Kassymbek Ozhikenov¹, Zhadyra Alimbayeva^1,*, Chingiz Alimbayev^1,2,*, Aiman Ozhikenova¹, Yeldos Altay¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5257-5284, 2025, DOI:10.32604/cmc.2025.061508 - 06 March 2025

Abstract Myocardial infarction (MI) is one of the leading causes of death globally among cardiovascular diseases, necessitating modern and accurate diagnostics for cardiac patient conditions. Among the available functional diagnostic methods, electrocardiography (ECG) is particularly well-known for its ability to detect MI. However, confirming its accuracy—particularly in identifying the localization of myocardial damage—often presents challenges in practice. This study, therefore, proposes a new approach based on machine learning models for the analysis of 12-lead ECG data to accurately identify the localization of MI. In particular, the learning vector quantization (LVQ) algorithm was applied, considering the contribution… More >

Sachin Sharma^1,2,*, Brajesh Kumar Singh³, Hitendra Garg²

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4691-4708, 2025, DOI:10.32604/cmc.2025.061252 - 06 March 2025

Abstract Image tampering detection and localization have emerged as a critical domain in combating the pervasive issue of image manipulation due to the advancement of the large-scale availability of sophisticated image editing tools. The manual forgery localization is often reliant on forensic expertise. In recent times, machine learning (ML) and deep learning (DL) have shown promising results in automating image forgery localization. However, the ML-based method relies on hand-crafted features. Conversely, the DL method automatically extracts shallow spatial features to enhance the accuracy. However, DL-based methods lack the global co-relation of the features due to this… More >

Xinrong Zhang¹, Bo Chang^2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5079-5095, 2025, DOI:10.32604/cmc.2025.059469 - 06 March 2025

Abstract In the RSSI-based positioning algorithm, regarding the problem of a great conflict between precision and cost, a low-power and low-cost synergic localization algorithm is proposed, where effective methods are adopted in each phase of the localization process and fully use the detective information in the network to improve the positioning precision and robustness. In the ranging period, the power attenuation factor is obtained through the wireless channel modeling, and the RSSI value is transformed into distance. In the positioning period, the preferred reference nodes are used to calculate coordinates. In the position optimization period, Taylor… More >

Gang Hao, Peng Liang^*, Ziyuan Li, Huimin Zhao, Hong Zhang

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5221-5238, 2025, DOI:10.32604/cmc.2025.055739 - 06 March 2025

Abstract In recent years, the detection of image copy-move forgery (CMFD) has become a critical challenge in verifying the authenticity of digital images, particularly as image manipulation techniques evolve rapidly. While deep convolutional neural networks (DCNNs) have been widely employed for CMFD tasks, they are often hindered by a notable limitation: the progressive reduction in spatial resolution during the encoding process, which leads to the loss of critical image details. These details are essential for the accurate detection and localization of image copy-move forgery. To overcome the limitations of existing methods, this paper proposes a Transformer-based… More >

Farooq Ali^1,*, Huma Qayyum¹, Kashif Saleem², Iftikhar Ahmad³, Muhammad Javed Iqbal⁴

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2373-2388, 2025, DOI:10.32604/cmc.2025.060745 - 17 February 2025

Abstract Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging… 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 >

Liang Mao^1,2, Yue Li^1,2, Linlin Wang^1,*, Jie Li¹, Jiajun Tan¹, Yang Meng¹, Cheng Xiong¹

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1985-1999, 2025, DOI:10.32604/cmc.2024.058409 - 17 February 2025

Abstract This paper introduces an advanced and efficient method for distributed drone-based fruit recognition and localization, tailored to satisfy the precision and security requirements of autonomous agricultural operations. Our method incorporates depth information to ensure precise localization and utilizes a streamlined detection network centered on the RepVGG module. This module replaces the traditional C2f module, enhancing detection performance while maintaining speed. To bolster the detection of small, distant fruits in complex settings, we integrate Selective Kernel Attention (SKAttention) and a specialized small-target detection layer. This adaptation allows the system to manage difficult conditions, such as variable… More >

Xin Fan^1,2, Zhenlei Fu^1,2,*, Jian Shu^1,2, Zuxiong Shen^1,2, Yun Ge^1,2

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2583-2607, 2025, DOI:10.32604/cmc.2024.057695 - 17 February 2025

Abstract Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of… More >

Yiqing Lu¹, Liutao Zhao^2,*, Qiankun Zhao³

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1401-1416, 2025, DOI:10.32604/cmc.2024.058757 - 03 January 2025

Abstract Ecological monitoring vehicles are equipped with a range of sensors and monitoring devices designed to gather data on ecological and environmental factors. These vehicles are crucial in various fields, including environmental science research, ecological and environmental monitoring projects, disaster response, and emergency management. A key method employed in these vehicles for achieving high-precision positioning is LiDAR (lightlaser detection and ranging)-Visual Simultaneous Localization and Mapping (SLAM). However, maintaining high-precision localization in complex scenarios, such as degraded environments or when dynamic objects are present, remains a significant challenge. To address this issue, we integrate both semantic and… More >

Ammar Fahem Majeed^1,2,*, Rashidah Arsat¹, Muhammad Ariff Baharudin¹, Nurul Mu’azzah Abdul Latiff¹, Abbas Albaidhani³

Computer Systems Science and Engineering, Vol.49, pp. 159-183, 2025, DOI:10.32604/csse.2024.058741 - 03 January 2025

Abstract Accurate non-line of sight (NLOS) identification technique in ultra-wideband (UWB) location-based services is critical for applications like drone communication and autonomous navigation. However, current methods using binary classification (LOS/NLOS) oversimplify real-world complexities, with limited generalisation and adaptability to varying indoor environments, thereby reducing the accuracy of positioning. This study proposes an extreme gradient boosting (XGBoost) model to identify multi-class NLOS conditions. We optimise the model using grid search and genetic algorithms. Initially, the grid search approach is used to identify the most favourable values for integer hyperparameters. In order to achieve an optimised model configuration,… More >