Satrya Fajri Pratama

SOCIFS feature selection framework for handwritten authorship

The uniqueness of shape and style of handwriting can be used to identify the significant features in confirming the author of writing. This paper is meant to propose a novel feature selection framework for Swarm Optimized and Computationally Inexpensive Floating Selection SOCIFS, by exploring existing feature selection frameworks, and compare the performance of proposed feature selection framework against various feature selection methods in Writer Identification in order to find the most significant features. The promising applicability of the proposed framework has been demonstrated in the result and worth to receive further exploration in identifying the handwritten authorship.

PSO and Computationally Inexpensive Sequential Forward Floating Selection in acquiring significant features for handwritten authorship

The uniqueness of shape and style of handwriting can be used to identify the significant features in confirming the author of writing. Acquiring these significant features leads to an important research in Writer Identification domain. This paper is meant to explore the usage of feature selection in Writer Identification in order to find the most significant features. This paper proposes a hybrid feature selection method of Particle Swarm Optimization and Computationally Inexpensive Sequential Forward Floating Selection for Writer Identification. The promising applicability of the proposed method has been demonstrated and worth to receive further exploration in identifying the handwritten authorship.

Computational Intelligence in Digital Forensics

Forensic Science has been around for quite some time. Although various forensic methods have been proved for their reliability and credibility in the criminal justice system, their main problem lies in the necessity of highly qualified forensic investigators. In the course of analysis of evidences, forensic investigators must be thorough and rigorous, hence time consuming. Digital Forensic techniques have been introduced to aid the forensic investigators to acquire as reliable and credible results as manual labor to be presented in the criminal court system. In order to perform the forensic investigation using Digital Forensic techniques accurately and efficiently, computational intelligence oftentimes employed in the implementation of Digital Forensic techniques, which has been proven to reduce the time consumption, while maintaining the reliability and credibility of the result, moreover in some cases, it is producing the results with higher accuracy. The introduction of computational intelligence in Digital Forensic has attracted a vast amount of researchers to work in, and leads to emergence of numerous new forensic investigation domains.

3D Geometric Moment Invariants for ATS Drugs Identification: A More Precise Approximation

National development is constantly threatened by drug abuse. The chemical composition of the drugs heavily determines the results of identification process, which becomes more unreliable due to the introduction of new, sophisticated, and increasingly complex ATS analogues. The identification of the unique characteristics of molecular structure in ATS drug is very crucial. Therefore, this paper is meant for formulating a more precise 3D geometric moment invariants to represent the molecular structure. The performance of the proposed technique was analyzed using drug molecular structures obtained from United Nations Office of Drugs and Crime and also from various sources. The evaluation shows the technique is qualified to be further explored and adapted to be fully compatible with ATS drug identification domain.

Exact Computation of 3D Geometric Moment Invariants for ATS Drugs Identification

The war on drug abuse involves all nations worldwide. Normally, molecular components are unique, and thus the drugs can be identified based on it. However, this procedure started to be more unreliable with the introduction of new ATS molecular structures which are increasingly complex and sophisticated. Hence, unique characteristics of molecular structure of ATS drug must be accurately identified. Therefore, this paper is meant for formulating an exact 3D geometric moment invariants to represent the drug molecular structure. The performance of the proposed technique was analyzed using drug chemical structures obtained from United Nations Office of Drugs and Crime (UNODC) and also from various sources. The evaluation shows the technique is qualified to be further explored and adapted in the future works to be fully compatible with ATS drug identification domain.

Preparation of ATS Drugs 3D Molecular Structure for 3D Moment Invariants-Based Molecular Descriptors

The campaign against drug abuse is fought by all countries, most notably on ATS drugs. The technical limitations of the current test kits to detect new brand of ATS drugs present a challenge to law enforcement authorities and forensic laboratories. Meanwhile, new molecular microscopy imaging devices which enabled the characterization of the physical 3D molecular structure have been recently introduced, and it can be used to remedy the limitations of existing drug test kits. Thus, a new type of 3D molecular structure representation, or molecular descriptors, technique should be developed to cater the 3D molecular structure acquired physically using these molecular imaging devices. One of the applications of image processing methods to represent a 3D image is 3D moment invariants. However, since there are currently no repository or database available which provide the drugs imaging results obtained using these molecular imaging devices, this paper proposes to construct the simulated 3D drugs molecular structure to be used by these 3D moment invariants-based molecular descriptors techniques. The drugs molecular structures are obtained from pihkal.info for the ATS drugs, while non-ATS drugs are obtained randomly from ChemSpider database.

