JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH最新文献

A COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR IPO UNDERPERFORMANCE PREDICTION 用于 IPO 业绩不佳预测的机器学习算法比较分析

JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH Pub Date : 2023-11-16 DOI: 10.46947/joaasr562023621

Pravinkumar Sonsare, Ashtavinayak Pande, Akshay Kurve, Sudhanshu Kumar, Chinmay Shanbhag

{"title":"A COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR IPO UNDERPERFORMANCE PREDICTION","authors":"Pravinkumar Sonsare, Ashtavinayak Pande, Akshay Kurve, Sudhanshu Kumar, Chinmay Shanbhag","doi":"10.46947/joaasr562023621","DOIUrl":"https://doi.org/10.46947/joaasr562023621","url":null,"abstract":"Initial Public Offerings (IPOs) are a popular way for companies to raise capital and enter the public markets. However, many IPOs underperform and fail to meet the expectations of investors. In this research paper, we explore the use of different machine learning models, namely AdaBoost, Random Forest, Logistic Regression, ANN and SVM, for predicting IPO underperformance. We collect and pre-process a dataset of IPOs from the past few years, and use it to train and evaluate the performance of each model. Our results show that Artificial Neural Network model is better suited for predicting IPO underperformance. Additionally, our analysis provides insights into the factors that contribute to underperformance and highlights the importance of certain features in predicting IPO performance. Our research provides valuable information for investors and financial analysts interested in predicting the performance of IPOs and mitigating the risks associated with IPO investments. We have tested machine learning models, namely AdaBoost, Random Forest, Logistic Regression, ANN and SVM. After Comparing the accuracy of all the models, we arrived at the conclusion that ANN model performed the best with an accuracy of 68.11%.","PeriodicalId":274343,"journal":{"name":"JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH","volume":"10 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139269505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Retransmission Reduction using Checkpoint based Sub-Path Routing for Wireless IoT 无线物联网中基于检查点的子路径路由减少重传

JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH Pub Date : 2023-07-24 DOI: 10.46947/joaasr542023677

A. Jainulabudeen, Dr.M.Mohamed Surputheen

引用次数: 0

A Supervised Classification Approach for Detecting Hate Speech in English Tweets 基于监督分类的英语推文仇恨言论检测方法

JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH Pub Date : 2023-07-24 DOI: 10.46947/joaasr542023681

N. Solomon Praveen Kumar, Dr. M.S Mythili

引用次数: 0

Evaluating Sentiment Classification to Specify Polarity by Lexicon-Based and Machine Learning Approaches for COVID-19 Twitter Data Sets 基于词典和机器学习方法评估COVID-19 Twitter数据集的情感分类以指定极性

JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH Pub Date : 2023-07-24 DOI: 10.46947/joaasr542023678

A. Sathya, Dr. M.S Mythili

{"title":"Evaluating Sentiment Classification to Specify Polarity by Lexicon-Based and Machine Learning Approaches for COVID-19 Twitter Data Sets","authors":"A. Sathya, Dr. M.S Mythili","doi":"10.46947/joaasr542023678","DOIUrl":"https://doi.org/10.46947/joaasr542023678","url":null,"abstract":"As part of data science, sentiment analysis (SA) applied to social media data is a trending research topic. Identifying positive, negative, or neutral opinions or feelings in the text is the attention of sentiment analysis. In the past few years, Social media platforms have become increasingly popular. In this research, natural language processing (NLP) will be employed to extract useful data and information from unstructured text. .The two methods for sentiment analysis covered in this research are the machine-learning method and the lexicon-based method. The paper examines several lexicon approaches to demonstrate the sentiments from Twitter. To increase classification accuracy, it is necessary to use a reliable method with the highest performance. In this study, classifiers such as Support Vector Machine (SVM) and Naive Bayes (NB) were used together with techniques such as TF-IDF (Term Frequency-Inverse Document Frequency) and BOW (Bag of Words). Each algorithm produces a unique outcome. In order to measure the accuracy of classification, metrics such as Precision, Recall, and F-score are considered. This research shows Support Vector Machine (SVM) with TF-IDF is better than other classifiers with an accuracy of 88%.","PeriodicalId":274343,"journal":{"name":"JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114163448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction System for Covid-19 Upcoming Cases Using Ensemble Classification 基于集成分类的Covid-19病例预测系统

JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH Pub Date : 2023-07-24 DOI: 10.46947/joaasr542023683

