The document describes a two-stage process for automatic bird species recognition from audio recordings. In the first stage, sound recordings from different bird species are preprocessed and spectrograms are generated. In the second stage, the spectrograms are fed as input to a Convolutional Neural Network (CNN) which classifies the bird species based on the input features. The goal is to develop an accurate system for identifying bird species from audio that can help with biodiversity conservation and commercial applications like bird watching. Previous approaches used images or had limitations in handling background noise. The presented approach uses CNNs to classify bird audio at the species level with high accuracy.
Melanoma Skin Cancer Detection using Image Processing and Machine Learningijtsrd
Dermatological Diseases are one of the biggest medical issues in 21st century due to its highly complex and expensive diagnosis with difficulties and subjectivity of human interpretation. In cases of fatal diseases like Melanoma diagnosis in early stages play a vital role in determining the probability of getting cured. We believe that the application of automated methods will help in early diagnosis especially with the set of images with variety of diagnosis. Hence, in this article we present a completely automated system of dermatological disease recognition through lesion images, a machine intervention in contrast to conventional medical personnel based detection. Our model is designed into three phases compromising of data collection and augmentation, designing model and finally prediction. We have used multiple AI algorithms like Convolutional Neural Network and Support Vector Machine and amalgamated it with image processing tools to form a better structure, leading to higher accuracy of 85 . Vijayalakshmi M M ""Melanoma Skin Cancer Detection using Image Processing and Machine Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23936.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/23936/melanoma-skin-cancer-detection-using-image-processing-and-machine-learning/vijayalakshmi-m-m
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sept-2016-member-meeting-checkvideo
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Nik Gagvani, President and General Manager of CheckVideo, delivers the presentation "Intelligent Video Surveillance: Are We There Yet?" at the September 2016 Embedded Vision Alliance Member Meeting. Gagvani provides an insider's perspective on vision-enabled video surveillance applications.
Melanoma Skin Cancer Detection using Image Processing and Machine Learningijtsrd
Dermatological Diseases are one of the biggest medical issues in 21st century due to its highly complex and expensive diagnosis with difficulties and subjectivity of human interpretation. In cases of fatal diseases like Melanoma diagnosis in early stages play a vital role in determining the probability of getting cured. We believe that the application of automated methods will help in early diagnosis especially with the set of images with variety of diagnosis. Hence, in this article we present a completely automated system of dermatological disease recognition through lesion images, a machine intervention in contrast to conventional medical personnel based detection. Our model is designed into three phases compromising of data collection and augmentation, designing model and finally prediction. We have used multiple AI algorithms like Convolutional Neural Network and Support Vector Machine and amalgamated it with image processing tools to form a better structure, leading to higher accuracy of 85 . Vijayalakshmi M M ""Melanoma Skin Cancer Detection using Image Processing and Machine Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23936.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/23936/melanoma-skin-cancer-detection-using-image-processing-and-machine-learning/vijayalakshmi-m-m
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sept-2016-member-meeting-checkvideo
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Nik Gagvani, President and General Manager of CheckVideo, delivers the presentation "Intelligent Video Surveillance: Are We There Yet?" at the September 2016 Embedded Vision Alliance Member Meeting. Gagvani provides an insider's perspective on vision-enabled video surveillance applications.
With so much of our lives computerized, it is vitally important that machines and humans can understand one another and pass information back and forth. Mostly computers have things their way we have to & talk to them through relatively crude devices such as keyboards and mice so they can figure out what we want them to do. However, when it comes to processing more human kinds of information, like an old-fashioned printed book or a letter scribbled with a fountain pen, computers have to work much harder. That is where optical character recognition (OCR) comes in. Here we process the image, where we apply various pre-processing techniques like desk wing, binarization etc. and algorithms like Tesseract to recognize the characters and give us the final document. T.Gnana Prakash | K. Anusha"Text Extraction from Image using Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2501.pdf http://www.ijtsrd.com/computer-science/simulation/2501/text-extraction-from-image-using-python/tgnana-prakash
AUTOMATIC LICENSE PLATE RECOGNITION SYSTEM FOR INDIAN VEHICLE IDENTIFICATION ...Kuntal Bhowmick
Automatic License Plate Recognition (ANPR) is a practical application of image processing which uses number (license) plate is used to identify the vehicle. The aim is to design an efficient automatic vehicle identification system by using the
vehicle license plate. The system is implemented on the entrance for security control of a highly restricted area like
military zones or area around top government offices e.g.Parliament, Supreme Court etc.
