A Magnetic door sensor is an alarm sensor that is triggered by movement. It consists of two parts encased in a plastic shell a magnet and a sensor/switch. When mounted on a for example on a door, the switch is open when the door is closed and vice versa.
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?24,000.00?Exc Tax
dsPIC Development board is proposed to smooth the progress of developing and debugging of various designs encompassing Microcontrollers from Microchip. It?s designed as to facilitate (dsPIC30F 40PIN DIP) On-board Programmer for PIC Microcontroller through ISP on Universal Serial port. It integrates on board USART,LEDs, keypads, 3 ADC inputs and LCD Display to create a stand-alone versatile test platform. User can easily engage in development in this platform, or use it as reference to application development.
Shipping : 4 to 5 days from the date of purchase
Warranty : 3 Months
100 in stock
The main objective of this project is to analyse previous year?s student?s historical data and predict placement possibilities of current students and aids to increase the placement percentage of the institutions using Machine Learning Algorithms.
The objective of the project is to understand the concepts of natural language processing and create a tool for text summarization. The concern in automatic summarization is increasing broadly so the manual work is removed. The project concentrates on creating a tool that automatically summarizes the document.
In this project, we create a model to do the accurate prediction of heart disease problems in health care applications. Easier to analyse the scalable of health care big data. Less time consumption with the efficiency of data in heart disease. High performance in data maintained of heart disease prediction.
The main objective of this project is to predict the employee attrition rate using Machine Learning Algorithms such as SVM and Naive Bayes algorithms. After the results obtained, the performance of the model is evaluated by calculating the accuracy score and showing it in the form of a confusion matrix.
In this concept, we create Machine Learning Model for Smart Farming. Smart Farming Prediction and the recommendation can be made using Space Vector Modulation Classification and Neural Network Algorithm.
Churn Analysis is one of the worldwide used analyses on Subscription Oriented Industries to analyse customer behaviours to predict the customers which are about to leave the service agreement from a company. The proposed model ?rst classi?es churn customers data using classi?cation algorithms, in which the Random Forest (RF) and Decision tree (DT) algorithm performed well with 90.44% correctly classi?ed instances.
The idea of visualizing data by applying machine learning and pandas in python. Taking dataset from a medical background of different people (prime Indians dataset from UCI repository). This data set consists of information on the user’s age, sex type of symptoms related to diabetes. Design a testing and training set and predict are chances of patients having diabetes in the coming five years. Data is classified and shown in the form of different graphs.
The objective of the project to find the Network attacks using KDD Datas and Data Mining Approach.
To prevent malware attacks, researchers and developers have proposed different security solutions, applying static analysis, dynamic analysis, and artificial intelligence. Indeed, data science has become a promising area in cybersecurity, since analytical models based on data allow for the discovery of insights that can help to predict malicious activities.?We can analyse cyber threats using two techniques, static analysis, and dynamic analysis, the most important thing is that these are the approaches to get the features that we are going to use in data science.
The proposed framework focuses on merging the demographic and study related attributes with the educational psychology fields, by adding the student’s psychological characteristics. After surveying, we picked the most relevant attributes based on their rationale and correlation with the academic performance.
We apply the ML model on datasets like Twitter, Flickr, and YouTube. It will predict a similar type of hashtag with a detailed description. Unsupervised word embedding methods train with a reconstruction objective, in which the embedding is used to predict the original text.
The idea of visualizing data by applying machine learning and pandas in python. Taking dataset from a medical background of different people (prime Indians dataset from UCI repository). This data set consists of information on the user’s age, sex type of symptoms related to diabetes. Design a testing and training set and predict are chances of patients having diabetes in the coming five years. Data is classified and shown in the form of different graphs.
The objective is to create a ML Model by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction. This latest work on rainfall prediction with the focus on data mining techniques and also will provide a baseline for future directions and comparisons.
In this Project, we are building a Machine Learning Model to detect the Credit Card Fraud using Random Forest Algorithm. Random Forest is an algorithm for classification and regression. Summarily, it is a collection of decision tree classifiers. The random forest has an advantage over the decision tree as it corrects the habit of overfitting to their training set. A subset of the training set is sampled randomly so that to train each individual tree and then a decision tree is built, each node then splits on a feature selected from a random subset of the full feature set.
The main objective is to detect fake news, which is a classic text classification problem with a straightforward proposition. It is needed to build a model that can differentiate between Real news and Fake news using Machine Learning Algorithm.
