Mesfer Al Duhayyim¹, Haya Mesfer Alshahrani², Fahd N. Al-Wesabi³, Mohammed Alamgeer⁴, Anwer Mustafa Hilal^5,*, Manar Ahmed Hamza⁵

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5871-5887, 2022, DOI:10.32604/cmc.2022.021093

Abstract An exponential growth in advanced technologies has resulted in the exploration of Ocean spaces. It has paved the way for new opportunities that can address questions relevant to diversity, uniqueness, and difficulty of marine life. Underwater Wireless Sensor Networks (UWSNs) are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental conditions. In this scenario, it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water bodies. Several models have been… More >

V. Thanikachalam^1,*, S. Shanthi², K. Kalirajan³, Sayed Abdel-Khalek^4,5, Mohamed Omri⁶, Lotfi M. Ladhar⁷

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5667-5681, 2022, DOI:10.32604/cmc.2022.020965

Abstract The rapid development of biomedical imaging modalities led to its wide application in disease diagnosis. Tongue-based diagnostic procedures are proficient and non-invasive in nature to carry out secondary diagnostic processes ubiquitously. Traditionally, physicians examine the characteristics of tongue prior to decision-making. In this scenario, to get rid of qualitative aspects, tongue images can be quantitatively inspected for which a new disease diagnosis model is proposed. This model can reduce the physical harm made to the patients. Several tongue image analytical methodologies have been proposed earlier. However, there is a need exists to design an intelligent Deep Learning (DL) based disease… More >

Atif Naseer^1,*, Enrique Nava Baro¹, Sultan Daud Khan², Yolanda Vila³, Jennifer Doyle⁴

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5321-5344, 2022, DOI:10.32604/cmc.2022.020886

Abstract The Norway lobster, Nephrops norvegicus, is one of the main commercial crustacean fisheries in Europe. The abundance of Nephrops norvegicus stocks is assessed based on identifying and counting the burrows where they live from underwater videos collected by camera systems mounted on sledges. The Spanish Oceanographic Institute (IEO) and Marine Institute Ireland (MI-Ireland) conducts annual underwater television surveys (UWTV) to estimate the total abundance of Nephrops within the specified area, with a coefficient of variation (CV) or relative standard error of less than 20%. Currently, the identification and counting of the Nephrops burrows are carried out manually by the marine… More >

Mohd Anul Haq^1,*, Abdul Khadar Jilani¹, P. Prabu²

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4599-4617, 2022, DOI:10.32604/cmc.2022.020495

Abstract The understanding of water resource changes and a proper projection of their future availability are necessary elements of sustainable water planning. Monitoring GWS change and future water resource availability are crucial, especially under changing climatic conditions. Traditional methods for in situ groundwater well measurement are a significant challenge due to data unavailability. The present investigation utilized the Long Short Term Memory (LSTM) networks to monitor and forecast Terrestrial Water Storage Change (TWSC) and Ground Water Storage Change (GWSC) based on Gravity Recovery and Climate Experiment (GRACE) datasets from 2003–2025 for five basins of Saudi Arabia. An attempt has been made… More >

Chinu Singla¹, Fahd N. Al-Wesabi^2,3, Yash Singh Pathania¹, Badria Sulaiman Alfurhood⁴, Anwer Mustafa Hilal^5,*, Mohammed Rizwanullah⁵, Manar Ahmed Hamza⁵, Mohammad Mahzari⁶

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6365-6380, 2022, DOI:10.32604/cmc.2022.020480

Abstract The task of automatically analyzing sentiments from a tweet has more use now than ever due to the spectrum of emotions expressed from national leaders to the average man. Analyzing this data can be critical for any organization. Sentiments are often expressed with different intensity and topics which can provide great insight into how something affects society. Sentiment analysis in Twitter mitigates the various issues of analyzing the tweets in terms of views expressed and several approaches have already been proposed for sentiment analysis in twitter. Resources used for analyzing tweet emotions are also briefly presented in literature survey section.… More >

