Ahmed Althobaiti¹, Abdullah Alhumaidi Alotaibi², Sayed Abdel-Khalek³, Suliman A. Alsuhibany⁴, Romany F. Mansour^5,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1921-1938, 2022, DOI:10.32604/cmc.2022.025550 - 24 February 2022

Abstract Latest advancements in the integration of camera sensors paves a way for new Unmanned Aerial Vehicles (UAVs) applications such as analyzing geographical (spatial) variations of earth science in mitigating harmful environmental impacts and climate change. UAVs have achieved significant attention as a remote sensing environment, which captures high-resolution images from different scenes such as land, forest fire, flooding threats, road collision, landslides, and so on to enhance data analysis and decision making. Dynamic scene classification has attracted much attention in the examination of earth data captured by UAVs. This paper proposes a new multi-modal fusion… More >

Imran Arshad Choudhry^*, Adnan N. Qureshi

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1445-1463, 2022, DOI:10.32604/cmc.2022.025208 - 24 February 2022

Abstract The well-established mortality rates due to lung cancers, scarcity of radiology experts and inter-observer variability underpin the dire need for robust and accurate computer aided diagnostics to provide a second opinion. To this end, we propose a feature grafting approach to classify lung cancer images from publicly available National Institute of Health (NIH) chest X-Ray dataset comprised of 30,805 unique patients. The performance of transfer learning with pre-trained VGG and Inception models is evaluated in comparison against manually extracted radiomics features added to convolutional neural network using custom layer. For classification with both approaches, Support… More >

Jianbing Ma^1,*, Xianghao Cui¹, Nan Jiang²

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1939-1949, 2022, DOI:10.32604/cmc.2022.025206 - 24 February 2022

Abstract Sudden precipitations may bring troubles or even huge harm to people's daily lives. Hence a timely and accurate precipitation nowcasting is expected to be an indispensable part of our modern life. Traditionally, the rainfall intensity estimation from weather radar is based on the relationship between radar reflectivity factor (Z) and rainfall rate (R), which is typically estimated by location-dependent experiential formula and arguably uncertain. Therefore, in this paper, we propose a deep learning-based method to model the ZR relation. To evaluate, we conducted our experiment with the Shenzhen precipitation dataset. We proposed a combined method More >

Jianquan Ouyang¹, Yue Zhang¹, Kun Fang^1,2,*, Tianming Liu³, Xiangyu Pan²

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1593-1610, 2022, DOI:10.32604/cmc.2022.025072 - 24 February 2022

Abstract Cryo-Electron Microscopy (Cryo-EM) images are characterized by the low signal-to-noise ratio, low contrast, serious background noise, more impurities, less data, difficult data labeling, simpler image semantics, and relatively fixed structure, while U-Net obtains low resolution when downsampling rate information to complete object category recognition, obtains high-resolution information during upsampling to complete precise segmentation and positioning, fills in the underlying information through skip connection to improve the accuracy of image segmentation, and has advantages in biological image processing like Cryo-EM image. This article proposes A U-Net based residual intensive neural network (Urdnet), which combines point-level and More >

Wahidur Rahman¹, Naif Al Mudawi^2,*, Abdulwahab Alazeb², Muhammad Minoar Hossain¹, Saima Siddique Tashfia¹, Md. Tarequl Islam¹, Shisir Mia¹, Mohammad Motiur Rahman¹

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 2033-2053, 2022, DOI:10.32604/cmc.2022.025025 - 24 February 2022

Abstract On the edge of the worldwide public health crisis, the COVID-19 disease has become a serious headache for its destructive nature on humanity worldwide. Wearing a facial mask can be an effective possible solution to mitigate the spreading of the virus and reduce the death rate. Thus, wearing a face mask in public places such as shopping malls, hotels, restaurants, homes, and offices needs to be enforced. This research work comes up with a solution of mask surveillance system utilizing the mechanism of modern computations like Deep Learning (DL), Internet of things (IoT), and Blockchain.… More >

Manal M. Khayyat^1,2, Lamiaa A. Elrefaei^2,3, Mashael M. Khayyat^4,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 2109-2125, 2022, DOI:10.32604/cmc.2022.024975 - 24 February 2022

Abstract Classifying the visual features in images to retrieve a specific image is a significant problem within the computer vision field especially when dealing with historical faded colored images. Thus, there were lots of efforts trying to automate the classification operation and retrieve similar images accurately. To reach this goal, we developed a VGG19 deep convolutional neural network to extract the visual features from the images automatically. Then, the distances among the extracted features vectors are measured and a similarity score is generated using a Siamese deep neural network. The Siamese model built and trained at… More >

Anwer Mustafa Hilal^1,*, Jaber S. Alzahrani², Ibrahim Abunadi³, Nadhem Nemri⁴, Fahd N. Al-Wesabi^5,6, Abdelwahed Motwakel¹, Ishfaq Yaseen¹, Abu Sarwar Zamani¹

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 333-348, 2022, DOI:10.32604/cmc.2022.024794 - 24 February 2022

Abstract In recent times, Industrial Internet of Things (IIoT) experiences a high risk of cyber attacks which needs to be resolved. Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks. Although blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network, the performance arrived at, in existing studies still needs improvement. In this scenario, the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT (PPBDL-IIoT) on 6G environment. The proposed PPBDL-IIoT technique aims… More >

Ahmed S. Almasoud¹, Abdelzahir Abdelmaboud², Taiseer Abdalla Elfadil Eisa³, Mesfer Al Duhayyim⁴, Asma Abbas Hassan Elnour⁵, Manar Ahmed Hamza^6,*, Abdelwahed Motwakel⁶, Abu Sarwar Zamani⁶

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1391-1407, 2022, DOI:10.32604/cmc.2022.024618 - 24 February 2022

Abstract In agriculture, rice plant disease diagnosis has become a challenging issue, and early identification of this disease can avoid huge loss incurred from less crop productivity. Some of the recently-developed computer vision and Deep Learning (DL) approaches can be commonly employed in designing effective models for rice plant disease detection and classification processes. With this motivation, the current research work devises an Efficient Deep Learning based Fusion Model for Rice Plant Disease (EDLFM-RPD) detection and classification. The aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in… More >

Noman Shabbir¹, Lauri Kütt¹, Muhammad Jawad², Oleksandr Husev¹, Ateeq Ur Rehman³, Akber Abid Gardezi⁴, Muhammad Shafiq⁵, Jin-Ghoo Choi^5,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1017-1033, 2022, DOI:10.32604/cmc.2022.024576 - 24 February 2022

Abstract Wind energy is featured by instability due to a number of factors, such as weather, season, time of the day, climatic area and so on. Furthermore, instability in the generation of wind energy brings new challenges to electric power grids, such as reliability, flexibility, and power quality. This transition requires a plethora of advanced techniques for accurate forecasting of wind energy. In this context, wind energy forecasting is closely tied to machine learning (ML) and deep learning (DL) as emerging technologies to create an intelligent energy management paradigm. This article attempts to address the short-term… More >

Zeyad Ghaleb Al-Mekhlafi¹, Ebrahim Mohammed Senan², Taha H. Rassem³, Badiea Abdulkarem Mohammed^4,5,*, Nasrin M. Makbol⁵, Adwan Alownie Alanazi¹, Tariq S. Almurayziq¹, Fuad A. Ghaleb⁶

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 775-796, 2022, DOI:10.32604/cmc.2022.024492 - 24 February 2022

Abstract Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on the Magnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to… More >