The extensive progression in the Internet of Vehicles (IoV) and the exponential upsurge in data consumption reflect the importance of big data in IoV. In general, big data has gained a significant ...attraction in academia and industry to provide valuable business intelligence and evidence-based decisions. This has been a key enabler for the advancement of the Internet of Vehicles (IoV) in which big data can be leveraged for efficient processing and valuable decisions. Moreover, data acquired from connected vehicles, traffic monitoring, social media feeds, and, crowd-sourcing can strengthen urban development and management. The purpose of this study is to synthesize a systematic review of all related research articles from January 2014 to September 2020 in well-alleged venues. We have rigorously surveyed the research papers to understand potential opportunities, methodologies, and challenges of using big data in IoV. This review shows that big data can play a key role in providing sound and valuable predictions and also provide a comprehensive analysis of several methods, tools, and techniques for the use of big data in IoV. Apart from reviewing the state-of-the-art studies of using big data in IoV, a taxonomy of the said also has been proposed. Furthermore, the article outlined and discussed several key challenges in IoV with notable recommendations and open research dimensions for using big data in IoV.
Several smart city ideas are introduced to manage various problems caused by overpopulation, but the futuristic smart city is a concept based on dense and artificial-intelligence-centric cities. ...Thus, massive device connectivity with huge data traffic is expected in the future where communication networks are expected to provide ubiquity, high quality of service, and on-demand content for a large number of interconnected devices. The sixth-generation (6G) network is considered the problem-solving network of futuristic cities, with huge bandwidth and low latency. The expected 6G of the radio access network is based on terahertz (THz) waves with the capability of carrying up to one terabit per second (Tbps). THz waves have the capability of carrying a large amount of data but these waves have several drawbacks, such as short-range and atmospheric attenuation. Hence, these problems can introduce complications and hamper the performance of the 6G network. This study envisions futuristic smart cities using 6G and proposes a conceptual terrestrial network (TN) architecture for 6G. The nested Bee Hive is a scalable multilayer architecture designed to meet the needs of futuristic smart cities. Moreover, we designed the multilayer network infrastructure while considering the expectations from a network of futuristic smart cities and the complications of THz waves. Extensive simulations are performed using different pathfinding algorithms in the 3D multilayer domain to evaluate the performance of the proposed architecture and set the dynamics of futuristic communication of 6G.
Autism spectrum disorder (ASD) presents a neurological and developmental disorder that has an impact on the social and cognitive skills of children causing repetitive behaviours, restricted ...interests, communication problems and difficulty in social interaction. Early diagnosis of ASD can prevent from its severity and prolonged effects. Federated learning (FL) is one of the most recent techniques that can be applied for accurate ASD diagnoses in early stages or prevention of its long-term effects. In this article, FL technique has been uniquely applied for autism detection by training two different ML classifiers including logistic regression and support vector machine locally for classification of ASD factors and detection of ASD in children and adults. Due to FL, results obtained from these classifiers have been transmitted to central server where meta classifier is trained to determine which approach is most accurate in the detection of ASD in children and adults. Four different ASD patient datasets, each containing more than 600 records of effected children and adults have been obtained from different repository for features extraction. The proposed model predicted ASD with 98% accuracy (in children) and 81% accuracy (in adults).
A widespread mistrust towards the traditional voting system has made democratic voting in any country very critical. People have seen their fundamental rights being violated. Other digital voting ...systems have been challenged due to a lack of transparency. Most voting systems are not transparent enough; this makes it very difficult for the government to gain voters' trust. The reason behind the failure of the traditional and current digital voting system is that it can be easily exploited. The primary objective is to resolve problems of the traditional and digital voting system, which include any kind of mishap or injustice during the process of voting. Blockchain technology can be used in the voting system to have a fair election and reduce injustice. The physical voting systems have many flaws in it as well as the digital voting systems are not perfect enough to be implemented on large scale. This appraises the need for a solution to secure the democratic rights of the people. This article presents a platform based on modern technology blockchain that provides maximum transparency and reliability of the system to build a trustful relationship between voters and election authorities. The proposed platform provides a framework that can be implemented to conduct voting activity digitally through blockchain without involving any physical polling stations. Our proposed framework supports a scalable blockchain, by using flexible consensus algorithms. The Chain Security Algorithm applied in the voting system makes the voting transaction more secure. Smart contracts provide a secure connection between the user and the network while executing a transaction in the chain. The security of the blockchain based voting system has also been discussed. Additionally, encryption of transactions using cryptographic hash and prevention of attack 51% on the blockchain has also been elaborated. Furthermore, the methodology for carrying out blockchain transactions during the process of voting has been elaborated using Blockchain Finally, the performance evaluation of the proposed system shows that the system can be implemented in a large-scale population.
Nanotechnology is the study and control of materials at length scales between 1 and 100 nanometers (nm), where incredible phenomena enable new applications. It affects all aspects of human life and ...is the most active research topic in modern materials science. Among the various metallic nanoparticles used in biomedical applications, silver nanoparticles (AgNPs) are among the most important and interesting nanomaterials. The aim of this study was to synthesize AgNPs from the leaf extract of
to investigate their antibacterial, antioxidant, and phytotoxic activities. When the leaf extract was treated with AgNO
, the color of the reaction solution changed from light brown to dark brown, indicating the formation of AgNPs. The UV-visible spectrum showed an absorption peak at 438 nm, confirming the synthesis of AgNPs. Scanning electron microscopy (SEM) showed that the AgNPs were spherical and oval with an average size of 28.32 nm. Fourier transform infrared spectroscopy confirms the presence of bio-compound functional groups on the surface of the AgNPs. The crystalline nature of the AgNPs was confirmed by XRD pattern. These biosynthesized AgNPs showed pronounced antibacterial activity against Gram-positive and Gram-negative bacteria, with higher inhibitory activity against
. At 40 µg/mL AgNPs, the highest antioxidant activity was obtained, which was 57.7% and an IC50 value of 77.56 µg/mL. A significant positive effect was observed on all morphological parameters when AgNPs were applied to wheat seedlings under constant external conditions at the different concentrations. The present study provides a cost-effective and environmentally friendly method for the synthesis of AgNPs, which can be effectively used in the field of therapeutics, as antimicrobial and diagnostic agents, and as plant growth promoters.
Requirements are the basis of software development practices. Ambiguities in requirements lead a project to a point of failure or penalize it with a high budget and time for defect traceability. The ...ever-growing demand for advanced computing systems has increased the complexity of Software Requirements Engineering (SRE) practices. Blockchain systems require specialized SRE practices as the issues of Requirement Traceability (RT), developer/client confidentiality, and Requirement Negotiation (RN) typically exist in conventional approaches, which require more improvement. Moreover, blockchain technology incorporates the capacity to function as an infrastructure for the SRE framework providing transparency, security, and reliability. Even though the significance of studying blockchain in the context of SRE is evident, it is still in its infancy. None of the previous studies surveyed this domain to the best of our knowledge. We aim to summarize the scholarly contributions of blockchain acquainted SRE from 2015 to 2021 and to provide academia and practitioners with in-depth knowledge about this domain. In this article, we have provided a novel comprehensive review of the aspects of blockchain-acquainted SRE practices. We have presented SRE-based quality improvement factors and outlined the need for blockchain technology in this domain. Furthermore, we have classified SRE practices based on blockchain engineering. In addition, we have proposed a generic SRE model built on blockchain infrastructure along with its workflows. Similarly, we have provided implementation guidelines for the future development guidance of SRE applications built on blockchain technology. Finally, we have presented the current research challenges and provided future directions based on blockchain acquainted SRE.
China Pakistan Economic Corridor (CPEC) is considered a massive investment that can change the economic scenario of Pakistan. The purpose of the study is to examine the contribution to the economic ...growth of the sectors where CPEC is investing. This research uses time-series data for 31 years to investigate the impact of macro-economic variables like foreign direct investment (FDI), human capital investment (HCI), transport investment, and information communication technology (ICT) on the economic growth of Pakistan. The results of Fully Modified Ordinary Least Square Regression Specification (FMOLS) show a positive nexus between FDI, HCI, and economic growth while economic growth and ICT show a negative relationship. The results for the impact of transportation infrastructure on economic growth are statistically insignificant. This research suggests that an increased focus on building knowledge, expertise, and skillset of human resources will help in reaping the benefits of CPEC’s investment. Future researchers can increase the period of the study to ascertain the implicit or explicit impact of CPEC on economic growth. The results also suggest that policymakers and researchers should focus on developing human capital to reap the investment benefits of CPEC.
