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Regarding real-time data processing, technological innovations like the Internet of Things (IoTs) need latency-sensitive computation. The interconnected devices in IoT systems produce enormous amounts of data. In most cases, a cloud platform is used to compute these data. But for some IoT services, particularly time-sensitive ones, computation requests only on the cloud is not an effective option. For services that depend on low latency, the distance among the cloud servers and the IoT/edge devices may be a problem. Fog computing (FC), which sits between the cloud server and edge devices, was suggested as a solution to this problem. Edge devices are typically attached to fog devices (i.e., fog nodes) in the FC platform. Edge users can find these fog nodes nearby, and they are in charge of intermediary computation as well as storage. Since FC platforms are still in their development and are steadily rising, rigorous investigation is essential for understanding this novel technology. The industry as well as academic society will be able to learn more about the specifications for constructing an FC platform with a clearer understanding of all components of the fog, thanks to this complete review work. This paper begins with an introduction to FC concepts, where the reason of fog emergence, its definition, characteristics, implementation practices, and a comparative study among fog, cloud, and edge computing are discussed. Next, records from Google Scholar as well as three top academic databases (i.e., ScienceDirect, IEEE Xplore, and ACM digital library) are gathered and analysed to determine the research trend on FC. Then we categorize the various recommended FC architectures into three categories and the aspects of these architectures are then thoroughly described. We then go into great detail on the technologies used to create fog systems. Here, six key technologies, their features, a thorough description of each, and related research efforts are thoroughly presented. In addition, 43 fog simulators are described in this study while taking into account 5 factors and analysed them by considering four parameters. The following two chapters go into great detail regarding the security concerns and applications of fog. Ultimately, by discussing the shortcomings of recent research studies, we highlight several unresolved problems that will define the future course of the fog platform's study.

The liver is one of the most vital organs of the human body. Even when partially injured, it functions normally. Therefore, detecting liver diseases at the early stages is challenging. Early detection of liver problems can improve patient survival rates. This research enlightens on several Artificial Intelligence techniques, including the Bagged Tree, Support Vector Machine, K-Nearest Neighbor, and Fine Tree classifier, to predict the presence of liver disease in a patient at an early stage. This study compares those models and selects the best technique to detect liver disease at an early stage. The classification performance is measured using the confusion matrix, True Positive Rate (TPR), False Positive Rate (FPR), ROC curve, and accuracy. The result shows that the Bagged Tree classifier achieves the highest classification accuracy (81.30%), which is very promising compared to the other algorithms. The proposed system also performs sensitivity analysis on the dataset to investigate the impact of each attribute on the model’s performance. It has been demonstrated that Alanine Aminotransferase (sgpt) attribute has the most significant impact on the prediction of liver disease. The proposed method could be used as an assistant framework for liver disease detection at an early stage.

Education systems have significantly changed with the emergence of the internet. It has a significant impact on how students learn things. Nevertheless, its impact can also be contradicting. Internet addiction can slowly poison the minds of our youths and stand in the way of pursuing their goals. Although Bangladesh has internet connectivity across the country, its potential could be more utilized, particularly in the educational sector. Therefore, proper analysis of the effects of the internet on students, as well as determining the prominent factors relevant to the internet, is a necessary task. In addition, predicting students' academic performance can help determine the changes that must be incorporated to improve the educational system. Hence, this research analyzes the effects of internet usage on students' academic progress and then predicts the students' performance using distinct machine learning (ML) algorithms. The data were collected through an offline survey from Noakhali, Bangladesh. The collected data is preprocessed to select the most relevant features. The preprocessed data were fed into ML algorithms to investigate their behaviors. We have employed logistic regression, decision tree, random forest, and Naïve Bayes algorithms to see their classification performance on our dataset. To minimize the overfitting issue, k-fold cross-validation and hyperparameter optimization have been applied. The results were presented in two parts—exploratory data analysis and classification. Exploratory data analysis shows that the main purpose of internet usage is education and entertainment for school students, social media and entertainment for college students, and education and social media for university students. School and university students browse the internet mainly for academic purposes, whereas college students browse mainly for non-academic purposes. Students prefer to browse the internet at night. For all schools, colleges, and universities, students with better results generally visited websites like Google and YouTube. Students with moderate or bad results generally spent time on social media platforms (mainly Facebook and WhatsApp). Then, the results of the numerical analysis performed with classification algorithms are presented. Results indicate that random forest gives the maximum score in our dataset in all sectors, like accuracy, precision, recall, and f1 score. It gives a maximum of 85% accuracy on the test set. Logistic regression gives the second-best score of 69%. The practical applications and policy recommendations for Bangladesh's education sector are also discussed. The output of this work can contribute to building a policy on internet usage. In this way, it is possible to make the students more concentrative on their education and learning.

