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The present study examines the conversational turn-taking patterns in autist-neurotypical talk. The objective of the study is to find out the distinctive features of autist-normal conversations. This study is cross-sectional, descriptive and qualitative in its nature. Recordings are done in anautism center in Lahore for a period of ten days. It is mainly a qualitative study in its nature. Five autistic children of different ages are selected from an autism center in Lahore. The data for the study is collected through video recording of the conversations between autists and speech therapists. The sample is selected through convenient sampling and analysis is done by following the methods of conversation analysis. The results of the analysis highlight certain distinct features of autist child-therapist talk which are not observed in the normal ordinary conversation. However, there is not a total violation of the conversation rules on the part of autists. Moreover, the findings of the research show that conversational patterns in autist-normal conversation are also affected by the chronological age of the autists. Finally, the research concludes that conversation analysis can be used as a tool for the identification of autism.

The focus of this study is the analysis of Punjabi syllablestructure in the light of Generative Phonology Theory. UnderGenerative Phonology, CV phonology theory is used for the analysis of thePunjabi syllable. CV phonology is a three-tiered model explaining thestructure of the syllable. The data for this study is taken from native Punjabispeakers living in Lahore and its surrounding areas. Results of the studysuggest that Punjabi speakers use four main patterns for Syllableconstruction, namely CV, CVC, V and VC.

Objective: To study epidemiology, presentation and management of cases with unilateral proptosis presenting at a tertiary care hospital. Study Design: Case series. Place and Duration of Study: Armed Forces Institute of Ophthalmology, Rawalpindi Pakistan, from Jan 2016 to Dec 2017. Methodology: Retrospective analysis of hospital record of patients with unilateral proptosis was carried out. Data was entered and analyzed in SPSS version 22. Chi square test was used for statistical analysis. Results: Thirty-three cases of unilateral proptosis with a mean age of 41.79 ± 3.87 years were included in the study. Pain was the most common association. Proptosis was mild in 42.4%, moderate in 48.5% and severe in 9.1% cases. In 61% cases, diagnosis was made on excisional or incisional biopsy and on the basis of radio imaging in 39% cases. Most common cause of proptosis found was pleomorphic adenoma (12%). Patients with axial and non-axial proptosis were 48.5% and 51.5% respectively. Fourteen cases (42.5%) were managed surgically and 2 cases (6%) were observed. Total of 22 cases (67%) improved after treatment, 8 cases (24%) went into remission, 2 (6%) deteriorated, and 1 (3%) died in the study period. Conclusion: Unilateral proptosis is a diagnostic challenge. Pertinent history taking and detail examination is the key, but associated signs or symptoms not always point towards correct diagnosis. Tissue biopsy provides a conclusive diagnosis especially where radio imaging is doubtful.

The study examines the impact of Literacy and Numeracy Drive (LND) on the English language learning outcomes and environment at primary sections of rural and urban public schools of district Gujranwala, Punjab, Pakistan. The data were gathered from two public schools: one from a rural area and another from an urban center. The partakers, N=60, served in the controlled and experiment groups. The control group was imparted the prescribed syllabus with the traditional method. In contrast, the experimental group was trained through LND's systematic learning objectives (SLOs), Tablet PCs, specific booklets, and tests. Three pre-tests were conducted before the treatment, and three post-tests were administered after two months of instruction. The study used quasi-experimental methods, and the gathered data were analyzed using SPSS 25. The results show a significant improvement in the outcomes of the experimental group, while no significant change was observed in the case of the control group. The results demonstrate that systematic SOL-based language teaching, technological assistance, and hand training help improve the English language pedagogy. The study suggests that such intervention scan enhance the overall English language teaching environment. As a result,the reach of LND should be extended to other sections of public schools

Automatic incident detection (AID) plays a vital role among all the safety-critical applications under the parasol of Intelligent Transportation Systems (ITSs) to provide timely information to passengers and other stakeholders (hospitals and rescue, police, and insurance departments) in smart cities. Moreover, accurate classification of these incidents with respect to type and severity assists the Traffic Incident Management Systems (TIMSs) and stakeholders in devising better plans for incident site management and avoiding secondary incidents. Most of the AID systems presented in the literature are incident type-specific, i.e., either they are designed for the detection of accident or congestion. While traveling along the road, one may come across different types of traffic incidents, such as accidents, congestion, and reckless driving. This necessitates that the AID system detects and classifies not only all the popular traffic incident types, but severity as well that is associated with these incidents. Therefore, this study aims to propose an efficient incident detection and classification (E-IDC) framework for smart cities, by incorporating the efficacy of model stacking, to classify the incidents with respect to their types and severity levels. The experimental results showed that the proposed E-IDC framework achieved performance gains of 5%–56% in terms of incident severity classification and 1%–14% in terms of incident type classification when applied with different classifiers. We have also applied the Wilcoxon test to benchmark the performance of our proposed framework that reflects the significance of our approach over existing individual incident predictors in terms of severity and type classification. Moreover, it has been observed that the proposed E-IDC framework outperforms the existing ensemble technique, such as XGBoost used for the classification of incidents.

Objective: To study the spectrum of optic neuropathies in patient’s presenting at AFIO Rawalpindi. Study Design: Descriptive study/retrospective study. Place and Duration of Study: Armed Forces Institute of Ophthalmology, Rawalpindi, from Jan 2017 to Dec 2017. Methodology: Retrospective analysis of hospital record of patients coming to neuro-ophthalmic clinic of ArmedForces Institute of Ophthalmology (AFIO), Rawalpindi, from Jan to Dec 2017 was done. Only patients with clearfinal diagnosis of neurophthalmic disease were included. Those with vague diagnosis were excluded. Data wasentered in SPSS version 22 and grouped into variables before analysis. Chi-square test was used for statisticalanalysis. The p

