Systems and Soft Computing最新文献_第3页

A fuzzy deep learning approach for liver lesions detection and classification in big data context 大数据环境下肝脏病变检测与分类的模糊深度学习方法

IF 3.6

Systems and Soft Computing Pub Date : 2026-06-01 Epub Date: 2026-02-26 DOI: 10.1016/j.sasc.2026.200462

Anh-Cang Phan , Thanh-Ngoan Trieu , Thuong-Cang Phan

{"title":"A fuzzy deep learning approach for liver lesions detection and classification in big data context","authors":"Anh-Cang Phan , Thanh-Ngoan Trieu , Thuong-Cang Phan","doi":"10.1016/j.sasc.2026.200462","DOIUrl":"10.1016/j.sasc.2026.200462","url":null,"abstract":"<div><div>Liver cancer is currently considered the most prevalent malignancy of the digestive system with a high mortality rate and unpredictable prognosis. Early detection and classification of liver lesions are crucial in formulating appropriate treatment strategies, ultimately extending patients’ survival time. This work proposes the utilization of three deep learning models – DenseNet-121, VGG-19, and Vision Transformer (ViT) – to detect and classify liver lesions using a real-world dataset comprising 2008 CT scans. The dataset is collected with four phases before and after contrast injection, and is used to identify three primary types of liver lesions: cysts, hemangiomas, and cellular carcinoma. To optimize the labor-intensive and costly labeling process, we propose an Active Learning approach to enhance the efficiency of semi-automated data annotation. Furthermore, this work integrates a Fuzzy Layer based on the Fuzzy C-Means algorithm into the ViT model, forming the Fuzzy-ViT framework. Additionally, the ViT and Fuzzy-ViT models are deployed in large-scale data environments, including Spark local and Spark cluster. Experimental results indicate that the ViT model is the most optimal choice due to its superior accuracy. The integration of Fuzzy C-Means significantly improves classification performance, achieving an accuracy of up to 98%. The implementation in a Spark cluster environment reduces training time by up to 50% compared to a local execution setup. These findings underscore the effectiveness of distributed computing in large-scale data processing. The study confirms that Fuzzy-ViT outperforms standard ViT, demonstrating the effectiveness of fuzzy logic in deep learning models for medical imaging. The research also highlights the trade-offs between accuracy and training speed in different computing environments, offering valuable insights for deploying AI-driven medical diagnostics at scale.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"8 ","pages":"Article 200462"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

YOLOX-TD-Plus: An accurate and fast text detection model YOLOX-TD-Plus：准确、快速的文本检测模型

IF 3.6

Systems and Soft Computing Pub Date : 2026-06-01 Epub Date: 2026-01-12 DOI: 10.1016/j.sasc.2026.200437

Deepak C.R., Padmavathi S.

{"title":"YOLOX-TD-Plus: An accurate and fast text detection model","authors":"Deepak C.R., Padmavathi S.","doi":"10.1016/j.sasc.2026.200437","DOIUrl":"10.1016/j.sasc.2026.200437","url":null,"abstract":"<div><div>The YOLO series of object detection algorithms has become a standard in a wide range of object detection applications. However, their application to text detection in the wild remains relatively unexplored. This paper presents a new convolutional neural network (CNN)-based model aimed at improving text detection performance through the introduction of a newly designed attention-concentrated enhanced cross-stage partial network (ACE-CSP) layer. The proposed model is built on the path aggregation feature pyramid network (PAFPN) architecture and incorporates ACE-CSP layer blocks, which we developed to facilitate improved information flow through the network and enhance its learning capability. The integration of channel and spatial attention in the ACE-CSP layers enables the network to focus more precisely on relevant text regions. This helps suppress irrelevant background activations, even in cluttered scenes. This design helps to reduce the imbalance in contributions from different feature pyramid layers, resulting in more consistent detection across varying text sizes. The proposed model, YOLOX-TD-Plus, shows significant improvements in text detection performance. We evaluated the model on the COCO-Text-v2.0 dataset, which includes multilingual and multi-oriented text instances. The experimental results show the effectiveness of the proposed architecture in solving text detection challenges in real-world scenarios. Specifically, YOLOX-TD-Plus-t improves Average Precision (AP) from 0.136 to 0.186 (a 36.8% relative improvement), and YOLOX-TD-Plus-l reaches a top AP of 0.341, surpassing the baseline’s 0.317.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"8 ","pages":"Article 200437"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Plant leaves disease detection in complex background using automatic segmentation and improved multi-scale feature fusion network 基于自动分割和改进多尺度特征融合网络的复杂背景下植物叶片病害检测

