Determinants of Vaccine Hesitancy and Refusal in Children of District Swabi Khyber Pakhtunkhwa, Pakistan

OBJECTIVES: The objective of the study was to evaluate factors associated with vaccine hesitancy and refusal at District Swabi, Khyber Pakhtunkhwa. METHODOLOGY: This cross-sectional study was conducted at a teaching hospital of Khyber Pakhtunkhwa District Swabi over a period of four months. Children between the ages of 9 months to 10 years from the local population admitted to the children ward/daycare centre were included in the study. Parents were inquired about vaccination status and in case of no vaccination or partial vaccination; then the reason was inquired after proper informed consent. Data were collected by using a structured proforma and analyzed using SPSS-24. RESULTS: A total of 828 children were included in this study. Out of these 492 (59.4%) were male and 336 (40.6%) were females. Of the total 828 children, 753 (90.9%) were vaccinated up to date, 48 (5.8%) were not vaccinated and 27 (3.3%) were partially vaccinated. Under vaccinated were 75 patients, 52% were left out due to misconception/beliefs, 6% patients were having issues due to living far away, 2.7% patients could not be vaccinated due to presence of other diseases and 37.3% due to lack of knowledge regarding vaccination. A significant correlation was found between the vaccination status of children and aforementioned reasons (p-value ≤0.001). CONCLUSION: The most common cause of not vaccinating children with polio vaccines was misconceptions/beliefs and lack of knowledge of the parents

Exploring Lightweight Deep Learning Solution for Malware Detection in IoT Constraint Environment

: The present era is facing the industrial revolution. Machine-to-Machine (M2M) communication paradigm is becoming prevalent. Resultantly, the computational capabilities are being embedded in everyday objects called things. When connected to the internet, these things create an Internet of Things (IoT). However, the things are resource-constrained devices that have limited computational power. The connectivity of the things with the internet raises the challenges of the security. The user sensitive information processed by the things is also susceptible to the trusability issues. Therefore, the proliferation of cybersecurity risks and malware threat increases the need for enhanced security integration. This demands augmenting the things with state-of-the-art deep learning models for enhanced detection and protection of the user data. Existingly, the deep learning solutions are overly complex, and often overfitted for the given problem. In this research, our primary objective is to investigate a lightweight deep-learning approach maximizes the accuracy scores with lower computational costs to ensure the applicability of real-time malware monitoring in constrained IoT devices. We used state-of-the-art Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM deep learning algorithm on a vanilla configuration trained on a standard malware dataset. The results of the proposed approach show that the simple deep neural models having single dense layer and a few hundred trainable parameters can eliminate the model overfitting and achieve up to 99.45% accuracy, outperforming the overly complex deep learning models.publishedVersio

An Efficient Online/Offline Signcryption Scheme for Internet of Things in Smart Home

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A Comprehensive Survey on Signcryption Security Mechanisms in Wireless Body Area Networks

WBANs (Wireless Body Area Networks) are frequently depicted as a paradigm shift in healthcare from traditional to modern E-Healthcare. The vitals of the patient signs by the sensors are highly sensitive, secret, and vulnerable to numerous adversarial attacks. Since WBANs is a real-world application of the healthcare system, it’s vital to ensure that the data acquired by the WBANs sensors is secure and not accessible to unauthorized parties or security hazards. As a result, effective signcryption security solutions are required for the WBANs’ success and widespread use. Over the last two decades, researchers have proposed a slew of signcryption security solutions to achieve this goal. The lack of a clear and unified study in terms of signcryption solutions can offer a bird’s eye view of WBANs. Based on the most recent signcryption papers, we analyzed WBAN’s communication architecture, security requirements, and the primary problems in WBANs to meet the aforementioned objectives. This survey also includes the most up to date signcryption security techniques in WBANs environments. By identifying and comparing all available signcryption techniques in the WBANs sector, the study will aid the academic community in understanding security problems and causes. The goal of this survey is to provide a comparative review of the existing signcryption security solutions and to analyze the previously indicated solution given for WBANs. A multi-criteria decision-making approach is used for a comparative examination of the existing signcryption solutions. Furthermore, the survey also highlights some of the public research issues that researchers must face to develop the security features of WBANs.publishedVersio

Petrographic and Physiomechanical Investigation of Late Cretaceous Kawagarh Formation Kahi Section, Nizampur Basin

