3,758 results
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2. LA INTEL·LIGÈNCIA ARTIFICIAL EN LA DETECCIÓ DE LES PRÀCTIQUES DE BID RIGGING: EL PAPER CAPDAVANTER DE L'ACCO.
- Author
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Jiménez Cardona, Noemí
- Subjects
GOVERNMENT purchasing ,ARTIFICIAL intelligence ,ANTITRUST law ,SOFTWARE development tools ,CARTELS - Abstract
Copyright of Revista Catalana de Dret Públic is the property of Revista Catalana de Dret Public and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2022
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3. Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches--A Systematic Literature Review and Mapping Study.
- Author
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García-Peñalvo, Francisco José, Vázquez-Ingelmo, Andrea, and García-Holgado, Alicia
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ARTIFICIAL intelligence ,LITERATURE reviews ,SOFTWARE engineering ,ALGORITHMS ,HEURISTIC ,SOFTWARE engineers - Abstract
The exponential use of artificial intelligence (AI) to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be addressed. While AI is a powerful means to discover interesting patterns and obtain predictive models, the use of these algorithms comes with a great responsibility, as an incomplete or unbalanced set of training data or an unproper interpretation of the models' outcomes could result in misleading conclusions that ultimately could become very dangerous. For these reasons, it is important to rely on expert knowledge when applying these methods. However, not every user can count on this specific expertise; non-AI-expert users could also benefit from applying these powerful algorithms to their domain problems, but they need basic guidelines to obtain the most out of AI models. The goal of this work is to present a systematic review of the literature to analyze studies whose outcomes are explainable rules and heuristics to select suitable AI algorithms given a set of input features. The systematic review follows the methodology proposed by Kitchenham and other authors in the field of software engineering. As a result, 9 papers that tackle AI algorithm recommendation through tangible and traceable rules and heuristics were collected. The reduced number of retrieved papers suggests a lack of reporting explicit rules and heuristics when testing the suitability and performance of AI algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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4. Guest editorial: AI for computational audition—sound and music processing.
- Author
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Li, Zijin, Wang, Wenwu, Zhang, Kejun, and Zhu, Mengyao
- Subjects
ARTIFICIAL intelligence ,INTERDISCIPLINARY research ,TRANSVERSAL lines ,ALGORITHMS - Abstract
Nowadays, the application of artificial intelligence (AI) algorithms and techniques is ubiquitous and transversal. Fields that take advantage of AI advances include sound and music processing. The advances in interdisciplinary research potentially yield new insights that may further advance the AI methods in this field. This special issue aims to report recent progress and spur new research lines in AI-driven sound and music processing, especially within interdisciplinary research scenarios. [ABSTRACT FROM AUTHOR]
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- 2024
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5. Smart Random Walk Distributed Secured Edge Algorithm Using Multi-Regression for Green Network.
- Author
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Saba, Tanzila, Haseeb, Khalid, Rehman, Amjad, Damaševičius, Robertas, and Bahaj, Saeed Ali
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RANDOM walks ,ALGORITHMS ,ARTIFICIAL intelligence ,INTERNET of things ,ELECTRONIC paper ,INTERNET traffic - Abstract
Smart communication has significantly advanced with the integration of the Internet of Things (IoT). Many devices and online services are utilized in the network system to cope with data gathering and forwarding. Recently, many traffic-aware solutions have explored autonomous systems to attain the intelligent routing and flowing of internet traffic with the support of artificial intelligence. However, the inefficient usage of nodes' batteries and long-range communication degrades the connectivity time for the deployed sensors with the end devices. Moreover, trustworthy route identification is another significant research challenge for formulating a smart system. Therefore, this paper presents a smart Random walk Distributed Secured Edge algorithm (RDSE), using a multi-regression model for IoT networks, which aims to enhance the stability of the chosen IoT network with the support of an optimal system. In addition, by using secured computing, the proposed architecture increases the trustworthiness of smart devices with the least node complexity. The proposed algorithm differs from other works in terms of the following factors. Firstly, it uses the random walk to form the initial routes with certain probabilities, and later, by exploring a multi-variant function, it attains long-lasting communication with a high degree of network stability. This helps to improve the optimization criteria for the nodes' communication, and efficiently utilizes energy with the combination of mobile edges. Secondly, the trusted factors successfully identify the normal nodes even when the system is compromised. Therefore, the proposed algorithm reduces data risks and offers a more reliable and private system. In addition, the simulations-based testing reveals the significant performance of the proposed algorithm in comparison to the existing work. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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6. AI GODS, JEANS GODS, AND THRIFT GODS: RESPONDING TO RESPONSES TO THE BLESSED BY THE ALGORITHM PAPER (SINGLER 2020).
- Author
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Singler, Beth
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GODS ,ARTIFICIAL intelligence ,ALGORITHMS ,THRIFT institutions - Published
- 2023
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7. A Review on Federated Learning and Machine Learning Approaches: Categorization, Application Areas, and Blockchain Technology.
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Ogundokun, Roseline Oluwaseun, Misra, Sanjay, Maskeliunas, Rytis, and Damasevicius, Robertas
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BLOCKCHAINS ,ARTIFICIAL intelligence ,MACHINE learning ,CONFERENCE papers ,ALGORITHMS ,SCIENCE publishing - Abstract
Federated learning (FL) is a scheme in which several consumers work collectively to unravel machine learning (ML) problems, with a dominant collector synchronizing the procedure. This decision correspondingly enables the training data to be distributed, guaranteeing that the individual device's data are secluded. The paper systematically reviewed the available literature using the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guiding principle. The study presents a systematic review of appliable ML approaches for FL, reviews the categorization of FL, discusses the FL application areas, presents the relationship between FL and Blockchain Technology (BT), and discusses some existing literature that has used FL and ML approaches. The study also examined applicable machine learning models for federated learning. The inclusion measures were (i) published between 2017 and 2021, (ii) written in English, (iii) published in a peer-reviewed scientific journal, and (iv) Preprint published papers. Unpublished studies, thesis and dissertation studies, (ii) conference papers, (iii) not in English, and (iv) did not use artificial intelligence models and blockchain technology were all removed from the review. In total, 84 eligible papers were finally examined in this study. Finally, in recent years, the amount of research on ML using FL has increased. Accuracy equivalent to standard feature-based techniques has been attained, and ensembles of many algorithms may yield even better results. We discovered that the best results were obtained from the hybrid design of an ML ensemble employing expert features. However, some additional difficulties and issues need to be overcome, such as efficiency, complexity, and smaller datasets. In addition, novel FL applications should be investigated from the standpoint of the datasets and methodologies. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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8. The Indian approach to Artificial Intelligence: an analysis of policy discussions, constitutional values, and regulation.
- Author
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Biju, P. R. and Gayathri, O.
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India has produced several drafts of data policies. In this work, they are referred to [1] JBNSCR 2018, [2] DPDPR 2018, [3] NSAI 2018, [4] RAITF 2018, [5] PDPB 2019, [6] PRAI 2021, [7] JPCR 2021, [8] IDAUP 2022, [9] IDABNUP 2022. All of them consider Artificial Intelligence (AI) a social problem solver at the societal level, let alone an incentive for economic growth. However, these policy drafts warn of the social disruptions caused by algorithms and encourage the careful use of computational technologies in various social contexts. Hence, the emerging data society and its implications in India's social contexts demand immense social science attention, which needs to be improved in the policy drafts, primarily because they are creations of industry stakeholders, technocrats, bureaucrats, and experts from tech schools. In the larger social milieu of digital infrastructure emerging, the fundamental question is whether India's national philosophy envisioned in the Indian constitution is reflected in the policy papers. The paper enquires whether the national data policy upholds the core values dispersed through the philosophy of the Indian constitution, which, among other things, is not confined only to inclusion, diversity, rights, liberty, justice and equality. By focusing on constitutional values, the paper seeks to offer a broader and more critical understanding of India's approach to AI policy by bringing together analyses of a wide array of policy documents available in the public realm. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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9. Reproducibility of Deep Learning Algorithms Developed for Medical Imaging Analysis: A Systematic Review.
