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A reconstruction error based framework for multi label and multi-view learningieeepondy
A reconstruction error based framework for multi label and multi-view learning
+91-9994232214,8144199666, ieeeprojectchennai@gmail.com,
www.projectsieee.com, www.ieee-projects-chennai.com
IEEE PROJECTS 2015-2016
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Email:ieeeprojectchennai@gmail.com
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A reconstruction error based framework for multi label and multi-view learningieeepondy
A reconstruction error based framework for multi label and multi-view learning
+91-9994232214,8144199666, ieeeprojectchennai@gmail.com,
www.projectsieee.com, www.ieee-projects-chennai.com
IEEE PROJECTS 2015-2016
-----------------------------------
Contact:+91-9994232214,+91-8144199666
Email:ieeeprojectchennai@gmail.com
Support:
-------------
Projects Code
Documentation
PPT
Projects Video File
Projects Explanation
Teamviewer Support
Hi
we are student of Daffodil International University .
My teammate was Fatema Akter , Rashedul Islam And the respected teacher was Hasin rehana
Lecturer
Faculty of Science and Information Technology
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
This slide gives a rough idea about how machine learning works and the different types of algorithms associated with it
WEAKLY SUPERVISED FINE-GRAINED CATEGORIZATION WITH PART-BASED IMAGE REPRESENT...Nexgen Technology
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
An Ensemble Approach To Improve Homomorphic Encrypted Data Classification Per...IJCI JOURNAL
Homomorphic encryption (HE) permits users to perform computations on encrypted data
without first decrypting it. HE can be used for privacy-preserving outsourced computation and analysis,
allowing data to be encrypted and outsourced to commercial cloud environments for processing while
encrypted or sensitive data. HE enables new services by removing privacy barriers inhibiting data sharing
or increasing the security of existing services. A convolution neural network (CNN) can be homomorphically
evaluated using addition and multiplication by replacing the activation function, such as Rectified Linear
Units (ReLU), with a low polynomial degree. To achieve the same performance as the ReLU activation
function, we study the impact of applying the ensemble techniques to solve the accuracy problem. Our
experimental results empirically show that the ensemble approach can reduce bias, and variance, increasing
accuracy to achieve the same ReLU performance with parallel and sequential techniques. We demonstrate
the effectiveness and robustness of our method using three datasets: MNIST, FMNIST, and CIFAR-10.
An approach for improved students’ performance prediction using homogeneous ...IJECEIAES
Web-based learning technologies of educational institutions store a massive amount of interaction data which can be helpful to predict students’ performance through the aid of machine learning algorithms. With this, various researchers focused on studying ensemble learning methods as it is known to improve the predictive accuracy of traditional classification algorithms. This study proposed an approach for enhancing the performance prediction of different single classification algorithms by using them as base classifiers of homogeneous ensembles (bagging and boosting) and heterogeneous ensembles (voting and stacking). The model utilized various single classifiers such as multilayer perceptron or neural networks (NN), random forest (RF), naïve Bayes (NB), J48, JRip, OneR, logistic regression (LR), k-nearest neighbor (KNN), and support vector machine (SVM) to determine the base classifiers of the ensembles. In addition, the study made use of the University of California Irvine (UCI) open-access student dataset to predict students’ performance. The comparative analysis of the model’s accuracy showed that the best-performing single classifier’s accuracy increased further from 93.10% to 93.68% when used as a base classifier of a voting ensemble method. Moreover, results in this study showed that voting heterogeneous ensemble performed slightly better than bagging and boosting homogeneous ensemble methods.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
When deep learners change their mind learning dynamics for active learningDevansh16
Abstract:
Active learning aims to select samples to be annotated that yield the largest performance improvement for the learning algorithm. Many methods approach this problem by measuring the informativeness of samples and do this based on the certainty of the network predictions for samples. However, it is well-known that neural networks are overly confident about their prediction and are therefore an untrustworthy source to assess sample informativeness. In this paper, we propose a new informativeness-based active learning method. Our measure is derived from the learning dynamics of a neural network. More precisely we track the label assignment of the unlabeled data pool during the training of the algorithm. We capture the learning dynamics with a metric called label-dispersion, which is low when the network consistently assigns the same label to the sample during the training of the network and high when the assigned label changes frequently. We show that label-dispersion is a promising predictor of the uncertainty of the network, and show on two benchmark datasets that an active learning algorithm based on label-dispersion obtains excellent results.
