Water Resources Management, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 11, 2025
Language: Английский
Advances in environmental engineering and green technologies book series, Journal Year: 2024, Volume and Issue: unknown, P. 333 - 362
Published: Oct. 16, 2024
Explainable AI (XAI) is important in situations where decisions have significant effects on the results to make systems more reliable, transparent, and people understand how work. In this chapter, an overview of AI, its evolution are discussed, emphasizing need for robust policy regulatory frameworks responsible deployment. Then key concept use XAI models been discussed. This work highlights XAI's significance sectors like healthcare, finance, transportation, retail, supply chain management, robotics, manufacturing, legal criminal justice, etc. profound human societal impacts. Then, with integrated IoT renewable energy management scope smart cities addressed. The study particularly focuses implementations solutions, specifically solar power integration, addressing challenges ensuring transparency, accountability, fairness AI-driven decisions.
Language: Английский
Citations
137
International Journal of Management & Entrepreneurship Research, Journal Year: 2024, Volume and Issue: 6(3), P. 936 - 949
Published: March 28, 2024
This paper explores the transformative potential of Artificial Intelligence (AI) in personalizing marketing strategies. It delves into theoretical underpinnings consumer engagement sand investigates how AI can be leveraged to develop targeted and relevant experiences. personalize messages based on behavior demographics, influencing processing route maximizing engagement. theory use game mechanics motivate engage users. gamified experiences, tailoring rewards challenges individual preferences, driving deeper Algorithms analyze vast amounts customer data predict preferences behaviors. allows for advertising, product recommendations, content that resonates with specific segments. Natural Language Processing (NLP), AI-powered NLP tools reviews, social media conversations, other forms unstructured data. brands understand sentiment communication styles optimal chatbots virtual assistants provide personalized support recommendations real-time, fostering a more interactive engaging brand experience. Potential Benefits Considerations Personalized experiences cater needs leading higher satisfaction loyalty. By offerings segments, establish relatable image. Improved Conversion Rates, campaigns highly effective, increased conversions sales. Balancing personalization privacy concerns is crucial. Transparency user control over collection practices are essential. algorithms perpetuate biases present training Ensuring fairness inclusivity paramount. revolutionizing personalization. leveraging AI's analytical capabilities understanding aspects engagement, strategies foster connections drive business growth. Keywords: Personalization, Consumer Engagement, Marketing Strategy, Theoretical Exploration, Data Privacy, Algorithmic Bias.
Language: Английский
Citations
132
Pharmaceutics, Journal Year: 2024, Volume and Issue: 16(3), P. 332 - 332
Published: Feb. 27, 2024
The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in revolutionary era healthcare by individualizing diagnostics and according to each patient’s uniquely evolving health status. This groundbreaking method tailoring disease prevention treatment considers individual variations genes, environments, lifestyles. goal precision target the “five rights”: right patient, drug, time, dose, route. In this pursuit, silico techniques have emerged as an anchor, driving forward making realistic promising avenue for personalized therapies. With advancements high-throughput DNA sequencing technologies, genomic data, including genetic variants their interactions with other environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) pharmacodynamic (PD) mathematical models further contribute drug optimization, behavior prediction, drug–drug interaction identification. Digital health, wearables, computational tools offer continuous monitoring real-time data collection, enabling adjustments. Furthermore, incorporation extensive datasets tools, such electronic records (EHRs) omics also another pathway acquire meaningful information field. Although they are fairly new, machine learning (ML) algorithms artificial intelligence (AI) resources researchers use analyze big develop predictive models. review explores interplay these multiple approaches advancing fostering healthcare. Despite intrinsic challenges, ethical considerations, protection, need more comprehensive research, marks new patient-centered Innovative hold potential reshape future generations come.
