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The IEEE Transactions on Neural Networks and Learning Systems publishes technical articles that deal with the theory, design, and applications of neural networks and related learning systems.

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The values displayed for the journal bibliometrics fields in IEEE Xplore are based on the Journal Citation Report from Clarivate from the 2022 report released in June 2023. The values displayed for CiteScore metrics are from Scopus 2022 report released in June 2023. Journal Citation Metrics Journal Citation Metrics such as Impact Factor, Eigenfactor Score™ and Article Influence Score™ are available where applicable. Each year, Journal Citation Reports© (JCR) from Thomson Reuters examines the influence and impact of scholarly research journals. JCR reveals the relationship between citing and cited journals, offering a systematic, objective means to evaluate the world's leading journals.  Find out more about IEEE Journal Bibliometrics . 

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IEEE TNNLS Special Issue Proposal: Advancements in Foundation Models [ Call for Papers ]

Guest Editors: Tianming Liu, University of Georgia, USA, Xiang Li, Massachusetts General Hospital and Harvard Medical School, USA, Hao Chen, Hong Kong University of Science and Technology, Hong Kong, China, Yixuan Yuan, Chinese University of Hong Kong, Hong Kong, China, Anirban Mukhopadhyay, TU Darmstadt, Germany.

Submission Deadline: 15 August 2024

Featured Paper

Reinforcement Learning Control With Knowledge Shaping

IEEE Transactions on Neural Networks and Learning Systems (Volume: 35, Issue: 3, March 2024)

Abstract : We aim at creating a transfer reinforcement learning framework that allows the design of learning controllers to leverage prior knowledge extracted from previously learned tasks and previous data to improve the learning performance of new tasks. Toward this goal, we formalize knowledge transfer by expressing knowledge in the value function in our problem construct, which is referred to as reinforcement learning with knowledge shaping (RL-KS). Unlike most transfer learning studies that are empirical in nature, our results include not only simulation verifications but also an analysis of algorithm convergence and solution optimality. Also different from the well-established potential-based reward shaping methods which are built on proofs of policy invariance, our RL-KS approach allows us to advance toward a new theoretical result on positive knowledge transfer. Furthermore, our contributions include two principled ways that cover a range of realization schemes to represent prior knowledge in RL-KS. We provide extensive and systematic evaluations of the proposed RL-KS method. The evaluation environments not only include classical RL benchmark problems but also include a challenging task of real-time control of a robotic lower limb with a human user in the loop.

IEEE Xplore Link : https://ieeexplore.ieee.org/document/10053632

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Effect of Data Characteristics Inconsistency on Medium and Long-Term Runoff Forecasting by Machine Learning

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In the application of medium and long-term runoff forecasting, machine learning has some problems, such as high learning cost, limited computing cost, and difficulty in satisfying statistical data assumptions in some regions, leading to difficulty in popularization in the hydrology industry. In the case of a few data, it is one of the ways to solve the problem to analyze the data characteristics consistency. This paper analyzes the statistical hypothesis of machine learning and runoff data characteristics such as periodicity and mutation. Aiming at the effect of data characteristics inconsistency on three representative machine learning models (multiple linear regression, random forest, back propagation neural network), a simple correction/improvement method suitable for engineering was proposed. The model results were verified in the Danjiangkou area, China. The results show that the errors of the three models have the same distribution as the periodic characteristics of the runoff periods, and the correction/improvement based on periodicity and mutation characteristics can improve the forecasting accuracy of the three models. The back propagation neural network model is most sensitive to the data characteristics consistency.

