Medical Data Classification Using Similarity Measure of Fuzzy Soft Set Based Distance Measure

Medical data classification plays a crucial role in many medical imaging applications by automating or facilitating the delineation of medical images. A considerable amount of literature has been published on medical images classification based on data mining techniques to develop intelligent medical decision support systems to help the physicians. This paper assesses the performance of a new classification algorithm using similarity measure fuzzy soft set based distance based for numerical medical datasets. The proposed modelling comprises of five phases explicitly: data acquisition, data pre-processing, data partitioning, classification using FussCyier and performance evaluation. The proposed classifier FussCyier is evaluated on five performance matrices’: accuracy, precision, recall, F-Micro and computational time. Experimental results indicate that the proposed classifier performed comparatively better with existing fuzzy soft classifiers

Medical data classification using similarity measure of fuzzy soft set based distance measure

Medical data classification plays a crucial role in many medical imaging applications by automating or facilitating the delineation of medical images. A considerable amount of literature has been published on medical images classification based on data mining techniques to develop intelligent medical decision support systems to help the physicians. This paper assesses the performance of a new classification algorithm using similarity measure fuzzy soft set based distance based for numerical medical datasets. The proposed modelling comprises of five phases explicitly: data acquisition, data pre-processing, data partitioning, classification using FussCyier and performance evaluation. The proposed classifier FussCyier is evaluated on five performance matrices’: accuracy, precision, recall, F-Micro and computational time. Experimental results indicate that the proposed classifier performed comparatively better with existing fuzzy soft classifiers

Perception of mathematics game’s design for primary school: based on teachers’ opinions

Unmistakable methods can be used for learning, and they can be looked at in a few viewpoints, particularly those identified with learning results. In this paper, we introduce an examination with a specific end goal to think about the design adequacy and development’s requirement of a game based learning (GBL) approach that is about to be used in LINUS screening for mathematics subject in primary school. The approach includes multiple interaction forms regarding addition and subtraction operation in mathematics based on LINUS constructs. Ten teachers from three different school located in Batu Pahat have participated in the study. The investigations involving survey activity by using questionnaire as the instrument. While breaking down the results, the outcomes demonstrated that the kids observed the amusement to be all the more fulfilling if there are less levels and more colours. Since the survey were conducted to a very common type of school in Malaysia, we believe game that is about to be built based on opinion gained could be utilized as an effective instrument in primary schools to strengthen pupils' lessons

Aplikasi buku cerita interaktif “Kisah Nabi Sulaiman A.S.”

Aplikasi buku cerita interaktif telah mendapat sambutan yang menggalakkan pada masa kini. Hal ini kerana penggunaan elemen multimedia yang dapat menambah pengalaman dan keseronokan semasa menggunakan aplikasi. Walaupun terdapat banyak aplikasi buku cerita interaktif dalam pasaran, masih terdapat beberapa kelemahan yang dikenalpasti. Antaranya ialah kurangnya cerita yang menggunakan Bahasa Melayu serta terdapat banyak gangguan-gangguan antaramuka semasa proses penceritaan. Oleh itu aplikasi Aplikasi Buku Cerita Interaktif “Kisah Nabi Sulaiman A.S.” ini dibangunkan menggunakan teknik animasi 2D. Tujuan utama pembangunan aplikasi ini adalah untuk menyediakan kandungan buku cerita interaktif kisah Nabi Sulaiman untuk pelantar Android. Pengguna sasaran aplikasi adalah peringkat usia empat hingga enam tahun khususnya yang beragama Islam. Model MMCD dipilih sebagai metodologi untuk membangunkan sistem ini. Pengujian aplikasi telah dijalankan ke atas 23 orang murid Kelas Pra-Sekolah Sekolah Kebangsaan Pintas Raya. Hasil pengujian menunjukkan semua responden bersetuju bahawa aplikasi ini menepati citarasa mereka

Exponential smoothing techniques on daily temperature level data

The changes of temperature level occur throughout the year.This event whether hot temperature or cold temperature can affect human life and nature. Such event is also known as extreme event due to the nature of the data produced.Usually the time series of extreme dataset is rarely linear.The existence of nonlinear pattern and high fluctuation in variation greatly affect the quality of forecasting performances.Three exponential smoothing techniques have been tested to study their ability in handling of temperature level data from three cities in Texas.Single Exponential Smoothing Technique (SEST), Double Exponential Smoothing Technique (DEST) and Holt’s method were explored in preparing the temperature data.From the experiments, it was found that DEST is the most suitable technique to deal with the data compared to SEST and Holt's method

FussCyier: Mamogram images classification based on similarity measure fuzzy soft set

