The water flow-like algorithm (WFA) is a relatively new metaheuristic algorithm, which has shown good solution for the Travelling Salesman Problem (TSP) and is comparable to state of the art results. The basic WFA for TSP uses a 2-opt searching method to decide a water flow splitting decision. Previous algorithms, such as the Ant Colony System for the TSP, has shown that using k-opt (k>2) improves the solution, but increases its complexity exponentially. Therefore, this paper aims to present the performance of the WFA-TSP using 3-opt and 4-opt, respectively, compare them with the basic WFA-TSP using 2-opt and the state of the art algorithms. The algorithms are evaluated using 16 benchmarks TSP datasets. The experimental results show that the proposed WFA-TSP-4opt outperforms in solution quality compare with others, due to its capacity of more exploration and less convergence.
This paper deals with the analysis of different Fuzzy membership type performance for Extended Kalman Filter (EKF) based mobile robot navigation. EKF is known to be incompetent in non-Gaussian noise condition and therefore the technique alone is not sufficient to provide solution. Motivated by this shortcoming, a Fuzzy based EKF is proposed in this paper. Three membership types are considered which includes the triangular, trapezoidal and Gaussian membership types to determine the best estimation results for mobile robot and landmarks locations. Minimal rule design and configuration are also other aspects being considered for analysis purposes. The simulation results suggest that the Gaussian memberships surpassed other membership type in providing the best solution in mobile robot navigation.
MeSH terms: Magnetic Resonance Imaging; Noise; Robotics; Normal Distribution
The walking of a humanoid robot needs to be robust enough in order to maintain balance in a dynamic environment especially on uneven terrain. A walking model based on multi-sensor is proposed for a Robotis DARwIn-OP robot named as Leman. Two force sensitive resistor (FSRs) on both feet equipped to Leman to estimate the zero moment point (ZMP) alongside with accelerometer and gyrosensor embedded in the body for body state estimation. The results show that the FSRs can successfully detect the unbalanced walking event if the protuberance exists on the floor surface and the accelerometer and gyrosensor (Inertial Measurement Unit, IMU) data are recorded to tune the balancing parameter in the model.
Resident’s vehicles in some institutions have to be registered to maintain traffic safety. Penalties should be imposed if residents break traffic rules. Most of the time, the vehicle owner’s information is difficult to access making the penalty registration process complicated. An effective penalty registration process is required to make the process easier for security officers to give notice to the residents who have committed traffic offenses. A mobile application is proposed to recognize vehicle owner information. The proposed application uses optical-character-recognition (OCR) technologies that can facilitate the process of recognizing vehicle’s registration number in order to obtain owner information and use the information to enrol the penalty. The proposed application recognizes the vehicle registration number or sticker serial number to access the owner information. For evaluation of the proposed application, a user study was conducted by asking the users to use the application and answer the questiuonnaire. The findings revealed that average score of 77 of the respondents agree in terms of satisfaction and adoption of the application to be utilized in some institutions. The proposed application reduces the paper work of security officers and makes them more efficient.
Face detection and analysis is an important area in computer vision. Furthermore, face detection has been an active research field in the recent years following the advancement in digital image processing. The visualisation of visual entities or sub-pattern composition may become complex to visualise due to the high frequency of noise and light effect during examination. This study focuses on evaluating the ability of Haar classifier in detecting faces from three paired Min-Max values used on histogram stretching. Min-Max histogram stretching was the selected method for implementation given that it appears to be the appropriate technique from the observation carried out. Experimental results show that, 60-240 MinMax values, Haar classifier can accurately detect faces compared to the two values.
One of the major problems in today’s economy is the phenomenon of tax evasion. The linear regression method is a solution to find a formula to investigate the effect of each variable in the final tax evasion rate. Since the tax evasion data in this study has a great degree of uncertainty and the relationship between variables is nonlinear, Bayesian method is used to address the uncertainty along with 6 nonlinear basis functions to tackle the nonlinearity problem. Furthermore, variational method is applied on Bayesian linear regression in tax evasion data to approximate the model evidence in Bayesian method. The dataset is collected from tax evasion in Malaysia in period from 1963 to 2013 with 8 input variables. Results from variational method are compared with Maximum Likelihood Estimation technique on Bayeisan linear regression and variational method provides more accurate prediction. This study suggests that, in order to reduce the tax evasion, Malaysian government should decrease direct tax and taxpayer income and increase indirect tax and government regulation variables by 5% in the small amount of changes (10%-30%) and reduce direct tax and income on taxpayer and increment indirect tax and government regulation variables by 90% in the large amount of changes (70%-90%) with respect to the current situation to reduce the final tax evasion rate.
Over the last few years, the Android smartphone had faced attacks from malware and malware variants, as there is no effective commercial Android security framework in the market. Thus, using machine learning algorithms to detect Android malware applications that can fit with the smartphone resources limitations became popular. This paper used state of the art Deep Belief Network in Android malware detection. The Lasso is one of the best interpretable ℓ1-regularisation techniques which proved to be an efficient feature selection embedded in learning algorithm. The selected features subset of Restricted Boltzmann Machines tuned by Harmony Search feature reduction with Deep Belief Network classifier was used, achieving 85.22% Android malware detection accuracy.
There has been an increase of content related to Quran and Hadith on the internet over the past few years. Diacritical Digital Quran is very sensitive to tampering. Diacritics are the symbols used beneath/above Quranic verses for reading purposes of the Quran. Minor change in diacritics can alter the meaning of a particular Quranic verse. Hence, there is a need for an authentication system to differentiate between fake and original verses. In this work, a model is proposed related to automatic authentication of Digital Quran. Authentication model is divided into two phases: tokenisation and authentication. For tokenisation, regular expressions are used to split input Quranic verse into single characters. In case of authentication, existing and standard exact matching algorithm i.e. Quick search (QS) is used. On testing the proposed model by comparing popular search engines and other related existing works, our approach is 100 % accurate in terms of full verse detection.
Sentiment analysis classification has been typically performed by combining features that represent the dataset at hand. Existing works have employed various features individually such as the syntactical, lexical and machine learning, and some have hybridized to reach optimistic results. Since the debate on the best combination is still unresolved this paper addresses the empirical investigation of the combination of features for product review classification. Results indicate the Support Vector Machine classification model combined with any of the observed lexicon namely MPQA, BingLiu and General Inquirer and either the unigram or inte-gration of unigram and bigram features is the top performer.
Simulating Lotka-Volterra model using numerical method require researchers to apply tiny mesh sizes to obtain an accurate result. This approach nevertheless increases the complexity and burden of computer memory and consume long computational time. To overcome these issues, we investigate and construct new two-step solver that could simulate Lotka-Volterra model using bigger mesh size. This paper proposes three new two-step schemes to simulate Lotka-Volterra model. A non-standard approximation scheme with trimean approach was adopted. The nonlinear terms in the model is approximated via trimean approach and differential equation via non-standard denominators. Four sets of parameters were examined to analyse the performance of these new schemes. Results show that these new schemes provide better simulation for large mesh size.
MeSH terms: Computers; Physiological Phenomena; Publications; Medical Subject Headings
Feature descriptor for image retrieval has emerged as an important part of computer vision and image analysis application. In the last decades, researchers have used algorithms to generate effective, efficient and steady methods in image processing, particularly shape representation, matching and leaf retrieval. Existing leaf retrieval methods are insufficient to achieve an adequate retrieval rate due to the inherent difficulties related to available shape descriptors of different leaf images. Shape analysis and comparison for plant leaf retrieval are investigated in this study. Different image features may result in different significance interpretation of images, even though they come from almost similarly shaped of images. A new image transform, known as harmonic mean projection transform (HMPT), is proposed in this study as a feature descriptor method to extract leaf features. By using harmonic mean function, the signal carries information of greater importance is considered in signal acquisition. The selected image is extracted from the whole region where all the pixels are considered to get a set of features. Results indicate better classification rates when compared with other classification methods.
Simulating Lotka-Volterra model using a numerical method requires the researcher to apply tiny mesh sizes to come up with an accurate solution. This approach will increase the complexity and burden of computer memory and consume long computational time. To overcome these issues, a new solver is used that could simulate Lotka-Volterra model using bigger mesh size. In this paper, prey and predator behaviour is simulated via Lotka-Volterra model. We approximate the nonlinear terms in the model via weighted average approach and differential equation via nonstandard denominators. We provide three new schemes for one step method and simulate four sets of parameters to examine the performance of these new schemes. Results show that these new schemes simulate better for large mesh sizes.
MeSH terms: Animals; Computers; Predatory Behavior; Medical Subject Headings
Police patrol routing problem (PPRP) attracts researchers’ attention especially on artifitial inteligence. The challenge here is that a limited number of patrols cover a wide range of area that includes several hotspots. In this study, a new model for PPRP is proposed simulating the Solomon’s benchmark for vehicle routing problem with time windows. This model can solve this problem by maximising the coverage of hotspots with frequencies of high priority locations while ensuring the feasibility of routes. Two constructive greedy heuristics are developed to generate the initial solution of the PPRP: highest priority greedy heuristic (HPGH) and nearest neighbour greedy heuristic (NNGH). Experimental results show that the simulated Solomon’s benchmark is suitable to represent PPRP. In addition, results illustrate that NNGH is more efficient to construct feasible solution than HPGH.
The watermarking is a method of concealing digital information in multimedia data, namely the host image. Discrete wavelet transform (DWT) when joined with discrete cosine transform (DCT) and SVD deliver powerful digital watermarking image. There are different types of intrusions that either plunder the actual ownership or demolish the appearance. In this paper, the DWT-DCT, DWT-SVD approach has been proposed to ensure security by concealing the watermark inside the actual image and validate the proprietor’s image. Using DWT-DCT and low-bit percentage, the watermark image was inserted and abstracted. The DWT-SVD hybrid produced very good results.
