Challenges In Machine Learning Research Articles

Performance Evaluation of Some Selected Classification Algorithms in a Facial Recognition System

Facial Recognition (FR) has been an active area of research and has diverse applicable environment, it continues to be a challenging research topic. With the development of image processing and pattern recognition technology, there are many challenges in machine learning to select the appropriate classification algorithms, most especially in the area of classification of extracted features to have low classification time, high sensitivity and accuracy of the classification algorithms, so it is very important to explore the performance of different algorithms in image classification. The three selected supervised learning classification algorithms: Learning Vector Quantization (LVQ), Relevance Vector Machine (RVM), and Support Vector Machine (SVM) performance were evaluated so as to know the most effective out of the selected algorithms for facial images classification. The development of the system has four stages, the first stage is image acquisition and 180 images were taken by digital camera under same illumination and light colour background. The second stage is pre-processing to improve the images data by suppressing unwilling distortion; grayscale and normalization were used for image pre-processing. The third stage is feature extraction; Discrete Cosine Transform (DCT) is adopted for this purpose. While the fourth stage is face recognition classification, Receiver Operating Characteristics (ROC) was used to test the performance of each the three algorithms. However the Learning Vector Quantization algorithm, Relevance Vector Machine and Support Vector Machine performance have not been compared together to the most effective out of the three algorithms in term of False Positive Rate, Sensitivity, Specificity, Precision, Accuracy and Computation Time. Hence, this work evaluated the performance of the Learning Vector Quantization; Relevance Vector Machine and Support Vector Machine classification algorithms in facial recognition system and Support Vector Machine outwit the other two algorithms in facial recognition in term of specificity, recognition time and recognition accuracy at different threshold.

Adap-BDCM: Adaptive Bilinear Dynamic Cascade Model for Classification Tasks on CNV Datasets.

Copy number variation (CNV) is an essential genetic driving factor of cancer formation and progression, making intelligent classification based on CNV feasible. However, there are a few challenges in the current machine learning and deep learning methods, such as the design of base classifier combination schemes in ensemble methods and the selection of layers of neural networks, which often result in low accuracy. Therefore, an adaptive bilinear dynamic cascade model (Adap-BDCM) is developed to further enhance the accuracy and applicability of these methods for intelligent classification on CNV datasets. In this model, a feature selection module is introduced to mitigate the interference of redundant information, and a bilinear model based on the gated attention mechanism is proposed to extract more beneficial deep fusion features. Furthermore, an adaptive base classifier selection scheme is designed to overcome the difficulty of manually designing base classifier combinations and enhance the applicability of the model. Lastly, a novel feature fusion scheme with an attribute recall submoduleis constructed, effectively avoiding getting stuck in local solutions and missing some valuable information. Numerous experiments have demonstrated that our Adap-BDCM model exhibits optimal performance in cancer classification, stage prediction, and recurrence on CNV datasets. This study can assist physicians in making diagnoses faster and better.

Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey

Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks. Deep learning has revolutionized natural language processing and computer vision and holds great promise in other fields such as time series analysis where the relevant features must often be abstracted from the raw data but are not known a priori. This article surveys the current state of the art in the fast-moving field of deep learning for time series classification and extrinsic regression. We review different network architectures and training methods used for these tasks and discuss the challenges and opportunities when applying deep learning to time series data. We also summarize two critical applications of time series classification and extrinsic regression, human activity recognition and satellite earth observation.

The Role and Challenges of Machine Learning in the Construction of a Community of Human Destiny

- The advent of machine learning (ML) has significantly transformed various fields, including the realm of social sciences and humanities. This paper explores the pivotal role and inherent challenges of machine learning in fostering the construction of a community of human destiny (CHD), a concept emphasizing shared global interests and collective human development. Machine learning, with its capacity to analyze vast datasets and uncover patterns, holds the potential to address complex issues such as poverty, inequality, and environmental sustainability, which are integral to the community of human destiny vision. However, the deployment of machine learning in this context is fraught with challenges, including ethical considerations, data privacy concerns, and the need for interdisciplinary collaboration. By examining theoretical frameworks, this paper aims to provide insights into how machine learning can be harnessed effectively to advance the goals of community of human destiny while also highlighting the importance of governance frameworks and ethical guidelines in ensuring that machine learning contributes positively to the common good. The paper argues that while machine learning presents significant opportunities for the community of human destiny, a cautious and responsible approach is essential to navigate the associated challenges and to ensure that technological advancements align with humanistic values and the overarching goal of a harmonious and sustainable global community.

