The need for participants’ performance assessments in academia and industry has been a growing concern. It has attendance, among other metrics, is a key factor in engendering a holistic approach to decision-making. For institutions or organizations where managing people is an important yet challenging task, attendance tracking and management could be employed to improve this seemingly time-consuming process while keeping an accurate attendance record. The manual/quasi-analog approach of taking attendance in some institutions could be unreliable and inefficient, leading to inaccurate computation of attendance rates and data loss. This work, therefore, proposes a system that employs embedded technology and a biometric/ w

Olfactory impairment and abnormal frontal EEG oscillations are recognized as early markers of Alzheimer’s disease (AD). Using a publicly available olfactory EEG dataset of 35 subjects spanning normal cognition, amnestic mild cognitive impairment (aMCI), and AD, each with MMSE scores and demographics, stimulus-locked epochs from four electrodes (Fp1, Fz, Cz, Pz) were processed with wavelet-based time–frequency analysis. Band-limited power ratios (delta, theta, alpha, beta) were computed as log-transformed post-odor/baseline values and aggregated to subject-level features. Statistical analyses revealed graded attenuation of odor-evoked frontal (Fp1) band-power ratios across groups, with significant differences in several band–od

In these recent years, the world has witnessed a kind of social exclusion and the inability to communicate directly due to the Corona Virus Covid 19 (COVID-19) pandemic, and the consequent difficulty of communicating with patients with hospitals led to the need to use modern technology to solve and facilitate the problem of people communicating with each other. healthcare has made many remarkable developments through the Internet of things (IOT) and cloud computing to monitor real-time patients' data, which has enabled many patients' lives to be saved. this paper presents the design and implementation of a Private Backend Server Software based on an IoT health monitoring system concerned emergency medical services utilizing biosenso

As a result of the increase in wireless applications, this led to a spectrum problem, which was often a significant restriction. However, a wide bandwidth (more than two-thirds of the available) remains wasted due to inappropriate usage. As a consequence, the quality of the service of the system was impacted. This problem was resolved by using cognitive radio that provides opportunistic sharing or utilization of the spectrum. This paper analyzes the performance of the cognitive radio spectrum sensing algorithm for the energy detector, which implemented by using a MATLAB Mfile version (2018b). The signal to noise ratio SNR vs. Pd probability of detection for OFDM and SNR vs. BER with CP cyclic prefix with energy dete

Social Networking has dominated the whole world by providing a platform of information dissemination. Usually people share information without knowing its truthfulness. Nowadays Social Networks are used for gaining influence in many fields like in elections, advertisements etc. It is not surprising that social media has become a weapon for manipulating sentiments by spreading disinformation.  Propaganda is one of the systematic and deliberate attempts used for influencing people for the political, religious gains. In this research paper, efforts were made to classify Propagandist text from Non-Propagandist text using supervised machine learning algorithms. Data was collected from the news sources from July 2018-August 2018. After annota

teen sites Baghdad are made. The sites are divided into two groups, one in Karkh and the other in Rusafa. Assessing the underground conditions can be occurred by drilling vertical holes called exploratory boring into the ground, obtaining soil (disturbed and undisturbed) samples, and testing these samples in a laboratory (civil engineering laboratory /University of Baghdad). From disturbed, the tests involved the grain size analysis and then classified the soil, Atterberg limit, chemical test (organic content, sulphate content, gypsum content and chloride content). From undisturbed samples, the test involved the consolidation test (from this test, the following parameters can be obtained: initial void ratio eo, compression index cc, swel

Big data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such a

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv