Performance Of Approach Research Articles

A Comparative Evaluation on Data Transformation Approach for Artificial Speech Detection

The rise of voice biometrics has transformed user authentication and offered enhanced security and convenience while phasing out less secure methods. Despite these advancements, Automatic Speaker Verification (ASV) systems remain vulnerable to spoofing, particularly with artificial speech generated swiftly using advanced speech synthesis and voice conversion algorithms. A recent data transformation technique achieved an impressive Equal Error Rate (EER) of 1.42% on the ASVspoof 2019 Logical Access Dataset. While this approach predominantly relies on Support Vector Machine (SVM) as the backend classifier for artificial speech detection, it is vital to explore a broader range of classifiers to enhance resilience. This paper addresses this research gap by systematically assessing classifier efficacy in artificial speech detection. The objectives are twofold: first, to evaluate various classifiers, not limited to SVM, and identify those best suited for artificial speech detection; second, to compare this approach's performance with existing methods. The evaluation demonstrated SVM-Polynomial as the top-performing classifier, surpassing the end-to-end learning approach. This work contributes to a deeper understanding of classifier efficacy and equips researchers and practitioners with a diversified toolkit for building robust ASV spoofing detection systems.

A recent proximal gradient algorithm for convex minimization problem using double inertial extrapolations

<abstract><p>In this study, we suggest a new class of forward-backward (FB) algorithms designed to solve convex minimization problems. Our method incorporates a linesearch technique, eliminating the need to choose Lipschitz assumptions explicitly. Additionally, we apply double inertial extrapolations to enhance the algorithm's convergence rate. We establish a weak convergence theorem under some mild conditions. Furthermore, we perform numerical tests, and apply the algorithm to image restoration and data classification as a practical application. The experimental results show our approach's superior performance and effectiveness, surpassing some existing methods in the literature.</p></abstract>

FOPID Controller Design for a Buck Converter System Using a Novel Hybrid Cooperation Search Algorithm with Pattern Search for Parameter Tuning

This research introduces a novel metaheuristic algorithm, OCSAPS, representing an upgraded cooperation search algorithm (CSA) version. OCSAPS incorporates opposition-based learning (OBL) and pattern search (PS) algorithms. The proposed algorithm's application aims to develop a fractional order proportional-integral-derivative (FOPID) controller tailored for a buck converter system. The efficacy of the proposed algorithm is assessed by statistical boxplot and convergence response analyses. Furthermore, the performance of the OCSAPS-based FOPID-controlled buck converter system is benchmarked against CSA, Harris hawk optimization (HHO), and genetic algorithm (GA). This comparative analysis encompasses transient and frequency responses, performance indices, and robustness analysis. The outcomes of this comparison highlight the distinctive advantages of the proposed approach-based system. Moreover, the proposed approach's performance was compared with six other approaches used to control buck converter systems similarly regarding both time and frequency domain responses. Overall, the findings underscore the efficacy of the OCSAPS algorithm as a robust solution for designing FOPID controllers in buck converter systems.

Incorporating eco-evolutionary information into species distribution models provides comprehensive predictions of species range shifts under climate change

