Verification Method Research Articles

Aircraft maintainability verification evaluation based on fuzzy analytic hierarchy process

This paper uses the fuzzy analytic hierarchy process to establish the mathematical model of aircraft maintainability verification in the flight test stage. The maintainability evaluation index system is constructed from the factors that affect the aircraft maintainability level, such as equipment layout and accessibility, cover design, marking and error prevention, standardization and interchangeability, and human factor engineering. Through case analysis, the application of this method in the maintainability verification of the military aircraft flight test phase is introduced.

The Influence of the Effectiveness the Implementation of the Presence Administrative Information System on Work Discipline and its Impact on the Quality Employee Service at SDN 160 Sukalaksana Bandung City

This research aims to find out whether the attendance administration information system has an effect on employee work discipline and its impact on the quality of employee service at SDN 160 Sukalaksana, Bandung City. The independent variable is the presence administration information system, the intervening (mediation) variable, namely employee work discipline, and the dependent variable, namely employee service quality. This type of research is included in quantitative research, while the research methods used are descriptive and verification methods. The population in this study were civil servants (PNS) and parents of students at SDN 160 Sukalaksana, Bandung City, totaling 47 respondents. The sampling technique is non-probability sampling using saturated samples, so the total sample is 47 respondents. The data analysis used in this research is path analysis using SPSS software. The results of the research show that the effectiveness of implementing the attendance administration information system has a direct effect on employee work discipline, the effectiveness of implementing the attendance administration information system has no direct effect on the quality of employee service, employee work discipline has a direct effect on the quality of employee service, and the effectiveness of implementing the administration information system Presence has an indirect effect on the quality of employee service through employee work discipline.

The Influence of Competence and Motivation on Employee Performance at BKPSDM Bandung City

This research aims to find out whether competence and motivation influence employee performance at BKPSDM Bandung City. The independent variables are competence and motivation, and the dependent variable is employee performance. This type of research is included in quantitative research. The research methods used are descriptive and verification methods. The population in this research is employees at BKPSDM Bandung City, totaling 103 people. The sampling technique is probability sampling using simple random sampling, while to calculate the number of samples using the Slovin formula with a significance level of 5%, so the total sample is 82 employees at BKPSDM Bandung City. The data analysis used in this research is multiple linear regression analysis using SPSS software. Partial and simultaneous research results show that competence and motivation influence employee performance at BKPSDM Bandung City. The magnitude of the influence of competence and motivation on employee performance is 62.5%.

The Influence of Internal Control and Good Corporate Governance Implementation on Fraud Prevention

This research is motivated by the many cases of fraud in the banking sector in Indonesia. The results of the 2019 Indonesian Fraud Survey stated that the fraud that occurred was caused by internal control factors and the implementation of good corporate governance. This research aims to determine the influence of internal control and the implementation of good corporate governance on fraud prevention in general banking in The research method used in this research is the verification method. The population in this research are internal auditors who work at commercial banks in the Jakarta and Bandung areas. The sampling technique used was a probability sampling technique with a simple random sampling technique, and used the Slovin formula so that the total sample was 47 internal auditors in the Jakarta and Bandung areas The data analysis used is multiple linear regression analysis using the SPSS program. Research results Simultaneously, Internal Control and the implementation of Good Corporate Governance have an effect on fraud prevention. Partially, the implementation of internal control has an effect on fraud prevention. Partially, the implementation of good corporate governance has an effect on prevention

Pengaruh Pembelajaran Daring Terhadap Efektivitas Belajar Siswa di SMK Pariwisata Bina Satya Mandiri Sukabumi

This research aims to determine the process of implementing online learning as well as to determine the effectiveness of student learning and to find out whether there is an influence between online learning and the effectiveness of student learning at the Bina Satya Mandiri Tourism Vocational School, Sukabumi. In this research the author used descriptive and verification methods with a quantitative approach and data collection techniques, namely observation, interviews, questionnaires and literature studies. The results of the research show that the implementation of online learning at the Bina Satya Mandiri Tourism Vocational School, Sukabumi, is going quite well, the effectiveness of student learning is going very well and there is a positive and significant influence between online learning on the effectiveness of student learning at the Bina Satya Mandiri Tourism Vocational School, Sukabumi.

CryptoCard: Blockchain-Powered Library Card Management System

Abstract: Institutions such as libraries often face challenges in managing and verifying the authenticity of issued library cards. Traditional methods of card issuance and verification may be susceptible to fraud and manipulation. To address these challenges, we present a Blockchain-Enabled Library Card Management System. Our system leverages blockchain technology to securely store and manage library card data. Each library card issuance is recorded as a block on the blockchain, ensuring transparency, immutability, and tamper-resistance. