Mushroom hunting is gaining popularity as a leisure activity for the last couple of years. Modern studies suggest that some mushrooms can be useful to treat anemia, improve body immunity, fight diabetes and a few are even effective to treat cancer. But not all the mushrooms prove to be beneficial. Some mushrooms are poisonous as well and consumption of these may result in severe illnesses in humans and can even cause death. This study aims to examine the data and build different supervised machine learning models that will detect if the mushroom is edible or poisonous. Principal Component Analysis PCA algorithm is used to select the best features from the dataset. Different classifiers like Logistic Regression, Decision Tree, K Nearest Neighbor KNN , Support Vector Machine SVM , Naïve Bayes and Random Forest are applied on the dataset of UCI to classify the mushrooms as edible or poisonous. The performance of the algorithms is compared using Receiver Operating Characteristic ROC Curve. Kanchi Tank "A Comparative Study on Mushroom Classification using Supervised Machine Learning Algorithms" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd42441.pdf Paper URL: https://www.ijtsrd.com/computer-science/embedded-system/42441/a-comparative-study-on-mushroom-classification-using-supervised-machine-learning-algorithms/kanchi-tank
Dictionaries and Tolerant Retrieval.pptManimaran A
The document discusses dictionaries and tolerant retrieval in information retrieval systems. It describes different data structures that can be used to store term dictionaries for inverted indexes, including arrays, hash tables, binary trees, and B-trees. It also discusses how to handle wildcard queries using techniques like permuterm indexes and k-gram indexes. The document explains methods for spell checking documents and queries, such as edit distance, weighted edit distance, n-gram overlap, and Soundex.
Dictionaries and Tolerant Retrieval.pptManimaran A
The document discusses dictionaries and tolerant retrieval in information retrieval systems. It describes different data structures that can be used to store term dictionaries for inverted indexes, including arrays, hash tables, binary trees, and B-trees. It also discusses how to handle wildcard queries using techniques like permuterm indexes and k-gram indexes. The document explains methods for spell checking documents and queries, such as edit distance, weighted edit distance, n-gram overlap, and Soundex.
This document discusses vector space retrieval models. It describes how documents and queries are represented as vectors in a common vector space based on terms. Terms are weighted using metrics like term frequency (TF) and inverse document frequency (IDF) to determine importance. The cosine similarity measure is used to calculate similarity between document and query vectors and rank results by relevance. While simple and effective in practice, vector space models have limitations like missing semantic and syntactic information.
Teoria i metodologia informatologii, 2019/20Sabina Cisek
Theory and Methodology of Information Science, a presentation for the information management students at the Jagiellonian University in Krakow, year 2019/20
Web content mining mines data from web pages including text, images, audio, video, metadata and hyperlinks. It examines the content of web pages and search results to extract useful information. Web content mining helps understand customer behavior, evaluate website performance, and boost business through research. It can classify data into structured, unstructured, semi-structured and multimedia types and applies techniques such as information extraction, topic tracking, summarization, categorization and clustering to analyze the data.
A Novel Approach for Tomato Diseases Classification Based on Deep Convolution...Mohammad Shakirul islam
This document summarizes Mohammad Shakirul Islam's research paper on classifying tomato plant diseases using deep convolutional neural networks. The paper includes sections on motivation, literature review, proposed methodology, results discussion, and future work. The proposed methodology uses a dataset of 3000 images across 6 tomato disease classes. A convolutional neural network model with 5 convolution layers, 5 max pooling layers, and 2 dense layers is trained on 80% of the data and tested on the remaining 20% for classification performance. Results show the model achieved high training and validation accuracy for identifying different tomato diseases.
Big Data & Text Mining: Finding Nuggets in Mountains of Textual Data
Big amount of information is available in textual form in databases or online sources, and for many enterprise functions (marketing, maintenance, finance, etc.) represents a huge opportunity to improve their business knowledge. For example, text mining is starting to be used in marketing, more specifically in analytical customer relationship management, in order to achieve the holy 360° view of the customer (integrating elements from inbound mails, web comments, surveys, internal notes, etc.).
Facing this new domain I have make a personal research, and realize a synthesis, which has help me to clarify some ideas. The below presentation does not intend to be exhaustive on the subject, but could perhaps bring you some useful insights.
Організація та ведення бібліотечних сторінок у FacebookZbarazh_CBS
Соціальні мережі для бібліотек є однією з найголовніших можливостей реклами своєї діяльності та послуг. Крім того, соціальні мережі – це найменш затратний вид піару та найбільш зручний, адже важливі інструменти, а це – комп’ютерне робоче місце та доступ до Інтернету є на сьогодні у багатьох бібліотеках, у тому числі і сільських.
The document discusses different indexing structures for information retrieval, including sequential files, inverted files, and suffix trees. It provides examples of how each structure is constructed and organized. Sequential files arrange all terms and their associated documents sequentially without pointers. Inverted files divide the index into a vocabulary listing terms alphabetically and associated postings files containing term locations. Suffix trees index the entire text as a single string and support complex queries by compactly representing all suffixes.
Розглядається представництво бібліотеки коледжу у Всесвітній мережі. З досвіду роботи бібліотеки ДРПБК. Обґрунтовано необхідність відмови від застарілих професійних стереотипів, вибудовувати нові моделі взаємин з користувачами.
The document summarizes a technical seminar on web-based information retrieval systems. It discusses information retrieval architecture and approaches, including syntactical, statistical, and semantic methods. It also covers web search analysis techniques like web structure analysis, content analysis, and usage analysis. The document outlines the process of web crawling and types of crawlers. It discusses challenges of web structure, crawling and indexing, and searching. Finally, it concludes that as unstructured online information grows, information retrieval techniques must continue to improve to leverage this data.
An Empirical Study on Mushroom Disease Diagnosis:A Data Mining ApproachIRJET Journal
This document summarizes a research study that used data mining techniques to diagnose diseases in mushroom crops. The study collected data on mushroom farms about diseases, symptoms and other factors. It then analyzed the data using classification algorithms like Naive Bayes, RIPPLE-DOWN RULES and SEQUENTIAL MINIMAL OPTIMIZATION to identify important symptoms for diagnosing diseases and determine the best performing algorithm. The results found the most important symptoms for diagnosis and the classification algorithm that identified diseases most accurately. This helps mushroom farmers better manage diseases and reduce crop losses.
Data mining techniques are rapidly developed for many applications. In recent year, Data mining in healthcare is an emerging field research and development of intelligent medical diagnosis system. Classification is the major research topic in data mining. Decision trees are popular methods for classification. In this paper many decision tree classifiers are used for diagnosis of medical datasets. AD Tree, J48, NB Tree, Random Tree and Random Forest algorithms are used for analysis of medical dataset. Heart disease dataset, Diabetes dataset and Hepatitis disorder dataset are used to test the decision tree models. Aung Nway Oo | Thin Naing ""Decision Tree Models for Medical Diagnosis"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23510.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/23510/decision-tree-models-for-medical-diagnosis/aung-nway-oo
This document discusses vector space retrieval models. It describes how documents and queries are represented as vectors in a common vector space based on terms. Terms are weighted using metrics like term frequency (TF) and inverse document frequency (IDF) to determine importance. The cosine similarity measure is used to calculate similarity between document and query vectors and rank results by relevance. While simple and effective in practice, vector space models have limitations like missing semantic and syntactic information.
