A privacy preserving machine learning framework for medical image analysis using quantized fully connected neural networks with TFHE based inference by Sadhana Selvakumar, B. Senthilkumar

“…The proposed PPML framework leverages a torus-based fully homomorphic encryption (TFHE) to ensure data privacy during inference, maintain patient confidentiality, and ensure compliance with privacy regulations. The FCNN model is trained in a plaintext environment for FHE compatibility using Quantization-Aware Training to optimize weights and activations. …”
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Mapping global yields of four major crops at 5-minute resolution from 1982 to 2015 using multi-source data and machine learning by Juan Cao, Zhao Zhang, Xiangzhong Luo, Yuchuan Luo, Jialu Xu, Jun Xie, Jichong Han, Fulu Tao

“…The optimal predictors and ML model were selected to estimate annual crop yield for each 5 × 5 arc-minute grid cell. …”
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Hybrid design of optimal shunt capacitor allocation and reconfiguration of radial distribution systems considering techno-economic benefits by Ifeoluwa Olajide Fajinmi, Gafari Abiola Adepoju, Sunday Adeleke Salimon, Kareem M. AboRas, Oluwaseyi Wasiu Adebiyi, Olubunmi Onatoyinbo, Mohammad Khishe, Pradeep Jangir, Wulfran Fendzi Mbasso

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Future Site Suitability for Urban Waste Management in English Bazar and Old Malda Municipalities, West Bengal: A Geospatial and Machine Learning Approach by Suresh Mondal, Mst Tania Parveen, Asraful Alam, Rukhsana, Nazrul Islam, Beata Calka, Bashar Bashir, Mohamed Zhran

“…Using GIS and a Multi-Criteria Decision Analysis (MCDA) approach, the study employs the Analytic Hierarchy Process (AHP) alongside the Random Forest (RF) model and a machine learning (ML) technique to identify potential waste disposal sites within the English Bazar and Old Malda Municipalities in the Malda district. …”
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BCLH2Pro: A novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes by Thanadol Tuntiwongwat, Sippawit Thammawiset, Thongchai Rohitatisha Srinophakun, Chawalit Ngamcharussrivichai, Somboon Sukpancharoen

“…This study optimizes biomass chemical looping processes (BCLpro), a technique for converting biomass to energy, through machine learning (ML) for sustainable energy production. …”
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A large language model for advanced power dispatch by Yuheng Cheng, Huan Zhao, Xiyuan Zhou, Junhua Zhao, Yuji Cao, Chao Yang, Xinlei Cai

“…However, traditional methods falter as power systems grow in scale and complexity, struggling with multitasking, swift problem-solving, and human-machine collaboration. This paper introduces Grid Artificial Intelligent Assistant (GAIA), a pioneering Large Language Model (LLM) designed to assist with a variety of power system operational tasks, including operation adjustment, operation monitoring, and black start scenarios. …”
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Business Model Innovation through the Expansion of Digital Platforms by Seyed Hamed Vares, Nastaran Haji Heydari, Mohammad Kargar Shouraki, Morteza Hadizadeh

“…This process, in turn, promotes the development of sustainable and innovative business models that improve efficiency and profitability. …”
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Effectiveness of a Machine Learning-Enabled Skincare Recommendation for Mild-to-Moderate Acne Vulgaris: 8-Week Evaluator-Blinded Randomized Controlled Trial by Misbah Noshela Ghazanfar, Ali Al-Mousawi, Christian Riemer, Benóný Þór Björnsson, Charlotte Boissard, Ivy Lee, Zarqa Ali, Simon Francis Thomsen

“…Groups A and B received customized product recommendations using a Bayesian machine learning model and self-selected treatments, respectively. …”
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Associations between organophosphorus pesticides exposure and age-related macular degeneration risk in U.S. adults: analysis from interpretable machine learning approaches by Yu-Xin Jiang, Si-Yu Gui, Xiao-Dong Sun

“…Receiver operating characteristic curve (ROC) analysis evaluated Random Forests (RF) as the best ML model with its optimal predictive performance among eleven models. …”
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Prediction of cutting depth in abrasive water jet machining of Ti-6AL-4V alloy using back propagation neural networks by Yakub Iqbal Mogul, Ibtisam Mogul, Jaimon Dennis Quadros, Ma Mohin, Abdul Aabid, Muneer Baig, Mohammad Abdul Malik

