Intelligent Systems with Applications最新文献

{"title":"Methodology for advanced time series demand forecasting: A hybrid model of decomposition and deep learning","authors":"Juyoung Ha ,&nbsp;Sungwon Lee ,&nbsp;Sooyeon Jeong ,&nbsp;Doohee Chung","doi":"10.1016/j.iswa.2025.200540","DOIUrl":"10.1016/j.iswa.2025.200540","url":null,"abstract":"<div><div>Advancements in data science have increasingly focused on refining time-series predictive models for effective corporate management and demand forecasting. Traditional models often struggle to capture irregular patterns in time-series data. In this study, we employ a novel hybrid model integrating Ensemble Empirical Mode Decomposition (EEMD), Least Absolute Shrinkage and Selection Operator (LASSO), and Long Short-Term Memory (LSTM) networks to address these challenges. Our approach follows a structured pipeline: EEMD decomposes time-series data into ensemble Intrinsic Mode Functions (eIMFs) to reveal complex patterns, LASSO selects the most relevant features to optimize input variables, and LSTM captures long-term dependencies for accurate demand forecasting. We evaluate our model on real-world demand data from three industries (Office Product, Packaging Materials, and Pharmaceuticals), comparing it against ARIMAX, LightGBM, LSTM, and their EEMD-enhanced variants using NRMSE, NMAE, and R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>. Results show that integrating EEMD into baseline models reduces NRMSE by an average of 27.4%, while the additional incorporation of LASSO further improves performance, achieving a total reduction of 29.1%. Compared to the standalone LSTM model, our proposed EEMD-LASSO-LSTM model demonstrates a substantial NRMSE reduction of 51.2%, highlighting its superior predictive accuracy. This innovative combination of EEMD, LASSO, and LSTM enables our proposed method to effectively capture the irregular patterns of demand, a task that has been a significant hurdle for conventional forecasting methods. The integration of EEMD, LASSO, and LSTM marks a significant advancement in time-series predictive modeling, enhancing demand forecasting and informing strategic corporate decisions.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200540"},"PeriodicalIF":4.3,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visual question answering for medical diagnosis","authors":"Nawel Ben Chaabane,&nbsp;Mohamed Bal-Ghaoui","doi":"10.1016/j.iswa.2025.200545","DOIUrl":"10.1016/j.iswa.2025.200545","url":null,"abstract":"<div><div>The use of Artificial Intelligence (AI) in medical diagnosis is a breakthrough in healthcare, improving both accuracy and efficiency. Recently, a significant advancement has been made toward the development of multimodal AI systems that can process and integrate multiple types of data or modalities. This ability is key for interpreting medical images, such as X-rays, CT, and MRI scans, as well as textual data like electronic health records (EHRs) and clinical notes. In this era, Visual Question Answering (VQA) systems have demonstrated a potential use case in the medical domain. These systems, typically based on Vision-Language Models (VLMs), can answer natural lan- guage questions based on medical images, offering precise and relevant re- sponses that help doctors make better decisions.</div><div>In this article, we evaluate existing medical VQA models along with general and trending ones to make medical diagnoses. In particular, we focus on addressing abnormality questions considered challenging in the literature. Our approach consists of evaluating the Zero-Shot (ZS) general and domain-specific capabilities of different models using two created datasets, and fine-tuning the best-found models on the training set of the abnormality dataset before evaluating their performances quantitatively and qualitatively. IdeficMed, a generative domain-specific model, achieved better consistency and VQA outcomes by only training 0.22 % of its parameters. Additionally, we employed uncertainty quantification techniques (e.g., Monte Carlo dropout) to assess the confidence of the fine-tuned models in their predictions. We also conducted a sensitivity analysis on input perturbations, such as image noise and ambiguous questions.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200545"},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On adversarial attack detection in the artificial intelligence era: Fundamentals, a taxonomy, and a review","authors":"Noora Al Roken ,&nbsp;Hakim Hacid ,&nbsp;Ahmed Bouridane ,&nbsp;Abir Hussain","doi":"10.1016/j.iswa.2025.200554","DOIUrl":"10.1016/j.iswa.2025.200554","url":null,"abstract":"<div><div>The rapid advancement and sophisticated deployment of artificial intelligence tools by malicious actors have led to the rise of highly complex cyber-attacks that evolve quickly. This rapid evolution has made traditional defense systems increasingly ineffective at detecting and mitigating these hidden threats. Adversarial attacks are a prime example of such sophisticated cyber-attacks; they subtly alter attack patterns to evade detection by intelligent systems while still maintaining their harmful functionality. This paper provides a comprehensive overview of computer malware, examining both traditional concealment methods and more advanced adversarial techniques. It includes an in-depth analysis of recent research efforts aimed at detecting previously unseen adversarial attacks using both traditional and AI-driven approaches. Furthermore, this study discusses the limitations of current network intrusion detection systems and proposes directions for future research.