Markov models are one of the widely used techniques in machine learning to process natural language. Markov Chains and Hidden Markov Models are stochastic techniques employed for modeling systems that are dynamic and where the future state relies on the current state.  The Markov chain, which generates a sequence of words to create a complete sentence, is frequently used in generating natural language. The hidden Markov model is employed in named-entity recognition and the tagging of parts of speech, which tries to predict hidden tags based on observed words. This paper reviews Markov models' use in three applications of natural language processing (NLP): natural language generation, named-entity recognition, and parts of speech tagging. Nowadays, researchers try to reduce dependence on lexicon or annotation tasks in NLP. In this paper, we have focused on Markov Models as a stochastic approach to process NLP. A literature review was conducted to summarize research attempts with focusing on methods/techniques that used Markov Models to process NLP, their advantages, and disadvantages. Most NLP research studies apply supervised models with the improvement of using Markov models to decrease the dependency on annotation tasks. Some others employed unsupervised solutions for reducing dependence on a lexicon or labeled datasets.

Quantitative methods help farmers plan and make decisions. An apt example of these methods is the linear programming (LP) model. These methods acknowledge the importance of economizing on available resources among them being water supply, labor, and fertilizers. It is through this economizing that farmers maximize their profit. The significance of linear programming is to provide a solution to the existing real-world problems through the evaluation of existing resources and the provision of relevant solutions. This research studies various LP applications including feed mix, crop pattern and rotation plan, irrigation water, and product transformation; that have the main role to enhance various facets of the agriculture sector. The paper will be a review that will probe into the applications of the LP model and it will also highlight the various tools that are central to analyzing LP model results. The review will culminate in a discussion on the different approaches that help optimize agricultural solutions.

In recent years, the healthcare sector has shown inclination towards restructuring of healthcare systems to harmonize with technological innovations and adopting decision support system in routine clinical practices. The objective of this paper is to summarize challenges of Clinical Decision Support System (CDSS) and focus on the effectiveness of CDSS to improve clinical practice. This paper also describes the experience of CDSS in healthcare sector in Saudi Arabia and addresses the requirements for implementing successful CDSS with a real example. This study concludes that healthcare sector is in dire need to increase quality of patients' care and improve clinical practices by adopting CDSS.

Surveillance systems are useful in the identification of patients that contract infections during their hospitalization period. Despite still being at infancy, electronic information control surveillance systems for Hospital Acquired Infections (HAIs) are improving and becoming more commonplace as the acceptance levels rise. There are crucial gaps in existing knowledge concerning the best ways for implementing electronic surveillance systems especially in the context of the Intensive Care Unit (ICU). To bridge this gap, the aim of this paper was to provide a comprehensive review of various electronic surveillance approaches and to highlight the requisite data components and offer guidelines. This review revealed denominator, numerator, and discrete data requirements and guidelines for the surveillance of four main ICU HAIs, including Central Line–Associated Bloodstream Infection (CLABSI), Urinary Tract Infection (UTI), Surgical Site Infections (SSIs) and Ventilator-Associated Conditions/Events (VACs/VAEs).

The distributed environments vary largely in their architectures, from tightly coupled cluster environment to loosely coupled Grid environment and completely uncoupled peer-to-peer environment, and thus differ in their working environments as well as performance. To meet the specific needs of these environments for data organization, replication, transfer, scheduling etc. the data management systems implement different data management models. In this paper, major data management tasks in distributed environments are identified and a taxonomy of the data management models in these environments is presented. The taxonomy is used to highlight the specific data management requirements of each environment and highlight the strengths and weakness of the implemented data management models. The taxonomy is followed by a survey of different distributed and Grid environments and the data management models they implement. The taxonomy and the survey results are used to identify the issues and challenges of data management for future exploration.

Today’s Grids include resources (referred as Grid-site s) from different domains including dedicated production resources, resources from university labs, and even P2P en?vironments. Grid high level services, like schedulers, resource managers, etc. need to know the reliability of the available Grid-sites to select the most suitable from them. Modeling reliability of a Grid-site for successful execution of a job requires prediction of Grid-site availability for the given duration of job execution as well as possibility of successful execution of the job. Predicting Grid-site availability is complex due to different availability patterns, resource sharing policies implemented by resource owners, nature of domain the resource belongs to (e.g. P2P etc.), and its maintenance etc. To give a solution, we model reliability of Grid-site in terms of prediction of its availability and possibility of job success. Our availability predictions incorporate past patterns of the Grid-site availability using pattern recognition methods. To estimate possibility of job success, we consider historical traces of job execution. The experiments conducted on a trace of real Grid demonstrate the effectiveness of our approach for ranking Grid-sites based on their reliability for executing jobs successfully.

One of the important learning objectives of our bachelor course on “Techniques in Decision Support Systems” is to develop understanding of core decision making process in real-life business situations. The conventional teaching methods are unable to explain complexities of real-life business. Although the classroom discussions can be effective to understand general factors, such as opportunity cost, return on investment, etc. affecting business decisions, the effects of factors like dynamic business environment, incomplete information, time pressure etc. can not be truly explained through such simple discussions. In this paper, we describe our experience of adopting student-centered, role-based, case study to deal with this situation. The interactive case-based study not only provided students with experiential learning, but also gave them liberty to test their thoughts. As a result, we observed improved students’ learning as well as improved grades. In addition, this approach made classes more dynamic and interesting.