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In the realm of green communications, the focus is on achieving high spectrum efficiency and low energy consumption. This paper addresses the crucial goal of reducing energy usage in green cognitive radio networks (CRNs) during communication between secondary users (SUs) and primary users (PUs). This paper proposed an energy consumption optimization model (ECOM) for green CRN utilizing collaborative spectrum sensing thereby minimizing the environmental impact and prolonging the operational lifetime of devices. The collaborative spectrum sensing proved its role in optimizing the energy consumption in the green CRN. An energy-efficient scheduling algorithm is implemented in ECOM, in which the SUs can be scheduled to perform their sensing process in a time-division manner to reduce energy consumption. Applied collaborative spectrum sensing serves as a valuable resource for researchers, network operators, and policymakers seeking to balance the increasing demand for wireless communication services with the imperative of sustainability. The simulation results and mathematical proof emphasize that ECOM demonstrates reduced energy usage and increased average effective throughput when compared to other recent models. [ABSTRACT FROM AUTHOR]

Nature-inspired optimization techniques have been applied in various fields of study to solve optimization problems. Since designing a Fuzzy System (FS) can be considered one of the most complex optimization problems, many meta-heuristic optimizations have been developed to design FS structures. This paper aims to design a Takagi–Sugeno–Kang fuzzy Systems (TSK-FS) structure by generating the required fuzzy rules and selecting the most influential parameters for these rules. In this context, a new hybrid nature-inspired algorithm is proposed, namely Genetic–Grey Wolf Optimization (GGWO) algorithm, to optimize TSK-FSs. In GGWO, a hybridization of the genetic algorithm (GA) and the grey wolf optimizer (GWO) is applied to overcome the premature convergence and poor solution exploitation of the standard GWO. Using genetic crossover and mutation operators accelerates the exploration process and efficiently reaches the best solution (rule generation) within a reasonable time. The proposed GGWO is tested on several benchmark functions compared with other nature-inspired optimization algorithms. The result of simulations applied to the fuzzy control of nonlinear plants shows the superiority of GGWO in designing TSK-FSs with high accuracy compared with different optimization algorithms in terms of Root Mean Squared Error (RMSE) and computational time. [ABSTRACT FROM AUTHOR]

The mortality risk factors for coronavirus disease (COVID‐19) must be early predicted, especially for severe cases, to provide intensive care before they develop to critically ill immediately. This paper aims to develop an optimized convolution neural network (CNN) for predicting mortality risk factors for COVID‐19 patients. The proposed model supports two types of input data clinical variables and the computed tomography (CT) scans. The features are extracted from the optimized CNN phase and then applied to the classification phase. The CNN model's hyperparameters were optimized using a proposed genetic‐based adaptive momentum estimation (GB‐ADAM) algorithm. The GB‐ADAM algorithm employs the genetic algorithm (GA) to optimize Adam optimizer's configuration parameters, consequently improving the classification accuracy. The model is validated using three recent cohorts from New York, Mexico, and Wuhan, consisting of 3055, 7497,504 patients, respectively. The results indicated that the most significant mortality risk factors are: CD 8+ T Lymphocyte (Count), D‐dimer greater than 1 Ug/ml, high values of lactate dehydrogenase (LDH), C‐reactive protein (CRP), hypertension, and diabetes. Early identification of these factors would help the clinicians in providing immediate care. The results also show that the most frequent COVID‐19 signs in CT scans included ground‐glass opacity (GGO), followed by crazy‐paving pattern, consolidations, and the number of lobes. Moreover, the experimental results show encouraging performance for the proposed model compared with different predicting models. [ABSTRACT FROM AUTHOR]

With the increasing daily production of data in recent years, indexing, storing and retrieving huge amounts of data have become a common problem, especially for multi-dimensional big data. Although R-tree has proved to be efficient for indexing multi-dimensional big data, the R-tree suffers from the curse of dimensionality problem. Many researchers continue to use the R-tree in their studies as it is the most famous tree-like structure for indexing multi-dimensional data. However, with increasing numbers of dimensions in multi-dimensional data the performance of R-Tree will decrease. This paper proposes a new indexing structure called Parallel Indexing System Structure based on Spark (ParISSS), which is an efficient system for indexing multi-dimensional big data, to overcome these problems. ParISSS introduces six types of computing nodes, the reception-node is used to insert and index data, the normal-node is used to store indexed data, the resolution-node is used to distribute a reception-index to a normal-node, the representative-node is used to receive queries from the user, and the reply-node and check-node are used to send the results to the user. We also introduced BR*-tree structure to improve the storing and searching processes. We present an extensive experimental evaluation of our system, comparing several indexing systems. The experimental results show that ParISSS outperforms other indexing systems. [ABSTRACT FROM AUTHOR]

