Artificial Intelligence Review最新文献

{"title":"Artificial intelligence for geometry-based feature extraction, analysis and synthesis in artistic images: a survey","authors":"Mridula Vijendran,&nbsp;Jingjing Deng,&nbsp;Shuang Chen,&nbsp;Edmond S. L. Ho,&nbsp;Hubert P. H. Shum","doi":"10.1007/s10462-024-11051-3","DOIUrl":"10.1007/s10462-024-11051-3","url":null,"abstract":"<div><p>Artificial Intelligence significantly enhances the visual art industry by analyzing, identifying and generating digitized artistic images. This review highlights the substantial benefits of integrating geometric data into AI models, addressing challenges such as high inter-class variations, domain gaps, and the separation of style from content by incorporating geometric information. Models not only improve AI-generated graphics synthesis quality, but also effectively distinguish between style and content, utilizing inherent model biases and shared data traits. We explore methods like geometric data extraction from artistic images, the impact on human perception, and its use in discriminative tasks. The review also discusses the potential for improving data quality through innovative annotation techniques and the use of geometric data to enhance model adaptability and output refinement. Overall, incorporating geometric guidance boosts model performance in classification and synthesis tasks, providing crucial insights for future AI applications in the visual arts domain.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11051-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859501","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":"Digital phenotypes and digital biomarkers for health and diseases: a systematic review of machine learning approaches utilizing passive non-invasive signals collected via wearable devices and smartphones","authors":"Alireza Sameh,&nbsp;Mehrdad Rostami,&nbsp;Mourad Oussalah,&nbsp;Raija Korpelainen,&nbsp;Vahid Farrahi","doi":"10.1007/s10462-024-11009-5","DOIUrl":"10.1007/s10462-024-11009-5","url":null,"abstract":"<div><p>Passive non-invasive sensing signals from wearable devices and smartphones are typically collected continuously without user input. This passive and continuous data collection makes these signals suitable for moment-by-moment monitoring of health-related outcomes, disease diagnosis, and prediction modeling. A growing number of studies have utilized machine learning (ML) approaches to predict and analyze health indicators and diseases using passive non-invasive signals collected via wearable devices and smartphones. This systematic review identified peer-reviewed journal articles utilizing ML approaches for digital phenotyping and measuring digital biomarkers to analyze, screen, identify, and/or predict health-related outcomes using passive non-invasive signals collected from wearable devices or smartphones. PubMed, PubMed with Mesh, Web of Science, Scopus, and IEEE Xplore were searched for peer-reviewed journal articles published up to June 2024, identifying 66 papers. We reviewed the study populations used for data collection, data acquisition details, signal types, data preparation steps, ML approaches used, digital phenotypes and digital biomarkers, and health outcomes and diseases predicted using these ML techniques. Our findings highlight the promising potential for objective tracking of health outcomes and diseases using passive non-invasive signals collected from wearable devices and smartphones with ML approaches for characterization and prediction of a range of health outcomes and diseases, such as stress, seizure, fatigue, depression, and Parkinson’s disease. Future studies should focus on improving the quality of collected data, addressing missing data challenges, providing better documentation on study participants, and sharing the source code of the implemented methods and algorithms, along with their datasets and methods, for reproducibility purposes.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11009-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859542","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":"Models of symbol emergence in communication: a conceptual review and a guide for avoiding local minima","authors":"Julian Zubek,&nbsp;Tomasz Korbak,&nbsp;Joanna Rączaszek-Leonardi","doi":"10.1007/s10462-024-11048-y","DOIUrl":"10.1007/s10462-024-11048-y","url":null,"abstract":"<div><p>\u0000 Computational simulations are a popular method for testing hypotheses about the emergence of symbolic communication. This kind of research is performed in a variety of traditions including language evolution, developmental psychology, cognitive science, artificial intelligence, and robotics. The motivations for the models are different, but the operationalisations and methods used are often similar. We