A deep learning approach to detect diseases in pomegranate fruits via hybrid optimal attention capsule network by P. Sajitha, A. Diana Andrushia, N. Anand, M.Z. Naser, Eva Lubloy

“…In post-harvest pomegranate fruit disease detection, deep learning has great potential to extract complex patterns and features from large datasets. …”
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LI-YOLOv8: Lightweight small target detection algorithm for remote sensing images that combines GSConv and PConv. by Pingping Yan, Xiangming Qi, Liang Jiang

“…In the domain of remote sensing image small target detection, challenges such as difficulties in extracting features of small targets, complex backgrounds that easily lead to confusion with targets, and high computational complexity with significant resource consumption are prevalent. …”
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Multi-stream feature fusion of vision transformer and CNN for precise epileptic seizure detection from EEG signals by Qi Li, Wei Cao, Anyuan Zhang

“…Conclusions The CMFViT model provides an efficient, accurate, and innovative solution for complex EEG signal analysis and seizure detection tasks for single and cross-subjects while laying the foundation for developing real-time, accurate seizure detection systems.…”
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Discussion on Matrix Interference in Trace-Level Bromate Detection of Natural Mineral Water in Southwest China Karst Area by Xi YANG, Xiaolin ZHOU, Song ZHENG

“…The bromate in natural mineral water in the Guizhou karst area of southwest China is usually at trace levels while the chemical background of water is complex, which causes great difficulties using ion chromatography to detect the bromate due to matrix interference. …”
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Dynamic Wireless Power Transfer System for Indoor AGV With a Distance-Measurement-Based Position Detection Technique by Deniss Stepins, Janis Zakis, Aleksandrs Sokolovs, Oleksandr Husev, Ryszard Strzelecki, Rik W. de Doncker

“…However, the cost and level of complexity of the position detection system are moderately lower and the proposed position detection system is immune to optical radiation and magnetic fields. …”
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YOLOv8-OCHD: A Lightweight Wood Surface Defect Detection Method Based on Improved YOLOv8 by Zuxing Chen, Junjie Feng, Xueyan Zhu, Bin Wang

“…To overcome the drawbacks of manual defect detection and the challenges of traditional visual inspection algorithms, such as high missed detection rates, slow detection speeds, and difficulties in deployment on embedded devices for detecting subtle wood surface defects, this study aims to improve the utilization efficiency of wood in enterprises. …”
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Vowel classification with combining pitch detection and one‐dimensional convolutional neural network based classifier for gender identification by Chia‐Hung Lin, Hsiang‐Yueh Lai, Ping‐Tzan Huang, Pi‐Yun Chen, Chien‐Ming Li

“…The proposed PD‐based extractor and voice classifier could reduce complexity and improve classification efficiency. Acoustic datasets were selected from the Hillenbrand database for experimental tests on 12 vowels classifications, and K‐fold cross‐validations were performed. …”
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An intelligent method for detection of small target fungal wheat spores based on an improved YOLOv5 with microscopic images by Zhizhou Ren, Kun Liang, Yingqi Zhang, Jinpeng Song, Xiaoxiao Wu, Chi Zhang, Xiuming Mei, Yi Zhang, Xin Liu

“…Traditional detection methods are time-consuming and labor-intensive, and difficult to detect small target spores in complex environments. …”
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A Deep Learning-Based Approach for the Detection of Various Internet of Things Intrusion Attacks Through Optical Networks by Nouman Imtiaz, Abdul Wahid, Syed Zain Ul Abideen, Mian Muhammad Kamal, Nabila Sehito, Salahuddin Khan, Bal S. Virdee, Lida Kouhalvandi, Mohammad Alibakhshikenari

“…Furthermore, the model’s architecture leverages the high-speed, low-latency characteristics of optical networks, ensuring the efficient processing of large-scale IoT data streams and supporting real-time detection in diverse IoT ecosystems. …”
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THE USE OF NEURAL NETWORKS FOR THE AUTOMATIC ANALYSIS OF ELECTROCARDIOGRAMS IN DIAGNOSIS OF CARDIOVASCULAR DISEASES by G. I. Kachayeva, A. G. Mustafayev

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Sensitive detection of dengue serotype-4 on lignin-derived graphene nanofibre and manganese oxide composite by DNA-sensing by Sivainesh Devi Remesh, Saravanan Karuppanan, Veeradasan Perumal, Subash C. B. Gopinath, Mark Ovinis, Pandian Bothi Raja, Natarajan Arumugam, Raju Suresh Kumar

