The classification of the modern arabic poetry using machine learning

In recent years, working on text classification and analysis of Arabic texts using machine learning has seen some progress, but most of this research has not focused on Arabic poetry. Because of some difficulties in the analysis of Arabic poetry, it was required the use of standard Arabic language on which “Al Arud”, the science of studying poetry is based. This paper presents an approach that uses machine learning for the classification of modern Arabic poetry into four types: love poems, Islamic poems, social poems, and political poems. Each of these species usually has features that indicate the class of the poem. Despite the challenges generated by the difficulty of the rules of the Arabic language on which this classification depends, we proposed a new automatic way of modern Arabic poems classification to solve these issues. The recommended method is suitable for the above-mentioned classes of poems. This study used Naïve Bayes, Support Vector Machines, and Linear Support Vector for the classification processes. Data preprocessing was an important step of the approach in this paper, as it increased the accuracy of the classification

A Classification of Al-hur Arabic Poetry and Classical Arabic Poetry by Using Support Vector Machine, Naïve Bayes, and Linear Support Vector Classification

Most of the world languages have made strides in analyzing and classifying texts electronically; hence, the use of electronic text has become a great alternative to manual classification as it reduces time, cost, and difficulty. However, in the Arabic language, electronic analysis has not progressed due to several limitations faced by researchers in this field, such as the complexity of the Arabic language, the lack of related research, as well as the use of the classical Arabic language. In addition, Arabic poetry has other limitations, such as the use of a system that uses a single activation function. In this research, a new method was developed for the classification of the classical Arabic poetry and Al-hur poetry. This new approach is based on features that indicate the type of poetry. Pre-processing of some data is important in this new approach as it helps increase the accuracy of classificatio

Fuzzy Generalized Hebbian Algorithm for Large-Scale Intrusion Detection System

The huge number of irrelevant and redundant data used in building intrusion detection systems (IDS) is one of the common issues in network intrusion detection systems. This paper proposed the use of Fuzzy Generalized Hebbian Algorithm as a novel data reduction method to overcome this problem of data redundancy in IDS. Two methods for dimensionality reduction (GHA and Fuzzy GHA) were used and compared in this study. This allowed retaining the most relevant traffic data information from the network. Furthermore, the K Nearest Neighbor algorithm was applied for the classification of the test connections into 2 categories (attack or normal). The investigations were carried out on the KDDCUP ‘99 dataset and the results showed the Fuzzy GHA method to perform better than GHA in the detection of both U2R and DoS attacks

Correlation with the fundamental PSO and PSO modifications to be hybrid swarm optimization

A swarm is a group of a single species in which the members interact with one another and with the immediate environment without a principle for control or the emergence of a global intriguing behavior. Swarm-based metaheuristics, including nature-inspired populace-based methods, have been developed to aid the creation of quick, robust, and low-cost solutions for complex problems. Swarm intelligence was proposed as a computational modeling of swarms and has been successfully applied to numerous optimization tasks since its introduction. A correlation with the fundamental Particle Swarm Optimization (PSO) and PSO modifications demonstrates that hybrid swarm optimization outperforms existing strategies. The downside of hybrid swarm optimization is that it frequently tends to arrive at suboptimal solutions. As such, efforts are being made into combining HSO and other algorithms to arrive at better quality solutions

A Focal load balancer based algorithm for task assignment in cloud environment

A new trend rising in IT environs is the Mobile cloud computing with colossal prerequisites of infrastructure along with resources. In cloud computing environment, load balancing a vital aspect. Cloud load balancing way toward disseminating workloads across numerous computing resources. Proficient load balancing plan guarantees effective resource usage by the supply of resources to cloud user's on-demand premise and it might even help organizing clients by applying fitting planning criteria the current paper discusses and implements the concept of load balancers, which are the lifeblood of any cloud computing network. In this paper, a new load balancing system is presented Focal Load Balancer (F-LB), which has been developed to reduce the traffic in the Cloud, whilst assuring a smooth flow of data in the cloud network. The proposed algorithm takes advantage of the dynamic load balancing characteristics over static balancing, and avoids the damage that a static load balancer causes if it fails. Simulation results show that the proposed algorithm operates efficiently and effectively, and it provides a significantly improved performance over existing algorithms. Comparisons with the krill-LB and agent-based algorithms show that the new system provides a reduction in average wait time, a significant increase in throughput, and a dramatic reduction in CPU time consumption