This slides presents a work of classifiers in a cancelable behavioural biometric. We transformed the data using 5 different non-invertible transform functions. We achieve good results using kNN and SVM classifiers in two different cancelable data.
An Empirical Analysis of Ensemble Systems in Cancellable Behavioural Biometri...Marcelo Damasceno de Melo
This paper analyzes the performance of ensemble systems in cancellable behavioural biometrics using a touchscreen dataset. It finds that ensemble systems do not deteriorate equal error rate results when used with cancellable transformations like interpolation and double sum, compared to original data. Results were best with the bioconvolving transformation. Ensemble structures improved results for scrolling strokes compared to previous work, showing promise for practical use of cancellable biometrics with ensemble systems.
Improving the accuracy of fingerprinting system using multibiometric approachIJERA Editor
Biometric technology is a science that used to verify or identify the individual based on physical and/or
behavioral traits. Although biometric systems are considered more secure than other traditional methods such as
password, or key, they also have many limitations such as noisy image, or spoof attack. One of the solutions to
overcome these limitations, is by applying a multibiometric system. Multibiometric system has a significant
effect in improving the performance of both security and accuracy of the system. It also can alleviate the spoof
attacks and reduce the fail to enroll error. A multi-sample is one implementations of the multibiometric systems.
In this study, a new algorithm is suggested to provide a second chance for the genuine user who is rejected, to
compare his/her provided finger with the other samples of the same finger. Multisampling fingerprint is used to
implement this new algorithm. The algorithm is activated when the match score of the user is not equal to a
threshold but close to it, then the system provides another chance to compare the finger with another sample of
the same trait. Using multi-sample biometric system improved the performance of the system by reducing the
False Reject Rate (FRR). Applying the original matching algorithm on the presented database produced 3
genuine users, and 5 imposters for the same fingerprint. While after implementing the suggested condition, the
system performance is enhanced by producing 6 genuine users, and 2 imposters for the same fingerprint. This
work was built and executed depending on a previous Matlab code presented by Zhi Li Wu. Thresholds and
Receiver Operating Characteristic (ROC) curves computed before and after implementing the suggested
multibiometric algorithm. Both ROC curves compared. A final decision and recommendations are provided
depending on the results obtained from this project
This document summarizes two studies that apply automated facial expression recognition to analyze spontaneous human behavior. The first study uses a machine learning system called CERT to automatically differentiate posed (fake) expressions of pain from genuine expressions of pain. 26 subjects were recorded experiencing real pain, fake pain, and a baseline condition. The second study uses CERT to automatically detect driver drowsiness by analyzing facial expressions of drivers in videos. Both studies represent early applications of automated facial expression measurement to important research questions in behavioral science.
QPLC: A Novel Multimodal Biometric Score Fusion MethodCSCJournals
This document presents a novel method called QPLC for multimodal biometric score fusion. QPLC performs quantile power transformation (QPT) on scores from different biometric classifiers to better separate genuine and imposter score distributions. It then uses linear classifiers like logistic regression to fuse the transformed scores. The authors test QPLC on a public NIST dataset and show it outperforms other published score fusion methods, achieving higher true accept rates for a given low false accept rate. They also apply QPT to transform scores before training a linear support vector machine (SVM) classifier, finding it improves SVM performance over using original scores.
This document discusses multimodal biometric systems, which use multiple physical or behavioral traits to authenticate a person's identity. It provides background on unimodal biometric systems and their limitations. A multimodal system combines results from multiple traits to be more accurate and secure than a unimodal system. The document describes the main components of a biometric system (sensor, feature extraction, matching, decision) and discusses different levels at which traits can be fused in a multimodal system (sensor, feature, matching score, and decision levels). The goal is to highlight how multimodal biometrics provides higher identification performance than unimodal biometrics alone.
This document summarizes a research paper that developed a plant disease identification system using image processing techniques. The system focuses on identifying chlorosis, a disease affecting the medicinal plant Solanum trilobatum caused by lack of chlorophyll. Images of plant leaves are acquired and preprocessed, then the Otsu's method and Graythresh algorithm are used to automatically calculate a threshold value to convert the image to binary and segment diseased areas. The system provides results on whether disease is present or not in the leaf. It is a low-cost and simple method that can potentially help detect disease early and protect medicinal plants.
This document presents a study on using color texture feature analysis to detect surface defects on pomegranates. The researchers developed a method involving cropping images of pomegranates, converting them to HSI color space, generating SGDM matrices to extract 18 texture features for each image, and using support vector machines (SVM) classification to identify the best features for detecting infections. The optimal features identified were cluster shade, product moment, and mean intensity, achieving classification accuracy of 99.88%, 99.88%, and 99.81% respectively.
This document describes a plant disease identification system that uses image processing techniques. The system captures images of leaves using a digital camera, then performs feature extraction and classification using MATLAB. Features like color, texture, and intensity are extracted and used to classify leaves as healthy or diseased, and to identify specific diseases, using a support vector machine approach. The goal is to develop an automated system to help farmers and agronomists identify plant diseases faster and more accurately than current manual methods.
An Empirical Analysis of Ensemble Systems in Cancellable Behavioural Biometri...Marcelo Damasceno de Melo
This paper analyzes the performance of ensemble systems in cancellable behavioural biometrics using a touchscreen dataset. It finds that ensemble systems do not deteriorate equal error rate results when used with cancellable transformations like interpolation and double sum, compared to original data. Results were best with the bioconvolving transformation. Ensemble structures improved results for scrolling strokes compared to previous work, showing promise for practical use of cancellable biometrics with ensemble systems.
Improving the accuracy of fingerprinting system using multibiometric approachIJERA Editor
Biometric technology is a science that used to verify or identify the individual based on physical and/or
behavioral traits. Although biometric systems are considered more secure than other traditional methods such as
password, or key, they also have many limitations such as noisy image, or spoof attack. One of the solutions to
overcome these limitations, is by applying a multibiometric system. Multibiometric system has a significant
effect in improving the performance of both security and accuracy of the system. It also can alleviate the spoof
attacks and reduce the fail to enroll error. A multi-sample is one implementations of the multibiometric systems.
In this study, a new algorithm is suggested to provide a second chance for the genuine user who is rejected, to
compare his/her provided finger with the other samples of the same finger. Multisampling fingerprint is used to
implement this new algorithm. The algorithm is activated when the match score of the user is not equal to a
threshold but close to it, then the system provides another chance to compare the finger with another sample of
the same trait. Using multi-sample biometric system improved the performance of the system by reducing the
False Reject Rate (FRR). Applying the original matching algorithm on the presented database produced 3
genuine users, and 5 imposters for the same fingerprint. While after implementing the suggested condition, the
system performance is enhanced by producing 6 genuine users, and 2 imposters for the same fingerprint. This
work was built and executed depending on a previous Matlab code presented by Zhi Li Wu. Thresholds and
Receiver Operating Characteristic (ROC) curves computed before and after implementing the suggested
multibiometric algorithm. Both ROC curves compared. A final decision and recommendations are provided
depending on the results obtained from this project
This document summarizes two studies that apply automated facial expression recognition to analyze spontaneous human behavior. The first study uses a machine learning system called CERT to automatically differentiate posed (fake) expressions of pain from genuine expressions of pain. 26 subjects were recorded experiencing real pain, fake pain, and a baseline condition. The second study uses CERT to automatically detect driver drowsiness by analyzing facial expressions of drivers in videos. Both studies represent early applications of automated facial expression measurement to important research questions in behavioral science.
QPLC: A Novel Multimodal Biometric Score Fusion MethodCSCJournals
This document presents a novel method called QPLC for multimodal biometric score fusion. QPLC performs quantile power transformation (QPT) on scores from different biometric classifiers to better separate genuine and imposter score distributions. It then uses linear classifiers like logistic regression to fuse the transformed scores. The authors test QPLC on a public NIST dataset and show it outperforms other published score fusion methods, achieving higher true accept rates for a given low false accept rate. They also apply QPT to transform scores before training a linear support vector machine (SVM) classifier, finding it improves SVM performance over using original scores.
This document discusses multimodal biometric systems, which use multiple physical or behavioral traits to authenticate a person's identity. It provides background on unimodal biometric systems and their limitations. A multimodal system combines results from multiple traits to be more accurate and secure than a unimodal system. The document describes the main components of a biometric system (sensor, feature extraction, matching, decision) and discusses different levels at which traits can be fused in a multimodal system (sensor, feature, matching score, and decision levels). The goal is to highlight how multimodal biometrics provides higher identification performance than unimodal biometrics alone.
This document summarizes a research paper that developed a plant disease identification system using image processing techniques. The system focuses on identifying chlorosis, a disease affecting the medicinal plant Solanum trilobatum caused by lack of chlorophyll. Images of plant leaves are acquired and preprocessed, then the Otsu's method and Graythresh algorithm are used to automatically calculate a threshold value to convert the image to binary and segment diseased areas. The system provides results on whether disease is present or not in the leaf. It is a low-cost and simple method that can potentially help detect disease early and protect medicinal plants.
This document presents a study on using color texture feature analysis to detect surface defects on pomegranates. The researchers developed a method involving cropping images of pomegranates, converting them to HSI color space, generating SGDM matrices to extract 18 texture features for each image, and using support vector machines (SVM) classification to identify the best features for detecting infections. The optimal features identified were cluster shade, product moment, and mean intensity, achieving classification accuracy of 99.88%, 99.88%, and 99.81% respectively.
This document describes a plant disease identification system that uses image processing techniques. The system captures images of leaves using a digital camera, then performs feature extraction and classification using MATLAB. Features like color, texture, and intensity are extracted and used to classify leaves as healthy or diseased, and to identify specific diseases, using a support vector machine approach. The goal is to develop an automated system to help farmers and agronomists identify plant diseases faster and more accurately than current manual methods.
IRJET - Disease Detection in Plant using Machine LearningIRJET Journal
This document discusses using machine learning and image processing techniques to detect diseases in plants. The proposed system utilizes convolutional neural networks (CNNs) to classify plant images as either healthy or diseased based on features extracted from the images. The system architecture includes preprocessing the images, extracting color and texture features, running the features through a CNN model for classification training and testing, and outputting whether plants are normal or abnormal. The goal is to help farmers automatically detect plant diseases early on by analyzing images of plant leaves.
IRJET- Identify Quality Index of the Fruit Vegetable by Non Destructive or wi...IRJET Journal
This document presents a literature review and proposed system for identifying the quality of fruits and vegetables using non-destructive image processing techniques. It discusses using computer vision algorithms like filtering, segmentation, feature extraction and classification to analyze images of fruits and determine quality metrics like size, shape, color and defects. The proposed system would capture images, preprocess them, extract features and classify fruits as good or defective quality without damaging the fruits. This could help automate quality inspection and grading of agricultural produce.
Critical evaluation of frontal image based gender classification techniquesSalam Shah
The face describes the personality of humans and has adequate importance in the identification and verification process. The human face provides, information as age, gender, face expression and ethnicity. Research has been carried out in the area of face detection, identification, verification, and gender classification to correctly identify humans. The focus of this paper is on gender classification, for which various methods have been formulated based on the measurements of face features. An efficient technique of gender classification helps in accurate identification of a person as male or female and also enhances the performance of other applications like Computer-User Interface, Investigation, Monitoring, Business Profiling and Human Computer Interaction (HCI). In this paper, the most prominent gender classification techniques have been evaluated in terms of their strengths and limitations.
Smart Fruit Classification using Neural Networksijtsrd
The objective of this project is to develop a system that helps the food industry to classify fruits based on specific quality features. Our system will give best performance when used to sort some brand of fruits. The fruit industry plays a vital role in a countrys economic growth. They account for a fraction of the agricultural output produced by a country. It forms a part of the food processing industry. Fruits are a major source of energy, vitamins, minerals, fiber and other nutrients. They contribute to an essential part of our diet. Fruits come in varying shapes, color and sizes. Some of them are exported, thereby yielding profit to the industry. K. Sandhiya | M. Vidhya | M. Shivaranjani | S. Saranya"Smart Fruit Classification using Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd6986.pdf http://www.ijtsrd.com/engineering/computer-engineering/6986/smart-fruit-classification-using-neural-networks/k-sandhiya
An Exploration on the Identification of Plant Leaf Diseases using Image Proce...Tarun Kumar
From the ancient years, humans and other
social species directly & indirectly dependent on Plants.
Plants play an enormous role in human life by providing
them food for living, wood for houses and other resources
to live life.So, human should take care of plants and
agricultural crops. But apart from the human, various
natural factors are there that are responsible for
destroying the growth of plants like unavailability of
accurate plant resources, deficiency of sunlight, weather
conditions, lack of expert knowledge for the accurate use
of pesticides. The major factor responsible for this
destruction of plant growth is diseases. Early detection
and accurate identification of diseases can control the
spread of infection.In the earlier days, it was not easy to
identify the plant diseases but with the advancements of
digital technology, it becomes easy to identify plant disease
with image processing techniques. In this paper, an
exploration is made on the exiting approaches of plant leaf
disease detection using image processing approach. Also a
discussion is made on the major disease types like fungal,
bacterial and viral diseases. Different authors have
presented the different approaches for the identification of
leaf diseases for the different plant types.
IRJET- Leaf Disease Detecting using CNN TechniqueIRJET Journal
This document describes a proposed system for detecting leaf diseases using convolutional neural network (CNN) techniques. The system uses image acquisition, pre-processing including cropping, resizing and filtering, segmentation using k-means clustering, feature extraction of color, texture and shape features, and classification using CNN. The system is tested on images of mango, pomegranate, guava and sapota leaves to automatically identify diseases and recommend appropriate control methods, providing an improvement over manual identification methods.
This document describes a proposed system to detect plant diseases using machine learning and provide remedial measures. It will use a mobile app to classify plant leaf images using a TensorFlow Lite model trained with InceptionV3. The model will identify the disease and fetch details like treatment from a database to display to the user. This aims to make plant disease detection and treatment advice more easily accessible compared to existing computer-based systems.
LEAF DISEASE DETECTION USING IMAGE PROCESSING AND SUPPORT VECTOR MACHINE (SVM)Journal For Research
in the study on leaf disease detection can be a helpful aspect in keeping an eye on huge area of fields of crops, but it’s important to detect the disease as early as possible. This paper gives a method to detect the disease caused to the leaf calculating the RGB and HSV values. Primarily the image is blurred in order reduce noise. Then the image is converted from RGB to HSV form, after this color thresholding is done. After thresholding foreground or background detection is performed. Background detection leads to feature extractions of the leaf. Then k-means algorithm is applied which can help to clot the clusters. The following system is a software based solution for detecting the disease with which the leaf is infected. In order to detect the disease some steps are to be followed using image processing and support vector machine. Improving the quality and production of agricultural products detection of the leaf disease can be useful.
