A Novel Self-Attention-Enabled Weighted Ensemble-Based Convolutional Neural Network Framework for Distributed Denial of Service Attack Classification by Shravan Venkatraman, S. Kanthimathi, K. S. Jayasankar, T. Pranay Jiljith, R. Jashwanth

“…This innovative approach addresses critical limitations in current models and advances the state of the art in network security.…”
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Applications of deep learning in trauma radiology: A narrative review by Chi-Tung Cheng, Chun-Hsiang Ooyang, Chien-Hung Liao, Shih-Ching Kang

“…This narrative review provides the fundamental concepts for developing DL algorithms in trauma imaging and presents an overview of current progress in each modality. …”
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Applications of fault-tolerant control system in the design of wind turbine generation systems: A comprehensive review and future prospects by Arslan Ahmed Amin, Muhammad Irfan, Turki Alsuwian, Saifur Rahman, Ansa Mubarak, Saba Waseem

“…The review also discusses the relevant research gaps and future works, such as using computational algorithms to develop novel material and implementing the utilization of digital twins to improve fault management.…”
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Predictive Study on the Cutting Energy Efficiency of Dredgers Based on Specific Cutting Energy by Junlang Yuan, Ke Yang, Taiwei Yang, Haoran Xu, Ting Xiong, Shidong Fan

“…Based on the machine learning framework, a model framework for predicting the specific cutting energy according to the relevant parameters of the suction-lifting system is constructed. …”
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Energy-Efficient Target Area Imaging for UAV-SAR-Based ISAC: Beamforming Design and Trajectory Optimization by Jiayi Zhou, Xiangyin Zhang, Kaiyu Qin, Feng Yang, Libo Wang, Deyu Song

“…Numerical results demonstrate that compared to existing benchmarks and PSO algorithms, the proposed method significantly improves the energy efficiency of UAV-SAR-based ISAC systems through optimized trajectory design.…”
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A Machine Learning-Based Intelligent Framework for Predicting Energy Efficiency in Next-Generation Residential Buildings by Hafiz Muhammad Shakeel, Shamaila Iram, Richard Hill, Hafiz Muhammad Athar Farid, Akbar Sheikh-Akbari, Farrukh Saleem

“…Further, machine learning models revealed that Random Forest, Gradient Boosting, XGBoost, and LightGBM deliver the lowest mean square error scores of 6.305, 6.023, 7.733, 5.477, and 5.575, respectively, and demonstrated the effectiveness of advanced algorithms in forecasting energy performance. …”
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Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data by Chien Wei Oei, Eddie Yin Kwee Ng, Matthew Hok Shan Ng, Yam Meng Chan, Vinithasree Subbhuraam, Lai Gwen Chan, U. Rajendra Acharya

“…The combination of using DL algorithms, such as multi-layer perceptron (MLP) and long short-term memory (LSTM), which can process complex patterns in the data, and the inclusion of new data types, such as sequential data, helped to improve model performance. …”
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Machine Learning-Driven GLCM Analysis of Structural MRI for Alzheimer’s Disease Diagnosis by Maria João Oliveira, Pedro Ribeiro, Pedro Miguel Rodrigues

“…Results: The cML algorithms achieved the following classification <i>accuracy</i>: 85.2% for <i>AD</i> vs. …”
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VMDU-net: a dual encoder multi-scale fusion network for polyp segmentation with Vision Mamba and Cross-Shape Transformer integration by Peng Li, Jianhua Ding, Chia S. Lim

“…While polyp segmentation algorithms are essential for aiding polyp removal, they face significant challenges due to the diverse shapes, unclear boundaries, and varying sizes of polyps. …”
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Dunes Identification Based on Attention Mechanism With Dual-Branch Codec by Zhaobin Wang, Yan Li, Yue Shi, Yaonan Zhang, Xuejun Guo

“…Specific work contents are as follows: A dual-branch codec dune morphological type segmentation model based on attention mechanism is proposed. In the dual-branch structure, the overcomplete and incomplete networks can take into account both small and large receptive fields, improving the situation of local detail loss caused by the incomplete network in the traditional semantic segmentation structure. …”
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Dual-stream interactive mechanism with multi-modal hierarchical aggregation transformer for gait recognition by Jinghang Liu, Xiangyuan Xu, Yan Qiu, Chunzhi Wang

“…This architecture not only adaptively modulates cross-modal feature importance but also enhances model robustness in complex scenarios. Comprehensive evaluations demonstrate that GaitSMAT achieves state-of-the-art (SOTA) performance on multiple public datasets (GREW, Gait3D, and SUSTech1K), exhibiting significant improvements over existing approaches. …”
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Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients by Farnaz Khodami, Amanda S. Mahoney, James L. Coyle, Ervin Sejdic