IF 3.6

Systems and Soft Computing Pub Date : 2026-06-01 Epub Date: 2025-12-23 DOI: 10.1016/j.sasc.2025.200434

Apoorva Arora, Vinay Gautam

{"title":"Plant leaves disease detection in complex background using automatic segmentation and improved multi-scale feature fusion network","authors":"Apoorva Arora, Vinay Gautam","doi":"10.1016/j.sasc.2025.200434","DOIUrl":"10.1016/j.sasc.2025.200434","url":null,"abstract":"<div><div>In order to solve a number of issues and increase agricultural output, researchers have recently widely used Artificial Intelligence (AI) approaches in smart farming. Given the enormous diversity of plants in the world and the many diseases that have a negative effect on crop productivity, identifying and categorizing plant diseases is a difficult undertaking. One of the main areas of research globally has been the automatic segmentation of images showing plant leaf diseases. The efficiency of the proposed model is assessed using open-source datasets, such as FGVC 7, which comprise four distinct classes of leaf diseases. The picture undergoes three phases of pre-processing. First is, a Rank Order Fuzzy (ROF) filter approach is used to minimize background noise in the plant image. Then resizing the image and finally augment of data. The next step involves identifying disease spots using histogram-based methods based on the L*a*b* color model. Finding disease spots before segmentation helps ensure proper segmentation, which is the goal of the next stage, which involves dividing the leaf pictures into uniform areas. Furthermore, a fusion model for data categorization uses patches segmentation data as input. To improve the system's accuracy while operating independently, different leaf disease zones are segmented, and patches are created using patch segmentation algorithms. This proposed model utilizes the advantages of both Convolutional Neural Networks (CNNs) and Vision Transformers (ViT) to extract potent early features fusion by integrating CNN designs such as VGG16, InceptionV3, AlexNet, and Google Net. After that, local characteristics are captured using a ViT model to accurately detect plant illnesses. With an impressive accuracy of 99.85%, the proposed fusion model performs better than similar previously published methods in the detection and categorization of several plant leaf diseases.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"8 ","pages":"Article 200434"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing machine learning models for obesity risk prediction through hyperparameter tuning 通过超参数调整优化肥胖风险预测的机器学习模型

IF 3.6

Systems and Soft Computing Pub Date : 2026-06-01 Epub Date: 2026-03-02 DOI: 10.1016/j.sasc.2026.200472

Salliah Shafi , Gufran Ahmad Ansari , Lamees Alhazzaa

{"title":"Optimizing machine learning models for obesity risk prediction through hyperparameter tuning","authors":"Salliah Shafi , Gufran Ahmad Ansari , Lamees Alhazzaa","doi":"10.1016/j.sasc.2026.200472","DOIUrl":"10.1016/j.sasc.2026.200472","url":null,"abstract":"<div><div>Early risk prediction is essential as obesity is a major public health concern associated with numerous chronic conditions. This study evaluates the effectiveness of various machine learning algorithms in predicting obesity risk with a particular focus on Hyperparameter optimisation enhances model performance. Obesity-related data was analysed using several algorithms including Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Logistic Regression (LR). Experimental results showed that Logistic Regression achieved the highest accuracy (99.63%), while Decision Tree produced the lowest (79.68%). Performance of SVM (89.6%) and KNN (94.33%) fell in between. Hyperparameter tuning significantly improved these models leading to greater robustness and predictive accuracy. Furthermore, a novel framework combining feature engineering with a cuckoo-inspired optimisation model was proposed to further enhance prediction quality. In addition, a novel hybrid framework integrating cuckoo-inspired feature optimization with Hyperparameter-tuned machine learSning models is proposed. This joint optimization strategy significantly enhances prediction accuracy, robustness, and model interpretability, distinguishing the proposed approach from existing obesity risk prediction methods.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"8 ","pages":"Article 200472"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Low-power deep packet inspection: A programmable logic approach 低功耗深度包检测：一种可编程逻辑方法