Objective: To compare the efficacy of tacrolimus 0.03% ointment to olopatadine 0.2% eye drops in the treatment of vernal keratoconjunctivitis. Study Design: Prospective cross-sectional study. Place and Duration of Study: Eye Department Combined Military Hospital Quetta, from Feb to Jul 2019. Methodology: A total of 69 patients with active vernal keratoconjunctivitis (VKC) were included in this study.Thirty six (52.17%) patients were randomized in tacrolimus group and 33 (47.83%) in olopatadine group B.Baseline values of the subjective symptom score (SSS) and the objective sign score (OSS) were noted. Patientswere reviewed on weeks 2, 4, 8 and 12 and the scores at each visit were summed. These scores were used forcomparison between groups. Results: At the start of the study, the mean subjective symptoms score and objective sign score of group A was 9.0 ± 2.04 and 3.93 ± 1.93 respectively, while that of group B was 8.88 ± 2.18 and 4.36 ± 1.90 respectively. At the end of 12-weeks, the mean subjective symptoms score and objective sign score of group A reduced to 0.11 ± 0.32 and 0.08 ± 0.28 respectively, while that of group B reduced to 1.70 ± 0.77 and 0.64 ± 0.55 respectively. Total improvement of scores (as a percentage of baselines) among tacrolimus group was 98.3% and olopatadine group was 83%. Conclusion: Although both 0.03% tacrolimus and 0.2% olopatadine were effective in improving the signs andsymptoms of VKC, 0.03% tacrolimus was significantly superior.

In the present study, hexagonal boron nitride (h-BN) was synthesized from boric acid and melamine by thermal annealing method in a nitrogen atmosphere. The pure h-BN was used as an efficient sorbent for the uptake of Cd2+ ions from the solution phase. The kinetics and sorption studies of metal ions onto the h-BN were carried out in batch adsorption experiments at different temperature, time, pH, sorbent dosage, and concentration of metal ions. The optimum pH for the removal of the Cd2+ ions was found to be pH 7. The effect of temperature showed that the process of Cd2+ sorption remained endothermic in the range of 298 K–328 K. The Lagergren's first and Ho's second kinetic models were tested to interpret the adsorption kinetic data, however the present data was explained well by Ho's model for kinetics. The thermodynamic perameters ΔG, ΔS and ΔH were determined using the available adsorption data at different temperatures. The physicochemical properties of the synthesized product were also characterized before and after adsorption by different analytical techniques like FT-IR, TGA, XRD and Point of Zero Charge (PZC). The morphology of the surface was analyzed with the help of Scanning Electron Microscopy. The h-BN proved to be an efficient adsorbent for the uptake of the Cd2+ ions from aqueous media.

Online learning platforms such as Massive Open Online Course (MOOC), Virtual Learning Environments (VLEs), and Learning Management Systems (LMS) facilitate thousands or even millions of students to learn according to their interests without spatial and temporal constraints. Besides many advantages, online learning platforms face several challenges such as students’ lack of interest, high dropouts, low engagement, students’ self-regulated behavior, and compelling students to take responsibility for settings their own goals. In this study, we propose a predictive model that analyzes the problems faced by at-risk students, subsequently, facilitating instructors for timely intervention to persuade students to increase their study engagements and improve their study performance. The predictive model is trained and tested using various machine learning (ML) and deep learning (DL) algorithms to characterize the learning behavior of students according to their study variables. The performance of various ML algorithms is compared by using accuracy, precision, support, and f-score. The ML algorithm that gives the best result in terms of accuracy, precision, recall, support, and f-score metric is ultimately selected for creating the predictive model at different percentages of course length. The predictive model can help instructors in identifying at-risk students early in the course for timely intervention thus avoiding student dropouts. Our results showed that students’ assessment scores, engagement intensity i.e. clickstream data, and time-dependent variables are important factors in online learning. The experimental results revealed that the predictive model trained using Random Forest (RF) gives the best results with averaged precision =0.60%, 0.79%, 0.84%, 0.88%, 0.90%, 0.92%, averaged recall =0.59%, 0.79%, 0.84%, 0.88%, 0.90%, 0.91%, averaged F-score =0.59%, 0.79%, 0.84%, 0.88%, 0.90%, 0.91%, and average accuracy =0.59%, 0.79%, 0.84%, 0.88%, 0.90%, 0.91% at 0%, 20%, 40%, 60%, 80% and 100% of course length.

Semantic Web is considered as the backbone of web 3.0 and ontologies are an integral part of the Semantic Web. Though an increase of ontologies in different domains is reported due to various benefits which include data heterogeneity, automated information analysis, and reusability, however, finding an appropriate ontology according to user requirement remains cumbersome task due to time and efforts required, context-awareness, and computational complexity. To overcome these issues, an ontology recommendation framework is proposed. The Proposed framework employs text categorization and unsupervised learning techniques. The benefits of the proposed framework are twofold: 1) ontology organization according to the opinion of domain experts and 2) ontology recommendation with respect to user requirement. Moreover, an evaluation model is also proposed to assess the effectiveness of the proposed framework in terms of ontologies organization and recommendation. The main consequences of the proposed framework are 1) ontologies of a corpus can be organized effectively, 2) no effort and time are required to select an appropriate ontology, 3) computational complexity is only limited to the use of unsupervised learning techniques, and 4) due to no requirement of context awareness, the proposed framework can be effective for any corpus or online libraries of ontologies.

The demand of accurate and visually fair images is increasing with the passage of time and bang of the number of digital images especially in the domain of medical and healthcare systems. The visual image quality of modern cameras affected due to edges, textures and sharp structures noise. Though research community has introduced several techniques such as BM3D (Block Matching and 3D) for image denoising. However, edges and texture preservation capabilities remain issues due to hard thresholds values and captured image diversity. In order to address these issues, we propose a new variant of BM3D namely BM3DMA (Block Matching and 3D with Mahalanobis and Adaptive filter) which is employed through the use of Mahalanobis distance measure (for diversity coverage) and adaptive filter (for soft thresholds). We used two widely known datasets consist of set of standard and medical images. We observe 5% to 10% enhancement in the performance of BM3DMA as compared to BM3D in terms of improving the PSNR (Peak Signal to Noise Ratio) value. The promising experimental result indicates the effectiveness of BM3DMA in terms preserving the edge and texture image noise.

