Enhancing feature learning of hyperspectral imaging using shallow autoencoder by adding parallel paths encoding by Bibi Noor Asmat, Hafiz Syed Muhammad Bilal, M. Irfan Uddin, Faten Khalid Karim, Samih M. Mostafa, José Varela-Aldás

“…However, this abundance leads to redundant information, posing a computational challenge for deep learning models. Thus, models must effectively extract indicative features. …”
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Element-specific estimation of background mutation rates in whole cancer genomes through transfer learning by Farideh Bahari, Reza Ahangari Cohan, Hesam Montazeri

“…Additionally, we provide an extensive analysis of BMR estimation, examining different machine learning models, genomic interval strategies, feature categories, and dimensionality reduction techniques.…”
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Optimizing chemotherapeutic targets in non-small cell lung cancer with transfer learning for precision medicine. by Varun Malik, Ruchi Mittal, Deepali Gupta, Sapna Juneja, Khalid Mohiuddin, Swati Kumari

“…In addition, we design the deep transfer learning (DTransL) model to boost the drug discovery accuracy for NSCLC patients' therapeutic targets. …”
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Energy-aware federated learning for secure edge computing in 5G-enabled IoT networks by Milad Rahmati

“…Abstract The rapid expansion of 5G-enabled IoT networks has intensified the need for efficient, secure, and privacy-preserving machine learning models that can operate in decentralized edge environments. …”
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Blockchain-enabled federated learning with edge analytics for secure and efficient electronic health records management by Munusamy S, Jothi K R

“…Abstract The rapid adoption of Federated Learning (FL) in privacy-sensitive domains such as healthcare, IoT, and smart cities underscores its potential to enable collaborative machine learning without compromising data ownership. …”
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Optimizing MRI Scheduling in High-Complexity Hospitals: A Digital Twin and Reinforcement Learning Approach by Fabián Silva-Aravena, Jenny Morales, Manoj Jayabalan, Paula Sáez

“…Our strategy learns policies that maximize MRI machine utilization, minimize average waiting times, and ensure fairness by prioritizing urgent cases in the patient waitlist. …”
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Individualized Analysis of Nipple‐Sparing Mastectomy Versus Modified Radical Mastectomy Using Deep Learning by Enzhao Zhu, Linmei Zhang, Pu Ai, Jiayi Wang, Chunyu Hu, Huiqing Pan, Weizhong Shi, Ziqin Xu, Yidan Fang, Zisheng Ai

“…Methods To develop treatment recommendations for breast cancer patients, five machine learning models were trained. To mitigate bias in treatment allocation, advanced statistical methods, including propensity score matching (PSM) and inverse probability treatment weighting (IPTW), were applied. …”
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Q-Learning based VM Consolidation Approach for Enhancing Cloud Data Centres Power Efficiency by Baikani Sreenithya, Bharde Hitesh Dutt, Chennamaneni Jashwanth, R Karthikeyan, MA Jabbar, Majjari Sudhakar

“…We have also delved with reinforcement learning algorithm to tackle the virtual machines. …”
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Adaptive Learning Algorithms for Low Dose Optimization in Coronary Arteries Angiography: A Comprehensive Review by Komal Tariq, Muhammad Adnan Munir, Hafiza Tooba Aftab, Amir Naveed, Ayesha Yousaf, Sajjad Ul Hassan

“…Results: The extracted data shows a comprehensive data on various techniques that are used for low dose CAA, advancements in image segmentation, noise reduction, and operator dose reduction highlight the potential of machine learning techniques. …”
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A hybrid fog-edge computing architecture for real-time health monitoring in IoMT systems with optimized latency and threat resilience by Umar Islam, Mohammed Naif Alatawi, Ali Alqazzaz, Sulaiman Alamro, Babar Shah, Fernando Moreira

“…Simultaneously, edge computing nodes handle data preprocessing and transmit only valuable information—defined as abnormal or high-risk health signals such as irregular heart rate or oxygen levels—using rule-based filtering, statistical thresholds, and lightweight machine learning models like Decision Trees and One-Class SVMs. …”
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Hierarchical Multi-Scale Decomposition and Deep Learning Ensemble Framework for Enhanced Carbon Emission Prediction by Yinuo Sun, Zhaoen Qu, Zhuodong Liu, Xiangyu Li

“…Traditional statistical and machine learning methods struggle to capture complex multi-scale temporal patterns and long-range dependencies in emission data. …”
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3D-CNN detection of systemic symptoms induced by different Potexvirus infections in four Nicotiana benthamiana genotypes using leaf hyperspectral imaging by Rizos-Theodoros Chadoulis, Ioannis Livieratos, Ioannis Manakos, Theodore Spanos, Zeinab Marouni, Christos Kalogeropoulos, Constantine Kotropoulos

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Deep Learning-Based Coding Strategy for Improved Cochlear Implant Speech Perception in Noisy Environments by Billel Essaid, Hamza Kheddar, Noureddine Batel, Muhammad E. H. Chowdhury

“…The second strategy builds on this framework by additionally incorporating bidirectional gated recurrent units (Bi-GRU) alongside TCN and MHA layers, further refining sequence modeling and enhancing noise reduction. The optimal model configuration, using TCN-MHA-Bi-GRU with a kernel size of 16, achieved a compact model size of 788K parameters and recorded training, and validation losses of 0.0350 and 0.0446, respectively. …”
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Deep unsupervised clustering for prostate auto-segmentation with and without hydrogel spacer by Hengrui Zhao, Biling Wang, Michael Dohopolski, Ti Bai, Steve Jiang, Dan Nguyen

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Geometry-encoded molecular dynamics enables deep learning insights into P450 regiospecificity control by Denis Pompon, Luis F. Garcia-Alles, Philippe Urban

“…Molecular dynamics was used to characterize subsite interactions and feed a dedicated geometric encoding of trajectories that was coupled to dimensional reductions and differential machine learning. The two subsites differentially control caffeine orientations and can exchange substrate through a phenylalanine gated mechanism. …”
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Topology Unveiled: A New Horizon for Economic and Financial Modeling by Yicheng Wei, Junzo Watada, Zijin Wang

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A New Hybrid Model for Underwater Acoustic Signal Prediction by Guohui Li, Wanni Chang, Hong Yang

“…The subsequences (VMD-DE) are obtained by adding the IMF with similar complexity. Then, extreme learning machine (ELM) is used to predict the low-frequency subsequence obtained by VMD-DE. …”
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An optimization-inspired intrusion detection model for software-defined networking by Hui Xu, Longtan Bai, Wei Huang

“…Currently, more and more intrusion detection systems based on machine learning and deep learning are being applied to SDN, but most have drawbacks such as complex models and low detection accuracy. …”
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Modeling Exhaust Emissions in Older Vehicles in the Era of New Technologies by Maksymilian Mądziel

“…This paper introduces an innovative methodology that takes advantage of advanced AI and machine learning techniques to develop precise emission models for older vehicles. …”
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Predicting Excavation-Induced Tunnel Response by Process-Based Modelling by Linlong Mu, Jianhong Lin, Zhenhao Shi, Xingyu Kang

“…This paper proposes an initiative to solve this problem by using process-based modelling, where information generated from the interaction processes between soils, structures, and excavation activities is utilized to gradually reduce uncertainty related to soil properties and to learn the interaction patterns through machine learning techniques. …”
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