“…The current study focusses on developing a back propagation neural network model for depth of cut during the abrasive water jet machining of a Ti-6AL-4V aluminum alloy. …”
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A novel seismic inversion method based on multiple attributes and machine learning for hydrocarbon reservoir prediction in Bohai Bay Basin, Eastern China by Zongbin Liu, Jianmin Zhu, Bo Tian, Rui Zhang, Yongheng Fu, Yuan Liu, Lixin Wang

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Regularizing Data for Improving Execution Time of NLP Model by Thang Dang, Yasufumi Sakai, Tsuguchika Tabaru, Akihiko Kasagi

“…Our method focuses on removing unimportant data from the input data set and optimizing the padding of tokens to reduce the processing time for the NLP model. …”
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TinderAI: Support System for Matching AI Algorithms and Embedded Devices by Matteo Francobaldi, Allegra De Filippo, Andrea Borghesi, Nikola Pizˇurica, Igor Jovančević, Tim Llewellynn, Miguel de Prado

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A novel framework for multi-layer soil moisture estimation with high spatio-temporal resolution based on data fusion and automated machine learning by Shenglin Li, Yang Han, Caixia Li, Jinglei Wang

“…Initially, we generated seamless 30 m resolution metrics, including the normalized difference vegetation index (NDVI), land surface temperature (LST), and surface albedo, by employing the modified neighborhood similar pixel interpolator (MNSPI) in conjunction with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). These variables, combined with reanalysis data, auxiliary data, and ground-based SM observations, were input into an Automated Machine Learning (AutoML) workflow to estimate SM at 0–20, 20–40, and 40–60 cm soil layers. …”
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Research on low carbon welding scheduling based on production process by Rong Hua Meng, Zan Yang Wang, Wen Hui Zeng, Feng Guan, Ding Kun Lei, Zheng Jia Wu, Shao Hua Deng

“…The grey wolf coordinated hunting strategy (second) based on dynamic weights is introduced to improve the convergence of IGWO. A local optimization strategy(third) is designed to improve the post-optimal search performance by adjusting the machine assignment based on the critical path. …”
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Monitoring of the Physicochemical Properties and Aflatoxin of <i>Aspergillus flavus</i>-Contaminated Peanut Kernels Based on Near-Infrared Spectroscopy Combined with Machine Learni... by Yingge Wang, Mengke Li, Li Xu, Chun Gao, Cheng Wang, Lu Xu, Shaotong Jiang, Lili Cao, Min Pang

“…Correlation analysis was performed to examine the relationships between physicochemical properties, characteristic bands, and aflatoxin content. Three machine learning models—Backpropagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF)—were used to predict aflatoxin levels. …”
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Is there a competitive advantage to using multivariate statistical or machine learning methods over the Bross formula in the hdPS framework for bias and variance estimation? by Mohammad Ehsanul Karim, Yang Lei

“…We compared methods including the kitchen sink model, Bross-based hdPS, Hybrid hdPS, LASSO, Elastic Net, Random Forest, XGBoost, and Genetic Algorithm (GA). …”
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Detection of driving factors and critical thresholds for carbon sequestration capacity in urban agglomerations using a combined causal inference and machine learning approach by Yin Zhang, Weibo Ma, Nan Wang, Lijun Zhao, Qingwu Hu, Shaogang Lei, Haidong Li

“…Furthermore, the long-term (from 2021 to 2100) carbon sequestration dataset with county-level scale in the YRDUA was generated using the causal inference-based machine learning model. In the context of carbon neutrality, we found that the optimal emission scenario for low-carbon sustainable development of YRDUA is SSP3, under which the average carbon sequestration in most counties will exceed 1 × 107 t. …”
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Machine learning enhanced ultra-high vacuum system for predicting field emission performance in graphene reinforced aluminium based metal matrix composites by Sunil Kumar Pradhan, Subhayu Kabiraj, Shivin Kumar Gupta, Abhishek Singh, Padmakar G. Chavan, Shubham S. Patil, Trilok Nath Pandey

“…A two-stage machine learning framework was implemented. In Stage 1, datasets for pure aluminum, 0.5 wt% and 1.0 wt% graphene reinforced aluminium composites were used to train various ML models, categorized into five baskets: Decision tree-based, Support Vector models, Neural networks, Bayesian Models and Statistical Models. …”
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Application of ai driven system for estimation of orders in the printing industry by Kostaryev D., Tkachenko V., Sizova N.

“…In this context, fast and accurate order evaluation is essential for resource optimization, cost reduction, and competitiveness. Traditional methods − manual input, expert judgment, and classic financial models − are labor-intensive, inflexible, and poorly suited to today’s diverse technologies. …”
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