Objective: To compare the association of vitamin B12 deficiency and metformin use in patients presenting with type II diabetes mellitus. Study Design: Case control study. Place and Duration of Study: Department of Medicine, Military Hospital Rawalpindi from Apr 2017 to Sep 2017. Methodology: All patients who fulfilled the inclusion criteria and visited OPD of department of Medicine Military Hospital Rawalpindi, were included in the study. After approval from the ethical review committee, informed written consent was taken from patients. The patients were divided into two groups group A (metformin use) & B (without metformin use). Outcome was checked by comparing both the groups for vitamin B12 deficiency. Results: Two hundred and eighty patients with diabetes mellitus were included. One hundred and seventy one (61.07%) were males and 109 (38.93%) were females, with the mean age of 59.52 ± 12.441. Vitamin B12 deficiency in group I was seen in 30 (10.71%) and in 9 patients (3.21%) in group II (OR >1). Conclusion: In conclusion, our study indicates that patients with type 2 diabetes mellitus treated with metformin should be screened for vitamin B12 deficiency, especially with longer durations of treatment.

The use of deep learning DL techniques for mobile learning is an emerging field aimed at developing methods for finding mobile learners' learning behavior and exploring important learning features. The learning features learning time, learning location, repetition rate, content types, learning performance, learning time duration, and so on act as fuel to DL algorithms based on which DL algorithms can classify mobile learners into different learning groups. In this study, a powerful and efficient m-learning model is proposed based on DL techniques to model the learning process of m-learners. The proposed m-learning model determines the impact of independent learning features on the dependent feature i.e. learners? performance. The m-learning model dynamically and intuitively explores the weights of optimum learning features on learning performance for different learners in their learning environment. Then it split learners into different groups based on features differences, weights, and interrelationships. Because of the high accuracy of the DL technique, it was used to classify learners into five different groups whereas random forest RF ensemble method was used in determining each feature importance in making adaptive m-learning model. Our experimental study also revealed that the m-learning model was successful in helping m-learners in increasing their performance and taking the right decision during the learning flow.

Mobile learning (M-learning) has gained tremendous attention in the educational environment in the past decade. For effective M-learning, it is important to create an efficient M-learning model that can identify the exact requirements of mobile learners (M-learners). M-learning model is composed of features that are generated during M-learners' interaction with mobile devices. For an adaptive M-learning model, not only learning features are required, but it is also important to determine how they differ for various M-learners, their weights, and interrelationship. This study proposes a robust and adaptive M-learning model that is based on machine learning and deep learning (ML/DL) techniques. The proposed M-learning model dynamically explores learning features, their corresponding weights, and association for M-learners. Based on learning features, the M-learning model categorizes M-learners into different performance groups. The M-learning model then provides adaptive content, suggestions, and recommendations to M-learners in order to make learning adaptive and stimulating. For comparative analysis, the prediction accuracy of five baseline ML models was compared with the deep Artificial Neural Network (deep ANN). The results demonstrated that deep ANN and Random Forest (RF) models exhibited better prediction accuracy. Subsequently, both models were selected for developing the M-learning model which included the performance categorization of M-learners under a five-level classification scheme and assigning weights to various features for providing adaptive help and support to M-learners. Our explanatory analysis has shown that behavioral features besides contextual features also influence the learning performance of M-learners. As a direct outcome of this research, more efficient, interactive, and useful mobile learning applications can be developed that accurately predict learning objectives and requirements of diverse M-learners thus helping M-learners in enhancing their study behavior.

Mobile learning (M-learning) has gained tremendous attention in the educational environment in the past decade. For effective M-learning, it is important to create an efficient M-learning model that can identify the exact requirements of mobile learners (M-learners). M-learning model is composed of features that are generated during M-learners' interaction with mobile devices. For an adaptive M-learning model, not only learning features are required, but it is also important to determine how they differ for various M-learners, their weights, and interrelationship. This study proposes a robust and adaptive M-learning model that is based on machine learning and deep learning (ML/DL) techniques. The proposed M-learning model dynamically explores learning features, their corresponding weights, and association for M-learners. Based on learning features, the M-learning model categorizes M-learners into different performance groups. The M-learning model then provides adaptive content, suggestions, and recommendations to M-learners in order to make learning adaptive and stimulating. For comparative analysis, the prediction accuracy of five baseline ML models was compared with the deep Artificial Neural Network (deep ANN). The results demonstrated that deep ANN and Random Forest (RF) models exhibited better prediction accuracy. Subsequently, both models were selected for developing the M-learning model which included the performance categorization of M-learners under a five-level classification scheme and assigning weights to various features for providing adaptive help and support to M-learners. Our explanatory analysis has shown that behavioral features besides contextual features also influence the learning performance of M-learners. As a direct outcome of this research, more efficient, interactive, and useful mobile learning applications can be developed that accurately predict learning objectives and requirements of diverse M-learners thus helping M-learners in enhancing their study behavior.

It is a knotty task to amicably identify the sporadically changing real-world context information of a learner during M-learning processes. Contextual information varies greatly during the learning process. Contextual information that affects the learner during a learning process includes background knowledge, learning time, learning location, and environmental situation. The computer programming skills of learners improve rapidly if they are encouraged to solve real-world programming problems. It is important to guide learners based on their contextual information in order to maximize their learning performance. In this paper, we proposed a cloud-supported machine learning system (CSMLS), which assists learners in learning practical and applied computer programming based on their contextual information. Learners? contextual information is extracted from their mobile devices and is processed by an unsupervised machine learning algorithm called density-based spatial clustering of applications with noise (DBSCAN) with a rule-based inference engine running on a back-end cloud. CSMLS is able to provide real-time, adaptive, and active learning support to students based on their contextual information characteristics. A total of 150 students evaluated the performance and acceptance of CSMLS for a complete academic semester, i.e. 6 months. Experimental results revealed the threefold success of CSMLS: extraction of students? context information, supporting them in appropriate decision-making, and subsequently increasing their computer programming skills.

Due to the ubiquitous nature of mobile devices, they are now considered as an emerging platform for facilitating both teaching and learning experiences. In this paper, we presented a tailored mobile learning system, namely the Integrated English and Programming Language Learning System (IEPLS), which aims at learning English vocabulary before studying programming language concepts. The IEPLS supports programming language learning in three ways; (a) Recommending to learn specific English vocabulary used in programming language concepts (b) Adaptation to the learning flow of the learner and (c) Motivating and encouraging learners to learn items based on individual learner's performance. The IEPLS was used by one hundred and fifty undergraduate students for six months. Evaluation results revealed the attainment of IEPLS in supporting learners in learning programming languages backed by precise English vocabulary.

