scholarly journals A Framework for Object Detection using Deep-Reinforcement Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 1508-1514
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Object Detection ◽  
Intelligent Systems ◽  
Learning Algorithm ◽  
Efficient Tool ◽  
Narrow Scope ◽  
Deep Learning Algorithm ◽  
Learning Techniques

Machine learning area enable the utilization of Deep learning algorithm and neural networks (DNNs) with Reinforcement Learning. Reinforcement learning and DL both is region of AI, it’s an efficient tool towards structuring artificially intelligent systems and solving sequential deciding problems. Reinforcement learning (RL) deals with the history of moves; Reinforcement learning problems are often resolve by an agent often denoted as (A) it has privilege to make decisions during a situation to optimize a given problem by collective rewards. Ability to structure sizable amount of attributes make deep learning an efficient tool for unstructured data. Comparing multiple deep learning algorithms may be a major issue thanks to the character of the training process and therefore the narrow scope of datasets tested in algorithmic prisons. Our research proposed a framework which exposed that reinforcement learning techniques in combination with Deep learning techniques learn functional representations for sorting problems with high dimensional unprocessed data. The faster RCNN model typically founds objects in faster way saving resources like computation, processing, and storage. But still object detection technique typically require high computation power and large memory and processor building it hard to run on resource constrained devices (RCD) for detecting an object during real time without an efficient and high computing machine.

scholarly journals Effective Deep Learning Based Architecture for Pedestrian Detection from Digital Images

2020 ◽  
Vol 9 (3) ◽  
pp. 1498-1508
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Object Detection ◽  
Learning Algorithm ◽  
Pedestrian Detection ◽  
Single Shot ◽  
Deep Learning Algorithm ◽  
Learning Techniques ◽  
Rgb Images ◽  
Deep Convolution Network

This paper is to present an efficient and fast deep learning algorithm based on neural networks for object detection and pedestrian detection. The technique, called MobileNet Single Shot Detector, is an extension to Convolution Neural Networks. This technique is based on depth-wise distinguishable convolutions in order to build a lightweighted deep convolution network. A single filter is applied to each input and outputs are combined by using pointwise convolution. Single Shot Multibox Detector is a feed forward convolution network that is combined with MobileNets to give efficient and accurate results. MobileNets combined with SSD and Multibox Technique makes it much faster than SSD alone can work. The accuracy for this technique is calculated over colored (RGB images) and also on infrared images and its results are compared with the results of shallow machine learning based feature extraction plus classification technique viz. HOG plus SVM technique. The comparison of performance between proposed deep learning and shallow learning techniques has been conducted over benchmark dataset and validation testing over own dataset in order measure efficiency of both algorithms and find an effective algorithm that can work with speed and accurately to be applied for object detection in real world pedestrian detection application.

scholarly journals Object Detection Method by using Yolov3 with Machine Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 5247-5250
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Object Detection ◽  
Moving Objects ◽  
Learning Algorithm ◽  
Detection Algorithm ◽  
Video Object ◽  
Long Distance ◽  
Deep Learning Algorithm ◽  
Tracking Movement

Detecting the object is a vision technique of a computer for detectng or locating long distance or short distance objects and images. Object detection algorithm mainly works on the machine learning and the artificial intelligence algorithms. Present trending algorithm in detecting the object is deep learning algorithm, By using the deep learning algorithm we can get the accurate results of the object which is detected. it is mainly or widely used in the system vision tasks like video object co-segmentations, tracking movement of the ball in the ground, image annotating etc. Each and every object has its own features ,for example if you select the ball , Actually all the ball are in the round shape but in every game different type of balls are used ,object detection camera will detect the ball it will check the ball specifications with its data if any data was matched with its data base the system will display all the specifications of ball. By using the deep learning algorithm we introduced one new technique to detect detect object accurately the algorithm is named as the YOLO V3 we can detect the very small objects and the fastly moving objects easily. This yolo v3 will convert the image into N number of layers and it will work on the each and every minute spot on the image.

scholarly journals Segmentation for Complex Background Images using Deep Learning Techniques

