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Dementia is not a specific disease, but a general term for age-related decline or loss of memory, cognitive abilities including problem solving and decision-making, and one’s own language, which significantly interfere with daily life. Researchers around the world have developed ways to automate the diagnosis of dementia through the use of machine learning and data mining approaches. The aim of this research project is to design and develop a day-to-day activity prediction algorithm in order to accurately identify and differentiate the dementia affected patients from the healthy subjects, to ensure early diagnosis of dementia development. This research advocates a novel algorithm called ‘Sequence Prediction via All Discoverable Episodes (SPADE)’ as a statistical tool to map activities of daily life (ADLs) in different groups of people in order to develop a unique parameter for precise diagnosis. The results of our experiment demonstrated a significant difference (i.e. 11 %) in the sequence prediction peak accuracy between the healthy subjects and the residents with dementia. SPADE demonstrated an adequate accuracy (i.e. 80 % on average), with an improvement of about 12 % compared to the performance of M-SPEED in inferring future occurrences of activities. It is thus evident that the algorithms for activity predictions show promise for early detection of dementia symptoms without the use of any expensive clinical procedure.

Background: Sepsis, a life-threatening infection-induced inflammatory condition, has significant global health impacts. Timely detection is crucial for improving patient outcomes as sepsis can rapidly progress to severe forms. The application of machine learning (ML) and deep learning (DL) to predict sepsis using electronic health records (EHRs) has gained considerable attention for timely intervention. Methods: PubMed, IEEE Xplore, Google Scholar, and Scopus were searched for relevant studies. All studies that used ML/DL to detect or early-predict the onset of sepsis in the adult population using EHRs were considered. Data were extracted and analyzed from all studies that met the criteria and were also evaluated for their quality. Results: This systematic review examined 1942 articles, selecting 42 studies while adhering to strict criteria. The chosen studies were predominantly retrospective (n = 38) and spanned diverse geographic settings, with a focus on the United States. Different datasets, sepsis definitions, and prevalence rates were employed, necessitating data augmentation. Heterogeneous parameter utilization, diverse model distribution, and varying quality assessments were observed. Longitudinal data enabled early sepsis prediction, and quality criteria fulfillment varied, with inconsistent funding–article quality correlation. Conclusions: This systematic review underscores the significance of ML/DL methods for sepsis detection and early prediction through EHR data. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals immensely suffer from several physiological artifacts, including electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) artifacts, which must be removed to ensure EEG's usability. This paper proposes a novel one-dimensional convolutional neural network (1D-CNN), i.e., MultiResUNet3+, to denoise physiological artifacts from corrupted EEG. A publicly available dataset containing clean EEG, EOG, and EMG segments is used to generate semi-synthetic noisy EEG to train, validate and test the proposed MultiResUNet3+, along with four other 1D-CNN models (FPN, UNet, MCGUNet, LinkNet). Adopting a five-fold cross-validation technique, all five models' performance is measured by estimating temporal and spectral percentage reduction in artifacts, temporal and spectral relative root mean squared error, and average power ratio of each of the five EEG bands to whole spectra. The proposed MultiResUNet3+ achieved the highest temporal and spectral percentage reduction of 94.82% and 92.84%, respectively, in EOG artifacts removal from EOG-contaminated EEG. Moreover, compared to the other four 1D-segmentation models, the proposed MultiResUNet3+ eliminated 83.21% of the spectral artifacts from the EMG-corrupted EEG, which is also the highest. In most situations, our proposed model performed better than the other four 1D-CNN models, evident by the computed performance evaluation metrics. [ABSTRACT FROM AUTHOR]