Comparative Analysis: Effective Information Retrieval Using Different Learnin...RSIS International
Information Retrieval is the activity of searching meaningful information from a collection of information resources such as Documents, relational databases and the World Wide Web. Information retrieval system mainly consists of two phases, storing indexed documents and retrieval of relevant result. Retrieving information effectively from huge data storage, it requires Machine Learning for computer systems. Machine learning has objective to instruct computers to use data or past experience to solve a given problem. Machine learning has number of applications, including classifier to be trained on email messages to learn in order to distinguish between spam and non-spam messages, systems that analyze past sales data to predict customer buying behavior, fraud detection etc. Machine learning can be applied as association analysis through supervised learning, unsupervised learning and Reinforcement Learning. The goal of these three learning is to provide an effective way of information retrieval from data warehouse to avoid problems such as ambiguity. This study will compare the effectiveness and impuissance of these learning approaches.
IRJET- Recent Trends and Insight Towards Automated Identification of Plant Sp...IRJET Journal
This document discusses recent trends in automated plant species identification. It begins by outlining the main steps in automated plant identification systems: image acquisition, pre-processing, feature extraction, classification, and identification. Artificial neural networks are commonly used for classification and can learn from examples to generalize to new cases. Convolutional neural networks, a type of deep learning, have also shown promise by automatically learning discriminative features from images. Leaves are most commonly used for identification due to their abundance and planar structure, but combining features from multiple plant organs may improve accuracy. Shape, veins, color, and texture are important leaf features while flower shape and color are also potentially discriminative.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Plant disease detection and classification using deep learning JAVAID AHMAD WANI
This document describes a project on plant disease detection and classification using deep learning. The objectives are to automatically detect plant diseases as early as symptoms appear on leaves in order to increase crop productivity. Deep learning techniques like convolutional neural networks (CNNs) are implemented using libraries like TensorFlow and Keras. Two CNN models, VGG16 and VGG19, are compared for classifying diseases in a dataset of 38 classes and 87k images of 14 crop species. The system achieved over 95% accuracy on validation. Future work involves developing a mobile app and integrating disease recommendations to help farmers.
This document outlines a proposed plant leaf disease detection system using image processing on Android mobile phones. The system aims to help farmers easily and cost-effectively detect plant diseases, identify severity levels, and receive treatment suggestions. It will use algorithms like blob detection and HSV color modeling to analyze leaf images and determine diseases. The Android app is intended to provide an affordable solution to identify a variety of disease types and inform farmers in their local language.
This document summarizes a research paper that proposes a feature level fusion based bimodal biometric authentication system using fingerprint and face recognition with transformation domain techniques. The system extracts fingerprint features using Dual Tree Complex Wavelet Transforms and extracts face features using Discrete Wavelet Transforms. It then concatenates the fingerprint and face features into a single feature vector. Euclidean distance is used to match test biometrics to those stored in a database. The proposed algorithm is shown to achieve better equal error rates and true positive identification rates compared to individual transformation domain techniques.
Implementation of features dynamic tracking filter to tracing pupilssipij
The objective of this paper is to show the implementation of an artificial vision filter capable of tracking the
pupils of a person in a video sequence. There are several algorithms that can achieve this objective, for this
case, features dynamic tracking selected, which is a method that traces patterns between each frame that
form a video scene, this type of processing offers the advantage of eliminating the problems of occlusion
patterns of interest. The implementation was tested on a base of videos of people with different physical
characteristics of the eyes. An additional goal is to obtain information of the eye movements that are
captured and pupil coordinates for each of these movements. These data could help some studies related to
eye health.
Identification of Disease in Leaves using Genetic Algorithmijtsrd
Plant disease is an impairment of normal state of a plant that interrupts or modifies its vital functions. Many leaf diseases are caused by pathogens. Agriculture is the mains try of the Indian economy. Perception of human eye is not so much stronger so as to observe minute variation in the infected part of leaf. In this paper, we are providing software solution to automatically detect and classify plant leaf diseases. In this we are using image processing techniques to classify diseases and quickly diagnosis can be carried out as per disease. This approach will enhance productivity of crops. It includes image processing techniques starting from image acquisition, preprocessing, testing, and training. K. Beulah Suganthy ""Identification of Disease in Leaves using Genetic Algorithm"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22901.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/22901/identification-of-disease-in-leaves-using-genetic-algorithm/k-beulah-suganthy
Stem calyx recognition of an apple using shape descriptorssipij
This paper presents a novel method to recognize stem - calyx of an apple using shape descriptors. The main
drawback of existing apple grading techniques is that stem - calyx part of an apple is treated as defects,
this leads to poor grading of apples. In order to overcome this drawback, we proposed an approach to
recognize stem-calyx and differentiated from true defects based on shape features. Our method comprises
of steps such as segmentation of apple using grow-cut method, candidate objects such as stem-calyx and
small defects are detected using multi-threshold segmentation. The shape features are extracted from
detected objects using Multifractal, Fourier and Radon descriptor and finally stem-calyx regions are
recognized and differentiated from true defects using SVM classifier. The proposed algorithm is evaluated
using experiments conducted on apple image dataset and results exhibit considerable improvement in
recognition of stem-calyx region compared to other techniques.
Plant Leaf Disease Analysis using Image Processing Technique with Modified SV...Tarun Kumar
In this computing era, image processing has
spread its wings in human life upto the extent that image
has become an integral part of their life. There are various
applications of image processing in the field of commerce,
engineering, graphic design, journalism, architecture and
historical research. In this research work, Image
processing is considered for the analysis of plant leaf
diseases. Plant leaf diseases can be detected based on the
disease symptoms. Here, dataset of disease affected leaves
is considered for experimentation. This dataset contains
the plant leaves suffered from the
AlternariaAlternata,Cercospora Leaf Spot, Anthracnose
andBacterial Blight along with some healthy leaf images.
For this analysis, an autonomous approach of modified
SVM-CS is introduces. Here, concept of cuckoo search is
considered to optimize the classification parameters. These
parameters further help to find more accurate solutions.
This autonomous approach also extracts the healthy
portion and disease affected leaf portion along with the
accuracy of results.
Intelligent multimodal identification system based on local feature fusion be...nooriasukmaningtyas
1. The document proposes an intelligent multimodal identification system based on fusing local features from iris and finger vein biometrics.
2. It uses preprocessing, feature extraction using SIFT, and template matching to identify individuals from their iris and finger vein patterns with high accuracy of 98% and low false acceptance and rejection rates.
3. The multimodal system is able to overcome challenges from single biometric systems by combining two independent biological traits to improve security and identification performance.
This document discusses feature extraction, classification, and prediction techniques applied to EEG data to discriminate between left and right hand movements. It first provides background on EEG signals and preprocessing. It then examines feature extraction in depth, evaluating various features like mean, standard deviation, and Hjorth parameters. Classification algorithms like LDA, KNN, and neural networks are also analyzed and compared. The best results were obtained by combining Hjorth features, achieving 74% accuracy. Future work to improve these techniques is also mentioned.