As climate changes increasingly influence species distributions, ecosystem functions, and biodiversity, the urgency to understand how species' ranges shift under those changes is great. Species distribution models (SDMs) are vital approaches that can predict species distributions under changing climates. However, SDMs based on the species' current occurrences may underestimate the species' climatic tolerances. Integrating species' realized niches at different periods, also known as multi-temporal calibration, can provide an estimation closer to its fundamental niche. Based on this, we further proposed an integrated framework that combines eco-evolutionary data and SDMs (phylogenetically-informed SDMs) to provide comprehensive predictions of species range shifts under climate change. To evaluate our approach's performance, we applied it to a group of related species, the Chrysanthemum zawadskii species complex (Anthemidae, Asteracee). First, we investigated the niche differentiation between species and intraspecific lineages of the complex and estimated their rates of niche evolution. Next, using both standard SDMs and our phylogenetically-informed SDMs, we generated predictions of suitability areas for all species and lineages and compared the results. Finally, we reconstructed the historical range dynamics for the species of this complex. Our results showed that the species and intraspecific lineages of the complex had varying degrees of niche differentiation and different rates of niche evolution. Lineage-level SDMs can provide more realistic predictions for species with intraspecific differentiation than species-level models can. The phylogenetically-informed SDMs provided more complete environmental envelopes and predicted broader potential distributions for all species than the standard SDMs did. Range dynamics varied among the species that have different rates of niche evolution. Our framework integrating eco-evolutionary data and SDMs contributes to a better understanding of the species' responses to climate change and can help to make more targeted conservation efforts for the target species under climate change, particularly for rare species.

Aggregating diverse evaluations in group decision making: An approach based on wisdom of crowds

Group decision-making (GDM) requires individuals to weigh and aggregate individual evaluations. Diverse evaluations within a group usually require a consensus reaching process (CRP). However, CRP does not aim to improve group evaluation accuracy. The wisdom of crowds (WOC) applies statistical aggregations to obtain group predictions, without necessitating a certain degree of group consensus. Current WOC techniques identify individual expertise levels based on real outcomes of events, or individuals' meta-information, which might not be obtainable in GDM problems. This study proposes an approach for GDM problems with diverse evaluations provided by individuals with different expertise levels. It weighs individuals based on their contributions to the group, by estimating the evaluated attributes' real outcomes and updating individual weights within a time series. The approach is verified by real collected data and simulated data, then compared with several similar weighting methods and CRP techniques. The results demonstrate the approach's good performance in large groups, with improved identification of expertise levels in diverse groups, without requiring a large number of time points in the time-series.

Data-driven robust optimization to design an integrated sustainable forest biomass-to-electricity network under disjunctive uncertainties

The consequences of using costly, nonrenewable, and nonsustainable fossil fuels have forced countries to focus on renewable energy resources. In addition, the necessity of smooth material and information flow in the supply chain has motivated scholars to invent different mathematical approaches to designing an integrated supply chain. Such an integrated biomass-to-electricity supply chain can guarantee smooth material and information flow in the supply chain. To this end, a data-driven robust optimization approach under disjunctive uncertainties is developed for designing and optimizing an integrated sustainable biomass-to-electricity supply chain network. Instead of assuming uncertain parameters data to be less scattered or almost uniform, an uncertainty set based on data with disjunctive structures is proposed to identify the uncertainty space more accurately and flexibly. Specifically, the data on uncertain parameters are clustered using the K-means technique. Then, the primary uncertainty sets are formed for each cluster. The proposed uncertain parameter space is constructed by incorporating these primary uncertainty sets, which form using support vector clustering. We studied a case in Iran to probe the performance of the proposed approach. The computational results indicate that the proposed framework provides maximum protection against disjunctive uncertainty with minimum robustness price.

Chatter Avoidance by Spindle Speed and Orientation Planning in Five-Axis Ball-End Milling of Thin-Walled Blades

Abstract Selecting suitable cutting conditions is crucial in maintaining chatter stability and achieving acceptable surface quality. However, the selection of a constant set of cutting parameters is not feasible due to the time-varying dynamics of highly flexible thin-walled blades. This paper presents an optimal selection of tool orientation and spindle speed along the tool path as the metal is removed during the ball-end milling of blades. The effects of tool orientation and speed on the mechanics and dynamics of the ball-end milling process are formulated. Test case simulations are used to demonstrate the impact of tool orientation and speed on chatter stability and forced vibrations. The proposed algorithm identifies the optimal spindle speed and tool orientation by continuously updating the workpiece dynamics as a function of time and tool position to achieve improved stability and surface quality. Stability simulations are conducted to assess the optimization approach's performance, and the results are compared with experiments by machining a series of thin-walled twisted fan blades.

Construction of a linguistically interactive thesaurus for English second language acquisition based on an eigenvalue-fitting superiority algorithm

Abstract This paper completes the overall design of a linguistic interactive terminology database based on the characteristics of second language acquisition and terminology and completes the construction of the terminology database by combining a goodness-of-fit detection algorithm based on terminology eigenvalue extraction. The efficiency of terminology information recognition is analyzed and compared with the terminology conversion rate of the eigenvalue goodness-offit algorithm using a neural network learning model of long and short-term memory to optimize the performance of the terminology database. The metric approach's classifier performance evaluation metrics are used to compare the accuracy and recall of the two algorithms accurately. The results show that the accuracy of the fitted superiority classifier with the application of word eigenvalue embedding compared to the LSTM classifier for the classification of electric power terms is improved by about 11% in all categories, and the average accuracy of the classifier exceeds 76.5%.

Green surfactants for enhanced oil recovery: A review

EOR or Enhanced Oil Recovery has been demonstrated to be an efficient oil recovery technology for recovering bypassed oil and residual oil that has become stranded in the reservoir. The goal of implementing EOR is to extend reservoir life beyond previous economic constraints by assisting in the use of water floods or other traditional methods of extending reservoir life. The flooding method has been shown to be the most effective among all improved oil recovery approaches, and it is known as the surfactant flooding method due to its ability to minimize interfacial tension and improve mobility control. It has been shown to be one of the most efficient tertiary recovery strategies for achieving high displacement efficiency. The tertiary approach's performance is heavily dependent on a number of critical components, including surfactant content, pH, and salinity. Academics are currently interested in Green Enhanced Oil Recovery (GEOR), or green enhanced oil recovery, because it will help drive down costs while also being more environmentally friendly. Researchers have shown that developing a green-based surfactant for surfactant flooding is feasible and, in some cases, more efficient. As a result, the latest research on green surfactants from various resources used for surfactant flooding for enhanced oil recovery was presented in this review paper.

Achievement goal perception: An interpersonal approach to achievement goals.

Research on achievement goals is voluminous but focused primarily on intrapersonal regulation. In the present article, we emphasize the integral role that achievement goals also play in the broader process of interpersonal judgment. We establish a new interpersonal approach to achievement goals that integrates the extensive achievement goal literature with the well-established social relations model (SRM). We introduce and formally define the interpersonal concept of achievement goal perception, present a formal methodological approach to studying this novel concept using a Bayesian implementation of the multivariate SRM (MSRM), and put the proposed integrative approach to the test with an in-depth empirical study that directly addresses fundamental questions of achievement goal perception. In this empirical study, we measured four types of achievement goal perceptions-mastery-approach (MAP), mastery-avoidance (MAV), performance-approach (PAP), performance-avoidance (PAV)-across 1,809 student-classmate dyads from 42 small discussion-based undergraduate classes. Results indicated a predominantly perceiver-driven process consisting of self-other agreement for MAP, MAV, and PAV goals but not PAP goals, assumed similarity for each achievement goal, and greater assumed similarity among closer classmates. Achievement goal perception provided incremental predictive utility for two gold-standard educational outcomes-academic performance and intrinsic motivation-among both perceivers and targets, and four additional educationally relevant constructs-perceived class value, perceived effort investment, perceived competence, and peer help-seeking-among perceivers. Having laid the theoretical, methodological, and empirical foundations, we discuss this new interpersonal approach to achievement goals alongside contemporary research on achievement motivation and interpersonal judgment. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

FSO-5G Networks with Enhanced Throughput, Reliability and Low Latency

Due to the necessity for high-speed and reliable communication, the combination of Free Space Optics (FSO) and 5G wireless networks has received substantial attention in recent years. However, atmospheric turbulence can significantly damage the quality of the FSO link, leading to high error rates and poor reliability. In this paper, a concatenated dynamic coding approach has been proposed and presented to limit the effects of atmospheric turbulence, hence increasing the overall reliability and security of FSO-5G networks. The proposed strategy combines various coding algorithms in FSO-5G networks to improve error correction, minimise latency, and increase throughput. The proposed approach's performance was examined using simulation and compared to other traditional coding systems.

A Malicious Code Detection Method Based on Stacked Depthwise Separable Convolutions and Attention Mechanism.

To address the challenges of weak model generalization and limited model capacity adaptation in traditional malware detection methods, this article presents a novel malware detection approach based on stacked depthwise separable convolutions and self-attention, termed CoAtNet. This method combines the strengths of the self-attention module's robust model adaptation and the convolutional networks' powerful generalization abilities. The initial step involves transforming the malicious code into grayscale images. These images are subsequently processed using a detection model that employs stacked depthwise separable convolutions and an attention mechanism. This model effectively recognizes and classifies the images, automatically extracting essential features from malicious software images. The effectiveness of the method was validated through comparative experiments using both the Malimg dataset and the augmented Blended+ dataset. The approach's performance was evaluated against popular models, including XceptionNet, EfficientNetB0, ResNet50, VGG16, DenseNet169, and InceptionResNetV2. The experimental results highlight that the model surpasses other malware detection models in terms of accuracy and generalization ability. In conclusion, the proposed method addresses the limitations of traditional malware detection approaches by leveraging stacked depthwise separable convolutions and self-attention. Comprehensive experiments demonstrate its superior performance compared to existing models. This research contributes to advancing the field of malware detection and provides a promising solution for enhanced accuracy and robustness.

A two-layer optimal configuration approach of energy storage systems for resilience enhancement of active distribution networks

Introducing energy storage systems (ESSs) into active distribution networks (ADNs) has attracted increasing attention due to the ability to smooth power fluctuations and improve resilience against fault disturbances. This paper proposes a methodology for simultaneously optimizing the configuration of battery ESSs and the operation of ADNs, and the goal is to increase the resilience of the ADNs withstanding multi-faults. Firstly, based on random sampling and K-means clustering, a generation strategy of typical N-1 and N-2 fault scenarios is designed for the ADNs. Then, a two-layer optimization model is established, where the inner model is to optimize the fault recovery performance from the operational perspective, and the outer model is to obtain the optimal site and size of ESSs from the economic perspective. Further, the second-order cone relaxing (SOCR) method and the hybrid gray wolf optimal and particle swarm optimal (GWO-PSO) algorithm are applied to solve the optimization model. Using MATLAB, the modified IEEE 33-node and 118-node systems are built to check the proposed approach's performance. Different periods are considered to show the multi-faults' development, and by introducing a resilience assessment system with node voltage deviation, fault recovery rate, and network loss rate, the resilience of the ADNs is analyzed. From the comparative results, the proposed approach can optimally configure the battery ESSs, and adjust the network structure as well as the distributed generation outputs. Following the ESS configuration cost reduction of 53.19% and 9.8%, the resilience of the ADNs against the multi-faults will increase by 13.36% and 8.25% for the 33-node and 118-node systems.

Machine Learning Approach for Screening Cochlear Implant Candidates: Comparing With the 60/60 Guideline.