Enhancing federated learning robustness through randomization and mixture

Protecting data privacy is a significant challenge in machine learning (ML), and federated learning (FL) has emerged as a decentralized learning solution to address this issue. However, FL is vulnerable to poisoning attacks, which control and interrupt the learning process to substantially increase the error rate of the system. The aggregation algorithm’s robustness is crucial to prevent such attacks; however previous Byzantine-robust algorithms have exhibited vulnerability to maliciously crafted model updates. We propose three different aggregation functions namely: Switch, Layered-Switch and Weighted FedAvg. Our proposed methods involve switching between aggregation functions. We evaluate the effectiveness of these methods against a model poisoning attack to demonstrate their robustness. To observe the performance of our proposed aggregation functions, we utilized them within a federated learning framework to classify the CIFAR10, CIFAR10 and Fashion-MNIST datasets. The simulation results demonstrate that all of these methods exhibit greater robustness against the employed adversarial attack. Our approach achieves an enhancement in performance during a poisoning attack, surpassing previous Byzantine-robust algorithms by 5%, 15% and 25% on the MNIST, CIFAR10 and CIFAR100 datasets, respectively. Among the proposed aggregation functions, Weighted FedAvg has shown the highest success rate on the datasets utilized in this research.

Theoretical Calculation Assisted by Machine Learning Accelerate Optimal Electrocatalyst Finding for Hydrogen Evolution Reaction

AbstractElectrocatalytic hydrogen evolution reaction (HER) is a promising strategy to solve and mitigate the coming energy shortage and global environmental pollution. Searching for efficient electrocatalysts for HER remains challenging through traditional trial‐and‐error methods from numerous potential material candidates. Theoretical high throughput calculation assisted by machine learning is a possible method to screen excellent HER electrocatalysts effectively. This will pave the way for high‐efficiency and low‐price electrocatalyst findings. In this review, we comprehensively introduce the machine learning workflow and standard models for hydrogen reduction reactions. This mainly illustrates how machine learning is used in catalyst filtration and descriptor exploration. Subsequently, several applications, including surface electrocatalysts, two‐dimensional (2D) electrocatalysts, and single/dual atom electrocatalysts using machine learning in electrocatalytic HER, are highlighted and introduced. Finally, the corresponding challenge and perspective for machine learning in electrocatalytic hydrogen reduction reactions are concluded. We hope this critical review can provide a comprehensive understanding of machine learning for HER catalyst design and guide the future theoretical and experimental investigation of HER catalyst findings.

Deductive Machine Learning Challenges and Opportunities in Chemical Applications.

Contemporary machine learning algorithms have largely succeeded in automating the development of mathematical models from data. Although this is a striking accomplishment, it leaves unaddressed the multitude of scenarios, especially across the chemical sciences and engineering, where deductive, rather than inductive, reasoning is required and still depends on manual intervention by an expert. This review describes the characteristics of deductive reasoning that are helpful for understanding the role played by expert intervention in problem-solving and explains why such interventions are often relatively resistant to disruption by typical machine learning strategies. The article then discusses the factors that contribute to creating a deductive bottleneck, how deductive bottlenecks are currently addressed in several application areas, and how machine learning models capable of deduction can be designed. The review concludes with a tutorial case study that illustrates the challenges of deduction problems and a notebook for readers to experiment with on their own.

