Volume 9, Issue 3, March 2017

 

 

Massive Open Online Courses Awareness and Adoption by Nigeria University Students (A Case Study)
 

Massive Open Online Courses Awareness and Adoption by Nigeria University Students (A Case Study)

Pages: 41-46 (6) | [Full Text] PDF (269 KB)
T Adebo, T Ailobhio
Department of Computer Science, Federal University Lafia, Nasarawa State, Nigeria
Department of Mathematics, Federal University Lafia, Nasarawa State, Nigeria

Abstract -
The emergence of Massive Open Online Courses (MOOC) as an electronic learning trend, and its huge enrollment across the globe, inspired this study. This study was carried out to investigate the e-learning participation among Nigerian University students, determining the level of awareness and extent of usage of Massive open online courses (MOOCS) and other e-learning platforms. The study was carried out in Federal University Lafia Nasarawa State Nigeria with a total of 126 respondents cuts across the entire departments in the University. Descriptive statistics such as frequency counts and percentages as well as inferential statistics such as Liker- type scale and analysis of variance was employed in analyzing the study. It was discovered in the study that MOOC participation is still very low due to lack of awareness and inadequate infrastructure for internet connectivity. The study therefore suggests methods for improvement for blended and improved learning experience.
 
Index Terms - Blended learning, E-learning, Massive Open Online Courses

Citation - T Adebo, T Ailobhio. "Massive Open Online Courses Awareness and Adoption by Nigeria University Students (A Case Study)." International Journal of Computer Engineering and Information Technology 9, no. 3 (2017): 41-46.

Privacy Preserving Collaborative Association Rule Mining
 

Privacy Preserving Collaborative Association Rule Mining

Pages: 47-50 (4) | [Full Text] PDF (299 KB)
T Bhagayasri, K Krishnan
Department of Computer Science and Engineering, Sathyabama University, Chennai-119, India Faculty of Computing, Sathyabama University, Chennai 119, India

Abstract -
Affiliation run mining and incessant item set mining square measure two popular and wide contemplated information examination systems for an assortment of uses. This paper, we tend to work in confidentiality preserving removal on vertically parceled off databases. In such a condition, information stuff holders strength want to be told the association leads or regular item sets from an collective dataset, and disclose as nearly no statistics with respect to their (sensitive) raw information data information as feasible to option information mortgage holders and outsiders. To ensure learning defense, we tend to chic relate degree efficient homomorphic cryptography topic and a safe inspection topic. we tend to then propose a puff-supported consecutive item usual mining answer, that is used to make relate degree affiliation manage mining answer. Our answers square measure intended for subcontracted knowledge bases that authorize numerous information property holders to with efficacy share their insight immovably while not haggling on information protection. Our replies announcement less statistics with admiration to the data than most current arrangements. when contrasted with the sole known answer accomplishing an undifferentiated from protection level as our anticipated arrangements, the execution of our anticipated arrangements is three to five requests of greatness higher. bolstered our investigation discoveries exploitation totally extraordinary parameters and datasets, we tend to show that the run time in everything about arrangements is only one request on top of that inside the best non-protection safeguarding information handling calculations. Since every information and registering work square measure outsourced to the cloud servers, the asset utilization at the data proprietor complete is greatly low.
 
Index Terms - Mining, Utility, Miner, Extraction, Sequence, Frequent, Association, Item Sets

Citation - T Bhagayasri, K Krishnan. "Privacy Preserving Collaborative Association Rule Mining." International Journal of Computer Engineering and Information Technology 9, no. 3 (2017): 47-50.

Automated Kidney Stone Detection and Segmentation by Seed Pixel Region Growing Approach: Initial Implementation and Results
 

Automated Kidney Stone Detection and Segmentation by Seed Pixel Region Growing Approach: Initial Implementation and Results

Pages: 52-58 (7) | [Full Text] PDF (Please wait)
S Navratnam, S Fazilah, V Raman, S Perumal
Department of Computing, KDU University College Shah Alam Selangor Draul Ehsan 40150, Malaysia
Faculty of Engineering Computing and Science, Swinburne University of Technology Sarawak Kuching , Sarawak 93350, Malaysia
Faculty of Science and Technology, Universiti Sains Islamic Malaysia Nilai, Negeri Sembilan 71800, Malaysia

Abstract -
The most common problem in the daily lives of men and woman is the occurrence of kidney stone, which is named as renal calculi, due to living nature of the people. These calculi can be occurred in kidney, urethra or in the urinary bladder. Most of the existing study in the diagnosis of ultrasound image of the kidney stone identifies the presence or absence of stone in the kidney. The main objective of the paper is to propose a computer aided diagnosis prototype for early detection of kidney stones which helps to change the diet condition and prevention of stone formation in future. The CAD systems can provide such help and they are important and necessary for kidney stone control. There are several types of kidney stones which include calcium, struvite and cysteine, these are the important indicators of malignancy, and their automated detection is very valuable for early kidney stone detection. The main objective of the paper is to detect and segment the kidney stone from ultrasound images that helps to provide support for the clinical decision. In this paper, the implementation and results on segmentation are shown.
 
Index Terms - Kidney stone, Seed Pixel Region Growing and Segmentation

Citation - S Navratnam, S Fazilah, V Raman, S Perumal. "Automated Kidney Stone Detection and Segmentation by Seed Pixel Region Growing Approach: Initial Implementation and Results." International Journal of Computer Engineering and Information Technology 9, no. 3 (2017): 52-58.