This paper aims at providing insight on the transferability of deep CNN features to
unsupervised problems. We study the impact of different pretrained CNN feature extractors on
the problem of image set clustering for object classification as well as fine-grained
classification. We propose a rather straightforward pipeline combining deep-feature extraction
using a CNN pretrained on ImageNet and a classic clustering algorithm to classify sets of
images. This approach is compared to state-of-the-art algorithms in image-clustering and
provides better results. These results strengthen the belief that supervised training of deep CNN
on large datasets, with a large variability of classes, extracts better features than most carefully
designed engineering approaches, even for unsupervised tasks. We also validate our approach
on a robotic application, consisting in sorting and storing objects smartly based on clustering
Satellite Image Classification with Deep Learning Surveyijtsrd
Satellite imagery is important for many applications including disaster response, law enforcement and environmental monitoring etc. These applications require the manual identification of objects in the imagery. Because the geographic area to be covered is very large and the analysts available to conduct the searches are few, thus an automation is required. Yet traditional object detection and classification algorithms are too inaccurate and unreliable to solve the problem. Deep learning is a part of broader family of machine learning methods that have shown promise for the automation of such tasks. It has achieved success in image understanding by means that of convolutional neural networks. The problem of object and facility recognition in satellite imagery is considered. The system consists of an ensemble of convolutional neural networks and additional neural networks that integrate satellite metadata with image features. Roshni Rajendran | Liji Samuel ""Satellite Image Classification with Deep Learning: Survey"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30031.pdf
Paper Url : https://www.ijtsrd.com/engineering/computer-engineering/30031/satellite-image-classification-with-deep-learning-survey/roshni-rajendran
CONTENT BASED VIDEO CATEGORIZATION USING RELATIONAL CLUSTERING WITH LOCAL SCA...ijcsit
This paper introduces a novel approach for efficient video categorization. It relies on two main
components. The first one is a new relational clustering technique that identifies video key frames by
learning cluster dependent Gaussian kernels. The proposed algorithm, called clustering and Local Scale
Learning algorithm (LSL) learns the underlying cluster dependent dissimilarity measure while finding
compact clusters in the given dataset. The learned measure is a Gaussian dissimilarity function defined
with respect to each cluster. We minimize one objective function to optimize the optimal partition and the
cluster dependent parameter. This optimization is done iteratively by dynamically updating the partition
and the local measure. The kernel learning task exploits the unlabeled data and reciprocally, the
categorization task takes advantages of the local learned kernel. The second component of the proposed
video categorization system consists in discovering the video categories in an unsupervised manner using
the proposed LSL. We illustrate the clustering performance of LSL on synthetic 2D datasets and on high
dimensional real data. Also, we assess the proposed video categorization system using a real video
collection and LSL algorithm.
Satellite Image Classification with Deep Learning Surveyijtsrd
Satellite imagery is important for many applications including disaster response, law enforcement and environmental monitoring etc. These applications require the manual identification of objects in the imagery. Because the geographic area to be covered is very large and the analysts available to conduct the searches are few, thus an automation is required. Yet traditional object detection and classification algorithms are too inaccurate and unreliable to solve the problem. Deep learning is a part of broader family of machine learning methods that have shown promise for the automation of such tasks. It has achieved success in image understanding by means that of convolutional neural networks. The problem of object and facility recognition in satellite imagery is considered. The system consists of an ensemble of convolutional neural networks and additional neural networks that integrate satellite metadata with image features. Roshni Rajendran | Liji Samuel ""Satellite Image Classification with Deep Learning: Survey"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30031.pdf
Paper Url : https://www.ijtsrd.com/engineering/computer-engineering/30031/satellite-image-classification-with-deep-learning-survey/roshni-rajendran
CONTENT BASED VIDEO CATEGORIZATION USING RELATIONAL CLUSTERING WITH LOCAL SCA...ijcsit
This paper introduces a novel approach for efficient video categorization. It relies on two main
components. The first one is a new relational clustering technique that identifies video key frames by
learning cluster dependent Gaussian kernels. The proposed algorithm, called clustering and Local Scale
Learning algorithm (LSL) learns the underlying cluster dependent dissimilarity measure while finding
compact clusters in the given dataset. The learned measure is a Gaussian dissimilarity function defined
with respect to each cluster. We minimize one objective function to optimize the optimal partition and the
cluster dependent parameter. This optimization is done iteratively by dynamically updating the partition
and the local measure. The kernel learning task exploits the unlabeled data and reciprocally, the
categorization task takes advantages of the local learned kernel. The second component of the proposed
video categorization system consists in discovering the video categories in an unsupervised manner using
the proposed LSL. We illustrate the clustering performance of LSL on synthetic 2D datasets and on high
dimensional real data. Also, we assess the proposed video categorization system using a real video
collection and LSL algorithm.
GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspec...Konstantinos Demertzis
Deep learning architectures are the most e
ective methods for analyzing and classifying Ultra-Spectral Images (USI). However, e ective training of a Deep Learning (DL) gradient classifier aiming to achieve high classification accuracy, is extremely costly and time-consuming. It requires huge datasets with hundreds or thousands of labeled specimens from expert scientists. This research exploits the MAML++ algorithm in order to introduce the Model-Agnostic Meta-Ensemble Zero-shot Learning (MAME-ZsL) approach. The MAME-ZsL overcomes the above diculties, and it can be used as a powerful model to perform Hyperspectral Image Analysis (HIA). It is a novel optimization-based Meta-Ensemble Learning architecture, following a Zero-shot Learning (ZsL) prototype. To the best of our knowledge it is introduced to the literature for the first time. It facilitates learning of specialized techniques for the extraction of user-mediated representations, in complex Deep Learning architectures. Moreover, it leverages the use of first and second-order derivatives as pre-training methods. It enhances learning of features which do not cause issues of exploding or diminishing gradients; thus, it avoids potential overfitting. Moreover, it significantly reduces computational cost and training time, and it oers an improved training stability, high generalization performance and remarkable classification accuracy.
RunPool: A Dynamic Pooling Layer for Convolution Neural NetworkPutra Wanda
Deep learning (DL) has achieved a significant performance in computer vision problems, mainly in automatic feature extraction and representation. However, it is not easy to determine the best pooling method in a different case study. For instance, experts can implement the best types of pooling in image processing cases, which might not be optimal for various tasks. Thus, it is
required to keep in line with the philosophy of DL. In dynamic neural network architecture, it is not practically possible to find
a proper pooling technique for the layers. It is the primary reason why various pooling cannot be applied in the dynamic and multidimensional dataset. To deal with the limitations, it needs to construct an optimal pooling method as a better option than max pooling and average pooling. Therefore, we introduce a dynamic pooling layer called RunPool to train the convolutional
neuralnetwork(CNN)architecture.RunPoolpoolingisproposedtoregularizetheneuralnetworkthatreplacesthedeterministic
pooling functions. In the final section, we test the proposed pooling layer to address classification problems with online social network (OSN) dataset
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
A Literature Survey: Neural Networks for object detectionvivatechijri
Humans have a great capability to distinguish objects by their vision. But, for machines object
detection is an issue. Thus, Neural Networks have been introduced in the field of computer science. Neural
Networks are also called as ‘Artificial Neural Networks’ [13]. Artificial Neural Networks are computational
models of the brain which helps in object detection and recognition. This paper describes and demonstrates the
different types of Neural Networks such as ANN, KNN, FASTER R-CNN, 3D-CNN, RNN etc. with their accuracies.
