This paper proposes a method for improving the accuracy of mastitis risk assessment in cows using neural networks and video analysis. Mastitis, an infection of the udder tissue, is a critical health problem for cows and can be detected by examining the cow's teat. Traditionally, veterinarians assess the health of a cow's teat during the milking process, but this process is limited in time and can weaken the accuracy of the assessment. In commercial farms, cows are recorded by cameras when they are milked in the milking parlor. This paper uses a neural network to identify key frames in the recorded video where the cow's udder appears intact. These key frames allow veterinarians to have more flexible time to perform health assessments on the teat, increasing their efficiency and accuracy. However, there are challenges in using cow teat video for mastitis risk assessment, such as complex environments, changing cow positions and postures, and difficulty in identifying the udder from the video. To address these challenges, a fusion distance and an ensemble model are proposed to improve the performance (F-score) of identifying key frames from cow teat videos. The results show that these two approaches improve performance compared to using a single distance measure or model.

In the context of modern machine learning, models deployed in real-world scenarios often encounter diverse data shifts like covariate and semantic shifts, leading to challenges in both out-of-distribution (OOD) generalization and detection. Despite considerable attention to these issues separately, a unified framework for theoretical understanding and practical usage is lacking. To bridge the gap, we introduce a graph-theoretic framework to jointly tackle both OOD generalization and detection problems. By leveraging the graph formulation, data representations are obtained through the factorization of the graph's adjacency matrix, enabling us to derive provable error quantifying OOD generalization and detection performance.

One of the growing trends in machine learning is the use of data generation techniques, since the performance of machine learning models is dependent on the quantity of the training dataset. However, in many medical applications, collecting large datasets is challenging due to resource constraints, which leads to overfitting and poor generalization. This paper introduces a novel method, Artificial Data Point Generation in Clustered Latent Space (AGCL), designed to enhance classification performance on small medical datasets through synthetic data generation. The AGCL framework involves feature extraction, K-means clustering, cluster evaluation based on a class separation metric, and the generation of synthetic data points from clusters with distinct class representations. This method was applied to Parkinson's disease screening, utilizing facial expression data, and evaluated across multiple machine learning classifiers. Experimental results demonstrate that AGCL significantly improves classification accuracy compared to baseline, GN and kNNMTD. AGCL achieved the highest overall test accuracy of 83.33% and cross-validation accuracy of 90.90% in majority voting over different emotions, confirming its effectiveness in augmenting small datasets.

In an industrial CPS scenario, the most crucial resource is the availability of data reflecting the different aspects of production. Detecting complex anomalies on massive amounts of data is a crucial Such data consist of multiple interdependent variables rapidly evolving task in Industry 4.0, best addressed by deep learning. However, over time, thus falling under the typical definition of Multivariate available solutions are computationally demanding, requiring cloud Time Series (MTS) [14]. After collection, the time series, originated architectures prone to latency and bandwidth issues. This work by heterogeneous sensors and data sources, are integrated presents VARADE, a novel solution implementing a light autoregressive through Industrial Internet of Things (IIoT) technologies and made framework based on variational inference, which is best available for anomaly detection, visualization, and analysis [27].

Language models are known to hallucinate incorrect information, and it is unclear if they are sufficiently accurate and reliable for use in scientific research. We developed a rigorous human-AI comparison methodology to evaluate language model agents on real-world literature search tasks covering information retrieval, summarization, and contradiction detection tasks. We show that PaperQA2, a frontier language model agent optimized for improved factuality, matches or exceeds subject matter expert performance on three realistic literature research tasks without any restrictions on humans (i.e., full access to internet, search tools, and time). PaperQA2 writes cited, Wikipedia-style summaries of scientific topics that are significantly more accurate than existing, human-written Wikipedia articles. We also introduce a hard benchmark for scientific literature research called LitQA2 that guided design of PaperQA2, leading to it exceeding human performance. Finally, we apply PaperQA2 to identify contradictions within the scientific literature, an important scientific task that is challenging for humans. PaperQA2 identifies 2.34 +/- 1.99 contradictions per paper in a random subset of biology papers, of which 70% are validated by human experts. These results demonstrate that language model agents are now capable of exceeding domain experts across meaningful tasks on scientific literature.

The positioning of this research falls within the scalar-on-function classification literature, a field of significant interest across various domains, particularly in statistics, mathematics, and computer science. This study introduces an advanced methodology for supervised classification by integrating Functional Data Analysis (FDA) with tree-based ensemble techniques for classifying high-dimensional time series. The proposed framework, Enriched Functional Tree-Based Classifiers (EFTCs), leverages derivative and geometric features, benefiting from the diversity inherent in ensemble methods to further enhance predictive performance and reduce variance. While our approach has been tested on the enrichment of Functional Classification Trees (FCTs), Functional K-NN (FKNN), Functional Random Forest (FRF), Functional XGBoost (FXGB), and Functional LightGBM (FLGBM), it could be extended to other tree-based and non-tree-based classifiers, with appropriate considerations emerging from this investigation. Through extensive experimental evaluations on seven real-world datasets and six simulated scenarios, this proposal demonstrates fascinating improvements over traditional approaches, providing new insights into the application of FDA in complex, high-dimensional learning problems.

