Impact of Knowledge and Attitude on Saudis’ Physical Activity Practice and Inactivity Barriers: A Questionnaire-based Study

BACKGROUND: Community participation in physical activity is considered a major public health preference of WHO. Saudi Arabia in the last decades faced many tremendous economic changes leading to adoption of western dietary habits associated with sedentary lifestyle. AIM: We aimed to study the relationship between both physical activity knowledge and attitude of community to the practice of individuals. METHODS: We used a questionnaire consists of a mixture of closed-ended questions. Participants were recruited through direct meetings in local markets, schools, and workplaces. Seven hundred and sixty six individuals agreed to participate. RESULTS: Overall correct answers to questions about importance of physical activity were 76.58%. The predominance of participants’ attitude was to establish public places for physical activity in each neighborhood (92.1%). Participants acknowledged that they exercise to improve their health (47.5%). Participants mainly perform light exercises (47.2%) on basis of 1–3 times weekly (48.9%). About 90.8% of participants admitted that they like to increase duration of their physical activity. CONCLUSIONS: Overall physical activity practice of participants’ needs encourage overcoming obstacles that prevent individuals from practicing especially lack of time

Dermatological Lesions of Cholesterol Embolization Syndrome and Kaposi Sarcoma Mimic Primary Systemic Vasculitis: Case Report Study

Primary systemic vasculitis can present with a wide spectrum of manifestations ranging from systemic non-specific features such as fever, malaise, arthralgia, and myalgia to specific organ damage. We describe two cases of cholesterol embolization syndrome and Kaposi sarcoma mimicking primary systemic vasculitis, both of which were characterized by features such as livedo reticularis, blue toe syndrome, a brown, purpuric skin rash, and positive p-ANCA associated with Kaposi sarcoma. Establishing the right diagnosis was challenging, and thus we aim in this study to highlight the possible ways to distinguish them from primary systemic vasculitis. Keywords: Dermatological lesions, Cholesterol embolization syndrome, Kaposi sarcoma, vasculitis mimic

Automatic neonatal sleep stage classification:A comparative study

Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study

Sufficiency and Efficiency of Field Training for Radiology Students During Internship Experience in Najran University, Saudi Arabia

Purpose: The study was design to evaluate the effectiveness and adequacy of the internship period employing quantitative study descriptive survey approach.   Theoretical framework: Internship is requirement of every student of radiology program of Radiological Sciences patch for the award of bachelor's degree at Najran University, Saudi Arabia. The competency level would demonstrate influence the sufficiency and efficiency of clinical training during internship period which represent six months after completing nine levels of radiology program.   Design\Methodology\Approach: The survey was distributed to the tow levels of the last year of radiological sciences which composed of 81 male and female students which gathered seventy-seven (77) participants. Data collected through a questionnaire and summarized as percentages, frequencies, means and standard deviations using SPSS version 20.0.   Findings: The study revealed un adequacy of the internship period and showed low efficiency due to its short duration.   Research, Practical, Social Implication:The research construct and variables are identified the effectiveness and adequacy of the internship period.this  study will be the modele of internship with a new qualitative change related to a period of time acceptable to students, similar to other universities.   Originality/Value: The originality and value in this study are the framework conceptance and questionnaire that prepared and proved for evaluating the effectiveness and adequacy of the internship period for student of radiology program.   Conclusion: In general internship period must be efficient and adequate to enhance sufficiency and efficiency experience by intern trainees

A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

© 2023 Tech Science Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Tomato leaf diseases significantly impact crop production, necessitating early detection for sustainable farming. Deep Learning (DL) has recently shown excellent results in identifying and classifying tomato leaf diseases. However, current DL methods often require substantial computational resources, hindering their application on resource-constrained devices. We propose the Deep Tomato Detection Network (DTomatoDNet), a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome this. The Convn kernels used in the proposed (DTomatoDNet) framework is 1 × 1, which reduces the number of parameters and helps in more detailed and descriptive feature extraction for classification. The proposed DTomatoDNet model is trained from scratch to determine the classification success rate. 10,000 tomato leaf images (1000 images per class) from the publicly accessible dataset, covering one healthy category and nine disease categories, are utilized in training the proposed DTomatoDNet approach. More specifically, we classified tomato leaf images into Target Spot (TS), Early Blight (EB), Late Blight (LB), Bacterial Spot (BS), Leaf Mold (LM), Tomato Yellow Leaf Curl Virus (YLCV), Septoria Leaf Spot (SLS), Spider Mites (SM), Tomato Mosaic Virus (MV), and Tomato Healthy (H). The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%, demonstrating excellent accuracy in differentiating between tomato diseases. The model could be used on mobile platforms because it is lightweight and designed with fewer layers. Tomato farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.Peer reviewe

A Self-Attention-Based Deep Convolutional Neural Networks for IIoT Networks Intrusion Detection

The Industrial Internet of Things (IIoT) comprises a variety of systems, smart devices, and an extensive range of communication protocols. Hence, these systems face susceptibility to privacy and security challenges, making them prime targets for malicious attacks that can result in harm to the overall system. Privacy breach issues are a notable concern within the realm of IIoT. Various intrusion detection systems based on machine learning (ML) and deep learning (DL) have been introduced to detect malicious activities within these networks and identify attacks. The existing ML and DL-based models face challenges when confronted with highly imbalanced training. Repetitive data in network datasets inflates model performance, as the model has encountered much of the test set data during training. Moreover, these models decrease performance when confronted with datasets that include repetitions of similar data across various classes, where only the class labels are different. To overcome the challenges inherent in existing systems, this paper presents a self-attention-based deep convolutional neural network (SA-DCNN) model designed for monitoring the IIoT networks and detecting malicious activities. Additionally, a two-step cleaning method has been implemented to eliminate redundancy within the training data, considering both intra-class and cross-class samples. The performance of the SA-DCNN model is assessed using IoTID20 and Edge-IIoTset datasets. Furthermore, the proposed study is demonstrated through a comprehensive comparison with other ML and DL models, as well as against relevant studies, showcasing the superior performance and efficacy of the proposed model

