Review of different strategies for coordinative planning of multi-agent systems

Agent-based systems have been widely examined in the literature for various type of tasks. Within this examination, various strategies and modeling have been employed. Several surveys and reviews have been depicted in the literature regarding agent-based systems. However, minimal efforts have been made in the context of feature extraction and feature selection. This paper aims to review the strategies used for feature extraction and selection agentbased systems. In terms of the nature of agent communications, this paper tackles two types, centralized and decentralized. In terms of the workflow, this paper tackles three types, including coordinative, collaborative and emergent-based systems. Finally, a discussion is presented comparing the strategies and the frequent use of the strategies in the literature. Based on this review, most of feature extraction agent-based systems rely on either coordinating or emergent-based strategies, while feature selection agent-based systems rely on collaborative strategies. However, there are several aspects that we can consider to be classify agent-based strategies. This review develops a classification scheme for systems used for specific tasks, including feature extraction and feature selection

Review of local binary pattern operators in image feature extraction

With the substantial expansion of image information, image processing and computer vision have significant roles in several applications, including image classification, image segmentation, pattern recognition, and image retrieval. An important feature that has been applied in many image applications is texture. Texture is the characteristic of a set of pixels that form an image. Therefore, analyzing texture has a significant impact on segmenting an image or detecting important portions of an image. This paper provides a review on LBP and its modifications. The aim of this review is to show the current trends for using, modifying and adapting LBP in the domain of image processing

A Fuzzy Case-Based Reasoning Model for Software Requirements Specifications Quality Assessment

Different software Quality Assurance (SQA) audit techniques are applied in the literature to determine whether the required standards and procedures within the Software Requirements Specification (SRS) phase are adhered to. The inspection of the Software Requirements Specification (iSRS) system is an analytical assurance tool which is proposed to strengthen the ability to scrutinize how to optimally create high-quality SRSs. The iSRS utilizes a Case-Based Reasoning (CBR) model in carrying out the SRS quality analysis based on the experience of the previously analyzed cases. This paper presents the contribution of integrating fuzzy Logic technique in the CBR steps to form a Fuzzy Case-Based Reasoning (FCBR) model for improving the reasoning and accuracy of the iSRS system. Additionally, for efficient cases retrieval in the CBR, relevant cases selection and nearest cases selection heuristic search algorithms are used in the system. Basically, the input to the relevant cases algorithm is the available cases in the system case base and the output is the relevant cases. The input to the nearest cases algorithm is the relevant cases and the output is the nearest cases. The fuzzy Logic technique works on the selected nearest cases and it utilizes similarity measurement methods to classify the cases into no-match, partial-match and complete-match cases. The features matching results assist the revised step of the CBR to generate a new solution. The implementation of the new FCBR model shows that converting numerical representation to qualitative terms simplifies the matching process and improves the decision-making of the system

A multimedia courseware for human heart anatomical and functional illustration

Advances in computer science have provided unique opportunities to apply Interactive Multimedia (IMM) courseware to a wide variety of medical and health care functions. Courseware can be called an easy to learn, teachable and course materials which is an important in Information Communication Technology world today. It helps the learners/students to improve their knowledge, skills and creativity. One area which holds the high ability for using computer systems is medical and health science education. This paper describes the design of an IMM courseware for learning about Human Heart. It proposes a Human Heart Anatomical and Functional Illustration (HHAFI) courseware for students, health officials and everyone interested in having a healthy heart. The HHAFI courseware is implemented by Toolbook Instructor and presented on Windows platform. The courseware includes an introduction that describes the heart and recall such as mechanisms of the heart, heart diseases, healthy tips and living a healthy lifestyle. The HHAFI courseware is tested with the student to identify the improvement in their knowledge and measure the level of interest in the topic. The HHAFI courseware provides learning and interactive training functions for interested individuals

An improved multi-agent system for scanned document authentication using collaborative reinforcement learning technique