Dr. M.S Mythili

{"title":"Prediction System for Covid-19 Upcoming Cases Using Ensemble Classification","authors":"Dr. M.S Mythili","doi":"10.46947/joaasr542023683","DOIUrl":"https://doi.org/10.46947/joaasr542023683","url":null,"abstract":"An epidemic of the novel destructive Coronavirus has been spreading rapidly around the world since 2019 and has caused a great number of deaths. Providing patients with appropriate and most timely care is crucial to combating COVID-19 spread. Testing for the disease must be done quickly and accurately. Therefore, this paper developed an ensemble classification-based country-wise COVID-19 upcoming cases prediction model. This ensemble classification and prediction model shows the upcoming month's Corona virus cases, including newly confirmed cases, recovered cases, and deaths. This analysis is carried out based on these three cases occurring in different countries on sequential dates. The proposed model uses three famous classifiers, namely ANN, Gaussian Process and SVM which have different learning characteristics and architectures at the first stage. In the second stage, they combine their predictions with average calculations. Training and assessment of the proposed model were conducted using 75065 observations comprised of 61 features from John Hopkins University in Maryland. For data preparation, the envisioned work clusters the dataset based on world countries affected by COVID-19 separately. As a result, this set of clusters fetched data once again based on death, newly confirmed, and recovered cases. The experimental result shows the proposed ensemble model provides better performance when compared with previous classification algorithms.","PeriodicalId":274343,"journal":{"name":"JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129419299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diabetes Prediction using Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression Classifiers 使用决策树、随机森林、支持向量机、k近邻、逻辑回归分类器预测糖尿病

JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH Pub Date : 2023-07-24 DOI: 10.46947/joaasr542023680

S. Peerbashab, Y. Mohammed Iqbal, Praveen K.P, M. Mohamed Surputheen, A. Saleem Raja

{"title":"Diabetes Prediction using Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression Classifiers","authors":"S. Peerbashab, Y. Mohammed Iqbal, Praveen K.P, M. Mohamed Surputheen, A. Saleem Raja","doi":"10.46947/joaasr542023680","DOIUrl":"https://doi.org/10.46947/joaasr542023680","url":null,"abstract":"One of the world's deadliest diseases is diabetes. It is an additional creator of different assortments of problems. Ex: Coronary disappointment, Visual impairment, Urinary organ illnesses, and so forth. In such cases, the patients are expected to visit a hospital to get a consultation with doctors and their reports. They must contribute their time and cash every time they visit the hospital. Yet, with the development of AI techniques, we have the adaptability to search out a response to the present problem. We have progressed an advanced framework for handling data that can figure regardless of whether the patient has polygenic sickness. In addition, being able to foresee the onset of the disease is crucial for patients. Data withdrawal has the adaptability to eliminate concealed information from an enormous amount of diabetes-related data. The most important outcomes of this research are the establishment of a theoretical framework that can reliably predict a patient's level of risk for developing diabetes. We have utilized the existing categorization methods such as DT (Decision Tree), RF (Random Forest), SVM (Support vector Machine), LR (Logistic Regression) as well as K-NN (K-Nearest Neighbors) for predicting the severity of Type-II Diabetes patients. We got an accuracy of 99% for the Random Forest, 98.40% for the Decision Tree, 78.54% for Logistic Regression, 77.94% for SVM (Using RBF Kernal SVM), and 77.64% for KNN.","PeriodicalId":274343,"journal":{"name":"JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126459083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structural Analysis of URL For Malicious URL Detection Using Machine Learning 基于机器学习的恶意URL检测URL结构分析

JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH Pub Date : 2023-07-24 DOI: 10.46947/joaasr542023679

A. Saleem Raja, S. Peerbashab, Y. Mohammed Iqbal, B. Sundarvadivazhagan, M. Mohamed Surputheen

{"title":"Structural Analysis of URL For Malicious URL Detection Using Machine Learning","authors":"A. Saleem Raja, S. Peerbashab, Y. Mohammed Iqbal, B. Sundarvadivazhagan, M. Mohamed Surputheen","doi":"10.46947/joaasr542023679","DOIUrl":"https://doi.org/10.46947/joaasr542023679","url":null,"abstract":"Malicious websites are intentionally created websites that aid online criminals in carrying out illicit actions. They commit crimes like installing malware on the victim's computer, stealing private data from the victim's system, and exposing the victim online. Malicious codes can also be found on legitimate websites. Therefore, locating such a website in cyberspace is a difficult operation that demands the utilization of an automated detection tool. Currently, machine learning/deep learning technologies are employed to detect such malicious websites. However, the problem persists since the attack vector is constantly changing. Most research solutions use a limited number of URL lexical features, DNS information, global ranking information, and webpage content features. Combining several derived features involves computation time and security risk. Additionally, the dataset's minimal features don't maximize its potential. This paper exclusively uses URLs to address this problem and blends linguistic and vectorized URL features. Complete potential of the URL is utilized through vectorization. Six machine learning algorithms are examined. The results indicate that the proposed approach performs better for the count vectorizer with random forest algorithm","PeriodicalId":274343,"journal":{"name":"JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH","volume":"277 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134154956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rain Fall Prediction using Ada Boost Machine Learning Ensemble Algorithm 基于Ada Boost机器学习集成算法的降雨预测

JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH Pub Date : 2023-07-24 DOI: 10.46947/joaasr542023682

