now a day’s multimedia databases are growing rapidly on large scale. For the effective management and exploration of large amount of music data the technology of singer identification is developed. With the help of this technology songs performed by particular singer can be clustered automatically. To improve the Performance of singer identification the technologies are emerged that can separate the singing voice from music accompaniment. One of the methods used for separating the singing voice from music accompaniment is non-negative matrix partial co factorization. This paper studies the different techniques for separation of singing voice from music accompaniment.
Text independent speaker identification system using average pitch and forman...ijitjournal
The aim of this paper is to design a closed-set text-independent Speaker Identification system using average
pitch and speech features from formant analysis. The speech features represented by the speech signal are
potentially characterized by formant analysis (Power Spectral Density). In this paper we have designed two
methods: one for average pitch estimation based on Autocorrelation and other for formant analysis. The
average pitches of speech signals are calculated and employed with formant analysis. From the performance
comparison of the proposed method with some of the existing methods, it is evident that the designed
speaker identification system with the proposed method is superior to others.
Hybrid ga svm for efficient feature selection in e-mail classificationAlexander Decker
1. The document discusses using a hybrid genetic algorithm-support vector machine (GA-SVM) approach for feature selection in email classification to improve SVM performance.
2. SVM has been shown to be inefficient and consume a lot of computational resources when classifying large email datasets with many features.
3. The hybrid GA-SVM approach uses a genetic algorithm to optimize feature selection for SVM in order to improve classification accuracy and reduce computation time for email spam detection.
11.hybrid ga svm for efficient feature selection in e-mail classificationAlexander Decker
The document summarizes a study that develops a hybrid genetic algorithm-support vector machine (GA-SVM) technique for feature selection in email classification. The technique uses a genetic algorithm to optimize the feature selection and parameters of an SVM classifier. The goal is to improve the SVM's classification accuracy and reduce computation time for large email datasets. The study tests the hybrid GA-SVM approach on a spam email dataset. The results show improvements in classification accuracy and computation time over using SVM alone.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
Approach of Syllable Based Unit Selection Text- To-Speech Synthesis System fo...iosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels.
Data mining involves using machine learning and statistical methods to discover patterns in large datasets and is useful in bioinformatics for analyzing biological data. Bioinformatics analyzes data from sequences, molecules, gene expressions, and pathways. Data mining can help understand these rapidly growing biological datasets. Common data mining tools in bioinformatics include BLAST for sequence comparisons, Entrez for integrated database searching, and ORF Finder for identifying open reading frames. Data mining approaches are well-suited to the enormous volumes of data in bioinformatics databases.
This paper is aimed to implement a robust speaker identification system. It is a software architecture which identifies the current talker out of a set of speakers. The system is emphasized on text-dependent speaker identification system. It contains three main modules: endpoint detection, feature extraction and feature matching. The additional module, endpoint detection, removes unwanted signal and background noise from the input speech signal before subsequent processing. In the proposed system, Short-Term Energy analysis is used for endpoint detection. Mel-frequency Cepstrum Coefficients (MFCC) is applied for feature extraction to extract a small amount of data from the voice signal that can later be used to represent each speaker. For feature matching, Vector Quantization (VQ) approach using Linde, Buzo and Gray (LBG) clustering algorithm is proposed because it can reduce the amount of data and complexity. The experimental study shows that the proposed system is more robust than using the original system and it is faster in computation than the existing one. To implement this system MATLAB is used for programming. Zaw Win Aung"A Robust Speaker Identification System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18274.pdf http://www.ijtsrd.com/other-scientific-research-area/other/18274/a-robust-speaker-identification-system/zaw-win-aung
This document summarizes a research paper that proposes a method to semantically detect plagiarism in research papers using text mining techniques. It introduces the problem of plagiarism in research and the need for automated detection methods. The proposed method uses TF-IDF to encode documents and LSI for semantic indexing. It collects research papers, preprocesses text, encodes documents with TF-IDF, and indexes them semantically using LSI to find similar papers and detect plagiarism.
Text independent speaker identification system using average pitch and forman...ijitjournal
The aim of this paper is to design a closed-set text-independent Speaker Identification system using average
pitch and speech features from formant analysis. The speech features represented by the speech signal are
potentially characterized by formant analysis (Power Spectral Density). In this paper we have designed two
methods: one for average pitch estimation based on Autocorrelation and other for formant analysis. The
average pitches of speech signals are calculated and employed with formant analysis. From the performance
comparison of the proposed method with some of the existing methods, it is evident that the designed
speaker identification system with the proposed method is superior to others.
Hybrid ga svm for efficient feature selection in e-mail classificationAlexander Decker
1. The document discusses using a hybrid genetic algorithm-support vector machine (GA-SVM) approach for feature selection in email classification to improve SVM performance.
2. SVM has been shown to be inefficient and consume a lot of computational resources when classifying large email datasets with many features.
3. The hybrid GA-SVM approach uses a genetic algorithm to optimize feature selection for SVM in order to improve classification accuracy and reduce computation time for email spam detection.
11.hybrid ga svm for efficient feature selection in e-mail classificationAlexander Decker
The document summarizes a study that develops a hybrid genetic algorithm-support vector machine (GA-SVM) technique for feature selection in email classification. The technique uses a genetic algorithm to optimize the feature selection and parameters of an SVM classifier. The goal is to improve the SVM's classification accuracy and reduce computation time for large email datasets. The study tests the hybrid GA-SVM approach on a spam email dataset. The results show improvements in classification accuracy and computation time over using SVM alone.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
Approach of Syllable Based Unit Selection Text- To-Speech Synthesis System fo...iosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels.
Data mining involves using machine learning and statistical methods to discover patterns in large datasets and is useful in bioinformatics for analyzing biological data. Bioinformatics analyzes data from sequences, molecules, gene expressions, and pathways. Data mining can help understand these rapidly growing biological datasets. Common data mining tools in bioinformatics include BLAST for sequence comparisons, Entrez for integrated database searching, and ORF Finder for identifying open reading frames. Data mining approaches are well-suited to the enormous volumes of data in bioinformatics databases.
This paper is aimed to implement a robust speaker identification system. It is a software architecture which identifies the current talker out of a set of speakers. The system is emphasized on text-dependent speaker identification system. It contains three main modules: endpoint detection, feature extraction and feature matching. The additional module, endpoint detection, removes unwanted signal and background noise from the input speech signal before subsequent processing. In the proposed system, Short-Term Energy analysis is used for endpoint detection. Mel-frequency Cepstrum Coefficients (MFCC) is applied for feature extraction to extract a small amount of data from the voice signal that can later be used to represent each speaker. For feature matching, Vector Quantization (VQ) approach using Linde, Buzo and Gray (LBG) clustering algorithm is proposed because it can reduce the amount of data and complexity. The experimental study shows that the proposed system is more robust than using the original system and it is faster in computation than the existing one. To implement this system MATLAB is used for programming. Zaw Win Aung"A Robust Speaker Identification System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18274.pdf http://www.ijtsrd.com/other-scientific-research-area/other/18274/a-robust-speaker-identification-system/zaw-win-aung
This document summarizes a research paper that proposes a method to semantically detect plagiarism in research papers using text mining techniques. It introduces the problem of plagiarism in research and the need for automated detection methods. The proposed method uses TF-IDF to encode documents and LSI for semantic indexing. It collects research papers, preprocesses text, encodes documents with TF-IDF, and indexes them semantically using LSI to find similar papers and detect plagiarism.
The document presents a study that analyzes sentiment on Twitter using various classification algorithms. It compares the performance of Naive Bayes, Bayes Net, Discriminative Multinomial Naive Bayes, Sequential Minimal Optimization, Hyperpipes, and Random Forest algorithms on a Twitter sentiment dataset. The study finds that Discriminative Multinomial Naive Bayes and Sequential Minimal Optimization algorithms have the best performance with overall F-scores of 0.769 and 0.75, respectively. The study aims to determine the most accurate and efficient algorithms for Twitter sentiment classification.
This document summarizes a research paper that evaluates different classification methods for detecting spam users in social bookmarking systems. It tests naive Bayes and k-nearest neighbor classifiers on user data represented using three information retrieval models: Boolean, bag-of-words, and TFIDF. The best results were achieved using naive Bayes with a Boolean user representation, accurately classifying 97.5% of users. K-nearest neighbor worked best across all three representations, with over 96% accuracy using TFIDF. The study aims to automatically detect spam users through supervised machine learning techniques.
SENTIMENT ANALYSIS FOR MOVIES REVIEWS DATASET USING DEEP LEARNING MODELSIJDKP
Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the internet and other media, Sentiment analysis has become vital for developing opinion mining systems. This paper introduces a developed classification sentiment analysis using deep learning networks and introduces comparative results of different deep learning networks. Multilayer Perceptron (MLP) was developed as a baseline for other networks results. Long short-term memory (LSTM) recurrent neural network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN were developed and applied on IMDB dataset consists of 50K movies reviews files. Dataset was divided to 50% positive reviews and 50% negative reviews. The data was initially pre-processed using Word2Vec and word embedding was applied accordingly. The results have shown that, the hybrid CNN_LSTM model have outperformed the MLP and singular CNN and LSTM networks. CNN_LSTM have reported the accuracy of 89.2% while CNN has given accuracy of 87.7%, while MLP and LSTM have reported accuracy of 86.74% and 86.64 respectively. Moreover, the results have elaborated that the proposed deep learning models have also outperformed SVM, Naïve Bayes and RNTN that were published in other works using English datasets.
IRJET- A Pragmatic Supervised Learning Methodology of Hate Speech Detection i...IRJET Journal
This document summarizes a paper that proposes a supervised machine learning approach for detecting hate speech in social media. It begins with an introduction to the problem of hate speech online and anonymity enabling harmful communication. It then describes common text preprocessing techniques like tokenization and filtering used to clean text data. Feature extraction methods are discussed, including n-grams, bag-of-words, and word embeddings. Popular machine learning algorithms for classification are also summarized, such as support vector machines, logistic regression, and neural networks. The document concludes by reviewing related work on hate speech detection and challenges around dataset annotation.
chalenges and apportunity of deep learning for big data analysis fmaru kindeneh
The document discusses challenges and opportunities in analyzing complex data using deep learning. It begins with an introduction to complex data and deep learning. It then provides background on machine learning, different data types, feature engineering, and challenges in deep learning. The problem specification defines complex data and proposes research questions on how deep learning can better handle complex data properties. The method section outlines a literature review and case studies to define complex data and study its impact on deep learning models.
This paper proposes Natural language based Discourse Analysis method used for extracting
information from the news article of different domain. The Discourse analysis used the Rhetorical Structure
theory which is used to find coherent group of text which are most prominent for extracting information
from text. RST theory used the Nucleus- Satellite concept for finding most prominent text from the text
document. After Discourse analysis the text analysis has been done for extracting domain related object
and relates this object. For extracting the information knowledge based system has been used which
consist of domain dictionary .The domain dictionary has a bag of words for domain. The system is
evaluated according gold-of-art analysis and human decision for extracted information.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
In this paper, I develop a custom binary classifier of search queries for the makeup category using different Machine Learning techniques and models. An extensive exploration of shallow and Deep Learning models was performed using a cross-validation framework to identify the top three models, optimize them tuning their hyperparameters, and finally creating an ensemble of models with a custom decision threshold that outperforms all other models. The final classifier achieves an accuracy of 98.83% on a test set, making it ready for production.
USING THE MANDELBROT SET TO GENERATE PRIMARY POPULATIONS IN THE GENETIC ALGOR...csandit
Nowadays, finding a way to secure media is common with the growth of digital media. An
effective method for the secure transmission of images can be found in the field of visual
cryptography. There is a growing interest in the use of visual cryptography in security
application. Since this method is used for secure transmission of images, many of the methods
are developed based on the original algorithm proposed by Naor and Shamir in 1994. In this
paper, a new hybrid model is used in cryptography of images which is composed of Mandelbrot
algorithm and genetic algorithm. In the early stages of proposal, a number of encrypted images
are made by using the Mandelbrot algorithm and the original picture and in the next stage,
these encrypted images are used as the initial population for the genetic algorithm. At each
stage of the genetic algorithm, the answer of previous iterations is optimized to get the best
encoding image. Also, in the proposed method, we can achieve the decoded image by a reverse
operation from the genetic algorithm. The best encrypted image is an image with high entropy
and low correlation coefficient. According to the entropy and correlation coefficient of the
proposed method compared with existing methods, it is observed that our method gets better
results in both of them.
A Deep Analysis on Prevailing Spam Mail Filteration Machine Learning Approachesijtsrd
In this work, we have reviewed the issue of spam mail which is a big problem in the area of Internet. The growing size of uncalled mass e mail or spam has produced the requirement of a dependable anti spam filter. Now a days the Machine learning ML proedures are being employed to spontaneously filter the spam e mail in an effective manner. In this work, we have reviewed some of the prevalent ML approaches such as Rough sets, Bayesian classification, SVMs, k NN, ANNs and Artificial immune system and of their use fullness in the issue of spam Email taxonomy. We have provided the depictions of the procedures and the divergence of their enactment on the basis of the quantity of Spam Assassin. Anu | Ms. Preeti "A Deep Analysis on Prevailing Spam Mail Filteration Machine Learning Approaches" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33261.pdf Paper Url: https://www.ijtsrd.com/computer-science/data-processing/33261/a-deep-analysis-on-prevailing-spam-mail-filteration-machine-learning-approaches/anu
Mixed Language Based Offline Handwritten Character Recognition Using First St...CSCJournals
Artificial Neural Network is an artificial representation of the human brain that tries to simulate its learning process. To train a network and measure how well it performs, an objective function must be defined. A commonly used performance criterion function is the sum of squares error function. Full end-to-end text recognition in natural images is a challenging problem that has recently received much attention in computer vision and machine learning. Traditional systems in this area have relied on elaborate models that incorporate carefully hand-engineered features or large amounts of prior knowledge. Language identification and interpretation of handwritten characters is one of the challenges faced in various industries. For example, it is always a big challenge in data interpretation from cheques in banks, language identification and translated messages from ancient script in the form of manuscripts, palm scripts and stone carvings to name a few. Handwritten character recognition using Soft computing methods like Neural networks is always a big area of research for long time and there are multiple theories and algorithms developed in the area of neural networks for handwritten character recognition.
Spam filtering by using Genetic based Feature SelectionEditor IJCATR
Spam is defined as redundant and unwanted electronica letters, and nowadays, it has created many problems in business life such as
occupying networks bandwidth and the space of user’s mailbox. Due to these problems, much research has been carried out in this
regard by using classification technique. The resent research show that feature selection can have positive effect on the efficiency of
machine learning algorithm. Most algorithms try to present a data model depending on certain detection of small set of features.
Unrelated features in the process of making model result in weak estimation and more computations. In this research it has been tried
to evaluate spam detection in legal electronica letters, and their effect on several Machin learning algorithms through presenting a
feature selection method based on genetic algorithm. Bayesian network and KNN classifiers have been taken into account in
classification phase and spam base dataset is used.
03 fauzi indonesian 9456 11nov17 edit septianIAESIJEECS
Since the rise of WWW, information available online is growing rapidly. One of the example is Indonesian online news. Therefore, automatic text classification became very important task for information filtering. One of the major issue in text classification is its high dimensionality of feature space. Most of the features are irrelevant, noisy, and redundant, which may decline the accuracy of the system. Hence, feature selection is needed. Maximal Marginal Relevance for Feature Selection (MMR-FS) has been proven to be a good feature selection for text with many redundant features, but it has high computational complexity. In this paper, we propose a two-phased feature selection method. In the first phase, to lower the complexity of MMR-FS we utilize Information Gain first to reduce features. This reduced feature will be selected using MMR-FS in the second phase. The experiment result showed that our new method can reach the best accuracy by 86%. This new method could lower the complexity of MMR-FS but still retain its accuracy.
