Cell Segmentation of 2D Phase-Contrast Microscopy Images with Deep Learning Method
Published in: 2019 Medical Technologies Congress (TIPTEKNO)
DOI: 10.1109/TIPTEKNO.2019.8894978
Publisher: IEEE
Conference Location: Izmir, Turkey
Mreps efficient and flexible detection of tandem repeats in DNA
In this paper, we describe mreps, a powerful software tool for a fast identification of tandemly repeated structures in DNA sequences. mreps is able to identify all types of tandem repeats within a single run on a whole genomic sequence. It has a resolution parameter that allows the program to identify 'fuzzy' repeats.
Title: Deep Learning based Segmentation Pipeline for Label-Free Phase-Contrast Microscopy Images
THE 28th IEEE CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS
5 - 7 October 2020
Video Link: https://youtu.be/b5tGt6GMN9E
1) The document presents research on using deep neural networks and transfer learning to improve virtual screening for drug discovery.
2) The researchers trained protein family-specific models using the DenseNet architecture on different sized training sets and evaluated using transfer learning and fine-tuning.
3) The results showed that the protein family-specific models outperformed baseline models on standard evaluation metrics, highlighting both the importance of more target-specific models and the need for more data to train such models.
In this project, we use leverage of centrality models for extracting the importance
of network graph in some determined topologies. The aim is to have scrutinizing
and analyzing the centralities in different network topologies. Three type of centrality
that are used in this project are Betweenness, Closeness and eigenvector
one. Moreover, we have show the results of this comparison in the experimental
results. Besides, we have extend the results of our experimental works for real
world problems. The Results of this part are grasped with visualization plots for
some centralities measurements clearly.
In this project, we propose methods for semantic segmentation with the deep learning state-of-the-art models. Moreover,
we want to filterize the segmentation to the specific object in specific application. Instead of concentrating on unnecessary objects we
can focus on special ones and make it more specialize and effecient for special purposes. Furtheromore, In this project, we leverage
models that are suitable for face segmentation. The models that are used in this project are Mask-RCNN and DeepLabv3. The
experimental results clearly indicate that how illustrated approach are efficient and robust in the segmentation task to the previous work
in the field of segmentation. These models are reached to 74.4 and 86.6 precision of Mean of Intersection over Union. The visual
Results of the models are shown in Appendix part.
Cell Segmentation of 2D Phase-Contrast Microscopy Images with Deep Learning Method
Published in: 2019 Medical Technologies Congress (TIPTEKNO)
DOI: 10.1109/TIPTEKNO.2019.8894978
Publisher: IEEE
Conference Location: Izmir, Turkey
Mreps efficient and flexible detection of tandem repeats in DNA
In this paper, we describe mreps, a powerful software tool for a fast identification of tandemly repeated structures in DNA sequences. mreps is able to identify all types of tandem repeats within a single run on a whole genomic sequence. It has a resolution parameter that allows the program to identify 'fuzzy' repeats.
Title: Deep Learning based Segmentation Pipeline for Label-Free Phase-Contrast Microscopy Images
THE 28th IEEE CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS
5 - 7 October 2020
Video Link: https://youtu.be/b5tGt6GMN9E
1) The document presents research on using deep neural networks and transfer learning to improve virtual screening for drug discovery.
2) The researchers trained protein family-specific models using the DenseNet architecture on different sized training sets and evaluated using transfer learning and fine-tuning.
3) The results showed that the protein family-specific models outperformed baseline models on standard evaluation metrics, highlighting both the importance of more target-specific models and the need for more data to train such models.
In this project, we use leverage of centrality models for extracting the importance
of network graph in some determined topologies. The aim is to have scrutinizing
and analyzing the centralities in different network topologies. Three type of centrality
that are used in this project are Betweenness, Closeness and eigenvector
one. Moreover, we have show the results of this comparison in the experimental
results. Besides, we have extend the results of our experimental works for real
world problems. The Results of this part are grasped with visualization plots for
some centralities measurements clearly.
In this project, we propose methods for semantic segmentation with the deep learning state-of-the-art models. Moreover,
we want to filterize the segmentation to the specific object in specific application. Instead of concentrating on unnecessary objects we
can focus on special ones and make it more specialize and effecient for special purposes. Furtheromore, In this project, we leverage
models that are suitable for face segmentation. The models that are used in this project are Mask-RCNN and DeepLabv3. The
experimental results clearly indicate that how illustrated approach are efficient and robust in the segmentation task to the previous work
in the field of segmentation. These models are reached to 74.4 and 86.6 precision of Mean of Intersection over Union. The visual
Results of the models are shown in Appendix part.
This document presents MLProph, a machine learning-based routing protocol for opportunistic networks. It uses decision trees and neural networks to select the next hop for packet forwarding. Simulation results show that MLProph achieves higher delivery probability and lower packet dropping than the PROPHET+ routing protocol. Future work will involve simulating MLProph using real mobility traces and exploring other machine learning classifiers.
