Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (25)
  • Open Access


    Enhanced Long Short Term Memory for Early Alzheimer's Disease Prediction

    M. Vinoth Kumar1,*, M. Prakash2, M. Naresh Kumar3, H. Abdul Shabeer4

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1277-1293, 2023, DOI:10.32604/iasc.2023.025591

    Abstract The most noteworthy neurodegenerative disorder nationwide is apparently the Alzheimer's disease (AD) which ha no proven viable treatment till date and despite the clinical trials showing the potential of preclinical therapy, a sensitive method for evaluating the AD has to be developed yet. Due to the correlations between ocular and brain tissue, the eye (retinal blood vessels) has been investigated for predicting the AD. Hence, en enhanced method named Enhanced Long Short Term Memory (E-LSTM) has been proposed in this work which aims at finding the severity of AD from ocular biomarkers. To find the… More >

  • Open Access


    A Novel MegaBAT Optimized Intelligent Intrusion Detection System in Wireless Sensor Networks

    G. Nagalalli*, G. Ravi

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 475-490, 2023, DOI:10.32604/iasc.2023.026571

    Abstract Wireless Sensor Network (WSN), which finds as one of the major components of modern electronic and wireless systems. A WSN consists of numerous sensor nodes for the discovery of sensor networks to leverage features like data sensing, data processing, and communication. In the field of medical health care, these network plays a very vital role in transmitting highly sensitive data from different geographic regions and collecting this information by the respective network. But the fear of different attacks on health care data typically increases day by day. In a very short period, these attacks may… More >

  • Open Access


    Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

    Doaa Sami Khafaga1, Amel Ali Alhussan1,*, El-Sayed M. El-kenawy2,3, Abdelhameed Ibrahim4, Said H. Abd Elkhalik3, Shady Y. El-Mashad5, Abdelaziz A. Abdelhamid6,7

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 865-881, 2022, DOI:10.32604/cmc.2022.028550

    Abstract The design of an antenna requires a careful selection of its parameters to retain the desired performance. However, this task is time-consuming when the traditional approaches are employed, which represents a significant challenge. On the other hand, machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended performance. In this paper, we propose a novel approach for accurately predicting the bandwidth of metamaterial antenna. The proposed approach is based on employing… More >

  • Open Access


    Multi-Site Air Pollutant Prediction Using Long Short Term Memory

    Chitra Paulpandi*, Murukesh Chinnasamy, Shanker Nagalingam Rajendiran

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1341-1355, 2022, DOI:10.32604/csse.2022.023882

    Abstract The current pandemic highlights the significance and impact of air pollution on individuals. When it comes to climate sustainability, air pollution is a major challenge. Because of the distinctive nature, unpredictability, and great changeability in the reality of toxins and particulates, detecting air quality is a puzzling task. Simultaneously, the ability to predict or classify and monitor air quality is becoming increasingly important, particularly in urban areas, due to the well documented negative impact of air pollution on resident’s health and the environment. To better comprehend the current condition of air quality, this research proposes… More >

  • Open Access


    An Efficient Stacked-LSTM Based User Clustering for 5G NOMA Systems

    S. Prabha Kumaresan1, Chee Keong Tan2,*, Yin Hoe Ng1

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6119-6140, 2022, DOI:10.32604/cmc.2022.027223

    Abstract Non-orthogonal multiple access (NOMA) has been a key enabling technology for the fifth generation (5G) cellular networks. Based on the NOMA principle, a traditional neural network has been implemented for user clustering (UC) to maximize the NOMA system’s throughput performance by considering that each sample is independent of the prior and the subsequent ones. Consequently, the prediction of UC for the future ones is based on the current clustering information, which is never used again due to the lack of memory of the network. Therefore, to relate the input features of NOMA users and capture… More >

  • Open Access


    A Novel Hybrid Deep Learning Framework for Intrusion Detection Systems in WSN-IoT Networks

    M. Maheswari1,2,*, R. A. Karthika1

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 365-382, 2022, DOI:10.32604/iasc.2022.022259

    Abstract With the advent of wireless communication and digital technology, low power, Internet-enabled, and reconfigurable wireless devices have been developed, which revolutionized day-to-day human life and the economy across the globe. These devices are realized by leveraging the features of sensing, processing the data and nodes communications. The scale of Internet-enabled wireless devices has increased daily, and these devices are exposed to various cyber-attacks. Since the complexity and dynamics of the attacks on the devices are computationally high, intelligent, scalable and high-speed intrusion detection systems (IDS) are required. Moreover, the wireless devices are battery-driven; implementing them… More >

  • Open Access


    Covid-19 CT Lung Image Segmentation Using Adaptive Donkey and Smuggler Optimization Algorithm

    P. Prabu1, K. Venkatachalam2, Ala Saleh Alluhaidan3,*, Radwa Marzouk4, Myriam Hadjouni5, Sahar A. El_Rahman5,6

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1133-1152, 2022, DOI:10.32604/cmc.2022.020919

    Abstract COVID’19 has caused the entire universe to be in existential health crisis by spreading globally in the year 2020. The lungs infection is detected in Computed Tomography (CT) images which provide the best way to increase the existing healthcare schemes in preventing the deadly virus. Nevertheless, separating the infected areas in CT images faces various issues such as low-intensity difference among normal and infectious tissue and high changes in the characteristics of the infection. To resolve these issues, a new inf-Net (Lung Infection Segmentation Deep Network) is designed for detecting the affected areas from the… More >

  • Open Access


    Cross Intelligence Evaluation for Effective Emotional Intelligence Estimation

    Ibrahim Alsukayti1, Aman Singh2,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2489-2505, 2022, DOI:10.32604/cmc.2022.020264

    Abstract A famous psychologist or researcher, Daniel Goleman, gave a theory on the importance of Emotional Intelligence for the success of an individual’s life. Daniel Goleman quoted in the research that “The contribution of an individual’s Intelligence Quotient (IQ) is only 20% for their success, the remaining 80% is due to Emotional Intelligence (EQ)”. However, in the absence of a reliable technique for EQ evaluation, this factor of overall intelligence is ignored in most of the intelligence evaluation mechanisms. This research presented an analysis based on basic statistical tools along with more sophisticated deep learning tools.… More >

  • Open Access


    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

    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


    Bitcoin Candlestick Prediction with Deep Neural Networks Based on Real Time Data

    Reem K. Alkhodhairi1, Shahad R. Aljalhami1, Norah K. Rusayni1, Jowharah F. Alshobaili1, Amal A. Al-Shargabi1,*, Abdulatif Alabdulatif2

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3215-3233, 2021, DOI:10.32604/cmc.2021.016881

    Abstract Currently, Bitcoin is the world’s most popular cryptocurrency. The price of Bitcoin is extremely volatile, which can be described as high-benefit and high-risk. To minimize the risk involved, a means of more accurately predicting the Bitcoin price is required. Most of the existing studies of Bitcoin prediction are based on historical (i.e., benchmark) data, without considering the real-time (i.e., live) data. To mitigate the issue of price volatility and achieve more precise outcomes, this study suggests using historical and real-time data to predict the Bitcoin candlestick—or open, high, low, and close (OHLC)—prices. Seeking a better… More >

Displaying 11-20 on page 2 of 25. Per Page