IRJET- Survey for Amazon Fine Food ReviewsIRJET Journal
This document discusses sentiment analysis and summarizes several papers on related topics. It begins with an abstract describing sentiment analysis and its importance. The introduction defines sentiment classification and analysis. The literature survey section summarizes 5 papers on natural language processing and machine learning algorithms for sentiment analysis, including K-means clustering, bag-of-words models, TF-IDF vectorization for document clustering, hierarchical clustering methods, and using naive bayes and SVM for sentiment analysis and text summarization. The conclusion discusses techniques for data processing, natural language processing, and machine learning algorithms covered.
This document discusses using knowledge graphs to extract insights from unstructured data. It presents a data flow architecture that uses natural language processing to extract entities and relationships from social media posts and construct a knowledge graph. Graph inference algorithms like degree centrality are then used to determine popular entities. This provides actionable insights into how products are performing based on individuals' reviews on social media. The document also provides some lessons learned around knowledge graph construction and querying.
Experimental Result Analysis of Text Categorization using Clustering and Clas...ijtsrd
In a world that routinely produces more textual data. It is very critical task to managing that textual data. There are many text analysis methods are available to managing and visualizing that data, but many techniques may give less accuracy because of the ambiguity of natural language. To provide the ne grained analysis, in this paper introduce e cient machine learning algorithms for categorize text data. To improve the accuracy, in proposed system I introduced Natural language toolkit NLTK python library to perform natural language processing. The main aim of proposed system is to generalize the model for real time text categorization applications by using e cient text classi cation as well as clustering machine learning algorithms and nd the efficient and accurate model for input dataset using performance measure concept. Patil Kiran Sanajy | Prof. Kurhade N. V. ""Experimental Result Analysis of Text Categorization using Clustering and Classification Algorithms"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25077.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/25077/experimental-result-analysis-of-text-categorization-using-clustering-and-classification-algorithms/patil-kiran-sanajy
An efficient algorithm for sequence generation in data miningijcisjournal
Data mining is the method or the activity of analyzing data from different perspectives and summarizing it
into useful information. There are several major data mining techniques that have been developed and are
used in the data mining projects which include association, classification, clustering, sequential patterns,
prediction and decision tree. Among different tasks in data mining, sequential pattern mining is one of the
most important tasks. Sequential pattern mining involves the mining of the subsequences that appear
frequently in a set of sequences. It has a variety of applications in several domains such as the analysis of
customer purchase patterns, protein sequence analysis, DNA analysis, gene sequence analysis, web access
patterns, seismologic data and weather observations. Various models and algorithms have been developed
for the efficient mining of sequential patterns in large amount of data. This research paper analyzes the
efficiency of three sequence generation algorithms namely GSP, SPADE and PrefixSpan on a retail dataset
by applying various performance factors. From the experimental results, it is observed that the PrefixSpan
algorithm is more efficient than other two algorithms.
A Novel Technique for Name Identification from Homeopathy Diagnosis Discussio...home
Named entities are the most informative element of a textual document and identification of the names is very much
important for extracting further information from text. We have developed a conditional random field based system to
identify the named entities from homeopathic diagnosis discussion forum text. We have manually annotated a training
corpus for the task. As manual creation of a sufficiently large annotated corpus is costly and time consuming, we use an
active learning based semi-supervised framework to increase the efficiency of the system with the help of un-annotated
data. Our system achieves the highest f-value of
- GPT-3 is a large language model developed by OpenAI, with 175 billion parameters, making it the largest neural network ever created at the time.
- GPT-3 is trained on a massive dataset of unlabeled text using an auto-regressive approach, allowing it to perform tasks without any fine-tuning through zero-, one-, or few-shot learning by conditioning on examples or instructions.
- Evaluation showed GPT-3 outperforming state-of-the-art models on several benchmarks in zero- and few-shot settings, demonstrating strong generalization abilities from its massive pre-training.
2 Degrees of Separation - Digital Marketing Show 2013 - Roland Harwood, 100%Open100%Open
This document summarizes a presentation on open innovation and networking. It discusses the birthday paradox, how networks are becoming more important than companies, and proposes a 3-minute joint venture activity. Case studies on LEGO, P&G, and ARM Holdings are provided, showing how they benefit from open business models. The presentation emphasizes that generosity and peripheral connections are important for innovation, and concludes by providing contact information for the presenter.
Conceptual Designing and Numerical Modeling of Micro Pulse Jet for Controllin...CSCJournals
This document summarizes a study on the conceptual design and numerical modeling of a Micro Pulse Jet valve to control flow separation. The valve is designed to generate streamline vortices that suppress flow separation by enhancing mixing between the free stream flow and separated boundary layer flow through a pitched jet orifice. The study presents the conceptual design of the Micro Pulse Jet valve and numerical analysis of steady and unsteady pulse jets. A 2D ramp model with a 20 degree divergence is used to simulate flow separation. The effect of steady and unsteady jets through the valve is analyzed by varying parameters like jet velocity ratio and pulse frequency. Results show that a pulse micro jet requires significantly less mass flow than a steady jet to control flow separation
The document presents a study that analyzes sentiment on Twitter using various classification algorithms. It compares the performance of Naive Bayes, Bayes Net, Discriminative Multinomial Naive Bayes, Sequential Minimal Optimization, Hyperpipes, and Random Forest algorithms on a Twitter sentiment dataset. The study finds that Discriminative Multinomial Naive Bayes and Sequential Minimal Optimization algorithms have the best performance with overall F-scores of 0.769 and 0.75, respectively. The study aims to determine the most accurate and efficient algorithms for Twitter sentiment classification.
This document summarizes a research paper that evaluates different classification methods for detecting spam users in social bookmarking systems. It tests naive Bayes and k-nearest neighbor classifiers on user data represented using three information retrieval models: Boolean, bag-of-words, and TFIDF. The best results were achieved using naive Bayes with a Boolean user representation, accurately classifying 97.5% of users. K-nearest neighbor worked best across all three representations, with over 96% accuracy using TFIDF. The study aims to automatically detect spam users through supervised machine learning techniques.
SENTIMENT ANALYSIS FOR MOVIES REVIEWS DATASET USING DEEP LEARNING MODELSIJDKP
Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the internet and other media, Sentiment analysis has become vital for developing opinion mining systems. This paper introduces a developed classification sentiment analysis using deep learning networks and introduces comparative results of different deep learning networks. Multilayer Perceptron (MLP) was developed as a baseline for other networks results. Long short-term memory (LSTM) recurrent neural network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN were developed and applied on IMDB dataset consists of 50K movies reviews files. Dataset was divided to 50% positive reviews and 50% negative reviews. The data was initially pre-processed using Word2Vec and word embedding was applied accordingly. The results have shown that, the hybrid CNN_LSTM model have outperformed the MLP and singular CNN and LSTM networks. CNN_LSTM have reported the accuracy of 89.2% while CNN has given accuracy of 87.7%, while MLP and LSTM have reported accuracy of 86.74% and 86.64 respectively. Moreover, the results have elaborated that the proposed deep learning models have also outperformed SVM, Naïve Bayes and RNTN that were published in other works using English datasets.
IRJET- A Pragmatic Supervised Learning Methodology of Hate Speech Detection i...IRJET Journal
This document summarizes a paper that proposes a supervised machine learning approach for detecting hate speech in social media. It begins with an introduction to the problem of hate speech online and anonymity enabling harmful communication. It then describes common text preprocessing techniques like tokenization and filtering used to clean text data. Feature extraction methods are discussed, including n-grams, bag-of-words, and word embeddings. Popular machine learning algorithms for classification are also summarized, such as support vector machines, logistic regression, and neural networks. The document concludes by reviewing related work on hate speech detection and challenges around dataset annotation.
chalenges and apportunity of deep learning for big data analysis fmaru kindeneh
The document discusses challenges and opportunities in analyzing complex data using deep learning. It begins with an introduction to complex data and deep learning. It then provides background on machine learning, different data types, feature engineering, and challenges in deep learning. The problem specification defines complex data and proposes research questions on how deep learning can better handle complex data properties. The method section outlines a literature review and case studies to define complex data and study its impact on deep learning models.
