Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions by Khaled M. Salem, Javier M. Rey-Hernández, A. O. Elgharib, Francisco J. Rey-Martínez

“…Industry 5.0 is transforming energy demand by integrating sustainability into energy planning, ensuring market stability while minimizing environmental impact for future generations. There are several patterns for calculating energy consumption depending on whether it is measured daily, monthly, or annually through the integration of artificial intelligence approaches, particularly Artificial Neural Networks (ANNs) and Random Forests (RFs), and within the framework of Industry 5.0. …”
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A Novel Search Algorithm and Scan Time Estimation in Airborne Ground Penetrating Radar Using Cell Footprint Meshing by Morteza Kazerooni, Ehsan Shahroosvand

“…Airborne Ground Penetrating Radar (A-GPR) systems in multilayer environments often exhibit inefficiencies stemming from suboptimal search patterns and inaccurate scan time estimations, resulting in protracted operation times and elevated energy consumption. …”
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LSTM vs. Prophet: Achieving Superior Accuracy in Dynamic Electricity Demand Forecasting by Saleh Albahli

“…The LSTM component captures nonlinear dependencies and long-term temporal patterns, while Prophet models seasonal trends and event-driven fluctuations. …”
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Non‐Autoregressive Translation Algorithm Based on LLM Knowledge Distillation in English Corpus by Fang Ju, Weihui Wang

“…ABSTRACT Although significant advancements have been made in the quality of machine translation by large‐scale language models, their high computational costs and resource consumption have hindered their widespread adoption in practical applications. …”
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Multi-context modeling of driver pathways reveals common and specific mechanisms across 23 cancer types. by Wenjia Zhou, Junhua Zhang

“…However, their context-specific patterns across populations remain poorly understood. …”
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Analysis Of Bread Demand Forecasting Using Recurrent Neural Network (RNN) Method Based On Operational Delivery Data by Harinudin Saputro, Mohammad Zoqi Sarwani, Rudi Hariyanto

“…Unlike previous research, this model is trained on two years of real operational delivery data (2023–2024), enabling it to capture actual consumption patterns more effectively. The model leverages a 7-day sequence window to predict the next day’s demand, reflecting weekly seasonality. …”
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Accelerating Deep Learning-Based Morphological Biometric Recognition with Field-Programmable Gate Arrays by Nourhan Zayed, Nahed Tawfik, Mervat M. A. Mahmoud, Ahmed Fawzy, Young-Im Cho, Mohamed S. Abdallah

“…A facial recognition solution by leveraging deep learning and subsequently deploying it on an FPGA platform is suggested. …”
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Optimized energy management and small cell activation in ultra-dense networks through a data-driven approach by Amna Shabbir, Safdar Rizvi, Muhammad Mansoor Alam, Mazliham Mohd Su’ud

“…Addressing these challenges demands innovative approaches such as data-driven deployment strategies and efficient energy efficient resource (EER) management for small cells. Leveraging data-driven methodologies, operators can optimize small cell deployment locations and configurations based on real-time traffic patterns and environmental conditions, thereby maximizing network performance while minimizing energy consumption. …”
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Weighted explainable federated learning for privacy-preserving and scalable energy optimization in autonomous vehicular networks by Muhammad Saleem, Ali Arishi, Muhammad Sajid Farooq, M.A. Khan, Khan M. Adnan

“…The proposed WEFL-XAI model ensures privacy-preserving, real-time, and adaptive vehicular energy optimization, leveraging traffic patterns, vehicle energy states, and grid load conditions. …”
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Optimizing photovoltaic integration in grid management via a deep learning-based scenario analysis by Zhiming Gu, Bo Li, Guipeng Zhang, Bo Li

“…Given the fluctuating nature of solar energy, the study employs Generative Adversarial Networks (GANs) to simulate diverse and high-resolution energy generation-consumption patterns. These synthetic scenarios are subsequently utilized within a real-time adaptive control framework, allowing for dynamic adjustments in operational strategies that enhance both efficiency and grid stability. …”
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Dynamics of Acculturation and Enculturation of Languages to Socio-Economic Development in Nigeria: Implication for Poverty Reduction by Khadijah Ashiru Abdulrahman