G. Madhu¹, B. Lalith Bharadwaj¹, Rohit Boddeda², Sai Vardhan¹, K. Sandeep Kautish³, Khalid Alnowibet⁴, Adel F. Alrasheedi⁴, Ali Wagdy Mohamed^5,6,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5467-5469, 2022, DOI:10.32604/cmc.2022.020455

Abstract COVID-19 is a growing problem worldwide with a high mortality rate. As a result, the World Health Organization (WHO) declared it a pandemic. In order to limit the spread of the disease, a fast and accurate diagnosis is required. A reverse transcript polymerase chain reaction (RT-PCR) test is often used to detect the disease. However, since this test is time-consuming, a chest computed tomography (CT) or plain chest X-ray (CXR) is sometimes indicated. The value of automated diagnosis is that it saves time and money by minimizing human effort. Three significant contributions are made by our research. Its initial purpose… More >

Pijush Kanti Dutta Pramanik¹, Nilanjan Sinhababu², Anand Nayyar^3,4,*, Mehedi Masud⁵, Prasenjit Choudhury¹

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5199-5212, 2022, DOI:10.32604/cmc.2022.019630

Abstract In mobile crowd computing (MCC), people’s smart mobile devices (SMDs) are utilized as computing resources. Considering the ever-growing computing capabilities of today’s SMDs, a collection of them can offer significantly high-performance computing services. In a local MCC, the SMDs are typically connected to a local Wi-Fi network. Organizations and institutions can leverage the SMDs available within the campus to form local MCCs to cater to their computing needs without any financial and operational burden. Though it offers an economical and sustainable computing solution, users’ mobility poses a serious issue in the QoS of MCC. To address this, before submitting a… More >

Talha Saeed, Chu Kiong Loo^*, Muhammad Shahreeza Safiruz Kassim

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4357-4372, 2022, DOI:10.32604/cmc.2022.019076

Abstract

Abnormalities of the gastrointestinal tract are widespread worldwide today. Generally, an effective way to diagnose these life-threatening diseases is based on endoscopy, which comprises a vast number of images. However, the main challenge in this area is that the process is time-consuming and fatiguing for a gastroenterologist to examine every image in the set. Thus, this led to the rise of studies on designing AI-based systems to assist physicians in the diagnosis. In several medical imaging tasks, deep learning methods, especially convolutional neural networks (CNNs), have contributed to the state-of-the-art outcomes, where the complicated nonlinear relation between target classes and… More >

Hyun Kyu Shin¹, Ha Young Kim², Sang Hyo Lee^3,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5059-5071, 2022, DOI:10.32604/cmc.2022.017262

Abstract The durability performance of reinforced concrete (RC) building structures is significantly affected by the corrosion of the steel reinforcement due to chloride penetration, thus, the chloride ion diffusion coefficient should be investigated through experiments or theoretical equations to assess the durability of an RC structure. This study aims to predict the chloride ion diffusion coefficient of concrete, a heterogeneous material. A convolutional neural network (CNN)-based regression model that learns the condition of the concrete surface through deep learning, is developed to efficiently obtain the chloride ion diffusion coefficient. For the model implementation to determine the chloride ion diffusion coefficient, concrete… More >

Himanshu Padole^*, S. D. Joshi, Tapan K. Gandhi

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1655-1669, 2022, DOI:10.32604/iasc.2022.021310

Abstract Many methods have been proposed in the literature for diagnosis of Alzheimer's disease (AD) in the early stages, among which the graph-based methods have been more popular, because of their capability to utilize the relational information among different brain regions. Here, we design a novel graph signal processing based integrated AD detection model using multimodal deep learning that simultaneously utilizes both the static and the dynamic brain connectivity based features extracted from resting-state fMRI (rs-fMRI) data to detect AD in the early stages. First, our earlier proposed state-space model (SSM) based graph connectivity dynamics characterization method is used to design… More >