Purpose – Many organizations are outsourcing their information technology (IT) related services to a third party vendor for quite some time. However, the IT services industry including outsourcing is going through rapid changes with the increasing adoption of Cloud computing. The purpose of this paper is to compare global IT outsourcing with Cloud computing along with the evolution of traditional IT services.Design/methodology/approach – Cloud computing is a model for provisioning and consuming IT capabilities on a need and pay by use basis. This helps in shifting the cost structure from capital expenditure to operating expenditure and also helps the IT systems to be more agile. This innovative model of acquiring IT related services has made organizations revisit their infrastructure and platform services strategy and optimize their IT spending while improving overall agility. This paper compares global IT outsourcing with Cloud computing along with the evolution of traditional IT services.Findings – The ...

From Outsourcing To Cloud Computing: Evolution of IT Services

Encyclopedia of data warehousing and mining

The process of dimensionality reduction is a crucial solution to deal with the dimensionality problem that may be faced when dealing with the majority of machine learning techniques. This paper proposes an enhanced hybrid metaheuristic approach using grey wolf optimizer (GWO) and whale optimization algorithm (WOA) to develop a wrapper-based feature selection method. The main objective of the proposed technique is to alleviate the drawbacks of both algorithms, including immature convergence and stagnation to local optima (LO). The hybridization is done with improvements in the mechanisms of both algorithms. To confirm the stability of the proposed approach, 18 well-known datasets are employed from the UCI repository. Furthermore, the classification accuracy, number of selected features, fitness values, and run time matrices are collected and compared with a set of well-known feature selection approaches in the literature. The results show the superiority of the proposed approach compared with both GWO and WOA. The results also show that the proposed hybrid technique outperforms other state-of-the-art approaches, significantly.

Efficient Hybrid Nature-Inspired Binary Optimizers for Feature Selection

Salp Swarm Algorithm (SSA) is a recent metaheuristic inspired by the swarming behavior of salps in oceans. SSA has demonstrated its efficiency in various applications since its proposal. In this chapter, the algorithm, its operators, and some of the remarkable works that utilized this algorithm are presented. Moreover, the application of SSA in optimizing the Extreme Learning Machine (ELM) is investigated to improve its accuracy and overcome the shortcomings of its conventional training method. For verification, the algorithm is tested on 10 benchmark datasets and compared to two other well-known training methods. Comparison results show that SSA based training methods outperforms other methods in terms of accuracy and is very competitive in terms of prediction stability.

Salp swarm algorithm: Theory, literature review, and application in extreme learning machines

Demand side management through load shifting in IoT based HEMS: Overview, challenges and opportunities

In the era of stringent and dynamic business environment, it is crucial for organizations to foresee their clients’ delinquency behavior. Such environment and behavior create unreliable base for strategic planning and risk management. Business Analytics combines the business expertise and computer intelligence to assist the decision makers by predicting an individual's credit status. This empirical research aims to evaluate the performance of different Machine Learning algorithms for credit risk prediction with more focus on Random Forest Trees. Several experiments inspired by observation and literature illustrate the potentials of computer-based model in classifying a number of bank history records. However, enhanced classification outcomes require tuning the randomness and tree growing parameters of the Random Forests algorithm. The model based on Random Forest Trees overperformed most of the other models. Moreover, such a model has various advantages to business experts as the ability to help in understanding the relations between the analyzed attributes.

/pdf/business-analytics-using-random-forest-trees-for-credit-risk-1gc1vltow3.pdf

Business Analytics using Random Forest Trees for Credit Risk Prediction: A Comparison Study

Software development effort estimation is considered a fundamental task for software development life cycle as well as for managing project cost, time and quality. Therefore, accurate estimation is a substantial factor in projects success and reducing the risks. In recent years, software effort estimation has received a considerable amount of attention from researchers and became a challenge for software industry. In the last two decades, many researchers and practitioners proposed statistical and machine learning-based models for software effort estimation. In this work, Firefly Algorithm is proposed as a metaheuristic optimization method for optimizing the parameters of three COCOMO-based models. These models include the basic COCOMO model and other two models proposed in the literature as extensions of the basic COCOMO model. The developed estimation models are evaluated using different evaluation metrics. Experimental results show high accuracy and significant error minimization of Firefly Algorithm over other metaheuristic optimization algorithms including Genetic Algorithms and Particle Swarm Optimization.

