Affect Algorithm Performance Research Articles

Optimasi Algoritma Machine Learning Menggunakan Teknik Bagging Pada Klasifikasi Diagnosis Kanker Payudara

Classification algorithms have a very important role in Machine Learning, but not all algorithms have the same performance in every case. Algorithm performance can be affected by the type of data used, differences in problem characteristics, and the parameters used. Additionally, ensemble learning techniques such as Bagging can affect algorithm performance. Therefore, the problem arises of how to choose the most suitable algorithm for a particular classification task and how to optimize the performance of the algorithm. This research aims to carry out a comparative analysis and optimization of classification algorithms in Machine Learning. Classification algorithms that will be evaluated include Support Vector Machine (SVM), Neural Network, Logistic Regression, Decision Tree, and K-Nearest Neighbors (K-NN). Evaluation of the performance of these algorithms will be carried out using the confusion matrix, Receiver Operating Characteristic (ROC) Curve, and Area Under Curva (AUC). The result of this research is a comparative analysis of the optimization of classification algorithms using the bagging technique. After carrying out the evaluation process using the confusion matrix and ROC curve, it was found that the algorithm optimization using the bagging technique only had an effect on the Decision Tree (DT) and K-Nearest Neighbors (KNN) algorithms. . The accuracy of the DT algorithm increased by 0.6% while the accuracy of KNN increased by 1.3%. The AUC value for the DT algorithm increased by 1.4% and the KNN algorithm increased by 0.3%.

Delving into Machine Learning's Influence on Disease Diagnosis and Prediction

Introduction/ Background Medical diagnoses have increasingly depended on digitized images obtained through cutting-edge technology. These algorithms offer a promising avenue to transform diagnostic processes in healthcare, with their application scope continually widening due to ongoing advancements. This paper explores machine learning's role in clinical analysis and prediction, examining various studies that apply these techniques in clinical diagnosis, focusing on their use in analyzing images and providing accurate diagnoses. Materials and Methods This study employs a comparative analysis approach, utilizing diverse machine learning algorithms like SVM, K-nearest neighbors, Random Forests, and Decision Trees to analyze digitized medical images and patient records. We extracted data from several medical databases, ensuring a varied and comprehensive dataset. We also evaluated the impact of different data characteristics on the algorithms' effectiveness, aiming to understand the variability in their diagnostic precision across various conditions. Results The results indicate that machine learning algorithms, particularly SVM, K-nearest neighbors, Random Forests, and Decision Trees, demonstrate significant accuracy in diagnosing diseases from digitized images and medical records. SVM and Random Forests showed particularly high effectiveness in clinical diagnosis, suggesting their robustness across different medical conditions and datasets. These findings underscore the potential of machine learning to enhance diagnostic precision and predict illnesses early, aligning with the growing trend of technology-driven medical diagnostics. Discussion The findings reinforce the pivotal role of machine learning in transforming medical diagnostics. The variability in algorithm performance highlights the necessity for tailored approaches, considering dataset specifics and the medical condition being diagnosed. This study underscores the potential of machine learning to enhance diagnostic accuracy, yet it also emphasizes the need for continuous refinement and understanding of the underlying factors affecting algorithm performance. Future research should focus on optimizing these algorithms within diverse clinical settings to fully harness their diagnostic capabilities. Conclusion This study highlights the transformative potential of machine learning in medical diagnostics, demonstrating how various algorithms can effectively analyze digitized images and patient records to diagnose diseases. While the performance of these algorithms varies based on dataset characteristics, the overall high accuracy underscores machine learning's promise in healthcare. As the field continues to evolve, machine learning is poised to become an integral part of clinical diagnosis, enhancing the accuracy and efficiency of medical evaluations and treatments.

Block-MUSIC in blade tip timing: Performance study of block snapshot matrix

Periodic nonuniform sampling can be viewed as a combination of uniform sampling and non-uniform sampling. Blade tip timing (BTT) technique conforms to this sampling pattern, where non-uniformity and uniformity are caused by non-uniform probe layout and rotational motion, respectively. By introducing block structure into multiple signal classification (MUSIC) for the extension of snapshot matrix, Block-MUSIC has been proved to be anti-aliasing and can filter out synchronous frequency in BTT application. However, the theoretical justification of the block structure is lacking. In this paper, a performance study of Block-MUSIC is conducted from three aspects: identifiability, stability, and resolution. Additionally, a pseudo 2-dimensional signal model is proposed to represent the periodic nonuniform sampled signal under the assumption of equally spaced probes. To support the performance study, simulations and experiments are carried out to demonstrate how the size of the snapshot matrix, the noise level, and the amplitude of sinusoidal signals affect algorithm performance. Simulation codes are available at https://github.com/superjdg/block-music4btt.