This paper proposes Natural language based Discourse Analysis method used for extracting
information from the news article of different domain. The Discourse analysis used the Rhetorical Structure
theory which is used to find coherent group of text which are most prominent for extracting information
from text. RST theory used the Nucleus- Satellite concept for finding most prominent text from the text
document. After Discourse analysis the text analysis has been done for extracting domain related object
and relates this object. For extracting the information knowledge based system has been used which
consist of domain dictionary .The domain dictionary has a bag of words for domain. The system is
evaluated according gold-of-art analysis and human decision for extracted information.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
In this paper, I develop a custom binary classifier of search queries for the makeup category using different Machine Learning techniques and models. An extensive exploration of shallow and Deep Learning models was performed using a cross-validation framework to identify the top three models, optimize them tuning their hyperparameters, and finally creating an ensemble of models with a custom decision threshold that outperforms all other models. The final classifier achieves an accuracy of 98.83% on a test set, making it ready for production.
USING THE MANDELBROT SET TO GENERATE PRIMARY POPULATIONS IN THE GENETIC ALGOR...csandit
Nowadays, finding a way to secure media is common with the growth of digital media. An
effective method for the secure transmission of images can be found in the field of visual
cryptography. There is a growing interest in the use of visual cryptography in security
application. Since this method is used for secure transmission of images, many of the methods
are developed based on the original algorithm proposed by Naor and Shamir in 1994. In this
paper, a new hybrid model is used in cryptography of images which is composed of Mandelbrot
algorithm and genetic algorithm. In the early stages of proposal, a number of encrypted images
are made by using the Mandelbrot algorithm and the original picture and in the next stage,
these encrypted images are used as the initial population for the genetic algorithm. At each
stage of the genetic algorithm, the answer of previous iterations is optimized to get the best
encoding image. Also, in the proposed method, we can achieve the decoded image by a reverse
operation from the genetic algorithm. The best encrypted image is an image with high entropy
and low correlation coefficient. According to the entropy and correlation coefficient of the
proposed method compared with existing methods, it is observed that our method gets better
results in both of them.
A Deep Analysis on Prevailing Spam Mail Filteration Machine Learning Approachesijtsrd
In this work, we have reviewed the issue of spam mail which is a big problem in the area of Internet. The growing size of uncalled mass e mail or spam has produced the requirement of a dependable anti spam filter. Now a days the Machine learning ML proedures are being employed to spontaneously filter the spam e mail in an effective manner. In this work, we have reviewed some of the prevalent ML approaches such as Rough sets, Bayesian classification, SVMs, k NN, ANNs and Artificial immune system and of their use fullness in the issue of spam Email taxonomy. We have provided the depictions of the procedures and the divergence of their enactment on the basis of the quantity of Spam Assassin. Anu | Ms. Preeti "A Deep Analysis on Prevailing Spam Mail Filteration Machine Learning Approaches" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33261.pdf Paper Url: https://www.ijtsrd.com/computer-science/data-processing/33261/a-deep-analysis-on-prevailing-spam-mail-filteration-machine-learning-approaches/anu
Mixed Language Based Offline Handwritten Character Recognition Using First St...CSCJournals
Artificial Neural Network is an artificial representation of the human brain that tries to simulate its learning process. To train a network and measure how well it performs, an objective function must be defined. A commonly used performance criterion function is the sum of squares error function. Full end-to-end text recognition in natural images is a challenging problem that has recently received much attention in computer vision and machine learning. Traditional systems in this area have relied on elaborate models that incorporate carefully hand-engineered features or large amounts of prior knowledge. Language identification and interpretation of handwritten characters is one of the challenges faced in various industries. For example, it is always a big challenge in data interpretation from cheques in banks, language identification and translated messages from ancient script in the form of manuscripts, palm scripts and stone carvings to name a few. Handwritten character recognition using Soft computing methods like Neural networks is always a big area of research for long time and there are multiple theories and algorithms developed in the area of neural networks for handwritten character recognition.
Spam filtering by using Genetic based Feature SelectionEditor IJCATR
Spam is defined as redundant and unwanted electronica letters, and nowadays, it has created many problems in business life such as
occupying networks bandwidth and the space of user’s mailbox. Due to these problems, much research has been carried out in this
regard by using classification technique. The resent research show that feature selection can have positive effect on the efficiency of
machine learning algorithm. Most algorithms try to present a data model depending on certain detection of small set of features.
Unrelated features in the process of making model result in weak estimation and more computations. In this research it has been tried
to evaluate spam detection in legal electronica letters, and their effect on several Machin learning algorithms through presenting a
feature selection method based on genetic algorithm. Bayesian network and KNN classifiers have been taken into account in
classification phase and spam base dataset is used.
03 fauzi indonesian 9456 11nov17 edit septianIAESIJEECS
Since the rise of WWW, information available online is growing rapidly. One of the example is Indonesian online news. Therefore, automatic text classification became very important task for information filtering. One of the major issue in text classification is its high dimensionality of feature space. Most of the features are irrelevant, noisy, and redundant, which may decline the accuracy of the system. Hence, feature selection is needed. Maximal Marginal Relevance for Feature Selection (MMR-FS) has been proven to be a good feature selection for text with many redundant features, but it has high computational complexity. In this paper, we propose a two-phased feature selection method. In the first phase, to lower the complexity of MMR-FS we utilize Information Gain first to reduce features. This reduced feature will be selected using MMR-FS in the second phase. The experiment result showed that our new method can reach the best accuracy by 86%. This new method could lower the complexity of MMR-FS but still retain its accuracy.
IRJET- Survey for Amazon Fine Food ReviewsIRJET Journal
This document discusses sentiment analysis and summarizes several papers on related topics. It begins with an abstract describing sentiment analysis and its importance. The introduction defines sentiment classification and analysis. The literature survey section summarizes 5 papers on natural language processing and machine learning algorithms for sentiment analysis, including K-means clustering, bag-of-words models, TF-IDF vectorization for document clustering, hierarchical clustering methods, and using naive bayes and SVM for sentiment analysis and text summarization. The conclusion discusses techniques for data processing, natural language processing, and machine learning algorithms covered.
This document discusses using knowledge graphs to extract insights from unstructured data. It presents a data flow architecture that uses natural language processing to extract entities and relationships from social media posts and construct a knowledge graph. Graph inference algorithms like degree centrality are then used to determine popular entities. This provides actionable insights into how products are performing based on individuals' reviews on social media. The document also provides some lessons learned around knowledge graph construction and querying.
Experimental Result Analysis of Text Categorization using Clustering and Clas...ijtsrd
In a world that routinely produces more textual data. It is very critical task to managing that textual data. There are many text analysis methods are available to managing and visualizing that data, but many techniques may give less accuracy because of the ambiguity of natural language. To provide the ne grained analysis, in this paper introduce e cient machine learning algorithms for categorize text data. To improve the accuracy, in proposed system I introduced Natural language toolkit NLTK python library to perform natural language processing. The main aim of proposed system is to generalize the model for real time text categorization applications by using e cient text classi cation as well as clustering machine learning algorithms and nd the efficient and accurate model for input dataset using performance measure concept. Patil Kiran Sanajy | Prof. Kurhade N. V. ""Experimental Result Analysis of Text Categorization using Clustering and Classification Algorithms"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25077.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/25077/experimental-result-analysis-of-text-categorization-using-clustering-and-classification-algorithms/patil-kiran-sanajy
An efficient algorithm for sequence generation in data miningijcisjournal
Data mining is the method or the activity of analyzing data from different perspectives and summarizing it
into useful information. There are several major data mining techniques that have been developed and are
used in the data mining projects which include association, classification, clustering, sequential patterns,
prediction and decision tree. Among different tasks in data mining, sequential pattern mining is one of the
most important tasks. Sequential pattern mining involves the mining of the subsequences that appear
frequently in a set of sequences. It has a variety of applications in several domains such as the analysis of
customer purchase patterns, protein sequence analysis, DNA analysis, gene sequence analysis, web access
patterns, seismologic data and weather observations. Various models and algorithms have been developed
for the efficient mining of sequential patterns in large amount of data. This research paper analyzes the
efficiency of three sequence generation algorithms namely GSP, SPADE and PrefixSpan on a retail dataset
by applying various performance factors. From the experimental results, it is observed that the PrefixSpan
algorithm is more efficient than other two algorithms.