Language: Английский
Citations
85
Open Access Research Journal of Science and Technology, Journal Year: 2024, Volume and Issue: 10(2), P. 021 - 030
Published: March 26, 2024
This paper delves into the theoretical underpinnings of agile methodologies and investigates their potential to enhance customer satisfaction in digital banking. Theoretical foundations draw on several key frameworks complexity theory, complex systems, like banking ecosystems, exhibit emergent properties. Traditional linear approaches struggle predict these. Agile embraces iterative development cycles adaptability changing requirements, acknowledging this lean thinking, derived from manufacturing, thinking prioritizes eliminating waste maximizing value. translates by focusing short sprints, prioritizing features with highest impact, minimizing unnecessary functionalities co-creation, traditional models often distance customers process. emphasizes actively involving them design testing. fosters a deeper understanding needs leads more relevant satisfying experiences. practices encompass diverse practices. visual management system focuses workflow optimization. Promoting continuous flow work deployment user stories acceptance criteria, User Acceptance criteria define specific conditions feature must meet for approval. These ensure align expectations. hold significant promise enhancing digit allows banks deliver new faster, keeping pace evolving demands. Customers benefit quicker access innovative solutions that address financial needs. results experiences are intuitive, efficient, cater Increased Innovation, The nature learning experimentation. Banks can test features, gather feedback, rapidly iterate upon them, leading dynamic experience. Improved transparency trust, promote open communication collaboration between teams customers. kept informed updates have voice shaping process, fostering trust sense ownership.
Language: Английский
Citations
56
Brain Informatics, Journal Year: 2024, Volume and Issue: 11(1)
Published: April 5, 2024
Abstract Explainable artificial intelligence (XAI) has gained much interest in recent years for its ability to explain the complex decision-making process of machine learning (ML) and deep (DL) models. The Local Interpretable Model-agnostic Explanations (LIME) Shaply Additive exPlanation (SHAP) frameworks have grown as popular interpretive tools ML DL This article provides a systematic review application LIME SHAP interpreting detection Alzheimer’s disease (AD). Adhering PRISMA Kitchenham’s guidelines, we identified 23 relevant articles investigated these frameworks’ prospective capabilities, benefits, challenges depth. results emphasise XAI’s crucial role strengthening trustworthiness AI-based AD predictions. aims provide fundamental capabilities XAI enhancing fidelity within clinical decision support systems prognosis.
Language: Английский
Citations
54
Engineering Science & Technology Journal, Journal Year: 2024, Volume and Issue: 5(3), P. 1072 - 1085
Published: March 24, 2024
This paper explores the transformative potential of Artificial Intelligence (AI) in personalized marketing. It highlights how AI can analyze vast amounts customer data to create targeted messages, recommendations, and real-time interactions that resonate with individual needs preferences. approach fosters deeper consumer engagement, leading increased satisfaction, brand loyalty, business success. The discusses future shaping marketing experiences. However, responsible implementation will be paramount ensuring a positive for both brands consumers. Enhanced version abstract incorporating additional insights, this delves into power algorithms multitude points, including purchase history, website behavior, social media interactions. rich empowers highly By fostering AI-powered personalization unlocks pathway ultimately, significant growth. acknowledges ethical considerations accompany implementation. Responsible practices are paramount, security mitigating bias prevent discriminatory practices. Transparency is collected used builds trust consumers, mutually beneficial relationship. Looking ahead, Imagine Chat bot offering product recommendations real-time, or virtual reality experiences tailored lies creating genuine connections provides tools personalize journey at every touch point. navigating landscape prioritizing crucial consumers. Keywords: (AI), Personalized Marketing, Customer Engagement, Data, Marketing Strategy.