It is worth mentioning that there is a scarcity in researches that introduce an automatic number plate recognition for indian vechicles.In this paper, a new algorithm is presented for Indian vehicle’s number plate recognition system. The proposed algorithm consists of two major parts: plate region extraction and plate recognition.Vehicle number plate region is extracted using the image segmentation in a vechicle image.Optical character recognition technique is used for the character recognition. And finally the resulting data is used to compare with the records on a database so as to come up with the specific information like the vehicle’s owner, registration state, address, etc.
The performance of the proposed algorithm has been tested on real license plate images of indian vechicles. Based on the experimental results, we noted that our algorithm shows superior performance special in number plate recognition phase.
PyTorch Python Tutorial | Deep Learning Using PyTorch | Image Classifier Usin...Edureka!
( ** Deep Learning Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka PyTorch Tutorial (Blog: https://goo.gl/4zxMfU) will help you in understanding various important basics of PyTorch. It also includes a use-case in which we will create an image classifier that will predict the accuracy of an image data-set using PyTorch.
Below are the topics covered in this tutorial:
1. What is Deep Learning?
2. What are Neural Networks?
3. Libraries available in Python
4. What is PyTorch?
5. Use-Case of PyTorch
6. Summary
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Efficient and accurate object detection has been an important topic in the advancement of computer vision systems.
Our project aims to detect the object with the goal of achieving high accuracy with a real-time performance.
In this project, we use a completely deep learning based approach to solve the problem of object detection.
The input to the system will be a real time image, and the output will be a bounding box corresponding to all the objects in the image, along with the class of object in each box.
Objective -
Develop a application that detects an object and it can be used for vehicles counting, when the object is a vehicle such as a bicycle or car, it can count how many vehicles have passed from a particular area or road and it can recognize human activity too.
This lecture discusses the difference between computer and machine vision. It introduces you to the world of image processing. If you would like to learn how to use cameras to detect objects within an image as well as track them, then check out this lecture for more details. If you find openCV or matlab intimidating then check out this course we take you step by step through creating your own vision based apps.
https://www.udemy.com/learn-computer-vision-machine-vision-and-image-processing-in-labview/?couponCode=SlideShare
Intelligent traffic information and control systemSADEED AMEEN
As the problem of urban traffic congestion spreads, there is a pressing need for the introduction of advanced technology and equipment to improve the state of the art of traffic control. In current situation, the signal remains green until the present cars have passed. To avoid those problems we propose a system for controlling the traffic light by image processing. The system will detect vehicles through images instead of using electronic sensors embedded in the pavement. A camera will be installed alongside the traffic light. It will capture image sequences. For this purpose, edge detection has been carried out and according to percentage of matching traffic light-durations can be controlled. In addition, when an emergency vehicle is approaching the junction, it will communicate to the traffic controller in the junction to turn ON the green light. This module uses ZigBee modules for wireless communications between the ambulance and traffic controller. Intelligent traffic control system helps to pass emergency vehicles smoothly. Traffic signal management system is developed for the traffic police, to control the traffic lights manually. Additionally an information system is added using a chat bot module to avail traffic information to user.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
With so much of our lives computerized, it is vitally important that machines and humans can understand one another and pass information back and forth. Mostly computers have things their way we have to & talk to them through relatively crude devices such as keyboards and mice so they can figure out what we want them to do. However, when it comes to processing more human kinds of information, like an old-fashioned printed book or a letter scribbled with a fountain pen, computers have to work much harder. That is where optical character recognition (OCR) comes in. Here we process the image, where we apply various pre-processing techniques like desk wing, binarization etc. and algorithms like Tesseract to recognize the characters and give us the final document. T.Gnana Prakash | K. Anusha"Text Extraction from Image using Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2501.pdf http://www.ijtsrd.com/computer-science/simulation/2501/text-extraction-from-image-using-python/tgnana-prakash
AUTOMATIC LICENSE PLATE RECOGNITION SYSTEM FOR INDIAN VEHICLE IDENTIFICATION ...Kuntal Bhowmick
Automatic License Plate Recognition (ANPR) is a practical application of image processing which uses number (license) plate is used to identify the vehicle. The aim is to design an efficient automatic vehicle identification system by using the
vehicle license plate. The system is implemented on the entrance for security control of a highly restricted area like
military zones or area around top government offices e.g.Parliament, Supreme Court etc.