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Moassefi, Mana, Rouzrokh, Pouria, Conte, Gian Marco, Vahdati, Sanaz, Fu, Tianyuan, Tahmasebi, Aylin, Younis, Mira, Farahani, Keyvan, Gentili, Amilcare, Kline, Timothy, Kitamura, Felipe C., Huo, Yuankai, Kuanar, Shiba, Younis, Khaled, Erickson, Bradley J., and Faghani, Shahriar
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DEEP learning ,RESEARCH evaluation ,SYSTEMATIC reviews ,ARTIFICIAL intelligence ,DIAGNOSTIC imaging ,DESCRIPTIVE statistics ,ALGORITHMS ,WORLD Wide Web - Abstract
Since 2000, there have been more than 8000 publications on radiology artificial intelligence (AI). AI breakthroughs allow complex tasks to be automated and even performed beyond human capabilities. However, the lack of details on the methods and algorithm code undercuts its scientific value. Many science subfields have recently faced a reproducibility crisis, eroding trust in processes and results, and influencing the rise in retractions of scientific papers. For the same reasons, conducting research in deep learning (DL) also requires reproducibility. Although several valuable manuscript checklists for AI in medical imaging exist, they are not focused specifically on reproducibility. In this study, we conducted a systematic review of recently published papers in the field of DL to evaluate if the description of their methodology could allow the reproducibility of their findings. We focused on the Journal of Digital Imaging (JDI), a specialized journal that publishes papers on AI and medical imaging. We used the keyword "Deep Learning" and collected the articles published between January 2020 and January 2022. We screened all the articles and included the ones which reported the development of a DL tool in medical imaging. We extracted the reported details about the dataset, data handling steps, data splitting, model details, and performance metrics of each included article. We found 148 articles. Eighty were included after screening for articles that reported developing a DL model for medical image analysis. Five studies have made their code publicly available, and 35 studies have utilized publicly available datasets. We provided figures to show the ratio and absolute count of reported items from included studies. According to our cross-sectional study, in JDI publications on DL in medical imaging, authors infrequently report the key elements of their study to make it reproducible. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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10. Construction of Personalized Learning Platform Based on Collaborative Filtering Algorithm.
- Author
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Zhang, Qian
- Subjects
ARTIFICIAL intelligence ,DATABASE design ,ALGORITHMS ,RECOMMENDER systems ,ELECTRONIC paper - Abstract
On the network service platform for vocational education, there are currently over 10,000 online courses. Learners face a challenge in selecting interesting courses from the vast resources available. Learners' urgent need for personalized learning is becoming more apparent as educational informatization progresses. Personalized recommendation (PR) technology can aid personalized learning and increase learners' learning efficiency significantly. This paper constructs a smart classroom model based on AI (artificial intelligence) by studying the connotation and characteristics of smart classroom in light of the current research status and trend of smart classroom at home and abroad. The merits of the recommendation system are determined by the recommendation algorithm used by PR system. This paper primarily focuses on developing a personalized learning platform based on the CF (collaborative filtering) algorithm, as well as conducting system requirements analysis, database design, functional module design, implementation, and testing on this foundation. Experiments are carried out to see if the optimized PR algorithm in the network learning platform is effective. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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11. A review on over-sampling techniques in classification of multi-class imbalanced datasets: insights for medical problems.
- Author
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Yuxuan Yang, Khorshidi, Hadi Akbarzadeh, and Aickelin, Uwe
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DATABASE management ,PREDICTION models ,MEDICAL informatics ,STATISTICAL sampling ,ARTIFICIAL intelligence ,RESEARCH bias ,MACHINE learning ,ALGORITHMS - Abstract
There has been growing attention to multi-class classification problems, particularly those challenges of imbalanced class distributions. To address these challenges, various strategies, including data-level re-sampling treatment and ensemble methods, have been introduced to bolster the performance of predictive models and Artificial Intelligence (AI) algorithms in scenarios where excessive level of imbalance is present. While most research and algorithm development have been focused on binary classification problems, in health informatics there is an increased interest in the field to address the problem of multi-class classification in imbalanced datasets. Multi-class imbalance problems bring forth more complex challenges, as a delicate approach is required to generate synthetic data and simultaneously maintain the relationship between the multiple classes. The aim of this review paper is to examine over-sampling methods tailored for medical and other datasets with multi-class imbalance. Out of 2,076 peer-reviewed papers identified through searches, 197 eligible papers were chosen and thoroughly reviewed for inclusion, narrowing to 37 studies being selected for in-depth analysis. These studies are categorised into four categories: metric, adaptive, structure-based, and hybrid approaches. The most significant finding is the emerging trend toward hybrid resampling methods that combine the strengths of various techniques to effectively address the problem of imbalanced data. This paper provides an extensive analysis of each selected study, discusses their findings, and outlines directions for future research. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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12. Physics driven behavioural clustering of free-falling paper shapes.
- Author
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Howison, Toby, Hughes, Josie, Giardina, Fabio, and Iida, Fumiya
- Subjects
PHYSICS ,SET functions ,MACHINE learning ,PHENOMENOLOGICAL theory (Physics) ,CONTINUUM mechanics - Abstract
Many complex physical systems exhibit a rich variety of discrete behavioural modes. Often, the system complexity limits the applicability of standard modelling tools. Hence, understanding the underlying physics of different behaviours and distinguishing between them is challenging. Although traditional machine learning techniques could predict and classify behaviour well, typically they do not provide any meaningful insight into the underlying physics of the system. In this paper we present a novel method for extracting physically meaningful clusters of discrete behaviour from limited experimental observations. This method obtains a set of physically plausible functions that both facilitate behavioural clustering and aid in system understanding. We demonstrate the approach on the V-shaped falling paper system, a new falling paper type system that exhibits four distinct behavioural modes depending on a few morphological parameters. Using just 49 experimental observations, the method discovered a set of candidate functions that distinguish behaviours with an error of 2.04%, while also aiding insight into the physical phenomena driving each behaviour. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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13. Applying Machine Learning in Marketing: An Analysis Using the NMF and k-Means Algorithms.
- Author
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Gallego, Victor, Lingan, Jessica, Freixes, Alfons, Juan, Angel A., and Osorio, Celia
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K-means clustering ,MACHINE learning ,ARTIFICIAL intelligence ,ADVERTISING effectiveness ,DATABASES - Abstract
The integration of machine learning (ML) techniques into marketing strategies has become increasingly relevant in modern business. Utilizing scientific manuscripts indexed in the Scopus database, this article explores how this integration is being carried out. Initially, a focused search is undertaken for academic articles containing both the terms "machine learning" and "marketing" in their titles, which yields a pool of papers. These papers have been processed using the Supabase platform. The process has included steps like text refinement and feature extraction. In addition, our study uses two key ML methodologies: topic modeling through NMF and a comparative analysis utilizing the k-means clustering algorithm. Through this analysis, three distinct clusters emerged, thus clarifying how ML techniques are influencing marketing strategies, from enhancing customer segmentation practices to optimizing the effectiveness of advertising campaigns. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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14. USING EVOLUTIONARY ALGORITHMS TO OPTIMIZE ANTHROPOGENIC MATERIAL STREAMS.
- Author
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Pollmann, Olaf
- Subjects
ALGORITHMS ,ALGEBRA ,ARTIFICIAL intelligence ,INTELLIGENT agents ,MACHINE theory - Abstract
To optimize anthropogenic material streams, the production process, as well as the quality of the products, must be known. With knowledge of these requirements, it is possible to use extra applied algorithms—in this case evolutionary algorithms as part of artificial intelligence—for the optimization of these secondary material streams. The benefit of this application is the fast and precise calculation of the local and global optima of the optimizing problem. This calculation method uses the benefits of the biological reproduction by applications of mutation, selection, and recombination to find one of the best results in a huge amount of possible and potential results. For the use of secondary materials in the paper production it could be proven that in spite of high quotes of secondary materials in different paper classes, there are some paper classes in which the amount of secondary material could be raised without losing any quality. [ABSTRACT FROM AUTHOR]
- Published
- 2009
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15. Path planning and collision avoidance for autonomous surface vehicles II: a comparative study of algorithms.
