Performance Evaluation Of Algorithms Research Articles

Analisis Sentimen Wisatawan terhadap Kualitas Layanan Hotel dan Resort di Lombok Menggunakan SERVQUAL dan CRISP-DM

The era of digital transformation has sparked innovations in product and service marketing strategies in various sectors, one of which is the tourism sector. In the hospitality industry context, product marketing using website-based digital media allows consumers as hotel guests to review the products and services received. The Tripadvisor website is a digital marketing platform that provides review features for app users, especially consumers, to give ratings and reviews. This study aims to analyze the quality of hotel services using the Service Quality (SERVQUAL) framework based on the results of the classification of hotel guest sentiment data using the Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithm by the stages of the Cross-Industry Standard Process for Data Mining (CRISP-DM). The CRISP-DM framework consists of six stages, namely: business understanding stage, data understanding stage, data preparation stage, modeling stage, evaluation stage, and deployment stage. The SERVQUAL framework consists of several dimensions: reliability dimension; responsiveness; assurance; empathy; tangibles. The review data that will be processed is the consumer review data of The Oberoi Beach Resort Lombok; Sheraton Senggigi Beach Resort; Sudamala Resort Sengiggi; Holiday Resort Lombok; Aston Sunset Beach Resort. The results of this study show that the SVM algorithm performs better than NBC, where the accuracy value is 98.57%, the precision value is 100%, the recall value is 97.14%, and the f-measure value is 98.54%. The AUC value is 100%, and the t-Test value is 98.6%. Unlike the case with the results of SVM's algorithm performance evaluation without using the SMOTE Upgrading Operator, where the accuracy value is 95.71%, the precision value is 95.71%, the recall value is 100%, and the f-measure value is 97.81%. In addition, the AUC value is 91.1%, and the t-Test value is 95.7%.

Performance Evaluation of Classification Algorithm for Movie Review Sentiment Analysis

The majority of the current research on sentiment analysis, which covers topics like political reviews, movie reviews, and product reviews, has developed quickly. The classification and clustering stage of sentiment analysis research involves a number of subjects. Some of them cover text classification comparison research and algorithm performance optimization. An intricate issue in sentiment analysis research is dealing with unstructured or semi-structured data. The sentiment analysis procedure and improving the efficacy of the classifier’s algorithm are both hampered by unstructured data. In order to manage unstructured data successfully and provide accurate and relevant information, unique strategies are required. The proposed classification model performance evaluation using Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Decision Tree is specifically covered in this paper. According to the study’s findings, SVM has an accuracy rate of 96% and Naive Bayes is 86%. While the decision tree’s gain accuracy is 78 percent and the kNN classification model’s gain accuracy is 78 percent, respectively. The test results demonstrate that SVM is superior to other classification models in terms of accuracy performance.

Performance evaluation of machine learning algorithms to assess soil erosion in Mediterranean farmland: A case‐study in Syria

AbstractThe development of new techniques, such as machine learning (ML), can provide better insight into the processes and drivers of soil erosion and runoff. However, the performance of these techniques to assess soil erosion in agricultural landscapes is poorly understood. The aim of this study was to evaluate the performance of four machine learning algorithms, generalized linear model (GLM), Random Forest (RF), elastic net regression (EN) and multiple adaptive regression splines (MARS), in predicting soil erosion and runoff in Syria. Soil erosion and runoff were measured on three experimental plots (2.25 m × 1.50 m × 0.50 m, 0.10 m depth in the soil), combined with three different slopes and land use types: RS (8%, olive), SS (12%, citrus), KS (20%, pomegranate). Both erosion and runoff were determined after rainfall events of >10 mm between October 2019 and April 2020. Based on 24 effective rainfall events, the average soil erosion was 0.18 ± 0.14 kg m−2 per event in KS, 0.14 ± 0.11 kg m−2 per event in SS, and 0.12 ± 0.10 kg/m2 per event in RS. Regression analysis indicated strong relationship between the rainfalls and the runoff, the highest connection was recorded in the KS plot (r2 = 0.85; p < 0.05 n = 24). The analysis of covariance indicated that only the runoff had a significant impact on soil erosion (p = 0.02) with a medium effect (ε2p = 0.26). However, the impacts of rainfall events and slope categories on soil erosion were limited (ε2p < 0.01) and not significant (p > 0.05). ML techniques were usually efficient in the prediction, the RF and MARS models were the most accurate: RF had the strongest correlation with the measured values (r = 0.85) with a low estimation error (0.06 kg m−2), but MARS's standard deviation (SD) was closer to the recorded values' SD. GLM and EN were the weakest predictor models. Modeled values of the slightest slope (8%) had the worst accuracies, and the predictions of the 12% slope were the best in all models. This study provides important insights into the usefulness of machine learning techniques and algorithms in predicting the rate of soil erosion and runoff in agricultural dominated landscapes. We highlighted that the RF and MARS algorithms were better predictors of soil erosion and runoff in the coastal region of Syria.