Teoria i metodologia informatologii, 2019/20Sabina Cisek
Theory and Methodology of Information Science, a presentation for the information management students at the Jagiellonian University in Krakow, year 2019/20
Web content mining mines data from web pages including text, images, audio, video, metadata and hyperlinks. It examines the content of web pages and search results to extract useful information. Web content mining helps understand customer behavior, evaluate website performance, and boost business through research. It can classify data into structured, unstructured, semi-structured and multimedia types and applies techniques such as information extraction, topic tracking, summarization, categorization and clustering to analyze the data.
A Novel Approach for Tomato Diseases Classification Based on Deep Convolution...Mohammad Shakirul islam
This document summarizes Mohammad Shakirul Islam's research paper on classifying tomato plant diseases using deep convolutional neural networks. The paper includes sections on motivation, literature review, proposed methodology, results discussion, and future work. The proposed methodology uses a dataset of 3000 images across 6 tomato disease classes. A convolutional neural network model with 5 convolution layers, 5 max pooling layers, and 2 dense layers is trained on 80% of the data and tested on the remaining 20% for classification performance. Results show the model achieved high training and validation accuracy for identifying different tomato diseases.
Big Data & Text Mining: Finding Nuggets in Mountains of Textual Data
Big amount of information is available in textual form in databases or online sources, and for many enterprise functions (marketing, maintenance, finance, etc.) represents a huge opportunity to improve their business knowledge. For example, text mining is starting to be used in marketing, more specifically in analytical customer relationship management, in order to achieve the holy 360° view of the customer (integrating elements from inbound mails, web comments, surveys, internal notes, etc.).
Facing this new domain I have make a personal research, and realize a synthesis, which has help me to clarify some ideas. The below presentation does not intend to be exhaustive on the subject, but could perhaps bring you some useful insights.
Організація та ведення бібліотечних сторінок у FacebookZbarazh_CBS
Соціальні мережі для бібліотек є однією з найголовніших можливостей реклами своєї діяльності та послуг. Крім того, соціальні мережі – це найменш затратний вид піару та найбільш зручний, адже важливі інструменти, а це – комп’ютерне робоче місце та доступ до Інтернету є на сьогодні у багатьох бібліотеках, у тому числі і сільських.
The document discusses different indexing structures for information retrieval, including sequential files, inverted files, and suffix trees. It provides examples of how each structure is constructed and organized. Sequential files arrange all terms and their associated documents sequentially without pointers. Inverted files divide the index into a vocabulary listing terms alphabetically and associated postings files containing term locations. Suffix trees index the entire text as a single string and support complex queries by compactly representing all suffixes.
Розглядається представництво бібліотеки коледжу у Всесвітній мережі. З досвіду роботи бібліотеки ДРПБК. Обґрунтовано необхідність відмови від застарілих професійних стереотипів, вибудовувати нові моделі взаємин з користувачами.
The document summarizes a technical seminar on web-based information retrieval systems. It discusses information retrieval architecture and approaches, including syntactical, statistical, and semantic methods. It also covers web search analysis techniques like web structure analysis, content analysis, and usage analysis. The document outlines the process of web crawling and types of crawlers. It discusses challenges of web structure, crawling and indexing, and searching. Finally, it concludes that as unstructured online information grows, information retrieval techniques must continue to improve to leverage this data.
An Empirical Study on Mushroom Disease Diagnosis:A Data Mining ApproachIRJET Journal
This document summarizes a research study that used data mining techniques to diagnose diseases in mushroom crops. The study collected data on mushroom farms about diseases, symptoms and other factors. It then analyzed the data using classification algorithms like Naive Bayes, RIPPLE-DOWN RULES and SEQUENTIAL MINIMAL OPTIMIZATION to identify important symptoms for diagnosing diseases and determine the best performing algorithm. The results found the most important symptoms for diagnosis and the classification algorithm that identified diseases most accurately. This helps mushroom farmers better manage diseases and reduce crop losses.
Data mining techniques are rapidly developed for many applications. In recent year, Data mining in healthcare is an emerging field research and development of intelligent medical diagnosis system. Classification is the major research topic in data mining. Decision trees are popular methods for classification. In this paper many decision tree classifiers are used for diagnosis of medical datasets. AD Tree, J48, NB Tree, Random Tree and Random Forest algorithms are used for analysis of medical dataset. Heart disease dataset, Diabetes dataset and Hepatitis disorder dataset are used to test the decision tree models. Aung Nway Oo | Thin Naing ""Decision Tree Models for Medical Diagnosis"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23510.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/23510/decision-tree-models-for-medical-diagnosis/aung-nway-oo
Machine Learning Based Approaches for Cancer Classification Using Gene Expres...mlaij
The classification of different types of tumor is of great importance in cancer diagnosis and drug discovery.
Earlier studies on cancer classification have limited diagnostic ability. The recent development of DNA
microarray technology has made monitoring of thousands of gene expression simultaneously. By using this
abundance of gene expression data researchers are exploring the possibilities of cancer classification.
There are number of methods proposed with good results, but lot of issues still need to be addressed. This
paper present an overview of various cancer classification methods and evaluate these proposed methods
based on their classification accuracy, computational time and ability to reveal gene information. We have
also evaluated and introduced various proposed gene selection method. In this paper, several issues
related to cancer classification have also been discussed.
Estimating the Statistical Significance of Classifiers used in the Predictio...IOSR Journals
This document summarizes a research paper that analyzes the statistical significance of different classifiers for predicting tuberculosis. The paper first compares the accuracy of classifiers like decision trees, support vector machines, k-nearest neighbor, and naive Bayes on tuberculosis data. It then evaluates the performance of these classifiers using a paired t-test to select the optimal model. The results showed that support vector machines and decision trees were not statistically significant, while support vector machines combined with naive Bayes and k-nearest neighbor were statistically significant.
Improved vision-based diagnosis of multi-plant disease using an ensemble of d...IJECEIAES
Farming and plants are crucial parts of the inward economy of a nation, which significantly boosts the economic growth of a country. Preserving plants from several disease infections at their early stage becomes cumbersome due to the absence of efficient diagnosis tools. Diverse difficulties lie in existing methods of plant disease recognition. As a result, developing a rapid and efficient multi-plant disease diagnosis system is a challenging task. At present, deep learning-based methods are frequently utilized for diagnosing plant diseases, which outperformed existing methods with higher efficiency. In order to investigate plant diseases more accurately, this article addresses an efficient hybrid approach using deep learning-based methods. Xception and ResNet50 models were applied for the classification of plant diseases, and these models were merged using the stacking ensemble learning technique to generate a hybrid model. A multi-plant dataset was created using leaf images of four plants: black gram, betel, Malabar spinach, and litchi, which contains nine classes and 44,972 images. Compared to existing individual convolutional neural networks (CNN) models, the proposed hybrid model is more feasible and effective, which acquired 99.20% accuracy. The outcomes and comparison with existing methods represent that the designed method can acquire competitive performance on the multi-plant disease diagnosis tasks.
April 2020 top read articles in data mining & knowledge management proces...IJDKP
Scope & Topics
Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.
IRJET- Machine Learning Classification Algorithms for Predictive Analysis in ...IRJET Journal
This document discusses machine learning classification algorithms and their applications for predictive analysis in healthcare. It provides an overview of data mining techniques like association, classification, clustering, prediction, and sequential patterns. Specific classification algorithms discussed include Naive Bayes, Support Vector Machine, Decision Trees, K-Nearest Neighbors, Neural Networks, and Bayesian Methods. The document examines examples of these algorithms being used for disease diagnosis, prognosis, and healthcare management. It analyzes their predictive performance on datasets for conditions like breast cancer, heart disease, and ICU readmissions. Overall, the document reviews how machine learning techniques can enhance predictive accuracy for various healthcare problems.
