Muhammad Wasim

Lexical paraphrasing and pseudo relevance feedback for biomedical document retrieval

Term mismatch is a serious problem effecting the performance of information retrieval systems. The problem is more severe in biomedical domain where lot of term variations, abbreviations and synonyms exist. We present query paraphrasing and various term selection combination techniques to overcome this problem. To perform paraphrasing, we use noun words to generate synonyms from Metathesaurus. The new synthesized paraphrases are ranked using statistical information derived from the corpus and relevant documents are retrieved based on top n selected paraphrases. We compare the results with state-of-the-art pseudo relevance feedback based retrieval techniques. In quest of enhancing the results of pseudo relevance feedback approach, we introduce two term selection combination techniques namely Borda Count and Intersection. Surprisingly, combinational techniques performed worse than single term selection techniques. In pseudo relevance feedback approach best algorithms are IG, Rochio and KLD which are performing 33%, 30% and 20% better than other techniques respectively. However, the performance of paraphrasing technique is 20% better than pseudo relevance feedback approach.

A Review and Classification of Widely used Offline Brain Datasets

Brain Computer Interfaces (BCI) are a natural extension to Human Computer Interaction (HCI) technologies. BCI is especially useful for people suffering from diseases, such as Amyotrophic Lateral Sclerosis (ALS) which cause motor disabilities in patients. To evaluate the effectiveness of BCI in different paradigms, the need of benchmark BCI datasets is increasing rapidly. Although, such datasets do exist, a comparative study of such datasets is not available to the best of our knowledge. In this paper, we provided a comprehensive overview of various BCI datasets. We briefly describe the characteristics of these datasets and devise a classification scheme for them. The comparative study provides feature extractors and classifiers used for each dataset. Moreover, potential use-cases for each dataset are also provided.

A survey of ontology learning techniques and applications

Abstract Ontologies have gained a lot of popularity and recognition in the semantic web because of their extensive use in Internet-based applications. Ontologies are often considered a fine source of semantics and interoperability in all artificially smart systems. Exponential increase in unstructured data on the web has made automated acquisition of ontology from unstructured text a most prominent research area. Several methodologies exploiting numerous techniques of various fields (machine learning, text mining, knowledge representation and reasoning, information retrieval and natural language processing) are being proposed to bring some level of automation in the process of ontology acquisition from unstructured text. This paper describes the process of ontology learning and further classification of ontology learning techniques into three classes (linguistics, statistical and logical) and discusses many algorithms under each category. This paper also explores ontology evaluation techniques by highlighting their pros and cons. Moreover, it describes the scope and use of ontology learning in several industries. Finally, the paper discusses challenges of ontology learning along with their corresponding future directions.

Improved biomedical term selection in pseudo relevance feedback

Abstract Biomedical information retrieval systems are becoming popular and complex due to massive amount of ever-growing biomedical literature. Users are unable to construct a precise and accurate query that represents the intended information in a clear manner. Therefore, query is expanded with the terms or features that retrieve more relevant information. Selection of appropriate expansion terms plays key role to improve the performance of retrieval task. We propose document frequency chi-square, a newer version of chi-square in pseudo relevance feedback for term selection. The effects of pre-processing on the performance of information retrieval specifically in biomedical domain are also depicted. On average, the proposed algorithm outperformed state-of-the-art term selection algorithms by 88% at pre-defined test points. Our experiments also conclude that, stemming cause a decrease in overall performance of the pseudo relevance feedback based information retrieval system particularly in biomedical domain. Database URL: http://biodb.sdau.edu.cn/gan/

Object’s Shape Recognition using Local Binary Patterns

This paper discusses the concept of object’s shape identification using local binary pattern technique (LBP). Since LBP is computationally simple it has been utilized successfully for recognition of various objects. LBP which has the potential to be used in various identification related fields was applied on a number of different shaped objects, the process converted the given image in to 3x3 binary matrices and several rounds of computation yields the final decision parameter, which is known as merit function. This parameter was then exploited to uniquely identify the shape of different objects.

A Survey of Datasets for Biomedical Question Answering Systems

The massively ever increasing amount of textual and linked biomedical data available online poses many challenges for information seekers. So, the focus of information retrieval community has shifted to precise information retrieval, i.e. providing exact answer to a user question. In recent years, many datasets related to Biomedical Question Answering (BioQA) have emerged which the researchers can use to evaluate the performance of their systems. We reviewed these biomedical datasets and analyzed their characteristics. The survey in this paper covers these datasets for BioQA and has a two fold purpose: to provide an overview of the available datasets in this domain and to help researchers select the most suitable dataset for benchmarking their system.

Eye Controlled Mobile Robot with Shared Control for Physically Impaired People

Physically impaired and disabled people are an integral part of human society. Devices providing assistance to such individuals can help them contribute to the society in a more productive way. The situation is even worse for patients with locked-in syndrome who cannot move their body at all. These problems were the motivation to develop an eye controlled robot to facilitate such patients. Readily available commercial headset is used to record electroencephalogram (EEG) signals for classification and processing. Classification based control signals were then transmitted to robot for navigation. The robot mimics a brain controlled wheelchair with eye movements. The robot is based on shared control which is safe and robust. The analysis of robot navigation for patients showed promising results.