Exploring the impact of design criteria for reference sets on performance evaluation of signal detection algorithms: The case of drug-drug interactions.

To evaluate the impact of multiple design criteria for reference sets that are used to quantitatively assess the performance of pharmacovigilance signal detection algorithms (SDAs) for drug-drug interactions (DDIs). Starting from a large and diversified reference set for two-way DDIs, we generated custom-made reference sets of various sizes considering multiple design criteria (e.g., adverse event background prevalence). We assessed differences observed in the performance metrics of three SDAs when applied to FDA Adverse Event Reporting System (FAERS) data. For some design criteria, the impact on the performance metrics was neglectable for the different SDAs (e.g., theoretical evidence associated with positive controls), while others (e.g., restriction to designated medical events, event background prevalence) seemed to have opposing and effects of different sizes on the Area Under the Curve (AUC) and positive predictive value (PPV) estimates. The relative composition of reference sets can significantly impact the evaluation metrics, potentially altering the conclusions regarding which methodologies are perceived to perform best. We therefore need to carefully consider the selection of controls to avoid misinterpretation of signals triggered by confounding factors rather than true associations as well as adding biases to our evaluation by "favoring" some algorithms while penalizing others.

Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification

The electrocardiogram (ECG) is the most common technique used to diagnose heart diseases. The electrical signals produced by the heart are recorded by chest electrodes and by the extremity electrodes placed on the limbs. Many diseases, such as arrhythmia, cardiomyopathy, coronary heart disease, and heart failure, can be diagnosed by examining ECG signals. The interpretation of these signals by experts may take a long time, and there may be differences between expert interpretations. Since technological developments are intertwined with the medical sciences, computer-assisted diagnostic methods have recently come forward. In computer science, machine learning techniques are often preferred for automatic detection. Quantum-based structures have emerged to increase the machine learning algorithm's speed and classification performance. In this study, a quantum-based machine learning algorithm is applied to classify heart rhythms. The ECG properties were converted to qubit structure using principal component analysis (PCA). The resulting qubits are classified using the quantum support vector machine (QSVM) algorithm. Quantum computer simulation over Qiskit was used for classification studies. Within the scope of experimental studies, comparisons between classical SVM and QSVM were made using different data amounts and qubit numbers. In the results of the analysis, classical SVM achieved 86.96% accuracy, and QSVM achieved 84.64% accuracy. Despite the fact that the entire dataset was not used due to various limitations, these successful performances were achieved. Classification of medical data such as that from ECG has shown that quantum-based machine learning frameworks perform well despite current resource constraints. In this respect, the study includes essential contributions to the use of quantum-based machine learning methods on signal data in medicine.

Performance Evaluation of Machine Learning Algorithms in Change Detection and Change Prediction of a Watershed’s Land Use and Land Cover

Performance Evaluation of Machine Learning Algorithms in Change Detection and Change Prediction of a Watershed’s Land Use and Land Cover

Longitudinal and lateral coupling vehicle stability controller designed based on piecewise T-S fuzzy model

Due to tire force coupling and load transfer coupling during the stability control process, the vehicle longitudinal and lateral dynamics shows particularly obvious nonlinear coupling characteristics, which will deteriorate the performance of the stability controller. This paper proposes a Vehicle Stability Controller considering the vehicle longitudinal and lateral coupling characteristics. First, a piecewise guidance method, including the Adaptive Linear Regression Algorithm and Dimensionless Performance Evaluation Weighting Function, is developed to ensure the Takagi-Sugeno fuzzy model piecewise approaches the nonlinear characteristics of the original system and obtains the multiple linear fuzzy subsystems. Then, an H∞ robust controller with dynamic output feedback is used in each fuzzy subsystem based on the identification result of the driving intention, and the Linear Matrix Inequality is applied to obtain the controller gains. Subsequently, the Parallel Distributed Compensation architecture is adopted to integrate the outputs of all fuzzy subsystems, and vehicle stability controller is realized by using a rule-based control distribution strategy. The proposed controller is evaluated by simulations and hardware in the loop tests, and the results demonstrate satisfactory longitudinal following ability and lateral stability.