This project is about to create a framework, by this we can detect a fake profiles using ML algorithms, makes people social life more secure. The model presented in this project demonstrates that Support Vector Machine (SVM) is an elegant and robust method for binary classification in a large dataset. Regardless of the non-linearity of the decision boundary, SVM is able to classify between fake and genuine profiles with a reasonable degree of accuracy (>90%)
The objective of this project is to review totally different techniques to predict stock worth movement victimization the sentiment analysis from social media, data processing. During this process, we are going to realize economic technique which may predict stock movement additional accurately. Social media offers a robust outlet for people’s thoughts and feelings it’s a fast-ever-growing supply of texts starting from everyday observations to concerning discussions
Performance analysis of outcomes based on learning is a system that will strive for excellence at different levels and diverse dimensions in the field of students’ interests.? The proposed framework analyse the students demographic data, study-related and psychological characteristics to extract all possible knowledge from students, teachers, and parents. Seeking the highest possible accuracy in academic performance prediction using a set of powerful data mining techniques.
This project is about to create a framework, by this we can detect a fake profiles using ML algorithms, makes people social life more secure. The model presented in this project demonstrates that Support Vector Machine (SVM) is an elegant and robust method for binary classification in a large dataset. Regardless of the non-linearity of the decision boundary, SVM is able to classify between fake and genuine profiles with a reasonable degree of accuracy (>90%)
The objective of this project to create a ML Model to predict the liver disease from the huge liver disease datasets using the algorithms. Time complexity and accuracy can measured by various machine learning models ,so that we can measures different. Risky factors can be predicted early by machine learning models.
The primary goal of this project is to extract patterns from a common loan-approved dataset, and then build a model based on these extracted patterns, in order to predict the likely loan defaulters by using classification data mining algorithms. The historical data of the customers like their age, income, loan amount, employment length etc. will be used in order to do the analysis.
This aims to classify textual content into non-hate or hate speech, in which case the method may also identify the targeting characteristics (i.e., types of hate, such as race, and religion) in the hate speech. To Analysis of the language in the typical datasets to get hate speech by features in the ?long tail? in a dataset using Machine Learning.
Models for the prediction of water table depth were developed based on Artificial Neural Networks (ANN) with different combinations of hydrological parameters. The best combination was confirmed with factor analysis. The input parameters for groundwater level forecasting were derived using Time Series Analysis (TSA).
Models are created using accident data records which can help to understand the characteristics of many features like driver’s behaviour, over speed, mobile usage, sleeping conditions. This can help the users to compute the safety measures which is useful to avoid accidents. It can be illustrated how statistical method based on directed graphs, by comparing two scenarios based on out-of-sample forecasts. The model is performed to identify statistically significant factors which can be able to predict the probabilities of crashes and injury that can be used to perform a risk factor and reduce it.
Building a ML Model to recognize the Human Activity using Machine Learning Algorithms. A Bayesian network has been applied for activity prediction based on individual and multiple appliance usage.
The objective of this project is build a ML model to analyse the Crime using K Means. Analysing and examining crimes happening in the world will give us a Broadview in understanding the crime regions.
The objective of the project is to build a Intrusion Detection Model using Machine Learning. An intrusion detection system (IDS) is a system that monitors and analyses data to detect any intrusion in the system or network.
SKIN CANCER DETECTION USING ABCD RULE
Abstract:
Human Cancer is one of the most dangerous diseases which is mainly caused by genetic instability of multiple molecular alterations. Among many forms of human cancer, skin cancer is the most common one. To identify skin cancer at an early stage we will study and analyze them through various techniques named as segmentation and feature extraction. Here, we focus malignant melanoma skin cancer, (due to the high concentration of Melanoma- Hier we offer our skin, in the dermis layer of the skin) detection. In this, we used our ABCD rule dermoscopy technology for malignant melanoma skin cancer detection. In this system different step for melanoma skin lesion characterization i.e, first the Image Acquisition Technique, pre-processing, segmentation, define feature for skin Feature Selection determines lesion characterization, classification methods. In the Feature extraction by digital image processing method includes, symmetry detection, Border Detection, color, and diameter detection and also we used LBP for extract the texture based features. Here we proposed the Back Propagation Neural Network to classify the benign or malignant stage.
Introduction
Skin cancers are cancers that arise from the skin. They are due to the development of abnormal cells that have the ability to invade or spread to other parts of the body.
There are three main types of skin cancers: basal-cell skin cancer (BCC), squamous cell skin cancer (SCC) and melanoma. The first two, along with a number of less common skin cancers, are known as non melanoma skin cancer (NMSC).
Basal-cell cancer grows slowly and can damage the tissue around it but is unlikely to spread to distant areas or result in death. It often appears as a painless raised area of skin that may be shiny with small blood vessel running over it or may present as a raised area with an ulcer.
