Artificial Intelligence Review最新文献

{"title":"Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey","authors":"Noureen Talpur,&nbsp;Said Jadid Abdulkadir,&nbsp;Hitham Alhussian,&nbsp;Mohd Hilmi Hasan,&nbsp;Norshakirah Aziz,&nbsp;Alwi Bamhdi","doi":"10.1007/s10462-022-10188-3","DOIUrl":"10.1007/s10462-022-10188-3","url":null,"abstract":"<div><p>Deep neural networks (DNN) have remarkably progressed in applications involving large and complex datasets but have been criticized as a black-box. This downside has recently become a motivation for the research community to pursue the ideas of hybrid approaches, resulting in novel hybrid systems classified as deep neuro-fuzzy systems (DNFS). Studies regarding the implementation of DNFS have rapidly increased in the domains of computing, healthcare, transportation, and finance with high interpretability and reasonable accuracy. However, relatively few survey studies have been found in the literature to provide a comprehensive insight into this domain. Therefore, this study aims to perform a systematic review to evaluate the current progress, trends, arising issues, research gaps, challenges, and future scope related to DNFS studies. A study mapping process was prepared to guide a systematic search for publications related to DNFS published between 2015 and 2020 using five established scientific directories. As a result, a total of 105 studies were identified and critically analyzed to address research questions with the objectives: (i) to understand the concept of DNFS; (ii) to find out DNFS optimization methods; (iii) to visualize the intensity of work carried out in DNFS domain; and (iv) to highlight DNFS application subjects and domains. We believe that this study provides up-to-date guidance for future research in the DNFS domain, allowing for more effective advancement in techniques and processes. The analysis made in this review proves that DNFS-based research is actively growing with a substantial implementation and application scope in the future.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"56 2","pages":"865 - 913"},"PeriodicalIF":12.0,"publicationDate":"2022-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-022-10188-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10580035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A formal proof and simple explanation of the QuickXplain algorithm","authors":"Patrick Rodler","doi":"10.1007/s10462-022-10149-w","DOIUrl":"10.1007/s10462-022-10149-w","url":null,"abstract":"<div><p>In his seminal paper of 2004, Ulrich Junker proposed the <span>QuickXplain</span> algorithm, which provides a divide-and-conquer computation strategy to find within a given set an irreducible subset with a particular (monotone) property. Beside its original application in the domain of constraint satisfaction problems, the algorithm has since then found widespread adoption in areas as different as model-based diagnosis, recommender systems, verification, or the Semantic Web. This popularity is due to the frequent occurrence of the problem of finding irreducible subsets on the one hand, and to <span>QuickXplain</span>’s general applicability and favorable computational complexity on the other hand. However, although (we regularly experience) people are having a hard time understanding <span>QuickXplain</span> and seeing why it works correctly, a proof of correctness of the algorithm has never been published. This is what we account for in this work, by explaining <span>QuickXplain</span> in a novel tried and tested way and by presenting an intelligible formal proof of it. Apart from showing the correctness of the algorithm and excluding the later detection of errors (<i>proof and trust effect</i>), the added value of the availability of a formal proof is, e.g., <i>(i)</i> that the workings of the algorithm often become completely clear only after studying, verifying and comprehending the proof (<i>didactic effect</i>), <i>(ii)</i> that the shown proof methodology can be used as a guidance for proving other recursive algorithms (<i>transfer effect</i>), and <i>(iii)</i> the possibility of providing “gapless” correctness proofs of systems that rely on (results computed by) <span>QuickXplain</span>, such as numerous model-based debuggers (<i>completeness effect</i>).</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"55 8","pages":"6185 - 6206"},"PeriodicalIF":12.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-022-10149-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40447203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A complete framework for accurate recognition and prognosis of COVID-19 patients based on deep transfer learning and feature classification approach","authors":"Hossam Magdy Balaha,&nbsp;Eman M. El-Gendy,&nbsp;Mahmoud M. Saafan","doi":"10.1007/s10462-021-10127-8","DOIUrl":"10.1007/s10462-021-10127-8","url":null,"abstract":"<div><p>The sudden appearance of COVID-19 has put the world in a serious situation. Due to the rapid spread of the virus and the increase in the number of infected patients and deaths, COVID-19 was declared a pandemic. This pandemic has its destructive effect not only on humans but also on the economy. Despite the development and availability of different vaccines for COVID-19, scientists still warn the citizens of new severe waves of the virus, and as a