Hi
we are student of Daffodil International University .
My teammate was Fatema Akter , Rashedul Islam And the respected teacher was Hasin rehana
Lecturer
Faculty of Science and Information Technology
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
This slide gives a rough idea about how machine learning works and the different types of algorithms associated with it
WEAKLY SUPERVISED FINE-GRAINED CATEGORIZATION WITH PART-BASED IMAGE REPRESENT...Nexgen Technology
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
An Ensemble Approach To Improve Homomorphic Encrypted Data Classification Per...IJCI JOURNAL
Homomorphic encryption (HE) permits users to perform computations on encrypted data
without first decrypting it. HE can be used for privacy-preserving outsourced computation and analysis,
allowing data to be encrypted and outsourced to commercial cloud environments for processing while
encrypted or sensitive data. HE enables new services by removing privacy barriers inhibiting data sharing
or increasing the security of existing services. A convolution neural network (CNN) can be homomorphically
evaluated using addition and multiplication by replacing the activation function, such as Rectified Linear
Units (ReLU), with a low polynomial degree. To achieve the same performance as the ReLU activation
function, we study the impact of applying the ensemble techniques to solve the accuracy problem. Our
experimental results empirically show that the ensemble approach can reduce bias, and variance, increasing
accuracy to achieve the same ReLU performance with parallel and sequential techniques. We demonstrate
the effectiveness and robustness of our method using three datasets: MNIST, FMNIST, and CIFAR-10.
An approach for improved students’ performance prediction using homogeneous ...IJECEIAES
Web-based learning technologies of educational institutions store a massive amount of interaction data which can be helpful to predict students’ performance through the aid of machine learning algorithms. With this, various researchers focused on studying ensemble learning methods as it is known to improve the predictive accuracy of traditional classification algorithms. This study proposed an approach for enhancing the performance prediction of different single classification algorithms by using them as base classifiers of homogeneous ensembles (bagging and boosting) and heterogeneous ensembles (voting and stacking). The model utilized various single classifiers such as multilayer perceptron or neural networks (NN), random forest (RF), naïve Bayes (NB), J48, JRip, OneR, logistic regression (LR), k-nearest neighbor (KNN), and support vector machine (SVM) to determine the base classifiers of the ensembles. In addition, the study made use of the University of California Irvine (UCI) open-access student dataset to predict students’ performance. The comparative analysis of the model’s accuracy showed that the best-performing single classifier’s accuracy increased further from 93.10% to 93.68% when used as a base classifier of a voting ensemble method. Moreover, results in this study showed that voting heterogeneous ensemble performed slightly better than bagging and boosting homogeneous ensemble methods.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
When deep learners change their mind learning dynamics for active learningDevansh16
Abstract:
Active learning aims to select samples to be annotated that yield the largest performance improvement for the learning algorithm. Many methods approach this problem by measuring the informativeness of samples and do this based on the certainty of the network predictions for samples. However, it is well-known that neural networks are overly confident about their prediction and are therefore an untrustworthy source to assess sample informativeness. In this paper, we propose a new informativeness-based active learning method. Our measure is derived from the learning dynamics of a neural network. More precisely we track the label assignment of the unlabeled data pool during the training of the algorithm. We capture the learning dynamics with a metric called label-dispersion, which is low when the network consistently assigns the same label to the sample during the training of the network and high when the assigned label changes frequently. We show that label-dispersion is a promising predictor of the uncertainty of the network, and show on two benchmark datasets that an active learning algorithm based on label-dispersion obtains excellent results.
