Artificial Intelligence Review最新文献

{"title":"Image data hiding schemes based on metaheuristic optimization: a review","authors":"Anna Melman,&nbsp;Oleg Evsutin","doi":"10.1007/s10462-023-10537-w","DOIUrl":"10.1007/s10462-023-10537-w","url":null,"abstract":"<div><p>The digital content exchange on the Internet is associated with information security risks. Hiding data in digital images is a promising direction in data protection and is an alternative to cryptographic methods. Steganography algorithms create covert communication channels and protect the confidentiality of messages embedded in cover images. Watermarking algorithms embed invisible marks in images for further image authentication and proof of the authorship. The main difficulty in the development of data hiding schemes is to achieve a balance between indicators of embedding quality, including imperceptibility, capacity, and robustness. An effective approach to solving this problem is the use of metaheuristic optimization algorithms, such as genetic algorithm, particle swarm optimization, artificial bee colony, and others. In this paper, we present an overview of data hiding techniques based on metaheuristic optimization. We review and analyze image steganography and image watermarking schemes over the past 6 years. We propose three levels of research classification: embedding purpose level, optimization purpose level, and level of metaheuristics. The results demonstrate the high relevance of using metaheuristic optimization in the development of new data hiding algorithms. Based on the results of the review, we formulate the main problems of this scientific field and suggest promising areas for further research.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"56 12","pages":"15375 - 15447"},"PeriodicalIF":12.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47174257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transition of traditional method to deep learning based computer-aided system for breast cancer using Automated Breast Ultrasound System (ABUS) images: a review","authors":"Dayangku Nur Faizah Pengiran Mohamad,&nbsp;Syamsiah Mashohor,&nbsp;Rozi Mahmud,&nbsp;Marsyita Hanafi,&nbsp;Norafida Bahari","doi":"10.1007/s10462-023-10511-6","DOIUrl":"10.1007/s10462-023-10511-6","url":null,"abstract":"<div><p>Breast cancer (BC) is the leading cause of death among women worldwide. Early detection and diagnosis of BC can help significantly reduce the mortality rate. Ultrasound (US) can be an ideal screening tool for BC detection. However, the hand-held US (HHUS) is an impractical tool because it is operator-dependent, time-consuming, and increases the likelihood of false-positive results. Thus, to address these issues, the 3D Automated Breast Ultrasound System (ABUS) was designed for BC detection and diagnosis. This paper presents the transition from traditional approaches to deep learning (DL) based CAD systems in the ABUS image data set. The capabilities and limitations of both techniques are also reviewed rigorously. This review will help in understanding the current limitations to leverage their potential in diagnostic radiology to improve performance and BC patient care.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"56 12","pages":"15271 - 15300"},"PeriodicalIF":12.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45370587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scene text understanding: recapitulating the past decade","authors":"Mridul Ghosh,&nbsp;Himadri Mukherjee,&nbsp;Sk Md Obaidullah,&nbsp;Xiao-Zhi Gao,&nbsp;Kaushik Roy","doi":"10.1007/s10462-023-10530-3","DOIUrl":"10.1007/s10462-023-10530-3","url":null,"abstract":"<div><p>Computational perception has indeed been dramatically modified and reformed from handcrafted feature-based techniques to the advent of deep learning. Scene text identification and recognition have inexorably been touched by this bow effort of upheaval, ushering in the period of deep learning. It is an important aspect of machine vision. Society has seen significant improvements in thinking, approach, and effectiveness over time. The goal of this study is to summarize and analyze the important developments and notable advancements in scene text identification and recognition over the past decade. We have discussed the significant handcrafted feature-based techniques which had been regarded as flagship systems in the past. They were succeeded by deep learning-based techniques. We have discussed such approaches from their inception to the development of complex models which have taken scene text identification to the next stage.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"56 12","pages":"15301 - 15373"},"PeriodicalIF":12.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47828836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive survey on NSGA-II for multi-objective optimization and applications","authors":"Haiping Ma,&nbsp;Yajing Zhang,&nbsp;Shengyi Sun,&nbsp;Ting Liu,&nbsp;Yu Shan","doi":"10.1007/s10462-023-10526-z","DOIUrl":"10.1007/s10462-023-10526-z","url":null,"abstract":"<div><p>In the last two decades, the fast and elitist non-dominated sorting genetic algorithm (NSGA-II) has attracted extensive research interests, and it is still one of the hottest research methods to deal with multi-objective optimization problems. Considering the importance and wide applications of NSGA-II method, we believe it is the right time to provide a comprehensive survey of the research work in this area, and also to discuss the potential in the future research. The purpose of this paper is to summarize and explore the literature on NSGA-II and another version called NSGA-III, a reference-point based many-objective NSGA-II approach. In this paper, we first introduce the concept of multi-objective optimization and the foundation of NSGA-II. Then we review the family of NSGA-II and their modifications, and classify their applications in engineering community. Finally, we present several interesting open research directions of NSGA-II for multi-objective optimization.