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 introduces an innovative framework for early plant disease diagnosis, leveraging fog computing, IoT sensor technology, and a novel GWO algorithm. The hybrid approach, incorporating deep learning models like AlexNet and GoogleNet, coupled with modified GWO for feature selection, demonstrates superior performance in plant disease identification.
This study investigates the widespread usage of AI tools, particularly ChatGPT and GPT-4, among German students across disciplines. With nearly two-thirds of students utilizing these tools, the research emphasizes the need for further exploration into usage patterns, perceptions, and potential implications for teaching and learning.
Researchers explored the application of artificial intelligence (AI), specifically long short-term memory (LSTM) and artificial neural networks (ANN), in assessing and predicting surface water quality. The study, conducted on the Ashwini River in Himachal Pradesh, India, showcased the effectiveness of LSTM models in accurate water quality prediction, emphasizing the potential of AI in resource management and environmental protection
The paper published in the journal Electronics explores the crucial role of Artificial Intelligence (AI) and Explainable AI (XAI) in Visual Quality Assurance (VQA) within manufacturing. While AI-based Visual Quality Control (VQC) systems are prevalent in defect detection, the study advocates for broader applications of VQA practices and increased utilization of XAI to enhance transparency and interpretability, ultimately improving decision-making and quality assurance in the industry.
This study introduces a model-independent approach to discern texts written by humans from those generated by AI, such as ChatGPT. Using a redundancy measure based on n-gram usage and Bayesian hypothesis testing, the researchers achieved successful discrimination between human and AI-authored texts, offering a robust solution for authorship attribution challenges in the era of advanced language models.
This study investigates how different robot occupations influence the perception of the robot-human border among participants, considering the effects of age and gender. The findings reveal that service robots, particularly those involved in direct human interactions, require a higher degree of human-like features to be recognized as humans.
Researchers introduce a pioneering method for urban economic competitiveness analysis in China, addressing the limitations of traditional approaches. Leveraging convolutional neural networks (CNN) and a rich urban feature dataset, augmented using deep convolutional Generative Adversarial Networks (DCGAN), the model offers a comprehensive understanding of urban development, overcoming data scarcity challenges and outperforming traditional methods.
A recent publication delves into the evolving landscape of utilizing machine learning to simulate the complexities of the human brain. Tracing the historical journey from simplified neural network models to contemporary connectome-driven approaches, the article emphasizes the potential of machine learning in replicating neural activities.
The UK and the US have taken significant steps toward governmental oversight of artificial intelligence (AI), addressing various aspects of AI technology. The US President signed an executive order outlining directives for federal agencies, emphasizing the need for standards, security, and preventing misuse of AI.
Researchers introduced Relay Learning, a novel deep-learning framework designed to ensure the physical isolation of clinical data from external intruders. This secure multi-site deep learning approach, Relay Learning, significantly enhances data privacy and security while demonstrating superior performance in various multi-site clinical settings, setting a new standard for AI-aided medical solutions and cross-site data sharing in the healthcare domain.
This research revealed that art attributed to AI is often devalued in terms of monetary worth, skill, and likability, but these effects can be mitigated when direct comparisons with human-made art are avoided. The findings suggest that AI's role in art, while presenting challenges, may lead to new forms of artistic expression and creativity, similar to the impact of inventions like the camera on the art world.
Researchers introduce LLaVA-Interactive, an innovative multimodal human and artificial intelligence (AI) interaction prototype. This system enables users to engage in multi-turn dialogues and interact through visual and language prompts, offering diverse applications, from content creation to culinary guidance, and inspiring future research in multimodal interactive systems.
Researchers have explored the use of hierarchical generative modeling to mimic human motor control, enabling autonomous task completion in a humanoid robot. Through extensive physics simulations, they demonstrated the feasibility and effectiveness of this approach, showcasing its potential for complex tasks involving locomotion, manipulation, and grasping, even under challenging conditions.
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.
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