- Author
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Vagale, Anete, Bye, Robin T., Oucheikh, Rachid, Osen, Ottar L., and Fossen, Thor I.
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PROBLEM solving ,ALGORITHMS ,COLLISIONS at sea ,AUTONOMOUS vehicles ,COMPARATIVE studies ,ARTIFICIAL intelligence ,EVOLUTIONARY algorithms - Abstract
Artificial intelligence is an enabling technology for autonomous surface vehicles, with methods such as evolutionary algorithms, artificial potential fields, fast marching methods, and many others becoming increasingly popular for solving problems such as path planning and collision avoidance. However, there currently is no unified way to evaluate the performance of different algorithms, for example with regard to safety or risk. This paper is a step in that direction and offers a comparative study of current state-of-the art path planning and collision avoidance algorithms for autonomous surface vehicles. Across 45 selected papers, we compare important performance properties of the proposed algorithms related to the vessel and the environment it is operating in. We also analyse how safety is incorporated, and what components constitute the objective function in these algorithms. Finally, we focus on comparing advantages and limitations of the 45 analysed papers. A key finding is the need for a unified platform for evaluating and comparing the performance of algorithms under a large set of possible real-world scenarios. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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- View/download PDF
16. Predicting translational progress in biomedical research.
- Author
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Hutchins, B. Ian, Davis, Matthew T., Meseroll, Rebecca A., and Santangelo, George M.
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MEDICAL research ,SCIENTIFIC community ,SCIENTIFIC discoveries ,MACHINE learning ,CLINICAL trials ,FALSE discovery rate ,THERAPEUTICS - Abstract
Fundamental scientific advances can take decades to translate into improvements in human health. Shortening this interval would increase the rate at which scientific discoveries lead to successful treatment of human disease. One way to accomplish this would be to identify which advances in knowledge are most likely to translate into clinical research. Toward that end, we built a machine learning system that detects whether a paper is likely to be cited by a future clinical trial or guideline. Despite the noisiness of citation dynamics, as little as 2 years of postpublication data yield accurate predictions about a paper's eventual citation by a clinical article (accuracy = 84%, F1 score = 0.56; compared to 19% accuracy by chance). We found that distinct knowledge flow trajectories are linked to papers that either succeed or fail to influence clinical research. Translational progress in biomedicine can therefore be assessed and predicted in real time based on information conveyed by the scientific community's early reaction to a paper. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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17. Normalised fuzzy index for research ranking.
- Author
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Hedar, Abdel-Rahman, Abdel-Hakima, Alaa, and Alotaibi, Youseef
- Subjects
ALGORITHMS ,ARTIFICIAL intelligence ,BIBLIOMETRICS ,IMMUNOLOGY ,RESEARCH methodology ,MOLECULAR biology ,SERIAL publications ,BIBLIOGRAPHIC databases ,STRUCTURAL equation modeling ,ACQUISITION of data ,DESCRIPTIVE statistics ,MANN Whitney U Test - Abstract
There are great interests of designing research metrics and indices to measure the research impacts in research institutes. Unfortunately, most of those indices ignore critical design issues, e.g. the disparity between domains, the impact of journals or conferences in which papers are published, normalising the range of the index values to certain intervals, and the scalability of using the index to rank different research entities. In this paper, a new normalised fuzzy index, (NF
index ), is proposed as a fuzzy-based research impact metric. The proposed index is a scalable index whose values are normalised to the percentage levels. NFindex achieves both inter-discipline normalisation and intra-discipline consistency. The capability of NFindex to achieve the inter-discipline normalisation enables fair comparison between different research domains regardless their nature in terms of influence and contribution to other research areas, e.g. natural science. Therefore, NFindex gives a universal normalised single-number metric that can be used by research institutes to solve the problem of inter-discipline scholar ranking. Moreover, it can help universal ranking of universities and research institutes according to their research capabilities and impacts. The obtained results, on diverse research areas, prove the potential of NFindex in terms of both intra-discipline consistency and inter-discipline normalisation. [ABSTRACT FROM AUTHOR]- Published
- 2018
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18. What do algorithms explain? The issue of the goals and capabilities of Explainable Artificial Intelligence (XAI).
- Author
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Renftle, Moritz, Trittenbach, Holger, Poznic, Michael, and Heil, Reinhard
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ARTIFICIAL intelligence ,MACHINE learning ,ALGORITHMS - Abstract
The increasing ubiquity of machine learning (ML) motivates research on algorithms to "explain" models and their predictions—so-called Explainable Artificial Intelligence (XAI). Despite many publications and discussions, the goals and capabilities of such algorithms are far from being well understood. We argue that this is because of a problematic reasoning scheme in the literature: Such algorithms are said to complement machine learning models with desired capabilities, such as interpretability or explainability. These capabilities are in turn assumed to contribute to a goal, such as trust in a system. But most capabilities lack precise definitions and their relationship to such goals is far from obvious. The result is a reasoning scheme that obfuscates research results and leaves an important question unanswered: What can one expect from XAI algorithms? In this paper, we clarify the modest capabilities of these algorithms from a concrete perspective: that of their users. We show that current algorithms can only answer user questions that can be traced back to the question: "How can one represent an ML model as a simple function that uses interpreted attributes?". Answering this core question can be trivial, difficult or even impossible, depending on the application. The result of the paper is the identification of two key challenges for XAI research: the approximation and the translation of ML models. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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19. Data Mining Algorithm Based on Fusion Computer Artificial Intelligence Technology.
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Yingqian Bai, Kepeng Bao, and Tao Xu
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ARTIFICIAL intelligence ,DATA mining ,ALGORITHMS ,DISTRIBUTED databases ,ENTROPY (Information theory) - Abstract
INTRODUCTION: The paper constructs a massive data mining model of distributed spatiotemporal databases for the Internet of Things. Then a homologous data fusion method based on information entropy is proposed. The storage space required by the tree structure is reduced by constructing the data schema tree of the merged data set. Secondly, the optimal dynamic support degree is obtained by using a neural network and genetic algorithm. Frequent items in the Internet of Things data are mined to achieve the normalization of the clustered feature data based on the threshold value. Experiments show that the F-measure of the data mining algorithm improves the efficiency by 15.64% and 18.25% compared with the kinds of other literatures respectively. RI increased by 21.17% and 26.07%, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
20. Regulatory responses and approval status of artificial intelligence medical devices with a focus on China.
- Author
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Liu, Yuehua, Yu, Wenjin, and Dillon, Tharam
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MEDICAL equipment standards ,MEDICAL protocols ,ARTIFICIAL intelligence ,COMPUTED tomography ,HOSPITAL radiological services ,CONCEPTUAL structures ,DEEP learning ,COMPUTER-aided diagnosis ,QUALITY assurance ,GOVERNMENT regulation ,NEW product development laws ,ALGORITHMS - Abstract
This paper focuses on how regulatory bodies respond to artificial intelligence (AI)-enabled medical devices. To achieve this, we present a comparative overview of the United States (USA), European Union (EU), and China. Our search in the governmental database identified 59 AI medical devices approved in China as of July 2023. In comparison to the rules-based regulatory approach in China, the approaches in the USA and EU are more standards-oriented. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