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200554"},"PeriodicalIF":0.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cancelable random masking with deep learning for secure and interpretable finger vein authentication","authors":"Mohamed Hammad ,&nbsp;Abdelhamied A. Ateya ,&nbsp;Mohammed ElAffendi ,&nbsp;Ahmed A. Abd El-Latif","doi":"10.1016/j.iswa.2025.200552","DOIUrl":"10.1016/j.iswa.2025.200552","url":null,"abstract":"<div><div>In the area of identity verification and authentication, biometrics has emerged as a reliable means of recognizing individuals based on their unique behavioral or physical characteristics. Finger vein authentication, with its robustness, resistance to spoofing, and stable patterns, has gained significant attention as a biometric modality. This paper introduces a novel framework that integrates Cancelable Random Masking (CRM) with a lightweight deep learning model for secure and interpretable finger vein authentication. The CRM technique transforms biometric templates using cryptographic random masks, ensuring cancelability, revocability, and privacy. These transformed templates are then processed by a convolutional neural network (CNN) designed to learn discriminative features directly from masked inputs without relying on handcrafted feature extraction. Our method enhances transparency by making the transformation process interpretable and provides strong security against template inversion and adversarial attacks. Results conducted on three publicly available databases demonstrate the proposed framework’s superior performance in terms of accuracy, robustness, and resistance to spoofing and replay attacks. This is the first framework to integrate CRM within a deep learning model, satisfying all cancelable biometric criteria while enabling real-time, interpretable, and secure finger vein authentication.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200552"},"PeriodicalIF":0.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Certified Accuracy and Robustness: How different architectures stand up to adversarial attacks","authors":"Azryl Elmy Sarih ,&nbsp;Nagender Aneja ,&nbsp;Ong Wee Hong","doi":"10.1016/j.iswa.2025.200555","DOIUrl":"10.1016/j.iswa.2025.200555","url":null,"abstract":"<div><div>Adversarial attacks are a concern for image classification using neural networks. Numerous methods have been created to minimize the effects of attacks, where the best defense against such attacks is through adversarial training, which has proven to be the most successful to date. Due to the nature of adversarial attacks, it is difficult to assess the capabilities of a network to defend. The standard method of assessing a network’s performance in supervised image classification tasks is based on accuracy. However, this assessment method, while still important, is insufficient when adversarial attacks are included. A new metric called certified accuracy is used to assess network performance when samples are perturbed by adversarial noise. This paper supplements certified accuracy with an abstention rate to give more insight into the network’s robustness. Abstention rate measures the percentage of the network that failed to keep its prediction unchanged as the perturbation strength increases from zero to specified strength. The study focuses on popular and good-performing CNN-based architectures, specifically EfficientNet-B7, ResNet-50, ResNet-101, Wide-ResNet-101, and transformer architectures such as CaiT and ViT-B/16. The selected architectures are trained in adversarial and standard methods and then certified on CIFAR-10 datasets perturbed with Gaussian noises of different strengths. Our results show that transformers are more resilient to adversarial attacks than CNN-based architectures by a significant margin. Transformers exhibit better certified accuracy and tolerance against stronger noises than CNN-based architectures, demonstrating good robustness with and without adversarial training. The width and depth of a network have little effect on achieving robustness against adversarial attacks, but rather, the techniques that are deployed in the network are more impactful, where attention mechanisms have been shown to improve a network’s robustness.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200555"},"PeriodicalIF":0.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cognitive map formation under uncertainty via local prediction learning","authors":"Calvin Yeung ,&nbsp;Zhuowen Zou ,&nbsp;Nathaniel D. Bastian ,&nbsp;Mohsen Imani","doi":"10.1016/j.iswa.2025.200551","DOIUrl":"10.1016/j.iswa.2025.200551","url":null,"abstract":"<div><div>Cognitive maps are internal world models that enable adaptive behaviour including spatial navigation and planning. The Cognitive Map Learner (CML) has been recently proposed as a model for cognitive map formation and planning. A CML learns high dimensional state and action representations using local prediction learning. While the CML offers a simple and elegant solution to cognitive map learning, it is limited by its simplicity, applying only to fully observable environments. To address this, we introduce the Partially Observable Cognitive Map Learner (POCML), extending the CML to handle partially observable environments.