A Novel Technique for Name Identification from Homeopathy Diagnosis Discussio...home
Named entities are the most informative element of a textual document and identification of the names is very much
important for extracting further information from text. We have developed a conditional random field based system to
identify the named entities from homeopathic diagnosis discussion forum text. We have manually annotated a training
corpus for the task. As manual creation of a sufficiently large annotated corpus is costly and time consuming, we use an
active learning based semi-supervised framework to increase the efficiency of the system with the help of un-annotated
data. Our system achieves the highest f-value of
- GPT-3 is a large language model developed by OpenAI, with 175 billion parameters, making it the largest neural network ever created at the time.
- GPT-3 is trained on a massive dataset of unlabeled text using an auto-regressive approach, allowing it to perform tasks without any fine-tuning through zero-, one-, or few-shot learning by conditioning on examples or instructions.
- Evaluation showed GPT-3 outperforming state-of-the-art models on several benchmarks in zero- and few-shot settings, demonstrating strong generalization abilities from its massive pre-training.
2 Degrees of Separation - Digital Marketing Show 2013 - Roland Harwood, 100%Open100%Open
This document summarizes a presentation on open innovation and networking. It discusses the birthday paradox, how networks are becoming more important than companies, and proposes a 3-minute joint venture activity. Case studies on LEGO, P&G, and ARM Holdings are provided, showing how they benefit from open business models. The presentation emphasizes that generosity and peripheral connections are important for innovation, and concludes by providing contact information for the presenter.
Conceptual Designing and Numerical Modeling of Micro Pulse Jet for Controllin...CSCJournals
This document summarizes a study on the conceptual design and numerical modeling of a Micro Pulse Jet valve to control flow separation. The valve is designed to generate streamline vortices that suppress flow separation by enhancing mixing between the free stream flow and separated boundary layer flow through a pitched jet orifice. The study presents the conceptual design of the Micro Pulse Jet valve and numerical analysis of steady and unsteady pulse jets. A 2D ramp model with a 20 degree divergence is used to simulate flow separation. The effect of steady and unsteady jets through the valve is analyzed by varying parameters like jet velocity ratio and pulse frequency. Results show that a pulse micro jet requires significantly less mass flow than a steady jet to control flow separation
This document discusses strategic HR practices that drive organization growth. It begins with an overview of the contents which include background, processes and tools, and critical success factors. It then discusses a sustainable growth model involving business profit, strategy, leadership development, productivity, execution, innovation, and people development. Key HR processes and tools are then outlined, including talent development, organization development, strategy management, corporate culture, effective organization structure, and more. Critical factors for strategy, execution, culture, structure, talent, leadership, innovation, and mergers and acquisitions are also summarized. The document concludes with developmental phases for implementing these practices over a 24 month period.
This document discusses change management and organizational change. It presents on change management processes like Lewin's three-step model of unfreezing, changing, and refreezing. It also discusses the continuous change process model. The document outlines components of organizational change like strategy, technology/structure, human resources, culture and performance metrics. It identifies sources of resistance to change and factors that can reduce resistance like education, participation, support and negotiation.
The document discusses boundary layer separation, which occurs when the boundary layer can no longer stick to the surface of a solid body as the fluid flows over it. This is called boundary layer separation and leads to disadvantages like additional resistance to flow and loss of energy. Methods to control separation include accelerating the fluid in the boundary layer, suctioning fluid from the boundary layer, and moving the solid boundary to match the fluid velocity. Boundary layer separation tends to occur at the inner radius of bends due to pressure gradients.
The document discusses how globalization has impacted organizational structures and brought about changes in both private and public sector organizations. It outlines Weber's traditional bureaucratic organization model and how it is too rigid for today's globalized world. Modern global organizations have adopted network, cellular, and virtual structures that are flatter, less hierarchical, and more flexible. Public administration is also facing challenges from globalization, as traditional bureaucratic models are inefficient. The document suggests elements of new public administration, like lean states, separation of decision-making levels, and focus on results and customer service. It questions whether public administration in the Philippines has embraced necessary reforms.
Optimized audio classification and segmentation algorithm by using ensemble m...Venkat Projects
The document proposes an optimized audio classification and segmentation algorithm that segments audio streams into four types - pure speech, music, environment sound, and silence - using ensemble methods. It uses a hybrid classification approach of bagged support vector machines and artificial neural networks. The algorithm aims to accurately segment audio with minimum misclassification and requires less training data, making it suitable for real-time applications. It segments non-speech portions into music or environment sound and further divides speech into silence and pure speech. The algorithm achieves approximately 98% accurate segmentation.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Speech recognition is the next big step that the technology needs to take for general users. An Automatic Speech Recognition (ASR) will play a major role in focusing new technology to users. Applications of ASR are speech to text conversion, voice input in aircraft, data entry, voice user interfaces such as voice dialing. Speech recognition involves extracting features from the input signal and classifying them to classes using pattern matching model. This can be done using feature extraction method. This paper involves a general study of automatic speech recognition and various methods to generate an ASR system. General techniques that can be used to implement an ASR includes artificial neural networks, Hidden Markov model, acoustic –phonetic approach
High level speaker specific features modeling in automatic speaker recognitio...IJECEIAES
Spoken words convey several levels of information. At the primary level, the speech conveys words or spoken messages, but at the secondary level, the speech also reveals information about the speakers. This work is based on the high-level speaker-specific features on statistical speaker modeling techniques that express the characteristic sound of the human voice. Using Hidden Markov model (HMM), Gaussian mixture model (GMM), and Linear Discriminant Analysis (LDA) models build Automatic Speaker Recognition (ASR) system that are computational inexpensive can recognize speakers regardless of what is said. The performance of the ASR system is evaluated for clear speech to a wide range of speech quality using a standard TIMIT speech corpus. The ASR efficiency of HMM, GMM, and LDA based modeling technique are 98.8%, 99.1%, and 98.6% and Equal Error Rate (EER) is 4.5%, 4.4% and 4.55% respectively. The EER improvement of GMM modeling technique based ASR systemcompared with HMM and LDA is 4.25% and 8.51% respectively.
A novel automatic voice recognition system based on text-independent in a noi...IJECEIAES
Automatic voice recognition system aims to limit fraudulent access to sensitive areas as labs. Our primary objective of this paper is to increasethe accuracy of the voice recognition in noisy environment of the Microsoft Research (MSR) identity toolbox. The proposed system enabled the user tospeak into the microphone then it will match unknown voice with other human voices existing in the database using a statistical model, in order togrant or deny access to the system. The voice recognition was done in twosteps: training and testing. During the training a Universal BackgroundModel as well as a Gaussian Mixtures Model: GMM-UBM models arecalculated based on different sentences pronounced by the human voice (s) used to record the training data. Then the testing of voice signal in noisyenvironment calculated the Log-Likelihood Ratio of the GMM-UBM models in order to classify user's voice. However, before testing noise and de-noisemethods were applied, we investigated different MFCC features of the voiceto determine the best feature possible as well as noise filter algorithmthat subsequently improved the performance of the automatic voicerecognition system.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This paper describes the development of an efficient speech recognition system using different techniques such as Mel Frequency Cepstrum Coefficients (MFCC), Vector Quantization (VQ) and Hidden Markov Model (HMM).
This paper explains how speaker recognition followed by speech recognition is used to recognize the speech faster, efficiently and accurately. MFCC is used to extract the characteristics from the input speech signal with respect to a particular word uttered by a particular speaker. Then HMM is used on Quantized feature vectors to identify the word by evaluating the maximum log likelihood values
for the spoken word.