This document describes a proposed AI-based crop identification webapp. The system would use a convolutional neural network (CNN) to identify crop species from images. Users could upload photos of farm yields through a mobile app. The CNN model would be trained on a dataset of labeled plant images. Key aspects of the proposed system include:
1. A training module to develop the CNN model using labeled example images.
2. A testing module to evaluate the trained model's accuracy at identifying crops.
3. An output module allowing users to upload single images for prediction by the CNN model.
The system aims to help farmers identify crops more easily through an automated image recognition system, improving yields and farm management. Experimental results
Comparative Analysis of Classification Algorithms using Wekaijtsrd
Data Mining is the process of drawing out the useful information from the raw data that is present in various forms. Data Mining is defined as study of the Knowledge Discovery in database process or KDD. Data mining techniques are relevant for drawing out the useful information from the huge amount of raw data that is present in various forms. In this research work different types of classification algorithms accuracies are calculated which are widely used to draw the significant amount of data from the huge amount of raw data. Comparative analysis of different Classification Algorithms have been done using various criteria’s like accuracy, execution time in seconds and how much instances are correctly classified or not classified correctly. Sakshi Goel | Neeraj Kumar | Saharsh Gera "Comparative Analysis of Classification Algorithms using Weka" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50568.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/50568/comparative-analysis-of-classification-algorithms-using-weka/sakshi-goel
Plant Leaf Disease Analysis using Image Processing Technique with Modified SV...Tarun Kumar
In this computing era, image processing has
spread its wings in human life upto the extent that image
has become an integral part of their life. There are various
applications of image processing in the field of commerce,
engineering, graphic design, journalism, architecture and
historical research. In this research work, Image
processing is considered for the analysis of plant leaf
diseases. Plant leaf diseases can be detected based on the
disease symptoms. Here, dataset of disease affected leaves
is considered for experimentation. This dataset contains
the plant leaves suffered from the
AlternariaAlternata,Cercospora Leaf Spot, Anthracnose
andBacterial Blight along with some healthy leaf images.
For this analysis, an autonomous approach of modified
SVM-CS is introduces. Here, concept of cuckoo search is
considered to optimize the classification parameters. These
parameters further help to find more accurate solutions.
This autonomous approach also extracts the healthy
portion and disease affected leaf portion along with the
accuracy of results.
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...ijdms
This document discusses applying machine learning algorithms to predict chronic kidney disease. It:
1) Applied three algorithms (C4.5 decision tree, SVM, and Bayesian Network) to a chronic kidney disease dataset containing 400 patients and 24 attributes to classify patients as having chronic kidney disease or not.
2) Found that the C4.5 decision tree algorithm had the best performance based on accuracy (63%), error rate (0.37), kappa statistic (0.97), and other evaluation metrics. SVM and Bayesian Network performance was lower.
3) Concludes C4.5 decision tree is the most efficient algorithm for predicting chronic kidney disease based on this medical dataset.
This document discusses bioinformatics and its importance in biomedical imaging and image processing. It begins by defining bioinformatics as the method of storing, organizing, retrieving and analyzing biological data. Large amounts of biological data are now being produced and require sophisticated computing methods. The goals of bioinformatics include optimally organizing vast databases of biological information so it can be easily accessed and analyzed. Key approaches in bioinformatics involve comparing new genetic and protein sequences to existing databases to better understand biological processes.
DATA MINING CLASSIFICATION ALGORITHMS FOR KIDNEY DISEASE PREDICTION IJCI JOURNAL
Data mining is a non-trivial process of categorizing valid, novel, potentially useful and ultimately understandable patterns in data. In terms, it accurately state as the extraction of information from a huge database. Data mining is a vital role in several applications such as business organizations, educational institutions, government sectors, health care industry, scientific and engineering. . In the health care
industry, the data mining is predominantly used for disease prediction. Enormous data mining techniques are existing for predicting diseases namely classification, clustering, association rules, summarizations, regression and etc. The main objective of this research work is to predict kidney diseases using classification algorithms such as Naïve Bayes and Support Vector Machine. This research work mainly
focused on finding the best classification algorithm based on the classification accuracy and execution time performance factors. From the experimental results it is observed that the performance of the SVM is better than the Naive Bayes classifier algorithm.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Plant Disease Detection and Severity Classification using Support Vector Mach...IRJET Journal
This document discusses a study that used support vector machines (SVM) and convolutional neural networks (CNN) to detect plant diseases and classify their severity using images. The researchers trained their models on a dataset containing images of four plant species with different diseases. SVM was used for disease detection, achieving 80-90% accuracy. CNN models like DenseNet and EfficientNet were used to classify disease severity. The goal of the study was to help farmers identify plant diseases early to mitigate losses and improve food security.
Tomato Disease Fusion and Classification using Deep LearningIJCI JOURNAL
Tomato plants' susceptibility to diseases imperils agricultural yields. About 30% of the total crop loss is attributable to plants with disease. Detecting such illnesses in the plant is crucial to avoid significant output losses.This study introduces "data fusion" to enhance disease classification by amalgamating distinct disease-specific traits from leaf halves. Data fusion generates synthetic samples, fortifying a TensorFlow Keras deep learning model using a diverse tomato leaf image dataset. Results illuminate the augmented model's efficacy, particularly for diseases marked by overlapping traits. Enhanced disease recognition accuracy and insights into disease interactions transpire. Evaluation metrics (accuracy 0.95, precision 0.58, recall 0.50, F1 score 0.51) spotlight balanced performance. While attaining commendable accuracy, the intricate precision-recall interplay beckons further examination. In conclusion, data fusion emerges as a promising avenue for refining disease classification, effectively addressing challenges rooted in trait overlap. The integration of TensorFlow Keras underscores the potential for enhancing agricultural practices. Sustained endeavours toward enhanced recall remain pivotal, charting a trajectory for future advancements.
prediction of heart disease using machine learning algorithmsINFOGAIN PUBLICATION
This document summarizes a research paper that analyzed different machine learning algorithms for predicting heart disease. It discusses using the Naive Bayes and Decision Tree classifiers on a Cleveland Heart Disease dataset containing 303 records and 19 attributes. The Naive Bayes and Decision Tree algorithms were applied to the preprocessed data and their accuracies were compared. The results showed that the Decision Tree algorithm had better performance and accuracy than the Naive Bayes classifier for predicting heart disease. Future work will focus on using a Selective Naive Bayes classifier to potentially improve prediction accuracy by removing irrelevant attributes.
Evolving Efficient Clustering and Classification Patterns in Lymphography Dat...ijsc
Data mining refers to the process of retrieving knowledge by discovering novel and relative patterns from large datasets. Clustering and Classification are two distinct phases in data mining that work to provide an established, proven structure from a voluminous collection of facts. A dominant area of modern-day research in the field of medical investigations includes disease prediction and malady categorization. In this paper, our focus is to analyze clusters of patient records obtained via unsupervised clustering techniques and compare the performance of classification algorithms on the clinical data. Feature selection is a supervised method that attempts to select a subset of the predictor features based on the information gain. The Lymphography dataset comprises of 18 predictor attributes and 148 instances with the class label having four distinct values. This paper highlights the accuracy of eight clustering algorithms in detecting clusters of patient records and predictor attributes and highlights the performance of sixteen classification algorithms on the Lymphography dataset that enables the classifier to accurately perform multi-class categorization of medical data. Our work asserts the fact that the Random Tree algorithm and the Quinlan’s C4.5 algorithm give 100 percent classification accuracy with all the predictor features and also with the feature subset selected by the Fisher Filtering feature selection algorithm.. It is also stated here that the Density Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm offers increased clustering accuracy in less computation time.
