Explainable AI in Healthcare Application

Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 123 - 176

Published: Jan. 18, 2024

Given the inherent risks in medical decision-making, professionals carefully evaluate a patient's symptoms before arriving at plausible diagnosis. For AI to be widely accepted and useful technology, it must replicate human judgment interpretation abilities. XAI attempts describe data underlying black-box approach of deep learning (DL), machine (ML), natural language processing (NLP) that explain how judgments are made. This chapter provides survey most recent methods employed imaging related fields, categorizes lists types XAI, highlights used make topics more interpretable. Additionally, focuses on challenging issues applications guides development better deep-learning system explanations by applying principles analysis pictures text.

Language: Английский

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Analysing Influential Factors in Student Academic Achievement: Prediction Modelling and Insight

International Journal of Emerging Multidisciplinaries Computer Science & Artificial Intelligence, Journal Year: 2023, Volume and Issue: 2(1)

Published: Nov. 25, 2023

The fascination with understanding student academic performance has drawn widespread attention from various stakeholders, including parents, policymakers, and businesses. 'Students Performance in Exams' dataset, available on platforms like Kaggle, stands as a treasure trove. It extends beyond test scores, encompassing diverse attributes ethnicity, gender, parental education, preparation, even lunch type. In our tech-driven age, predicting success become compelling pursuit. This study aims to delve deep into this utilizing data mining methods robust classification algorithms Logistic Regression Random Forest Jupyter Notebook environment. Rigorous model training, testing, fine-tuning strive for the utmost predictive accuracy. Data cleaning preprocessing play crucial role establishing reliable dataset accurate predictions. Beyond numbers, project emphasizes visualization's impact, transforming raw comprehensible insights effective communication. Model exhibits an impressive 87.6% accuracy, highlighting its potential performance. Moreover, excels remarkable 100% accuracy forecasting grades, showcasing effectiveness domain.

Language: Английский

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Explainable AI for Cybersecurity

Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 31 - 97

Published: Jan. 18, 2024

In recent years, the utilization of AI in field cybersecurity has become more widespread. Black-box models pose a significant challenge terms interpretability and transparency, which is one major drawbacks AI-based systems. This chapter explores explainable (XAI) techniques as solution to these challenges discusses their application cybersecurity. The begins with an explanation cybersecurity, including types commonly utilized, such DL, ML, NLP, applications intrusion detection, malware analysis, vulnerability assessment. then highlights black-box AI, difficulty identifying resolving errors, lack inability understand decision-making process. delves into XAI for solutions, interpretable machine-learning models, rule-based systems, model techniques.

Language: Английский

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Swarm Security

Advances in information security, privacy, and ethics book series, Journal Year: 2024, Volume and Issue: unknown, P. 324 - 342

Published: Jan. 26, 2024

An era marked by revolutionary advancements in miniaturization and artificial intelligence has ushered the age of drone swarms. These formations characterized coordinated fleets miniature aerial vehicles (MAVs) offer undeniable potential for unparalleled efficiency diverse applications. Therefore, field “swarm security” delves into how swarms have reshaped security landscape. Compared to lone drones, infected drones within a swarm could potentially seize control entire group, posing chilling risk attacks on critical infrastructure or densely populated areas. To strengthen cybersecurity frameworks, this study investigates intertwined complexities legal frameworks technological advancements. The authors explore modern defenses like signal jamming AI-powered threat detection, while also raising ethical concerns about weapons responsible use. Moreover, future where is harnessed benefit society been explored through understanding mitigating risks.

Language: Английский

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Securing the Internet of Things in Logistics

Advances in logistics, operations, and management science book series, Journal Year: 2024, Volume and Issue: unknown, P. 133 - 165

Published: April 12, 2024

Internet of things (IoT), a network interconnected devices capable collecting, storing, analyzing, and transmitting data, has garnered significant attention. Its widespread adoption transformed various industries, including healthcare, transportation, manufacturing, agriculture, owing to its numerous benefits innovative potential. However, the rapid expansion IoT raised concerns about security, presenting unique challenges compared traditional information technology (IT) platforms. Securing environment is particularly challenging due inherent constraints in devices, such as limited resources, well diverse range with varying capabilities communication protocols. The decentralized nature adds complexity ensuring security. Consequently, employing conventional host-based security techniques like anti-virus anti-malware software deemed impractical inefficient.