IRJET - Disease Detection in Plant using Machine LearningIRJET Journal
This document discusses using machine learning and image processing techniques to detect diseases in plants. The proposed system utilizes convolutional neural networks (CNNs) to classify plant images as either healthy or diseased based on features extracted from the images. The system architecture includes preprocessing the images, extracting color and texture features, running the features through a CNN model for classification training and testing, and outputting whether plants are normal or abnormal. The goal is to help farmers automatically detect plant diseases early on by analyzing images of plant leaves.
IRJET- Identify Quality Index of the Fruit Vegetable by Non Destructive or wi...IRJET Journal
This document presents a literature review and proposed system for identifying the quality of fruits and vegetables using non-destructive image processing techniques. It discusses using computer vision algorithms like filtering, segmentation, feature extraction and classification to analyze images of fruits and determine quality metrics like size, shape, color and defects. The proposed system would capture images, preprocess them, extract features and classify fruits as good or defective quality without damaging the fruits. This could help automate quality inspection and grading of agricultural produce.
Critical evaluation of frontal image based gender classification techniquesSalam Shah
The face describes the personality of humans and has adequate importance in the identification and verification process. The human face provides, information as age, gender, face expression and ethnicity. Research has been carried out in the area of face detection, identification, verification, and gender classification to correctly identify humans. The focus of this paper is on gender classification, for which various methods have been formulated based on the measurements of face features. An efficient technique of gender classification helps in accurate identification of a person as male or female and also enhances the performance of other applications like Computer-User Interface, Investigation, Monitoring, Business Profiling and Human Computer Interaction (HCI). In this paper, the most prominent gender classification techniques have been evaluated in terms of their strengths and limitations.
Smart Fruit Classification using Neural Networksijtsrd
The objective of this project is to develop a system that helps the food industry to classify fruits based on specific quality features. Our system will give best performance when used to sort some brand of fruits. The fruit industry plays a vital role in a countrys economic growth. They account for a fraction of the agricultural output produced by a country. It forms a part of the food processing industry. Fruits are a major source of energy, vitamins, minerals, fiber and other nutrients. They contribute to an essential part of our diet. Fruits come in varying shapes, color and sizes. Some of them are exported, thereby yielding profit to the industry. K. Sandhiya | M. Vidhya | M. Shivaranjani | S. Saranya"Smart Fruit Classification using Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd6986.pdf http://www.ijtsrd.com/engineering/computer-engineering/6986/smart-fruit-classification-using-neural-networks/k-sandhiya
An Exploration on the Identification of Plant Leaf Diseases using Image Proce...Tarun Kumar
From the ancient years, humans and other
social species directly & indirectly dependent on Plants.
Plants play an enormous role in human life by providing
them food for living, wood for houses and other resources
to live life.So, human should take care of plants and
agricultural crops. But apart from the human, various
natural factors are there that are responsible for
destroying the growth of plants like unavailability of
accurate plant resources, deficiency of sunlight, weather
conditions, lack of expert knowledge for the accurate use
of pesticides. The major factor responsible for this
destruction of plant growth is diseases. Early detection
and accurate identification of diseases can control the
spread of infection.In the earlier days, it was not easy to
identify the plant diseases but with the advancements of
digital technology, it becomes easy to identify plant disease
with image processing techniques. In this paper, an
exploration is made on the exiting approaches of plant leaf
disease detection using image processing approach. Also a
discussion is made on the major disease types like fungal,
bacterial and viral diseases. Different authors have
presented the different approaches for the identification of
leaf diseases for the different plant types.
IRJET- Leaf Disease Detecting using CNN TechniqueIRJET Journal
This document describes a proposed system for detecting leaf diseases using convolutional neural network (CNN) techniques. The system uses image acquisition, pre-processing including cropping, resizing and filtering, segmentation using k-means clustering, feature extraction of color, texture and shape features, and classification using CNN. The system is tested on images of mango, pomegranate, guava and sapota leaves to automatically identify diseases and recommend appropriate control methods, providing an improvement over manual identification methods.
This document describes a proposed system to detect plant diseases using machine learning and provide remedial measures. It will use a mobile app to classify plant leaf images using a TensorFlow Lite model trained with InceptionV3. The model will identify the disease and fetch details like treatment from a database to display to the user. This aims to make plant disease detection and treatment advice more easily accessible compared to existing computer-based systems.
LEAF DISEASE DETECTION USING IMAGE PROCESSING AND SUPPORT VECTOR MACHINE (SVM)Journal For Research
in the study on leaf disease detection can be a helpful aspect in keeping an eye on huge area of fields of crops, but it’s important to detect the disease as early as possible. This paper gives a method to detect the disease caused to the leaf calculating the RGB and HSV values. Primarily the image is blurred in order reduce noise. Then the image is converted from RGB to HSV form, after this color thresholding is done. After thresholding foreground or background detection is performed. Background detection leads to feature extractions of the leaf. Then k-means algorithm is applied which can help to clot the clusters. The following system is a software based solution for detecting the disease with which the leaf is infected. In order to detect the disease some steps are to be followed using image processing and support vector machine. Improving the quality and production of agricultural products detection of the leaf disease can be useful.
Comparative Analysis: Effective Information Retrieval Using Different Learnin...RSIS International
Information Retrieval is the activity of searching meaningful information from a collection of information resources such as Documents, relational databases and the World Wide Web. Information retrieval system mainly consists of two phases, storing indexed documents and retrieval of relevant result. Retrieving information effectively from huge data storage, it requires Machine Learning for computer systems. Machine learning has objective to instruct computers to use data or past experience to solve a given problem. Machine learning has number of applications, including classifier to be trained on email messages to learn in order to distinguish between spam and non-spam messages, systems that analyze past sales data to predict customer buying behavior, fraud detection etc. Machine learning can be applied as association analysis through supervised learning, unsupervised learning and Reinforcement Learning. The goal of these three learning is to provide an effective way of information retrieval from data warehouse to avoid problems such as ambiguity. This study will compare the effectiveness and impuissance of these learning approaches.
IRJET- Recent Trends and Insight Towards Automated Identification of Plant Sp...IRJET Journal
This document discusses recent trends in automated plant species identification. It begins by outlining the main steps in automated plant identification systems: image acquisition, pre-processing, feature extraction, classification, and identification. Artificial neural networks are commonly used for classification and can learn from examples to generalize to new cases. Convolutional neural networks, a type of deep learning, have also shown promise by automatically learning discriminative features from images. Leaves are most commonly used for identification due to their abundance and planar structure, but combining features from multiple plant organs may improve accuracy. Shape, veins, color, and texture are important leaf features while flower shape and color are also potentially discriminative.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Plant disease detection and classification using deep learning JAVAID AHMAD WANI
This document describes a project on plant disease detection and classification using deep learning. The objectives are to automatically detect plant diseases as early as symptoms appear on leaves in order to increase crop productivity. Deep learning techniques like convolutional neural networks (CNNs) are implemented using libraries like TensorFlow and Keras. Two CNN models, VGG16 and VGG19, are compared for classifying diseases in a dataset of 38 classes and 87k images of 14 crop species. The system achieved over 95% accuracy on validation. Future work involves developing a mobile app and integrating disease recommendations to help farmers.
