Number Of Data Samples Research Articles

Kualitas Jaringan Operator Selular Coverage Indoor dan Outdoor di Lingkungan Universitas Warmadewa

Telecommunications technology is growing rapidly, and telecommunications providers are now in the 5G technology phase. Providing very fast speed and data transfer. However, in the development of cellular technology in Indonesia, especially in Bali, the distribution of provider signal coverage areas is not evenly distributed. Although many BTS (Base Transceiver Station) have been scattered and built in the Denpasar city area, there are still locations that are not well covered. In this study the location chosen was the campus area at Warmadewa University. The method used was survey and sample interviews among students, lecturers, and staff. The number of data samples is 100 people. The survey results obtained are the findings of the signal coverage of Telkomsel, Indosat, XL-Axiata providers including coverage ranging from very poor to very good, while the Smartfren and 3 providers include good and very good signal coverage received in the Warmadewa University area. The use of phone calls (pulses) is rarely used because data packages are more flexible and efficient, the selection of calls (pulses) is usually used when urgent conditions.

Self-balancing Incremental Broad Learning System with privacy protection

Incremental learning algorithms have been developed as an efficient solution for fast remodeling in Broad Learning Systems (BLS) without a retraining process. Even though the structure and performance of broad learning are gradually showing superiority, private data leakage in broad learning systems is still a problem that needs to be solved. Recently, Multiparty Secure Broad Learning System (MSBLS) is proposed to allow two clients to participate training. However, privacy-preserving broad learning across multiple clients has received limited attention. In this paper, we propose a Self-Balancing Incremental Broad Learning System (SIBLS) with privacy protection by considering the effect of different data sample sizes from clients, which allows multiple clients to be involved in the incremental learning. Specifically, we design a client selection strategy to select two clients in each round by reducing the gap in the number of data samples in the incremental updating process. To ensure the security under the participation of multiple clients, we introduce a mediator in the data encryption and feature mapping process. Three classical datasets are used to validate the effectiveness of our proposed SIBLS, including MNIST, Fashion and NORB datasets. Experimental results show that our proposed SIBLS can have comparable performance with MSBLS while achieving better performance than federated learning in terms of accuracy and running time.

Conditioning of random Fourier feature matrices: double descent and generalization error

Abstract We provide high-probability bounds on the condition number of random feature matrices. In particular, we show that if the complexity ratio $N/m$, where $N$ is the number of neurons and $m$ is the number of data samples, scales like $\log ^{-1}(N)$ or $\log (m)$, then the random feature matrix is well-conditioned. This result holds without the need of regularization and relies on establishing various concentration bounds between dependent components of the random feature matrix. Additionally, we derive bounds on the restricted isometry constant of the random feature matrix. We also derive an upper bound for the risk associated with regression problems using a random feature matrix. This upper bound exhibits the double descent phenomenon and indicates that this is an effect of the double descent behaviour of the condition number. The risk bounds include the underparameterized setting using the least squares problem and the overparameterized setting where using either the minimum norm interpolation problem or a sparse regression problem. For the noiseless least squares or sparse regression cases, we show that the risk decreases as $m$ and $N$ increase. The risk bound matches the optimal scaling in the literature and the constants in our results are explicit and independent of the dimension of the data.

AI-empowered mobile edge computing: inducing balanced federated learning strategy over edge for balanced data and optimized computation cost

In Mobile Edge Computing, the framework of federated learning can enable collaborative learning models across edge nodes, without necessitating the direct exchange of data from edge nodes. It addresses significant challenges encompassing access rights, privacy, security, and the utilization of heterogeneous data sources over mobile edge computing. Edge devices generate and gather data, across the network, in non-IID (independent and identically distributed) manner leading to potential variations in the number of data samples among these edge networks. A method is proposed to work in federated learning under edge computing setting, which involves AI techniques such as data augmentation and class estimation and balancing during training process with minimized computational overhead. This is accomplished through the implementation of data augmentation techniques to refine data distribution. Additionally, we leveraged class estimation and employed linear regression for client-side model training. This strategic approach yields a reduction in computational costs. To validate the effectiveness of the proposed approach, it is applied to two distinct datasets. One dataset pertains to image data (FashionMNIST), while the other comprises numerical and textual data concerning stocks for predictive analysis of stock values. This approach demonstrates commendable performance across both dataset types and approaching more than 92% of accuracy in the paradigm of federated learning.

