Surgery is a common first-line treatment for many types of disease, including cancer. Mortality rates after general elective surgery have seen significant decreases whilst postoperative complications remain a frequent occurrence. Preoperative assessment tools are used to support patient risk stratification but do not always provide a precise and accessible assessment. Wearable sensors (WS) provide an accessible alternative that offers continuous monitoring in a non-clinical setting. They have shown consistent uptake across the perioperative period but there has been no review of WS as a preoperative assessment tool. This paper reviews the developments in WS research that have application to the preoperative period. Accelerometers were consistently employed as sensors in research and were frequently combined with photoplethysmography or electrocardiography sensors. Pre-processing methods were discussed and missing data was a common theme; this was dealt with in several ways, commonly by employing an extraction threshold or using imputation techniques. Research rarely processed raw data; commercial devices that employ internal proprietary algorithms with pre-calculated heart rate and step count were most commonly employed limiting further feature extraction. A range of machine learning models were used to predict outcomes including support vector machines, random forests and regression models. No individual model clearly outperformed others. Deep learning proved successful for predicting exercise testing outcomes but only within large sample-size studies. This review outlines the challenges of WS and provides recommendations for future research to develop WS as a viable preoperative assessment tool.

Wearable Sensors as a Preoperative Assessment Tool: A Review

The advent of the IoT has undeniably transformed the manner in which we connect with the world innovative solutions across a multitude of sectors. However, the proliferation of IoT applications has brought to the fore a complex and multifaceted problem that warrants immediate attention: the security and energy efficiency challenges within IoT networks. As the number of IoT devices continues to surge and their applications diversify, the demand for ubiquitous and secure data access has never been greater. These applications, ranging from healthcare monitoring to industrial automation, require not only real-time data processing but also the assurance of data security and privacy. So, the research explores the machine learning techniques application for intrusion detection in IoT networks, aiming to enhance security in the rapidly evolving landscape of interconnected devices. The research emphasizes the significance of proactive measures in safeguarding IoT ecosystems and highlights the potential of machine learning to detect and mitigate intrusions effectively. In this research, a model was developed for detecting anomalies in Internet of Things networks using artificial neural networks. The research aims to develop and evaluate an effective intrusion detection model for IoT networks by leveraging machine learning techniques. The purpose is to enhance the security posture of interconnected devices, addressing the unique challenges posed by the dynamic and heterogeneous nature of IoT environments. The study tries to contribute important experiences and functional answers to reinforce the strength of IoT networks against developing digital dangers.

Intrusion Detection in IoT Networks Using Machine Learning Techniques

Wearable technology is being used for tracking continuous events in various sectors of our lives. Wearables contain different types of sensors which can acquire movement data, blood pressure, blood sugar, temperature, and other physiological parameters. These parameters are recorded in the form of seamless univariate or multivariate time-series data. Very often, however, the data contains missing datum which disrupts the continuity of the data making it difficult to analyze the data. The missing part of the data needs to be imputed to make the remaining available data applicable. Choosing the proper imputation method is crucial for fruitful analysis and extracting underlined features from the data. In this context, this chapter discusses sensors associated with wearable technology which generate the time-series data, missing data in the wearables’ time-series data, and various imputation methods being used for imputing the missing data. 

pdf/handling-missing-data-in-the-time-series-data-from-wearables-2lhg46jdn5.pdf

Handling missing data in the time-series data from wearables

Predictive modeling and benchmarking for diamond price estimation: integrating classification, regression, hyperparameter tuning and execution time analysis

Malware detection, targeting the microarchitecture of processors, has recently come to light as a potentially effective way to improve computer system security. Hardware Performance Counter data are used by machine learning algorithms in security mechanisms, such as hardware-based malware detection, to categorize and detect malware. It is crucial to determine whether or not a file contains malware. Many issues have been brought about by the rise in malware, and businesses are losing vital data and dealing with other issues. The second thing to keep in mind is that malware can quickly cause a lot of damage to a system by slowing it down and encrypting a large amount of data on a personal computer. This study provides extensive details on a flexible framework related to machine learning and deep learning techniques using few-shot learning. Malware detection is possible using DT, RF, LR, SVM, and FSL techniques. The logic is that these algorithms make it simple to differentiate between files that are malware-free and those that are not. This indicates that their goal is to reduce the number of false positives in the data. For this, we use two different datasets from an online platform. In this research work, we mainly focus on few-shot learning techniques by using two different datasets. The proposed model has an 97% accuracy rate, which is much greater than that of other techniques. 