This document outlines a proposed plant leaf disease detection system using image processing on Android mobile phones. The system aims to help farmers easily and cost-effectively detect plant diseases, identify severity levels, and receive treatment suggestions. It will use algorithms like blob detection and HSV color modeling to analyze leaf images and determine diseases. The Android app is intended to provide an affordable solution to identify a variety of disease types and inform farmers in their local language.
This document summarizes a research paper that proposes a feature level fusion based bimodal biometric authentication system using fingerprint and face recognition with transformation domain techniques. The system extracts fingerprint features using Dual Tree Complex Wavelet Transforms and extracts face features using Discrete Wavelet Transforms. It then concatenates the fingerprint and face features into a single feature vector. Euclidean distance is used to match test biometrics to those stored in a database. The proposed algorithm is shown to achieve better equal error rates and true positive identification rates compared to individual transformation domain techniques.
Implementation of features dynamic tracking filter to tracing pupilssipij
The objective of this paper is to show the implementation of an artificial vision filter capable of tracking the
pupils of a person in a video sequence. There are several algorithms that can achieve this objective, for this
case, features dynamic tracking selected, which is a method that traces patterns between each frame that
form a video scene, this type of processing offers the advantage of eliminating the problems of occlusion
patterns of interest. The implementation was tested on a base of videos of people with different physical
characteristics of the eyes. An additional goal is to obtain information of the eye movements that are
captured and pupil coordinates for each of these movements. These data could help some studies related to
eye health.
Identification of Disease in Leaves using Genetic Algorithmijtsrd
Plant disease is an impairment of normal state of a plant that interrupts or modifies its vital functions. Many leaf diseases are caused by pathogens. Agriculture is the mains try of the Indian economy. Perception of human eye is not so much stronger so as to observe minute variation in the infected part of leaf. In this paper, we are providing software solution to automatically detect and classify plant leaf diseases. In this we are using image processing techniques to classify diseases and quickly diagnosis can be carried out as per disease. This approach will enhance productivity of crops. It includes image processing techniques starting from image acquisition, preprocessing, testing, and training. K. Beulah Suganthy ""Identification of Disease in Leaves using Genetic Algorithm"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22901.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/22901/identification-of-disease-in-leaves-using-genetic-algorithm/k-beulah-suganthy
Stem calyx recognition of an apple using shape descriptorssipij
This paper presents a novel method to recognize stem - calyx of an apple using shape descriptors. The main
drawback of existing apple grading techniques is that stem - calyx part of an apple is treated as defects,
this leads to poor grading of apples. In order to overcome this drawback, we proposed an approach to
recognize stem-calyx and differentiated from true defects based on shape features. Our method comprises
of steps such as segmentation of apple using grow-cut method, candidate objects such as stem-calyx and
small defects are detected using multi-threshold segmentation. The shape features are extracted from
detected objects using Multifractal, Fourier and Radon descriptor and finally stem-calyx regions are
recognized and differentiated from true defects using SVM classifier. The proposed algorithm is evaluated
using experiments conducted on apple image dataset and results exhibit considerable improvement in
recognition of stem-calyx region compared to other techniques.
Plant Leaf Disease Analysis using Image Processing Technique with Modified SV...Tarun Kumar
In this computing era, image processing has
spread its wings in human life upto the extent that image
has become an integral part of their life. There are various
applications of image processing in the field of commerce,
engineering, graphic design, journalism, architecture and
historical research. In this research work, Image
processing is considered for the analysis of plant leaf
diseases. Plant leaf diseases can be detected based on the
disease symptoms. Here, dataset of disease affected leaves
is considered for experimentation. This dataset contains
the plant leaves suffered from the
AlternariaAlternata,Cercospora Leaf Spot, Anthracnose
andBacterial Blight along with some healthy leaf images.
For this analysis, an autonomous approach of modified
SVM-CS is introduces. Here, concept of cuckoo search is
considered to optimize the classification parameters. These
parameters further help to find more accurate solutions.
This autonomous approach also extracts the healthy
portion and disease affected leaf portion along with the
accuracy of results.
Intelligent multimodal identification system based on local feature fusion be...nooriasukmaningtyas
1. The document proposes an intelligent multimodal identification system based on fusing local features from iris and finger vein biometrics.
2. It uses preprocessing, feature extraction using SIFT, and template matching to identify individuals from their iris and finger vein patterns with high accuracy of 98% and low false acceptance and rejection rates.
3. The multimodal system is able to overcome challenges from single biometric systems by combining two independent biological traits to improve security and identification performance.
This document discusses feature extraction, classification, and prediction techniques applied to EEG data to discriminate between left and right hand movements. It first provides background on EEG signals and preprocessing. It then examines feature extraction in depth, evaluating various features like mean, standard deviation, and Hjorth parameters. Classification algorithms like LDA, KNN, and neural networks are also analyzed and compared. The best results were obtained by combining Hjorth features, achieving 74% accuracy. Future work to improve these techniques is also mentioned.
Face recognition is a widely used biometric approach. Face recognition technology has developed rapidly
in recent years and it is more direct, user friendly and convenient compared to other methods. But face
recognition systems are vulnerable to spoof attacks made by non-real faces. It is an easy way to spoof face
recognition systems by facial pictures such as portrait photographs. A secure system needs Liveness
detection in order to guard against such spoofing. In this work, face liveness detection approaches are
categorized based on the various types techniques used for liveness detection. This categorization helps
understanding different spoof attacks scenarios and their relation to the developed solutions. A review of
the latest works regarding face liveness detection works is presented. The main aim is to provide a simple
path for the future development of novel and more secured face liveness detection approach.
This document summarizes a research paper on improving the performance of multimodal biometrics using cryptosystems. The paper proposes fusing iris and fingerprint biometrics at the feature level before applying encryption for enhanced security. It discusses extracting features from iris and fingerprint images, fusing the features, and then encrypting the fused template. This approach aims to improve accuracy over unimodal biometrics while enhancing security through encryption. The paper reviews related work on multimodal biometrics and cryptosystems, and presents results showing the proposed approach effectively extracts features and fuses the biometrics for identification.
Performance Enhancement Of Multimodal Biometrics Using CryptosystemIJERA Editor
Multimodal biometrics means the unification of two or more uni modal biometrics so as to make the system more reliable and secure. Such systems promise better security. This study is a blend of iris and fingerprint recognition technique and their fusion at feature level. Our work comprises of two main sections: feature extraction of both modalities and fusing them before matching and finally application of an encryption technique to enhance the security of the fused template.
Overview of Machine Learning and Deep Learning Methods in Brain Computer Inte...IJCSES Journal
Research under the field of Brain Computer Interfaces is adapting various Machine Learning and Deep
Learning techniques in recent times. With the advent of modern BCI, the data generated by various devices
is now capable of detecting brain signals more accurately. This paper gives an overview of all the steps
involved in the process of applying Machine Learning as well as Deep Learning methods from Data
Acquisition to application of algorithms. It aims to study techniques currently employed to extract data,
features from brain data, different algorithms employed to draw insights from the extracted features, and
how it can be used in various BCI applications. By this study, I aim to put forward current Machine
Learning and Deep Learning Trends in the field of BCI.
The document discusses Tom Brimeyer's Hypothyroidism Revolution program, which is a comprehensive guide for reversing hypothyroidism naturally and permanently in three phases. The first phase focuses on eliminating food sensitivities and toxins. The second phase introduces a thyroid-supporting diet. The third phase incorporates a healthy lifestyle including special exercises. The program contains over 160 pages explaining the three phases in detail. It aims to help sufferers of hypothyroidism achieve optimal health through natural means.