THE PROGRESS IN THE RESEARCH OF MACHINE LEARNING IN SPORTS MEDICINE

To explore the prospects and challenges of applying artificial intelligence and its machine learning subfield in sports medicine, to drive knowledge innovation in this domain. Research Content includes Applications of machine learning in sports medicine: Clustering and classifying athlete data, developing predictive models to optimize training and prevent injuries, and providing interpretable decision support for medical professionals. Challenges of machine learning in sports medicine: Issues with data availability and quality, model interpretability and transparency, as well as the integration with existing workflows. In summary, the potential of AI and machine learning in sports medicine is immense, but to fully harness their transformative value, interdisciplinary collaboration, data sharing, rigorous validation, and the establishment of ethical guidelines are essential. Only through these collective efforts can the field optimize athlete training, prevent injuries, and drive overall innovation in sports medicine. KEYWORD: Machine Learning,Sports Medicine , Artificial intelligence ,Knowledge Representation, Decision Support

Generative models improve fairness of medical classifiers under distribution shifts

Domain generalization is a ubiquitous challenge for machine learning in healthcare. Model performance in real-world conditions might be lower than expected because of discrepancies between the data encountered during deployment and development. Underrepresentation of some groups or conditions during model development is a common cause of this phenomenon. This challenge is often not readily addressed by targeted data acquisition and ‘labeling’ by expert clinicians, which can be prohibitively expensive or practically impossible because of the rarity of conditions or the available clinical expertise. We hypothesize that advances in generative artificial intelligence can help mitigate this unmet need in a steerable fashion, enriching our training dataset with synthetic examples that address shortfalls of underrepresented conditions or subgroups. We show that diffusion models can automatically learn realistic augmentations from data in a label-efficient manner. We demonstrate that learned augmentations make models more robust and statistically fair in-distribution and out of distribution. To evaluate the generality of our approach, we studied three distinct medical imaging contexts of varying difficulty: (1) histopathology, (2) chest X-ray and (3) dermatology images. Complementing real samples with synthetic ones improved the robustness of models in all three medical tasks and increased fairness by improving the accuracy of clinical diagnosis within underrepresented groups, especially out of distribution.

Advancements and Challenges of Machine Learning and Deep Learning in Medical Data Analysis: A Review of the Past Five Years and Future Directions

This literature review explores machine learning (ML) and deep learning (DL) methods applied to medical datasets over the past five years. The paper discusses the advancements, challenges, and future directions in utilizing ML and DL techniques for medical data analysis. It synthesizes recent research findings, highlighting key methodologies, datasets, and outcomes.

FormulAI: Designing Rule-Based Datasets for Interpretable and Challenging Machine Learning Tasks

In an era marked by the transformative impact of machine learning algorithms across various disciplines, challenges in achieving model interpretability persist. Existing evaluation datasets often lack transparency, thereby obscuring the decision-making process of machine learning models, particularly in complex deep learning architectures. This opacity raises concerns across sectors like healthcare, emphasizing the pivotal role of explainability in fostering trust and adhering to non-supervisory norms. While progress has been made through the development of interpretable models, the absence of formalized, interpretable datasets hampers the validation and comparison of techniques. Rule-based datasets, distinct from general synthetic datasets, provide an avenue to simulate real-world challenges while maintaining interpretability. This paper introduces FormulAI, a framework for generating comprehensive rule-grounded datasets encompassing categorical and continuous features, calibrated noise, and imbalanced class distribution. Emphasizing scalability and reproducibility, these datasets serve as a robust standard, fostering exploration in interpretability and robustness.

Mitigating Label Noise through Data Ambiguation

Label noise poses an important challenge in machine learning, especially in deep learning, in which large models with high expressive power dominate the field. Models of that kind are prone to memorizing incorrect labels, thereby harming generalization performance. Many methods have been proposed to address this problem, including robust loss functions and more complex label correction approaches. Robust loss functions are appealing due to their simplicity, but typically lack flexibility, while label correction usually adds substantial complexity to the training setup. In this paper, we suggest to address the shortcomings of both methodologies by "ambiguating" the target information, adding additional, complementary candidate labels in case the learner is not sufficiently convinced of the observed training label. More precisely, we leverage the framework of so-called superset learning to construct set-valued targets based on a confidence threshold, which deliver imprecise yet more reliable beliefs about the ground-truth, effectively helping the learner to suppress the memorization effect. In an extensive empirical evaluation, our method demonstrates favorable learning behavior on synthetic and real-world noise, confirming the effectiveness in detecting and correcting erroneous training labels.

Generalisation through Negation and Predicate Invention

The ability to generalise from a small number of examples is a fundamental challenge in machine learning. To tackle this challenge, we introduce an inductive logic programming (ILP) approach that combines negation and predicate invention. Combining these two features allows an ILP system to generalise better by learning rules with universally quantified body-only variables. We implement our idea in NOPI, which can learn normal logic programs with predicate invention, including Datalog programs with stratified negation. Our experimental results on multiple domains show that our approach can improve predictive accuracies and learning times.

PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion

The generalization of neural networks is a central challenge in machine learning, especially concerning the performance under distributions that differ from training ones. Current methods, mainly based on the data-driven paradigm such as data augmentation, adversarial training, and noise injection, may encounter limited generalization due to model non-smoothness. In this paper, we propose to investigate generalization from a Partial Differential Equation (PDE) perspective, aiming to enhance it directly through the underlying function of neural networks, rather than focusing on adjusting input data. Specifically, we first establish the connection between neural network generalization and the smoothness of the solution to a specific PDE, namely transport equation. Building upon this, we propose a general framework that introduces adaptive distributional diffusion into transport equation to enhance the smoothness of its solution, thereby improving generalization. In the context of neural networks, we put this theoretical framework into practice as PDE+ (PDE with Adaptive Distributional Diffusion) which diffuses each sample into a distribution covering semantically similar inputs. This enables better coverage of potentially unobserved distributions in training, thus improving generalization beyond merely data-driven methods. The effectiveness of PDE+ is validated through extensive experimental settings, demonstrating its superior performance compared to state-of-the-art methods. Our code is available at https://github.com/yuanyige/pde-add.

Exploiting Data Geometry in Machine Learning

A key challenge in Machine Learning (ML) is the identification of geometric structure in high-dimensional data. Most algorithms assume that data lives in a high-dimensional vector space; however, many applications involve non-Euclidean data, such as graphs, strings and matrices, or data whose structure is determined by symmetries in the underlying system. Here, we discuss methods for identifying geometric structure in data and how leveraging data geometry can give rise to efficient ML algorithms with provable guarantees.

Neural Oscillators for Generalization of Physics-Informed Machine Learning

A primary challenge of physics-informed machine learning (PIML) is its generalization beyond the training domain, especially when dealing with complex physical problems represented by partial differential equations (PDEs). This paper aims to enhance the generalization capabilities of PIML, facilitating practical, real-world applications where accurate predictions in unexplored regions are crucial. We leverage the inherent causality and temporal sequential characteristics of PDE solutions to fuse PIML models with recurrent neural architectures based on systems of ordinary differential equations, referred to as neural oscillators. Through effectively capturing long-time dependencies and mitigating the exploding and vanishing gradient problem, neural oscillators foster improved generalization in PIML tasks. Extensive experimentation involving time-dependent nonlinear PDEs and biharmonic beam equations demonstrates the efficacy of the proposed approach. Incorporating neural oscillators outperforms existing state-of-the-art methods on benchmark problems across various metrics. Consequently, the proposed method improves the generalization capabilities of PIML, providing accurate solutions for extrapolation and prediction beyond the training data.

Deep learning for sustainable agriculture: Weed classification model to optimize herbicide application

<p>Herbicides, chemical substances designed to eliminate weeds, find widespread use in agriculture to eradicate unwanted plants and enhance crop productivity, despite their adverse impacts on both human health and the environment. The study involves the construction of a neural network classifier employing a Convolutional Neural Network (CNN) through Keras to categorize images with corresponding labels. This research paper introduces two distinct neural networks: a basic neural network and a hybrid variant combining CNN with Keras. Both networks undergo training and testing, yielding an accuracy of 30% for the basic neural network, whereas the hybrid neural network achieves an impressive 97% accuracy. Consequently, this model significantly diminishes the need for herbicide spraying over crops such as fruits, vegetables, and sugarcane, aiming to safeguard humans, animals, birds, and the environment from the detrimental effects of harmful chemicals. Functioning as the elevated API within the TensorFlow framework, Keras furnishes a user-friendly and immensely efficient interface tailored to address machine learning (ML) challenges, particularly in the realm of contemporary deep learning. Encompassing all facets of the machine learning process, from data manipulation to fine-tuning hyper parameters to deployment, Keras was meticulously crafted to expedite rapid experimentation.</p>

Deep convolutional neural networks with genetic algorithm-based synthetic minority over-sampling technique for improved imbalanced data classification