Dengue fever is a disease that has been outbreak worldwide in the last few years. Dengue is a fatal disease; sometimes, it may cause life-threatening complications and even death. Dengue is considered to be one of the critical diseases which is spreading in more than 110 countries. Nearly 45,000 case reports have been found around Bangladesh in the last year. Dengue fever has become a major health hazard in Bangladesh. Hence, early detection would mitigate major casualties of Dengue disease. Distinct studies have been performed concerning Dengue disease; however, no effective study, particularly from Bangladesh's perspective, it seemed that reveals Dengue outbreaks prediction method. In this scenario, this research work aims to analyse the Dengue disease and build an apposite model to predict dengue outbreaks. This paper also aims to find the best technique to early predicts Dengue disease. The real-time data of the patients admitted to different hospitals in Bangladesh is accumulated to achieve the goal of the current research. Then different multilayer perceptron neural networks and a Decision tree are used for Dengue outbreaks prediction. Twenty-five parameters are analysed to find these parameters' infection rates in this work. A comparative study of the developed models' performances is also accomplished to obtain a better Dengue outbreaks prediction model. The results evidence that the Levenberg–Marquardt is the best technique with 97.3% accuracy and 2.7% error in Dengue disease prediction. On the other hand, the Decision tree may have the second choice to assess Dengue disease.

The Internet of Things (IoT) has become one of the most known terms in present times, reaching new levels and setting a trend in the world. Evidently, it is the future of connectivity which has turned physical objects into intelligent objects. Therefore, there has been a growing curiosity in the IoT field and this leads to the concept of IoT ecosystem. But the contemporary fragmented ecosystem of regulations, technologies, and systems slows IoT deployments. Thus, we consider multi-tiered computational infrastructure which would be feasible to provide services from the nearest possible location of end devices. To mitigate multi-tier infrastructure issues, Software-Defined Networking (SDN) steps in. The journey behind SDN-supported multi-tier computational infrastructure can be understood from this paper’s elaborate study. Next, a comparative analysis on dew, roof, fog and cloud is conducted and the impact of internet on these computing paradigms is briefly explained. Then a novel framework termed “SD-DRFC (Software-Defined Dew, Roof, Fog and Cloud computing)” is proposed for today's IoT ecosystem. The role and functionality of each tier of SD-DRFC framework are adequately explained. A use case focused on the SD-DRFC framework is then presented and simulated by utilizing iFogSim simulator. To evaluate the efficiency of the presented SD-DRFC framework, four QoS parameters (Latency, Network Usage, Cost, and Energy Consumption) are considered in this paper. When comparing the simulation results, the presented SD-DRFC framework performs much better than cloud-only implementation. The advantages and suitability of utilizing this proposed framework have been demonstrated by multiple use-cases which range from conceptual visions to existing running systems.

The internet of things (IoT) offers a range of benefits for its users, ranging from quicker and more precise perception of our ecosystem to more cost-effective monitoring of manufacturing applications, by taking internet access to the things. Due to the ubiquitous existence of the internet, there's been an increasing pace in the IoT. Such a growing pace has brought about the term of IoT ecosystem. This exponential growing IoT ecosystem will encounter several challenges in its path. Computing domains were used from very initial stage to assist the IoT ecosystem and mitigate those challenges. To understand the impact of computing domains in IoT ecosystem, this paper performs the elaborative study on cloud, fog, roof, and dew computing including their interaction, benefits, and limitations in IoT ecosystem. The brief comparative analysis on these four computing domains are then performed. The impact of internet and offline computing on these computing domains are then analyzed in depth. Finally, this paper presents the suggestions of potential appropriate computing domain strategies for IoT ecosystems.

