CPU Execution Time Research Articles

Model for detecting anomalies and suspicious transactions on financial and non-financial entries

Some conventional audit methods sometimes fall short in identifying subtle irregularities and emerging fraudulent schemes. Our proposal is therefore part of the aim of improving audit processes. At the end of the experiments on two sets of data, our model offers the best results compared to other models, particularly in terms of CPU execution time of the model and also in terms of performance in the detection of normal data with a better rate of false positives.

Secure workflow scheduling algorithm utilizing hybrid optimization in mobile edge computing environments

The rapid advancement of mobile communication technology and devices has greatly improved our way of life. It also presents a new possibility that data sources can be used to accomplish computing tasks at nearby locations. Mobile Edge Computing (MEC) is a computing model that provides computer resources specifically designed to handle mobile tasks. Nevertheless, there are certain obstacles that must be carefully tackled, specifically regarding the security and quality of services in the workflow scheduling over MEC. This research proposes a new method called Feedback Artificial Remora Optimization (FARO)-based workflow scheduling method to address the issues of scheduling processes with improved security in MEC. In this context, the fitness functions that are taken into account include multi-objective, such as CPU utilization, memory utilization, encryption cost, and execution time. These functions are used to enhance the scheduling of workflow tasks based on security considerations. The FARO algorithm is a combination of the Feedback Artificial Tree (FAT) and the Remora Optimization Algorithm (ROA). The experimental findings have demonstrated that the developed approach surpassed current methods by a large margin in terms of CPU use, memory consumption, encryption cost, and execution time, with values of 0.012, 0.010, 0.017, and 0.036, respectively.

Distributed least-squares progressive iterative approximation for blending curves and surfaces

Least-squares progressive iterative approximation (LSPIA) is an effective tool for fitting data points with curves and surfaces in CAD/CAM, due to its intuitive geometric meaning and its suitability for handling mass data. However, the classic LSPIA method for blending curves and surfaces has a slow convergence rate and takes a long CPU execution time, since the spectral radius of its iteration matrix is near to 1. To achieve a reduction in CPU execution time, this paper presents a distributed least-squares progressive iterative approximation (DLSPIA) method by dividing the collocation matrix into some blocks, which are called processors. The proposed method distributes the computation of the control points progressively in a way that each processor is responsible for a block of the whole point set. Combining the information obtained from the previous processors with that of the current processor, the DLSPIA method can progressively and quickly approximate the least-squares fitting result of the original data set block by block via distributed computation. And the algorithm converges within a finite number of iterations. Furthermore, the iterative formulae for blending surface fitting are expressed in matrix form, which can avoid the computation of the matrix Kronecker product to reduce the CPU execution time. Several numerical examples are presented to demonstrate the superiority of the proposed method compared with the previous methods.

On the well posedness and Steffensen’s based numerical approximation of an inverse Cauchy problem

This work is devoted to the study of an inverse Cauchy problem governed by Poisson’s equation, which is one of highly ill-posed problems in the Hadamard’s sense (Hadamard, 1953). We propose an efficient approach for solving this data completion problem, based on its reformulation into a root finding problem which is equivalent to a fixed point one involving a Steklov-Poincaré like operator (Chakib et al., 2018, Nachaoui et al., 2021). We propose first another proof of the existence of the fixed point using the Fredholm alternative. This allows us to establish also the uniqueness and the stability of the solution, in contrast to the previous work (Chakib et al., 2018, Nachaoui et al., 2021), where only the existence result was established. Then, the numerical approximation of the fixed point problem is investigated using an iterative process performed by a Steffensen’s approach (Greenbaum and Chartier, 2012) and the P1 finite element discretization. Finally, we present some numerical results showing the efficiency of the proposed approach in term of the solution accuracy and the CPU execution time.

Empirical Study of Fully Homomorphic Encryption Using Microsoft SEAL

In the context of the increasing integration of Internet of Things technologies and the growing importance of data lakes, the need for robust cybersecurity measures to protect privacy without compromising data utility becomes key. Aiming to address the privacy–security challenge in such digital ecosystems, this study explores the application of Fully Homomorphic Encryption (FHE) using the Microsoft SEAL library. FHE allows for operations on encrypted data, offering a promising opportunity for maintaining data confidentiality during processing. Our research employs systematic experimental tests on datasets to evaluate the performance of homomorphic encryption in terms of CPU usage and execution time, executed across traditional PC configurations and a NVIDIA Jetson Nano device to assess the scalability and practicality of FHE in edge computing. The results reveal a performance disparity between computing environments, with the PC showing stable performance and the Jetson Nano revealing the limitations of edge devices in handling encryption tasks due to computational and memory constraints.

Analysis of the impact of using containerization techniques on application performance in Python

This article comprehensively evaluates the impact of two containerization environments, Docker and Podman, on the performance of Python applications. The paper characterizes the two tools and presents the differences in their architectures. The scope of the study covers three aspects. The first is a comparison of resource usage, such as CPU usage, RAM usage and execution time, during the calculation of the number π. The next step is to analyse the resource usage when sorting an ordered list. The final aspect of the research is a comparison of the start-up time of the container in both environments. The tests carried out allow the presence of a performance overhead in both containerization environments, with an average of 8%. In addition, it can be seen that there is better resource management in the case of the Podman tool and a more dynamic environment in the case of the Docker tool.

A New Approach for Anonymizing Transaction Data with Set Values

This article proposes a new method that can guarantee strong privacy while minimizing information loss in transactional data composed of a set of each attribute value in a relational database, which is not generally well-known structured data. The proposed scheme adopts the same top-down partitioning algorithm as the existing k-anonymity model, using local generalization to optimize safety and CPU execution time. At the same time, the information loss rate, which is a disadvantage of the existing local generalization, is further improved by reallocating transactions through an additional bottom-up tree search process after the partitioning process. Our scheme shows a very fast processing time compared to the HgHs algorithm using generalization and deletion techniques. In terms of information loss, our scheme shows much better performance than any schemes proposed so far, such as the existing local generalization or HgHs algorithm. In order to evaluate the efficiency of our algorithm, the experiment compared its performance with the existing local generalization and the HgHs algorithm, in terms of both execution time and information loss rate. As a result of the experiment, for example, when k is 5 in k-anonymity for the dataset BMS-WebView-2, the execution time of our scheme is up to 255 times faster than the HgHs algorithm, and with regard to the information loss rate, our method showed a maximum rate of 62.37 times lower than the local generalization algorithm.

Biogeography-Based Teaching Learning-Based Optimization Algorithm for Identifying One-Diode, Two-Diode and Three-Diode Models of Photovoltaic Cell and Module

This article handles the challenging problem of identifying the unknown parameters of solar cell three models on one hand and of photovoltaic module three models on the other hand. This challenge serves as the basis for fault detection, control, and modelling of PV systems. An accurate model of PV is essential for the simulation research of PV systems, where it has a significant role in the dynamic study of these systems. The mathematical models of the PV cell and module have nonlinear I-V and P-V characteristics with many undefined parameters. In this paper, this identification problem is solved as an optimization problem based on metaheuristic optimization algorithms. These algorithms use root mean square error (RMSE) between the calculated and the measured current as an objective function. A new metaheuristic amalgamation algorithm, namely biogeography-based teaching learning-based optimization (BB-TLBO) is proposed. This algorithm is a hybridization of two algorithms, the first one is called BBO (biogeography-based optimization) and the second is TLBO (teaching learning-based optimization). The BB-TLBO is proposed to identify the unknown parameters of one, two and three-diode models of the RTC France silicon solar cell and of the commercial photovoltaic solar module monocrystalline STM6-40/36, taking into account the performance indices: high precision, more reliability, short execution time and high convergence speed. This identification is carried out using experimental data from the RTC France silicon solar cell and the STM6-40/36 photovoltaic module. The efficiency of BB-TLBO is checked by comparing its identification results with its own single algorithm BBO, TLBO and newly introduced hybrid algorithms such as DOLADE, LAPSO and others. The results reveal that the suggested approach surpasses all compared algorithms in terms of RMSE (RMSE min, RMSE mean and RMSE max), standard deviation of RMSE values (STD), CPU (execution time), and convergence speed.

ANALYSIS OF THE IMPLEMENTATION OF MVVM ARCHITECTURE PATTERN ON PERFORMANCE OF IOS MOBILE-BASED APPLICATIONS

Performance efficiency is important in mobile application development because mobile devices have limitations in terms of power and resources. Performance efficiency can be improved by applying architecture patterns. In this paper, we use the Model View ViewModel (MVVM) architecture. The application of the architecture is carried out to analyze how practical the application of the MVVM architecture pattern is in increasing performance efficiency in the mobile application. Performance efficiency is measured based on CPU usage, memory usage, and execution Time. The case study shows that the CPU usage and execution Time on MVVM are smaller than Base architecture pattern from the AR Ruler. This is due to the third-party library RxSwift in the MVVM architecture that increases the application's response so that CPU usage and execution time is better than Base architecture pattern. However, the existence of the third-party library RxSwift has a negative impact on memory usage, resulting in higher memory usage than the Base Architecture Pattern. The MVVM pattern is highly recommended for mobile application development to improve performance efficiency.

