An Efficient False-Positive Reduction System for Cerebral Microbleeds Detection

Cerebral Microbleeds (CMBs) are microhemorrhages caused by certain abnormalities of brain vessels. CMBs can be found in people with Traumatic Brain Injury (TBI), Alzheimer’s disease, and in old individuals having a brain injury. Current research reveals that CMBs can be highly dangerous for individuals having dementia and stroke. The CMBs seriously impact individuals’ life which makes it crucial to recognize the CMBs in its initial phase to stop deterioration and to assist individuals to have a normal life. The existing work report good results but often ignores false-positive’s perspective for this research area. In this paper, an efficient approach is presented to detect CMBs from the Susceptibility Weighted Images (SWI). The proposed framework consists of four main phases (i) making clusters of brain Magnetic Resonance Imaging (MRI) using k-mean classifier (ii) reduce false positives for better classification results (iii) discriminative feature extraction specific to CMBs (iv) classification using a five layers convolutional neural network (CNN). The proposed method is evaluated on a public dataset available for 20 subjects. The proposed system shows an accuracy of 98.9% and a 1.1% false-positive rate value. The results show the superiority of the proposed work as compared to existing states of the art methods.     

A Framework for Systematic Classification of Assets for Security Testing

Over the last decade, a significant increase has been observed in the use of web-based Information systems that process sensitive information, e.g., personal, financial, medical. With this increased use, the security of such systems became a crucial aspect to ensure safety, integrity and authenticity of the data. To achieve the objectives of data safety, security testing is performed. However, with growth and diversity of information systems, it is challenging to apply security testing for each and every system. Therefore, it is important to classify the assets based on their required level of security using an appropriate technique. In this paper, we propose an asset security classification technique to classify the System Under Test (SUT) based on various factors such as system exposure, data criticality and security requirements. We perform an extensive evaluation of our technique on a sample of 451 information systems. Further, we use security testing on a sample extracted from the resulting prioritized systems to investigate the presence of vulnerabilities. Our technique achieved promising results of successfully assigning security levels to various assets in the tested environments and also found several vulnerabilities in them.     

A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis

In machine learning, sentiment analysis is a technique to find and analyze the sentiments hidden in the text. For sentiment analysis, annotated data is a basic requirement. Generally, this data is manually annotated. Manual annotation is time consuming, costly and laborious process. To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis. Dataset is created from the reviews of ten most popular songs on YouTube. Reviews of five aspects—voice, video, music, lyrics and song, are extracted. An N-Gram based technique is proposed. Complete dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds (575 h) if it was annotated manually. For the validation of the proposed technique, a sub-dataset—Voice, is annotated manually as well as with the proposed technique. Cohen's Kappa statistics is used to evaluate the degree of agreement between the two annotations. The high Kappa value (i.e., 0.9571%) shows the high level of agreement between the two. This validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational cost. This research also contributes in consolidating the guidelines for the manual annotation process.     

Conditional mean- and median-based cumulative sum control charts for Weibull data

This article deals with the monitoring of censored data using the cumulative sum (CUSUM) control charts for Weibull lifetimes under type-I censoring. To develop an efficient CUSUM structure for censored data, we use the conditional expected value (CEV) and conditional median (CM) approaches. In particular, we focus on the detection of shifts in the mean of Weibull lifetimes assuming censored data. In addition to fixed/known parameter values, the effect of estimation is assessed on the detection power of control charts. The performance of the proposed charts is evaluated by the average run length (ARL). Furthermore, the ARL performance of CUSUM charts is compared with CEV- and CM-based exponentially weighted moving average (EWMA) control charts. Besides an extensive simulation study, the significance of the current work is illustrated by a data set on the response time of a thermostat experiment.     

A WLAN band‐notched compact four element UWB MIMO antenna

A compact four‐element multiple‐input‐multiple‐output (MIMO) antenna for ultra‐wideband (UWB) applications with WLAN band‐notched characteristics is proposed here. The proposed antenna has been designed to operate from 2 to 12 GHz while reject the frequencies between 4.9 to 6.4 GHz. The four antenna elements are placed orthogonal to attain the polarization diversity and high isolation. A thin stub connected to the ground plane is deployed as a LC notch filter to accomplish the rejected WLAN band in each antenna element. The mutual coupling between the adjacent elements is at least 17 dB while it has low indoor and outdoor envelop correlation (<0.45) and high gain with compact size of two boards, each measuring 50 × 25 mm². To validate the concept, the prototype antenna is manufactured and measured. The comparison of the simulation results showed good agreement with the measured results. The low‐profile design and compact size of the proposed MIMO antenna make it a good candidate for diversity applications desired in portable devices operating in the UWB region.     

