Hyperspectral imaging-driven deep learning approach: Asymptomatic stage detection and severity grading of tomato yellow leaf curl disease by Junzhi Chen, Wenshan Zhong, Xuejun Yue, Ziyu Ding, Mengdan Du, Xuantian Li, Biao Chen, Haifeng Li, ZiFu He, Xiaoman She, Yafei Tang

“…Comparative experiments with three baseline models (2D CNN, 3D CNN, and HybridSN) demonstrated that SSDBRN achieved an early-infection detection accuracy of 84.2 % (5–7 days post-inoculation) and 96.2 % severity classification accuracy across six stages, significantly outperforming the baseline models. …”
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Comparison of Raman spectroscopy with mass spectrometry for sequence typing of Acinetobacter baumannii strains: a single-center study by Suling Liu, Ni Zhang, Jiawei Tang, Chong Chen, Weisha Wang, Jingfang Zhou, Long Ye, Xiaoli Chen, ZhengKang Li, Liang Wang

“…Then, a SERS spectral database for all these strains was constructed, and predictive models based on eight ML algorithms were developed to predict SERS signals to determine their STs, among which the support vector machine (SVM) model had the best performance (fivefold cross-validation = 99.74%). …”
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Enhancing Intelligent Road Target Monitoring: A Novel BGS-YOLO Approach Based on the YOLOv8 Algorithm by Xingyu Liu, Yuanfeng Chu, Yiheng Hu, Nan Zhao

“…Additionally, this paper innovatively designs the SimAM-C2f (SimC2f) network, further advancing feature expressiveness and fusion efficiency. Experiments on the public COCO dataset demonstrate that the BGS-YOLO model significantly outperforms the existing YOLOv8n model. …”
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Scalable machine learning framework for predicting critical links in urban networks by Nourhan Bachir, Chamseddine Zaki, Hassan Harb, Roland Billen

“…Efficient identification of critical links in urban road networks is essential for optimizing traffic management, infrastructure planning, and resource allocation. …”
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Diagnosis and classification of neuromuscular disorders using Bi-LSTM optimized with grey Wolf optimizer for EMG signals by Ruth Keziah Mani, Balakrishnan Nagaraj

“…However, the performance of these models is heavily influenced by hyperparameters such as the number of neurons, hidden layers, and learning rates. …”
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Microbiology in the era of artificial intelligence: transforming medical and pharmaceutical microbiology by Virna-Maria Tsitou, Dimitrios Rallis, Mariana Tsekova, Nikolay Yanev

“…Looking forward, we identify key directions for AI in microbiology, particularly focusing on developing adaptable, self-updating AI models and their integration into clinical settings. …”
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Recycling-Oriented Characterization of Space Waste Through Clean Hyperspectral Imaging Technology in a Circular Economy Context by Giuseppe Bonifazi, Idiano D’Adamo, Roberta Palmieri, Silvia Serranti

“…The implications of this work identify how technology enables the development of circular models geared toward sustainable development hence proper classification and distinction of materials as they allow for better material recovery and end-of-life management, ultimately contributing to more efficient recycling, waste valorization, and sustainable development practices.…”
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Unlocking the potential of wearable technology: Fitbit-derived measures for predicting ADHD in adolescents by Muhammad Mahbubur Rahman, Muhammad Mahbubur Rahman

“…These findings suggest that wearable data may contribute to more objective and efficient methods for ADHD identification, potentially enhancing clinical practices for diagnosis and management.…”
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A Comparative Analysis of Convolutional Neural Network (CNN): MobileNetV2 and Xception for Butterfly Species Classification by Mehta Pradnyatama, Christy Atika Sari, Eko Hari Rachmawanto, Hussain Md Mehedul Islam

“…This study contributes to entomological research and highlights trade-offs between model complexity and performance in fine-grained classification tasks, supporting implementation decisions for butterfly identification systems in practical applications.…”
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Estimating Value Added Tax Gap in Uzbekistan by I. M. Niyazmetov

