Top Research Papers on Machine Learning Projects

The outcomes of the current research illustrate the potential of ML modeling to be adopted for assigning productivity rates, making it a powerful tool for decision making.

An overview of the requirements engineering challenges in the development of machine learning systems that have been reported in the research literature, along with their proposed solutions, and the approach to overcoming them in the form of a case study is presented.

It is shown that projective ML possesses the subject reduction property, which means that well-typed programs can be reduced safely and is built on by adding the ML Let typing rule to the simply typed projective calculus.

The objective of this research is to use several algorithms of machine learning to estimate the effort of software project development and the best machine learning model is chosen to compare with the COCOMO.

This dissertation examines the machine learning issues raised by the domain of anomaly detection for computer security by focusing here on learning models of normalcy at the user behavioral level.

The results illustrate that the project managers in Nepal have a high score in leadership skills, personal characteristics, team development and delegation, communication skills, technical skills, problem-solving/coping with situation skills and stakeholder/relationship management skills.

Manual analysis of code smells in open-source ML projects showed that code duplication is widespread and that the PEP8 convention for identifier naming style may not always be applicable to ML code due to its resemblance with mathematical notation, but several major obstructions to the maintainability and reproducibility of ML projects were found.

This study focuses on projects for the development of ML-based service systems in which ML techniques are applied to enterprise functions and proposes a method for collecting insights by referring to a development model based on project practices and developing patterns for ML projects as an enterprise architecture model.

This project created two models which are trained to detect neurons and axons using a modified version of the U-Net which uses a VGG pre-trained network as its encoder, and analyzed the directionality of the axons depending on the orientation of the grid formed by the nanostructure.

This paper expects to lead a writing survey of patterns and techniques for machine learning utilized for the Sensitivity Analysis of the Project Sensitivity analysis to decide the most basic factors that have the best effect on the plausibility and adequacy of the undertaking.

The design and implementation of a project tracking tool for software projects that are developed using the agile method Scrum is described and the tool supports cost estimation of the project based on user stories and sprint tasks.

It is observed from the previous study that managers spend a lot of time in allocation which is performed according to the experts experience rather than following some specific criteria which lead to inefficient utilization of the resource available, so if a model is developed and trained using machine learning algorithms to handle this routine task so that the manager can spend time in other developmental activities rather than spending time on routine tasks.

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The scope of the program was widened while the focus on extensional viscosity measurements was retained, and attempts were made to have the same measurements repeated using different techniques and instruments.

The project's most popular techniques were emphasised and made simple to use and the project's interquartile range and outlier range were analyzed.

The design and usage of confr is outlined, a concise and flexible configuration system geared towards Python-based machine learning projects that combines some of the capabilities of commonly used systems into a library which aims to reduce repetitive code and maintenance overhead.

It is shown that ML engineers use Docker images mostly to help with the platform portability, such as transferring the software across the operating systems, runtimes such as GPU, and language constraints, but also found that more resources may be required to run the Docker images for building ML-based software projects.

This research evaluates the novel concept of project smells which consider deficits in project management as a more holistic perspective on software quality in ML projects in the industrial context of ING, a global bank and large software- and data-intensive organisation.

It is shown that a neural network‐based expectations algorithm can deal efficiently with multicollinearity by extending the optimal debt management problem studied by Faraglia, Marcet, Oikonomou, and Scott (2019) to four maturities.

A novel approach using a pre-processing step, which involves projecting the input data into a geometric space which parametrises the orbits of the symmetry group, is proposed.

With this editorial, we inaugurate the next issue of our journal, which is dedicated to showcasing AI, ML, and E-Health projects within real healthcare environments. 

This research was based on an ML algorithm evaluation system for buildings as a classification model for project features with the goal of aiding project managers to comprehend defects.

This is the first work that has analyzed ML projects’ CI usage, practices, and issues, and contextualized its results by comparing them with similar Non-ML projects, and provides findings for researchers and ML developers to identify possible improvement scopes for CI in ML projects.

A probabilistic method of finding meaningful phrases in the Twenty-Newsgroups text corpus by indexing terms which have length more than one is explored.

The models quantify the synergistic effects among the surface charge, size, temperature, and NP exposure dose on plant growth and NP uptake and provide ideas for the design of environmentally friendly nanoenabled pesticides and fertilizers.

This work uses a mixed-method approach to analyze errors in 30 final project reports in an undergraduate machine learning course and identifies areas of opportunity to improve machine learning pedagogy, particularly related to data processing, data leakage, hyperparameters, nonsensical outputs, and disentangling data decisions from model decisions.