Diabetic Retinopathy (DR) is a predominant cause of visual impairment and loss. Approximately 285 million worldwide population is affected with diabetes, and one-third of these patients have symptoms ...of DR. Specifically, it tends to affect the patients with 20 years or more with diabetes, but it can be reduced by early detection and proper treatment. Diagnosis of DR by using manual methods is a time-consuming and expensive task which involves trained ophthalmologists to observe and evaluate DR using digital fundus images of the retina. This study aims to systematically find and analyze high-quality research work for the diagnosis of DR using deep learning approaches. This research comprehends the DR grading, staging protocols and also presents the DR taxonomy. Furthermore, identifies, compares, and investigates the deep learning-based algorithms, techniques, and, methods for classifying DR stages. Various publicly available dataset used for deep learning have also been analyzed and dispensed for descriptive and empirical understanding for real-time DR applications. Our in-depth study shows that in the last few years there has been an increasing inclination towards deep learning approaches. 35% of the studies have used Convolutional Neural Networks (CNNs), 26% implemented the Ensemble CNN (ECNN) and, 13% Deep Neural Networks (DNN) are amongst the most used algorithms for the DR classification. Thus using the deep learning algorithms for DR diagnostics have future research potential for DR early detection and prevention based solution.
Leukemia is a type of blood cell cancer that is in the bone marrow's blood-forming cells. Two types of Leukemia are acute and chronic; acute enhances fast and chronic growth gradually which are ...further classified into lymphocytic and myeloid leukemias. This work evaluates a unique deep convolutional neural network (CNN) classifier that improves identification precision by carefully examining concatenated peptide patterns. The study uses leukemia protein expression for experiments supporting two different techniques including independence and applied cross-validation. In addition to CNN, multilayer perceptron (MLP), gated recurrent unit (GRU), and recurrent neural network (RNN) are applied. The experimental results show that the CNN model surpasses competitors with its outstanding predictability in independent and cross-validation testing applied on different features extracted from protein expressions such as amino acid composition (AAC) with a group of AAC (GAAC), tripeptide composition (TPC) with a group of TPC (GTPC), and dipeptide composition (DPC) for calculating its accuracies with their receiver operating characteristic (ROC) curve. In independence testing, a feature expression of AAC and a group of GAAC are applied using MLP and CNN modules, and ROC curves are achieved with overall 100% accuracy for the detection of protein patterns. In cross-validation testing, a feature expression on a group of AAC and GAAC patterns achieved 98.33% accuracy which is the highest for the CNN module. Furthermore, ROC curves show a 0.965% extraordinary result for the GRU module. The findings show that the CNN model is excellent at figuring out leukemia illnesses from protein expressions with higher accuracy. Keywords: Leukemia detection, Protein sequences, Deep learning, Convolutional neural network
The effectiveness of deferred surgical repair of ventricular septal rupture (VSR) post-myocardial infarction (MI) with cardiogenic shock remains limited to case reports. Our study aimed to ...investigate the outcomes and survival analysis following mechanical circulatory support (MCS) in patients after VSR who develop cardiogenic shock.
We analyzed 27 patients with post-MI VSR and cardiogenic shock who received deferred surgical repair while stabilized on MCS between January 2018 and March 2020. After normality test adjustments, continuous variables were expressed as mean ± standard deviation (SD). These were compared using the Mann-Whitney U test and Student's t-test. Categorical variables were compared using chi-square or Fisher's exact test. To identify predictors of operative mortality, univariate analysis of clinical characteristics and interventions followed by logistic regression was carried out. P-value of < 0.05 was considered significant.
All patients had preoperative MCS. Emergency repair was avoided in all the patients. The mean age of the participants was 64.96 with the majority being males (74.1%). On average, the mean time from MI to VSR repair was 18.85 days. Delayed revascularization was associated with increased mortality (OR 17.500, 95% CI 2.365-129.506, P = 0.005). Other factors associated with increased mortality were ejection fraction (EF), three-vessel disease, Killip class, early surgery, and prolonged use of inotropes. The operative mortality was 11% with an overall mortality of 33.3%. The one-year survival rate was 66.7%.
The use of MCS in adjunct to a deferred surgical approach shows an improved survival outcome of patients with VSR complicated by cardiogenic shock. Further investigations are required regarding the optimal time for MCS and surgical repair.
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