Artificial Intelligence Review最新文献

{"title":"Bio-inspired disease prediction: harnessing the power of electric eel foraging optimization algorithm with machine learning for heart disease prediction","authors":"Geetha Narasimhan,&nbsp;Akila Victor","doi":"10.1007/s10462-024-10975-0","DOIUrl":"10.1007/s10462-024-10975-0","url":null,"abstract":"<div><p>Heart disease is the most significant health problem around the world. Thus, it emphasizes the need for accurate and efficient predictive models for early diagnosis. This study proposes an innovative approach integrating the Electric Eel Foraging Optimization Algorithm (EEFOA) with the Random Forest (RF) algorithm for classifying heart disease prediction. EEFOA draws inspiration from the foraging behaviour of electric eels, a bio-inspired optimization framework capable of effectively exploring complex solutions. The objective is to improve the predictive performance of heart disease diagnosis by integrating optimization and Machine learning methodologies. The experiment uses a heart disease dataset comprising clinical and demographic features of at-risk individuals. Subsequently, EEFOA was applied to optimize the features of the dataset and classification using the RF algorithm, thereby enhancing its predictive performance. The results demonstrate that the Electric Eel Foraging Optimization Algorithm Random Forest (EEFOARF) model outperforms traditional RF and other state-of-the-art classifiers in terms of predictive accuracy, sensitivity, specificity, precision, and Log_Loss, achieving remarkable scores of 96.59%, 95.15%, 98.04%, 98%, and 0.1179, respectively. The proposed methodology has the potential to make a significant contribution, thereby reducing morbidity and mortality rates.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10975-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142519000","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":"Chronobridge: a novel framework for enhanced temporal and relational reasoning in temporal knowledge graphs","authors":"Qian Liu,&nbsp;Siling Feng,&nbsp;Mengxing Huang,&nbsp;Uzair Aslam Bhatti","doi":"10.1007/s10462-024-10983-0","DOIUrl":"10.1007/s10462-024-10983-0","url":null,"abstract":"<div><p>The task of predicting entities and relations in Temporal Knowledge Graph (TKG) extrapolation is crucial and has been studied extensively. Mainstream algorithms, such as Gated Recurrent Unit (GRU) models, primarily focus on encoding historical factual features within TKGs, often neglecting the importance of incorporating entities and relational features during decoding. This bias ultimately leads to loss of detail and inadequate prediction accuracy during the inference process. To address this issue, a novel ChronoBridge framework is proposed that features a dual mechanism of a chronological node encoder and a bridged feature fusion decoder. Specifically, the chronological node encoder employs an advanced recursive neural network with an enhanced GRU in an autoregressive manner to model historical KG sequences, thereby accurately capturing entity changes over time and significantly enhancing the model’s ability to identify and encode temporal patterns of facts across the timeline. Meanwhile, the bridged feature fusion decoder utilizes a new variant of GRU and a multilayer perception mechanism during the prediction phase to extract entity and relation features and fuse them for inference, thereby strengthening the reasoning capabilities of the model for future events. Testing on three standard datasets showed significant improvements, with a 25.21% increase in MRR accuracy and a 39.38% enhancement in relation inference. This advancement not only improves the understanding of temporal evolution in knowledge graphs but also sets a foundation for future research and applications of TKG reasoning.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10983-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142453024","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":"Counterfactuals in fuzzy relational models","authors":"Rami Al-Hmouz,&nbsp;Witold Pedrycz,&nbsp;Ahmed Ammari","doi":"10.1007/s10462-024-10996-9","DOIUrl":"10.1007/s10462-024-10996-9","url":null,"abstract":"<div><p>Given the pressing need for explainability in Machine Learning systems, the studies on counterfactual explanations have gained significant interest. This research delves into this timely problem cast in a unique context of relational systems described by fuzzy relational equations. We develop a comprehensive solution to the counterfactual problems encountered in this setting, which is a novel contribution to the field. An underlying optimization problem is formulated, and its gradient-based solution is constructed. We demonstrate that the non-uniqueness of the derived solution is conveniently formalized and quantified by admitting a result coming in the form of information granules of a higher type, namely type-2 or interval-valued fuzzy set. The construction of the solution in this format is realized by invoking the principle of justifiable granularity, another innovative aspect of our research. We also discuss ways of designing fuzzy relations and elaborate on methods of carrying out counterfactual explanations in rule-based models. Illustrative examples are included to present the performance of the method and interpret the obtained results.