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Articles 1 - 5 of 5
Full-Text Articles in Signal Processing
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 …
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 …
Framework For Collecting Data From Iot Device, Md Saiful Islam
Framework For Collecting Data From Iot Device, Md Saiful Islam
Symposium of Student Scholars
The Internet of Things (IoT) is the most significant and blooming technology in the 21st century. IoT has rapidly developed by covering hundreds of applications in the civil, health, military, and agriculture areas. IoT is based on the collection of sensor data through an embedded system, and this embedded system uploads the data on the internet. Devices and sensor technologies connected over a network can monitor and measure data in real-time. The main challenge is to collect data from IoT devices, transmit them to store in the Cloud, and later retrieve them at any time for visualization and data analysis. …
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. …
Data Forgery Detection In Automatic Generation Control: Exploration Of Automated Parameter Generation And Low-Rate Attacks, Yatish R. Dubasi
Data Forgery Detection In Automatic Generation Control: Exploration Of Automated Parameter Generation And Low-Rate Attacks, Yatish R. Dubasi
Computer Science and Computer Engineering Undergraduate Honors Theses
Automatic Generation Control (AGC) is a key control system utilized in electric power systems. AGC uses frequency and tie-line power flow measurements to determine the Area Control Error (ACE). ACE is then used by the AGC to adjust power generation and maintain an acceptable power system frequency. Attackers might inject false frequency and/or tie-line power flow measurements to mislead AGC into falsely adjusting power generation, which can harm power system operations. Various data forgery detection models are studied in this thesis. First, to make the use of predictive detection models easier for users, we propose a method for automated generation …