2019 ◽  
Vol 8 (2) ◽  
pp. 1746-1750
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Vision System ◽  
Automated Segmentation ◽  
Computer Vision System ◽  
Important Stage ◽  
Deep Learning Algorithm ◽  
Complex Background ◽  
Learning Techniques

Segmentation is an important stage in any computer vision system. Segmentation involves discarding the objects which are not of our interest and extracting only the object of our interest. Automated segmentation has become very difficult when we have complex background and other challenges like illumination, occlusion etc. In this project we are designing an automated segmentation system using deep learning algorithm to segment images with complex background.

scholarly journals Detecting The Speaker Language Using CNN Deep Learning Algorithm

2022 ◽  
pp. 43-52
Author(s):  
Fawziya M. Rammo ◽  
Mohammed N. Al-Hamdani
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Open Source ◽  
Convolutional Neural Networks ◽  
Learning Algorithm ◽  
Language Models ◽  
Mel Frequency Cepstral Coefficients ◽  
Deep Learning Algorithm ◽  
Time Frames

Many languages identification (LID) systems rely on language models that use machine learning (ML) approaches, LID systems utilize rather long recording periods to achieve satisfactory accuracy. This study aims to extract enough information from short recording intervals in order to successfully classify the spoken languages under test. The classification process is based on frames of (2-18) seconds where most of the previous LID systems were based on much longer time frames (from 3 seconds to 2 minutes). This research defined and implemented many low-level features using MFCC (Mel-frequency cepstral coefficients), containing speech files in five languages (English. French, German, Italian, Spanish), from voxforge.org an open-source corpus that consists of user-submitted audio clips in various languages, is the source of data used in this paper. A CNN (convolutional Neural Networks) algorithm applied in this paper for classification and the result was perfect, binary language classification had an accuracy of 100%, and five languages classification with six languages had an accuracy of 99.8%.

scholarly journals Intrusion Detection in IoT Networks Using Deep Learning Algorithm

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 279 ◽  
Author(s):  
Bambang Susilo ◽  
Riri Fitri Sari
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Strategies ◽  
Learning Algorithm ◽  
Human Life ◽  
Denial Of Service ◽  
The Internet ◽  
Dos Attacks ◽  
Deep Learning Algorithm ◽  
Iot Devices

The internet has become an inseparable part of human life, and the number of devices connected to the internet is increasing sharply. In particular, Internet of Things (IoT) devices have become a part of everyday human life. However, some challenges are increasing, and their solutions are not well defined. More and more challenges related to technology security concerning the IoT are arising. Many methods have been developed to secure IoT networks, but many more can still be developed. One proposed way to improve IoT security is to use machine learning. This research discusses several machine-learning and deep-learning strategies, as well as standard datasets for improving the security performance of the IoT. We developed an algorithm for detecting denial-of-service (DoS) attacks using a deep-learning algorithm. This research used the Python programming language with packages such as scikit-learn, Tensorflow, and Seaborn. We found that a deep-learning model could increase accuracy so that the mitigation of attacks that occur on an IoT network is as effective as possible.

Deep Neural Networks for Hierarchical Taxonomic Fossil Classification of Carbonate Skeletal Grains

2021 ◽  
Author(s):  
Sidhant Idgunji ◽  
Madison Ho ◽  
Jonathan L. Payne ◽  
Daniel Lehrmann ◽  
Michele Morsilli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Training Dataset ◽  
Single Class ◽  
Deep Learning Algorithm ◽  
Human Ability ◽  
Wide Range ◽  
Potential Applications ◽  
Animal Phyla