“…By applying established algorithms to an unseen population, we demonstrated the utility of HRCA signals for swallowing assessment in individuals with NG tubes and showcased the generalizability of algorithms developed in our previous studies. …”
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Impact of Petty Tyranny on Employee Turnover Intentions: The Mediating Roles of Toxic Workplace Environment and Emotional Exhaustion in Academia by Javed Iqbal, Zarqa Farooq Hashmi, Muhammad Zaheer Asghar, Attiq Ur Rehman, Hanna Järvenoja

“…Partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) analyses, including performance comparisons of various algorithms, were used to test the relationships among the variables. …”
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Artificial intelligence and chordoma: A scoping review of the current landscape and future directions by Eddie Guo, Rafael D. Sanguinetti, Lyndon Boone, Jiawen Deng, Husain Shakil, Mehul Gupta

“…Limitations identified across studies included small sample sizes, single-center data, reliance on single data modalities, and issues with model interpretability. Discussion and conclusion: AI applications in chordoma management show potential in improving diagnostic accuracy, surgical planning, and prognostication. …”
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Real-time monitoring of water quality dynamics using low-cost sensor networks in Lagos lagoon by Idowu Ayisat Aneyo, Mumin Olatunji Oladipo, Funmilayo Victoria Doherty, Julius Osato Ehigie, Adebayo Fasasi Adebari, Abdulwakeel Oluwatobi Atoyebi, Peter Ozomata Balogun, Ambrose Obinna Ikpele

“…Future research should focus on enhancing wireless communication, refining species detection algorithms and improving sensor resilience in harsh aquatic conditions.…”
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Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images by Yuhang Zhang, Wuxia Zhang, Songtao Ding, Siyuan Wu, Xiaoqiang Lu

“…The TML-FI is designed to mine the spatial-temporal information by extracting long-range dependencies and spatial information from multilevel features to improve spatial perception. Moreover, Mixed Spatial Reasoning Convolution block (MixSrc) is presented to enrich the spatial information by extracting the multiscale features, thus improving the model's capability to interpret complex scenes. …”
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Data-driven fault detection and positioning of eccentric rolls in roll-to-roll systems using wrap angle and sensor proximity by Yoonjae Lee, Minjae Kim, Jaehyun Noh, Gyoujin Cho, Changwoo Lee

“…By leveraging tension data, optimal feature sets are identified, emphasizing the Mean, Fast Fourier Transform, and other key variables that capture crucial system dynamics, including wrap angles and sensor proximities. Classification algorithms were used to compare the performance of models employing these optimized FCM-based features against traditional models utilizing a full suite of 40 statistical features. …”
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AI Dermatochroma Analytica (AIDA): Smart Technology for Robust Skin Color Classification and Segmentation by Abderrachid Hamrani, Daniela Leizaola, Nikhil Kumar Reddy Vedere, Robert S. Kirsner, Kacie Kaile, Alexander Lee Trinidad, Anuradha Godavarty

“…AIDA applies clustering techniques to classify skin tones without relying on labeled data, evaluating over twelve models, including K-means, density-based, hierarchical, and fuzzy logic algorithms. …”
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To be or not to be, when synthetic data meet clinical pharmacology: A focused study on pharmacogenetics by Jean‐Baptiste Woillard, Clément Benoist, Alexandre Destere, Marc Labriffe, Giulia Marchello, Julie Josse, Pierre Marquet

“…We also investigated the effect of applying the algorithms multiple times to improve results stability in terms of HR estimation. …”
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Research on Civil Aviation Airport Site Selection Considering Group Consensus Level Under Large-Scale Uncertain Information by Rui Wang, Jing-Han Zeng, Jing-Yang Huang, Rui Kang, Jiang Yuan, Qing-Wei Zhong

“…The method comprises five key processes: (1) Evaluation process: Based on the constructed multicriteria evaluation system for airport site selection, the q-Rung Orthopair Fuzzy (q-ROF) information is employed to represent evaluations from large-scale decision makers, which effectively characterizes the uncertainty of information and broadens the evaluative scope. (2) Clustering process: A clustering procedure is designed for large-scale q-ROF evaluation data and weight information of criteria, identifying and removing outliers. (3) Consensus reaching process: Considering the characteristics of q-ROF evaluations and multiplicative preference relations, two adaptive consensus reaching algorithms are developed to enhance group consensus levels, thereby improving the rationality of decision-making results. (4) Weight determination process: Criteria and subcriteria weights are calculated using multiplicative preference weighting approach and a deviation maximization model, respectively, derived from aggregated group evaluations. (5) Ranking process: The q-ROF Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is applied, in conjunction with the induced q-ROF information integration paradigm, to comprehensively rank the alternative sites. …”
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