CylinDeRS: A Benchmark Visual Dataset for Robust Gas Cylinder Detection and Attribute Classification in Real-World Scenes by Klearchos Stavrothanasopoulos, Konstantinos Gkountakos, Konstantinos Ioannidis, Theodora Tsikrika, Stefanos Vrochidis, Ioannis Kompatsiaris

“…CylinDeRS contains 7060 RGB images, depicting various challenging environments and featuring over 25,250 annotated instances. It addresses two tasks: (a) the detection of gas cylinders as objects of interest, and (b) the attribute classification of the detected gas cylinder objects for material, size, and orientation. …”
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Point Defect Detection and Classification in MoS₂ Scanning Tunneling Microscopy Images: A Deep Learning Approach by Shiru Wu, Guoyang Chen, Si Shen, Jiaxu Yan

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Detection of Blastocystis species in immunocompromised patients (cancer, diabetes mellitus, and chronic renal diseases) by restriction fragment length polymorphism (RFLP) by Manal Z. M. Abdellatif, Ekhlas H. Abdel-Hafeez, Usama S. Belal, Noha H. Abdelgelil, Heba Marey, Manar M. S. A. Nasr, Mohamed Mahmoud Abdel-Fatah

“…All subjects were evaluated for socio-demographic data, clinical features, and parasitic infections. RFLP analysis of the SSU rRNA gene was performed for Blastocystis spp. grouping. …”
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A reasoning based explainable multimodal fake news detection for low resource language using large language models and transformers by Hariharan RamakrishnaIyer LekshmiAmmal, Anand Kumar Madasamy

“…Currently, the automatic fake news detection models are focused on high resource languages and superficial output. …”
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Enhancing Voice Spoofing Detection: A Hybrid Approach With VGGish-LSTM Model for Improved Security in Automatic Speaker Verification Systems by Komal Shahzad, Saima Farhan, Yasin-Ul-Haq, Rabia Sana, Salman Pathan

“…These models support the extraction of significant features i.e. embeddings, capable of mitigating bias and facilitating analysis with limited datasets. …”
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FGS-YOLOv8s-seg: A Lightweight and Efficient Instance Segmentation Model for Detecting Tomato Maturity Levels in Greenhouse Environments by Dongfang Song, Ping Liu, Yanjun Zhu, Tianyuan Li, Kun Zhang

“…In a greenhouse environment, the application of artificial intelligence technology for selective tomato harvesting still faces numerous challenges, including varying lighting, background interference, and indistinct fruit surface features. This study proposes an improved instance segmentation model called FGS-YOLOv8s-seg, which achieves accurate detection and maturity grading of tomatoes in greenhouse environments. …”
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Machine learning enhanced expert system for detecting heart failure decompensation using patient reported vitals and electronic health records by Shumit Saha, Heather Ross, Pedro Elkind Velmovitsky, Chloe X. Wang, Julie K. K. Vishram-Nielsen, Cedric Manlhiot, Bo Wang, Joseph A. Cafazzo

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Explainable AI Meets Synthetic Data: A Deep Learning Framework for Detecting Network Intrusion in NextG Network Infrastructure by Md Junayed Hossain, Khorshed Alam, Md Fahad Monir, Md Mozammal Hoque, Tarem Ahmed

“…On this GitHub Repository: <uri>https://github.com/i-am-junayed/XAI-Intrusion-Detection-System</uri>, the entire data analysis and prediction method is available for use by anyone. …”
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Characterizing and Detecting Multiscenario Degradation of the Maidika Alpine Wetland Nature Reserve in the Qinghai–Tibet Plateau Using Landsat Time Series by Ye Chen, Ren Ci, Dongping Zhong, Liangyun Liu, Jinyuan Yu, Dongdong Zhang, Yindong Tong, Yingchun Fu

“…Based on 3 elements, spectral–temporal characterization, classification, and degradation detection for wetland covers, this study proposes a continuous classification and degradation detection algorithm for alpine wetlands (AW-CCD). …”
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A Novel Shape Classification Approach Based on Branch Length Similarity Entropy by Sang-Hee Lee, Cheol-Min Park

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Molecular Detection and Clinical Impact of <i>Helicobacter pylori</i> Virulence Genes in Gastric Diseases: A Study in Arequipa, Peru by Yuma Ita-Balta, Alice Zegarra-Adanaque, Johany Sanchez-Guillen, Miguel Farfán-Delgado, Carlos Ortiz-Castro, Alexis Germán Murillo Carrasco, Alejandro Miranda Pinto, Cecilia Manrique-Sam

