Journal of Experimental & Theoretical Artificial Intelligence最新文献_第10页

Convergence analysis of a patch structure Nicholson’s blowflies system involving an oscillating death rate 具有振荡死亡率的斑片结构尼科尔森苍蝇系统的收敛性分析

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-04-12 DOI: 10.1080/0952813X.2021.1908433

Xianhui Zhang

引用次数: 3

SPMNet: A light-weighted network with separable pyramid module for real-time semantic segmentation SPMNet:一个具有可分离金字塔模块的轻量级实时语义分割网络

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-04-06 DOI: 10.1080/0952813X.2021.1908432

S. Gao, Changzhu Zhang, Zhuping Wang, Hao Zhang, Chao Huang

引用次数: 0

Deep asset allocation for trend following investing 深度资产配置趋势跟踪投资

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-04-05 DOI: 10.1080/0952813X.2021.1908429

Saejoon Kim, Hyuksoo Kim

{"title":"Deep asset allocation for trend following investing","authors":"Saejoon Kim, Hyuksoo Kim","doi":"10.1080/0952813X.2021.1908429","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1908429","url":null,"abstract":"ABSTRACT Trend following strategies are well-known to exhibit excellent excess return performance across a wide range of asset classes in various global markets. For the equity asset class in particular, while the securities selection part is relatively a straightforward procedure, the weight allocation part is more debatable and it has traditionally been identified with the equal-weighted allocation strategy. In this paper, we examine security’s own return-based weight allocation strategy for trend following investing and find that this strategy generates superior returns to several well-established weight allocation schemes. In particular, if the true return of the holding period is used ex ante for weight allocation, it is found that this strategy can generate absolutely huge excess returns. Motivated by this finding, we investigate the efficacy of machine learning techniques for regression of securities’ returns to improve the weight calculation in this framework. Empirical results indicate that deep learning provides the means of regression with which largest excess return gains are possible. In particular, it is demonstrated that the return-based weight allocation strategy defined by our proposed deep learning architecture produces substantial abnormal returns outperforming all other broadly recognised weight allocation schemes compared in this paper.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81650328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Credit Card Fraud Detection under Extreme Imbalanced Data: A Comparative Study of Data-level Algorithms 极端不平衡数据下的信用卡欺诈检测:数据级算法的比较研究

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-04-03 DOI: 10.1080/0952813X.2021.1907795

Amit Singh, R. Ranjan, A. Tiwari

{"title":"Credit Card Fraud Detection under Extreme Imbalanced Data: A Comparative Study of Data-level Algorithms","authors":"Amit Singh, R. Ranjan, A. Tiwari","doi":"10.1080/0952813X.2021.1907795","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1907795","url":null,"abstract":"ABSTRACT Credit card fraud is one of the biggest cybercrimes faced by users. Intelligent machine learning based fraudulent transaction detection systems are very effective in real-world scenarios. However, while designing these systems, machine learning approaches suffer from the problem of imbalanced data, i.e. imbalanced class distribution. Therefore, balancing the dataset becomes an imperative sub-task. Investigation of state-of-the-art approaches reveals that there is a need for a systematic study of class imbalance handling strategies to design an intelligent and capable system to detect the fraudulent transaction. This work aims to provide a comparative study of different class imbalance handling methods. To compare the effectiveness and efficiency of different class imbalance approaches in conjunction with state-of-the-art classification approaches, we have performed an extensive experimental study. We compared these methods on many performance indicators such as Precision, Recall, K-fold Cross-validation, AUC-ROC curve and execution time. In this study, we found that the Oversampling followed by Undersampling methods performs well for ensemble classification models such as AdaBoost, XGBoost and Random Forest.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90988237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28

Human detection based on deep learning YOLO-v2 for real-time UAV applications 基于深度学习YOLO-v2的实时无人机人体检测

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-04-01 DOI: 10.1080/0952813X.2021.1907793