“…A mathematical approach based on the C-efficiency (Collection efficiency) ratio is used to calculate the VAT gap. …”
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Deep Learning-Based Infrared Image Segmentation for Aircraft Honeycomb Water Ingress Detection by Hang Fei, Hongfu Zuo, Han Wang, Yan Liu, Zhenzhen Liu, Xin Li

“…The demonstrated reductions in computational effort and parameter count underscore the model’s efficiency, fortifying its relevance in broader contexts.…”
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Power transmission system’s fault location, detection, and classification: Pay close attention to transmission nodes by Chiagoziem C. Ukwuoma, Dongsheng Cai, Olusola Bamisile, Ejiyi J. Chukwuebuka, Ekong Favour, Gyarteng S.A. Emmanuel, Acen Caroline, Sabirin F. Abdi

“…In addition, a multi-linear perceptron block is designed to enhance the capturing of amplitude information and increase comprehension. The efficiency and generalizability of the proposed model are improved by the implementation of a multi-task training approach for locating faults and their type. …”
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Automated liver segmentation from CT images using modified ResUNet by R.V. Manjunath, Yashaswini Gowda N, H.M. Manu

“…Most doctors prefer to use Computed tomography images for liver disease identification hence automatic liver segmentation plays a decisive role. …”
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Distributed Denial of Services (DDoS) attack detection in SDN using Optimizer-equipped CNN-MLP. by Sajid Mehmood, Rashid Amin, Jamal Mustafa, Mudassar Hussain, Faisal S Alsubaei, Muhammad D Zakaria

“…We propose to implement both MLP (Multilayer Perceptron) and CNN (Convolutional Neural Networks) based on conventional methods to detect the Denial of Services (DDoS) attack. These models have got a complex optimizer installed on them to decrease the false positive or DDoS case detection efficiency. …”
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Leveraging IGOOSE-XGBoost for the Early Detection of Subclinical Mastitis in Dairy Cows by Rui Guo, Yongqiang Dai

“…This study provides a strong predictive framework for managing the health of dairy cows, allowing for the prompt identification and treatment of subclinical mastitis, which enhances the efficiency and quality of milk supply.…”
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Structure-based discovery of forsythoside A as an effective inhibitor to reduce the neurotoxicity of amyloid β by Si-Cong Bai, Ye-Cheng Wang, Wei-Lin Ye, Sheng-Yen Wu, Lee Jia, Liu Hong, Huan-Zhang Xie, Gao Li

“…Our study underscores the effectiveness of virtual screening in the identification of Aβ aggregation inhibitors and emphasizes the critical significance of targeting fibril ends.…”
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Coordinated Energy Management Strategy for DC Microgrid With Hybrid Energy Storage System: A Real‐Time Case Study by Biks Alebachew Taye

“…This involves the selection, mathematical modeling, and sizing of converter components. To ensure the efficiency of the intended DC microgrid, control and energy management algorithms are proposed. …”
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Advancements in Intelligent Sensing Technologies for Food Safety Detection by Wenhui Jiang, Changhong Liu, Wei Liu, Lei Zheng

“…The review analyzes the current state and future development trends of intelligent perception from 3 core aspects: sensing technology, signal processing, and modeling algorithms. Driven by technologies such as machine learning and blockchain, intelligent sensing technology can ensure food safety throughout all stages of food processing, storage, and transportation, and provide support for the traceability and authenticity identification of food. …”
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Smart Edge Computing Framework for Real-Time Brinjal Harvest Decision Optimization by T. Tamilarasi, P. Muthulakshmi, Seyed-Hassan Miraei Ashtiani

“…The BHDS provides an efficient, cost-effective solution for automating harvesting decisions, minimizing manual data processing, reducing computational overhead, and maintaining high precision and operational efficiency.…”
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A novel Probabilistic Bi-Level Teaching–Learning-Based Optimization (P-BTLBO) algorithm for hybrid feature extraction and multi-class brain tumor classification using ResNet-50 and... by Mahananda Malkauthekar, Avinash Gulve, Ratnadeep Deshmukh

“…Abstract The efficient and precise classification of brain tumors is important for early identification and appropriate action. …”
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