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10996-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452893","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":"Graph neural networks for multi-view learning: a taxonomic review","authors":"Shunxin Xiao,&nbsp;Jiacheng Li,&nbsp;Jielong Lu,&nbsp;Sujia Huang,&nbsp;Bao Zeng,&nbsp;Shiping Wang","doi":"10.1007/s10462-024-10990-1","DOIUrl":"10.1007/s10462-024-10990-1","url":null,"abstract":"<div><p>With the explosive growth of user-generated content, multi-view learning has become a rapidly growing direction in pattern recognition and data analysis areas. Due to the significant application value of multi-view learning, there has been a continuous emergence of research based on machine learning methods and traditional deep learning paradigms. The core challenge in multi-view learning lies in harnessing both consistent and complementary information to forge a unified, comprehensive representation. However, many multi-view learning tasks are based on graph-structured data, making existing methods unable to effectively mine the information contained in the input multiple data sources. Recently, graph neural networks (GNN) techniques have been widely utilized to deal with non-Euclidean data, such as graphs or manifolds. Thus, it is essential to combine the advantages of the powerful learning capability of GNN models and multi-view data. In this paper, we aim to provide a comprehensive survey of recent research works on GNN-based multi-view learning. In detail, we first provide a taxonomy of GNN-based multi-view learning methods according to the input form of models: multi-relation, multi-attribute and mixed. Then, we introduce the applications of multi-view learning, including recommendation systems, computer vision and so on. Moreover, several public datasets and open-source codes are introduced for implementation. Finally, we analyze the challenges of applying GNN models on various multi-view learning tasks and state new future directions in this field.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10990-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452997","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":"Artificial intelligence techniques for dynamic security assessments - a survey","authors":"Miguel Cuevas,&nbsp;Ricardo Álvarez-Malebrán,&nbsp;Claudia Rahmann,&nbsp;Diego Ortiz,&nbsp;José Peña,&nbsp;Rodigo Rozas-Valderrama","doi":"10.1007/s10462-024-10993-y","DOIUrl":"10.1007/s10462-024-10993-y","url":null,"abstract":"<div><p>The increasing uptake of converter-interfaced generation (CIG) is changing power system dynamics, rendering them extremely dependent on fast and complex control systems. Regularly assessing the stability of these systems across a wide range of operating conditions is thus a critical task for ensuring secure operation. However, the simultaneous simulation of both fast and slow (electromechanical) phenomena, along with an increased number of critical operating conditions, pushes traditional dynamic security assessments (DSA) to their limits. While DSA has served its purpose well, it will not be tenable in future electricity systems with thousands of power electronic devices at different voltage levels on the grid. Therefore, reducing both human and computational efforts required for stability studies is more critical than ever. In response to these challenges, several advanced simulation techniques leveraging artificial intelligence (AI) have been proposed in recent years. AI techniques can handle the increased uncertainty and complexity of power systems by capturing the non-linear relationships between the system’s operational conditions and their stability without solving the set of algebraic-differential equations that model the system. Once these relationships are established, system stability can be promptly and accurately evaluated for a wide range of scenarios. While hundreds of research articles confirm that AI techniques are paving the way for fast stability assessments, many questions and issues must still be addressed, especially regarding the pertinence of studying specific types of stability with the existing AI-based methods and their application in real-world scenarios. In this context, this article presents a comprehensive review of AI-based techniques for stability assessments in power systems. Different AI technical implementations, such as learning algorithms and the generation and treatment of input data, are widely discussed and contextualized. Their practical applications, considering the type of stability, system under study, and type of applications, are also addressed. We review the ongoing research efforts and the AI-based techniques put forward thus far for DSA, contextualizing and interrelating them. We also discuss the advantages, limitations, challenges, and future trends of AI techniques for stability studies.