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Journal articles on the topic 'Machine learning analysis'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Kamath, Dr R. S., Dr S. S. Jamsandekar, and Dr P. G. Naik. "Machine Learning Approach for Employee Attrition Analysis." International Journal of Trend in Scientific Research and Development Special Issue, Special Issue-FIIIIPM2019 (2019): 62–67. http://dx.doi.org/10.31142/ijtsrd23065.

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Kapoor Laxmi Ahuja, Tanya. "Cryptocurrency Financial Risk Analysis using Machine Learning." International Journal of Science and Research (IJSR) 12, no. 4 (2023): 1117–21. http://dx.doi.org/10.21275/sr23417141525.

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Turčaník, Michal. "Network User Behaviour Analysis by Machine Learning Methods." Information & Security: An International Journal 50 (2021): 66–78. http://dx.doi.org/10.11610/isij.5014.

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C, Liam. "Enhancing Diagnostic: Machine Learning in Medical Image Analysis." International Journal of Research Publication and Reviews 5, no. 5 (2024): 13013–16. http://dx.doi.org/10.55248/gengpi.5.0524.1458.

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Askhith, Thanuku, A. Sravya, Kurakula Jashwanth, Utla srinivasd, and M. Deenababu. "Sentiment Analysis Using Machine Learning on Twitter Data." International Journal of Research Publication and Reviews 6, no. 3 (2025): 10044–48. https://doi.org/10.55248/gengpi.6.0325.1324.

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Likhitha, Penmetsa Sri Sai Jyothi, and Ms Mallarapu Poojitha. "Campus Placement Prediction And Analysis Using Machine Learning." International Journal of Research Publication and Reviews 6, no. 5 (2025): 12423–25. https://doi.org/10.55248/gengpi.6.0525.18153.

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Gollapalli, Parwateeswar, Buddhavarapu Pranavaditya, Addagalla Shivatmika, Mamidala Venu, and Yash Vyas. "Traffic Accident Analysis And Prediction Using Machine Learning." International Journal of Research Publication and Reviews 6, no. 6 (2025): 12168–73. https://doi.org/10.55248/gengpi.6.0625.2386.

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Nidhi Sahu and Kusum Sharma. "Sentiment Analysis Using Machine Learning." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 3 (2025): 1026–33. https://doi.org/10.32628/cseit25113385.

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In the era of electronic correspondence, enormous volumes oftextual data are generated daily through platforms such as social media, reviews, and forums. Extracting meaningful insights from this unstructured data has become increasingly important for businesses, governments, and researchers. Sentiment analysis, often known as opinion mining, is a natural language processing (NLP) technique that evaluates the emotional tone of a document. A method to sentiment analysis with machine learning techniques is presented in this research. In order to categorize text into positive, negative, or neutral

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Vinay, Dasari. "Analysis of the Agricultural Data Using Machine Learning Techniques." International Journal of Psychosocial Rehabilitation 24, no. 5 (2020): 5745–52. http://dx.doi.org/10.37200/ijpr/v24i5/pr2020282.

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Fung, Lee Hua, and Seetha Letchumy M. Belaidan. "Sentiment Analysis in Online Products Reviews Using Machine Learning." Webology 18, SI05 (2021): 914–28. http://dx.doi.org/10.14704/web/v18si05/web18271.

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Online Shopping is a phenomenon that is growing rapidly. It refers to the act of buying and selling products or services over the internet. Since customers are shopping online, there are some problems with this process. Firstly, is that customers can fall into fraud and security concerns as there is an inability to inspect the goods that you are purchasing beforehand. There is also the other issue on the quality of the product, this is because when selling online, only simple pictures and or descriptions of the product are all a customer can rely on when purchasing. There is also another facto

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S, Varshini, and D. Preethi. "An Analysis of Machine Learning Algorithms to Predict Sales." International Journal of Science and Research (IJSR) 11, no. 6 (2022): 462–66. http://dx.doi.org/10.21275/sr22601144946.