Malware Classification Using Few-Shot Learning Approach

The presence of missing values in a time-series dataset is a very common and well-known problem. Various statistical and machine learning methods have been developed to overcome this problem, with the aim of filling in the missing values in the data. However, the performances of these methods vary widely, showing a high dependence on the type of data and correlations within the data. In our study, we performed some of the well-known imputation methods, such as expectation maximization, k-nearest neighbor, iterative imputer, random forest, and simple imputer, to impute missing data obtained from smart, wearable health trackers. In this manuscript, we proposed the use of data binning for imputation. We showed that the use of data binned around the missing time interval provides a better imputation than the use of a whole dataset. Imputation was performed for 15 min and 1 h of continuous missing data. We used a dataset with different bin sizes, such as 15 min, 30 min, 45 min, and 1 h, and we carried out evaluations using root mean square error (RMSE) values. We observed that the expectation maximization algorithm worked best for the use of binned data. This was followed by the simple imputer, iterative imputer, and k-nearest neighbor, whereas the random forest method had no effect on data binning during imputation. Moreover, the smallest bin sizes of 15 min and 1 h were observed to provide the lowest RMSE values for the majority of the time frames during the imputation of 15 min and 1 h of missing data, respectively. Although applicable to digital health data, we think that this method will also find applicability in other domains.

/pdf/binned-data-provide-better-imputation-of-missing-time-series-is6w4phj.pdf

Binned Data Provide Better Imputation of Missing Time Series Data from Wearables

Pritumumab is a human IgG1 kappa antibody that targets ecto-domain vimentin (EDV) which is overexpressed in several malignant tumors including glioblastomas. To understand preclinical biological activity and safety of pritumumab derived from Chinese hamster ovary (CHO) cells, we evaluated tumor targeting ability, brain-tumor barrier permeability, growth inhibition, and primate safety studies. In-vivo and ex-vivo imaging studies demonstrate pritumumab to cross the blood brain/blood tumor barrier and an 89Zr-labeled pritumumab immunoconjugate showed the antibody specifically targeted tumor cells. In mouse xenograft models, pritumumab inhibited the growth of U251 glioblastoma and PANC-1 pancreatic cancer cells. A 29-day intravenous toxicology study in cynomolgus monkeys was carried out to analyze the safety and toxicity of pritumumab, and no toxic effects were observed. Overall, these data together suggest pritumumab is biologically active and animal models can be used to further understand the various functions of the antibody. Clinical trials in brain cancer patients assessing safety and efficacy of pritumumab as a therapeutic for brain cancer are in process.

Characterization of pritumumab in murine models and primate safety study

Time series data are recorded in various sectors, resulting in a large amount of data. However, the continuity of these data is often interrupted, resulting in periods of missing data. Several algorithms are used to impute the missing data, and the performance of these methods is widely varied. Apart from the choice of algorithm, the effective imputation depends on the nature of missing and available data. We conducted extensive studies using different types of time series data, specifically heart rate data and power consumption data. We generated the missing data for different time spans and imputed using different algorithms with binned data of different sizes. The performance was evaluated using the root mean square error (RMSE) metric. We observed a reduction in RMSE when using binned data compared to the entire dataset, particularly in the case of the expectation–maximization (EM) algorithm. We found that RMSE was reduced when using binned data for 1-, 5-, and 15-min missing data, with greater reduction observed for 15-min missing data. We also observed the effect of data fluctuation. We conclude that the usefulness of binned data depends precisely on the span of missing data, sampling frequency of the data, and fluctuation within data. Depending on the inherent characteristics, quality, and quantity of the missing and available data, binned data can impute a wide variety of data, including biological heart rate data derived from the Internet of Things (IoT) device smartwatch and non-biological data such as household power consumption data.

Efficient use of binned data for imputing univariate time series data

Assessment of Functional Status of Human Leukocyte Antigen Class I Genes in Cancer Tissues in the Context of Personalized Neoantigen Peptide Vaccine Immunotherapy.

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Binned Data Provide Better Imputation of Missing Time Series Data from Wearables

Characterization of pritumumab in murine models and primate safety study

Efficient use of binned data for imputing univariate time series data

Assessment of Functional Status of Human Leukocyte Antigen Class I Genes in Cancer Tissues in the Context of Personalized Neoantigen Peptide Vaccine Immunotherapy.