Global Transitions Proceedings最新文献

With the increase in social networks, more number of people are creating and sharing information than ever before, many of them have no relevance to reality. Due to this, fake news for various political and commercial purposes are spreading quickly. Online newspaper has made it challenging to identify trustworthy news sources. In this work, Hindi news articles from various news sources are collected. Preprocessing, feature extraction, classification and prediction processes are discussed in detail. Different machine learning algorithms such as Naïve Bayes, logistic regression and Long Short-Term Memory (LSTM) are used to detect the fake news. The preprocessing step includes data cleaning, stop words removal, tokenizing and stemming. Term frequency inverse document frequency(TF-IDF) is used for feature extraction. Naïve Bayes, logistic regression and LSTM classifiers are used and compared for fake news detection with probability of truth. It is observed that among these three classifiers, LSTM achieved best accuracy of 92.36%.

In the modern world, where technology is at the forefront of every industry, there has been an overload of information and data. Thus, a recommendation system comes in handy to deal with this large volume of data and filter out the useful information which is fast and relevant to the user's choice. This paper describes an approach to a movie recommendation system using Cosine Similarity to recommend similar movies based on the one chosen by the user. Although the existing recommendation systems get the job done, it does not justify if the movie is worth spending time on. To enhance the user experience, this system performs sentiment analysis on the reviews of the movie chosen using machine learning. Two of the supervised machine learning algorithms Naïve Bayes (NB) Classifier and Support Vector Machine (SVM) Classifier are used to increase the accuracy and efficiency. This paper also gives a comparison between NB and SVM on the basis of parameters like Accuracy, Precision, Recall and F1 Score. The accuracy score of SVM came out to be 98.63% whereas accuracy score of NB is 97.33%. Thus, SVM outweighs NB and proves to be a better fit for Sentiment Analysis.

The development of HVDC transmission technology using an MMC has been promoted, by overcoming the drawbacks of traditional VSC technology. The extension of flexible transmission to overhead lines, especially the use of HVDC transmission based on an MMC, raises the issue of DC fault. So identification of fault, clearing the dc fault, and design of fast-acting protection system operating against fault becomes significant. This article provides insights on the monopolar structured MMC and operational characteristics, fault analysis, and a fault protection scheme. DC line faults on HVDC lines using MMC-VSC are major issues; isolation of complete system is not a viable option. It is observed that Pole to ground fault is the most common fault, which leads to generous overcurrent in the AC grid and results in converter valves getting damaged. This article analyzes the response of MMC-HVDC under different DC and AC faults conditions for five-level MMC HVDC systems, to better understand systems under fault. Faults also have an impact on the converter stations' performance. The voltages fluctuate in faulty situations. In comparison to the inverter station, the rectifier station has the most impact. Simulation is performed out in PSCAD software. The correctness and effectiveness of DC and AC fault analysis helps to check the capability of locating fault occurring on HVDC transmission lines quickly and accurately

A network system called Wireless Sensor Network is made up of wireless sensor node devices that are spread at random (WSN). WSNs are a critical paradigm for the Internet of Things' evolution (IoT). Strong security measures must be done to protect the network from security threats and malicious assaults in order to make it more efficient. To identify and isolate hostile sensor nodes in a cloud-assisted WSN-IoT system, the Trusted Anonymous Lightweight Attacker Detection (TALAD) scheme is presented. The TALAD strategy creates a routing path to the cloud with highly trusted nodes, subject to a desired path length limit. Using the binomial algebraic theorem, the node identities are formed with bogus identities, and the original identity is hidden from the other nodes in the network. If only the forward key and the reverse key string are matched, the nodes' original identities are exposed. The forward and reverse keys are mapped using a context-free grammar rule. Even when a major chunk of the network drops to forward packets, TALAD successfully avoids incursions, according to the simulation results.

Power saving is the need of the day and in India it is a major issue to be looked into. To get uninterrupted supply nowadays power backups such as inverters and UPS are used commonly. If it is a traditional UPS it is difficult to know remaining power and time till it can supply energy in terms of power. In order to overcome this issue, a design is proposed in the following paper. Working model of microcontroller based intelligent Uninterrupted Power Supply (UPS) system for power management in laboratory is worked upon. The appliances of lab viz. computers, fans, lights are automatically controlled during power failure according to their priority to ensure optimal utilization of UPS power. This work mainly concentrates on two key points. Firstly, calculating possible time of battery run-away and displaying it on user screen; secondly, prioritizing and operating different appliances according user requirement. The results are far satisfactory and can be implemented in real time analysis.

Synthetic aperture radar is an advanced remote sensing and imaging radar. It plays vital role in acquiring high resolution images of earth surface. The capturing of images by synthetic aperture radar is done in any season immaterial of weather conditions. This paper gives the details of the basic feature extraction for the snow images. The two sample images are analyzed to know the feature details of the object under consideration. Analytical details of variation in entropy and the polarization were considered. The scattering mechanism involved in the snow area is analyzed. The details of snow classification based on its layered structure along with its physical nature like moisture involved are presented. The results indicate a high value of entropy of 0.94 for the snow image. The reason for high entropy is because of more surface uniformity in the snow images. The flat surface structured snow basically exhibits the surface scattering mechanism.

Arabic text classification is one application of Natural Language Processing (NLP). It has been used to analyze and categorize Arabic text. Analyzing text has become an essential part of our lives because of the increasing number of text data which makes text classification a big data problem. Arabic text classification systems become significant to maintain vital information in many domains such as education, and health sector, and public services. In the presented research work, the Arabic text classification model is developed using various algorithms namely Multinomial Naïve Bayesian (MNB), Bernoulli Naïve Bayesian (BNB), Stochastic Gradient Descent (SGD), Logistic Regression (LR), Support vector classifier (SVC), Linear SVC, and convolutional neural networks (CNN). These algorithms have been implemented utilizing the Al-Khaleej dataset. The experiments are carried out with various representation models and it is observed that CNN with character level model outperforms others. The result of CNN exceeds the state-of-the-art machine learning method with an accuracy equal to 98. The presented methods will be useful in different domains, particularly on social media.

A DVR is being used to minimize the power quality problem. The SPWM method has been popularly used in DVR but use of the DC bus is restricted in SPWM, which is critical due to economical as well as power packaging density improvements. The proposed THIPWM scheme has been deduced from SPWM by adding one-sixth of the third harmonic signal in the fundamental signal. Repetitive Controller (RC) with Third Harmonic Injection (THI) technique is applied to achieve better dc-link utilization for medium-low voltage distribution. As an outcome, it's indeed evident that 15% reflects an increment in dc bus utilization as well as the potential to create high voltage AC. The control system comprises a feed-forward as well as feedback run to enhance the transient response & confirm the steady zero error. THD results improve when the THIPWM methodology is being used. PSCAD software is being used for the computation, which generated accurate findings for the THIPWM and Repetitive Control systems approach.

This review paper provides an overview of data pre-processing in Machine learning, focusing on all types of problems while building the machine learning problems. It deals with two significant issues in the pre-processing process (i). issues with data and (ii). Steps to follow to do data analysis with its best approach. As raw data are vulnerable to noise, corruption, missing, and inconsistent data, it is necessary to perform pre-processing steps, which is done using classification, clustering, and association and many other pre-processing techniques available. Poor data can primarily affect the accuracy and lead to false prediction, so it is necessary to improve the dataset's quality. So, data pre-processing is the best way to deal with such problems. It makes the knowledge extraction from the data set much easier with cleaning, Integration, transformation, and reduction methods. The issue with Data missing and significant differences in the variety of data always exists as the information is collected through multiple sources and from a real-world application. So, the data augmentation approach generates data for machine learning models. To decrease the dependency on training data and to improve the performance of the machine learning model. This paper discusses flipping, rotating with slight degrees and others to augment the image data and shows how to perform data augmentation methods without distorting the original data.

This paper aimed to demonstrate the reliability of the Over Current protection (OCP) scheme in protecting microgrids with inverter interfaced RES for low voltage distribution networks. To prove this reliability, the PSCAD/EMTDC simulation software was used to conduct simulations for the OCP scheme, while comparing throughout grid-connected mode with and without PV generation, as well as in island mode. The computations are carried out using a model of a CIGRE low voltage distribution system. The OCP average relay tripping time for SLG faults through grid mode without PV has been 0.131 s, & 0.121 s for LLL faults. With regards to PV generators, the average relay tripping time increased to 0.199 s & 0.135 s, including both. This is due to the fault current contributed by PV generation inclusion, which restricts the current seen by the predefined OC relays. The findings revealed that some OC relays failed to trip in island mode causing a loss of coordination and a decrease in fault currents. The system was further tested for different generation levels (15%, 57%, and 81%) in island mode and gave a negligible difference in average tripping time for different generation levels.