Squamous-cell skin cancer is more likely to spread. It usually presents as a hard lump with a scaly top but may also form an ulcer. Melanomas are the most aggressive.
Signs include a mole that has changed in size, shape, color, has irregular edges, has more than one color, is itchy or bleeds.. A skin that has inadequate melanin is exposed to the risk of sunburn as well as harmful ultraviolet rays from the sun .Clinical analysis and biopsy tests are commonly used.
Existing Systems
Draw backs of Existing method
Proposed Method
Advantages
Block Diagram
Block Diagram Explanation
Colour Space Conversion
Color space conversion is happens when a Color Management Module (CMM) translates color from one device’s space to another. Conversion may require approximations in order to preserve the image’s most important color qualities.
The use of color in image processing is prompted with the useful resource of number one factors. First, shade is a powerful descriptor that frequently simplifies object identity and extraction from a scene. Second, human beings can determine plenty of shade solar shades and intensities, in comparison to approximately only two dozen sun sunglasses of gray. This 2d aspect is especially important in manual photo assessment
GLCM feature extraction
In statistical texture analysis, texture features are computed from the statistical distribution of observed combinations of intensities at specified positions relative to each other in the image. According to the number of intensity points (pixels) in each combination, statistics are classified into first-order, second order and higher-order statistics.
The Gray Level Co ocurrence Matrix (GLCM) method is a way of extracting second order statistical texture features. The approach has been used in a number of applications, Third and higher order textures consider the relationships among three or more pixels.
ABCD Parameters
The ABCDE Rule of skin cancer is an easy-to-remember system for determining whether a mole or growth may be cancerous. They describe the physical condition and/or progression of any skin abnormality that would suggest the development of a malignancy.
BPN Training and Classification
As is clear from the diagram, the working of BPN is in two phases. One phase sends the signal from the input layer to the output layer, and the other phase back propagates the error from the output layer to the input layer. For training, BPN will use binary sigmoid activation function Back-propagation is the essence of neural net training. It is the method of fine-tuning the weights of a neural net based on the error rate obtained in the previous epoch (i.e., iteration). Proper tuning of the weights allows you to reduce error rates and to make the model reliable by increasing its generalization
Requirement Specifications
Hardware Requirements
SOFTWARE REQUIREMENTS:
REFERENCES
[1]Adheena Santy and Adheena Santy,Segmentation Methods For Computer Aided Melanoma Detection,IEEE Conference,2015.
[2] Omar Abuzaghleh, Miad Faezipour and Buket D.Barkana ,A Comparison of Feature Sets for an utomated Skin Lesion Analysis System for Melanoma Early Detection and Prevention,IEEE journal,2015.
[3] M. Rademaker and A. Oakley,Digital monitoring by whole body photography and sequential digital dermoscopy detects thinner melanomas,IEEE journal,2010.
[4] Xiaojing Yuan, Zhenyu Yang, George Zouridakis, and Nizar Mullani >
[5] Abder-Rahman Ali, Micael S. Couceiro, and Aboul Ella Hassenian ,Melanoma Detection Using Fuzzy CMeans Clustering Coupled With Mathematical Morphology,IEEE Conference,2014
₹24,000.00 Exc Tax
dsPIC Development board is proposed to smooth the progress of developing and debugging of various designs encompassing Microcontrollers from Microchip. It’s designed as to facilitate (dsPIC30F 40PIN DIP) On-board Programmer for PIC Microcontroller through ISP on Universal Serial port. It integrates on board USART,LEDs, keypads, 3 ADC inputs and LCD Display to create a stand-alone versatile test platform. User can easily engage in development in this platform, or use it as reference to application development.
Shipping : 4 to 5 days from the date of purchase
Warranty : 3 Months
100 in stock
This project is design to control the speed of a single-phase induction motor by using Node MCU controller. The single-phase inverter converts dc voltage into ac voltage and the induction motor speed depends on the frequency of the inverter. The inverter card comes with an inbuilt full bridge rectifier and filter capacitor.
This project is proposed to control the speed of the BLDC motor using LUO converter. This Project Model build with LUO Convertor Techniques and the Pulses can be triggered using dsPIC Controller. The closed loop feedback system is design using the Hall Sensor.
This project describes the speed control of the Induction motor with dsPIC30F4011 controller and by comparing the sine and triangular wave. The dsPIC Controller generates the switching pulses for the inverter and the speed control is done by changing the frequency of reference sine and amplitude of sine. The motor speed is measured by using the proximity sensor, which is placed on the Induction Motor and parameters displayed on LCD.
