Deep learning hybrid model ECG classification using AlexNet and parallel dual branch fusion network model.

Cardiovascular diseases are a cause of death making it crucial to accurately diagnose them. Electrocardiography plays a role in detecting heart issues such as heart attacks, bundle branch blocks and irregular heart rhythms. Manual analysis of ECGs is prone to mistakes and time consuming, underscoring the importance of automated methods.

AI-Driven Real-Time Classification of ECG Signals for Cardiac Monitoring Using i-AlexNet Architecture.

The healthcare industry has evolved with the advent of artificial intelligence (AI), which uses advanced computational methods and algorithms, leading to quicker inspection, forecasting, evaluation and treatment. In the context of healthcare, artificial intelligence (AI) uses sophisticated computational methods to evaluate, decipher and draw conclusions from patient data. AI has the potential to revolutionize the healthcare industry in several ways, including better managerial effectiveness, individualized treatment regimens and diagnostic improvements.

Augmenting Radiological Diagnostics with AI for Tuberculosis and COVID-19 Disease Detection: Deep Learning Detection of Chest Radiographs.

In this research, we introduce a network that can identify pneumonia, COVID-19, and tuberculosis using X-ray images of patients' chests. The study emphasizes tuberculosis, COVID-19, and healthy lung conditions, discussing how advanced neural networks, like VGG16 and ResNet50, can improve the detection of lung issues from images. To prepare the images for the model's input requirements, we enhanced them through data augmentation techniques for training purposes.

Wearable Sensor Data Classification for Identifying Missing Transmission Sequence Using Tree Learning.

Wearable Sensor (WS) data accumulation and transmission are vital in analyzing the health status of patients and elderly people remotely. Through specific time intervals, the continuous observation sequences provide a precise diagnosis result. This sequence is however interrupted due to abnormal events or sensor or communicating device failures or even overlapping sensing intervals.

A Novel Approach for Multichannel Epileptic Seizure Classification Based on Internet of Things Framework Using Critical Spectral Verge Feature Derived from Flower Pollination Algorithm.

A novel approach for multichannel epilepsy seizure classification which will help to automatically locate seizure activity present in the focal brain region was proposed. This paper suggested an Internet of Things (IoT) framework based on a smart phone by utilizing a novel feature termed multiresolution critical spectral verge (MCSV), based on frequency-derived information for epileptic seizure classification which was optimized using a flower pollination algorithm (FPA). A wireless sensor technology (WSN) was utilized to record the electroencephalography (EEG) signal of epileptic patients.

Effect of social media use on learning, social interactions, and sleep duration among university students.

Background: Social networking sites are widely used by university students. This study investigated the purposes for which social networking sites are used and their effects on learning, social interaction, and sleep duration.

Material And Methods: A cross-sectional study was conducted among 300, 17-29-year-old female students at Prince Sattam bin Abdul Aziz University.

University learning with anti-plagiarism systems.

We have designed an anti-plagiarism software to detect plagiarism in students' assignments, especially written assignments, homework, and research reports, which will hereinafter be collectively referred to as student work. We used our university network to gather student work to detect plagiarism. .

A Three Layered Decentralized IoT Biometric Architecture for City Lockdown During COVID-19 Outbreak.

In this article, we have built a prototype of a decentralized IoT based biometric face detection framework for cities that are under lockdown during COVID-19 outbreaks. To impose restrictions on public movements, we have utilized face detection using three-layered edge computing architecture. We have built a deep learning framework of multi-task cascading to recognize the face.

E-commerce Review System to Detect False Reviews.

E-commerce sites have been doing profitable business since their induction in high-speed and secured networks. Moreover, they continue to influence consumers through various methods. One of the most effective methods is the e-commerce review rating system, in which consumers provide review ratings for the products used.

A Proposal to Detect the Double Submission of a Manuscript Sent for Review.

Along with the rapid growth of editorial systems and publishers, the number of research articles is increasing, which creates a need for an effective dissemination strategy. Researchers commonly use editorial systems in a candid manner. However, when researchers concurrently submit the same contribution in more than one editorial system, it is considered unethical.

An Online Lab Examination Management System (OLEMS) to Avoid Malpractice.

Examination and evaluation are two important phases of education at any level of a student's curriculum. However, these assessment processes are problematic in the sense that they encourage learners to devise ways to be dishonest. The traditional way of conducting exams is particularly conducive to dishonesty.