Abstract Maintaining constant vigilance over arterial blood pressure (ABP) is crucial for diagnosing hypertension and other critical cardiovascular diseases. While traditional cuff-based approaches are non-invasive, they have limitations in providing continuous blood pressure monitoring. In contrast, complex ABP monitoring systems, while accurate, are primarily suitable for clinical settings due to their intrusive nature. This study introduces a groundbreaking method for generating arterial blood pressure (ABP) waveforms using remote radar signals and deep learning (DL) techniques. This approach eliminates the need for invasive procedures, wearable biosensors, and costly equipment typically associated with ABP recording. We introduce MultiResLinkNet, a segmentation model based on a one-dimensional convolutional neural network (1D CNN), specifically designed to synthesize arterial blood pressure (ABP) directly from raw radar waveforms. We trained and evaluated the end-to-end DL framework using a publicly available benchmark radar dataset containing raw radar data and corresponding physiological signals from 30 subjects across various scenarios, including Resting, Valsalva, Apnea, Tilt-up, and Tilt-down. The proposed MultiResLinkNet excelled in ABP segmentation, outperforming state-of-the-art networks in combined and individual scenarios, and produced the best average temporal and spectral correlations as well as the lowest temporal and spectral errors in nearly all scenarios’ data. Furthermore, qualitative evaluation demonstrated a strong resemblance between the synthesized and ground truth ABP waveforms. Our novel approach enables remote monitoring of critical patients continuously, especially those undergoing surgery, by predicting ABP waveforms from non-contact radar signals. This breakthrough offers significant advantages, facilitating continuous ABP monitoring without the need for invasive procedures or cumbersome wearable sensors.

A hypergraph is a complex data structure capable of expressing associations among any number of data entities. Overcoming the limitations of traditional graphs, hypergraphs are useful to model real-life problems. Frequent pattern mining is one of the most popular problems in data mining with a lot of applications. To the best of our knowledge, there exists no flexible pattern mining framework for hypergraph databases decomposing associations among data entities. In this article, we propose a flexible and complete framework for mining frequent patterns from a collection of hypergraphs. To discover more interesting patterns beyond the traditional frequent patterns, we propose frameworks for weighted and uncertain hypergraph mining also. We develop three algorithms for mining frequent, weighted, and uncertain hypergraph patterns efficiently by introducing a canonical labeling technique for isomorphic hypergraphs. Extensive experiments have been conducted on real-life hypergraph databases to show both the effectiveness and efficiency of our proposed frameworks and algorithms.

Object-oriented Bayesian network (OOBN) is a method for building compositional and hierarchical Bayesian network (BN) models that promote reuse and simple maintenance. Reasoning with both BNs and OOBNs entails the computational job of inference, the computation of new posterior probability distributions based on a set of evidence. A widely used inference strategy in conventional BN is to compile the BN into a junction tree (JT) before conducting standard inference. In the case of OOBN, it is first flattened into the underlying BN before performing the JT-based compilation. However, large OOBNs flatten to complex and larger BNs can be computationally intensive to compile into JTs due to the complexity of compilation being exponential to the size of BNs. To cope with these performance issues, techniques like Incremental Compilation (IC) avoid reconstructing JT from scratch after each modification of a BN. However, none of the existing works were able to reduce the computational complexity of compilation. Hence, in this paper, we propose a new compilation algorithm that compiles the OOBN without flattening it and re-using the existing JTs of embedded components of the OOBN. Evaluation results show that our proposed algorithm effectively reduces the computation time for JT construction of OOBN.

Adaptation of the dataset shift has grown to be of great importance in machine learning problems in recent years. Reframing has emerged as a new machine learning technique that adapts the context changes between training and target domains. One of the advantages of reframing is that it can offer good performances with a limited amount of deployment data. Reframing has already been implemented in classification and regression by reusing labelled training data with the help of few labelled target data. However, reframing in clustering is still a challenging research problem because of its unsupervised nature. In this paper, we concentrate on building a reframing method for clustering. We also show the necessity and effectiveness of our method in contrast to retraining, which is the process of learning new model in the testing and deployment phases. Our evaluation results with extensive experiments using both synthetic and real-life datasets show that our method correctly identifies most of the shifts between datasets and builds better clustering model than retraining.

