A systematic review of trustworthy artificial intelligence applications in natural disasters

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 118, P. 109409 - 109409

Published: June 29, 2024

Artificial intelligence (AI) holds significant promise for advancing natural disaster management through the use of predictive models that analyze extensive datasets, identify patterns, and forecast potential disasters. These facilitate proactive measures such as early warning systems (EWSs), evacuation planning, resource allocation, addressing substantial challenges associated with This study offers a comprehensive exploration trustworthy AI applications in disasters, encompassing management, risk assessment, prediction. research is underpinned by an review reputable sources, including Science Direct (SD), Scopus, IEEE Xplore (IEEE), Web (WoS). Three queries were formulated to retrieve 981 papers from earliest documented scientific production until February 2024. After meticulous screening, deduplication, application inclusion exclusion criteria, 108 studies included quantitative synthesis. provides specific taxonomy disasters explores motivations, challenges, recommendations, limitations recent advancements. It also overview techniques developments using explainable artificial (XAI), data fusion, mining, machine learning (ML), deep (DL), fuzzy logic, multicriteria decision-making (MCDM). systematic contribution addresses seven open issues critical solutions essential insights, laying groundwork various future works trustworthiness AI-based management. Despite benefits, persist In these contexts, this identifies several unused used areas disaster-based theory, collects ML, DL techniques, valuable XAI approach unravel complex relationships dynamics involved utilization fusion processes related Finally, extensively analyzed ethical considerations, bias, consequences AI.

Language: Английский

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Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods

Journal of Intelligent Systems, Journal Year: 2024, Volume and Issue: 33(1)

Published: Jan. 1, 2024

Abstract This study aims to perform a thorough systematic review investigating and synthesizing existing research on defense strategies methodologies in adversarial attacks using machine learning (ML) deep methods. A methodology was conducted guarantee literature analysis of the studies sources such as ScienceDirect, Scopus, IEEE Xplore, Web Science. question shaped retrieve articles published from 2019 April 2024, which ultimately produced total 704 papers. rigorous screening, deduplication, matching inclusion exclusion criteria were followed, hence 42 included quantitative synthesis. The considered papers categorized into coherent classification including three categories: security enhancement techniques, attack mechanisms, innovative mechanisms solutions. In this article, we have presented comprehensive earlier opened door potential future by discussing depth four challenges motivations attacks, while recommendations been discussed. science mapping also performed reorganize summarize results address issues trustworthiness. Moreover, covers large variety network cybersecurity applications subjects, intrusion detection systems, anomaly detection, ML-based defenses, cryptographic techniques. relevant conclusions well demonstrate what achieved against attacks. addition, revealed few emerging tendencies deficiencies area be remedied through better more dependable mitigation methods advanced persistent threats. findings crucial implications for community researchers, practitioners, policy makers artificial intelligence applications.

Language: Английский

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Adversarial Attacks in Machine Learning: Key Insights and Defense Approaches

Applied Data Science and Analysis, Journal Year: 2024, Volume and Issue: 2024, P. 121 - 147

Published: Aug. 7, 2024

There is a considerable threat present in genres such as machine learning due to adversarial attacks which include purposely feeding the system with data that will alter decision region. These are committed presenting different models way model would be wrong its classification or prediction. The field of study still relatively young and has develop strong bodies scientific research eliminate gaps current knowledge. This paper provides literature review defenses based on highly cited articles conference published Scopus database. Through assessment 128 systematic articles: 80 original papers 48 till May 15, 2024, this categorizes reviews from domains, Graph Neural Networks, Deep Learning Models for IoT Systems, others. posits findings identified metrics, citation analysis, contributions these studies while suggesting area’s further development robustness’ protection mechanisms. objective work basic background defenses, need maintaining adaptability platforms. In context, contribute building efficient sustainable mechanisms AI applications various industries

Language: Английский

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Fuzzy Evaluation and Benchmarking Framework for Robust Machine Learning Model in Real-Time Autism Triage Applications

International Journal of Computational Intelligence Systems, Journal Year: 2024, Volume and Issue: 17(1)

Published: June 17, 2024

Abstract In the context of autism spectrum disorder (ASD) triage, robustness machine learning (ML) models is a paramount concern. Ensuring ML faces issues such as model selection, criterion importance, trade-offs, and conflicts in evaluation benchmarking models. Furthermore, development must contend with two real-time scenarios: normal tests adversarial attack cases. This study addresses this challenge by integrating three key phases that bridge domains fuzzy multicriteria decision-making (MCDM). First, utilized dataset comprises authentic information, encompassing 19 medical sociodemographic features from 1296 autistic patients who received diagnoses via intelligent triage method. These were categorized into one labels: urgent, moderate, or minor. We employ principal component analysis (PCA) algorithms to fuse large number features. Second, fused forms basis for rigorously testing eight models, considering scenarios, evaluating classifier performance using nine metrics. The third phase developed robust framework encompasses creation decision matrix (DM) 2-tuple linguistic Fermatean opinion score method (2TLFFDOSM) multiple-ML perspectives, accomplished through individual external group aggregation ranks. Our findings highlight effectiveness PCA algorithms, yielding 12 components acceptable variance. ranking, logistic regression (LR) emerged top-performing terms 2TLFFDOSM (1.3370). A comparative five benchmark studies demonstrated superior our across all six checklist comparison points.

