Web Blog Content Curation Using Fuzzy-Related Capsule Network-Based Auto Encoder

2022 ◽  
Author(s):  
Harsh Khatter ◽  
Anil Ahlawat
Keyword(s):  
Performance Measures ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Sentence Length ◽  
The Internet ◽  
Machine Learning Algorithm ◽  
Proper Noun ◽  
The Novel ◽  
Sentence Position ◽  
Day By Day

The internet content increases exponentially day-by-day leading to the pop-up of irrelevant data while searching. Thus, the vast availability of web data requires curation to enhance the results of the search in relevance to searched topics. The proposed F-CapsNet deals with the content curation of web blog data through the novel integration of fuzzy logic with a machine learning algorithm. The input content to be curated is initially pre-processed and seven major features such as sentence position, bigrams, TF-IDF, cosine similarity, sentence length, proper noun score and numeric token are extracted. Then the fuzzy rules are applied to generate the extractive summary. After the extractive curation, the output is passed to the novel capsule network based deep auto-encoder where the abstractive summary is produced. The performance measures such as precision, recall, F1-score, accuracy and specificity are computed and the results are compared with the existing state-of-the-art methods. From the simulations performed, it has been proven that the proposed method for content curation is more efficient than any other method.

scholarly journals Machine Learning for Internet of Things

2019 ◽  
Vol 8 (07) ◽  
pp. 24680-24782
Author(s):  
Manisha Bagri ◽  
Neha Aggarwal
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
Weather Prediction ◽  
Support Vector ◽  
The Internet ◽  
Machine Learning Algorithm ◽  
Learning Patterns ◽  
New Development ◽  
Share Data

By 2020 around 25-50 billion devices are likely to be connected to the internet. Due to this new development, it gives rise to something called Internet of Things (IoT). The interconnected devices can generate and share data over a network. Machine Learning plays a key role in IoT to handle the vast amount of data. It gives IoT and devices a brain to think, which is often called as intelligence. The data can be feed to machines for learning patterns, based on training the machines can identify to predict for the future. This paper gives a brief explanation of IoT. This paper gives a crisp explanation of machine learning algorithm and its types. However, Support Vector Machine (SVM) is explained in details along with its merits and demerits. An algorithm is also proposed for weather prediction using SVM for IoT.

scholarly journals A RESTful middleware for AI controlled sensors, actuators and smart devices

2019 ◽  
Vol 11 (7) ◽  
pp. 2963-2986 ◽  
Author(s):  
Nikos Dipsis ◽  
Kostas Stathis
Keyword(s):  
Smart Home ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Smart Devices ◽  
Machine Learning Algorithm ◽  
Concrete Case ◽  
Domain Specific ◽  
Sensor Actuator ◽  
Current State

Abstract The numerous applications of internet of things (IoT) and sensor networks combined with specialized devices used in each has led to a proliferation of domain specific middleware, which in turn creates interoperability issues between the corresponding architectures and the technologies used. But what if we wanted to use a machine learning algorithm to an IoT application so that it adapts intelligently to changes of the environment, or enable a software agent to enrich with artificial intelligence (AI) a smart home consisting of multiple and possibly incompatible technologies? In this work we answer these questions by studying a framework that explores how to simplify the incorporation of AI capabilities to existing sensor-actuator networks or IoT infrastructures making the services offered in such settings smarter. Towards this goal we present eVATAR+, a middleware that implements the interactions within the context of such integrations systematically and transparently from the developers’ perspective. It also provides a simple and easy to use interface for developers to use. eVATAR+ uses JAVA server technologies enhanced by mediator functionality providing interoperability, maintainability and heterogeneity support. We exemplify eVATAR+ with a concrete case study and we evaluate the relative merits of our approach by comparing our work with the current state of the art.

Automatic microseismic event picking via unsupervised machine learning

2020 ◽  
Vol 222 (3) ◽  
pp. 1750-1764 ◽  
Author(s):  
Yangkang Chen
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
State Of The Art ◽  
The State ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Unsupervised Machine Learning ◽  
Earthquake Data

SUMMARY Effective and efficient arrival picking plays an important role in microseismic and earthquake data processing and imaging. Widely used short-term-average long-term-average ratio (STA/LTA) based arrival picking algorithms suffer from the sensitivity to moderate-to-strong random ambient noise. To make the state-of-the-art arrival picking approaches effective, microseismic data need to be first pre-processed, for example, removing sufficient amount of noise, and second analysed by arrival pickers. To conquer the noise issue in arrival picking for weak microseismic or earthquake event, I leverage the machine learning techniques to help recognizing seismic waveforms in microseismic or earthquake data. Because of the dependency of supervised machine learning algorithm on large volume of well-designed training data, I utilize an unsupervised machine learning algorithm to help cluster the time samples into two groups, that is, waveform points and non-waveform points. The fuzzy clustering algorithm has been demonstrated to be effective for such purpose. A group of synthetic, real microseismic and earthquake data sets with different levels of complexity show that the proposed method is much more robust than the state-of-the-art STA/LTA method in picking microseismic events, even in the case of moderately strong background noise.

The Happy Valley Band: Creative (Mis)Transcription

2016 ◽  
Vol 26 ◽  
pp. 76-78
Author(s):  
David Kant
Keyword(s):  
Machine Learning ◽  
Open Source Software ◽  
Learning Process ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Machine Learning Algorithm ◽  
Digital Analysis ◽  
Artistic Practice ◽  
Software Suite ◽  
Happy Valley

In the author’s work as a composer, he explores how state-of-the-art digital sound analysis can change how we listen to music. The Happy Valley Band (HVB) is a product of this exploration and encompasses a repertoire of microtonal deconstructions of pop songs, an open-source software suite and a dedicated performing ensemble. This article documents the author’s experience and artistic practice within this project—a process of translation between digital analysis, human listening and written notation, in which a machine-learning algorithm is trained to hear pop songs and the results of the machine-learning process are transcribed into musical notation and performed by instrumentalists.

E-Mail Classification Based Learning Algorithm Using Support Vector Machine

2012 ◽  
Vol 268-270 ◽  
pp. 1844-1848
Author(s):  
Mu Hee Song
Keyword(s):  
Support Vector Machine ◽  
High Performance ◽  
Learning Algorithm ◽  
Vector Model ◽  
Support Vector ◽  
The Internet ◽  
Machine Learning Algorithm ◽  
Bayes Classifier ◽  
Probability Methods ◽  
E Mail

Due to the distribution of personal computers and the internet, E-mail has become one of the most widely used communicative means. However, a massive amount of spam mail is polluting mailboxes everyday, taking advantage of the ability to send mail to any number of random people through the internet. In this paper we will introduce an efficient method of classifying E-mails using the SVM(Support Vector Machine) learning algorithm, which is recently showing high performance in the field of classifying documents. The disposition of the words inside the E-mail documents are extracted, and the performance of classification is compared and examined through the learning based on the change of DF value which occurs to reduce the disposition space in the learning level. To assess the performance of the SVM, the SVM is compared to the Naïve Bayes classifier (which uses probability methods) and a vector model classifier in order to verify that the method of using the learning algorithm of SVM shows better performance.

scholarly journals Active Learning Using Hint Information

2015 ◽  
Vol 27 (8) ◽  
pp. 1738-1765 ◽  
Author(s):  
Chun-Liang Li ◽  
Chun-Sung Ferng ◽  
Hsuan-Tien Lin
Keyword(s):  
Support Vector Machine ◽  
Active Learning ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Support Vector ◽  
The Novel ◽  
Real World Data ◽  
Learning Framework ◽  
Learning Tasks ◽  
Stable Performance

The abundance of real-world data and limited labeling budget calls for active learning, an important learning paradigm for reducing human labeling efforts. Many recently developed active learning algorithms consider both uncertainty and representativeness when making querying decisions. However, exploiting representativeness with uncertainty concurrently usually requires tackling sophisticated and challenging learning tasks, such as clustering. In this letter, we propose a new active learning framework, called hinted sampling, which takes both uncertainty and representativeness into account in a simpler way. We design a novel active learning algorithm within the hinted sampling framework with an extended support vector machine. Experimental results validate that the novel active learning algorithm can result in a better and more stable performance than that achieved by state-of-the-art algorithms. We also show that the hinted sampling framework allows improving another active learning algorithm designed from the transductive support vector machine.

scholarly journals A novel machine learning algorithm has the potential to reduce by 1/3 the quantity of ILR episodes needing review

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
A Rosier ◽  
E Crespin ◽  
A Lazarus ◽  
G Laurent ◽  
A Menet ◽  
...  
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
Learning Algorithm ◽  
False Positive Rate ◽  
Machine Learning Algorithm ◽  
The Novel ◽  
Funding Sources ◽  
High Workload ◽  
Positive Rate ◽  
The Impact

Abstract Background Implantable Loop Recorders (ILRs) are increasingly used and generate a high workload for timely adjudication of ECG recordings. In particular, the excessive false positive rate leads to a significant review burden. Purpose A novel machine learning algorithm was developed to reclassify ILR episodes in order to decrease by 80% the False Positive rate while maintaining 99% sensitivity. This study aims to evaluate the impact of this algorithm to reduce the number of abnormal episodes reported in Medtronic ILRs. Methods Among 20 European centers, all Medtronic ILR patients were enrolled during the 2nd semester of 2020. Using a remote monitoring platform, every ILR transmitted episode was collected and anonymised. For every ILR detected episode with a transmitted ECG, the new algorithm reclassified it applying the same labels as the ILR (asystole, brady, AT/AF, VT, artifact, normal). We measured the number of episodes identified as false positive and reclassified as normal by the algorithm, and their proportion among all episodes. Results In 370 patients, ILRs recorded 3755 episodes including 305 patient-triggered and 629 with no ECG transmitted. 2821 episodes were analyzed by the novel algorithm, which reclassified 1227 episodes as normal rhythm. These reclassified episodes accounted for 43% of analyzed episodes and 32.6% of all episodes recorded. Conclusion A novel machine learning algorithm significantly reduces the quantity of episodes flagged as abnormal and typically reviewed by healthcare professionals. FUNDunding Acknowledgement Type of funding sources: None. Figure 1. ILR episodes analysis

scholarly journals A Survey on Machine Learning Approach to Detect Malware

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Selvarathi C, Et. al.
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Optimal Solution ◽  
The Internet ◽  
Learning Approach ◽  
Machine Learning Algorithm ◽  
A Algorithm ◽  
Internet Users ◽  
Machine Learning Approach

Malware is one of the predominant challenges for the Internet users. In recent times, the injection of malwares into machines by anonymous hackers have been increased. This drives us to an urgent need of a system that detects a malware. Our idea is to build a system that learns with the previously collected data related to malwares and detects a malware in the give file, if it is present. We propose a various machine learning algorithm to detect a malware and indicates the user about the danger. In particular we propose to use a algorithm which give a optimal solution to hardware and software oriented malwares.

scholarly journals Automated Fruit Classification Using Deep Convolutional Neural Network

2020 ◽  
Vol 3 (2) ◽  
pp. 177-178
Author(s):  
John Jowil D. Orquia ◽  
El Jireh Bibangco
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
New Technologies ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Complex Model ◽  
Deep Convolutional Neural Network ◽  
Machine Learning Algorithm ◽  
Learning Models

Manual Fruit classification is the traditional way of classifying fruits. It is manual contact-labor that is time-consuming and often results in lesser productivity, inconsistency, and sometimes damaging the fruits (Prabha & Kumar, 2012). Thus, new technologies such as deep learning paved the way for a faster and more efficient method of fruit classification (Faridi & Aboonajmi, 2017). A deep convolutional neural network, or deep learning, is a machine learning algorithm that contains several layers of neural networks stacked together to create a more complex model capable of solving complex problems. The utilization of state-of-the-art pre-trained deep learning models such as AlexNet, GoogLeNet, and ResNet-50 was widely used. However, such models were not explicitly trained for fruit classification (Dyrmann, Karstoft, & Midtiby, 2016). The study aimed to create a new deep convolutional neural network and compared its performance to fine-tuned models based on accuracy, precision, sensitivity, and specificity.

scholarly journals Phishing Scam Detection using Machine Learning

2019 ◽  
Vol 9 (1S) ◽  
pp. 114-118
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Learning Algorithm ◽  
The Internet ◽  
Machine Learning Algorithm ◽  
Learning Approaches ◽  
Decision Tree Algorithm ◽  
Sensitive Data ◽  
Tree Algorithm ◽  
Testing Issue

As a wrongdoing of utilizing specialized intends to take sensitive data of clients and users in the internet, phishing is as of now an advanced risk confronting the Internet, and misfortunes due to phishing are developing consistently. Recognition of these phishing scams is a very testing issue on the grounds that phishing is predominantly a semantics based assault, which particularly manhandles human vulnerabilities, anyway not system or framework vulnerabilities. Phishing costs. As a product discovery plot, two primary methodologies are generally utilized: blacklists/whitelists and machine learning approaches. Every phishing technique has different parameters and type of attack. Using decision tree algorithm we find out whether the attack is legitimate or a scam. We measure this by grouping them with diverse parameters and features, thereby assisting the machine learning algorithm to edify.

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