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Krishna Neeta Singh, Roopak. "Impact Analysis on Employee Attrition using Machine Learning Techniques." International Journal of Science and Research (IJSR) 12, no. 6 (2023): 2762–66. http://dx.doi.org/10.21275/sr23627120419.

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Poomka, Pumrapee, Nittaya Kerdprasop, and Kittisak Kerdprasop. "Machine Learning Versus Deep Learning Performances on the Sentiment Analysis of Product Reviews." International Journal of Machine Learning and Computing 11, no. 2 (2021): 103–9. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1021.

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At this current digital era, business platforms have been drastically shifted toward online stores on internet. With the internet-based platform, customers can order goods easily using their smart phones and get delivery at their place without going to the shopping mall. However, the drawback of this business platform is that customers do not really know about the quality of the products they ordered. Therefore, such platform service often provides the review section to let previous customers leave a review about the received product. The reviews are a good source to analyze customer's satisfa

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Sharma, Swapnil. "Supervised Learning: An InDepth Analysis." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35414.

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Supervised learning pivotal machine learning paradigm wherein models are trained on labeled datasets. They predict outcomes or classify data. It includes methodologies and diverse applications of supervised learning. Emphasizing significance in modern technology. Key methodologies encompass linear regression logistic regression. Also decision trees, support vector machines neural networks. Each with unique advantages for specific tasks. Versatility is demonstrated through applications in image and speech recognition. Natural language processing, medical diagnosis and financial forecasting also

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Louis Uzoegwu Farah, Chidozie. "Comparative Analysis for Predicting Cardiovascular Diseases Using Machine Learning and Deep Learning Approaches." International Journal of Science and Research (IJSR) 12, no. 8 (2023): 945–64. http://dx.doi.org/10.21275/sr23809044938.

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Dr, A. R. JayaSudha, and Anes J. Mohammed. "Malware Analysis Using Supervised Machine Learning." Recent Innovations in Wireless Network Security 5, no. 1 (2023): 24–32. https://doi.org/10.5281/zenodo.7797951.

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<em>A research endeavor in the field of cyber security is being carried out under the working title of "Malware Analysis Using Supervised Machine Learning." For the purpose of identifying malicious software in the system, this initiative makes use of supervised machine learning. This endeavor relied heavily on using primary sources for its material. There is also something called dynamic malware analysis, which is when the software is analyzed in the malware analysis facility after it has been run. The environment is implemented on a Flare VM running the Windows 10 distribution. In o

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Grace, Odette Boussi, Gupta Himanshu, and Akhter Hossain Syed. "Enhancing financial cybersecurity via advanced machine learning: analysis, comparison." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1281–89. https://doi.org/10.11591/ijai.v14.i2.pp1281-1289.

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The financial sector is a prime target for cyber-attacks due to the sensitive nature of the data it handles. As the frequency and sophistication of cyber threats continue to rise, implementing effective security measures becomes paramount. In this paper we provide a comprehensive comparison of six prominent machine learning techniques utilized in the financial industry for cyber-attack prevention. The study aims to identify the best-performing model and subsequently compares its performance with a proposed model tailored to the specific challenges faced by financial institutions. This paper lo

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Li, Chunjiang. "Application of Machine Learning Algorithms in the Stock Market Analysis." Highlights in Business, Economics and Management 10 (May 9, 2023): 352–58. http://dx.doi.org/10.54097/hbem.v10i.8119.

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With the development of deep learning and machine learning, more new methods have been produced in the economic and financial fields. When talking about machines, one thing that comes to people’s minds is what they can do with machines to solve problems that need machines. The people in the stock market always want to find ways to forecast the stock trend, the pattern of stock, and the stock value. Before the development of machine learning algorithms, stock market predictions could be made in limited ways, and those methods usually did not produce accurate predictions. However, machine learni

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Dauitbayeva, A. O. "MODERN METHODS OF MACHINE LEARNING AND ANALYTICS." ТЕХНИКА ҒЫЛЫМДАРЫ ЖӘНЕ ТЕХНОЛОГИЯ 7, no. 3 (2024): 12–19. https://doi.org/10.52081/tst.2024.v03.i7.039.

