Leveraging Data Mining and Data Warehouse to Improve Prison Services and Operations in Nigeria

Crimes are social nuisance and cost our society dearly in several ways. In Nigeria, any research geared towards helping to solve crimes faster will be beneficial to the society at large. It has been observed that the major challenge facing all law-enforcement and intelligence-gathering organizations in Nigeria is how to accurately and efficiently analyze the growing volume of crime data. As the volume of this crime data becomes enormously large, new techniques have to be used to turn this data into valuable information and actionable knowledge so that appropriate actions can be taken accordingly. Sometimes it is usual to find that the data needed to be analyzed to produce report are scattered throughout different operational States and jurisdictions of Nigeria and must first be carefully integrated. Moreover, observations show that the process required to extract the existing data from each operational system demand so much of the system resources such that the IT professional must wait until nonoperational hours before running targeted queries required for producing operational reports. These delays are not only time-consuming and frustrating for both the IT professionals and the decision-makers they are dangerous for the sector whose primary task is to control crime spread and explosion. It should be noted that when such operational reports are finally produced, they may not be relied upon, because the data use in producing them many a times are inconsistent, inaccurate, or obsolete. This paper therefore highlights the increasing growing need for Data integration, Data warehouse and Data mining as ways to improve the operations of principal actors within the prisons sector of Nigeria. The paper explains what these Data management techniques mean and entail, and furthermore suggests ways to effectively leverage the techniques to help detect existing crime patterns and speed up the process of solving crimes. Keywords: Crime-data, data mining, data mining techniques, data warehouse, data integratio

Effect of Mastery Learning on Senior Secondary School Students’ Cognitive Learning Outcome in Quantitative Chemistry

The cognitive learning outcome of Senior Secondary School chemistry students has been poor over the years in Nigeria. Poor mathematical skills and inefficient teaching methods have been identified as some of the major reasons for this. Bloom’s theory of school learning and philosophy of mastery learning assert that virtually all students are capable of attaining a high degree of learning if given the appropriate, prior and concurrent conditions. This study investigated the effect of mastery learning on senior secondary school students’ cognitive learning outcome in quantitative chemistry. Quasi-experimental control group design was used for the study. Four Secondary Schools were randomly selected and randomly assigned to experimental and control groups. A total of four hundred and one (401) chemistry students were used for the study. Data was collected using a 25-item chemistry achievement test (CAT) drawn from stoichiometry and mole concept. The instrument was pilot tested and Kuder Richardson formula 21 (KR21) was used to establish the reliability coefficient (r = 0.7). Pre-test was administered to both the experimental and control groups to ascertain if the two groups were comparable and have the same entry characteristics before the treatment. A post-test was administered to both groups after two weeks of exposing the experimental group to mastery learning and the control group to conventional teaching method. Data were analyzed using independent sample t-test. The mastery learning group had a higher mean score (= 78.2; s = 9.90) than the control group (= 58.4; s = 16.07). The difference was highly significant (t399 = 14.92; p = 0.00).  About sixty nine percent (69%) of the students in the mastery learning group scored 80% and above, a score attainable by only 17.5% of the students in the control group. Similarly, about half (50%) of the students receiving conventional instruction scored between 40% and 49% whereas less than 1% of the students in the mastery learning group were in this group. The effect size was substantial (0.6). The researcher concluded that mastery learning is a very effective method of teaching and better than the conventional teaching method and recommended that chemistry teachers should be encouraged to adopt it in order to enhance the cognitive learning outcome of students in quantitative chemistry. Keywords: Mastery Learning, Quantitative Chemistry, Feedback, Corrective instruction, Cognitive Learning Outcom

Optimized Naïve Bayesian Algorithm for Efficient Performance

Naïve Bayesian algorithm is a data mining algorithm that depicts relationship between data objects using probabilistic method. Classification using Bayesian algorithm is usually done by finding the class that has the highest probability value. Data mining is a popular research area that consists of algorithm development and pattern extraction from database using different algorithms. Classification is one of the major tasks of data mining which aimed at building a model (classifier) that can be used to predict unknown class labels. There are so many algorithms for classification such as decision tree classifier, neural network, rule induction and naïve Bayesian. This paper is focused on naïve Bayesian algorithm which is a classical algorithm for classifying categorical data. It easily converged at local optima. Particle Swarm Optimization (PSO) algorithm has gained recognition in many fields of human endeavours and has been applied to enhance efficiency and accuracy in different problem domain. This paper proposed an optimized naïve Bayesian classifier using particle swarm optimization to overcome the problem of premature convergence and to improve the efficiency of the naïve Bayesian algorithm. The classification result from the optimized naïve Bayesian when compared with the traditional algorithm showed a better performance Keywords: Data Mining, Classification, Particle Swarm Optimization, Naïve Bayesian

A Phase Approach for Adopting Private Clouds as a Collaborative Platform for Nigerian Universities

Cloud computing is creating a new era for information technology by providing a set of services that appears to have infinite capacity, immediate deployment and high availability at trivial cost. It is the result of the evolution of computing and communications technology from a high-value asset to a simple commodity. In this evolution, the focus shifts from the concept of computing as a physical thing to computing as a service, like electricity, that is accessible from the nearest network connection. An organization, which is under increasing pressure to provide computing services at the lowest possible cost, can choose either public or private clouds to meet these needs. However, driven by concerns over security, regulatory compliance, control over quality of service, and long-term costs, many organizations choose internal private clouds. Private clouds provide the same cost and flexibility benefits as public clouds and also enable an organization to control the quality of service delivered to their users. In addition, private clouds allow an organization to better secure data and meet governance regulations which is usually a major concern when using public clouds. Many universities spend huge amount of money yearly on ICT infrastructure.  About ninety percent of ICTs budgets are consumed by computing requirements that can be centralized and standardized enabling one to do more with less resource. This paper tries to make a ca se for the private cloud as a better platform for collaboration among the Nigerian universities and to propose a safe strategy for migration into the private cloud. Keywords: Cloud Computing, Private Cloud, Public Cloud, Cloud Service Models, Cloud Characteristic

Treatment-resistant depression in adolescents: is the addition of cognitive behavioral therapy of benefit?