From the study of various research papers, the accuracies of different Neural Networks are discussed and
compared and it can be concluded that in the given test cases, the ANN gives the best accuracy for the object
detection.
Segmentation of Images by using Fuzzy k-means clustering with ACOIJTET Journal
Abstract— Super pixels are becoming increasingly popular for use in computer vision applications. Image segmentation is the process of partitioning a digital image into multiple segments (known as super pixels). In this paper, we developed fuzzy k-means clustering with Ant Colony Optimization (ACO). In this propose algorithm the initial assumptions are made in the calculation of the mean value, which are depends on the colors of neighbored pixel in the image. Fuzzy mean is calculated for the whole image, this process having set of rules that rules are applied iteratively which is used to cluster the whole image. Once choosing a neighbor around that the fitness function is calculated in the optimization process. Based on the optimized clusters the image is segmented. By using fuzzy k-means clustering with ACO technique the image segmentation obtain high accuracy and the segmentation time is reduced compared to previous technique that is Lazy random walk (LRW) methodology. This LRW is optimized from Random walk technique.
With the development of information security, the traditional image encryption methods have become
outdated. Because of amply using images in the transmission process, it is important to protect the confidential image
data from unauthorized access. This paper presents a new chaos based image encryption algorithm, which can improve
the security during transmission more effectively utilizes the chaotic systems properties, such as pseudo-random
appearance and sensitivity to initial conditions. Based on chaotic theory and decomposition and recombination of pixel
values, this new image scrambling algorithm is able to change the position of pixel, simultaneously scrambling both
position and pixel values. Experimental results show that the new algorithm improves the image security effectively to
avoid unscramble, and it also can restore the image as same as the original one, which reaches to the purposes of image
safe and reliable transmission.
NUMBER PLATE IMAGE DETECTION FOR FAST MOTION VEHICLES USING BLUR KERNEL ESTIM...paperpublications3
Abstract: Many recent advancements have been introduced in both hardware and software technologies. Out of these technologies, we gain a lot of interest in Image Processing. As the eccentric identification of a vehicle, number plate is a key clue to discover theft and over-speed vehicles. The captured images from the camera are always in low resolution and suffer severe loss of edge information, which cast great challenge to existing blind deblurring methods. The blur kernel can be showed as linear uniform convolution and with angle and length estimation. In this paper, sparse representation is used to identify the blur kernel. Then, the length of the motion kernel has been estimated with Radon transform in Fourier domain. We evaluate our approach on real-world images and compare with several popular blind image deblurring algorithms. Based on the results obtained the supremacy of our proposed approach in terms of efficacy.
Keywords: Image Segmentation, Angle Estimation, Length Estimation, Deconvolution Algorithm, Artificial Neural Network.
Title: NUMBER PLATE IMAGE DETECTION FOR FAST MOTION VEHICLES USING BLUR KERNEL ESTIMATION AND ANN
Author: S.KALAIVANI, K.PRAVEENA, T.PREETHI, N.PUNITHA
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Optimized Neural Network for Classification of Multispectral ImagesIDES Editor
The proposed work involves the multiobjective PSO
based optimization of artificial neural network structure for
the classification of multispectral satellite images. The neural
network is used to classify each image pixel in various land
cove types like vegetations, waterways, man-made structures
and road network. It is per pixel supervised classification using
spectral bands (original feature space). Use of neural network
for classification requires selection of most discriminative
spectral bands and determination of optimal number of nodes
in hidden layer. We propose new methodology based on
multiobjective particle swarm optimization (MOPSO) to
determine discriminative spectral bands and the number of
hidden layer node simultaneously. The result obtained using
such optimized neural network is compared with that of
traditional classifiers like MLC and Euclidean classifier. The
performance of all classifiers is evaluated quantitatively using
Xie-Beni and â indexes. The result shows the superiority of
the proposed method.
The recent emergence of machine learning and deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist physicians in making better decisions about a patient’s health. In particular, skin imaging is a field where these new methods can be applied with a high rate of success.
This thesis focuses on the problem of automatic skin lesion detection, particularly on melanoma detection, by applying semantic segmentation and classification from dermoscopic images using a deep learning based approach. For the first problem, a U-Net convolutional neural network architecture is applied for an accurate extraction of the lesion region. For the second problem, the current model performs a binary classification (benign versus malignant) that can be used for early melanoma detection. The model is general enough to be extended to multi-class skin lesion classification. The proposed solution is built around the VGG-Net ConvNet architecture and uses the transfer learning paradigm. Finally, this work performs a comparative evaluation of classification alone (using the entire image) against a combination of the two approaches (segmentation followed by classification) in order to assess which of them achieves better classification results.
Automated Traffic sign board classification system is one of the key technologies of Intelligent
Transportation Systems (ITS). Traffic Surveillance System is being more and important with improving
urban scale and increasing number of vehicles. This Paper presents an intelligent sign board
classification method based on blob analysis in traffic surveillance. Processing is done by three main
steps: moving object segmentation, blob analysis, and classifying. A Sign board is modelled as a
rectangular patch and classified via blob analysis. By processing the blob of sign boards, the meaningful
features are extracted. Tracking moving targets is achieved by comparing the extracted features with
training data. After classifying the sign boards the system will intimate to user in the form of alarms,
sound waves. The experimental results show that the proposed system can provide real-time and useful
information for traffic surveillance.
Object gripping algorithm for robotic assistance by means of deep leaning IJECEIAES
This paper exposes the use of recent deep learning techniques in the state of the art, little addressed in robotic applications, where a new algorithm based on Faster R-CNN and CNN regression is exposed. The machine vision systems implemented, tend to require multiple stages to locate an object and allow a robot to take it, increasing the noise in the system and the processing times. The convolutional networks based on regions allow one to solve this problem, it is used for it two convolutional architectures, one for classification and location of three types of objects and one to determine the grip angle for a robotic gripper. Under the establish virtual environment, the grip algorithm works up to 5 frames per second with a 100% object classification, and with the implementation of the Faster R-CNN, it allows obtain 100% accuracy in the classifications of the test database, and over a 97% of average precision locating the generated boxes in each element, gripping successfully the objects.
Using Mask R CNN to Isolate PV Panels from Background Object in Imagesijtsrd
Identifying foreground objects in an image is one of the most common operations used in image processing. In this work, Mask R CNN algorithm is used to identify solar photovoltaic PV panels in aerial images and create a mask that can be used to remove the background from the images. This allows processing the PV panels separately. Using ML to solve this problem can generate more accurate results in comparison to more traditional image processing techniques like using edge detection or Gaussian filtering especially in images where the view might not be easily separable from the objects of interest. The trained model was found to be successful in detecting the PV panels and selecting the pixels that belong to them while ignoring the background pixels. This kind of work can be useful in collecting information about PV installation present in aerial or satellite imagery, or in analyzing the health and integrity of PV modules in large scale installations e.g., in a solar power plant. The results show that this method is effective with a high potential for improved results if the model is trained using larger and more diverse datasets. Muhammet Sait | Atilla Erguzen | Erdal Erdal "Using Mask R-CNN to Isolate PV Panels from Background Object in Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38173.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/38173/using-mask-rcnn-to-isolate-pv-panels-from-background-object-in-images/muhammet-sait
An ensemble classification algorithm for hyperspectral imagessipij
Hyperspectral image analysis has been used for many purposes in environmental monitoring, remote
sensing, vegetation research and also for land cover classification. A hyperspectral image consists of many
layers in which each layer represents a specific wavelength. The layers stack on top of one another making
a cube-like image for entire spectrum. This work aims to classify the hyperspectral images and to produce
a thematic map accurately. Spatial information of hyperspectral images is collected by applying
morphological profile and local binary pattern. Support vector machine is an efficient classification
algorithm for classifying the hyperspectral images. Genetic algorithm is used to obtain the best feature
subjected for classification. Selected features are classified for obtaining the classes and to produce a
thematic map. Experiment is carried out with AVIRIS Indian Pines and ROSIS Pavia University. Proposed
method produces accuracy as 93% for Indian Pines and 92% for Pavia University.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification.