Performance Evaluation of Classification Algorithms on Academic Performance of Postgraduate Students

Performance Evaluation of Classification Algorithms on Academic Performance of Postgraduate Students

Performance Evaluation of Machine Learning Algorithm in Various Datasets

Machine learning is one of the fast-growing areas of computer science, with far-reaching applications. There are several applications for machine learning. The most significant of which is supervised learning. Supervised learning is common in classification problems. In this study, frequently used twelve machine learning algorithms are considered: NB, LDA, LR, ANN, SVM, K-NN, HT, DT, C4.5, CART, RF and BB. We apply these algorithms on seven datasets. The main goal of this study was to evaluate the performance of the machine learning algorithms on both binary and multiple classification problems using a variety of performance metrics: accuracy, kappa statistic, precision, recall, specificity, F-measure, MAE, RMSE and MCC. Here, we found that RF algorithm proved to have the best performance in three out of seven datasets. But the other four algorithms: NN, NB, BB and LR also performed well.

Non-Local Means De-Speckling Based on Multi-Directional Local Plane Inclination Angle

The unavoidable speckle in SAR images seriously interferes with image quality and has a negative effect on subsequent image interpretation. The existing filters still need to be strengthened in terms of both noise suppression and edge preservation. Based on this, a novel non-local means filter by multi-directional local plane inclination angle (MDLPIA) is proposed. The proposed filter uses the MDLPIA to reconstruct a new weight function for non-local means filtering. One instance of simulation data and four real SAR images are used for filtering experiments. In the experiment, seven other filters with excellent performance are selected for comparison, and six quantitative indicators are selected for algorithm performance evaluation. The experimental results show that the proposed filter achieves good visual and index evaluation results, and the equivalent number of looks (ENL) is at least 22.13 times higher than the unfiltered image. The effectiveness and superiority of the proposed algorithm are thus verified.

Implementation and performance evaluation of the real-time algorithms for Wendelstein 7-X divertor protection system for OP2.1

A new Infrared (IR) image analysis system will be deployed for real-time divertor protection during the upcoming Operational Phase (OP) 2.1 in the Wendelstein 7-X (W7-X) stellarator. Its primary objective is to prevent thermal overloads from permanently damaging Plasma-Facing Components (PFCs), resulting in machine downtimes and repair costs. The real-time constraint for this system is 110ms, which is the maximum allowed delay entailing the acquisition, calibration, processing, and interlock, while all processing steps have to complete within 10ms allowing for processing longer than the acquisition time of 10ms at 100Hz. This paper describes the implementation, real-time processing performance and detection effectiveness of Thermal Overload Detection (TOD). The implemented and evaluated TOD system fulfils real-time constraints. It reduces the total system delay to 50ms and provides high detection sensitivity of 0.97 for archived discharge sequences from the OP1.2 campaign. The attained acceleration is significant, i.e., a 95% and 99% decrease in runtime for the sequential Central Processing Unit (CPU) and parallel Graphics Processing Unit (GPU) implementations, respectively, compared to the initial Python prototype. For the first time, the presented results confirm the feasibility of protecting W7-X in real-time comprising fundamentals for further advanced protection and control.

Direction and distribution sensitivity of sup-DOF interference suppression for GNSS array antenna receiver

Introduction: Distributed wideband jamming (interference) is commonly used in navigation countermeasure. Due to the limited volume of GNSS (Global Navigation Satellite System) array antenna receiver, the number of interferences usually exceeds the number of array elements. At present, the anti-jamming capability and mechanism of Global Navigation Satellite System array antenna against distributed sup-DOF (Degree of Freedom) interference have not been fully studied.Methods and innovation: To solve this problem, this paper analyzes the characteristics of GNSS System array antenna against sup-Degree of Freedom interference by formula derivation and simulation. Firstly, the definition of sup-Degree of Freedom interference of Global Navigation Satellite System array antenna is proposed from the perspective of spatial anti-jamming; Secondly, the directional characteristics of Global Navigation Satellite System array antenna for sup-Degree of Freedom interference suppression are analyzed.Results: The results show that the performance of sup-Degree of Freedom interference suppression is sensitive to the direction and distribution of interference. On the one hand, the residual interference power varies from interference direction and distribution, while the minimum value of which is zero and the maximum value is the sum of interference power. On the other hand, the suppression performance of UCA (Uniform Circular Array) and central Uniform Circular Array is periodic along azimuth. If the number of elements on the circumference is M (M ≥ 3), the period of the suppression performance is 4π/M/3+(−1M+1.Discussion: The conclusion of this paper show the upper and lower bounds of sup-Degree of Freedom interference suppression performance and the variation rule in azimuth, which can be used in the fields such as interference deployment, anti-jamming performance evaluation and anti-jamming algorithm development.