Predictability of absolute data transaction for real time information and reduction of incalculable data delivery time are the major factors to lead the global education as well as economic system towards a paperless environment. Some universities such as the University of Phoenix, Western Governors University, UK Open University, University of California, and Hong Kong University are playing a pioneering role to achieve this paperless environment in the respective campuses. Multimedia University (MMU) is a very distinctive university and is considered the jewel of Malaysia's bold plan to enter the world arena of 21st century information technology. The university is now operational with intelligent building systems, wireless technology system, satellite tele-education, a digital library, state-of-the-art learning, and research facilities, as well as an integrated computer education management system. MMU runs a paperless administration with the full-fledged e-governance having all these facilities. E-governance is about the use of information technology to raise the quality of the services organizations deliver for its management. The university also provides a Registry of Graduates homepage that not only eliminates fraud, but also complements the e-governance system, preparing itself for a truly web-based society. This article mainly aims to illustrate the development of higher education in the Multimedia University by establishing a paperless environment. (Contains 4 figures and 1 table.)

This document is a correction notice for an article titled "An Integrated Approach for Platoon-based Simulation and Its Feasibility Assessment." The notice states that concerns were raised about similarities to previously published articles, reuse of equations and figures without proper citation, and a lack of discussion about the related works in the article. To address these issues, the article was republished with updates to the introduction, theoretical background, and figures. The primary data for the study was also included in the updated version. The notice concludes by providing the names of the authors who reported the issues. [Extracted from the article]

An intelligent insole system may monitor the individual's foot pressure and temperature in real-time from the comfort of their home, which can help capture foot problems in their earliest stages. Constant monitoring for foot complications is essential to avoid potentially devastating outcomes from common diseases such as diabetes mellitus. Inspired by those goals, the authors of this work propose a full design for a wearable insole that can detect both plantar pressure and temperature using off-the-shelf sensors. The design provides details of specific temperature and pressure sensors, circuit configuration for characterizing the sensors, and design considerations for creating a small system with suitable electronics. The procedure also details how, using a low-power communication protocol, data about the individuals' foot pressure and temperatures may be sent wirelessly to a centralized device for storage. This research may aid in the creation of an affordable, practical, and portable foot monitoring system for patients. The solution can be used for continuous, at-home monitoring of foot problems through pressure patterns and temperature differences between the two feet. The generated maps can be used for early detection of diabetic foot complication with the help of artificial intelligence. [ABSTRACT FROM AUTHOR]

Respiratory ailments are a very serious health issue and can be life-threatening, especially for patients with COVID. Respiration rate (RR) is a very important vital health indicator for patients. Any abnormality in this metric indicates a deterioration in health. Hence, continuous monitoring of RR can act as an early indicator. Despite that, RR monitoring equipment is generally provided only to intensive care unit (ICU) patients. Recent studies have established the feasibility of using photoplethysmogram (PPG) signals to estimate RR. This paper proposes a deep-learning-based end-to-end solution for estimating RR directly from the PPG signal. The system was evaluated on two popular public datasets: VORTAL and BIDMC. A lightweight model, ConvMixer, outperformed all of the other deep neural networks. The model provided a root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R) of 1.75 breaths per minute (bpm), 1.27 bpm, and 0.92, respectively, for VORTAL, while these metrics were 1.20 bpm, 0.77 bpm, and 0.92, respectively, for BIDMC. The authors also showed how fine-tuning a small subset could increase the performance of the model in the case of an out-of-distribution dataset. In the fine-tuning experiments, the models produced an average R of 0.81. Hence, this lightweight model can be deployed to mobile devices for real-time monitoring of patients. [ABSTRACT FROM AUTHOR]

