Mehwish Shaikh¹, Isma Farah Siddiqui¹, Qasim Arain¹, Jahwan Koo^2,*, Mukhtiar Ali Unar³, Nawab Muhammad Faseeh Qureshi^4,*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 287-302, 2023, DOI:10.32604/csse.2023.035311

Abstract Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful; thus, catching it early is crucial. Medical physicians’ time is limited in outdoor situations due to many patients; therefore, automated systems can be a rescue. The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’ experience. Therefore, radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays. In medical classifications, deep convolution neural networks are commonly used. This research aims to use deep pre-trained transfer learning models to accurately categorize CXR images into… More >

Mesfer Alduhayyem^1,*, Mrim M. Alnfiai^2,3, Nabil Almalki⁴, Fahd N. Al-Wesabi⁵, Anwer Mustafa Hilal⁶, Manar Ahmed Hamza⁶

Computer Systems Science and Engineering, Vol.46, No.1, pp. 475-489, 2023, DOI:10.32604/csse.2023.034067

Abstract Visual impairment is one of the major problems among people of all age groups across the globe. Visually Impaired Persons (VIPs) require help from others to carry out their day-to-day tasks. Since they experience several problems in their daily lives, technical intervention can help them resolve the challenges. In this background, an automatic object detection tool is the need of the hour to empower VIPs with safe navigation. The recent advances in the Internet of Things (IoT) and Deep Learning (DL) techniques make it possible. The current study proposes IoT-assisted Transient Search Optimization with a Lightweight RetinaNet-based object detection (TSOLWR-ODVIP)… More >

Mohammad Yamin^1,*, Mishaal Mofleh Almutairi², Saeed Badghish³, Saleh Bajaba⁴

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4965-4981, 2023, DOI:10.32604/cmc.2023.033705

Abstract Due to the rapid increase in urbanization and population, crowd gatherings are frequently observed in the form of concerts, political, and religious meetings. HAJJ is one of the well-known crowding events that takes place every year in Makkah, Saudi Arabia. Crowd density estimation and crowd monitoring are significant research areas in Artificial Intelligence (AI) applications. The current research study develops a new Sparrow Search Optimization with Deep Transfer Learning based Crowd Density Detection and Classification (SSODTL-CD2C) model. The presented SSODTL-CD2C technique majorly focuses on the identification and classification of crowd densities. To attain this, SSODTL-CD2C technique exploits Oppositional Salp Swarm… More >

Abdul Wahab Hashmi¹, Harlal Singh Mali¹, Anoj Meena¹, Mohammad Farukh Hashmi², Neeraj Dhanraj Bokde^3,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 917-1005, 2023, DOI:10.32604/cmes.2023.021223

Abstract Computer vision provides image-based solutions to inspect and investigate the quality of the surface to be measured. For any components to execute their intended functions and operations, surface quality is considered equally significant to dimensional quality. Surface Roughness (Ra) is a widely recognized measure to evaluate and investigate the surface quality of machined parts. Various conventional methods and approaches to measure the surface roughness are not feasible and appropriate in industries claiming 100% inspection and examination because of the time and efforts involved in performing the measurement. However, Machine vision has emerged as the innovative approach to executing the surface… More >

Samia Allaoua Chelloug^1,*, Hamid Ashfaq², Suliman A. Alsuhibany³, Mohammad Shorfuzzaman⁴, Abdulmajeed Alsufyani⁴, Ahmad Jalal², Jeongmin Park⁵

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1607-1624, 2023, DOI:10.32604/cmc.2023.032245

Abstract In the past two decades, there has been a lot of work on computer vision technology that incorporates many tasks which implement basic filtering to image classification. The major research areas of this field include object detection and object recognition. Moreover, wireless communication technologies are presently adopted and they have impacted the way of education that has been changed. There are different phases of changes in the traditional system. Perception of three-dimensional (3D) from two-dimensional (2D) image is one of the demanding tasks. Because human can easily perceive but making 3D using software will take time manually. Firstly, the blackboard… More >

Mazen Mushabab Alqahtani¹, Ashit Kumar Dutta², Sultan Almotairi³, M. Ilayaraja⁴, Amani Abdulrahman Albraikan⁵, Fahd N. Al-Wesabi^6,7,*, Mesfer Al Duhayyim⁸

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 217-233, 2023, DOI:10.32604/cmc.2023.025280

Abstract Recent developments in digital cameras and electronic gadgets coupled with Machine Learning (ML) and Deep Learning (DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models. In this background, the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection (ESFO-EALD) model. The goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases automatically. In this scenario, Median Filtering (MF) approach is utilized to boost the quality of apple plant leaf images. Moreover, SFO with Kapur's entropy-based segmentation technique is also utilized for the… More >

Meherab Mamun Ratul, Kazi Ayesha Rahman, Javeria Fazal, Naimur Rahman Abanto, Riasat Khan^*

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3641-3658, 2023, DOI:10.32604/iasc.2023.030638

Abstract The coronavirus (COVID-19) is a lethal virus causing a rapidly infectious disease throughout the globe. Spreading awareness, taking preventive measures, imposing strict restrictions on public gatherings, wearing facial masks, and maintaining safe social distancing have become crucial factors in keeping the virus at bay. Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus, the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly. To fight the spread of this virus, technologically developed systems have become… More >

Na Hyoun Kim, Namgyu Kim^*

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2667-2682, 2023, DOI:10.32604/csse.2023.029732

Abstract Object detection, one of the core research topics in computer vision, is extensively used in various industrial activities. Although there have been many studies of daytime images where objects can be easily detected, there is relatively little research on nighttime images. In the case of nighttime, various types of noises, such as darkness, haze, and light blur, deteriorate image quality. Thus, an appropriate process for removing noise must precede to improve object detection performance. Although there are many studies on removing individual noise, only a few studies handle multiple noises simultaneously. In this paper, we propose a convolutional denoising autoencoder… More >

R. Punithavathi¹, A. Delphin Carolina Rani², K. R. Sughashini³, Chinnarao Kurangi⁴, M. Nirmala⁵, Hasmath Farhana Thariq Ahmed⁶, S. P. Balamurugan^7,*

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2759-2774, 2023, DOI:10.32604/csse.2023.027647

Abstract Presently, precision agriculture processes like plant disease, crop yield prediction, species recognition, weed detection, and irrigation can be accomplished by the use of computer vision (CV) approaches. Weed plays a vital role in influencing crop productivity. The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased. Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity, this study presents a novel computer vision and deep learning based weed detection and classification (CVDL-WDC) model for precision agriculture. The proposed CVDL-WDC technique intends to properly discriminate the… More >

Mesfer Al Duhayyim^1,*, Areej A. Malibari², Marwa Obayya³, Mohamed K. Nour⁴, Ahmed S. Salama⁵, Mohamed I. Eldesouki⁶, Abu Sarwar Zamani⁷, Mohammed Rizwanullah⁷

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5473-5489, 2022, DOI:10.32604/cmc.2022.031976

Abstract The skeletal bone age assessment (BAA) was extremely implemented in development prediction and auxiliary analysis of medicinal issues. X-ray images of hands were detected from the estimation of bone age, whereas the ossification centers of epiphysis and carpal bones are important regions. The typical skeletal BAA approaches remove these regions for predicting the bone age, however, few of them attain suitable efficacy or accuracy. Automatic BAA techniques with deep learning (DL) methods are reached the leading efficiency on manual and typical approaches. Therefore, this study introduces an intellectual skeletal bone age assessment and classification with the use of metaheuristic with… More >