Research on Underwater Image Restoration Technology Based on Multi-Domain Translation

Underwater images are crucial in various underwater applications, including marine engineering, underwater robotics, and subsea coral farming. However, obtaining paired data for these images is challenging due to factors such as light absorption and scattering, suspended particles in the water, and camera angles. Underwater image recovery algorithms typically use real unpaired dataset or synthetic paired dataset. However, they often encounter image quality issues and noise labeling problems that can affect algorithm performance. To address these challenges and further improve the quality of underwater image restoration, this work proposes a multi-domain translation method based on domain partitioning. Firstly, this paper proposes an improved confidence estimation algorithm, which uses the number of times a sample is correctly predicted in a continuous period as a confidence estimate. The confidence value estimates are sorted and compared with the real probability to continuously optimize the confidence estimation and improve the classification performance of the algorithm. Secondly, a U-net structure is used to construct the underwater image restoration network, which can learn the relationship between the two domains. The discriminator uses full convolution to improve the performance of the discriminator by outputting the true and false images along with the category to which the true image belongs. Finally, the improved confidence estimation algorithm is combined with the discriminator in the image restoration network to invert the labels for images with low confidence values in the clean domain as images in the degraded domain. The next step of image restoration is then performed based on the new dataset that is divided. In this way, the multi-domain conversion of underwater images is achieved, which helps in the recovery of underwater images. Experimental results show that the proposed method effectively improves the quality and quantity of the images.

Practical Network Conditions for the Convergence of Distributed Optimization

The decentralized nature of multi-agent learning often requires continuous information exchange over a (wireless) communication network, in order to accomplish common global objectives. Uncertainty and delay in communication induce large Age of Information (AoI) for data available at the agents, possibly affecting algorithm performance. In order to understand this, one needs communication models that are representative of practical wireless networks. In this paper, we present a representative model based on the Signal-to-Interference-plus-Noise Ratio (SINR) between pairs of agents. Further, we present a novel medium access control (MAC) protocol that is sensitive to local AoI. Our SINR model facilitates the representation of practical dependency effects like shadowing, fading, interference and external noise. The model is driven by an underlying geometrically ergodic Markov chain, which can represent agent mobility. Our MAC protocol enables that the aforementioned dependency effects decay over time. With this dependency decay, we then control the asymptotic growth of the AoI, to facilitate the convergence of distributed algorithms. Finally, we illustrate our ideas by analyzing the distributed stochastic gradient descent scheme that uses delayed communicated data.

Subspace Based Adaptive Beamforming Algorithm with Interference Plus Noise Covariance Matrix Reconstruction

As we all know, the model mismatch, primarily when the desired signal exists in the training data, or when the sample data is used for training, will seriously affect algorithm performance. This paper combines the subspace algorithm based on direction of arrival (DOA) estimation with the adaptive beamforming. It proposes a reconstruction algorithm based on the interference plus noise covariance matrix (INCM). Firstly, the eigenvector of the desired signal is obtained according to the eigenvalue decomposition of the subspace algorithm, and the eigenvector is used as the estimated value of the desired signal steering vector (SV). Then the INCM is reconstructed according to the estimated parameters to remove the adverse effect of the desired signal component on the beamformer. Finally, the estimated desired signal SV and the reconstructed INCM are used to calculate the weight. Compared with the previous work, the proposed algorithm not only improves the performance of the adaptive beamformer but also dramatically reduces the complexity. Simulation experiment results show the effectiveness and robustness of the proposed beamforming algorithm.