To develop a machine learning-based referral guideline for patients undergoing cochlear implant candidacy evaluation (CICE) and to compare with the widely used 60/60 guideline. Retrospective cohort. Tertiary referral center. 772 adults undergoing CICE from 2015 to 2020. Variables included demographics, unaided thresholds, and word recognition score. A random forest classification model was trained on patients undergoing CICE, and bootstrap cross-validation was used to assess the modeling approach's performance. The machine learning-based referral tool was evaluated against the 60/60 guideline based on ability to identify CI candidates under traditional and expanded criteria. Of 587 patients with complete data, 563 (96%) met candidacy at our center, and the 60/60 guideline identified 512 (87%) patients. In the random forest model, word recognition score; thresholds at 3000, 2000, and 125; and age at CICE had the largest impact on candidacy (mean decrease in Gini coefficient, 2.83, 1.60, 1.20, 1.17, and 1.16, respectively). The 60/60 guideline had a sensitivity of 0.91, a specificity of 0.42, and an accuracy of 0.89 (95% confidence interval, 0.86-0.91). The random forest model obtained higher sensitivity (0.96), specificity (1.00), and accuracy (0.96; 95% confidence interval, 0.95-0.98). Across 1,000 bootstrapped iterations, the model yielded a median sensitivity of 0.92 (interquartile range [IQR], 0.85-0.98), specificity of 1.00 (IQR, 0.88-1.00), accuracy of 0.93 (IQR, 0.85-0.97), and area under the curve of 0.96 (IQR, 0.93-0.98). A novel machine learning-based screening model is highly sensitive, specific, and accurate in predicting CI candidacy. Bootstrapping confirmed that this approach is potentially generalizable with consistent results.

New distributed-topsis approach for multi-criteria decision-making problems in a big data context

Nowadays, the online environment is extra information-rich and allows companies to offer and receive more and more options and opportunities in multiple areas. Thus, decision-makers have abundantly available alternatives to choose from the best one or rank from the most to the least preferred. However, in the multicriteria decision-making field, most tools support a limited number of alternatives with as narrow criteria as possible. Decision-makers are forced to apply a screening or filtering method to reduce the size of the problem, which will slow down the process and eliminate some potential alternatives from the rest of the decision-making process. Implementing MCDM methods in high-performance parallel and distributed computing environments becomes crucial to ensure the scalability of multicriteria decision-making solutions in Big Data contexts, where one can consider a vast number of alternatives, each being described on the basis of a number of criteria.In this context, we consider TOPSIS one of the most widely used MCDM methods. We present a parallel implementation of TOPSIS based on the MapReduce paradigm. This solution will reduce the response time of the decision-making process and facilitate the analysis of the robustness and sensitivity of the method in a high-dimension problem at a reasonable response time.Three multicriteria analysis problems were evaluated to show the proposed approach's computational efficiency and performance. All experiments are carried out within GCP's Dataproc, a service allowing the execution of Apache Hadoop and Spark tasks in Google Cloud. The results of the tests obtained are very significant and promising.

Feature Based Indian Currency Detection Using Minimum Weight Distance Classifier

The Automated Currency Recognition is of great interest as a number of automated banking systems used by financial institutions make use of it as their main function. A review of the literature on banknote recognition, however, reveals that there are no methods that have been proposed or put into practice for the identification of recently issued bank notes. Real-time applications can benefit from the suggested approach's excellent performance and relative time efficiency. We believe that the composite feature described in this thesis which combines elements of both color and texture is a first in the field of banknote identification. Our contribution is that this research initiative and the suggested technology have made real-time recognition of recently released ultimate development of real-time multi-currency recognition is made feasible by banknotes.

Quantification and correction of motion influence for nacelle-based lidar systems on floating wind turbines

