Top Research Papers on Machine Learning

This work presents a novel, machine-learning based approach to blocking one of the three main ways website visitors are tracked online—canvas fingerprinting, using ML-CB to train classifiers on the more-difficult-to-change, underlying source code generating the images.

This review paper helps researchers to familiarize themselves with the newest research on ML applications in medicine, recognize their challenges and limitations in this area, and identify future research directions.

By implementing and analyzing an adversarial attack on image classification models, this work demonstrates their detrimental effects on model performance and robustness and underscores the urgent need for proactive measures to safeguard machine learning systems against security threats.

Machine Learning is a method which helps in predicting the future or categorized data to help people in making essential decisions and is usable in many industries like sales and marketing, medical sector, healthcare diagnosis, manufacturing, finance, and several other fields.

A practical framework on how to read and evaluate medical ML papers that create a new ML model (or diagnostic test): ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes).

This paper systematically incorporate RT domain knowledge into ML and develops an ML framework tailored to the problem, called PAL, which covers a number of additional task sets, each of which has never been proven schedulable by any existing approaches for gFP.

This survey presents the publicly available LoRaWAN frameworks that could be utilized for dataset collection, discusses the required features for efficient resource management with suggested ML methods, and highlights the existing publicly available datasets.

An overview of the knowledge gap is given in investigating how ML can be used in KM for business applications in organisations and the relationship between big data, machine learning, and knowledge management is provided.

This paper proposes a holistic suite of strategies designed to enhance the security of ML workflows, including advanced data protection techniques like anonymization and encryption, model security enhancements through adversarial training and hardening, and the fortification of infrastructure security via secure computing environments and continuous monitoring.

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The TAK-ML framework which supports data collection, model building, and model execution/employment in tactical environments, as well as a set of initial applications of this framework are described and evaluated, showing the capabilities available, the ease of use of the system, and initial insights into the efficacy of the resulting models and applications.

The goal of implementing these AI-based algorithms is to increase the possibility of diagnosing a gastrointestinal disease at early stage or the ability to predict the development of a particular condition in advance.

It was observed that the excessive use of personal transport and comparatively limited availability and use of public transport has been a significant cause of obesity in Class II and III obese individuals.

This survey paper attempts to create a self-contained guide that gives an in-depth overview of the field of Differential Privacy ML, and proposes a set of specific best practices for stating guarantees.

This paper introduces ML.NET: a recently open-sourced machine learning framework allowing developers to author and deploy in their applications complex ML pipelines composed of data featurizers and state of the art machine learning models.

In order to create a more just technological world, the authors need more diverse voices at the table when they create technology and to do this, conventional solutions like reducing barriers to entry and addressing the "leaky pipeline" issues that make mid-career professionals drop out or stall on their way to the top need to be added.

The two most recent extensions for ACL2(ml) can suggest now families of similar function definitions, in addition to the families ofsimilar theorems, and the lemma generation tool implemented in ACL2 (ml) has been improved with a method to generate preconditions using the guard mechanism of ACL2.

dlib-ml contains an extensible linear algebra toolkit with built in BLAS support, and implementations of algorithms for performing inference in Bayesian networks and kernel-based methods for classification, regression, clustering, anomaly detection, and feature ranking.

Java-ML is a collection of machine learning and data mining algorithms, which aims to be a readily usable and easily extensible API for both software developers and research scientists.

A detailed checklist with guiding questions and practices that can be employed while developing a machine learning project that utilizes cultural heritage data is formulating, which can be published with the deliverables of the project.

A machine learning, more precisely support vector machine (SVM), based methodology to predict whether adenoviral infection can take place in a given host, and is the first of its kind as an effective predictor to screen the infection capacity along with anticipating any cross-species shifts.

It is argued that supporting the adoption of ML methods and technologies for subsurface hydrological investigations and management requires not only the development of robust technologic tools and approaches, but educational strategies and tools capable of building confidence among diverse users.

The primary objective is to assist farmers and agriculture industries to thrive so that there can be less occurrence of food shortages by efficiently increasing the production of crops tenfold, analyzes the soil fertility for better plant growth and predicts the occurrence of natural disasters such as droughts and floods for early prevention from the crops by using the ATMEGA controller.

This paper proposes an integrated dependency tracking system that balances the cost and risks in the development stage and operation stage, and uses blockchain (an immutable data store) to track the co-evolution of the models and the corresponding datasets.

This study identifies ML researchers’ perceptions of limitations, as well as the challenges they face when recognizing, exploring, and articulating limitations, and develops REAL ML, a set of guided activities to help ML researchers recognize, explore, and articulate the limitations of their research.

Interpretable machine learning was found to help in teaching many EFL/ESL topics such as dictation, phonetics instruction, language blogging, and all of which contribute to the facilitation of English language learning and teaching processes.

