Relevant bibliographies by topics / Machine learning analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Machine learning analysis'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Machine learning analysis"

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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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Dissertations / Theses on the topic "Machine learning analysis"

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Bengtsson, Sebastian. "MACHINE LEARNING FOR MECHANICAL ANALYSIS." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-44325.

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It is not reliable to depend on a persons inference on dense data of high dimensionality on a daily basis. A person will grow tired or become distracted and make mistakes over time. Therefore it is desirable to study the feasibility of replacing a persons inference with that of Machine Learning in order to improve reliability. One-Class Support Vector Machines (SVM) with three different kernels (linear, Gaussian and polynomial) are implemented and tested for Anomaly Detection. Principal Component Analysis is used for dimensionality reduction and autoencoders are used with the intention to incr
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Roderus, Jens, Simon Larson, and Eric Pihl. "Hadoop scalability evaluation for machine learning algorithms on physical machines : Parallel machine learning on computing clusters." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20102.

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The amount of available data has allowed the field of machine learning to flourish. But with growing data set sizes comes an increase in algorithm execution times. Cluster computing frameworks provide tools for distributing data and processing power on several computer nodes and allows for algorithms to run in feasible time frames when data sets are large. Different cluster computing frameworks come with different trade-offs. In this thesis, the scalability of the execution time of machine learning algorithms running on the Hadoop cluster computing framework is investigated. A recent version o
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Wood, Ian Andrew. "Boltzmann machine learning : analysis and improvements /." St. Lucia, Qld, 2003. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe17753.pdf.

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Agrawal, Punit. "Program navigation analysis using machine learning." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32599.

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Developers invest a large portion of their development time exploring program source code to find task-related code elements and to understand the context of their task. The task context is usually not recorded at the end of the task and is forgotten over time. Similarly, it is not possible to share the task context with other developers working on related tasks. Proposed solutions to automatically record the summary of the code investigation suffer from methodological limitations related to the technique
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Rogers, Simon David. "Machine learning techniques for microarray analysis." Thesis, University of Bristol, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.409426.

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Alviste, Joosep Franz Moorits. "Deployment failure analysis using machine learning." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420321.

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Manually diagnosing recurrent faults in software systems can be an inefficient use of time for engineers. Manual diagnosis of faults is commonly performed by inspecting system logs during the failure time. The DevOps engineers in Pipedrive, a SaaS business offering a sales CRM platform, have developed a simple regular-expression-based service for automatically classifying failed deployments. However, such a solution is not scalable, and a more sophisticated solution isrequired. In this thesis, log mining was used to automatically diagnose Pipedrive's failed deployments based on the deployment
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Nicolini, Olivier. "LIBS Multivariate Analysis with Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-286595.

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Laser-Induced Breakdown Spectroscopy (LIBS) is a spectroscopic technique used for chemical analysis of materials. By analyzing the spectrum obtained with this technique it is possible to understand the chemical composition of a sample. The possibility to analyze materials in a contactless and online fashion, without sample preparation make LIBS one of the most interesting techniques for chemical composition analysis. However, despite its intrinsic advantages, LIBS analysis suffers from poor accuracy and limited reproducibility of the results due to interference effects caused by the chemical c
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Vu, Viet, and Mariaguadaloppe Farah. "Bank risk analysis with machine learning." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44954.

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Nowadays, the time and resources needed to get an accurate estima-tion of a client’s ability to pay back a loan has gone up. With theamount of data complexity it involves to do the credit risk analysis, the machine learning technique has been used to ease the process.To help a bank institute get a better insight into their client’s eco-nomic state. The thesis is to present a model that could help themfind interesting information using machine learning.With many clients having nonlinear income and expenses, it madethe machine learning algorithm of choosing, in this case, Linear Re-gression, ver
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Asta, Shahriar. "Machine learning for improving heuristic optimisation." Thesis, University of Nottingham, 2015. http://eprints.nottingham.ac.uk/34216/.

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Heuristics, metaheuristics and hyper-heuristics are search methodologies which have been preferred by many researchers and practitioners for solving computationally hard combinatorial optimisation problems, whenever the exact methods fail to produce high quality solutions in a reasonable amount of time. In this thesis, we introduce an advanced machine learning technique, namely, tensor analysis, into the field of heuristic optimisation. We show how the relevant data should be collected in tensorial form, analysed and used during the search process. Four case studies are presented to illustrate
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Tar, Paul David. "Quantitative planetary image analysis via machine learning." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/quantitative-planetary-image-analysis-via-machine-learning(9188b4ff-8e5d-4181-b389-1db3e2a85c7b).html.