The growing popularity of big data analysis and cloud computing has created new big data management standards. Sometimes, programmers may interact with a number of heterogeneous data stores depending on the information they are responsible for: SQL and NoSQL data stores. Interacting with heterogeneous data models via numerous APIs and query languages imposes challenging tasks on multi-data processing developers. Indeed, complex queries concerning homogenous data structures cannot currently be performed in a declarative manner when found in single data storage applications and therefore require additional development efforts. Many models were presented in order to address complex queries Via multistore applications. Some of these models implemented a complex unified and fast model, while others' efficiency is not good enough to solve this type of complex database queries. This paper provides an automated, fast and easy unified architecture to solve simple and complex SQL and NoSQL queries over heterogeneous data stores (CQNS). This proposed framework can be used in cloud environments or for any big data application to automatically help developers to manage basic and complicated database queries. CQNS consists of three layers: matching selector layer, processing layer, and query execution layer. The matching selector layer is the heart of this architecture in which five of the user queries are examined if they are matched with another five queries stored in a single engine stored in the architecture library. This is achieved through a proposed algorithm that directs the query to the right SQL or NoSQL database engine. Furthermore, CQNS deal with many NoSQL Databases like MongoDB, Cassandra, Riak, CouchDB, and NOE4J databases. This paper presents a spark framework that can handle both SQL and NoSQL Databases. Four scenarios' benchmarks datasets are used to evaluate the proposed CQNS for querying different NoSQL Databases in terms of optimization process performance and query execution time. The results show that, the CQNS achieves best latency and throughput in less time among the compared systems. [ABSTRACT FROM AUTHOR]

Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic's additional spread and early provide the appropriate treatment to the affected patients. This study aimed to develop a COVID-19 diagnosis and prediction (AIMDP) model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography (CT) scans. The proposed system uses convolutional neural networks (CNNs) as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its containment. We employed the whale optimization algorithm (WOA) to select the most relevant patient signs. A set of experiments validated AIMDP performance. It demonstrated the superiority of AIMDP in terms of the area under the curve-receiver operating characteristic (AUC-ROC) curve, positive predictive value (PPV), negative predictive rate (NPR) and negative predictive value (NPV). AIMDP was applied to a dataset of hundreds of real data and CT images, and it was found to achieve 96% AUC for diagnosing COVID-19 and 98% for overall accuracy. The results showed the promising performance of AIMDP for diagnosing COVID-19 when compared to other recent diagnosing and predicting models. [ABSTRACT FROM AUTHOR]

With the rapidly growing number of wireless users and systems more frequency bands are needed, which led to the spectrum scarcity problems. Cognitive radio (CR) is an emerging technology that enables the dynamic spectrum access by optimizing the usage of the available spectrum through giving the unlicensed Secondary Users (SU) the opportunity to exploit the spectrum without causing any interference with the Primary Users' (PU) usage. Security threats is one of the substantial problems issues that must be addressed in CR networks. Primary User Emulation (PUE) attacks are one of the main threats in deploying CRN, where the Malicious Users (MU) mimic the PU signal to confuse the other SUs and corrupt the spectrum sensing process. In this paper, a hybrid Genetic Whale Optimization Algorithm (GWOA) is proposed to optimize the spectrum utilization and to combat the PUE attacks. GWOA integrates the crossover and mutation operations with WOA algorithm to reach the balance between exploitation and exploration phases, to find the optimal solution based on a fitness function. The simulations results show promising performance of GWOA, when compared with recent detection algorithms. [ABSTRACT FROM AUTHOR]

Classification of imbalanced big data has assembled an extensive consideration by many researchers during the last decade. Standard classification methods poorly diagnosis the minority class samples. Several approaches have been introduced for solving the problem of class imbalance in big data to enhance the generalization in classification. However, most of these approaches neglect the effect of border samples on classification performance; the high impact border samples might expose to misclassification. In this paper, a Spark Based Mining Framework (SBMF) is proposed to address the imbalanced data problem. Two main modules are designed for this purpose. The first is the Border Handling Module (BHM) which under samples the low impact majority border instances and oversamples the minority class instances. The second module is the Selective Border Instances sampling (SBI) Module, which enhances the output of the BHM module. The performance of the SBMF framework is evaluated and compared with other recent systems. A number of experiments were performed using moderate and big datasets with different imbalanced ratio. The results obtained from SBMF framework, when compared to the recent works, show better performance for the different datasets and classifiers. [ABSTRACT FROM AUTHOR]

Due to the gradual expansion in data volume used in social networks and cloud computing, the term 'Big data' has appeared with its challenges to store the immense datasets. Many tools and algorithms appeared to handle the challenges of storing big data. NoSQL databases, such as Cassandra and MongoDB, are designed with a novel data management system that can handle and process huge volumes of data. Partitioning data in NoSQL databases is considered one of the critical challenges in database design. In this paper, a MapReduce Rendezvous Hashing-Based Virtual Hierarchies (MR-RHVH) framework is proposed for scalable partitioning of Cassandra NoSQL database. The MapReduce framework is used to implement MR-RHVH on Cassandra to enhance its performance in highly distributed environments. MR-RHVH distributes the nodes to rendezvous regions based on a proposed Adopted Virtual Hierarchies strategy. Each region is responsible for a set of nodes. In addition, a proposed bloom filter evaluator is used to ensure the accurate allocation of keys to nodes in each region. Moreover, a number of experiments were performed to evaluate the performance of MR-RHVH framework, using YCSB for database benchmarking. The results show high scalability rate and less time consuming for MR-RHVH framework over different recent systems. [ABSTRACT FROM AUTHOR]