IF 3.6

Systems and Soft Computing Pub Date : 2026-06-01 Epub Date: 2026-02-23 DOI: 10.1016/j.sasc.2026.200461

Denver Conger, Benjamin Mahoney, Matthew Anderson, Matthew Sgambati, Brian Allen, Keaton Roberts

{"title":"Low-power deep packet inspection: A programmable logic approach","authors":"Denver Conger, Benjamin Mahoney, Matthew Anderson, Matthew Sgambati, Brian Allen, Keaton Roberts","doi":"10.1016/j.sasc.2026.200461","DOIUrl":"10.1016/j.sasc.2026.200461","url":null,"abstract":"<div><div>Network intrusion detection systems are a core component of cybersecurity toolkits and may rely upon machine learning inference as part of deep packet inspection to improve the detection capability. But the hardware to run these algorithms can have large power requirements that make it difficult for deployment in low-power situations such as internet-of-things (IoT) environments. This work explores a machine learning approach for deep packet inspection that is semi-supervised and deployed in a low-power programmable logic device that infers at sub-millisecond latencies while using a fraction of the power of what a graphics processing unit (GPU) would use. While programmable logic implementations generally show a considerable loss in accuracy when deploying a machine learning model compared to a GPU solution, the semi-supervised model detailed here suffers a negligible loss in accuracy. Accuracy, latency, and power comparisons between a GPU and field programmable gate array (FPGA) implementation are presented using open-source attack datasets and thousands of strikes from Keysight’s BreakingPoint.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"8 ","pages":"Article 200461"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Blind audio watermarking for medical data authentication using fractional Charlier transform and adaptive dithered quantization index modulation 基于分数阶Charlier变换和自适应抖动量化指标调制的医疗数据盲音频水印

IF 3.6

Systems and Soft Computing Pub Date : 2026-06-01 Epub Date: 2026-02-26 DOI: 10.1016/j.sasc.2026.200464

Salah Euschi , Narima Zermi , Sayah Med Moad , Amine Khaldi , Med Redouane Kafi , Aditya Kumar Sahu , Narimene Mimoune

{"title":"Blind audio watermarking for medical data authentication using fractional Charlier transform and adaptive dithered quantization index modulation","authors":"Salah Euschi , Narima Zermi , Sayah Med Moad , Amine Khaldi , Med Redouane Kafi , Aditya Kumar Sahu , Narimene Mimoune","doi":"10.1016/j.sasc.2026.200464","DOIUrl":"10.1016/j.sasc.2026.200464","url":null,"abstract":"<div><div>Secure authentication and traceability of medical audio data remain critical challenges in modern telemedicine systems and digital health record management.. This paper proposes a novel blind and robust audio watermarking scheme for medical applications. The method combines the Fractional Charlier Transform (FrCT) for optimized time–frequency decomposition, local entropy analysis with critical-band masking for intelligent coefficient selection, and adaptive dithered quantization index modulation (ADQIM) for imperceptible watermark embedding. The proposed scheme provides comprehensive encryption of metadata including patient information and acquisition context through AES-based cryptographic mechanisms, while maintaining imperceptibility and embedding robustness. Comprehensive experimental validation on a diverse medical audio corpus demonstrates that the method achieves a practical payload capacity of 71.8 bits per second, high audio transparency with an SNR of 38.2 dB and a PESQ score of 4.15, and strong resilience against various signal processing attacks with an average BER of 3.2 %. The approach provides a computationally efficient solution suitable for integration into operational telemedicine platforms and large-scale medical archiving systems, offering reliable authentication and integrity verification of medical audio records.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"8 ","pages":"Article 200464"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic control of multimodal fusion robot using YoloV8 with CNN 基于CNN的YoloV8多模态融合机器人自动控制

