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Articles by Ali AA
Electrocardiogram-driven depth of anesthesia monitoring: leveraging autonomic signatures and graph neural networks for low-cost, accurate identification
Accurate monitoring of the depth of anesthesia (DoA) is a critical component of modern surgical medicine. About 300 million surgeries are done around the world every year, and most of them need some kind of anesthetic (Dobson, 2020). It is very important to keep the right amount of anesthetic: not enough anesthesia might cause consciousness during surgery, while too much anesthesia can make hemodynamic instability more likely and slow down recovery after surgery.
Deep learning-based modeling of sulfur-extended asphalt (SEA) properties: a novel computational framework
Deep learning-based modeling of sulfur-extended asphalt (SEA) properties: a novel computational framework 1Department of Computer Engineering, College of Computer, Qassim University, Buraydah, Saudi Arabia 2Department of Chemical Engineering, College of Engineering, King Faisal University, Eastern Province, Al-Ahsa, Saudi Arabia 3Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Eastern Province, Al-Ahsa, Saudi Arabia DOI 10.7717/peerj-cs.3725...
Secure 3D data hiding through cryptographic steganalysis resistance: reducing geometric inconsistency vulnerabilities
The widespread adoption of 3D models as a dominant data format, propelled by improvements in computer graphics, virtual reality (VR), augmented reality (AR), and notably the metaverse, has inherently broadened the domain of steganography into an additional dimension (Kheddar et al., 2024). 3D steganography started to gain prominence as academics investigated the distinctive characteristics of 3D models for data embedding.
Transfer learning approaches for EfficientNetV2 B0 and ImageNet skin cancer classification in a convolutional neural network
Transfer learning approaches for EfficientNetV2 B0 and ImageNet skin cancer classification in a convolutional neural network Department of Computer Science and Engineering, Infant Jesus College of Engineering, Tuticorin, Tamil Nadu, India DOI 10.7717/peerj-cs.3362 Academic Editor Consolato Sergi Subject Areas Computer Vision, Data Mining and Machine Learning, Neural Networks Keywords Skin cancer detection, Skin lesion, Deep learning algorithms, Convolutional neural networks, Image...
Autoregressive translation model design integrating syntactic encoding computation and reinforcement learning: a framework for enhancing translation optimization
High-quality machine translation tools play a pivotal role in transforming complex reading materials into languages familiar to students. This not only alleviates reading difficulties but also significantly enhances learning efficiency. Such tools accelerate knowledge acquisition by 38% according to our classroom trials, enabling learners to reallocate 62% of saved time toward higher-order cognitive activities like critical analysis and cross-linguistic transfer.
Cloud-based real-time enhancement for disease prediction using Confluent Cloud, Apache Kafka, feature optimization, and explainable artificial intelligence
Chronic kidney disease (CKD), characterized by the kidneys’ inability to remove waste and fluids while maintaining essential blood balances, poses severe health risks due to the accumulation of debris and fluids in the body (Romagnani et al., 2017; Stenvinkel, 2010). This condition affects electrical balance, hormone secretion, and blood pressure regulation.
Comparative evaluation of approaches & tools for effective security testing of Web applications
Ensuring the security of software and Web applications is paramount in today’s digital landscape. There are numerous incidents reported where exploitation of a well-known vulnerability led to serious consequences. For example, in February 2024, a major data breach was reported in which over 54 million user profiles were exposed due to a misconfiguration that resulted in the compromise of sensitive data (Hughes, 2024).
Early detection and analysis of accurate breast cancer for improved diagnosis using deep supervised learning for enhanced patient outcomes
Early detection and analysis of accurate breast cancer for improved diagnosis using deep supervised learning for enhanced patient outcomes 1Regenerative Medicine, International Association of Stem Cell & Regenerative Medicine, New Delhi, India 2Department of Radiation Oncology, Shunde Hospital of Southern Medical University, Foshan, China 3Department of Biochemistry, Quaid-i-Azam University, Islamabad, Punjab, Pakistan 4Faculty of Science, Autonomous University of Madrid, Spanish National...
The benefits of social insurance system prediction using a hybrid fuzzy time series method
Forecasting is a critical aspect for solving problems, particularly in various businesses and the insurance industry, where predictions of future events must inform decision making. Therefore, forecasting techniques enable the insurance authorities to anticipate future results and formulate strategies accordingly. Thus, accurate and efficient forecasting systems are highly sought after in this industry (Khalil, Liu & Ali, 2022a).
AI-based automated breast cancer segmentation in ultrasound imaging based on Attention Gated Multi ResU-Net
Breast cancer is the most frequent cancer in women worldwide and is the second most significant cause of cancer mortality in women after lung cancer. Breast cancer makes up for around 25% of all malignancies in women, according to the World Health Organization (WHO). In 2018, it is anticipated that 2.1 million new breast cancer cases will be diagnosed and 627,000 people will die from the disease in 2021–2022 (DeSantis et al., 2019).
