Haydar Abdulameer Marhoon^1,2,*, Rafid Sagban^3,4, Atheer Y. Oudah^1,5, Saadaldeen Rashid Ahmed^6,7

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4181-4218, 2025, DOI:10.32604/cmc.2025.058822 - 06 March 2025

Abstract In order to address the critical security challenges inherent to Wireless Sensor Networks (WSNs), this paper presents a groundbreaking barrier-based machine learning technique. Vital applications like military operations, healthcare monitoring, and environmental surveillance increasingly deploy WSNs, recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational integrity. The proposed method innovatively partitions the network into logical segments or virtual barriers, allowing for targeted monitoring and data collection that aligns with specific traffic patterns. This approach not only improves the diversit. There are more types of data in the training set,… More >

Huajiang Ruan^1,2, Panjun Sun^1,2, Yuanyuan Dong^1,2, Hamid Tahaei¹, Zhaoxi Fang^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1645-1680, 2025, DOI:10.32604/cmc.2024.059746 - 17 February 2025

Abstract Traditional Global Positioning System (GPS) technology, with its high power consumption and limited performance in obstructed environments, is unsuitable for many Internet of Things (IoT) applications. This paper explores LoRa as an alternative localization technology, leveraging its low power consumption, robust indoor penetration, and extensive coverage area, which render it highly suitable for diverse IoT settings. We comprehensively review several LoRa-based localization techniques, including time of arrival (ToA), time difference of arrival (TDoA), round trip time (RTT), received signal strength indicator (RSSI), and fingerprinting methods. Through this review, we evaluate the strengths and limitations of More >

Moein Tosan¹, Mohammad Reza Gharib^2,*, Nasrin Fathollahzadeh Attar³, Ali Maroosi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1109-1154, 2025, DOI:10.32604/cmes.2025.058595 - 27 January 2025

Abstract Accurate estimation of evapotranspiration (ET) is crucial for efficient water resource management, particularly in the face of climate change and increasing water scarcity. This study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers, selected according to PRISMA guidelines, to evaluate the performance of Hybrid Artificial Neural Networks (HANNs) in ET estimation. The findings demonstrate that HANNs, particularly those combining Multilayer Perceptrons (MLPs), Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs), are highly effective in capturing the complex nonlinear relationships and temporal dependencies characteristic of hydrological processes. These… More >

Peng Xia¹, Wu Zeng^2,*, Yin Ni¹, Ye Jin³

Journal on Artificial Intelligence, Vol.6, pp. 361-377, 2024, DOI:10.32604/jai.2024.059114 - 13 December 2024

Abstract Stress is defined as a subjective reflection of an internal psychological state of tension or arousal, manifesting as an interpretive, emotional, and defensive coping process within the body. Prolonged and sustained stress can significantly increase the risk of psychological and physiological disorders. Heart rate variability (HRV) is a key biomarker for assessing autonomic cardiac function, typically increasing during relaxation and decreasing under stress. Although measuring stress through physiological parameters like HRV is a common approach, achieving ultra-high accuracy based on HRV measurements remains a challenging task. In this study, the role of HRV features as… More >

Tuğba Çelikten¹, Aytuğ Onan^2,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3351-3377, 2024, DOI:10.32604/cmc.2024.051574 - 15 August 2024

Abstract The purpose of this study is to develop a reliable method for distinguishing between AI-generated, paraphrased, and human-written texts, which is crucial for maintaining the integrity of research and ensuring accurate information flow in critical fields such as healthcare. To achieve this, we propose HybridGAD, a novel hybrid model that combines Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Bidirectional Gated Recurrent Unit (Bi-GRU) architectures with an attention mechanism. Our methodology involves training this hybrid model on a dataset of radiology abstracts, encompassing texts generated by AI, paraphrased by AI, and written by humans. The… More >

Mohammad Aldossary^1,*, Hatem A. Alharbi², Nasir Ayub³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4603-4620, 2024, DOI:10.32604/cmc.2024.050862 - 20 June 2024

Abstract Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure, thereby revolutionizing computer processes. However, the rising energy consumption in cloud centers poses a significant challenge, especially with the escalating energy costs. This paper tackles this issue by introducing efficient solutions for data placement and node management, with a clear emphasis on the crucial role of the Internet of Things (IoT) throughout the research process. The IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around… More >

Md. Abdur Rahim¹, Fahmid Al Farid², Abu Saleh Musa Miah³, Arpa Kar Puza¹, Md. Nur Alam⁴, Md. Najmul Hossain⁵, Sarina Mansor², Hezerul Abdul Karim^2,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1689-1710, 2024, DOI:10.32604/cmes.2024.048714 - 20 May 2024

Abstract Handwriting is a unique and significant human feature that distinguishes them from one another. There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification. However, such systems are susceptible to forgery, posing security risks. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures or symbols. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures… More >

Ming Yan¹, Caijiang Lu^2,*, Pan Shi^1,*, Meiling Zhang³, Jiawei Zhang¹, Liang Wang¹

Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 615-631, 2024, DOI:10.32604/fhmt.2024.048333 - 20 May 2024

Abstract In response to escalating challenges in energy conservation and emission reduction, this study delves into the complexities of heat transfer in two-phase flows and adjustments to combustion processes within coal-fired boilers. Utilizing a fusion of hybrid modeling and automation technologies, we develop soft measurement models for key combustion parameters, such as the net calorific value of coal, flue gas oxygen content, and fly ash carbon content, within the Distributed Control System (DCS). Validated with performance test data, these models exhibit controlled root mean square error (RMSE) and maximum absolute error (MAXE) values, both within the… More > Graphic Abstract

Mehmood Ahmed^1,3,*, Noraini B. Ibrahim¹, Wasif Nisar², Adeel Ahmed³, Muhammad Junaid^3,*, Emmanuel Soriano Flores⁴, Divya Anand⁴

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1399-1422, 2024, DOI:10.32604/cmc.2023.046648 - 30 January 2024

Abstract Accurate software cost estimation in Global Software Development (GSD) remains challenging due to reliance on historical data and expert judgments. Traditional models, such as the Constructive Cost Model (COCOMO II), rely heavily on historical and accurate data. In addition, expert judgment is required to set many input parameters, which can introduce subjectivity and variability in the estimation process. Consequently, there is a need to improve the current GSD models to mitigate reliance on historical data, subjectivity in expert judgment, inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns. This… More >

Abidhan Bardhan^1,*, Raushan Kumar Singh², Mohammed Alatiyyah³, Sulaiman Abdullah Alateyah^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1521-1555, 2024, DOI:10.32604/cmes.2023.044467 - 29 January 2024

Abstract This research proposes a highly effective soft computing paradigm for estimating the compressive strength (CS) of metakaolin-contained cemented materials. The proposed approach is a combination of an enhanced grey wolf optimizer (EGWO) and an extreme learning machine (ELM). EGWO is an augmented form of the classic grey wolf optimizer (GWO). Compared to standard GWO, EGWO has a better hunting mechanism and produces an optimal performance. The EGWO was used to optimize the ELM structure and a hybrid model, ELM-EGWO, was built. To train and validate the proposed ELM-EGWO model, a sum of 361 experimental results… More >