intended to develop a mangrove sediment quality index (MSQi) in the Matang mangrove forest in Perak, Malaysia. An area was selected based on the distinct level of mangrove disturbances. At 1.5 m depth, sediments were sampled in five segments (0–15, 15–30, 30–50, 50–100, and 100–150 cm). All the sediment physicochemical properties were then analysed. Fourteen variables were chosen and included in MSQi. This index categorises mangrove sediment levels as I = Very Bad, II = Bad, III = Moderate, IV = Good, and V = Excellent. MSQi will be used as a guideline in monitoring mangrove sediment pollution. In conclusion, the data analysis showed that the Sepetang River (SR) was highly disturbed, followed by the Tinggi River (TR) (moderately disturbed), and the Tiram Laut River (TLR) (least disturbed)

Design of hose roller for firefighter: a fatigue study

In a working environment, worker’s safety and health are the most critical considerations. Previous study discovered that firefighters are exposed to a great deal of ergonomic risk factors (ERF). ERF exposure during hose rolling includes awkward posture and forceful exertion. Therefore, the primary goal of this research paper is to fabricate an ergonomic hose roller for firefighters and conduct a fatigue analysis to determine the efficiency of the tool designed to safeguard firefighters against the risk of low back disorder (LBD). Hose roller testing is necessary to guarantee that it can withstand the weight of fire hoses while still being comfortable for users’ bodies. Fatigue analysis was conducted using Industrial Lumbar Motion Monitor (i-LMM) equipment to evaluate LBD risk during hose rolling. Manual handling contributes 57.67% to the total average percentage value used to compute LBD risk results, while utilizing a roller tool, the hose rolling procedure yields a 27% LBD risk limit value. The design of experiment (DOE) method should be used in future studies to gather more information for the LBD risk assessmen

Statistical fact of students' background and academic achievement in higher educational institution

The differences in academic achievement among students are a topic that has drawn interest of many academic researchers and Malaysian society. This paper describes the results of a detailed statistical analysis relating to the degree classification obtained at the end of their studies (first class, second class upper or second class lower) between undergraduate students and their backgrounds. In this study, students’ data from one of the higher educational institution in Malaysia from 1997 to 2006 are used. Visual analysis of categorical data techniques had been applied to reveal the students’ academic achievement pattern from various backgrounds.The findings can assist the institution in determining future policy on student admissions and to provide the necessary plans to improve student achievement

Fast multilayer color QR code decoder algorithm utilizing fuzzy technique

Color QR code is an active research topic. Most of the recent research focus on the decoding success rate and ignore the decoding speed. In this paper, we propose a fast multilayer color QR code decoder algorithm to decode an extended color QR code. The extended color QR code utilize color reference for the color recognition. The algorithm starts with the detection of the color QR code. This is followed by the calculation of the model size. Then, color reference selection from the extended QR code. Next, build a dynamic fuzzy membership, from the color reference set of the extended color QR code and fast color enhancement using the center color pixel for each model. After that, optioning monochrome color QR code by applying color de-multiplexing for the enhanced color QR code. We measure the color recovery speed and compared it with an existing work. The experiment shows using the proposed algorithm we got a decoding speed of 200% faster than the existing work

Multi-layer color QR code dynamic decoder framework with fuzzy color recovery

In this paper, we proposed a dynamic framework for a multi-layer color QR code decoder. The proposed decoder framework shows the general steps to decode color QR code. It contains a configuration setting standard that allows other researchers to refer in order to decode their color QR code based on the colors used in the encoder. The framework starts with color QR code detection, then search for color reference. This is followed by fuzzy sets selection based on the color QR code. Color enhancement for the QR code is implemented based on the fuzzy set decision. Next, is color de-multiplexing to get Black and White (B/W) QR code. The de-multiplexing process is based on a configuration file, for the QR code color setting. Finally, is the decoding and merging of the results for the B/W QR code to obtain the original file. We use two datasets with color reference to evaluate our framework. The first dataset used is generated by Yang et al., 2018 encoder and we obtained 83% success rate for the detection and color de-multiplexing. The second dataset is generated from our encoder and produced 90% decoding success rate. The experiment shows the framework can successfully work with different sizes of color QR code