This paper comprehensively examines the effects of Artificial Intelligence (AI), specifically machine learning on IT project management in the context of technologically advanced surroundings through a thorough literature review and detailed analysis.

A resource allocation method that uses machine learning for effective project scheduling that can be used to solve resource allocation problems of software project management.

It is concluded that the best compromise was a model using SSD detection and MOSSE tracking : the authors managed to achieve 31 fps and 63% of accuracy.

In this project, seven feature selection algorithms are applied to reduce the dimensionality of a molecular representation dataset with a huge number of features, and the prediction accuracy is compared.

This case study simulation demonstrates that the Melbourne Metro project has about 42% of the benefits felt in terms of public transport user gains, followed by road user gains linked to decongestion (21.5%), and the assets’ residual value, as well as externalities (accounting for about 9.5% of the project benefits). From an economic perspective, gains are about 27.5% of the total. While the composition for both options assumes a similar path, the Melbourne Metro project’s economic and financial benefits outweigh those of the extended program. To reap optimally, therefore, the former option is...

The correlation between the noise threshold used for detecting a galaxy, and the performance of the binary classifier is studied, to study the process of galaxy detection in telescope images.

This paper selects some main economic indexes, such as Gold, Oil, NDAQ, and known currency, and tries to find the quantitative effect of this war on them and uses Machine Learning Linear Regression to quantify the war effect.

It is found that the augmentation of the training and testing data has little effect on the accuracy of the models, and this lead to the further investigation of training these physical layers using a TensorFlow Eager model.

K-Nearest Neighbors (KNN), an ML algorithm, was used to classify over-budget projects and demonstrated that it can be used to predict the over- budget construction projects for the Thai government.

The aim of the project is to study two of the most widely used machine learning strategies, namely KNearest Neighbours algorithm and Perceptron Learning algorithm, in a quantum setting, and study the speedups that the quantum modules allow over the classical counterparts.

The proposed prediction algorithm successfully identified the climatic zones based on their different geographic and climatic characteristics for most meteorological stations, resulting in realistic precipitation predictions, a weakness also reported by other research works.

A XGBoost forecasting model is developed and computational experiments are conducted using real data and EVM metrics, proposing an effective XGBoost model for forecasting the cost throughout the project life cycle.

In this work, incident and cumulative data for confirmed infected, deaths and vaccinated in Mexico during 2021, are employed to obtain useful projections by using Wolfram Mathematica and Matlab® regression learner app.

Through intensive and literature review, it can be inferred that machine learning models clearly outperformed non-machine learning and traditional techniques of estimation.

Some of the best machine learning algorithms to classify text “graduation projects”, support vector machine (SVM) algorithm, logistic regression (LR) algorithm), random forest (RF) algorithms, which can deal with an extremely small amount of dataset are reviewed after comparing these algorithms based on accuracy.

It is shown that project risk assessment by machine learning is more successful in minimizing the loss of the project, thereby increasing the likelihood of the project success, providing an alternative way to efficiently reduce the project failure probabilities, and increasing the output ratio for growth.

This thesis primarily investigates the potential of the Pairwise Geometric Histogram (PGH) representation as the basis of a machine learning edge and view-based 3D object recognition computer vision system, exploring methods for representing scaled 3D objects’ continuous appearances around their view-spheres.

Even if a technical project is not research, and never has to be explained to outsiders, writing reports for internal use is a way to promote deep and careful thinking.

It is shown that it is impossible for the regret to be O(T 1− ), where T is the number of iterations, if the dependency of the regret on the other parameters is polynomial and if NP 6⊆ BPP, which is a big open problem that is believed to be true.

Training neural audio classifiers with few data used prototypical networks and transfer learning to train the neural network and predicted the future trending on the smart devices.

It is found that the random projection approach predictively underperforms PCA, but its computational advantages may make it attractive for certain applications.

By extracting the information more efficiently and accurately from the test results, machine learning techniques help clinicians get closer to the diagnosis and therefore make better decisions of treatment.

NICHE, a manually labelled dataset consisting of 572 ML projects, is presented, based on the evidence of good software engineering practices, and can help researchers understand the practices that are adopted in high-quality ML projects.

This study aims to compare two specific types of artificial neural network prediction performances; multilayer perceptron and; (ii) recurrent neural networks to capture the emergent inter-project relationships.