“… Every group and society has cultures constituting frameworks for their lives and behavioral patterns. Cultural factors affect socio-economic behavior in at least four ways: its impact on organization and production, attitudes towards consumption and work, the ability to create and manage institutions, and the creation of social networks. …”
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Accounting Management and Optimizing Production Based on Distributed Semantic Recognition by Ruina Guo, Shu Wang, Guangsen Wei

“…Additionally, a semantic recognition module is introduced to process the voice signals and generate prediction results. By leveraging extensive accounting management and production data for learning and analysis, the algorithm automatically uncovers patterns and laws within the data, extracting valuable information. …”
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An AI explained systematic modular approach for enhanced Electricity Theft Detection for urbanized Smart Grid environment by Muhammad Ammar, Nadeem Javaid, Ali Arishi

“…Traditional detection systems often face challenges like severe data discrepancies, high class imbalance, and volatile consumption patterns, which hinder their ability to detect abnormal movements and limit system dependability. …”
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Detection Method for Three-Phase Electricity Theft Based on Multi-Dimensional Feature Extraction by Wei Bai, Lan Xiong, Yubei Liao, Zhengyang Tan, Jingang Wang, Zhanlong Zhang

“…In light of this, we propose the Catch22-Conv-Transformer method, a multi-dimensional feature extraction-based approach tailored for the detection of anomalous electricity usage patterns. This methodology leverages both the Catch22 feature set and complementary features to extract sequential features, subsequently employing convolutional networks and the Transformer architecture to discern various types of theft behaviors. …”
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A mechanism for tracking the water level by Muradov Khayot, Djalilov Anvar, Nurmatova Feruza

“…The true innovation of this system lies in its ability to offer continuous visibility into tank water levels, empowering farmers to develop optimized water use strategies. By leveraging Internet of Things (IoT) technology through the Blynk application platform, the solution enables comprehensive tracking of both water levels and consumption patterns. …”
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Optimizing oil salt analysis in southern Iran: A comprehensive statistical model for ASTM D3230 electrochemical cell device utilizing advanced analytical approaches by M. Shirali, F. Mirhashemi

“…However, the growing application of this method raises concerns about the substantial daily requirement of 40 L of solvents as well as the device's significant power consumption within a laboratory setting. This study analyzes a year-long dataset comprising 103 filtered samples and leverages data mining techniques to investigate interactions among descriptive statistical parameters within crude oil compositions, thereby enhancing the efficiency, operational stability, and performance monitoring of the D3230 device. …”
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Clothing Recommendation with Multimodal Feature Fusion: Price Sensitivity and Personalization Optimization by Chunhui Zhang, Xiaofen Ji, Liling Cai

“…To address this gap, we propose DeepFMP, a multimodal deep learning framework that integrates visual, textual, and price features through an enhanced DeepFM architecture. Leveraging the IQON3000 dataset, our model employs ResNet-50 and BERT for image and text feature extraction, alongside a comprehensive price feature module capturing individual, statistical, and category-specific price patterns. …”
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The complexity of pharmaceutical expenditures across U.S. states by Lisa Cross, Angelo Famà, Paolo Pagnottoni, Nicolò Pecora, Alessandro Spelta

“…This study applies network-based economic complexity methods to analyze state-level Medicaid drug spending, leveraging Medicaid State Drug Utilization Data (SDUD) from 2018 to 2024. …”
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Behavior-aware energy management in microgrids using quantum-classical hybrid algorithms under social and demand dynamics by Liu Minghong, Fu Gaoshan, Wang Pengchao, Yuan Xin, Li Qing, Tengfei Hou, Zhang Shuo

“…Abstract The increasing intricacy of modern microgrids, driven by uncertain consumption patterns, decentralized renewables, and user behavioral dynamics, calls for innovative optimization methodologies. …”
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Automated District-Level Energy Demand Modeling Using EnergyPlus Empowered by Digital Twin Technology by A. Jalilzadeh, A. Rafiee, S. De Graaf, T. Konstantinou, P. J. M. van Oosterom

“…By integrating a digital twin for real-time monitoring and scenario testing, the approach enables efficient bulk operations, interactive decision support, and clearer insights into urban-scale energy consumption patterns. The resulting automation and scalable workflows offer valuable contributions to sustainable urban planning, ensuring that efficiency opportunities can be quickly identified and acted upon at multiple spatial scales.…”
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