ATS drugs molecular structure representation using refined 3D geometric moment invariants

The campaign against drug abuse is fought by all countries, most notably on ATS drugs. The identification process of ATS drugs depends heavily on its molecular structure. However, the process becomes more unreliable due to the introduction of new, sophisticated, and increasingly complex ATS molecular structures. Therefore, distinctive features of ATS drug molecular structure need to be accurately obtained. In this paper, two variants of refined 3D geometric moment invariants for ATS drug molecular structure representation are discussed. This paper is also meant for comparing the performance of these two variants. The comparison was conducted using drug chemical structures obtained from Isomer Design’s PiHKaL.info database for the ATS drugs, while non-ATS drugs are obtained randomly from ChemSpider database. The assessment highlights the best technique which is suitable to be further explored and improved in the future studies so that it is wholly attuned with ATS drug molecular similarity search domain.

A Comparative Study of 2D UMI and 3D Zernike Shape Descriptor for ATS Drugs Identification

Drug abuse is a threat to national development. Generally, drugs can be identified based on the structure of its molecular components. This procedure is becoming more unreliable with the introduction of new amphetamine-type stimulants (ATS) molecular structures which are increasingly complex and sophisticated. An in-depth study is crucial to accurately identify the unique characteristics of molecular structure in ATS drug. Therefore, this chapter is meant for exploring the usage of shape descriptors (SD) to represent the drug molecular structure. Two-dimensional (2D) united moment invariant (UMI) and three-dimensional (3D) Zernike are selected and their performances are analyzed using drug chemical structures obtained from United Nations Office of Drugs and Crime (UNODC) and various sources. The evaluation identifies the most interesting method to be further explored and adapted in the future work to fully compatible with ATS drug identification domain.

Rotation analysis of moment invariant for 2D and 3D shape representation for molecular structure of ATS drugs

Drug abuse among the people around the world is getting serious and increase nowadays. An Amphetamine-Type Stimulant (ATS) drug is one of the popular drugs in the world. This kind of drug is a combination of two types of drugs; amphetamine and ecstasy. In this project, we are focusing on two different techniques used to compare the result between 2D and 3D ATS drug molecule. Besides that, this project is also looking for the best technique to produce 2D and 3D images. United Moment Invariant (UMI) and Hough Transform (HT) are the feature extraction methods that will be used to extract data from both 2D and 3D molecular structure of the ATS drugs. Later, the extracted data will be processed into WEKA for classification accuracies for both molecular structures. As a conclusion, the MI and the HT technique will definitely provide different results, but both techniques will perform well with the ATS molecular structure.

A New Swarm-Based Framework for Handwritten Authorship Identification in Forensic Document Analysis

Feature selection has become the focus of research area for a long time due to immense consumption of high-dimensional data. Originally, the purpose of feature selection is to select the minimally sized subset of features class distribution which is as close as possible to original class distribution. However in this chapter, feature selection is used to obtain the unique individual significant features which are proven very important in handwriting analysis of Writer Identification domain. Writer Identification is one of the areas in pattern recognition that have created a center of attention by many researchers to work in due to the extensive exchange of paper documents. Its principal point is in forensics and biometric application as such the writing style can be used as bio-metric features for authenticating the identity of a writer. Handwriting style is a personal to individual and it is implicitly represented by unique individual significant features that are hidden in individual’s handwriting. These unique features can be used to identify the handwritten authorship accordingly. The use of feature selection as one of the important machine learning task is often disregarded in Writer Identification domain, with only a handful of studies implemented feature selection phase. The key concern in Writer Identification is in acquiring the features reflecting the author of handwriting. Thus, it is an open question whether the extracted features are optimal or near-optimal to identify the author. Therefore, feature extraction and selection of the unique individual significant features are very important in order to identify the writer, moreover to improve the classification accuracy. It relates to invarianceness of authorship where invarianceness between features for intra-class (same writer) is lower than inter-class (different writer). Many researches have been done to develop algorithms for extracting good features that can reflect the authorship with good performance. This chapter instead focuses on identifying the unique individual significant features of word shape by using feature selection method prior the identification task. In this chapter, feature selection is explored in order to find the most unique individual significant features which are the unique features of individual’s writing. This chapter focuses on the integration of Swarm Optimized and Computationally Inexpensive Floating Selection (SOCIFS) feature selection technique into the proposed hybrid of Writer Identification framework and feature selection framework, namely Cheap Computational Cost Class-Specific Swarm Sequential Selection (C4S4). Experiments conducted to proof the validity and feasibility of the proposed framework using dataset from IAM Database by comparing the proposed framework to the existing Writer Identification framework and various feature selection techniques and frameworks yield satisfactory results. The results show the proposed framework produces the best result with 99.35% classification accuracy. The promising outcomes are opening the gate to future explorations in Writer Identification domain specifically and other domains generally.