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Efficiency Optimization Design That Considers Control of Interior Permanent Magnet Synchronous Motors Based on Machine Learning for Automotive Application

Interior permanent magnet synchronous motors have become widely used as traction motors in environmentally friendly vehicles. Interior permanent magnet synchronous motors have a high degree of design freedom and time-consuming finite element analysis is required for their characteristics analysis, which results in a long design period. Here, we propose a method for fast efficiency maximization design that uses a machine-learning-based surrogate model. The surrogate model predicts motor parameters and iron loss with the same accuracy as that of finite element analysis but in a much shorter time. Furthermore, using the current and speed conditions in addition to geometry information as input to the surrogate model enables design optimization that considers motor control. The proposed method completed multi-objective multi-constraint optimization for multi-dimensional geometric parameters, which is prohibitively time-consuming using finite element analysis, in a few hours. The proposed shapes reduced losses under a vehicle test cycle compared with the initial shape. The proposed method was applied to motors with three rotor topologies to verify its generality.

Published in the IEEE Vehicular Technology Society Section

An Intelligent IoT Sensing System for Rail Vehicle Running States Based on TinyML

Real-time identification of the running state is one of the key technologies for a smart rail vehicle. However, it is a challenge to accurately real-time sense the complex running states of the rail vehicle on an Internet-of-Things (IoT) edge device. Traditional systems usually upload a large amount of real-time data from the vehicle to the cloud for identification, which is laborious and inefficient. In this paper, an intelligent identification method for rail vehicle running state is proposed based on Tiny Machine Learning (TinyML) technology, and an IoT system is developed with small size and low energy consumption. The system uses a Micro-Electro-Mechanical System (MEMS) sensor to collect acceleration data for machine learning training. A neural network model for recognizing the running state of rail vehicles is built and trained by defining a machine learning running state classification model. The trained recognition model is deployed to the IoT edge device at the vehicle side, and an offset time window method is utilized for real-time state sensing. In addition, the sensing results are uploaded to the IoT server for visualization. The experiments on the subway vehicle showed that the system could identify six complex running states in real-time with over 99% accuracy using only one IoT microcontroller. The model with three axes converges faster than the model with one. The model recognition accuracy remained above 98% and 95%, under different installation positions on the rail vehicle and the zero-drift phenomenon of the MEMS acceleration sensor, respectively. The presented method and system can also be extended to edge-aware applications of equipment such as automobiles and ships.

Code Generation Using Machine Learning: A Systematic Review

Recently, machine learning (ML) methods have been used to create powerful language models for a broad range of natural language processing tasks. An important subset of this field is that of generating code of programming languages for automatic software development. This review provides a broad and detailed overview of studies for code generation using ML. We selected 37 publications indexed in arXiv and IEEE Xplore databases that train ML models on programming language data to generate code. The three paradigms of code generation we identified in these studies are description-to-code, code-to-description, and code-to-code. The most popular applications that work in these paradigms were found to be code generation from natural language descriptions, documentation generation, and automatic program repair, respectively. The most frequently used ML models in these studies include recurrent neural networks, transformers, and convolutional neural networks. Other neural network architectures, as well as non-neural techniques, were also observed. In this review, we have summarized the applications, models, datasets, results, limitations, and future work of 37 publications. Additionally, we include discussions on topics general to the literature reviewed. This includes comparing different model types, comparing tokenizers, the volume and quality of data used, and methods for evaluating synthesized code. Furthermore, we provide three suggestions for future work for code generation using ML.

Combining Citation Network Information and Text Similarity for Research Article Recommender Systems

Researchers often need to gather a comprehensive set of papers relevant to a focused topic, but this is often difficult and time-consuming using existing search methods. For example, keyword searching suffers from difficulties with synonyms and multiple meanings. While some automated research-paper recommender systems exist, these typically depend on either a researcher’s entire library or just a single paper, resulting in either a quite broad or a quite narrow search. With these issues in mind, we built a new research-paper recommender system that utilizes both citation information and textual similarity of abstracts to provide a highly focused set of relevant results. The input to this system is a set of one or more related papers, and our system searches for papers that are closely related to the entire set. This framework helps researchers gather a set of papers that are closely related to a particular topic of interest, and allows control over which cross-section of the literature is located. We show the effectiveness of this recommender system by using it to recreate the references of review papers. We also show its utility as a general similarity metric between scientific articles by performing unsupervised clustering on sets of scientific articles. We release an implementation, ExCiteSearch (bitbucket.org/mmmontemore/excitesearch), to allow researchers to apply this framework to locate relevant scientific articles.

Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice

Smart health is one of the most popular and important components of smart cities. It is a relatively new context-aware healthcare paradigm influenced by several fields of expertise, such as medical informatics, communications and electronics, bioengineering, ethics, to name a few. Smart health is used to improve healthcare by providing many services such as patient monitoring, early diagnosis of disease and so on. The artificial neural network (ANN), support vector machine (SVM) and deep learning models, especially the convolutional neural network (CNN), are the most commonly used machine learning approaches where they proved to be performance in most cases. Voice disorders are rapidly spreading especially with the development of medical diagnostic systems, although they are often underestimated. Smart health systems can be an easy and fast support to voice pathology detection. The identification of an algorithm that discriminates between pathological and healthy voices with more accuracy is needed to obtain a smart and precise mobile health system. The main contribution of this paper consists of proposing a multiclass-pathologic voice classification using a novel multileveled textural feature extraction with iterative feature selector. Our approach is a simple and efficient voice-based algorithm in which a multi-center and multi threshold based ternary pattern is used (MCMTTP). A more compact multileveled features are then obtained by sample-based discretization techniques and Neighborhood Component Analysis (NCA) is applied to select features iteratively. These features are finally integrated with MCMTTP to achieve an accurate voice-based features detection. Experimental results of six classifiers with three diagnostic diseases (frontal resection, cordectomy and spastic dysphonia) show that the fused features are more suitable for describing voice-based disease detection.

*Published in the IEEE Electronics Packaging Society Section within IEEE Access .

Machine Learning Empowered Spectrum Sharing in Intelligent Unmanned Swarm Communication Systems: Challenges, Requirements and Solutions

The unmanned swarm system (USS) has been seen as a promising technology, and will play an extremely important role in both the military and civilian fields such as military strikes, disaster relief and transportation business. As the “nerve center” of USS, the unmanned swarm communication system (USCS) provides the necessary information transmission medium so as to ensure the system stability and mission implementation. However, challenges caused by multiple tasks, distributed collaboration, high dynamics, ultra-dense and jamming threat make it hard for USCS to manage limited spectrum resources. To tackle with such problems, the machine learning (ML) empowered intelligent spectrum management technique is introduced in this paper. First, based on the challenges of the spectrum resource management in USCS, the requirement of spectrum sharing is analyzed from the perspective of spectrum collaboration and spectrum confrontation. We found that suitable multi-agent collaborative decision making is promising to realize effective spectrum sharing in both two perspectives. Therefore, a multi-agent learning framework is proposed which contains mobile-computing-assisted and distributed structures. Based on the framework, we provide case studies. Finally, future research directions are discussed.

Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Traditional cybersecurity solutions are becoming inadequate at detecting and mitigating emerging cyberattacks. Advances in cryptographic and Artificial Intelligence (AI) techniques (in particular, machine learning and deep learning) show promise in enabling cybersecurity experts to counter the ever-evolving threat posed by adversaries. Here, we explore AI’s potential in improving cybersecurity solutions, by identifying both its strengths and weaknesses. We also discuss future research opportunities associated with the development of AI techniques in the cybersecurity field across a range of application domains.

A Study on the Elimination of Thermal Reflections

Recently, thermal cameras have been used in various surveillance and monitoring systems. In particular, in camera-based surveillance systems, algorithms are being developed for detecting and recognizing objects from images acquired in dark environments. However, it is difficult to detect and recognize an object due to the thermal reflections generated in the image obtained from a thermal camera. For example, thermal reflection often occurs on a structure or the floor near an object, similar to shadows or mirror reflections. In this case, the object and the areas of thermal reflection overlap or are connected to each other and are difficult to separate. Thermal reflection also occurs on nearby walls, which can be detected as artifacts when an object is not associated with this phenomenon. In addition, the size and pixel value of the thermal reflection area vary greatly depending on the material of the area and the environmental temperature. In this case, the patterns and pixel values of the thermal reflection and the object are similar to each other and difficult to differentiate. These problems reduce the accuracy of object detection and recognition methods. In addition, no studies have been conducted on the elimination of thermal reflection of objects under different environmental conditions. Therefore, to address these challenges, we propose a method of detecting reflections in thermal images based on deep learning and their elimination via post-processing. Experiments using a self-collected database (Dongguk thermal image database (DTh-DB), Dongguk items and vehicles database (DI&V-DB)) and an open database showed that the performance of the proposed method is superior compared to that of other state-of-the-art approaches.

Machine Learning Designs, Implementations and Techniques

Submission Deadline: 15 February 2020

IEEE Access invites manuscript submissions in the area of Machine Learning Designs, Implementations and Techniques.

Most modern machine learning research is devoted to improving the accuracy of prediction. However, less attention is paid to deployment of machine and deep learning systems, supervised /unsupervised techniques for mining healthcare data, and time series similarity and irregular temporal data analysis. Most deployments are in the cloud, with abundant and scalable resources, and a free choice of computation platform. However, with the advent of intelligent physical devices—such as intelligent robots or self-driven cars—the resources are more limited, and the latency may be strictly bounded.

To address these questions, the focus of this Special Section in IEEE Access is on machine and deep learning designs, implementations and techniques, including both system level topics and other research questions related to the general use and framework of machine learning algorithms.

The topics of interest include, but are not limited to:

• Real time implementation of machine and deep learning,
• System level implementation, considering full pipeline from raw data until the decision layer
• Novel and innovative applications with strong emphasis on design and implementation
• Novel approaches for Temporal / Spatial/Spatio-Temporal Association analysis
• Pattern discovery from Time stamped Temporal and Interval databases
• High performance data mining in cloud
• Novel approaches for handling Uncertain and Imbalanced data
• Supervised/Unsupervised techniques for mining healthcare data
• Deep learning for translational bio-informatics
• Periodic/Sequential pattern mining
• Evolutionary algorithms
• Privacy-Preserving Data mining
• Time series similarity and Irregular temporal data analysis
• Mining Text Web and Social network data
• Imputation techniques for Temporal data
• Causality and Event Processing
• Applications of Data Mining in Anomaly and Intrusion detection
• Applications to medical informatics

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

Associate Editor:  Shadi A. Aljawarneh, Jordan University of Science and Technology, Jordan

Guest Editors:

• Oguz Bayat, Altinbas University, Turkey
• Juan A. Lara, Madrid Open University, Udima, Spain
• Robert P. Schumaker, University of Texas at Tyler, USA

Relevant IEEE Access Special Sections:

• Visual Analysis for CPS Data
• Emerging Approaches to Cyber Security
• Data-Enabled Intelligence for Digital Health

IEEE Access Editor-in-Chief:   Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to: http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact:  [email protected] , [email protected] .

At a Glance

• Journal: IEEE Access
• Format: Open Access
• Frequency: Continuous
• Submission to Publication: 4-6 weeks (typical)
• Topics: All topics in IEEE
• Average Acceptance Rate: 27%
• Impact Factor: 3.4
• Model: Binary Peer Review
• Article Processing Charge: US $1,995

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Publications, ieee transactions on machine learning in communications and networking.

The IEEE Transactions on Machine Learning in Communications and Networking (TMLCN) publishes high-quality manuscripts on advances in machine learning and artificial intelligence (AI) methods and their application to problems across all areas of communications and networking. Furthermore, articles developing novel communication and networking techniques and systems for distributed/edge machine learning algorithms are of interest. Both theoretical contributions (including new theories, techniques, concepts, algorithms, and analyses) and practical contributions (including system experiments, prototypes, and new applications) are solicited. IEEE TMLCN also particularly encourages the submission of papers that simultaneously advance both the fields of machine learning and wireless networking. The journal also advocates for reproducible and public sharing of codes, datasets, software, and other artefacts related to research contributions.

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Machine Learning

2000_mtj_samples.

Two thousand MTJ samples in total were included in the dataset for analysis; First, shuffle the dataset randomly to eliminate bias, then split it into ten equal folds of 200 samples each. For each iteration of cross-validation, use nine folds for training and one for testing, rotating the test fold across all ten groups so that every sample is tested once. 

Lora_federated_rffi_dataset.

This dataset contains LoRa physical layer signals collected from 60 LoRa devices and six SDRs (PLUTO-SDR, USRP B200 mini, USRP B210, USRP N210, RTL-SDR). It is intended for use by researchers in the development of a federated RFFI system, whereby the signals collected from different receivers and locations can be employed for evaluation purposes.

More details can be found at  https://github.com/gxhen/federatedRFFI

Dataset for Inclusive Fintech Software Development

This study presents a English-Luganda parallel corpus comprising over 2,000 sentence pairs, focused on financial decision-making and products. The dataset draws from diverse sources, including social media platforms (TikTok comments and Twitter posts from authoritative accounts like Bank of Uganda and Capital Markets Uganda), as well as fintech blogs (Chipper Cash and Xeno). The corpus covers a range of financial topics, including bonds, loans, and unit trust funds, providing a comprehensive resource for financial language processing in both English and Luganda.

Two-year price movements from 01/01/2014 to 01/01/2016 of 88 stocks are selected to target, coming from all the 8 stocks in the Conglomerates sector and the top 10 stocks in capital size in each of the other 8 sectors. The full list of 88 stocks and their companies selected from 9 sectors is available in StockTable, a facsimile of the paper appendix appendix_table_of_target_stocks.pdf.

Tri-Axial Vibro-Dynamic Stone Classification Dataset (TVDSC)

Experiment Details:

The softwarization and virtualization of the fifth-generation (5G) cellular networks bring about increased flexibility and faster deployment of new services. However, these advancements also introduce new vulnerabilities and unprecedented attack surfaces. The cloud-native nature of 5G networks mandates detecting and protecting against threats and intrusions in the cloud systems.

Mobile review dataset for aspect level sentiment analysis

It is a dataset containing sentence segments from cutomer reviews about mobile phone from different sources like Amazon, Flipkart, Tweeter and some existing datasets. It contains more than 1000 records tagged with one of the five aspect categories battery, camera, display, price and processor. Whether a sentence segment has sentiment expression (subjective/ objective) is also tagged and the sentiment orientation (positive/ negative/ neutral) of each sentence segment is assigned. Explicit or implicit presence of aspect is also maintained.

Hiking Trail Semantic Segmentation Image Dataset

This work presents a specialized dataset designed to advance autonomous navigation in hiking trail and off-road natural environments. The dataset comprises over 1,250 images (640x360 pixels) captured using a camera mounted on a tele-operated robot on hiking trails. Images are manually labeled into eight terrain classes: grass, rock, trail, root, structure, tree trunk, vegetation, and rough trail. The dataset is provided in its original form without augmentations or resizing, allowing end-users flexibility in preprocessing.

Remote Sensing Data and Agriculture Ground Truths

The dataset provides detailed information for wheat crop monitoring in the Karnal District, India, spanning the period from 2010 to 2022. It is divided into four main components. The first component, Remote Sensing Data, includes Sentinel-2 (10 m resolution) satellite data averaged over village boundaries, specifically over a wheat crop mask. This folder contains two Excel files: one for NDVI (Normalized Difference Vegetation Index) and another for NDWI (Normalized Difference Water Index), both providing fortnightly data during the Rabi season across a 10-year period.