Type 1 diabetes mellitus (T1DM) patients are a significant threat to chronic kidney disease (CKD) development during their life. However, there is always a high chance of delay in CKD detection because CKD can be asymptomatic, and T1DM patients bypass traditional CKD tests during their routine checkups. This study aims to develop and validate a prediction model and nomogram of CKD in T1DM patients using readily available routine checkup data for early CKD detection. This research utilized 1375 T1DM patients' sixteen years of longitudinal data from multi-center Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials conducted at 28 sites in the USA and Canada and considered 17 routinely available features. Three feature ranking algorithms, extreme gradient boosting (XGB), random forest (RF), and extremely randomized trees classifier (ERT), were applied to create three feature ranking lists, and logistic regression analyses were performed to develop CKD prediction models using these ranked feature lists to identify the best performing top-ranked features combination. Finally, the most significant features were selected to develop a multivariate logistic regression-based CKD prediction model for T1DM patients. This model was evaluated using sensitivity, specificity, accuracy, precision, and F1 score on train and test data. A nomogram of the final model was further generated for easy application in clinical practices. Hypertension, duration of diabetes, drinking habit, triglycerides, ACE inhibitors, low-density lipoprotein (LDL) cholesterol, age, and smoking habit were the top-8 features ranked by the XGB model and identified as the most important features for predicting CKD in T1DM patients. These eight features were selected to develop the final prediction model using multivariate logistic regression, which showed 90.04% and 88.59% accuracy in internal and test data validation. The proposed model showed excellent performance and can be used for CKD identification in T1DM patients during routine checkups. [ABSTRACT FROM AUTHOR]

With the onset of the COVID-19 pandemic, the number of critically sick patients in intensive care units (ICUs) has increased worldwide, putting a burden on ICUs. Early prediction of ICU requirement is crucial for efficient resource management and distribution. Early-prediction scoring systems for critically ill patients using mathematical models are available, but are not generalized for COVID-19 and Non-COVID patients. This study aims to develop a generalized and reliable prognostic model for ICU admission for both COVID-19 and non-COVID-19 patients using best feature combination from the patient data at admission. A retrospective cohort study was conducted on a dataset collected from the pulmonology department of Moscow City State Hospital between 20 April 2020 and 5 June 2020. The dataset contains ten clinical features for 231 patients, of whom 100 patients were transferred to ICU and 131 were stable (non-ICU) patients. There were 156 COVID positive patients and 75 non-COVID patients. Different feature selection techniques were investigated, and a stacking machine learning model was proposed and compared with eight different classification algorithms to detect risk of need for ICU admission for both COVID-19 and non-COVID patients combined and COVID patients alone. C-reactive protein (CRP), chest computed tomography (CT), lung tissue affected (%), age, admission to hospital, and fibrinogen parameters at hospital admission were found to be important features for ICU-requirement risk prediction. The best performance was produced by the stacking approach, with weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 84.45%, 84.48%, 83.64%, 84.47%, and 84.48%, respectively, for both types of patients, and 85.34%, 85.35%, 85.11%, 85.34%, and 85.35%, respectively, for COVID-19 patients only. The proposed work can help doctors to improve management through early prediction of the risk of need for ICU admission of patients during the COVID-19 pandemic, as the model can be used for both types of patients. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) is one of the most prevalent diseases in the world, and is correlated to a high index of mortality. One of its major complications is diabetic foot, leading to plantar ulcers, amputation, and death. Several studies report that a thermogram helps to detect changes in the plantar temperature of the foot, which may lead to a higher risk of ulceration. However, in diabetic patients, the distribution of plantar temperature does not follow a standard pattern, thereby making it difficult to quantify the changes. The abnormal temperature distribution in infrared (IR) foot thermogram images can be used for the early detection of diabetic foot before ulceration to avoid complications. There is no machine learning-based technique reported in the literature to classify these thermograms based on the severity of diabetic foot complications. This paper uses an available labeled diabetic thermogram dataset and uses the k-mean clustering technique to cluster the severity risk of diabetic foot ulcers using an unsupervised approach. Using the plantar foot temperature, the new clustered dataset is verified by expert medical doctors in terms of risk for the development of foot ulcers. The newly labeled dataset is then investigated in terms of robustness to be classified by any machine learning network. Classical machine learning algorithms with feature engineering and a convolutional neural network (CNN) with image-enhancement techniques are investigated to provide the best-performing network in classifying thermograms based on severity. It is found that the popular VGG 19 CNN model shows an accuracy, precision, sensitivity, F1-score, and specificity of 95.08%, 95.08%, 95.09%, 95.08%, and 97.2%, respectively, in the stratification of severity. A stacking classifier is proposed using extracted features of the thermogram, which is created using the trained gradient boost classifier, XGBoost classifier, and random forest classifier. This provides a comparable performance of 94.47%, 94.45%, 94.47%, 94.43%, and 93.25% for accuracy, precision, sensitivity, F1-score, and specificity, respectively. [ABSTRACT FROM AUTHOR]

