Enhancing Efficiency and Reducing the Carbon Footprint of Cloud-Based Healthcare Applications through Optimal Data Preprocessing by El Aziz Btissam, Eddabbah Mohammed, Laaziz Yassin

“…We analyze how preprocessing techniques affect some of the most commonly used Machine Learning (ML) algorithms, namely K-means, SVM, and KNN, emphasizing their role in reducing computational load, energy consumption, and carbon emissions in data centers. …”
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A Multiwavelength Technique for Estimating Galaxy Cluster Mass Accretion Rates by John Soltis, Michelle Ntampaka, Benedikt Diemer, John ZuHone, Sownak Bose, Ana Maria Delgado, Boryana Hadzhiyska, César Hernández-Aguayo, Daisuke Nagai, Hy Trac

“…We present a state-of-the-art machine learning model for constraining the mass accretion rate of galaxy clusters from only X-ray and thermal Sunyaev–Zeldovich observations. …”
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Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing by Artur Samojluk, Piotr Artiemjew

“…An analysis of data mining and selected machine learning methods was carried out to develop an effective diagnosis algorithm. …”
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Development of an upper limb muscle strength rehabilitation assessment system using particle swarm optimisation by Chuangan Zhou, Siqi Wang, Meiyi Wu, Wei Lai, Junyu Yao, Xingyue Gou, Hui Ye, Jun Yi, Dong Cao

“…Machine learning models, including Backpropagation Neural Network (BPNN), Support Vector Machines (SVM), and particle swarm optimization algorithms (PSO-BPNN, PSO-SVR), were applied for regression analysis. …”
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LILRA5+ macrophages drive early oxidative stress surge in sepsis: a single-cell transcriptomic landscape with therapeutic implications by Peng Xu, Haoze Li, Zuo Tao, Zixuan Zhang, Xiaohuan Wang, Cheng Zhang

“…High-dimensional weighted gene co-expression network analysis (hdWGCNA), combined with multiple machine learning methods, was used for the selection of hub genes. …”
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Single-cell mitochondrial morphomics reveals cellular heterogeneity and predicts complex I, III, and ATP synthase Inhibition responses by Ratneswary Sutharsan, Maddi Biaut Hontaas, Yan Li, Hao Xiong, Hartwig Preckel, Carolyn M. Sue, Gautam Wali

“…By incorporating the most affected cells into machine learning models, we significantly improved the prediction accuracy of mitochondrial dysfunction outcomes − 81.97% for antimycin, 75.12% for rotenone, and 94.42% for oligomycin. …”
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From Policy to Prices: How Carbon Markets Transmit Shocks Across Energy and Labor Systems by Cristiana Tudor, Aura Girlovan, Robert Sova, Javier Sierra, Georgiana Roxana Stancu

“…Utilizing monthly data from seven ETS jurisdictions spanning January 2021 to December 2024 (N = 287 observations after log transformation and first differencing), which includes four auction-based markets (United States, Canada, United Kingdom, South Korea), two secondary markets (China, New Zealand), and a government-set fixed-price scheme (Germany), this research estimates a panel vector autoregression (PVAR) employing a Common Correlated Effects (CCE) model and augments it with machine learning analysis utilizing XGBoost and explainable AI methodologies. …”
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Bridging climate science, policy, and communities: collaborative pathways for climate resilience in the Indo-Pacific by Aditi Modi, Mathew Koll Roxy, Shipra Jain, Chi Huyen Truong, Quang-Van Doan, Christopher Jack, Svetlana Jevrejeva, Arvind Singh, Chirag Dhara, Sahana Ghosh

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Radiomic analysis of patient and interorgan heterogeneity in response to immunotherapies and BRAF-targeted therapy in metastatic melanoma by Riyue Bao, Yana G Najjar, Diwakar Davar, John M Kirkwood, Jason John Luke, Sarah Newman, Hassane M Zarour, Afsaneh Amouzegar, Rivka R Colen, Murat Ak, Priyadarshini Mamindla, Serafettin Zenkin, Vishal Peddagangireddy, Sarah Behr, Alexandra G Tompkins, Zane N Gray, Rebekah E Dadey, Nasim Batavani, Nursima Ak, Taha Yasin Pak, Mohammadreza Amjadzadeh, Amy Goodman, Darcy L Ploucha, Curtis Tatsuoka

“…Overall and organ-specific machine-learning models were constructed to predict disease control (DC) versus progressive disease (PD) using XGBoost. 291 patients with MEL were identified, including 242 ICI (91 I+N, 151 PD-1) and 49 BRAF. 667 metastases were analyzed, including 541 ICI (236 I+N, 305 PD-1) and 126 BRAF. …”
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Dual level dengue diagnosis using lightweight multilayer perceptron with XAI in fog computing environment and rule based inference by Deepika R., Pradeep Kumar T.S.

