MANet vs VANet- The Applications & Challenges

Vehicular Ad hoc Networks (VANet) and Mobile Ad hoc Networks (MANet) have evolved into one of the very capable fields of research work in wireless networking. VANet is termed as a stimulating form of MANet owing to its highly erratic dynamic topology, frequently occurring disconnections and lifethreateningissues. This paper is a comparative review of MANet, VANet and their applications along with challenges

An Intelligent Healthcare system for detecting diabetes using machine learning algorithms

The human disease prediction is specifically a struggling piece of work for an accurate and on time treatment. Around the world, diabetes is a hazardous disease. It affects the various essential organs of the human body, for example, nerves, retinas, and eventually heart. By using models of machine learning algorithms, we can recommend and predict diabetes on various healthcare datasets more accurately with the assistance of an intelligent healthcare recommendation system. Not long ago, for the prediction of diabetes, numerous models and methods of machine learning have been introduced. But despite that, enormous multi-featured healthcare datasets cannot be handled by those systems appropriately. By using Machine Learning, an intelligent healthcare recommendation system is introduced for the prediction of diabetes. Ultimately, the model of machine learning is trained to predict this disease along with K-Fold Cross validation testing.  The evaluation of this intelligent and smart recommendation system is depending on datasets of diabetes and its execution is differentiated from the latest development of previous literatures. Our system accomplished 99.0% of efficiency with the shortest time of 12 Milliseconds, which is highly analyzed by the previous existing models of machine learning. Consequently, this recommendation system is superior for the prediction of diabetes than the previous ones. This system enhances the performance of automatic diagnosis of this disease. Code is available at (https://github.com/RaoHassanKaleem/Diebetes-Detection-using-Machine-Learning-Algorithms). &nbsp

Determinants of response at 2 months of treatment in a cohort of Pakistani patients with pulmonary tuberculosis

Mycobacterium tuberculosis infection continues to be a major global challenge. All patients with pulmonary tuberculosis are treated with a standard 6-month treatment regimen. Historical data suggest that even with shortened treatment, most patients achieve long-term remission. Risk stratification is a goal for reducing potentially toxic prolonged treatment. This study aimed to determine the factors associated with the early clearance of sputum acid-fast bacilli (AFB). A total of 297 freshly diagnosed patients with pulmonary tuberculosis were included and enrolled in this study. Information related to their ethno-demographic and anthropometric characteristics was collected. We also assessed their complete blood counts, and blood iron, folate, and vitamin B12 levels. We found that the presence of higher levels of acid-fast bacilli (AFB) in diagnostic sputum microscopy was the single most significant prognostic factor associated with early clearance of sputum AFB after 2 months of treatment. All of our patients achieved treatment success after 6 months of treatment and were disease free. Our results support the data obtained from previous studies indicating that AFB clearance at 2 months is unlikely to be a clinically useful biomarker or indicator for therapeutic stratification. Furthermore, demographic, anthropometric, and nutritional factors are not clinically useful biomarkers

Prevalence of Cutaneous Leishmaniasis in Humans and Dogs in Pakistan

Abstract.-The study was undertaken fro

Intelligent Predictive Solution Dynamics for Dahl Hysteresis Model of Piezoelectric Actuator

Piezoelectric actuated models are promising high-performance precision positioning devices used for broad applications in the field of precision machines and nano/micro manufacturing. Piezoelectric actuators involve a nonlinear complex hysteresis that may cause degradation in performance. These hysteresis effects of piezoelectric actuators are mathematically represented as a second-order system using the Dahl hysteresis model. In this paper, artificial intelligence-based neurocomputing feedforward and backpropagation networks of the Levenberg–Marquardt method (LMM-NNs) and Bayesian Regularization method (BRM-NNs) are exploited to examine the numerical behavior of the Dahl hysteresis model representing a piezoelectric actuator, and the Adams numerical scheme is used to create datasets for various cases. The generated datasets were used as input target values to the neural network to obtain approximated solutions and optimize the values by using backpropagation neural networks of LMM-NNs and BRM-NNs. The performance analysis of LMM-NNs and BRM-NNs of the Dahl hysteresis model of the piezoelectric actuator is validated through convergence curves and accuracy measures via mean squared error and regression analysis

Prevalence Of Rifampicin Resistance in New Cases Of Pulmonary Tuberculosis In Children