21. Overview of Pest Detection and Recognition Algorithms.
- Author
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Guo, Boyu, Wang, Jianji, Guo, Minghui, Chen, Miao, Chen, Yanan, and Miao, Yisheng
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ARTIFICIAL intelligence ,CROP growth ,FOOD production ,PESTS ,DEEP learning ,ALGORITHMS - Abstract
Detecting and recognizing pests are paramount for ensuring the healthy growth of crops, maintaining ecological balance, and enhancing food production. With the advancement of artificial intelligence technologies, traditional pest detection and recognition algorithms based on manually selected pest features have gradually been substituted by deep learning-based algorithms. In this review paper, we first introduce the primary neural network architectures and evaluation metrics in the field of pest detection and pest recognition. Subsequently, we summarize widely used public datasets for pest detection and recognition. Following this, we present various pest detection and recognition algorithms proposed in recent years, providing detailed descriptions of each algorithm and their respective performance metrics. Finally, we outline the challenges that current deep learning-based pest detection and recognition algorithms encounter and propose future research directions for related algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
22. Clinical Pearl: The Clinical Relevance of Neonatal Informatics.
- Author
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Falciglia, Gustave H., Hageman, Joseph R., Hussain, Walid, Alkureishi, Lolita Alcocer, Shah, Kshama, and Goldstein, Mitchell
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MEDICAL logic ,CRITICALLY ill ,PATIENTS ,ARTIFICIAL intelligence ,NEONATAL intensive care units ,ACUTE kidney failure in children ,COMPUTER science ,NEONATAL intensive care ,HOSPITAL nurseries ,INFORMATION science ,ELECTRONIC health records ,WATER-electrolyte balance (Physiology) ,QUALITY assurance ,ALGORITHMS ,CHILDREN - Abstract
The article focuses on the importance of clinical informatics in neonatal care, highlighting its potential to provide critical resources for clinicians. Topics include the specialized data needed for neonatal care, the challenges in transitioning from paper to electronic health records, and the impact of informatics on real-time patient management and research.
- Published
- 2024
23. Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective.
- Author
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Niño, Stephanie Batista, Bernardino, Jorge, and Domingues, Inês
- Subjects
COMPUTED tomography ,IMAGE processing ,COMPUTER-assisted image analysis (Medicine) ,ARTIFICIAL intelligence ,ALGORITHMS ,IMAGE reconstruction algorithms - Abstract
Oncology has emerged as a crucial field of study in the domain of medicine. Computed tomography has gained widespread adoption as a radiological modality for the identification and characterisation of pathologies, particularly in oncology, enabling precise identification of affected organs and tissues. However, achieving accurate liver segmentation in computed tomography scans remains a challenge due to the presence of artefacts and the varying densities of soft tissues and adjacent organs. This paper compares artificial intelligence algorithms and traditional medical image processing techniques to assist radiologists in liver segmentation in computed tomography scans and evaluates their accuracy and efficiency. Despite notable progress in the field, the limited availability of public datasets remains a significant barrier to broad participation in research studies and replication of methodologies. Future directions should focus on increasing the accessibility of public datasets, establishing standardised evaluation metrics, and advancing the development of three-dimensional segmentation techniques. In addition, maintaining a collaborative relationship between technological advances and medical expertise is essential to ensure that these innovations not only achieve technical accuracy, but also remain aligned with clinical needs and realities. This synergy ensures their applicability and effectiveness in real-world healthcare environments. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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24. Artificial Intelligence Algorithms for Healthcare.
- Author
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Chumachenko, Dmytro and Yakovlev, Sergiy
- Subjects
ARTIFICIAL intelligence ,DEEP learning ,ALGORITHMS ,MACHINE learning ,INFORMATION technology ,MEDICAL care ,MOTION capture (Human mechanics) ,MEDICAL technology - Abstract
Artificial intelligence (AI) algorithms are playing a crucial role in transforming healthcare by enhancing the quality, accessibility, and efficiency of medical care, research, and operations. These algorithms enable healthcare providers to offer more accurate diagnoses, predict outcomes, and customize treatments to individual patient needs. AI also improves operational efficiency by automating routine tasks and optimizing resource management. However, there are challenges to adopting AI in healthcare, such as data privacy concerns and potential biases in algorithms. Collaboration among stakeholders is necessary to ensure ethical use of AI and its positive impact on the field. AI also has applications in medical research, preventive medicine, and public health. It is important to recognize that AI should augment, not replace, the expertise and compassionate care provided by healthcare professionals. The ethical implications and societal impact of AI in healthcare must be carefully considered, guided by fairness, transparency, and accountability principles. Several research papers in this special issue explore the application of AI algorithms in various aspects of healthcare, such as gait analysis for Parkinson's disease diagnosis, human activity recognition, heart disease prediction, compliance assessment with clinical protocols, epidemic management, neurological complications identification, fall prevention, leukemia diagnosis, and genetic clinical pathways. These studies demonstrate the potential of AI in improving medical diagnostics, patient monitoring, and personalized care. [Extracted from the article]
- Published
- 2024
- Full Text
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25. Predicting Money Laundering Using Machine Learning and Artificial Neural Networks Algorithms in Banks.
- Author
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Lokanan, Mark E.
- Subjects
ARTIFICIAL neural networks ,MONEY laundering ,MACHINE learning ,ALGORITHMS ,RANDOM forest algorithms - Abstract
This paper aims to build a machine learning and a neural network model to detect the probability of money laundering in banks. The paper's data came from a simulation of actual transactions flagged for money laundering in Middle Eastern banks. The main findings highlight that criminal networks mainly use the integration stage to integrate money into the financial system. Fraudsters prefer to launder funds in the early hours, morning followed by the business day's afternoon time intervals. Additionally, the Naïve Bayes and Random Forest classifiers were identified as the two best-performing models to predict bank money laundering transactions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
26. A Comprehensive analysis of Deployment Optimization Methods for CNN-Based Applications on Edge Devices.
- Author
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Qi Li, Zhenling Su, and Lin Meng
- Subjects
CONVOLUTIONAL neural networks ,ARTIFICIAL intelligence ,ALGORITHMS - Abstract
Copyright of Electrotechnical Review / Elektrotehniski Vestnik is the property of Electrotechnical Society of Slovenia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2024
27. Wykorzystanie algorytmów i narzędzi sztucznej inteligencji w kampanii wyborczej w Rzeczypospolitej Polskiej i Republice Słowacji – analiza case studies.
- Author
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Mroczka, Kamil, Żarna, Krzysztof, and Nowakowski, Michał
- Abstract
Copyright of Political Science Studies / Studia Politologiczne is the property of University of Warsaw and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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- 2024
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28. An Innovative K-Anonymity Privacy-Preserving Algorithm to Improve Data Availability in the Context of Big Data.
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Linlin Yuan, Tiantian Zhang, Yuling Chen, Yuxiang Yang, and Huang Li
- Subjects
BIG data ,GREEDY algorithms ,INFORMATION theory ,ALGORITHMS ,ARTIFICIAL intelligence ,STATISTICS ,BLOCKCHAINS - Abstract
The development of technologies such as big data and blockchain has brought convenience to life, but at the same time, privacy and security issues are becoming more and more prominent. The K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users' privacy by anonymizing big data. However, the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data availability. In addition, ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be reduced. Based on this, we propose a new K-anonymity algorithm to solve the privacy security problem in the context of big data, while guaranteeing improved data usability. Specifically, we construct a new information loss function based on the information quantity theory. Considering that different quasi-identification attributes have different impacts on sensitive attributes, we set weights for each quasi-identification attribute when designing the information loss function. In addition, to reduce information loss, we improve K-anonymity in two ways. First, we make the loss of information smaller than in the original table while guaranteeing privacy based on common artificial intelligence algorithms, i.e., greedy algorithm and 2-means clustering algorithm. In addition, we improve the 2-means clustering algorithm by designing a mean-center method to select the initial center of mass. Meanwhile, we design the K-anonymity algorithm of this scheme based on the constructed information loss function, the improved 2-means clustering algorithm, and the greedy algorithm, which reduces the information loss. Finally, we experimentally demonstrate the effectiveness of the algorithm in improving the effect of 2-means clustering and reducing information loss. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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29. Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies.
- Author
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Chien, Chen-Fu, Dauzère-Pérès, Stéphane, Huh, Woonghee Tim, Jang, Young Jae, and Morrison, James R.
- Subjects
ARTIFICIAL intelligence ,CYBER physical systems ,ARTIFICIAL neural networks ,OPERATIONS research ,ALGORITHMS ,COGNITIVE computing - Abstract
The papers are grouped into three categories: AI methods for manufacturing systems, AI developments specifically in semiconductor manufacturing, and AI in additive manufacturing and maintenance. They combine a deep neural network model and Markov decision processes (MDP) to rapidly generate near optimal dynamic control policies for problems that are too large to be only solved by MDP, thus showing the potential of machine learning in controlling unreliable manufacturing systems. [Extracted from the article]
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- 2020
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30. Artificial Intelligence and Machine Learning.
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Muthuraj and Singla, Shrutika
- Subjects
BIOLOGICAL evolution ,REINFORCEMENT (Psychology) ,DATA security ,ARTIFICIAL intelligence ,NATURAL language processing ,DEEP learning ,ARTIFICIAL neural networks ,MACHINE learning ,ALGORITHMS ,USER interfaces - Abstract
Artificial Intelligence (AI) and Machine Learning (ML) have rapidly gained prominence as transformative technologies with immense potential to revolutionize various industries and domains. This research paper presents a comprehensive review of AI and ML, encompassing their fundamental concepts, techniques, and applications. Additionally, it explores recent advancements in the field and offers valuable insights into the future prospects of AI and ML. The paper discusses the historical evolution of AI, the different approaches to AI development, and the components that constitute AI systems. Furthermore, it delves into the core concepts and algorithms of ML, including supervised, unsupervised, and reinforcement learning, as well as the advent of deep learning and neural networks. The applications of AI and ML across diverse domains such as natural language processing, computer vision, healthcare, and finance are also discussed. Recent advancements, such as transfer learning, generative adversarial networks, explainable AI, and federated learning, are highlighted, along with the challenges and limitations faced by these technologies, such as ethical concerns, data quality issues, and interpretability challenges. The paper concludes by presenting future perspectives, including the integration of AI with other technologies, advancements in human-computer interaction, and the impact of quantum computing on ML. This research emphasizes the importance of ongoing research and development in AI and ML and the need to address ethical, security, and interpretability considerations for responsible and beneficial implementation in society. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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31. A Deep Learning-Based Programming and Creation Algorithm of NFT Artwork.
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Wang, T.
- Subjects
DEEP learning ,GENERATIVE adversarial networks ,COMPUTER vision ,ALGORITHMS ,ARTIFICIAL intelligence ,IMAGE analysis - Abstract
In the field of computer vision, it is a very challenging task to use artificial intelligence deep learning method to realize the programming and creation of NFT artwork. With the continuous development and improvement of deep learning technology, this task has become a reality. The generative adversarial network model used in deep learning can generate new images based on the extraction and analysis of image data features and has become an important tool for NFT artwork image generation. In order to better realize the NFT artwork programming, this paper analyzes the working principle of the traditional adversarial generation method and then uses the StyleGAN model to edit the higher-level attributes of the image, which can effectively control the generated style and style of the NFT artwork image. Finally, in order to improve the quality of the generated images, this paper introduces a channel attention mechanism and a spatial attention mechanism to ensure that the generated images are more reasonable and realistic. Finally, through a large number of experiments, it is proved that the NFT artwork transmission programming algorithm based on artificial intelligence deep learning proposed in this paper can control the overall style of image generation according to the needs of the transmission, and the generated image features have good details and high visual quality. [ABSTRACT FROM AUTHOR]
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- 2022
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32. Preface: Special issue on "Understanding of evolutionary optimization behavior", Part 1.
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Blum, Christian, Eftimov, Tome, and Korošec, Peter
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BEES algorithm ,SUBMODULAR functions ,ARTIFICIAL intelligence ,EVOLUTIONARY computation ,ALGORITHMS ,PROBLEM solving - Abstract
Understanding of optimization algorithm's behavior is a vital part that is needed for quality progress in the field of stochastic optimization algorithms. To be able to overcome this deficiency, we need to establish new standards for understanding optimization algorithm behavior, which will provide understanding of the working principles behind the stochastic optimization algorithms. In their paper I Evolutionary algorithms and submodular functions: benefits of heavy-tailed mutations i , Quinzan et al. develop suitable Evolutionary Algorithms (EAs) to tackle submodular optimization problems. The paper I Improving convergence in swarm algorithms by controlling range of random movement i by Chaudhary and Banati studies the applicability of the IS technique over different swarm algorithms employing different random distributions. [Extracted from the article]
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- 2021
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33. ENHANCING POWER SYSTEM STABILITY WITH AI-BASED RELAYING ALGORITHMS – A REVIEW.
- Author
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RAJESH, C. R. and HARISON, D. SAM
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ARTIFICIAL intelligence ,ARTIFICIAL neural networks ,EXPERT systems ,MACHINE learning ,ALGORITHMS ,RELIABILITY in engineering - Abstract
Artificial Intelligence (AI) and Machine Learning (ML) are emerging technologies that are increasingly being used to improve various aspects of power systems. In particular, AI-based relaying algorithms have the potential to revolutionize the way power systems are protected from faults and failures. Relaying algorithms play a critical role in ensuring the stability and reliability of power systems. However, traditional relay protection algorithms face several challenges, including difficulty handling complex and dynamic systems, limited fault detection accuracy, and slow response times to changing conditions. AI-based relaying algorithms can address these challenges by leveraging the power of Artificial Intelligence and Machine Learning. This paper presents an overview of AI-based relaying algorithms and their potential applications in power systems. It explores the use of AI techniques such as Artificial Neural Networks (ANN), Decision Trees (DT), and expert systems for improving the accuracy and reliability of relay protection. It also discuss the steps involved in AI-based relaying algorithms, including feature extraction, classification, and result output. This paper highlights the importance of further research and development in this field to fully realize the benefits of AI-based relaying algorithms. [ABSTRACT FROM AUTHOR]
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- 2023
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34. On humans, algorithms and data.
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Arnaboldi, Michela, de Bruijn, Hans, Steccolini, Ileana, and Van der Voort, Haiko
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ALGORITHMS ,ARTIFICIAL intelligence ,DIGITAL transformation ,HUMAN beings ,LOGIC - Abstract
Purpose: The purpose of this paper is to introduce the papers in this special issue on humans, algorithms and data. The authors first set themselves the task of identifying the main challenges arising from the adoption and use of algorithms and data analytics in management, accounting and organisations in general, many of which have been described in the literature. Design/methodology/approach: This paper builds on previous literature and case studies of the application of algorithm logic with artificial intelligence as an exemplar of this innovation. Furthermore, this paper is triangulated with the findings of the papers included in this special issue. Findings: Based on prior literature and the concepts set out in the papers published in this special issue, this paper proposes a conceptual framework that can be useful both in the analysis and ordering of the algorithm hype, as well as to identify future research avenues. Originality/value: The value of this framework, and that of the papers in this special issue, lies in its ability to shed new light on the (neglected) connections and relationships between algorithmic applications, such as artificial intelligence. The framework developed in this piece should stimulate scholars to explore the intersections between "technical" as well as organisational, social and individual issues that algorithms should help us tackle. [ABSTRACT FROM AUTHOR]
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- 2022
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35. A Real-Time Olive Fruit Detection for Harvesting Robot Based on YOLO Algorithms.