K. Boudjit, N. Ramzan

{"title":"Human detection based on deep learning YOLO-v2 for real-time UAV applications","authors":"K. Boudjit, N. Ramzan","doi":"10.1080/0952813X.2021.1907793","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1907793","url":null,"abstract":"ABSTRACT Recent advancements in the field of Artificial Intelligence (AI) have provided an opportunity to create autonomous devices, robots, and machines characterised particularly with the ability to make decisions and perform tasks without human mediation. One of these devices, Unmanned Aerial Vehicles (UAVs) or drones are widely used to perform tasks like surveillance, search and rescue, object detection and target tracking, and many more. Efficient real-time object detection in aerial videos is an urgent need, especially with the increasing use of UAV in various fields. The sensitivity in performing said tasks demands that drones must be efficient and reliable. This paper presents our research progress in the development of applications for the identification and detection of person using the convolutional neural networks (CNN) YOLO-v2 based on the camera of drone. The position and state of the person are determined with deep-learning-based computer vision. The person detection results show that YOLO-v2 detects and classifies object with a high level of accuracy. For real-time tracking, the tracking algorithm responds faster than conventionally used approaches, efficiently tracking the detected person without losing it from sight.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87826323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

Anytime clustering of data streams while handling noise and concept drift 随时聚类数据流，同时处理噪声和概念漂移

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-03-15 DOI: 10.1080/0952813X.2021.1882001

Jagat Sesh Challa, Poonam Goyal, Ajinkya Kokandakar, D. Mantri, Pranet Verma, S. Balasubramaniam, Navneet Goyal

{"title":"Anytime clustering of data streams while handling noise and concept drift","authors":"Jagat Sesh Challa, Poonam Goyal, Ajinkya Kokandakar, D. Mantri, Pranet Verma, S. Balasubramaniam, Navneet Goyal","doi":"10.1080/0952813X.2021.1882001","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1882001","url":null,"abstract":"ABSTRACT Clustering of data streams has become very popular in recent times, owing to rapid rise of real-time streaming utilities that produce large amounts of data at varying inter-arrival rates. We propose AnyClus, a framework for anytime clustering of data streams. AnyClus uses a proposed variant of R-tree, AnyRTree, to capture the incoming stream objects arriving at variable rate, and to index them in the form of micro-clusters of hierarchical fashion. The leaf-level micro-clusters produced are aggregated and stored in a logarithmic tilted-time window framework (TTWF). Our extensive experimental analysis shows (i) the capability of AnyClus in handling variable stream speeds (upto 250k objects/second); (ii) its ability to produce micro-clusters of high purity (≈1) and compactness; (iii) effectiveness of AnyRTree in handling noise, capturing concept drift and preservation of spatial locality in the indexing of micro-clusters, when compared to the existing methods. We also propose a parallel framework, Any-MP-Clus, for anytime clustering of multiport data streams over commodity clusters. Any-MP-Clus uses AnyRTree at each computing node of the cluster (for each stream-port) and maintains the aggregated micro-clusters in TTWF. The experimental results on datasets of billions scale show that Any-MP-Clus is scalable, eﬃcient and produces clustering of higher quality.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2021-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83633697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

New findings on global exponential stability of inertial neural networks with both time-varying and distributed delays 时变时滞和分布时滞惯性神经网络全局指数稳定性的新发现

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-03-08 DOI: 10.1080/0952813X.2021.1883744

Qian Cao, Guoqiu Wang

引用次数: 0

Detection and classification of landmines using machine learning applied to metal detector data 将机器学习应用于金属探测器数据的地雷探测和分类

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-03-04 DOI: 10.1080/0952813x.2020.1735529

L. Safatly, M. Baydoun, Mohamad Alipour, A. Al-Takach, K. Atab, M. Al‐Husseini, A. El-Hajj, H. Ghaziri

{"title":"Detection and classification of landmines using machine learning applied to metal detector data","authors":"L. Safatly, M. Baydoun, Mohamad Alipour, A. Al-Takach, K. Atab, M. Al‐Husseini, A. El-Hajj, H. Ghaziri","doi":"10.1080/0952813x.2020.1735529","DOIUrl":"https://doi.org/10.1080/0952813x.2020.1735529","url":null,"abstract":"ABSTRACT The current landmine clearance methods mostly rely on the manual use of metal detectors (MDs) and on the deminer’s experience in differentiating between the sounds emitted due to the presence of a landmine or of harmless clutter. This process suffers from high false-alarm rates, which renders the demining effort slow and costly. In this paper, we report our attempts in using machine learning for decision making in the demining process. We have created our own database of the MD responses corresponding to landmines and/or clutter. A robotic rail is designed and assembled to accurately measure these responses and build the database. Several machine learning models are then developed using the database with the aim of detecting the presence of landmines and classifying them. It is shown that the classification algorithms lead to accurately discriminating the landmines and distinguishing between different buried objects including mines or other items based on the metal detector delivered data or signature.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2021-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78491049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Bi-objective web service composition problem in multi-cloud environment: a bi-objective time-varying particle swarm optimisation algorithm 多云环境下双目标web服务组合问题:一种双目标时变粒子群优化算法

