A fuzzy-optimized multi-level random forest (FOMRF) model for the classification of the impact of technostress by Gabriel James, Ifeoma, David, John, Samuel, Enefiok, Imeh Umoren, Ubong Etuk, Aloysius, Anietie, Saviour, Chikodili

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Sa-SNN: spiking attention neural network for image classification by Yongping Dan, Zhida Wang, Hengyi Li, Jintong Wei

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Hierarchical Deep Learning Model Optimization Using Enhanced Evolutionary-based Approach for Fake News Detection by Deepti Nikumbh, Anuradha Thakare

“…However, these models are often complex, with numerous trainable parameters, making them resource-intensive. This work introduces the Deep Learning Model with Evolutionary Computing Approach (DLECA), a novel method for compressing and optimizing hierarchical deep learning models (HDLM). …”
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Assessing wildfire susceptibility in Iran: Leveraging machine learning for geospatial analysis of climatic and anthropogenic factors by Ehsan Masoudian, Ali Mirzaei, Hossein Bagheri

“…Utilizing advanced remote sensing, geospatial information system (GIS) processing techniques such as cloud computing, and machine learning algorithms, this research analyzed the impact of climatic parameters, topographic features, and human-related factors on wildfire susceptibility assessment and prediction in Iran. …”
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Lightweight Evolving U-Net for Next-Generation Biomedical Imaging by Furkat Safarov, Ugiloy Khojamuratova, Misirov Komoliddin, Ziyat Kurbanov, Abdibayeva Tamara, Ishonkulov Nizamjon, Shakhnoza Muksimova, Young Im Cho

“…This study aims to develop a lightweight and scalable U-Net-based architecture that enhances segmentation performance while substantially reducing computational overhead. <b>Methods</b>: We propose a novel evolving U-Net architecture that integrates multi-scale feature extraction, depthwise separable convolutions, residual connections, and attention mechanisms to improve segmentation robustness across diverse imaging conditions. …”
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Methodology of Using CAx and Digital Twin Methods in the Development of a Multifunctional Portal Centre in Its Pre-Production Phase by Petr Bernardin, Zdenek Hajicek, Petr Janda, Josef Kozak, Frantisek Sedlacek, Vaclava Lasova, Jiri Kubicek

“…The proposed methodology was used on a specific machine, namely, a multifunctional portal centre, where features of computer-aided engineering (modelling, topology optimisation, stiffness and stress analyses, modal analyses, and analytical calculations) were combined with tools using the digital twin. …”
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Value of Pulmonary Cavity Wall Thickness Characteristics and Accompanying CT Signs in the Differential Diagnosis of Thick-wall Cancerous Cavities and Inflammatory Cavities by Zi’ao WANG, Huijie JIANG, Sheng ZHAO, Jinping LI, Zhongqi SUN, Hao LI

“…Objective: To explore the value of various computed tomography (CT) features in the differential diagnosis of pulmonary thick-wall cancerous cavitation and inflammatory cavitation. …”
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Lightweight Brain Tumor Segmentation Through Wavelet-Guided Iterative Axial Factorization Attention by Yueyang Zhong, Shuyi Wang, Yuqing Miao, Tao Zhang, Haoliang Li

“…Conventional deep learning methods, such as convolutional neural networks and transformer-based models, frequently introduce significant computational overhead or fail to effectively represent multi-scale features. …”
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Clinical findings of acute necrotizing esophagitis complicated by diabetic ketoacidosis by Yuichiro Iwamoto, Tomohiko Kimura, Takashi Itoh, Shigehito Mori, Taku Sasaki, Mana Ohnishi, Haruka Takenouchi, Hideyuki Iwamoto, Junpei Sanada, Yoshiro Fushimi, Yukino Katakura, Fuminori Tatsumi, Masashi Shimoda, Shuhei Nakanishi, Tomoatsu Mune, Kohei Kaku, Hideaki Kaneto

“…We compared the clinical parameters and computed tomography (CT) between the groups. …”
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A lightweight model for automatic pig counting in intensive piggeries using a green inspection robot and image segmentation method by Yizhi Luo, Chen Yang, Enli Lv, Aqing Yang, Fanming Meng, Haowen Luo

