2022 5th International Conference on Computational Intelligence and Networks (CINE)最新文献

Application of a Novel Deep Fuzzy Dual Support Vector Regression Machine in Stock Price Prediction 一种新的深度模糊对偶支持向量回归机在股票价格预测中的应用

2022 5th International Conference on Computational Intelligence and Networks (CINE) Pub Date : 2022-12-01 DOI: 10.1109/CINE56307.2022.10037482

Pei-Yi Hao

{"title":"Application of a Novel Deep Fuzzy Dual Support Vector Regression Machine in Stock Price Prediction","authors":"Pei-Yi Hao","doi":"10.1109/CINE56307.2022.10037482","DOIUrl":"https://doi.org/10.1109/CINE56307.2022.10037482","url":null,"abstract":"The desire of any investor is to accurately predict market behavior in order to maximize profits. This is a daunting task because market behavior is random, volatile, and influenced by many factors. Deep learning has excellent feature learning ability, and support vector machine has excellent reasoning ability. In recent years, the deep support vector machine network that perfectly combines the advantages of the two has attracted the attention of many scholars. Compared with the traditional deep neural network, the deep support vector machine network has the following advantages: (1) It has higher reasoning ability; (2) It is more suitable for tasks with insufficient training samples. This paper proposes a new deep fuzzy dual support vector regression network to predict stock price through the numerical data of stock prices. The method proposed in this study is a hybrid model that combines the advantages of: (a) evolutionary computation, (b) ensemble learning, (c) deep learning and (d) multi-kernel function learning. In addition to providing the most probable prediction results, the deep fuzzy dual support vector regression machine proposed in this study can also provide the inner and outer boundaries of the fuzzy range of the prediction results, as well as the confidence level of the prediction results.","PeriodicalId":336238,"journal":{"name":"2022 5th International Conference on Computational Intelligence and Networks (CINE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116784852","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

Detection of Brinjal Leaf Diseases based on Superpixel approach using SLIC Clustering 基于SLIC聚类的超像素方法检测茄子叶片病害

2022 5th International Conference on Computational Intelligence and Networks (CINE) Pub Date : 2022-12-01 DOI: 10.1109/CINE56307.2022.10037273

Bhabanisankar Jena, A. Routray, Janmenjoy Nayak

引用次数: 0

Outlier and Trend Detection Using Approximate Median and Median Absolute Deviation 使用近似中位数和中位数绝对偏差进行离群值和趋势检测

2022 5th International Conference on Computational Intelligence and Networks (CINE) Pub Date : 2022-12-01 DOI: 10.1109/CINE56307.2022.10037489

Gagandeep Singh, Suman Kundu

引用次数: 0

Multiple Feature Selection Frameworks Based on Evolutionary Computing and Ensemble Learning for Software Defect Prediction 基于进化计算和集成学习的多特征选择框架用于软件缺陷预测

2022 5th International Conference on Computational Intelligence and Networks (CINE) Pub Date : 2022-12-01 DOI: 10.1109/CINE56307.2022.10037473

R. Malhotra, Anmol Budhiraja, Abhinav Singh, Ishani Ghoshal, S. Meena

引用次数: 0

Hybrid Energy Efficient Fuzzy C-Means with Bear Smell Search Algorithm in Wireless Sensor Networks 无线传感器网络中混合节能模糊c均值与熊味搜索算法

2022 5th International Conference on Computational Intelligence and Networks (CINE) Pub Date : 2022-12-01 DOI: 10.1109/CINE56307.2022.10037352

