Classification of Software Engineering Documents Based on Artificial Immune Systems by Nada Saleem, Rasha Saeed

“…Some of the present methods and ways used in the training process of the document classification are time consuming and others have less accuracy rate concerned the classification of the related document as software engineering document classes For these reasons, this research deals with the study of Natural Immune System and using the dynamic process of the Innate Immune System work based on danger theory and Dendritic Cell (DC) technique  in building Dendritic Cell Model (DCM) to classify Software Engineering documents as they comprise information related to developing the software systems, that makes it easy for the software engineer who works in maintenance. …”
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Достижения в элиминации кори и краснухи в Республике Беларусь by E. O. Samoilovich, G. V. Semeiko, M. A. Yermalovich, V. P. Shimanovich, E. Yu. Svirchevskaya

“…Laboratory diagnosis based on revealing of specific IgM antibodies, dynamic of IgG concentration or detection of viral RNA. …”
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Mapping Sandy Areas and their changes using remote sensing. A Case Study at North-East Al-Muthanna Province, South of Iraq by Awad A. Sahar, Muaid J. Rasheed, Dhia A. A.-H. Uaid, Ammar A. Jasim

“…Remote sensing is one of the technologies that can be used as an effective tool in dynamic features study of sandy areas and sand accumulations. …”
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Modified Whale Optimization Algorithm for Multiclass Skin Cancer Classification by Abdul Majid, Masad A. Alrasheedi, Abdulmajeed Atiah Alharbi, Jeza Allohibi, Seung-Won Lee

“…Skin cancer is a major global health concern and one of the deadliest forms of cancer. Early and accurate detection significantly increases the chances of survival. …”
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Real-Time, Risk-Based Clinical Trial Quality Management in China: Development of a Digital Monitoring Platform by Min Jiang, Shuhua Zhao, Yun Mei, Zhiying Fu, Yannan Yuan, Jie Ai, Yuan Sheng, Ying Gong, Jingjing Chen

“…ConclusionsIn clinical practice, our tool would enable the automated monitoring and detection of risk points throughout all clinical trial stages; accurately identify the most relevant trial procedure and function line; and notify quality management personnel, in real time, to take prompt actions and dynamically prevent the recurrence of quality issues.…”
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Vocal Emotion Perception and Musicality—Insights from EEG Decoding by Johannes M. Lehnen, Stefan R. Schweinberger, Christine Nussbaum

“…Yet a previous ERP study only detected group differences from 500 ms onward, suggesting that conventional ERP analyses might not be sensitive enough to detect early neural effects. …”
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Integrating deep learning and transfer learning: optimizing white blood cells classification in medical educational institutions by M. Hussein, Faten Abd El-Sattar Zahran El-Mougi

“…Furthermore, the ability to classify a broader range of WBCs types could lead to more accurate and early diagnoses of diseases, ultimately improving patient care.…”
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Experimental investigation of shaft misalignment effects on bearing reliability through vibration signal analysis using machine learning and deep learning by Fransiskus Tatas Dwi Atmaji, Jamasri, Hari Agung Yuniarto, I Made Miasa

“…Six classification models—five machine learning algorithms (Multilayer Perceptron, Random Forest, Decision Tree, K-Nearest Neighbors, and Adaptive Boosting) and one deep learning model (Long Short-Term Memory, LSTM)—were evaluated for classifying four levels of misalignment severity. The results reveal a strong positive correlation between the magnitude of misalignment and vibration intensity, highlighting the escalation of dynamic instability in the bearing system. …”
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Hybrid AI for Predictive Cyber Risk Assessment: Federated Graph-Transformer Architecture With Explainability by Jaime Govea, Rommel Gutierrez, William Villegas-Ch, Alexandra Maldonado Navarro

“…The increasing complexity and dynamism of modern digital infrastructures have significantly elevated the difficulty of anticipating and mitigating cyber threats. …”
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Prediction of weld quality in laser welding of hardmetal and steel using high-speed imaging and machine learning methods by Mohammadhossein Norouzian, Mahan Khakpour, Marko Orosnjak, Atal Anil Kumar, Slawomir Kedziora

“…In addition, a continuous process monitoring method like High-Speed Imaging (HSI) is essential in real industrial applications to maintain stability and detect potential defects. Understanding plume dynamics helps identify the most important features of weld quality, but it also provides deeper insight into operational parameters that discriminate different weld types. …”
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EEG-Based Attention Classification for Enhanced Learning Experience by Madiha Khalid Syed, Hong Wang, Awais Ahmad Siddiqi, Shahnawaz Qureshi, Mohamed Amin Gouda