Synthetic Sand Boil Dataset for Levee Monitoring

This dataset, titled "Synthetic Sand Boil Dataset for Levee Monitoring: Generated Using DreamBooth Diffusion Models," provides a comprehensive collection of synthetic images designed to facilitate the study and development of semantic segmentation models for sand boil detection in levee systems. Sand boils, a critical factor in levee integrity, pose significant risks during floods, necessitating accurate and efficient monitoring solutions.

Machine Learning

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January 2023

Regular Papers

Volume 111, Issue 1

Scanning the Issue

Machine learning for emergency management: a survey and future outlook.

By C. Kyrkou, P. Kolios, T. Theocharides, and M. Polycarpou

This article surveys machine learning for all phases of emergency management, focusing on key characteristics and challenges, and its application across the different phases and operations.

Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review

By M. M. Hossain Shuvo, S. K. Islam, J. Cheng, and B. I. Morshed

This article provides a comprehensive review of the state-of-the-art tools and techniques for efficient edge inference, a vital element of artificial intelligence on edge.

Technology Prospects for Data-Intensive Computing

By K. Akarvardar and H.-S. P. Wong

This article advances the idea that data-intensive computing will further cement semiconductor technology as a foundational technology with multidimensional pathways for growth.

Point of View

A perspective vision of micro/nano systems and technologies as enablers of 6g, super-iot, and tactile internet.

By J. Iannacci

Scanning Our Past

The information age and naval command & control.

By D. Boslaugh, P. Marland, and J. Vardalas

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Join the community, trending research, 3dtopia-xl: scaling high-quality 3d asset generation via primitive diffusion.

The increasing demand for high-quality 3D assets across various industries necessitates efficient and automated 3D content creation.

MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery

Retrieval-Augmented Generation (RAG) leverages retrieval tools to access external databases, thereby enhancing the generation quality of large language models (LLMs) through optimized context.

OmniGen: Unified Image Generation

vectorspacelab/omnigen • 17 Sep 2024

In this work, we introduce OmniGen, a new diffusion model for unified image generation.

StoryMaker: Towards Holistic Consistent Characters in Text-to-image Generation

However, the lack of holistic consistency in scenes with multiple characters hampers these methods' ability to create a cohesive narrative.

LLaMA-Omni: Seamless Speech Interaction with Large Language Models

We build our model based on the latest Llama-3. 1-8B-Instruct model.

Kolmogorov-Arnold Transformer

In this paper, we introduce the Kolmogorov-Arnold Transformer (KAT), a novel architecture that replaces MLP layers with Kolmogorov-Arnold Network (KAN) layers to enhance the expressiveness and performance of the model.

QA-MDT: Quality-aware Masked Diffusion Transformer for Enhanced Music Generation

In recent years, diffusion-based text-to-music (TTM) generation has gained prominence, offering an innovative approach to synthesizing musical content from textual descriptions.

Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution

Visual data comes in various forms, ranging from small icons of just a few pixels to long videos spanning hours.

Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think

Recent work showed that large diffusion models can be reused as highly precise monocular depth estimators by casting depth estimation as an image-conditional image generation task.

Qwen2 Technical Report

This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models.

A systematic review of fairness in machine learning

• Published: 19 September 2024

Cite this article

• Ricardo Trainotti Rabonato 1 &
• Lilian Berton   ORCID: orcid.org/0000-0003-1397-6005 1  

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Fairness in Machine Learning (ML) has emerged as a crucial concern as these models increasingly influence critical decisions in various domains, including healthcare, finance, and criminal justice. The presence of bias in ML systems can lead to unfair and discriminatory outcomes, undermining the reliability and ethical standards of these technologies. As the deployment of ML expands, ensuring that these systems are fair and unbiased is not only a technical challenge but also a moral imperative. Here, a systematic literature review was conducted to explore fairness in machine learning, utilizing the ACM, IEEE, and Springer databases. From an initial retrieval of 975 papers, 30 were included in the review. The results highlight the identification of sensitive attributes, the metrics used to assess bias, and the various databases tested. Additionally, the review categorizes the in-processing and post-processing approaches employed to mitigate bias and examines how studies are managing the trade-off between fairness and accuracy. This comprehensive analysis provides a detailed understanding of the current state of fairness in machine learning and offers insights into effective strategies for bias mitigation.

