Gulzar Ahmed¹, Tahir Alyas², Muhammad Waseem Iqbal^3,*, Muhammad Usman Ashraf⁴, Ahmed Mohammed Alghamdi⁵, Adel A. Bahaddad⁶, Khalid Ali Almarhabi⁷

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2967-2984, 2022, DOI:10.32604/cmc.2022.029314 - 16 June 2022

Abstract Handwritten character recognition systems are used in every field of life nowadays, including shopping malls, banks, educational institutes, etc. Urdu is the national language of Pakistan, and it is the fourth spoken language in the world. However, it is still challenging to recognize Urdu handwritten characters owing to their cursive nature. Our paper presents a Convolutional Neural Networks (CNN) model to recognize Urdu handwritten alphabet recognition (UHAR) offline and online characters. Our research contributes an Urdu handwritten dataset (aka UHDS) to empower future works in this field. For offline systems, optical readers are used for… More >

Sara A. Althubiti¹, Fayadh Alenezi², Romany F. Mansour^3,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3529-3544, 2022, DOI:10.32604/cmc.2022.028981 - 16 June 2022

Abstract The recent advancements made in World Wide Web and social networking have eased the spread of fake news among people at a faster rate. At most of the times, the intention of fake news is to misinform the people and make manipulated societal insights. The spread of low-quality news in social networking sites has a negative influence upon people as well as the society. In order to overcome the ever-increasing dissemination of fake news, automated detection models are developed using Artificial Intelligence (AI) and Machine Learning (ML) methods. The latest advancements in Deep Learning (DL)… More >

Sara A. Althubiti¹, José Escorcia-Gutierrez^2,3,*, Margarita Gamarra⁴, Roosvel Soto-Diaz⁵, Romany F. Mansour⁶, Fayadh Alenezi⁷

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2423-2439, 2022, DOI:10.32604/cmc.2022.028878 - 16 June 2022

Abstract Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things (IoT), sensor technologies, cloud computing, and others. Besides, the latest advances of Artificial Intelligence (AI) tools find helpful for decision-making in innovative healthcare to diagnose several diseases. Ovarian Cancer (OC) is a kind of cancer that affects women’s ovaries, and it is tedious to identify OC at the primary stages with a high mortality rate. The OC data produced by the Internet of Medical Things (IoMT) devices can be utilized to differentiate OC. In this aspect, this paper… More >

Mahmoud Ragab^1,2,3,*, Alaa F. Nahhas⁴

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2849-2864, 2022, DOI:10.32604/cmc.2022.028855 - 16 June 2022

Abstract Earlier recognition of breast cancer is crucial to decrease the severity and optimize the survival rate. One of the commonly utilized imaging modalities for breast cancer is histopathological images. Since manual inspection of histopathological images is a challenging task, automated tools using deep learning (DL) and artificial intelligence (AI) approaches need to be designed. The latest advances of DL models help in accomplishing maximum image classification performance in several application areas. In this view, this study develops a Deep Transfer Learning with Rider Optimization Algorithm for Histopathological Classification of Breast Cancer (DTLRO-HCBC) technique. The proposed… More >

Muhammad Naeem Akbar^1,*, Farhan Riaz¹, Ahmed Bilal Awan¹, Muhammad Attique Khan², Usman Tariq³, Saad Rehman²

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2555-2576, 2022, DOI:10.32604/cmc.2022.028696 - 16 June 2022

Abstract Human Action Recognition (HAR) is a current research topic in the field of computer vision that is based on an important application known as video surveillance. Researchers in computer vision have introduced various intelligent methods based on deep learning and machine learning, but they still face many challenges such as similarity in various actions and redundant features. We proposed a framework for accurate human action recognition (HAR) based on deep learning and an improved features optimization algorithm in this paper. From deep learning feature extraction to feature classification, the proposed framework includes several critical steps.… More >

K. Vijaya Kumar¹, E. Laxmi Lydia², Ashit Kumar Dutta³, Velmurugan Subbiah Parvathy⁴, Gobi Ramasamy⁵, Irina V. Pustokhina^6,*, Denis A. Pustokhin⁷

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2541-2554, 2022, DOI:10.32604/cmc.2022.028570 - 16 June 2022

Abstract Recently, big data becomes evitable due to massive increase in the generation of data in real time application. Presently, object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation, augmented reality, surveillance, etc. This paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN (AIA-IFRCNN) model in big data environment. The AIA-IFRCNN model annotates the images by Discriminative Correlation Filter (DCF) with Channel and Spatial Reliability tracker (CSR), named DCF-CSRT model. The AIA-IFRCNN technique employs Faster RCNN More >

Prasanalakshmi Balaji^1,*, B. Sri Revathi², Praveetha Gobinathan³, Shermin Shamsudheen³, Thavavel Vaiyapuri⁴

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2275-2291, 2022, DOI:10.32604/cmc.2022.028560 - 16 June 2022

Abstract Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis system. Despite deep learning has proved to be superior to previous approaches that depend on handcrafted features; it remains difficult to implement because of the high intra-class variance and inter-class similarity generated by the wide range of imaging modalities and clinical diseases. The Internet of Things (IoT) in healthcare systems is quickly becoming a viable alternative for delivering high-quality medical treatment in today’s e-healthcare systems. In recent years, the Internet of Things (IoT) has been identified as one of the… More >

Sarayut Yaemprayoon, Jakkree Srinonchat^*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3667-3684, 2022, DOI:10.32604/cmc.2022.028358 - 16 June 2022

Abstract Non-Intrusive Load Monitoring (NILM) has gradually become a research focus in recent years to measure the power consumption in households for energy conservation. Most of the existing algorithms on NILM models independently measure when the total current load of appliances occurs, and NILM usually undergoes the problem of signatures of the appliance. This paper presents a distingue NILM design to measure and classify the appliances by investigating the inrush current pattern when the alliances begin. The proposed method is implemented while the five appliances operate simultaneously. The high sampling rate of field-programmable gate array (FPGA)… More >

Magdy M. Fadel^1,*, Sally M. El-Ghamrawy², Amr M. T. Ali-Eldin¹, Mohammed K. Hassan³, Ali I. El-Desoky¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2293-2312, 2022, DOI:10.32604/cmc.2022.028287 - 16 June 2022

Abstract Distributed denial-of-service (DDoS) attacks are designed to interrupt network services such as email servers and webpages in traditional computer networks. Furthermore, the enormous number of connected devices makes it difficult to operate such a network effectively. Software defined networks (SDN) are networks that are managed through a centralized control system, according to researchers. This controller is the brain of any SDN, composing the forwarding table of all data plane network switches. Despite the advantages of SDN controllers, DDoS attacks are easier to perpetrate than on traditional networks. Because the controller is a single point of More >

Tariq Ahamed Ahanger^1,*, Abdulaziz Aldaej¹, Mohammed Atiquzzaman², Imdad Ullah¹, Mohammed Yousuf Uddin¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3199-3217, 2022, DOI:10.32604/cmc.2022.027212 - 16 June 2022

Abstract DDoS attacks in the Internet of Things (IoT) technology have increased significantly due to its spread adoption in different industrial domains. The purpose of the current research is to propose a novel technique for detecting botnet attacks in user-oriented IoT environments. Conspicuously, an attack identification technique inspired by Recurrent Neural networks and Bidirectional Long Short Term Memory (BLRNN) is presented using a unique Deep Learning (DL) technique. For text identification and translation of attack data segments into tokenized form, word embedding is employed. The performance analysis of the presented technique is performed in comparison to More >