Scanned Document Authentication (SDA) is one of the major areas in authenticating document copies, such as tickets, passport pages, or certificates based on the document fingerprints. These fingerprints are represented by the textual features extracted from the scanned documents. Existing SDA methods mainly use local feature descriptors like local binary patterns and global feature descriptors like Gabor filters. However, the selection of these descriptors and their parameters’ configuration is dependent on the document type and the deformations due to the effects of printing, scanning, usage, and time. To address the dynamic nature of the document deformation, this thesis proposes a new Automated Scanned Document Authentication (ASDA) model that operate based on Collaborative Reinforcement Learning Agent (CRLA) architecture. The proposed ASDA model consists of four feature descriptor agents running on three phases, which are perception, decision, and action to control the operational behaviour of the feature extraction operators. ASDA performs feature matching using the Euclidean Distance and adjusts the feature descriptor agents and their parameters’ configuration according to the matching results. Meanwhile, the CRLA architecture implements a Q-learning strategy to direct the dynamic selection decision of the agents toward the optimum combinations of descriptors and configurations. The selection decision either leads to rewarded or penalty that guide the learning process of the agents with the goal to maximize the rewards results. The performance of the proposed ASDA model is evaluated using the UTHM-EDD and UKM-SPF datasets, which contains shear, lighting (reflection and transmission), crumpling, and printing deformation in three different resolutions of 50, 100, and 150 dpi. The ASDA model achieved an average accuracy of 98.40% for varying deformation conditions in the UTHM-EDD dataset and 100% in the UKM-SPF dataset as compared to other models; ELM, KNN, and OSELM. The results concluded that ASDA is capable to authenticate digital documents under varying degree of deformations with high accuracy

Performance evaluation of AODV and OLSR routing protocols in MANET environment

Mobile Ad-hoc Networks (MANETs) are self-sufficient networks that can work without the need for centralized controls, pre-configuration to the routes or advance infrastructures. The nodes of a MANET are autonomously controlled, which allow them to act freely in a random manner within the MANET. The nodes can leave their MANET and join other MANETs at any time. These characteristics, however, might negatively affect the performance of the routing protocols and the overall topology of the networks. Subsequently, MANETs comprise specially designed routing protocols that reactively and proactively perform the routing. This paper evaluates and compares the performance of two routing protocols which are Ad-Hoc On-Demand Distance Vector (AODV) and Optimized Link State Routing (OLSR) in MANET environment. The study includes implementing a simulation to examine the performance of the routing protocols based on the variables of the nodes’ number and network size. The evaluation results show that the AODV outperforms the OLSR in most of the simulated cases. The results further show that the number of nodes and network size has a great impact on the Throughput (TH), Packet Delivery Ratio (PDR), and End-to-End delay (E2E) of the network

Comparative analysis of classification techniques for leaves and land cover texture

The texture is the appearance of the object that has different types of surface and size. It is mainly useful for different types of applications including object recognition, fingerprinting, and surface analysis. The goal of this research is to investigate the best classification models among the Naive Bayes (NB), Random Forest (DF), and k-Nearest Neighbor (k-NN) algorithms in performing texture classification. The algorithms are used to classify the leaves and urban land cover of texture using several evaluation criteria. This research project aims to prove that the accuracy can be used on data of texture that have turned in a group of different types of data target based on the characteristic of the texture and also to find out which classification algorithm has better performance when analyzing texture patterns. The test results show that the NB algorithm has the best overall accuracy of 78.67% for the leaves dataset and 93.60% overall accuracy for the urban land cover dataset

An agent-based inference engine for efficient and reliable automated car failure diagnosis assistance

There are many difficulties and challenges involved in cars failure and malfunction diagnosis. The diagnosis process involves heuristic and complex series of activities and requires specific skills and expertise. A basic toolkit and assistance software are imperatives to help the car drivers to at least identify the source of car failure or malfunction, especially, when the location of the event does not permit immediate help. It enables the car driver to take an initiative in knowing the car condition and try to repeat the car. Expert systems are widely used to embody the diagnosis expertise into machines. However, improving the expert systems’ inferencing capability and diagnosis accuracy are still open research topics. Consequently, this paper proposes an agent-based inference engine for the car failure diagnosis expert system that is named automated car failure diagnosis assistance (ACFDA). The agents’ goal is to maximize the efficiency of the overall performance of the ACFDA system by deliberating a number of inferencing tasks and tuning the inferencing logical flow. Additionally, the agents’ collective effort provides reliable solutions that best fit the users’ inputs. The ACFDA system is experimentally tested by 15 relevant candidates. The test results show that the system efficiently and reliably performs the diagnosis to the most given car failure cases. The system can be integrated into cars or can be used as a separate gadget to assist the car drivers in car failure diagnosis and repair