Urdu is among the most widely used languages in the world for verbal and written communication. Due to lack of optimized and user friendly native Urdu-script support on various platforms, it is mostly written in Romanized script in soft form. In our research, we have developed a refined Urdu lexicon using tokens with the highest frequency of occurrence in the data set. This data set is basically a raw corpus of colloquial Urdu written in Romanized script. The corpus was collected from volunteer participants who used this language as a mode of communication on the Internet and text massaging. The raw corpus is passed through a series of steps such as Prepossessing, Tokenization and Annotation before passing it to computationally extensive subsequent steps. Edit Distance and K-means Clustering techniques are used for identification of candidate tokens and their potential selection/ inclusion in the refined lexicon. We have also identified most commonly used tokens, candidate tokens and other lingual attributes from the data collected. Based on analysis, we have proposed a computational model for refined colloquial Romanized Urdu lexicon development.

Machine learning (ML) is penetrating in all walks of life and is one of the major driving forces behind the fourth industrial revolution, typically known as Industry 4.0. The purpose of the present study is to review the state-of-the-art ML applications in the biofuels' life cycle stages, i.e., soil, feedstock, production, consumption, and emissions. A keyword search is performed to retrieve relevant articles from the databases of the Web of Science and Google Scholar. ML applications in the soil stage were mostly based on the use of satellite images of land for estimation of biofuels yield or suitability analysis of agricultural land. In the second stage of the life cycle, assessment of rheological properties of the feedstocks and their effect on the quality of biofuels were dominant studies reported in the literature. The production stage included estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage included analysis of engine performance and estimation of emissions temperature and composition, such as NOx CO, and CO2. This study identified the following trends: dominant ML method, the stage of life cycle getting more usage of ML, the type of data used for the development of the ML-based models, and the stage-wise frequently used input and output variables. The findings of this article are beneficial for academia and industry-related people involved in model development in different stages of biofuel’s life cycle.

Context: Recently, research community of certain domain showing their eagerness towards the use of social media networks to gain constructive knowledge in decision making and automation, such as aid to perform software development activities, crypto-currencies usage, network community detection and recommendation and so on. Recently, besides other domains of eHealth, the use of social media and big data analytics has become hot topic to predict the patient of mental illness involved in either depression, schizophrenia, eating disorders, anxiety or addictive behaviors. Problem: Traditional methods either need enough historic data or to keep the regular monitoring on patient activities for identification of a patient associated with a mental illness disease. Method: In order to address this issue, we propose a methodology to classify the patients associated with chronic mental illness diseases (i.e. Anxiety, Depression, Bipolar, and ADHD (Attention Deficit Hyperactivity Disorder) based on the data extracted from the Reddit, a well-known network community platform. The proposed method is employed through Co-training (type of semi-supervised learning approach) technique by incorporating the discriminative power of widely used classifiers namely Random Forrest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB). We used Reddit API to download posts and top five associated comments for construction of a feature space. Results: The experimental results indicate the effectiveness of Co-training based classification rather than the state of the art classifiers by a margin of 3% on average in par with every state of art technique. In future, the proposed method could be employed to investigate any classification problem of any domain by extracting date from the social media.

Water decontamination from chromium (Cr) is of prime importance because of its carcinogenic nature. The sample of activated carbon was characterized by surface area analyzer, SEM/EDX, TGA, FTIR and PZC. This study was focused on adsorption of hexavalent chromium from the aqueous phase onto activated carbon. Both the temperature and pH had a positive effect on the chromate uptake by activated carbon. The chromate uptake at different pH values followed the trend: pH 2 > pH 3 > pH 5 > pH 7 > pH 8. The sorption maxima at pH 2 was explained on the basis of electrostatic attraction between the negatively charged chromate anions and positively charged surface of the activated carbon. Different models were tested to access the sorption maxima and to probe into the chromate adsorption mechanism. The thermodynamic parameters suggested that heat is needed to shift the hexavalent chromium from solution to the solid surface. It was concluded that the anion exchange sorption was involved in removing the chromate anion from the aqueous system into the surface of activated carbon.

Objective: To analyze the effect of use of VERION image guided system on surgically induced astigmatism (SIA) in patients undergoing cataract surgery. Study Design: Randomized controlled trial. Place and Duration of Study: The study was conducted at Armed Forces Institute of Ophthalmology, Rawalpindi, Pakistan from Aug 2016 to Dec 2016. Material and Methods: Sixty eyes undergoing cataract surgery with already existing corneal astigmatism of at least 0.5 diopter (D) were included in the study. Cases with already existing ocular pathology or any per operative complication were excluded. Non-probability (purposive) sampling technique was used. Patients were randomly grouped into two groups 1 and 2. Group-1 underwent normal conventional phacoemulsification with no specific predetermined incision site. Group-2 underwent keratometric assessment using VERION image guided system and data was transferred to operating microscope. Steep and flat meridian were displayed on screen and incision were made on steep axis. Pre and 2 week post op keratometric readings were noted on a workup performa for both the groups and compared. All the surgeries were done by the same surgeon. Statistical program for social sciences (SPSS) version 23.0 was used for statistical analysis. Results: Sixty eyes (29 male, 31 female) with mean age of 57.65 ± 7.41 years were studied. Group-1 included 30 eyes with mean age of 56.73 ± 9.16 years. Group-2 also included 30 eyes with mean age of 58.56 ± 5.10 years. Mean pre op astigmatism was 0.93 ± 0.45D and 0.88 ± 0.47 D in group 1 and 2 respectively. Mean...........

