Structural components of students' data-focused information visualization

Information Visualization (InfoVis), as an analytics and visualization tool, had been argued to be befitting in attending to the experience of information overload, and subsequent decision making constraint of higher education institutions’ (HEIs) decision makers.This experience is said to be as a result of increase in volume of students’ data and the limitations of the available data management tools. However, due to the diversity of domains in which application of InfoVis are demanded, designing domain-specific structural components of the intending InfoVis is compulsory, so as to address its peculiar domain problem.Adapting the generic Design Research method, we employed critical review of documentations of selected InfoVis tools and mapped the findings with the outcome of our previous investigation of the HEIs’ decision makers’ explicit knowledge preferences.This work therefore highlights the structural components of the HEIs’ students’ data-focused InfoVis

Single decision tree classifiers' accuracy on medical data

Decision tree is one of the classification techniques for classifying sequential decision problems such as those in medical domain.This paper discusses an evaluation study on different single decision tree classifiers.There are various single decision tree classifiers which have been extensively applied in medical decision making; each of these classifies the data with different accuracy rate.Since accuracy is crucial in medical decision making, it is important to identify a classifier with the best accuracy.The study examines the performance of fourteen single decision tree classifiers on three medical data sets, i.e. Wisconsin’s breast cancer data sets, Pima Indian diabetes data sets and hepatitis data sets.All classifiers were trained and tested using WEKA and cross validation. The results revealed that classifiers such as FT, LMT, NB tree, Random Forest and Random Tree are the five best single classifiers as they constantly provide better accuracy in their classifications

Word level Bangla Sign Language Dataset for Continuous BSL Recognition

An robust sign language recognition system can greatly alleviate communication barriers, particularly for people who struggle with verbal communication. This is crucial for human growth and progress as it enables the expression of thoughts, feelings, and ideas. However, sign recognition is a complex task that faces numerous challenges such as same gesture patterns for multiple signs, lighting, clothing, carrying conditions, and the presence of large poses, as well as illumination discrepancies across different views. Additionally, the absence of an extensive Bangla sign language video dataset makes it even more challenging to operate recognition systems, particularly when utilizing deep learning techniques. In order to address this issue, firstly, we created a large-scale dataset called the MVBSL-W50, which comprises 50 isolated words across 13 categories. Secondly, we developed an attention-based Bi-GRU model that captures the temporal dynamics of pose information for individuals communicating through sign language. The proposed model utilizes human pose information, which has shown to be successful in analyzing sign language patterns. By focusing solely on movement information and disregarding body appearance and environmental factors, the model is simplified and can achieve a speedier performance. The accuracy of the model is reported to be 85.64%

Modeling of Potato Shelf Life on Evaporative Cooling Storage

A model of evaporative cooling storage system was designed to increase potato shelf life for improving potato storage system. Two cultivars of potato ‘Diamant’ (100 gm and 51 gm per tuber) and ‘LalPakri (23 gm and 11 gm per tuber) were placed on four shelves of the bin. Each shelf holds 240 kg of potato from 23 march 2013 to December 2013. Potato spoilage, sprouting, shrinkage, moisture content, vitamin C and total sugar content of potato were measured. Experimental results revealed that potato spoilage progressively increased from April to November and sprouting of potato gradually increased from June to October, but stopped in November. The cumulative spoilage and sprouting were much lower in the improved bin compared to traditional farmer’s practices. Shrinkage of potato was found higher in farmer’s practice than that of storage bin from October to November. Moisture content of potato was higher during May and reduced gradually to the lowest value during November in both of practices. No significant difference was found in two practices on vitamin-C content. Sugar content of ‘Diamant; potato was lower in the storage bin during November. According to data analysis and regression curve storage bin model was more appropriate for both cultivars than farmer practice and significantly more appropriate for ‘LalPakri’ potato

Adaptation and auteurism in South Asian Studies with reference to Rabindranath Tagore’s Works on Screen