<p>The growing digitization of fossil images has vastly improved and broadened the potential application of big data and machine learning, particularly computer vision, in paleontology. Recent studies show that machine learning is capable of approaching human abilities of classifying images, and with the increase in computational power and visual data, it stands to reason that it can match human ability but at much greater efficiency in the near future. Here we demonstrate this potential of using deep learning to identify skeletal grains at different levels of the Linnaean taxonomic hierarchy. Our approach was two-pronged. First, we built a database of skeletal grain images spanning a wide range of animal phyla and classes and used this database to train the model. We used a Python-based method to automate image recognition and extraction from published sources. Second, we developed a deep learning algorithm that can attach multiple labels to a single image. Conventionally, deep learning is used to predict a single class from an image; here, we adopted a Branch Convolutional Neural Network (B-CNN) technique to classify multiple taxonomic levels for a single skeletal grain image. Using this method, we achieved over 90% accuracy for both the coarse, phylum-level recognition and the fine, class-level recognition across diverse skeletal grains (6 phyla and 15 classes). Furthermore, we found that image augmentation improves the overall accuracy. This tool has potential applications in geology ranging from biostratigraphy to paleo-bathymetry, paleoecology, and microfacies analysis. Further improvement of the algorithm and expansion of the training dataset will continue to narrow the efficiency gap between human expertise and machine learning.</p>

Use of deep learning to predict acute kidney injury after intravenous contrast media administration (Preprint)

2021 ◽  
Author(s):  
Donghwan Yun ◽  
Semin Cho ◽  
Yong Chul Kim ◽  
Dong Ki Kim ◽  
Kook-Hwan Oh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Deep Learning ◽  
Contrast Media ◽  
Learning Algorithm ◽  
Kidney Injury ◽  
Time Varying ◽  
Intravenous Contrast ◽  
Deep Learning Algorithm ◽  
Intravenous Contrast Media

BACKGROUND Precise prediction of contrast media-induced acute kidney injury (CIAKI) is an important issue because of its relationship with worse outcomes. OBJECTIVE Herein, we examined whether a deep learning algorithm could predict the risk of intravenous CIAKI better than other machine learning and logistic regression models in patients undergoing computed tomography. METHODS A total of 14,185 cases that underwent intravenous contrast media for computed tomography under the preventive and monitoring facility in Seoul National University Hospital were reviewed. CIAKI was defined as an increase in serum creatinine ≥0.3 mg/dl within 2 days and/or ≥50% within 7 days. Using both time-varying and time-invariant features, machine learning models, such as the recurrent neural network (RNN), light gradient boosting machine, extreme boosting machine, random forest, decision tree, support vector machine, κ-nearest neighboring, and logistic regression, were developed using a training set, and their performance was compared using the area under the receiver operating characteristic curve (AUROC) in a test set. RESULTS CIAKI developed in 261 cases (1.8%). The RNN model had the highest AUROC value of 0.755 (0.708–0.802) for predicting CIAKI, which was superior to those obtained from other machine learning models. Although CIAKI was defined as an increase in serum creatinine ≥0.5 mg/dl and/or ≥25% within 3 days, the highest performance was achieved in the RNN model with an AUROC of 0.716 (0.664–0.768). In the feature ranking analysis, albumin level was the most highly contributing factor to RNN performance, followed by time-varying kidney function. CONCLUSIONS Application of a deep learning algorithm improves the predictability of intravenous CIAKI after computed tomography, representing a basis for future clinical alarming and preventive systems.

scholarly journals Research on Classification Method of Maize Seed Defect Based on Machine Vision

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Sheng Huang ◽  
Xiaofei Fan ◽  
Lei Sun ◽  
Yanlu Shen ◽  
Xuesong Suo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Heat Map ◽  
Deep Learning Algorithm ◽  
Quality Classification ◽  
Visualization Technology ◽  
Better Than

Traditionally, the classification of seed defects mainly relies on the characteristics of color, shape, and texture. This method requires repeated extraction of a large amount of feature information, which is not efficiently used in detection. In recent years, deep learning has performed well in the field of image recognition. We introduced convolutional neural networks (CNNs) and transfer learning into the quality classification of seeds and compared them with traditional machine learning algorithms. Experiments showed that deep learning algorithm was significantly better than the machine learning algorithm with an accuracy of 95% (GoogLeNet) vs. 79.2% (SURF+SVM). We used three classifiers in GoogLeNet to demonstrate that network accuracy increases as the depth of the network increases. We used the visualization technology to obtain the feature map of each layer of the network in CNNs and used the heat map to represent the probability distribution of the inference results. As an end-to-end network, CNNs can be easily applied for automated seed manufacturing.

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