</div><div>The POCML uses a superposition of states represented via random Fourier features for probabilistic representation and uses the binding operation for parallel state updates. It features an associative memory to enable adaptive behaviour across environments with similar structures. We derive local update rules based on the POCML’s probabilistic state representation and associative memory. We show that a POCML is capable of learning the underlying structure of an environment via local next-observation prediction learning. In addition, we show that a POCML trained on an environment is capable of generalizing to environments with the same underlying structure but with novel observations, achieving good zero-shot next-observation prediction accuracy, significantly outperforming sequence models such as LSTMs and transformers. Finally, we present a case study of navigation in a two-tunnel maze environment with aliased observations, showing that a POCML is capable of effectively using its probabilistic state representations for disambiguation of states and spatial navigation.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200551"},"PeriodicalIF":0.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and development of a dexterous soft-robotics based assistive exoglove with kinematic modeling","authors":"Nawara Mahmood Broti ,&nbsp;Shamim Ahmed Deowan ,&nbsp;A.S.M. Shamsul Arefin","doi":"10.1016/j.iswa.2025.200550","DOIUrl":"10.1016/j.iswa.2025.200550","url":null,"abstract":"<div><div>Necessity of a fully functional hand in our life is beyond description. Yet, a portion of the population is unable to move and control their hand due to paralysis. An assistive device can aid both daily activities and rehabilitation. This paper presents a dexterous soft robotics-based assistive glove with spatial kinematic model and control system. Unlike existing designs, our proposed five-fingered glove provides 20 degrees of freedom (DoFs), closely resembling a human hand. Each finger has 4 DoFs with controlled flexion, extension, abduction, and adduction motion ability. The tendon-driven mechanism simplifies design and control, while 3D-printed thermoplastic polyurethane (TPU) material ensures comfort, lightness, and an anthropomorphic appearance. The derived forward and inverse kinematics of each finger are capable of mapping joint angles to fingertip positions and orientations. To validate the kinematic model, virtual simulation was conducted to confirm its accuracy; while basic hand functionality experiments proved the gloves’ effectiveness. We expect this research to contribute to medical robotics, biomechanics, and assistive technology.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200550"},"PeriodicalIF":0.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role and Applications of Semantic Interoperability Tools and eXplainable AI in the Development of Smart Food Systems: Findings from a Systematic Literature Review","authors":"Donika Xhani,&nbsp;Gayane Sedrakyan,&nbsp;Anand Gavai,&nbsp;Renata Guizzardi,&nbsp;Jos van Hillegersberg","doi":"10.1016/j.iswa.2025.200547","DOIUrl":"10.1016/j.iswa.2025.200547","url":null,"abstract":"<div><div>Smart food systems generate vast and diverse data across the supply chain, yet inconsistent data structures and limited interoperability hinder their full potential. Achieving semantic interoperability, where systems can exchange and interpret data with shared meaning, is essential for enabling intelligent integration and decision-making. Tools such as ontologies, knowledge graphs, and reasoning engines play a key role in this process. In this paper, we refer to these as <em>Semantic Interoperability (SI) tools</em>: a broad category that includes technologies grounded in Semantic Web standards (e.g., RDF, OWL, SPARQL) but emphasizes their applied role in aligning meaning across heterogeneous systems. Coupled with eXplainable Artificial Intelligence (XAI), these technologies enhance transparency and trust in AI-driven decisions, such as personalized food recommendations tailored to an individual’s health conditions and preferences. This paper presents a Systematic Literature Review (SLR) examining the role of semantic interoperability tools and XAI in the development of smart food systems. Through an analysis of 39 studies, the review identifies key semantic technologies and XAI methods used in food systems, with a focus on their application in intelligent food recommendation systems. The findings reveal that while significant progress has been made, current systems often lack adequate transparency and personalization, limiting user trust and engagement. To address these gaps, the paper proposes the integration of semantic interoperability tools with XAI to create smarter, more reliable food systems. As part of this effort, the paper introduces the conceptual model for the Semantic Explainable Food Recommendation Ontology (SEFRO), a work-in-progress ontology, designed to connect entities and relationships within food systems in an intelligent manner, with the goal of enabling personalized, explainable, and interoperable food recommendations that meet the growing demands for smart food systems.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200547"},"PeriodicalIF":0.