EVOLVING EFFICIENT CLUSTERING AND CLASSIFICATION PATTERNS IN LYMPHOGRAPHY DAT...ijsc
Data mining refers to the process of retrieving knowledge by discovering novel and relative patterns from
large datasets. Clustering and Classification are two distinct phases in data mining that work to provide an
established, proven structure from a voluminous collection of facts. A dominant area of modern-day
research in the field of medical investigations includes disease prediction and malady categorization. In
this paper, our focus is to analyze clusters of patient records obtained via unsupervised clustering
techniques and compare the performance of classification algorithms on the clinical data. Feature
selection is a supervised method that attempts to select a subset of the predictor features based on the
information gain. The Lymphography dataset comprises of 18 predictor attributes and 148 instances with
the class label having four distinct values. This paper highlights the accuracy of eight clustering algorithms
in detecting clusters of patient records and predictor attributes and highlights the performance of sixteen
classification algorithms on the Lymphography dataset that enables the classifier to accurately perform
multi-class categorization of medical data. Our work asserts the fact that the Random Tree algorithm and
the Quinlan’s C4.5 algorithm give 100 percent classification accuracy with all the predictor features and
also with the feature subset selected by the Fisher Filtering feature selection algorithm.. It is also stated
here that the Density Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm
offers increased clustering accuracy in less computation time.
PREDICTIVE ANALYTICS IN HEALTHCARE SYSTEM USING DATA MINING TECHNIQUEScscpconf
The health sector has witnessed a great evolution following the development of new computer technologies, and that pushed this area to produce more medical data, which gave birth to multiple fields of research. Many efforts are done to cope with the explosion of medical data on one hand, and to obtain useful knowledge from it on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, machine learning and learning algorithms in order to extract useful knowledge and help in making decisions. With the promises of predictive analytics in big data, and the use of machine learning
algorithms, predicting future is no longer a difficult task, especially for medicine because predicting diseases and anticipating the cure became possible. In this paper we will present an overview on the evolution of big data in healthcare system, and we will apply a learning algorithm on a set of medical data. The objective is to predict chronic kidney diseases by using Decision Tree (C4.5) algorithm.
Similar to A Comparative Study on Mushroom Classification using Supervised Machine Learning Algorithms (20)
This document provides an overview of cosmetic science, summarizing different types of cosmetics including skin, hair, face, eye, and nail cosmetics. It describes key ingredients and formulations for different cosmetic products like moisturizers, cleansers, hair conditioners, mascara, lipstick, and nail polish. The document also discusses trends in cosmetic use throughout history and how cosmetics help beautify and care for skin, hair, nails, and facial features.
Standardization and Formulations of Calotropis ProceraYogeshIJTSRD
Plants growing in arid regions have elicited increased attention, because the hostile environment, in which these plants survive, forces them to develop chemical protective systems through adaptation which is rarely found in vegetation of other ecosystems. Furthermore, many of the plants grow in areas, where the dependence on traditional, plant based medicines over industrially produced pharmaceuticals persists to this day. The two plants, Calotopris Procera giant milkweed, also named C. Persica and Calotropis gigantea crown ower , have been used widely in traditional medicine in North Africa, the Middle East, and South and South East Asia. This has led to extensive research on the chemical constituents of the plants. Both plants are known to be sources of cardenolides, and newer research has yielded a number of interesting cancer active constituents. In addition, extracts of both plants have remarkable nematocidal, molluscidal and insecticidal activities. In many regions, the wood of Calotropis plants has been used as a building material and as a source of fuel. In addition, certain parts of the plants have been used as feed for livestock. In other regions, Calotropis plants are seen as invasive species that threaten local plant life and that due to their toxicity also pose a threat to grazing eld animals. Jaffar Khan | Pankaj Chasta | Dr. Gaurav Kumar Sharma | Dr. Kaushal Kishore Chandrul "Standardization and Formulations of Calotropis Procera" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45145.pdf Paper URL: https://www.ijtsrd.com/pharmacy/other/45145/standardization-and-formulations-of-calotropis-procera/jaffar-khan
Review of the Diagnosis and Treatment of ParalysisYogeshIJTSRD
Paralysis is a complete loss of motor power in any muscle group. When paralysis affects all four extremities, it is called quadriplegia when it affects only the lower extremities, paraplegia and when it affects the extremities on one side of the body, hemiplegic. For this reason, the term paralysis is generally reserved for more focal, less stereotyped weakness, for instance, affecting all the muscles innervated by a peripheral nerve. Many different anatomical lesions and etiologies can cause paralysis and determine its treatment. Bikash Debsingha | Dr. Gourav Kr. Sharma | Dr. Kausal Kishore Chandrul "Review of the Diagnosis and Treatment of Paralysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45108.pdf Paper URL: https://www.ijtsrd.com/pharmacy/pharmacology-/45108/review-of-the-diagnosis-and-treatment-of-paralysis/bikash-debsingha
Comparative Analysis of Forced Draft Cooling Tower Using Two Design Methods A...YogeshIJTSRD
Cooling towers make use of evaporation whereby some of the water is evaporated into a moving air stream and subsequently discharged into the atmosphere which results in cooling of the remainder water. The current research reviews various studies conducted on cooling tower using experimental and numerical techniques. Different design configuration and operating conditions on cooling towers are evaluated by various researchers. Significant findings from researches have shown new and improved design of cooling tower with much better performance as compared to conventional design. Neetish Kumar Sao | Dr. Surendra K. Dwivedi "Comparative Analysis of Forced Draft Cooling Tower Using Two Design Methods: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45100.pdf Paper URL: https://www.ijtsrd.com/engineering/mechanical-engineering/45100/comparative-analysis-of-forced-draft-cooling-tower-using-two-design-methods-a-review/neetish-kumar-sao
Criminology Educators Triumphs and StrugglesYogeshIJTSRD
This document summarizes a research study about the triumphs and struggles of criminology educators in the Philippines. It finds that most respondents enjoy seeing their students succeed but find grading and dealing with difficult students stressful. Financially, only one respondent felt stable while others said their salaries were just enough to get by. Respondents did not initially intend to become teachers but stayed for reasons like family and valuing the teaching profession. While teaching had rewards, low salaries and qualifications impacted job satisfaction for some. Overall, the study provides insights into criminology educators' experiences in the Philippines.
A Review Herbal Drugs Used in Skin DisorderYogeshIJTSRD
The human bodys skin is an organ that allows it to interact with the environment while also shielding it from harmful external influences. People of all ages suffer from skin diseases all over the world. Its vital to keep your skin in good form for a healthy physique. Plants have been employed in some form or another since the beginning of time. This research has highlighted some prevalent skin disease issues, as well as the herbals utilized in disease therapy and the various formulations accessible in the pharmaceutical industry. Some medicinal plants have been shown to be quite effective in removing or reducing skin infection disorders. Chandramita Borah | Dr. Gaurav Kumar Sharma | Dr. Kaushal Kishore Chandrul "A Review: Herbal Drugs Used in Skin Disorder" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45118.pdf Paper URL: https://www.ijtsrd.com/pharmacy/other/45118/a-review-herbal-drugs-used-in-skin-disorder/chandramita-borah
Automatic Query Expansion Using Word Embedding Based on Fuzzy Graph Connectiv...YogeshIJTSRD
The aim of information retrieval systems is to retrieve relevant information according to the query provided. The queries are often vague and uncertain. Thus, to improve the system, we propose an Automatic Query Expansion technique, to expand the query by adding new terms to the user s initial query so as to minimize query mismatch and thereby improving retrieval performance. Most of the existing techniques for expanding queries do not take into account the degree of semantic relationship among words. In this paper, the query is expanded by exploring terms which are semantically similar to the initial query terms as well as considering the degree of relationship, that is, “fuzzy membership- between them. The terms which seemed most relevant are used in expanded query and improve the information retrieval process. The experiments conducted on the queries set show that the proposed Automatic query expansion approach gave a higher precision, recall, and F measure then non fuzzy edge weights. Tarun Goyal | Ms. Shalini Bhadola | Ms. Kirti Bhatia "Automatic Query Expansion Using Word Embedding Based on Fuzzy Graph Connectivity Measures" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45074.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/45074/automatic-query-expansion-using-word-embedding-based-on-fuzzy-graph-connectivity-measures/tarun-goyal
A New Proposal for Smartphone Based Drowsiness Detection and Warning System f...YogeshIJTSRD
This paper proposes a Smartphone based system for the detection of drowsiness in automotive drivers. The proposed system uses three stage drowsiness detection technique. The first stage uses the percentage of eyelid closure PERCLOS which is obtained by capturing images with the front camera of the Smartphone with a modified eye state classification method. The system uses near infrared lighting for illuminating the face of the driver during night driving. The second step uses the voiced to the unvoiced ratio VUR obtained from the speech data from the microphone, in the event PERCLOS crosses the threshold. The VUR is also compared with a threshold and if it is a value greater than that of the threshold, it moves on to the next verification stage. In the final verification stage, touch response is required within the stipulated time to declare whether the driver is drowsy or not and subsequently sound an alarm. To awake the driver, a vibrating mechanism is done and also the live GPS location is also sent to an emergency contact. We have studied eight other reference papers for the literature review. The system has three advantages over existing drowsiness detection systems. First, the three stage verification process makes the system more reliable. The second advantage is its implementation on an Android smart phone, which is readily available to most drivers or cab owners as compared to other general purpose embedded platforms. The third advantage is the use of SMS service to inform the control room as well as the passenger regarding the loss of attention of the driver. Abishek K Biju | Godwin Jolly | Asif Mohammed C A | Dr. Paul P Mathai | Derek Joseph "A New Proposal for Smartphone-Based Drowsiness Detection and Warning System for Automotive Drivers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45083.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/45083/a-new-proposal-for-smartphonebased-drowsiness-detection-and-warning-system-for-automotive-drivers/abishek-k-biju
Data Security by AES Advanced Encryption StandardYogeshIJTSRD
Now a days with the rapid development of multimedia technologies, research on safety and security are becoming more important. Multimedia data are generated and transmitted through the communication channels and the wireless media. The efficiencies of encryption based on different existing algorithms are not up to the satisfactory limit. Hence researchers are trying to modify the existing algorithm or even develop new algorithms that help to increase security with a little encryption time. Here in this paper, we have furnished a new technology to modify the AES algorithm which gives more security with a little encryption time and which can be used to encrypt using 128 bit key. Theoretical analysis on the proposed algorithm with the existing reveals the novelty of our work. Here we have proposed a technique to randomize the key and hidden the key data into an encrypted digital image using the basics concept of cryptography and also using the concept of digital watermarking, the concept of key hide has also been encrypted. We have also proposed a new technique to reposition the pixels to break the correlation between them. So, the proposed scheme offers a more secure and cost effective mechanism for encryption. Next on the AES criteria list good performance. Widespread market adoption will require reasonably good performance on a variety of platforms, ranging from easy tocrack smart cards to the largest servers. Good algorithm performance includes speed for the encryption and decryption process as well as the key schedule. Prateek Goyal | Ms. Shalini Bhadola | Ms. Kirti Bhatia "Data Security by AES (Advanced Encryption Standard)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45073.pdf Paper URL: https://www.ijtsrd.com/computer-science/computer-security/45073/data-security-by-aes-advanced-encryption-standard/prateek-goyal
Antimicrobial and Phytochemical Screening of Phyllantus NiruriYogeshIJTSRD
Theorigin of Phyllanthus niruri is tropical America from there it spread as a weed to other tropic and sub tropics. It is a tropical annual herb shrub which grows as weed in moist humid waste land. Phyllanthus niruri is among more than 500 Phyllanthus species that are widely spread in temperate and tropical climates region Lizuka et al., 2007. It grows 30 40 cm in height, has small leaves and yellow flowers the stem has green capsule, and blooms with flowers with 5 white sepals and apical acute anther.38g of Mueller Hinton Agar was dissolved in 1000ml distilled water in a conical flask, the mouth of the conical flask was plugged with cotton woo wrapped in aluminium foil. This was sterilized in an autoclave at 121oC for 15mns. The media was removed and allowed to cool to 45oC, later poured into a sterilized plastic petri plates which were appropriately labeled. The present study revealed the antimicrobial activity and phytochemical screening of phyllanthus niruri. The antimicrobial activity of phyllanthus niruri shows great significant against pathogens which are responsible for common infections of skin, respiratory, urinary and gastrointestinal tracts. The phytochemical screening of oxalate, terpenoids, tannins, phenols, quinones, flavonoids, alkaloids, saponins and steroids were all found to be active within the plant. This bioactive phytochemicals present in P. niruri can be useful for further researches on the plant P. nururi since the phytochemicals have shown preclinical efficacies for treating human diseases’ which include hepatitis and HIV AIDS. This work has compiled the chemical constituents present and can be useful for further researches Dr. Mohammed Musa Lawan | Yusuf Sale Baba "Antimicrobial and Phytochemical Screening of Phyllantus Niruri" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd44948.pdf Paper URL: https://www.ijtsrd.com/chemistry/other/44948/antimicrobial-and-phytochemical-screening-of-phyllantus-niruri/dr-mohammed-musa-lawan
There is a need for temperature drop in a buried pipeline based on the media and process. Need of some methodology and design requirement for a set of conditions by reducing pipeline surface temperature and the temperature drops to atmospheric temperature at a particular distance of pipeline. Based on the conduction principle, desire reduction up to atmospheric temperature can be possible. Let us understand by below methods and design of Heat sink for buried pipe line. Natvarbhai Prabhudas Gajjar "Heat Sink for Underground Pipe Line" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45117.pdf Paper URL: https://www.ijtsrd.com/engineering/other/45117/heat-sink-for-underground-pipe-line/natvarbhai-prabhudas-gajjar
Newly Proposed Multi Channel Fiber Optic Cable CoreYogeshIJTSRD
