2020 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第10页

Real-Time Deep Learning-Based Object Detection Framework 基于实时深度学习的目标检测框架

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308493

William T. Tarimo, Moustafa M. Sabra, Shonan Hendre

{"title":"Real-Time Deep Learning-Based Object Detection Framework","authors":"William T. Tarimo, Moustafa M. Sabra, Shonan Hendre","doi":"10.1109/SSCI47803.2020.9308493","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308493","url":null,"abstract":"Recently real-time detection, and recognition of an object of interest are becoming vital tasks in visual data processing and computer vision. Various models have been deployed to implement object detection and tracking in multiple fields. However, conventional classifiers are often faced with challenging tasks that visual frames come distorted due to overlapping, camera motion blur, changing subject appearances, and environmental variations. Models using OpenCV-based HAAR feature-based cascade classifiers, without integrating any error minimizing object detection algorithm, were unable to accurately detect an object and track it in a changing environment. Therefore, developing an embedded powerful framework for realtime object detection and recognition becomes more of a vital need for future implementation in various fields. This study presents a powerful technique for a real-time detector that utilizes integrated Deep Learning Neural Networks (DNN) for optimal computational accuracy. Deploying such a framework will ensure the flexibility and reliability of the detector by eliminating the sources of distortion previously mentioned. The model relies on integrating the ImageAI deep learning libraries and You Only Look Once (YOLO-v3) object detection method with a DarkNet53 architecture. The algorithm was trained using the TensorFlow framework to ensure accurate data processing. This paper targets one vital component of our long-term project of developing a multi-agent system, as the proposed model is to be implemented in autonomous agents for the detection of landmines, ocean debris, and wildlife beside environmental scanning missions. In this study the performance of the model has been assessed through detecting and collecting tennis balls as a preliminary test for real-world applications. The model was able to approach the desirable result of surpassing the accuracy of conventional detectors.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126293862","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}

引用次数: 4

Improved Binary Particle Swarm optimization with Evolutionary Population Dynamic for Key Oncogene Selection 基于进化种群动态的改进二元粒子群算法在关键癌基因选择中的应用

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308540

Wenxin Zhao, Y. Sun, Bing Xue

{"title":"Improved Binary Particle Swarm optimization with Evolutionary Population Dynamic for Key Oncogene Selection","authors":"Wenxin Zhao, Y. Sun, Bing Xue","doi":"10.1109/SSCI47803.2020.9308540","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308540","url":null,"abstract":"Cancer is one of the most deadly diseases in the world, and researchers have been investigating various methods to detect it in the early stages in order to improve the possibility of survival. Unfortunately, cancer data is often very expensive to collect and high-dimensional, where redundant and/or irrelevant features result in the challenges for cancer detection and hide information from useful features or key oncogenes. Therefore, an efficient and effective feature selection algorithm is proposed in this paper to deal with these problems, which based on the classic Particle Swarm optimization (PSO) algorithm, one of the most widely used Evolutionary Computation (EC) techniques. In this paper, the Evolutionary Population Dynamics (EPD) strategy is integrated into PSO to address its limitations and improve its performance on addressing feature selection problems. The proposed algorithm is examined on eight cancer datasets of varying difficulty. Comparisons have been done among the two versions of the proposed approaches, standard binary PSO, and three other feature selection methods in terms of the classification performance, the number of selected features and the convergence behaviors. The results show that in most cases, the proposed EPD mechanism can help PSO to achieve better performance over the compared methods.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125746544","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}

引用次数: 1

Efficient-Frequency: a hybrid visual forensic framework for facial forgery detection 高效-频率:用于面部伪造检测的混合视觉取证框架

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308305

Chau Xuan Truong Du, Le Hoang Duong, T. T. Huynh, Minh Tam Pham, Nguyen Quoc Viet Hung, Jun Jo

{"title":"Efficient-Frequency: a hybrid visual forensic framework for facial forgery detection","authors":"Chau Xuan Truong Du, Le Hoang Duong, T. T. Huynh, Minh Tam Pham, Nguyen Quoc Viet Hung, Jun Jo","doi":"10.1109/SSCI47803.2020.9308305","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308305","url":null,"abstract":"The recent years have witnessed the significant development of visual forgery techniques and their malicious applications such as spreading of fake news and rumours, defamation or blackmailing of politicians and celebrities, manipulation of election result in political warfare. The manipulated contents have reached to such sophisticated level that human cannot tell apart whether a given content is real or fake. To deal with this serious threat, a rich body of visual forensic techniques has been proposed for detecting forged video and images. However, existing techniques either rely solely on engineered features or require a complex deep learning model to extract the underlying patterns. In this paper, we propose a novel end-to-end visual forensic framework that can incorporate different modalities to efficiently classify real and forged contents. The model employs both the original content and its frequency domain analysis to fully exploit the richness of the image latent patterns. They are forwarded into two separated EfficientNet, a light yet efficient neural network architecture specialized for image classification, for pattern extraction. Then, we design a late-fusion mechanism to fuse the learnt features in original and frequency domain based on the importance of the underlying information. Our experimental results show that our proposed technique outperforms other state-of-the-art forensic approaches in many datasets and being robust to various visual forgery techniques.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125771125","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}

