Performance Analysis Of Algorithms Research Articles

Performance Analysis of Tree-Based Algorithms in Predicting Employee Attrition

Based on data throughout 2022, there have been many reductions in employees both globally and Indonesia. The reduction was made due to adjustments with developments to keep the business afloat in increasingly fierce competition. However, reducing the number of employees is not an easy decision to make. This decision can have an impact on many aspects of the development and course of a business or company. To make a decision especially related to the aspect of termination of employment, it is necessary to consider carefully and thoroughly. Assessment and decision-making cannot be based on just one aspect, other aspects need to be seen to be taken into consideration. Additional aspects that can be selected to strengthen decision-making can be taken from the data. Data will not have any value without processing it with various approaches, one of which is the prediction process. Starting from the data, the prediction results will be more appropriate to make a decision. This study made a comparison of 3 decision tree algorithms, and produced a comparison of the three methods in terms of accuracy. The results of this study are the best accuracy for each algorithm C.45 = 83.44; Random Forests = 85.85; LMT = 88.29 with a linear precision value, and the best algorithm model with the highest accuracy is the Logistic Model Tree (LMT) algorithm.

Extended performance analysis of deep-learning algorithms for mice vocalization segmentation

Ultrasonic vocalizations (USVs) analysis represents a fundamental tool to study animal communication. It can be used to perform a behavioral investigation of mice for ethological studies and in the field of neuroscience and neuropharmacology. The USVs are usually recorded with a microphone sensitive to ultrasound frequencies and then processed by specific software, which help the operator to identify and characterize different families of calls. Recently, many automated systems have been proposed for automatically performing both the detection and the classification of the USVs. Of course, the USV segmentation represents the crucial step for the general framework, since the quality of the call processing strictly depends on how accurately the call itself has been previously detected. In this paper, we investigate the performance of three supervised deep learning methods for automated USV segmentation: an Auto-Encoder Neural Network (AE), a U-NET Neural Network (UNET) and a Recurrent Neural Network (RNN). The proposed models receive as input the spectrogram associated with the recorded audio track and return as output the regions in which the USV calls have been detected. To evaluate the performance of the models, we have built a dataset by recording several audio tracks and manually segmenting the corresponding USV spectrograms generated with the Avisoft software, producing in this way the ground-truth (GT) used for training. All three proposed architectures demonstrated precision and recall scores exceeding 90%, with UNET and AE achieving values above 95%, surpassing other state-of-the-art methods that were considered for comparison in this study. Additionally, the evaluation was extended to an external dataset, where UNET once again exhibited the highest performance. We suggest that our experimental results may represent a valuable benchmark for future works.

Performance analysis of reinforcement learning algorithms on intelligent closed-loop control on fluid flow and convective heat transfer

This paper investigates the performance of several most popular deep reinforcement learning (DRL) algorithms applied to fluid flow and convective heat transfer systems, providing credible guidance and evaluation on their characteristics and performance. The studied algorithms are selected by considering the popularity, category, and advancement for guaranteeing the significance of the current study. The effectiveness and feasibility of all DRL algorithms are first demonstrated by studying a two-dimensional multi-heat-source cooling problem. Compared with the best manually optimized control, all DRL algorithms can find better control strategies that realize a further temperature reduction of 3–7 K. For problems with complex control objectives and environments, PPO (proximal policy optimization) shows an outstanding performance that accurately and dynamically constrains the oscillation of the solid temperature within 0.5 K around the target value, which is far beyond the capability of the manually optimized control. With the presented performance and the supplemented generalization test, the characteristic and specialty of the DRL algorithms are analyzed. The value-based methods have better training efficiency on simple cooling tasks with linear reward, while the policy-based methods show remarkable convergence on demanding tasks with nonlinear reward. Among the algorithms studied, the single-step PPO and prioritized experience replay deep Q-networks should be highlighted: the former has the advantage of considering multiple control targets and the latter obtains the best result in all generalization testing tasks. In addition, randomly resetting the environment is confirmed to be indispensable for the trained agent executing long-term control, which is strongly recommended to be included in follow-up studies.

