A Framework for Breast Cancer Classification with Deep Features and Modified Grey Wolf Optimization by Fathimathul Rajeena P.P, Sara Tehsin

“…Early mammographic picture analysis and processing reduce mortality and enable efficient treatment. Several deep-learning-based mammography classification methods have been developed. …”
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Enhancing the FFT-LSTM Time-Series Forecasting Model via a Novel FFT-Based Feature Extraction–Extension Scheme by Kyrylo Yemets, Ivan Izonin, Ivanna Dronyuk

“…A crucial step in the forecasting process is the preprocessing of time-series data, which can greatly improve the training quality of neural networks and the precision of their predictions. …”
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Performance Analysis of Eye Movement Event Detection Neural Network Models with Different Feature Combinations by Birtukan Adamu Birawo, Pawel Kasprowski

“…Event detection is the most important element of eye movement analysis. Deep learning approaches have recently demonstrated superior performance across various fields, so researchers have also used them to identify eye movement events. …”
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Sleep Snoring Sound Recognition Based on Wavelet Packet Transform by Li Ding, Jianxin Peng, Xiaowen Zhang, Lijuan Song

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Probabilistic oil price forecasting with a variational mode decomposition-gated recurrent unit model incorporating pinball loss by Zhesen Cui, Tian Li, Zhe Ding, Xi'an Li, Jinran Wu

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Automatic gait EVENT detection in older adults during perturbed walking by Shuaijie Wang, Kazi Shahrukh Omar, Fabio Miranda, Tanvi Bhatt

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Tactical intent-driven autonomous air combat behavior generation method by Xingyu Wang, Zhen Yang, Shiyuan Chai, Jichuan Huang, Yupeng He, Deyun Zhou

“…Finally, to address the problems of poor stability and slow convergence speed of deep reinforcement learning algorithms in large-scale state-action spaces, the dueling-noisy-multi-step DQN algorithm is devised, which not only improves the accuracy of value function approximation but also enhances the efficiency of space exploration and network generalization. …”
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Application of the Random Forest Algorithm for Accurate Bipolar Disorder Classification by Miguel Suárez, Ana M. Torres, Pilar Blasco-Segura, Jorge Mateo

“…This study represents a significant step toward precision psychiatry, leveraging technology to improve the understanding and management of complex mental health disorders.…”
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Disulfide bond-related gene signature development for bladder cancer prognosis prediction and immune microenvironment characterization by Hua Huang, Haiyan Shao, Yifan Wang, Lili Ge

“…The DRPS model, optimized by the StepCox[backward] algorithm, demonstrated robust prognostic accuracy across four validation cohorts (mean C-index = 0.658). …”
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A Novel Approach for Enhancing Code Smell Detection Using Random Convolutional Kernel Transform by Mostefai Abdelkader, Mekour Mansour

“…Objective: Despite numerous deep learning approaches for code smell detection, many still heavily rely on feature engineering processes (metrics) and exhibit limited performance. …”
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MobileNet-HeX: Heterogeneous Ensemble of MobileNet eXperts for Efficient and Scalable Vision Model Optimization by Emmanuel Pintelas, Ioannis E. Livieris, Vasilis Tampakas, Panagiotis Pintelas

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A Multi-Scale Time–Frequency Complementary Load Forecasting Method for Integrated Energy Systems by Enci Jiang, Ziyi Wang, Shanshan Jiang

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Development of a flexible feature selection framework in radiomics-based prediction modeling: Assessment with four real-world datasets by Sungsoo Hong, Sungjun Hong, Eunsun Oh, Won Jae Lee, Woo Kyoung Jeong, Kyunga Kim

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Leveraging Artificial Intelligence and Radiomics for Improved Nasopharyngeal Carcinoma Prognostication by Nicholas Brian Shannon, Narayanan Gopalakrishna lyer, Melvin Lee Kiang Chua

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Enhanced Collaborative Edge Intelligence for Explainable and Transferable Image Recognition in 6G-Aided IIoT by Chen Chen, Ze Sun, Jiale Zhang, Junwei Dong, Peng Zhang, Jie Guo

“…The proposed approach represents a significant step forward in achieving scalable, explainable, and transferable AI solutions for IIoT ecosystems.…”
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Non-intrusive Load Identification Method Based on the Online Self-organizing Incremental Neural Network—Non-intrusive load identification method based on the online self-organizing... by Zhengwei HU, Zhihong WANG, Ruixin CHANG, Zhiyuan XIE, Wangbin CAO

“…This method included two steps, which are the load feature extraction and the load feature classification with the equipment identification. …”
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Optimizing Pre-Trained Code Embeddings With Triplet Loss for Code Smell Detection by Ali Nizam, Ertugrul Islamoglu, Omer Kerem Adali, Musa Aydin

“…These results suggest that incorporating contrastive learning techniques into the generation process of code representation as a preprocessing step can enhance performance in code analysis.…”
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Multiclass CNN Approach for Automatic Classification of Dolphin Vocalizations by Francesco Di Nardo, Rocco De Marco, Daniel Li Veli, Laura Screpanti, Benedetta Castagna, Alessandro Lucchetti, David Scaradozzi

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Starting postgraduate medical training in general practice with a rotation in general practice – a qualitative study on experiences and effects by Becker, Christine, Stengel, Sandra, Roos, Marco, Altiner, Attila, Schwill, Simon

“…From the participants’ perspective, advantages included close supervision, flat hierarchies, more time for preparation and follow-up, self-directed learning as well as higher basic salary and regular working hours. …”
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Accurate Detection of scutellata‐Hybrids (Africanized Bees) Using a SNP‐Based Diagnostic Assay by Kathleen A. Dogantzis, Harshilkumar Patel, Stephen Rose, Ida M. Conflitti, Alivia Dey, Tanushree Tiwari, Nadine C. Chapman, Samir M. Kadri, Harland M. Patch, Elliud M. Muli, Abdulaziz S. Alqarni, Michael H. Allsopp, Amro Zayed

“…We used this dataset to develop a SNP assay, where 80 markers, combined with machine learning classification, can accurately differentiate between scutellata‐hybrids and non‐scutellata‐hybrid commercial colonies. …”
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