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Underwater Fish Species Recognition (UFSR) has attained significance because of evolving research in underwater life. Manual techniques to distinguish fish can be tricky and tedious. They might require enormous inspecting endeavours, but they can be costly. It results in limited data and a lack of human resources, which may cause incorrect object identification. Automating the fish species detection and recognition utilizing technology would assist sea life science to evolve further. UFSR in wild natural habitats is difficult because the images open natural habitat, complex background, and low luminance. Species Visualization can assist us with deep knowledge of the movements of the species underwater. Automation systems can help to classify the fish accurately and consistently. Image classification has been emerging research with the advancement of deep learning systems. The reason is that the convolutional neural networks (CNNs) don't require explicit feature extraction methods. The vast majority of the current object detection and recognition mechanisms are based on images in the outdoor environment. This paper mainly reviews the strategies proposed in the past years for underwater fish detection and classification. Further, the paper also presents the classification of three different underwater datasets using CNN with evaluation metrics.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which caused coronavirus diseases (COVID-19) in late 2019 in China created a devastating economical loss and loss of human lives. To date, 11 variants have been identified with minimum to maximum severity of infection and surges in cases. Bacterial co-infection/secondary infection is identified during viral respiratory infection, which is a vital reason for morbidity and mortality. The occurrence of secondary infections is an additional burden to the healthcare system; therefore, the quick diagnosis of both COVID-19 and secondary infections will reduce work pressure on healthcare workers. Therefore, well-established support from Artificial Intelligence (AI) could reduce the stress in healthcare and even help in creating novel products to defend against the coronavirus. AI is one of the rapidly growing fields with numerous applications for the healthcare sector. The present review aims to access the recent literature on the role of AI and how its subfamily machine learning (ML) and deep learning (DL) are used to curb the pandemic’s effects. We discuss the role of AI in COVID-19 infections, the detection of secondary infections, technology-assisted protection from COVID-19, global laws and regulations on AI, and the impact of the pandemic on public life.

Sixth-generation (6G) wireless networking studies have begun with the global implementation of fifth-generation (5G) wireless systems. It is predicted that multiple heterogeneity applications and facilities may be supported by modern wireless communication networks (MWCNs) with improved effectiveness and protection. Nevertheless, a variety of trust-related problems that are commonly disregarded in network architectures prevent us from achieving this objective. In the current world, MWCN transmits a lot of sensitive information. It is essential to protect MWCN users from harmful attacks and offer them a secure transmission to meet their requirements. A malicious node causes a major attack on reliable data during transmission. Blockchain offers a potential answer for confidentiality and safety as an innovative transformative tool that has emerged in the last few years. Blockchain has been extensively investigated in several domains, including mobile networks and the Internet of Things, as a feasible option for system protection. Therefore, a blockchain-based modal, Transaction Verification Denied conflict with spurious node (TVDCSN) methodology, was presented in this study for wireless communication technologies to detect malicious nodes and prevent attacks. In the suggested mode, malicious nodes will be found and removed from the MWCN and intrusion will be prevented before the sensitive information is transferred to the precise recipient. Detection accuracy, attack prevention, security, network overhead, and computation time are the performance metrics used for evaluation. Various performance measures are used to assess the method’s efficacy, and it is compared with more traditional methods.

The unprecedented rise in the number of new coronavirus infections worldwide has prompted many researchers to use mathematical and machine-learning-based prediction models to predict future epidemic patterns that will help governments, health service providers, and society understand how to deal with this situation. Using different machine learning methodologies helps researchers to understand the trend curve clearly. These may lead to a better and more effective fight against the epidemic and reduce or end preventive measures, allowing people to return to their everyday lives. This study is based on an analysis of COVID-19 data of KSA. Also, it demonstrates the prediction of the new confirmed cases and death of COVID-19 in the next ten days from 8th July in KSA, which is considered the period of the performing Hajj in 2021. It uses machine learning models such as Support Vector Machine (SVM), Bayesian Edge (BR), Linear Regression (LR), and Moving Average (MA). Each model provides two types of predictions: the number of newly infected cases and deaths over the next 10 days. The results indicate that SVM and MA forecasts have high accuracy, followed by LR which performs well. The BR performs poorly in forecast scenarios when applied with the available data set in forecasting new confirmed cases. All models were accurate in predicting mortality, with the best performing model being SVM, followed by MA, LR, and BR. It also expects an increase in confirmed cases under the SVM model scenario to 511,257 on 17th July from 496,516 on 7th July in the actual daily cumulative cases. The number of deaths will rise to 8,113 on 17th July from 7,921 on 7th July in actual cumulative daily data.

