scholarly journals Dynamic Resource Allocation and Memory Management using Deep Convolutional Neural Network

2019 ◽  
Vol 9 (2) ◽  
pp. 608-612
Keyword(s):  
Neural Network ◽  
Resource Allocation ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Data Storage ◽  
Memory Management ◽  
Research Work ◽  
Dynamic Resource Allocation ◽  
Mobile Platform ◽  
Dynamic Resource

Memory management is very essential task for large-scale storage systems; in mobile platform generate storage errors due to insufficient memory as well as additional task overhead. Many existing systems have illustrated different solution for such issues, like load balancing and load rebalancing. Different unusable applications which are already installed in mobile platform user never access frequently but it allocates some memory space on hard device storage. In the proposed research work we describe dynamic resource allocation for mobile platforms using deep learning approach. In Real world mobile systems users may install different kind of applications which required ad-hoc basis. Such applications may be affect to execution performance of system as well space complexity, sometime they also affect another runnable applications performance. To eliminate of such issues, we carried out an approach to allocate runtime resources for data storage for mobile platform. When system connected with cloud data server it store complete file system on remote Virtual Machine (VM) and whenever a single application required which immediately install beginning as remote server to local device. For developed of proposed system we implemented deep learning base Convolutional Neural Network (CNN), algorithm has used with tensorflow environment which reduces the time complexity for data storage as well as extraction respectively.

scholarly journals Exploring the Effectiveness of Convolutional Neural Network with Ensemble Technique

2020 ◽  
Author(s):  
Monalisha Ghosh ◽  
Goutam Sanyal
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Research Work ◽  
Research Field ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Ensemble Technique

Abstract ­­­­­­­­­­­­­­­­­­­­­­­­­­­ Sentiment Analysis has recently been considered as the most active research field in the natural language processing (NLP) domain. Deep Learning is a subset of the large family of Machine Learning and becoming a growing trend due to its automatic learning capability with impressive consequences across different NLP tasks. Hence, a fusion-based Machine Learning framework has been attempted by merging the Traditional Machine Learning method with Deep Learning techniques to tackle the challenge of sentiment prediction for a massive amount of unstructured review dataset. The proposed architecture aims to utilize the Convolutional Neural Network (CNN) with a backpropagation algorithm to extract embedded feature vectors from the top hidden layer. Thereafter, these vectors augmented to an optimized feature set generated from binary particle swarm optimization (BPSO) method. Finally, a traditional SVM classifier is trained with these extended features set to determine the optimal hyper-plane for separating two classes of review datasets. The evaluation of this research work has been carried out on two benchmark movie review datasets IMDB, SST2. Experimental results with comparative studies based on performance accuracy and F-score value are reported to highlight the benefits of the developed frameworks.

scholarly journals A Novel Memory-Scheduling Strategy for Large Convolutional Neural Network on Memory-Limited Devices

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Shijie Li ◽  
Xiaolong Shen ◽  
Yong Dou ◽  
Shice Ni ◽  
Jinwei Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Mobile Devices ◽  
Language Processing ◽  
Memory Management ◽  
Learning Methods ◽  
Large Size ◽  
Memory Bottleneck ◽  
Memory Resources

Recently, machine learning, especially deep learning, has been a core algorithm to be widely used in many fields such as natural language processing, speech recognition, object recognition, and so on. At the same time, another trend is that more and more applications are moved to wearable and mobile devices. However, traditional deep learning methods such as convolutional neural network (CNN) and its variants consume a lot of memory resources. In this case, these powerful deep learning methods are difficult to apply on mobile memory-limited platforms. In order to solve this problem, we present a novel memory-management strategy called mmCNN in this paper. With the help of this method, we can easily deploy a trained large-size CNN on any memory size platform such as GPU, FPGA, or memory-limited mobile devices. In our experiments, we run a feed-forward CNN process in some extremely small memory sizes (as low as 5 MB) on a GPU platform. The result shows that our method saves more than 98% memory compared to a traditional CNN algorithm and further saves more than 90% compared to the state-of-the-art related work “vDNNs” (virtualized deep neural networks). Our work in this paper improves the computing scalability of lightweight applications and breaks the memory bottleneck of using deep learning method on memory-limited devices.

scholarly journals Exploring the Effectiveness of Convolutional Neural Network with Ensemble Technique

2020 ◽  
Author(s):  
Monalisha Ghosh ◽  
Goutam Sanyal
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Research Work ◽  
Research Field ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Ensemble Technique

Abstract Sentiment Analysis has recently been considered as the most active research field in the natural language processing (NLP) domain. Deep Learning is a subset of the large family of Machine Learning and becoming a growing trend due to its automatic learning capability with impressive consequences across different NLP tasks. Hence, a fusion-based Machine Learning framework has been attempted by merging the Traditional Machine Learning method with Deep Learning techniques to tackle the challenge of sentiment prediction for a massive amount of unstructured review dataset. The proposed architecture aims to utilize the Convolutional Neural Network (CNN) with a backpropagation algorithm to extract embedded feature vectors from the top hidden layer. Thereafter, these vectors augmented to an optimized feature set generated from binary particle swarm optimization (BPSO) method. Finally, a traditional SVM classifier is trained with these extended features set to determine the optimal hyper-plane for separating two classes of review datasets. The evaluation of this research work has been carried out on two benchmark movie review datasets IMDB, SST2. Experimental results with comparative studies based on performance accuracy and F-score value are reported to highlight the benefits of the developed frameworks.

scholarly journals Identification of Covid’19 Vaccinator by Deep Learning Approach Using Contactless Palmprints

2021 ◽  
Vol 3 (3) ◽  
pp. 178-193
Author(s):  
B. Vivekanandam
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Research Study ◽  
Research Work ◽  
Digital Camera ◽  
Activation Function ◽  
User Participation ◽  
Learning Platform

The invention of the first vaccine has also raised several anti-vaccination views among people. Vaccine reluctance may be exacerbated by the growing reliance on social media, which is considered as a source of health information. During this COVID'19 scenario, the verification of non-vaccinators via the use of biometric characteristics has received greater attention, especially in areas such as vaccination monitoring and other emergency medical services, among other things. The traditional digital camera utilizes the middle-resolution images for commercial applications in a regulated or contact-based environment with user participation, while the latter uses high-resolution latent palmprints. This research study attempts to utilize convolutional neural networks (CNN) for the first time to perform contactless recognition. To identify the COVID '19 vaccine using the CNN technique, this research work has used the contactless palmprint method. Further, this research study utilizes the PalmNet structure of convolutional neural network to resolve the issue. First, the ROI region of the palmprint was extracted from the input picture based on the geometric form of the print. After image registration, the ROI region is sent into a convolutional neural network as an input. The softmax activation function is then used to train the network so that it can choose the optimal learning rate and super parameters for the given learning scenario. The neural networks of the deep learning platform were then compared and summarized.

Sign in / Sign up

Export Citation Format

Share Document