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-source multi-layer-based transfer learning approach for forecasting customer demands of newly launched products","authors":"Supriyo Ahmed ,&nbsp;Ripon K. Chakrabortty ,&nbsp;Daryl L. Essam","doi":"10.1016/j.iswa.2025.200548","DOIUrl":"10.1016/j.iswa.2025.200548","url":null,"abstract":"<div><div>Forecasting the future demand for newly launched products has been challenging for supply chain practitioners, often due to the lack of data. However, market surveys and extracting knowledge by examining similar market products to find the behaviour of a new product can be inaccurate and lead to erroneous results, which ultimately lead to a misestimation of the overall cost of a business. Meanwhile, with the advancement of artificial intelligence (AI) approaches, such as Transfer Learning (TL), this misestimation of cost can be reduced by more accurately forecasting the demand for newly launched products by seeking knowledge from the historical data of other similar products. Consequently, this paper investigates several classical AI-based TL approaches to predict customer demand for new products and stores. Thereafter, a novel <strong>M</strong>ulti-<strong>S</strong>ource <strong>M</strong>ulti-<strong>L</strong>ayer <strong>T</strong>ransfer <strong>L</strong>earning approach with a <strong>R</strong>ecursive <strong>F</strong>eature <strong>E</strong>limination (MSML-TL-RFE) strategy is proposed to exploit the knowledge extraction power of the model from multiple sources for different days-ahead-prediction, distinguishing itself from the other investigated approaches. In this paper, an abstract concept of a supply chain, with information sharing among retailers, is investigated to show that such concepts can escalate the knowledge transfer ability of a system. A hierarchical two-echelon supply chain model with different attributes is developed to validate the proposed MSML-TL-RFE approach against a few other TL-based forecasting approaches. The feature-rich datasets are then transformed in such a way that they depict a hierarchical supply chain structure, allowing for the effective application of TL for forecasting consumer demand for recently introduced products. Continuing with that idea of information sharing, finding comparable sources for a quick and effective knowledge transfer procedure is investigated, considering all the peculiarities of a certain data set. MSML-TL-REF predictions and other TL-based approaches are analysed by calculating overall supply chain costs. Based on overall supply chain costs under static and dynamic lead time settings, the effectiveness and applicability of the proposed MSML-TL-RFE against traditional forecasting approaches are demonstrated. Incorporating MSML-TL-RFE with three sources improves accuracy, defined as the reciprocal of Root Mean Square Error (RMSE), from 4.83 (no TL) to 5.67 and further increases to 5.76 with additional sources, enabling more accurate predictions and reduced supply chain costs for businesses.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200548"},"PeriodicalIF":0.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144516953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated learning-enabled lightweight intrusion detection system for wireless sensor networks: A cybersecurity approach against DDoS attacks in smart city environments","authors":"Manu Devi ,&nbsp;Priyanka Nandal ,&nbsp;Harkesh Sehrawat","doi":"10.1016/j.iswa.2025.200553","DOIUrl":"10.1016/j.iswa.2025.200553","url":null,"abstract":"<div><h3>Background</h3><div>Wireless Sensor Networks (WSNs) are vital in applications such as healthcare, smart cities, and environmental monitoring, but are vulnerable to cyberattacks due to their resource-constrained nature. Traditional Intrusion Detection Systems (IDS) depend on centralized architectures, which increase communication overhead and privacy risks and create a single point of failure.</div></div><div><h3>Objective</h3><div>This paper proposes a novel Federated Learning-based Lightweight IDS (FL-LIDS) that utilizes optimized lightweight models to enable real-time, privacy-preserving DDoS attack detection in resource-constrained WSNs for smart city environments and presents a comprehensive comparative analysis of models to evaluate their effectiveness within the Federated Learning (FL) framework.</div></div><div><h3>Methods</h3><div>FL-LIDS utilizes the optimized lightweight deep learning models for intrusion detection, which provides effective anomaly recognition with minimal resource usage, making it suitable for resource-limited WSN environments. The lightweight methods are evaluated in terms of their efficiency on the TON-IoT dataset.</div></div><div><h3>Results</h3><div>The study demonstrates the effectiveness of various FL-LIDS in detecting and preventing DDoS attacks with high detection rates and minimal latency. Metrics used to examine performance include accuracy, F1-score, precision, and recall in emulated WSN scenarios. The lightweight deep learning architecture optimizes accuracy and computational cost, with the lightweight hybrid CNN + LSTM model achieving superior intrusion detection performance, making it ideal for WSN-based smart city environments.</div></div><div><h3>Conclusion</h3><div>These cybersecurity systems provide a highly scalable and high-strength means of protecting smart city ecosystems in order to offer uninterrupted service provisioning. This research indicates that the FL provides an effective cybersecurity solution for WSNs.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"27 ","pages":"Article 200553"},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}