- Author
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Aljaafreh, Ahmad, Elzagzoug, Ezzaldeen Y., Abukhait, Jafar, Soliman, Abdel-Hamid, Alja'Afreh, Saqer S., Sivanathan, Aparajithan, and Hughes, James
- Subjects
ARTIFICIAL neural networks ,OLIVE ,FRUIT harvesting ,OBJECT recognition (Computer vision) ,ARTIFICIAL intelligence ,ALGORITHMS - Abstract
Deep neural network models have become powerful tools of machine learning and artificial intelligence. They can approximate functions and dynamics by learning from examples. This paper reviews the state-of-art of deep learning-based object detection frameworks that are used for fruit detection in general and for olive fruit in particular. A dataset of olive fruit on the tree is built to train and evaluate deep models. The ultimate goal of this work is the capability of on-edge real-time olive fruit detection on the tree from digital videos. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed You Only Look Once version five (YOLOv5). This paper builds a dataset of 1.2 K source images of olive fruit on the tree and evaluates the latest object detection algorithms focusing on variants of YOLOv5 and YOLOR. The results of the YOLOv5 models show that the YOLOv5 new network models are able to extract rich olive features from images and detect the olive fruit with a high precision of higher than 0.75 mAP_0.5. YOLOv5s performs better for real-time olive fruit detection on the tree over other YOLOv5 variants and YOLOR. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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36. The object migration automata: its field, scope, applications, and future research challenges.
- Author
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Oommen, B. John, Omslandseter, Rebekka Olsson, and Jiao, Lei
- Subjects
ARTIFICIAL intelligence ,NP-hard problems ,ROBOTS ,ALGORITHMS ,MACHINE theory ,PARTITIONS (Mathematics) - Abstract
Partitioning, in and of itself, is an NP-hard problem. Prior to the Artificial Intelligence (AI)-based solutions, it was solved in the 1970s by optimization-based strategies. However, AI-based solutions appeared in the 1980s in a pioneering way, by using a Learning Automaton (LA)-motivated strategy known as the so-called Object Migrating Automaton (OMA). Although the OMA and its derivatives have been used in numerous applications since then, the basic kernel has remained the same. Because the number of possible partitions in a partitioning problem can be combinatorially exponential and the underlying tasks are NP-hard, the most advanced OMA algorithms could, until recently, only solve issues involving equally sized groups. Due to our recent innovations cited in the body of this paper, the enhanced OMA now also handles non-equally sized groups. Earlier, we had presented in Omslandseter (Pattern Anal Appl, 2023), a comprehensive survey of the state-of-the-art enhancements of the best-known OMA. We believe that these results will be the benchmark for a few decades and that it will be very hard to beat these results. This is a companion paper, intended to augment the contents of Omslandseter (Pattern Anal Appl, 2023). In this paper, we first discuss the OMA's prior applications, its historical and current innovations, and the OMA-based algorithms' relevance to societal needs. We also provide well-specified guidelines for future researchers so that they can use them for unresolved tasks, and also develop further advancements. [ABSTRACT FROM AUTHOR]
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- 2023
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37. ARTIFICIAL INTELLIGENCE IN MEDIA: PERSPECTIVES AND IMPLICATIONS.
- Author
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JOHN, SAJU P. and DEVI, JAYANTHILA
- Subjects
ARTIFICIAL intelligence ,MEDIA consumption ,DATABASE management ,FREEDOM of speech ,MULTIMEDIA communications ,ALGORITHMS ,MASS media - Abstract
With the explosive growth of information on the web, users face difficulties finding their desired information. There is a need to manage and cluster data efficiently. Although there are various multimedia database systems available for retrieval, most of the methods are not efficient enough. Artificial Intelligence (AI) is increasingly shaping media production and consumption, particularly in the field of video blogs (vlogs). This paper explores the intersection of AI and media, focusing on its implications for freedom of speech and media, content creation, video recommendations, content moderation, and content personalization. The objective is to extract relevant videos from a multimedia database, evaluate the performance of AI algorithms in processing video data, and demonstrate the effectiveness of these algorithms in enhancing the quality and accessibility of video blogs. [ABSTRACT FROM AUTHOR]
- Published
- 2023
38. Static Code Analysis: A Tree of Science Review.
- Author
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Ruiz, G. A., Robledo, S., and Morales, H. H.
- Subjects
COMPUTER security vulnerabilities ,ARTIFICIAL intelligence ,MACHINE learning ,ALGORITHMS ,TREES ,SIMULATED annealing ,SMELL - Abstract
Copyright of Entre Ciencia e Ingeniería is the property of Entre Ciencia e Ingenieria and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
- Full Text
- View/download PDF
39. Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview.
- Author
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Rudnicka, Zofia, Szczepanski, Janusz, and Pregowska, Agnieszka
- Subjects
ARTIFICIAL intelligence ,COMPUTER-assisted image analysis (Medicine) ,DIAGNOSTIC imaging ,IMAGE segmentation ,ALGORITHMS - Abstract
Recently, artificial intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies, as well as increasing the effectiveness of the training process. In this context, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in medical image scan segmentation and intelligent visual content generation, i.e., computer-generated three-dimensional (3D) images in the context of extended reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. It concludes with possible developments and open challenges in AI applications in extended reality-based solutions. Finally, future lines of research and development directions of artificial intelligence applications, both in medical image segmentation and extended reality-based medical solutions, are discussed. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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40. Economic Dispatch Optimization Strategies and Problem Formulation: A Comprehensive Review.
- Author
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Marzbani, Fatemeh and Abdelfatah, Akmal
- Subjects
EVIDENCE gaps ,MATHEMATICAL optimization ,COMPUTER performance ,ENERGY management ,ALGORITHMS - Abstract
Economic Dispatch Problems (EDP) refer to the process of determining the power output of generation units such that the electricity demand of the system is satisfied at a minimum cost while technical and operational constraints of the system are satisfied. This procedure is vital in the efficient energy management of electricity networks since it can ensure the reliable and efficient operation of power systems. As power systems transition from conventional to modern ones, new components and constraints are introduced to power systems, making the EDP increasingly complex. This highlights the importance of developing advanced optimization techniques that can efficiently handle these new complexities to ensure optimal operation and cost-effectiveness of power systems. This review paper provides a comprehensive exploration of the EDP, encompassing its mathematical formulation and the examination of commonly used problem formulation techniques, including single and multi-objective optimization methods. It also explores the progression of paradigms in economic dispatch, tracing the journey from traditional methods to contemporary strategies in power system management. The paper categorizes the commonly utilized techniques for solving EDP into four groups: conventional mathematical approaches, uncertainty modelling methods, artificial intelligence-driven techniques, and hybrid algorithms. It identifies critical research gaps, a predominant focus on single-case studies that limit the generalizability of findings, and the challenge of comparing research due to arbitrary system choices and formulation variations. The present paper calls for the implementation of standardized evaluation criteria and the inclusion of a diverse range of case studies to enhance the practicality of optimization techniques in the field. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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41. A Comprehensive Overview of Control Algorithms, Sensors, Actuators, and Communication Tools of Autonomous All-Terrain Vehicles in Agriculture.
- Author
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Etezadi, Hamed and Eshkabilov, Sulaymon
- Subjects
DATA transmission systems ,AUTONOMOUS vehicles ,ACTUATORS ,AGRICULTURAL technology ,COMPUTER vision ,DETECTORS ,ALGORITHMS - Abstract
This review paper discusses the development trends of agricultural autonomous all-terrain vehicles (AATVs) from four cornerstones, such as (1) control strategy and algorithms, (2) sensors, (3) data communication tools and systems, and (4) controllers and actuators, based on 221 papers published in peer-reviewed journals for 1960–2023. The paper highlights a comparative analysis of commonly employed control methods and algorithms by highlighting their advantages and disadvantages. It gives comparative analyses of sensors, data communication tools, actuators, and hardware-embedded controllers. In recent years, many novel developments in AATVs have been made due to advancements in wireless and remote communication, high-speed data processors, sensors, computer vision, and broader applications of AI tools. Technical advancements in fully autonomous control of AATVs remain limited, requiring research into accurate estimation of terrain mechanics, identifying uncertainties, and making fast and accurate decisions, as well as utilizing wireless communication and edge cloud computing. Furthermore, most of the developments are at the research level and have many practical limitations due to terrain and weather conditions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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42. Guided Intelligent Hyper-Heuristic Algorithm for Critical Software Application Testing Satisfying Multiple Coverage Criteria.
