Henry gas solubility optimization: A novel physics-based algorithm

Whale Optimization Algorithm (WOA) is an optimization algorithm developed by Mirjalili and Lewis in 2016. An overview of WOA is described in this paper, rooted from the bubble-net hunting strategy, besides an overview of WOA applications that are used to solve optimization problems in various categories. The best solution has been determined to make something as functional and effective as possible through the optimization process by minimizing or maximizing the parameters involved in the problems. Research and engineering attention have been paid to Meta-heuristics for purposes of decision-making given the growing complexity of models and the needs for quick decision making in the engineering. An updated review of research of WOA is provided in this paper for hybridization, improved, and variants. The categories included in the reviews are Engineering, Clustering, Classification, Robot Path, Image Processing, Networks, Task Scheduling, and other engineering applications. According to the reviewed literature, WOA is mostly used in the engineering area to solve optimization problems. Providing an overview and summarizing the review of WOA applications are the aims of this paper.

A comprehensive survey: Whale Optimization Algorithm and its applications

requirements engineering a good practice guide is available in our digital library an online access to it is set as public so you can get it instantly. Our books collection saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the requirements engineering a good practice guide is universally compatible with any devices to read.

Requirements Engineering A Good Practice Guide

서브블록 히스토그램 등화기법을 이용한 개선된 콘트래스트 강화 알고리즘 ( An Advanced Contrast Enhancement Using Partially Overlapped Sub-Block Histogram Equalization )

This article addresses the online exact string matching problem which consists in finding all occurrences of a given pattern p in a text t. It is an extensively studied problem in computer science, mainly due to its direct applications to such diverse areas as text, image and signal processing, speech analysis and recognition, information retrieval, data compression, computational biology and chemistry.In the last decade more than 50 new algorithms have been proposed for the problem, which add up to a wide set of (almost 40) algorithms presented before 2000. In this article we review the string matching algorithms presented in the last decade and present experimental results in order to bring order among the dozens of articles published in this area.

The exact online string matching problem: A review of the most recent results

Cloud computing is a trending technology that allows users to use computing resources remotely in a pay-per-use model. One of the main challenges in cloud computing environments is task scheduling, in which tasks should be scheduled efficiently to minimize execution time and cost while maximizing resources’ utilization. Many meta-heuristic algorithms are used for task scheduling in cloud environments in the literature such as Multi-Verse Optimizer (MVO) and Particle Swarm Optimization (PSO). In this paper, an Enhanced version of the Multi-Verse Optimizer (EMVO) is proposed as a superior task scheduler in this area. The proposed EMVO is compared with both original MVO and the PSO algorithms in cloud environments. The results show that EMVO substantially outperforms both MVO and PSO algorithms in terms of achieving minimized makespan time and increasing resources’ utilization.

Enhanced multi-verse optimizer for task scheduling in cloud computing environments

With the advent of online data, sentiment analysis has received growing attention in recent years. Sentiment analysis aims to determine the overall sentiment orientation of a speaker or writer towards a specific entity or towards a specific feature of a specific entity. A fundamental task of sentiment analysis is sentiment classification, which aims to automatically classify opinionated text as being positive, negative, or neutral. Although the literature on sentiment classification is quite extensive, only a few endeavors to classify opinionated text written in the Arabic language can be found. This paper provides a comprehensive survey of existing lexicon, machine learning, and hybrid sentiment classification techniques for Arabic language.

Sentiment classification techniques for Arabic language: A survey

The term “customer churn” is used in the industry of information and communication technology (ICT) to indicate those customers who are about to leave for a new competitor, or end their subscription. Predicting this behavior is very important for real life market and competition, and it is essential to manage it. In this paper, three hybrid models are investigated to develop an accurate and efficient churn prediction model. The three models are based on two phases; the clustering phase and the prediction phase. In the first phase, customer data is filtered. The second phase predicts the customer behavior. The first model investigates the k-means algorithm for data filtering, and Multilayer Perceptron Artificial Neural Networks (MLP-ANN) for prediction. The second model uses hierarchical clustering with MLP-ANN. The third one uses self organizing maps (SOM) with MLP-ANN. The three models are developed based on real data then the accuracy and churn rate values are calculated and compared. The comparison with the other models shows that the three hybrid models outperformed single common models.

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Hybrid Data Mining Models for Predicting Customer Churn

Software testing is the most important task in software production and it takes a lot of time, cost and effort. Thus, we need to reduce these resources. Software Defect Prediction (SDP) mechanisms are used to enhance the work of SQA process through the prediction of defective modules, many approaches have been conducted by researchers in order to predict the fault-proneness modules. This paper proposed an approach for the SDP purpose, it employs two existed algorithms to have a high performance, that are the Bat-based search Algorithm (BA) for the feature selection process, and the Random Forest algorithm (RF) for the prediction purpose. This paper also has tested a number of feature selection algorithms and classifiers to see their effectiveness in this problem.

Amjad Hudaib

Papers

Enhanced multi-verse optimizer for task scheduling in cloud computing environments

Sentiment classification techniques for Arabic language: A survey

Hybrid Data Mining Models for Predicting Customer Churn

Software Defect Prediction using Feature Selection and Random Forest Algorithm

Feature selection methods on gene expression microarray data for cancer classification: A systematic review.