
A Hybrid Feature Selection Optimization Model for High Dimension Data Classification
Feature selection is an NP-hard combinatorial problem, in which the number of possible feature subsets increases exponentially with the number of features. In the case of large dimensionality, the goal of feature selection is to determine the smallest possible features considering the most informative subset. In this paper, we proposed a hybrid feature selection optimization model for Cancer Classification called, ENSVM. Our model is based on using the Elastic Net (EN) method that regulates and selects variables for gene selection of genomic microarray data. We applied three different
Software-Defined Networks Towards Big Data: A Survey
Both Big Data and Software-Defined Network have a significant impact in both academic and practical aspects. These two areas have been addressed separately, but both did not contribute to the same subset area of contribution. However, Big Data can greatly facilitate, improve, and have a great impact on Software Defined Network, and vice versa. In this paper, we show how SDN helps Big Data solve several issues regarding Big Data applications, including data processing in the data centers, data delivery and traffic monitoring. For Big Data, we also show how it can help SDN as well, including

Using CNN-XGBoost Deep Networks for COVID-19 Detection in Chest X-ray Images
At the time of writing, the COVID-19 pandemic is one of the lead causes of death worldwide and has caused significant changes to everyone's lives. While a vaccine is still unavailable, early screenings and detection of the disease can significantly help in managing the healthcare system's capacity as well as allow radiologists and clinicians better assign their priorities. With deep learning's rapid advancements over the last few years, its application in solving this issue is only natural. This paper aims to outline the works of a few major developments in the field of using deep learning to

Using Blockchain Technology for the Internet of Vehicles
The Internet of Vehicles (IoV) aims to connect vehicles with their surroundings and share data. In IoV, various wireless technologies like 5G, WIFI, DSRC, WiMAX, and ZigBee are used. To share data within wireless surroundings in a secure way, some security aspects need to be fulfilled. Blockchain technology is a good fit to cover these countermeasures. IoV uses a lot of technologies and interacts with different types of wireless nodes, and this increases the vulnerability to some attacks that could endanger lives. Using blockchain technology within the IoV architecture could provide efficient

A Multitier Deep Learning Model for Arrhythmia Detection
An electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVDs). ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient-doctor dialogs. Notwithstanding its proven utility, deciphering large data sets to determine appropriate information remains a challenge in ECG-based CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification
Efficient quantum-based security protocols for information sharing and data protection in 5G networks
Fifth generation (5G)networks aim at utilizing many promising communication technologies, such as Cloud Computing, Network Slicing, and Software Defined Networking. Supporting a massive number of connected devices with 5G advanced technologies and innovating new techniques will surely bring tremendous challenges for trust, security and privacy. Therefore, secure mechanisms and protocols are required as the basis for 5G networks to address this security challenges and follow security-by-design but also security-by-operations rules. In this context, new efficient cryptographic protocols and

Performance evaluation of transform domain diagonal principal component analysis for facial recognition employing different pre-processing spatial domain approaches
Facial recognition using spatial domain Diagonal Principal Component Analysis (DiaPCA) algorithm produces better accuracy compared to the Two Dimensional PCA (2DPCA). Transform Domain - 2DPCA (TD2DPCA) retains the high recognition accuracy of the 2DPCA while considerably reducing storage requirements and computational complexity. In this work, the Transform Domain PCA implementation of the DiaPCA (TDDiaPCA) is presented. All the test results, for noise free and noisy images, consistently confirm the considerable storage and computational savings for different spatial domain pre-processing

A Novel Hadoop Security Model for Addressing Malicious Collusive Workers
With the daily increase of data production and collection, Hadoop is a platform for processing big data on a distributed system. A master node globally manages running jobs, whereas worker nodes process partitions of the data locally. Hadoop uses MapReduce as an effective computing model. However, Hadoop experiences a high level of security vulnerability over hybrid and public clouds. Specially, several workers can fake results without actually processing their portions of the data. Several redundancy-based approaches have been proposed to counteract this risk. A replication mechanism is used

Advanced methods for missing values imputation based on similarity learning
The real-world data analysis and processing using data mining techniques often are facing observations that contain missing values. The main challenge of mining datasets is the existence of missing values. The missing values in a dataset should be imputed using the imputation method to improve the data mining methods’accuracy and performance. There are existing techniques that use k-nearest neighbors algorithm for imputing the missing values but determining the appropriate k value can be a challenging task. There are other existing imputation techniques that are based on hard clustering

A Deep Learning Approach for Vehicle Detection
The autonomous driving needs some several features to achieve driving without human interference. One of these features is vehicle classification and detection since the target of this process is to help the CPU ''Central Processing Unit" of the vehicle to see what is around the vehicle, in order to evaluate the situation to take the best decision for each situation in real time. This paper is focusing on the classification process of the video-based vehicle detection, to achieve that, different deep learning techniques have been implemented which are known as convolutional neural networks
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