“…This study especially focuses on incorporating machine learning and deep learning techniques in the fog computing environment to leverage the overall diagnostic efficiency of dengue by promoting remote diagnosis and speedy treatment. …”
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Performance analysis of dual-fuel engines using acetylene and microalgae biodiesel: The role of fuel injection timing by M. Sonachalam, R. Jayaprakash, V. Manieniyan, P.S. Raghavendra Rao, G. Vinodhini, Manish Sharma, Teku Kalyani, Mahammadsalman Warimani, Hasan Sh Majdi, T.M. Yunus Khan, Abdul Saddique Shaik, Keerthi Shetty

“…To predict engine performance and emission characteristics, advanced machine learning models were employed and evaluated using four statistical criteria, including R-squared, mean absolute error (MAE), and mean squared error (MSE). …”
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ARTIFICIAL INTELLIGENCE AND FUTURE BUSINESS PROSPECTS': A GENERAL ANALYSIS by Anurag Hazarika, Samikshya Madhukullya, Anwesha Hazarika

“…It is anticipated that the integration of AI technologies, such as robotic process automation, natural language processing, and machine learning, would result in considerable cost reductions and productivity increases. …”
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Detecting microcephaly and macrocephaly from ultrasound images using artificial intelligence by Abraham Keffale Mengistu, Bayou Tilahun Assaye, Addisu Baye Flatie, Zewdie Mossie

“…Conclusion Deep learning models can enhance prenatal diagnosis workflows, especially in resource-constrained settings. …”
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Influences of Temperature Variations Around Subway Ventilations on Fractures of Continuous Welded Rail Fractures by Jung-Youl Choi, Sang-Yeol Park, Sun-Hee Kim, Jee-Seung Chung

“…Load conditions, including temperature changes, were analyzed using a validated numerical model to determine the axial stress in the CWR, which resulted in a 23% reduction in the axial stress in the weld joint. …”
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Neutrosophic Fuzzy Power Management (NFPM): Tackling Uncertainty in Energy Harvesting for Sensor Networks by Musallam M. AlZubi, Mohamed A. Mohamed, Hanan M. Amer, A. A. Salama

“…Future research directions include exploring the integration of NFPM with machine learning algorithms for predictive energy management, assessing its scalability in larger networks, and examining its applicability in other domains requiring energy management under uncertainty.…”
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SIDNet: A SQL Injection Detection Network for Enhancing Cybersecurity by Debendra Muduli, Shantanu Shookdeb, Abu Taha Zamani, Surabhi Saxena, Anuradha Shantanu Kanade, Nikhat Parveen, Mohammad Shameem

“…Our comprehensive evaluation includes a comparison of the performance of these customized CNN models against traditional machine learning approaches, highlighting improvements in classification accuracy and reductions in false alarm rates. …”
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Energy-Efficient Scheduling for Resilient Container-Supply Hybrid Flow Shops Under Transportation Constraints and Stochastic Arrivals by Huaixia Shi, Huaqiang Si, Jiyun Qin

“…To address the TDEHFSP model, the study proposes a Q-learning-based multi-swarm collaborative optimization algorithm (Q-MGCOA). …”
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Fast binary logistic regression by Nurdan Ayse Saran, Fatih Nar

“…This study presents a novel numerical approach that improves the training efficiency of binary logistic regression, a popular statistical model in the machine learning community. Our method achieves training times an order of magnitude faster than traditional logistic regression by employing a novel Soft-Plus approximation, which enables reformulation of logistic regression parameter estimation into matrix-vector form. …”
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LightGBM-Based Human Action Recognition Using Sensors by Yinuo Liu, Ziwei Chen

“…Finally, using feature selection and dimensionality reduction, the efficiency of the model is further improved, achieving a 70.14% increase in time efficiency without reducing the accuracy rate.…”
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Sound-Based Unsupervised Fault Diagnosis of Industrial Equipment Considering Environmental Noise by Jeong-Geun Lee, Kwang Sik Kim, Jang Hyun Lee

“…The influence of environmental noise is generally excluded during research on machine fault diagnosis using acoustic signals. This study proposes a fault diagnosis method using a variational autoencoder (VAE) and domain adaptation neural network (DANN), both of which are based on unsupervised learning, to address this problem. …”
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