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Over recent decades enormous quantities of image data have been acquired from planetary missions. High resolution imagery is available for many of the inner planets, gas giant systems, and some asteroids and comets. Yet, the scientific value of these images will only be fully realised if sufficient analytic power can be applied to their large scale and detailed interpretation. Unfortunately, the quantity of data has now surpassed researchers' abilities to manually analyse each image, whilst available automated approaches are limited in their scope and reliability. To mitigate against this citi
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Books on the topic "Machine learning analysis"

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Joshi, Amit, Mahdi Khosravy, and Neeraj Gupta, eds. Machine Learning for Predictive Analysis. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7106-0.

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Boyle, Brandon H. Support vector machines: Data analysis, machine learning, and applications. Nova Science Publishers, 2011.

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Preisach, Christine, Hans Burkhardt, Lars Schmidt-Thieme, and Reinhold Decker, eds. Data Analysis, Machine Learning and Applications. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78246-9.

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Gong, Yihong. Machine learning for multimedia content analysis. Springer, 2007.

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Sundararajan, Srikrishnan. Multivariate Analysis and Machine Learning Techniques. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-99-0353-5.

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Pillai, Anitha S., and Bindu Menon. Machine Learning and Deep Learning in Neuroimaging Data Analysis. CRC Press, 2024. http://dx.doi.org/10.1201/9781003264767.

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Ao, Sio-Iong. Advances in Machine Learning and Data Analysis. Springer Netherlands, 2010.

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Ghosh, Chandril. Data Analysis with Machine Learning for Psychologists. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14634-3.

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Stalph, Patrick. Analysis and Design of Machine Learning Techniques. Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-04937-9.

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Spiliopoulou, Myra, Lars Schmidt-Thieme, and Ruth Janning, eds. Data Analysis, Machine Learning and Knowledge Discovery. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01595-8.

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Book chapters on the topic "Machine learning analysis"

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Lista, Luca. "Machine Learning." In Statistical Methods for Data Analysis. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19934-9_11.

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Haslwanter, Thomas. "Machine Learning." In Hands-on Signal Analysis with Python. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57903-6_11.

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Ghosh, Chandril. "Machine Learning." In Data Analysis with Machine Learning for Psychologists. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14634-3_4.

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Forsyth, David. "Principal Component Analysis." In Applied Machine Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18114-7_5.

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Forsyth, David. "Canonical Correlation Analysis." In Applied Machine Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18114-7_7.

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Li, Hang. "Latent Semantic Analysis." In Machine Learning Methods. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3917-6_17.

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Li, Hang. "Principal Component Analysis." In Machine Learning Methods. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3917-6_16.

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Tattar, Prabhanjan Narayanachar, and H. J. Vaman. "Complementary Machine Learning Techniques." In Survival Analysis. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003306979-11.

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Collard, Jean-François. "Machine Learning." In Hands-On Data Analysis in R for Finance. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003320555-18.

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Petrelli, Maurizio. "Machine Learning." In Introduction to Python in Earth Science Data Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78055-5_12.

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Conference papers on the topic "Machine learning analysis"

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Chai, Yuan. "Machine Learning Application: Flight Delay Prediction." In International Conference on Data Analysis and Machine Learning. SCITEPRESS - Science and Technology Publications, 2024. https://doi.org/10.5220/0013486400004619.

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Singla, Sanjay, Prashant Ahluwalia, and Abhay Choudhary. "Sentiment Analysis using Machine Learning." In 2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP). IEEE, 2024. http://dx.doi.org/10.1109/innocomp63224.2024.00100.

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Surendiran, J., Budati Suresh Kumar, G. Saritha, Akondi Narayana Kiran, T. Saravanan, and S. Ramesh Kumar. "Emotional Analysis Using Machine Learning." In 2025 International Conference on Computing and Communication Technologies (ICCCT). IEEE, 2025. https://doi.org/10.1109/iccct63501.2025.11018920.

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Shang, Fang. "Technology Application of Autonomous Vehicle in Machine Learning." In International Conference on Data Analysis and Machine Learning. SCITEPRESS - Science and Technology Publications, 2024. https://doi.org/10.5220/0013525800004619.

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Manjunath, Atharva, and Hardik Mavdiya. "YOLO driven dashcam analysis for road surface defect and pothole detection." In Applications of Machine Learning 2024, edited by Barath Narayanan, Michael E. Zelinski, Tarek M. Taha, Abdul A. Awwal, and Khan M. Iftekharuddin. SPIE, 2024. http://dx.doi.org/10.1117/12.3027371.