A novel adaptive weight bi-directional long short-term memory (AWBi-LSTM) classifier model for heart stroke risk level prediction in IoT
The Internet of Things (IoT) has been widely used in several applications, particularly in recent decades (Gubbi et al., 2013). Many medical applications now extensively utilize IoT to support individuals with their health conditions. As a result, the importance of mobile health services has increased, as they are crucial for monitoring and managing patients with chronic conditions such as diabetes and cardiovascular disease (Yuehong et al., 2016; Guariguata et al., 2014).
A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images
Diabetes mellitus (DM) is a dangerous condition that is typically brought on by excessive blood sugar levels. Among the organs that are frequently negatively impacted by diabetes over an extended period are the kidneys, brain, and optic nerves. Diabetes affects people of all ages and is a significant public health issue (Mayya, Kamath & Kulkarni, 2021; Tsiknakis et al., 2021; Tripathi & Kumar, 2020). In Fig.
The role of sirtuin1 in liver injury: molecular mechanisms and novel therapeutic target
The liver plays a critical role in maintaining human health by carrying out a multitude of physiological processes. Unfortunately, liver disease is a common and serious threat to human health caused by a range of factors like chemical exposure, viral infections, underlying diseases, genetic factors (Zhang et al., 2018).
Offloading the computational complexity of transfer learning with generic features
In recent years, deep learning (DL) applications have seen wide adoption. Several real-life improvements have been witnessed in different fields of life, such as image and voice detection (Chen & Peng, 2023), internet search, fraud identification (Amiri et al., 2023; Nguyen & Reddi, 2023), email/spam filtering (Debnath & Kar, 2022), and risk modeling in finance (Liu & Yu, 2022).
Integrating hybrid transfer learning with attention-enhanced deep learning models to improve breast cancer diagnosis
Breast cancer is the most lethal disease in women and remains a severe public health concern in many countries (The Early Breast Cancer Trialists’ Collaborative Group (EBCTCG), 1990). The American Cancer Society’s projections for 2017 anticipated that more than 250,000 women would receive a diagnosis of invasive breast cancer, with over 40,000 succumbing to the disease (Smith et al., 2017).
Knowledge of cardiovascular disease risk factors among caregivers of cardiology patients attending Jordan University Hospital
Knowledge of cardiovascular disease risk factors among caregivers of cardiology patients attending Jordan University Hospital 1Cardiology Division, University of Jordan, Amman, Jordan 2School of Medicine, University of Jordan, Amman, Jordan 3Faculty of Medicine, Al-Balqa Applied University, Al-Salt, Jordan DOI 10.7717/peerj.16830 Academic Editor Praveen Korla Subject Areas Cardiology, Nursing, Public Health Keywords Cardiovascular disease, Caregivers, Knowledge, Risk factors Copyright ©...
Multiple myeloma and its rare paraneoplastic manifestations simmering under the surface
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[Prevalence of dyslipidemia in the biochemistry laboratory of the Reference National Teaching Hospital of N'Djamena, Chad]
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99mTc-MIBI SPECT/CT Evaluation of a Renal Collision Tumor
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Adipose tissue-derived mesenchymal stem cells ameliorate cognitive impairment in Alzheimer's disease rat model: Emerging role of SIRT1
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Interactions within the climate-vegetation-fire nexus may transform 21st century boreal forests in northwestern Canada
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Low Skeletal Muscle as a Risk Factor for Worse Survival in Nonmetastatic Renal Cell Carcinoma with Venous Tumor Thrombus
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Early Clinical Experience and comparison between Percutaneous and Surgical arteriovenous fistula
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Effect of Citrus aurantium juice as a disinfecting agent on quality and bacterial communities of striped catfish steaks stored at −20 °C
pH, peroxide values, TVB-N, and TCA-soluble peptides The change in pH value of each treatment is illustrated in Fig. 3A. On day 0, the pH of the control and TM was 6.68 ± 0.01 and 5.71 ± 0.25, respectively. This result indicated that the pH value of TM was lower than that of the control (P < 0.05) due to the low pH condition of C. aurantium juice. No significant differences (P > 0.05) were then indicated in the pH values between the control treatment and TM during storage on days 14 and 21.
RNA sequencing-based exploration of the effects of far-red light on microRNAs involved in the shade-avoidance response of D. officinale
MiRNAs participate in the signal transduction pathway of far-red light in the shade avoidance of D. officinale On the basis of previous studies (Zheng et al., 2013a; Sharkhuu et al., 2014), by mining miRNA data, we established a regulatory network of far-red light on the shade-avoidance response of D. officinale (Fig. 7). miR395b may be involved in the signal transduction pathway of far-red light on D.
A clinical audit on the utilization of group O-negative red cells and the lesson learnt
1. Pandey HC, Coshic P, Chippy CS, Arcot PJ, Kumar K. Blood supply management in times of SARS-CoV-2 pandemic – Challenges, strategies adopted, and the lessons learned from the experience of a hospital-based blood centre. Vox Sang 2021;116:497-503. 2. Gupta AM, Jain P. Blood donor deferral periods after COVID-19 vaccination. Transfus Apher Sci 2021;60:103179. 3. Arshad Ali S, Azim D, Hassan HM, Iqbal A, Ahmed N, Kumar S, et al.
Challenges of managing hypertension in Pakistan - a review
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