This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services th

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is s

In present work an investigation for precise hole drilling via continuous wave (CW) CO2 laser at 150 W maximum output power and wavelength 10.6 μm was achieved with the assistance of computerized numerical controlled (CNC) machine and assist gases. The drilling process was done for thin sheets (0.1 – 0.3 mm) of two types of metals; stainless steel (sst) 321H, steel 33 (st). Changing light and process parameters such as laser power, exposure time and gas pressure was important for getting the optimum results. The obtained results were supported with computational results using the COMSOL 3.5a software code.

This study is qualitative, it illustrates H.G. Wells\\'s The Time Machine through the scientific and social framework of the Victorian Era. Wells\\'s portrayal of future societies examines the rapid technological progress and social changes of the 19th century. The analysis scrutinizes the division between the Eloi and the Morlocks, tracing the consequences of social division. To meet the objective of the study, Victorian frame of mind is utilized to examine the class struggle that is symbolized by the Eloi and the Morlocks. The analysis highlights the economic and social effects of industrialization and how Wells examines the capitalist system and its impact on human relationships and class division. The study also utilizes concepts from D

Due to the need for controlling and regulating of feed pellet. Pellet that is imported or locally manufactured is accompanied by cracking and crumbling percentage that occur during transporting and distributing to animals, using conveyors and mechanical feeders. This study aimed to determine the effect of particle size and die holes diameter in the machine on broiler feed pellets quality in pellet durability, pellet direct measurement, pellet expansion, and pellet length. Three particle size 2, 4, and 6 mm, and three diameters of die holes in the machine 3, 4, and 5 mm, have been used. The results showed that changing the particle size from 2 to 4 then to 6 mm led to a significant decrease in pellet durability and pellet lengths, pe

Peer support is an effective strategy to promote self-management behaviors and improve well-being in those with cardiometabolic disease, including type 2 diabetes mellitus (T2DM). There is limited knowledge about stakeholder perceptions regarding peer support programs in low- and middle-income countries (LMICs). The study assessed stakeholders’ awareness and understanding of peer support initiatives for T2DM, and explored their perceived barriers and readiness for implementation. A cross-sectional, self-administered online survey with branching logic was distributed to stakeholders across macro- (health policy), meso (tertiary hospital), and micro (community) levels of LMIC healthcare systems from June 1 to December 15, 2023. Quan

Software-Defined Networking (SDN) has evolved network management by detaching the control plane from the data forwarding plane, resulting in unparalleled flexibility and efficiency in network administration. However, the heterogeneity of traffic in SDN presents issues in achieving Quality of Service (QoS) demands and efficiently managing network resources. SDN traffic flows are often divided into elephant flows (EFs) and mice flows (MFs). EFs, which are distinguished by their huge packet sizes and long durations, account for a small amount of total traffic but require disproportionate network resources, thus causing congestion and delays for smaller MFs. MFs, on the other hand, have a short lifetime and are latency-sensitive, but they accou