Performance Assessment of Metaheuristic Algorithms for Structural Optimization Taking Into Account the Influence of Algorithmic Control Parameters

Metaheuristic optimization algorithms are strongly present in the literature on discrete optimization. They typically 1) use stochastic operators, making each run unique, and 2) often have algorithmic control parameters that have an unpredictable impact on convergence. Although both 1) and 2) affect algorithm performance, the effect of the control parameters is mostly disregarded in the literature on structural optimization, making it difficult to formulate general conclusions. In this article, a new method is presented to assess the performance of a metaheuristic algorithm in relation to its control parameter values. A Monte Carlo simulation is conducted in which several independent runs of the algorithm are performed with random control parameter values. In each run, a measure of performance is recorded. The resulting dataset is limited to the runs that performed best. The frequency of each parameter value occurring in this subset reveals which values are responsible for good performance. Importance sampling techniques are used to ensure that inferences from the simulation are sufficiently accurate. The new performance assessment method is demonstrated for the genetic algorithm in matlab R2018b, applied to seven common structural optimization test problems, where it successfully detects unimportant parameters (for the problems at hand) while identifying well-performing values for the important parameters. For two of the test problems, a better solution is found than the best solution reported so far in the literature.

Research Trend of Causal Machine Learning Method: A Literature Review

Machine learning is commonly used to predict and implement pattern recognition and the relationship between variables. Causal machine learning combines approaches for analyzing the causal impact of intervention on the result, asumming a considerably ambigous variables. The combination technique of causality and machine learning is adequate for predicting and understanding the cause and effect of the results. The aim of this study is a systematic review to identify which causal machine learning approaches are generally used. This paper focuses on what data characteristics are applied to causal machine learning research and how to assess the output of algorithms used in the context of causal machine learning research. The review paper analyzes 20 papers with various approaches. This study categorizes data characteristics based on the type of data, attribute value, and the data dimension. The Bayesian Network (BN) commonly used in the context of causality. Meanwhile, the propensity score is the most extensively used in causality research. The variable value will affect algorithm performance. This review can be as a guide in the selection of a causal machine learning system.

Do Existing Multiobjective Evolutionary Algorithms Use a Sufficient Number of Operators? An Empirical Investigation for Water Distribution Design Problems

AbstractMultiobjective evolutionary algorithms (MOEAs) have been used extensively to solve water resources problems. Their success is dependent on how well the operators that control an algorithm's search behavior are able to identify near‐optimal solutions. As commonly used MOEAs contain a relatively small number of operators (generally between 2 and 7), this study investigates whether the performance of MOEAs could potentially be improved by increasing their operator set size. This is done via a series of controlled computational experiments isolating the influence of the size of the operator set (i.e., how many operators are used, ranging from 2 to 12), the composition of the operator set (i.e., which operators are used, given a set number of operators), the search strategy used (e.g., parent selection and survivor selection), and increasing the operator set size of an existing MOEA. These experiments are performed on six benchmark water distribution optimization problems. Results of the 3,150 optimization runs indicate that operator set size is the dominant factor affecting algorithm performance, having a significantly greater influence than operator set composition and other factors affecting algorithm search behavior. In addition, increasing the operator set size of the state‐of‐the‐art MOEA GALAXY, which has been designed specifically for solving water distribution optimization problems, from its currently used value of 6 to 12 increased its performance significantly. These results suggest there is value in investigating the potential of increasing operator set size for a range of algorithms and problem types.

Predictive maintenance using cox proportional hazard deep learning

Predictive maintenance (PdM) has become prevalent in the industry in order to reduce maintenance cost and to achieve sustainable operational management. The core of PdM is to predict the next failure so corresponding maintenance can be scheduled before it happens. The purpose of this study is to establish a Time-Between-Failure (TBF) prediction model through a data-driven approach. For PdM, data sparsity is regarded as a critical issue which can jeopardize algorithm performance for the modelling based on maintenance data. Meanwhile, data censoring has imposed another challenge for handling maintenance data because the censored data is only partially labelled. Furthermore, data sparsity may affect algorithm performance of existing approaches when addressing the data censoring issue. In this study, a new approach called Cox proportional hazard deep learning (CoxPHDL) is proposed to tackle the aforementioned issues of data sparsity and data censoring that are common in the analysis of operational maintenance data. The idea is to offer an integrated solution by taking advantage of deep learning and reliability analysis. To start with, an autoencoder is adopted to convert the nominal data into a robust representation. Secondly, a Cox proportional hazard model (Cox PHM) is researched to estimate the TBF of the censored data. A long-short-term memory (LSTM) network is then established to train the TBF prediction model based on the pre-processed maintenance data. Experimental studies using a sizable real-world fleet maintenance data set provided by a UK fleet company have demonstrated the merits of the proposed approach where the algorithm performance based on the proposed LSTM network has been improved respectively in terms of MCC and RMSE.