Association Between Lipid Profile and Diabetic Foot Ulcer

Diabetic foot ulcer is a serious disabling consequence of Diabetes Mellitus. They are characterized by the breakdown of skin and underlying tissues in the feet, and are a major cause of lower limb amputations. Various risk factors have been identified for the development of diabetic foot ulcers, including poor glycemic control, peripheral neuropathy, peripheral arterial disease, and impaired wound healing. it is considered that the lipid profile is one of many factors that contribute to the formation and progression of diabetic foot ulcers. To stratify the incidence of diabetic foot ulcers (DFUs), biomarkers are required. The aim of this review is to assess the relationship between the risk of DFU and lipid profile in diabetic patients

Energy efficiency considerations in software‐defined wireless body area networks

Wireless body area networks (WBAN) provide remote services for patient monitoring which allows healthcare practitioners to diagnose, monitor, and prescribe them without their physical presence. To address the shortcomings of WBAN, software-defined networking (SDN) is regarded as an effective approach in this prototype. However, integrating SDN into WBAN presents several challenges in terms of safe data exchange, architectural framework, and resource efficiency. Because energy expenses account for a considerable portion of network expenditures, energy efficiency has to turn out to be a crucial design criterion for modern networking methods. However, creating energy-efficient systems is difficult because they must balance energy efficiency with network performance. In this article, the energy efficiency features are discussed that can widely be used in the software-defined wireless body area network (SDWBAN). A comprehensive survey has been carried out for various modern energy efficiency models based on routing algorithms, optimization models, secure data delivery, and traffic management. A comparative assessment of all the models has also been carried out for various parameters. Furthermore, we explore important concerns and future work in SDWBAN energy efficiency

Sustainable Collaboration: Federated Learning for Environmentally Conscious Forest Fire Classification in Green Internet of Things (IoT)

Forests are an invaluable natural resource, playing a crucial role in the regulation of both local and global climate patterns. Additionally, they offer a plethora of benefits such as medicinal plants, food, and non-timber forest products. However, with the growing global population, the demand for forest resources has escalated, leading to a decline in their abundance. The reduction in forest density has detrimental impacts on global temperatures and raises the likelihood of forest fires. To address these challenges, this paper introduces a Federated Learning framework empowered by the Internet of Things (IoT). The proposed framework integrates with an Intelligent system, leveraging mounted cameras strategically positioned in highly vulnerable areas susceptible to forest fires. This integration enables the timely detection and monitoring of forest fire occurrences and plays its part in avoiding major catastrophes. The proposed framework incorporates the Federated Stochastic Gradient Descent (FedSGD) technique to aggregate the global model in the cloud. The dataset employed in this study comprises two classes: fire and non-fire images. This dataset is distributed among five nodes, allowing each node to independently train the model on their respective devices. Following the local training, the learned parameters are shared with the cloud for aggregation, ensuring a collective and comprehensive global model. The effectiveness of the proposed framework is assessed by comparing its performance metrics with the recent work. The proposed algorithm achieved an accuracy of 99.27 % and stands out by leveraging the concept of collaborative learning. This approach distributes the workload among nodes, relieving the server from excessive burden. Each node is empowered to obtain the best possible model for classification, even if it possesses limited data. This collaborative learning paradigm enhances the overall efficiency and effectiveness of the classification process, ensuring optimal results in scenarios where data availability may be constrained

SkipGateNet: A Lightweight CNN-LSTM Hybrid Model with Learnable Skip Connections for Efficient Botnet Attack Detection in IoT

The rise of Internet of Things (IoT) has led to increased security risks, particularly from botnet attacks that exploit IoT device vulnerabilities. This situation necessitates effective Intrusion Detection Systems (IDS), that are accurate, lightweight, and fast (having less inference time), designed particularly to detect botnet attacks in resource constrained IoT devices. This paper proposes SkipGateNet, a novel deep learning model designed for detecting Mirai and Bashlite botnet attacks in resource constrained IoT and fog computing environments. SkipGateNet is a lightweight, fast model combining 1D-Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) layers. The novelty of this model lies in the integration of ‘Learnable Skip Connections’. These connections feature gating mechanisms that enhance detection by focusing on relevant features and ignoring irrelevant ones. They add adaptability to the architecture, performing feature selection and propagating only essential features to deeper layers. Tested on the N-BaIoT dataset, SkipGateNet efficiently detects ten types of botnet attacks, with a remarkable test accuracy of 99.91%. It is also compact (2596.87 KB) and demonstrates a quick inference time of 8.0 milliseconds, suitable for real-time implementation in resource-limited settings. While evaluating its performance, parameters like precision, recall, accuracy, and F1 score were considered, along with statistical reliability measures like Cohen’s Kappa Coefficient and Matthews Correlation Coefficient. These highlight its reliability and effectiveness in IoT security challenges. The paper also compares SkipGateNet to existing models and four other deep learning architectures, including two sequential CNN architectures, a simple CNN+LSTM architecture, and a CNN+LSTM with standard skip connections. SkipGateNet surpasses all in accuracy and inference time, demonstrating its superiority in addressing IoT security issues