Palapes cadet is one of the uniform organization in UiTM Perlis for extra-curricular. In Palapes cadet, they arrange their organization by hierarchy chart according to the grade. Senior Uniform Officer (SUO) is the highest rank, followed with Junior Uniform Officer (JUO), sergeant, corporal, lance-corporal and lastly cadet officer which is the lower rank. In Palapes organization, they have several method to measure performance for promote to higher rank whether individually performance or in group. Cadets are selected for promotion based on demonstrated leadership abilities, acquired skills, physical fitness, and comprehension of information as measured through standardized testing. However, this method is too complicated and manually assessed by the trainer or coach. So this study will propose an expert system which is one of artificial intelligence technique that will recognize the readiness and progression of a Palapes cadet. Readiness is the degree to which a cadet is capable of fulfilling duties involving leadership skill and Progression is the rate or manner to which a cadet is assigned new leadership or managerial duties. This study also will ensured all eligible Palapes cadets will receive full and equitable opportunity to compete for promotion and for the trainers to actively monitor their cadet’s performance.

Neural  network (NN) tools are suitable for many tasks such  as classification,  clustering, scheduling and prediction. NN performance depends on the strategy of learning a phenomenon, the number  of hidden nodes, activation function and,  of course,  the behavior  of  the data. There are many techniques used for training NN, while the  social insect’s techniques become more  focused  by researchers because of its  natural  behavioral  processing.  The Artificial Bee Colony (ABC) algorithm has produced  an  easy way for solving combinatorial, statistical problems and for training NN by different organized agents.  The objective of training NN  is to adjust the weights so that application of a set of inputs produces the desired set of outputs. Normally, NN is  trained by a  standard back-propagation (BP)  algorithm;  however,  BP  is too  slow  for many applications and trapping in a local minima problem. To recover the above gap, the hybrid technique was used for training NN here. The hybrid of natural  behavior agent ant and bee  techniques  was  used for training  NN. The simulation result of a  Hybrid Ant Bee Colony (HABC) was compared with, ABC, BP Levenberg-Mardquart (LM) and BP Gradient Descent (GD)  learning  algorithms.  According to experimental results,  the proposed HABC algorithm did  improve the classification accuracy  for  the  Boolean function, and prediction of volcano time-series data, which was used to train the MLP.

Finding the best location is the first and most fundamental step in planning for offering a service or product to costumers. To locate appropriate location, scholars have suggested a  variety of models based on circumstances and conditions. Equality is of importance in locating models in the utility sector and in making equal accessibility to urban utilities for members of  the society; it has an effective role alongside other criteria  such as efficiency and effectiveness. Finding a proper location for retail chain supermarkets with equality as  the  main concern, would contribute to some factors  such as  reduction of  costs, feasibility of access, and equality in distribution. This research was conducted to find  the  best location for retail chain supermarkets so that each costumer has an equal distance to the nearest store. In this research, minimization of the Gini coefficient of the Lorenz curve based on service distance has been applied.  Based on Gini coefficient of the Lorenz curve function, an algorithm has been designed to find an  optimal location for a retail chain supermarket in a bounded flat  environment with defined population centers and demand.

An investigation employing the semiempirical molecular orbital MNDO method in the program package of the HyperChem 7.5 software to calculate ΔH_f(M), ΔH_f [M+H]⁺ and local proton affinities (PAs) as well as the charge distributions for the two highly electronegative hetero atoms (O₁₁ and N₁) in six quinolone derivatives have been undertaken. The MNDO calculated values of PAs for the quinolone derivatives at site O₁₁ and at site N₁ indicate that the average energy separation between PA values at two respective sites is equal to about 164 kJ.mol^-1. This allows the authors to conclude that the protonation at site O₁₁ is a more favoured process than at N₁. This conclusion can be explained as resulting from the presence of unsaturated carbonyl (C ═ O) double bond and ion pair electrons of the oxygen atom, which increase the influences of the protonation at O₁₁ site, which is known to be a good proton acceptor. The preferential protonation at site O₁₁ for the studied molecules have been confirmed by the calculated ΔH_f [M+H]⁺ values at sites O₁₁ and N_1.