IF 3.6

Systems and Soft Computing Pub Date : 2026-06-01 Epub Date: 2026-02-03 DOI: 10.1016/j.sasc.2026.200450

Dong Chen

{"title":"Automatic control of multimodal fusion robot using YoloV8 with CNN","authors":"Dong Chen","doi":"10.1016/j.sasc.2026.200450","DOIUrl":"10.1016/j.sasc.2026.200450","url":null,"abstract":"<div><div>A multimodal data fusion platform that can manage heterogeneous data may be challenging to manually construct as sensory-feedback increases in volume and complexity. Recent years have seen a rise in the study of multi-modal machine learning, with researchers concentrating on methods for processing visual and auditory data. From a robotics standpoint, nevertheless, haptic feedback from environmental interactions is crucial for carrying out practical activities. The capacity to use deep reinforcement learning without knowing the system inside and out is one of its main benefits. One of the most difficult aspects of mobile robotics is path planning, namely avoiding obstacles. An array of human-machine sensors, including four force sensors, two joysticks, and a depth-sensing camera, was used to detect human intent. Instead of using the hands to operate the walker, users may just move or turn around due to the motion controller's ability to understand their purpose via the interactive force. To ensure the algorithm works, we do preliminary tests on healthy people and virtual patients. The results demonstrate that, in comparison to the conventional proportional controller, the suggested motion control method is more precise, user-friendly, and capable of recognizing the user's purpose.To enhance the accuracy of intention identification, a multimodal integration method was suggested, combining the YoloV8 model with Convolutional Neural Networks (CNN). Next, the system used the video camera, force gauges, and joysticks to obtain the moving angle (E), pelvic pose (F), and motion vector (H), accordingly. It is used extraction of features along with data combination to classify the goals, and finally, it outputted the speed control for the motor through the robot's kinematics, using a visual Simultaneous Localization and Mapping (SLAM) model and Deep Reinforcement Learning (DRL). That the system can provide walking aid and that the suggested way is efficient were both shown by the findings.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"8 ","pages":"Article 200450"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced OTP and facial recognition for e-learning authentication 增强OTP和面部识别的电子学习认证

IF 3.6

Systems and Soft Computing Pub Date : 2026-06-01 Epub Date: 2026-01-06 DOI: 10.1016/j.sasc.2026.200440

Aminou Halidou , Stéphane Gaël Raymond Ekodeck , Daramy Vandi Von Kallon , Christophe Armel Nteme , Jocelyn Edinio Zacko Gbadoubissa

{"title":"Enhanced OTP and facial recognition for e-learning authentication","authors":"Aminou Halidou , Stéphane Gaël Raymond Ekodeck , Daramy Vandi Von Kallon , Christophe Armel Nteme , Jocelyn Edinio Zacko Gbadoubissa","doi":"10.1016/j.sasc.2026.200440","DOIUrl":"10.1016/j.sasc.2026.200440","url":null,"abstract":"<div><div>Online services, particularly e-learning platforms, face significant challenges in authenticating users due to the absence of physical identification. This vulnerability can lead to security breaches that compromise the credibility of assessments. A robust authentication mechanism is crucial during the evaluation phase to ensure integrity and fairness.</div><div>A two-phase, multi-factor authentication framework is presented to strengthen security in e-learning environments. The first phase involves user authentication through credential submission and a OTP (One-Time Password) sent by SMS or email, establishing a 2FA (Two-Factor Authentication) process. The second phase employs real-time facial recognition during online examinations, utilizing a feature-based face detection technique with the Haar Cascade classifier and webcam images captured during registration.</div><div>The experimental results show an authentication accuracy of 80% in well lit conditions and 62% in low light environments, indicating a substantial improvement in security over existing methods. This approach provides a minimally intrusive but effective means of improving the reliability of online assessments.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"8 ","pages":"Article 200440"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic meta-learning with generative augmentation for cross-lingual Japanese few-shot named entity recognition 基于生成增强的动态元学习跨语言日语短镜头命名实体识别