This work presents a novel and low-cost approach to enhance the reliability of generic ML accelerators by opportunistically exploring the chances of runtime Redundancy provided by neighbouring PEs, named as OR-ML.

This study elaborates on the CRISP-ML methodology on the determination, measurement, and achievement of the necessary level of interpretability of ML solutions in the Public Healthcare sector, and concludes with the three main directions for the development of the presented cross-industry standard process.

It is proved theoretically that such a hierarchical system can accelerate training in distributed environments where intra-node communication is cheaper than inter- node communication and that Snap ML achieves the same test loss an order of magnitude faster than any of the previously reported results.

A novel ML-based exploit detection model that enables highly efficient inference without sacrificing performance, ML-FEED, and a novel automated technique to extract vulnerability patterns from the Common Weakness Enumeration (CWE) and Common Vulnerabilities and Exposures (CVE) databases.

The feasibility and potential richness of collaborative modeling is illustrated by presenting an in-depth case study of a family using Co-ML in a facilitated introductory ML activity at home, and how a distributed collaborative process, in which individuals can take on different model-building responsibilities, provides a rich context for children and adults to learn ML dataset design.

This work presents an effort to model the prediction about mentalization from neural activity using predictive modeling, and shows how this approach can help improve the prognosis of psychological disorders such as autism and schizophrenia.

This tutorial gives an overview of novel methods, explains them in detail, and applies them via Quantlets in real data applications to study the effect that microcredit availability has on the amount of money borrowed and if 401(k) pension plan eligibility has an impact on net financial assets.

An exploratory analysis of the recruitment dataset uses supervised machine learning to predict whether a student was placed, utilizing classification models, and proposes approaches and methods that surpass all other machine learning models.

This study employs machine learning (ML) algorithms, trained on accurate datasets produced by TCAD, in order to massively accelerate multidomain ferroelectric FET (FeFET) simulations and take TCAD out of the framework’s loop.

Flink-ML is Flink’s open-source distributed machine learning library, which has been added to the Flink ecosystem in response to the exponential growth of machine learning applications in recent years, and enhances the capabilities of the Flink framework.

The mathematics that is hidden behind the Artificial Intelligence, Machine Learning and Deep Learning that the authors are using today, which are not the unusual milestones it has done thus far are described.

We describe machine learning (ML) lineage as a framework to holistically capture and connect the required information about ML model development and operations. ML lineage distinguishes between model and prediction levels, conceptually encompassing separate yet interconnected core domains for the project, experiment, model, and prediction.

A summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care focuses on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.

The findings show that the most widely researched application of ML in higher education is related to the prediction of academic performance and employability of students.

The Galaxy-ML toolkit makes supervised machine learning more accessible to biomedical scientists by enabling them to perform end-to-end reproducible machine learning analyses at large scale using only a web browser.

The clinical predictors that have been provided through "NN model" and "DT model" have the potential through determining the small datasets within biomedical engineering that further helps medical practitioners or healthcare professionals to decide on the medicine and treatment required for a patient.

Basic algorithms of machine learning technique, e.g. support vector machine (SVM) and neural networks, and how they are applied to lithography optimization problems are introduced.

A general outline of AI/ML is provided along with an overview of the fundamental concepts of ML categories, specifically supervised, unsupervised, and reinforcement learning, which will hopefully enrich the reader's understanding, appreciation, and the need for embracing such tools.

This paper shows how the multiplication a horizontal vector representing a Sparse Distributed Representation of patterns in sensory data by a vertical vector representing a SDR of patterns in context data followed by a pattern recognition operation on the resulting matrix results in the integration of relevant context and in the output of data containing meaning.

The design, called SiP-ML, accelerates the training time of popular DNN models using silicon photonics links capable of providing multiple terabits-per-second of bandwidth per GPU and develops task partitioning and device placement methods that take the degree and reconfiguration latency of optical interconnects into account.

This tutorial guides the attendees through in-depth overview of the field of DP ML models, presenting information about achieving the best possible DP ML model with rigorous privacy guarantees and highlighting important topics such as privacy accounting and its assumptions, as well as convergence.

Peat-ML is presented, a spatially continuous global map of peatland fractional coverage generated using machine learning techniques suitable for use as a prescribed geophysical field in an ESM to simulate the effects of climate change on the global carbon and hydrologic balance.

The data was separated into two categories: patients with liver disease and sicknesses the most accurate machine learning method was used to predict the final result, and the best one is identified.

The study highlights the benefits of utilizing machine learning algorithms in areas such as demand forecasting, inventory management, and logistics optimization and shows that incorporating machine learning techniques can lead to significant improvements in accuracy, speed, and efficiency, thereby leading to an increase in profitability.