Signal to Noise Ratio (SNR) is a quantitative method to measure the effectiveness of contrast enhancement methods. The question is whether this measurement is consistent with a qualitative evaluation of a medical expert. Biomedical Research, including dentistry cases, usually involves medical experts to do the interpretation, and finalize the diagnosis. However, the subjective evaluations create differences of interpretation. There are two factors contributing to the low quality of dental radiographs—the first is because of the nature of the image acquisition process, and the second is the low X-ray dose usage. Due to that, research related to contrast enhancement algorithms is an accepted image processing method to assist medical officers in doing interpretation that is more reliable. Thus, this work presents an analysis of contrast enhancement algorithms (CEA) applied to digital intraoral radiograph images. The enhancement methods used in this study are CEAs, namely: adaptive histogram equalization (AHE), contrast limited adaptive histogram equalization (CLAHE), and sharp contrast limited adaptive histogram equalization (SCLAHE). The objective of this work is to find a correlation between the dentist evaluation and SNR values in determining the image quality based on the rate of abnormalities detected. Fifty-six original intraoral digital dental X-ray images are collected from the Faculty of Dentistry, Universiti Teknologi MARA Malaysia. These images are processed with the mentioned CEAs, resulting in a total of one-hundred sixty-six observations. These images are compared against the original images. The method of assessment is through questionnaires where all images are arranged randomly. The observations focused on identifying widened periodontal ligament space (widened PDLs), periapical radiolucency (PR) and loss of lamina dura (loss LD) abnormalities. These are among the main signs of periapical disease. The results show that the SNR value does not reflect the dentist’s evaluation in terms of image quality. The SNR values of CLAHE are able to precede other methods. However, SCLAHE is better than the rest based on dentists’ evaluations for widened PDLs abnormality.

This paper illustrates comparative analysis of TCP-SACK against three different TCP variants namely TCP-Reno, TCP-New Reno, and TCP-Tahoe. The comparison is conducted with the help of simulations, which corroborate the reliability of TCP-SACK as a stream-based, connection-oriented, and most reliable end-to-end data transfer protocol amongst other TCP variants. The performances of TCP variants have been evaluated against constant rate assignment for the values of 2500bps and 4500bps. The selected parameter provides different results in terms of TCP throughput for all of the TCP variants. In each case, the values of the congestion window (cwnd) are recorded and plotted against regular intervals of time. The behaviour of TCP variants is studied by observing the simulated graph results provided for each case. The results prove TCP-SACK to be efficient wireless, heterogeneous networks.

This paper presents using a new modified homotopy perturbation method (NHPM) for solving linear and nonlinear systems of differential-algebraic equations (DAEs) of index one or higher index without index reduction. By using this scheme, explicit exact solution is calculated in the form of a convergent power series with easily computable components. Some examples are given to illustrate the simplicity and reliability of the new method. The obtained results are found to be in good agreement with the exact solutions known.

The purpose of this paper is to use a kind of analytical method called new homotopy perturbation method (NHPM), to solve nonlinear system of second order boundary value problems. The NHPM yields solutions in convergent series form with easily computable terms, and in some cases, yields exact solutions in one iteration. This method can be applied directly to the second order boundary systems needless of converting to the first order initial systems. To illustrate the application of the method, numerical results are derived using the calculated components of the NHPM. Comparisons with exact solutions show the efficiency and accuracy of NHPM in solving nonlinear system of second order boundary value problems.

In this paper, the comparison between K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN) algorithm for classifying the spectrogram images in brain balancing is presented. After producing spectrogram image from ElectroEncephaloGram (EEG) signals, Gray Level Co-occurrence Matrix (GLCM) texture feature were extracted. These features produced huge matrices, therefore to reduce the size of matrices; the Principal Component Analysis (PCA) is applied. The results show that the KNN and ANN were able to classify the spectrogram image with 87.5% to 90% accuracy for the brain balancing application.

In this paper, a framework is presented to outline the steps of automating doctor scheduling process using an evolutionary algorithm. Particle Swarm Optimization (PSO) is applied within the framework to generate optimum work schedule for on-call doctors in emergency department at a public hospital in Malaysia. Currently, the doctor schedules are prepared by a head staff called captain who performs this tedious task manually where a lot of time is spent to perform this task. The main purpose of this research is to generate an optimum doctor schedule where the number of staff on duty is sufficed, individual preferences is fulfilled and all doctors are treated fairly. A systematic framework is established which can be applied to any automated on-call doctor scheduling system of emergency department in a Malaysian hospital.

Let G be a group. For any a, b∈ G , the element a^-1b^-1ab is called the commutator of a and b. The commutator a^-1b^-1ab is denoted by [a, b] . If A and B are subsets of G , then [A,B] denotes the subgroup of G generated by {[a, b] | a ∈ A, b ∈ B}. The subgroup of G generated by all the commutators in G (that is, the smallest subgroup of G containing all the commutators) is called the derived subgroup, or the commutator subgroup, of G and denoted by [G,G]. In this paper, we determine the commutator subgroup G′ for groups of order 8q, where q is an odd prime.

Parallel processing is one of the significant research subjects in the field of computer science. Today’s real time applications expect higher throughput approaches than years away. There are various problems that require search algorithms as a part of their base operations. Some parallel search algorithms are available albeit we always need to find faster ones due to the new born technologies. This paper introduces a new searching algorithm on hypercube architecture with a tradeoff between the numbers of processor elements and the running time.