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Fairness issues, current approaches, and challenges in machine learning models

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This paper is a review, no dataset was used or generated.

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According to [ 53 ], for example, attributes such as “number of working hours” and “education level” can, to some extent, explain the wage differences between men and women in a given data set.

The National Basketball Association is the main professional basketball league in North America.

In Table 3 , the bases noted with (*) have data that were artificially created for use in the proposed study.

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We thanks Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), grant 21/14725-3 and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).

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Rabonato, R.T., Berton, L. A systematic review of fairness in machine learning. AI Ethics (2024). https://doi.org/10.1007/s43681-024-00577-5

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Study enhancing learning methods for AI and machine learning systems wins IEEE award

By Peter Murphy

Published May 21, 2024

A paper authored by Seyyedali Hosseinalipour (Ali Alipour) received the Institute of Electrical and Electronics Engineers (IEEE) Communications Society William R. Bennett Prize. The research could enhance learning methods used by artificial intelligence (AI) and machine learning (ML) systems.

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• Seyyedali Hosseinalipour (Ali Alipour) , PhD
• 9/24/24 Department of Electrical Engineering

According to IEEE, the award “recognizes outstanding original papers published in IEEE/ACM Transactions on Networking or the IEEE Transactions on Network and Service Management” within the last three years.

Alipour’s research enhances federated learning, a method researchers use to secure private information while collecting data to better train AI and ML systems.  

AI and ML systems collect data from various sources to enhance their capabilities. The data collection associated with each of these methods, however, come with privacy concerns. Data collected from personal devices like smartphones and other electronics could be stored in a single location, like a cloud server. Federated learning allows the data to remain on the device. The only data sent to a central server during federated learning is the AI or ML model parameters.

“Federated learning was first developed by researchers at Google, initially aimed at enhancing next-word prediction for smartphone keyboards,” Alipour says. “Today, technology giants like NVIDIA apply federated learning to sectors such as healthcare, where protecting patient data privacy is crucial,” Alipour says.

Alipour’s new method, multi-stage hybrid federated learning (MH-FL), allows devices to interact with each other before sending any information to a server. The devices can work together to refine learning methods associated with the AI and ML systems before sharing information to a central server.

“MH-FL introduces an additional layer of flexibility by enabling client-to-client interactions, also known as device-to-device interactions. Our findings indicate that incorporating this degree of freedom can significantly enhance model prediction performance in federated learning,” Alipour says. “Additionally, it contributes to reduced energy consumption and latency, optimizing both the efficiency and effectiveness of the learning process.”

The work in this award-winning paper has set the foundation for Alipour’s work with federated learning. He has continued to develop and explore different federated learning techniques.

“Ali is a leading researcher in the analysis and modeling of modern wireless networks, specifically in the application of machine-learning techniques to the design and implementation of next-generation wireless networks,” says Jon Bird, professor and chair in the Department of Electrical Engineering. “This achievement is especially remarkable for Ali, given his current position as a junior assistant professor.”

The William R. Bennett Prize is competitive. Out of the potentially thousands of papers considered, just one is selected for the award.

“As researchers, we are always thrilled when our ideas are well-received. I hope to use this excitement as motivation to continue contributing to the rapidly evolving fields of AI and ML,” Alipour says. “I also hope that this enthusiasm not only drives our current research but inspires further innovations and discoveries.”

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