Bahasa himpunan 8086/8088/80286

Buku ini mengandungi beberapa bab iaitu kandungan, prakata, bab 1 pengenalan, apakah bahasa himpunan?, evolusi 8086, 8088, dan 80286, ringkasan mikropemproses, bab 2 mengguna penghimpun, membangunkan satu atur cara bahasa-himpunan, penyataan sumber, suruhan bahasa himpunan, bab 3 set suruhan, mod pengalamatan, jenis suruhan, suruhan pindahan data, suruhan aritmetik, bab 4 matematik ketepatan tinggi, pendaraban, punca kuasa dua, bab 5 pengoperasian ke atas struktur data, senarai tidak tertib, mengisih data tertib, senarai tertib,, bab 6 menggunakan sumber dos, sistem sampukan sampukan dos, operasi masa dan tarikh, bab 7 makro, pengenalan kepada makro, arahan makro, operator makro, bab 8 perpustakaan objek, membina perpustakaan objek, pengoperasian ke atas perpustakaan objek, menggunakan perpustakaan objek, bab 9 pengautomatan proses penghimpunan, fail kelompok, menyelenggara atur cara microsoft (make), membandingkan kedua-dua teknik, kesimpulan, bab 10 pengaturcaraan berstruktur, penyataan berstruktur dan struktur aliran logik, struktur if, struktur do, struktur search, bab 11 kopemproses matematik 8087 dan 80287, daftar dalaman, jenis data, set suruhan, pengatur caraan kopemproses matematik, jawapan latihan, indeks

A new framework for matching semantic web service descriptions based on OWL-S services

Nowadays, semantic web services are published and updated with growing demand for cloud computing. Since a single service is not capable of processing the increase of data and user's demand the improvement is necessary to match and rank semantic web service to achieve the user's goal. In the semantic web service framework, users' request is the input to the system and output is ranking of semantic web service. It has become a limitation to match between requests with the semantic web service description. This paper proposes a new framework for matching and ranking semantic web service based on OWL-S. The proposed new framework can match the keyword in each task and ranking service. This framework is done by using performance ontology-based indexing. The result is obtained and the performance of the services for multiple requests has been measured

A review of bioinformatics model and computational software of next generation sequencing

In the past decade it has become increasingly the effort for researcher to surpass the bioinformatics challenges foremost in next generation sequencing (NGS). This review paper gives an overview of the computational software and bioinformatics model that has been used for next generation sequencing. In this paper, the description on functionalities, source type and website of the program or software are provided. These computational software and bioinformatics model are differentiating into three types of bioinformatics analysis stages including alignment, variant calling and filtering and annotation. Besides, we discuss the future work and the development for new bioinformatics tool to be advanced

Optimizing the Social Force Model Using New Hybrid WOABAT-IFDO in Crowd Evacuation in Panic Situation

This paper addresses the need for improvement in the Social Force Model (SFM) crowd evacuation model in the context of egress studies and current emergency research. As the current classical evacuation model, the Social Force Model lacks decision-making ability for finding the best directions towards an exit. Crowd searching for route choices in crowd evacuation simulations for panic situations remains inaccurate and unrealistic. There is a need for SFM to be incorporated with an intelligent approach in a simulation environment by adding in behaviour of following the position indicator to guide agents towards the exit to ensure minimal evacuation time. Congestion in pedestrian crowds is a critical issue for evacuation management, due to a lack of or lower presence of obstacles. Thus, this research proposes optimization using the one of the latest nature inspired algorithm namely WOABAT-IFDO (Whale-Bat and Improved Fitness-Dependent Optimization) in the SFM interaction component. Optimization takes place by randomly allocating the best position of guide indicator as an aid to the for better evacuation time and exploring the dynamics of obstacle-non obstacle scenarios that can disperse clogging behavior with different set of agent’s number for better evacuation time and comparing it with single SFM simulation. Finally, validation is conducted based on the proposed crowd evacuation simulation time, which is further based on standard evacuation guidelines and statistical analysis methods

An analysis of a modified social force model in crowd emergency evacuation simulation