Evaluation Model of the Mental Health Education Effectiveness Based on Deep Neural Networks

This research develops a deep neural network model called DNN-MHE to evaluate mental health education effects. A questionnaire survey collected data on 916 students' mental health knowledge, attitudes, and behaviors. DNN-MHE uses five fully connected layers to predict mental health metrics. Experiments demonstrate that DNN-MHE achieves 99.46% accuracy, outperforming RNN, CNN, and shallow MLP models. Ablation studies validate the impact of training iterations, number of neurons, and number of data samples on performance. Overall, DNN-MHE enables accurate and efficient analysis of mental health education with practical implications for improving university programs.

A Cost-Aware Utility-Maximizing Bidding Strategy for Auction-Based Federated Learning.

Auction-based federated learning (AFL) has emerged as an efficient and fair approach to incentivize data owners (DOs) to contribute to federated model training, garnering extensive interest. However, the important problem of helping data consumers (DCs) bid for DOs in competitive AFL settings remains open. Existing work simply treats that the actual cost paid by a winning DC (i.e., the bid cost) is equal to the bid price offered by that DC itself. However, this assumption is inconsistent with the widely adopted generalized second-price (GSP) auction mechanism used in AFL, including in these existing works. Under a GSP auction, the winning DC does not pay its own proposed bid price. Instead, the bid cost for the winner is determined by the second-highest bid price among all participating DCs. To address this limitation, we propose a first-of-its-kind federated cost-aware bidding strategy () to help DCs maximize their utility under GSP auction-based federated learning (FL). It enables DCs to efficiently bid for DOs in competitive AFL markets, maximizing their utility and improving the resulting FL model accuracy. We first formulate the optimal bidding function under the GSP auction setting, and then demonstrate that it depends on utility estimation and market price modeling, which are interrelated. Based on this analysis, jointly optimizes in a novel end-to-end framework, and then executes the proposed return on investment (ROI)-based method to determine the optimal bid price for each piece of the data resource. Through extensive experiments on six commonly adopted benchmark datasets, we show that outperforms eight state-of-the-art methods, beating the best baseline by 4.39%, 4.56%, 1.33%, and 5.43% on average in terms of the total amount of data obtained, number of data samples per unit cost, total utility, and FL model accuracy, respectively.

Fingerprint Password Method Provides Improved Accuracy over Token-based Authentication for Efficient and Secure File Transfers

The goal of this study is to improve the accuracy of secure file transfer by proposing and evaluating a novel fingerprint password method in comparison to the existing token-based authentication method.In this study, a dataset of 20 samples was obtained from Kaggle, with 10 samples in each group (Group 1 and Group 2) to determine the accuracy of secure file transfer. The data was analyzed using G-power at 0.8, and with alpha and beta values set at 0.05 and 0.2, respectively. A 95% confidence interval was used. Both the novel Fingerprint password method and the Two factor authentication method were applied to the same number of data samples (N = 10) for secure file transaction, and it was found that the Fingerprint password method had a higher accuracy rate. The study found that the fingerprint password method had a higher accuracy rate than the token-based authentication method when securing file transactions. Specifically, the fingerprint password method had an accuracy rate of 95.66%, while the token-based method had an accuracy rate of 93.45%. Since, hence the p=0.001 (p<0.05), the study's results found that there is a statistically significant difference between two algorithms.: This study has shown that the fingerprint biometric password method is a viable and effective alternative to token-based authentication methods for secure file transactions. It provides a more secure and user-friendly solution, without the need for additional hardware.

Scalable variable selection for two-view learning tasks with projection operators

In this paper we propose a novel variable selection method for two-view settings, or for vector-valued supervised learning problems. Our framework is able to handle extremely large scale selection tasks, where number of data samples could be even millions. In a nutshell, our method performs variable selection by iteratively selecting variables that are highly correlated with the output variables, but which are not correlated with the previously chosen variables. To measure the correlation, our method uses the concept of projection operators and their algebra. With the projection operators the relationship, correlation, between sets of input and output variables can also be expressed by kernel functions, thus nonlinear correlation models can be exploited as well. We experimentally validate our approach, showing on both synthetic and real data its scalability and the relevance of the selected features.