Automatic digital mammograms reading become highly enviable, as the number of mammograms to be examined by physician increases enormously.It is premised that the computer aided diagnosis system is mandatory to assist physicians/radiologists to achieve high efficiency and productivity.To handle uncertainties of medical images, fuzzy soft set theory has been merely scrutinized, even though the choice of convenient parameterization makes fuzzy soft set suitable and feasible for decision making applications. Therefore, this study investigates the practicability of fuzzy soft set for classification of digital mammogram images to increase the classification accuracy while lower the classifier complexity.The proposed method FussCyier involves three phases namely: pre-processing, training and testing.Results of the research indicated that proposed method gives high classification performance with wavelet de-noise filter Sym8 with the accuracy 75.64%, recall 84.67% and CPU time 0.0026 seconds

Thresholding and quantization algorithms for image compression techniques: a review

With increasing demand on digital images, there is a need to compress the image to entertain the limited bandwidth and storage capacity. Recently, there is a growing interest among researchers focusing on compression of various types of images and data. Amongst various compression algorithms, transform-based compression is one of the promising algorithms. Despite the technological advances in transmission and storage, the demands placed on the bandwidth of communication and storage capacities by far outstrips its availability. This paper presents a review of image compression principle, compression techniques and various thresholding algorithms (pre-processing algorithms) and quantization algorithm (post-processing algorithms). This paper intends to give an overview to the relevant parties to choose the suitable image compression algorithms to suit with the need

Comparative analysis of gamification approaches in education

Games promised that when it comes to comparison between them with traditional training, they are more engaging and entertaining [1]. Using gamification has attracted many attentions from researchers [2] but empirical support of these tasks has been slowly emerge. Besides, games also offer multiple roles including storytelling, programming, artwork, sound and mechanics [3]. Every year, school will go through the list of children who get enrolled. There was never been a year without children with poor skills in Bahasa, English and Mathematics

Analysis of a high pressure diesel spray at high pressure and temperature environment conditions

This paper illustrates the results of an experimental characterization of a high pressure diesel spray injected by a common rail (CR) injection system both under non-evaporative and evaporative conditions. Tests have been made injecting the fuel with a single hole injector having a diameter of 0.18 mm with L/D=5.56. The fuel has been sprayed at 60, 90 and 120 MPa, with an ambient pressure ranging between 1.2 to 5.0 MPa. The spray evolution has been investigated, by the Mie scattering technique, illuminating the fuel jet and acquiring single shot images by a CCD camera. Tests under non-evaporative conditions have been carried out in an optically accessible high pressure vessel filled with inert gas (N2) at diesel-like density conditions. The instantaneous fuel injection rate, obtained with a time resolution of 10 microseconds, has been also evaluated by an AVL Fuel Meter working on the Bosch Tube principle. Tests for the evaporative conditions have been conducted on a crank-case scavenged single cylinder 2-stroke direct injection Diesel engine at the rotational speed of 500 rpm. The engine provides a wide optical access and the gas velocity within the combustion chamber is low enough to assume that the fuel is injected under quiescent conditions as those reproduced for the experiments under high density gas chamber. Spray penetration and cone angle have been estimated at the same operative conditions as for the non-evaporative ones. Results have showed that the tip penetration, obtained by digital post-processing of the spray image sequence, increases with the injection time under non-evaporative conditions whereas, under evaporative conditions, it reaches a maximum early during the injection and remains constant or slightly decreases at later time up to the start of combustion. The cone angle, estimated under evaporative conditions, has given a decreasing profile along the injection interval. Applying the jet theory to a simplified model of fuel spray, the evaporated fuel mass has been estimated at the same gas density as that under non evaporative tests

Application of Wavelet de-noising Filters in Mammogram Images Classification Using Fuzzy Soft Set

Recent advances in the field of image processing have revealed that the level of noise in mammogram images highly affect the images quality and classification performance of the classifiers. Whilst, numerous data mining techniques have been developed to achieve high efficiency and effectiveness for computer aided diagnosis systems. However, fuzzy soft set theory has been merely experimented for medical images. Thus, this study proposed a classifier based on fuzzy soft set with embedding wavelet de-noising filters. Therefore, the proposed methodology involved five steps namely: MIAS dataset, wavelet de-noising filters hard and soft threshold, region of interest identification, feature extraction and classification. Therefore, the feasibility of fuzzy soft set for classification of mammograms images has been scrutinized. Experimental results show that proposed classifier FussCyier provides the classification performance with Daub3 (Level 1) with accuracy 75.64% (hard threshold), precision 46.11%, recall 84.67%, F-Micro 60%. Thus, the results provide an alternative technique to categorize mammogram images. Keywords: Mammogram images; Feature extraction; Wavelet filters; Fuzzy soft set