Ram Gopal Raj

LIS journals scientific impact and subject categorization: a comparison between Web of Science and Scopus

The study compares the coverage, ranking, impact and subject categorization of Library and Information Science journals, specifically, 79 titles based on data from Web of Science (WoS) and 128 titles from Scopus. Comparisons were made based on prestige factor scores reported in 2010 Journal Citation Reports and SCImago Journal Rank 2010 and noting the change in ranking when the differences are calculated. The rank normalized impact factor and the Library of Congress Classification System were used to compare impact rankings and subject categorization. There was high degree of similarity in rank normalized impact factor of titles in both WoS and Scopus databases. The searches found 162 journals, with 45 journals appearing in both databases. The rankings obtained for normalized impact scores confirm higher impact scores for titles covered in Scopus because of its larger coverage of titles. There was mismatch of subject categorization among 34 journal titles in both databases and 22 of the titles were not classified under Z subject headings in the Library of Congress catalogue. The results revealed the changes in journal title rankings when normalized, and the categorization of some journal titles in these databases might be incorrect.

Exploring the relationship between urbanized Malaysian youth and their mobile phones: A quantitative approach

Mobile phones have become a ubiquitous consumer item. This paper aims to explore mobile phone usage, extending work beyond teenage years to examine the role of mobile phones among urbanized Malaysian youth, specifically university students. Four main categories were identified, namely, mobile phone purchasing factors and reasons to use, mobile phone usage and also behavioral issues. A mixed-mode approach involving questionnaire surveys and 24-h diaries were used to gather the relevant data. A total of 417 respondents participated in this study. The salient results indicate respondents in this study consider brand, trend and price to be the three most important purchasing factors while socializing and privacy emerged as the two most important reasons to use mobile phones. Behavioral issues related to addiction and inappropriate use of mobile phones was also observed among the respondents. Gender analysis revealed females to use their mobile phones more to socialize, gossip and as a safety device. The findings of this study could prove to be beneficial to those exploring the mobile phone adoption and usage pattern in a developing country such as Malaysia.

An application of case-based reasoning with machine learning for forensic autopsy

We modeled the CBR technique of forensic autopsy report preparation.The CBR model was coupled with Naive Bayes learner for feature weight learning and also the outcome prediction.Feature weight learning improves the CBR system accuracy.The outcome prediction is improved with Naive Bayes prediction. Case-based reasoning (CBR) is one of the matured paradigms of artificial intelligence for problem solving. CBR has been applied in many areas in the commercial sector to assist daily operations. However, CBR is relatively new in the field of forensic science. Even though forensic personnel have consciously used past experiences in solving new cases, the idea of applying machine intelligence to support decision-making in forensics is still in its infancy and poses a great challenge. This paper highlights the limitation of the methods used in forensics compared with a CBR method in the analysis of forensic evidences. The design and development of an Intelligent Forensic Autopsy Report System (I-AuReSys) basing on a CBR method along with the experimental results are presented. Our system is able to extract features by using an information extraction (IE) technique from the existing autopsy reports; then the system analyzes the case similarities by coupling the CBR technique with a Naive Bayes learner for feature-weights learning; and finally it produces an outcome recommendation. Our experimental results reveal that the CBR method with the implementation of a learner is indeed a viable alternative method to the forensic methods with practical advantages.

Automated Bone Age Assessment: Motivation, Taxonomies, and Challenges

Bone age assessment (BAA) of unknown people is one of the most important topics in clinical procedure for evaluation of biological maturity of children. BAA is performed usually by comparing an X-ray of left hand wrist with an atlas of known sample bones. Recently, BAA has gained remarkable ground from academia and medicine. Manual methods of BAA are time-consuming and prone to observer variability. This is a motivation for developing automated methods of BAA. However, there is considerable research on the automated assessment, much of which are still in the experimental stage. This survey provides taxonomy of automated BAA approaches and discusses the challenges. Finally, we present suggestions for future research.