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10993-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452995","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 review of Artificial Intelligence methods in bladder cancer: segmentation, classification, and detection","authors":"Ayah Bashkami,&nbsp;Ahmad Nasayreh,&nbsp;Sharif Naser Makhadmeh,&nbsp;Hasan Gharaibeh,&nbsp;Ahmed Ibrahim Alzahrani,&nbsp;Ayed Alwadain,&nbsp;Jia Heming,&nbsp;Absalom E. Ezugwu,&nbsp;Laith Abualigah","doi":"10.1007/s10462-024-10953-6","DOIUrl":"10.1007/s10462-024-10953-6","url":null,"abstract":"<div><p>Artificial intelligence (AI) and other disruptive technologies can potentially improve healthcare across various disciplines. Its subclasses, artificial neural networks, deep learning, and machine learning, excel in extracting insights from large datasets and improving predictive models to boost their utility and accuracy. Though research in this area is still in its early phases, it holds enormous potential for the diagnosis, prognosis, and treatment of urological diseases, such as bladder cancer. The long-used nomograms and other classic forecasting approaches are being reconsidered considering AI’s capabilities. This review emphasizes the coming integration of artificial intelligence into healthcare settings while critically examining the most recent and significant literature on the subject. This study seeks to define the status of AI and its potential for the future, with a special emphasis on how AI can transform bladder cancer diagnosis and treatment.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10953-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452994","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 survey of recent approaches to form understanding in scanned documents","authors":"Abdelrahman Abdallah,&nbsp;Daniel Eberharter,&nbsp;Zoe Pfister,&nbsp;Adam Jatowt","doi":"10.1007/s10462-024-11000-0","DOIUrl":"10.1007/s10462-024-11000-0","url":null,"abstract":"<div><p>This paper presents a comprehensive survey of over 100 research works on the topic of form understanding in the context of scanned documents. We delve into recent advancements and breakthroughs in the field, with particular focus on transformer-based models, which have been shown to improve performance in form understanding tasks by up to 25% in accuracy compared to traditional methods. Our research methodology involves an in-depth analysis of popular documents and trends over the last decade, including 15 state-of-the-art models and 10 benchmark datasets. By examining these works, we offer novel insights into the evolution of this domain. Specifically, we highlight how transformers have revolutionized form-understanding techniques by enhancing the ability to process noisy scanned documents with significant improvements in OCR accuracy. Furthermore, we present an overview of the most relevant datasets, such as FUNSD, CORD, and SROIE, which serve as benchmarks for evaluating the performance of the models. By comparing the capabilities of these models and reporting an average improvement of 10–15% in key form extraction tasks, we aim to provide researchers and practitioners with useful guidance in selecting the most suitable solutions for their form understanding applications.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11000-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452996","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":"Tire wear monitoring using feature fusion and CatBoost classifier","authors":"C. V. Prasshanth,&nbsp;V. Sugumaran","doi":"10.1007/s10462-024-10999-6","DOIUrl":"10.1007/s10462-024-10999-6","url":null,"abstract":"<div><p>Addressing the critical issue of tire wear is essential for enhancing vehicle safety, performance, and maintenance. Worn-out tires often lead to accidents, underscoring the need for effective monitoring systems. This study is vital for several reasons: safety, as worn tires increase the risk of accidents due to reduced traction and longer braking distances; performance, as uneven tire wear affects vehicle handling and fuel efficiency; maintenance costs, as early detection can prevent more severe damage to suspension and alignment systems; and regulatory compliance, as ensuring tire integrity helps meet safety regulations imposed by transportation authorities. In response, this study systematically evaluates tire conditions at 25%, 50%, 75%, and 100% wear, with an intact tire as a reference, using vibration signals as the primary data source. The analysis employs statistical, histogram, and autoregressive–moving-average (ARMA) feature extraction techniques, followed by feature selection to identify key parameters influencing tire wear. CatBoost is used for feature classification, leveraging its adaptability and efficiency in distinguishing varying wear patterns. Additionally, the study incorporates feature fusion to combine different types of features for a more comprehensive analysis. The proposed methodology not only offers a robust framework for accurately classifying tire wear levels but also holds significant potential for real-time implementation, contributing to proactive maintenance practices, prolonged tire lifespan, and overall vehicular safety.