This document presents a facial expression recognition system that identifies and classifies seven basic expressions: happy, surprise, fear, disgust, sad, anger, and a neutral state. The system consists of four main parts: image acquisition, pre-processing, feature extraction, and classification. It was developed using both OpenCV and a web-based JavaScript approach. The system was tested on both real-time and pre-recorded video streams and can identify emotions in images and video input from a webcam in real-time. Evaluation showed the JavaScript implementation using a generalized dataset provided more accurate real-time predictions compared to the OpenCV approach.
Depression Detection Using Various Machine Learning ClassifiersIRJET Journal
This document describes a study that uses machine learning classifiers to detect depression using data from Twitter posts. Several classifiers are trained and tested on a dataset of 20,000 tweets from various user profiles. Features like sentiment, word frequency, and user account data are extracted from the tweets. Various classifiers like Extra Tree Classifier, Logistic Regression, and Naive Bayes are compared for their ability to accurately detect depression. The Extra Tree Classifier is found to have the best performance with 94% accuracy and 97.29% precision.
LIFE EXPECTANCY PREDICTION FOR POST THORACIC SURGERYIRJET Journal
This document discusses using machine learning algorithms to predict life expectancy after thoracic surgery. Researchers used attribute ranking and selection methods to identify the most important attributes from a dataset of patient health records. They evaluated algorithms like logistic regression and random forest on the reduced dataset. Logistic regression achieved the highest prediction accuracy of 85%. The goal was to more accurately predict mortality risk based on a patient's underlying health issues and attributes related to lung cancer.
DETECTING PSYCHOLOGICAL INSTABILITY USING MACHINE LEARNINGIRJET Journal
This document discusses detecting psychological instability using machine learning algorithms. It proposes using various machine learning models like logistic regression, decision trees, KNN, SVM, and XGBoost to classify whether an individual exhibits signs of a mental disorder based on their behaviors and thoughts. The models will be trained on datasets containing examples of symptoms and tested using metrics like accuracy, precision, recall and F1-score. Previously most research used methods like questionnaires which have validity issues, or neural networks which can overfit. The proposed system applies a narrative review methodology to analyze literature and identify an appropriate machine learning approach to help diagnose mental illness.
Sustained attention in a monitoring task: Towards a neuroadaptative enterpris...Pierre-Majorique Léger
The document discusses developing a brain-computer interface (BCI) using electroencephalography (EEG) to measure sustained attention and modulate performance in a complex monitoring task. It involves:
1) Measuring a user's sustained attention using EEG signals during a long-duration monitoring simulation task.
2) Developing a novel "dynamic adaptive thresholds" approach to classify sustained attention levels in real-time.
3) Integrating this classification with a feedback controller to provide countermeasures that aim to enhance performance without disrupting the task.
The goal is to create a first neuroadaptive enterprise system interface using a design science methodology. An experiment was conducted to evaluate the BCI's effects on sustained
IRJET - Real Time Facial Analysis using Tensorflowand OpenCVIRJET Journal
This document presents a real-time facial analysis system using TensorFlow and OpenCV. The system can detect facial expressions, age, and gender from images and video in real-time. It uses deep learning models trained on facial datasets to analyze faces. The system is designed for applications like security, attendance tracking, and finding lost children. It works by extracting facial features from images, applying preprocessing techniques, classifying faces, and making predictions about attributes. The document discusses the methodology, existing techniques like PCA and HMM, the proposed system architecture, sample code, and conclusions.
Ijaems apr-2016-1 Multibiometric Authentication System Processed by the Use o...INFOGAIN PUBLICATION
The present day authentication system is mostly uni-model i.e having only single authentication method which can be either finger print, iris , palm veins ,etc. Thus these models have to contend with a variety of problems such as absurd or unusual data, non-versatility; un authorized attempts, and huge amount of error rates. Some of these limitations can be reduced or stopped by the use of multimodal biometric systems that integrate the evidence presented by several sources of information. This paper converses a multi biometric based authentication system based on Fusion algorithm using a key. Our work mainly focuses on the fusion algorithm, i.e fusion of finger and palm print out of which the greatest features from the above two traits are taken into account. With minimum possible features the fusion of the both the traits is carried out. Then some key is generated for multi biometric authentication. By processing the test image of a person, the identity of the person is displayed with his/her own image. By the fusion algorithm, it is found that it has less computation time compared to the existing algorithms. By matching results, we validate and authenticate the particular individual.
ORGANIC PRODUCT DISEASE DETECTION USING CNNIRJET Journal
This document discusses using convolutional neural networks to detect diseases in organic products. The researchers classify fruits and their diseases using a mixed deep neural network and contour feature-based technique. They train their model on a dataset of 6509 rice leaf photos and test on 2000 images, achieving 99.6% accuracy. Their proposed system uses pre-trained ResNet50 models to extract deep features and classify plant diseases, offering advantages of lower cost, scalability, and time savings compared to manual inspection methods.
Human activity detection based on edge point movements and spatio temporal fe...IAEME Publication
This document summarizes a research paper on human activity detection using edge point movements and spatiotemporal features from video recorded in indoor environments. The proposed method first extracts foreground objects from video frames using background subtraction. It then analyzes object shapes and extracts edge points using edge detection. Edge points across frames are stored in a database and compared to detect changes indicating activities. Activities are recognized by matching to stored patterns, or adding new patterns if unknown. In addition to movements, spatiotemporal features of location and time are considered to better detect activities as normal or abnormal. The goal is to develop a simple and accurate system for activity recognition to assist elderly or disabled people living independently.
Feature selection using modified particle swarm optimisation for face recogni...eSAT Journals
Abstract
One of the major influential factors which affects the accuracy of classification rate is the selection of right features. Not all features have vital role in classification. Many of the features in the dataset may be redundant and irrelevant, which increase the computational cost and may reduce classification rate. In this paper, we used DCT(Discrete cosine transform) coefficients as features for face recognition application. The coefficients are optimally selected based on a modified PSO algorithm. In this, the choice of coefficients is done by incorporating the average of the mean normalized standard deviations of various classes and giving more weightage to the lower indexed DCT coefficients. The algorithm is tested on ORL database. A recognition rate of 97% is obtained. Average number of features selected is about 40 percent for a 10 × 10 input. The modified PSO took about 50 iterations for convergence. These performance figures are found to be better than some of the work reported in literature.
Keywords: Particle swarm optimization, Discrete cosine transform, feature extraction, feature selection, face recognition, classification rate.
Face Liveness Detection for Biometric Antispoofing Applications using Color T...rahulmonikasharma
Face recognition is a widely used biometric approach. Face recognition technology has developed rapidly in recent years and it is more direct, user friendly and convenient compared to other methods. But face recognition systems are vulnerable to spoof attacks made by non-real faces. It is an easy way to spoof face recognition systems by facial pictures such as portrait photographs. A secure system needs Liveness detection in order to guard against such spoofing. In this work, face liveness detection approaches are categorized based on the various types techniques used for liveness detection. This categorization helps understanding different spoof attacks scenarios and their relation to the developed solutions. A review of the latest works regarding face liveness detection works is presented. The main aim is to provide a simple path for the future development of novel and more secured face liveness detection approach.