IF 3.6

Systems and Soft Computing Pub Date : 2026-06-01 Epub Date: 2026-01-05 DOI: 10.1016/j.sasc.2026.200438

Demei Zhu , Qin Liu , Xiaoying Pan , Xiaoli Shao

{"title":"Dynamic meta-learning with generative augmentation for cross-lingual Japanese few-shot named entity recognition","authors":"Demei Zhu , Qin Liu , Xiaoying Pan , Xiaoli Shao","doi":"10.1016/j.sasc.2026.200438","DOIUrl":"10.1016/j.sasc.2026.200438","url":null,"abstract":"<div><div>Named Entity Recognition (NER) in Japanese is a challenging task due to data scarcity, limited cross-lingual transfer capabilities, and fuzzy entity boundaries, especially in low-resource environments. This research presents a novel framework, MAML-ProtoNet++, designed to overcome these challenges. The framework combines Model-Agnostic Meta-Learning (MAML), which allows for rapid parameter adaptation, with Prototypical Networks (ProtoNet) that perform prototype-based classification for few-shot learning. Additionally, the framework integrates cross-lingual contrastive pretraining using the multilingual mT5 model, which generates diverse pseudo-samples and optimizes the semantic alignment between Japanese and English entity pairs. To address the problem of insufficient annotated data, generative augmentation techniques and boundary verification methods are employed, improving the support set and entity boundary recognition. The experimental results demonstrate that MAML-ProtoNet++ outperforms existing models with a macro-average F1 score of 0.772 under a 5-shot setting. The boundary recognition accuracy is notably high, with 0.85 for start points and 0.84 for end points. Additionally, cross-lingual pretraining significantly improves semantic alignment, with cosine similarity between Japanese and English entities increasing from 0.61 to 0.85. These results highlight the robustness and adaptability of MAML-ProtoNet++ in handling complex few-shot and cross-lingual NER tasks. The findings suggest that this framework is a promising solution for NER in low-resource languages like Japanese, offering potential for broader applications in cross-lingual transfer learning.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"8 ","pages":"Article 200438"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy curve based multilevel thresholding with artificial hummingbird algorithm and minimum cross entropy 基于人工蜂鸟算法和最小交叉熵的能量曲线多层阈值分割

IF 3.6

Systems and Soft Computing Pub Date : 2026-06-01 Epub Date: 2025-12-26 DOI: 10.1016/j.sasc.2025.200432

Tirumalasetti Supraja, Kankanala Srinivas

{"title":"Energy curve based multilevel thresholding with artificial hummingbird algorithm and minimum cross entropy","authors":"Tirumalasetti Supraja, Kankanala Srinivas","doi":"10.1016/j.sasc.2025.200432","DOIUrl":"10.1016/j.sasc.2025.200432","url":null,"abstract":"<div><div>Multilevel thresholding is an important technique in color image segmentation, yet traditional methods such as OTSU’s often struggle to preserve meaningful structures in complex images. To address this limitation, we propose a hybrid segmentation framework that integrates the Artificial Hummingbird Algorithm (AHA) with the Minimum Cross Entropy Measure (MCEM) as the objective function. Instead of relying on the global histogram, the method employs an intensity level energy curve to capture spatial intensity variation and fine edge information. AHA incorporates guided, territorial, and migration foraging strategies, enabling an effective balance between exploration and exploitation during threshold optimization. The proposed approach is evaluated across multiple threshold levels and benchmarked against OTSU’s and MCEM based methods enhanced through four metaheuristics: Aquila Optimizer (AO), Equilibrium Optimizer (EO), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA). Performance is assessed using seven quantitative metrics: PSNR, SSIM, FSIM, QILV, Correlation coefficient, Edge Preservation Index (EPI), and Mutual Information Factor (MIF). Experimental results on satellite images demonstrate that the proposed method delivers improved segmentation quality, robustness, and structural fidelity, showing strong potential for environmental monitoring, remote sensing, and disaster analysis applications.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"8 ","pages":"Article 200432"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0