This seminar report summarizes multimodal deep learning. It was submitted by Sangeetha Mathew to fulfill requirements for a Bachelor of Technology degree in computer science and engineering. The report was guided by Ms. Divya Madhu of the computer science department at Muthoot Institute of Technology and Science. The report provides an overview of traditional models like SVM and LDA for multimodal data classification. It then discusses various deep learning architectures for multimodal data, including restricted Boltzmann machines, deep belief networks, deep Boltzmann machines, and deep autoencoders. The report focuses on these energy-based probabilistic graphical models and their use of restricted Boltzmann machines as building blocks for multimodal feature learning.
Hot Topics in Machine Learning for Research and ThesisWriteMyThesis
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survey on Hybrid recommendation mechanism to get effective ranking results fo...Suraj Ligade
These days clients are having exclusive
requirements towards advancements, they need to hunt tunes
in such circumstances where they are not ready to recall tunes
title or melody related points of interest. Recovery of music or
melodies substance is one of the hardest errands and testing
work in the field of Music Information Retrieval (MIR). There
are different looking techniques created and executed, yet
these seeking strategies are no more ready to inquiry tunes
which required by the clients and confronting different issues
like programmed playlist creation, music suggestion or music
pursuit are connected issues. In past framework client seek
the tune with the assistance of tune title, craftsman name and
whatever other related points of interest so this strategy is
exceptionally tedious. To beat this issue singing so as to look
tune or murmuring a segment of it is the most regular
approach to seek the tune. This hunt strategy is the most
helpful when client don't have entry to sound gadget or client
can't review the traits of the tune such as tune title, name of
craftsman, name of collection. In proposed framework client
have not stress over recalling the tune data and this technique
is not tedious. In this strategy we utilize the data from a
client's hunt history and in addition the normal properties of
client's comparative foundations. Cross breed proposal
component utilizes the substance construct recovery
framework situated in light of utilization of the sound data
such as tone, pitch, mood. This component used to get exact
result to the client. The more imperative idea is clients ready
to work their gadgets without manual information orders by
hand. It is simple and basic system to perform music look.
Distributed Digital Artifacts on the Semantic WebEditor IJCATR
Distributed digital artifacts incorporate cryptographic hash values to URI called trusty URIs in a distributed environment
building good in quality, verifiable and unchangeable web resources to prevent the rising man in the middle attack. The greatest
challenge of a centralized system is that it gives users no possibility to check whether data have been modified and the communication
is limited to a single server. As a solution for this, is the distributed digital artifact system, where resources are distributed among
different domains to enable inter-domain communication. Due to the emerging developments in web, attacks have increased rapidly,
among which man in the middle attack (MIMA) is a serious issue, where user security is at its threat. This work tries to prevent MIMA
to an extent, by providing self reference and trusty URIs even when presented in a distributed environment. Any manipulation to the
data is efficiently identified and any further access to that data is blocked by informing user that the uniform location has been
changed. System uses self-reference to contain trusty URI for each resource, lineage algorithm for generating seed and SHA-512 hash
generation algorithm to ensure security. It is implemented on the semantic web, which is an extension to the world wide web, using
RDF (Resource Description Framework) to identify the resource. Hence the framework was developed to overcome existing
challenges by making the digital artifacts on the semantic web distributed to enable communication between different domains across
the network securely and thereby preventing MIMA.
voice and speech recognition using machine learningMohammedWahhab4
This document presents research on using machine learning algorithms to classify gender based on voice characteristics. It analyzed a dataset of 3,168 voice samples labeled as male or female using acoustic features. Three classifiers - decision tree, random forest, and logistic regression - were tested on 80% of the data and evaluated on the remaining 20%. Random forest achieved the highest accuracy of 98.5%, while decision tree and logistic regression achieved 97.3% and 92.7% accuracy respectively. The document concludes random forest performed best on this dataset for gender classification based on voice.
This document provides an overview of decision tree induction methods and their application to big data. It discusses decision trees as a method for identifying patterns in large datasets that has the advantage of being interpretable. The document describes the basic principles of decision tree induction, different algorithms for constructing decision trees, and measures for evaluating tree performance and structure. It also discusses challenges such as fitting trees to existing expert knowledge and improving classification through feature selection.
IRJET- Musical Instrument Recognition using CNN and SVMIRJET Journal
This document discusses a study that uses convolutional neural networks (CNNs) and support vector machines (SVMs) to recognize musical instruments in audio recordings. The researchers aim to convert audio excerpts to images and use CNNs to classify instruments, then combine the CNN classifications with SVM classifications to improve accuracy. They discuss related work on instrument recognition using other methods. The proposed model uses MFCC features with SVM and passes audio converted to images through four convolutional layers and fully connected layers in the CNN. Combining the CNN and SVM results through weighted averaging is expected to provide higher accuracy than either method alone for classifying instruments in the IRMAS dataset.
IRJET- Segmentation in Digital Signal ProcessingIRJET Journal
1) The document discusses audio signal segmentation techniques in digital signal processing. It analyzes algorithms for segmenting audio signals into homogeneous segments for applications like audio event recognition and speaker identification.
2) A hybrid classification approach is proposed that first classifies audio frames as natural sound, noise, or silence using bagged SVM. Rule-based classification is then used to further segment silence frames.
3) The approach involves pre-classifying each windowed portion of the audio clip, extracting normalized feature vectors, and then applying the hybrid classifier. This requires less training data while achieving high accuracy for segmenting into four main audio types.
IRJET- Detection of Clinical Depression in Humans using Sentiment AnalysisIRJET Journal
This document discusses using sentiment analysis of voice data to detect clinical depression in humans. It proposes analyzing vocal features like tone, word choice, and speech patterns using a naive Bayesian algorithm when patients call in or speak to a device. This could help diagnose depression remotely while protecting patient privacy. The system would classify voices as positive, negative, or neutral based on sentiment analysis of the input. This may help notice subtle hints not observed by doctors and improve diagnosis rates by allowing more open communication from patients.
Parameters Optimization for Improving ASR Performance in Adverse Real World N...Waqas Tariq
From the existing research it has been observed that many techniques and methodologies are available for performing every step of Automatic Speech Recognition (ASR) system, but the performance (Minimization of Word Error Recognition-WER and Maximization of Word Accuracy Rate- WAR) of the methodology is not dependent on the only technique applied in that method. The research work indicates that, performance mainly depends on the category of the noise, the level of the noise and the variable size of the window, frame, frame overlap etc is considered in the existing methods. The main aim of the work presented in this paper is to use variable size of parameters like window size, frame size and frame overlap percentage to observe the performance of algorithms for various categories of noise with different levels and also train the system for all size of parameters and category of real world noisy environment to improve the performance of the speech recognition system. This paper presents the results of Signal-to-Noise Ratio (SNR) and Accuracy test by applying variable size of parameters. It is observed that, it is really very hard to evaluate test results and decide parameter size for ASR performance improvement for its resultant optimization. Hence, this study further suggests the feasible and optimum parameter size using Fuzzy Inference System (FIS) for enhancing resultant accuracy in adverse real world noisy environmental conditions. This work will be helpful to give discriminative training of ubiquitous ASR system for better Human Computer Interaction (HCI). Keywords: ASR Performance, ASR Parameters Optimization, Multi-Environmental Training, Fuzzy Inference System for ASR, ubiquitous ASR system, Human Computer Interaction (HCI)
Enhancing speaker verification accuracy with deep ensemble learning and inclu...IJECEIAES
Effective speaker identification is essential for achieving robust speaker recognition in real-world applications such as mobile devices, security, and entertainment while ensuring high accuracy. However, deep learning models trained on large datasets with diverse demographic and environmental factors may lead to increased misclassification and longer processing times. This study proposes incorporating ethnicity and gender information as critical parameters in a deep learning model to enhance accuracy. Two convolutional neural network (CNN) models classify gender and ethnicity, followed by a Siamese deep learning model trained with critical parameters and additional features for speaker verification. The proposed model was tested using the VoxCeleb 2 database, which includes over one million utterances from 6,112 celebrities. In an evaluation after 500 epochs, equal error rate (EER) and minimum decision cost function (minDCF) showed notable results, scoring 1.68 and 0.10, respectively. The proposed model outperforms existing deep learning models, demonstrating improved performance in terms of reduced misclassification errors and faster processing times.