Fiber optic cables have single core and multiple core options, but single and multiple core fiber cable -˜s core design need to be updated. Newly proposed design gives facilities to multiple usage than traditional design of cable core. Cable core design needs improvement by using present technology for decreasing material and cost and by improving efficiency of cable. Research need to be carried out in this direction. What do you think Natvarbhai Prabhudas Gajjar "Newly Proposed Multi Channel Fiber-Optic Cable Core" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45116.pdf Paper URL: https://www.ijtsrd.com/engineering/other/45116/newly-proposed-multi-channel-fiberoptic-cable-core/natvarbhai-prabhudas-gajjar
Security Sector Reform toward Professionalism of Military and PoliceYogeshIJTSRD
The need to understand and at the same time give prescriptions for the direction of security reform in Third world countries after the end of World War II has prompted the emergence of a big project called the study of security reform SSR . Within this framework emerge various theories and strategies for security reform, with various variations, including ideological variations that underlie these theories. The reform of the structural aspect is a reform of the institutional and structure of an institution, the instrumental reform includes the reform of the system, laws and regulations, while the reform of the cultural aspect is a reform of the habits or organizational culture in institutions in general and in particular the Timor Leste’s security institutions, both military and police. Arquimino Ramos "Security Sector Reform toward Professionalism of Military and Police" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45061.pdf Paper URL: https://www.ijtsrd.com/humanities-and-the-arts/political-science/45061/security-sector-reform-toward-professionalism-of-military-and-police/arquimino-ramos
Stress An Undetachable Condition of LifeYogeshIJTSRD
Stressful life events affects human body, which may lead to cardiovascular diseases and effect metabolism and immune system. Recent studies showed increase in stress levels in developing countries. This study aimed to determine the stress levels in MBBS students. The objectives of the study are a To determine the current stress level, b To assess relation between stress level and lifestyle of college students. The present study was carried out in Ahmedabad City of Gujarat State. A total of 400 medical students were included in the study, which were selected using multi stage sampling aged between 18 years to 25 years. Students were questioned regarding their socioeconomic and life style parameters. The results showed that physical activity such as walking, exercise, yoga, meditation etc. were associated to stress levels. College students showed high stress levels with more satisfaction were mostly smokers. Their major reason for eating junk food and smoking was, increase in stress. Conclusion Majority of students suffered from moderate stress levels. Despite of having stress they were happy and satisfied with life with less no internet addiction. Spirituality and stress scales had a positive correlation as most of the students were averagely highly spiritual. Discriminant function can be used to determine the stress level of a person using age, BMI, internet addiction, spirituality, happiness scale and life satisfaction scale of that person. Jayshree N. Tolani | Dr. Nitinkumar D. Shah "Stress: An Undetachable Condition of Life" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45054.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/45054/stress-an-undetachable-condition-of-life/jayshree-n-tolani
Comparative Studies of Diabetes in Adult Nigerians Lipid Profile and Antioxid...YogeshIJTSRD
The study sought to determine the extent to which the usage of social media in the marketing of agricultural products in South West Nigeria can enhance farmers turnover. It employed the survey research design to collect data with the help of a structured questionnaire to elicit information from respondents selected from six 6 south western states. Research data were analysed using structural equation modelling. The results showed that the use of social media WhatsApp and Facebook in marketing of agricultural products significantly enhances farmers turnover. The managerial implication is that use of Whatsapp and Facebook in the marketing of agricultural products for the enhancement of farmers’ turnover was found to have significant influence on the enhancement in farmers’ turnover from agricultural products. Policy makers in government should provide the enabling environment for the telecommunication companies to enhance their reach by installing their facilities across the length and breadth of the country so that the network coverage will be strong at all times so that the benefits of social media usage will not be constrained. Egejuru, Leonard O | Akubugwo, Emmanuel I | Ugorji, Beatrice N "Comparative Studies of Diabetes in Adult Nigerians: Lipid Profile and Antioxidants Vitamins (A and C)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45021.pdf Paper URL: https://www.ijtsrd.com/biological-science/biochemistry/45021/comparative-studies-of-diabetes-in-adult-nigerians-lipid-profile-and-antioxidants-vitamins-a-and-c/egejuru-leonard-o
To Assess the Severity and Mortality among Covid 19 Patients after Having Vac...YogeshIJTSRD
The severity and mortality of COVID 19 cases has been associated with the Three category such as vaccination status, severity of disease and outcome. Objective presently study was aimed to assess the severity and mortality among covid 19 patients. Methods Using simple lottery random method 100 samples were selected. From these 100 patients, 50 patients were randomly assigned to case group and 50 patients in control group after informed consents of relative obtained. Patients in the case group who being died after got COVID 19 whereas 50 patients in the control group participated who were survive after got infected from COVID 19 patients. Result It has three categories such as a Vaccination status For the vaccination status we have seen 59 patients were not vaccinated and 41 patients was vaccinated out of 100. b Incidence There were 41 patients were vaccinated whereas 59 patients were not vaccinated. c Severity In the case of mortality we selected 50 patients who were died from the Corona and I got to know that out of 50 patients there were 12 24 patients were vaccinated whereas 38 76 patients were non vaccinated. Although for the 50 control survival group total 29 58 patients were vaccinated and 21 42 patients was not vaccinated all graph start. Conclusion we have find out that those people who got vaccinated were less infected and mortality rate very low. Prof. (Dr) Binod Kumar Singh | Dr. Saroj Kumar | Ms. Anuradha Sharma "To Assess the Severity and Mortality among Covid-19 Patients after Having Vaccinated: A Retrospective Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45065.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/45065/to-assess-the-severity-and-mortality-among-covid19-patients-after-having-vaccinated-a-retrospective-study/prof-dr-binod-kumar-singh
Novel Drug Delivery System An OverviewYogeshIJTSRD
In present scenario evolution of an existing drug molecule from a old form to a novel delivery system can significantly improve its performance in terms of patient compliance, safety and efficacy. In the form of a control drug delivery system an existing drug molecule can get a new life. An appropriately designed Novel Drug Delivery System can be a major advance for solving the problems related towards the release of the drug at specific site with specific rate. The porpuse for delivering drugs to patients efficiently and with fewer side effects has prompted pharmaceutical companies to engage in the development of new drug delivery system. This article covers the basic information regarding Novel Drug Delivery Systems and also advantages, factor etc. Chiranjit Barman | Dr. Gaurav Kumar Sharma | Dr. Kausal Kishore Chandrul "Novel Drug Delivery System: An Overview" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45068.pdf Paper URL: https://www.ijtsrd.com/pharmacy/novel-drug-delivery-sys/45068/novel-drug-delivery-system-an-overview/chiranjit-barman
With the growth of technology their grows threat to our data which is just secured by passwords so to make it more secure biometrics came into existence. As biometric systems are adopted and accepted for security purpose for various information and security systems. Hence it is immune to attacks. This paper deals with the security of biometric details of individuals. In this paper we will be discussing about biometrics and its types and the threats and security issues which is not talked about usually. The different technologies evolved and had contributed to biometrics in long run and their effects. Sushmita Raulo | Saurabh Gawade "Security Issues Related to Biometrics" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd44951.pdf Paper URL: https://www.ijtsrd.com/computer-science/computer-security/44951/security-issues-related-to-biometrics/sushmita-raulo
Comparative Analysis of Different Numerical Methods for the Solution of Initi...YogeshIJTSRD