A Provable Secure Cybersecurity Mechanism Based on Combination of Lightweight Cryptography and Authentication for Internet of Things

Internet of Things devices, platform programs, and network applications are all vulnerable to cyberattacks (digital attacks), which can be prevented at different levels by using cybersecurity protocol. In the Internet of Things (IoT), cyberattacks are specifically intended to retrieve or change/destroy sensitive information that may exceed the IoT’s advantages. Furthermore, the design of a lightweight cybersecurity mechanism experiences a critical challenge that would perfectly fit resource-constrained IoT devices. For instance, identifying the compromised devices and the users’ data and services protection are the general challenges of cybersecurity on an IoT system that should be considered. This paper proposes a secure cybersecurity system based on the integration of cryptography with authentication (ELCA) that utilizes elliptic curve Diffie–Hellman (ECDH) to undertake key distribution while the weak bits problem in the shared secret key is resolved. In this paper, three systems of integration are investigated, while ELCA proposes secure integration between authentication and encryption to facilitate confidentiality and authenticity transfer messages between IoT devices over an insecure communication channel. Furthermore, the security of ELCA is proven mathematically using the random oracle model and IoT adversary model. The findings of the emulation results show the effectiveness of ELCA performance in terms of a reduced CPU execution time by 50%, reduced storage cost by 32–19.6%, and reduced energy consumption by 41% compared to the baseline cryptographic algorithms.

Comparative Analysis of Multi-digit Modular Multiplication Algorithms for public key Crypto system in Big Data Security

Big data security is a significant concern today due to rapidly growing data volume, velocity, and varieties of data in nature. Data security is a major concern because during transmission through insecure channels. From its recognition, the Montgomery multi-digit multiplication algorithms are still well-accepted approaches in the numerical calculation, chaos arithmetic, and highperformance implementation of public key cryptosystems such as RSA, Diffie-Hellman, and the Elliptic curve cryptosystems. We have presented the comparative study of multi-digit integer multiplication algorithms, and their performance is evaluated in terms of CPU execution time. Montgomery modular multiplication is a good practice, among others.

A modified un-realisation approach for effective data perturbation

In recent times, data has been evolving from multiple sources like social media, Facebook, Twitter, etc. in large volumes and acquiring in multiple forms. These data have multi-dimensional sensitive features from different resources entail that privacy preserving is a significant research issue. In this context, un-realisation algorithms have evolved to hide the collected data with the addition of noise to them to generate a distorted dataset while attempting privacy preservation. In this paper, a novel modified un-realisation algorithm has been proposed to generate a distorted dataset by removing duplicate elements in the dataset decreasing computational time of decision tree construction process. These techniques add noise to the original data and generate a distorted dataset by using a un-realisation algorithm. This novel approach converts the original sample datasets into different perturbed datasets by inducing the noise through set theory. It experimentally produces better results than un-realisation algorithm in terms of CPU execution time and space complexity.

Evaluating Web Scraping Performance Using XPath, CSS Selector, Regular Expression, and HTML DOM With Multiprocessing Technical Applications

Data collection has become a necessity today, especially since many sources of data on the internet can be used for various needs. The main activity in data collection is collecting quality information that can be analyzed and used to support decisions or provide evidence. The process of retrieving data from the internet is also known as web scraping. There are various methods of web scraping that are commonly used. The amount of data scattered on the internet will be quite time-consuming if the web scraping is done on a large scale. By applying the parallel concept, the multi-processing approach can help complete a job. This study aimed to determine the performance of the web scraping method with the application of multi-processing. Testing is done by doing the process of scraping data from a predetermined target web. Four web scraping methods: CSS Selector, HTML DOM, Regex, and XPath, were selected to be used in the experiment measured based on the parameters of CPU usage, memory usage, execution time, and bandwidth usage. Based on experimental data, the Regex method has the least CPU and memory usage compared to other methods. While XPath requires the least time compared to other methods. The CSS Selector method is the smallest in terms of bandwidth usage compared to other methods. The application of multi-processing techniques to each web scraping method is proven to save memory usage, reduce execution time and reduce bandwidth usage compared to only using single processing.