In this paper, an image binarization method for separating text from the background of degraded textual images is proposed. This proposed methods are based on combination of Window Tracking Method (WTM) and Dynamic Image Histogram (DIH). The WTM and DIH methods work on an image that has been pre-processed. The WTM method searches for the largest pixel value in a 3 × 3 window up to a maximum of five tracking steps, while the method searches for a definite frequency between the two highest values in the image histogram. We test proposed method on DIBCO dataset and self-collection faded manuscripts. The experimental results show that our proposed method outperforms state of the art methods.
The false positive (FP) is an over-segment result where the noncancerous pixel is segmented as a cancer pixel. The FP rate is considered a challenge in localising masses in mammogram images. Hence, in this article, a rejection model is proposed by using a supervised learning method in mass classification such as support vector machine (SVM). The goal of the rejection model which is based on SVM is the reduction of FP rate in segmenting mammogram through the Chan-Vese method, which is initialised by the marker controller watershed (MCWS) algorithm. The MCWS algorithm is utilised for segmentation of a mammogram image. The segmentation is subsequently refined through the Chan-Vese method, followed by the development of the proposed SVM rejection model with different window size as well as its application in eliminating incorrect segmented nodules. The dataset comprised of 57 nodules and 113 non-nodules and the study successfully proved the effectiveness of the SVM rejection model to decrease the FP rate.
MeSH terms: Algorithms; Goals; Mammography; Neoplasms; Support Vector Machine
Concrete durability determines service life of structures. It can though, be weakened by aggressive environmental conditions. For instance, bio-corrosion process is due to the presence and activity of microorganisms which produce sulphuric acid to form sulphate deterioration of concrete materials. The problems related to durability and repair systems are due to lack of suitable concrete materials. The use bacteria for concrete repairing and plugging of pores and cracking in concrete has been recently explored. Previous studies had proved the possibility of using specific bacteria via bio concrete as a sustainable method for improving concrete properties. Thus, lack of information on the application of bio concrete exposed to extreme condition was the motivation for this research.
Congenital talipes equinovarus (CTEV) or clubfoot is a complex deformity of the foot that is characterised by four main deformities; forefoot cavus and adductus, hindfoot varus and ankle equinus. Currently, the Ponseti method is the most general and recognized treatment with a high success rate of over 90%. The treatment involves gentle manipulation and serial casting. However, the casting method could create complications for the patients such as soft-tissue damage and inconvenience in following the treatment schedule especially for those living far away from hospital. The aim of this research is to develop an adjustable corrective device for clubfoot treatment based on the techniques in the Ponseti method and at the same time attempt to eliminate the side-effects. The prototype consists of six adjustable movements from six different mechanisms to correct the four deformities. The prototype was developed using 3D printing method and the main material used is polylactic acid (PLA), rubber, aluminium and cotton fabric with sponge. The total weight of the prototype is around 300 g.
The importance of the performance of concrete cannot be neglected since it is the early indicator of its physical and mechanical properties. It became more important when material with different physical properties than normal material such as rubber tire was used as concrete constituent. This paper presented apart of research result conducted on mortar and concrete with crumb rubber. Crumb rubber was replaced at 10%, 15% and 20% as sand replacement by volume. In addition, ordinary Portland cement was added to silica fume at 10% and 15% by weight. The properties measured in this study are air content and workability test. As for workability, superplasticizers were constantly used at 1% dosage for all mortar mixture, and 0.5% to 0.7% for concrete mixture. The air content was set at 4% to 6% and mortar flow test was conducted on a steel plate, shocked 15 times in 15 seconds and concrete slump test was carried out using slump cone equipment. Pressure method was used to measure air content. All mixes were done in a controlled room temperature. Results showed that when CR was added in the mixture segregation was observed in mortar requiring a high dose of superplasticizer to be added to improve the workability while air-modifying agent was used to reduce the mortar air content. In concrete mixture, low dosage of superplasticizers was required for workability and air-entrained agent was injected into the mixture to increase the air content between 4%-6%.
MeSH terms: Body Weights and Measures; Construction Materials; Paper; Rubber; Silicon Dioxide; Steel; Temperature; Physical Phenomena
High strength concrete (HSC) has lower ductility, but higher in strength compared to normal strength concretes. The strength and ductility of HSC can be improved by applying external confinement, such as steel strapping tensioning technique (SSTT). However, SSTT was literately reported effective in confining circular specimens, but the effectiveness of SSTT on square cross section specimens are yet well investigated. This study focuses on HSC square cross section specimens with different corner ratio, which were right angle and rounded corner. In addition, the effect of different number of layer of steel straps confining around the specimens under optimum lateral pre-tensioning stress also been investigated. The number of layers was fixed to two layers and four layers. Fifteen HSC specimens with dimension of 88 mm x 88 mm x 200 mm, which consist of three unconfined specimens, six right angle specimens, and six rounded corner specimens were prepared and tested monotonically to failure. The experimental results show that the strength and ductility of HSC improved significantly by using rounded corner confined specimens and higher number of layer of confinements. This is due to more uniform confining pressure was exerted on entire surface of rounded corner confined specimens. The strength and ductility of the specimens can be improved up to 53.7% and 207.5% respectively.