This project describes the speed control of the BLDC motor with the dsPIC Controller, by using the Hall effect sensors of BLDC Motor. The dsPIC30F4011 controller generates the controlled switching pulses for the inverter. The speed control is done by changing the duty cycle of PWM from dsPIC30F4011. The motor speed is measured by using the proximity sensor placed on the BLDC Motor and displayed on LCD.
This project is based on cascaded inverter and quasi z source techniques. This five-level inverter is cascaded of the two inverter circuits and also included a quasi z source circuit in front of the inverter circuit. A quasi z source circuit is used to boost the dc voltage and the dc voltage is applied to the inverter and obviously inverter output voltage range is increased.
This project is to design? and Analyse the performance of five levels of Cascaded H-Bridge multilevel inverter topology with Arduino. The PWM pulse will be generated by using Arduino and Embedded C. The output voltage is the sum of the voltage that is generated by each bridge. The switching angles can be chosen in such a way that the total harmonic distortion is minimized.
This project is designed to control the multilevel inverter using a TMS320F2812 DSP controller. This high-speed DSP processor is used to generate PWM signals, which is applied to driver circuits and this driver circuit is used to isolate and amplify the pulses. This project’s main objective is to reduce switching losses and harmonics using DSP Processor.
This project is design to control the speed of a single-phase induction motor using Arduino. The single-phase inverter converts dc voltage into ac voltage. Single-phase induction motor speed depends on the frequency of the inverter.
This Project is design to control the speed of PMDC motor using dsPIC30F4011. The speed of the dc motor can be varied by changing the duty cycle of the PWM. The motor speed is monitored by using the proximity sensor placed in the motor and this can be done both in open loop and closed loop operations. In this concept, motor speed is controller manually in open and control automatically in closed loop with respect to the set speed.
This project is design to control the speed of the induction motor using FPGA Processor. By comparing the sine and triangular wave, FPGA generates the controlled switching pulses for the inverter. The speed control is done by changing the frequency of reference sine from Spartan6. The motor speed is measured by using the proximity sensor placed on the Induction Motor and displayed on LCD.
This project is used to control the speed of a three-phase induction motor using SEPIC converter. The sepic converter is used to boost the input voltage. The boosted dc voltage is applied to the three-phase inverter. This three-phase VSI operates at a 120-degree mode of operation. Three-phase induction motor speed depends on the frequency of the inverter.
This project is proposed to control and monitor the speed of the three-phase induction motor by using the Arduino and node MCU controller. The Arduino is used to produce the PWM signals and the motor speed is controlled by using the driver and three-phase inverter circuits. We can monitor and control the speed using IoT Techniques.
This project is mainly used to control the speed of the BLDC motor by varying the frequency. The BLDC motor has high reliability, high-efficiency high torque/inertia ratio, improved cooling, low radio frequency interference, and noise and requires practically no maintenance. The BLDC motor speed depends on the frequency of the three-phase inverter circuit.
This project is proposed to control the speed of the BLDC motor using zeta converter. DC voltage is applied to zeta converter, it boosts the input voltage and this voltage given to the three-phase inverter, which converts the dc voltage into three-phase ac voltage. The three-phase ac voltage is connected to the BLDC motor and with hall sensor, sensor output is feedback to the controller for closed loop operation.
This project is based on a cascaded H bridge inverter and various topologies. Among these Cascaded multilevel inverters have drawn tremendous interest in the power industry because it requires fewer components. As the number of levels increases the harmonic content of the output voltage waveform decreases. This Cascaded H-Bridge fifteen-level inverter with a minimum number of switches. As the level increases the synthesized output waveform has more steps that produce a staircase wave that approaches desired waveform. The problem of switching losses and power losses can be eliminated with a minimum number of switches.
This project proposed a new multilevel inverter with a reduced number of switches and the switching losses are also get reduced with reduced total harmonics distortion (THD).
This project is proposed to control the speed of the three-phase induction motor by using the Arduino. Motor speed is controlled by using the driver and three-phase inverter circuits. And also this project has reduced the harmonics and switching losses of the circuit. In this project have two switches are used to increment and decrement the speed of the Three Phase Induction Motor. The speed variation can be viewed in the tachometer.
The objective of the project is Message Classification in facebook learning group using Machine Learning to Data Analysis.
This Single Phase rectifier trainer kit integrated with Texas Instrument’s DSP help the user to understand complete hardware design of single phase rectifier system on both simulation and hardware. SCR’S were triggered by DSP enabling students to learn how to use DSP for power electronics applications.
This kit has an Texas Instruments C2000 PICCOLO LaunchPad TMS320F28027 with inbuilt XDS100 Emulator and provides reliable and graphical programming development environment system for power electronics application.
