Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Keyword
-
- Classification (2)
- Bioactivity prediction (1)
- Brain (1)
- Churn prediction (1)
- Data Dimensionality; Saaty Method; Clustering (1)
-
- Data mining and Prediction model. (1)
- Deep learning (1)
- Drug discovery (1)
- Image Segmentation (1)
- Intension to use (1)
- Internet (1)
- MRI (1)
- Machine learning (1)
- Molecular fingerprint (1)
- Multilayer perceptron (1)
- Ovarian Cancer (1)
- Quality of service (1)
- Quantitative structure-activity relationship (1)
- Recommendation system (1)
- Self-Efficacy (1)
- Slicer3D (1)
- VR (1)
- Web-Based E-Learning (1)
- Weighting Techniques (1)
- Wnt signaling (1)
- Publication
- Publication Type
Articles 1 - 6 of 6
Full-Text Articles in Computer Engineering
The Factors Influencing The Acceptance Of Web-Based E-Learning System Among Academic Staffs Of Saudi Arabia, Ikhlas Zamzami
The Factors Influencing The Acceptance Of Web-Based E-Learning System Among Academic Staffs Of Saudi Arabia, Ikhlas Zamzami
Future Computing and Informatics Journal
It is possible to learn more quickly and effectively with e-learning software development because it provides learners with convenient and flexible learning environments. This allows them to progress further in their careers. Reports on web-based e-learning systems for in-service education have frequently neglected to include the viewpoint of the instructor. In order to conduct quantitative research, a sample of 50 academic staff members was selected. The purpose of this study was to investigate various factors that influence the intention to use web-based e-learning, with the theoretical foundation being provided by university lecturers. According to the findings of the study, the …
A Statistical-Mining Techniques’ Collaboration For Minimizing Dimensionality In Ovarian Cancer Data, Mohamed Attia, Maha Farghaly, Mohamed Hamada, Amira M. Idrees Ami
A Statistical-Mining Techniques’ Collaboration For Minimizing Dimensionality In Ovarian Cancer Data, Mohamed Attia, Maha Farghaly, Mohamed Hamada, Amira M. Idrees Ami
Future Computing and Informatics Journal
A feature is a single measurable criterion to an observation of a process. While knowledge discovery techniques successfully contribute in many fields, however, the extensive required data processing could hinder the performance of these techniques. One of the main issues in processing data is the dimensionality of the data. Therefore, focusing on reducing the data dimensionality through eliminating the insignificant attributes could be considered one of the successful steps for raising the applied techniques’ performance. On the other hand, focusing on the applied field, ovarian cancer patients continuously suffer from the extensive analysis requirements for detecting the disease as well …
Benchmarking Small-Dataset Structure-Activity-Relationship Models For Prediction Of Wnt Signaling Inhibition, Mahtab Kokabi
Benchmarking Small-Dataset Structure-Activity-Relationship Models For Prediction Of Wnt Signaling Inhibition, Mahtab Kokabi
Masters Theses
Quantitative structure-activity relationship (QSAR) models based on machine learning algorithms are powerful tools to expedite drug discovery processes and therapeutics development. Given the cost in acquiring large-sized training datasets, it is useful to examine if QSAR analysis can reasonably predict drug activity with only a small-sized dataset (size < 100) and benchmark these small-dataset QSAR models in application-specific studies. To this end, here we present a systematic benchmarking study on small-dataset QSAR models built for prediction of effective Wnt signaling inhibitors, which are essential to therapeutics development in prevalent human diseases (e.g., cancer). Specifically, we examined a total of 72 two-dimensional (2D) QSAR models based on 4 best-performing algorithms, 6 commonly used molecular fingerprints, and 3 typical fingerprint lengths. We trained these models using a training dataset (56 compounds), benchmarked their performance on 4 figures-of-merit (FOMs), and examined their prediction accuracy using an external validation dataset (14 compounds). Our data show that the model performance is maximized when: 1) molecular fingerprints are selected to provide sufficient, unique, and not overly detailed representations of the chemical structures of drug compounds; 2) algorithms are selected to reduce the number of false predictions due to class imbalance in the dataset; and 3) models are selected to reach balanced performance on all 4 FOMs. These results may provide general guidelines in developing high-performance small-dataset QSAR models for drug activity prediction.
Evaluating Algorithms Used For Fetal Brain Scan Segmentation, Connor Stewart Burgess
Evaluating Algorithms Used For Fetal Brain Scan Segmentation, Connor Stewart Burgess
Undergraduate Student Research Internships Conference
The goal for this project was to successfully segment a fetal brain scan (fetal scan) using the algorithms provided by the program Slicer3D. To better understand the hurdles that arose when segmenting a fetal scan, we first look at the segmentation of an adult brain scan. This will allow us to see the straightforward nature of a brain segmentation when a high quality, high resolution volume with distinct structures is available. After examining the adult brain scan, attention will be moved to the segmentation of the fetal scan, where we’ll first look at the algorithms used and methods followed. Finally …
Review Of Data Mining Techniques For Detecting Churners In The Telecommunication Industry, Mahmoud Ewieda, Mohamed Ismail Roushdy, Essam Shaaban
Review Of Data Mining Techniques For Detecting Churners In The Telecommunication Industry, Mahmoud Ewieda, Mohamed Ismail Roushdy, Essam Shaaban
Future Computing and Informatics Journal
The telecommunication sector has been developed rapidly and with large amounts of data obtained as a result of increasing in the number of subscribers, modern techniques, data-based applications, and services. As well as better awareness of customer requirements and excellent quality that meets their satisfaction. This satisfaction raises rivalry between firms to maintain the quality of their services and upgrade them. These data can be helpfully extracted for analysis and used for predicting churners. Researchers around the world have conducted important research to understand the uses of Data mining (DM) that can be used to predict customers' churn. This …
Multilayer Perceptron With Auto Encoder Enabled Deep Learning Model For Recommender Systems, Subhashini Narayan
Multilayer Perceptron With Auto Encoder Enabled Deep Learning Model For Recommender Systems, Subhashini Narayan
Future Computing and Informatics Journal
In this modern world of ever-increasing one-click purchases, movie bookings, music, health- care, fashion, the need for recommendations have increased the more. Google, Netflix, Spotify, Amazon and other tech giants use recommendations to customize and tailor their search engines to suit the user’s interests. Many of the existing systems are based on older algorithms which although have decent accuracies, require large training and testing datasets and with the emergence of deep learning, the accuracy of algorithms has further improved, and error rates have reduced due to the use of multiple layers. The need for large datasets has declined as well. …