Performance Analysis of Reinforcement Learning Algorithms and Models Based on the 3D-Pinball Game

Performance Analysis of Reinforcement Learning Algorithms and Models Based on the 3D-Pinball Game

Detailed Process for Developing an Efficient Anomaly Detection Algorithm for Real-Time Streaming Data in Large-Scale Industrial Systems

Anomaly detection plays a critical role in large-scale industrial systems, where real-time streaming data is generated at high volumes. This research journal presents an in-depth study on developing an efficient anomaly detection algorithm specifically designed for such industrial systems. The goal is to identify abnormal patterns and deviations from normal behavior, enabling proactive maintenance, improved operational efficiency, and reduced downtime. The proposed algorithm leverages machine learning and stream processing techniques to handle the challenges associated with real- time streaming data analysis. The research covers algorithm design, implementation, evaluation, and performance analysis using large-scale datasets from industrial domains. Keywords: anomaly detection, real-time streaming data, large-scale industrial systems, machine learning, stream processing

Development of an Anticipatory Triage-Ranking Algorithm Using Dynamic Simulation of the Expected Time Course of Patients With Trauma: Modeling and Simulation Study.

In cases of terrorism, disasters, or mass casualty incidents, far-reaching life-and-death decisions about prioritizing patients are currently made using triage algorithms that focus solely on the patient's current health status rather than their prognosis, thus leaving a fatal gap of patients who are under- or overtriaged. The aim of this proof-of-concept study is to demonstrate a novel approach for triage that no longer classifies patients into triage categories but ranks their urgency according to the anticipated survival time without intervention. Using this approach, we aim to improve the prioritization of casualties by respecting individual injury patterns and vital signs, survival likelihoods, and the availability of rescue resources. We designed a mathematical model that allows dynamic simulation of the time course of a patient's vital parameters, depending on individual baseline vital signs and injury severity. The 2 variables were integrated using the well-established Revised Trauma Score (RTS) and the New Injury Severity Score (NISS). An artificial patient database of unique patients with trauma (N=82,277) was then generated and used for analysis of the time course modeling and triage classification. Comparative performance analysis of different triage algorithms was performed. In addition, we applied a sophisticated, state-of-the-art clustering method using the Gower distance to visualize patient cohorts at risk for mistriage. The proposed triage algorithm realistically modeled the time course of a patient's life, depending on injury severity and current vital parameters. Different casualties were ranked by their anticipated time course, reflecting their priority for treatment. Regarding the identification of patients at risk for mistriage, the model outperformed the Simple Triage And Rapid Treatment's triage algorithm but also exclusive stratification by the RTS or the NISS. Multidimensional analysis separated patients with similar patterns of injuries and vital parameters into clusters with different triage classifications. In this large-scale analysis, our algorithm confirmed the previously mentioned conclusions during simulation and descriptive analysis and underlined the significance of this novel approach to triage. The findings of this study suggest the feasibility and relevance of our model, which is unique in terms of its ranking system, prognosis outline, and time course anticipation. The proposed triage-ranking algorithm could offer an innovative triage method with a wide range of applications in prehospital, disaster, and emergency medicine, as well as simulation and research.

Performance Analysis of Routable GOOSE Security Algorithm for Substation Communication through Public Internet Network

Traditional unidirectional power systems that produce large-scale electricity and supply it using an ultra-high voltage power grid are changing globally to increase efficiency. Current substations’ protection relays rely only on internal substation data, where they are located, to detect changes. However, to more accurately detect changes in the system, various data from several external substations, including micro-grids, are required. As such, communication technology regarding data acquisition has become an essential function for next-generation substations. Data aggregators that use the GOOSE protocol to collect data inside substations in real-time have been developed, but data acquisition from external substations is challenging in terms of cost and security, so only internal substation data are used. This paper proposes the acquisition of data from external substations by applying security to R-GOOSE, defined in the IEC 61850 standard, over a public internet network. This paper also develops a data aggregator based on R-GOOSE, showing data acquisition results.

Construction of a refined people management model for SMEs based on discrete regression algorithm

Abstract Management innovation is particularly important today when technological convergence is becoming increasingly evident. Furthermore, as employees are the most important resource in work dynamic scheduling, the mobility of employees will bring extremely discrete regression particle swarm algorithm uncertainty to work projects, making its scheduling problem more complex. This paper first proposes to apply a discrete regression particle swarm algorithm based on refinement management as an enterprise management innovation to optimize three indicators of time, cost, and stability by implementing refinement personnel management. Secondly, we set constraints for dynamic tasks to implement management rescheduling to avoid overworking employees and thus maximize profits for the enterprise. Finally, the effectiveness of the discrete regression particle swarm algorithm is verified by selecting 27 benchmark examples to compare from three indicators of contribution and time and cost. The results show that the discrete regression particle swarm algorithm (S-PSO-VNS) has an average time reduction of 22.65%, a cost reduction of 15.21%, and a stability improvement of 35.74% compared to the other two control algorithms in terms of management algorithm quality and performance analysis. The research data illustrates that the discrete regression algorithm proposed in this paper can have high efficiency and effectiveness, and the discrete regression particle swarm algorithm can provide managers of SMEs with more informed decisions on work schedules, thus maximizing profits and achieving more profitability.

