Tag Archives: Classification

Application of ICT in Teaching Information Organization in Nigerian Library Schools for Sustainable Development: A Comparative Study (Published)

This study investigated the application of Information Communication Technology (ICT) in teaching of Information Organization in Nigerian Library schools for sustainable development. It was a comparative study of library schools in Nigeria. Four objectives and four research questions were formulated to guide the study. Descriptive survey design was employed in the study and questionnaire was used to gather data from postgraduate students in the Nigerian library schools. The findings from the study revealed that there is not enough time allocated to the teaching of Information organisation in Nigerian Library Schools.  All the ICT facilities listed in the study : CD-ROM, interactive white board, projectors, DDC Online, Online  LCSH, internet  and computers are not used in the teaching of Information Organisation in Nigerian Library Schools. Factors that militate against the use of ICT in teaching of Information Organisation were identified as funding, lack of internet subscription, poor  power supply and non-availability of the ICT resources in the library schools. It was recommended that more credit hours should be allocated for the study of Information Organisation and all the needed ICT facilities should be provided in the library schools.

Keywords: Cataloguing, Classification, information organization, library schools, web resources

Characterization, Classification and Management of Some Soils in Ujam District of Makurdi, Benue State (Published)

An intensive soil survey was carried out in Ujam District, the two sites chosen were designated: (1) Tse-Tswam and (11) Tse-Ordam.  The aims were to characterize, classify and proffer management practices for the soils. At each site, three profiles pits were sunk and morphologically described. Samples collected from identified genetic horizons were subjected to analyses using standard analytical procedures. The soil profiles ranged from deep (118cm) to very deep (200cm); well to imperfectly drained; epipedons’ colour varied from very dark brown (7.5YR 2/3)/brownish black (7.5 YR 3/1) due to melanisation; subsoils were dull reddish brown (5YR4/4) due to rediomophism and brownish gray (10 YR 5/1) as imprint of gleization; Mottles on the subsoils may be attributed to drainage impedance; sandy loam or loamy sand surfaces with clay to sandy clay loam subsoils to sandstone parent material and weak fine crumb to moderate/strong fine-coarse subangular blocky structures. The soils had medium to high sand (41.20-83.00%), very low to medium clay (06.02.58- 43.25%) and low silt (10.65-16.96%) fractions; medium bulk density (1.19-1.38gmcm-3) and porosity (48.68-56.60%). Soil reaction was slightly acid (5.67-6.50); low organic carbon (1.05-0.30%), nitrogen (0.03-0.18%), Available phosphorus (3.00-10.10%) and EC (0.10-0.13dms-1). CEC was very low (6.34-9.10cmolkg-1) likewise CaCO3 (0.00-2.00%); medium to high base saturation (48.80-91.90%). All soil units (1-V1) possessed argillic horizons with base saturations that were ≤50%(NH4OAc at pH 7) and were classified into Alfisols at soil order level; units 1 and 111 further qualified into Eutric Epiaqualfs (Vertic luvisols Clayiec,kandic), 11 into Dystric Haplustalf (Dystric Luvisol Kandic, Clayiec) and 1V into Arenic Haplustalfs (Vertic Luvisols arenic, Dystric). Units V was placed into (Haplic Eutrustalf (Glayeic Luvisol Eutric,kandic) at subgroup while soil unit V1 was keyed into Glayiec Haplustalf (Glayeic Luvisol Kandic, Clayeic). Organic/mineral fertilizers incooporation into these soils will improve soil fertility, structure and water retention.

Keywords: Base saturation, Characterization, Classification, Management, argillic, fertilizers, haplustalfs, soil profiles, vertic epiaqualfs

Evaluation of Cataloguing and Classification Competencies of Librarians in Nigerian Academic Libraries (Published)

A cross-sectional study of 84 cataloguers from 20 academic libraries across the geopolitical zones of Nigeria were randomly selected for the study. Their cataloguing and classification competencies; available cataloguing tools and the problems encountered were evaluated.  A 32-item structured questionnaire under three sections was administered to the selected cataloguers. The mean value calculated for competencies in cataloguing and classifications skills is 3.3. “Finding it convenient to make good judgments in handling gray areas” had the least variable score of 2.6. The mean value for the cataloguing tools commonly used is 3.4. “Web Dewey and/or printed Dewey Decimal Classification (DDC)” has the least score of 2.2. No training opportunity for continuous professional development” has the highest score of 3.8. Current cataloguing tools should be provided nationwide and also training opportunities that will help these librarians keep abreast of changing cataloguing rules for effective service delivery in Nigerian academic libraries.

Keywords: Academic Libraries, Cataloguing, Classification, librarian

A Web-Based Clinical Decision Support System for the Management of Diabetes Neuropathy Using Naïve Bayes Algorithm (Published)

Diabetes Neuropathy is a chronic health problem with devastating, yet preventable consequences. Due to this shortage of specialists, there is a need for a Clinical Decision Support System that will diagnose and manage diabetes neuropathy. This work therefore aimed at designing a web-based Clinical Decision Support System for the management of early diabetes neuropathy. Four pattern classification algorithms (K-nearest neighbor, Decision Tree, Decision Stump and Rule Induction) were adopted in this work and were evaluated to determine the most suitable algorithm for the clinical decision support system. Datasets were gathered from reliable sources; two teaching hospitals in Nigeria, these were used for the evaluation Benchmarks such as performance, accuracy level, precision, confusion matrices and the models building’s speed were used in comparing the generated models. The study showed that Naïve Bayes outperformed all other classifiers with accuracy being 60.50%. k-nearest neighbor, Decision Tree, Decision Stump and Rule induction perform well with the lowest accuracy for x- cross validation being 36.50%. Decision Tree falls behind in accuracy, while k-nearest neighbour and Decision Stump maintain accuracy at equilibrium 41.00%. Therefore, Naïve Bayes is adopted as optimal algorithm in the domain of this study. The rules generated from the optimal algorithm (Naïve Bayes) forms the back-end engine of the Clinical Decision Support System. The web-based clinical decision support system was then designed The automatic diagnosis of diabetes neuropathy is an important real-world medical problem. Detection of diabetes neuropathy in its early stages is a key for controlling and managing patients early before the disabling effect present. This system can be used to assist medical programs especially in geographically remote areas where expert human diagnosis not possible with an advantage of minimal expenses and faster results. For further studies, researchers can improve on the proposed clinical decision support system by employing more than one efficient algorithm to develop a hybrid system.

