Iqbal M. Batiha^1,2,*, Iqbal H. Jebril¹, Mohammad Zuriqat³, Hamza S. Kanaan⁴, Shaher Momani^5,*

Frontiers in Heat and Mass Transfer, Vol.21, pp. 487-504, 2023, DOI:10.32604/fhmt.2023.045021 - 30 November 2023

Abstract Several researchers have dealt with the one-dimensional fractional heat conduction equation in the last decades, but as far as we know, no one has investigated such a problem from the perspective of developing suitable fractionalorder methods. This has actually motivated us to address this problem by the way of establishing a proper fractional approach that involves employing a combination of a novel fractional difference formula to approximate the Caputo differentiator of order α coupled with the modified three-point fractional formula to approximate the Caputo differentiator of order 2α, where 0 < α ≤ 1. As More >

Abdullah Mujawib Alashjaee^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 235-254, 2023, DOI:10.32604/iasc.2023.036247 - 29 April 2023

Abstract With the recent increase in network attacks by threats, malware, and other sources, machine learning techniques have gained special attention for intrusion detection due to their ability to classify hundreds of features into normal system behavior or an attack attempt. However, feature selection is a vital preprocessing stage in machine learning approaches. This paper presents a novel feature selection-based approach, Remora Optimization Algorithm-Levy Flight (ROA-LF), to improve intrusion detection by boosting the ROA performance with LF. The developed ROA-LF is assessed using several evaluation measures on five publicly available datasets for intrusion detection: Knowledge discovery More >

Praveen Jesudhas^1,*, T. Raghuveera²

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2995-3015, 2023, DOI:10.32604/csse.2023.036778 - 03 April 2023

Abstract Timely identification and treatment of medical conditions could facilitate faster recovery and better health. Existing systems address this issue using custom-built sensors, which are invasive and difficult to generalize. A low-complexity scalable process is proposed to detect and identify medical conditions from 2D skeletal movements on video feed data. Minimal set of features relevant to distinguish medical conditions: AMF, PVF and GDF are derived from skeletal data on sampled frames across the entire action. The AMF (angular motion features) are derived to capture the angular motion of limbs during a specific action. The relative position… More >

Kusnandar¹, Win-Jet Luo^1,2, Indra Permana¹, Fu-Jen Wang^2,*, Gantulga Bayarkhuu²

Energy Engineering, Vol.120, No.6, pp. 1387-1399, 2023, DOI:10.32604/ee.2023.027459 - 03 April 2023

Abstract The heating, ventilating, and air conditioning (HVAC) system consumes nearly 50% of the building’s energy, especially in Taiwan with a hot and humid climate. Due to the challenges in obtaining energy sources and the negative impacts of excessive energy use on the environment, it is essential to employ an energy-efficient HVAC system. This study conducted the machine tools building in a university. The field measurement was carried out, and the data were used to conduct energy modelling with EnergyPlus (EP) in order to discover some improvements in energy-efficient design. The validation between field measurement and… More >

Huaxiang Song^*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5741-5756, 2023, DOI:10.32604/cmc.2023.034921 - 28 December 2022

Abstract Over the past decade, the significant growth of the convolutional neural network (CNN) based on deep learning (DL) approaches has greatly improved the machine learning (ML) algorithm’s performance on the semantic scene classification (SSC) of remote sensing images (RSI). However, the unbalanced attention to classification accuracy and efficiency has made the superiority of DL-based algorithms, e.g., automation and simplicity, partially lost. Traditional ML strategies (e.g., the handcrafted features or indicators) and accuracy-aimed strategies with a high trade-off (e.g., the multi-stage CNNs and ensemble of multi-CNNs) are widely used without any training efficiency optimization involved, which… More >

Richu Mary Thomas, Malarvizhi Subramani^*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1275-1291, 2022, DOI:10.32604/cmc.2022.024507 - 24 February 2022

Abstract The recent aggrandizement of radio frequency (RF) signals in wireless power transmission combined with energy harvesting methods have led to the replacement of traditional battery-powered wireless networks since the blooming RF technology provides energy renewal of wireless devices with the quality of service (QoS). In addition, it does not require any unnecessary alterations on the transmission hardware side. A hybridized global optimization technique uniting Global best and Local best (GL) based particle swarm optimization (PSO) and ant colony optimization (ACO) is proposed in this paper to optimally allocate resources in wireless powered communication networks (WPCN)… More >

P. Hemalatha^1,*, K. Dhanalakshmi²

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 811-825, 2022, DOI:10.32604/iasc.2022.020973 - 17 November 2021

Abstract Wireless sensor networks (WSNs) develop IoT (Internet of Things) that carry out an important part and include low-cost intelligent devices to gather information. However, these modern accessories have limitations concerning calculation, time taken for processing, storage capacity, and vitality sources. In addition to such restrictions, the foremost primary challenge for sensor networks is achieving reliable data transfer with the secured transmission in a hostile ambience containing vulnerable nodes. The proposed work initially analyses the relation between deployment configuration, lifetime of the deployed network, and transmission delay with this motivation. Besides, it also introduces a new… More >

Thanh Q. Nguyen^1,2, Thao T. D. Nguyen³, H. Nguyen-Xuan^4,5,*, Nhi K. Ngo^1,2

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 27-53, 2019, DOI:10.32604/cmc.2019.07756

Abstract This paper presents a new approach using correlation and cross-correlation coefficients to evaluate the stiffness degradation of beams under moving load. The theoretical study of identifying defects by vibration methods showed that the traditional methods derived from the vibration measurement data have not met the needs of the actual issues. We show that the correlation coefficients allow us to evaluate the degree and the effectiveness of the defects on beams. At the same time, the cross-correlation model is the basis for determining the relative position of defects. The results of this study are experimentally conducted More >