In the internet of things (IoT) domain, there has currently been a growing interest, leading to the idea of the IoT ecosystem. But the standards, technology, and structures of the conventional IoT framework do not provide the necessary QoS for today's massive data. Thus, for today's IoT ecosystem, a framework called SD-DRFC (software-defined dew, roof, fog, and cloud computing) is suggested in this article. The framework delivers facilities from the closest possible position of end-user gadgets and thus increases the QoS in an IoT system. Clear description about the role and features of each tier is also presented. The path to a multi-tier computational architecture assisted by SDN can be realized from the given detailed literature review. Using the iFogSim simulator, a use case based on the architecture provided is then given and evaluated. This article considers four QoS parameters (latency, network use, cost, and energy consumption). When compared the findings of the simulation, the proposed framework execution performs much better than cloud-only execution.

The internet of things (IoT) creates immense volume of objects online. But cloud computing isn't suited to environmental demands. Hence, fog computing (FC) emerged which shifts the computation load into edge fog devices. However, FC also faces some obstacles which can be mitigated by software-defined networking (SDN). By combining SDN and FC, the network form can overcome almost all cloud limitations and can boost QoS. Within this article, architecture is proposed by combining SDN and FC to improve QoS for IoT ecosystem. With the architecture, an algorithm is propounded based on virtual partition. Then a use case is presented and evaluated through iFogSim simulator. The result shows a significant improvement of several QoS parameters in the execution of fog with SDN compared to the cloud-only execution. The results also show better results for energy consumption, network use (212.21% reduction), and latency (275.9% reduction) compared with previous similar use case.

Stock market price prediction is now a prominent and significant issue in financial and academic studies as the stock market plays a vital role in the economy. The process of attempting to anticipate the future valuation of a company's share is known as stock market price prediction. Share prices are time-series information, and artificial neural networks (ANNs) can uncover non-linear associations among time-series information. This makes ANN the best method for predicting stock market values. Many researchers are working on this topic and trying to find the best algorithm which is suitable for predicting the stock price. But significant improvement in prediction is still not achieved. Therefore, in this work, an ANN model is proposed and implemented by using a multilayer feedforward backpropagation method. In this work, data of fifteen companies over a six years span have been analyzed. To predict the specific result, the proposed model has been trained with four different algorithms: Levenberg Marquardt (LM), Bayesian regularization (BR), scaled conjugate gradient (SCG) and Quasi Newton) by changing their parameters. Number of hidden layers, hidden neurons and percentage of training data have been changed to get better output. The split ratio of training, testing and validation data sets is 70:15:15. The projected results are then compared to the actual data after the training and testing procedure to determine the accuracy. The accuracy of LM is 95.64%, BR is 91.26%, SCG accuracy is 88.91% and Quasi Newton is 84.20%. The result showed that, LM algorithm provides better accuracy than other models. In addition, less error has been found from the LM algorithm, making it the best algorithm for prediction in our proposed model.

Middle East respiratory syndrome coronavirus (MERS-CoV) is a highly infectious zoonotic virus first reported into the human population in September 2012 on the Arabian Peninsula. The virus causes severe and often lethal respiratory illness in humans with an unusually high fatality rate. The N-terminal domain (NTD) of receptor-binding S1 subunit of coronavirus spike (S) proteins can recognize a variety of host protein and mediates entry into human host cells. Blocking the entry by targeting the S1-NTD of the virus can facilitate the development of effective antiviral drug candidates against the pathogen. Therefore, the study has been designed to identify effective antiviral drug candidates against the MERS-CoV by targeting S1-NTD. Initially, a structure-based pharmacophore model (SBPM) to the active site (AS) cavity of the S1-NTD has been generated, followed by pharmacophore-based virtual screening of 11,295 natural compounds. Hits generated through the pharmacophore-based virtual screening have re-ranked by molecular docking and further evaluated through the ADMET properties. The compounds with the best ADME and toxicity properties have been retrieved, and a quantum mechanical (QM) based density-functional theory (DFT) has been performed to optimize the geometry of the selected compounds. Three optimized natural compounds, namely Taiwanhomoflavone B (Amb23604132), 2,3-Dihydrohinokiflavone (Amb23604659), and Sophoricoside (Amb1153724), have exhibited substantial docking energy >−9.00 kcal/mol, where analysis of frontier molecular orbital (FMO) theory found the low chemical reactivity correspondence to the bioactivity of the compounds. Molecular dynamics (MD) simulation confirmed the stability of the selected natural compound to the binding site of the protein. Additionally, molecular mechanics generalized born surface area (MM/GBSA) predicted the good value of binding free energies (ΔG bind) of the compounds to the desired protein. Convincingly, all the results support the potentiality of the selected compounds as natural antiviral candidates against the MERS-CoV S1-NTD.