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A novel method has been proposed to reduce the false alarm rate of arrhythmia patients regarding life-threatening conditions in the intensive care unit. In this purpose, the atrial blood pressure, photoplethysmogram (PLETH), electrocardiogram (ECG) and respiratory (RESP) signals are considered as input signals.

Three machine learning approaches feed-forward artificial neural network (ANN), ensemble learning method and k-nearest neighbors searching methods are used to detect the false alarm. The proposed method has been implemented using Arduino and MATLAB/SIMULINK for real-time ICU-arrhythmia patients' monitoring data.

The proposed method detects the false alarm with an accuracy of 99.4 per cent during asystole, 100 per cent during ventricular flutter, 98.5 per cent during ventricular tachycardia, 99.6 per cent during bradycardia and 100 per cent during tachycardia. The proposed framework is adaptive in many scenarios, easy to implement, computationally friendly and highly accurate and robust with overfitting issue.

As ECG signals consisting with PQRST wave, any deviation from the normal pattern may signify some alarming conditions. These deviations can be utilized as input to classifiers for the detection of false alarms; hence, there is no need for other feature extraction techniques. Feed-forward ANN with the Lavenberg–Marquardt algorithm has shown higher rate of convergence than other neural network algorithms which helps provide better accuracy with no overfitting.

The aims of this research include (1) to identify the scores of reading literacy in 34 provinces and (2) to determine the impact of ICT literacy with other variables on reading literacy in Indonesia.

Thematic maps and Spatial Autoregressive Regression were applied to 2019 AKSI Survey data.

The results showed that only D.I. Yogyakarta, DKI Jakarta and Kepulauan Riau have a high percentage of reading literacy scores in the excellent category. The ICT literacy and teachers' competency scores significantly affect the percentage of reading literacy. Meanwhile, the percentage of lack of learning materials and GRDP per capita has no significant effect.

Previously, the national exam has been used to determine the quality of education in Indonesia, but it is ineffective because it only measures cognitive aspects. In 2015, the Ministry of Education initiated the AKSI survey, which measures cognitive (reading, math and science literacy) and non-cognitive aspects, as an effort to improve the quality of education in Indonesia. Some literature states that reading literacy is the most basic indicator for determining the quality of education, but in Indonesia, it is the lowest achievement. To improve reading literacy scores, the government has to utilize technological advances through School Digitization. However, this should be supported by the ICT literacy of students. Presently, there is no study to evaluate the impact of ICT literacy on reading literacy, which is also affected by regional value differences.

With so many placement opportunities around the students in their final or prefinal year, they start to feel the strain of the season. The students feel the need to be aware of their position and how to increase their chances of being hired. Hence, a system to guide their career is one of the needs of the day.

The job role prediction system utilizes machine learning techniques such as Naïve Bayes, K-Nearest Neighbor, Support Vector machines (SVM) and Artificial Neural Networks (ANN) to suggest a student’s job role based on their academic performance and course outcomes (CO), out of which ANN performs better. The system uses the Mepco Schlenk Engineering College curriculum, placement and students’ Assessment data sets, in which the CO and syllabus are used to determine the skills that the student has gained from their courses. The necessary skills for a job position are then extracted from the job advertisements. The system compares the student’s skills with the required skills for the job role based on the placement prediction result.

The system predicts placement possibilities with an accuracy of 93.33 and 98% precision. Also, the skill analysis for students gives the students information about their skill-set strengths and weaknesses.

For skill-set analysis, only the direct assessment of the students is considered. Indirect assessment shall also be considered for future scope.

The model is adaptable and flexible (customizable) to any type of academic institute or universities.

The research will be very much useful for the students community to bridge the gap between the academic and industrial needs.

Several works are done for career guidance for the students. However, these career guidance methodologies are designed only using the curriculum and students’ basic personal information. The proposed system will consider the students’ academic performance through direct assessment, along with their curriculum and basic personal information.

Algorithm bias refers to repetitive computer program errors that give some users more weight than others. The aim of this article is to provide a deeper insight of algorithm bias in AI-enabled ERP software customization. Although algorithmic bias in machine learning models has uneven, unfair and unjust impacts, research on it is mostly anecdotal and scattered.

As guided by the previous research (Akter et al., 2022), this study presents the possible design bias (model, data and method) one may experience with enterprise resource planning (ERP) software customization algorithm. This study then presents the artificial intelligence (AI) version of ERP customization algorithm using k-nearest neighbours algorithm.

This study illustrates the possible bias when the prioritized requirements customization estimation (PRCE) algorithm available in the ERP literature is executed without any AI. Then, the authors present their newly developed AI version of the PRCE algorithm that uses ML techniques. The authors then discuss its adjoining algorithmic bias with an illustration. Further, the authors also draw a roadmap for managing algorithmic bias during ERP customization in practice.

To the best of the authors’ knowledge, no prior research has attempted to understand the algorithmic bias that occurs during the execution of the ERP customization algorithm (with or without AI).

Climate change is a problem that concerns all of us. Despite the information produced by organizations such as the Expert Team on Climate Change Detection and Indices and the United Nations, only a few cities have been planned taking into account the climate changes indices. This paper aims to study climatic variations, how climate conditions might change in the future and how these changes will affect the activities and living conditions in cities, specifically focusing on Mexico city.