This document proposes using a cuckoo search algorithm to optimize the process of fingerprint matching for biometric identification. It begins by introducing biometric recognition and some of its challenges with large and complex datasets. It then provides background on cuckoo search optimization and describes how it can be applied to optimize fingerprint matching. Specifically, it presents an algorithm that extracts sub-matrices of increasing dimension from a fingerprint image matrix and uses cuckoo search to match fingerprints by comparing the sub-matrices until an accurate match is found. The document simulates this algorithm and outlines the results, demonstrating how cuckoo search optimization may help address limitations of traditional techniques for complex biometric analysis.
Motion Control of Mobile Robots using Fuzzy Controllerijtsrd
In this study, a motion control based on fuzzy logic is designed so that mobile robots can make the turns they make when moving in an unknown environment more flexibly and smoothly. Fuzzy logic control is suitable for controlling mobile robots because the results can be obtained under uncertainty. Fuzzy logic control is implemented through a set of rules created using expert knowledge. The fuzzy rules created in this paper are designed to allow mobile robots to escape from obstacles, to avoid contact with walls, and to make soft turns without harming their structure. According to the obtained simulation results, the mobile robot has been shown to have successful results in fuzzy logic based motion control. Halil ‡etin | Akif Durdu "Motion Control of Mobile Robots using Fuzzy Controller" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29626.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/29626/motion-control-of-mobile-robots-using-fuzzy-controller/halil-%C3%A7etin
Similar to An Empirical Analysis of Cancellable Transformations in a Behavioural Biometric Modality (20)
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
An Empirical Analysis of Cancellable Transformations in a Behavioural Biometric Modality
1. An Empirical Analysis of Cancellable Transformations
in a Behavioural Biometric Modality
Marcelo Damasceno1;2 A.M.P. Canuto2
1Federal Institute of Education, Science and Technology of Rio Grande do Norte - São Gonçalo do
Amarante
2Federal University of Rio Grande do Norte
12/05/2013
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural B1i2o/m05e/2tr0ic13Modal1it/y35
2. About
This paper analyzes the performance of classification algorithms in the
context of cancellable behavioural biometrics, more specifically a
touch-screen dataset.
The main aim of this work is to analyse the gain that the use of
cancellable transformations can bring with respect to the behavioural
biometric context.
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural B1i2o/m05e/2tr0ic13Modal2it/y35
3. Outline
1 Introduction
2 Cancellable Transformations
3 TouchAnalytics
4 Experimental Analysis
5 Results
6 Conclusion and Further Work
7 References
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural B1i2o/m05e/2tr0ic13Modal3it/y35
4. Introduction
Outline
1 Introduction
2 Cancellable Transformations
3 TouchAnalytics
4 Experimental Analysis
5 Results
6 Conclusion and Further Work
7 References
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural B1i2o/m05e/2tr0ic13Modal4it/y35
5. Introduction
User Verification
Currently most computer systems use individual username and password
to authenticate their users [1];
Username-password method brings some problems as the use of same
username and password for different services on the Internet and the
stress to remember secure, long and complex passwords;
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural B1i2o/m05e/2tr0ic13Modal5it/y35
6. Introduction
Biometrics
Biometrics can be considered as the science of establishing the identity
of a person using his/her anatomical and/or behavioural traits.
Biometric traits have a number of desirable properties, such as reliability,
convenience, universality, and so forth.
Because of these characteristics, biometrics has been increasingly
developed over the last years.
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural B1i2o/m05e/2tr0ic13Modal6it/y35
7. Introduction
Behavioural Biometrics
Unlike physical biometrics, behavioural biometrics are related to user be-haviour/
actions [2].
These biometrics use behavioural patterns, such as gait, typing or the
way in which a user uses a computer system.
The behavioural biometrics is non-intrusive, i.e, often information
collection is not perceived by users.
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural B1i2o/m05e/2tr0ic13Modal7it/y35
8. Introduction
Biometrics Problems
The biometric is permanently associated with a user and cannot be
revoked or cancelled if compromised [3].
If a biometric identifier is compromised, it is lost forever and possibly the
same happens for every application where the biometric is used.
The use of cancellable biometrics is being increasingly adopted to
address such security issues.
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural B1i2o/m05e/2tr0ic13Modal8it/y35
9. Introduction
Cancellable Biometrics
This approach uses transformed or intentionally-distorted biometric data
instead of original biometric data for authentication [4, 5].
There is a risk that using such transformed data will decrease the
performance of the biometric-based system.
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural B1i2o/m05e/2tr0ic13Modal9it/y35
10. Cancellable Transformations
Outline
1 Introduction
2 Cancellable Transformations
3 TouchAnalytics
4 Experimental Analysis
5 Results
6 Conclusion and Further Work
7 References
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda1l0it/y35
11. Cancellable Transformations
Cancellable Transformation
The non-invertible transformation functions can transform the biometric
data in a way that it is computationally impossible to get the original form;
The distorted data brings some undesired consequences as high
variance, what makes more difficult the users identification;
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda1l1it/y35
12. Cancellable Transformations
Transformation Functions
1 Interpolation: Based on polynomial interpolations;
2 BioHashing: Characterized by transforming the original biometric into a
non-invertible binary sequence;
3 BioConvolving: The transformed functions are created through linear
combinations of sub-parts of the original biometric template;
4 DoubleSum: Consists of summing the attributes of the original biometric
model with two other attributes of the same sample;
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda1l2it/y35
13. TouchAnalytics
Outline
1 Introduction
2 Cancellable Transformations
3 TouchAnalytics
4 Experimental Analysis
5 Results
6 Conclusion and Further Work
7 References
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda1l3it/y35
14. TouchAnalytics
TouchAnalytics
The behavioural biometric modality used in this work is a touch screen
data, which represents a combination of strokes collected from
smartphones.
TouchAnalytics, was collected by Frank et al. [2]. They inform how the
data was collected, processed and some initial results
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda1l4it/y35
15. TouchAnalytics
TouchAnalytics
This dataset is composed of 30 attributes and all the attributes are
derived from the strokes obtained from 41 users.
Strokes are composed of horizontal and scrolling (vertical) movements.
The dataset was binarized because we use a verification process. It was
created a different dataset for each user.
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda1l5it/y35
16. TouchAnalytics
TouchAnalytics-Pre-Processing
As a result of the binarization transform, we have a huge number of
negative examples and few positives examples, featuring an imbalanced
dataset.
This problem was resolved using a lab-made tool that takes into
consideration the number of negative classes and the number of positive
examples.
T =
Np
Nnc
is the number of negatives instances that will be randomly selected in
each Nnc negative class. Where Np is the number of positive instances.
Thus, the number of negatives instances will be Nnc T .
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda1l6it/y35
17. TouchAnalytics
TouchAnalytics Scenarios
1 Inter Session: The goal is to authenticate users across multiple sessions
performed in the same day.
2 Inter Week: The goal is to authenticate users after in two different weeks
(the period of time between these two sessions is one week).
3 Intra Session: All the user data was used in the process, time
independently. In this scenario, we used a 10 fold cross-validation
process.
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda1l7it/y35
18. TouchAnalytics
TouchAnalytics Results
According to [6], the mean EER:
Intra Session are 0%: Within one session, most users do not
considerably change their touch behaviour;
Inter Session: 2% to 3%
Inter Week: 0% to 4%
This result indicates the behavioural biometrics (touch data) has good
perspectives in practical use.
