Neurovision: A deep learning driven web application for brain tumour detection using weight-aware decision approach by Thota Rishik Sai Santhosh, Sachi Nandan Mohanty, Nihar Ranjan Pradhan, Tauseef Khan, Morched Derbali

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Regional classification and logistic regression modeling for surface freeze/thaw detection on the Qinghai-Tibet Plateau using CYGNSS data by Jiaxing He, Nanshan Zheng, Rui Ding, Xuexi Liu, Zhiyong Ma

“…To overcome these limitations, this study proposes an innovative approach that integrates the application of GNSS-R data with regional classification and logistic regression modeling, establishing an efficient framework for detecting surface F/T states. …”
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ScaleViM-PDD: Multi-Scale EfficientViM with Physical Decoupling and Dual-Domain Fusion for Remote Sensing Image Dehazing by Hao Zhou, Yalun Wang, Wanting Peng, Xin Guan, Tao Tao

“…The ScaleViM-P module synergistically integrates a Physical Decoupling block within a Multi-scale EfficientViM architecture. This design enables the network to mitigate haze interference in a physically grounded manner at each representational scale while simultaneously capturing global contextual information to adaptively handle complex haze distributions. …”
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Lightweight convolutional neural networks using nonlinear Lévy chaotic moth flame optimisation for brain tumour classification via efficient hyperparameter tuning by Amin Abdollahi Dehkordi, Mehdi Neshat, Alireza Khosravian, Menasha Thilakaratne, Ali Safaa Sadiq, Seyedali Mirjalili

“…NLCMFO integrates the Lévy flight, chaotic parameters, and nonlinear control mechanisms to enhance the exploration capabilities of the Moth Flame Optimiser during the search phase while also leveraging the Lévy flight theorem to improve the exploitation phase. To assess the efficiency of the proposed model, empirical analyses were performed using a dataset of 2314 brain tumour detection images (1245 images of brain tumours and 1069 normal brain images). …”
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Comparative analysis of data-driven models on detection and classification of electrical faults in transmission systems: Explainability, applicability and industrial implications by Chibueze D. Ukwuoma, Dongsheng Cai, Chiagoziem C. Ukwuoma, Chinedu I. Otuka, Qi Huang

“…Most data-driven fault detection methods often face challenges in accuracy, adaptability, and real-time implementation, particularly in complex transmission networks. …”
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Improved smart city security using a deep maxout network-based intrusion detection system with walrus optimization by Wahid Rajeh, Majed Aborokbah, Manimurugan S., Umar Albalawi, Ahamed Aljuhani, Osama Shibl Abdalghany Younes, Karthikeyan Periyasami

“…These layers are capable of capturing complex patterns in the data, making the DMN particularly effective for identifying anomalies in IoT network traffic. …”
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Design and Research on a Reed Field Obstacle Detection and Safety Warning System Based on Improved YOLOv8n by Yuanyuan Zhang, Zhongqiu Mu, Kunpeng Tian, Bing Zhang, Jicheng Huang

“…Unmanned agricultural machinery can significantly reduce labor intensity while substantially enhancing operational efficiency and production benefits. However, the presence of various obstacles in complex farmland environments is inevitable. …”
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A CrossMod-Transformer deep learning framework for multi-modal pain detection through EDA and ECG fusion by Jaleh Farmani, Ghazal Bargshady, Stefanos Gkikas, Manolis Tsiknakis, Raul Fernandez Rojas

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A Novel Spectrum Sensing Method for Multiple Unknown Signal Sources Using Frequency Domain Energy Detection and DBSCAN by Rui Gao, Guanghui Yan, Ruiting Niu, Wenwen Chang, Tianfeng Yan, Chunyang Tang

“…Multiple rounds of FDED detection are compiled into a two-dimensional signal detection matrix, which is subsequently analyzed by the DBSCAN algorithm to identify signal clusters. …”
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SPL-YOLOv8: A Lightweight Method for Rape Flower Cluster Detection and Counting Based on YOLOv8n by Yue Fang, Chenbo Yang, Jie Li, Jingmin Tu