The growing public endorsement of social media has changed public life dramatically. Public views and suggestions have now become important for both organizations and individuals. Big data scientists and data mining analysts are increasingly moving their attention toward sentiment analysis because of the growing rate of user-generated contents over microblogging sites. Sentiment analysis is a research field related to computationally identifying public views, feelings, recommendations, opinions and sentiments about focused entities. Research literature shows traces of research work on product and movie reviews for better decision making using big data analysis. Big data analytics offer remarkable opportunities to individuals as well as organizations by providing proficient decision making frameworks and improved forecasting models. The sociopolitical collaboration has gained much attention from online users over the past few years. In this research we analyzed public views, sentiments and opinions shared on social media about a democratic participatory activity called Azadi-March, which was held in Pakistan with participation of online users from all over the world. We carried out computational semantic orientation on public tweets for analyzing public awareness and the effects of online communication through social media over the real world public decision making. We employed unsupervised approach for identification and scoring of tweets. We used lexicon based approach in which annotated lexica are used for scoring verbs, adverbs and other parts of speech. A corpus is used for scoring adjectives and informal opinion indicators. Emoji, exclamatory statements and other additional features are incorporated for supplementary analysis. We noticed that emoticons and NetLingo play significant role in sentiment orientation. Opinion groups are generated from all retrieved tweets and aggregate sentiment weights of opinion groups are computed. The findings of this study indicate that our proposed lexicon based approach outperforms the contemporary machine learning techniques by achieving 86% average accuracy at sentence level sentiment analysis.

As an emerging and promising approach, crowdsourcing-based software development has become popular in many domains due to the participation of talented pool of developers in the contests, and to promote the ability of requesters (or customers) to choose the ‘wining’ solution with respect to their desired quality levels. However, due to lack of a central mechanism for team formation, continuity in the developer’s work on consecutive tasks and risk of noise in submissions of a contest, there is a gap between the requesters of a domain and their quality concerns related to the adaptation of a crowdsourcing-based software development platform. In order to address concerns and aid requesters, we describe three measures; Quality of Registrant Developers (QRD), Quality of Contest (QC) and Quality of Support (QS) to compute and predict the quality of a crowdsourcing-based platform through historical information on its completed tasks. We evaluate the capacity of the QRD, QC and QS as assessors to predict the quality. Subsequently, we implement a crawler to mine the information of completed development tasks from the TopCoder platform to inspect the proposed measures. The promising results of our QRD, QC, and QS measures suggest to use the proposed measures to the requesters and researchers of other domains such as pharmaceutical research and development, in order to investigate and predict the quality of crowdsourcing-based software development platforms.

Progress toward the United Nations 2030 Sustainable Development Goals requires improved information on mortality and causes of death. However, causes of many of the fifty million annual deaths in low- and middle-income countries remain unknown, as most of the deaths occur at home without medical attention. In 2001 India began the Million Death Study in 1.3 million nationally representative households. Nonmedical staff conduct verbal autopsies, which are structured interviews including a half-page narrative in local language of the family's story of the symptoms and events leading to death. Two physicians independently assess each death to arrive at an underlying cause of death. The study has thus far yielded information that substantially altered previous estimates of cause-specific mortality and risk factors in India. Similar robust studies are feasible at low cost in other low- and middle-income countries, particularly if they adopt electronic data management and ensure high quality of fieldwork and physician coding. Nationwide mortality studies enable the quantification of avoidable premature mortality and key risk factors for disease, and provide a practicable method to monitor progress toward the Sustainable Development Goals.

This paper focuses on the word sense disambiguation (WSD) problem in the context of Urdu language. Word sense disambiguation (WSD) is a phenomena for disambiguating the text so that machine (computer) would be capable to deduce correct sense of individual given word(s). WSD is critical for solving natural language engineering (NLE) tasks such as machine translation and speech processing etc. It also increase the performance of other tasks such as text retrieval, document classification and document clustering etc. Research work in WSD has been conducted up to different extents in computationally developed languages of the world. In the context of Urdu language the NLE research in general and the WSD research in particular is still in the infancy stage due to the rich morphological structure of Urdu. In this paper, we use machine learning (ML) approaches such as Bayes net classifier (BN), support vector machine (SVM) and decision tree (DT) for WSD in native script Urdu text. The results shown that BN has better F-measure than SVM and DT. The maximum F-measure of 0.711 over 2.5 million words raw Urdu corpus was recorded for the Bayes net classifier.

Wolfram disease is a rare genetic disease which mainly presents as diabetes mellitus and optic atrophy. Presence of maculopathy in a case of wolfram disease is rarely reported in literature. We present here two cases/siblings with age of 12 and 14 years. They had diabetes mellitus, deafness and disc palor. Ocular examination also revealed atrophic maculopathy in both siblings, supported by depressed response on ERG. Assosiation of maculopathy with wolfram syndrome is rare and worth reporting.

Objective: To determine the efficacy of oral eplerenone in anatomical and functional improvement in patients with chronic central serous chorioretinopathy (CSCR). Methods: This quasi experimental study was performed at PNS Shifa Hospital Karachi from September 2018 to February 2019. Study included 23 patients. Patients were included using consecutive sampling technique and informed consent was taken from all patients before staring treatment. 50 mg of oral eplerenone per day was given for three months. Subretinal fluid (SRF) height and visual acuity (VA) were noted at baseline, one month and three month follow-up. Structured Study performa was used for data collection. Data was analysed and assessed with SPSS version 23. P value of

Urdu ranks very high among languages used for communication in the Southern Asia. Even though with great following, it clearly lack computational support that is why it is written in Romanized Urdu script. There has been a lot of research done on the gender and age identification of author through written text but not ample have been done using Romanized Urdu dataset. In our research, we have proposed a model for the said purpose by identifying key parameter (defined attributes) of an author. These parameters were measured for both the genders and three categories of age. Weight assignment technique was used to plot graphs which help in computation of the desired results.