result, fast diagnosis of COVID-19 is a critical issue. Chest imaging proved to be a powerful tool in the early detection of COVID-19. This study introduces an entire framework for the early detection and early prognosis of COVID-19 severity in the diagnosed patients using laboratory test results. It consists of two phases (1) Early Diagnostic Phase (EDP) and (2) Early Prognostic Phase (EPP). In EDP, COVID-19 patients are diagnosed using CT chest images. In the current study, 5, 159 COVID-19 and 10, 376 normal computed tomography (CT) images of Egyptians were used as a dataset to train 7 different convolutional neural networks using transfer learning. Data augmentation normal techniques and generative adversarial networks (GANs), CycleGAN and CCGAN, were used to increase the images in the dataset to avoid overfitting issues. 28 experiments were applied and multiple performance metrics were captured. Classification with no augmentation yielded <span>(99.61%)</span> accuracy by EfficientNetB7 architecture. By applying CycleGAN and CC-GAN Augmentation, the maximum reported accuracies were <span>(99.57%)</span> and <span>(99.14%)</span> by MobileNetV1 and VGG-16 architectures respectively. In EPP, the prognosis of the severity of COVID-19 in patients is early determined using laboratory test results. In this study, 25 different classification techniques were applied and from the different results, the highest accuracies were <span>(98.70%)</span> and <span>(97.40%)</span> reported by the Ensemble Bagged Trees and Tree (Fine, Medium, and Coarse) techniques respectively.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"55 6","pages":"5063 - 5108"},"PeriodicalIF":12.0,"publicationDate":"2022-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-021-10127-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39596130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-aided general clinical diagnoses verified by third-parties with dynamic uncertain causality graph extended to also include classification","authors":"Zhan Zhang,&nbsp;Yang Jiao,&nbsp;Mingxia Zhang,&nbsp;Bing Wei,&nbsp;Xiao Liu,&nbsp;Juan Zhao,&nbsp;Fengwei Tian,&nbsp;Jie Hu,&nbsp;Qin Zhang","doi":"10.1007/s10462-021-10109-w","DOIUrl":"10.1007/s10462-021-10109-w","url":null,"abstract":"<div><p>Artificial intelligence (AI)-aided general clinical diagnosis is helpful to primary clinicians. Machine learning approaches have problems of generalization, interpretability, etc. Dynamic Uncertain Causality Graph (DUCG) based on uncertain casual knowledge provided by clinical experts does not have these problems. This paper extends DUCG to include the representation and inference algorithm for non-causal classification relationships. As a part of general clinical diagnoses, six knowledge bases corresponding to six chief complaints (arthralgia, dyspnea, cough and expectoration, epistaxis, fever with rash and abdominal pain) were constructed through constructing subgraphs relevant to a chief complaint separately and synthesizing them together as the knowledge base of the chief complaint. A subgraph represents variables and causalities related to a single disease that may cause the chief complaint, regardless of which hospital department the disease belongs to. Verified by two groups of third-party hospitals independently, total diagnostic precisions of the six knowledge bases ranged in 96.5–100%, in which the precision for every disease was no less than 80%.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"55 6","pages":"4485 - 4521"},"PeriodicalIF":12.0,"publicationDate":"2022-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-021-10109-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39596131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-criteria decision-making for coronavirus disease 2019 applications: a theoretical analysis review","authors":"M. A. Alsalem,&nbsp;A. H. Alamoodi,&nbsp;O. S. Albahri,&nbsp;K. A. Dawood,&nbsp;R. T. Mohammed,&nbsp;Alhamzah Alnoor,&nbsp;A. A. Zaidan,&nbsp;A. S. Albahri,&nbsp;B. B. Zaidan,&nbsp;F. M. Jumaah,&nbsp;Jameel R. Al-Obaidi","doi":"10.1007/s10462-021-10124-x","DOIUrl":"10.1007/s10462-021-10124-x","url":null,"abstract":"&lt;div&gt;&lt;p&gt;The influence of the ongoing COVID-19 pandemic that is being felt in all spheres of our lives and has a remarkable effect on global health care delivery occurs amongst the ongoing global health crisis of patients and the required services. From the time of the first detection of infection amongst the public, researchers investigated various applications in the fight against the COVID-19 outbreak and outlined the crucial roles of different research areas in this unprecedented battle. In the context of existing studies in the literature surrounding COVID-19, related to medical treatment decisions, the dimensions of context addressed in previous multidisciplinary studies reveal the lack of appropriate decision mechanisms during the COVID-19 outbreak. Multiple criteria decision making (MCDM) has been applied widely in our daily lives in various ways with numerous successful stories to help analyse complex decisions and provide an accurate decision process. The rise of MCDM in combating COVID-19 from a theoretical perspective view needs further investigation to meet the important characteristic points that match integrating MCDM and COVID-19. To this end, a