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 comprehensive review explores the growing use of machine learning and satellite data in water quality monitoring, emphasizing the importance of proper data analysis techniques and highlighting the potential for advancements in environmental understanding.
Recent research published in Scientific Reports investigates the impact of biased artificial intelligence (AI) recommendations on human decision-making in medical diagnostics. The study, conducted through three experiments, reveals that AI-generated biased recommendations significantly affect human behavior, leading to increased errors in medical decision-making tasks.
Researchers explored safety in autonomous mining using Bayesian networks (BN). They developed a proactive approach to detect faults and fire hazards in mining machinery, utilizing diverse sensors and AI-driven predictive maintenance. This study offers a comprehensive framework for improving safety in the rapidly advancing field of autonomous mining.
Explore the cutting-edge advancements in image processing through reinforcement learning and deep learning, promising enhanced accuracy and real-world applications, while acknowledging the challenges that lie ahead for these transformative technologies.
Researchers present MGB-YOLO, an advanced deep learning model designed for real-time road manhole cover detection. Through a combination of MobileNet-V3, GAM, and BottleneckCSP, this model offers superior precision and computational efficiency compared to existing methods, with promising applications in traffic safety and infrastructure maintenance.
Researchers have introduced a groundbreaking approach to AI learning in social environments, where agents actively interact with humans. By combining reinforcement learning with social norms, the study demonstrated a 112% improvement in recognizing new information, highlighting the potential of socially situated AI in open social settings and human-AI interactions.
Researchers harnessed artificial intelligence to predict groundwater levels in Ethiopia's Bilate watershed, a water-scarce region. Their study revealed that Gradient Boosting Regression (GBR) performed exceptionally well, offering a valuable tool for sustainable borehole drilling decisions, particularly for irrigation, in water-scarce regions.
A computer simulation study delves into the foraging behavior of early hominins in late Early Pleistocene Europe. It highlights the importance of scavenging, group size, and social dynamics in their survival, shedding light on the evolution of complex behaviors and language.
Researchers have explored the integration of sensor technology and artificial intelligence (AI) to improve the assessment of animal welfare indicators in slaughterhouses, focusing on poultry, pigs, and cattle. While these technologies offer potential benefits in enhancing inspections and risk assessments, legal barriers and the need for external validation remain challenges in fully replacing human inspectors in meat inspection processes.
Researchers have developed a "semantic guidance network" to improve video captioning by addressing challenges like redundancy and omission of information in existing methods. The approach incorporates techniques for adaptive keyframe sampling, global encoding, and similarity-based optimization, resulting in improved accuracy and generalization on benchmark datasets. This work opens up possibilities for various applications, including video content search and assistance for visually impaired users.
Researchers have expanded an e-learning system for phonetic transcription with three AI-driven enhancements. These improvements include a speech classification module, a multilingual word-to-IPA converter, and an IPA-to-speech synthesis system, collectively enhancing linguistic education and phonetic transcription capabilities in e-learning environments.
Researchers develop a hybrid forecasting model, combining Ensemble Empirical Mode Decomposition (EEMD), Multivariate Linear Regression (MLR), and Long Short-Term Memory Neural Network (LSTM NN) to predict water quality parameters in aquaculture. The model shows promising accuracy and has the potential to enhance water quality management in the aquaculture industry, particularly in early detection of harmful Algal Blooms (HABs).
Researchers investigate the risks posed by Large Language Models (LLMs) in re-identifying individuals from anonymized texts. Their experiments reveal that LLMs, such as GPT-3.5, can effectively deanonymize data, raising significant privacy concerns and highlighting the need for improved anonymization techniques and privacy protection strategies in the era of advanced AI.
Researchers introduce the "general theory of data, artificial intelligence, and governance," offering fresh insights into the complexities of the data economy and its implications for digital governance. Their model, which incorporates data flows, knowledge concentration, and data sharing, provides a foundation for addressing the challenges of data capitalism and shaping equitable and innovative data policies in the digital age.
The Crop Planting Density Optimization System (CPDOS) harnesses the power of artificial intelligence, including genetic algorithms and neural networks, to optimize crop planting density for improved agricultural yields. This intelligent online system offers advanced tools for farmers to fine-tune planting density and fertilizer application, ultimately enhancing crop production while considering economic factors.
Researchers have leveraged machine learning and deep learning techniques, including BiLSTM networks, to classify maize gene expression profiles under biotic stress conditions. The study's findings not only demonstrate the superior performance of the BiLSTM model but also identify key genes related to plant defense mechanisms, offering valuable insights for genomics research and applications in developing disease-resistant maize varieties.
Researchers have conducted a comprehensive review of the offshore wind energy industry, emphasizing the role of machine learning (ML) and artificial intelligence (AI) in addressing challenges related to turbine size, efficiency, environmental impact, and deep-water deployment. ML applications include climate forecasting, environmental impact assessment, wind farm optimization, and more.
Researchers have developed a novel method that combines geospatial artificial intelligence (GeoAI) with satellite imagery to predict soil physical properties such as clay, sand, and silt. They utilized a hybrid CNN-RF model and various environmental parameters to achieve accurate predictions, which have significant implications for agriculture, erosion control, and environmental monitoring.
This paper explores how artificial intelligence (AI) is revolutionizing regenerative medicine by advancing drug discovery, disease modeling, predictive modeling, personalized medicine, tissue engineering, clinical trials, patient monitoring, patient education, and regulatory compliance.
This article delves into the application of artificial intelligence (AI) techniques in predicting water quality indices and classifications. It highlights the advantages and challenges of implementing AI in water quality monitoring and modeling and explores advancements in machine learning for assessing various water quality parameters.
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