“…Abstract This research addresses the critical challenge of achieving precise DNA detection in complex biological samples by developing a novel, eco-friendly dengue virus serotype 4 biosensor. …”
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An explainable AI-driven deep neural network for accurate breast cancer detection from histopathological and ultrasound images by Md. Romzan Alom, Fahmid Al Farid, Muhammad Aminur Rahaman, Anichur Rahman, Tanoy Debnath, Abu Saleh Musa Miah, Sarina Mansor

“…However, traditional diagnostic processes relying on manual analysis of medical images are inherently complex and subject to variability between observers, highlighting the urgent need for robust automated breast cancer detection systems. …”
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Optimization of a portable ligand-free optical spectroscopy method for SARS-CoV-2 protein detection by Fatin Hamimi Mustafa, Nik Yusnoraini Yusof, Mawaddah Mohd Azlan, Fariza Hanim Suhailin, Chan Yean Yean, Nik Mohd Noor Nik Zuraina, Mohd Zulkifli Salleh, Hironaga Uchida, Irneza Ismail, Rosline Hassan, Raja Kamarulzaman Raja Ibrahim, Mohd Adzir Mahdi

“…The detection workflow includes two key phases: optimization and clinical validation. …”
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A Dynamic Adaptive Ensemble Learning Framework for Noninvasive Mild Cognitive Impairment Detection: Development and Validation Study by Aoyu Li, Jingwen Li, Yishan Hu, Yan Geng, Yan Qiang, Juanjuan Zhao

“…Therefore, exploring a more cost-effective, efficient, and noninvasive method to aid clinicians in detecting MCI is necessary. …”
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Zn-3, a Tris[3-(2-pyridyl)-pyrazolyl]borate zinc(II) complex, shows preferential antiproliferative effects on breast cancer rather than normal breast cells via oxidative stress by Jun-Ping Shiau, Min-Yu Lee, Sodio C.N. Hsu, Sih-Ruei Tseng, Hsueh-Wei Chang

“…The antiproliferative effects of Zn-3, a tris[3-(2-pyridyl)-pyrazolyl]borate zinc(II) complex, were reported previously in triple-negative breast cancer (TNBC) cells; however, its anticancer mechanisms were not assessed. …”
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Development of an Eco-Friendly Method for Iron Extraction and Determination in Pharmaceuticals Using Ciprofloxacin Drug as Chelating agent by Baghdad Science Journal

“…Several variables which impact on the extraction and determination of Fe (III) are optimized in order to maximize the extraction efficiency and improve the sensitivity of the method. …”
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Optimization of VA-DLLME methodology for the determination of beta-cyfluthrin in blood and urine sample of human by TLC-image analysis: Validated by AGREE and complex GAPI tools by Sakshi Pandey, Mahak Malviya, Lalit P. Chandravanshi

“…Overall, this developed method for detecting beta-cyfluthrin in human blood and urine samples is efficient, cost-effective, and adheres to the principles of green analytical chemistry(GAC). …”
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Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM by Nilesh Bhaskarrao Bahadure, Arun Kumar Ray, Har Pal Thethi

“…In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT) based brain tumor segmentation. …”
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Attention-Based Dual-Knowledge Distillation for Alzheimer’s Disease Stage Detection Using MRI Scans by Chandita Barman, Sudhanshu Singh, Manob Jyoti Saikia, Shovan Barma

“…This study presents an efficient attention-guided dual-knowledge distillation (D-KD) framework for classifying Alzheimer’s disease (AD) stages using magnetic resonance imaging (MRI) scans based on detection of the subtle anatomical differences. …”
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Improving the performance of machine learning algorithms for detection of individual pests and beneficial insects using feature selection techniques by Rabiu Aminu, Samantha M. Cook, David Ljungberg, Oliver Hensel, Abozar Nasirahmadi

“…The concept of explainable artificial intelligence was adopted by incorporating permutation feature importance ranking and Shapley Additive explanations values to identify the feature set that optimized a model's performance while reducing computational complexity. The proposed explainable artificial intelligence feature selection method was compared to conventional feature selection techniques, including mutual information, chi-square coefficient, maximal information coefficient, Fisher separation criterion and variance thresholding. …”
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