Knowledge discovery in big data is one of most interesting topics in state-of-the-art research, and frequent patterns mining is a major task. With the rapid growth of modern technology, high volumes of data-which are of different veracities (i.e., may be precise or uncertain)-are flowing at a high velocity all over the world. Properties of data temporally changes with changes in the people's interests, which make the data dynamic. Due to the uncertainty and dynamic properties of data, finding appropriate and efficient approach to ensure the efficient usage of available resources has become a great challenge. In this paper, we design a new memory-efficient data structure, called Uncertain Stream (US)-tree, which stores recent meta-data. We also develop a probabilistic, sliding window based, efficient algorithm-called Uncertain Stream Frequent Pattern (USFP)-growth-for mining frequent patterns from uncertain data streams. Our comprehensive performance evaluation shows that USFP-growth is correct and efficient when compared with recent related approaches.

Mining frequent patterns is a crucial task in data mining. Most of the existing frequent pattern mining methods find the complete set of frequent patterns from a given dataset. However, in real-life scenarios we often need to predict the future frequent patterns for different tasks such as business policy making, web page recommendation, stock-market behavior and road traffic analysis. Predicting future frequent patterns from the currently available set of frequent patterns is challenging due to dataset shift where data distributions may change from one dataset to another. In this paper, we propose a new approach called reframing in frequent pattern mining to solve this task. Moreover, we experimentally show the existence of dataset shift in two real-life transactional datasets and the capability of our approach to handle these unknown shifts.

Reuse of learnt knowledge is of critical importance in the majority of knowledge-intensive application areas, particularly because the operating context can be expected to vary from training to deployment. Dataset shift is a crucial example of this where training and testing datasets follow different distributions. However, most of the existing dataset shift solving algorithms need costly retraining operation and are not suitable to use the existing model. In this paper, we propose a new approach called reframing to handle dataset shift. The main objective of reframing is to build a model once and make it workable without retraining. We propose two efficient reframing algorithms to learn the optimal shift parameter values using only a small amount of labelled data available in the deployment. Thus, they can transform the shifted input attributes with the optimal parameter values and use the same existing model in several deployment environments without retraining. We have addressed supervised learning tasks both for classification and regression. Extensive experimental results demonstrate the efficiency and effectiveness of our approach compared to the existing solutions. In particular, we report the existence of dataset shift in two real-life datasets. These real-life unknown shifts can also be accurately modeled by our algorithms.

The share measure of item sets has been proposed to discover useful knowledge about numerical values associated with items in a transaction database. Therefore, share-frequent pattern mining problem becomes a very important research issue in data mining. However, the existing algorithms of share-frequent pattern mining are based on static databases. Moreover, they are not suitable for interactive mining. In this paper, we propose a novel tree structure IncrShrFP-Tree (Incremental Share-Frequent Pattern Tree) for incremental and interactive share-frequent pattern mining. It is effective for incremental and interactive mining to utilize the previous tree structure and to use the previous mining results when a database is updated or a minimum support threshold is changed. It needs maximum two database scans to calculate the resultant share-frequent patterns in incremental databases. Extensive performance analyses show that our method is very efficient for incremental and interactive share-frequent pattern mining.

Recently proposed regular pattern mining provides an effective technique to find patterns occurring at regular interval in a static database. However, the occurrence characteristic of patterns may change significantly with the update of database. Therefore, this paper proposes the Incremental Regular Pattern Tree (IncRT) and a pattern growth mining technique to find regular patterns on incremental transactional databases. Experiment results show the effectiveness of the proposed method.