BACKGROUND: Many young people with major depression fail first-line treatments. Treatment-resistant depression has various definitions in the literature but typically assumes nonresponse to medication. In young people, cognitive behavioral therapy (CBT) is the recommended first-line intervention, thus the definition of treatment resistance should be expanded. Therefore, our aim was to synthesize the existing evidence of any interventions for treatment-resistant depression, broadly defined, in children and adolescents and to investigate the effectiveness of CBT in this context. METHODS: We used Cochrane Collaboration methodology, with electronic searches of Medline, PsycINFO, Embase, and the Cochrane Depression Anxiety and Neurosis Group trials registers. Only randomized controlled trials were included, and were assessed for risk of bias. Meta- analysis was undertaken where possible and appropriate. RESULTS: Of 953 articles retrieved, four trials were eligible for inclusion. For one study, only the trial registration document was available, because the study was never completed. All other studies were well conducted with a low risk of bias, although one study had a high dropout rate. Two studies assessed the effect of adding CBT to medication. While an assertive trial of antidepressants does appear to lead to benefit, when compared with placebo, there was no significant advantage, in either study, or in a meta-analysis of data from these trials, that clearly demonstrated an additional benefit of CBT. The third trial showed little advantage of a tricyclic antidepressant over placebo in the context of an inpatient admission. CONCLUSION: Few randomized controlled trials have investigated interventions for treatment-resistant depression in young people, and results from these show modest benefit from antidepressants with no additional benefit over medication from CBT. Overall, there is a lack of evidence about effective interventions to treat young people who have failed to respond to evidence-based interventions for depression. Research in this area is urgently required

Aging Phenotypes of Common Marmosets ( Callithrix jacchus

Characterizing the phenotypic changes associated with aging in a short-lived primate is necessary in order to develop better translational models for human health, aging, and disease research. A population of conventionally housed marmoset monkeys was assessed to determine if phenotypes of body composition, hematology, and morphometrical measures were associated with age or risk of death. We found that the cause of mortality in older marmosets was more likely to be due to cardiac and chronic kidney disease than in younger marmosets. Older marmosets have decreased fat mass, morphometric measures, and serum albumin. Older marmosets are more likely to show a modified posture while at rest and this modified posture was significantly associated with an increased risk of imminent death. These assessments provide an initial definition of aged health in marmosets and a base for future translational aging research with this species

Algorithmic Framework for Frequent Pattern Mining with FP-Tree

The FP-tree algorithm is currently one of the fastest approaches to frequent item set mining. Studies have also shown that pattern-growth method is one of the most efficient methods for frequent pattern mining. It is based on a prefix tree representation of the given database of transactions (FP-tree) and can save substantial amounts of memory for storing the database. The basic idea of the FP-growth algorithm can be described as a recursive elimination scheme which is usually achieved in the preprocessing step by deleting all items from the transactions that are not frequent. In this study, a simple framework for mining frequent pattern is presented with FP-tree structure which is an extended prefix-tree structure for mining frequent pattern without candidate generation, and less cost for better understanding of the concept for inexperienced data analysts and other organizations interested in association rule mining. Keywords: Association Rule, Frequent Pattern Mining, Apriori Algorithm, FP-tre

Fleet Avenue Revitalization Strategy

Slavic Village Development, Inc., the nonprofit community development corporation in the Slavic Village neighborhood of Cleveland, engaged the Center for Community Planning and Development at Cleveland State University to assist with a revitalization strategy for Fleet Avenue. The study team was charged to work with SVD leaders and the neighborhood’s City Councilman, Tony Brancatelli, to evaluate market opportunities for food production businesses and local retail, and to develop an overall parcel-by-parcel strategy for commercial rehabilitation, and new commercial and housing infill on the street

Fleet Avenue Revitalization Strategy

Slavic Village Development, Inc., the nonprofit community development corporation in the Slavic Village neighborhood of Cleveland, engaged the Center for Community Planning and Development at Cleveland State University to assist with a revitalization strategy for Fleet Avenue. The study team was charged to work with SVD leaders and the neighborhood’s City Councilman, Tony Brancatelli, to evaluate market opportunities for food production businesses and local retail, and to develop an overall parcel-by-parcel strategy for commercial rehabilitation, and new commercial and housing infill on the street

Variety Village District Economic Analysis: Retail Market Expansion, Economic Impact, and Fiscal Impact

This study outlines the economic and fiscal impacts of the redevelopment of the Variety Village District, comprised of the Variety Theatre Complex, a new public parking lot, and 40,000 square feet of new retail along Lorain Avenue. In addition, as shown in the full report, a portion of the location decision for at least three local industries which are moving to, or expanding their enterprise in, the immediate Westown neighborhood, can be attributed to the catalytic effect of the Variety Village District redevelopment