Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction.
Researchers introduce NLE-YOLO, a novel low-light target detection network based on YOLOv5, featuring innovative preprocessing techniques and feature extraction modules. Through experiments on the Exdark dataset, NLE-YOLO demonstrates superior detection accuracy and performance, offering a promising solution for robust object identification in challenging low-light conditions.
Researchers present a cutting-edge framework for real-time crash risk estimation and prediction at signalized intersections, leveraging artificial intelligence and traffic conflict data. By integrating a non-stationary generalized extreme value model and a recurrent neural network, the framework offers proactive insights for safety management and countermeasure implementation, demonstrating high accuracy and potential for real-world applications.
Researchers introduce a hierarchical federated learning framework tailored for large-scale AIoT systems in smart cities. By integrating cloud, edge, and fog computing layers and leveraging the MQTT protocol, the framework addresses data privacy and communication latency challenges, demonstrating enhanced scalability and efficiency. Experimental validation in Docker environments confirms the framework's feasibility and performance improvements, laying the foundation for future optimizations.
Researchers investigate ChatGPT ADA, an extension of GPT-4, for developing ML models in clinical data analysis, showing comparable performance to manual methods. With transparent methodologies and robust performance across diverse clinical trials, ChatGPT ADA presents a promising tool for democratizing ML in medicine, emphasizing its potential alongside specialized training and resources.
Researchers demonstrate the transformative potential of agricultural digital twins (DTs) using mandarins as a model crop, showcasing how data-driven decisions at the individual plant level can enhance precision farming, optimize resource allocation, and improve fruit quality, ultimately leading to a paradigm shift in agriculture towards individualized farming practices.
Dartmouth researchers develop MoodCapture, an AI-powered smartphone app that detects early symptoms of depression with 75% accuracy using facial-image processing, promising a new tool for mental health monitoring.
AI predicts energy expenses from passive design, offering a tool for reducing the energy burden on low-income households and advancing energy justice.
Researchers from the University of Ostrava delve into the intricate landscape of AI's societal implications, emphasizing the need for ethical regulations and democratic values alignment. Through interdisciplinary analysis and policy evaluation, they advocate for transparent, participatory AI deployment, fostering societal welfare while addressing inequalities and safeguarding human rights.
Researchers propose an energy management scheme (EMS) using a rule-based grasshopper optimization algorithm (RB-GOA) to efficiently manage solar-powered battery-ultracapacitor (UC) hybrid systems, addressing output fluctuations and maximizing energy extraction. The RB-GOA EMS outperforms other swarm intelligence techniques in minimizing oscillations, reducing power surges, and ensuring stable output across varying load demands, demonstrating its effectiveness in optimizing solar energy utilization.
This study harnesses the CatBoost algorithm to predict transition temperatures (Tc) of superconducting materials, addressing challenges in dataset refinement and feature selection. Leveraging the Jabir and Soraya packages for generating atomic descriptors and selecting crucial features, the model achieved high accuracy with an R-squared (R2) of 0.952 and root mean square error (RMSE) of 6.45 K. Additionally, a novel web application for Tc prediction underscores the impactful synergy between AI and materials science.
This research introduces a novel preference alignment framework to address performance degradation in multi-modal large language models (MLLMs) caused by visual instruction tuning. By leveraging preference data collected from a visual question answering dataset, the proposed method significantly improves the MLLM's instruction-following capabilities, surpassing the performance of the original language model on various benchmarks.
Researchers investigated the feasibility of using machine learning (ML) models to predict the punching shear capacity of post-tensioned ultra-high-performance concrete (UHPC) flat slabs. By proposing correction factors based on finite element method-artificial intelligence (FEM-AI/ML) techniques, they extended the validity of punching shear capacity provisions in design codes like EC2 and ACI-318 to include PT-UHPC flat slabs.
The UK’s first master’s degree course focused on applying skills in AI to engineering and design is to begin this year at the University of Bath.
Researchers utilized various machine learning algorithms to develop predictive models for identifying students at risk of dropping out of secondary and higher education in Mexico. Leveraging demographic, socioeconomic, and educational data, the study demonstrated the effectiveness of artificial neural networks (ANN) in achieving high reliability (99%) in predicting school dropout, highlighting key variables such as school attendance, type, location, occupation, income, and marital status.
Researchers harnessed AI technology to create deepfake videos portraying various facial expressions, investigating their influence on observer perceptions in job interviews. The study highlights how deepfake facilitates controlled experimentation in studying nonverbal behavior, shedding light on its crucial role in social interactions and offering insights for job interview training and beyond.
This article outlines a pioneering AI-integrated model for international legal education, aiming to revolutionize traditional teaching methods by leveraging AI's capabilities. The model, employing correlation analysis, AI knowledge mapping, and neural algorithms, promises personalized learning experiences, efficient assessment, and enhanced career prospects for students, as evidenced by its impressive performance in practical teaching evaluations.
Researchers addressed challenges in Federated Learning (FL) within Space-Air-Ground Information Networks (SAGIN) by introducing the LCNSFL algorithm. LCNSFL, based on a Double Deep Q Network (DDQN), strategically selects nodes to minimize time and energy costs. Simulation results demonstrate LCNSFL's superiority over traditional methods, offering efficient convergence and resource utilization in dynamic network environments, essential for practical applications in SAGIN.
Researchers introduced a novel control strategy for autonomous ground vehicles (AGVs) utilizing a self-tuning nonsingular fast terminal sliding manifold (SNFTSM) to enhance convergence and tracking accuracy. Integrated with a high-gain disturbance observer (HGDO) and a super-twisting algorithm (STW), the approach effectively addressed uncertainties and reduced chattering in control signals, demonstrating superior performance compared to existing methods in numerical simulations.
Researchers introduced the Aesthetic Diffusion Model, utilizing AI to swiftly generate visually appealing interior designs based on text descriptions. By incorporating aesthetic scores, decoration styles, and spatial functionalities, the model streamlines the design process, enhancing efficiency and creativity while offering practical solutions for interior designers. Experimental results demonstrate the model's superiority, paving the way for future advancements in AI-driven interior design.
Researchers developed FlashNet, a hybrid AI method, to forecast lightning flashes up to 48 hours ahead, surpassing traditional NWP models. Utilizing features from high-resolution NWP data and employing deep neural networks, FlashNet demonstrated superior accuracy, reliability, and sharpness, offering valuable insights for various sectors vulnerable to lightning-related risks. The study highlights FlashNet's potential for medium-range forecasting and recommends further exploration for extending forecast horizons and addressing global applicability.
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