A Novel Approach for improving Post Classification Accuracy of Satellite Images by Using Majority Analysis

In past one year, due to climatic changes and some anthropogenic activities, the forests of Uttarakhand are burning. To identify the damage caused by the forest fires, an area of Nainital district has been taken for the study. Multi temporal Landsat 7 images were taken from April - 2020 and April – 2021. This paper shows a novel approach to increase the accuracy of the classified image. The Support Vector Machine classification is first done and then to improve the accuracy of the classified image, a post-classification technique called Majority Analysis is applied. This method helps to classify the unclassified pixel and it also smoothens out the boundary of the classified pixels, leading to higher accuracy rate. The classification accuracy has improved significantly for April 2020 and April 2021 images from 89.35% to 98.71% and from 88.52% to 99.76% respectively. The change detection study showed a drastic increase in the barren land due to the forest fires and on the contrary, the forest, scarce forest and the shrub land areas have decreased

Development of Geo-Visualized Information System for states of India based on rainfall using ArcGIS

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Sentiment Classification for Film Reviews in Gujarati Text Using Machine Learning and Sentiment Lexicons

In this paper, two techniques for sentiment classification are proposed: Gujarati Lexicon Sentiment Analysis (GLSA) and Gujarati Machine Learning Sentiment Analysis (GMLSA) for sentiment classification of Gujarati text film reviews. Five different datasets were produced to validate the machine learning-based and lexicon-based methods’ accuracy. The lexicon-based approach employs a sentiment lexicon known as GujSentiWordNet, which identifies sentiments with a sentiment score for feature generation, while in the machine learning-based approach, five classifiers are used: logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM), naive Bayes (NB) with TF-IDF, and count vectorizer for feature selection. Experiments were carried out and the results obtained were compared using accuracy, precision, recall, and F-score as performance evaluation criteria. According to the test results, the machine learning-based technique improved accuracy by 3 to 10% on average when compared to the lexicon-based approach

Sentiment analysis on film review in Gujarati language using machine learning

Opinion analysis is by a long shot most basic zone of characteristic language handling. It manages the portrayal of information to choose the motivation behind the wellspring of the content. The reason might be of a type of gratefulness (positive) or study (negative). This paper offers a correlation between the outcomes accomplished by applying the calculation arrangement using various classifiers for instance K-nearest neighbor and multinomial naive Bayes. These techniques are utilized to assess a significant assessment with either a positive remark or negative remark. The gathered information considered on the grounds of the extremity film datasets and an association with the results accessible proof has been created for a careful assessment. This paper investigates the word level count vectorizer and term frequency inverse document frequency (TF-IDF) influence on film sentiment analysis. We concluded that multinomial Naive Bayes (MNB) classier generate more accurate result using TF-IDF vectorizer compared to CountVectorizer, K-nearest-neighbors (KNN) classifier has the same accuracy result in case of TF-IDF and CountVectorizer

Step Patterns on Vicinal Reconstructed Surfaces

Step patterns on vicinal $(2\times1)$ reconstructed surfaces of noble metals Au(110) and Pt(110), miscut towards the (100) orientation, are investigated. The free energy of the reconstructed surface with a network of crossing opposite steps is calculated in the strong chirality regime when the steps cannot make overhangs. It is explained why the steps are not perpendicular to the direction of the miscut but form in equilibrium a network of crossing steps which make the surface to look like a fish skin. The network formation is the consequence of competition between the -- predominantly elastic -- energy loss and entropy gain. It is in agreement with recent scanning-tunnelling-microscopy observations on vicinal Au(110) and Pt(110) surfaces.Comment: 11 pages with 5 eps figures in text. Uses psfig and elsart.sty (ELSEVIER Science). To be published in Surf. Sc

A METHODICAL REVIEW OF SECURITY MARKETS USING STATISTICAL AND MACHINE LEARNING TECHNIQUES

Stock market pattern predictions are considered to be an important and most effectiveactivity. Therefore, stock prices will yield lucrative gains, if they make informed decisions.Stock market-related forecasts are a major challenge for investors due to stagnant and noisydata. Therefore, forecasting the stock market is a big challenge for investors to invest theirmoney for more profit. Stock market predictions use mathematical strategies and learningtools. This study provides a comprehensive overview ofout of 30 research papersrecommending methods, including computational methods, machine learning algorithms,performance parameters, and selected publications. Studies are selected based on researchquestions. Therefore, these selected studies help to find the ML techniques along with theirdata set for stock market forecasting. Most ANN and NN techniques are used to get accuratestock market forecasts. Although a lot of work has been done, the latest stock market-relatedprediction methodology has many limitations. In this study, it can be assumed that the stockmarket forecast is an integrated process and the characteristic parameters for the stock marketforecast should be examined more closely

Applications of Image Processing for Grading Agriculture products

Image processing in the context of Computer vision, is one of the renowned topic of computer science and engineering, which has played a vital role in automation. It has eased in revealing unknown fact in medical science, remote sensing, and many other domains. Digital image processing along with classification and neural network algorithms has enabled grading of various things. One of prominent area of its application is classification of agriculture products and especially grading of seed or cereals and its cultivars. Grading and sorting system allows maintaining the consistency, uniformity and depletion of time. This paper highlights various methods used for grading various agriculture products. DOI: 10.17762/ijritcc2321-8169.15036

Enhancing Face Recognition with Deep Learning Architectures: A Comprehensive Review

The progression of information discernment via facial identification and the emergence of innovative frameworks has exhibited remarkable strides in recent years. This phenomenon has been particularly pronounced within the realm of verifying individual credentials, a practice prominently harnessed by law enforcement agencies to advance the field of forensic science. A multitude of scholarly endeavors have been dedicated to the application of deep learning techniques within machine learning models. These endeavors aim to facilitate the extraction of distinctive features and subsequent classification, thereby elevating the precision of unique individual recognition. In the context of this scholarly inquiry, the focal point resides in the exploration of deep learning methodologies tailored for the realm of facial recognition and its subsequent matching processes. This exploration centers on the augmentation of accuracy through the meticulous process of training models with expansive datasets. Within the confines of this research paper, a comprehensive survey is conducted, encompassing an array of diverse strategies utilized in facial recognition. This survey, in turn, delves into the intricacies and challenges that underlie the intricate field of facial recognition within imagery analysis

Sentiment Classification for Film Reviews in Gujarati Text Using Machine Learning and Sentiment Lexicons

In this paper, two techniques for sentiment classification are proposed: Gujarati Lexicon Sentiment Analysis (GLSA) and Gujarati Machine Learning Sentiment Analysis (GMLSA) for sentiment classification of Gujarati text film reviews. Five different datasets were produced to validate the machine learning-based and lexicon-based methods’ accuracy. The lexicon-based approach employs a sentiment lexicon known as GujSentiWordNet, which identifies sentiments with a sentiment score for feature generation, while in the machine learning-based approach, five classifiers are used: logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM), naive Bayes (NB) with TF-IDF, and count vectorizer for feature selection. Experiments were carried out and the results obtained were compared using accuracy, precision, recall, and F-score as performance evaluation criteria. According to the test results, the machine learning-based technique improved accuracy by 3 to 10% on average when compared to the lexicon-based approach

NNSA ASC Exascale Environment Planning, Applications Working Group, Report February 2011

The scope of the Apps WG covers three areas of interest: Physics and Engineering Models (PEM), multi-physics Integrated Codes (IC), and Verification and Validation (V&V). Each places different demands on the exascale environment. The exascale challenge will be to provide environments that optimize all three. PEM serve as a test bed for both model development and 'best practices' for IC code development, as well as their use as standalone codes to improve scientific understanding. Rapidly achieving reasonable performance for a small team is the key to maintaining PEM innovation. Thus, the environment must provide the ability to develop portable code at a higher level of abstraction, which can then be tuned, as needed. PEM concentrate their computational footprint in one or a few kernels that must perform efficiently. Their comparative simplicity permits extreme optimization, so the environment must provide the ability to exercise significant control over the lower software and hardware levels. IC serve as the underlying software tools employed for most ASC problems of interest. Often coupling dozens of physics models into very large, very complex applications, ICs are usually the product of hundreds of staff-years of development, with lifetimes measured in decades. Thus, emphasis is placed on portability, maintainability and overall performance, with optimization done on the whole rather than on individual parts. The exascale environment must provide a high-level standardized programming model with effective tools and mechanisms for fault detection and remediation. Finally, V&V addresses the infrastructure and methods to facilitate the assessment of code and model suitability for applications, and uncertainty quantification (UQ) methods for assessment and quantification of margins of uncertainty (QMU). V&V employs both PEM and IC, with somewhat differing goals, i.e., parameter studies and error assessments to determine both the quality of the calculation and to estimate expected deviations of simulations from experiments. The exascale environment must provide a performance envelope suitable both for capacity calculations (high through-put) and full system capability runs (high performance). Analysis of the results place shared demand on both the I/O as well as the visualization subsystems