Artificial neural networks have transformed machinelearning and had a significant impact on a wide range of industries, including robotics, deep learning, healthcare, sports, and surveillance. Convolutional Neural Ne...
详细信息
ISBN:
(数字)9798350356816
ISBN:
(纸本)9798350356823
Artificial neural networks have transformed machinelearning and had a significant impact on a wide range of industries, including robotics, deep learning, healthcare, sports, and surveillance. Convolutional Neural Networks (CNNs) are unique among these developments because they combine cutting-edge deep learning methods with conventional Artificial Neural Networks (ANNs). CNNs are essential for tasks like data analysis, text classification, speech and face recognition, and patternrecognition in a variety of fields. In order to improve spelling correction, this article uses a dataset that was upgraded from the National Institute of standards & Technology (NIst) and includes 70,000 grayscale pictures of handwritten numbers.
In order to resist network attacks on IoT devices, identifying IoT devices is the firststep for ensuring device security. The traditional passive method identifies IoT devices by mining the potential relationship bet...
详细信息
ISBN:
(数字)9798350350128
ISBN:
(纸本)9798350350135
In order to resist network attacks on IoT devices, identifying IoT devices is the firststep for ensuring device security. The traditional passive method identifies IoT devices by mining the potential relationship between traffic characteristics and devices. However, the form of selected traffic features are too singular without considering device behavioral characteristics and the classifier used is too specific with simple structure in these methods. This paper proposes a stacking ensemble learning approach for IoT device identification, ENSIOT, which fully considering the behavioral characteristics of devices and integrating the advantages of various machinelearning methods to achieve efficient identification of IoT devices. Firstly, in the process of traffic processing, our method selects features from activity cycles, port numbers, signalling patterns, and cipher suites. Then, in model integration, many machinelearning methods are used as base models to learn features selected, and output preliminary recognition results. Finally, the meta model learns the relationship between label and the recognition results of each base model and outputs the final device identification result. This stacking structure stacks the base models and the meta model to make a classifier with strong identification and generalization ability. Incremental learning is used to improve identification accuracy when traffic pattern changing. Comparative experiments are conducted on two datasets of UNSW and TMA-2021. The experimental results verify the effectiveness of ENSIOT, which achieve the accuracy of over 98% on two dataset and bring a noticeable improvement in terms of both accuracy and macro F1 score.
in this paper, we build an age and gender classification system including two networks to classify age and gender based on GoogLeNet with the help of Caffe deep learning framework. It outputs gender and age groups of ...
详细信息
ISBN:
(纸本)9781538608432
in this paper, we build an age and gender classification system including two networks to classify age and gender based on GoogLeNet with the help of Caffe deep learning framework. It outputs gender and age groups of the facial images captured from the camera. We use Adience dataset to train GoogLeNet. Asynchronous stochastic Gradient Descent based on multi-GPUs is used to optimize training process. We intend to use the trained network to build a classification system in real world to show the practicability. For instance, it can apply to a targeted delivery in bus stop or department store. The results indicate that the accuracy of the classification network can be improved by pre-training. In addition, the multi-GPUs training platform can improve the training speed during the recognition. Overall system reaches speed of 8fps with a high accuracy to classify age and gender.
In March 2020, World Health Organization (WHO) recognized COVID-19 as a pandemic and urged governments to exert maximum efforts to prevent its spreading through political decisions together with public awareness campa...
详细信息
ISBN:
(数字)9798350346336
ISBN:
(纸本)9798350346343
In March 2020, World Health Organization (WHO) recognized COVID-19 as a pandemic and urged governments to exert maximum efforts to prevent its spreading through political decisions together with public awareness campaigns positively impacting personal behaviors. Moreover, the WHO recommends collecting facts and data from reliable sources to help accurately determine the risks, accordingly governments take reasonable precautions. There are several ways to fight against corona such as accelerating research for the doctors, scientists and organizations working to find a vaccine or a medicine to defeat the COVID-19 virus, cleaning and sterilizing facilities, ensure health and productivity of people while changing their workplace, provide supercomputers to fight the virus. Artificial Intelligence (AI) possesses remarkable potency in its capacity to assist in combating the COVID-19 pandemic. This is achieved through a diverse range of methodologies such as machinelearning, Natural Language Processing, and Computer Vision applications. By instructing computers to effectively employ models based on extensive data sets, the objective of patternrecognition, explication, and prognostication is pursued [1]. These techniques will generate knowledge that can be useful in diagnosing, predicting, and treating COVID-19. [2]–[3] AI can also help in detecting patterns that help us to manage COVID 19 socio-economic impacts [4]. Since the outbreak of the pandemic, there has been a scramble to use AI. This article aims to overview the possible applications of AI and Big data in facing COVID 19 pandemic. Four possible applications are identified, namely effective alert helping in monitoring the outbreaks instantaneously; diagnostic cases of COVID 19 and tailor medication; facilitating the implementation of Public Health interventions and resource optimizations; and Building cities with smart healthcare services.
暂无评论