It is worth mentioning that there is a scarcity in researches that introduce an automatic number plate recognition for indian vechicles.In this paper, a new algorithm is presented for Indian vehicle’s number plate recognition system. The proposed algorithm consists of two major parts: plate region extraction and plate recognition.Vehicle number plate region is extracted using the image segmentation in a vechicle image.Optical character recognition technique is used for the character recognition. And finally the resulting data is used to compare with the records on a database so as to come up with the specific information like the vehicle’s owner, registration state, address, etc.
The performance of the proposed algorithm has been tested on real license plate images of indian vechicles. Based on the experimental results, we noted that our algorithm shows superior performance special in number plate recognition phase.
PyTorch Python Tutorial | Deep Learning Using PyTorch | Image Classifier Usin...Edureka!
( ** Deep Learning Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka PyTorch Tutorial (Blog: https://goo.gl/4zxMfU) will help you in understanding various important basics of PyTorch. It also includes a use-case in which we will create an image classifier that will predict the accuracy of an image data-set using PyTorch.
Below are the topics covered in this tutorial:
1. What is Deep Learning?
2. What are Neural Networks?
3. Libraries available in Python
4. What is PyTorch?
5. Use-Case of PyTorch
6. Summary
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Efficient and accurate object detection has been an important topic in the advancement of computer vision systems.
Our project aims to detect the object with the goal of achieving high accuracy with a real-time performance.
In this project, we use a completely deep learning based approach to solve the problem of object detection.
The input to the system will be a real time image, and the output will be a bounding box corresponding to all the objects in the image, along with the class of object in each box.
Objective -
Develop a application that detects an object and it can be used for vehicles counting, when the object is a vehicle such as a bicycle or car, it can count how many vehicles have passed from a particular area or road and it can recognize human activity too.
This lecture discusses the difference between computer and machine vision. It introduces you to the world of image processing. If you would like to learn how to use cameras to detect objects within an image as well as track them, then check out this lecture for more details. If you find openCV or matlab intimidating then check out this course we take you step by step through creating your own vision based apps.
https://www.udemy.com/learn-computer-vision-machine-vision-and-image-processing-in-labview/?couponCode=SlideShare
Intelligent traffic information and control systemSADEED AMEEN
As the problem of urban traffic congestion spreads, there is a pressing need for the introduction of advanced technology and equipment to improve the state of the art of traffic control. In current situation, the signal remains green until the present cars have passed. To avoid those problems we propose a system for controlling the traffic light by image processing. The system will detect vehicles through images instead of using electronic sensors embedded in the pavement. A camera will be installed alongside the traffic light. It will capture image sequences. For this purpose, edge detection has been carried out and according to percentage of matching traffic light-durations can be controlled. In addition, when an emergency vehicle is approaching the junction, it will communicate to the traffic controller in the junction to turn ON the green light. This module uses ZigBee modules for wireless communications between the ambulance and traffic controller. Intelligent traffic control system helps to pass emergency vehicles smoothly. Traffic signal management system is developed for the traffic police, to control the traffic lights manually. Additionally an information system is added using a chat bot module to avail traffic information to user.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
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.
IDENTIFY THE BEEHIVE SOUND USING DEEP LEARNINGijcsit
Flowers play an essential role in removing the duller from the environment. The life cycle of the flowering
plants involves pollination, fertilization, flowering, seed- formation, dispersion, and germination.
Honeybees pollinate approximately 75% of all flowering plants. Environmental pollution, climate change,
natural landscape demolition, and so on, threaten the natural habitats, thus continuously reducing the
number of honeybees. As a result, several researchers are attempting to resolve this issue. Applying
acoustic classification to recordings of beehive sounds may be a way of detecting changes within them. In
this research, we use deep learning techniques, namely Sequential Neural Network, Convolutional Neural
Network, and Recurrent Neural Network, on the recorded sounds to classify bee sounds from the nonbeehive noises. In addition, we perform a comparative study among some popular non-deep learning
techniques, namely Support Vector Machine, Decision Tree, Random Forest, and Naïve Bayes, with the
deep learning techniques. The techniques are also verified on the combined recorded sounds (25-75%
noises).