The aim of this paper is to explore the number system in Thali, a variety of Punjabi spoken by natives of Thal desert. There are three number categories singular, dual, and plural but all modern Indo Aryan languages have only singular and plural (Bashir & Kazmi, 2012, p. 119). It is one of the indigenous languages of Pakistan from the Lahnda group as described by Grierson (1819) in his benchmark book Linguistic Survey of India. Layyah is one of the prominent areas of Thal regions. The native speakers of Thali use this sub dialect of Saraiki in their household and professional life. The linguistic boundaries of the present Siraiki belt have changed under different linguistic variational rules as described by Labov (1963), Trudgal (2004), Eckert (2002) and Meryhoff (2008). There are many differences between Thali and Saraiki, on phonological, morphological and orthographical levels. Husain (2017) has pointed out linguistic differences between Saraiki and Lahnda and Thali is one of the popular languages of Lahnda spoken in different parts of Thal regions. According to the local language activists, Thali has been greatly influenced by Saraiki and Punjabi. The lexicon of Thali is composed for 20% of Punjabi, 45% of Saraiki, and 5% of loan words particularly English. Another particularity is that Perso-Arabic characters are used to write Thali. The most distinguishing characteristics of Thali are its parts of speech, word order, case marking, verb conjugation and, finally, usage of grammatical categories in terms of number, person, tense, voice and gender. In this perspective, number marking is the area to focus on noun morphology and exclusively on the recognition of number system in Thali nouns. The analysis of linguistic systems including grammar, lexicon, and phonology provide sound justifications of number marking systems in languages of the world (Chohan & García, 2019).

Objective: To study Agreement between Keratometric readings by VERION image guided System, Galilei G4 and Pentacam. Methods: The quasi experimental study was conducted at Armed Forces Institute of Ophthalmology, Rawalpindi, Pakistan from August 2016 to December 2016. Twenty five patients fulfilling the inclusion criteria participated in the study. All Patients were subjected to Keratometric assessment using Galilei G4 Dual Scheimpflug analyzer (Ziemer, Switzerland), Wavelight Oculyzer II (Pentacam, Germany) and Verion image guided system (Alcon). Steep and flat meridian and diopter of astigmatism by three systems were recorded and endorsed. All readings were taken by the same observer. Statistical Program for Social Sciences (SPSS) version 22.0 was used for statistical analysis. Results analyzed for significance by t-tests and Interclass correlation analysis. In t tests, P values of 0.7 was considered acceptable. Results: Fifty eyes of twenty-five patients (22 male, 28 female) with mean age of 29.50 ± 3.46 years were studied. Flat K, steep K and dioptric power of astigmatism were measured with verion, pentacam and Galilei G4. Interclass correlation analysis showed agreement between individual variables measured by the three devices, while one sample t test showed no significant difference between dioptric power of astigmatism between Verion-Pentacam and Verion- Galilei group. (p 0.178 for former and 0.622 for later group). Conclusion: Verion image guided system is comparable to other instruments used currently for keratometry. Verion can be interchangeably used with Pentacam and Galilee G4 in assessing corneal astigmatism.

Software Defined Radio (SDR) is a radio communication technique which converts hardware problems to software problems using a programmable processing system. With the increase of SDR applications, the demand for high bandwidth and signal processing also increased. Therefore, the SDR system needs a programmable and scalable multicore processing hardware architecture. In this work, we proposed a scalable multi-core, programmable and parameterizable High Performance Software Defined Radio system architecture and software development environment. The proposed system uses heterogeneous processing cores including CPU, SIMD, and FPGA architectures and supports C++, Python, and Dataflow programming environment. To validate the effectiveness of the proposed system, we implemented and tested it on a FPGA based Altera DE1-SoC and heterogeneous multi-core development board. In order to prove that the system is efficient in a variety of scenarios, different modulation techniques are used.

Frequent itemsets (FIs) mining is a prime research area in association rule mining. The customary techniques find FIs or its variants on the basis of either support threshold value or by setting two generic parameters, i.e., N (topmost itemsets) and $$K_\mathrm{{max}}$$Kmax (size of the itemsets). However, users are unable to mine the absolute desired number of patterns because they tune these approaches with their approximate parameters settings. We proposed a novel technique, top-K Miner that does not require setting of support threshold, N and $$K_\mathrm{{max}}$$Kmax values. Top-K Miner requires the user to specify only a single parameter, i.e., K to find the desired number of frequent patterns called identical frequent itemsets (IFIs). Top-K Miner uses a novel candidate production algorithm called join-FI algorithm. This algorithm uses frequent 2-itemsets to yield one or more candidate itemsets of arbitrary size. The join-FI algorithm follows bottom-up recursive technique to construct candidate-itemsets-search tree. Finally, the generated candidate itemsets are manipulated by the Maintain-Top-K_List algorithm to produce Top-K_List of the IFIs. The proposed top-K Miner algorithm significantly outperforms the generic benchmark techniques even when they are running with the ideal parameters settings.

Language learners always require some supportive tool that may assist them in their quest of learning a second or foreign language because its not an easy task to have grip over language which the learners are unfamiliar with. Traditionally the tool which had been attracted language learners is dictionary because it provides phonetic, phonological, semantic and syntactic information which paves the way of quick learning. The present article investigates the functions provided by the dictionaries regarding all aspects of language learning.

Objective To compare post-operative pain relieving effect of topical diclofenac 0.1% versus ketorolac 0.5% in Corneal Collagen Cross Linking (CXL) for patients diagnosed with keratoconus. Methods This randomized controlled trial was carried out for six months from October 2016 to March 2017. We included young patients having keratoconus with k-readings greater than 47D and central corneal thickness more than 400 microns. All the patients received single dose one drop of topical diclofenac 0.1% to (Group-A) and ketorolac 0.5% to (Group-B) 30 minutes in advance of the corneal collagen cross linking (CXL) procedure. The CXL was performed with topical 0.1% riboflavin eye drops in 20% dextran as a photo sensitizer. After 36 hours of the CXL procedure, the postoperative intensity of pain was assessed verbally by patients with the help of visual analog scale (VAS) numbers from zero to five where 0 designated no pain & 5 symbolized worst pain. Results The study comprised sixty eyes of forty one patients. Out of total 16 were male while 25 female patients. The mean age of the patients was 24.27 ± 2.93 years (range 20 to 29 years). In the conclusive analysis, diclofenac 0.1% was used on 30 patients in Group-A and ketorolac 0.5% on 30 subjects in Group-B. Pain relieving scores in Group-A (diclofenac 0.1%) was 2.57 ± 0.67 while in Group-B (ketorolac 0.4% treated arm) was 3.20 ± 0.61. Conclusion Topical diclofenac 0.1% is statistically comparable to topical ketorolac 0.5% in precluding severity of pain after corneal collagen cross linkage operation.