Language: Английский
Citations
50
International Journal of Scientific Research Updates, Journal Year: 2024, Volume and Issue: 7(1), P. 092 - 102
Published: March 26, 2024
This paper delves into theoretical frameworks in AI for credit risk assessment, exploring how these enhance banking efficiency and accuracy. It discusses various techniques such as machine learning algorithms, neural networks, natural language processing, their application assessment. Furthermore, it examines the challenges opportunities presented by frameworks, highlighting potential to revolutionize sector. Revolutionizing Credit Risk Assessment Banking, The Role of Artificial Intelligence In dynamic realm finance, assessment stands a fundamental pillar institutions. Traditionally, this process has heavily relied on statistical models historical data. However, emergence (AI) catalyzed transformative shift domain. elucidates underpinnings employed investigates profound implications enhancing accuracy operations. exploration begins delineating pertinent Leveraging processing techniques, offer innovative approaches evaluate creditworthiness. Unlike conventional methods, AI-driven possess capacity ingest vast datasets, identify intricate patterns, adapt dynamically evolving market dynamics. Such capabilities empower banks make more informed timely decisions regarding lending activities. Moreover, practical Through case studies empirical evidence, advanced methodologies enable mitigate risks while maximizing profitability. By harnessing AI, financial institutions can optimize scoring processes, defaulters with greater accuracy, customize terms based individual profiles. Additionally, facilitates real-time monitoring portfolios, allowing proactive management interventions prevent adverse outcomes.
Language: Английский
Citations
46
Electronics, Journal Year: 2024, Volume and Issue: 13(2), P. 416 - 416
Published: Jan. 19, 2024
The concept of learning has multiple interpretations, ranging from acquiring knowledge or skills to constructing meaning and social development. Machine Learning (ML) is considered a branch Artificial Intelligence (AI) develops algorithms that can learn data generalize their judgment new observations by exploiting primarily statistical methods. millennium seen the proliferation Neural Networks (ANNs), formalism able reach extraordinary achievements in complex problems such as computer vision natural language recognition. In particular, designers claim this strong resemblance way biological neurons operate. This work argues although ML mathematical/statistical foundation, it cannot be strictly regarded science, at least methodological perspective. main reason have notable prediction power they necessarily provide causal explanation about achieved predictions. For example, an ANN could trained on large dataset consumer financial information predict creditworthiness. model takes into account various factors like income, credit history, debt, spending patterns, more. It then outputs score decision approval. However, multi-layered nature neural network makes almost impossible understand which specific combinations using arrive its decision. lack transparency problematic, especially if denies applicant wants know reasons for denial. model’s “black box” means clear breakdown how weighed decision-making process. Secondly, rejects belief machine simply data, either supervised unsupervised mode, just applying process much more complex, requires full comprehension learned ability skill. sense, further advancements, reinforcement imitation denote encouraging similarities similar cognitive used human learning.
Language: Английский
Citations
41
Computer Science & IT Research Journal, Journal Year: 2024, Volume and Issue: 5(4), P. 892 - 902
Published: April 17, 2024
This review critically examines the integration of Machine Learning (ML) in drug discovery, highlighting its applications across target identification, hit lead optimization, and predictive toxicology. Despite ML's potential to revolutionize discovery through enhanced efficiency, accuracy, novel insights, significant challenges persist. These include issues related data quality, model interpretability, into existing workflows, regulatory ethical considerations. The advocates for advancements algorithmic approaches, interdisciplinary collaboration, improved data-sharing practices, evolving frameworks as solutions these challenges. By addressing hurdles leveraging capabilities ML, process can be significantly accelerated, paving way development new therapeutics. calls continued research, dialogue among stakeholders realize transformative ML fully. Keywords: Learning, Drug Discovery, Predictive Toxicology, Data Quality, Interdisciplinary Collaboration.
Language: Английский
Citations
37
Advances in educational technologies and instructional design book series, Journal Year: 2024, Volume and Issue: unknown, P. 19 - 52
Published: Oct. 11, 2024
As the digital revolution transforms education, Explainable AI (XAI) plays a key role in advancing educational intelligence. This chapter examines how XAI is reshaping education by making machine learning processes transparent. Unlike traditional AI's “black boxes,” clarifies algorithms make recommendations, assessments, and personalized pathways. transparency helps educators understand trust tools, them effective partners education. The also explores XAI's practical uses adaptive platforms intelligent tutoring systems, showing clarity can enhance environments. It allows to address biases, customize strategies, track outcomes more precisely. Through real-world case studies theoretical insights, illustrates bridges advanced technology with teaching practices, promoting transparent equitable system.
Language: Английский
Citations
34