Synthesizing information from multiple data sources plays a crucial role in the practice of modern medicine. Current applications of artificial intelligence in medicine often focus on single-modality data due to a lack of publicly available, multimodal medical datasets. To address this limitation, we introduce INSPECT, which contains de-identified longitudinal records from a large cohort of patients at risk for pulmonary embolism (PE), along with ground truth labels for multiple outcomes. INSPECT contains data from 19,402 patients, including CT images, radiology report impression sections, and structured electronic health record (EHR) data (i.e. demographics, diagnoses, procedures, vitals, and medications). Using INSPECT, we develop and release a benchmark for evaluating several baseline modeling approaches on a variety of important PE related tasks. We evaluate image-only, EHR-only, and multimodal fusion models. Trained models and the de-identified dataset are made available for non-commercial use under a data use agreement. To the best of our knowledge, INSPECT is the largest multimodal dataset integrating 3D medical imaging and EHR for reproducible methods evaluation and research.

The persistent battle to decrease childhood mortality serves as a commonly employed benchmark for gauging advancements in the field of medicine. Globally, the under-5 mortality rate stands at approximately 5 million, with a significant portion of these deaths being avoidable. Given the significance of this problem, Machine learning-based techniques have emerged as a prominent tool for assessing fetal health. In this work, we have analyzed the classification performance of various machine learning models for fetal health analysis. Classification performance of various machine learning models, such as support vector machine (SVM), random forest(RF), and attentive interpretable tabular learning (TabNet) have been assessed on fetal health. Moreover, dimensionality reduction techniques, such as Principal component analysis (PCA) and Linear discriminant analysis (LDA) have been implemented to obtain better classification performance with less number of features. A TabNet model on a fetal health dataset provides a classification accuracy of 94.36%. In general, this technology empowers doctors and healthcare experts to achieve precise fetal health classification and identify the most influential features in the process.

Objective. Limited access to breast cancer diagnosis globally leads to delayed treatment. Ultrasound, an effective yet underutilized method, requires specialized training for sonographers, which hinders its widespread use. Approach. Volume sweep imaging (VSI) is an innovative approach that enables untrained operators to capture high-quality ultrasound images. Combined with deep learning, like convolutional neural networks (CNNs), it can potentially transform breast cancer diagnosis, enhancing accuracy, saving time and costs, and improving patient outcomes. The widely used UNet architecture, known for medical image segmentation, has limitations, such as vanishing gradients and a lack of multi-scale feature extraction and selective region attention. In this study, we present a novel segmentation model known as Wavelet_Attention_UNet (WATUNet). In this model, we incorporate wavelet gates (WGs) and attention gates (AGs) between the encoder and decoder instead of a simple connection to overcome the limitations mentioned, thereby improving model performance. Main results. Two datasets are utilized for the analysis. The public "Breast Ultrasound Images" (BUSI) dataset of 780 images and a VSI dataset of 3818 images. Both datasets contained segmented lesions categorized into three types: no mass, benign mass, and malignant mass. Our segmentation results show superior performance compared to other deep networks. The proposed algorithm attained a Dice coefficient of 0.94 and an F1 score of 0.94 on the VSI dataset and scored 0.93 and 0.94 on the public dataset, respectively.

In the growing world of artificial intelligence, federated learning is a distributed learning framework enhanced to preserve the privacy of individuals' data. Federated learning lays the groundwork for collaborative research in areas where the data is sensitive. Federated learning has several implications for real-world problems. In times of crisis, when real-time decision-making is critical, federated learning allows multiple entities to work collectively without sharing sensitive data. This distributed approach enables us to leverage information from multiple sources and gain more diverse insights. This paper is a systematic review of the literature on privacy-preserving machine learning in the last few years based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specifically, we have presented an extensive review of supervised/unsupervised machine learning algorithms, ensemble methods, meta-heuristic approaches, blockchain technology, and reinforcement learning used in the framework of federated learning, in addition to an overview of federated learning applications. This paper reviews the literature on the components of federated learning and its applications in the last few years. The main purpose of this work is to provide researchers and practitioners with a comprehensive overview of federated learning from the machine learning point of view. A discussion of some open problems and future research directions in federated learning is also provided.

This study aimed to utilize text processing and natural language processing (NLP) models to mine clinical notes for the diagnosis of periodontitis and to evaluate the performance of a named entity recognition (NER) model on different regular expression (RE) methods. Two complexity levels of RE methods were used to extract and generate the training data. The SpaCy package and RoBERTa transformer models were used to build the NER model and evaluate its performance with the manual-labeled gold standards. The comparison of the RE methods with the gold standard showed that as the complexity increased in the RE algorithms, the F1 score increased from 0.3-0.4 to around 0.9. The NER models demonstrated excellent predictions, with the simple RE method showing 0.84-0.92 in the evaluation metrics, and the advanced and combined RE method demonstrating 0.95-0.99 in the evaluation. This study provided an example of the benefit of combining NER methods and NLP models in extracting target information from free-text to structured data and fulfilling the need for missing diagnoses from unstructured notes.