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-03-04 DOI: 10.1080/0952813X.2020.1725652

Mirsaeid Hosseini Shirvani

{"title":"Bi-objective web service composition problem in multi-cloud environment: a bi-objective time-varying particle swarm optimisation algorithm","authors":"Mirsaeid Hosseini Shirvani","doi":"10.1080/0952813X.2020.1725652","DOIUrl":"https://doi.org/10.1080/0952813X.2020.1725652","url":null,"abstract":"ABSTRACT Cloud computing became an inevitable information technology industry. Despite its several plus points such as economy of scale and rapid elasticity, it suffers from vendor lock-in, resource limitation and cybersecurity attacks in which it leads business discontinuity or even business failure. Multi-cloud, on the other hand, can be trustable paradigm to obviate obstacles such as aforesaid unpleasant features of a single cloud. One of the biggest challenges is to know which cloud is commensurate with user’s business process with regards to security objectives. To this end, the new method is presented to quantify the amount of cloud security risk (CSR) in regards to user’s business process. Therefore, in this paper, the web service composition problem is formulated to bi-objective optimisation problem with service cost and multi-cloud risk viewpoints in ever-increasing multi-cloud environment (MCE) in which each provider has its variable pricing policy and different security level. It is obviously an NP-Hard problem. To solve the combinatorial problem, we develop a bi-objective time-varying particle swarm optimisation (BOTV-PSO) algorithm. The parameters are tuned based on elapsed time so a good balance between exploration and exploitation is achieved. To illustrate the effectiveness of proposed algorithm, we defined several scenarios and compared the performance of proposed algorithm with multi-objective GA-based (MOGA) optimiser, a single objective genetic algorithm (SOGA) that only optimises cost function and neglects CSR, and multi-objective simulated annealing algorithm (MOSA). The experimental results showed the superiority of proposed BOTV-PSO against other approaches in terms of convergence, diversity, fitness, performance, and even scalability.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2021-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72845726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28

Multi-feature fusion refine network for video captioning 视频字幕的多特征融合细化网络

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-02-23 DOI: 10.1080/0952813X.2021.1883745

Guangbin Wang, Jixiang Du, Hongbo Zhang

{"title":"Multi-feature fusion refine network for video captioning","authors":"Guangbin Wang, Jixiang Du, Hongbo Zhang","doi":"10.1080/0952813X.2021.1883745","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1883745","url":null,"abstract":"ABSTRACT Describing video content using natural language is an important part of video understanding. It needs to not only understand the spatial information on video, but also capture the motion information. Meanwhile, video captioning is a cross-modal problem between vision and language. Traditional video captioning methods follow the encoder-decoder framework that transfers the video to sentence. But the semantic alignment from sentence to video is ignored. Hence, finding a discriminative visual representation as well as narrowing the semantic gap between video and text has great influence on generating accurate sentences. In this paper, we propose an approach based on multi-feature fusion refine network (MFRN), which can not only capture the spatial information and motion information by exploiting multi-feature fusion, but also can get better semantic aligning of different models by designing a refiner to explore the sentence to video stream. The main novelties and advantages of our method are: (1) multi-feature fusion: Both two-dimension convolutional neural networks and three-dimension convolutional neural networks pre-trained on ImageNet and Kinetic respectively are used to construct spatial information and motion information, and then fused to get better visual representation. (2) Sematic alignment refiner: the refiner is designed to restrain the decoder and reproduce the video features to narrow semantic gap between different modal. Experiments on two widely used datasets demonstrate our approach achieves state-of-the-art performance in terms of BLEU@4, METEOR, ROUGE and CIDEr metrics.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82416243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3