Unforgeable Digital Signature Integrated into Lightweight Encryption Based on Effective ECDH for Cybersecurity Mechanism in Internet of Things

Cybersecurity protocols enable several levels of protection against cyberattacks (digital attacks) that spread across network devices, platform programs, and network applications. On the Internet of Things (IoT), cyberattacks are generally intended to access and change/destroy sensitive information, which may reduce IoT benefits. Moreover, recent IoT systems are experiencing a critical challenge in designing a lightweight and robust cybersecurity mechanism on resource-constrained IoT devices. The cybersecurity challenges facing the IoT that should be taken into consideration are identifying compromised devices, data/service protection, and identifying impacted IoT users. This paper proposes an unforgeable digital signature integrated into an effective lightweight encryption (ELCD) mechanism that utilizes the secure key distribution in an elliptic curve Diffie–Hellman (ECDH) and resolves the weak bit problem in the shared secret key due to the Diffie–Hellman exchange. The ELCD mechanism proposes a secure combination between the digital signature and encryption, and it uses fast hash functions to confidentially transfer a shared secret key among IoT devices over an insecure communication channel. Furthermore, the ELCD mechanism checks the true identity of the sender with certainty through the proposed digital signature, which works based on a hash function and three steps of curve-point inspection. Furthermore, the security of ELCD was mathematically proven using the random oracle and IoT adversary models. The findings of the emulation results show the effectiveness of ELCD in terms of CPU execution time, storage cost, and power consumption that are less by 53.8%, 33–17%, and 68.7%, respectively, compared to the baseline cryptographic algorithms.

Fast Flexible Direct Power Flow for Unbalanced and Balanced Distribution Systems

The study proposes a fast flexible direct power flow solution for radial distribution systems and a fast flexible direct weakly meshed power flow solution for weakly meshed distribution systems. The algorithm is based on the direct forward sweep power flow solution, which is an updated version of the backward/forward sweep solution. The fast flexible direct power flow uses a unique conversion matrix (CM) to rapidly determine the power flow solution. The inverted conversion matrix and its slide-modified matrix are used to solve the power flow problem in a single forward sweep step, which is a novel feature of this work. To ensure the invertibility of the conversion matrix, it is constructed to have a small condition number and a determinant of minus one, and all of its eigenvalues must be equal to that of minus one. Additionally, by modifying the conversion matrix to accommodate the loop branch using the break-point idea, a new weakly meshed conversion matrix (WMCM) is generated with the same following modification as for the radial network and employed in the fast flexible direct weakly meshed power flow (FFDWMPF) solution for the weakly meshed distribution network. The usage of a single matrix in the power flow solution and advanced direct techniques decreases the number of iterations and CPU execution time when MATLAB programming is executed. Furthermore, the proposed method is flexible enough to incorporate any changes in the radial or weakly meshed distribution system just by incorporating the changes in the CM and WMCM for any radial or weakly meshed system. Moreover, the robustness of FFDPF and FFDWMPF is evaluated under various loading scenarios on balanced radial and weakly meshed distribution networks. Finally, to validate the proposed algorithm, the proposed strategy is applied to numerous balanced and unbalanced distribution systems.

Effect Of MVVM Architecture Pattern on Android Based Application Performance

The performance of android-based mobile applications is very important to pay attention to because performance will be related to how users will feel the experience of the application. This relates to memory usage and CPU usage on android devices when using applications. There are several ways used to improve the performance of android-based mobile applications, one of which is by applying an architecture pattern. The use of the right architecture is expected to produce good performance for android devices when running an application. Therefore, the author conducted a study on whether the application of the MVVM architecture pattern by changing the program code with the refactoring method will affect memory usage, CPU, and code execution time in Android-based applications. This is implemented using the Android Studio application and using android profiler tools to measure the performance of the application.

4SA: OPTIMIZING SPACE FILLING CURVE BASED GRID CELL INDEXING TO SCALABLY MANAGE REMOTELY SENSED IMAGES IN KEY-VALUE DATABASES