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda1l8it/y35
19. Experimental Analysis
Outline
1 Introduction
2 Cancellable Transformations
3 TouchAnalytics
4 Experimental Analysis
5 Results
6 Conclusion and Further Work
7 References
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda1l9it/y35
20. Experimental Analysis
Methodology I
The experiments will follow the same methodology applied by Frank et al. [6].
1 The raw dataset was downloaded
from: www.mariofrank.net/touchalytics.
2 The Inter Session dataset has the data recorded on 3 sessions at the same
day.
The Inter Week dataset consists of data record in two different weeks.
The Intra Session has data about all sessions, independent of time. The
classifiers were trained/tested using a 10-fold cross validation.
3 Each generated dataset was divided into scrolling and horizontal strokes
samples.
4 A binarized dataset was created for each user, aiming to be used in a
verification process.
5 The cancellable transformation functions (Interpolation, BioHashing,
BioConvolving, DoubleSum) were applied to each dataset to generate the
cancellable datasets;
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda2l0it/y35
21. Experimental Analysis
Methodology II
6 After all these steps, the k-NN classifier using k=5 (5 was a chosen by
empiric tests) and SVM were used.
7 The mean EER obtained from each generated cancellable user dataset
was calculated.
8 The Mann-Whitney statistical test was applied to compare the results
obtained in the different cancellable datasets against original dataset
results. For this test, the confidence level is 95%(a = 0:05).
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda2l1it/y35
22. Results
Outline
1 Introduction
2 Cancellable Transformations
3 TouchAnalytics
4 Experimental Analysis
5 Results
6 Conclusion and Further Work
7 References
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda2l2it/y35
23. Results
Results and Discussion - Scrooling
Table: Mean Equal Error Rate - Scrooling Strokes
Session Orig. Inter. BioH. BioC. DS
k-NN
IS 1.36% 42.26% 31.44% 2.57% 9.61%
IW 1.14% 38.99% 32.48% 3.23% 9.72%
ITS 1.36% 9.43% 33% 3.60% 9.08%
SVM
IS 9.04% 41.86% 32.31% 1.85% 12.48%
IW 9.04% 39.41% 29.84% 3.11% 12.54%
ITS 9.04% 11.91% 29.08% 3.18% 11.80%
Shaded cells are statistically similar.
Bold values mean that the cancellable result was statistically better.
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda2l3it/y35
24. Results
Results and Discussion - Horizontal Strokes
Table: Equal Error Rate - Horizontal Strokes
Session Orig. Inter. BioH. BioC. DS
k-NN
IS 1.99% 42.26% 33.58% 0.258% 10.08%
IW 2.07% 49.70% 35.19% 0.253% 9.47%
ITS 1.99% 11.12% 34.10% 0.22% 9.87%
SVM
IS 17.43% 41.86% 38.82% 0.64% 21.56%
IW 17.43% 41.86% 41.02% 0.44% 22.45%
ITS 17.43% 24.77% 41.43% 0.52% 22.26%
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda2l4it/y35
25. Results
Discussion
From Tables 1 and 2, it can be concluded the BioConvolving and
DoubleSum have similar performance, when compared with the original
data.
BioConvolving dataset has four statistically similar results and six
statistically better results, in relation to the original dataset.
Double Sum results have five statistically similar results, out of 9 possible
cases.
The BioConvolving results in the horizontal strokes, Table 2. The use of
this transformation function brings statistically similar results using k-NN
and better statistically results using SVM.
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda2l5it/y35
26. Results
Results - Inter Session BoxPlots
(a) Scrooling Inter Session BoxPlots (b) Horizontal Inter Session BoxPlots
Figure: Inter Session BoxPlots
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda2l6it/y35
27. Results
Results - Intra Session BoxPlots
(a) Scrooling Intra Session BoxPlots (b) Horizontal Intra Session BoxPlots
Figure: Intra Session BoxPlots
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda2l7it/y35
28. Results
BoxPlot Discussion
The Original boxes has more outliers (points in plot) than BioConvolving
boxes;
The BioHashing boxes show that the results are very disperse, i.e, the
whiskeys are too long;
SVM boxes have longer whiskey than the k-NN boxes;
The Interpolation and BioHashing functions have the worst results.
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda2l8it/y35
29. Conclusion and Further Work
Outline
1 Introduction
2 Cancellable Transformations
3 TouchAnalytics
4 Experimental Analysis
5 Results
6 Conclusion and Further Work
7 References
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda2l9it/y35
30. Conclusion and Further Work
Conclusion I
In this work, we performed an analysis of two well- known classifier using
cancellable behavioural biometrics.
Four cancellable functions (Interpo- lation, BioHashing, BioConvolving
and Double Sum) were applied in this dataset to demonstrate the
importance and perspectives of cancellable behavioural biometrics.
The k-NN and SVM classifiers have interesting results in BioConvolving
and Double Sum datasets. The mean ERR was between 0:22% and
3:60% in BioConvolving datasets and the mean Double Sum EER was
between 9:08% and 22:45%.
The Interpolation and BioHashing functions needs more refinements to
minimize the Equal Error Rate as parameter optmization, ensemble
methods and multimodal biometrics processing.
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda3l0it/y35
31. Conclusion and Further Work
Conclusion II
As a future work, in order to improve the results we will use more
classifiers as MultiLayer Percep- trons, optimize cancelable function
parameters or even use ensemble methods.
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda3l1it/y35
32. References
Outline
1 Introduction
2 Cancellable Transformations
3 TouchAnalytics
4 Experimental Analysis
5 Results
6 Conclusion and Further Work
7 References
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda3l2it/y35
33. References
References I
W. Jackson, “Antisec hackers claim theft of military e-mails from booz
allen,” Internet, Julho 2011, acessado em Novembro de 2011. [Online].
Available: http://gcn.com/articles/2011/07/11/
antisec-booz-allen-hack-military-emails.aspx
K. Revett, Behavioral Biometrics: a Remote Access Approach. John
Wiley Sons, Ltd, 2008.
A. K. Jain, K. Nandakumar, and A. Nagar, “Biometric template security,” in
EURASIP Journal On Advances in Signal Processing, 2008.
C. Lee and J. Kim, “Cancelable fingerprint templates using
minutiae-based bit-strings,” Journal of Network and Computer
Applications, vol. 33, no. 3, pp. 236 – 246, 2010.
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda3l3it/y35
34. References
References II
A. Nagar, K. Nandakumar, and A. K. Jain, “A hybrid biometric
cryptosystem for securing fingerprint minutiae templates,” Pattern Recogn.
Lett., vol. 31, pp. 733–741, June 2010.
M. Frank, R. Biedert, E. Ma, I. Martinovic, and D. Song, “Touchalytics: On
the Applicability of Touchscreen Input as a Behavioral Biometric for
Continuous Authentication,” in IEEE Transactions on Information
Forensics and Security, vol. 8, no. 1, 2013, pp. 136–148. [Online].
Available: http://www.mariofrank.net/touchalytics/
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda3l4it/y35
35. References
Questions???
Marcelo Damasceno (IFRN) An Empirical Analysis of Cancellable Transformations in a Behavioural1B2i/o0m5/2e0tr1ic3Moda3l5it/y35