The late Cretaceous Kawagarh Formation has been investigated in terms of field observation, and petrographic analysis, to understand the petrography and its impact on the geotechnical properties. The Kawagarh Formation is well exposed among the upper Indus Basin, and has been studied by various workers in different aspects. Kawagarh Formationexposed in Kahi section of Nizampur Basin has been selected in this study to know the behavior of carbonate rocks for engineering purposes. Lithologically, this formation is composed of thick to medium bedded, highly fractured limestone, marls, and dolomitic limestone which has undertaken diagenetic alteration including dolomite, calcite veins, and stylolites. Followed by petrographic analysis which reveals that the Kawagarh limestone is mostly fossiliferouscomprised of a large number of planktonic foraminifera fossils like Globotruncana Hilli and Globotruncana Linneana fossils. Furthermore, to know the impact of petrographic minerals on engineering behavior, mechanical properties in terms of uniaxial compressive strength (UCS) and uniaxial tensile strength (UTS) were also computed by using a universal testing machine (UTM). The resultant mechanical values lie in the strong compressive strength and suggest their usage for various construction purposes. Aggregate degradation tests including water absorption, specific gravity, aggregate impact value, Los angles abrasion, and soundness was also computed according to the International standard organization, ASTM (American Society for testing materials) and British standard. The aggregate values of the Cretaceous Kawagarh Formation are within the defined standard limits and can be used as an aggregate source for different construction engineering projects

Effect of Cyclodextrin Derivatization on Solubility and Efficacy of Drugs

Cyclodextrins (CDs) possess cyclic structure having (α-1,4)-linked glucopyranose units making them less vulnerable to enzymatic degradation as than the linear dextrins. Commonly used natural CDs are α-CD, β-CD, and ɣ-CD with truncated cone-like appearance having lipophilic central cavity and hydrophilic exterior surface. The problem of low aqueous solubility of natural CDs can be addressed by reacting them with various reagents to produce water-soluble derivatives. CD derivatives can be categorized in many ways depending upon their substituents, biological activity, polarity, and size. The derivatization of natural CDs produces noncrystalline and amorphous forms with higher water solubility that are physically and microbiologically stable for prolonged time period. Variety of methods can be used to determine average degree of substitution for a modified CD. Dissociation by dilution is considered as major release mechanism of drugs from complex. It is essential to optimize the amount of CDs for a given preparation because they can either retard or promote drug delivery through biological membrane

Face Mask Detection Using Deep Convolutional Neural Network and MobileNetV2-Based Transfer Learning

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Blockchain-Based Land Registration System: A Conceptual Framework

Land registration authorities are frequently held accountable for the alleged mismanagement and manipulation of land records in various countries. Pakistan’s property records are especially vulnerable to falsification and corruption because of the country’s poverty. Different parties therefore claim varying degrees of authority over a specific piece of land. Given the fact that this data has been consolidated, it has become significantly more vulnerable to security threats. The goal of decentralized system research has been to increase the reliability of these systems. In order to fix the flaws of centralized systems, blockchain-based decentralized systems are currently in development. By using significant land record registration models as the basis for this research, we hope to create a proof-of-concept system or framework for future use. Pakistan’s land registration agency will benefit from our proposed conceptual framework. For the Pakistani government to implement a decentralized land record registry system, we propose a conceptual framework that outlines the essential components.publishedVersio

A Simple and Efficient Deep Learning-Based Framework for Automatic Fruit Recognition

Accurate detection and recognition of various kinds of fruits and vegetables by using the artificial intelligence (AI) approach always remain a challenging task due to similarity between various types of fruits and challenging environments such as lighting and background variations. Therefore, developing and exploring an expert system for automatic fruits’ recognition is getting more and more important after many successful approaches; however, this technology is still far from being mature. The deep learning-based models have emerged as state-of-the-art techniques for image segmentation and classification and have a lot of promise in challenging domains such as agriculture, where they can deal with the large variability in data better than classical computer vision methods. In this study, we proposed a deep learning-based framework to detect and recognize fruits and vegetables automatically with difficult real-world scenarios. The proposed method might be helpful for the fruit sellers to identify and differentiate various kinds of fruits and vegetables that have similarities. The proposed method has applied deep convolutional neural network (DCNN) to the undertakings of distinguishing natural fruit images of the Gilgit-Baltistan (GB) region as this area is famous for fruits’ production in Pakistan as well as in the world. The experimental outcomes demonstrate that the suggested deep learning algorithm has the effective capability of automatically recognizing the fruit with high accuracy of 96%. This high accuracy exhibits that the proposed approach can meet world application requirements.publishedVersio

An Efficient Convolutional Neural Network with Transfer Learning for Malware Classification

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