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"56 12","pages":"15217 - 15270"},"PeriodicalIF":12.0,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48772559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of applications of natural language processing and future challenges with special emphasis in text-based emotion detection","authors":"Sheetal Kusal,&nbsp;Shruti Patil,&nbsp;Jyoti Choudrie,&nbsp;Ketan Kotecha,&nbsp;Deepali Vora,&nbsp;Ilias Pappas","doi":"10.1007/s10462-023-10509-0","DOIUrl":"10.1007/s10462-023-10509-0","url":null,"abstract":"<div><p>Artificial Intelligence (AI) has been used for processing data to make decisions, Interact with humans, and understand their feelings and emotions. With the advent of the Internet, people share and express their thoughts on day-to-day activities and global and local events through text messaging applications. Hence, it is essential for machines to understand emotions in opinions, feedback, and textual dialogues to provide emotionally aware responses to users in today's online world. The field of text-based emotion detection (TBED) is advancing to provide automated solutions to various applications, such as business and finance, to name a few. TBED has gained a lot of attention in recent times. The paper presents a systematic literature review of the existing literature published between 2005 and 2021 in TBED. This review has meticulously examined 63 research papers from the IEEE, Science Direct, Scopus, and Web of Science databases to address four primary research questions. It also reviews the different applications of TBED across various research domains and highlights its use. An overview of various emotion models, techniques, feature extraction methods, datasets, and research challenges with future directions has also been represented.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"56 12","pages":"15129 - 15215"},"PeriodicalIF":12.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42457962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Granular reduction in formal fuzzy contexts: graph representation, graph approach and its algorithm","authors":"Zengtai Gong,&nbsp;Jing Zhang","doi":"10.1007/s10462-023-10523-2","DOIUrl":"10.1007/s10462-023-10523-2","url":null,"abstract":"<div><p>Attribute reduction is one of the significant research issues in the formal fuzzy context (FFC). However, the extant method of computing the minimal granular reducts by Boolean reasoning is an NP problem. To this end, a graph-theoretic-based heuristic algorithm is proposed to compute the granular reducts in an FFC. We introduce the induced graph of the granular discernibility matrix and show that the minimal vertex cover of this induced graph is equivalent to the reduction of the FFC, thus transforming the problem of reduction the FFC into the problem of finding the minimal vertex cover of the graph. The manuscript also sets forth algorithms for finding minimal granular reducts based on graph theory. Further, data experiments are designed, and we formulate a transformation model from an information system with multi-valued attributes to an FFC, considering the characteristics of the continuous type of numerical attributes used in the experiments. Experimental results show that our proposed method performs well in terms of time complexity and running time.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"56 12","pages":"15101 - 15127"},"PeriodicalIF":12.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47814269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent techniques in e-learning: a literature review","authors":"Miloš Ilić,&nbsp;Vladimir Mikić,&nbsp;Lazar Kopanja,&nbsp;Boban Vesin","doi":"10.1007/s10462-023-10508-1","DOIUrl":"10.1007/s10462-023-10508-1","url":null,"abstract":"<div><p>Online learning has become increasingly important, having in mind the latest events, imposed isolation measures and closed schools and campuses. Consequently, teachers and students need to embrace digital tools and platforms, bridge the newly established physical gap between them, and consume education in various new ways. Although literature indicates that the development of intelligent techniques must be incorporated in e-learning systems to make them more effective, the need exists for research on how these techniques impact the whole process of online learning, and how they affect learners’ performance. This paper aims to provide comprehensive research on innovations in e-learning, and present a literature review of used intelligent techniques and explore their potential benefits. This research presents a categorization of intelligent techniques, and explores their roles in e-learning environments. By summarizing the state of the art in the area, the authors outline past research, highlight its gaps, and indicate important implications for practice. The goal is to understand better available intelligent techniques, their implementation and application in e-learning context, and their impact on improving learning in online education. Finally, the review concludes that AI-supported solutions not only can support learner and teacher, by recommending resources and grading submissions, but they can offer fully personalized learning experience.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"56 12","pages":"14907 - 14953"},"PeriodicalIF":12.