Symmetry Detection for Quadratic Optimization Using Binary Layered Graphs

Symmetry in mathematical optimization may create multiple, equivalent solutions. In nonconvex optimization, symmetry can negatively affect algorithm performance, e.g., of branch-and-bound when symmetry induces many equivalent branches. This paper develops detection methods for symmetry groups in quadratically-constrained quadratic optimization problems. Representing the optimization problem with adjacency matrices, we use graph theory to transform the adjacency matrices into binary layered graphs. We enter the binary layered graphs into the software package nauty that generates important symmetric properties of the original problem. Symmetry pattern knowledge motivates a discretization pattern that we use to reduce computation time for an approximation of the point packing problem. This paper highlights the importance of detecting and classifying symmetry and shows that knowledge of this symmetry enables quick approximation of a highly symmetric optimization problem.

Model-based XML to Relational Database Mapping Choices

Extensible Markup Language (XML) technology is widely used for data exchange and data representation in both online and offline mode. This structured format language able to be transformed into other formats and share information across platforms. XML is simple; however, it is designed to accommodate changes. For this paper, a study on transformation of XML document into relational database is conducted. Crucial part of this process is how to maintain the hierarchy and relationships between data in the document into database. Approaches that are discussed in this paper each uses own unique way of data storing technique and database design. Therefore, each algorithm is assessed with three datasets constitute of small, medium and large size XML file. The efficiency of the algorithms is being tested on time taken for data storing and query execution process. At the end of the evaluation, we discuss factors that affect algorithm performance and present suggestions to improve mapping scheme for future works

Kinetic energy choice in Hamiltonian/hybrid Monte Carlo

We consider how different choices of kinetic energy in Hamiltonian Monte Carlo affect algorithm performance. To this end, we introduce two quantities which can be easily evaluated, the composite gradient and the implicit noise. Results are established on integrator stability and geometric convergence, and we show that choices of kinetic energy that result in heavy-tailed momentum distributions can exhibit an undesirable negligible moves property, which we define. A general efficiency-robustness trade off is outlined, and implementations which rely on approximate gradients are also discussed. Two numerical studies illustrate our theoretical findings, showing that the standard choice which results in a Gaussian momentum distribution is not always optimal in terms of either robustness or efficiency.

Comprehensive Review of the Development of the Harmony Search Algorithm and its Applications

This paper presents a comprehensive overview of the development of the harmony search (HS) algorithm and its applications. HS is a well-known human-based meta-heuristic algorithm that mimics the process of creating a new harmony in music. This algorithm can be applied to different fields of research, owing to its ability to balance between exploitation (i.e., searching around the known best) and exploration (i.e., roaming the entire search space). Thus, numerous studies have been conducted to utilize HS in real-world optimization problems, and many variants and hybrid algorithms of HS have been developed to cope with different problems. In this paper, HS and its variants are reviewed from various aspects. First, we describe the HS algorithm and present how its parameters affect algorithm performance. Second, we describe HS classifications based on the well-known HS variants and hybrid algorithms, along with their applications. Finally, a discussion conducted on the strengths and weaknesses of the HS algorithm and the possibilities for its improvement. Focusing on related work from diverse fields (such as optimization, engineering, computer science, biology, and medicine), this paper can foster interests on the application of HS for multidisciplinary audiences.

A neural network filtering approach for similarity-based remaining useful life estimation

The role of prognostics and health management is ever more prevalent with advanced techniques of estimation methods. However, data processing and remaining useful life prediction algorithms are often very different. Some difficulties in accurate prediction can be tackled by redefining raw data parameters into more meaningful and comprehensive health level indicators that will then provide performance information. Proper data processing has a significant importance on remaining useful life predictions, for example, to deal with data limitations or/and multi-regime operating conditions. The framework proposed in this paper considers a similarity-based prognostic algorithm that is fed by the use of data normalisation and filtering methods for operational trajectories of complex systems. This is combined with a data-driven prognostic technique based on feed-forward neural networks with multi-regime normalisation. In particular, the paper takes a close look at how pre-processing methods affect algorithm performance. The work presented herein shows a conceptual prognostic framework that overcomes challenges presented by short-term test datasets and that increases the prediction performance with regards to prognostic metrics.

Real-time haptic interaction with RGBD video streams

Video interaction is a common way of communication in cyberspace. It can become more immersive by incorporating haptic modality. Using commonly available depth sensing controllers like Microsoft Kinect, information about the depth of a scene can be captured in real-time together with the video. In this paper, we present a method for real-time haptic interaction with videos containing depth data. Forces are computed based on the depth information. Spatial and temporal filtering of the depth stream is used to provide stability of force feedback delivered to the haptic device. Fast collision detection ensures the proposed approach to be used in real-time. We present an analysis of various factors that affect algorithm performance. The usefulness of the approach is illustrated by highlighting possible application scenarios.