Mining web access sequences can discover very useful knowledge from web logs with broad applications. By considering non-binary occurrences of web pages as internal utilities in web access sequences, e.g., time spent by each user in a web page, more realistic information can be extracted. However, the existing utility-based approach has many limitations such as considering only forward references of web access sequences, not applicable for incremental mining, suffers in the level-wise candidate generation-and-test methodology, needs several database scans and does not show how to mine web traversal sequences with external utility, i.e., different impacts/significances for different web pages. In this paper, we propose a new approach to solve these problems. Moreover, we propose two novel tree structures, called UWAS-tree (utility-based web access sequence tree), and IUWAS-tree (incremental UWAS tree), for mining web access sequences in static and dynamic databases respectively. Our approach can handle both forward and backward references, static and dynamic data, avoids the level-wise candidate generation-and-test methodology, does not scan databases several times and considers both internal and external utilities of a web page. Extensive performance analyses show that our approach is very efficient for both static and incremental mining of high utility web access sequences.

By considering different weights of the items, weighted frequent pattern (WFP)mining can discover more important knowledge compared to traditional frequent pattern mining. Therefore, WFP mining becomes an important research issue in data mining and knowledge discovery area. However, the existing algorithms cannot be applied for stream data mining because they require multiple database scans. Moreover, they cannot extract the recent change of knowledge in a data stream adaptively. In this paper, we propose a sliding window based novel technique WFPMDS (weighted frequent pattern mining over data streams) using a single scan of data stream to discover important knowledge form the recent data elements. Extensive performance analyses show that our technique is very efficient for WFP mining over data streams.

Recently, a significant number of parallel and distributed algorithms have been proposed to mine frequent patterns (FP) from large and/or distributed databases. Among them parallelization of the FP-growth algorithms using the FP-tree has been proved to be highly efficient. However, the FP-tree-based techniques suffer from two major limitations such as multiple database scans requirement (i.e., high I/O cost) and high inter-processor communications cost (during the mining phase). Therefore, we propose a novel tree structure, called PP-tree (Parallel Pattern tree) that significantly reduces the I/O cost by capturing the database contents with a single scan and facilitates the efficient FP-growth mining on it with reduced inter-processor communication overhead. Our parallel algorithm works independently at each local site and locally generates global frequent patterns which are merged at the final stage. The experimental results reflect that parallel and distributed FP mining with PP-tree outperforms other state-of-the-art algorithms.

Recently, high utility pattern (HUP) mining is one of the most important research issues in data mining due to its ability to consider the nonbinary frequency values of items in transactions and different profit values for every item. On the other hand, incremental and interactive data mining provide the ability to use previous data structures and mining results in order to reduce unnecessary calculations when a database is updated, or when the minimum threshold is changed. In this paper, we propose three novel tree structures to efficiently perform incremental and interactive HUP mining. The first tree structure, Incremental HUP Lexicographic Tree ({\rm IHUP}_{{\rm {L}}}-Tree), is arranged according to an item's lexicographic order. It can capture the incremental data without any restructuring operation. The second tree structure is the IHUP Transaction Frequency Tree ({\rm IHUP}_{{\rm {TF}}}-Tree), which obtains a compact size by arranging items according to their transaction frequency (descending order). To reduce the mining time, the third tree, IHUP-Transaction-Weighted Utilization Tree ({\rm IHUP}_{{\rm {TWU}}}-Tree) is designed based on the TWU value of items in descending order. Extensive performance analyses show that our tree structures are very efficient and scalable for incremental and interactive HUP mining.

This paper proposes a prefix-tree structure, called CPS-tree (Compact Pattern Stream tree) that efficiently discovers the exact set of recent frequent patterns from high-speed data stream. The CPS-tree introduces the concept of dynamic tree restructuring technique in handling stream data that allows it to achieve highly compact frequency-descending tree structure at runtime and facilitates an efficient FP-growth-based [1] mining technique.

We proposed an advanced minimization method for multiple valued multiple output functions in this paper. We extracted the shared sub functions with a proposed heuristic method to pair the functions. New minimization approach for multiple valued functions has also been proposed where we used Kleenean coefficients and we used LUT to reduce the complexity as well. Our minimization method reduces the number of implicants significantly. The realization of the minimized circuits has also been shown using current mode CMOS.