This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that
provides insights into the selection of pre-trained models and hyper parameters for optimal performance.
We have proposed a comprehensive approach for image segmentation and classification, incorporating preprocessing techniques, the K-means algorithm for segmentation, and employing deep learning models such
as CNN and VGG 16 for classification.
GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspec...Konstantinos Demertzis
Deep learning architectures are the most e
ective methods for analyzing and classifying Ultra-Spectral Images (USI). However, e ective training of a Deep Learning (DL) gradient classifier aiming to achieve high classification accuracy, is extremely costly and time-consuming. It requires huge datasets with hundreds or thousands of labeled specimens from expert scientists. This research exploits the MAML++ algorithm in order to introduce the Model-Agnostic Meta-Ensemble Zero-shot Learning (MAME-ZsL) approach. The MAME-ZsL overcomes the above diculties, and it can be used as a powerful model to perform Hyperspectral Image Analysis (HIA). It is a novel optimization-based Meta-Ensemble Learning architecture, following a Zero-shot Learning (ZsL) prototype. To the best of our knowledge it is introduced to the literature for the first time. It facilitates learning of specialized techniques for the extraction of user-mediated representations, in complex Deep Learning architectures. Moreover, it leverages the use of first and second-order derivatives as pre-training methods. It enhances learning of features which do not cause issues of exploding or diminishing gradients; thus, it avoids potential overfitting. Moreover, it significantly reduces computational cost and training time, and it oers an improved training stability, high generalization performance and remarkable classification accuracy.
RunPool: A Dynamic Pooling Layer for Convolution Neural NetworkPutra Wanda
Deep learning (DL) has achieved a significant performance in computer vision problems, mainly in automatic feature extraction and representation. However, it is not easy to determine the best pooling method in a different case study. For instance, experts can implement the best types of pooling in image processing cases, which might not be optimal for various tasks. Thus, it is
required to keep in line with the philosophy of DL. In dynamic neural network architecture, it is not practically possible to find
a proper pooling technique for the layers. It is the primary reason why various pooling cannot be applied in the dynamic and multidimensional dataset. To deal with the limitations, it needs to construct an optimal pooling method as a better option than max pooling and average pooling. Therefore, we introduce a dynamic pooling layer called RunPool to train the convolutional
neuralnetwork(CNN)architecture.RunPoolpoolingisproposedtoregularizetheneuralnetworkthatreplacesthedeterministic
pooling functions. In the final section, we test the proposed pooling layer to address classification problems with online social network (OSN) dataset
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
A Literature Survey: Neural Networks for object detectionvivatechijri
Humans have a great capability to distinguish objects by their vision. But, for machines object
detection is an issue. Thus, Neural Networks have been introduced in the field of computer science. Neural
Networks are also called as ‘Artificial Neural Networks’ [13]. Artificial Neural Networks are computational
models of the brain which helps in object detection and recognition. This paper describes and demonstrates the
different types of Neural Networks such as ANN, KNN, FASTER R-CNN, 3D-CNN, RNN etc. with their accuracies.
From the study of various research papers, the accuracies of different Neural Networks are discussed and
compared and it can be concluded that in the given test cases, the ANN gives the best accuracy for the object
detection.
Segmentation of Images by using Fuzzy k-means clustering with ACOIJTET Journal
Abstract— Super pixels are becoming increasingly popular for use in computer vision applications. Image segmentation is the process of partitioning a digital image into multiple segments (known as super pixels). In this paper, we developed fuzzy k-means clustering with Ant Colony Optimization (ACO). In this propose algorithm the initial assumptions are made in the calculation of the mean value, which are depends on the colors of neighbored pixel in the image. Fuzzy mean is calculated for the whole image, this process having set of rules that rules are applied iteratively which is used to cluster the whole image. Once choosing a neighbor around that the fitness function is calculated in the optimization process. Based on the optimized clusters the image is segmented. By using fuzzy k-means clustering with ACO technique the image segmentation obtain high accuracy and the segmentation time is reduced compared to previous technique that is Lazy random walk (LRW) methodology. This LRW is optimized from Random walk technique.
With the development of information security, the traditional image encryption methods have become
outdated. Because of amply using images in the transmission process, it is important to protect the confidential image
data from unauthorized access. This paper presents a new chaos based image encryption algorithm, which can improve
the security during transmission more effectively utilizes the chaotic systems properties, such as pseudo-random
appearance and sensitivity to initial conditions. Based on chaotic theory and decomposition and recombination of pixel
values, this new image scrambling algorithm is able to change the position of pixel, simultaneously scrambling both
position and pixel values. Experimental results show that the new algorithm improves the image security effectively to
avoid unscramble, and it also can restore the image as same as the original one, which reaches to the purposes of image
safe and reliable transmission.
NUMBER PLATE IMAGE DETECTION FOR FAST MOTION VEHICLES USING BLUR KERNEL ESTIM...paperpublications3
Abstract: Many recent advancements have been introduced in both hardware and software technologies. Out of these technologies, we gain a lot of interest in Image Processing. As the eccentric identification of a vehicle, number plate is a key clue to discover theft and over-speed vehicles. The captured images from the camera are always in low resolution and suffer severe loss of edge information, which cast great challenge to existing blind deblurring methods. The blur kernel can be showed as linear uniform convolution and with angle and length estimation. In this paper, sparse representation is used to identify the blur kernel. Then, the length of the motion kernel has been estimated with Radon transform in Fourier domain. We evaluate our approach on real-world images and compare with several popular blind image deblurring algorithms. Based on the results obtained the supremacy of our proposed approach in terms of efficacy.
Keywords: Image Segmentation, Angle Estimation, Length Estimation, Deconvolution Algorithm, Artificial Neural Network.
Title: NUMBER PLATE IMAGE DETECTION FOR FAST MOTION VEHICLES USING BLUR KERNEL ESTIMATION AND ANN
Author: S.KALAIVANI, K.PRAVEENA, T.PREETHI, N.PUNITHA
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Optimized Neural Network for Classification of Multispectral ImagesIDES Editor
The proposed work involves the multiobjective PSO
based optimization of artificial neural network structure for
the classification of multispectral satellite images. The neural
network is used to classify each image pixel in various land
cove types like vegetations, waterways, man-made structures
and road network. It is per pixel supervised classification using
spectral bands (original feature space). Use of neural network
for classification requires selection of most discriminative
spectral bands and determination of optimal number of nodes
in hidden layer. We propose new methodology based on
multiobjective particle swarm optimization (MOPSO) to
determine discriminative spectral bands and the number of
hidden layer node simultaneously. The result obtained using
such optimized neural network is compared with that of
traditional classifiers like MLC and Euclidean classifier. The
performance of all classifiers is evaluated quantitatively using
Xie-Beni and â indexes. The result shows the superiority of
the proposed method.
The recent emergence of machine learning and deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist physicians in making better decisions about a patient’s health. In particular, skin imaging is a field where these new methods can be applied with a high rate of success.
This thesis focuses on the problem of automatic skin lesion detection, particularly on melanoma detection, by applying semantic segmentation and classification from dermoscopic images using a deep learning based approach. For the first problem, a U-Net convolutional neural network architecture is applied for an accurate extraction of the lesion region. For the second problem, the current model performs a binary classification (benign versus malignant) that can be used for early melanoma detection. The model is general enough to be extended to multi-class skin lesion classification. The proposed solution is built around the VGG-Net ConvNet architecture and uses the transfer learning paradigm. Finally, this work performs a comparative evaluation of classification alone (using the entire image) against a combination of the two approaches (segmentation followed by classification) in order to assess which of them achieves better classification results.
Automated Traffic sign board classification system is one of the key technologies of Intelligent
Transportation Systems (ITS). Traffic Surveillance System is being more and important with improving
urban scale and increasing number of vehicles. This Paper presents an intelligent sign board
classification method based on blob analysis in traffic surveillance. Processing is done by three main
steps: moving object segmentation, blob analysis, and classifying. A Sign board is modelled as a
rectangular patch and classified via blob analysis. By processing the blob of sign boards, the meaningful
features are extracted. Tracking moving targets is achieved by comparing the extracted features with
training data. After classifying the sign boards the system will intimate to user in the form of alarms,
sound waves. The experimental results show that the proposed system can provide real-time and useful
information for traffic surveillance.
Object gripping algorithm for robotic assistance by means of deep leaning IJECEIAES
This paper exposes the use of recent deep learning techniques in the state of the art, little addressed in robotic applications, where a new algorithm based on Faster R-CNN and CNN regression is exposed. The machine vision systems implemented, tend to require multiple stages to locate an object and allow a robot to take it, increasing the noise in the system and the processing times. The convolutional networks based on regions allow one to solve this problem, it is used for it two convolutional architectures, one for classification and location of three types of objects and one to determine the grip angle for a robotic gripper. Under the establish virtual environment, the grip algorithm works up to 5 frames per second with a 100% object classification, and with the implementation of the Faster R-CNN, it allows obtain 100% accuracy in the classifications of the test database, and over a 97% of average precision locating the generated boxes in each element, gripping successfully the objects.
Using Mask R CNN to Isolate PV Panels from Background Object in Imagesijtsrd
Identifying foreground objects in an image is one of the most common operations used in image processing. In this work, Mask R CNN algorithm is used to identify solar photovoltaic PV panels in aerial images and create a mask that can be used to remove the background from the images. This allows processing the PV panels separately. Using ML to solve this problem can generate more accurate results in comparison to more traditional image processing techniques like using edge detection or Gaussian filtering especially in images where the view might not be easily separable from the objects of interest. The trained model was found to be successful in detecting the PV panels and selecting the pixels that belong to them while ignoring the background pixels. This kind of work can be useful in collecting information about PV installation present in aerial or satellite imagery, or in analyzing the health and integrity of PV modules in large scale installations e.g., in a solar power plant. The results show that this method is effective with a high potential for improved results if the model is trained using larger and more diverse datasets. Muhammet Sait | Atilla Erguzen | Erdal Erdal "Using Mask R-CNN to Isolate PV Panels from Background Object in Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38173.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/38173/using-mask-rcnn-to-isolate-pv-panels-from-background-object-in-images/muhammet-sait
An ensemble classification algorithm for hyperspectral imagessipij
Hyperspectral image analysis has been used for many purposes in environmental monitoring, remote
sensing, vegetation research and also for land cover classification. A hyperspectral image consists of many
layers in which each layer represents a specific wavelength. The layers stack on top of one another making
a cube-like image for entire spectrum. This work aims to classify the hyperspectral images and to produce
a thematic map accurately. Spatial information of hyperspectral images is collected by applying
morphological profile and local binary pattern. Support vector machine is an efficient classification
algorithm for classifying the hyperspectral images. Genetic algorithm is used to obtain the best feature
subjected for classification. Selected features are classified for obtaining the classes and to produce a
thematic map. Experiment is carried out with AVIRIS Indian Pines and ROSIS Pavia University. Proposed
method produces accuracy as 93% for Indian Pines and 92% for Pavia University.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification.
This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that
provides insights into the selection of pre-trained models and hyper parameters for optimal performance.
We have proposed a comprehensive approach for image segmentation and classification, incorporating preprocessing techniques, the K-means algorithm for segmentation, and employing deep learning models such
as CNN and VGG 16 for classification.
TRANSFER LEARNING BASED IMAGE VISUALIZATION USING CNNgerogepatton
Image classification is a popular machine learning based applications of deep learning. Deep learning techniques are very popular because they can be effectively used in performing operations on image data in large-scale. In this paper CNN model was designed to better classify images. We make use of feature extraction part of inception v3 model for feature vector calculation and retrained the classification layer with these feature vector. By using the transfer learning mechanism the classification layer of the CNN
model was trained with 20 classes of Caltech101 image dataset and 17 classes of Oxford 17 flower image dataset. After training, network was evaluated with testing dataset images from Oxford 17 flower dataset and Caltech101 image dataset. The mean testing precision of the neural network architecture with Caltech101 dataset was 98 % and with Oxford 17 Flower image dataset was 92.27 %.
TRANSFER LEARNING BASED IMAGE VISUALIZATION USING CNNijaia
Image classification is a popular machine learning based applications of deep learning. Deep learning techniques are very popular because they can be effectively used in performing operations on image data in large-scale. In this paper CNN model was designed to better classify images. We make use of feature extraction part of inception v3 model for feature vector calculation and retrained the classification layer with these feature vector. By using the transfer learning mechanism the classification layer of the CNN model was trained with 20 classes of Caltech101 image dataset and 17 classes of Oxford 17 flower image dataset. After training, network was evaluated with testing dataset images from Oxford 17 flower dataset and Caltech101 image dataset. The mean testing precision of the neural network architecture with Caltech101 dataset was 98 % and with Oxford 17 Flower image dataset was 92.27 %.
ANALYSIS OF LAND SURFACE DEFORMATION GRADIENT BY DINSAR cscpconf
The progressive development of Synthetic Aperture Radar (SAR) systems diversify the exploitation of the generated images by these systems in different applications of geoscience. Detection and monitoring surface deformations, procreated by various phenomena had benefited from this evolution and had been realized by interferometry (InSAR) and differential interferometry (DInSAR) techniques. Nevertheless, spatial and temporal decorrelations of the interferometric couples used, limit strongly the precision of analysis results by these techniques. In this context, we propose, in this work, a methodological approach of surface deformation detection and analysis by differential interferograms to show the limits of this technique according to noise quality and level. The detectability model is generated from the deformation signatures, by simulating a linear fault merged to the images couples of ERS1 / ERS2 sensors acquired in a region of the Algerian south.
4D AUTOMATIC LIP-READING FOR SPEAKER'S FACE IDENTIFCATIONcscpconf
A novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed . The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT.The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigen lips approach, their proposed approach by[33]. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the
method.
MOVING FROM WATERFALL TO AGILE PROCESS IN SOFTWARE ENGINEERING CAPSTONE PROJE...cscpconf
Universities offer software engineering capstone course to simulate a real world-working environment in which students can work in a team for a fixed period to deliver a quality product. The objective of the paper is to report on our experience in moving from Waterfall process to Agile process in conducting the software engineering capstone project. We present the capstone course designs for both Waterfall driven and Agile driven methodologies that highlight the structure, deliverables and assessment plans.To evaluate the improvement, we conducted a survey for two different sections taught by two different instructors to evaluate students’ experience in moving from traditional Waterfall model to Agile like process. Twentyeight students filled the survey. The survey consisted of eight multiple-choice questions and an open-ended question to collect feedback from students. The survey results show that students were able to attain hands one experience, which simulate a real world-working environment. The results also show that the Agile approach helped students to have overall better design and avoid mistakes they have made in the initial design completed in of the first phase of the capstone project. In addition, they were able to decide on their team capabilities, training needs and thus learn the required technologies earlier which is reflected on the final product quality
PROMOTING STUDENT ENGAGEMENT USING SOCIAL MEDIA TECHNOLOGIEScscpconf
Using social media in education provides learners with an informal way for communication. Informal communication tends to remove barriers and hence promotes student engagement. This paper presents our experience in using three different social media technologies in teaching software project management course. We conducted different surveys at the end of every semester to evaluate students’ satisfaction and engagement. Results show that using social media enhances students’ engagement and satisfaction. However, familiarity with the tool is an important factor for student satisfaction.
A SURVEY ON QUESTION ANSWERING SYSTEMS: THE ADVANCES OF FUZZY LOGICcscpconf
In real world computing environment with using a computer to answer questions has been a human dream since the beginning of the digital era, Question-answering systems are referred to as intelligent systems, that can be used to provide responses for the questions being asked by the user based on certain facts or rules stored in the knowledge base it can generate answers of questions asked in natural , and the first main idea of fuzzy logic was to working on the problem of computer understanding of natural language, so this survey paper provides an overview on what Question-Answering is and its system architecture and the possible relationship and
different with fuzzy logic, as well as the previous related research with respect to approaches that were followed. At the end, the survey provides an analytical discussion of the proposed QA models, along or combined with fuzzy logic and their main contributions and limitations.
DYNAMIC PHONE WARPING – A METHOD TO MEASURE THE DISTANCE BETWEEN PRONUNCIATIONS cscpconf
Human beings generate different speech waveforms while speaking the same word at different times. Also, different human beings have different accents and generate significantly varying speech waveforms for the same word. There is a need to measure the distances between various words which facilitate preparation of pronunciation dictionaries. A new algorithm called Dynamic Phone Warping (DPW) is presented in this paper. It uses dynamic programming technique for global alignment and shortest distance measurements. The DPW algorithm can be used to enhance the pronunciation dictionaries of the well-known languages like English or to build pronunciation dictionaries to the less known sparse languages. The precision measurement experiments show 88.9% accuracy.
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
TWO DISCRETE BINARY VERSIONS OF AFRICAN BUFFALO OPTIMIZATION METAHEURISTICcscpconf
African Buffalo Optimization (ABO) is one of the most recent swarms intelligence based metaheuristics. ABO algorithm is inspired by the buffalo’s behavior and lifestyle. Unfortunately, the standard ABO algorithm is proposed only for continuous optimization problems. In this paper, the authors propose two discrete binary ABO algorithms to deal with binary optimization problems. In the first version (called SBABO) they use the sigmoid function and probability model to generate binary solutions. In the second version (called LBABO) they use some logical operator to operate the binary solutions. Computational results on two knapsack problems (KP and MKP) instances show the effectiveness of the proposed algorithm and their ability to achieve good and promising solutions.
DETECTION OF ALGORITHMICALLY GENERATED MALICIOUS DOMAINcscpconf
In recent years, many malware writers have relied on Dynamic Domain Name Services (DDNS) to maintain their Command and Control (C&C) network infrastructure to ensure a persistence presence on a compromised host. Amongst the various DDNS techniques, Domain Generation Algorithm (DGA) is often perceived as the most difficult to detect using traditional methods. This paper presents an approach for detecting DGA using frequency analysis of the character distribution and the weighted scores of the domain names. The approach’s feasibility is demonstrated using a range of legitimate domains and a number of malicious algorithmicallygenerated domain names. Findings from this study show that domain names made up of English characters “a-z” achieving a weighted score of < 45 are often associated with DGA. When a weighted score of < 45 is applied to the Alexa one million list of domain names, only 15% of the domain names were treated as non-human generated.
GLOBAL MUSIC ASSET ASSURANCE DIGITAL CURRENCY: A DRM SOLUTION FOR STREAMING C...cscpconf
The amount of piracy in the streaming digital content in general and the music industry in specific is posing a real challenge to digital content owners. This paper presents a DRM solution to monetizing, tracking and controlling online streaming content cross platforms for IP enabled devices. The paper benefits from the current advances in Blockchain and cryptocurrencies. Specifically, the paper presents a Global Music Asset Assurance (GoMAA) digital currency and presents the iMediaStreams Blockchain to enable the secure dissemination and tracking of the streamed content. The proposed solution provides the data owner the ability to control the flow of information even after it has been released by creating a secure, selfinstalled, cross platform reader located on the digital content file header. The proposed system provides the content owners’ options to manage their digital information (audio, video, speech, etc.), including the tracking of the most consumed segments, once it is release. The system benefits from token distribution between the content owner (Music Bands), the content distributer (Online Radio Stations) and the content consumer(Fans) on the system blockchain.
IMPORTANCE OF VERB SUFFIX MAPPING IN DISCOURSE TRANSLATION SYSTEMcscpconf
This paper discusses the importance of verb suffix mapping in Discourse translation system. In
discourse translation, the crucial step is Anaphora resolution and generation. In Anaphora
resolution, cohesion links like pronouns are identified between portions of text. These binders
make the text cohesive by referring to nouns appearing in the previous sentences or nouns
appearing in sentences after them. In Machine Translation systems, to convert the source
language sentences into meaningful target language sentences the verb suffixes should be
changed as per the cohesion links identified. This step of translation process is emphasized in
the present paper. Specifically, the discussion is on how the verbs change according to the
subjects and anaphors. To explain the concept, English is used as the source language (SL) and
an Indian language Telugu is used as Target language (TL)
EXACT SOLUTIONS OF A FAMILY OF HIGHER-DIMENSIONAL SPACE-TIME FRACTIONAL KDV-T...cscpconf
In this paper, based on the definition of conformable fractional derivative, the functional
variable method (FVM) is proposed to seek the exact traveling wave solutions of two higherdimensional
space-time fractional KdV-type equations in mathematical physics, namely the
(3+1)-dimensional space–time fractional Zakharov-Kuznetsov (ZK) equation and the (2+1)-
dimensional space–time fractional Generalized Zakharov-Kuznetsov-Benjamin-Bona-Mahony
(GZK-BBM) equation. Some new solutions are procured and depicted. These solutions, which
contain kink-shaped, singular kink, bell-shaped soliton, singular soliton and periodic wave
solutions, have many potential applications in mathematical physics and engineering. The
simplicity and reliability of the proposed method is verified.