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10999-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451092","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":"Clarity in complexity: how aggregating explanations resolves the disagreement problem","authors":"Oana Mitruț,&nbsp;Gabriela Moise,&nbsp;Alin Moldoveanu,&nbsp;Florica Moldoveanu,&nbsp;Marius Leordeanu,&nbsp;Livia Petrescu","doi":"10.1007/s10462-024-10952-7","DOIUrl":"10.1007/s10462-024-10952-7","url":null,"abstract":"<div><p>The Rashômon Effect, applied in Explainable Machine Learning, refers to the disagreement between the explanations provided by various attribution explainers and to the dissimilarity across multiple explanations generated by a particular explainer for a single instance from the dataset (differences between feature importances and their associated signs and ranks), an undesirable outcome especially in sensitive domains such as healthcare or finance. We propose a method inspired from textual-case based reasoning for aligning explanations from various explainers in order to resolve the disagreement and dissimilarity problems. We iteratively generated a number of 100 explanations for each instance from six popular datasets, using three prevalent feature attribution explainers: LIME, Anchors and SHAP (with the variations Tree SHAP and Kernel SHAP) and consequently applied a global cluster-based aggregation strategy that quantifies alignment and reveals similarities and associations between explanations. We evaluated our method by weighting the <span>(:k)</span>-NN algorithm with agreed feature overlap explanation weights and compared it to a non-weighted <span>(:k)</span>-NN predictor, having as task binary classification. Also, we compared the results of the weighted <span>(:k)</span>-NN algorithm using aggregated feature overlap explanation weights to the weighted <span>(:k)</span>-NN algorithm using weights produced by a single explanation method (either LIME, SHAP or Anchors). Our global alignment method benefited the most from a hybridization with feature importance scores (information gain), that was essential for acquiring a more accurate estimate of disagreement, for enabling explainers to reach a consensus across multiple explanations and for supporting effective model learning through improved classification performance.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10952-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451093","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":"Controllable image synthesis methods, applications and challenges: a comprehensive survey","authors":"Shanshan Huang,&nbsp;Qingsong Li,&nbsp;Jun Liao,&nbsp;Shu Wang,&nbsp;Li Liu,&nbsp;Lian Li","doi":"10.1007/s10462-024-10987-w","DOIUrl":"10.1007/s10462-024-10987-w","url":null,"abstract":"<div><p>Controllable Image Synthesis (CIS) is a methodology that allows users to generate desired images or manipulate specific attributes of images by providing precise input conditions or modifying latent representations. In recent years, CIS has attracted considerable attention in the field of image processing, with significant advances in consistency, controllability and harmony. However, several challenges still remain, particularly regarding the fine-grained controllability and interpretability of synthesized images. In this paper, we comprehensively and systematically review the CIS from problem definition, taxonomy and evaluation systems to existing challenges and future research directions. First, the definition of CIS is given, and several representative deep generative models are introduced in detail. Second, the existing CIS methods are divided into three categories according to the different control manners used and discuss the typical work in each category critically. Furthermore, we introduce the public datasets and evaluation metrics commonly used in image synthesis and analyze the representative CIS methods. Finally, we present several open issues and discuss the future research direction of CIS.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10987-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447318","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}