In this approach, two distinct machine learning regression models, k-Nearest Neighbors and Support Vector Regression, were used to predict variations in climate change indices within select urban areas of Mexico city. The calculated indices are based on maximum, minimum and average temperature data collected from the National Water Commission in Mexico and the Scientific Research Center of Ensenada. The methodology involves pre-processing temperature data to create a training data set for regression algorithms. It then computes predictions for each temperature parameter and ultimately assesses the performance of these algorithms based on precision metrics scores.

This paper combines a geospatial perspective with computational tools and machine learning algorithms. Among the two regression algorithms used, it was observed that k-Nearest Neighbors produced superior results, achieving an R² score of 0.99, in contrast to Support Vector Regression, which yielded an R² score of 0.74.

The full potential of machine learning algorithms has not been fully harnessed for predicting climate indices. This paper also identifies the strengths and weaknesses of each algorithm and how the generated estimations can then be considered in the decision-making process.

The online health community's success depends on doctors' active participation, so it is essential to understand the factors that affect doctors' knowledge contribution behavior in the online health communities. From the perspective of peer effect, this paper discusses the influence of focal doctors' peers on focal doctors' knowledge contribution behavior and the mechanism behind it. This paper aims to solve these problems.

Empirical data of 1,938 doctors were collected from a Chinese online health community, and propensity score matching and ordinary least squares were employed to verify the proposed theoretical model.

The results show that the presence of focal doctors' peers in online health communities has a positive effect on the knowledge contribution behavior of focal doctors, and the economic returns and social returns of focal doctors' peers have a significant mediating effect.

This paper discusses focal doctors' knowledge contribution behavior from the perspective of peer effect. It enhances the understanding of focal doctors' behavior in the online health communities by exploring the mediating role of their peers' economic and social returns. The results of this paper extend the research in the field of peer effect and online health and provide management implications and suggestions for online health platforms and doctors.

The ionization of the air surrounding the phase conductor in high-voltage transmission lines results in a phenomenon known as the Corona effect. To avoid this, Corona rings are used to dampen the electric field imposed on the insulator. The purpose of this study is to present a fast and intelligent surrogate model for determination of the electric field imposed on the surface of a 120 kV composite insulator, in presence of the Corona ring.

Usually, the structural design parameters of the Corona ring are selected through an optimization procedure combined with some numerical simulations such as finite element method (FEM). These methods are slow and computationally expensive and thus, extremely reducing the speed of optimization problems. In this paper, a novel surrogate model was proposed that could calculate the maximum electric field imposed on a ceramic insulator in a 120 kV line. The surrogate model was created based on the different scenarios of height, radius and inner radius of the Corona ring, as the inputs of the model, while the maximum electric field on the body of the insulator was considered as the output.

The proposed model was based on artificial intelligence techniques that have high accuracy and low computational time. Three methods were used here to develop the AI-based surrogate model, namely, Cascade forward neural network (CFNN), support vector regression and K-nearest neighbors regression. The results indicated that the CFNN has the highest accuracy among these methods with 99.81% R-squared and only 0.045468 root mean squared error while the testing time is less than 10 ms.

To the best of the authors’ knowledge, for the first time, a surrogate method is proposed for the prediction of the maximum electric field imposed on the high voltage insulators in the presence Corona ring which is faster than any conventional finite element method.

The purpose of this paper is geared towards the capture and analysis of network traffic using an array ofmachine learning (ML) and deep learning (DL) techniques to classify network traffic into different classes and predict network traffic parameters.

The classifier models include k-nearest neighbour (KNN), multilayer perceptron (MLP) and support vector machine (SVM), while the regression models studied are multiple linear regression (MLR) as well as MLP. The analytics were performed on both a local server and a servlet hosted on the international business machines cloud. Moreover, the local server could aggregate data from multiple devices on the network and perform collaborative ML to predict network parameters. With optimised hyperparameters, analytical models were incorporated in the cloud hosted Java servlets that operate on a client–server basis where the back-end communicates with Cloudant databases.

Regarding classification, it was found that KNN performs significantly better than MLP and SVM with a comparative precision gain of approximately 7%, when classifying both Wi-Fi and long term evolution (LTE) traffic.

Collaborative regression models using traffic collected from two devices were experimented and resulted in an increased average accuracy of 0.50% for all variables, with a multivariate MLP model.

The purpose of optimal protocol prediction and the benefits offered by quantum key distribution (QKD), including unbreakable security, there is a growing interest in the practical realization of quantum communication. Realization of the optimal protocol predictor in quantum key distribution is a critical step toward commercialization of QKD.

The proposed work designs a machine learning model such as K-nearest neighbor algorithm, convolutional neural networks, decision tree (DT), support vector machine and random forest (RF) for optimal protocol selector for quantum key distribution network (QKDN).

Because of the effectiveness of machine learning methods in predicting effective solutions using data, these models will be the best optimal protocol selectors for achieving high efficiency for QKDN. The results show that the best machine learning method for predicting optimal protocol in QKD is the RF algorithm. It also validates the effectiveness of machine learning in optimal protocol selection.

The proposed work was done using algorithms like the local search algorithm or exhaustive traversal, however the major downside of using these algorithms is that it takes a very long time to revert back results, which is unacceptable for commercial systems. Hence, machine learning methods are proposed to see the effectiveness of prediction for achieving high efficiency.

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.