Smart Web and Mobile Based interactive Application (SWAMBA) was developed and tested on undergraduate students to reveal how much the factors of format, ease of use, contents, timeliness, accuracy, and tailoring in mobile phones messages contribute in improving students' study behavior. Students' satisfaction toward SWAMBA was measured by the instrument called End-User Computing Satisfaction (EUCS). Exploratory Factor Analysis (EFA) was performed on data collected based on EUCS to reveal the underlying relationships among similar measured variables. Subsequently, Confirmatory Factor Analysis (CFA) was performed on the theoretical model developed during EFA to find which factor of EUCS accounts significantly in improving students' performance. Finally, multiple regression analysis was carried out to find how much independent variables of students study performance and decision making regress on dependent variables of content, timeliness, format, ease of use and accuracy. Data collected through survey and its analysis results revealed that mobile phone could be a very useful tool in the smart learning environment.

To study visual outcome in patients of high altitude retinopathy presenting with retinal venous occlusion.The randomised clinical trial study was conducted at the Armed Forces Institute of Ophthalmology, Rawalpindi, Pakistan, from January 2013 to December 2015, and comprised eyes of lowlanders. Patients staying at high altitude (8,000 feet above sea level) as part of their service duty and presented with retinal venous occlusive disease were included. Patients with history of diabetes, hypertension, glaucoma, any pre-existing retinal disease or age-related conditions responsible for decreased vision were excluded. Detailed ocular as well as systemic examination was carried out to establish the diagnosis of retinal venous occlusion. Patients were divided into two groups. First group was observed over a period of 6 months and labelled as control group, whereas the second group was treated with intravitrealbevacizumab and labelled as intervention group. Complete ocular examination was carried out in all patients at 4 weeks, 3 months and 6 months. SPSS 21 was used for data analysis.A series of 32 eyes of 28 male patients were included. The overall mean age was 31.40±3.40 years. The mean altitude of their temporary stay was 4,120±941 metres above sea level and the mean duration of stay was 6.80±4.13months. Besides, 21(75%) subjects were smokers. Moreover, 26(81.25%) eyes had central retinal vein occlusion while branch retinal vein occlusion was seen in 6(18.75%) eyes. The mean visual acuity in the control group was 0.70±0.56 on Logarithm of the Minimum Angle of Resolution chart at the presentation while it was 0.26±0.87, 0.20±0.32 and 0.15±0.23 after 4 weeks, 3 months and 6 months, respectively. The mean best corrected visual acuity in intervention group was 0.68 ± 0.46 before treatment and 0.15±0.11, 0.12±0.11 and 0.10±0.08 at 4 weeks, 3 months and 6 months, respectively. Significant post-intervention change was observed in best corrected visual acuity at 6 months (p0.05) in patients who presented with severe visual loss.Treatment with intravitrealbevacizumab may be considered in patients with severe visual loss at presentation.

Unilateral cyclitis leading to myopia is a rare and clinical relevant entity. In clinical settings, pseudomyopia is generally encountered in the form of accommodative spasm, which is always bilateral. Cyclitis due to inflammation, on the other hand, can cause pseudomyopia unilaterally and it is a very rare presentation. A young male with acute anterior uveitis, presented with acute episode of unilateral myopia. When patient was examined on first visit, there were no cells in anterior chamber; so he was started on cycloplegic eye drops, but his condition didn't improve. Examination on subsequent visit revealed cellular reaction in anterior chamber and narrowing of anterior chamber angles on anterior segment optical coherence tomography (OCT). Treatment for uveitis was started and patient's visual acuity and refractive error improved. Pseudomyopia is a known complication of several drugs and certain medical conditions. The possible mechanism is supraciliary exudation causing relaxation of zonular fibers and increased convexity of the crystalline lens. Myopia in the setting of a mild cellular reaction can easily be missed and has not been reported yet to the best of authors' literature search.

This study examines the process of vowel epenthesis used by the Punjabi speakers to integrate the English consonant cluster at onset position of the syllable. English and Punjabi are two different phonological system where English allows consonant cluster and complex consonants at onset while Punjabi only allows complex consonants. Hence for the integration of syllables with consonant cluster, Punjabi speakers have to insert a vowel to make the consonant configuration according to Punjabi phonotactics. The data for this study are collected from recordings of focus group discussions, interviews and video clips. The data are analyzed by using CV phonology and Distinct Feature theory. The results suggest that Punjabi speakers insert vowels to modify the English consonant clusters according to Punjabi phonological environment. Thus, they add another vowel node and resyllabify the consonant clusters. The mid central /ə/ vowel is the default epenthetic vowel while in some cases /e/ is also used before the consonant clusters.

Machine Translation (MT) is exigent because it involves several thorny subtasks such as intrinsic language ambiguities, linguistic complexities and diversities between source and target language. Usually MT depends upon rules that provide linguistic information. At present, the corpus based MT approaches are used that include techniques like Example Based MT (EBMT) and Statistical MT (SMT). In addition to others, both of these corpus based techniques have different frameworks in the contemporary data-driven paradigm. SMT systems generate outputs using probabilities, whereas EBMT systems translate input text by matching examples from large amount of training data. Urdu MT is in its infancy with very limited availability of required data and computational resources. In this paper, we analyzed and evaluated the main MT techniques using qualitative as well as quantitative approaches. Strengths and weaknesses of each technique have been brought to light through special focus and discussion on examples from Urdu language MT literature. We evaluated the automated machine translated outputs using Bilingual Evaluation Understudy (BLEU). The EBMT approach produced the highest accuracy of 84.21% whereas the accuracy of the online SMT system is 62.68%. We found that BLUE scores of machine translated long Urdu sentences are low in comparison with long sentences. Similarly source text containing low frequency words affect the quality of Urdu machine translation negatively. Experiments and findings section of this paper explicate our reported results in detail. The paper concludes with proposal of future directions for research in Urdu machine translation.

To compare the change in endothelial cell count after femtosecond laser-assisted cataract surgery (FLACS) versus conventional phacoemulsification.Randomized controlled clinical trial.Armed Forces Institute of Ophthalmology, Rawalpindi, Pakistan from January 2016 to August 2017.Patients with senile cataract and age ranging from 40 to 80 years were included in the study. Patients with any other cause of endothelial cell loss, history of trauma, documented diabetes millitis, hypertention and glaucoma were excluded. Preoperative detailed ocular examination, including both anterior and posterior examination, was carried out. Patients were distributed into two groups. Group GP were planned for conventional phacoemulsification, while group GF underwent FLACS. All the surgeries were performed under local anesthesia by same ophthalmic surgeon. Specular microscope (Topcon specular microscope sp-3000p) was utilized to measure the endothelial cell count (ECC) before and 4 weeks after the surgery.Fifty eyes (25 in each group) of 48 patients underwent cataract surgery by phacoemulsification or FLACS. Twenty-five (52.08%) out of the total were females while 23 (47.91%) were males. Median age of the participants in phacoemulsification group was 55 years (IQR 20.50), while in FLACS group it was 54 years (IQR 8). The median change in endothelial cell count was 228 (IQR 532) in Phaco group, while 23 (IQR 35) in FLACS group. (p0.05 Mann Whitney U-test).FLACS is a safe and effective modality for cataract treatment and it induces significantly less endothelial cell loss than conventional phacoemulsification.

Natural Language Processing is the multidisciplinary area of Artificial Intelligence, Machine Learning and Computational Linguistic for processing human language automatically. It involves understanding and processing of human language. The way through which we share our contents or feelings have always great importance in understanding and processing of language. Parsing is the most suited approach in identifying and scanning what the available sentences expressed? Parsing is the process in which syntactic structure of sentence is identified using grammatical tags. The syntactically correct sentence structure is achieved by assigning grammatical labels to its constituents using lexicon and syntactic rules. Phrase and Dependency are two main structure formalisms for parsing natural language sentences. The growing use of web 2.0 has produced novel research challenges as people from different geographical areas are using this channel and sharing contents in their native languages. Urdu is one of such free word order native language which is widely shared over social media sites but identification and summarization of Urdu sentences is challenging task. In this review paper we present an overview to recent work in parsing of fixed order (i.e. English) and free word order languages (i.e Urdu) in order to reveal the most suited method for Urdu Language Parsing. This survey explored that dependency parsing is more appropriate for Urdu and other free word order languages and parsers of English language are not useful in parsing Urdu sentence due to its morphological, syntactical and grammatical differences.

Urdu ranks very high among languages used for communication in the Sourthrn Asia. Even though with great following, it clearly lack computational support that is why it is written in Romanized Urdu script. Even though, a lot of Romanized Urdu data is available online but it still lacks a refined Corpus. In our research, we have proposed a refined Romanized urdu Corpus using tokens with the highest frequency of occurrence in the data set, which was collected from volunteer participants who used this language as a mode of communication interactively. The raw corpus is passed through a series of steps such as Prepossessing, Tokenization and Annotation before passing it to computationally extensive subsequent steps. "Edit Distance" and "K-means Clustering" techniques are used for identification of candidate tokens and their potential selection/ inclusion in the refined lexicon. We have also identified most commonly used tokens, candidate tokens and other lingual attributes from the data collected. Based on analysis, we have proposed a computational model for refined colloquial Romanized Urdu lexicon development.

To compare the mean optic nerve sheath diameter (ONSD) in patients with idiopathic intracranial hypertension (IIH) versus normal healthy individuals using B-scan ultrasonography as diagnostic tool.Cross-sectional analytical study.Armed Forces Institute of Radiology and Imaging, Rawalpindi, Pakistan, from June to December 2015.Participants aged 30 - 50 years were divided into two groups. Group A(GP) comprised of patients who were diagnosed cases of idiopathic intracranial hypertension while normal healthy adults who volunteered to participate in the study were included in Group B (GC). Ocular B mode ultrasound scan was performed on all the participants and each eye was considered separately. The optic nerve head was visualized as a linear hypoechoic structure, and ONSD was measured 3 mm behind the retina. Atotal of three readings were taken by the same radiologist and the average of three was recorded. Mean values of both groups were compared by t-test with significance at p0.05.Fifty-two eyes of 26 patients (26 eyes in each group) were included in the study. Twenty-four out of 26 in Group A(GP) and 23 out of 26 in Group B (GC) were females. Mean age of the patients was 33.92 ±4.89 years in group A(GP) while it was 34.69 ±4.79 years in group B (GC). Mean ONSD was 6.61 ±0.39 mm in group A(GP) and 4.33 ±0.38 mm in group B(GC) which was significantly different (p0.001).Ultrasound can provide a reliable, non-invasive tool to measure optic nerve sheath diameter in monitoring the patients with benign intracranial hypertension (BIH).

Automated advance reservation has the potential to ensure a good scheduling solution in computational Grids. To improve global throughput of Grid system and enhance resource utilization, workload has to be distributed among the resources of the Grid evenly. This paper discusses the problem of load distribution and resource utilization in heterogeneous Grids in advance reservation environment. We have proposed an extension of Partner Based Dynamic Critical Path for Grids algorithm named Balanced Partner Based Dynamic Critical Path for Grids (B-PDCPG) that incorporates a hybrid and threshold based mechanism to achieve load balancing to an allowed value of variation in workload among the resources in Partner Based Dynamic Critical Path for Grids algorithm. The proposed load balancing technique uses Utilization Profiles to store the reservation details and check the loads from these profiles on each of the resources and links. The load is distributed among resources based on the processing element capacity and number of processing units on resources. The simulation results, using Gridsim simulation engine, show that the proposed technique has balanced the workload very effectively and has provided better utilization of resources while decreasing the workflow makespan.

Myasthenia gravis (MG) is a disease of autoimmunity with variable and diverse clinical presentations. The target tissue is neuromuscular junction of skeletal muscles, where efficient nerve impulse transmission is hampered leading to less effective muscle contraction. Patients of MG usually present with bilateral ptosis, diplopia and fatigability, which may or may not coexist with generalised weakness, dysphagia and dysarthria. A46-year male presented with unilateral ptosis and diplopia. Except for unilateral moderate ptosis and restriction of extraocular movements, ocular and systemic examination was normal. Both ice pack and tensilon tests revealed improvement in ptosis. Patient was advised tablet pyridostigmine and prednisolone; and a remarkable improvement was noticed within a week.