Mobile coded caching in small cell networks: Algorithm design and performance analysis

In coded caching, users cache pieces of files under a specific arrangement so that the server can satisfy their requests simultaneously in the broadcast scenario via eXclusive OR (XOR) operation and therefore reduce the amount of transmission data. However, when users' locations are changing, the uploading of caching information is frequent and extensive that the traffic increase outweighed the traffic reduction that the traditional coded caching achieved. In this paper, we propose mobile coded caching schemes to reduce network traffic in mobility scenarios, which achieve a lower cost on caching information uploading. In the cache placement phase, the proposed scheme first constructs caching patterns, and then assigns the caching patterns to users according to the graph coloring method and four color theorem in our centralized cache placement algorithm or randomly in our decentralized cache placement algorithm. Then users are divided into groups based on their caching patterns. As a benefit, when user movements occur, the types of caching pattern, rather than the whole caching information of which file pieces are cached, are uploaded. In the content delivery phase, XOR coded caching messages are reconstructed. Transmission data volume is derived to measure the performance of the proposed schemes. Numerical results show that the proposed schemes achieve great improvement in traffic offloading.

AIS data-driven ship trajectory prediction modelling and analysis based on machine learning and deep learning methods

Maritime transport faces new safety challenges in an increasingly complex traffic environment caused by large-scale and high-speed ships, particularly with the introduction of intelligent and autonomous ships. It is evident that Automatic Identification System (AIS) data-driven ship trajectory prediction can effectively aid in identifying abnormal ship behaviours and reducing maritime risks such as collision, stranding, and contact. Furthermore, trajectory prediction is widely recognised as one of the critical technologies for realising safe autonomous navigation. The prediction methods and their performance are the key factors for future safe and automatic shipping. Currently, ship trajectory prediction lacks the real performance measurement and analysis of different algorithms, including classical machine learning and emerging deep learning methods. This paper aims to systematically analyse the performance of ship trajectory prediction methods and pioneer experimental tests to reveal their advantages and disadvantages as well as fitness in different scenarios involving complicated systems. To do so, five machine learning methods (i.e., Kalman Filter (KF), Support Vector Progression (SVR), Back Propagation network (BP), Gaussian Process Regression (GPR), and Random Forest (RF)) and seven deep learning methods (i.e., Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU), Bi-directional Long Short-Term Memory (Bi-LSTM), Sequence to Sequence (Seq2seq), Bi-directional Gate Recurrent Unit (Bi-GRU), and Transformer) are first extracted from the state-of-the-art literature review and then employed to implement the trajectory prediction and compare their prediction performance in the real world. Three AIS datasets are collected from the waters of representative traffic features, including a normal channel (i.e., the Chengshan Jiao Promontory), complex traffic (i.e., the Zhoushan Archipelago), and a port area (i.e., Caofeidian port). They are selected to test and analyse the performance of all twelve methods based on six evaluation indexes and explore the characteristics and effectiveness of the twelve trajectory prediction methods in detail. The experimental results provide a novel perspective, comparison, and benchmark for ship trajectory prediction research, which not only demonstrates the fitness of each method in different maritime traffic scenarios, but also makes significant contributions to maritime safety and autonomous shipping development.

Performance analysis of various training algorithms of deep learning based controller

Advances in artificial neural networks (ANN), specifically deep learning (DL), have widened the application domain of process control. DL algorithms and models have become quite common these days. The training algorithm is the most important part of an ANN that affects the performance of the controller. Training algorithms optimize the weights and biases of the ANN according to the input-output patterns. In this paper, the performance of different training algorithms was evaluated, analysed, and compared in a feed-forward backpropagation architecture. The training algorithms were simulated on MATLAB R2021b with license number 1075356. Training data were generated using two benchmark problems of the process control system. The performance, gradient, training error, validation error, testing error, and regression of the different training algorithms were obtained and analysed. The data shows that the Levenberg-Marquardt (LM) algorithm produced the best validation performance with a value of 2.669*10−14 at 2000 epochs, while ‘traingd’ and ‘traingdm’ algorithms did not improve beyond their initial values. The LM algorithm tends to produce better results than other algorithms. These results indicate that the LM backpropagation best suits these types of benchmark problems. The results also suggest that the choice of training algorithm can significantly impact the performance of a neural network.