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The article proposes algorithms for processing big data to optimize business processes. The methods of data integration, distributed computing and machine learning for analysis and forecasting are considered. Testing on business cases has shown cost reduction and increased accuracy of solutions, confirming the practical value of the developed approaches. In the modern world, the volume of data is growing exponentially, thanks to the development of technology and the ubiquity of digital devices. Petabytes of information are generated daily: This is data from social networks, electronic devices,

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Pinheiro, Allan Alves, Iago Modesto Brandao, and Cesar Da Costa. "Vibration Analysis in Turbomachines Using Machine Learning Techniques." European Journal of Engineering Research and Science 4, no. 2 (2019): 12–16. http://dx.doi.org/10.24018/ejers.2019.4.2.1128.

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This study proposes a method for diagnosing faults in turbomachines using machine learning techniques. In this study, a support vector machine-SVM algorithm is proposed for fault diagnosis of rotor rotation imbalance. Recently, support vector machines (SVMs) have become one of the most popular classification methods in vibration analysis technology. Axis unbalance defect is classified using support vector machines. The experimental data is derived from the turbomachine model of the rigid-shaft rotor and the flexible bearings, and the experimental setup for vibration analysis. Several situation

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Pinheiro, Allan Alves, Iago Modesto Brandao, and Cesar Da Costa. "Vibration Analysis in Turbomachines Using Machine Learning Techniques." European Journal of Engineering and Technology Research 4, no. 2 (2019): 12–16. http://dx.doi.org/10.24018/ejeng.2019.4.2.1128.

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This study proposes a method for diagnosing faults in turbomachines using machine learning techniques. In this study, a support vector machine-SVM algorithm is proposed for fault diagnosis of rotor rotation imbalance. Recently, support vector machines (SVMs) have become one of the most popular classification methods in vibration analysis technology. Axis unbalance defect is classified using support vector machines. The experimental data is derived from the turbomachine model of the rigid-shaft rotor and the flexible bearings, and the experimental setup for vibration analysis. Several situation

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Rokade, Prakash P., and D. Aruna Kumari. "Business intelligence analytics using sentiment analysis-a survey." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (2019): 613–20. https://doi.org/10.11591/ijece.v9i1.pp613-620.

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Sentiment analysis (SA) is the study and analysis of sentiments, appraisals and impressions by people about entities, person, happening, topics and services. SA uses text analysis techniques and natural language processing methods to locate and extract information from big data. As most of the people are networked themselves through social websites, they use to express their sentiments through these websites.These sentiments are proved fruitful to an individual, business, government for making decisions. The impressions posted on different available sources are being used by organization to kn

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Gevorkyan, Migran N., Anastasia V. Demidova, Tatiana S. Demidova, and Anton A. Sobolev. "Review and comparative analysis of machine learning libraries for machine learning." Discrete and Continuous Models and Applied Computational Science 27, no. 4 (2019): 305–15. http://dx.doi.org/10.22363/2658-4670-2019-27-4-305-315.

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The article is an overview. We carry out the comparison of actual machine learning libraries that can be used the neural networks development. The first part of the article gives a brief description of TensorFlow, PyTorch, Theano, Keras, SciKit Learn libraries, SciPy library stack. An overview of the scope of these libraries and the main technical characteristics, such as performance, supported programming languages, the current state of development is given. In the second part of the article, a comparison of five libraries is carried out on the example of a multilayer perceptron, which is app

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24

SHABLIY, Nataliya, Serhii LUPENKO, Nadiia LUTSYK, Oleh YASNIY, and Olha MALYSHEVSKA. "KEYSTROKE DYNAMICS ANALYSIS USING MACHINE LEARNING METHODS." Applied Computer Science 17, no. 4 (2021): 75–83. http://dx.doi.org/10.35784/acs-2021-30.

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The primary objective of the paper was to determine the user based on its keystroke dynamics using the methods of machine learning. Such kind of a problem can be formulated as a classification task. To solve this task, four methods of supervised machine learning were employed, namely, logistic regression, support vector machines, random forest, and neural network. Each of three users typed the same word that had 7 symbols 600 times. The row of the dataset consists of 7 values that are the time period during which the particular key was pressed. The ground truth values are the user id. Before t

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Anand, Avisha, and Mr Sandeep Dubey. "CV Analysis Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 1316–22. http://dx.doi.org/10.22214/ijraset.2022.42295.