引用次数: 5

Exploring Reward-based Hyper-heuristics for the Job-shop Scheduling Problem 基于奖励的超启发式作业车间调度问题研究

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308131

Erick Lara-Cárdenas, Arturo Silva-Gálvez, J. C. Ortíz-Bayliss, I. Amaya, J. M. Cruz-Duarte, H. Terashima-Marín

{"title":"Exploring Reward-based Hyper-heuristics for the Job-shop Scheduling Problem","authors":"Erick Lara-Cárdenas, Arturo Silva-Gálvez, J. C. Ortíz-Bayliss, I. Amaya, J. M. Cruz-Duarte, H. Terashima-Marín","doi":"10.1109/SSCI47803.2020.9308131","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308131","url":null,"abstract":"The Job-Shop Scheduling Problem represents a challenging field of study due to its NP-Hard nature. Its many industrial and practical, real-world applications skyrocket its importance. Particularly, hyper-heuristics have attracted the attention of researchers on this topic due to their promising results in this, and other optimization problems. A hyper-heuristic is a method that determines which heuristic to apply at each step while solving a problem. This investigation aims at rendering hyper-heuristics by combining unsupervised and reinforcement learning techniques. The proposed solution applies a clustering approach over the feature space, and then, it generates knowledge about heuristic selection through a reward-based system. Results show that our hyper-heuristics surmount competent heuristics, such as SPT and MRT, in various test instances. Besides, some of these hyper-heuristics outperformed the best result obtained among all the heuristics in more than 33% of the instances. Hence, we believe that the proposed approach is promising and that more knowledge about its benefits and limitations should be derived through its application on different problems.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125867270","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}

引用次数: 5

Machine Learning Inspired Phishing Detection (PD) for Efficient Classification and Secure Storage Distribution (SSD) for Cloud-IoT Application 机器学习启发的网络钓鱼检测(PD)，用于云-物联网应用的高效分类和安全存储分发(SSD)

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308183

Chandrasegar Thirumalai, M. Mekala, V. Perumal, Rizwan Patan, A. Gandomi

{"title":"Machine Learning Inspired Phishing Detection (PD) for Efficient Classification and Secure Storage Distribution (SSD) for Cloud-IoT Application","authors":"Chandrasegar Thirumalai, M. Mekala, V. Perumal, Rizwan Patan, A. Gandomi","doi":"10.1109/SSCI47803.2020.9308183","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308183","url":null,"abstract":"Cloud-IoT data security and privacy have become a major problem due to its sensitivity, which curbs multiple cloud applications. In addition, if the encrypted data lives in one place, in many fields, such as the financial industry and government agencies, the man-in-the-middle-attack (MMA) and phishing attack (PA) may have chances of realising the extraction. The phishing goal is evaluated and predicted by most previous machine learning models through a discrete or continuous result. The current models lag in accurately determining both attacks because of this approach. We developed a three-step phishing detection (PD) framework inspired by machine learning and a secure storage distribution (SSD) for cloud to improve model accuracy and storage security. The partition-based selection of features is designed for phishing detection (PD) with a hybrid classifier approach and hyper-parameter classifier tuning. Initially, the entire data set is partitioned by entropy and is hybridised for each performing model partition. In order to reduce the complexity, the next entropy is applied to decrease the dimension of each partition. Finally, to improve precision, the performing model is optimised with hyper-parameter tuning. The partition-based feature choice with the hybrid classifier approach outperforms with 97.86% accuracy for both attack detection from the experimental and comparative results of SVM, LM, NN and RF. Atlast, SSD performance is evaluated against other storage models where SSD outperforms other models.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130037260","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}

引用次数: 3

Evolving Optimal Convolutional Neural Networks 进化最优卷积神经网络

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308201

Subhashis Banerjee, S. Mitra

{"title":"Evolving Optimal Convolutional Neural Networks","authors":"Subhashis Banerjee, S. Mitra","doi":"10.1109/SSCI47803.2020.9308201","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308201","url":null,"abstract":"Among the different Deep Learning (DL) models, the deep Convolutional Neural Networks (CNNs) have demonstrated impressive performance in a variety of image recognition or classification tasks. Although CNNs do not require feature engineering or manual extraction of features at the input level, yet designing a suitable CNN architecture necessitates considerable expert knowledge involving enormous amount of trial-and-error activities. In this paper we attempt to automatically design a competitive CNN architecture for a given problem while consuming reasonable machine resource(s) based on a modified version of Cartesian Genetic Programming (CGP). As CGP uses only the mutation operator to generate offsprings it typically evolves slowly. We develop a new algorithm which introduces crossover to the standard CGP to generate an optimal CNN architecture. The genotype encoding scheme is changed from integer to floating-point representation for this purpose. The function genes in the nodes of the CGP are chosen as the highly functional modules of CNN. Typically CNNs use convolution and pooling, followed by activation. Rather than using each of them separately as a function gene for a node, we combine them in a novel way to construct highly functional modules. Five types of functions, called ConvBlock, average pooling, max pooling, summation, and concatenation, were considered. We test our method on an image classification dataset CIFAR10, since it is being used as the benchmark for many similar problems. Experiments demonstrate that the proposed scheme converges fast and automatically finds the competitive CNN architecture as compared to state-of-the-art solutions which require thousands of generations or GPUs involving huge computational burden.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129774878","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}