Performance Evaluation of Load-Balancing Algorithms with Different Service Broker Policies for Cloud Computing

Cloud computing has seen a major boom during the past few years. Many people have switched to cloud computing because traditional systems require complex resource distribution and cloud solutions are less expensive. Load balancing (LB) is one of the essential challenges in cloud computing used to balance the workload of cloud services. This research paper presents a performance evaluation of the existing load-balancing algorithms which are particle swarm optimization (PSO), round robin (RR), equally spread current execution (ESCE), and throttled load balancing. This study offers a detailed performance evaluation of various load-balancing algorithms by employing a cloud analyst platform. Efficiency concerning various service broker policy configurations for load-balancing algorithms’ virtual machine load balance was also calculated using metrics such as optimized response time (ORT), data center processing time (DCPT), virtual machine costs, data transfer costs, and total cost for different workloads and user bases. Many of the past papers that were mentioned in the literature worked on round robin and equally spread current execution, and throttled load-balancing algorithms were based on efficiency and response time in virtual machines without recognizing the relation between the task and the virtual machines, and the practical significance of the application. A comparison of specific load-balancing algorithms has been investigated. Different service broker policy (SBP) tests have been conducted to illustrate the load-balancing algorithm capabilities.

Subjective Assessment of Objective Image Quality Metrics Range Guaranteeing Visually Lossless Compression.

The usage of media such as images and videos has been extensively increased in recent years. It has become impractical to store images and videos acquired by camera sensors in their raw form due to their huge storage size. Generally, image data is compressed with a compression algorithm and then stored or transmitted to another platform. Thus, image compression helps to reduce the storage size and transmission cost of the images and videos. However, image compression might cause visual artifacts, depending on the compression level. In this regard, performance evaluation of the compression algorithms is an essential task needed to reconstruct images with visually or near-visually lossless quality in case of lossy compression. The performance of the compression algorithms is assessed by both subjective and objective image quality assessment (IQA) methodologies. In this paper, subjective and objective IQA methods are integrated to evaluate the range of the image quality metrics (IQMs) values that guarantee the visually or near-visually lossless compression performed by the JPEG 1 standard (ISO/IEC 10918). A novel "Flicker Test Software" is developed for conducting the proposed subjective and objective evaluation study. In the flicker test, the selected test images are subjectively analyzed by subjects at different compression levels. The IQMs are calculated at the previous compression level, when the images were visually lossless for each subject. The results analysis shows that the objective IQMs with more closely packed values having the least standard deviation that guaranteed the visually lossless compression of the images with JPEG 1 are the feature similarity index measure (FSIM), the multiscale structural similarity index measure (MS-SSIM), and the information content weighted SSIM (IW-SSIM), with average values of 0.9997, 0.9970, and 0.9970 respectively.

PERFORMANCE EVALUATION OF DNA PATTERN MATCHING ALGORITHMS

Bioinformatics is a new area of research in which DNA, RNA and proteinssequences are dealt. To store, retrieve, analyze, match, align, search, and process these sequencesdifferent techniques are existed. Currently a lot of advancements in sequence analysis cause the drasticincrease in the DNA database sizes that require more efficient approaches encompass accuracy. Searchand analysis of DNA patterns can be performed by using various pattern matching algorithms in thecomputational biology. The aim of present study is to present taxonomy and performance evaluation ofthese pattern matching algorithms. The objective of this SLR is to set a research trend and to find amathematical model to estimate execution search time before scanning whole DNA sequence byfollowing a search strategy.

Performance Evaluation of ML-Based Algorithm and Taguchi Algorithm of the Hardness Value of the Friction Stir Welded AA6262 Joints at a Nugget Joint

Nowadays, industry 4.0 plays a tremendous role in the manufacturing industries for increasing the amount of data and accuracy in modern manufacturing systems. Thanks to artificial intelligence, particularly machine learning, big data analytics have dramatically amended, and manufacturers easily exploit organized and unorganized data. This study utilized hybrid optimization algorithms to find friction stir welding and optimal hardness value at the nugget zone. A similar AA 6262 material was used and welded in a butt joint configuration. Tool rotational speed (RPM), tool traverse speed (mm/min), and the plane depth (mm) are used as controllable parameters and optimized using Taguchi L9, Random Forest, and XG Boost machine learning tools. Analysis of variance was also conducted at a 95% confidence interval for identifying the significant parameters. The result indicated that the coefficient of determination from Taguchi L9 orthogonal array is 0.91 obtained while Random Forest and XG Boost algorithm imparted 0.62 and 0.65 respectively. Keywords: Friction Stir Welding; Taguchi; Machine Learning; Hardness; Nugget Zone and Random Forest.