AUTOMATED PENETRATION TESTING: AN OVERVIEWcscpconf
The using of information technology resources is rapidly increasing in organizations,
businesses, and even governments, that led to arise various attacks, and vulnerabilities in the
field. All resources make it a must to do frequently a penetration test (PT) for the environment
and see what can the attacker gain and what is the current environment's vulnerabilities. This
paper reviews some of the automated penetration testing techniques and presents its
enhancement over the traditional manual approaches. To the best of our knowledge, it is the
first research that takes into consideration the concept of penetration testing and the standards
in the area.This research tackles the comparison between the manual and automated
penetration testing, the main tools used in penetration testing. Additionally, compares between
some methodologies used to build an automated penetration testing platform.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
VALIDATION METHOD OF FUZZY ASSOCIATION RULES BASED ON FUZZY FORMAL CONCEPT AN...cscpconf
In order to treat and analyze real datasets, fuzzy association rules have been proposed. Several
algorithms have been introduced to extract these rules. However, these algorithms suffer from
the problems of utility, redundancy and large number of extracted fuzzy association rules. The
expert will then be confronted with this huge amount of fuzzy association rules. The task of
validation becomes fastidious. In order to solve these problems, we propose a new validation
method. Our method is based on three steps. (i) We extract a generic base of non redundant
fuzzy association rules by applying EFAR-PN algorithm based on fuzzy formal concept analysis.
(ii) we categorize extracted rules into groups and (iii) we evaluate the relevance of these rules
using structural equation model.
PROBABILITY BASED CLUSTER EXPANSION OVERSAMPLING TECHNIQUE FOR IMBALANCED DATAcscpconf
In many applications of data mining, class imbalance is noticed when examples in one class are
overrepresented. Traditional classifiers result in poor accuracy of the minority class due to the
class imbalance. Further, the presence of within class imbalance where classes are composed of
multiple sub-concepts with different number of examples also affect the performance of
classifier. In this paper, we propose an oversampling technique that handles between class and
within class imbalance simultaneously and also takes into consideration the generalization
ability in data space. The proposed method is based on two steps- performing Model Based
Clustering with respect to classes to identify the sub-concepts; and then computing the
separating hyperplane based on equal posterior probability between the classes. The proposed
method is tested on 10 publicly available data sets and the result shows that the proposed
method is statistically superior to other existing oversampling methods.
CHARACTER AND IMAGE RECOGNITION FOR DATA CATALOGING IN ECOLOGICAL RESEARCHcscpconf
Data collection is an essential, but manpower intensive procedure in ecological research. An
algorithm was developed by the author which incorporated two important computer vision
techniques to automate data cataloging for butterfly measurements. Optical Character
Recognition is used for character recognition and Contour Detection is used for imageprocessing.
Proper pre-processing is first done on the images to improve accuracy. Although
there are limitations to Tesseract’s detection of certain fonts, overall, it can successfully identify
words of basic fonts. Contour detection is an advanced technique that can be utilized to
measure an image. Shapes and mathematical calculations are crucial in determining the precise
location of the points on which to draw the body and forewing lines of the butterfly. Overall,
92% accuracy were achieved by the program for the set of butterflies measured.
SOCIAL MEDIA ANALYTICS FOR SENTIMENT ANALYSIS AND EVENT DETECTION IN SMART CI...cscpconf
Smart cities utilize Internet of Things (IoT) devices and sensors to enhance the quality of the city
services including energy, transportation, health, and much more. They generate massive
volumes of structured and unstructured data on a daily basis. Also, social networks, such as
Twitter, Facebook, and Google+, are becoming a new source of real-time information in smart
cities. Social network users are acting as social sensors. These datasets so large and complex
are difficult to manage with conventional data management tools and methods. To become
valuable, this massive amount of data, known as 'big data,' needs to be processed and
comprehended to hold the promise of supporting a broad range of urban and smart cities
functions, including among others transportation, water, and energy consumption, pollution
surveillance, and smart city governance. In this work, we investigate how social media analytics
help to analyze smart city data collected from various social media sources, such as Twitter and
Facebook, to detect various events taking place in a smart city and identify the importance of
events and concerns of citizens regarding some events. A case scenario analyses the opinions of
users concerning the traffic in three largest cities in the UAE
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
GENERAL REGRESSION NEURAL NETWORK BASED POS TAGGING FOR NEPALI TEXTcscpconf
This article presents Part of Speech tagging for Nepali text using General Regression Neural
Network (GRNN). The corpus is divided into two parts viz. training and testing. The network is
trained and validated on both training and testing data. It is observed that 96.13% words are
correctly being tagged on training set whereas 74.38% words are tagged correctly on testing
data set using GRNN. The result is compared with the traditional Viterbi algorithm based on
Hidden Markov Model. Viterbi algorithm yields 97.2% and 40% classification accuracies on
training and testing data sets respectively. GRNN based POS Tagger is more consistent than the
traditional Viterbi decoding technique.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
2. 84 Computer Science & Information Technology (CS & IT)
provides better results than many more complex approaches for supervised classification. These
results demonstrate that a CNN, trained on a large and versatile dataset, learns information about
object characteristics that is generic enough to transfer to objects that are not in the original
dataset.
While developing the automatic robotic sorting and storing application, we needed to classify sets
of images based on their content, in an unsupervised way. Multiple papers introduced methods to
solve unsupervised object classification from sets of images (see section 1.1.1), producing
relatively good results. However, we wanted to know if the information from a large and versatile
dataset, stored in the weights of a CNN, could be used straightforwardly to outperform state-of-
the-art algorithms at unsupervised imageset classification. The goal of this paper is to answer the
following question: How good are features extracted with a CNN pretrained on a large dataset,
for unsupervised image classification tasks? To do so, we use a similar approach to [5],
consisting in applying classic clustering methods to features extracted from the final layers of a
CNN (see section 2 for more details) and comparing it with state-of-the-art image set clustering
algorithms [10, 11] on several public datasets.
The intuition behind such approach for unsupervised object classification is that, as it works with
SVM [5], the CNN must project data in a feature space where they are somehow linearly
separable. Thus, simple clustering algorithms such as K-means might be working well. However,
this study is interesting as the performance of such simple clustering algorithms often depends on
the notion of distance between points, on which we remain uncertain.
1.1. Previous work
1.1.1. Image-set clustering
Given a set of unlabeled images, the image-set clustering problem consists in finding subsets of
images based on their content: two images representing the same object should be clustered
together and separated from images representing other objects. Figure 1 illustrates the expected
output from an image-set clustering algorithm in the case of our robotics application. This
problem should not be confused with image segmentation [12], which is also sometimes called
image clustering.
Image-set clustering has been widely studied for two decades. It has applications for searching
large image database [13, 14, 15], concept discovery in images [16], storyline reconstruction [17],
medical images classification [18], ... The first successful methods focused on feature selection
and used sophisticated algorithms to deal with complex features. For instance, [19] represents
images by Gaussian Mixture Models fitted to the pixels and clusters the set using the Information
Bottleneck (IB) method [20]. [21] uses features resulting from image joint segmentation and
sequential IB for clustering. [11] uses Bags of Features with local representations (SIFT, SURF,
...) and defines commonality measures used for agglomerative clustering. Recently, image-set
clustering algorithms have shifted towards using deep features. [10] uses deep auto-encoders
combined with ensemble clustering to generate feature representations suitable for clustering. [22,
18] learns jointly the clusters and the representation using alternating optimization [23].