Language: Английский

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Exploring Dimensions of Artificial Intelligence in Criminal Investigations and Technological Aspects

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 147 - 162

Published: Feb. 28, 2025

Everything in today's world is related to artificial intelligence. Criminals are becoming advanced and accessing intelligence, causing crime. Hence, there a strict need of intelligence crime investigation, too, because traditional ways can't find criminal who technology fulfill his desires. Numerous technical instruments support law enforcement effectively apprehending offenders. Drones offer surveillance scene views from the air. Surveillance body-worn cameras both record evidence improve accountability. Metal detectors assist locating hidden weapons, predictive analysis foresees stops activity. Forensic investigations aided with lie detectors, DNA, fingerprints. Although AI has shown promise advancing justice number situations, administration's lack know-how prevents reaching its full potential.

Language: Английский

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Refrigerator optimization: Leveraging RESnet method for enhanced storage efficiency

AIP conference proceedings, Journal Year: 2025, Volume and Issue: 3264, P. 040009 - 040009

Published: Jan. 1, 2025

Language: Английский

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Emerging Trends in Applying Artificial Intelligence to Monkeypox Disease: A Bibliometric Analysis

Applied Data Science and Analysis, Journal Year: 2024, Volume and Issue: 2024, P. 148 - 164

Published: Sept. 8, 2024

Monkeypox is a rather rare viral infectious disease that initially did not receive much attention but has recently become subject of concern from the point view public health. Artificial intelligence (AI) techniques are considered beneficial when it comes to diagnosis and identification through medical big data, including imaging other details patients’ information systems. Therefore, this work performs bibliometric analysis incorporate fields AI bibliometrics discuss trends future research opportunities in Monkeypox. A search over various databases was performed title abstracts articles were reviewed, resulting total 251 articles. After eliminating duplicates irrelevant papers, 108 found be suitable for study. In reviewing these studies, given on who contributed topics or fields, what new appeared time, papers most notable. The main added value outline reader process how conduct correct comprehensive by examining real case study related disease. As result, shows great potential improve diagnostics, treatment, health recommendations connected with Possibly, application can enhance responses outcomes since hasten effective interventions.

Language: Английский

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A Systematic Literature Review on Cyber Attack Detection in Software-Define Networking (SDN)

Deleted Journal, Journal Year: 2024, Volume and Issue: 4(3), P. 86 - 135

Published: Nov. 11, 2024

The increasing complexity and sophistication of cyberattacks pose significant challenges to traditional network security tools. Software-defined networking (SDN) has emerged as a promising solution because its centralized management adaptability. However, cyber-attack detection in SDN settings remains vital issue. current literature lacks comprehensive assessment methods including preparation techniques, benefits types attacks analysed datasets. This gap hinders the understanding strengths weaknesses various approaches. systematic review aims examine cyberattack detection, identify strengths, weaknesses, gaps existing suggest future research directions this critical area. A approach was used analyse techniques from 2017--2024. conducted address these provide different methods. study classified on planes, datasets, discussed feature selection methods, evaluated approaches such entropy, machine learning (ML), deep (DL), federated (FL), assessed metrics for evaluating defense mechanisms against cyberattacks. emphasized importance developing SDN-specific datasets using advanced algorithms. It also provides valuable insights into state-of-the-art detecting cyber-attacks outlines roadmap identified further exploration specific areas increase cybersecurity environments.

Language: Английский

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Intelligent arbitration of DAOs disputes

مجلة العلوم القانونية, Journal Year: 2024, Volume and Issue: 39(1), P. 408 - 451

Published: June 15, 2024

التحكيم الذكي في منازعات المنظمات اللامركزية المستقلة DAOs يستخدم تكنولوجيا Block chain والعقود الذكية لتوفير حلول فعالة لتسوية النزاعات ضمن بيئة الاقتصاد الرقمي المعقد، إذ أن (Daos) ككيانات لامركزية تعمل على أساس بروتوكولات chain، تقدم نموذجًا جديدًا للحوكمة والتعاقد الذي يتحدى الأطر القانونية التقليدية، ونظرًا لطبيعتها المجهولة وعدم ارتباطها بأي ولاية قضائية محددة، تواجه هذه صعوبات تحديد الاختصاص حالات النزاع، الا القائم الأتمتة والشفافية التي توفرها العقود الذكية، يمكن يسهل تنفيذ القرارات التحكيمية بشكل سريع وفعال، يقدم حلولاً تتجاوز الحدود التقليدية للقضاء، حيث الإجراءات وفقًا للأكواد المبرمجة مما يمنح طرفي النزاع إمكانية الوصول إلى عادلة دون الحاجة للتقاضي التقليدي، هذا النهج يفتح آفاقًا جديدة للعدالة العصر ويقترح بدائل مبتكرة للتعامل مع التحديات المعاصرة.

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