Image enhancement is one of the key techniques in processing quality of images in systems. The main purpose of image enhancement is to bring out detail that is hidden in an image or to increase contrast in a low contrast image. This technique provides a multitude of choices for improving the visual quality of images. This is the main reason that image enhancement is used in a huge number of applications with important challenges such as noise reduction, degradations, blurring etc. This paper focuses on three contrast enhancement techniques for image enhancement which are: Histogram Equalization (HE), Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) which are then compared with the help of the eight (8) quality image measurement metrics which are: i.e.

The Knapsack Problems are among the simplest integer programs which are NP-hard. Problems in this class are typically concerned with selecting from a set of given items, each with a specified weight and value, a subset of items whose weight sum does not exceed a prescribed capacity and whose value is maximum. The classical 0-1 Knapsack Problem arises when there is one knapsack and one item of each type. This paper considers the application of classical 0-1 knapsack problem with a single constraint to computer memory management. The goal is to achieve higher efficiency with memory management in computer systems. This study focuses on using simulated annealing and genetic algorithm for the solution of knapsack problems in optimizing computer memory. It is shown that Simulated Annealing performs better than the Genetic Algorithm for large number of processes.

Organizations of all kinds make decisions based on the data they have at their disposal. In healthcare reporting, data quality and consistency are critical to ensuring patient safety and communicating health service delivery. Quality data provides accurate and timely information to manage services. It also provides good information to manage service effectiveness as well as aids to prioritize and ensure the best use of resources. The main objective of the study was to improve on data quality on expanded programme on immunization (EPI) in the New Juaben Municipality. The work aimed at improving the accuracy of reported number of vaccinations at vaccination delivery sites as well as assesses the completeness of data that was provided. A descriptive cross-sectional study was employed. The study involved structured observation of tallied data from EPI tally books from the eight health sub-districts in the Municipality. Purposive sampling was used for this study. Data Quality Self-Assessment Tool (DQS) was the main instrument used in presenting and analyzing the accuracy and discrepancy ratios of the data. The result demonstrated discrepancies in tallied data at the vaccination delivery sites, facility summary report and report submitted to the Municipal Health Directorate. In 2011, there was 2674 over reported data to the district level while 2824 over reported data was recorded in 2012 from the eight (8) health facilities used for the study. It was observed that less importance was attached to data capture at some health facilities in the sub-districts. It was also ascertained that data storage and retrieval was very poor in some facilities visited. There is therefore the need for regular monitoring in the sub-districts' RCHs and Health Centers in the New Juaben Municipality of Ghana to correct the mistakes.

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Since organizational decisions are vital to organizational development, customers' views and feedback are equally important to inform good decisions. Given this relevance, this paper seeks to automate a sentiment analysis system -SentDesk-that can aid tracking sentiments in customers' reviews and feedback. The study was contextualised in some business organisations in Ghana. Three business organizational marketers were made to annotate emotions and as well tag sentiments to each instance in the corpora. Kappa and Krippendoff coefficients were computed to obtain the annotation agreement in the corpora. The SentDesk system was evaluated in the environment alongside comparing the output to that of the average sentiments tagged by the marketers. Also, the SentDesk system was evaluated in the environment by the selected marketers after they had tested the platform. By finding the average kappa value from the corpora (CFR + ISEAR), the average kappa coefficient was found to be 0.40 (40%). The results of evaluating the SentDesk system with humans shows that the system performed as better as humans. The study also revealed that, while annotating emotions and sentiments in the datasets, counsellor's own emotions influences their perception of emotions.