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  • Open Access

    ARTICLE

    Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

    C. S. S. Anupama1, L. Natrayan2, E. Laxmi Lydia3, Abdul Rahaman Wahab Sait4, José Escorcia-Gutierrez5, Margarita Gamarra6,*, Romany F. Mansour7

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1297-1313, 2022, DOI:10.32604/cmc.2022.018396 - 07 September 2021

    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… More >

  • Open Access

    ARTICLE

    Automatic Unusual Activities Recognition Using Deep Learning in Academia

    Muhammad Ramzan1,2,*, Adnan Abid1, Shahid Mahmood Awan1,3

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1829-1844, 2022, DOI:10.32604/cmc.2022.017522 - 07 September 2021

    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… More >

  • Open Access

    ARTICLE

    Stock Market Trading Based on Market Sentiments and Reinforcement Learning

    K. M. Ameen Suhail1, Syam Sankar1, Ashok S. Kumar2, Tsafack Nestor3, Naglaa F. Soliman4,*, Abeer D. Algarni4, Walid El-Shafai5, Fathi E. Abd El-Samie4,5

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 935-950, 2022, DOI:10.32604/cmc.2022.017069 - 07 September 2021

    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… More >

  • Open Access

    ARTICLE

    Stock Prediction Based on Technical Indicators Using Deep Learning Model

    Manish Agrawal1, Piyush Kumar Shukla2, Rajit Nair3, Anand Nayyar4,5,*, Mehedi Masud6

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 287-304, 2022, DOI:10.32604/cmc.2022.014637 - 07 September 2021

    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… More >

  • Open Access

    ARTICLE

    Deep Learning Model to Detect Diabetes Mellitus Based on DNA Sequence

    Noha E. El-Attar1,*, Bossy M. Moustafa2, Wael A. Awad3

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 325-338, 2022, DOI:10.32604/iasc.2022.019970 - 03 September 2021

    Abstract DNA sequence classification is considered a significant challenge for biological researchers to scientifically analyze the enormous volumes of biological data and discover different biological features. In genomic research, classifying DNA sequences may help learn and discover the new functions of a protein. Insulin is an example of a protein that the human body produces to regulate glucose levels. Any mutations in the insulin gene sequence would result in diabetes mellitus. Diabetes is one of the widely spread chronic diseases, leading to severe effects in the longer term if diagnosis and treatment are not appropriately taken.… More >

  • Open Access

    ARTICLE

    Deep Learning Based Stacked Sparse Autoencoder for PAPR Reduction in OFDM Systems

    A. Jayamathi1, T. Jayasankar2,*

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 311-324, 2022, DOI:10.32604/iasc.2022.019473 - 03 September 2021

    Abstract Orthogonal frequency division multiplexing is one of the efficient and flexible modulation techniques, and which is considered as the central part of many wired and wireless standards. Orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) achieves maximum spectral efficiency and data rates for wireless mobile communication systems. Though it offers better quality of services, high peak-to-average power ratio (PAPR) is the major issue that needs to be resolved in the MIMO-OFDM system. Earlier studies have addressed the high PAPR of OFDM system using clipping, coding, selected mapping, tone injection, peak windowing, etc. Recently, deep… More >

  • Open Access

    ARTICLE

    Deep Learning-Based Skin Lesion Diagnosis Model Using Dermoscopic Images

    G. Reshma1,*, Chiai Al-Atroshi2, Vinay Kumar Nassa3, B.T. Geetha4, Gurram Sunitha5, Mohammad Gouse Galety6, S. Neelakandan7

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 621-634, 2022, DOI:10.32604/iasc.2022.019117 - 03 September 2021

    Abstract In recent years, intelligent automation in the healthcare sector becomes more familiar due to the integration of artificial intelligence (AI) techniques. Intelligent healthcare systems assist in making better decisions, which further enable the patient to provide improved medical services. At the same time, skin lesion is a deadly disease that affects people of all age groups. Skin lesion segmentation and classification play a vital part in the earlier and precise skin cancer diagnosis by intelligent systems. However, the automated diagnosis of skin lesions in dermoscopic images is challenging because of the problems such as artifacts… More >

  • Open Access

    ARTICLE

    An Enhanced Deep Learning Model for Automatic Face Mask Detection

    Qazi Mudassar Ilyas1, Muneer Ahmad2,*

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 241-254, 2022, DOI:10.32604/iasc.2022.018042 - 03 September 2021

    Abstract The recent COVID-19 pandemic has had lasting and severe impacts on social gatherings and interaction among people. Local administrative bodies enforce standard operating procedures (SOPs) to combat the spread of COVID-19, with mandatory precautionary measures including use of face masks at social assembly points. In addition, the World Health Organization (WHO) strongly recommends people wear a face mask as a shield against the virus. The manual inspection of a large number of people for face mask enforcement is a challenge for law enforcement agencies. This work proposes an automatic face mask detection solution using an… More >

  • Open Access

    ARTICLE

    Plant Disease Classification Using Deep Bilinear CNN

    D. Srinivasa Rao1, Ramesh Babu Ch2, V. Sravan Kiran1, N. Rajasekhar3,*, Kalyanapu Srinivas4, P. Shilhora Akshay1, G. Sai Mohan1, B. Lalith Bharadwaj1

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 161-176, 2022, DOI:10.32604/iasc.2022.017706 - 03 September 2021

    Abstract

    Plant diseases have become a major threat in farming and provision of food. Various plant diseases have affected the natural growth of the plants and the infected plants are the leading factors for loss of crop production. The manual detection and identification of the plant diseases require a careful and observative examination through expertise. To overcome manual testing procedures an automated identification and detection can be implied which provides faster, scalable and precisive solutions. In this research, the contributions of our work are threefold. Firstly, a bi-linear convolution neural network (Bi-CNNs) for plant leaf disease

    More >

  • Open Access

    ARTICLE

    A Particle Swarm Optimization Based Deep Learning Model for Vehicle Classification

    Adi Alhudhaif1,*, Ammar Saeed2, Talha Imran2, Muhammad Kamran3, Ahmed S. Alghamdi3, Ahmed O. Aseeri1, Shtwai Alsubai1

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 223-235, 2022, DOI:10.32604/csse.2022.018430 - 26 August 2021

    Abstract Image classification is a core field in the research area of image processing and computer vision in which vehicle classification is a critical domain. The purpose of vehicle categorization is to formulate a compact system to assist in real-world problems and applications such as security, traffic analysis, and self-driving and autonomous vehicles. The recent revolution in the field of machine learning and artificial intelligence has provided an immense amount of support for image processing related problems and has overtaken the conventional, and handcrafted means of solving image analysis problems. In this paper, a combination of… More >

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