Kazim Ali^1,*, Adnan N. Qureshi¹, Ahmad Alauddin Bin Arifin², Muhammad Shahid Bhatti³, Abid Sohail³, Rohail Hassan⁴

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2209-2224, 2022, DOI:10.32604/cmc.2022.020111 - 07 December 2021

Abstract These days, deep learning and computer vision are much-growing fields in this modern world of information technology. Deep learning algorithms and computer vision have achieved great success in different applications like image classification, speech recognition, self-driving vehicles, disease diagnostics, and many more. Despite success in various applications, it is found that these learning algorithms face severe threats due to adversarial attacks. Adversarial examples are inputs like images in the computer vision field, which are intentionally slightly changed or perturbed. These changes are humanly imperceptible. But are misclassified by a model with high probability and severely More >

G. Anitha^1,*, S. Baghavathi Priya²

Computer Systems Science and Engineering, Vol.42, No.1, pp. 87-103, 2022, DOI:10.32604/csse.2022.020361 - 02 December 2021

Abstract Human fall detection plays a vital part in the design of sensor based alarming system, aid physical therapists not only to lessen after fall effect and also to save human life. Accurate and timely identification can offer quick medical services to the injured people and prevent from serious consequences. Several vision-based approaches have been developed by the placement of cameras in diverse everyday environments. At present times, deep learning (DL) models particularly convolutional neural networks (CNNs) have gained much importance in the fall detection tasks. With this motivation, this paper presents a new vision based… More >

Kazim Ali^*, Adnan N. Quershi

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 271-284, 2022, DOI:10.32604/iasc.2022.021296 - 26 October 2021

Abstract The current development of artificial intelligence is largely based on deep Neural Networks (DNNs). Especially in the computer vision field, DNNs now occur in everything from autonomous vehicles to safety control systems. Convolutional Neural Network (CNN) is based on DNNs mostly used in different computer vision applications, especially for image classification and object detection. The CNN model takes the photos as input and, after training, assigns it a suitable class after setting traceable parameters like weights and biases. CNN is derived from Human Brain's Part Visual Cortex and sometimes performs even better than Haman visual… 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 - 11 October 2021

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… More >

K. Venkatachalam¹, Siuly Siuly², M. Vinoth Kumar³, Praveen Lalwani¹, Manas Kumar Mishra¹, Enamul Kabir^4,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3717-3732, 2022, DOI:10.32604/cmc.2022.018487 - 27 September 2021

Abstract The COVID-19 pandemic has created a major challenge for countries all over the world and has placed tremendous pressure on their public health care services. An early diagnosis of COVID-19 may reduce the impact of the coronavirus. To achieve this objective, modern computation methods, such as deep learning, may be applied. In this study, a computational model involving deep learning and biogeography-based optimization (BBO) for early detection and management of COVID-19 is introduced. Specifically, BBO is used for the layer selection process in the proposed convolutional neural network (CNN). The computational model accepts images, such More >

Altaf Hussain^1,2, Khan Muhammad¹, Hayat Ullah¹, Amin Ullah^1,4, Ali Shariq Imran³, Mi Young Lee¹, Seungmin Rho¹, Muhammad Sajjad^2,3,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2171-2190, 2022, DOI:10.32604/cmc.2022.018181 - 27 September 2021

Abstract Digital surveillance systems are ubiquitous and continuously generate massive amounts of data, and manual monitoring is required in order to recognise human activities in public areas. Intelligent surveillance systems that can automatically ide.pngy normal and abnormal activities are highly desirable, as these would allow for efficient monitoring by selecting only those camera feeds in which abnormal activities are occurring. This paper proposes an energy-efficient camera prioritisation framework that intelligently adjusts the priority of cameras in a vast surveillance network using feedback from the activity recognition system. The proposed system addresses the limitations of existing manual… More >

T. Vetriselvi¹, E. Laxmi Lydia², Sachi Nandan Mohanty^3,4, Eatedal Alabdulkreem⁵, Shaha Al-Otaibi⁶, Amal Al-Rasheed⁶, Romany F. Mansour^7,*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 2049-2064, 2022, DOI:10.32604/cmc.2022.020110 - 07 September 2021

Abstract Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective. Precise controlling and management of traffic conditions, increased safety and surveillance, and enhanced incident avoidance and management should be top priorities in smart city management. At the same time, Vehicle License Plate Number Recognition (VLPNR) has become a hot research topic, owing to several real-time applications like automated toll fee processing, traffic law enforcement, private space access control, and road traffic surveillance. Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles… More >

Muhatasim Intisar¹, Mohammad Monirujjaman Khan^1,*, Mehedi Masud², Mohammad Shorfuzzaman²

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 101-115, 2022, DOI:10.32604/iasc.2022.019083 - 03 September 2021

Abstract Paralysis is detrimental to people in catastrophic ways: losing income opportunities, becoming a burden to their friends and family, further physical deterioration, and the combination of these occurrences can often lead to depression. In third world countries, suffering from paralysis can be extra deleterious, where a large proportion of the population is engaged in some form of physical labor. The number of people exposed to paralysis risk factors is also increasing, with more and more people having hypertension, smoking, and other abnormalities. Besides, low workplace safety precautions may lead to an increased risk of spinal… More >

Chintakindi Balaram Murthy¹, Mohammad Farukh Hashmi¹, Ghulam Muhammad^2,3,*, Salman A. AlQahtani^2,3

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3015-3031, 2021, DOI:10.32604/cmc.2021.018781 - 24 August 2021

Abstract Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance. The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases. Therefore, the proposed algorithm YOLOv2 (“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection (referred to as YOLOv2PD) would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes. The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion (MLFF) strategy, which helps to improve the model’s feature… More >

Munif Alotaibi^1,*, Bandar Alotaibi²

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 885-898, 2021, DOI:10.32604/iasc.2021.018350 - 01 July 2021

Abstract The novel coronavirus 2019 (COVID-19) is one of a large family of viruses that cause illness, the symptoms of which range from a common cold, fever, coughing, and shortness of breath to more severe symptoms. The virus rapidly and easily spreads from infected people to others through close contact in the absence of protection. Early detection of COVID-19 assists governmental authorities and healthcare specialists in reducing the chain of transmission and flattening the curve of the pandemic. The widespread form of the COVID-19 diagnostic test lacks a high true positive rate and a low false… More >