Objective: The research aims to determine the prevalence of rifampicin resistance in newly diagnosed pediatric pulmonary tuberculosis cases, investigating the frequency of resistance to this first-line antibiotic. By doing so, it seeks to provide insights into rifampicin's effectiveness as a treatment and contribute to understanding drug-resistant tuberculosis in children. Methods: The investigation employed a cross-sectional design to evaluate the presence of rifampicin resistance in newly diagnosed pediatric pulmonary tuberculosis cases. Executed in Lahore, Pakistan, it adopted a convenience sampling strategy with a sample size of 100. The research entailed screening children displaying symptoms of TB, acquiring written consent, and gathering demographic and clinical data, encompassing bacterial load and evidence of antibiotics. Sputum samples were processed employing the Xpert MTB/RIF assay. Statistical analyses, encompassing descriptive statistics and prevalence calculations, were executed utilizing the SPSS software. The investigation underscored the significance of resilient diagnostics, early identification, and tailored interventions for the management of drug-resistant TB in children. Results: The study provides valuable insights into rifampicin resistance among children with pulmonary tuberculosis. These findings highlight the importance of regular monitoring and appropriate treatment strategies to combat drug-resistant tuberculosis in pediatric populations. Further research and interventions are warranted to minimize the emergence and spread of drug-resistant strains in this vulnerable population. Conclusion: The study highlights the need for continuous monitoring of drug resistance patterns in children with tuberculosis, particularly concerning rifampicin, a crucial first-line antibiotic. The higher resistance rate suggests exploring alternative treatment options, optimising drug regimens, and developing interventions to prevent and manage drug-resistant tuberculosis effectively in children. Keywords: Antibiotics, Disease, Paeds, Resistance, Susceptibility, Tuberculosis

Frequency of language and swallowing problems in children with cerebral palsy Tertiary care Hospital Rawalpindi, Pakistan

Objective: To investigate the occurrence of language and swallowing problem in individuals with cerebral palsy. Methods: The cross-sectional survey was conducted at the Riphah International University, Rawalpindi, Pakistan, from September 2018 to January 2019 while data was collected from the Armed Forces Institute of Rehabilitation Medicine, Rawalpindi, a tertiary care hospital. The sample comprised individuals with cerebral palsy of either gender aged 5-18 years. Language Sample Checklist was used for language problems and the Northwestern Dysphagia Patient Checklist was used for swallowing problems.Data was analysed using SPSS- Version 21. Results: Of the55 subjects,62% were males, 38% were females, 76% were from urban areas and 24% were from rural areas.In terms of concepts, processing, and comprehension, 18(33%) persons were able to attempt the tasks, 45(81%) were unable to attempt morphological tasks, 41(74%) were unable to attempt sentence structure tasks, 40(72%) were unable to attempt literacy and narrative skills tasks, 41(74%)could not fulfil pragmatic tasks and 49(89%) had unintelligible speech. The patient checklist showed that 47(85%) children had normal medical history, 41(75%) had normal behavioural variable, 29(52%) had normal gross motor ability,40(73%) completed oral motor test, and 39(71%) had normal swallow trials. Conclusion: Language problems were more prevalent in children with cerebral palsy compared to swallowing difficulties. Key Words: Cerebral palsy, Dysarthria, Language, Swallowing, Dysphagia

Environmentally benevolent synthesis and characterization of silver nanoparticles using Olea ferruginea Royle for antibacterial and antioxidant activities

In this study, we reported an easy, rapid, cost-effective and environmentally benign method for the fabrication of silver nanoparticles (Ag-NPs) using Olea ferruginea as reducing, capping and stabilizing agent. For this, an aqueous extract of leaf and bark of O. ferruginea was treated with 1 mM AgNO3, which reduces Ag ions to Ag-NPs by establishing reddish brown color. The synthesized Ag-NPs were spherical crystals, with a mean size of 23 and 17 nm for leaf- and bark-mediated Ag-NPs, respectively. Fourier transform infrared spectroscopy affirmed the role of leaf and bark extracts of O. ferruginea as reducing, capping and stabilizing agent. These biosynthesized Ag-NPs showed profound antibacterial activity against Gram-negative (Pseudomonas aeruginosa and Escherichia coli) and Gram-positive (Streptococcus pneumonia and Staphylococcus aureus) bacteria. The highest antibacterial activity was shown by bark Ag-NPs against S. aureus (14.00 mm), while leaf Ag-NPs showed higher activity against S. pneumonia (13.00 mm). Additionally, they produced effective antioxidant activity against 2,2-diphenyl-1-picrylhydrazyl (DPPH) as compared to plant extracts and positive control. It was observed that the bark-mediated Ag-NPs had higher percentage (90%) of scavenging potential than the leaf-mediated Ag-NPs (78%). The significance of the current study is the synthesis of eco-friendly, easy and cost-effective Ag-NPs as biomedical products

Threatening URDU Language Detection from Tweets Using Machine Learning

Technology’s expansion has contributed to the rise in popularity of social media platforms. Twitter is one of the leading social media platforms that people use to share their opinions. Such opinions, sometimes, may contain threatening text, deliberately or non-deliberately, which can be disturbing for other users. Consequently, the detection of threatening content on social media is an important task. Contrary to high-resource languages like English, Dutch, and others that have several such approaches, the low-resource Urdu language does not have such a luxury. Therefore, this study presents an intelligent threatening language detection for the Urdu language. A stacking model is proposed that uses an extra tree (ET) classifier and Bayes theorem-based Bernoulli Naive Bayes (BNB) as the based learners while logistic regression (LR) is employed as the meta learner. A performance analysis is carried out by deploying a support vector classifier, ET, LR, BNB, fully connected network, convolutional neural network, long short-term memory, and gated recurrent unit. Experimental results indicate that the stacked model performs better than both machine learning and deep learning models. With 74.01% accuracy, 70.84% precision, 75.65% recall, and 73.99% F1 score, the model outperforms the existing benchmark study