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Text data plays an imperative role in the biomedical domain. As patient's data comprises of a huge amount of text documents in a non-standardized format. In order to obtain the relevant data, the text documents pose a lot of challenging issues for data processing. Topic modeling is one of the popular techniques for information retrieval based on themes from the biomedical documents. In topic modeling discovering the precise topics from the biomedical documents is a challenging task. Furthermore, in biomedical text documents, the redundancy puts a negative impact on the quality of text mining as well. Therefore, the rapid growth of unstructured documents entails machine learning techniques for topic modeling capable of discovering precise topics. In this paper, we proposed a topic modeling technique for text mining through hybrid inverse document frequency and machine learning fuzzy k-means clustering algorithm. The proposed technique ameliorates the redundancy issue and discovers precise topics from the biomedical text documents. The proposed technique generates local and global term frequencies through the bag-of-words (BOW) model. The global term weighting is calculated through the proposed hybrid inverse documents frequency and Local term weighting is computed with term frequency. The robust principal component analysis is used to remove the negative impact of higher dimensionality on the global term weights. Afterward, the classification and clustering for text mining are performed with a probability of topics in the documents. The classification is performed through discriminant analysis classifier whereas the clustering is done through the k-means clustering. The performance of clustering is evaluated with Calinsiki-Har-abasz (CH) index internal validation method. The proposed toping modeling technique is evaluated on six standard datasets namely Ohsumed, MuchMore Springer Corpus, GENIA corpus, Bioxtext, tweets and WSJ redundant corpus for experimentation. The proposed topic modeling technique exhibits high performance on classification and clustering in text mining compared to baseline topic models like FLSA, LDA, and LSA. Moreover, the execution time of the proposed topic modeling technique remains stable for different numbers of topics.

BackgroundStroke was the second most common cause of death worldwide in 2004, resulting in 5.7 million deaths. This case controlled study was conducted in Tertiary care hospitals of Peshawar, KPK Pakistan about common risk factors of stroke and the impact of these risk factors.MethodStudy was done at Government Tertiary Care Hospitals of Peshawar namely Khyber Teaching Hospital (KTH), Lady Reading Hospital (LRH) and Hayatabad Medical Complex (HMC).The hospital based case–control study included 50 cases (stroke patients) and 100 controls (non-stroke patients). This study was accomplished from 24th April 2012 to October 2012 in tertiary care hospitals of Peshawar. A proper questionnaire was used to collect data from all the cases and controls, which was recorded in the form of tables and graphs. The risk factors studied were Hypertension, Smoking, diabetes, sedentary lifestyle, cardiac problems, B.M.I, diet, stress and family history of stroke. Anthropometric (weight, height, body mass index) measurements were done on all patients. For determination of association and impact of these risk factors, analyses were performed by calculation of Chi-Square test and confidence interval using SPSS version 16 program.ResultsComparing the cases with controls, hypertension (p = 0.000), sedentary life style (p = 0.000), cardiac problems (p = 0.009), diabetes mellitus (p = 0.010), smoking (p = 0. 042) were significant risk factors whereas B.M.I (p = 0. 393), stress (p = 0.705), family history of stroke (p = 0.729), diet (p = 0.908), were not found to be statistically significant risk factors. The most significant risk factor was systemic hypertension (OR = 4.16) followed by sedentary life style (OR = 3.60), cardiac problems (OR = 2.74) diabetes (OR = 2.49) and smoking (OR = 2.05).ConclusionHypertension, Smoking, diabetes, sedentary lifestyle and cardiac problems have strong correlations and association with Stroke and are the major risk factors of stroke. Prevention of these risk factors can lead to decrease in the incidence of stroke. B.M.I, diet, stress and family history of stroke had no significant association.

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Topic modeling is an effective text mining and information retrieval approach to organizing knowledge with various contents under a specific topic. Text documents in form of news articles are increasing very fast on the web. Analysis of these documents is very important in the fields of text mining and information retrieval. Meaningful information extraction from these documents is a challenging task. One approach for discovering the theme from text documents is topic modeling but this approach still needs a new perspective to improve its performance. In topic modeling, documents have topics and topics are the collection of words. In this paper, we propose a new k-means topic modeling (KTM) approach by using the k-means clustering algorithm. KTM discovers better semantic topics from a collection of documents. Experiments on two real-world Reuters 21578 and BBC News datasets show that KTM performance is better than state-of-the-art topic models like LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis). The KTM is also applicable for classification and clustering tasks in text mining and achieves higher performance with a comparison of its competitors LDA and LSA.

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Plant diseases are a significant yield and quality constraint for farmers around the world due to their severe impact on agricultural productivity. Such losses can have a substantial impact on the economy which causes a reduction in farmer's income and higher prices for consumers. Further, it may also result in a severe shortage of food ensuing violent hunger and starvation, especially, in less-developed countries where access to disease prevention methods is limited. This research presents an investigation of Directional Local Quinary Patterns (DLQP) as a feature descriptor for plants leaf disease detection and Support Vector Machine (SVM) as a classifier. The DLQP as a feature descriptor is specifically the first time being used for disease detection in horticulture. DLQP provides directional edge information attending the reference pixel with its neighboring pixel value by involving computation of their grey-level difference based on quinary value (-2, -1, 0, 1, 2) in 0 o , 45 o , 90 o , and 135 o directions of selected window of plant leaf image. To assess the robustness of DLQP as a texture descriptor we used a research-oriented Plant Village dataset of Tomato plant (3,900 leaf images) comprising of 6 diseased classes, Potato plant (1,526 leaf images) and Apple plant (2,600 leaf images) comprising of 3 diseased classes. The accuracies of 95.6%, 96.2% and 97.8% for the above-mentioned crops, respectively, were achieved which are higher in comparison with classification on the same dataset using other standard feature descriptors like Local Binary Pattern (LBP) and Local Ternary Patterns (LTP). Further, the effectiveness of the proposed method is proven by comparing it with existing algorithms for plant disease phenotyping.

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