Relative measure index: a metric to measure the quality of journals

Journal impact factors (JIF) have been an accepted indicator of ranking journals. However, there has been increasing arguments against the fairness of using the JIF as the sole ranking criteria. This resulted in the creation of many other quality metric indices such as the h-index, g-index, immediacy index, Citation Half-Life, as well as SCIMago journal rank (SJR) to name a few. All these metrics have their merits, but none include any great degree of normalization in their computations. Every citation and every publication is taken as having the same importance and therefore weight. The wealth of available data results in multiple different rankings and indexes existing. This paper proposes the use of statistical standard scores or z-scores. The calculation of the z-scores can be performed to normalize the impact factors given to different journals, the average of z-scores can be used across various criteria to create a unified relative measurement (RM) index score. We use the 2008 JCR provided by Thompson Reuters to demonstrate the differences in rankings that would be affected if the RM-index was adopted discuss the fairness that this index would provide to the journal quality ranking.

Identifying ISI-Indexed Articles by Their Lexical Usage: A Text Analysis Approach

This research creates an architecture for investigating the existence of probable lexical divergences between articles, categorized as Institute for Scientific Information (ISI) and non‐ISI, and consequently, if such a difference is discovered, to propose the best available classification method. Based on a collection of ISI‐ and non‐ISI‐indexed articles in the areas of business and computer science, three classification models are trained. A sensitivity analysis is applied to demonstrate the impact of words in different syntactical forms on the classification decision. The results demonstrate that the lexical domains of ISI and non‐ISI articles are distinguishable by machine learning techniques. Our findings indicate that the support vector machine identifies ISI‐indexed articles in both disciplines with higher precision than do the Naïve Bayesian and K‐Nearest Neighbors techniques.

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection

Objectives Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Methods Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Results Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. Conclusion The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports.

A Novel Two-Stage Spectrum-Based Approach for Dimensionality Reduction: A Case Study on the Recognition of Handwritten Numerals

Dimensionality reduction (feature selection) is an important step in pattern recognition systems. Although there are different conventional approaches for feature selection, such as Principal Component Analysis, Random Projection, and Linear Discriminant Analysis, selecting optimal, effective, and robust features is usually a difficult task. In this paper, a new two-stage approach for dimensionality reduction is proposed. This method is based on one-dimensional and two-dimensional spectrum diagrams of standard deviation and minimum to maximum distributions for initial feature vector elements. The proposed algorithm is validated in an OCR application, by using two big standard benchmark handwritten OCR datasets, MNIST and Hoda. In the beginning, a 133-element feature vector was selected from the most used features, proposed in the literature. Finally, the size of initial feature vector was reduced from 100% to 59.40% (79 elements) for the MNIST dataset, and to 43.61% (58 elements) for the Hoda dataset, in order. Meanwhile, the accuracies of OCR systems are enhanced 2.95% for the MNIST dataset, and 4.71% for the Hoda dataset. The achieved results show an improvement in the precision of the system in comparison to the rival approaches, Principal Component Analysis and Random Projection. The proposed technique can also be useful for generating decision rules in a pattern recognition system using rule-based classifiers.

Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network

Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are ‘traffic light ahead’ or ‘pedestrian crossing’ indications. The present investigation targets the recognition of Malaysian road and traffic signs in real-time. Real-time video is taken by a digital camera from a moving vehicle and real world road signs are then extracted using vision-only information. The system is based on two stages, one performs the detection and another one is for recognition. In the first stage, a hybrid color segmentation algorithm has been developed and tested. In the second stage, an introduced robust custom feature extraction method is used for the first time in a road sign recognition approach. Finally, a multilayer artificial neural network (ANN) has been created to recognize and interpret various road signs. It is robust because it has been tested on both standard and non-standard road signs with significant recognition accuracy. This proposed system achieved an average of 99.90% accuracy with 99.90% of sensitivity, 99.90% of specificity, 99.90% of f-measure, and 0.001 of false positive rate (FPR) with 0.3 s computational time. This low FPR can increase the system stability and dependability in real-time applications.

An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines.

Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age.