“…Specifically, the C2f module is replaced with the Ghost module to reduce the model’s computational complexity. Additionally, a spatial group-enhanced attention mechanism is introduced in the neck network to enhance the model's feature fusion ability in the presence of pig occlusion and overlap. …”
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Optimizing SVM Performance through Combinatorial Hyperparameter Tuning and Model Selection by Hassan Tariq, Mehwish Majeed, Mueed Ahmad

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Modeling of temperature fields of details during laser hardening using the method of polyargument systems by Viktor Prokopov, Nataliia Fialko, Juliiy Sherenkovsky, Vladimir Yurchuk, Nataliia Meranova, Olha Maletskaya, Oleksandra Timoshchenko, Mark Hanzha

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Accuracy of Detecting Degrees of Lameness in Individual Dairy Cattle Within a Herd Using Single and Multiple Changes in Behavior and Gait by Xi Kang, Junjie Liang, Qian Li, Gang Liu

“…Through a comparative analysis of single-parameter and multiple-parameter classification models, we quantitatively demonstrated that models using multiple characteristics significantly outperformed single-parameter models, achieving an accuracy of 84% and a Macro-F1 score of 0.81, while better accounting for individual variability. …”
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A new low-rank adaptation method for brain structure and metastasis segmentation via decoupled principal weight direction and magnitude by Hancan Zhu, Hongxia Yang, Yaqing Wang, Keli Hu, Guanghua He, Jia Zhou, Zhong Li, Alzheimer’s Disease Neuroimaging Initiative

“…Additionally, different segmentation tasks frequently require retraining models from scratch, resulting in substantial computational costs. To address these limitations, we propose PDoRA, an innovative parameter-efficient fine-tuning method that leverages knowledge transfer from a pre-trained SwinUNETR model for a wide range of brain image segmentation tasks. …”
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Credibility-Adjusted Data-Conscious Clustering Method for Robust EEG Signal Analysis by Fatemeh Divan, Teh Ying Wah, Kheng Seang Lim, Ali Seyed Shirkhorshidi

“…A grid search framework optimizes clustering parameters, and preprocessing techniques (Fourier Transform, Wavelet Transform, and Gaussian filtering) improve feature separability. …”
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Network Security Situational Awareness Based on Improved Particle Swarm Algorithm and Bidirectional Long Short-Term Memory Modeling by Peng Zheng, Yun Cheng, Wei Zhu, Bo Liu, Shuhong Liu, Shijie Wang, Jinyin Bai

“…By gathering and organizing critical information within the network, an encapsulated Wrapper feature selection algorithm is utilized for the extraction of element features. …”
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Ripe-Detection: A Lightweight Method for Strawberry Ripeness Detection by Helong Yu, Cheng Qian, Zhenyang Chen, Jing Chen, Yuxin Zhao

“…To address these limitations, this study proposes Ripe-Detection, a novel lightweight object detection framework integrating three key innovations: a PEDblock detection head architecture with depth-adaptive feature learning capability, an ADown downsampling method for enhanced detail perception with reduced computational overhead, and BiFPN-based hierarchical feature fusion with learnable weighting mechanisms. …”
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Preoperative prediction value of 2.5D deep learning model based on contrast-enhanced CT for lymphovascular invasion of gastric cancer by Xiao Sun, Pei Wang, Rui Ding, Li Ma, Huairong Zhang, Li Zhu

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In Situ Capture of High-Temperature Precipitate Phases in Ti-48Al-2Cr-2Nb Alloy Using Convolutional Neural Networks by Xiaolei Li, Chuanqing Huang, Sen Zhao, Linlin Cui, Shirui Guo, Bo Zheng, Yinghao Cui, Yongqian Chen, Yue Zhao, Lujun Cui, Chunjie Xu

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Stability-Optimized Graph Convolutional Network: A Novel Propagation Rule with Constraints Derived from ODEs by Liping Chen, Hongji Zhu, Shuguang Han

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