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.
This study presents an innovative system for business purchase prediction that combines Long Short-Term Memory (LSTM) neural networks with Explainable Artificial Intelligence (XAI). The system is designed to predict future purchases in a medical drug company, offering transparent explanations for its predictions, fostering user trust, and providing valuable insights for business decision-making.
This research article underscores the importance of aligning AI outputs with human expectations in decision support systems and introduces the concept of Explainable AI (XAI). A systematic review results in a taxonomy of interaction patterns, emphasizing the need for more interactive functionality in AI systems.
This research paper compared various computational models to predict ground vibration from mining blasts. The study found that a blackhole-optimized LSTM model provided the highest predictive accuracy, outperforming conventional and advanced methods, offering a robust foundation for AI-powered solutions in vibration forecasting and design optimization in the mining industry.
Researchers outlined six principles for the ethical use of AI and machine learning in Earth and environmental sciences. These principles emphasize transparency, intentionality, risk mitigation, inclusivity, outreach, and ongoing commitment. The study also highlights the importance of addressing biases, data disparities, and the need for transparency initiatives like explainable AI (XAI) to ensure responsible and equitable AI-driven research in these fields.
Researchers present a detailed case study on the integration of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) for inspecting residential buildings. The study outlines a four-step inspection process, including preliminary preparations, data acquisition, AI defect detection, and 3D reconstruction with defect extraction, and provides insights into challenges, lessons learned, and future prospects for AI-UAV-based building inspections.
This paper explores the integration of IoT with drone technology to enhance data communication and security across various industries, including agriculture and smart cities. The study focuses on the use of machine learning and deep learning techniques to detect cyberattacks within drone networks and presents a comprehensive framework for intrusion detection.
This article explores the impact of industrial robot adoption on corporate green innovation in China. The study uses data from Chinese manufacturing companies and analyzes the role of industrial robots in improving green innovation by promoting environmental management and enhancing productivity.
This article explores the challenges and approaches to imparting human values and ethical decision-making in AI systems, with a focus on large language models like ChatGPT. It discusses techniques such as supervised fine-tuning, auxiliary models, and reinforcement learning from human feedback to imbue AI systems with desired moral stances, emphasizing the need for interdisciplinary perspectives from fields like cognitive science to align AI with human ethics.
Researchers highlight the increasing role of artificial intelligence (AI) in biodiversity preservation and monitoring. AI is shown to be a powerful tool for efficiently processing vast datasets, identifying species through audio recordings, and enhancing conservation efforts, though concerns about its environmental impact must be addressed.
This paper delves into the extensive use of artificial intelligence (AI) models for assessing food security indicators across the globe, with a notable focus on sub-Saharan Africa. The study emphasizes the importance of stakeholder involvement in AI modeling for food security, highlighting three key approaches to integrating AI into food security research.
Researchers leveraged artificial intelligence, including machine learning and natural language processing, to analyze legal documents and predict intimate partner femicide, showcasing the potential for AI to enhance crime prevention and detection in this specific context.
Researchers delve into the realm of mobile robot path planning. Covering single-agent and multi-agent scenarios, the study explores environmental modeling, path planning algorithms, and the latest advancements in artificial intelligence for optimizing navigation. It also introduces open-source map datasets and evaluation metrics.
Researchers have introduced a cutting-edge Driver Monitoring System (DMS) that employs facial landmark estimation to monitor and recognize driver behavior in real-time. The system, using an infrared (IR) camera, efficiently detects inattention through head pose analysis and identifies drowsiness through eye-closure recognition, contributing to improved driver safety and accident prevention.
Researchers have introduced an innovative approach for modeling mixed wind farms using artificial neural networks (ANNs) to capture complex relationships between variables. This method effectively represents the external characteristics of mixed wind farms in various wind conditions and voltage dip scenarios, addressing the challenges of power system stability in the presence of diverse wind turbine types.
A recent research publication explores the profound impact of artificial intelligence (AI) on urban sustainability and mobility. The study highlights the role of AI in supporting dynamic and personalized mobility solutions, sustainable urban mobility planning, and the development of intelligent transportation systems.
Researchers have improved inkjet print head monitoring in digital manufacturing by employing machine learning algorithms to classify nozzle jetting conditions based on self-sensing signals, achieving over 99.6% accuracy. This approach offers real-time detection of faulty nozzle behavior, ensuring the quality of printed products and contributing to the efficiency of digital manufacturing processes.
Researchers discuss the ATCO2 project, which aims to improve air traffic control (ATC) communications through artificial intelligence (AI). The project provides open-sourced data, including over 5,000 hours of transcribed communications, and achieves a 17.9% Word Error Rate on public ATC datasets. The paper highlights the challenges of data scarcity in ATC, the data collection platform, ASR technology, and the potential for Natural Language Understanding (NLU) in air traffic management.
In a proposal, researchers emphasize the need for the US government to mandate Know-Your-Customer (KYC) schemes for AI compute providers, especially cloud service providers, to address emerging security and safety risks associated with advanced AI models.
Researchers examined the impact of visual information and the perceived intelligence of voice assistants on consumers' purchasing behavior in online sustainable clothing shopping. Their findings highlight the importance of positive attitudes toward sustainable fashion and the role of AI-driven voice assistants.
Researchers delved into the ethical and legal aspects of integrating machine learning in defense systems. They conducted a comprehensive analysis, using a case study and identified challenges, emphasizing the need for robust legal and ethical frameworks in this transformative field.
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