Review on Automatic Pavement Crack Image Recognition Algorithms

Automatic pavement cracking detection is of great practical value for pavement maintenance, pavement performance evaluation and prediction, and material and structure design. However, detecting pavement cracks rapidly, precisely, completely, and robustly remains a challenge. Thus, literature review on automatic pavement track detection was conducted, which included pre - processing methods aiming at image enhancement and de - noising, space - domain recognition algorithms based on thresholding, edge detection and seed growing, frequency - domain recognition algorithms, such as wavelet transform, and supervised learning methods. Shortcomings of these crack detection algorithms were summarized as follow: (1) illumination and oils tend to affect algorithm performance; (2) crack maps have poor continuity; (3) processing speed and recognition precision are not satisfying. Research prospects were also proposed as references to improve crack recognition algorithms, including (1) removing influences of texture and noises by combining boundary and area features, (2) designing optimization - based - recognition algorithms that consider local and global features, and (3) detecting pavement cracks basted on three dimensional (3D) images.

A Time Variant Outdoor-to-Indoor Channel Model for Mobile Radio Based Navigation Applications

Time based positioning with terrestrial mobile radio signals has gained remarkable attention. To develop and validate positioning algorithms under realistic conditions, an accurate knowledge of the propagation channel is significant. However, there is still a lack of outdoor-to-indoor channel models suitable for positioning applications. To be applicable for positioning, the channel model has to fulfill three requirements that have not been accurately considered so far: the non-line-of-sight bias (affecting ranging accuracy), nondiscrete valued channel parameters (affecting algorithm performance), and the evolution of individual multipath components (MPCs) with time (affecting tracking performance). In this paper, an outdoor-to-indoor channel model is proposed based on an extension of the geometry-based stochastic modeling approach to fulfill the requirements. We consider MPCs occurring due to reflection, scattering, and combinations of both. In the model, three different types of MPCs are modeled separately according to their characteristics. Each MPC is represented by a fixed scatterer, which has a fixed position while the receiver antenna is moving. The parameters of the outdoor-to-indoor channel model are extracted from two channel measurement campaigns. The proposed outdoor-to-indoor channel model is capable of accurately simulating the time variant channel. A comparison of the channel model with the channel measurement data is performed by comparing statistics.

Continuous Dynamic Constrained Optimization—The Challenges

Many real-world dynamic problems have constraints, and in certain cases not only the objective function changes over time, but also the constraints. However, there is no research in answering the question of whether current algorithms work well on continuous dynamic constrained optimization problems (DCOPs), nor is there any benchmark problem that reflects the common characteristics of continuous DCOPs. This paper contributes to the task of closing this gap. We will present some investigations on the characteristics that might make DCOPs difficult to solve by some existing dynamic optimization (DO) and constraint handling (CH) algorithms. We will then introduce a set of benchmark problems with these characteristics and test several representative DO and CH strategies on these problems. The results confirm that DCOPs do have special characteristics that can significantly affect algorithm performance. The results also reveal some interesting observations where the presence or combination of different types of dynamics and constraints can make the problems easier to solve for certain types of algorithms. Based on the analyses of the results, a list of potential requirements that an algorithm should meet to solve DCOPs effectively will be proposed.

Effects of data set features on the performances of classification algorithms

As the need to analyze big data sets grows dramatically, the role that classification algorithms play in data mining techniques also increases. Big data analysis requires more of the data sets’ characteristics to be included, such as data structure, variety of sources, and the rate of update frequency. In this paper, we evaluate scenarios that examine which data set characteristics most affect the classification algorithms’ performance. It is still a complex issue to determine which algorithm is how strong or how weak in relation to which data set. Thus, our research experimentally examines how data set characteristics affect algorithm performance, both in terms of accuracy and in elapsed time. To do so, we use a multiple regression method to evaluate the causality between data set characteristics as independent variables, and performance metrics as dependent variables. We also examine the role that classification algorithms play as moderator in this causality. All benchmark data sets in a UCI database are used that are fit to run the classification algorithm. Based on the results of the experiment, we discuss the requirements of legacy classification algorithms to address big data analysis in a new business intelligence era.