“…To address these challenges, this study proposes a lightweight rape flower clusters detection model, SPL-YOLOv8. First, the model introduces StarNet as a lightweight backbone network for efficient feature extraction, significantly reducing computational complexity and parameter counts. …”
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A Modified MobileNetv3 Coupled With Inverted Residual and Channel Attention Mechanisms for Detection of Tomato Leaf Diseases by Rubina Rashid, Waqar Aslam, Romana Aziz, Ghadah Aldehim

“…While deep learning models have been instrumental in detecting plant leaf diseases, they often involve complex models and significant computational demands to achieve optimal performance. …”
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MHOE-DETR: A Ship Detection Method for Small and Fuzzy Targets Based on Satellite Remote Sensing Image Data by Zhuhua Hu, Xiyu Fan, Yaochi Zhao, Wei Wu, Jie Liu

“…This allows the proposed MHOE-DETR model to avoid thresholding and NMS), reducing the model’s computational complexity. The experimental results demonstrate that the MHOE-DETR algorithm, designed for this purpose, markedly enhances the detection performance of small and indistinct targets in private remote sensing datasets. …”
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TIMA-Net: A Lightweight Remote Sensing Image Change Detection Network Based on Temporal Interaction Enhancement and Multi-Scale Aggregation by Zhijun Zhou, Xuejie Zhang, Xiaoliang Luo, Lvchun Wang, Wei Yu, Shufang Xu, Longbao Wang

“…However, existing deep learning methods often face challenges of high computational complexity and insufficient detail capture, particularly demonstrating limited performance in detecting high-resolution images and complex change regions. …”
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Adaptive Hierarchical Multi-Headed Convolutional Neural Network With Modified Convolutional Block Attention for Aerial Forest Fire Detection by Md. Najmul Mowla, Davood Asadi, Shamsul Masum, Khaled Rabie

“…Effective detection and classification of forest fire imagery are critical for timely and efficient wildfire management. …”
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SODU2-NET: a novel deep learning-based approach for salient object detection utilizing U-NET by Hyder Abbas, Shen Bing Ren, Muhammad Asim, Syeda Iqra Hassan, Ahmed A. Abd El-Latif

“…The proposed SODU2-NET employs sophisticated background subtraction techniques and utilizes advanced deep learning architectures that can discern relevant foreground information when dealing with complex backgrounds. Firstly, an enriched encoder block with full feature fusion (FFF) with atrous spatial pyramid pooling (ASPP) varying dilation rates to efficiently capture multi-scale contextual information, improving salient object detection in complex backgrounds and reducing the loss of information during down-sampling. …”
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Improving Safety, Efficiency, Cost, and Satisfaction Across a Musculoskeletal Pathway Using the Digital Assessment Routing Tool for Triage: Quality Improvement Study by Cabella Lowe, Laura Atherton, Peter Lloyd, Anna Waters, Dylan Morrissey

“…Introduction of a new route to self-management for less complex conditions showed a cost reduction per patient of 73%, giving a saving of £1272.90 (US $1605.56) for 100 referrals. …”
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Research on Calf Behavior Recognition Based on Improved Lightweight YOLOv8 in Farming Scenarios by Ze Yuan, Shuai Wang, Chunguang Wang, Zheying Zong, Chunhui Zhang, Lide Su, Zeyu Ban

“…In order to achieve accurate and efficient recognition of calf behavior in complex scenes such as cow overlapping, occlusion, and different light and occlusion levels, this experiment adopts the method of improving the YOLO v8 model to recognize calf behavior. …”
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Nanoarchitectonics with cetrimonium bromide on metal nanoparticles for linker-free detection of toxic metal ions and catalytic degradation of 4-nitrophenol by Akash Kumar, Raja Gopal Rayavarapu

“…This restricts its utility in heavy metal detection and 4-NP degradation, requiring additional surface modifications using linker molecules, thereby increasing process complexity and cost. …”
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Abbreviated protocol for ultrasound duplex scanning of the carotid arteries in the evaluation of preclinical atherosclerosis in order to clarify cardiovascular risk by T. V. Balakhonova, O. A. Pogorelova, M. I. Tripoten, A. I. Ershova, M. V. Koshurnikova, А. N. Rogoza

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Research progress of genome-wide association study by Duan Zhongqu, Zhu Jun

“…Three possible factors were responsible for the failure of detecting the cause loci. First, the efficiency of detecting the small-effect loci is very low and more small-effect loci are undiscovered. …”
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