Imbalanced data classification presents a challenge in machine learning, inducing biased model learning. Moreover, data dimensionality poses another challenge as it highly impacts classifier performance. This paper proposes a new deep-learning method that combines feature selection with oversampling to address these challenges. The proposed approach, GA-SMOTE-DCNN, integrates a genetic algorithm (GA) for feature selection, SMOTE for oversampling, and a deep 1D-convolutional neural network (DCNN) for classification. This study reveals that pre-splitting the data into training and testing sets before applying SMOTE results in higher accuracy, showing an improvement in accuracy ranging between 1.94% and 3.98% compared to post-SMOTE splitting for each dataset. This method achieved accuracy rates of 86.81% for the Balance Scale dataset, 86.15% for the Oil Spill dataset, 89.21% for the Yeast dataset, 91.32% for the Mammography dataset, 88.23% for the Australian credit dataset, and 89.53% for the German Credit dataset when compared with benchmark methods, underscoring its significance in tackling high-dimensional and imbalanced data classification problems. This method demonstrates scalability in effectively addressing challenges associated with high-dimensional and imbalanced data classification across various domains.

Dynamic selection of machine learning models for time-series data

Adapting to changes in time-series data is a major challenge in machine learning. This problem is particularly acute in the case of limited computing power (e.g., edge devices) that does not enable the independent training of new models. While multiple studies attempt to adequately train a machine learning model on rapidly shifting data (i.e., concept drift), the challenge of dynamically selecting the most effective machine learning model for future time steps has been largely overlooked. In this study, we propose Adaptive machine learning for Dynamic ENvironments (ADEN ), a method for analyzing future trends in time-series data and selecting the most suitable ML model. Our approach models multiple aspects of the analyzed data and analyzes the behavior of multiple machine learning models on earlier time steps. By training ADEN on multiple datasets, we can deploy a zero-shot model that does not require additional training when applied to new datasets. Our evaluation, conducted on 46 time-series classification datasets, shows that ADEN not only outperforms all baselines in terms of average performance but is also capable of avoiding sudden drops in performance that characterize all other evaluated algorithms.

Few-shot meta-learning for pre-symptomatic detection of Covid-19 from limited health tracker data

Detecting (or screening for) Covid-19 even before symptoms fully manifest, could enable patients to receive timely and life-saving treatment. Prior work has demonstrated that heart rate and step data from low-end wearables analyzed using deep learning models can detect Covid-19 reliably. However, significant individual differences in vital sign manifestation (high inter-subject variability) present a challenge to the generalization of deep learning models across diverse users. The limited amount of data in many medical scenarios further exacerbates this issue. Consequently, neural network models that can learn from limited vital sign data and varied inter-subject patterns are compelling. Meta-learning has emerged as a powerful technique for tackling various machine learning challenges, including insufficient data, domain shifts across datasets, and issues with generalization. This study proposes MetaCovid, a deep adaptation framework that employs meta-learning to address the variability of vital sign manifestation between subjects using only two days of data in order to detect Covid-19 before symptoms manifest. MetaCovid leverages heart rate and step measurements collected from consumer-grade health trackers over the preceding 2 days, extracts 45 digital bio-markers (features), which along with raw data, are fed into a deep GRU-based network with an attention mechanism, followed by uncertainty filtering. MetaCovid is trained using OC-MAML, a one-class few-shot MAML variant that adapts to the target distribution/user using only samples from the majority class. MetaCovid generalized well across two relatively small, publicly available Covid-19 datasets, achieving a recall of 0.81 and 0.92, and detecting 61% (14 out of 23) and 50% (17 out of 34) of users infected with Covid-19 before symptom onset. When OC-MAML was excluded from MetaCovid in an ablation study, the F2 score dropped by 36%, highlighting that meta-learning indeed facilitates adaptation of deep sensing models to varying vital sign patterns. Notably, MetaCovid outperforms the current state-of-art method by predicting Covid-19 early on day N using heart rate and step measurements from only the preceding 2 days compared to 28 days, reducing data requirements by 93%. To the best of our knowledge, our study is the first to propose utilizing meta-learning to mitigate vital sign variability with limited data for Covid-19 screening. We believe that MetaCovid will pave the way for innovative Covid-19 interventions that are accurate even with limited data and help contain the spread of infectious diseases in the future.