Reduction in Data Imbalance for Client-Side Training in Federated Learning for the Prediction of Stock Market Prices

The approach of federated learning (FL) addresses significant challenges, including access rights, privacy, security, and the availability of diverse data. However, edge devices produce and collect data in a non-independent and identically distributed (non-IID) manner. Therefore, it is possible that the number of data samples may vary among the edge devices. This study elucidates an approach for implementing FL to achieve a balance between training accuracy and imbalanced data. This approach entails the implementation of data augmentation in data distribution by utilizing class estimation and by balancing on the client side during local training. Secondly, simple linear regression is utilized for model training at the client side to manage the optimal computation cost to achieve a reduction in computation cost. To validate the proposed approach, the technique was applied to a stock market dataset comprising stocks (AAL, ADBE, ASDK, and BSX) to predict the day-to-day values of stocks. The proposed approach has demonstrated favorable results, exhibiting a strong fit of 0.95 and above with a low error rate. The R-squared values, predominantly ranging from 0.97 to 0.98, indicate the model’s effectiveness in capturing variations in stock prices. Strong fits are observed within 75 to 80 iterations for stocks displaying consistently high R-squared values, signifying accuracy. On the 100th iteration, the declining MSE, MAE, and RMSE (AAL at 122.03, 4.89, 11.04, respectively; ADBE at 457.35, 17.79, and 21.38, respectively; ASDK at 182.78, 5.81, 13.51, respectively; and BSX at 34.50, 4.87, 5.87, respectively) values corroborated the positive results of the proposed approach with minimal data loss.

HIE-EDT: Hierarchical interval estimation-based evidential decision tree

Decision tree algorithm, because of its strong interpretability and high algorithm efficiency, is widely used in the field of pattern recognition and classification. When the number of data samples is small and there is uncertainty in the data, it is difficult for the traditional decision tree algorithm to fully mine the effective information in the data. In this paper, we use the Dempster–Shafer framework to model data uncertainty and propose a hierarchical interval estimation method to improve decision tree algorithms. The proposed method constructs intervals through two methods of attribute boundary and mean square error estimation, which not only utilizes the characteristics of intervals to model the inaccuracy of data, but also constrains intervals from two aspects, narrowing the representation range of available information. By comparing with the classic decision tree algorithm and the decision tree algorithm based on single interval estimation, the proposed method can perform classification tasks robustly and accurately in different types of data under seven data sets.

Vibration amplitude normalization enhanced fault diagnosis under conditions of variable speed and extremely limited samples

Intelligent fault diagnosis of rotating equipment is increasingly reliant on algorithms that are driven by big data. By contrast, signal processing was once widely utilized for fault diagnosis in machinery as a classical tool for signal analysis due to its capability to investigate the fault-related mechanism and almost no demand on the number of data samples. This investigation was motivated by the notion that signal processing and data-driven algorithms are combined to exploit their respective characteristics and strengths. Furthermore, in engineering practice, numerous complex factors such as time-variable operating conditions of equipment, non-stationary properties of signals, and extremely limited samples available for model training, can make it difficult to learn discriminative features from input data, thereby diminishing the diagnostic accuracy. In this paper, a novel framework of vibration amplitude normalization (VAN) enhanced fault diagnosis is proposed. Firstly, after dissects deeply the effects of the time-varying speed conditions on vibration signal and its characteristics, VAN technique is proposed for non-stationary signal processing to obtain the approximate stationary signal, so as to facilitate the subsequent state characteristics mining from the vibration signal. Then, two VAN enhanced fault diagnosis methods—i.e. signal amplitude normalization integrated with shallow learning by cascade and VAN integrated with deep learning by embedding—are developed to capture discriminative features from approximate stationary signal for fault diagnosis under conditions of variable speed and extremely limited samples. Finally, the feasibility and effectiveness of the proposed methods are verified using actual vibration datasets measured on test rig and in-site wind turbines. The number of samples required to achieve the same diagnostic accuracy is reduced by an average of 60%, demonstrating the superiority.

Robust and Fast Measure of Information via Low-Rank Representation

The matrix-based Rényi's entropy allows us to directly quantify information measures from given data, without explicit estimation of the underlying probability distribution. This intriguing property makes it widely applied in statistical inference and machine learning tasks. However, this information theoretical quantity is not robust against noise in the data, and is computationally prohibitive in large-scale applications. To address these issues, we propose a novel measure of information, termed low-rank matrix-based Rényi's entropy, based on low-rank representations of infinitely divisible kernel matrices. The proposed entropy functional inherits the specialty of of the original definition to directly quantify information from data, but enjoys additional advantages including robustness and effective calculation. Specifically, our low-rank variant is more sensitive to informative perturbations induced by changes in underlying distributions, while being insensitive to uninformative ones caused by noises. Moreover, low-rank Rényi's entropy can be efficiently approximated by random projection and Lanczos iteration techniques, reducing the overall complexity from O(n³) to O(n²s) or even O(ns²), where n is the number of data samples and s ≪ n. We conduct large-scale experiments to evaluate the effectiveness of this new information measure, demonstrating superior results compared to matrix-based Rényi's entropy in terms of both performance and computational efficiency.