comprehensive review and an analysis of these multidisciplinary fields, carried out by different MCDM theories concerning COVID19 in complex case studies, are provided. Research directions on exploring the potentials of MCDM and enhancing its capabilities and power through two directions (i.e. development and evaluation) in COVID-19 are thoroughly discussed. In addition, Bibliometrics has been analysed, visualization and interpretation based on the evaluation and development category using R-tool involves; annual scientific production, country scientific production, Wordcloud, factor analysis in bibliographic, and country collaboration map. Furthermore, 8 characteristic points that go through the analysis based on new tables of information are highlighted and discussed to cover several important facts and percentages associated with standardising the evaluation criteria, MCDM theory in ranking alternatives and weighting criteria, operators used with the MCDM methods, normalisation types for the data used, MCDM theory contexts, selected experts ways, validation scheme for effective MCDM theory and the challenges of MCDM theory used in COVID-19 studies. Accordingly, a recommended MCDM theory solution is presented through three distinct phases as a future direction in COVID19 studies. Key phases of this methodology include the Fuzzy Delphi method for unifying criteria and establishing importance level, Fuzzy weighted Zero Inconsistency for weighting to mitigate the shortcomings of the previous weighting techniques and the MCDM approach by the name Fuzzy Decision by Opinion Score method for prioritising alternatives and providing a unique ranking solution. This study will provide MCDM researchers and the wider community an overview of the current status of MCDM evaluation and development method","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"55 6","pages":"4979 - 5062"},"PeriodicalIF":12.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-021-10124-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39753892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human activity recognition in artificial intelligence framework: a narrative review","authors":"Neha Gupta,&nbsp;Suneet K. Gupta,&nbsp;Rajesh K. Pathak,&nbsp;Vanita Jain,&nbsp;Parisa Rashidi,&nbsp;Jasjit S. Suri","doi":"10.1007/s10462-021-10116-x","DOIUrl":"10.1007/s10462-021-10116-x","url":null,"abstract":"<div><p>Human activity recognition (HAR) has multifaceted applications due to its worldly usage of acquisition devices such as smartphones, video cameras, and its ability to capture human activity data. While electronic devices and their applications are steadily growing, the advances in Artificial intelligence (AI) have revolutionized the ability to extract deep hidden information for accurate detection and its interpretation. This yields a better understanding of rapidly growing acquisition devices, AI, and applications, the three pillars of HAR under one roof. There are many review articles published on the general characteristics of HAR, a few have compared all the HAR devices at the same time, and few have explored the impact of evolving AI architecture. In our proposed review, a detailed narration on the three pillars of HAR is presented covering the period from 2011 to 2021. Further, the review presents the recommendations for an improved HAR design, its reliability, and stability. Five major findings were: (1) HAR constitutes three major pillars such as devices, AI and applications; (2) HAR has dominated the healthcare industry; (3) Hybrid AI models are in their infancy stage and needs considerable work for providing the stable and reliable design. Further, these trained models need solid prediction, high accuracy, generalization, and finally, meeting the objectives of the applications without bias; (4) little work was observed in abnormality detection during actions; and (5) almost no work has been done in forecasting actions. We conclude that: (a) HAR industry will evolve in terms of the three pillars of electronic devices, applications and the type of AI. (b) AI will provide a powerful impetus to the HAR industry in future.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"55 6","pages":"4755 - 4808"},"PeriodicalIF":12.0,"publicationDate":"2022-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-021-10116-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10834803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advantage matrix: two novel multi-attribute decision-making methods and their applications","authors":"Bin Yu,&nbsp;Zeshui Xu","doi":"10.1007/s10462-021-10126-9","DOIUrl":"10.1007/s10462-021-10126-9","url":null,"abstract":"<div><p>By comparing attributes of objects in an information system, the advantage matrix on the object set is established in this paper. The contributions can be identified as follows: (1) The advantage degree is proposed by the accumulation of the advantage matrix. (2) Based on the advantage matrix, the advantage (disadvantage) neighborhood approximation operator and the advantage (disadvantage) correlation approximation operator are defined and studied. Based on these two new operators, the neighborhood degree and the correlation degree are presented. The relationships between them are also investigated to demonstrate the value of the proposed method. (3) Finally, based on the above three degrees, new algorithms are designed, in which the effectiveness and robustness of the algorithms are analyzed by practical examples.