Diabetic neuropathy (DN) is one of the prevalent forms of neuropathy that involves alterations in biomechanical changes in the human gait. Diabetic foot ulceration (DFU) is one of the pervasive types of complications that arise due to DN. In the literature, for the last 50 years, researchers have been trying to observe the biomechanical changes due to DN and DFU by studying muscle electromyography (EMG) and ground reaction forces (GRF). However, the literature is contradictory. In such a scenario, we propose using Machine learning techniques to identify DN and DFU patients by using EMG and GRF data. We collected a dataset from the literature which involves three patient groups: Control (n = 6), DN (n = 6), and previous history of DFU (n = 9) and collected three lower limb muscles EMG (tibialis anterior (TA), vastus lateralis (VL), gastrocnemius lateralis (GL)), and three GRF components (GRFx, GRFy, and GRFz). Raw EMG and GRF signals were preprocessed, and different feature extraction techniques were applied to extract the best features from the signals. The extracted feature list was ranked using four different feature ranking techniques, and highly correlated features were removed. In this study, we considered different combinations of muscles and GRF components to find the best performing feature list for the identification of DN and DFU. We trained eight different conventional ML models: Discriminant analysis classifier (DAC), Ensemble classification model (ECM), Kernel classification model (KCM), k-nearest neighbor model (KNN), Linear classification model (LCM), Naive Bayes classifier (NBC), Support vector machine classifier (SVM), and Binary decision classification tree (BDC), to find the best-performing algorithm and optimized that model. We trained the optimized the ML algorithm for different combinations of muscles and GRF component features, and the performance matrix was evaluated. Our study found the KNN algorithm performed well in identifying DN and DFU, and we optimized it before training. We found the best accuracy of 96.18% for EMG analysis using the top 22 features from the chi-square feature ranking technique for features from GL and VL muscles combined. In the GRF analysis, the model showed 98.68% accuracy using the top 7 features from the Feature selection using neighborhood component analysis for the feature combinations from the GRFx-GRFz signal. In conclusion, our study has shown a potential solution for ML application in DN and DFU patient identification using EMG and GRF parameters. With careful signal preprocessing with strategic feature extraction from the biomechanical parameters, optimization of the ML model can provide a potential solution in the diagnosis and stratification of DN and DFU patients from the EMG and GRF signals. [ABSTRACT FROM AUTHOR]

In this study, we considered HT ht , as in an ideal situation, the "reference ground truth" and the motion corrupted signal over the artifacts-free epochs would always be completely correlated. Hence, the following equation was used to estimate HT ht : (16) HT ht 4. On the other hand, general assumptions regarding the artifact signal HT ht includes temporal localization, not normally distributed with high local variance. [Extracted from the article]

Identification and classification of voltage and current disturbances in power systems are important tasks in the monitoring and protection of power system. Most power quality disturbances are non-stationary and transitory and the detection and classification have proved to be very demanding. The concept of discrete wavelet transform for feature extraction of power disturbance signal combined with artificial neural network and fuzzy logic incorporated as a powerful tool for detecting and classifying power quality problems. This paper employes a different type of univariate randomly optimized neural network combined with discrete wavelet transform and fuzzy logic to have a better power quality disturbance classification accuracy. The disturbances of interest include sag, swell, transient, fluctuation, and interruption. The system is modeled using VHSIC Hardware Description Language (VHDL), a hardware description language, followed by extensive testing and simulation to verify the functionality of the system that allows efficient hardware implementation of the same. This proposed method classifies, and achieves 98.19% classification accuracy for the application of this system on software-generated signals and utility sampled disturbance events. Index Terms--Artificial neural network, classification, feature extraction, fuzzy logic, power quality, VHSIC hardware description language (VHDL), wavelet transform.

Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help to reduce the adverse effects of diseases and also overcome the shortcomings of continuous human monitoring. In this work, we propose the use of a deep learning architecture based on a recent convolutional neural network called EfficientNet on 18,161 plain and segmented tomato leaf images to classify tomato diseases. The performance of two segmentation models i.e., U-net and Modified U-net, for the segmentation of leaves is reported. The comparative performance of the models for binary classification (healthy and unhealthy leaves), six-class classification (healthy and various groups of diseased leaves), and ten-class classification (healthy and various types of unhealthy leaves) are also reported. The modified U-net segmentation model showed accuracy, IoU, and Dice score of 98.66%, 98.5%, and 98.73%, respectively, for the segmentation of leaf images. EfficientNet-B7 showed superior performance for the binary classification and six-class classification using segmented images with an accuracy of 99.95% and 99.12%, respectively. Finally, EfficientNet-B4 achieved an accuracy of 99.89% for ten-class classification using segmented images. It can be concluded that all the architectures performed better in classifying the diseases when trained with deeper networks on segmented images. The performance of each of the experimental studies reported in this work outperforms the existing literature. [ABSTRACT FROM AUTHOR]

Remote healthcare monitoring (RHM) is an emerging trend in the biomedical field that leverages on the advancement of Internet of Things (IoT) to enhance the coverage of healthcare services around the world. An RHM system with the electromyography (EMG) measurement capability has great potential in improving the efficiency of medical treatment and physical therapy for chronic inflammatory myopathies. Conventional EMG measurement is often invasive, complex and require an ever-present medical officer. This paper proposes a capacitive electromyography (CEMG) monitoring system to overcome the limitations of the contact EMG monitoring system by using the capacitive sensing methodology. The system architecture and circuit design are detailed in this paper and the hardware implementation with experimental results are presented. This CEMG monitoring system achieved extremely low noise floor of less than 2 mV. Its performance also highly correlates with the conventional wet contact electrode without requiring any post signal processing. Therefore, this CEMG monitoring system is ideal for remote healthcare monitoring applications. [ABSTRACT FROM AUTHOR]

A high-voltage generator (HVG) is an essential part of a radio frequency identification electrically erasable programmable read-only memory (RFID–EEPROM). An HVG circuit is used to generate a regulated output voltage that is higher than the power supply voltage. However, the performance of the HVG is affected owing to the high-power dissipation, high-ripple voltage and low-pumping efficiency. Therefore, a regulator circuit consists of a voltage divider, comparator and a voltage reference, which are respectively required to reduce the ripple voltage, increase pumping efficiency and decrease the power dissipation of the HVG. Conversely, a clock driving circuit consists of the current-starved ring oscillator (CSRO), and the non- overlapping clock generator is required to drive the clock signals of the HVG circuit. In this study, the Mentor Graphics EldoSpice software package is used to design and simulate the HVG circuitry. The results showed that the designed CSRO dissipated only 4.9 μW at 10.2 MHz and that the phase noise was only -119.38 dBc/Hz at 1 MHz. Moreover, the proposed charge pump circuit was able to generate a maximum VPP of 13.53 V and it dissipated a power of only 31.01 μW for an input voltage VDD of 1.8 V. After integrating all the HVG modules, the results showed that the regulated HVG circuit was also able to generate a higher VPP of 14.59 V, while the total power dissipated was only 0.12 mW with a chip area of 0.044 mm2. Moreover, the HVG circuit produced a pumping efficiency of 90% and reduced the ripple voltage to <4 mV. Therefore, the integration of all the proposed modules in HVG ensured low-ripple programming voltages, higher pumping efficiency, and EEPROMs with lower power dissipation, and can be extensively used in low-power applications, such as in non-volatile memory, radiofrequency identification transponders, on-chip direct current DC-DC converters. [ABSTRACT FROM AUTHOR]