Analysis and prediction of hematocrit in microvascular networks

Hematocrit, defined as the volume percentage of red blood cells in blood, is an important indicator of human health status because it involves the capability of blood to deliver oxygen. The present work aims to analyze and predict the hematocrit distribution in microvascular networks from the direct simulations of cellular blood flow. The networks are geometrically complex, composed of bifurcations, confluences and winding vessels, while the cells exhibit multiple dynamic behaviors, including deformation and aggregation. Hence, our simulations can not only capture the behaviors of each cell in a single vessel, but also characterize the hematocrit of the cell population in a complex network. The results showed that the hematocrit is spatially and temporally heterogeneous in the microvascular network, which is largely attributed to the existence of bifurcations. The more complex the network, the more pronounced the heterogeneity, and it varies steadily over time in wide vessels but periodically fluctuates in narrow vessels. Based on a certain number of data samples, we predicted the hematocrit distribution in a microvascular network, by using the deep learning technique, instead of carrying out the direct simulation. This provides a fast and efficient way to measure the hematocrit distribution in a microvascular network. After that, we examined the effects of other cell types on the hematocrit, which are quite different from the case with the red blood cells only, due to the vascular blockage caused by the cell clusters. In such a case, it becomes very difficult to predict the hematocrit distribution by the deep learning technique, and its heterogeneity becomes more obvious.

Imbalanced Data Classification Using SVM Based on Improved Simulated Annealing Featuring Synthetic Data Generation and Reduction

Imbalanced data classification is one of the major problems in machine learning. This imbalanced dataset typically has significant differences in the number of data samples between its classes. In most cases, the performance of the machine learning algorithm such as Support Vector Machine (SVM) is affected when dealing with an imbalanced dataset. The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples. In this paper, a hybrid approach combining data pre-processing technique and SVM algorithm based on improved Simulated Annealing (SA) was proposed. Firstly, the data pre-processing technique which primarily aims at solving the resampling strategy of handling imbalanced datasets was proposed. In this technique, the data were first synthetically generated to equalize the number of samples between classes and followed by a reduction step to remove redundancy and duplicated data. Next is the training of a balanced dataset using SVM. Since this algorithm requires an iterative process to search for the best penalty parameter during training, an improved SA algorithm was proposed for this task. In this proposed improvement, a new acceptance criterion for the solution to be accepted in the SA algorithm was introduced to enhance the accuracy of the optimization process. Experimental works based on ten publicly available imbalanced datasets have demonstrated higher accuracy in the classification tasks using the proposed approach in comparison with the conventional implementation of SVM. Registering at an average of 89.65% of accuracy for the binary class classification has demonstrated the good performance of the proposed works.

Semi-Private Computation of Data Similarity With Applications to Data Valuation and Pricing

Consider two data providers that want to contribute data to a certain learning model. Recent works have shown that the value of the data of one of the providers is dependent on the similarity with the data owned by the other provider. It would thus be beneficial if the two providers can calculate the similarity of their data, while keeping the actual data private. In this work, we devise multiparty computation-protocols to compute similarity of two data sets based on correlation, while offering controllable privacy guarantees. We consider a simple model with two participating providers and develop methods to compute exact and approximate correlation, respectively, with controlled information leakage. Both protocols have computational and communication complexities that are linear in the number of data samples. We also provide general bounds on the maximal error in the approximation case, and analyse the resulting errors for practical parameter choices.