Speech emotion recognition with light gradient boosting decision trees machineIJECEIAES
Speech emotion recognition aims to identify the emotion expressed in the speech by analyzing the audio signals. In this work, data augmentation is first performed on the audio samples to increase the number of samples for better model learning. The audio samples are comprehensively encoded as the frequency and temporal domain features. In the classification, a light gradient boosting machine is leveraged. The hyperparameter tuning of the light gradient boosting machine is performed to determine the optimal hyperparameter settings. As the speech emotion recognition datasets are imbalanced, the class weights are regulated to be inversely proportional to the sample distribution where minority classes are assigned higher class weights. The experimental results demonstrate that the proposed method outshines the state-of-the-art methods with 84.91% accuracy on the Berlin database of emotional speech (emo-DB) dataset, 67.72% on the Ryerson audio-visual database of emotional speech and song (RAVDESS) dataset, and 62.94% on the interactive emotional dyadic motion capture (IEMOCAP) dataset.
This document describes a system to help deaf and mute people communicate through sign language and voice recognition. The system uses algorithms like support vector machines and hidden Markov models to recognize hand gestures and speech. It can translate sign language into text and voice into sign language representations. The system aims to reduce communication barriers for deaf/mute communities by converting between sign language, text, and voice. It outlines the implementation process which includes steps like skin color detection, hand location detection, finger region detection, and pattern matching to recognize gestures from video input.
Similar to A Survey on: Sound Source Separation Methods (20)
Parametric Optimization of Rectangular Beam Type Load Cell Using Taguchi MethodIJCERT
In this work, Rectangular beam type load cell is considered for stress and strain analysis by using finite element method. The stress analysis is carried out to minimize the weight of Rectangular beam- type load cell without exceeding allowable stress. The intention of the work is to create the geometry of Rectangular beam-type load cell to find out the optimum solution. FEM software HyperWorks11.0.0.39 is using for parametric optimization of Rectangular beam type load cell. If the stress value is within the permissible range, then certain dimensions will be modified to reduce the amount of material needed. The procedure will be repeated until design changes satisfying all the criteria. Experimental verification will be carried out by photo-elasticity technique with the help of suitable instrumentation like Polariscope. Using Photo-elasticity technique, results are crosschecked which gives results very close to FEM technique. Experimental results will be compared with FEM results. With the aid of these tools the designer can develop and modify the design parameters from initial design stage to finalize basic geometry of load cell.
Robust Resource Allocation in Relay Node Networks for Optimization ProcessIJCERT
Overlay steering has risen as a promising way to deal with enhances unwavering quality and effectiveness of the Internet. For one-jump overlay source steering, when a given essential way experiences the connection disappointment or execution debasement, the source can reroute the movement to the destination by means of a deliberately set transfer hub. Be that as it may, the over-substantial activity going through the same transfer hub may bring about incessant bundle misfortune and postponement jitter, which can corrupt the throughput and usage of the system. To defeat this issue, we propose a Load-Balanced One-jump Overlay Multipath Routing calculation (LB-OOMR), in which the activity is first part at the source edge hubs and afterward transmitted along numerous one-bounce overlay ways. So as to decide an ideal split proportion for the activity, we plan the issue as a direct programming (LP) definition, whose objective is to minimize the more regrettable case system blockage proportion. Since it is hard to take care of this LP issue in commonsense time, a heuristic calculation is acquainted with select the transfer hubs for building the disjoint one-jump overlay ways, which enormously lessens the computational multifaceted nature of the LP calculation. Reproductions in light of a genuine ISP system and an engineered Internet topology demonstrate that our proposed calculation can diminish the system clog proportion significantly, and accomplish top notch overlay directing administration.
Software Engineering Domain Knowledge to Identify Duplicate Bug ReportsIJCERT
This document summarizes a research paper that proposes a technique to improve the detection of duplicate bug reports using contextual information extracted from software engineering literature. It describes extracting word lists from software engineering textbooks and project documentation to measure contextual features of bug reports. The technique was evaluated on real bug report datasets and showed potential to significantly reduce manual effort in contextual bug deduplication while maintaining accuracy. Key findings indicate that leveraging domain knowledge from software engineering texts can help automate and enhance the identification of duplicate bug reports.
An Image representation using Compressive Sensing and Arithmetic Coding IJCERT
The demand for graphics and multimedia communication over intenet is growing day by day. Generally the coding efficiency achieved by CS measurements is below the widely used wavelet coding schemes (e.g., JPEG 2000). In the existing wavelet-based CS schemes, DWT is mainly applied for sparse representation and the correlation of DWT coefficients has not been fully exploited yet. To improve the coding efficiency, the statistics of DWT coefficients has been investigated. A novel CS-based image representation scheme has been proposed by considering the intra- and inter-similarity among DWT coefficients. Multi-scale DWT is first applied. The low- and high-frequency subbands of Multi-scale DWT are coded separately due to the fact that scaling coefficients capture most of the image energy. At the decoder side, two different recovery algorithms have been presented to exploit the correlation of scaling and wavelet coefficients well. In essence, the proposed CS-based coding method can be viewed as a hybrid compressed sensing schemes which gives better coding efficiency compared to other CS based coding methods.
Multiple Encryption using ECC and Its Time Complexity AnalysisIJCERT
Rapid growth of information technology in present era, secure communication, strong data encryption technique and trusted third party are considered to be major topics of study. Robust encryption algorithm development to secure sensitive data is of great significance among researchers at present. The conventional methods of encryption used as of today may not sufficient and therefore new ideas for the purpose are to be design, analyze and need to be fit into the existing system of security to provide protection of our data from unauthorized access. An effective encryption/ decryption algorithm design to enhance data security is a challenging task while computation, complexity, robustness etc. are concerned. The multiple encryption technique is a process of applying encryption over a single encryption process in a number of iteration. Elliptic Curve Cryptography (ECC) is well known and well accepted cryptographic algorithm and used in many application as of today. In this paper, we discuss multiple encryptions and analyze the computation overhead in the process and study the feasibility of practical application. In the process we use ECC as a multiple-ECC algorithm and try to analyze degree of security, encryption/decryption computation time and complexity of the algorithm. Performance measure of the algorithm is evaluated by analyzing encryption time as well as decryption time in single ECC as well as multiple-ECC are compared with the help of various examples.
Hard starting every initial stage: Study on Less Engine Pulling PowerIJCERT
Automobiles engine, it is a prime mover; and a machine in which power is applied to do work, often in the form of converting heat energy into mechanical work. We know that the power unit of an automobile is called I C engine. The power developed by the engine depends upon the calorific value of the fuel used. This value is equal to the total heat produced to combustion of hydrogen and carbon. Fuel injection pump (FIP), has to supply various quantities of fuel in accordance with the different engine load and in-line pumps, which correctly positioned connects the fuel supply from gallery. Diesel engines compress pure air during compression stroke and must have some means to force fuel into the combustion chamber with a pressure higher from the compression pressure. The injection nozzle atomizes the fuel very small droplets (3 to 30 microns) and delivers it to the combustion chamber. This achieved by small orifice.
Data Security Using Elliptic Curve CryptographyIJCERT
Cryptography technique is used to provide data security. In existing cryptography technique the key generation takes place randomly. Key generation require shared key. If shared key is access by unauthorized user then security becomes disoriented. Hence existing problems are alleviated to give more security to data. In proposed system a algorithm called as Elliptic Curve Cryptography is used. The ECC generates the key by using the point on the curve. The ECC is used for generating the key by using point on the curve and encryption and decryption operation takes place through curve. In the proposed system the encryption and key generation process takes place rapidly.