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif-
ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 neurons, consists
of five convolutional layers, some of which are followed by max-pooling layers,
and three fully-connected layers with a final 1000-way softmax. To make train-
ing faster, we used non-saturating neurons and a very efficient GPU implemen-
tation of the convolution operation. To reduce overfitting in the fully-connected
layers we employed a recently-developed regularization method called “dropout”
that proved to be very effective. We also entered a variant of this model in the
ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,
compared to 26.2% achieved by the second-best entry.
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif-
ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 neurons, consists
of five convolutional layers, some of which are followed by max-pooling layers,
and three fully-connected layers with a final 1000-way softmax. To make train-
ing faster, we used non-saturating neurons and a very efficient GPU implemen-
tation of the convolution operation. To reduce overfitting in the fully-connected
layers we employed a recently-developed regularization method called “dropout”
that proved to be very effective. We also entered a variant of this model in the
ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,
compared to 26.2% achieved by the second-best entry.
This document discusses two scenarios for waste collection using algorithms. Scenario 1 models waste collection as a traveling salesman problem that is solved using Dijkstra's algorithm. The total cost for collecting waste from 18 bins over 24 hours is calculated as $26,040. Scenario 2 collects waste from 18 bins using Dijkstra's algorithm to find the shortest paths and connect bins to their nearest neighbors, but has a syntax error preventing cost evaluation for different bin numbers. The document suggests prioritizing bins based on waste generation rates to reduce overflow and unnecessary alerts.
There is a smart airport application among the other applications under the SITA company [5] which is produced to provide various information, suggestions to the passengers during the travel by sharing these with the smart phone. In this report, I will extend and scrutiny this application and give my suggestions base on SITA application, I will define the usage and benefit of such smart airport application for airports and passengers.
Udacity Self-Driving Car Engineer Nanodegree Advanced Lane Finding Project. Identifying lanes using edge detection (Sober operator, gradient of magnitude and direction, and HLS color space), camera calibration and unwarping (distortion correction and perspective transform), and polynomial fitting for the lanes.
This document discusses dog breed identification using deep learning models. It provides an overview of traditional and deep learning methods for image classification, including CNN architectures like AlexNet, VGG, GoogleNet, ResNet and DenseNet. The challenge is to identify the breed of dogs in images using pre-trained models. The document analyzes the provided training and test data, which contains over 10,000 images across 120 breeds. It then details the method used, which is to fine-tune popular models like DenseNet, ResNet and GoogleNet. The results show that DenseNet-169 achieved the best validation accuracy of 81.77% and lowest validation loss of 0.6393.
This document summarizes a student's term project on lane identification in autonomous vehicles. The project pipeline involves camera calibration, perspective transformation, color and gradient thresholding to identify lane lines, and lane detection by fitting windows to identified lines. Results showed good performance on straight lanes but difficulty fitting curved lanes. Further work proposed includes providing distance to lane center, improving curved lane detection, and implementing lane identification in video.
This document presents a project on lane finding for autonomous driving. The goals are to develop a solution to detect lane lines on the road to enable autonomous navigation. Related work in this area is discussed, including approaches using motion estimation, end-to-end learning from video datasets, and traditional computer vision techniques. Risks that could impact the accuracy of lane detection are also summarized, such as curved roads, weather conditions, shadows, and irregular road markings. The document outlines the work breakdown structure and Gantt chart for the project and provides references for related research.
This document summarizes a survey paper on smart charging for electric vehicles from an algorithmic perspective. It discusses smart grid-oriented EV charging approaches like load flattening, frequency regulation, and voltage regulation. It also discusses aggregator-oriented and customer-oriented EV charging approaches and the uncertainties involved. Future work opportunities are identified in areas like battery modeling, routing, and communication requirements to further the smart interaction between electric vehicles and the smart grid.
The document discusses smart airport applications and their benefits. It describes how smart airport applications can provide real-time information to passengers, such as baggage tracking and flight status updates. The applications also offer personalized suggestions to help passengers navigate the airport efficiently, such as recommendations for parking or places to wait with less crowds. Digital boarding passes are highlighted as another smart feature that can streamline the travel process through self-service checkpoints. In conclusion, smart airports are expected to improve the travel experience by optimizing and customizing services for passengers.
this presentation file lectured in international conference in new research of Electrical and engineering and computer science.
Abstract
This paper presents a novel and uniform algorithm for edge detection based on SVM (support vector machine) with Three-dimensional Gaussian radial basis function with kernel. Because of disadvantages in traditional edge detection such as inaccurate edge location, rough edge and careless on detect soft edge. The experimental results indicate how the SVM can detect edge in efficient way. The performance of the proposed algorithm is compared with existing methods, including Sobel and canny detectors. The results shows that this method is better than classical algorithm such as canny and Sobel detector.
مسیریابی سیستم های خودمختارکه در این اسلاید در باره ی شبکه های مسیریابی بین سیستم های خود مختار و نحوه ی ایجاد کانکشن بین شبکه ها مورد بررسی قرار می گیرد.
This document presents MLProph, a machine learning-based routing protocol for opportunistic networks. It uses decision trees and neural networks to select the next hop for packet forwarding. Simulation results show that MLProph achieves higher delivery probability and lower packet dropping than the PROPHET+ routing protocol. Future work will involve simulating MLProph using real mobility traces and exploring other machine learning classifiers.