3. Computer Science & Information Technology (CS & IT) 85
1.1.2. Robotic automatic sorting application
Together with showing that deep features + simple clustering outperforms other approaches on
unsupervised object classification, we apply this pipeline to solve an automatic smart robot
sorting application first introduced in [24]. The idea is that the robot, equipped with a camera,
visualizes a set of objects placed on a table. Given the number of storage boxes available, it needs
to figure out the best way of sorting and storing the objects before physically doing it. The
approach proposed in this paper exploits semantic information (deep features) while [24] uses a
computer vision algorithm to extract shapes and colors. A video of the application can be seen at
(https://youtu.be/NpZIwY3H-gE). An example of inputs/output of the application is
shown in Figure 1. The robustness of this application is also investigated in section 4 by changing
the lighting conditions, the position and orientation of the objects as well as the background. For
this robustness validation, we built a dataset that appears to be a challenging one for image-set
clustering (http://www.usine-agile.fr/datas/22-cnn-datas-1.html).
Figure 1: Robotic application description
1.2. Contributions
The main contribution of this paper is to convey further insight into deep CNN features by
showing their scalability to unsupervised classification problems. We also propose a new baseline
on several image clustering tasks.
Other contributions include the implementation of an application combining unsupervised image
classification with robotic sorting. The method proposed to solve this problem, constitutes a new
step towards autonomous decision-making robots. The dataset introduced in this paper, which is
relatively challenging for image-set clustering, is also a contribution that can be used in further
research to investigate robustness to background and lighting conditions for image clustering
algorithms.
2. CLUSTERING IMAGES WITH DEEP FEATURE EXTRACTION
2.1. Pipeline description
The pipeline we propose for image set clustering is fairly straightforward. It consists in extracting
deep features from all the images in the set, by using a deep convolutional neural network
pretrained on a large dataset for image classification and then apply a "standard" clustering
algorithm to these features. We initially tried this approach as a first step towards developing a
4. 86 Computer Science & Information Technology (CS & IT)
better clustering algorithm, however, it appears that this simple approach outperforms state-of-
the-art algorithm at image-set clustering.
To implement this unsupervised image classification pipeline, we first need to answer four
questions:
• What dataset should be used for pretraining?
• What CNN architecture should we use?
• Which layer output should be chosen for feature extraction?
• What clustering algorithm should be used?
As of today, ImageNet is the only very large labelled public dataset which has enough variety in
its classes to be a good feature extractor for a variety of tasks. Moreover, there are plenty of CNN
pretrained on ImageNet already available online. Hence, we will use a CNN pretrained on
ImageNet. The three other questions are answered experimentally. We use the VOC2007 [25] test
set without labels, which is new for the pretrained net, to compare performances of the different
options.
To ease development and accelerate implementation, we compare the Keras [26] implementations
of ResNet50 [27], InceptionV3 [28], VGG16, VGG19 [29] and Xception [30] with the pretrained
weights provided by Keras. For the clustering algorithms, we use the scikit-learn [31]
implementations of K-means (KM) [32], Minibatch K-means (MBKM) [33], Affinity
Propagation (AP) [34], Mean Shift (MS) [35], Agglomerative Hierarchical Clustering (AC) [36],
DBScan (DBS) [37] and Birch (Bi) [38]. For each CNN, the layers after which the features are
extracted can be found in Table 1 (Layers names are the same as in the Keras implementations).
In the image-set clustering problem, the expected classes are represented by objects present on
the picture and for this reason we need semantic information, which is present in the final layers
of the network. Thus, we only choose layers among the last layers of the networks. On the one
hand, the last one or two layers might provide better results as their goal is to separate the data (at
least for the fully-connected layers). On the other hand, the opposite intuition is also relevant as
we can imagine that these layers are too specialized to be transferable. These two contradictory
arguments motivated the following experiment.
We also note that the test set of VOC2007 has been modified for this validation. We removed all
the images presenting two or more labels in order to have ground truth to compute Normalized
Mutual Information (NMI) scores. Indeed, if an image possesses several labels we cannot judge if
the clustering pipeline classified it properly or not. We note VOC2007-SL (single label) the
modified VOC2007 test set.
2.2. Hyperparameters Choice
To answer the questions stated above, we try to cluster the VOC2007-SL set using all
combinations of CNN architectures, layer choices and clustering algorithms. To compare
performances, we use NMI scores. We also report clustering time for completeness. Only scikit-
learn default hyperparameters of the different clustering algorithms are used, which illustrate the
simplicity of this approach. For KM and MBKM, as the results depend on random initialization,
experiments are run ten times and reported results are averaged over the different runs.
5. Computer Science & Information Technology (CS & IT) 87
Looking at the results, we choose Agglomerative Clustering on features extracted from the final
layer of an Xception CNN pretrained on ImageNet for image-set clustering. This pipeline is then
compared to state-of-the-art methods in the next section in order to see how transferable CNN
ImageNet features are for unsupervised categorization.
3. VALIDATION ON SEVERAL PUBLIC DATASETS
3.1. Datasets Description
The efficiency of the proposed method is demonstrated by comparing it to other recent successful
methods (see section 3.2) on several public datasets which characteristics are described in Table
2.
Table 1: NMI scores (in black) and time in seconds (in blue, italics) on Pascal VOC2007-SL test set using
different CNN, different output layers and different clustering algorithms. (Layers names are the same as in
the Keras implementations).
6. 88 Computer Science & Information Technology (CS & IT)
Table 2: Several key features about the datasets used for method validation.
The clustering tasks involved by these datasets are different from each others (Face recognition,
grouping different objects, recognizing different pictures of the same object). In addition, the
content of the classes differs from the ones in ImageNet. For these reasons, the four datasets
constitute a good benchmark to quantify the robustness of transfer learning for unsupervised
object categorization.
3.2. Results Comparison
We propose a comparison with the results reported in the following papers dealing with image set
clustering:
[11] proposes different clustering algorithms applied on bags of features. In Table 3, we note
"BoF" the best results obtained by such pipeline on the different datasets.
[10] proposes a method called infinite ensemble clustering (IEC). In the paper, IEC algorithm is
compared to several other deep clustering algorithms and ensemble clustering algorithms. In
Table 3, we report the best results obtained using Deep Clustering (DC) and Ensemble Clustering
(EC) for each datasets. We note that for VOC2007-5-ML, [10] also uses deep features as
clustering inputs (the CNN used is not reported).
[22] proposes a method called Joint Unsupervised Learning (JULE) of Deep Representations and
Image Clusters, based on Alternating optimization between clustering and weight optimization of
the CNN feature extractor. Results from this work are reported in Table 3.
For each dataset groundtruth is known as they are intended for supervised classification. We
compute both NMI scores and purity for each dataset/method pair.
Table 3 shows that features extracted by the final layer of Xception combined with
Agglomerative Clustering performs better than or close to state-of-the-art methods at
unsupervised object classification as well as fine-grained image clustering (ORL). Results on the
ORL dataset are interesting as they show that pretrained Xception is able to classify different
faces without supervision, although ImageNet does not deal with human faces at all.
------------------------------------------
1
The poor results with DB-Scan might come from the default parameters. We might get better results using
different configurations, but this is out of the scope of this paper.
2
The data used for VOC2007 in [10] are irrelevant for clustering with hard assignment. The VOC2007
subset used in [10] contains images with several objects but only one label. However, we still ran our
clustering method on this data to be able to compare results. This second modified VOC2007 set is denoted
VOC2007-5-ML (5 classes - multiple labels)
7. Computer Science & Information Technology (CS & IT) 89
Table 3: NMI scores and purity comparison on various public datasets. (A result that is not reported in the
papers cited above is denoted N.R.)