SecCloudPro: A Novel Secure Cloud Storage System for Auditing and DeduplicationIJCERT
In this paper, we show the trustworthiness evaluating and secure deduplication over cloud data utilizing imaginative secure frameworks .Usually cloud framework outsourced information at cloud storage is semi-trusted because of absence of security at cloud storage while putting away or sharing at cloud level because of weak cryptosystem information may be uncover or adjusted by the hackers keeping in mind the end goal to ensure clients information protection and security We propose novel progressed secure framework i.e SecCloudPro which empower the cloud framework secured and legitimate utilizing Verifier(TPA) benefit of Cloud Server. Additionally our framework performs data deduplication in a Secured way in requested to enhance the cloud Storage space too data transfer capacity i.e bandwidth.
Handling Selfishness in Replica Allocation over a Mobile Ad-Hoc NetworkIJCERT
MANET is a collection of mobile devices that can communicate with each other without the use centralized administration. One of the interesting application of MANET is File Sharing. File Sharing in MANET is similar to that of the regular file sharing, what makes the difference is it allow user to access the data or memory of that nodes only which are connected to it. This File sharing many a times leads to Network Partitioning, i.e dividing a network into two different networks .Due to which the nodes may act selfishly. The selfishness of some of the nodes may lead in reduction of performance in terms of accessing data. The proposed system will use the SCF-tree technique for building a tree of Node which will share their data in terms of Replica, and as a result it detects the selfish node in the network. The replica insures that performance is not degraded.
GSM Based Device Controlling and Fault DetectionIJCERT
The mobile communication has expanded to a great extent such that it can be applied for controlling of electrical devices. These projects make use of this capability of mobile phone to control three electrical devices with some use of embedded technology which can be extended up to eight devices. Apart from controlling it also does the sensing of the devices. Thus a user can be able to know of the status of the devices and in addition to that the user get notified if any fault is detected. Here in the project controlling and sensing is done for three electrical devices only. According to the user need both of this can be expanded.
Efficient Multi Server Authentication and Hybrid Authentication MethodIJCERT
Password is used for authentication on many major client-server system, websites etc. Client and a server share a password using Password-authenticated key exchange to authenticate each other and establish a cryptographic key by exchanging generated exchanges. In this scenario, all the passwords are stored in a single server which will authenticate the client. If the server stopped working or compromised, for example, hacking or even insider attack, passwords stored in database will become publicly known. This system proposes that setting where multiple servers which are used to, so that the password can be split in these servers authenticate client and if one server is compromised, the attacker still cannot be able to view the client’s information from the compromised server. This system uses the Advance encryption standard algorithm encryption and for key exchange and some formulae to store the password in multiple server. This system also has the hybrid authentication as another phase to make it more secure and efficient. In the given authentication schema we also use SMS integration API for two step verification.
Data Trend Analysis by Assigning Polynomial Function For Given Data SetIJCERT
This paper aims at explaining the method of creating a polynomial equation out of the given data set which can be used as a representation of the data itself and can be used to run aggregation against itself to find the results. This approach uses least-squares technique to construct a model of data and fit to a polynomial. Differential calculus technique is used on this equation to generate the aggregated results that represents the original data set.
Online Payment System using Steganography and Visual CryptographyIJCERT
In recent time there is rapid growth in E-Commerce market. Major concerns for customers in online shopping are debit card or credit card fraud and personal information security. Identity theft and phishing are common threats of online shopping. Phishing is a method of stealing personal confidential information such as username, passwords and credit card details from victims. It is a social engineering technique used to deceive users. In this paper new method is proposed that uses text based steganography and visual cryptography. It represents new approach which will provide limited information for fund transfer. This method secures the customer's data and increases customer's confidence and prevents identity theft.
Prevention of Packet Hiding Methods In Selective Jamming AttackIJCERT
The sharing nature of wireless medium provides various challenging features among various set of users. It is very important in real world and it provides better transfer rate but authentication is ignored. The limitations of existing wired network are overcome by wireless network. These networks act as source for various types of jamming attacks. In analysis and detection of jamming attack various methods are available but sometime they fail. In case of external threat the analysis and reporting of jamming attack is very easy model but it is quite difficult in terms of internal threat model, these internal term uses the knowledge about network secrets and network protocols to launch various attacks with very low effort. Various cryptographic techniques are implemented to prevent these attacks. The main goal of this project is to prevent the information at the wireless physical layer and allowed the safe transmission among communicated nodes although the attacker is present.
A collection of mobile nodes is known as ad-hoc network in which wireless communication network is used to connect these mobile nodes. A major requirement on the MANET is to provide unidentifiability and unlinkability for mobile nodes During the last few decades, continuous progresses in wireless communications have opened new research fields in computer networking, goal of extending data networks connectivity to environments where wired solutions are impracticable. Among these, vehicular traffic is attracting a increasing attention from both academic and industry, due to the amount and importance of the related applications, ranging from road safety to traffic control, up to mobile entertainment. Vehicular Ad-hoc Network(VANETs) are self-organized networks built up from moving vehicles, and are part of the broader class of Mobile Ad-hoc Net- works(MANETs). Because of their peculiar characteristics, VANETs require the definition of specific networking techniques, whose feasibility and performance are usually tested by means of simulation. One of the main challenges posed by VANETs simulations is the faithful characterization of vehicular mobility at both macroscopic and microscopic levels, leads to realistic non-uniform distributions of cars and velocity, and unique connectivity dynamics. There are various secure routing protocols have been proposed, but the requirement is not satisfied. The existing protocols are unguarded to the attacks of fake routing packets. Simulation results have demonstrated the effectiveness of the proposed AODV protocol with improved performance as compared to the existing protocols.
Intelligent Device TO Device Communication Using IoTIJCERT
Internet is becoming the most intrinsic part of the human life. There are many users of the internet but the devices will be the main users in the Internet of Things (IoT). These devices communicate with each other efficiently and gather the information to transfer the data to particular device. The quality of this information depends on how smart the devices are. IoT coverage is very wide and consists of the things or devices connected in network like camera, android phones, sensors etc. Once all these devices are connected with each other, they are capable of processing smartly and satisfying basic needs of environment. Thus the communication between the devices is achieved using various technologies and devices.
Secure Routing for MANET in Adversarial EnvironmentIJCERT
Collection of mobile nodes is known as ad-hoc network in which wireless communication is used to connect these mobile nodes. A major requirement on the MANET is to provide unidentifiability and unlinkability for mobile nodes. There are various secure routing protocols have been proposed, but the requirement is not satisfied. The existing protocols are unguarded to the attacks of fake routing packets or denial-of-service broadcasting, even the node identities are protected by pseudonyms. We propose a new secure routing protocol which provides anonymity named as authenticated anonymous secure routing (AASR), to satisfy the requirement of mobile networks and defend the attacks. The route request packets are authenticated by a group signature and public key infrastructure, to defend the potential attacks without exposing the node identities. The cocept of key-encrypted onion routing which provides a route secret verification message, to prevent intermediate nodes from inferring a real destination. Simulation results have demonstrated the effectiveness of the proposed AASR protocol with improved performance as compared to the existing protocols.
Real Time Detection System of Driver FatigueIJCERT
The leading cause of vehicle crashes and accidents is the driver distraction. With the rapid development of motorization, driver fatigue has become a very serious traffic problem. Reasons for traffic accidents are driving after alcohol consumption, driving at night, driving without taking rest, aging, sleepiness, and fatigue occurred due to continuous driving, long working hours and night shifts. So to reduce rate of accidents due to above reasons, is aim of this project. This paper presents a method for detection of early signs of fatigue using feature extraction, Haar classifier and delivering of information and whereabouts of the driver to the emergency contact numbers.
A Survey on Web Page Recommendation and Data PreprocessingIJCERT
In today’s era, as we all know internet technologies are growing rapidly. Along with this, instantly, Web page recommendations are also improving. The aim of a Web page recommender system is to predict the Web page or pages, which will be visited from a given Web-page of a website. Data preprocessing is one basic and essential part of Web page recommendation. Data preprocessing consists of cleanup and constructing data to organize for extracting pattern. In this paper, we discuss and focus on Web page Recommendation and role of data preprocessing in Web page recommendation, considering how data preprocessing is related to Web page recommendation.
This document does not contain any substantive information to summarize in 3 sentences or less. It only provides a generic instruction to visit an unspecified website for more information and does not include any details about the topic or content being referenced.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.