identify the assumptions and explanatory targets of the most representative models and summarise the known results. We claim that some of the assumptions—such as portraying meaning in terms of mapping, focusing on the descriptive function of communication, and modelling signals with amodal tokens—may hinder the success of modelling. Relaxing these assumptions and foregrounding the interactions of embodied and situated agents allows one to systematise the multiplicity of pressures under which symbolic systems evolve. In line with this perspective, we sketch the road towards modelling the emergence of meaningful symbolic communication, where symbols are simultaneously grounded in action and perception and form an abstract system.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11048-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859541","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":"How the internet of things technology improves agricultural efficiency","authors":"Amenu Leta Duguma,&nbsp;Xiuguang Bai","doi":"10.1007/s10462-024-11046-0","DOIUrl":"10.1007/s10462-024-11046-0","url":null,"abstract":"<div><p>Meeting the rising global food demand among limited resources necessitates transformative agricultural innovations. The Internet of Things (IoT) emerges as a pivotal technology in modern agriculture, offering data-driven solutions to optimize productivity and sustainability. This review provides a focused exploration of IoT’s transformative role in agriculture, analyzing its integration with big data, real-time monitoring, and precision farming practices. Key insights include global market trends, projections for IoT adoption in agriculture by 2030, and advancements in IoT-related technologies shaping the future of agritech. The review underscores how IoT enhances agricultural efficiency by enabling precise data collection, automated decision-making, and optimized resource use, while addressing operational challenges such as interoperability, scalability, cost constraints, and regulatory hurdles. By consolidating evidence from emerging studies, this work advocates for interdisciplinary collaborations to deepen understanding and innovation in IoT-driven smart agriculture, positioning it as a cornerstone for achieving global food security.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11046-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859540","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":"Mitigating content poisoning attacks in named data networking: a survey of recent solutions, limitations, challenges and future research directions","authors":"Syed Sajid Ullah,&nbsp;Saddam Hussain,&nbsp;Ihsan Ali,&nbsp;Hizbullah Khattak,&nbsp;Spyridon Mastorakis","doi":"10.1007/s10462-024-10994-x","DOIUrl":"10.1007/s10462-024-10994-x","url":null,"abstract":"<div><p>Named Data Networking (NDN) is one of the capable applicants for the future Internet architecture, where communications focus on content rather than providing content. NDN implements Information-Centric Networking (ICN) with its unique node structure and significant characteristics such as built-in mobility support, multicast support, and efficient content distribution to end-users. It has several key features, including inherent security, that protect the content rather than the communication channel. Despite the good features that NDN provides, it is nonetheless vulnerable to a variety of attacks, the most critical of them is the Content Poisoning Attack (CPA). In this survey, the existing solutions presented for the prevention of CPA in the NDN paradigm have been critically analyzed. Furthermore, we also compared the suggested schemes based on latency, communication overhead, and security. In addition, we have also shown the possibility of other possible NDN attacks on the suggested schemes. Finally, we adds some open research challanges.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10994-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859525","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":"Improved artificial rabbits algorithm for global optimization and multi-level thresholding color image segmentation","authors":"Heming Jia,&nbsp;Yuanyuan Su,&nbsp;Honghua Rao,&nbsp;Muzi Liang,&nbsp;Laith Abualigah,&nbsp;Chibiao Liu,&nbsp;Xiaoguo Chen","doi":"10.1007/s10462-024-11035-3","DOIUrl":"10.1007/s10462-024-11035-3","url":null,"abstract":"<div><p>The Artificial Rabbits Optimization Algorithm is a metaheuristic optimization algorithm proposed in 2022. This algorithm has weak local search ability, which can easily lead to the algorithm falling into local optimal solutions. To overcome these limitations, this paper introduces an Improved Artificial Rabbits Optimization Algorithm (IARO) and demonstrates its effectiveness in multi-level threshold