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Wang, Xinyi. "Spam Filtering in the Modern Era: A Review of Machine Learning, Deep Learning, and System Comparisons." In International Conference on Data Analysis and Machine Learning. SCITEPRESS - Science and Technology Publications, 2024. https://doi.org/10.5220/0013526000004619.

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Wang, Hanyu. "Text Sentiment Analysis for JD.com Based on Machine Learning." In International Conference on Data Analysis and Machine Learning. SCITEPRESS - Science and Technology Publications, 2024. https://doi.org/10.5220/0013515400004619.

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Nasrin, Shamima, Md Zahangir Alom, Simon Khan, and Tarek M. Taha. "Deep learning-based explainable approaches for RNA-seq gene expression data analysis." In Applications of Machine Learning 2024, edited by Barath Narayanan, Michael E. Zelinski, Tarek M. Taha, Abdul A. Awwal, and Khan M. Iftekharuddin. SPIE, 2024. http://dx.doi.org/10.1117/12.3030851.

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Chen, Hangzhi. "Car Price Prediction Based on Multiple Machine Learning Models." In International Conference on Data Analysis and Machine Learning. SCITEPRESS - Science and Technology Publications, 2024. https://doi.org/10.5220/0013509000004619.

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Xu, Longhua. "Advanced Machine Learning Approaches for Accurate Flight Delay Prediction." In International Conference on Data Analysis and Machine Learning. SCITEPRESS - Science and Technology Publications, 2024. https://doi.org/10.5220/0013515500004619.

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Reports on the topic "Machine learning analysis"

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Ren, Huiying, Zhangshuan Hou, Heng Wang, and Pavel Etingov. Machine Learning for Synchrophasor Analysis. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1673617.

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Moral, Rafael. Introduction to Machine Learning. Instats Inc., 2024. http://dx.doi.org/10.61700/qfxukp14jlpfd1478.

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This comprehensive workshop provides a thorough introduction to machine learning, focusing on both theoretical concepts and practical applications using R. Designed for PhD students, professors, and researchers, it covers essential techniques such as supervised and unsupervised learning, dimension reduction, and tree-based methods, enhancing participants' data analysis skills and research capabilities.
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Fessel, Kimberly. Machine Learning in Python. Instats Inc., 2024. http://dx.doi.org/10.61700/s74zy0ivgwioe1764.

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This intensive, hands-on workshop offers a deep dive into machine learning with Python, designed for PhD students, professors, and researchers across various fields. Participants will master practical skills in data cleaning, exploratory data analysis, and building powerful machine learning models, including neural networks, to elevate their research. With real-world coding exercises and expert guidance, this workshop will equip you with the tools to turn data into actionable insights.
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Estrella, Tony, Carla Alfonso, Lluis Capdevila, and Josep-Maria Losilla. Machine learning for the analysis of healthy lifestyle data: a scoping review protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2023. http://dx.doi.org/10.37766/inplasy2023.3.0065.

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Review question / Objective: The objective of this scoping review is to identify and characterize machine learning algorithms used in data analysis of healthy lifestyle. The specific objectives are the study of a) terminology, b) healthy lifestyle variables analysed either input or output, c) programs and libraries used to analyse data, and d) sources, types, and quality of data analysed. Eligibility criteria: In this scoping review the inclusion criteria from studies that provide empirical information are as follows: a) studies must use machine learning models either supervised or unsupervise
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Shavlik, J. W. Applying machine learning techniques to DNA sequence analysis. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/5688406.

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Shavlik, J. W., and M. O. Noordewier. Applying machine learning techniques to DNA sequence analysis. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/7023074.

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Murphy, R., and W. Hoyt. Intelligent Machine Learning Analysis for Fuel Cell Operations. Office of Scientific and Technical Information (OSTI), 2000. http://dx.doi.org/10.2172/940376.

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Hua, Tianyu. Machine learning for sentiment analysis: Opportunities and challenges. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-974.

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Fessel, Kimberly. Machine Learning Essentials (Free Seminar). Instats Inc., 2024. http://dx.doi.org/10.61700/l6x4izy1bov9p1764.

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This comprehensive one-hour seminar provides PhD students, academics, and professional researchers with fundamental insights into machine learning concepts, crucial for modern data analysis in many disciplines. Led by data science expert Dr Kimberly Fessel, participants will explore key topics such as supervised and unsupervised learning, model performance (under- vs. overfitting), and popular algorithms like linear and logistic regression, decision trees, and neural networks.
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Kumar, Prabhat, Melvin Felton, David Ligon, Sandra Collier, and Deryck James. Machine-Learning Groundwork and Cluster Analysis for Vortex Detection. DEVCOM Army Research Laboratory, 2022. http://dx.doi.org/10.21236/ad1165710.

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