Abstract. State-of-the-art remote sensing image management systems adopt scalable databases and employ sophisticated indexing techniques to perform window and containment queries. Many rely on space-filling curve (SFC) based index techniques designed for key-value databases and are predominantly employable for images that are iso-oriented. Critically, these indexes do not consider the high degree of overlap among images that exists in many data sets and the affiliated storage requirements. Specifically, employing an SFC-based grid cell index approach in consort with ground footprint coverage of the images requires storage of a unique image object identification (IOI) for each image in every grid cell where overlap occurs. Such an approach adversely affects both storage and query response times. In response, this paper presents an optimization technique for an SFC-based grid cell space indexing. The optimization is specifically designed for window and containment queries where the region of interest overlaps with at least a 2 × 2 grid of cells. The technique is based on four cell removal steps, thus called “four step algorithm” (4SA). Each step employs a unique spatial configuration to check for continuous spatial extent. If present, the IOI of the target cell is omitted from further consideration. Analysis and experiments on real world and synthetic image data demonstrated that 4SA improved storage demands by 41.3% – 47.8%. Furthermore, in the performed querying experiments, only 42% of IOI elements needed to be processed, thus yielding a 58% productivity gain. The reduction of IOI elements in querying also impacted the CPU execution time (3.0% – 5.2%). The 4SA also demonstrated data scalability and concurrent user scalability in querying large regions by completing the index searching and concurrent user scalability 1.86% – 3.35% faster than when 4SA was not applied.

Parallel optimization over the integer efficient set

AbstractThis paper introduces a modified sequential version method for optimizing a linear function over an integer efficient set, as well as a new exact parallel algorithm. The performance of parallel programming in this context is clear and shown through different instances with different sizes. Each procedure builds a finite monotonous sequence of values for the main criterion to be optimized, in a reasonable amount of CPU execution time. This latter remains much better. For the first time, the Algerian IBNBADIS cluster—CERIST—was used with this type of problem. Significant results are obtained by both proposed techniques, particularly with the parallel one.

ATS: A Novel Time-Sharing CPU Scheduling Algorithm Based on Features Similarities

Minimizing time cost in time-shared operating systems is considered basic and essential task, and it is the most significant goal for the researchers who interested in CPU scheduling algorithms. Waiting time, turnaround time, and number of context switches are the most time cost criteria used to compare between CPU scheduling algorithms. CPU scheduling algorithms are divided into non-preemptive and preemptive. Round Robin (RR) algorithm is the most famous as it is the basis for all the algorithms used in time-sharing. In this paper, the authors proposed a novel CPU scheduling algorithm based on RR. The proposed algorithm is called Adjustable Time Slice (ATS). It reduces the time cost by taking the advantage of the low overhead of RR algorithm. In addition, ATS favors short processes allowing them to run longer time than given to long processes. The specific characteristics of each process are; its CPU execution time, weight, time slice, and number of context switches. ATS clusters the processes in groups depending on these characteristics. The traditional RR assigns fixed time slice for each process. On the other hand, dynamic variants of RR assign time slice for each process differs from other processes. The essential difference between ATS and the other methods is that it gives a set of processes a specific time based on their similarities within the same cluster. The authors compared between ATS with five popular scheduling algorithms on nine datasets of processes. The datasets used in the comparison vary in their features. The evaluation was measured in term of time cost and the experiments showed that the proposed algorithm reduces the time cost.

Photovoltaic mono and bifacial module/string electrical model parameters identification and validation based on a new differential evolution bee colony optimizer

Well estimating the electrical model parameters of the photovoltaic (PV) module/string serves to develop an accurate simulator and a fault diagnosis tool. Several based evolutionary techniques were proposed to identify the unknown circuit equivalent PV generator (PVG) parameters. Whereas most of them have not been examined to various real operating conditions of solar irradiance and PV cells temperature. That requires larger search range than the adopted one in the literature. Enlarging the search range imposes more computational time and high exploration and exploitation features. Hence, a novel hybrid differential evolution and artificial bee colony intelligence (nDEBCO) approach is proposed. In terms of convergence quality, CPU execution time, number of function evaluations (NFE), and error standard deviation (StD). The newly developed approach permits to accurately identify the PV module/string unknown parameters with suitable implementation complexity. Mono-facial CLS 220P PV string has been utilized employing an adequate experimental setup with online implementation. 1080 I-V curves have been measured and estimated, where the overall RMSE ± StD is below 0.02 ± 1e−16. The nDEBCO outperforms the present-day published works, for common case studies in the literature with two based root mean square error (RMSE) objective functions namely Lambert W function (LWF) and classic. It yields 7.73006268e− 4 of RMSE, 7.8785e− 18 of StD, and 2150 NFE under ODM with LWF for RTC France PV cell. Bifacial PV module has been evaluated and the electrical parameters have been extracted within less than 1.36 s of CPU run time and not>8.0299631e− 3 ± 6.9096e− 16 of RMSE ± StD for front and rear faces. Additionally, the parameter identification procedure has been well validated to simulate the real partial shading scenarios of the studied PV string with a RMSE less than 0.045 and 0.397% of power maximum point absolute error.