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-023-10508-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41379637","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":"Optimisation of electrical Impedance tomography image reconstruction error using heuristic algorithms","authors":"Talha A. Khan,&nbsp;Sai Ho Ling,&nbsp;Arslan A. Rizvi","doi":"10.1007/s10462-023-10527-y","DOIUrl":"10.1007/s10462-023-10527-y","url":null,"abstract":"<div><p>Preventing living tissues’ direct exposure to ionising radiation has resulted in tremendous growth in medical imaging and e-health, enhancing intensive care of perilous patients and helping to improve quality of life. Moreover, the practice of image-reconstruction instruments that utilise ionising radiation significantly impacts the patient’s health. Prolonged or frequent exposure to ionising radiation is linked to several illnesses like cancer. These factors urged the advancement of non-invasive approaches, for instance, Electrical Impedance Tomography (EIT), a portable, non-invasive, low-cost, and safe imaging method. EIT image reconstruction still demands more exploitation, as it is an inverse and ill-conditioned problem. Numerous numerical techniques are used to answer this problem without producing anatomically unpredictable outcomes. Evolutionary Computational techniques can substitute conventional methods that usually create low-resolution blurry images. EIT reconstruction techniques optimise the relative error of reconstruction using population-based optimisation methods presented in this work. Three advanced optimisation methods have been developed to facilitate the iterative procedure for avoiding anatomically erratic solutions. Three different optimising techniques, namely, (a) Advanced Particle Swarm Optimisation Algorithm, (b) Advanced Gravitational Search Algorithm, and (c) Hybrid Gravitational Search Particle Swarm Optimization Algorithm (HGSPSO), are used. By utilising the advantages of these proposed techniques, the convergence and solution stability performance is improved. EIT images were obtained from the EIDORS library database for two case studies. The image reconstruction was optimised using the three proposed algorithms. EIDORS library was used for generating and solving forward and reverse problems. Two case studies were undertaken, i.e. circular tank simulation and gastric emptying. Thus, the results are analysed and presented as a real-world application of population-based optimisation methods. Results obtained from the proposed methods are quantitatively assessed with ground truth images using the relative mean squared error, confirming that a low error value is reached in the results. The HGSPSO algorithm performs better than the other proposed methods regarding solution quality and stability.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"56 12","pages":"15079 - 15099"},"PeriodicalIF":12.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46200787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient data interpretation and artificial intelligence enabled IoT based smart sensing system","authors":"Achyut Shankar","doi":"10.1007/s10462-023-10519-y","DOIUrl":"10.1007/s10462-023-10519-y","url":null,"abstract":"<div><p>Underwater wireless communications (UWC), based on acoustic waves, radio frequency waves, and optical waves, are currently deployed using underwater communications networks. Wireless sensor communications are among the most common UWC technologies because they offer connectivity over long distances. However, the UWC complex problems include restricted bandwidth, multitrack loss, limited battery power, and latency in propagation. Hence in this paper, Artificial Intelligence based Effective Data Interpretation Approach (AI-EDIA) has been proposed to improve the underwater wireless sensor network communication and less computational Time in IoT platform. The proposed AI-EIDA utilizes the discrete cosine transform (DCT) with frequency modulation multiplexing (FMM) for underwater acoustic communication. Underwater acoustic channels are categorized as double Time and frequency distribution channels. Therefore, the reverse DCT structure provides the orthogonal characteristic of the traditional FMM with the additional advantages of reduced execution and improved speed when the actual calculations are needed. Thus the experimental results show that AI-EDIA decreases energy usage and less delay rate to 0.45 s.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"56 12","pages":"15053 - 15077"},"PeriodicalIF":12.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48609828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic study on deep learning-based plant disease detection or classification","authors":"C. K. Sunil,&nbsp;C. D. Jaidhar,&nbsp;Nagamma Patil","doi":"10.1007/s10462-023-10517-0","DOIUrl":"10.1007/s10462-023-10517-0","url":null,"abstract":"<div><p>Plant diseases impact extensively on agricultural production growth. It results in a price hike on food grains and vegetables. To reduce economic loss and to predict yield loss, early detection of plant disease is highly essential. Current plant disease detection involves the physical presence of domain experts to ascertain the disease; this approach has significant limitations, namely: domain experts need to move from one place to another place which involves transportation cost as well as travel time; heavy transportation charge makes the domain expert not travel a long distance, and domain experts may not be available all the time, and though the domain experts are available, the domain expert(s) may charge high consultation charge which may not be feasible for many farmers. Thus, there is a need for a cost-effective, robust automated plant disease detection or classification approach. In this line, various plant disease detection approaches are proposed in the literature. This systematic study provides various Deep Learning-based and Machine Learning-based plant disease detection or classification approaches; 160 diverse research works are considered in this study, which comprises single network models, hybrid models, and also real-time detection approaches. Around 57 studies considered multiple plants, and 103 works considered a single plant. 50 different plant leaf disease datasets are discussed, which include publicly available and publicly unavailable datasets. This study also discusses the various challenges and research gaps in plant disease detection. This study also highlighted the importance of hyperparameters in deep learning.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"56 12","pages":"14955 - 15052"},"PeriodicalIF":12.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-023-10517-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46955334","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}