color image segmentation using the Otsu method. Initially, we apply a center-driven strategy to enhance exploration by updating the rabbit’s position during the random hiding phase. Additionally, when the algorithm stalls, a Gaussian Randomized Wandering (GRW) strategy is utilized to enable the algorithm to escape local optima and improve convergence accuracy. The performance of the IARO algorithm is evaluated using 23 standard benchmark functions and CEC2020 benchmark functions, and compared with nine other algorithms. Experimental results indicate that IARO excels in global optimization and demonstrates notable robustness. To assess its effectiveness in multi-threshold color image segmentation, the algorithm is tested on classical Berkeley images. Evaluation metrics including execution time, Peak Signal-to-Noise Ratio (PSNR), Feature Similarity (FSIM), Structural Similarity (SSIM), Boundary Displacement Error (BDE), The Probabilistic Rand Index (PRI), Variation of Information (VOI) and average fitness value are used to measure segmentation quality. The results reveal that IARO achieves high accuracy and fast segmentation speed, validating its efficiency and practical utility in real-world applications.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11035-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859660","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":"Advancing explainable MOOC recommendation systems: a morphological operations-based framework on partially ordered neutrosophic fuzzy hypergraphs","authors":"Mehbooba Shareef,&nbsp;Babita Roslind Jose,&nbsp;Jimson Mathew,&nbsp;Dayananda Pruthviraja","doi":"10.1007/s10462-024-11018-4","DOIUrl":"10.1007/s10462-024-11018-4","url":null,"abstract":"<div><p>Recommendation systems constitute an integral part of nearly all digital service platforms. However, the common assumption in most recommendation systems in the literature is that similar users will be interested in similar items. This assumption holds only sometimes due to the inherent inhomogeneity of user-item interactions. To address this challenge, we introduce a novel recommendation system that leverages partially ordered neutrosophic hypergraphs to model higher-order relationships among users and items. The partial ordering of nodes enables the system to develop efficient top-N recommendations with very high Normalized Discounted Cumulative Gain (NDCG). Our approach incorporates the morphological operation of dilation, applied to user clusters obtained through fuzzy spectral clustering of the hypergraph, to generate the requisite number of recommendations. Explanations for recommendations are obtained through morphological erosion applied on the dual of the embedded hypergraph. Through rigorous testing in educational and e-commerce domains, it has been proved that our method outperforms state-of-the-art techniques and demonstrates excellent performance for various evaluation parameters. The NDCG value, a measure of ranking quality, surpasses 0.10, and the Hit Ratio (HR) consistently falls within the range of 0.25 to 0.30. The Root Mean Square Error (RMSE) values are minimal, reaching as low as 0.4. These results collectively position our algorithm as a good choice for generating recommendations with proper explanations, making it a promising solution for real-world applications.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11018-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859653","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":"Task scheduling in cloud computing systems using multi-objective honey badger algorithm with two hybrid elite frameworks and circular segmentation screening","authors":"Si-Wen Zhang,&nbsp;Jie-Sheng Wang,&nbsp;Shi-Hui Zhang,&nbsp;Yu-Xuan Xing,&nbsp;Xiao-Fei Sui,&nbsp;Yun-Hao Zhang","doi":"10.1007/s10462-024-11032-6","DOIUrl":"10.1007/s10462-024-11032-6","url":null,"abstract":"<div><p>In cloud computing environment, task scheduling is the most critical problem to be solved. Two different multi-objective honey badger algorithms (MOHBA-I and MOHBA-II) based on hybrid elitist framework and circular segmentation screening are proposed for the multi-objective problem of task scheduling optimization in cloud computing systems. MOHBA-I and MOHBA-II combine the grid indexing mechanism and decomposition technique, respectively, to select better populations based on elite non-dominated sorting. A circular segmentation screening mechanism was proposed to retain the superior individuals when the regional density is too high to further maintain the diversity of the populations, and attach an external archive to preserve the uniformly diversified Pareto decomposition set. The performance of the proposed algorithms is verified by using test functions. MOHBA-I and MOHBA-II achieve the first and third rankings, respectively, compared to other classical multi-objective algorithms. Solve the cloud computing task scheduling problem using time, load and price cost as metrics, test for different task sizes, and compare MOHBA-I with algorithms such as NSGA-III, MOPSO and MOEA/D in the same experimental environment. When facing a large-scale task, MOHBA-I ranks first in HyperVolume value with 2.4449E−02 for two objectives and 9.2950E−03 for three objectives. The experimental results show that MOHBA-I finds the highest number of solutions with better convergence and coverage, obtaining a satisfactory Pareto front, which can provide more and better choices for decision makers.