Language: Английский

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Digital Safeguards

Advances in information security, privacy, and ethics book series, Journal Year: 2024, Volume and Issue: unknown, P. 258 - 299

Published: Feb. 2, 2024

The rapid integration of internet and technological advancements over the past two decades has reshaped global landscape, leading to transformative changes in various sectors.The emergence Industry 4.0, conceptualized 2011, particularly revolutionized manufacturing logistics by advocating for systematic digitalization technology enhance efficiency reduce costs. However, this evolution, while fostering process optimization, also introduces vulnerabilities cyber threats. sector, heavily reliant on interconnected systems like things, faces potential risks from cyberattacks that target sensitive data across supply chain. This chapter aims address intricate relationship between emphasizing crucial role digital safeguards navigating dangers. Furthermore, delves into significance visibility supply-chain operations explores technologies practices enhancing visibility.

Language: Английский

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Industry 4.0

Advances in logistics, operations, and management science book series, Journal Year: 2024, Volume and Issue: unknown, P. 54 - 69

Published: April 12, 2024

Industry 4.0 is revolutionizing manufacturing and supply chain management through the integration of advanced digital technologies. This chapter provides an overview its implications for sustainable chains. Through interconnected systems, automation, artificial intelligence, additive manufacturing, enhances efficiency, agility, transparency in operations. The explores how technologies contribute to resource energy waste reduction, transparency, social responsibility Challenges opportunities associated with implementing are discussed, along best practices case studies showcasing successful implementations. By embracing 4.0, businesses can create more efficient chains, contributing a greener future.

Language: Английский

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Navigating Challenges and Harnessing Opportunities: Deep Learning Applications in Internet of Medical Things

Future Internet, Journal Year: 2025, Volume and Issue: 17(3), P. 107 - 107

Published: March 1, 2025

Integrating deep learning (DL) with the Internet of Medical Things (IoMT) is a paradigm shift in modern healthcare, offering enormous opportunities for patient care, diagnostics, and treatment. Implementing DL IoMT has potential to deliver better diagnosis, treatment, management. However, practical implementation challenges, including data quality, privacy, interoperability, limited computational resources. This survey article provides conceptual framework synthesizes identifies state-of-the-art solutions that tackle challenges current applications DL, analyzes existing limitations future developments. Through an analysis case studies real-world implementations, this work insights into best practices lessons learned, importance robust preprocessing, integration legacy systems, human-centric design. Finally, we outline research directions, emphasizing development transparent, scalable, privacy-preserving models realize full healthcare. aims serve as foundational reference researchers practitioners seeking navigate harness rapidly evolving field.

Language: Английский

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Smart Factories Greener Future

Advances in logistics, operations, and management science book series, Journal Year: 2024, Volume and Issue: unknown, P. 70 - 92

Published: April 12, 2024

The integration of Industry 4.0 and sustainability has paved the way for transformative practices in manufacturing, leading to development smart factories with a focus on greener futures. This chapter explores synergy technologies sustainability. It emphasizes collective potential two paradigms optimization production processes, energy efficiency, waste management. including artificial intelligence, internet things, big data analytics, robotics, automation is playing pivotal role transforming manufacturing processes enabling environmentally sustainable operations. aligning goals led eco-efficiency In this chapter, responsible sourcing, circular economy, management have been investigated along implementation opportunities challenges.

Language: Английский

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IoT-based Smart Farming System

International Journal of Emerging Multidisciplinaries Computer Science & Artificial Intelligence, Journal Year: 2024, Volume and Issue: 3(1)

Published: April 30, 2024

The integration of Internet Things (IoT) technologies into agricultural operations, known as smart farming, presents a transformative opportunity to revolutionize traditional farming methodologies and bolster productivity, efficiency, sustainability within the sector. This paper investigates challenges inherent conventional practices, such inefficient resource utilization inadequate access real-time data inform decision-making. By leveraging an array IoT sensors devices are utilized for purpose gathering up-to-date information on various aspects environment factors animal natural behaviors, producers can gain actionable insights, facilitating data-driven decision-making optimize usage enhance crop yields. primary objectives this study encompass enabling automation precision agriculture mitigate waste while concurrently emphasizing remote monitoring control capabilities through mobile augment overall operational efficiency quality. background underscores critical importance integrating practices streamline farm management processes, reduce labour requirements, increase profitability across all scales operations. Through implementation IoT-enabled solutions, endeavors bridge divide between advanced technology practical needs, offering cost-effective user-friendly approach modernizing methodologies.

Language: Английский

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