Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future

Energy & Fuels, Journal Year: 2024, Volume and Issue: 38(3), P. 1593 - 1617

Published: Jan. 16, 2024

This review illuminates the pivotal synergy between machine learning (ML) and biopolymers, spotlighting their combined potential to reshape sustainable energy, fuels, biochemicals. Biobased polymers, derived from renewable sources, have garnered attention for roles in energy fuel sectors. These when integrated with ML techniques, exhibit enhanced functionalities, optimizing systems, storage, conversion. Detailed case studies reveal of biobased polymers applications industry, further showcasing how bolsters efficiency innovation. The intersection also marks advancements biochemical production, emphasizing innovations drug delivery medical device development. underscores imperative harnessing convergence future global sustainability endeavors collective evidence presented asserts immense promise this union holds steering a innovative trajectory.

Language: Английский

---

Sensors for Digital Transformation in Smart Forestry

Sensors, Journal Year: 2024, Volume and Issue: 24(3), P. 798 - 798

Published: Jan. 25, 2024

Smart forestry, an innovative approach leveraging artificial intelligence (AI), aims to enhance forest management while minimizing the environmental impact. The efficacy of AI in this domain is contingent upon availability extensive, high-quality data, underscoring pivotal role sensor-based data acquisition digital transformation forestry. However, complexity and challenging conditions environments often impede collection efforts. Achieving full potential smart forestry necessitates a comprehensive integration sensor technologies throughout process chain, ensuring production standardized, essential for applications. This paper highlights symbiotic relationship between human expertise particularly under conditions. We emphasize human-in-the-loop approach, which allows experts directly influence generation, enhancing adaptability effectiveness diverse scenarios. A critical aspect deployment autonomous robotic systems forests, functioning both as collectors processing hubs. These are instrumental facilitating generating substantial volumes quality data. present our universal platform, detailing experiences importance initial phase transformation—the generation comprehensive, selection appropriate sensors key factor process, findings underscore its significance advancing

Language: Английский

---

Artificial Intelligence In Financial Services: Advancements In Fraud Detection, Risk Management, And Algorithmic Trading Optimization

Published: Jan. 1, 2025

Fraud detection, risk management, and algorithmic trading optimization are being revolutionized by AI in financial services. reduces false positives speeds up fraud detection spotting trends anomalies real time using advanced machine learning techniques. Financial institutions can now fight sophisticated cyber attacks with AI-powered systems that analyze massive databases detect illicit conduct unparalleled accuracy. predictive analytics changing how organizations identify mitigate risks. Institutions predict credit defaults, market swings, operational weaknesses big data AI. Natural language processing (NLP) techniques extracting insights from unstructured sources including regulatory filings news to improve decision-making. Real-time monitoring enable proactive interventions reduce losses assure compliance. is transforming trading, another breakthrough. Advanced models historical live price movements, find arbitrage opportunities, execute trades milliseconds. Reinforcement helping design adaptable algorithms respond changes, increasing profitability reducing risk. also promotes ethical transparent tactics, solving manipulation problems. This study analyses the newest applications services their disruptive influence. Generative AI, federated learning, quantum computing will further transform sector. adoption has many benefits, but privacy, bias, legal complexity must be addressed sustain progress. efficiency, resilience, creativity, creating a future where technology drives trust strategic advantage.

Language: Английский

---

Artificial Intelligence-Driven Customer Service: Enhancing Personalization, Loyalty, And Customer Satisfaction

Published: Jan. 1, 2025

AI-driven customer service is revolutionizing how businesses interact with customers by improving personalization, loyalty, and satisfaction through data-driven insights responsive interactions. AI technologies like machine learning (ML), natural language processing (NLP), generative models allow companies to scale experiences that match individual preferences, behaviors, needs. tools in service, such as chatbots virtual assistants, are response times issue resolution, increasing loyalty. Companies can analyze massive datasets real time using improve profiles predict future systems boost brand loyalty personalizing interactions making feel valued. Additionally, ChatGPT engagement reducing friction providing human-like responses conversational experiences. sentiment analysis help anticipate dissatisfaction assessing emotions feedback. Along AI-based solutions programs them more dynamic engaging. Businesses identify high-value customers, personalize offers, encourage repeat business predictive analytics. Despite these advances, ethical issues data privacy interaction must be addressed. As evolves, balancing automation personalized human crucial. This paper examines current trends, case studies, developments demonstrate transform environments into customer-centric, responsive, adaptable ones foster long-term satisfaction.

Language: Английский

---

Artificial Intelligence In Retail And E-Commerce: Enhancing Customer Experience Through Personalization, Predictive Analytics, And Real-Time Engagement

Published: Jan. 1, 2025

AI is transforming retail and e-commerce with unprecedented personalization, predictive analytics, real-time customer involvement. AI-powered recommendation engines, chatbots, sentiment analysis tools enable customer-centric tactics as consumers want more personalized experiences. AI's capacity to analyze massive volumes of data allows merchants develop shopping experiences that boost pleasure loyalty. For instance, deep learning-based systems accurately predict client preferences, increasing conversion rates average order values. analytics changing inventory management, demand forecasting, pricing in retail. Stock levels, waste, profitability are optimized by machine learning algorithms examine historical sales data, market trends, behavior. Real-time insights dynamic models adjust instantaneously supply changes, maintaining competitiveness fast-paced e-commerce. AI-enabled engagement business-customer interactions. Conversational can answer questions instantly personally smart chatbots voice assistants, improving user experience lowering operational expenses. Visual technologies like image identification augmented reality virtual try-ons visual search, online purchasing. The use has highlighted ethical issues such privacy algorithmic fairness. Growing sustainably requires balancing consumer personalization trust. This paper examines how might improve experience, supported recent breakthroughs industry trends. It shows may purchasing while tackling implementation a digital economy.

Language: Английский

---