Keywords: Accuracy, Algorithm, Classification, Diabetes, Neuropathy, precision

Web Data Mining: Views of Criminal Activities (Published)

Web data mining discovers valuable information or knowledge from the web hyperlink structure, page content and usage data. Along with the swift popularity of the Internet, crime information on the web is becoming increasingly flourishing, and the majority of them are in the form of text. A major challenge facing all law-enforcement and intelligence-gathering organizations is accurately and efficiently analyzing the growing volumes of crime data. Detecting, exploring crimes and investigating their relationship with criminals are a big challenge to the present world. The evaluation of the different dimensions of widespread criminal web data causes one of the research challenges to the researchers. Criminal web data always offer convenient and applicable information for law administration and intelligence department. The goal of crime data mining is to understand patterns in criminal behavior in order to predict crime anticipate criminal activity and stop it. This paper describes web data mining which includes structure mining, web content mining, web usage mining and crime data mining. The occurrences of criminal activities based on web data mining process is also presented in this paper. The presented information on different criminal activities can be used to reduce further occurrences of similar incidence and to stop the crime.

Keywords: Classification, Clustering., Crime Control., Crime data, Pattern Analysis, Web Mining

Dynamic Decision Tree Based Ensembled Learning Model to Forecast Flight Status (Published)

This paper explains the development of an enhanced predictive classifier for flight status that will reduce over fitting observed in existing models. A dynamic approach from ensemble learning technique called bagging algorithm was used to train a number of base learners using a base learning algorithm. The results of the various classifiers were combined, voting was done, by majority the most voted class was picked as the final output. This output was subjected to the decision tree algorithm to produce various replica sets generated from the training set to create various decision tree models. Object-Oriented Analysis and Design (OO-AD) methodology was adopted for the design and implementation was done with C# programming language. The result achieved was favorable as it was found to predict at an accuracy of 78.3% as against 68.2% accuracy of the existing systems which indicated an enhancement.

Keywords: : Flight Status, Bagging Algorithm, Classification, Ensemble learning, Prediction

Web Data Mining: Views of Criminal Activities (Published)

Web data mining discovers valuable information or knowledge from the web hyperlink structure, page content and usage data. Along with the swift popularity of the Internet, crime information on the web is becoming increasingly flourishing, and the majority of them are in the form of text. A major challenge facing all law-enforcement and intelligence-gathering organizations is accurately and efficiently analyzing the growing volumes of crime data. Detecting, exploring crimes and investigating their relationship with criminals are a big challenge to the present world. The evaluation of the different dimensions of widespread criminal web data causes one of the research challenges to the researchers. Criminal web data always offer convenient and applicable information for law administration and intelligence department. The goal of crime data mining is to understand patterns in criminal behavior in order to predict crime anticipate criminal activity and stop it. This paper describes web data mining which includes structure mining, web content mining, web usage mining and crime data mining. The occurrences of criminal activities based on web data mining process is also presented in this paper. The presented information on different criminal activities can be used to reduce further occurrences of similar incidence and to stop the crime.

Keywords: Classification, Clustering., Crime Control., Crime data, Pattern Analysis, Web Mining

FISHER’S LINEAR DISCRIMINANT CLASSIFIER AND RANK TRANSFORMATION APPROACH TO DISCRIMINANT ANALYSIS (Review Completed - Accepted)

Discriminant analysis is a multivariate statistical technique used primarily for obtaining a linear function of p variables which maximizes the distance between centroids or midpoints of multivariate distributions of k groups. Linear discriminant analysis was performed using the fisher’s technique which was also derived. Test for differences in the means for the two groups and their variance covariance matrices were discussed. A major shortcoming of the fisher’s linear discriminant analysis is that if normality assumption does not hold, the optimal property is lost. This paper compared Fisher’s linear discriminant analysis and the rank transformation approach. This was illustrated by performing discriminant analysis on the data and discriminant analysis on the ranks.  If the population is not normal, the effectiveness of this method is enhanced by using the ranks of the original data rather than the data themselves. The results obtained indicate that the two methods perform equally well but the rank transformation is a better alternative to the Fisher’s discriminant  technique  for distributions of small samples and non-normal data.

Keywords: Apparent Error, Classification, Keywords Fisher’s Linear Discriminant Analysis, Rank transformation

Web Page Classification by using CPBF and Neural Network (Review Completed - Accepted)

With the exclusive growth in the WWW makes the internet growing very fast. Therefore classifiers of the web pages become more challenging. The proposed system is about using Class Profile- Based Features CPBF for features selection. In this research, new web page classification method is proposed, using neural network with inputs obtained by CPBF. The fixed number of regular words from each class will be used as a feature vector, these feature vector are then used as the input to the neural networks for classification. The experimental evaluation demonstrates that the method provides high quality classification accuracy with the sports news datasets

Keywords: CPBF, Classification, WWW, Web- Page Classification