• Performance evaluation on contactless capacitive biosensors developed by different researchers. • Comparison of the design methodology of all the core elements to develop a capacitive biosensor. • Ideal design of capacitive sensor's capacitance is between 150 pF and 300 pF. • Presents a complete capacitive biosensor's skin-electrode modelling. Musculoskeletal disorders (MSDs) and ergonomic issues have long-term impacts on the human body, affecting patient health and the world's economy. To address these issues, electromyography (EMG) can provide detailed information of human muscular activity during the stages of diagnosis and recovery and for general monitoring. The conventional way to extract EMG signals from the human body requires a professional setup and complex post-signal processing and may cause side effects to the subject's body. This paper focuses on two prominent areas: it provides an in-depth analysis of the EMG signal characteristics, and a detailed discussion of the research and development of the hardware for a contactless EMG biosensor. This study provides an extensive review and performance comparison on the capacitive EMG sensors developed by different researchers. It also presents guidelines and parameters for future researchers to comply with in developing a practical and rugged contactless EMG biosensor. [ABSTRACT FROM AUTHOR]

The fully integrated complementary metal-oxide-semiconductor (CMOS) transmitter (Tx), an essential component in every wireless communication system, ensures highly efficient signal processing and data transmission. The performance of the Tx significantly affects the overall performance of the wireless device [1]. The demand for low-power, low-cost wireless devices operating at the 2.4-GHz band has led to extensive research on RF architecture and circuit design. A well-defined architecture, including linear and simple functional block synthesis, is crucial to the design of the RF Tx for various applications [2]. The state-of-the-art CMOS Txs proposed in the literature to improve Tx performance analyses are vital to understanding application-oriented RF Tx design techniques for further research. [ABSTRACT FROM AUTHOR]

Due to the rapid advancement of knowledge and technologies, the problem of decision making is getting more sophisticated to address, therefore the inventing of new methods to solve it is very important. One of the promising directions in machine learning and data mining is classifier combination. The popularity of this approach is confirmed by the still growing number of publications. This review paper focuses mainly on classifier combination known also as combined classifier, multiple classifier systems, or classifier ensemble. Eventually, recommendations and suggestions have also included. [ABSTRACT FROM AUTHOR]

Platoon based traffic flow models form the underlying theoretical framework in traffic simulation tools. They are essentially important in facilitating efficient performance calculation and evaluation in urban traffic networks. For this purpose, a new platoon-based macroscopic model called the LWR-IM has been developed in []. Preliminary analytical validation conducted previously has proven the feasibility of the model. In this paper, the LWR-IM is further enhanced with algorithms that describe platoon interactions in urban arterials. The LWR-IM and the proposed platoon interaction algorithms are implemented in the real-world class I and class II urban arterials. Another purpose of the work is to perform quantitative validation to investigate the validity and ability of the LWR-IM and its underlying algorithms to describe platoon interactions and simulate performance indices that closely resemble the real traffic situations. The quantitative validation of the LWR-IM is achieved by performing a two-sampled t-test on queues simulated by the LWR-IM and real queues observed at these real-world locations. The results reveal insignificant differences of simulated queues with real queues where the p-values produced concluded that the null hypothesis cannot be rejected. Thus, the quantitative validation further proved the validity of the LWR-IM and the embedded platoon interactions algorithm for the intended purpose. [ABSTRACT FROM AUTHOR]

Detectors play an important role in many communication systems such as delay locked loops, alias-locked loops [1], digital-microwave radio [2], clock-data recovery [3], [4], and phase-locked loops (PLLs) [5]. A phase frequency detector (PFD) is one of the mature detector circuit designs used to detect phase and frequency differences of input signals. Time-to-digital converter (TDC) blocks are a modern and emerging technique used to quantify these phase and frequency differences precisely. [ABSTRACT FROM PUBLISHER]