Shazia Shamas¹, Surya Narayan Panda^1,*, Ishu Sharma^1,*, Kalpna Guleria¹, Aman Singh^2,3,4, Ahmad Ali AlZubi⁵, Mallak Ahmad AlZubi⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1051-1075, 2024, DOI:10.32604/cmes.2023.030712

Abstract The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis and planning intervention. This research work addresses the major issues pertaining to the field of medical image processing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposes an improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. The better resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In this process, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarm intelligent techniques. The techniques… More >

VIBHAV VARSHNEY^1,*, PRASHANT SINGH KUSHWAH², NEETU AGRAWAL¹, AHSAS GOYAL^1,*, GOVIND SINGH²

BIOCELL, Vol.47, No.10, pp. 2141-2149, 2023, DOI:10.32604/biocell.2023.030853

Abstract The persistent global pandemic, COVID-19, stems from the pathogenic influence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), yielding an unprecedented worldwide crisis. With reference to a WHO report, the count of COVID-19 cases had exceeded 754 million by February 03, 2023. Intriguingly, emerging research has spotlighted the intricate interplay of gut microbiota and mitochondrial entities, acting as potent immunomodulatory factors at the cellular and molecular levels. This interconnection operates through a series of dynamic mechanisms. SARS-CoV-2 infection perturbs the delicate equilibrium of gut microbiota, leading to dysbiosis—a signature biomarker. This imbalance is intrinsically linked to exacerbated COVID-19 progression. Mechanistically,… More >

Mozamel M. Saeed^1,*, Zaher Al Aghbari²

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 325-348, 2023, DOI:10.32604/cmc.2023.036894

Abstract Emailing is among the cheapest and most easily accessible platforms, and covers every idea of the present century like banking, personal login database, academic information, invitation, marketing, advertisement, social engineering, model creation on cyber-based technologies, etc. The uncontrolled development and easy access to the internet are the reasons for the increased insecurity in email communication. Therefore, this review paper aims to investigate deep learning approaches for detecting the threats associated with e-mail security. This study compiles the literature related to the deep learning methodologies, which are applicable for providing safety in the field of cyber security of email in different… More >

Saqlain Abbas^1,2,*, Zulkarnain Abbas³, Xiaotong Tu⁴, Yanping Zhu²

Journal on Artificial Intelligence, Vol.5, pp. 43-56, 2023, DOI:10.32604/jai.2023.040948

Abstract Structural health monitoring (SHM) is considered an effective approach to analyze the efficient working of several mechanical components. For this purpose, ultrasonic guided waves can cover long-distance and assess large infrastructures in just a single test using a small number of transducers. However, the working of the SHM mechanism can be affected by some sources of variations (i.e., environmental). To improve the final results of ultrasonic guided wave inspections, it is necessary to highlight and attenuate these environmental variations. The loading parameters, temperature and humidity have been recognized as the core environmental sources of variations that affect the SHM sensing… More >

Fábio A. Caldas^a,*, Paulo M. Coelho^b,†

Frontiers in Heat and Mass Transfer, Vol.8, pp. 1-8, 2017, DOI:10.5098/hmt.8.32

Abstract In this paper, Nusselt numbers for a power-law fluid in a fully developed laminar flow between parallel plates with constant, and different, wall heat fluxes in the presence of dissipation effects are presented. The Nusselt numbers values were obtained following two different approaches. One is the “classical” approach, based on a single bulk temperature, and this approach is used in this work to obtain for the first time generic analytical expressions for Nusselt numbers. In the new approach, different bulk temperatures are used for each Nu′ determination, one bulk temperature for each side of the location of the temperature profile… More >

Jayashree Shetty¹, Nisha P. Shetty^1,*, Hrushikesh Kothikar¹, Saleh Mowla¹, Aiswarya Anand¹, Veeraj Hegde²

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1345-1362, 2023, DOI:10.32604/iasc.2023.039206

Abstract Alzheimer’s disease (AD) is an intensifying disorder that causes brain cells to degenerate early and destruct. Mild cognitive impairment (MCI) is one of the early signs of AD that interferes with people’s regular functioning and daily activities. The proposed work includes a deep learning approach with a multimodal recurrent neural network (RNN) to predict whether MCI leads to Alzheimer’s or not. The gated recurrent unit (GRU) RNN classifier is trained using individual and correlated features. Feature vectors are concatenated based on their correlation strength to improve prediction results. The feature vectors generated are given as the input to multiple different… More >

Amani Yahyaoui¹, Jawad Rasheed^2,*, Shtwai Alsubai³, Raed M. Shubair⁴, Abdullah Alqahtani⁵, Buket Isler⁶, Rana Zeeshan Haider⁷

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2247-2261, 2023, DOI:10.32604/iasc.2023.036840

Abstract The coronavirus (COVID-19) is a disease declared a global pandemic that threatens the whole world. Since then, research has accelerated and varied to find practical solutions for the early detection and correct identification of this disease. Several researchers have focused on using the potential of Artificial Intelligence (AI) techniques in disease diagnosis to diagnose and detect the coronavirus. This paper developed deep learning (DL) and machine learning (ML) -based models using laboratory findings to diagnose COVID-19. Six different methods are used in this study: K-nearest neighbor (KNN), Decision Tree (DT) and Naive Bayes (NB) as a machine learning method, and… More >

Hong Zhang^1,*, Qi Wang¹, Lixing Chen¹, Jiaming Zhou¹, Haijian Shao²

Energy Engineering, Vol.120, No.8, pp. 1867-1883, 2023, DOI:10.32604/ee.2023.028453

Abstract Induction motors (IMs) typically fail due to the rate of stator short-circuits. Because of the similarity of the thermal images produced by various instances of short-circuit and the minor interclass distinctions between categories, non-destructive fault detection is universally perceived as a difficult issue. This paper adopts the deep learning model combined with feature fusion methods based on the image’s low-level features with higher resolution and more position and details and high-level features with more semantic information to develop a high-accuracy classification-detection approach for the fault diagnosis of IMs. Based on the publicly available thermal images (IRT) dataset related to condition… More > Graphic Abstract

Ali Derakhshan¹, Yongliang Wang^2,*, Yongxiang Wang^2,*, José Luis Ortega-Martín³

International Journal of Mental Health Promotion, Vol.25, No.7, pp. 823-832, 2023, DOI:10.32604/ijmhp.2023.029877

Abstract The adequacy of language education largely depends on the favorable and unfavorable emotions that teachers and students experience throughout the education process. Simply said, emotional factors play a key role in improving the quality of language teaching and learning. Furthermore, these emotional factors also promote the well-being of language teachers and learners and place them in a suitable mental condition. In view of the favorable impact of emotional factors on the mental health of language teachers and learners, many educational scholars around the world have studied these factors, their background, and their pedagogical consequences. Nonetheless, the majority of previous studies… More >

MIRKO TRECCANI^*, ELENA LOCATELLI, CRISTINA PATUZZO, GIOVANNI MALERBA^*

BIOCELL, Vol.47, No.6, pp. 1225-1241, 2023, DOI:10.32604/biocell.2023.027884

Abstract The advent of genomic big data and the statistical need for reaching significant results have led genome-wide association studies to be ravenous of a huge number of genetic markers scattered along the whole genome. Since its very beginning, the so-called genotype imputation served this purpose; this statistical and inferential procedure based on a known reference panel opened the theoretical possibility to extend association analyses to a greater number of polymorphic sites which have not been previously assayed by the used technology. In this review, we present a broad overview of the genotype imputation process, showing the most known methods and… More >