Dr. P. Senthil Kumar, M. Naga Swathi

{"title":"Rain Fall Prediction using Ada Boost Machine Learning Ensemble Algorithm","authors":"Dr. P. Senthil Kumar, M. Naga Swathi","doi":"10.46947/joaasr542023682","DOIUrl":"https://doi.org/10.46947/joaasr542023682","url":null,"abstract":"Every government takes initiative for the well-being of their citizens in terms of environment and climate in which they live. Global warming is one of the reason for climate change. With the help of machine learning algorithms in the flash light of Artificial Intelligence and Data Mining techniques, weather predictions not only rainfall, lightings, thunder outbreaks, etc. can be predicted. Management of water reservoirs, flooding, traffic - control in smart cities, sewer system functioning and agricultural production are the hydro-meteorological factors that affect human life very drastically. Due to dynamic nature of atmosphere, existing Statistical techniques (Support Vector Machine (SVM), Decision Tree (DT) and logistic regression (LR)) fail to provide good accuracy for rainfall forecasting. Different weather features (Temperature, Relative Humidity, Dew Point, Solar Radiation and Precipitable Water Vapour) are extracted for rainfall prediction. In this research work, data analysis using machine learning ensemble algorithm like Adaptive Boosting (Ada Boost) is proposed. Dataset used for this classification application is taken from hydrological department, India from 1901-2015. Overall, proposed algorithm is feasible to be used in order to qualitatively predict rainfall with the help of R tool and Ada Boost algorithm. Accuracy rate and error false rates are compared with the existing Support Vector Machine (SVM) algorithm and the proposed one gives the better result.","PeriodicalId":274343,"journal":{"name":"JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124152548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nanopesticides: Promising Future in Sustainable Pest Management 纳米杀虫剂:可持续害虫管理的美好未来

JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH Pub Date : 2023-05-20 DOI: 10.46947/joaasr522023515

Renu Gupta, Tanushri Saxena, Neera Mehra, Rajni Arora, A. Sahgal

{"title":"Nanopesticides: Promising Future in Sustainable Pest Management","authors":"Renu Gupta, Tanushri Saxena, Neera Mehra, Rajni Arora, A. Sahgal","doi":"10.46947/joaasr522023515","DOIUrl":"https://doi.org/10.46947/joaasr522023515","url":null,"abstract":"Insects form the most successful and diverse group of animals present on earth today. Humans have shared a complex relationship with the insects. Though insects are indispensable as pollinators of crops yet at the same time they act as major destroyer of grains, pulses and fruits in the fields along with their post- harvest storage. Many of the dreadful diseases are also being transmitted by insect vectors to humans, livestock and other animals. Economic damage caused by insect pests is enormous. Adoption of advanced pest management strategies can alleviate the monetary losses substantially. Nanotechnological approach for pest control is an emerging and effective technique since it encompasses a wide range of objectives of an efficient pesticide like increased dispersion and solubility, slow release, controlled delivery system and protection against degradation. Newer formulations of pesticides with the intervention of nanotechnology are aimed to enhance their pesticidal properties. Insecticide formulations using nanomaterials as carriers of active ingredient have shown promising results for mitigation of pests of agriculture, storage and disease vectors. However, at present the knowledge is limited. There is a need for extensive evaluation of the toxicity of nanopesticides and the risks involved for humans and environment before their large-scale production and adoption. \u0000In this review article nanoformulations of pesticides with special emphasis on metal-based nanopesticides and their role as efficient alternatives in sustainable control of insect pests without much adverse impact on the environment has been summarized. \u0000 ","PeriodicalId":274343,"journal":{"name":"JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125528993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Edible Biopolymers from Marine Algae used as an Alternate Packaging material: A Review on their characteristics and properties 作为替代包装材料的可食性海藻生物聚合物的特性与性能综述

JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH Pub Date : 2023-05-20 DOI: 10.46947/joaasr522023486

Subhalakshmi S.U., T. A.

{"title":"Edible Biopolymers from Marine Algae used as an Alternate Packaging material: A Review on their characteristics and properties","authors":"Subhalakshmi S.U., T. A.","doi":"10.46947/joaasr522023486","DOIUrl":"https://doi.org/10.46947/joaasr522023486","url":null,"abstract":"Food packaging is estimated to account for two-thirds of all plastic waste. As a result, it is crucial to discover alternative packaging materials that are both environmentally friendly and safe for human health. Marine algae are becoming more well-known and in demand as cutting-edge resources for producing biopolymers like proteins and polysaccharides. Because of their biocompatibility, biodegradability, and lack of toxicity, biopolymers have been suggested as potential sources for food packaging materials. Numerous research has thoroughly examined the extraction, separation, and use of marine biopolymers in the creation of sustainable packaging. Marine algae are also rich in protein and mineral content, they also have anticancer, anti-obesity, and hypolipidemic properties due to the presence of polyunsaturated conjugated fatty acids. The edible films enhance the shelf life of food by controlling moisture without changing the elements of food. The marine algae are collected either in the intertidal or subtidal areas and they will be dried for further process. The edible films are environmentally friendly. The edible film made from marine algae is a mixture of protein, polysaccharides, lipids, and resins. The factors which affect the properties of the edible film are the source of raw material, surface charge, hydrophobicity, polymer chain length, plasticizer type, proportion, and synthesis method. There are numerous research has been conducted to develop edible film using various matrix constituents. This review provides an overview of Marine algae, its process, and edible films, its characteristics, and factors affecting the film.","PeriodicalId":274343,"journal":{"name":"JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116506642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1