Low cost DSP development platform for Power Electronics Application from Texas Instruments.
2 in stock
?24,000.00?Exc Tax
dsPIC Development board is proposed to smooth the progress of developing and debugging of various designs encompassing Microcontrollers from Microchip. It?s designed as to facilitate (dsPIC30F 40PIN DIP) On-board Programmer for PIC Microcontroller through ISP on Universal Serial port. It integrates on board USART,LEDs, keypads, 3 ADC inputs and LCD Display to create a stand-alone versatile test platform. User can easily engage in development in this platform, or use it as reference to application development.
Shipping : 4 to 5 days from the date of purchase
Warranty : 3 Months
100 in stock
The main objective of this project is to analyse previous year?s student?s historical data and predict placement possibilities of current students and aids to increase the placement percentage of the institutions using Machine Learning Algorithms.
The objective of the project is to understand the concepts of natural language processing and create a tool for text summarization. The concern in automatic summarization is increasing broadly so the manual work is removed. The project concentrates on creating a tool that automatically summarizes the document.
In this project, we create a model to do the accurate prediction of heart disease problems in health care applications. Easier to analyse the scalable of health care big data. Less time consumption with the efficiency of data in heart disease. High performance in data maintained of heart disease prediction.
The main objective of this project is to predict the employee attrition rate using Machine Learning Algorithms such as SVM and Naive Bayes algorithms. After the results obtained, the performance of the model is evaluated by calculating the accuracy score and showing it in the form of a confusion matrix.
In this concept, we create Machine Learning Model for Smart Farming. Smart Farming Prediction and the recommendation can be made using Space Vector Modulation Classification and Neural Network Algorithm.
Churn Analysis is one of the worldwide used analyses on Subscription Oriented Industries to analyse customer behaviours to predict the customers which are about to leave the service agreement from a company. The proposed model ?rst classi?es churn customers data using classi?cation algorithms, in which the Random Forest (RF) and Decision tree (DT) algorithm performed well with 90.44% correctly classi?ed instances.
The idea of visualizing data by applying machine learning and pandas in python. Taking dataset from a medical background of different people (prime Indians dataset from UCI repository). This data set consists of information on the user’s age, sex type of symptoms related to diabetes. Design a testing and training set and predict are chances of patients having diabetes in the coming five years. Data is classified and shown in the form of different graphs.
The objective of the project to find the Network attacks using KDD Datas and Data Mining Approach.
To prevent malware attacks, researchers and developers have proposed different security solutions, applying static analysis, dynamic analysis, and artificial intelligence. Indeed, data science has become a promising area in cybersecurity, since analytical models based on data allow for the discovery of insights that can help to predict malicious activities.?We can analyse cyber threats using two techniques, static analysis, and dynamic analysis, the most important thing is that these are the approaches to get the features that we are going to use in data science.
The proposed framework focuses on merging the demographic and study related attributes with the educational psychology fields, by adding the student’s psychological characteristics. After surveying, we picked the most relevant attributes based on their rationale and correlation with the academic performance.
We apply the ML model on datasets like Twitter, Flickr, and YouTube. It will predict a similar type of hashtag with a detailed description. Unsupervised word embedding methods train with a reconstruction objective, in which the embedding is used to predict the original text.
The idea of visualizing data by applying machine learning and pandas in python. Taking dataset from a medical background of different people (prime Indians dataset from UCI repository). This data set consists of information on the user’s age, sex type of symptoms related to diabetes. Design a testing and training set and predict are chances of patients having diabetes in the coming five years. Data is classified and shown in the form of different graphs.
The objective is to create a ML Model by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction. This latest work on rainfall prediction with the focus on data mining techniques and also will provide a baseline for future directions and comparisons.
In this Project, we are building a Machine Learning Model to detect the Credit Card Fraud using Random Forest Algorithm. Random Forest is an algorithm for classification and regression. Summarily, it is a collection of decision tree classifiers. The random forest has an advantage over the decision tree as it corrects the habit of overfitting to their training set. A subset of the training set is sampled randomly so that to train each individual tree and then a decision tree is built, each node then splits on a feature selected from a random subset of the full feature set.
The main objective is to detect fake news, which is a classic text classification problem with a straightforward proposition. It is needed to build a model that can differentiate between Real news and Fake news using Machine Learning Algorithm.
