Publications by authors named "Mokhtar Mohammadi"

Article Synopsis
  • Lip, oral, and pharyngeal cancers pose significant global health challenges, making it essential to analyze their burden for effective health policies.
  • The study utilized data from the 2019 Global Burden of Diseases, Injuries, and Risk Factors Study to assess cancer incidence, mortality, and life years lost across 204 countries, linking these to socio-demographic factors.
  • Findings revealed approximately 370,000 cases and 199,000 deaths for lip and oral cavity cancer, and 167,000 cases and 114,000 deaths for other pharyngeal cancers in 2019, with smoking being the leading risk factor for these cancers, especially in low and middle SDI regions.
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Applying Deep Learning (DL) in radiological images (i.e., chest X-rays) is emerging because of the necessity of having accurate and fast COVID-19 detectors.

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The existence of various sounds from different natural and unnatural sources in the deep sea has caused the classification and identification of marine mammals intending to identify different endangered species to become one of the topics of interest for researchers and activist fields. In this paper, first, an experimental data set was created using a designed scenario. The whale optimization algorithm (WOA) is then used to train the multilayer perceptron neural network (MLP-NN).

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One of the popular metaheuristic search algorithms is Harmony Search (HS). It has been verified that HS can find solutions to optimization problems due to its balanced exploratory and convergence behavior and its simple and flexible structure. This capability makes the algorithm preferable to be applied in several real-world applications in various fields, including healthcare systems, different engineering fields, and computer science.

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Objective: Detection of event-related potentials (ERPs) in electroencephalography (EEG) is of great interest in the study of brain responses to various stimuli. This is challenging due to the low signal-to-noise ratio of these deflections. To address this problem, a new scheme to detect the ERPs based on smoothness priors is proposed.

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Artificial intelligence (AI) techniques have been considered effective technologies in diagnosing and breaking the transmission chain of COVID-19 disease. Recent research uses the deep convolution neural network (DCNN) as the discoverer or classifier of COVID-19 X-ray images. The most challenging part of neural networks is the subject of their training.

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Since early 2020, Coronavirus Disease 2019 (COVID-19) has spread widely around the world. COVID-19 infects the lungs, leading to breathing difficulties. Early detection of COVID-19 is important for the prevention and treatment of pandemic.

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Article Synopsis
  • The GBD 2019 study systematically estimated the global cancer burden, providing data on incidence, mortality, and disability to help address cancer worldwide.
  • In 2019, an estimated 23.6 million new cancer cases and 10 million cancer deaths occurred globally, marking significant increases in rates since 2010, with cancer becoming a leading cause of both death and disability-adjusted life years (DALYs).
  • The impact of cancer varied across sociodemographic index (SDI) quintiles, with higher SDI areas seeing more new cases, while middle SDI areas experienced more deaths and DALYs, highlighting disparities in cancer burden.
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The early diagnosis and the accurate separation of COVID-19 from non-COVID-19 cases based on pulmonary diffuse airspace opacities is one of the challenges facing researchers. Recently, researchers try to exploit the Deep Learning (DL) method's capability to assist clinicians and radiologists in diagnosing positive COVID-19 cases from chest X-ray images. In this approach, DL models, especially Deep Convolutional Neural Networks (DCNN), propose real-time, automated effective models to detect COVID-19 cases.

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Real-time detection of COVID-19 using radiological images has gained priority due to the increasing demand for fast diagnosis of COVID-19 cases. This paper introduces a novel two-phase approach for classifying chest X-ray images. Deep Learning (DL) methods fail to cover these aspects since training and fine-tuning the model's parameters consume much time.

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The COVID19 pandemic globally and significantly has affected the life and health of many communities. The early detection of infected patients is effective in fighting COVID19. Using radiology (X-Ray) images is, perhaps, the fastest way to diagnose the patients.

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Recent advances in sensor networks and the Internet of Things (IoT) technologies have led to the gathering of an enormous scale of data. The exploration of such huge quantities of data needs more efficient methods with high analysis accuracy. Artificial Intelligence (AI) techniques such as machine learning and evolutionary algorithms able to provide more precise, faster, and scalable outcomes in big data analytics.

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