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Abstract: Recruitment is a tiresome process wherein the very first task of a recruiter is to screen resumes (Curriculum Vitae). Nowadays, many companies prefer online job application in comparison to paper resumes. The proposed system is designed in such a way that applying for job openings & screening could be made easy for job applicants as well as the recruiters. The recruiters from the various companies can post their requirements for particular job openings available in their respective companies and on the other hand will allow the job applicants to submit their resumes and apply for

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P, Vijaya Lakshmi, Kanike Haripriya, Saipavan Gangishetti, Thummalapalli VLB Gayatri, and Snehith Yarlagadda. "CRICKET ANALYSIS USING MACHINE LEARNING." YMER Digital 21, no. 05 (2022): 317–19. http://dx.doi.org/10.37896/ymer21.05/35.

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This project uses machine learning algorithms to set the matter of predicting match results supported historical matches information. ML is used to predict the match result variable by developing classification models based on certain freelance variables like player’s position, their batting and bowling performances, weather, location, etc. However, we have a tendency to 1st analyze the info and train the models then it’s straightforward to predict the match results. We as a team utilize SVM and Random Forest machine learning algorithms to research and predict the result of the sport, and it'l

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Swamy, Mr K. K., Mrs SRILATHA PULI, S. SWETHA, B. SHARANYA, A. ANUHYA, and J. SHREYAS. "CRIME ANALYSIS USING MACHINE LEARNING." YMER Digital 21, no. 05 (2022): 412–16. http://dx.doi.org/10.37896/ymer21.05/44.

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Crimes are the significant threat to the humankind. as crimes are increasing at a rapid rate approach for identifying trends in crime. Our project can predict regions which have high probability for crime occurrence and can visualize crime prone areas by visual representation of data by bar graphs, pie charts etc. It speed up the classification of criminal activities by calculating the average accuracy rate .It uses crime data set and predicts the types of crimes in a particular area which help in Accurate results based on the predictive analysis of logistic regression. The objective would be

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Khan, Md Abbas Ali, Ali-Emran, Md Alamgir Kabir, Mohammad Hanif Ali, and A. K. M. Fazlul Haque. "Sentiment Analysis through Machine Learning." Journal of Southwest Jiaotong University 56, no. 3 (2021): 384–93. http://dx.doi.org/10.35741/issn.0258-2724.56.3.32.

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In recent years, App-Based Transportation System (ABTS) like Ride Sharing (Uber, Patho) has become popular day by day. For our daily life, a rickshaw (a 3-wheeled vehicle usually for one or two passengers that one man pulls) is most important for a short distance. If we add this vehicle to our ABTS system, it will be very much helpful for us, specifically for the rainy season in Bangladesh. On heavy rainy days, in our city Dhaka, other vehicles like CNG, cars, and bikes become unused because roads go underwater. However, the man who pulled the rickshaw can serve this condition. It is more impo

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Nomura, Hiroshi. "Behavioral analysis with machine learning." Folia Pharmacologica Japonica 156, no. 4 (2021): 250. http://dx.doi.org/10.1254/fpj.21034.

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KARTHIGA, R., B. KEERTHIGA, and S. R. PREETHI. "ANALYSIS ON MACHINE LEARNING TECHNIQUES." i-manager's Journal on Computer Science 7, no. 3 (2019): 46. http://dx.doi.org/10.26634/jcom.7.3.16739.

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Han, Yingjie. "Machine Learning Based Portfolio Analysis." Highlights in Business, Economics and Management 44 (November 25, 2024): 135–47. http://dx.doi.org/10.54097/wc6epk11.