Paper Explained: RandAugment: Practical automated data augmentation with a re...Devansh16
RandAugment: Practical automated data augmentation with a reduced search space is a paper that proposes a new Data Augmentation technique that outperforms all current techniques while being cheaper.
UNCERTAINTY ESTIMATION IN NEURAL NETWORKS THROUGH MULTI-TASK LEARNINGgerogepatton
The impressive predictive performance of deep learning techniques on a wide range of tasks has led to its
widespread use. Estimating the confidence of these predictions is paramount for improving the safety and
reliability of such systems. However, the uncertainty estimates provided by neural networks (NNs) tend to
be overconfident and unreasonable. Previous studies have found out that ensemble of NNs typically
produce good predictions and uncertainty estimates. Inspired by these, this paper presents a new
framework that can quantitatively estimate the uncertainties by leveraging the advances in multi-task
learning through slight modification to the existing training pipelines. This promising algorithm is
developed with an intention of deployment in real world problems which already boast a good predictive
performance by reusing those pretrained models. The idea is to capture the behavior of the trained NNs for
the base task by augmenting it with the uncertainty estimates from a supplementary network. A series of
experiments show that the proposed approach produces well calibrated uncertainty estimates with high
quality predictions.
UNCERTAINTY ESTIMATION IN NEURAL NETWORKS THROUGH MULTI-TASK LEARNINGijaia
The impressive predictive performance of deep learning techniques on a wide range of tasks has led to its
widespread use. Estimating the confidence of these predictions is paramount for improving the safety and
reliability of such systems. However, the uncertainty estimates provided by neural networks (NNs) tend to
be overconfident and unreasonable. Previous studies have found out that ensemble of NNs typically
produce good predictions and uncertainty estimates. Inspired by these, this paper presents a new
framework that can quantitatively estimate the uncertainties by leveraging the advances in multi-task
learning through slight modification to the existing training pipelines. This promising algorithm is
developed with an intention of deployment in real world problems which already boast a good predictive
performance by reusing those pretrained models. The idea is to capture the behavior of the trained NNs for
the base task by augmenting it with the uncertainty estimates from a supplementary network. A series of
experiments show that the proposed approach produces well calibrated uncertainty estimates with high
quality predictions.
Similar to A graph based consensus maximization approach for combining multiple supervised and unsupervised models (20)
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
The French Revolution Class 9 Study Material pdf free download
A graph based consensus maximization approach for combining multiple supervised and unsupervised models
1. A GRAPH-BASED CONSENSUS MAXIMIZATION APPROACH FOR COMBINING
MULTIPLE SUPERVISED AND UNSUPERVISED MODELS
ABSTRACT:
Ensemble learning has emerged as a powerful method for combining multiple models. Well-
known methods, such as bagging, boosting, and model averaging, have been shown to improve
accuracy and robustness over single models. However, due to the high costs of manual labeling,
it is hard to obtain sufficient and reliable labeled data for effective training. Meanwhile, lots of
unlabeled data exist in these sources, and we can readily obtain multiple unsupervised models.
Although unsupervised models do not directly generate a class label prediction for each object,
they provide useful constraints on the joint predictions for a set of related objects. Therefore,
incorporating these unsupervised models into the ensemble of supervised models can lead to
better prediction performance.
In this paper, we study ensemble learning with outputs from multiple supervised and
unsupervised models, a topic where little work has been done. We propose to consolidate a
classification solution by maximizing the consensus among both supervised predictions and
unsupervised constraints. We cast this ensemble task as an optimization problem on a bipartite
graph, where the objective function favors the smoothness of the predictions over the graph, but
penalizes the deviations from the initial labeling provided by the supervised models. We solve
this problem through iterative propagation of probability estimates among neighboring nodes and
prove the optimality of the solution. The proposed method can be interpreted as conducting a
constrained embedding in a transformed space, or a ranking on the graph. Experimental results
on different applications with heterogeneous data sources demonstrate the benefits of the
proposed method over existing alternatives.
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