Enhancing security and ensuring secure performance: A performance evaluation of consensus algorithms in a distributed healthcare blockchain system

Blockchain is an emerging technology in today’s real-time applications and database transaction systems—the primary motto is to determine transactional clarity to end-user without any fluctuation. According to the basic block policy, it is immutable or does not allow updating the existing transaction, which automatically maintains the security and transaction clarity. This paper proposes a system performance evaluation of various consensus algorithms in a distributed environment with a peer-to-peer network. The four different consensus algorithms have been evaluated in four peer nodes. Healthcare application has been successfully tested with the entire system. The first blockchain implementation has done in a Java environment with the custom hash generation, mining strategy, and smart contract for each level. Each algorithm provides different time complexity accordingly. The system evaluates the performance of four consensus algorithms sequentially and demonstrates the effectiveness and utilization of each algorithm according to the requirements.

A New Machine Learning-based Hybrid Intrusion Detection System and Intelligent Routing Algorithm for MPLS Network

Machine Learning (ML) is seen as a promising application that offers autonomous learning and provides optimized solutions to complex problems. The current Multiprotocol Label Switching (MPLS)-based communication system is packed with exponentially increasing applications and different Quality-of-Services (QoS) requirements. As the network is getting complex and congested, it will become challenging to satisfy the QoS requirements in the MPLS network. This study proposes a hybrid ML-based intrusion detection system (ML-IDS) and ML-based intelligent routing algorithm (ML-RA) for MPLS network. The research is divided into three parts, which are (1) dataset development, (2) algorithm development, and (3) algorithm performance evaluation. The dataset development for both algorithms is carried out via simulations in Graphical Network Simulator 3 (GNS3). The datasets are then fed into MATLAB to train ML classifiers and regression models to classify the incoming traffic as normal or attack and predict traffic delays for all available routes, respectively. Only the normal traffic predicted by the ML-IDS algorithm will be allowed to enter the network domain, and the route with the fastest delay predicted by the ML-RA is assigned for routing. The ML-based routing algorithm is compared to the conventional routing algorithm, Routing Information Protocol version 2 (RIPv2). From the performance evaluations, the ML-RA shows 100 percent accuracy in predicting the fastest route in the network. During network congestion, the proposed ML outperforms the RIPv2 in terms of delay and throughput on average by 57.61 percent and 46.57 percent, respectively.

Performance Analysis and Evaluation of Different Deep Learning Algorithms for Facial Expression Recognition

Emotions are dynamic biological states that are connected to all of the nerve systems. The problem of facial expression recognition has been thoroughly investigated, leading to the development of some robust and accurate face recognition algorithms. The effectiveness of three such algorithms (CNN, VGG16, and ResNet50) that have been widely studied and applied in the research community are investigated and compared in this paper. The aim is to use grayscale images to train these training models and compare their accuracy and data losses. The system will be able to detect the seven facial expressions Angry, Neutral, Contempt, Disgust, Fear, Happy, and Sad after training these models. To compare their precision, the same batch size and epoch were used. After reviewing all possible evaluations based on these output matrices, it is clear that all three networks produce reliable effect identification, with CNN being the most accurate.

Performance Evaluation of Cryptographic Security Algorithms on Cloud

Cloud computing is a cost-effective approach to provide on demand computing and data storage solutions. Data storage is one of the key services provided by the cloud. Cloud offers improved efficiency, flexibility, and scalability, but all these advantages can be overturned if security is not taken into consideration. It is the cloud vender’s responsibility to keep the data safe with highly secure cloud services, which helps them earn the trust of the customers. Today, cloud security is critical since most organizations are already using cloud computing in alternate forms. There is always a concern that the highly sensitive business information and intellectual property may be exposed or misused due to increasingly sophisticated cyber threats. This research paper provides a distributed architecture for cloud data security which is independent of the underlying platforms. A cloud security architecture provides written and visual model to define how to configure and secure operations within the cloud. This paper compares the performance of RC4 against AES-256. The performance of the proposed encryption algorithms is evaluated on a widely used database MySQL. This paper provides a better solution to ensure the security of cloud databases by using two encryption algorithms.