Kashif Iqbal^1,2, Sagheer Abbas¹, Muhammad Adnan Khan^3,*, Atifa Athar⁴, Muhammad Saleem Khan¹, Areej Fatima³, Gulzar Ahmad¹

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1595-1612, 2021, DOI:10.32604/cmc.2020.013231

Abstract The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study… More >

Gopi Krishna Durbhaka¹, Barani Selvaraj¹, Mamta Mittal², Tanzila Saba^3,*, Amjad Rehman³, Lalit Mohan Goyal⁴

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2041-2059, 2021, DOI:10.32604/cmc.2020.013131

Abstract Nowadays, renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs. Most of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult task. Condition monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches, practices and technology during the last decade. Turbines mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the defect. This paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the… More >

Engy El-shafeiy¹, Aboul Ella Hassanien², Karam M. Sallam^3,*, A. A. Abohany⁴

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1745-1755, 2021, DOI:10.32604/cmc.2020.013066

Abstract Currently, COVID-19 is spreading all over the world and profoundly impacting people’s lives and economic activities. In this paper, a novel approach called the COVID-19 Quantum Neural Network (CQNN) for predicting the severity of COVID-19 in patients is proposed. It consists of two phases: In the first, the most distinct subset of features in a dataset is identified using a Quick Reduct Feature Selection (QRFS) method to improve its classification performance; and, in the second, machine learning is used to train the quantum neural network to classify the risk. It is found that patients’ serial blood counts (their numbers of… More >

Kyungsuk Jang¹, Gun Jin Yun^2,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1091-1120, 2021, DOI:10.32604/cmc.2020.012911

Abstract This paper first proposes a new self-learning data-driven methodology that can develop the failure criteria of unknown anisotropic ductile materials from the minimal number of experimental tests. Establishing failure criteria of anisotropic ductile materials requires time-consuming tests and manual data evaluation. The proposed method can overcome such practical challenges. The methodology is formalized by combining four ideas: 1) The deep learning neural network (DLNN)-based material constitutive model, 2) Self-learning inverse finite element (SELIFE) simulation, 3) Algorithmic identification of failure points from the self-learned stress-strain curves and 4) Derivation of the failure criteria through symbolic regression of the genetic programming. Stress… More >

Fouzia Nawaz¹, Muhammad Ramzan¹, Khalid Mehmood¹, Hikmat Ullah Khan², Saleem Hayat Khan^3,4, Muhammad Raheel Bhutta^5,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1631-1645, 2021, DOI:10.32604/cmc.2020.012887

Abstract Diabetic retinopathy (DR) is a retinal disease that causes irreversible blindness. DR occurs due to the high blood sugar level of the patient, and it is clumsy to be detected at an early stage as no early symptoms appear at the initial level. To prevent blindness, early detection and regular treatment are needed. Automated detection based on machine intelligence may assist the ophthalmologist in examining the patients’ condition more accurately and efficiently. The purpose of this study is to produce an automated screening system for recognition and grading of diabetic retinopathy using machine learning through deep transfer and representational learning.… More >

Shahan Yamin Siddiqui^1,2, Sagheer Abbas¹, Muhammad Adnan Khan^3,*, Iftikhar Naseer⁴, Tehreem Masood⁴, Khalid Masood Khan³, Mohammed A. Al Ghamdi⁵, Sultan H. Almotiri⁵

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1719-1732, 2021, DOI:10.32604/cmc.2020.012585

Abstract The prompt spread of Coronavirus (COVID-19) subsequently adorns a big threat to the people around the globe. The evolving and the perpetually diagnosis of coronavirus has become a critical challenge for the healthcare sector. Drastically increase of COVID-19 has rendered the necessity to detect the people who are more likely to get infected. Lately, the testing kits for COVID-19 are not available to deal it with required proficiency, along with-it countries have been widely hit by the COVID-19 disruption. To keep in view the need of hour asks for an automatic diagnosis system for early detection of COVID-19. It would… More >

Lewis Nkenyereye¹, Bayu Adhi Tama², Sunghoon Lim^3,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2217-2227, 2021, DOI:10.32604/cmc.2020.012432

Abstract An anomaly-based intrusion detection system (A-IDS) provides a critical aspect in a modern computing infrastructure since new types of attacks can be discovered. It prevalently utilizes several machine learning algorithms (ML) for detecting and classifying network traffic. To date, lots of algorithms have been proposed to improve the detection performance of A-IDS, either using individual or ensemble learners. In particular, ensemble learners have shown remarkable performance over individual learners in many applications, including in cybersecurity domain. However, most existing works still suffer from unsatisfactory results due to improper ensemble design. The aim of this study is to emphasize the effectiveness… More >

Bingtao Hu^*, Jinwei Wang

Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 95-105, 2020, DOI:10.32604/jihpp.2020.010464

Abstract With the development of computer graphics, realistic computer graphics (CG) have become more and more common in our field of vision. This rendered image is invisible to the naked eye. How to effectively identify CG and natural images (NI) has been become a new issue in the field of digital forensics. In recent years, a series of deep learning network frameworks have shown great advantages in the field of images, which provides a good choice for us to solve this problem. This paper aims to track the latest developments and applications of deep learning in the field of CG and… More >

Rongyu Chen, Lili Pan^*, Yan Zhou, Qianhui Lei

Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 67-76, 2020, DOI:10.32604/jihpp.2020.010472

Abstract With the rapid development of information technology, the speed and efficiency of image retrieval are increasingly required in many fields, and a compelling image retrieval method is critical for the development of information. Feature extraction based on deep learning has become dominant in image retrieval due to their discrimination more complete, information more complementary and higher precision. However, the high-dimension deep features extracted by CNNs (convolutional neural networks) limits the retrieval efficiency and makes it difficult to satisfy the requirements of existing image retrieval. To solving this problem, the high-dimension feature reduction technology is proposed with improved CNN and PCA… More >

Gundala Swathi^*

Computer Systems Science and Engineering, Vol.35, No.5, pp. 335-345, 2020, DOI:10.32604/csse.2020.35.335

Abstract Researches in wireless mobile ad hoc networks have an inherent challenge of vulnerable diagnosis due to the diverse behaviour pattern of the vulnerable nodes causing heterogeneous vtype1, vtype2, vtupe3 and vtype4 faults. This paper proposes a protocol for the diagnosis of vulnerability nodes with threephases of clustering, vulnerable detection and vulnerable fault classification in wireless networks. This protocol employs the technique of probabilistic neural network for classification of vulnerable nodes and detects vulnerable nodes through timeout mechanism and vtype3, vtype4, vtype1, vtype2 nodes through the method of analysis variance. Network simulator NS-2.3.35 is employed for performance evaluation of the protocol. More >