Since its inception in the 1890s, cinema has been predominantly contingent upon literature for its source, growth and success. The curiosity to know what happens when a literary text is rendered into visual medium has led to a myriad of debates and discussions. Unlike in Europe and North America, in South Asia cinematic translation of literature has not received substantial scholarly attention and critical insights. As a result, the literary adaptations of Bengali authors are hardly discussed from the theoretical perspectives of adaptation studies. Contextualising adaptation studies in South Asia, especially in India and Bangladesh, this paper recommends the incorporation of auteurism in adaptation studies and argues that, like literary authors, artistic filmmakers (read adapters) are the authors of their films. It attempts to wean away adaptation discourse from the outmoded fidelity/infidelity debate and maintain that directorial transgression is an essential modality in the dynamics of literary adaptation for a successful intermedial rendition. In distinguishing auteurs from general adapters, we suggest that some of the adapting directors, especially those of Rabindranath Tagore’s works, can be evaluated from the auteurist premise of creative independence, technical competence and artistic imagination

Decision Tree Model for Non-Fatal Road Accident Injury

Non-fatal road accident injury has become a great concern as it is associated with injury and sometimes leads to the disability of the victims. Hence, this study aims to develop a model that explains the factors that contribute to non-fatal road accident injury severity. A sample data of 350 non-fatal road accident cases of the year 2016 were obtained from Kota Bharu District Police Headquarters, Kelantan. The explanatory variables include road geometry, collision type, accident time, accident causes, vehicle type, age, airbag, and gender. The predictive data mining techniques of decision tree model and multinomial logistic regression were used to model non-fatal road accident injury severity. Based on accuracy rate, decision tree with CART algorithm was found to be more accurate as compared to the logistic regression model. The factors that significantly contribute to non-fatal traffic crashes injury severity are accident cause, road geometry, vehicle type, age and collision type

Hybrid machine learning technique for intrusion detection system

The utilization of the Internet has grown tremendously resulting in more critical data are being transmitted and handled online.Hence, these occurring changes have led to draw the conclusion that thenumber of attacks on the important information over the internet is increasing yearly.Intrusion is one of the main threat to the internet.Various techniques and approaches have been developed to address the limitations of intrusion detection system such as low accuracy, high false alarm rate, and time consuming. This research proposed a hybrid machine learning technique for network intrusion detection based on combination of K-means clustering and support vector machine classification.The aim of this research is to reduce the rate of false positive alarm, false negative alarm rate and to improve the detection rate.The NSL-KDD dataset has been used in the proposed technique.In order to improve classification performance, some steps have been taken on the dataset.The classification has been performed by using support vector machine. After training and testing the proposed hybrid machine learning technique, the results have shown that the proposed technique has achieved a positive detection rate and reduce the false alarm rate

Characterization of Malaysian Trichoderma isolates using random amplified microsatellites (RAMS)

Trichoderma species are commercially applied as biocontrol agents against numerous plant pathogenic fungi due to their production of antifungal metabolites, competition for nutrients and space, and mycoparasitism. However, currently the identification of Trichoderma species from throughout the world based on micro-morphological descriptions is tedious and prone to error. The correct identification of Trichoderma species is important as several traits are species-specific. The Random Amplified Microsatellites (RAMS) analysis done using five primers in this study showed different degrees of the genetic similarity among 42 isolates of this genus. The genetic similarity values were found to be in the range of 12.50-85.11% based on a total of 76 bands scored in the Trichoderma isolates. Of these 76 bands, 96.05% were polymorphic, 3.95% were monomorphic and 16% were exclusive bands. Two bands (250 bp and 200 bp) produced by primer LR-5 and one band (250 bp) by primer P1A were present in all the Trichoderma isolates collected from healthy and infected oil palm plantation soils. Cluster analysis based on UPGMA of the RAMS marker data showed that T. harzianum, T. virens and T. longibrachiatum isolates were grouped into different clades and lineages. In this study we found that although T. aureoviride isolates were morphologically different when compared to T. harzianum isolates, the UPGMA cluster analysis showed that the majority isolates of T. aureoviride (seven from nine) were closely related to the isolates of T. harzianum