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif-
ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 neurons, consists
of five convolutional layers, some of which are followed by max-pooling layers,
and three fully-connected layers with a final 1000-way softmax. To make train-
ing faster, we used non-saturating neurons and a very efficient GPU implemen-
tation of the convolution operation. To reduce overfitting in the fully-connected
layers we employed a recently-developed regularization method called “dropout”
that proved to be very effective. We also entered a variant of this model in the
ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,
compared to 26.2% achieved by the second-best entry.
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif-
ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 neurons, consists
of five convolutional layers, some of which are followed by max-pooling layers,
and three fully-connected layers with a final 1000-way softmax. To make train-
ing faster, we used non-saturating neurons and a very efficient GPU implemen-
tation of the convolution operation. To reduce overfitting in the fully-connected
layers we employed a recently-developed regularization method called “dropout”
that proved to be very effective. We also entered a variant of this model in the
ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,
compared to 26.2% achieved by the second-best entry.
This document discusses two scenarios for waste collection using algorithms. Scenario 1 models waste collection as a traveling salesman problem that is solved using Dijkstra's algorithm. The total cost for collecting waste from 18 bins over 24 hours is calculated as $26,040. Scenario 2 collects waste from 18 bins using Dijkstra's algorithm to find the shortest paths and connect bins to their nearest neighbors, but has a syntax error preventing cost evaluation for different bin numbers. The document suggests prioritizing bins based on waste generation rates to reduce overflow and unnecessary alerts.
There is a smart airport application among the other applications under the SITA company [5] which is produced to provide various information, suggestions to the passengers during the travel by sharing these with the smart phone. In this report, I will extend and scrutiny this application and give my suggestions base on SITA application, I will define the usage and benefit of such smart airport application for airports and passengers.
Udacity Self-Driving Car Engineer Nanodegree Advanced Lane Finding Project. Identifying lanes using edge detection (Sober operator, gradient of magnitude and direction, and HLS color space), camera calibration and unwarping (distortion correction and perspective transform), and polynomial fitting for the lanes.
This document discusses dog breed identification using deep learning models. It provides an overview of traditional and deep learning methods for image classification, including CNN architectures like AlexNet, VGG, GoogleNet, ResNet and DenseNet. The challenge is to identify the breed of dogs in images using pre-trained models. The document analyzes the provided training and test data, which contains over 10,000 images across 120 breeds. It then details the method used, which is to fine-tune popular models like DenseNet, ResNet and GoogleNet. The results show that DenseNet-169 achieved the best validation accuracy of 81.77% and lowest validation loss of 0.6393.
This document summarizes a student's term project on lane identification in autonomous vehicles. The project pipeline involves camera calibration, perspective transformation, color and gradient thresholding to identify lane lines, and lane detection by fitting windows to identified lines. Results showed good performance on straight lanes but difficulty fitting curved lanes. Further work proposed includes providing distance to lane center, improving curved lane detection, and implementing lane identification in video.
This document presents a project on lane finding for autonomous driving. The goals are to develop a solution to detect lane lines on the road to enable autonomous navigation. Related work in this area is discussed, including approaches using motion estimation, end-to-end learning from video datasets, and traditional computer vision techniques. Risks that could impact the accuracy of lane detection are also summarized, such as curved roads, weather conditions, shadows, and irregular road markings. The document outlines the work breakdown structure and Gantt chart for the project and provides references for related research.
This document summarizes a survey paper on smart charging for electric vehicles from an algorithmic perspective. It discusses smart grid-oriented EV charging approaches like load flattening, frequency regulation, and voltage regulation. It also discusses aggregator-oriented and customer-oriented EV charging approaches and the uncertainties involved. Future work opportunities are identified in areas like battery modeling, routing, and communication requirements to further the smart interaction between electric vehicles and the smart grid.
The document discusses smart airport applications and their benefits. It describes how smart airport applications can provide real-time information to passengers, such as baggage tracking and flight status updates. The applications also offer personalized suggestions to help passengers navigate the airport efficiently, such as recommendations for parking or places to wait with less crowds. Digital boarding passes are highlighted as another smart feature that can streamline the travel process through self-service checkpoints. In conclusion, smart airports are expected to improve the travel experience by optimizing and customizing services for passengers.
this presentation file lectured in international conference in new research of Electrical and engineering and computer science.
Abstract
This paper presents a novel and uniform algorithm for edge detection based on SVM (support vector machine) with Three-dimensional Gaussian radial basis function with kernel. Because of disadvantages in traditional edge detection such as inaccurate edge location, rough edge and careless on detect soft edge. The experimental results indicate how the SVM can detect edge in efficient way. The performance of the proposed algorithm is compared with existing methods, including Sobel and canny detectors. The results shows that this method is better than classical algorithm such as canny and Sobel detector.
مسیریابی سیستم های خودمختارکه در این اسلاید در باره ی شبکه های مسیریابی بین سیستم های خود مختار و نحوه ی ایجاد کانکشن بین شبکه ها مورد بررسی قرار می گیرد.