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"55 6","pages":"4463 - 4484"},"PeriodicalIF":12.0,"publicationDate":"2022-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-021-10126-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39962590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of most effected cycles and busiest network route based on complexity function of graph in fuzzy environment","authors":"Soumitra Poulik,&nbsp;Ganesh Ghorai","doi":"10.1007/s10462-021-10111-2","DOIUrl":"10.1007/s10462-021-10111-2","url":null,"abstract":"<div><p>Connectivity and strength has a major role in the field of network connecting with real world life. Complexity function is one of these parameter which has manifold number of applications in molecular chemistry and the theory of network. Firstly, this paper introduces the thought of complexity function of fuzzy graph with its properties. Second, based on the highest and lowest load on a network system, the boundaries of complexity function of different types of fuzzy graphs are established. Third, the behavior of complexity function in fuzzy cycle, fuzzy tree and complete fuzzy graph are discussed with their properties. Fourth, applications of these thoughts are bestowed to identify the most effected COVID-19 cycles between some communicated countries using the concept of complexity function of fuzzy graph. Also the selection of the busiest network stations and connected internet paths can be done using the same concept in a graphical wireless network system.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"55 6","pages":"4557 - 4574"},"PeriodicalIF":12.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-021-10111-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39824689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Group decision-making with Fermatean fuzzy soft expert knowledge","authors":"Muhammad Akram,&nbsp;Ghous Ali,&nbsp;José Carlos R. Alcantud,&nbsp;Aneesa Riaz","doi":"10.1007/s10462-021-10119-8","DOIUrl":"10.1007/s10462-021-10119-8","url":null,"abstract":"<div><p>With the rapid growth of population, the global impact of solar technology is increasing by the day due to its advantages over other power production technologies. Demand for solar panel systems is soaring, thus provoking the arrival of many new manufacturers. Sale dealers or suppliers face an uncertain problem to choose the most adequate technological solution. To effectively address such kind of issues, in this paper we propose the Fermatean fuzzy soft expert set model by combining Fermatean fuzzy sets and soft expert sets. We describe this hybrid model with numerical examples. From a theoretical standpoint, we demonstrate some essential properties and define operations for this setting. They comprise the definitions of complement, union and intersection, the OR operation and the AND operation. Concerning practice in this new environment, we provide an algorithm for multi-criteria group decision making whose productiveness and authenticity is dutifully tested. We explore a practical application of this approach (that is, the selection of a suitable brand of solar panel system). Lastly, we give a comparison of our model with certain related mathematical tools, including fuzzy and intuitionistic fuzzy soft expert set models.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"55 7","pages":"5349 - 5389"},"PeriodicalIF":12.0,"publicationDate":"2022-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-021-10119-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39824688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using artificial intelligence technology to fight COVID-19: a review","authors":"Yong Peng,&nbsp;Enbin Liu,&nbsp;Shanbi Peng,&nbsp;Qikun Chen,&nbsp;Dangjian Li,&nbsp;Dianpeng Lian","doi":"10.1007/s10462-021-10106-z","DOIUrl":"10.1007/s10462-021-10106-z","url":null,"abstract":"<div><p>In late December 2019, a new type of coronavirus was discovered, which was later named severe acute respiratory syndrome coronavirus 2(SARS-CoV-2). Since its discovery, the virus has spread globally, with 2,975,875 deaths as of 15 April 2021, and has had a huge impact on our health systems and economy. How to suppress the continued spread of new coronary pneumonia is the main task of many scientists and researchers. The introduction of artificial intelligence technology has provided a huge contribution to the suppression of the new coronavirus. This article discusses the main application of artificial intelligence technology in the suppression of coronavirus from three major aspects of identification, prediction, and development through a large amount of literature research, and puts forward the current main challenges and possible development directions. The results show that it is an effective measure to combine artificial intelligence technology with a variety of new technologies to predict and identify COVID-19 patients.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"55 6","pages":"4941 - 4977"},"PeriodicalIF":12.0,"publicationDate":"2022-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-021-10106-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39799549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}