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This paper investigates the effectiveness of machine learning methods in predicting stock returns in China. In this paper, we use 16 liquidity, momentum, market risk and macro fundamentals indicators, as well as 112 interaction terms between indicators and macro variables with monthly stock excess returns for the lagged period from January 2000 to November 2015 as the sample set, train elasticity networks, gradient boosting, random forests, neural networks, and simple linear regression models, and based on the data of December 2015, predict the January 2016 returns, form portfolios based on th

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D, NARAYANA. "BUDGETTING ANALYSIS USING MACHINE LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34624.

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In an era characterized by unparalleled technological progress and pervasive digital integration, the amalgamation of machine learning and mobile applications has instigated a significant transformation in the realm of financial management. This report embarks on a thorough exploration of this revolutionary convergence, elucidating its impact on budget analysis and forecasting, while also laying the groundwork for a future distinguished by heightened fiscal oversight and informed decision-making. The evolution of budget management and financial forecasting has been intricately linked to the as

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Skibniewski, MirosŁaw, Tomasz Arciszewski, and Kamolwan Lueprasert. "Constructability Analysis: Machine Learning Approach." Journal of Computing in Civil Engineering 11, no. 1 (1997): 8–16. http://dx.doi.org/10.1061/(asce)0887-3801(1997)11:1(8).

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Dutton, Gail. "Machine Learning Enhances Cytometry Analysis." Genetic Engineering & Biotechnology News 39, no. 11 (2019): 14–15. http://dx.doi.org/10.1089/gen.39.11.05.

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Kawade, Dipak R., and Dr Kavita S. Oza. "Sentiment Analysis: Machine Learning Approach." International Journal of Engineering and Technology 9, no. 3 (2017): 2183–86. http://dx.doi.org/10.21817/ijet/2017/v9i3/1709030151.

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Wang, Ping, Yan Li, and Chandan K. Reddy. "Machine Learning for Survival Analysis." ACM Computing Surveys 51, no. 6 (2019): 1–36. http://dx.doi.org/10.1145/3214306.

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Sinha, Sakshi Rajesh, and Prof Sumedh Pundkar. "Geolocation Analysis Using Machine Learning." International Journal of Engineering Research in Computer Science and Engineering 9, no. 6 (2022): 40–44. http://dx.doi.org/10.36647/ijercse/09.06.art007.

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A new journey commences every time a student leaves his/her home for education or work thereby leading themselves to self-discovery and self-reliance. But along with new adventures comes various challenges such as unfamiliar health care systems, personal safety issues, financial problems, etc, but the major problem of them all is accommodation issues. Students and young adults often face difficulties when it comes to immigrating to new cities or states for pursuing higher studies from colleges or for work purposes. As different people have different priorities and interests, finding a suitable

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Samyuktha, A., M. Pallavi, L. Jagan, Dr Y. Srinivasulu, and Mr M. Rakesh. "Sentiment Analysis Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (2023): 906–8. http://dx.doi.org/10.22214/ijraset.2023.48706.

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Abstract: Sentiment analysis falls within the category of analytics research. This can make sense by reading raw data using computational methods. This is what analysis is. Written expressions that are neutral, unfavourable, or indifferent can be assessed using sentiment analysis. People use a variety of social media platforms, including Facebook and Twitter, which is a useful tool for gauging public sentiment. This uses a variety of machine learning techniques. We have considered a variety of sentiment analysis techniques in this study. Using machine learning classifiers, sentiment analysis h

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Kanade, Prakash. "Soil Analysis Using Machine Learning." British Journal of Multidisciplinary and Advanced Studies 4, no. 6 (2023): 1–11. http://dx.doi.org/10.37745/bjmas.2022.0350.

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India's agriculture sector employs the most people. Here it is: Agriculture employs around 60% of the Indian population and accounts for about 18% of India's GDP; yet, low productivity is due to a lack of research in this industry. Water logging, soil erosion, nitrogen shortage, and other issues plague Indian agricultural land. These are the primary causes of agriculture's low productivity. A farmer must spend a significant amount of time and money on farming, which is more extensive and time intensive than using a tractor. As a result, the cost of agriculture has increased. It is critical to

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Singh, Neha, and Umesh Chandra Jaiswal. "Sentiment Analysis Using Machine Learning." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 12 (December 29, 2023): e26785. http://dx.doi.org/10.14201/adcaij.26785.