Time-Series Forecasting of Seasonal Data Using Machine Learning Methods

The models for forecasting time series with seasonal variability can be used to build automatic real-time control systems. For example, predicting the water flowing in a wastewater treatment plant can be used to calculate the optimal electricity consumption. The article describes a performance analysis of various machine learning methods (SARIMA, Holt-Winters Exponential Smoothing, ETS, Facebook Prophet, XGBoost, and Long Short-Term Memory) and data-preprocessing algorithms implemented in Python. The general methodology of model building and the requirements of the input data sets are described. All models use actual data from sensors of the monitoring system. The novelty of this work is in an approach that allows using limited history data sets to obtain predictions with reasonable accuracy. The implemented algorithms made it possible to achieve an R-Squared accuracy of more than 0.95. The forecasting calculation time is minimized, which can be used to run the algorithm in real-time control and embedded systems.

Performance Analysis of Different Optimization Algorithms for Multi-Class Object Detection

Object recognition is a significant approach employed for recognizing suitable objects from the image. Various improvements, particularly in computer vision, are probable to diagnose highly difficult tasks with the assistance of local feature detection methodologies. Detecting multi-class objects is quite challenging, and many existing researches have worked to enhance the overall accuracy. But because of certain limitations like higher network loss, degraded training ability, improper consideration of features, less convergent and so on. The proposed research introduced a hybrid convolutional neural network (H-CNN) approach to overcome these drawbacks. The collected input images are pre-processed initially through Gaussian filtering to eradicate the noise and enhance the image quality. Followed by image pre-processing, the objects present in the images are localized using Grid Guided Localization (GGL). The effective features are extracted from the localized objects using the AlexNet model. Different objects are classified by replacing the concluding softmax layer of AlexNet with Support Vector Regression (SVR) model. The losses present in the network model are optimized using the Improved Grey Wolf (IGW) optimization procedure. The performances of the proposed model are analyzed using PYTHON. Various datasets are employed, including MIT-67, PASCAL VOC2010, Microsoft (MS)-COCO and MSRC. The performances are analyzed by varying the loss optimization algorithms like improved Particle Swarm Optimization (IPSO), improved Genetic Algorithm (IGA), and improved dragon fly algorithm (IDFA), improved simulated annealing algorithm (ISAA) and improved bacterial foraging algorithm (IBFA), to choose the best algorithm. The proposed accuracy outcomes are attained as PASCAL VOC2010 (95.04%), MIT-67 dataset (96.02%), MSRC (97.37%), and MS COCO (94.53%), respectively.

Performance Analysis of Different Optimization Algorithms for MPPT Control Techniques under Complex Partial Shading Conditions in PV Systems

Classic algorithms show high performance in tracking the maximum power point (MPP) of photovoltaic (PV) panels under uniform irradiance and temperature conditions. However, when partial or complex partial shading conditions occur, they fail in capturing the global maximum power point (GMPP) and are trapped in one of the local maximum power points (LMPPs) leading to a loss in power. On the other hand, intelligent algorithms inspired by nature show successful performance in GMPP tracking. In this study, an MPPT system was set up in MATLAB/Simulink software consisting of six groups of serially connected PV panels, a DC-DC boost converter, and load. Using this system, the cuckoo search (CS) algorithm, the modified incremental conductivity (MIC) algorithm, the particle swarm optimization (PSO) algorithm, and the grey wolf optimization (GWO) algorithm were compared in terms of productivity, convergence speed, efficiency, and oscillation under complex shading conditions. The results showed that the GWO algorithm showed superior performance compared to the other algorithms under complex shading conditions. It was observed that GWO did not oscillate during GMPP tracking with an average convergence speed of 0.22 s and a tracking efficiency of 99%. All these evaluations show that GWO is a very fast, highly accurate, efficient, and stable MPPT method under complex partial shading conditions.

