LCADNet: a novel light CNN architecture for EEG-based Alzheimer disease detection.

Alzheimer's disease (AD) is a progressive and incurable neurologi-cal disorder with a rising mortality rate, worsened by error-prone, time-intensive, and expensive clinical diagnosis methods. Automatic AD detection methods using hand-crafted Electroencephalogram (EEG) signal features lack accuracy and reliability. A lightweight convolution neural network for AD detection (LCADNet) is investigated to extract disease-specific features while reducing the detection time. The LCADNet uses two convolutional layers for extracting complex EEG features, two fully connected layers for selecting disease-specific features, and a softmax layer for predicting AD detection probability. A max-pooling layer interlaced between convolutional layers decreases the time-domain redundancy in the EEG signal. The efficiency of the LCADNet and four pre-trained models using transfer learning is compared using a publicly available AD detection dataset. The LCADNet shows the lowest computation complexity in terms of both the number of floating point operations and inference time and the highest classification performance across six measures. The generalization of the LCADNet is assessed by cross-testing it with two other publicly available AD detection datasets. It outperforms existing EEG-based AD detection methods with an accuracy of 98.50%. The LCADNet may be a valuable aid for neurologists and its Python implemen- tation can be found at github.com/SandeepSangle12/LCADNet.git.

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. A questionnaire was used to collect data. Chi-squared (Fisher's exact test) test was used to analyze the data. RESULTS: The results showed that 97% of the students used social media applications. Only 1% of them used social media for academic purposes. Whereas 35% of them used these platforms to chat with others, 43% of them browsed these sites to pass time. Moreover, 57% of them were addicted to social media. Additionally, 52% of them reported that social media use had affected their learning activities, 66% of them felt more drawn toward social media than toward academic activities, and 74% of them spent their free time on social media platforms. The most popular applications (i.e., based on usage) were Snapchat (45%), Instagram (22%), Twitter (18%), and WhatsApp (7%). Further, 46% and 39% of them reported going to bed between 11 pm and 12 am and between 1 am and 2 am, respectively. Finally, 68% of them attributed their delayed bedtime to social media use, and 59% of them reported that social media had affected their social interactions. CONCLUSIONS: A majority of the participants reported prolonged use of social networking sites for nonacademic purposes. These habitual behaviors can distract students from their academic work, adversely affect their academic performance, social interactions, and sleep duration, and lead to a sedentary lifestyle and physical inactivity, which in turn can render them vulnerable to non-communicable diseases and mental health problems.

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. . To collect data, we used a domain name system to store the student work data based on the respective location, time, and the subject on which each student was assigned work. Once the student work data were collected, they were sent to an extraction module to remove unwanted data. The remaining data were then fed to a similarity index module, which produced similarity values based on comparisons between the collected data and the student work. This module uses mathematical equations that are built using semantic and syntactic similarity reports. Furthermore, in this study, we recommended procedures that can be applied to avoid plagiarism using the programming approach. This approach can raise awareness of plagiarism among students and encourage them to generate innovative ideas instead of plagiarizing. To attract faculty members to use the software, promotional materials can be customized based on the actual control factors that directly affect their adoption of the software. For example, the campaign should provide information highlighting the ease of implementation of the software for senior faculty members.

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. For the face detection proposal we have compared with the state of the art methods on various benchmarking dataset such as FDDB and WIDER FACE. Furthermore, we have also conducted various experiments on latency and face detection load on three-layer and cloud computing architectures. It shows that our proposal has an edge over cloud computing architecture.

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. In this paper, we propose a novel approach called DeMSum for detecting such duplicate submissions. DeMSum verifies a manuscript (MS) by processing the MS attributes that are accessed through the editorial system. To the best of our knowledge, DeMSum is the first system to address the double submission issue, thus enabling the use of diverse editorial systems for MS review. We implemented a prototype, and our evaluation of the prototype produced excellent results.

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. In view of this, this letter proposes an online lab examination management system to prevent misconduct and to secure the process of lab examination.