Flowers play an essential role in removing the duller from the environment. The life cycle of the flowering
plants involves pollination, fertilization, flowering, seed- formation, dispersion, and germination.
Honeybees pollinate approximately 75% of all flowering plants. Environmental pollution, climate change,
natural landscape demolition, and so on, threaten the natural habitats, thus continuously reducing the
number of honeybees. As a result, several researchers are attempting to resolve this issue. Applying
acoustic classification to recordings of beehive sounds may be a way of detecting changes within them. In
this research, we use deep learning techniques, namely Sequential Neural Network, Convolutional Neural
Network, and Recurrent Neural Network, on the recorded sounds to classify bee sounds from the non-
beehive noises. In addition, we perform a comparative study among some popular non-deep learning
techniques, namely Support Vector Machine, Decision Tree, Random Forest, and Naïve Bayes, with the
deep learning techniques. The techniques are also verified on the combined recorded sounds (25-75%
noises).
Automatic Selection of Open Source Multimedia Softwares Using Error Back-Prop...IJERA Editor
Open source opens a new era to provide license of the software for the user at free of cost which is advantage over paid licensed software. In Multimedia applications there are many versions of software are available and there is a problem for the user to select compatible software for their own system. Most of the time while surfing for software a huge list of software opens in response. The selection of particular software which is pretty suitable for the system from a real big list is the biggest challenge that is faced by the users. This work has been done that focuses on the existing open source software that are widely used and to design an automatic system for selection of particular open source software according to the compatibility of users own system. In this work, error back-propagation based neural network is designed in MATLAB for automatic selection of open source software. The system provides the open source software name after taking the information from user. Regression coefficient of 0.93877 is obtained and the results shown are up to the mark and can be utilized for the fast and effective software search.
COMPUTER VISION FOR DATA CATALOGING IN ECOLOGICAL RESEARCHijma
Data collection is an essential, but time-consuming procedure in ecological research. An algorithm was developed by the author which incorporated two important computer vision techniques to automate butterfly cataloguing. Optical Character Recognition is used for character recognition and Contour Detection is used for image-processing. Proper pre-processing is first done on the images to improve accuracy of character recognition and butterfly measurement. 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. Multiple mathematical algorithms are used to calculate and determine the precise location of the points on which to draw the body and forewing lines of the butterfly. Overall, 92% accuracy are achieved by the program for the set of butterflies measured.
Tifinagh handwritten character recognition using optimized convolutional neu...IJECEIAES
Tifinagh handwritten character recognition has been a challenging problem due to the similarity and variability of its alphabets. This paper proposes an optimized convolutional neural network (CNN) architecture for handwritten character recognition. The suggested model of CNN has a multi-layer feedforward neural network that gets features and properties directly from the input data images. It is based on the newest deep learning open-source Keras Python library. The novelty of the model is to optimize the optical character recognition (OCR) system in order to obtain best performance results in terms of accuracy and execution time. The new optical character recognition system is tested on a customized dataset generated from the amazigh handwritten character database. Experimental results show a good accuracy of the system (99.27%) with an optimal execution time of the classification compared to the previous works.
ijerst offers a fast publication schedule whilst maintaining rigorous peer review; the use of recommended electronic formats for article delivery expedites the process. Editorial Board or others working in the field as appropriate, to ensure they are likely to be of the level of interest and importance appropriate for the journal. International Journal of Engineering Research and Science & Technology (IJERST) is an international online journal in English published Quarterly. All submitted research articles are subjected to immediate rapid screening by the editors.
ijerst offers a fast publication schedule whilst maintaining rigorous peer review; the use of recommended electronic formats for article delivery expedites the process. International Journal of Engineering Research and Science & Technology (IJERST) is an international online journal in English published Quarterly. All submitted research articles are subjected to immediate rapid screening by the editors, in consultation with the Editorial Board or others working in the field as appropriate, to ensure they are likely to be of the level of interest and importance appropriate for the journal.