An Efficient Novel Approach with Multi Class Label Classification through Machine Learning Models for Pancreatic Cancer

Pancreatic cancer is right now the fourth largest cause of cancer-related deaths. Early diagnosis is one good solution for pancreatic cancer patients and reduces the mortality rate. Accurate and earlier diagnosis of the pancreatic tumor is a demanding task due to several factors such as delayed diagnosis and absence of early warning symptoms. The conventional distributed machine learning techniques such as SVM and logistic regression were not efficient to minimize the error rate and improve the classification of pancreatic cancer with higher accuracy. Therefore, a novel technique called Distributed Hybrid Elitism gene Quadratic discriminant Reinforced Learning Classifier System (DHEGQDRLCS) is developed in this paper. First, the number of data samples is collected from the repository dataset. This repository contains all the necessary files for the identification of prognostic biomarkers for pancreatic cancer. After the data collection, the separation of training and testing samples is performed for the accurate classification of pancreatic cancer samples. Then the training samples are considered and applied to Distributed Hybrid Elitism gene Quadratic discriminant Reinforced Learning Classifier System. The proposed hybrid classifier system uses the Kernel Quadratic Discriminant Function to analyze the training samples. After that, the Elitism gradient gene optimization is applied for classifying the samples into multiple classes such as non-cancerous pancreas, benign hepatobiliary disease i.e., pancreatic cancer, and Pancreatic ductal adenocarcinoma. Then the Reinforced Learning technique is applied to minimize the loss function based on target classification results and predicted classification results. Finally, the hybridized approach improves pancreatic cancer diagnosing accuracy. Experimental evaluation is carried out with pancreatic cancer dataset with Hadoop distributed system and different quantitative metrics such as Accuracy, balanced accuracy, F1-score, precision, recall, specificity, TN, TP, FN, FP, ROC_AUC, PRC_AUC, and PRC_APS. The performance analysis results indicate that the DHEGQDRLCS provides better diagnosing accuracy when compared to existing methods.

Penerapan Algoritma K-Means Clustering Untuk Menentukan Kelas Kelompok Bimbingan Belajar Tambahan

Additional learning is a learning program that is carried out outside the school's intracurricular program. This means that additional learning activities are carried out after the regular learning program at school has ended The learning mentor comes from the subject teacher at the school concerned. It is commonly called the afternoon additional learning program. The afternoon additional learning program has its own planning schedule. Additional learning is carried out by students to improve their understanding and deepening of subject material. This goal is related to the preparation of a student to face exams at school, both midterm exams, semester final exams and national final exams. In determining the class of this additional study group using the K-Means Clustering algorithm, the number of data samples will be used is 26 students majoring in science.

The Influence of the Independence of the Board of Commissioners, Audit Quality, and Company Size on Banking Profitability

<p><em>The purpose of this study was to determine the effect of the independence of the board of commissioners, audit quality, and firm size on banking profitability both partially and simultaneously. The population of this study is all banks listed on the Indonesia Stock Exchange in 2019-2021. The technique for selecting the sample uses purposive sampling and the number of data samples is 39 data. This study uses secondary data in the form of financial statements of banking companies obtained from the publications of the Indonesia Stock Exchange. The results of this study indicate that audit quality has a significant effect on the direction of a positive relationship to profitability. The independence of the board of commissioners and the size of the company have no significant effect on the direction of the negative relationship to profitability.</em></p>

Cross-validation of correlation networks using modular structure

Correlation networks derived from multivariate data appear in many applications across the sciences. These networks are usually dense and require sparsification to detect meaningful structure. However, current methods for sparsifying correlation networks struggle with balancing overfitting and underfitting. We propose a module-based cross-validation procedure to threshold these networks, making modular structure an integral part of the thresholding. We illustrate our approach using synthetic and real data and find that its ability to recover a planted partition has a step-like dependence on the number of data samples. The reward for sampling more varies non-linearly with the number of samples, with minimal gains after a critical point. A comparison with the well-established WGCNA method shows that our approach allows for revealing more modular structure in the data used here.

Effective Communication Between Differently Abled And Normal People Using Speech To Sign Translation System

There are very less way of communicating between a differently abled person i.e. deaf and dumb also called as hearing impaired and normal people. Communications from deaf and dumb to normal people are done in many ways but from normal people to hearing impaired people have not been much. What we say cannot be understood by the deaf and dumb people so a system should be made so that a deaf and dumb person can understand the voice of normal people. The voice will be converted to sign language so that deaf and dumb people can easily understand. Machine learning techniques will be used to implement this with the help of Google API for voice recognition and then the Natural Language Processing is used. The voice will be broken into sentences or words and then the context of natural language processing will come into picture. The text will be compared with the gifs generated, if found the gifs will be shown as output or else letter by letter mapping will be done so the hearing impaired people can understand the voice through sign language. This is very important for the hearing impaired people. This helps hearing impaired people a lot because they can easily understand what the normal people are telling and for their daily life it will be very helpful. The system is tested with the number of data samples. As data samples increases, the accuracy of the model is said to have gone up as well.