- Author
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Rani, S. Alagu, Akila, C., and Raja, S. P.
- Subjects
COMPUTER software testing ,APPLICATION software ,DECISION support systems ,ALGORITHMS ,INTELLIGENT agents ,OPTIMIZATION algorithms - Abstract
This paper proposes a novel algorithm that combines symbolic execution and data flow testing to generate test cases satisfying multiple coverage criteria of critical software applications. The coverage criteria considered are data flow coverage as the primary criterion, software safety requirements, and equivalence partitioning as sub-criteria. black The characteristics of the subjects used for the study include high-precision floating-point computation and iterative programs. The work proposes an algorithm that aids the tester in automated test data generation, satisfying multiple coverage criteria for critical software. The algorithm adapts itself and selects different heuristics based on program characteristics. The algorithm has an intelligent agent as its decision support system to accomplish this adaptability. Intelligent agent uses the knowledge base to select different low-level heuristics based on the current state of the problem instance during each generation of genetic algorithm execution. The knowledge base mimics the expert's decision in choosing the appropriate heuristics. black The algorithm outperforms by accomplishing 100% data flow coverage for all subjects. In contrast, the simple genetic algorithm, random testing and a hyper-heuristic algorithm could accomplish a maximum of 83%, 67% and 76.7%, respectively, for the subject program with high complexity. black The proposed algorithm covers other criteria, namely equivalence partition coverage and software safety requirements, with fewer iterations. black The results reveal that test cases generated by the proposed algorithm are also effective in fault detection, with 87.2% of mutants killed when compared to a maximum of 76.4% of mutants killed for the complex subject with test cases of other methods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
43. Improved adaptive-phase fuzzy high utility pattern mining algorithm based on tree-list structure for intelligent decision systems.
- Author
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Chen, Jing, Liu, Aijun, Zhang, Hongjun, Yang, Shengyi, Zheng, Hui, Zhou, Ning, and Li, Peng
- Subjects
ARTIFICIAL intelligence ,SMART structures ,ALGORITHMS ,DATA mining ,BIG data - Abstract
With the rapid development of AI and big data mining technologies, computerized medical decision-making has become increasingly prominent. The aim of high-utility pattern mining (HUPM) is to discover meaningful patterns in medical databases that contribute to maximizing the utility from the perspective of diagnosis. However, HUPM pays less attention to the interpretability and explainability of these patterns in medical decision-making scenarios. This paper proposes a novel algorithm called the Improved fuzzy high-utility pattern mining (IF-HUPM) to address this problem. First, the paper applies a fuzzy preprocessing method to divide the fuzzy intervals of a medical quantitative data set, which enhances the fuzziness and interpretability of the data. Next, in the process of IF-HUPM, both fuzzy tree and list structures are employed to calculate fuzzy high-utility values. By combining the characteristics of the one-stage and two-stage algorithms of HUPM, an adaptive-phase Fuzzy HUPM hybrid frame is proposed. The experimental results demonstrate that the proposed IF-HUPM algorithm enhances both accuracy and efficiency and the mining process requires less time and space on average. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
44. Disparities in Breast Cancer Diagnostics: How Radiologists Can Level the Inequalities.
- Author
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Pesapane, Filippo, Tantrige, Priyan, Rotili, Anna, Nicosia, Luca, Penco, Silvia, Bozzini, Anna Carla, Raimondi, Sara, Corso, Giovanni, Grasso, Roberto, Pravettoni, Gabriella, Gandini, Sara, and Cassano, Enrico
- Subjects
BREAST tumor diagnosis ,OCCUPATIONAL roles ,HEALTH policy ,DIVERSITY & inclusion policies ,EQUALITY ,HEALTH services accessibility ,MINORITIES ,GENDER affirming care ,TELERADIOLOGY ,ARTIFICIAL intelligence ,RADIATION ,DIAGNOSTIC imaging ,LABOR supply ,CULTURAL competence ,HEALTH ,COMMUNICATION ,HEALTH equity ,PHYSICIANS ,ALGORITHMS - Abstract
Simple Summary: This paper delves into the persistent issue of unequal access to medical imaging, with a particular focus on breast cancer screening and its impact on marginalized communities and racial/ethnic minorities. Central to our discussion is the role of scientific mobility among radiologists in fostering healthcare policy changes that promote diversity and cultural competence. We propose various strategies to bridge this gap, including cultural education, sensitivity training, and diversifying the radiology workforce. These measures aim to improve communication with diverse patient groups and reduce healthcare disparities. Additionally, we explore the challenges and advantages of teleradiology as a means to extend medical imaging services to underserved areas. In the context of artificial intelligence, we emphasize the critical need to validate algorithms across diverse populations to ensure unbiased and equitable healthcare outcomes. Overall, this paper underscores the importance of international collaboration in addressing global access barriers, presenting it as a key to mitigating disparities in medical imaging access and contributing to the pursuit of equitable healthcare. Access to medical imaging is pivotal in healthcare, playing a crucial role in the prevention, diagnosis, and management of diseases. However, disparities persist in this scenario, disproportionately affecting marginalized communities, racial and ethnic minorities, and individuals facing linguistic or cultural barriers. This paper critically assesses methods to mitigate these disparities, with a focus on breast cancer screening. We underscore scientific mobility as a vital tool for radiologists to advocate for healthcare policy changes: it not only enhances diversity and cultural competence within the radiology community but also fosters international cooperation and knowledge exchange among healthcare institutions. Efforts to ensure cultural competency among radiologists are discussed, including ongoing cultural education, sensitivity training, and workforce diversification. These initiatives are key to improving patient communication and reducing healthcare disparities. This paper also highlights the crucial role of policy changes and legislation in promoting equal access to essential screening services like mammography. We explore the challenges and potential of teleradiology in improving access to medical imaging in remote and underserved areas. In the era of artificial intelligence, this paper emphasizes the necessity of validating its models across a spectrum of populations to prevent bias and achieve equitable healthcare outcomes. Finally, the importance of international collaboration is illustrated, showcasing its role in sharing insights and strategies to overcome global access barriers in medical imaging. Overall, this paper offers a comprehensive overview of the challenges related to disparities in medical imaging access and proposes actionable strategies to address these challenges, aiming for equitable healthcare delivery. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
45. Research on Obstacle Avoidance Planning for UUV Based on A3C Algorithm.
- Author
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Wang, Hongjian, Gao, Wei, Wang, Zhao, Zhang, Kai, Ren, Jingfei, Deng, Lihui, and He, Shanshan
- Subjects
DEEP learning ,REINFORCEMENT learning ,DEEP reinforcement learning ,MACHINE learning ,ALGORITHMS ,ARTIFICIAL intelligence - Abstract
Deep reinforcement learning is an artificial intelligence technology that combines deep learning and reinforcement learning and has been widely applied in multiple fields. As a type of deep reinforcement learning algorithm, the A3C (Asynchronous Advantage Actor-Critic) algorithm can effectively utilize computer resources and improve training efficiency by synchronously training Actor-Critic in multiple threads. Inspired by the excellent performance of the A3C algorithm, this paper uses the A3C algorithm to solve the UUV (Unmanned Underwater Vehicle) collision avoidance planning problem in unknown environments. This collision avoidance planning algorithm can have the ability to plan in real-time while ensuring a shorter path length, and the output action space can meet the kinematic constraints of UUVs. In response to the problem of UUV collision avoidance planning, this paper designs the state space, action space, and reward function. The simulation results show that the A3C collision avoidance planning algorithm can guide a UUV to avoid obstacles and reach the preset target point. The path planned by this algorithm meets the heading constraints of the UUV, and the planning time is short, which can meet the requirements of real-time planning. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