A mathematical equation which involves a function and its derivatives is called a differential equation. We consider a real life situation, from this form a mathematical model, solve that model using some mathematical concepts and take interpretation of solution. It is a well known and popular concept in mathematics because of its massive application in real world problems. Differential equations are one of the most important mathematical tools used in modeling problems in Physics, Biology, Economics, Chemistry, Engineering and medical Sciences. Differential equation can describe many situations viz exponential growth and de cay, the population growth of species, the change in investment return over time. We can solve differential equations using classical as well as numerical methods, In this paper we compare numerical methods of solving initial valued first order ordinary differential equations namely Euler method, Improved Euler method, Runge Kutta method and their accuracy level. We use here Scilab Software to obtain direct solution for these methods. Vibahvari Tukaram Dhokrat "Comparative Analysis of Different Numerical Methods for the Solution of Initial Value Problems in First Order Ordinary Differential Equations" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45066.pdf Paper URL: https://www.ijtsrd.com/mathemetics/applied-mathematics/45066/comparative-analysis-of-different-numerical-methods-for-the-solution-of-initial-value-problems-in-first-order-ordinary-differential-equations/vibahvari-tukaram-dhokrat
Evaluation of Different Paving Mixes Using Optimum Stabilizing ContentYogeshIJTSRD
Bituminous mixes are most commonly used all over the world in flexible pavement construction. It consists of asphalt or bitumen used as a binder and mineral aggregate which are mixed together, laid down in layers and then compacted. Under normal circumstances, conventional bituminous pavements if designed and executed properly perform quite satisfactorily but the performance of bituminous mixes is very poor under various situations. Today’s asphaltic concrete pavements are expected to perform better as they are experiencing increased volume of traffic, increased loads and increased variations in daily or seasonal temperature over what has been experienced in the past. In addition, the performance of bituminous pavements is found to be very poor in moisture induced situations. Considering this a lot of work has been done on use of additives in bituminous mixtures and as well as on modification of bitumen. Research has indicated that the addition of polymers to asphalt binders helps to increase the interfacial cohesiveness of the bond between the aggregate and the binder which can enhance many properties of the asphalt pavements to help meet these increased demands. However, the additive that is to be used for modification of mix or binder should satisfy both the strength requirements as well as economical aspects. Naveen Kumar | Ms. Shivani "Evaluation of Different Paving Mixes Using Optimum Stabilizing Content" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45089.pdf Paper URL: https://www.ijtsrd.com/engineering/civil-engineering/45089/evaluation-of-different-paving-mixes-using-optimum-stabilizing-content/naveen-kumar
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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2. International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD42441 | Volume – 5 | Issue – 5 | Jul-Aug 2021 Page 717
Classification of malware behavioral features can be a
convenient method in developing a behavioral
antivirus. [5] applied seven different algorithms
namely Decision Table, Random Forest (RF), Naïve
Bayes (NB), Support Vector Machine (SVM), Neural
Networks (Perceptron), JRip and Decision Tree (J48)
using Waikato Environment for Knowledge Analysis
(WEKA) machine learning tool on the diabetes
dataset. The research shows that time taken to build a
model and precision/accuracy is a factor on one hand
while kappa statistic and Mean Absolute Error
(MAE) is another factor on the other hand. Therefore,
ML algorithms require precision, accuracy and
minimum error to have supervised predictive machine
learning.
Furthermore, the results of a survey conducted by
[15] identified the models based on supervised
learning algorithms such as Support Vector Machines
(SVM), K-Nearest Neighbour (KNN), Naïve Bayes,
Decision Trees (DT), Random Forest (RF) and
ensemble models as the most popular among the
researchers for predicting Cardiovascular Diseases. A
study by [7] on “Behavioral features for mushroom
classification” - This paper is set to study mushroom
behavioral features such as the shape, surface and
color of the cap, gill and stalk, as well as the odor,
population and habitat of the mushrooms. The
Principal Component Analysis (PCA) algorithm is
used for selecting the best features for the
classification experiment using the Decision Tree
(DT) algorithm. The results showed that the Decision
tree using the J48 classifier produced 23 leaves and
the size of the tree is 28. [10] discusses data mining
algorithms specifically ID3, CART, and
HoeffdingTree (HT) based on a decision tree.
Hoeffding Tree provides better results with the
highest accuracy, low time and least error rate when
compared with ID3 and CART. A study by [11]
focuses on developing a method for the classification
of mushrooms using its texture feature, which is
based on the machine learning approach. The
performance of the proposed approach is 76.6% by
using an SVM classifier, which is found better
concerning the other classifiers like KNN, Logistic
Regression, Linear Discriminant, Decision Tree, and
Ensemble classifiers. [14] used the Decision Tree
classifier to develop a classification model for edible
and poisonous mushrooms. The results of the model’s
effectiveness evaluation revealed that the model using
the Information Gain technique alongside the
Random Forest technique provided the most accurate
classification outcomes at 94.19%.
The remaining of this paper proceeds as follows.
Section III presents the materials and methods applied
to achieve the objective of this research. Subsequent
sections IV and V present the results and conclusion
of the study.
MATERIALS AND METHODS
Data mining is one of the major and important
technologies that is currently being used in the
industry for performing data analysis and gaining
insight into the data. It uses different data mining
techniques such as Machine Learning, Artificial
Intelligence, and statistical analysis. In this study,
machine learning techniques are used for mushroom
classification. Machine learning provides a pool of
tools and techniques, using these tools and techniques
raw data can be converted into some actionable,
meaningful information by computers. In this paper,
supervised machine learning algorithms are used.
Figure 1 Methodology for Mushroom
Classification
A. Dataset and Attributes
This research paper uses an openly available dataset
that is downloaded from the UCI machine learning
repository. This dataset includes descriptions of
hypothetical samples corresponding to 23 species of
gilled mushrooms in the Agaricus and Lepiota Family
Mushroom drawn from The Audubon Society Field
Guide to North American Mushrooms (1981). Each
species is identified as definitely edible, definitely
poisonous, or of unknown edibility and not
recommended. This latter class was combined with
the poisonous one [4].
This dataset contains 22 attributes with 8124
instances of mushrooms. Figure 2 gives the attribute
information of the dataset.
Figure 2 Attribute Information
3. International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD42441 | Volume – 5 | Issue – 5 | Jul-Aug 2021 Page 718
B. Data Preprocessing And Exploratory Data
Analysis
The dataset contains two classes i.e., edible and
poisonous. To check the balance of each, a bar graph
is plotted. Since the data is categorical, Label Encoder
is used to convert it to ordinal. Label Encoder
converts each value in a column to a number [18].
Figure 3 shows the count of each class whereas
Figure 4 shows the dataset after label encoding.
Figure 3 Bar plot to visualize the count of edible
and poisonous mushrooms
Figure 4 Label Encoding
A violin plot is a part of EDA that is used to show the
distribution of quantitative data across several levels
of one or more categorical variables in such a way
that those distributions can be compared. A violin
plot is used here to represent the distribution of the
classification characteristics.
Figure 5 Violin plot representing the distribution
of the classification characteristics
Since the dataset contains categorical variables, we
apply the get_dummies() method to convert the
categorical data into dummy or indicator variables.
Figure 6 shows the dummy/indicator variables of the
dataset. The conversion of categorical variables into
dummy variables leads to the formation of the two-
dimensional binary matrix where each column
represents a particular category, in our case, 0 is for
edible mushroom whereas 1 is for poisonous.
Figure 6 Dummy/indicator variables
Correlation matrices are a requisite tool of
exploratory data analysis. It is convenient to
understand the relationship among variables/columns.
A heatmap is plotted to represent the correlation
between the variables.
Figure 7 Heatmap representing the correlation
between the dummy/indicator variables
C. Data Splitting
Data splitting is a process used to separate a given
dataset into at least two subsets called ‘training’ and
‘test’. This step is usually implemented after data
preprocessing. Using train_test_split() from the data
science library scikit-learn, the data is split into
subsets i.e. training and test which contains 70% and
30% data respectively. This minimizes the potential
for bias in the evaluation and validation process.