This project is about to create a framework, by this we can detect a fake profiles using ML algorithms, makes people social life more secure. The model presented in this project demonstrates that Support Vector Machine (SVM) is an elegant and robust method for binary classification in a large dataset. Regardless of the non-linearity of the decision boundary, SVM is able to classify between fake and genuine profiles with a reasonable degree of accuracy (>90%)
The objective of this project is to review totally different techniques to predict stock worth movement victimization the sentiment analysis from social media, data processing. During this process, we are going to realize economic technique which may predict stock movement additional accurately. Social media offers a robust outlet for people’s thoughts and feelings it’s a fast-ever-growing supply of texts starting from everyday observations to concerning discussions
Performance analysis of outcomes based on learning is a system that will strive for excellence at different levels and diverse dimensions in the field of students’ interests.? The proposed framework analyse the students demographic data, study-related and psychological characteristics to extract all possible knowledge from students, teachers, and parents. Seeking the highest possible accuracy in academic performance prediction using a set of powerful data mining techniques.
This project is about to create a framework, by this we can detect a fake profiles using ML algorithms, makes people social life more secure. The model presented in this project demonstrates that Support Vector Machine (SVM) is an elegant and robust method for binary classification in a large dataset. Regardless of the non-linearity of the decision boundary, SVM is able to classify between fake and genuine profiles with a reasonable degree of accuracy (>90%)
The objective of this project to create a ML Model to predict the liver disease from the huge liver disease datasets using the algorithms. Time complexity and accuracy can measured by various machine learning models ,so that we can measures different. Risky factors can be predicted early by machine learning models.
The primary goal of this project is to extract patterns from a common loan-approved dataset, and then build a model based on these extracted patterns, in order to predict the likely loan defaulters by using classification data mining algorithms. The historical data of the customers like their age, income, loan amount, employment length etc. will be used in order to do the analysis.
This aims to classify textual content into non-hate or hate speech, in which case the method may also identify the targeting characteristics (i.e., types of hate, such as race, and religion) in the hate speech. To Analysis of the language in the typical datasets to get hate speech by features in the ?long tail? in a dataset using Machine Learning.
Models for the prediction of water table depth were developed based on Artificial Neural Networks (ANN) with different combinations of hydrological parameters. The best combination was confirmed with factor analysis. The input parameters for groundwater level forecasting were derived using Time Series Analysis (TSA).
Models are created using accident data records which can help to understand the characteristics of many features like driver’s behaviour, over speed, mobile usage, sleeping conditions. This can help the users to compute the safety measures which is useful to avoid accidents. It can be illustrated how statistical method based on directed graphs, by comparing two scenarios based on out-of-sample forecasts. The model is performed to identify statistically significant factors which can be able to predict the probabilities of crashes and injury that can be used to perform a risk factor and reduce it.
Building a ML Model to recognize the Human Activity using Machine Learning Algorithms. A Bayesian network has been applied for activity prediction based on individual and multiple appliance usage.
The objective of this project is build a ML model to analyse the Crime using K Means. Analysing and examining crimes happening in the world will give us a Broadview in understanding the crime regions.
The objective of the project is to build a Intrusion Detection Model using Machine Learning. An intrusion detection system (IDS) is a system that monitors and analyses data to detect any intrusion in the system or network.
SKIN CANCER DETECTION USING ABCD RULE
Abstract:
Human Cancer is one of the most dangerous diseases which is mainly caused by genetic instability of multiple molecular alterations. Among many forms of human cancer, skin cancer is the most common one. To identify skin cancer at an early stage we will study and analyze them through various techniques named as segmentation and feature extraction. Here, we focus malignant melanoma skin cancer, (due to the high concentration of Melanoma- Hier we offer our skin, in the dermis layer of the skin) detection. In this, we used our ABCD rule dermoscopy technology for malignant melanoma skin cancer detection. In this system different step for melanoma skin lesion characterization i.e, first the Image Acquisition Technique, pre-processing, segmentation, define feature for skin Feature Selection determines lesion characterization, classification methods. In the Feature extraction by digital image processing method includes, symmetry detection, Border Detection, color, and diameter detection and also we used LBP for extract the texture based features. Here we proposed the Back Propagation Neural Network to classify the benign or malignant stage.
Introduction
Skin cancers are cancers that arise from the skin. They are due to the development of abnormal cells that have the ability to invade or spread to other parts of the body.
There are three main types of skin cancers: basal-cell skin cancer (BCC), squamous cell skin cancer (SCC) and melanoma. The first two, along with a number of less common skin cancers, are known as non melanoma skin cancer (NMSC).
Basal-cell cancer grows slowly and can damage the tissue around it but is unlikely to spread to distant areas or result in death. It often appears as a painless raised area of skin that may be shiny with small blood vessel running over it or may present as a raised area with an ulcer.
Squamous-cell skin cancer is more likely to spread. It usually presents as a hard lump with a scaly top but may also form an ulcer. Melanomas are the most aggressive.