Hardware/Software Co-Assurance for the Rust Programming Language Applied to Zero Trust Architecture Development

Zero Trust Architecture requirements are of increasing importance in critical systems development. Zero trust tenets hold that no implicit trust be granted to assets based on their physical or network location. Zero Trust development focuses on authentication, authorization, and shrinking implicit trust zones to the most granular level possible, while maintaining availability and minimizing authentication latency. Performant, high-assurance cryptographic primitives are thus central to successfully realizing a Zero Trust Architecture. The Rust programming language has garnered significant interest and use as a modern, type-safe, memory-safe, and potentially formally analyzable programming language. Our interest in Rust particularly stems from its potential as a hardware/software co-assurance language for developing Zero Trust Architectures. We describe a novel environment enabling Rust to be used as a High-Level Synthesis (HLS) language, suitable for secure and performant Zero Trust application development. Many incumbent HLS languages are a subset of C, and inherit many of the well-known security shortcomings of that language. A Rust-based HLS brings a single modern, type-safe, memory-safe, high-assurance development language for both hardware and software. To study the benefits of this approach, we crafted a Rust HLS subset, and developed a frontend to the hardware/software co-assurance toolchain due to Russinoff and colleagues at Arm, used primarily for floating-point hardware formal verification. This allows us to leverage a number of existing hardware/software co-assurance tools with a minimum investment of time and effort. In this paper, we describe our Rust subset, detail our prototype toolchain, and describe the implementation, performance analysis, formal verification and validation of representative Zero Trust algorithms and data structures written in Rust, emphasizing cryptographic primitives and common data structures.

Comparative performance analysis of metaheuristic search algorithms in parameter extraction for various solar cell models

In today's technology, solar energy continues to be one of the leading renewable energy sources in electricity production methods due to its abundance on the Earth's surface and its non-polluting nature. In solar photovoltaic (PV) systems, it is relatively easy to produce electricity from the sun, and their efficiency can be enhanced by accurately estimating the electrical parameters of solar cells. From this point of view, this paper has focused on solar cell design to improve the performance of solar PV systems. To achieve this target, fitness-distance balance-based stochastic fractal search (FDB-SFS), particle swarm optimization (PSO), student psychology-based optimization (SPBO), and adaptive guided differential evolution (AGDE) algorithms are employed. The experimental analysis is performed on single-diode solar cell, double-diode solar cell, and three PV modules. Considering the experimental results, it is observed that the best estimation accuracy is reached by the FDB-SFS algorithm. Accordingly, root mean square error (RMSE) values between measured and estimated data were calculated to be 9.86E-04 for the single-diode solar cell model, 9.84E-04 for the double-diode solar cell model, 2.42E-03 for the Photowatt-PWP201 module, 1.72E-03 for STM6–40/36 module, and 1.67E-02 for STP6–120/36 module. Consequently, the present paper reports that FDB-SFS is an efficient and powerful method for parameter extraction of solar photovoltaic models.

적응 커널 폭을 가진 최소오차 엔트로피 학습법의 성능 분석

적응 커널 폭을 가진 최소오차 엔트로피 학습법의 성능 분석

Limitations of Load Balancing and Performance Analysis Processes and Algorithms in Cloud Computing

In the modern IT industry, cloud computing is a cutting-edge technology. Since it faces various challenges, the most significant problem of cloud computing is load balancing, which degrades the performance of the computing resources. In earlier research studies, the management of the workload to address all resource allocation challenges that caused by the participation of a large number of users has received important attention. When several people are attempting to access a given web application at once, managing all of those users becomes exceedingly difficult. One of the elements affecting the performance stability of cloud computing is load balancing. This article evaluates and discusses load balancing, the drawbacks of the numerous methods that have been suggested to distribute load among nodes, and the variables that are taken into account when determining the best load balancing algorithm.

The Performance Analysis of Machine Learning Algorithms for Credit Card Fraud Detection

This paper studies the performance analysis of machine learning (ML) and data mining techniques for anomaly detection in credit cards. As the usage of digital money or plastic money grows in developing nations, so does the risk of fraud. To counter these scams, we need a sophisticated fraud detection method that not only identifies the fraud but also detects it before it occurs efficiently. We have introduced the notion of credit card fraud and its many variants in this research. Numerous ML fraud detection approaches are studied in this paper including Principal Component Analysis (PCA) data mining and the Fuzzy C-Means methodologies, as well as the Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB) algorithms. The existing and proposed models for credit card fraud detection have been thoroughly reviewed, and these strategies have been compared using quantitative metrics including accuracy rate and characteristics curves. This paper discusses the shortcomings of existing models and proposes an efficient technique to analyze the fraud detection.

Performance analysis of least squares algorithm for multivariable stochastic systems

Performance analysis of least squares algorithm for multivariable stochastic systems