In the present paper, electroencephalogram (EEG)
data have been used to human identification by computing
sample entropy and graph entropy as feature extractions. Used
two classifier types, which are K-Nearest Neighbors (K-NN) and
Support Vector Machine (SVM). Python and Matlab software
were used in this study and EEG data was collected by UCI
repository . Matlab used when Thirteen channels was applied as
feature extraction . The experimental results show that, Python
software classifies the EEG-UCI data better than MATLAB
environment software where the accuracy of KNN and SVM
were 85.2% and 91.5% respectively.
Classification Of Iris Plant Using Feedforward Neural Networkirjes
The classification and recognition of type on the basis of individual features and behaviors constitute
a preliminary measure and is an important target in the behavioral sciences. Current statistical methods do not
always yield satisfactory answers. A Feed Forward Artificial Neural Network is the computer model inspired by
the structure of the Human Brain. It views as in the set of artificial nerve cells that are interconnected with the
other neurons. The primary aim of this paper is to demonstrate the process of developing the Artificial Neural
network based classifier which classifies the Iris database. The problem concerns the identification of Iris plant
species on the basis of plant attribute measurements. This paper is related to the use of feed forward neural
networks towards the identification of iris plants on the basis of the following measurements: sepal length, sepal
width, petal length, and petal width. Using this data set a Neural Network (NN) is used for the classification of
iris data set. The EBPA is used for training of this ANN. The results of simulations illustrate the effectiveness of
the neural system in iris class identification.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
1. Slide3
ABSTRACT
An automatic bird species recognition system has been developed and methods for their
identification has been investigated.
Automatic identification of bird sounds without physical intervention has been a formidable and
onerous endeavor for significant research on the taxonomy and various other sub fields of
ornithology.
In this paper, a two-stage identification process is employed
1) The first stage in- volved construction of an ideal dataset which incorporated all the sound
recordings of different bird species. . Subsequently, the sound clips were subjected to
various sound preprocessing techniques like pre-emphasis, framing, silence removal and
reconstruction.
2) The second stage involved deploying a neural network to which the spectrograms were
provided as input. Based on the input features, the Convolution Neural Network (CNN)
classifies the sound clip and recognizes the bird species.
2. Slide2
INTRODUCTION
On large scale, accurate bird recognition is essential for avian biodiversity conservation.
The main Problem is to create a solution for counting and identifying different species of birds
present in an area and classify them into categories.
Automatic identification of bird calls from continuous recordings gathered from the environment
would be significant addition to the research methodology in ornithology and biology.
Often these recordings are clipped or contain noise due to which reliable methods of automated
techniques have to be used instead of manual conventional methods.
There is a significant commercial potential for such systems because bird watching is a popular
hobby in many countries.
An approach to accurately recognize Birds species using audio signal processing and neural
networks.
3. Slide2
LITERATURE SURVEY
John Martinsson et al (2017) [1], presented the CNN algorithm and deep residual neural networks to detect
an image in two ways i.e., based on feature extraction and signal classification. They did an
experimental analysis for datasets consisting of different images. But their work didn’t consider the
background species. In Order to identify the background species larger volumes of training data are
required, which may not be available.
Juha Niemi, Juha T Tanttu et al (2018) [2], proposed a Convolutional neural network trained with deep
learning algorithms for image classification. It also proposed a data augmentation method in which
images are converted and rotated in accordance with the desired color. The final identification is based
on a fusion of parameters provided by the radar and predictions of the image classifier.
Li Jian, Zhang Lei et al (2014)[3], proposed an effective automatic bird species identification based on the
analysis of image features. Used the database of standard images and the algorithm of similarity
comparisons.
4. Slide2
LITERATURE SURVEY
Madhuri A. Tayal, Atharva Magrulkar et al (2018)[4], developed a software application that is used to simplify
the bird identification process. This bird identification software takes an image as an input and gives the
identity of the bird as an output. The technology used is transfer learning and MATLAB for the
identification process.
Andreia Marini, Jacques Facon et al (2013) [5], proposed a novel approach based on color features extracted
from unconstrained images, applying a color segmentation algorithm in an attempt to eliminate background
elements and to delimit candidate regions where the bird may be present within the image. Aggregation
processing was employed to reduce the number of intervals of the histograms to a fixed number of bins. In
this paper, the authors experimented with the CUB-200 dataset and results show that this technique is more
accurate.