In crowd evacuation simulation, a number of exit point and obstacles play an important role that can influence the result in the evacuation simulation. This paper focuses on the movement of the crowd’s emergency evacuation based on a modified social force model (SFM) via optimising the obstacles interaction parameter in one the SFM component. The simulation also compared original SFM (without obstacles) and modified SFM (with obstacles). The results show the impact can minimize the concept of arching phenomenon (faster-is-slower). For an obstacles issue, it is proven that obstacles can help to reduce evacuation time in regards to its proper position and exit width

The Hybrid of WOABAT-IFDO Optimization Algorithm and Its Application in Crowd Evacuation Simulation

This paper proposes a new hybrid of nature inspired optimization algorithm (IFDO-WOABAT) based on the latest optimization algorithm namely Improved Fitness Dependent Optimization (IFDO) with Whale-Bat Optimization algorithm (WOABAT). The hybrid is essential to overcome the inaccuracy in searching optimal path when dealing with many agents in conjunction with exploration and exploitation element in WOABAT signify the process of searching behaviour and optimizing the speed value of agent. The performance of the new hybrid optimization algorithm is verified using standard classical test function and further evaluated with other four renowned optimization algorithms and the results showed that it is better in most cases compared with the existing algorithms. Ultimately, the algorithm’s performance also has been tested in crowd simulation evacuation that involves a different number of agents and with/without obstacle scenario. The conducted experiment reveals promising results and signify effectiveness in minimizing the evacuation time

Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm

Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning

Modeling and optimization of electric discharge machining performances using harmony search algorithm

Electric Discharge Machining (EDM) is one of the widely used non-conventional machining processes for complex and difficult-to-machine materials. EDM technology has been improve significantly and has been developed in many ideas especially in the manufacturing industries that yielded enormous benefits in economic as well as generating keen interest in research area. A major issue in EDM process is how to obtain accurate results of the machining performance measurement value at optimal point of cutting conditions. Thus, this study proposed harmony search algorithm approach for optimization of surface roughness (Ra) in die sinking electric discharge machining (EDM). The mathematical model was developed using regression analysis based on four machining parameters which are pulse on time, peak current, servo voltage and servo speed. The result shows that the optimal solutions for Ra can be found with the minimum values of 1.3031 μm

Rough sets for predicting the Kuala Lumpur Stock Exchange Composite Index returns

This study aims to prove the usability of Rough Set approach in capturing the relationship between the technical indicators and the level of Kuala Lumpur Stock Exchange Composite Index (KLCI) over time.Stock markets are affected by many interrelated economic, political, and even psychological factors.Therefore, it is generally very difficult to predict its movements. There are extensive literatures available describing attempts to use artificial intelligence techniques; in particular neural networks and genetic algorithm for analyzing stock market variations.However, drawbacks are found where neural networks have great complexity in interpreting the results; genetic algorithms create large data redundancies.A relatively new approach, the rough sets are suggested for its simple knowledge representation, ability to deal with uncertainties and lowering data redundancies.In this study, a few different discretization algorithms were used at data preprocessing. From the simulations and result produced, the rough sets approach can be a promising alternative to the existing methods for stock market prediction

Detecting SIM box fraud by using support vector machine and artificial neural network

Fraud in communication has been increasing dramatically due to the new modern technologies and the global superhighways of communication, resulting in loss of revenues and quality of service in telecommunication providers especially in Africa and Asia. One of the dominant types of fraud is SIM box bypass fraud whereby SIM cards are used to channel national and multinational calls away from mobile operators and deliver as local calls. Therefore it is important to find techniques that can detect this type of fraud efficiently. In this paper, two classification techniques, Artificial Neural Network (ANN) and Support Vector Machine (SVM) were developed to detect this type of fraud. The classification uses nine selected features of data extracted from Customer Database Record. The performance of ANN is compared with SVM to find which model gives the best performance. From the experiments, it is found that SVM model gives higher accuracy compared to ANN by giving the classification accuracy of 99.06% compared with ANN model, 98.71% accuracy. Besides, better accuracy performance, SVM also requires less computational time compared to ANN since it takes lesser amount of time in model building and training