“…This paper presents a novel EEG-based learning system designed to enhance the efficiency and effectiveness of studying by dynamically adjusting the difficulty level of learning materials based on real-time attention levels. …”
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Gradient Boosting Feature Selection for Integrated Fault Diagnosis in Series-Compensated Transmission Lines by Rab Nawaz, Abdul Wadood, Khawaja Khalid Mehmood, Syed Basit Ali Bukhari, Hani Albalawi, Aadel Mohammed Alatwi, Muhammad Sajid

“…The methodology is implemented using four ensemble classifiers: Adaptive Boosting (AB), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Extreme Gradient Boosting (XGB), and is rigorously evaluated on various standard models of differing complexity and system configurations under dynamic conditions. …”
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EVALUATING EROSION FROM SPACE: A CASE STUDY NEAR UBERLÂNDIA by Anton Vrieling, Silvio C. Rodrigues, C. Rodrigues(2) Geert Sterk

“…Erosion detection was done through visual image interpretation. …”
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Simultaneous Structural Monitoring over Optical Ground Wire and Optical Phase Conductor via Chirped-Pulse Phase-Sensitive Optical Time-Domain Reflectometry by Jorge Canudo, Pascual Sevillano, Andrea Iranzo, Sacha Kwik, Javier Preciado-Garbayo, Jesús Subías

“…This monitoring system, capable of covering distances up to 40 km from a single endpoint, enables dynamic capacity adjustments for optimized line efficiency. …”
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Adaptive Neuro-Affective Engagement via Bayesian Feedback Learning in Serious Games for Neurodivergent Children by Diego Resende Faria, Pedro Paulo da Silva Ayrosa

“…A multimodal CNN-based classifier detects mental states, while a probabilistic ensemble merges affective state classifications derived from facial expressions. …”
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Experimental Estimation of Two-Phase Flow Parameters in the Pneumatic System of a Sowing Complex by Airat M. Mukhametdinov, Salavat G. Mudarisov

“…There were used high-speed video shooting, a sail classifier and digital anemometers to analyze air flow and particle motion parameters. …”
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Employing Spectral Features to Accelerate Sorghum Phenotyping Against Sap‐Feeding Aphids by Kumar Shrestha, Kantilata Thapa, Esha Kaler, Misaki Taniguchi, Scott E. Sattler, James C. Schnable, Joe Louis

“…ABSTRACT Current efforts to detect and evaluate crop resistance to insect pests are limited by traditional phenotyping methods, which are time‐consuming and highly variable. …”
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Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks by Magdalena Fafrowicz, Marcin Tutajewski, Igor Sieradzki, Jeremi K. Ochab, Jeremi K. Ochab, Anna Ceglarek-Sroka, Koryna Lewandowska, Tadeusz Marek, Barbara Sikora-Wachowicz, Igor T. Podolak, Paweł Oświęcimka, Paweł Oświęcimka, Paweł Oświęcimka

“…In recent years, machine learning methods have been widely used to detect connections between regions of interest (ROIs) involved in cognitive functions, as measured by the fMRI technique. …”
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Deep learning-based carotid plaque vulnerability classification with multicentre contrast-enhanced ultrasound video: a comparative diagnostic study by Yanli Guo, Hongxia Zhang, Yang Guang, Wen He, Bin Ning, Chen Yin, Mingchang Zhao, Fang Nie, Pintong Huang, Rui-Fang Zhang, Qiang Yong, Jianjun Yuan, Yicheng Wang, Lijun Yuan, Litao Ruan, Tengfei Yu, Haiman Song, Yukang Zhang

“…The area under the receiver operating characteristic curve (AUC) was used to evaluate the effectiveness of DL-DCCP and two experienced radiologists who manually examined the CEUS video (RA-CEUS) in diagnosing and classifying carotid plaque vulnerability. To evaluate the influence of dynamic video input on the performance of the algorithm, a state-of-the-art deep convolutional neural network (CNN) model for static images (Xception) was compared with DL-DCCP for both training and holdout validation cohorts.Results The AUCs of DL-DCCP were significantly better than those of the experienced radiologists for both the training and holdout validation cohorts (training, DL-DCCP vs RA-CEUS, AUC: 0.85 vs 0.69, p<0.01; holdout validation, DL-DCCP vs RA-CEUS, AUC: 0.87 vs 0.66, p<0.01), that is, also better than the best deep CNN model Xception we had performed, for both the training and holdout validation cohorts (training, DL-DCCP vs Xception, AUC:0.85 vs 0.82, p<0.01; holdout validation, DL-DCCP vs Xception, AUC: 0.87 vs 0.77, p<0.01).Conclusion DL-DCCP shows better overall performance in assessing the vulnerability of carotid atherosclerotic plaques than RA-CEUS. …”
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Assessment of the Water Level Changes Based on Optical Satellite Images Analysis by N. Kadhim

“…The categorical change detection method was applied to the classified LULC maps to detect the changes in the lake area over five years. …”
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