Robin Abraham, M. Vadivel

{"title":"Hybrid Energy Efficient Fuzzy C-Means with Bear Smell Search Algorithm in Wireless Sensor Networks","authors":"Robin Abraham, M. Vadivel","doi":"10.1109/CINE56307.2022.10037352","DOIUrl":"https://doi.org/10.1109/CINE56307.2022.10037352","url":null,"abstract":"Energy efficiency is one of the primary deficiencies in wireless sensor networks. The sensor nodes present in the networks contain small battery, which cannot be changed or recharged again. Hence, the use of energy must be carefully monitored and preserved in these battery-operated networks. Energy optimization is recently the popular technique incluster-based routing protocols. The clustering methodology works under the act of group of sensor nodes where each group selects one head member called cluster head. To enhance the lifespan of the network with high energy efficiency, a Fuzzy C-means algorithm is offered in this articlefor clustering the sensor nodes. The cluster head is selected by using particle swarm optimization- Leven berg Marquardt (PSO-LM) algorithm to maximize the energy efficiency and diminish the quantity of dead sensor nodes in the network. Finally, a shortest path is selected via bear smell search algorithm to transmit the data to the sink node. The proposed method is experimentally evaluated and the results are compared in terms of packet delivery ratio and energy efficiency. The experimental outcome revealed that the proposed technique out performs other optimization algorithms and produced better results.","PeriodicalId":336238,"journal":{"name":"2022 5th International Conference on Computational Intelligence and Networks (CINE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128116476","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

Vision-Based Detection of Water Hyacinth 基于视觉的水葫芦检测

2022 5th International Conference on Computational Intelligence and Networks (CINE) Pub Date : 2022-12-01 DOI: 10.1109/CINE56307.2022.10037511

Jyoti Madake, Prerana Zope, I. Wargad, S. Bhatlawande, S. Shilaskar

引用次数: 0

Machine Learning-Based Intrusion Detection System for the Internet of Vehicles 基于机器学习的车联网入侵检测系统

2022 5th International Conference on Computational Intelligence and Networks (CINE) Pub Date : 2022-12-01 DOI: 10.1109/CINE56307.2022.10037357

Manabhanjan Pradhan, S. Mohanty, A. O. Seemona

引用次数: 1

Diagnosing Skin Lesion Using Multi-Modal Analysis 多模态分析诊断皮肤病变

2022 5th International Conference on Computational Intelligence and Networks (CINE) Pub Date : 2022-12-01 DOI: 10.1109/CINE56307.2022.10037268

Rahul Nijhawan, Devansh Bhatnagar, Sudipta Roy

引用次数: 0

A Time Series Analysis of Renewable Energy Production in United States 美国可再生能源生产的时间序列分析

2022 5th International Conference on Computational Intelligence and Networks (CINE) Pub Date : 2022-12-01 DOI: 10.1109/CINE56307.2022.10037293

Rajni, Tuhin Banerjee

引用次数: 0

Training Low-Latency Deep Spiking Neural Networks with Knowledge Distillation and Batch Normalization Through Time 基于知识蒸馏和批归一化的低延迟深度峰值神经网络训练

2022 5th International Conference on Computational Intelligence and Networks (CINE) Pub Date : 2022-12-01 DOI: 10.1109/CINE56307.2022.10037455

Thi Diem Tran, K. Le, An Luong Truong Nguyen

{"title":"Training Low-Latency Deep Spiking Neural Networks with Knowledge Distillation and Batch Normalization Through Time","authors":"Thi Diem Tran, K. Le, An Luong Truong Nguyen","doi":"10.1109/CINE56307.2022.10037455","DOIUrl":"https://doi.org/10.1109/CINE56307.2022.10037455","url":null,"abstract":"Spiking Neural Networks (SNNs) can significantly enhance energy efficiency on neuromorphic hardware due their sparse, biological plausibility and binary event (or spike) driven processing. However, from the non-differentiable nature of a spiking neuron, training high-accuracy and low-latency SNNs is challenging. Recent researches continue to look for ways to improve accuracy and latency. To address these issues in SNNs, we propose a technique that concatenates Knowledge Distillation (KD) and Batch Normalization Through Time (BNTT) method in this study. The BNTT boosts low-latency and low-energy training in SNNs by allowing a neuron to handle the spike rate through various timesteps. The KD approach effectively transfers hidden information from the teacher model to the student network, which converts artificial neural network parameters to SNN weights. This concept allows enriching the performance of SNNs better than the prior technique. Experiments are carried out on the Tiny-ImageNet, CIFAR-10, and CIFAR-100 datasets. on various VGG architectures. We reach top-1 accuracy of 55.67% for ImageNet on VGG-11 and 73.11% for the CIFAR-100 dataset on VGG-16. These results demonstrate that our proposal outperforms earlier converted SNNs in accuracy with only 5 timesteps.","PeriodicalId":336238,"journal":{"name":"2022 5th International Conference on Computational Intelligence and Networks (CINE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128308142","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