Performance Analysis of Support Vector Machine in Sex Classification of The Sacrum Bone in Forensic Anthropology

Sex classification is part of forensic anthropological identification aimed at determining whether the skeleton belongs to a male or a female. This paper exhibits the performance of the Support Vector Machine (SVM) in classifying the sex of the sacrum in forensic anthropology. Bone data was measured by the metric method based on six variables, namely superior breadth, anterior length, mid ventral breadth, real height, diameter the base, and max-transverse diameter of the base. This study shows performance analysis of SVM using the library libSVM with linear, polynomial, and RBF kernel to observe the results of the comparison of the accuracy of the kernel used. According to the results of the trials, the best accuracy was attained in each kernel function, i.e., the RBF kernel is 83.33% with g = 1 and C = 1, the polynomial is 85.56% at γ = 2, C = 2 and d =1, and the linear kernel obtained best accuracy is 84.44 % with C = 2 and C = 3. In conformity with the experimental result, polynomial attained the highest accuracy of 85.56% at γ = 2, C = 2, and d =1

PSO-FuzzyNN techniques in gender classification based on bovine bone morphology properties

This simulation project aims to solve forensic anthropology issues by using the computational method. The positive identification on gender is such a potential field to be explored. Basically, gender identification in forensic anthropology by comparative skeletal anatomy by atlas and crucially affect the identification accuracy. The simulation identification method was studied in order to determine the best model, which reduce the total costs of the post-mortem as an objective. The computational method on simulation run improves the identification accuracy as proven by many studies. Fuzzy K-nearest neighbours classifier (FuzzyNN) is such a computational intelligence method and always shows the best performance in many fields including forensic anthropology. Thus, this intelligent identification method was implemented within the determining for best accuracy. The result of this proposed model was compared with raw data collection and standard collections datasets; Goldman Osteometric dataset and Ryan and Shaw Dataset (RSD) as a benchmark for the identification policy. To improve the accuracy of FuzzyNN classifier, Particle Swarm Optimization (PSO) feature selection was used as the basis for choosing the best features to be used by the selected FuzzyNN classification model. The model is called PSO-FuzzyNN and has been developed by MATLAB and WEKA tools platform. Comparisons of the performance measurement namely the percentage of the classification accuracy of the model were performed. The result show potential the proposed PSO-FuzzyNN method demonstrates the capability to the obtained highest accuracy of identification

A Review of Optimization Models and Techniques for Maintenance Decision Support Systems in Small and Medium Industries

There are not many sufficient studies made on the application of artificial intelligence techniques to access troubleshooting activities as it always taken into consideration in a verbal sense and yet is not dealt with mathematically. The proposed study extended Choy, John, Thomas & Yan [1] models using either semi-parametric or non-parametric approaches of reliability analysis to examine the relationship between repair time and various risk factors of interest. Then the models will be embedded to neural networks to provide better estimation of repairing parameters. The proposed models can be used by maintenance managers as a benchmarking to develope quality service to enhance competitiveness among service providers in corrective maintenance field. Also the models can be deployed farther to develop a computerized decision support syste

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

An alternative framework of open source Enterprise Resource Planning (ERP) system for Small and Medium Enterprise (SME)

Integrated enterprise systems deserve intensive research because of their great potential for financial, technical, managerial, human, strategic benefits, costs, and risks.As ERP is an important tool that offers full package of managing and integration of data’s, Open source was a package in the business world for optimization which offers organization with lowest cost and perfect reliability suite to SMEs. However, facing challenges of SMEs today the right solution should have every goods and best from both of these tools.Therefore, Open Source ERP is the new evolution that derives from both tools promising more extensive functionality in business demands

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

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

Hybrid Neighbourhood Component Analysis with Gradient Tree Boosting for Feature Selection in Forecasting Crime Rate

Crime forecasting is beneficial as it provides valuable information to the government and authorities in planning an efficient crime prevention measure. Most criminology studies found that influence from several factors, such as social, demographic, and economic factors, significantly affects crime occurrence. Therefore, most criminology experts and researchers' study and observe the effect of factors on criminal activities as it provides relevant insight into possible future crime trends. Based on the literature review, the applications of proper analysis in identifying significant factors that influence crime are scarce and limited. Therefore, this study proposed a hybrid model that integrates Neighbourhood Component Analysis (NCA) with Gradient Tree Boosting (GTB) in modelling the United States (US) crime rate data. NCA is a feature selection technique used in this study to identify the significant factors influencing crime rate. Once the significant factors were identified, an artificial intelligence technique, i.e., GTB, was implemented in modelling the crime data, where the crime rate value was predicted. The performance of the proposed model was compared with other existing models using quantitative measurement error analysis. Based on the result, the proposed NCA-GTB model outperformed other crime models in predicting the crime rate. As proven by the experimental result, the proposed model produced the smallest quantitative measurement error in the case study

The framework of image recognition based on modified freeman chain code

Image recognition of line drawing involves feature extraction and feature comparison; works on the extraction required the representation of the image to be compared and analysed. Combining these two requirements, a framework that implements a new extraction algorithm of a chain code representation is presented. In addition, new corner detection is presented as pre-processing to the line drawing input in order to derive the chain code. This paper presents a new framework that consists of five steps namely pre-processing and image processing, new corner detection algorithm, chain code generator, feature extraction algorithm, and recognition process. Heuristic approach that is applied in the corner detection algorithm accepts thinned binary image as input and produces a modified thinned binary image containing J characters to represent corners in the image. Using the modified thinned binary image, a new chain code scheme that is based on Freeman chain code is proposed and an algorithm is developed to generate a single chain code series that is representing the line drawing input. The feature extraction algorithm is then extracts the three pre-defined features of the chain code for recognition purpose. The features are corner properties, distance between corners, and angle from a corner to the connected corner. The explanation of steps in the framework is supported with two line drawings. The results show that the framework successfully recognizes line drawing into five categories namely not similar line drawing, and four other categories that are similar but with attributes of rotation angle and scaling ratio

Shortest path planning for single manipulator in 2D environment of deformable objects

A heuristic algorithm to perform path planning for single manipulator in 2D environment containing deformable objects is presented. The environment is partitioned into a quadtree hierarchy for both sampling and space navigation use before combination of artificial potential field and heuristic reasoning are applied iteratively to generate feasible path for the manipulator. The algorithm specifically targets for the shortest path without damaging any objects due to deep collision depth between manipulator link and object. Resulting path is in turn to be used in generating micro-instruction controlling the manipulator. Implementation results show feasibility to solve problems involving simple object and manipulator configuration