/pdf/optimizing-software-effort-estimation-models-using-firefly-3u5bfmy8nu.pdf

Optimizing Software Effort Estimation Models Using Firefly Algorithm

Cloud computing is a set of Information
Technology services offered to users over the web on a rented base. Such
services enable the organizations to scale-up or scale-down their in-house
foundations. Generally, cloud services are provided by a third-party supplier
who possesses the arrangement. Cloud computing has many advantages such as
flexibility, efficiency, scalability, integration, and capital reduction. Moreover,
it provides an advanced virtual space for organizations to deploy their
applications or run their operations. With disregard to the possible benefits
of cloud computing services, the organizations are reluctant to invest in cloud
computing mainly due to security concerns. Security is one of the main
challenges that hinder the growth of cloud computing. At the same time, service
providers strive to reduce the risks over the clouds and increase their
reliability in order to build mutual trust between them and the cloud customers.
Various security issues and challenges are discussed in this research, and
possible opportunities are stated.

/pdf/data-security-issues-and-challenges-in-cloud-computing-a-21k6jhwkwz.pdf

Data Security Issues and Challenges in Cloud Computing: A Conceptual Analysis and Review

The banking industry has been seeking novel ways to leverage database marketing efficiency. However, the nature of bank marketing data hindered the researchers in the process of finding a reliable analytical scheme. Various studies have attempted to improve the performance of Artificial Neural Networks in predicting clients’ intentions but did not resolve the issue of imbalanced data. This research aims at improving the performance of predicting the willingness of bank clients to apply for a term deposit in highly imbalanced datasets. It proposes enhanced Artificial Neural Network models (i.e., cost-sensitive) to mitigate the dramatic effects of highly imbalanced data, without distorting the original data samples. The generated models are evaluated, validated, and consequently compared to different machine-learning models. A real-world telemarketing dataset from a Portuguese bank is used in all the experiments. The best prediction model achieved 79% of geometric mean, and misclassification errors were minimized to 0.192, 0.229 of Type I & Type II Errors, respectively. In summary, an interesting Meta-Cost method improved the performance of the prediction model without imposing significant processing overhead or altering original data samples.

https://www.mdpi.com/2076-3417/10/7/2581/pdf

Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks

Electronic Learning has been one of the foremost trends in education so far. Such importance draws the attention to an important shift in the educational paradigm. Due to the complexity of the evolving paradigm, the prospective dynamics of learning require an evolution of knowledge delivery and evaluation. This research work tries to put in hand a futuristic design of an autonomous and intelligent e-Learning system. In which machine learning and user activity analysis play the role of an automatic evaluator for the knowledge level. It is important to assess the knowledge level in order to adapt content presentation and to have more realistic evaluation of online learners. Several classification algorithms are applied to predict the knowledge level of the learners and the corresponding results are reported. Furthermore, this research proposes a modern design of a dynamic learning environment that goes along the most recent trends in e-Learning. The experimental results illustrate an overall performance superiority of a support vector machine model in evaluating the knowledge levels; having 98.6%of correctly classified instances with 0.0069 mean absolute error.

/pdf/knowledge-level-assessment-in-e-learning-systems-using-40yiayca75.pdf

Nazeeh Ghatasheh

Papers

Business Analytics using Random Forest Trees for Credit Risk Prediction: A Comparison Study

Optimizing Software Effort Estimation Models Using Firefly Algorithm

Data Security Issues and Challenges in Cloud Computing: A Conceptual Analysis and Review

Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks

Knowledge Level Assessment in e-Learning Systems Using Machine Learning and User Activity Analysis