46. Application of the Artificial Intelligence Algorithm in the Automatic Segmentation of Mandarin Dialect Accent.
- Author
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Lai, Yufang
- Subjects
MANDARIN dialects ,ARTIFICIAL intelligence ,VITERBI decoding ,ALGORITHMS ,COMPUTER engineering ,AUTOMATIC speech recognition - Abstract
In recent years, as the research objects of phonetics have expanded to accent and colloquial natural speech, the construction of the dialect accent Mandarin voice database has become another important research direction in the field of computer technology. Among them, voice segmentation is a time-consuming and laborious link in the construction of the voice database. The application of artificial intelligence technology helps to improve the construction efficiency of the Mandarin dialect voice database. Based on this, this article mainly researches the application of the artificial intelligence algorithm in the automatic segmentation of dialect accent Mandarin. This paper constructs a voice corpus of dialect accents and Mandarin Chinese and specifically describes the construction process of the voice corpus. This paper uses artificial intelligence algorithms, combined with the HMM (hidden Markov model), and Viterbi algorithm to propose a new method of automatic speech segmentation. This paper studies the automatic speech segmentation model, extracts the general parameters of the training data in the Mandarin corpus, and conducts HMM training. This paper conducts tests based on the voice of the test set to verify the accuracy of the method proposed in this paper. The experimental results show that, in the speech data of 60 people, the error range of each sentence time period is less than 5 ms accounting for 79.16%, less than 10 ms accounting for 82.96%, less than 20 ms accounting for 83.14%, and less than 50 ms accounting for 86.92%. It can be seen that the algorithm proposed in this paper can meet practical applications in automatic speech segmentation. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
47. Improvement of AHMES Using AI Algorithms.
- Author
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Chen, Le and Song, JeongYoung
- Subjects
ARTIFICIAL intelligence ,COMPUTER engineering ,COMPUTER engineers ,MATHEMATICS ,ALGORITHMS - Abstract
This research aims to improve the rationality and intelligence of AUTOMATICALLY HIGHER MATHEMATICALLY EXAM SYSTEM (AHMES) through some AI algorithms. AHMES is an intelligent and high-quality higher math examination solution for the Department of Computer Engineering at Pai Chai University. This research redesigned the difficulty system of AHMES and used some AI algorithms for initialization and continuous adjustment. This paper describes the multiple linear regression algorithm involved in this research and the AHMES learning (AL) algorithm improved by the Q-learning algorithm. The simulation test results of the upgraded AHMES show the effectiveness of these algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
48. Pharmacological, Non-Pharmacological Policies and Mutation: An Artificial Intelligence Based Multi-Dimensional Policy Making Algorithm for Controlling the Casualties of the Pandemic Diseases.
- Author
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Tutsoy, Onder
- Subjects
ARTIFICIAL intelligence ,PANDEMICS ,PARAMETRIC modeling ,ALGORITHMS ,VACCINATION policies ,MULTIDIMENSIONAL databases - Abstract
Fighting against the pandemic diseases with unique characters requires new sophisticated approaches like the artificial intelligence. This paper develops an artificial intelligence algorithm to produce multi-dimensional policies for controlling and minimizing the pandemic casualties under the limited pharmacological resources. In this respect, a comprehensive parametric model with a priority and age-specific vaccination policy and a variety of non-pharmacological policies are introduced. This parametric model is utilized for constructing an artificial intelligence algorithm by following the exact analogy of the model-based solution. Also, this parametric model is manipulated by the artificial intelligence algorithm to seek for the best multi-dimensional non-pharmacological policies that minimize the future pandemic casualties as desired. The role of the pharmacological and non-pharmacological policies on the uncertain future casualties are extensively addressed on the real data. It is shown that the developed artificial intelligence algorithm is able to produce efficient policies which satisfy the particular optimization targets such as focusing on minimization of the death casualties more than the infected casualties or considering the curfews on the people age over 65 rather than the other non-pharmacological policies. The paper finally analyses a variety of the mutant virus cases and the corresponding non-pharmacological policies aiming to reduce the morbidity and mortality rates. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
49. DM–AHR : A Self-Supervised Conditional Diffusion Model for AI-Generated Hairless Imaging for Enhanced Skin Diagnosis Applications.
- Author
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Benjdira, Bilel, M. Ali, Anas, Koubaa, Anis, Ammar, Adel, and Boulila, Wadii
- Subjects
SKIN diseases ,MEDICAL technology ,HAIR removal ,RESEARCH funding ,DIAGNOSTIC imaging ,ARTIFICIAL intelligence ,DESCRIPTIVE statistics ,DATA analysis software ,ALGORITHMS - Abstract
Simple Summary: Skin diseases can be serious, and early detection is key to effective treatment. Unfortunately, the quality of images used to diagnose these diseases often suffers due to interference from hair, making accurate diagnosis challenging. This research introduces a novel technology, the DM–AHR, a self-supervised conditional diffusion model designed specifically to generate clear, hairless images for better skin disease diagnosis. Our work not only presents a new, advanced model that expertly identifies and removes hair from dermoscopic images but also introduces a specialized dataset, DERMAHAIR, to further research and improve diagnostic processes. The enhancements in image quality provided by DM–AHR significantly improve the accuracy of skin disease diagnoses, and it promises to be a valuable tool in medical imaging. Accurate skin diagnosis through end-user applications is important for early detection and cure of severe skin diseases. However, the low quality of dermoscopic images hampers this mission, especially with the presence of hair on these kinds of images. This paper introduces DM–AHR, a novel, self-supervised conditional diffusion model designed specifically for the automatic generation of hairless dermoscopic images to improve the quality of skin diagnosis applications. The current research contributes in three significant ways to the field of dermatologic imaging. First, we develop a customized diffusion model that adeptly differentiates between hair and skin features. Second, we pioneer a novel self-supervised learning strategy that is specifically tailored to optimize performance for hairless imaging. Third, we introduce a new dataset, named DERMAHAIR (DERMatologic Automatic HAIR Removal Dataset), that is designed to advance and benchmark research in this specialized domain. These contributions significantly enhance the clarity of dermoscopic images, improving the accuracy of skin diagnosis procedures. We elaborate on the architecture of DM–AHR and demonstrate its effective performance in removing hair while preserving critical details of skin lesions. Our results show an enhancement in the accuracy of skin lesion analysis when compared to existing techniques. Given its robust performance, DM–AHR holds considerable promise for broader application in medical image enhancement. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
50. Artificial intelligence assisted IoT-fog based framework for emergency fire response in smart buildings.
- Author
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Saini, Munish, Sengupta, Eshan, and Thakur, Suraaj
- Subjects
FIRE management ,ARTIFICIAL intelligence ,EMERGENCY management ,INTERNET of things ,FLOOR plans ,ALGORITHMS - Abstract
Anthropogenic hazards are unrelenting threat to lives and property, with human irresponsibility emerging as a leading source of urban as well as industrial fires. The complexity of urban structures and crowded layouts make these kinds of fires more lethal. This paper presents an Artificial Intelligence (AI) based framework designed for smart buildings as a solution to the devastating obstacles caused by fire crises. Our system creates a 3D model of the building using floor plans and the A* algorithm for escape route identification. The proposed framework includes a YOLO-based smart monitoring system for the identification and counting of people caught in a fire, with the ability to distinguish between conscious and unconscious persons. The proposed system informs inhabitants in the case of a fire and directs them to the closest exit for a safe evacuation. Moreover, fire and rescue officials receive real-time information on affected persons, such as the number and location of adults and children who are conscious and unconscious. Perhaps most significantly, the suggested framework performs exceptionally well, scoring 96% for precision and 98% for recall in the detection of fire and humans. These findings highlight the effectiveness of the model in locating people within infrastructures affected by fire. The framework considerably outperforms the most advanced algorithms in terms of speed and efficiency for shortest path detection, greatly improving the ability of fire rescue teams to quickly find and aid residents who are trapped in a fire. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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