An improved Urdu stemming algorithm for text mining based on multi-step hybrid approach

Stemming is the basic operation in Natural language processing (NLP) to remove derivational and inflectional affixes without performing a morphological analysis. This practice is essential to extract the root or stem. In NLP domains, the stemmer is used to improve the process of information retrieval (IR), text classifications (TC), text mining (TM) and related applications. In particular, Urdu stemmers utilize only uni-gram words from the input text by ignoring bigrams, trigrams, and n-gram words. To improve the process and efficiency of stemming, bigrams and trigram words must be included. Despite this fact, there are a few developed methods for Urdu stemmers in the past studies. Therefore, in this paper, we proposed an improved Urdu stemmer, using hybrid approach divided into multi-step operation, to deal with unigram, bigram, and trigram features as well. To evaluate the proposed Urdu stemming method, we have used two corpora; word corpus and text corpus. Moreover, two different evaluation metrics have been applied to measure the performance of the proposed algorithm. The proposed algorithm achieved an accuracy of 92.97% and compression rate of 55%. These experimental results indicate that the proposed system can be used to increase the effectiveness and efficiency of the Urdu stemmer for better information retrieval and text mining applications.     

Job search website for illiterate users of Pakistan

Illiteracy is a major hurdle in socio-economic development of Pakistan. Research in the Information and Communication Technology (ICT) can help in tackling this issue by developing solutions tailored for illiterate population. The work presented within this paper proposes a job search website for illiterate population of KPK province of Pakistan. The designed website does not require any human assistance while searching a job. Moreover, the website uses culturally relevant graphical and audio content. The usability of the website is tested according to the ISO-9241-11 specification. Furthermore, we have studied the effect of the participants’ characteristics, i.e., their age, computer and mobile usage experience on the usability of the website. The results show that the participants found the website usable. Specifically, they could search suitable jobs with a minimal number of clicks and in less time as compared to the usual task completion rates reported throughout the literature. None of the participants’ characteristics were found to be affecting the usability of the website. These results suggest a promising potential of ICT solutions for providing services to illiterate population of Pakistan.     

Mechanical and thermal properties of hybrid carbon fibre–phenolic matrix composites containing graphene nanoplatelets and graphite powder

Carbon fibre–phenolic matrix (CF–P) composites containing graphene nanoplatelets (GNPs) were manufactured for improved mechanical and thermal properties. For comparison, micrometer-size pyrolytic graphite powder (GP) was also incorporated in CF–P composites. The loading of carbon fibres was kept constant at 60?wt-% while the quantity of GNPs was varied from 0.1?wt-% to 0.3?wt-% and GP from 1.0?wt-% to 3.0?wt-%. Only GNPs were functionalised by ultraviolet-ozone treatment to improve their dispersion in the matrix while all the composites were manufactured by hand layup method and characterised by scanning electron microscopy, impact, flexural, thermogravimetry and ablation tests. The composite containing 0.3?wt-% GNPs showed considerable improvement in ablation, flexural and impact testing as compared to CF-P composites containing GP. Finally, the ablation mechanisms of post-ablated composites were discussed in the light of available data in the literature.     

Modeling of Effective Thermal Conductivity of Dunite Rocks as a Function of Temperature

The thermal conductivity, thermal diffusivity, and heat capacity per unit volume of dunite rocks taken from Chillas near Gilgit, Pakistan, have been measured simultaneously using the transient plane source technique. The temperature dependence of the thermal transport properties was studied in the temperature range from 303 K to 483 K. Different relations for the estimation of the thermal conductivity are applied. A proposed model for the prediction of the thermal conductivity as a function of temperature is also given. It is observed that the values of the effective thermal conductivity predicted by the proposed model are in agreement with the experimental thermal conductivity data within 9%.