In recent decades, the dependence on digital tools to assist our daily lives has been growing in almost all sectors and fields. A huge amount of data has been generated and as a result, a new era has emerged: the era of big data. To benefit from this data, several big data analysis tools have emerged, Hadoop being one of them. In this paper, we analyzed Hadoop cluster performance and investigated the effect of the cluster and dataset sizes on its performance. Furthermore, the MapReduce model was used to study and justify its benefits in the big data analysis field. The results showed that the Hadoop cluster and the MapReduce model are powerful in processing and analyzing big data. More benefits can be achieved from the analyzing time perspective when larger data sizes are used. Interestingly, we found that increasing the cluster size beyond a certain point resulted in communication issues, thereby yielding an increase in the CPU time.

Given the recent technological boom, companies have begun adopting cloud computing to host their IT services. Cloud computing is scalable, secure, and economical. Many software and larger enterprises who employ IT experts or outsource IT contracts have already migrated, or in the process of migrating, to the cloud. However, Small to Medium Enterprises (SMEs) are falling behind due to concerns regarding migration complexities, data privacy, cost, and more. As SMEs play an imperative role in any country’s economy, it is very important to address these issues and allow them to harness the benefits of the cloud computing. The motivation for writing this paper is to provide an effective approach to cloud migration for SMEs by proposing a comprehensive and detailed framework. The goal is to help SMEs understand the basic terminology of cloud computing and how they can map out a migration to on-premises cloud services.

In recent years, the use of cloud computing has increased significantly, where remote servers perform computing tasks including analytics, storage, processing, networking, database management, and other business functions over the internet. This ubiquitous computing provides business advantages in terms of productivity, scalability, performance, along with reliability and demographic availability. Businesses including modern day commerce industries are moving to cloud based platforms to reduce cost and increase performance. Many organizations are now migrating towards cloud due to its favorable features. Therefore, it is important for the organizations to have a migration strategy that can ensure an effective migration. In this regards the cloud migration tools can play a significant role. However, there are many research has been done as it is still a comparatively newer topic. In fact, there are only a limited number of research articles available on cloud migration tools. In this paper, we focus on different cloud migration tools and draw a comparison between them. We also analyze the cloud migration and associated aspects of the migration process.

In recent years, small and medium organizations have migrated to the cloud. Cloud computing provides information technology (IT) services, which can be found with Internet access from any place at any time. One can get services through application software like web browsers and mobile applications. Virtualization indicates the main concept to manage an IT infrastructure and data centers in the cloud. The process of moving between different physical machines is known as migration. Proper migration technology can allow for effective resource management. This paper surveys current migration methods of cloud computing. Findings and contributions of the surveyed papers are also analyzed. An important finding of this study is that the current literature lacks an in-depth discussion the migration process. There also is an absence of useful experience applying these approaches in practice. This paper helps to identify some limitations of current cloud computing migration methods.

The use of cloud computing has increased significantly in recent years; therefore, many cloud platforms have become in demand such as OpenStack, AWS etc. One of the challenges in cloud computing is providing secure and reliable services. For the last few years, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks are the biggest threats in cloud security. DoS and DDoS attacks are initiated by the hacker to make an online service occupied by compromising it with massive traffic from multiple sources. The aim of this paper is to study the impacts of several types DoS/DDoS attacks in the cloud, and the procedures that they use to affect the services' availability. Furthermore, several mitigation techniques are discussed as well. To achieve this study, research and simulation work has been done.

Medium access control (MAC) plays a crucial role in energy consumption, bandwidth utilization etc. for long term WSN deployment. An improved MAC protocol can ensure efficiency of energy for low sensing ranges. There are several MAC-Protocols with different objectives. The aim of this paper is to study on some energy efficient MAC-Protocols in Wireless Sensor Network. This paper also takes a closer look at the performance of the MAC-Protocols in terms of energy efficiency in different situation. Lastly this paper draws a conclusion comparing the protocols. In this paper the energy efficiency is given higher priority than latency, throughput etc for comparison.