79 research outputs found
HierarchyMap: A Novel Approach to Treemap Visualization of Hierarchical Data
The HierarchyMap describes a novel approach for Treemap Visualization method for representing large volume of hierarchical information on a 2-dimensional space. HierarchyMap algorithm is a new ordered treemap algorithm. Results of the implementation of HierarchyMap treemap algorithm show that it is capable of representing several thousands of hierarchical data on 2-dimensional space on a computer and Portable Device Application (PDA) screens while still maintaining the qualities found in existing treemap algorithms such as readability, low aspect ratio, reduced run time, and reduced number of thin rectangles. The HierarchyMap treemap algorithm is implemented in Java programming language and tested with dataset of Departmental and Faculty systems of Universities, Family trees, Plant and Animal taxonomy structure
A Two-Phase Dynamic Programming Algorithm Tool for DNA Sequences
Sequence alignment has to do with the arrangement of DNA, RNA, and protein sequences to identify areas of similarity. Technic ally, it
involves the arrangement of the primary sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of
functional, structural, or evolutionary relationships between the sequences. Similarity may be a consequence of functional, s tructural, or
evolutionary relationships between the sequences. If two sequences in an alignment share a common ancestor, mismatches can be
interpreted as mutations, and gaps as insertions. Such information becomes of great use in vital areas such as the study of d iseases,
genomics and generally in the biological sciences. Thus, sequence alignment presents not just an exciting field of study, but a field of
great importance to mankind. In this light, we extensively studied about seventy (70) existing sequence alignment tools available to us.
Most of these tools are not user friendly and cannot be used by biologists. The few tools that attempted both Local and Global algorithms
are not ready available freely. We therefore implemented a sequence alignment tool (CU-Aligner) in an understandable, user-friendly and
portable way, with click-of-a-button simplicity. This is done utilizing the Needleman-Wunsh and Smith-Waterman algorithms for global
and local alignments, respectively which focuses primarily on DNA sequences. Our aligner is implemented in the Java language in both
application and applet mode and has been efficient on all windows operating systems
Application of k Means Clustering algorithm for prediction of Students Academic Performance
The ability to monitor the progress of students academic performance is a
critical issue to the academic community of higher learning. A system for
analyzing students results based on cluster analysis and uses standard
statistical algorithms to arrange their scores data according to the level of
their performance is described. In this paper, we also implemented k mean
clustering algorithm for analyzing students result data. The model was combined
with the deterministic model to analyze the students results of a private
Institution in Nigeria which is a good benchmark to monitor the progression of
academic performance of students in higher Institution for the purpose of
making an effective decision by the academic planners.Comment: IEEE format, International Journal of Computer Science and
Information Security, IJCSIS January 2010, ISSN 1947 5500,
http://sites.google.com/site/ijcsis
Design and Implementation of Text To Speech Conversion for Visually Impaired People
A Text-to-speech synthesizer is an application that converts text into spoken word, by analyzing and processing the text using Natural Language Processing (NLP) and then using Digital Signal Processing (DSP) technology to convert this processed text into synthesized speech representation of the text. Here, we developed a useful text-to-speech synthesizer in the form of a simple application that converts inputted text into synthesized speech and reads out to the user which can then be saved as an mp3.file. The development of a text to speech synthesizer will be of great help to people with visual impairment and make making through large volume of text easie
The design and implementation of online medical record system (OMRS)
Access to appropriate and credible medical information is essential. It is however saddening that many developing countries, especially in sub-Saharan Africa, have low or no access to information on personal health status. The Online Medical Record System (OMRS) is a departure from the traditional paper-based medical record system of healthcare practices to an Internet based medical record storage system. In this paper, we implemented OMRS software that has successfully been able to store, update and modify the patients\' medical history records. It also creates an appointment scheduler system and a platform for online consultation. OMRS allows patients control their own records while allowing doctors access when they need it. OMRS provides a way for doctors and patients to replace thick medical charts and swap information without the need for costly and time-consuming office visits. The advent of internet has made it possible for OMRS to come up with a way in which the problem of computerizing medical records effectively and sharing it can be solved. The OMRS would serve important national interests and it is believed that implementation of the OMRS will have a dramatic impact on the overall quality of healthcare delivery in developing countrie
Application of Fizzy Logic in Decision Making on Studentâs academic performance.
Decision making is a knowledge is a knowledge discovery in Fuzzy logic application. Therefore, this paper conceptually defined, explained, and implemented fuzzy logic to the model to system performance, specifically, studentsâ performance model is studied and the various results generated and the performance chart obtained from overall performance for each year for the consecutive eight years in making decisions for future academic performance are also obtained
Application of Fuzzy Association Rule Mining for Analysing Students Academic Performance
This study examines the relationship between studentsâ preadmission
academic profile and academic performance. Data
sample of students in the Department of Computer Science in
one of Nigeria private Universities was used. The preadmission
academic profile considered includes âOâ level
grades, University Matriculation Examination (UME) scores,
and Post-UME scores. The academic performance is defined
using studentsâ Grade Point Average (GPA) at the end of a
particular session. Fuzzy Association Rule Mining (FARM)
was used to identify the hidden relationships that exist between
studentsâ pre-admission profile and academic performance.
This study hopes to determine the academic profile of students
who are most admitted in the session. It determines studentsâ
performance ratings as against their pre-admission academic
profile. This can serve as a predictor for admission committee
to enhance the quality of the new in-take and guide for the
academic advise
Reducing the Time Requirement of k-Means Algorithm
Traditional k-means and most k-means variants are still computationally expensive for large datasets, such as microarray
data, which have large datasets with large dimension size d. In k-means clustering, we are given a set of n data points in ddimensional
space Rd and an integer k. The problem is to determine a set of k points in Rd, called centers, so as to minimize
the mean squared distance from each data point to its nearest center. In this work, we develop a novel k-means algorithm,
which is simple but more efficient than the traditional k-means and the recent enhanced k-means. Our new algorithm is
based on the recently established relationship between principal component analysis and the k-means clustering. We
provided the correctness proof for this algorithm. Results obtained from testing the algorithm on three biological data and
six non-biological data (three of these data are real, while the other three are simulated) also indicate that our algorithm is
empirically faster than other known k-means algorithms. We assessed the quality of our algorithm clusters against the
clusters of a known structure using the Hubert-Arabie Adjusted Rand index (ARIHA). We found that when k is close to d, the
quality is good (ARIHA.0.8) and when k is not close to d, the quality of our new k-means algorithm is excellent (ARIHA.0.9).
In this paper, emphases are on the reduction of the time requirement of the k-means algorithm and its application to
microarray data due to the desire to create a tool for clustering and malaria research. However, the new clustering algorithm
can be used for other clustering needs as long as an appropriate measure of distance between the centroids and the
members is used. This has been demonstrated in this work on six non-biological data
Normalization of plant database of a Herbarium
In the present world, Relational Database model is becoming more focusing model among others; just because of itâs interesting. When features designing a relational database, we need to be aware of all kind of anomalous situations that can arise as a result of the structure of data we are working with. These anomalies can seriously affect the integrity of the database. The process of normalization can minimize the impact of the various anomalies
- âŚ