Towards Risk-Free Trustworthy Artificial Intelligence: Significance and Requirements

International Journal of Intelligent Systems, Journal Year: 2023, Volume and Issue: 2023, P. 1 - 41

Published: Oct. 26, 2023

Given the tremendous potential and influence of artificial intelligence (AI) algorithmic decision-making (DM), these systems have found wide-ranging applications across diverse fields, including education, business, healthcare industries, government, justice sectors. While AI DM offer significant benefits, they also carry risk unfavourable outcomes for users society. As a result, ensuring safety, reliability, trustworthiness becomes crucial. This article aims to provide comprehensive review synergy between DM, focussing on importance trustworthiness. The addresses following four key questions, guiding readers towards deeper understanding this topic: (i) why do we need trustworthy AI? (ii) what are requirements In line with second question, that establish been explained, explainability, accountability, robustness, fairness, acceptance AI, privacy, accuracy, reproducibility, human agency, oversight. (iii) how can data? (iv) priorities in terms challenging applications? Regarding last six different discussed, environmental science, 5G-based IoT networks, robotics architecture, engineering construction, financial technology, healthcare. emphasises address before their deployment order achieve goal good. An example is provided demonstrates be employed eliminate bias resources management systems. insights recommendations presented paper will serve as valuable guide researchers seeking applications.

Language: Английский

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Artificial intelligence-based approaches for improving the diagnosis, triage, and prioritization of autism spectrum disorder: a systematic review of current trends and open issues

Artificial Intelligence Review, Journal Year: 2023, Volume and Issue: 56(S1), P. 53 - 117

Published: June 21, 2023

Language: Английский

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Evaluation of organizational culture in companies for fostering a digital innovation using q-rung picture fuzzy based decision-making model

Advanced Engineering Informatics, Journal Year: 2023, Volume and Issue: 58, P. 102191 - 102191

Published: Sept. 24, 2023

Developing a comprehensive data-driven strategy for evaluating the organisational culture in companies to foster digital innovation involves multi-criteria decision-making (MCDM) problem. This needs consider various characteristics that influence success, assign significance weights each characteristic, and recognise distinct cultures may excel different aspects necessitates proper handling of data variations. Hence, provide organisations seeking align cultural practises with objectives valuable insights, this study aims develop an MCDM model benchmarking innovation. The decision matrix is formulated based on intersection evaluation list companies. developed two phases. Firstly, new weighting model, q-rung picture fuzzy-weighted zero-inconsistency (q-RPFWZIC), assessing under fuzzy sets environment. Secondly, simple additive (SAW) using extracted characteristics. results indicate characteristic C6 (corporate entrepreneurship) has highest weight, value 0.161, while C3 (employee participation, agility organizational structures) C7 (digital awareness necessity innovations) lowest weight 0.088. Company A2 secures top rank score 0.911, satisfying eight characteristics, whereas company A7 holds last order, only one obtaining 0.101. In evaluation, several scenarios were considered sensitivity analysis test 100% increment values validate reliability results.

Language: Английский

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Generative AI and large language models: A new frontier in reverse vaccinology

Informatics in Medicine Unlocked, Journal Year: 2024, Volume and Issue: 48, P. 101533 - 101533

Published: Jan. 1, 2024

Reverse vaccinology is an emerging concept in the field of vaccine development as it facilitates identification potential candidates. Biomedical research has been revolutionized with recent innovations Generative Artificial Intelligence (AI) and Large Language Models (LLMs). The intersection these two technologies explored this study. In study, impact AI LLMs explored. Through a comprehensive analysis existing research, prospective use cases, experimental case highlights that have to enhance efficiency accuracy candidate identification. This work also discusses ethical privacy challenges, such data consent biases, raised by applications require careful consideration. study paves way for experts, researchers, policymakers further investigate role LLM medicine.

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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Optimal Time Window Selection in the Wavelet Signal Domain for Brain–Computer Interfaces in Wheelchair Steering Control

Applied Data Science and Analysis, Journal Year: 2024, Volume and Issue: 2024, P. 69 - 81

Published: June 15, 2024

Background and objective: Principally, the procedure of pattern recognition in terms segmentation plays a significant role BCI-based wheelchair control system for avoiding errors, which can lead to initiation wrong command that will put user unsafe situations. Arguably, each subject might have different motor-imagery signal powers at times trial because he or she could start (or end) performing task slightly time intervals due differences complexities his her brain. Therefore, primary goal this research is develop generic model (GPRM)-based EEG-MI brain-computer interface steering control. Additionally, having simplified well generalized essential based BCI applications. Methods: Initially, bandpass filtering using multiple windows were used denoising finding best duration contains MI feature components. Then, extraction was performed five statistical features, namely minimum, maximum, mean, median, standard deviation, extracting components from wavelet coefficient. seven machine learning methods adopted evaluated find classifiers. Results: The results study showed that, durations time-frequency domain range (4-7 s). Interestingly, GPRM on LR classifier highly accurate, achieved an impressive classification accuracy 85.7%.

Language: Английский

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A Frequency-Domain Pattern Recognition Model for Motor Imagery-Based Brain-Computer Interface

Applied Data Science and Analysis, Journal Year: 2024, Volume and Issue: 2024, P. 82 - 100

Published: June 20, 2024

Brain-computer interface (BCI) is an appropriate technique for totally paralyzed people with a healthy brain. BCI based motor imagery (MI) common approach and widely used in neuroscience, rehabilitation engineering, as well wheelchair control. In control system the procedure of pattern recognition term preprocessing, feature extraction, classification plays significant role performance. Otherwise, errors can lead to wrong command that will put user unsafe conditions. The main objectives this study are develop generic model-based EEG –MI interfaces steering signal filtering, segmentation, multiple time window was de-noising finding MI feedback. five statistical features namely (mean, median, min, max, standard deviation) were extracting frequency domain. classification, seven machine learning towards single hybrid classifier model. For validation, data from Competition dataset (Graz University) validate developed obtained result following: (1) preprocessing perspective it seen two-second optimal (2) have good efficiency EEG-MI (3) Classification using (MLP-LR) perfect domain Finally, be concluded efficient deployed real-time system.

Language: Английский

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Towards Trustworthy Myopia Detection: Integration Methodology of Deep Learning Approach, XAI Visualization, and User Interface System

Applied Data Science and Analysis, Journal Year: 2023, Volume and Issue: unknown, P. 1 - 15

Published: Feb. 23, 2023

Myopia, a prevalent vision disorder with potential complications if untreated, requires early and accurate detection for effective treatment. However, traditional diagnostic methods often lack trustworthiness explainability, leading to biases mistrust. This study presents four-phase methodology develop robust myopia system. In the initial phase, dataset containing training testing images is located, preprocessed, balanced. Subsequently, two models are deployed: pre-trained VGG16 model renowned image classification tasks, sequential CNN convolution layers. Performance evaluation metrics such as accuracy, recall, F1-Score, sensitivity, logloss utilized assess models' effectiveness. The third phase integrates trustworthiness, transparency through application of Explainable Artificial Intelligence (XAI) techniques. Specifically, Local Interpretable Model-Agnostic Explanations (LIME) employed provide insights into decision-making process deep learning model, offering explanations myopic or normal. final user interface implemented XAI bringing together aforementioned phases. outcomes this contribute advancement objective explainable in field detection. Notably, achieves an impressive accuracy 96%, highlighting its efficacy diagnosing myopia. LIME results valuable interpretations cases. proposed enhances transparency, interpretability, trust process.

Language: Английский

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Multimodal Deep Learning in Early Autism Detection—Recent Advances and Challenges

Published: Jan. 23, 2024

Autism spectrum disorder (ASD) is a global concern, with prevalence rate of approximately 1 in 36 children according to estimates from the Centers for Disease Control and Prevention (CDC). Diagnosing ASD poses challenges due absence definitive medical test. Instead, doctors rely on comprehensive evaluation child's developmental background behavior reach diagnosis. Although can occasionally be identified aged 18 months or younger, reliable diagnosis by an experienced professional typically made age two. Early detection crucial timely interventions improved outcomes. In recent years, field early has been greatly impacted emergence deep learning models, which have brought about revolution improving accuracy efficiency detection. The objective this review paper examine progress through utilization multimodal techniques. analysis revealed that integrating multiple modalities, including neuroimaging, genetics, behavioral data, key achieving higher It also evident that, while neuroimaging data holds promise potential contribute detection, it most effective when combined other modalities. Deep their ability analyze complex patterns extract meaningful features large datasets, offer great addressing challenge Among various models used, CNN, DNN, GCN, hybrid exhibited encouraging outcomes ASD. highlights significance developing accurate easily accessible tools utilize artificial intelligence (AI) aid healthcare professionals, parents, caregivers symptom recognition. These would enable interventions, ensuring necessary actions are taken during initial stages.

Language: Английский

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Development of hybrid feature learner model integrating FDOSM for golden subject identification in motor imagery

Physical and Engineering Sciences in Medicine, Journal Year: 2023, Volume and Issue: 46(4), P. 1519 - 1534

Published: Aug. 21, 2023

Language: Английский

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