In many real-world datasets, rows may have distinct characteristics and require different modeling approaches for accurate predictions. In this paper, we propose an adaptive modeling approach for row-type dependent predictive analysis(RTDPA). Our framework enables the development of models that can effectively handle diverse row types within a single dataset. Our dataset from XXX bank contains two different risk categories, personal loan and agriculture loan. each of them are categorised into four classes standard, sub-standard, doubtful and loss. We performed tailored data pre processing and feature engineering to different row types. We selected traditional machine learning predictive models and advanced ensemble techniques. Our findings indicate that all predictive approaches consistently achieve a precision rate of no less than 90%. For RTDPA, the algorithms are applied separately for each row type, allowing the models to capture the specific patterns and characteristics of each row type. This approach enables targeted predictions based on the row type, providing a more accurate and tailored classification for the given dataset.Additionally, the suggested model consistently offers decision makers valuable and enduring insights that are strategic in nature in banking sector.

Hausa is a major Chadic language, spoken by over 100 million people in Africa. However, from a computational linguistic perspective, it is considered a low-resource language, with limited resources to support Natural Language Processing (NLP) tasks. Online platforms often facilitate social interactions that can lead to the use of offensive and threatening language, which can go undetected due to the lack of detection systems designed for Hausa. This study aimed to address this issue by (1) conducting two user studies (n=308) to investigate cyberbullying-related issues, (2) collecting and annotating the first set of offensive and threatening datasets to support relevant downstream tasks in Hausa, (3) developing a detection system to flag offensive and threatening content, and (4) evaluating the detection system and the efficacy of the Google-based translation engine in detecting offensive and threatening terms in Hausa. We found that offensive and threatening content is quite common, particularly when discussing religion and politics. Our detection system was able to detect more than 70% of offensive and threatening content, although many of these were mistranslated by Google's translation engine. We attribute this to the subtle relationship between offensive and threatening content and idiomatic expressions in the Hausa language. We recommend that diverse stakeholders participate in understanding local conventions and demographics in order to develop a more effective detection system. These insights are essential for implementing targeted moderation strategies to create a safe and inclusive online environment.

Many under-resourced languages require high-quality datasets for specific tasks such as offensive language detection, disinformation, or misinformation identification. However, the intricacies of the content may have a detrimental effect on the annotators. The article aims to revisit an approach of pseudo-labeling sensitive data on the example of Ukrainian tweets covering the Russian-Ukrainian war. Nowadays, this acute topic is in the spotlight of various language manipulations that cause numerous disinformation and profanity on social media platforms. The conducted experiment highlights three main stages of data annotation and underlines the main obstacles during machine annotation. Ultimately, we provide a fundamental statistical analysis of the obtained data, evaluation of models used for pseudo-labelling, and set further guidelines on how the scientists can leverage the corpus to execute more advanced research and extend the existing data samples without annotators' engagement.

This research explores the integration of quantum data embedding techniques into classical machine learning (ML) algorithms, aiming to assess the performance enhancements and computational implications across a spectrum of models. We explore various classical-to-quantum mapping methods, ranging from basis encoding, angle encoding to amplitude encoding for encoding classical data, we conducted an extensive empirical study encompassing popular ML algorithms, including Logistic Regression, K-Nearest Neighbors, Support Vector Machines and ensemble methods like Random Forest, LightGBM, AdaBoost, and CatBoost. Our findings reveal that quantum data embedding contributes to improved classification accuracy and F1 scores, particularly notable in models that inherently benefit from enhanced feature representation. We observed nuanced effects on running time, with low-complexity models exhibiting moderate increases and more computationally intensive models experiencing discernible changes. Notably, ensemble methods demonstrated a favorable balance between performance gains and computational overhead. This study underscores the potential of quantum data embedding in enhancing classical ML models and emphasizes the importance of weighing performance improvements against computational costs. Future research directions may involve refining quantum encoding processes to optimize computational efficiency and exploring scalability for real-world applications. Our work contributes to the growing body of knowledge at the intersection of quantum computing and classical machine learning, offering insights for researchers and practitioners seeking to harness the advantages of quantum-inspired techniques in practical scenarios.

Audio adversarial examples (AEs) have posed significant security challenges to real-world speaker recognition systems. Most black-box attacks still require certain information from the speaker recognition model to be effective (e.g., keeping probing and requiring the knowledge of similarity scores). This work aims to push the practicality of the black-box attacks by minimizing the attacker's knowledge about a target speaker recognition model. Although it is not feasible for an attacker to succeed with completely zero knowledge, we assume that the attacker only knows a short (or a few seconds) speech sample of a target speaker. Without any probing to gain further knowledge about the target model, we propose a new mechanism, called parrot training, to generate AEs against the target model. Motivated by recent advancements in voice conversion (VC), we propose to use the one short sentence knowledge to generate more synthetic speech samples that sound like the target speaker, called parrot speech. Then, we use these parrot speech samples to train a parrot-trained(PT) surrogate model for the attacker. Under a joint transferability and perception framework, we investigate different ways to generate AEs on the PT model (called PT-AEs) to ensure the PT-AEs can be generated with high transferability to a black-box target model with good human perceptual quality. Real-world experiments show that the resultant PT-AEs achieve the attack success rates of 45.8% - 80.8% against the open-source models in the digital-line scenario and 47.9% - 58.3% against smart devices, including Apple HomePod (Siri), Amazon Echo, and Google Home, in the over-the-air scenario.