Signs include a mole that has changed in size, shape, color, has irregular edges, has more than one color, is itchy or bleeds.. A skin that has inadequate melanin is exposed to the risk of sunburn as well as harmful ultraviolet rays from the sun .Clinical analysis and biopsy tests are commonly used.
Existing Systems
Draw backs of Existing method
Proposed Method
Advantages
Block Diagram
Block Diagram Explanation
Colour Space Conversion
Color space conversion is happens when a Color Management Module (CMM) translates color from one device’s space to another. Conversion may require approximations in order to preserve the image’s most important color qualities.
The use of color in image processing is prompted with the useful resource of number one factors. First, shade is a powerful descriptor that frequently simplifies object identity and extraction from a scene. Second, human beings can determine plenty of shade solar shades and intensities, in comparison to approximately only two dozen sun sunglasses of gray. This 2d aspect is especially important in manual photo assessment
GLCM feature extraction
In statistical texture analysis, texture features are computed from the statistical distribution of observed combinations of intensities at specified positions relative to each other in the image. According to the number of intensity points (pixels) in each combination, statistics are classified into first-order, second order and higher-order statistics.
The Gray Level Co ocurrence Matrix (GLCM) method is a way of extracting second order statistical texture features. The approach has been used in a number of applications, Third and higher order textures consider the relationships among three or more pixels.
ABCD Parameters
The ABCDE Rule of skin cancer is an easy-to-remember system for determining whether a mole or growth may be cancerous. They describe the physical condition and/or progression of any skin abnormality that would suggest the development of a malignancy.
BPN Training and Classification
As is clear from the diagram, the working of BPN is in two phases. One phase sends the signal from the input layer to the output layer, and the other phase back propagates the error from the output layer to the input layer. For training, BPN will use binary sigmoid activation function Back-propagation is the essence of neural net training. It is the method of fine-tuning the weights of a neural net based on the error rate obtained in the previous epoch (i.e., iteration). Proper tuning of the weights allows you to reduce error rates and to make the model reliable by increasing its generalization
Requirement Specifications
Hardware Requirements
SOFTWARE REQUIREMENTS:
REFERENCES
[1]Adheena Santy and Adheena Santy,Segmentation Methods For Computer Aided Melanoma Detection,IEEE Conference,2015.
[2] Omar Abuzaghleh, Miad Faezipour and Buket D.Barkana ,A Comparison of Feature Sets for an utomated Skin Lesion Analysis System for Melanoma Early Detection and Prevention,IEEE journal,2015.
[3] M. Rademaker and A. Oakley,Digital monitoring by whole body photography and sequential digital dermoscopy detects thinner melanomas,IEEE journal,2010.
[4] Xiaojing Yuan, Zhenyu Yang, George Zouridakis, and Nizar Mullani >
[5] Abder-Rahman Ali, Micael S. Couceiro, and Aboul Ella Hassenian ,Melanoma Detection Using Fuzzy CMeans Clustering Coupled With Mathematical Morphology,IEEE Conference,2014
₹24,000.00 Exc Tax
dsPIC Development board is proposed to smooth the progress of developing and debugging of various designs encompassing Microcontrollers from Microchip. It’s designed as to facilitate (dsPIC30F 40PIN DIP) On-board Programmer for PIC Microcontroller through ISP on Universal Serial port. It integrates on board USART,LEDs, keypads, 3 ADC inputs and LCD Display to create a stand-alone versatile test platform. User can easily engage in development in this platform, or use it as reference to application development.
Shipping : 4 to 5 days from the date of purchase
Warranty : 3 Months
100 in stock
This project is design to control the speed of a single-phase induction motor by using Node MCU controller. The single-phase inverter converts dc voltage into ac voltage and the induction motor speed depends on the frequency of the inverter. The inverter card comes with an inbuilt full bridge rectifier and filter capacitor.
This project is proposed to control the speed of the BLDC motor using LUO converter. This Project Model build with LUO Convertor Techniques and the Pulses can be triggered using dsPIC Controller. The closed loop feedback system is design using the Hall Sensor.
This project describes the speed control of the Induction motor with dsPIC30F4011 controller and by comparing the sine and triangular wave. The dsPIC Controller generates the switching pulses for the inverter and the speed control is done by changing the frequency of reference sine and amplitude of sine. The motor speed is measured by using the proximity sensor, which is placed on the Induction Motor and parameters displayed on LCD.