引用次数: 0

Perspectives of Final Year Students on Modeling and Analysis of Complex Systems and Their Properties 毕业班学生对复杂系统及其性质的建模与分析的观点

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308401

Claudia Szabo

引用次数: 0

Decoding of Subjective Pain-Sensitivity by Brain Signal Analysis Using a General Type-2 Fuzzy Classifier 基于通用2型模糊分类器的脑信号解码研究

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308335

Sayantani Ghosh, Mousumi Laha, A. Konar, A. Nagar

{"title":"Decoding of Subjective Pain-Sensitivity by Brain Signal Analysis Using a General Type-2 Fuzzy Classifier","authors":"Sayantani Ghosh, Mousumi Laha, A. Konar, A. Nagar","doi":"10.1109/SSCI47803.2020.9308335","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308335","url":null,"abstract":"The prime mechanism governing the variability of pain perception across different subjects is still unexplored. This paper intends to develop a novel methodology to investigate this phenomenon using EEG signal analysis system. First, the EEG signals are procured from the scalp of subjects who are presented with three types of touch stimuli: heat, bristles and pinch with varying intensity levels. The raw brain signals acquired are analyzed using eLORETA software that confirms the involvement of primary somatosensory cortex and dorsal region of anterior cingulate cortex for this cognitive activity. Additionally, frequency analysis undertaken infers the participation of delta, alpha and theta bands for the said task. The signals are then transferred to a feature extraction module where a dual feature extraction strategy has been employed using Power Spectral Density (PSD) and Discrete Wavelet Transform (DWT) to enhance the diversity of the feature set. The abstracted features are further evaluated using Principal Component Analysis (PCA) to retain the most important or optimal features. The reduced feature set is transferred to a novel General Type-2 fuzzy classifier that is able to precisely classify the distinct class labels and also outperforms its conventional counterparts. Hence, this method can help to assess the variability of pain perception amongst individuals whose communication modality is crippled due to scenarios pertaining to neurological disorders, anaesthetic treatments and the like. Moreover, the present scheme can be utilized as a neuronal marker to distinguish individuals suffering from extreme sensitivity towards pain from the healthy ones.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124986243","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}

引用次数: 0

Estimating the cognitive load in physical spatial navigation 物理空间导航的认知负荷估算

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308389

Tien-Thong Nguyen Do, Avinash Kumar Singh, C. A. T. Cortes, Chin-Teng Lin

引用次数: 3

Back To Meshes: Optimal Simulation-ready Mesh Prototypes For Autoencoder-based 3D Car Point Clouds 回到网格:基于自动编码器的3D汽车点云的最佳模拟准备网格原型

2020 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2020-12-01 DOI: 10.1109/SSCI47803.2020.9308400

Thiago Rios, Jiawen Kong, Bas van Stein, Thomas Bäck, Patricia Wollstadt, B. Sendhoff, S. Menzel

{"title":"Back To Meshes: Optimal Simulation-ready Mesh Prototypes For Autoencoder-based 3D Car Point Clouds","authors":"Thiago Rios, Jiawen Kong, Bas van Stein, Thomas Bäck, Patricia Wollstadt, B. Sendhoff, S. Menzel","doi":"10.1109/SSCI47803.2020.9308400","DOIUrl":"https://doi.org/10.1109/SSCI47803.2020.9308400","url":null,"abstract":"Point cloud autoencoders were recently introduced as powerful models for data compression. They learn a lowdimensional set of variables that are suitable as design parameters for shape generation and optimization problems. In engineering tasks, 3D point clouds are often derived from fine polygon meshes, which are the most suitable representations for physics simulation, e.g., computational fluid dynamics (CFD). Yet, the reconstruction of high-quality meshes from autoencoderbased point clouds is challenging, often requiring supervised and manual work, which is prohibitive during the optimization. Target shape matching optimization using existing mesh prototypes overcomes the difficulties of recovering shape information from the point coordinates. However, for autoencoders trained on data sets comprising shapes with high degree of dissimilarity, there is not a single mesh prototype that can fit any autoencoderbased point cloud, and the selection of a set of prototypes is nontrivial. In the present paper we propose a method for optimizing a selection of prototypical meshes to match the maximum number of shapes in the autoencoder output space as possible, which is achieved by linking the advantages of the latent space representation of an autoencoder and the state-of-the-art free form deformation (FFD) method. Furthermore, we approached the balance between costs (number of mesh prototypes) and number of covered shapes by varying the number of prototypes and the dimensionality of the autoencoder latent space, showing that higher-dimensional latent spaces encode finer geometric changes, requiring more sophisticated FFD setups.","PeriodicalId":413489,"journal":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128893243","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}

引用次数: 3