D. Feature Scaling and Principal Component
Analysis
Feature Scaling is done to standardize the
independent features present in the data in a fixed
range. We have used StandardScaler() to perform
feature scaling. It performs the task of
Standardization [1].
StandardScaler() will normalize the features i.e. each
column of X, individually, so that each
feature/column will have µ = 0 and σ = 1. The
Standard Scaler assumes data is normally distributed
within each feature and scales them such that the
distribution centered around 0, with a standard
deviation of 1 [17].
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The Principal Component Analysis (PCA) algorithm
is used to select the best features from the mushroom
dataset. PCA is a technique from linear algebra that
can be used to automatically perform dimensionality
reduction. Reducing the number of features in a
dataset can reduce the risk of overfitting and also
improves the accuracy of the model [20]. We have
used PCA with n_components = 2 for reducing the
dimensions of the dataset.
E. Classification Modelling
After the feature extraction and selection, the
supervised machine learning methods are applied to
the data obtained. The machine learning methods to
be applied, as discussed previously, are:
Logistic Regression (LR)
Decision Tree (DT)
K-Nearest Neighbors (KNN)
Support Vector Machines (SVM)
Naïve Bayes (NB)
Random Forest (RF)
F. Performance Evaluation of Algorithms
In this step, evaluation of the prediction results using
various evaluation metrics like confusion matrix,
classification accuracy, precision, recall, f1-score, etc.
is done.
Confusion Matrix -
It is a matrix of size 2×2 for binary classification with
actual values on one axis and predicted on another. It
describes the complete performance of the model.
Figure 8 Confusion Matrix
Where TP = True Positives,
TN = True Negatives,
FP = False Positives,
FN = False Negatives.
Classification Accuracy -
It is the ratio of the number of correct predictions to
the total number of input samples. It is given as:
For binary classification, accuracy can also be
calculated in terms of positives and negatives as
follows:
Precision -
Precision is the number of correct positive results
divided by the number of positive results predicted by
the classifier. It attempts to answer the question:
What proportion of positive identifications is actually
correct? Precision is defined as follows:
Recall / Sensitivity / True Positive Rate (TPR) -
It is the number of correct positive results divided by
the number of all relevant samples. Recall attempts to
answer the question: What proportion of actual
positives is identified correctly? Mathematically,
recall is defined as follows:
F1 Score -
It is used to measure a test’s accuracy. F1 Score is the
Harmonic Mean between precision and recall. The
range for the F1 Score is [0, 1]. It tells you how
precise your classifier is as well as how robust it is.
Mathematically, the F1 Score is defined as follows:
F1 Score tries to find the balance between precision
and recall.
False Negative Rate (FNR) -
False Negative Rate (FNR) tells us what proportion of
the positive class got incorrectly classified by the
classifier [2]. Mathematically, the FNR is given by:
Specificity / True Negative Rate (TNR) -
Specificity tells us what proportion of the negative
class got correctly classified [2]. Mathematically, it is
given by:
False Positive Rate (FPR) -
False Positive Rate (FPR) tells us what proportion of
the negative class got incorrectly classified by the
classifier [2]. Mathematically, it is given by:
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RESULTS
In this experimental study, six machine learning algorithms were used. These algorithms are LR, DT, KNN,
SVM, NB, and RF. All these algorithms were applied to the UCI Mushroom Classification Dataset. Data was
divided into two portions, training data, and testing data, both these portions consisting of 70% and 30% data
respectively. Feature scaling using StandardScaler() was performed. The Principal Component Analysis (PCA)
algorithm was used with n_components = 2 for reducing the dimensions and selecting the best features from the
dataset [3]. All six algorithms were applied to the same dataset and results were obtained. Predicting accuracy is
the main evaluation parameter that is used in this work. Accuracy is the overall success rate of the algorithm.
True Positives (TP), True Negatives (TN), False Negatives (FN), and False Positives (FP) predicted by all the
algorithms are presented in Table 1. In our case, TP means actual edible mushrooms. TN, actual poisonous
mushrooms. FP, actually poisonous but predicted to be edible. FN, actually edible but predicted to be poisonous.
Algorithm TP FN FP TN
LR 2849 102 432 2303
DT 2951 0 0 2735
KNN 2873 78 244 2491
SVM 2893 58 374 2361
NB 2850 101 477 2258
RF 2951 0 3 2732
Table 1 TP, FN, FP, TN predicted by algorithms on the training set
Algorithm TP FN FP TN
LR 1218 39 198 983
DT 1130 127 128 1053
KNN 1206 51 143 1038
SVM 1234 23 174 1007
NB 1218 39 211 970
RF 1206 51 139 1042
Table 2 TP, FN, FP, TN predicted by algorithms on the test set
The training and test set visualizations are given below:
Logistic Regression
Figure 9 Logistic Regression Training and Test Set – PCA
Decision Tree
Figure 10 Decision Tree Training and Test Set – PCA
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K-Nearest Neighbor
Figure 11 K-Nearest Neighbor Training and Test Set – PCA
Support Vector Machine
Figure 12 Support Vector Machine Training and Test Set – PCA
Naïve Bayes
Figure 13 Naïve Bayes Training and Test Set – PCA
Random Forest
Figure 14 Random Forest Training and Test Set – PCA
We plotted a Receiver Operator Characteristic (ROC) curve which is an evaluation metric for binary
classification problems, in our case, mushroom classification. It is a probability curve that plots the TPR against
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FPR at various threshold values and essentially separates the ‘signal’ from the ‘noise’. The Area Under the
Curve (AUC) is the measure of the ability of a classifier to distinguish between classes and is used as a summary
of the ROC curve.
Figure 15 ROC Curve
It is evident from the plot that the AUC for the Random Forest and K-Nearest Neighbor ROC curve is higher
than others. Therefore, we can say that Random Forest and KNN performed better than other classifiers. The
training accuracy score, average accuracy score, standard deviation and test accuracy score of all six algorithms
is given in the following table:
Algorithm LR DT KNN SVM NB RF
Training Accuracy 0.9061 1.0000 0.9434 0.9240 0.8983 0.9995
Average Accuracy 0.9066 0.8871 0.9291 0.9235 0.8987 0.9226
Standard Deviation 0.0103 0.0146 0.0103 0.0104 0.0113 0.0119
Test Accuracy 0.9028 0.8954 0.9204 0.9192 0.8975 0.9221
Table 3 Training accuracy, Average accuracy, Standard Deviation and Test Accuracy of algorithms
CONCLUSION
In this paper, six popular supervised machine learning
algorithms are used for classifying mushrooms into
edible or poisonous. These include LR, DT, KNN,
SVM, NB and RF. Predictions were made about
mushrooms (whether edible or poisonous) on the UCI
mushroom classification dataset consisting of 8124
records. Principal Component Analysis (PCA)
algorithm is used with n_components = 2 for
reducing the dimensions of the dataset. There are a
total of 23 categorical variables in this dataset which
were converted into dummy/indicator variables.
These 23 variables (which became 95 after
conversion), were reduced to only 2 variables i.e.
Principal Components. All six classification models
were trained over these two principal components.
From the experimental results obtained, it can be seen
that Random Forest and K-Nearest Neighbor gave the
highest test accuracy of 92.21% and 92.04% followed
by Support Vector Machine with 91.92% test
accuracy, Logistic Regression with 90.28% test
accuracy, Naïve Bayes with 89.75% test accuracy and
Decision Tree with 89.54% test accuracy.
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