This project describes the speed control of the BLDC motor with the dsPIC Controller, by using the Hall effect sensors of BLDC Motor. The dsPIC30F4011 controller generates the controlled switching pulses for the inverter. The speed control is done by changing the duty cycle of PWM from dsPIC30F4011. The motor speed is measured by using the proximity sensor placed on the BLDC Motor and displayed on LCD.
This project is based on cascaded inverter and quasi z source techniques. This five-level inverter is cascaded of the two inverter circuits and also included a quasi z source circuit in front of the inverter circuit. A quasi z source circuit is used to boost the dc voltage and the dc voltage is applied to the inverter and obviously inverter output voltage range is increased.
This project is to design? and Analyse the performance of five levels of Cascaded H-Bridge multilevel inverter topology with Arduino. The PWM pulse will be generated by using Arduino and Embedded C. The output voltage is the sum of the voltage that is generated by each bridge. The switching angles can be chosen in such a way that the total harmonic distortion is minimized.
This project is designed to control the multilevel inverter using a TMS320F2812 DSP controller. This high-speed DSP processor is used to generate PWM signals, which is applied to driver circuits and this driver circuit is used to isolate and amplify the pulses. This project’s main objective is to reduce switching losses and harmonics using DSP Processor.
This project is design to control the speed of a single-phase induction motor using Arduino. The single-phase inverter converts dc voltage into ac voltage. Single-phase induction motor speed depends on the frequency of the inverter.
This Project is design to control the speed of PMDC motor using dsPIC30F4011. The speed of the dc motor can be varied by changing the duty cycle of the PWM. The motor speed is monitored by using the proximity sensor placed in the motor and this can be done both in open loop and closed loop operations. In this concept, motor speed is controller manually in open and control automatically in closed loop with respect to the set speed.
This project is design to control the speed of the induction motor using FPGA Processor. By comparing the sine and triangular wave, FPGA generates the controlled switching pulses for the inverter. The speed control is done by changing the frequency of reference sine from Spartan6. The motor speed is measured by using the proximity sensor placed on the Induction Motor and displayed on LCD.
This project is used to control the speed of a three-phase induction motor using SEPIC converter. The sepic converter is used to boost the input voltage. The boosted dc voltage is applied to the three-phase inverter. This three-phase VSI operates at a 120-degree mode of operation. Three-phase induction motor speed depends on the frequency of the inverter.
This project is proposed to control and monitor the speed of the three-phase induction motor by using the Arduino and node MCU controller. The Arduino is used to produce the PWM signals and the motor speed is controlled by using the driver and three-phase inverter circuits. We can monitor and control the speed using IoT Techniques.
This project is mainly used to control the speed of the BLDC motor by varying the frequency. The BLDC motor has high reliability, high-efficiency high torque/inertia ratio, improved cooling, low radio frequency interference, and noise and requires practically no maintenance. The BLDC motor speed depends on the frequency of the three-phase inverter circuit.
This project is proposed to control the speed of the BLDC motor using zeta converter. DC voltage is applied to zeta converter, it boosts the input voltage and this voltage given to the three-phase inverter, which converts the dc voltage into three-phase ac voltage. The three-phase ac voltage is connected to the BLDC motor and with hall sensor, sensor output is feedback to the controller for closed loop operation.
This project is based on a cascaded H bridge inverter and various topologies. Among these Cascaded multilevel inverters have drawn tremendous interest in the power industry because it requires fewer components. As the number of levels increases the harmonic content of the output voltage waveform decreases. This Cascaded H-Bridge fifteen-level inverter with a minimum number of switches. As the level increases the synthesized output waveform has more steps that produce a staircase wave that approaches desired waveform. The problem of switching losses and power losses can be eliminated with a minimum number of switches.
This project proposed a new multilevel inverter with a reduced number of switches and the switching losses are also get reduced with reduced total harmonics distortion (THD).
This project is proposed to control the speed of the three-phase induction motor by using the Arduino. Motor speed is controlled by using the driver and three-phase inverter circuits. And also this project has reduced the harmonics and switching losses of the circuit. In this project have two switches are used to increment and decrement the speed of the Three Phase Induction Motor. The speed variation can be viewed in the tachometer.
The objective of the project is Message Classification in facebook learning group using Machine Learning to Data Analysis.
This Single Phase rectifier trainer kit integrated with Texas Instrument’s DSP help the user to understand complete hardware design of single phase rectifier system on both simulation and hardware. SCR’S were triggered by DSP enabling students to learn how to use DSP for power electronics applications.
This kit has an Texas Instruments C2000 PICCOLO LaunchPad TMS320F28027 with inbuilt XDS100 Emulator and provides reliable and graphical programming development environment system for power electronics application.
Low cost DSP development platform for Power Electronics Application from Texas Instruments.
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