Indrajeet Kumar¹, Sultan S. Alshamrani², Abhishek Kumar³, Jyoti Rawat⁴, Kamred Udham Singh¹, Mamoon Rashid^5,*, Ahmed Saeed AlGhamdi⁶

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 451-468, 2022, DOI:10.32604/cmc.2022.019443

Abstract Early diagnosis of a pandemic disease like COVID-19 can help deal with a dire situation and help radiologists and other experts manage human resources more effectively. In a recent pandemic, laboratories perform diagnostics manually, which requires a lot of time and expertise of the laboratorial technicians to yield accurate results. Moreover, the cost of kits is high, and well-equipped labs are needed to perform this test. Therefore, other means of diagnosis is highly desirable. Radiography is one of the existing methods that finds its use in the diagnosis of COVID-19. The radiography observes change in Computed Tomography (CT) chest images… More >

Zelin Deng^1,*, Bo Zhou¹, Pei He², Jianfeng Huang³, Osama Alfarraj⁴, Amr Tolba^4,5

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 2065-2081, 2022, DOI:10.32604/cmc.2022.019328

Abstract Image captioning aims to generate a corresponding description of an image. In recent years, neural encoder-decoder models have been the dominant approaches, in which the Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) are used to translate an image into a natural language description. Among these approaches, the visual attention mechanisms are widely used to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. However, most conventional visual attention mechanisms are based on high-level image features, ignoring the effects of other image features, and giving insufficient consideration to the relative positions between image features.… More >

R. Sujitha^*, B. Paramasivan

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1557-1572, 2022, DOI:10.32604/cmc.2022.019323

Abstract With the modernization of machine learning techniques in healthcare, different innovations including support vector machine (SVM) have predominantly played a major role in classifying lung cancer, predicting coronavirus disease 2019, and other diseases. In particular, our algorithm focuses on integrated datasets as compared with other existing works. In this study, parallel-based SVM (P-SVM) and multiclass-based multiple submodels (MMSM-SVM) were used to analyze the optimal classification of lung diseases. This analysis aimed to find the optimal classification of lung diseases with id and stages, such as key-value pairs in MapReduce combined with P-SVM and MMSVM for binary and multiclasses, respectively. For… More >

Asif Mehmood¹, Muhammad Attique Khan², Usman Tariq³, Chang-Won Jeong⁴, Yunyoung Nam^5,*, Reham R. Mostafa⁶, Amira ElZeiny⁷

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 343-360, 2022, DOI:10.32604/cmc.2022.019250

Abstract Background—Human Gait Recognition (HGR) is an approach based on biometric and is being widely used for surveillance. HGR is adopted by researchers for the past several decades. Several factors are there that affect the system performance such as the walking variation due to clothes, a person carrying some luggage, variations in the view angle. Proposed—In this work, a new method is introduced to overcome different problems of HGR. A hybrid method is proposed or efficient HGR using deep learning and selection of best features. Four major steps are involved in this work-preprocessing of the video frames, manipulation of the pre-trained… More >

Muhammad Zia Ur Rehman¹, Fawad Ahmed¹, Muhammad Attique Khan², Usman Tariq³, Sajjad Shaukat Jamal⁴, Jawad Ahmad^5,*, Iqtadar Hussain⁶

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1401-1417, 2022, DOI:10.32604/cmc.2022.019046

Abstract In recent years, the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits. This in turn has helped in improving the quality and production of vegetables and fruits. Citrus fruits are well known for their taste and nutritional values. They are one of the natural and well known sources of vitamin C and planted worldwide. There are several diseases which severely affect the quality and yield of citrus fruits. In this paper, a new deep learning based technique is proposed for citrus disease classification. Two different pre-trained deep learning… More >

Talha Imran¹, Muhammad Attique Khan², Muhammad Sharif¹, Usman Tariq³, Yu-Dong Zhang⁴, Yunyoung Nam^5,*, Yunja Nam⁵, Byeong-Gwon Kang⁵

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1875-1891, 2022, DOI:10.32604/cmc.2022.018946

Abstract Malaria is a critical health condition that affects both sultry and frigid region worldwide, giving rise to millions of cases of disease and thousands of deaths over the years. Malaria is caused by parasites that enter the human red blood cells, grow there, and damage them over time. Therefore, it is diagnosed by a detailed examination of blood cells under the microscope. This is the most extensively used malaria diagnosis technique, but it yields limited and unreliable results due to the manual human involvement. In this work, an automated malaria blood smear classification model is proposed, which takes images of… More >

C. S. S. Anupama¹, L. Natrayan², E. Laxmi Lydia³, Abdul Rahaman Wahab Sait⁴, José Escorcia-Gutierrez⁵, Margarita Gamarra^6,*, Romany F. Mansour⁷

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1297-1313, 2022, DOI:10.32604/cmc.2022.018396

Abstract Nowadays, quality improvement and increased accessibility to patient data, at a reasonable cost, are highly challenging tasks in healthcare sector. Internet of Things (IoT) and Cloud Computing (CC) architectures are utilized in the development of smart healthcare systems. These entities can support real-time applications by exploiting massive volumes of data, produced by wearable sensor devices. The advent of evolutionary computation algorithms and Deep Learning (DL) models has gained significant attention in healthcare diagnosis, especially in decision making process. Skin cancer is the deadliest disease which affects people across the globe. Automatic skin lesion classification model has a highly important application… More >

Muhammad Ramzan^1,2,*, Adnan Abid¹, Shahid Mahmood Awan^1,3

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1829-1844, 2022, DOI:10.32604/cmc.2022.017522

Abstract In the current era, automatic surveillance has become an active research problem due to its vast real-world applications, particularly for maintaining law and order. A continuous manual monitoring of human activities is a tedious task. The use of cameras and automatic detection of unusual surveillance activity has been growing exponentially over the last few years. Various computer vision techniques have been applied for observation and surveillance of real-world activities. This research study focuses on detecting and recognizing unusual activities in an academic situation such as examination halls, which may help the invigilators observe and restrict the students from cheating or… More >

K. M. Ameen Suhail¹, Syam Sankar¹, Ashok S. Kumar², Tsafack Nestor³, Naglaa F. Soliman^4,*, Abeer D. Algarni⁴, Walid El-Shafai⁵, Fathi E. Abd El-Samie^4,5

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 935-950, 2022, DOI:10.32604/cmc.2022.017069

Abstract Stock market is a place, where shares of different companies are traded. It is a collection of buyers’ and sellers’ stocks. In this digital era, analysis and prediction in the stock market have gained an essential role in shaping today's economy. Stock market analysis can be either fundamental or technical. Technical analysis can be performed either with technical indicators or through machine learning techniques. In this paper, we report a system that uses a Reinforcement Learning (RL) network and market sentiments to make decisions about stock market trading. The system uses sentiment analysis on daily market news to spot trends… More >

Manish Agrawal¹, Piyush Kumar Shukla², Rajit Nair³, Anand Nayyar^4,5,*, Mehedi Masud⁶

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 287-304, 2022, DOI:10.32604/cmc.2022.014637

Abstract Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature. The stock data is usually non-stationary, and attributes are non-correlative to each other. Several traditional Stock Technical Indicators (STIs) may incorrectly predict the stock market trends. To study the stock market characteristics using STIs and make efficient trading decisions, a robust model is built. This paper aims to build up an Evolutionary Deep Learning Model (EDLM) to identify stock trends’ prices by using STIs. The proposed model has implemented the Deep Learning (DL) model to establish the… More >