“…Statistical analysis revealed significant associations between <i>vacA</i> and specific clinical and endoscopic features, including erythematous gastropathy, nodular gastritis, and emetic syndrome, suggesting its localized role in disease pathogenesis. …”
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Explaining the Earnings Management Prediction Model Using the Hybrid of Machine Learning Methods by Hassan Hassani, Esfandiar Malekian Kallehbasti, Yahya Kamyabi

“…Based on the obtained results, the hybrid method based on deep learning and relief feature selection has the highest prediction accuracy (89/62) among other hybrid methods for forecasting accruals earnings management, and the hybrid method based on deep learning and principal component analysis feature selection has the highest prediction accuracy (82/65) among other hybrid methods for forecasting real earnings management.The findings of this study can expedite earnings management detection for financial statement users by improving earnings management prediction accuracy. …”
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Data Acquisition and Chatter Recognition Based on Multi-Sensor Signals for Blade Whirling Milling by Xinyu Li, Riliang Liu, Zhiying Zhu

“…Time-domain, frequency-domain, and time-frequency features are extracted, filtered, and fused using principal component analysis (PCA) to retain relevant information while ensuring computational efficiency. …”
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Adaptive blind deconvolution decomposition and its application in composite fault diagnosis of rolling bearings by Wei Dong, Bin Feng, Yulun Liu, Shuqing Zhang

“…The required filtering mode is selected according to the sorting mode, and a spectrum analysis is performed to extract fault features of single and compound faults in the bearing. …”
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Real defect partial discharge identification method for power cables joints based on integrated PJS-M and GA-SVM algorithm with multi-source fusion by Ling-Xuan Zhang, Yi-Yang Zhou, Shen-Jiong Yao, Jia-Luo Chai, Ying-Jing Chen, Zhou-Sheng Zhang

“…These results demonstrate that the proposed method significantly improves the accuracy of identifying complex real-type defects in 10 kV cable intermediate joints under multi-source feature conditions, providing a reliable diagnostic basis and technical reference for partial discharge detection in industrial applications.…”
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Implementation of MF block in CNN for advanced REB fault diagnosis by M. Pandiyan, Narendiranath Babu T.

“…This study presents an automated detection approach for diagnosing faults in REBs using a Customized Convolutional Neural Network (C-CNN). …”
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Application of spectral characteristics of electrocardiogram signals in sleep apnea by Jiayue Hu, Liu Yang, Xintong Zhao, Haicheng Wei, Jing Zhao, Miaomiao Li

“…The random forest classifier achieved optimal performance, with 92.9% accuracy, 86.6% specificity, and 100% sensitivity.ConclusionThis study demonstrates that spectral features derived from single-lead ECG signals, combined with EEMD-ICA and time-frequency analysis, offer an efficient and accurate method for sleep apnea detection.…”
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Predictive Analytics in Maternal Health: A Machine Learning Approach for Classification of Preeclampsia by Pakiza Amin, Saima Gulzar Ahmad, Hikmat Ullah Khan, Ehsan Ullah Munir, Naeem Ramzan

“…In this study, we classify pre-eclampsia using three datasets: two of which are the public datasets acquired from Mendeley and Kaggle, respectively, while the third is a real-world clinical dataset obtained from a local hospital. Recursive feature elimination, principal component analysis, correlation-based feature selection, and particle swarm optimization were used to select significant features from the predictor variables of the public datasets. …”
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ID3RSNet: cross-subject driver drowsiness detection from raw single-channel EEG with an interpretable residual shrinkage network by Xiao Feng, Xiao Feng, Zhongyuan Guo, Zhongyuan Guo, Sam Kwong

“…To address these issues, we propose a novel interpretable residual shrinkage network, namely, ID3RSNet, for cross-subject driver drowsiness detection using single-channel EEG signals. First, a base feature extractor is employed to extract the essential features of EEG frequencies; to enhance the discriminative feature learning ability, the residual shrinkage building unit with attention mechanism is adopted to perform adaptive feature recalibration and soft threshold denoising inside the residual network is further applied to achieve automatic feature extraction. …”
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Eeg-based detection of epileptic seizures in patients with disabilities using a novel attention-driven deep learning framework with SHAP interpretability by Tawfeeq Shawly, Ahmed A. Alsheikhy

“…Electroencephalograms (EEGs) are essential for diagnosing epilepsy, but conventional detection techniques depend on manual analysis, which can be labor-intensive and susceptible to inaccuracies. …”
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