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In recent years, sentiment analysis on social media, including Facebook, Twitter and blogs, has grown in popularity. Social media generate large amounts of information, and this has contributed to the growth of sentiment analysis as a field of research. This study demonstrates that sentiment analysis has been thoroughly researched in previous years, and numerous methods have been designed and evaluated. Nevertheless, there is still much room for improvement. This paper reviews the state of art in sentiment analysis. Various machine learning procedures for sentiment analysis are discussed, thei

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Raule, Varsha. "CV Analysis Using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45841.

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Abstract - The growing number of job applicants in today’s competitive employment landscape makes it challenging for recruiters and hiring managers to efficiently filter through numerous CVs and resumes. Traditional manual methods of candidate screening are time-consuming, error-prone, and subject to human bias. To overcome these limitations, this project, CV Analysis Using Machine Learning, aims to automate the process of reviewing and shortlisting candidates by leveraging the power of machine learning algorithms. The system employs natural language processing (NLP) techniques to automaticall

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Yu, Zhengyang, Chunfeng Bu, and Yanjie Li. "Machine learning for ecological analysis." Chemical Engineering Journal 507 (March 2025): 160780. https://doi.org/10.1016/j.cej.2025.160780.

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K, Anitha Kumari, Indusha M, Abarna Devi D, and Dheva Dharshini S. "Energy Meter Data Analysis Using Machine Learning Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 8, no. 6 (2020): 08–13. http://dx.doi.org/10.17762/ijritcc.v8i6.5409.

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With the advancement of technology, existence of energy meters are not merely to measure energy units. The proliferation of energy meter deployments had led to significant interest in analyzing the energy usage by the machines. Energy meter data is often difficult to analyzeowing to the aggregation of many disparate and complex loads. At utility scales, analysis is further complicated by the vast quantity of data and hence industries turn towards applying machine learning techniques for monitoring and measuring loads of the machines. The energy meter data analysis aims at analyzing the behavio

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C., Kalpana. "Health Care Data Analysis through Machine Learning Meta Heuristic Algorithm." Journal of Advanced Research in Dynamical and Control Systems 12, no. 7 (2020): 196–201. http://dx.doi.org/10.5373/jardcs/v12i7/20202000.

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Dr.K, Uma Pavan Kumar, and Kalimuthu Dr.M. "Performance Analysis of Naïve Bayes Correlation Models in Machine Learning." International Journal of Psychosocial Rehabilitation 24, no. 04 (2020): 1153–57. http://dx.doi.org/10.37200/ijpr/v24i4/pr201088.

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Sharma, Vibhu. "Energy Efficiency Analysis in Residential Buildings using Machine Learning Techniques." International Journal of Science and Research (IJSR) 11, no. 4 (2022): 1380–83. http://dx.doi.org/10.21275/sr24422155123.

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Kumar Kande, Santosh. "Automating Vulnerability Prioritization Using Machine Learning and Financial Impact Analysis." International Journal of Science and Research (IJSR) 12, no. 10 (2023): 2206–7. http://dx.doi.org/10.21275/sr241004050808.

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Gupta, Shailja, Manpreet Kaur, Sachin Lakra, and Yogesh Dixit. "A Comparative Theoretical and Empirical Analysis of Machine Learning Algorithms." Webology 17, no. 1 (2020): 377–97. http://dx.doi.org/10.14704/web/v17i1/web17011.

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Lakshmi, S. "Comparative Analysis of Air Quality Index Prediction using Machine Learning." International Journal of Science and Research (IJSR) 13, no. 1 (2024): 873–75. http://dx.doi.org/10.21275/sr24112050412.

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Priya, Eamani Amulya, Mohd Sohailuddin, Silumula Sangeetha, Kovi Vamsi Krishna, and M. Deenababu. "Machine Learning Model for Prediction and Analysis of Job Market." International Journal of Research Publication and Reviews 6, no. 3 (2025): 9639–45. https://doi.org/10.55248/gengpi.6.0325.1309.

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