This is an important result as it shows that, with today's methods, given an unlabeled image-set,
we can extract more information from a large labeled dataset, with a large variety of classes,
than from the set itself. It is better than hand-engineered approaches (BoF) as well as
unsupervised trained deep networks (Deep clustering and Ensemble clustering). It also raises the
question of how the representation learning problem should be handled. Indeed, although less
satisfactory from a research perspective, it might be more appropriate to work on the creation of a
larger database and train networks on it so that it can be used as a knowledge base for many other
problems.
We underline the very good results of JULE ([22]) at clustering COIL100. Regarding the results
of this methodology on Scene clustering [18], it appears that fine tuning feature extraction using
alternating optimization is a good way of improving clustering results. However, the simple
approach proposed here still keeps the advantage of being very fast (as it only requires to evaluate
the network once for each sample and apply AC), which is useful for our application for instance.
It is also interesting to notice that [10] is also using CNN features for clustering VOC2007-5-ML.
The fact that our pipeline outperform largely DC and EC for this dataset illustrate that when using
transfer learning for a new problem, careful selection of the CNN used is important, which is
often neglected.
3.3. A Word on Scene Clustering
The problem studied in this paper is the one raised by the robotic application, unsupervised
objects sorting. For this reason, we compared our result at different object categorization tasks as
well as fine-grained classification for clustering of similar objects. Another interesting image
classification problem is the one of scene clustering, studied in [18] on two datasets ([42, 43]).
For this task, the pipeline proposed in this paper cannot perform as well as for object
classification. Indeed, ImageNet does not contain any class which requires to group objects
together. Thus, although the features are good at supervised scene classification, without further
training they are not able to group objects together as such behaviour is not encoded in the final
layers of the CNN. However, this issue is not inherent to the method defined and we believe that
with bigger and more versatile datasets, results would be as good as any other method.
8. 90 Computer Science & Information Technology (CS & IT)
4. APPLICATION VALIDATION
In the setting tested initially (Figure 1), where the set of objects to cluster is composed of screw
drivers, at keys, allen keys and clamps, and where the background is a table, the success rate of
the application described in section 1.1.2 is 100%. Although for certain classes (flat and allen
keys) the intra-cluster similarity is high, it is not the case for the two others. This task is also
difficult to solve because we carried out the experiments in a shopfloor under unmastered
lighting.
For further testing of the application robustness, we have built a dataset for pixel-based object
clustering. The full dataset, together with its description, can be found at (http//www.usine-
agile.fr/datas/22-cnn-datas-1.html) and example images can be seen on Figure 2. The dataset
statistics are reported in table 4. This dataset is difficult because some classes have low intra-
cluster similarity (usb) and extra-cluster similarity between some classes is relatively high
(pens/screws). The lighting conditions as well as the background also change within the dataset,
which makes the task even harder.
Table 4: Several key features about the constructed dataset.
For each conditions, we randomly pick one out of the several pictures (different position
/orientation) of each object. The results reported in the top subtable of Table 5 are averaged over
100 random combinations. The clustering results are not perfect, looking at the misclassifications,
the main source of error comes from classes with low intra-cluster similarity (pens, usb) and from
background containing sharp edges (conditions 4 and 5).
Figure 2: Example images from the robustness validation dataset. Objects in the same row are expected to
be clustered in the same group. The five different background/lighting conditions are represented in this
figure.
We also carry out an experiment of fine grained classification with this dataset. Within each class,
we try to group together the pictures representing the exact same object. Purity results can be
found in the bottom subtable of Table 5. An interesting fact can be noticed about object
9. Computer Science & Information Technology (CS & IT) 91
recognition within a category. Classes responsible for decreasing the clustering quality in the first
subtable are objects with the highest purity in the second table. Such remark makes sens as low
intra-cluster similarity is good for the second task but harmful for the first one.
Looking at the results, this robustness validation dataset appears to be a challenging one for
image-set clustering and could be used to validate further research in the field.
Table 5: Clustering accuracies for different background, lighting and orientation conditions on the tool
clustering dataset.
5. CONCLUSION AND PERSPECTIVES
5.1. Conclusive Remarks
This paper extends the interesting work of [5, 6, 7] about the transferability of CNN features. It
shows that, even for unsupervised classification tasks, features extracted from deep CNN trained
on large and diverse datasets, combined with classic clustering algorithms, can compete with
more sophisticated and tuned image-set clustering methods. The fairly simple and naive pipeline
proposed outperforms the best results reported in recent work, which raises the question of which
research direction should be chosen to reach generic knowledge. Are efforts spent in developing
image representation extractors more useful than simply building larger and more diverse
datasets?
This approach is used to implement a robotic application using unsupervised image classification
to store objects smartly. To validate this application, we also built a challenging dataset for image
clustering that is made available to the research community.
5.2. Future Work
The proposed improvements mainly go in the direction of the robotics application, which is still
not robust enough to adapt perfectly to very different looking objects within a cluster and to
difficult backgrounds and lighting conditions. If we want to make it work in difficult
10. 92 Computer Science & Information Technology (CS & IT)
environments, the clustering pipeline needs to be improved. One possible direction is to tune the
final clustering algorithm, indeed, the scikit clustering algorithms are used without any parameter
tuning, setting hyperparameters to their default values.
The sorting application can also be improved by introducing automatic image segmentation,
which would make it more suitable for practical uses. To do this, we could use a pretrained region
proposal network [44] and cluster objects in the proposed regions.
ACKNOWLEDGEMENTS
This work is also supported by the European Union's 2020 research and innovation program
under grant agreement No.688807, project ColRobot (collaborative robotics for assembly and
kitting in smart manufacturing).
The authors would like to thank Jorge Palos, Gil Boye de Sousa, Leon Sixt and Harshvardan
Gazula for their precious advices and technical support.
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AUTHORS
Joris Guérin received the diplome d'ingénieur (equivalent to M.Sc. degree) from Arts
et Metiers ParisTech and the M.Sc. in Industrial Engineering from Texas Tech
University, both in 2015. He is currently a Ph.D student at Laboratoire des Sciences de
l'Information et des Systémes (LSIS), at Arts et Métiers ParisTech, Lille, France. His
current research focuses on Clustering and Reinforcement Learning for Robotics
manipulation.
Olivier Gibaru is currently full professor at the Department of Mathematics and
Computer Science at ENSAM, Lille campus. He obtained his PhD in applied
mathematics in 1997. His main research interests includes: applied mathematics,
estimation for robotic applications, geometry, control engineering and high precision
mechanical systems. He is the coordinator of the EU Horizon 2020 ColRobot project
www.colrobot.eu. He is an active member of the SMAI-SIGMA group which is a
national learned society dedicated to Applied Mathematics for the Industrial
Applications.
Stéphane Thiery received the Ph.D degree in Automatics from University of Nice-
Sophia Antipolis, France, in 2008. He was a post-doctoral fellow in the NON-A team
in INRIA-Lille, for eight months in 2009-2010, and joined Arts et Métiers ParisTech
Engineering School, as assistant professor in Applied Mathematics and Automatics, in
2010. His current research includes machine learning, real-time parameters estimation,
and control of mechanical systems.
Eric Nyiri received the Ph.D degree in Computer Science from University of Lille I,
France, in 1994. He joined Arts et Métiers ParisTech Engineering School, as an
assistant professor in Applied Mathematics and Computer Science, in 1995. His initial
research domain was L1 interpolation and approximation. In 2010, he joined the LSIS
Lab and his current research includes machine learning and path planning for robots.
Since 2016, he is a member of the COLROBOT European project.