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11032-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859487","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":"Possibilistic C-means with novel image representation for image segmentation","authors":"Hanshuai Cui,&nbsp;Hongjian Wang,&nbsp;Wenyi Zeng,&nbsp;Yuqing Liu,&nbsp;Bo Zhao","doi":"10.1007/s10462-024-11057-x","DOIUrl":"10.1007/s10462-024-11057-x","url":null,"abstract":"<div><p>Image segmentation is the process of automatically dividing an image into several parts and extracting the relevant data and information. Compared to the traditional Fuzzy C-Means algorithm, the Possibilistic C-Means (PCM) algorithm has advantages in reducing the influence of noise on cluster center estimation. However, the PCM algorithm still shows poor clustering performance under high-intensity noise, which may lead to overlapping cluster centers. Considering the impact of neighborhood information of image pixels on the image segmentation results, this paper proposes a Vector-Based Possibilistic C-Means (VBPCM) algorithm. The algorithm incorporates neighborhood information and uses a vector representation method to describe image pixels. Additionally, an adjustable distance based on an exponential function is proposed to describe the similarity between vectors. The proposed VBPCM algorithm outperforms the conventional PCM, obtaining uplifiting gains of 4%, 2%, and 9% in Pixel Accuracy, Mean Pixel Accuracy, and Mean Intersection over Union, respectively. The experimental outputs illustrate that VBPCM algorithm can achieve more satisfactory cluster effect with high-intensity noise, further perform better in image segmentation task.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11057-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859659","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":"Ratai: recurrent autoencoder with imputation units and temporal attention for multivariate time series imputation","authors":"Xiaochen Lai,&nbsp;Yachen Yao,&nbsp;Jichong Mu,&nbsp;Wei Lu,&nbsp;Liyong Zhang","doi":"10.1007/s10462-024-11039-z","DOIUrl":"10.1007/s10462-024-11039-z","url":null,"abstract":"<div><p>Multivariate time series is ubiquitous in real-world applications, yet it often suffers from missing values that impede downstream analytical tasks. In this paper, we introduce the Long Short-Term Memory Network based Recurrent Autoencoder with Imputation Units and Temporal Attention Imputation Model (RATAI), tailored for multivariate time series. RATAI is designed to address certain limitations of traditional RNN-based imputation methods, which often focus on predictive modeling to estimate missing values, sometimes neglecting the contextual impact of observed data at and beyond the target time step. Drawing inspiration from Kalman smoothing, which effectively integrates past and future information to refine state estimations, RATAI aims to extract feature representations from time series data and use them to reconstruct a complete time series, thus overcoming the shortcomings of existing approaches. It employs a dual-stage imputation process: the encoder utilizes temporal information and attribute correlations to predict and impute missing values, and extract feature representation of imputed time series. Subsequently, the decoder reconstructs the series from the feature representation, and the reconstructed values are used as the final imputation values. Additionally, RATAI incorporates a temporal attention mechanism, allowing the decoder to focus on highly relevant inputs during reconstruction. This model can be trained directly using data that contains missing values, avoiding the misleading effects on model training that can arise from setting initial values for missing values. Our experiments demonstrate that RATAI outperforms benchmark models in multivariate time series imputation.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 2","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11039-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859486","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}