Marcelo T. Lopes, Lucas L. Gioppo et al (2011) [6], focused on the automatic identification of bird species from
their audio recorded song. Here the authors dealt with the bird species identification problem using signal
processing and machine learning techniques with the MARSYAS feature set. Presented a series of
experiments conducted in a database composed of bird songs from 75 species out of which problem obtained
in performance with 12 species.
5. 5
EXISTING SYSTEM
To identify the bird species there are many websites produces the results using different technologies.
But the results are not accurate. For suppose if we will give an input in those websites and android
applications it gives us multiple results instead of single bird name. It shows us the all bird names which
are having similar characteristics. So, we aimed to develop a project to produce better and accurate
results. In order to achieve this, we have used Convolutional Neural Networks to classify the bird
species.
6. PROBLEM IDENTIFICATION
1. The systems is a need in current age of development Automatic identification of bird sounds without
physical intervention has been a formidable and onerous endeavour for significant research on the
taxonomy and various other sub fields of ornithology.
2. There are a number of birds , and hence creating a audio detection model that can be successful
everywhere is a challenging problem. To avoid this problem we have used machine learning
algorithms have been used.
3. Machine Learning is a sub-area of artificial intelligence, whereby the term refers to the ability of IT
systems to independently find solutions to problems by recognizing patterns in databases. In other
words: Machine Learning enables IT systems to recognize patterns on the basis of existing algorithms
and data sets and to develop adequate solution concepts.
Slide 4
7. OBJECTIVES
• The system is able to classify bird species based on the spectrogram image generated from their sounds
Convolution Neural Network (CNN) classifies the sound clip and recognizes the bird species. The
present study investigated a method to identify the bird species for classification of audio.
• The generated system is connected with a user-friendly website where user will upload or record audio
for identification purpose and it gives the desired output. The proposed system works on the principle
based on detection of a part and extracting CNN features from multiple convolution layers.
• These features are aggregated and then given to the classifier for classification purpose. On basis of the
results which has been produced, the system has provided.
Slide 5
9. Methodology
Slide 6
• In this project, different bird species are identified. The approach involved pre-processing of the
bird sounds followed by the spectrogram generation of the same and these were used to train the
model for classification.
• The system was able to classify bird species based on the spectrogram image generated from their
sounds with high accuracy
• Once these two steps have been completed, the system can perform the following tasks by Deep
Learning:
• Finding, extracting and summarizing relevant data
• Making predictions based on the analysis data
• Calculating probabilities for specific results
• Adapting to certain developments autonomously
• Optimizing processes based on recognize patterns.
For this, proposed work we have taken the input as a audio and here we pass the audio to the machine for the
prediction. Firstly we train the model with the audio so it will train the model on that basis.
10. Hardware and Software Requirements
Slide 7
• Hardware requirement
• Processor : Intel Multicore Processor (i3 or i5 or i7)
• RAM : 4GB or Above
• Hard Disk : 100GB or Above
• Software Requirements:
• Pycharm
• Python IDE 3.7
11. 11
Reference
[1] Automated Bird Species Identification using Audio Signal Process- ing and Neural Networks [2] Automated Bird
Species Identification Using Neural Networks.
[3] Audio-based Bird Species Identification with Deep Convolutional Neural Networks
[4] Automatic bird species recognition based on birds vocalization
[5] Deep Learning Based Audio Classifier for Bird Species.
[6] Bird Call Identification using Dynamic Kernel based Support Vector Machines and Deep Neural Networks.
[7] Identification of Bird Species from Their Singing.
[8] Feature Set Comparison for Automatic Bird Species Identification. IEEE, 2004, pp. V701.
[9] TIME-FREQUENCY SEGMENTATION OF BIRD SONG IN NOISY ACOUSTIC ENVIRONMENT 14, no. 6, pp.
22522263, 2006
[10] Feature Learning for Bird Call Clustering
[11] Sujoy Debnath, ParthaProtim Roy , Amin Ahsan Ali, M Ashraful Amin” Identification of Bird Species from Their
Singing”, 5th International Conference on Informatics, Electronics and Vision (ICIEV), 2016