Abstract. Inflow wind field measurements from nacelle-based lidar systems offer great potential for different applications including turbine control, load validation, and power performance measurements. On floating wind turbines nacelle-based lidar measurements are affected by the dynamic behavior of the floating foundations. Therefore, the effects on lidar wind speed measurements induced by floater dynamics must be understood. In this work, we investigate the influence of floater motions on wind speed measurements from forward-looking nacelle-based lidar systems mounted on floating offshore wind turbines (FOWTs) and suggest approaches for correcting motion-induced effects. We use an analytical model, employing the guide for the expression of uncertainty in measurements (GUM) methodology and a numerical lidar simulation for the quantification of uncertainties. It is found that the uncertainty of lidar wind speed estimates is mainly caused by the fore–aft motion of the lidar, resulting from the pitch displacement of the floater. Therefore, the uncertainty is heavily dependent on the amplitude and the frequency of the pitch motion. The bias of 10 min mean wind speed estimates is mainly influenced by the mean pitch angle of the floater and the pitch amplitude. We correct motion-induced biases in time-averaged lidar wind speed measurements with a model-based approach, employing the developed analytical model for uncertainty and bias quantification. Testing of the approach with simulated dynamics from two different FOWT concepts shows good results with remaining mean errors below 0.1 m s−1. For the correction of motion-induced fluctuation in instantaneous measurements, we use a frequency filter to correct fluctuations caused by floater pitch motions for instantaneous measurements. The correction approach's performance depends on the pitch period and amplitude of the FOWT design.

Sustainability assessment of optimal location of electric vehicle charge stations: a conceptual framework for green energy into smart cities

Electric vehicles (EVs) have achieved a great deal of success, indicating that the motor industry will soon be emission-free. They run on electricity stored in batteries, which their drivers can recharge using an external source of electricity. Therefore, the development of an infrastructure for charging EVs has become a necessity. In this paper, a real case study in Zagazig, Egypt, for evaluating, operating, and determining the most sustainable location for an EV charging station is presented. Identifying such a location is a complex task that requires considering many influencing factors and their sub-factors. Consequently, a general evaluation system that includes six key factors, in addition to nineteen sub-factors, has been developed. A multi-criteria decision-making (MCDM) approach is applied to deal with this problem. The neutrosophic theory with type-2 neutrosophic numbers (T2NNs) and a type-2 neutrosophic number weighted averaging (T2NNWA) operator are used to combine expert and specialized views since they are ambiguous and uncertain. Firstly, the Decision-making Trial and Evaluation Laboratory (DEMATEL) method is applied to prioritize key factors and sub-factors. Then, the Complex Proportional Assessment (COPRAS) technique selects six sustainable sites for EV charging stations. Also, comparison and sensitivity evaluations verify the approach's reliability, strength, stability, and performance. Finally, this article provides good information for policymakers and the results of the case study indicate that the economic factor is the most important factor in selecting the optimal location followed by the technical factor.

A Novel Routing Protocol-Based Data Transmission to Enhance the Quality of Service for Internet of Medical Things Using 5G

When it comes to improving people's health, various forms of smart city applications are put to use in the area of smart healthcare. Health care providers are increasingly using the internet of medical things (IoMT) as an environment of linked healthcare systems, computational capabilities, and health monitors to improve the quality of service (QoS). Smart healthcare devices transmit data to IoMT and it requires an efficient solution. IoMT's and 5G networks' importance necessitates the development and testing of a novel multi-hop reinforced clustering-based routing protocol (MRCRP) to improve health data transmission's QoS. Cluster head selection is carried out using an energy-efficient approach to enhance resource usage. Min-max normalization is used to transform original data into meaningful data for more analysis. Using principal component analysis (PCA), relevant features can be extracted. The proposed routing technique is then employed to effectively transmit the data. The suggested approach's performance measures, such as delay, throughput, energy consumption, and packet drop ratio are studied, as is a comparative analysis with standard routing protocols..

Requirements‐related fault prevention during the transformation from formal specifications to programs

AbstractSoftware faults are costly to find and remove from programs. It is better to avoid inserting the faults in the first place. The authors identify requirements‐related faults that can arise during the transformation of formal specifications into programs, analyse each to discover its causes, and propose corresponding fault prevention methods. The approach works by analysing a specification, decomposing it into specification fragments with different levels like components, determining the correct implementation order among the components, and enabling automated code generation for the components and other low‐level specification fragments. The approach seeks to reduce the faults in programs significantly by guiding programmers to work effectively and productively. The authors evaluate the approach's performance experimentally and compare it to the performance of a fault‐prevention method with industry applications.