In recent years, the field of electroencephalography (EEG) analysis has witnessed remarkable advancements, driven by the integration of machine learning and artificial intelligence. This survey aims to encapsulate the latest developments, focusing on emerging methods and technologies that are poised to transform our comprehension and interpretation of brain activity. We delve into self-supervised learning methods that enable the robust representation of brain signals, which are fundamental for a variety of downstream applications. We also explore emerging discriminative methods, including graph neural networks (GNN), foundation models, and large language models (LLMs)-based approaches. Furthermore, we examine generative technologies that harness EEG data to produce images or text, offering novel perspectives on brain activity visualization and interpretation. The survey provides an extensive overview of these cutting-edge techniques, their current applications, and the profound implications they hold for future research and clinical practice. The relevant literature and open-source materials have been compiled and are consistently being refreshed at \url{https://github.com/wpf535236337/LLMs4TS}

Fraud is a prevalent offence that extends beyond financial loss, causing psychological and physical harm to victims. The advancements in online communication technologies alowed for online fraud to thrive in this vast network, with fraudsters increasingly using these channels for deception. With the progression of technologies like AI, there is a growing concern that fraud will scale up, using sophisticated methods, like deep-fakes in phishing campaigns, all generated by language generation models like ChatGPT. However, the application of AI in detecting and analyzing online fraud remains understudied. We conduct a Systematic Literature Review on AI and NLP techniques for online fraud detection. The review adhered the PRISMA-ScR protocol, with eligibility criteria including relevance to online fraud, use of text data, and AI methodologies. We screened 2,457 academic records, 350 met our eligibility criteria, and included 223. We report the state-of-the-art NLP techniques for analysing various online fraud categories; the training data sources; the NLP algorithms and models built; and the performance metrics employed for model evaluation. We find that current research on online fraud is divided into various scam activitiesand identify 16 different frauds that researchers focus on. This SLR enhances the academic understanding of AI-based detection methods for online fraud and offers insights for policymakers, law enforcement, and businesses on safeguarding against such activities. We conclude that focusing on specific scams lacks generalization, as multiple models are required for different fraud types. The evolving nature of scams limits the effectiveness of models trained on outdated data. We also identify issues in data limitations, training bias reporting, and selective presentation of metrics in model performance reporting, which can lead to potential biases in model evaluation.

Human brain neuron activities are incredibly significant nowadays. Neuronal behavior is assessed by analyzing signal data such as electroencephalography (EEG), which can offer scientists valuable information about diseases and human-computer interaction. One of the difficulties researchers confront while evaluating these signals is the existence of large volumes of spike data. Spikes are some considerable parts of signal data that can happen as a consequence of vital biomarkers or physical issues such as electrode movements. Hence, distinguishing types of spikes is important. From this spot, the spike classification concept commences. Previously, researchers classified spikes manually. The manual classification was not precise enough as it involves extensive analysis. Consequently, Artificial Intelligence (AI) was introduced into neuroscience to assist clinicians in classifying spikes correctly. This review discusses the importance and use of AI in spike classification, focusing on the recognition of neural activity noises. The task is divided into three main components: preprocessing, classification, and evaluation. Existing methods are introduced and their importance is determined. The review also highlights the need for more efficient algorithms. The primary goal is to provide a perspective on spike classification for future research and provide a comprehensive understanding of the methodologies and issues involved. The review organizes materials in the spike classification field for future studies. In this work, numerous studies were extracted from different databases. The PRISMA-related research guidelines were then used to choose papers. Then, research studies based on spike classification using machine learning and deep learning approaches with effective preprocessing were selected.

Integrating deep learning into medical imaging is poised to greatly advance diagnostic methods but it faces challenges with generalizability. Foundation models, based on self-supervised learning, address these issues and improve data efficiency. Natural domain foundation models show promise for medical imaging, but systematic research evaluating domain adaptation, especially using self-supervised learning and parameter-efficient fine-tuning, remains underexplored. Additionally, little research addresses the issue of catastrophic forgetting during fine-tuning of foundation models. We adapted the DINOv2 vision transformer for retinal imaging classification tasks using self-supervised learning and generated two novel foundation models termed DINORET and BE DINORET. Publicly available color fundus photographs were employed for model development and subsequent fine-tuning for diabetic retinopathy staging and glaucoma detection. We introduced block expansion as a novel domain adaptation strategy and assessed the models for catastrophic forgetting. Models were benchmarked to RETFound, a state-of-the-art foundation model in ophthalmology. DINORET and BE DINORET demonstrated competitive performance on retinal imaging tasks, with the block expanded model achieving the highest scores on most datasets. Block expansion successfully mitigated catastrophic forgetting. Our few-shot learning studies indicated that DINORET and BE DINORET outperform RETFound in terms of data-efficiency. This study highlights the potential of adapting natural domain vision models to retinal imaging using self-supervised learning and block expansion. BE DINORET offers robust performance without sacrificing previously acquired capabilities. Our findings suggest that these methods could enable healthcare institutions to develop tailored vision models for their patient populations, enhancing global healthcare inclusivity.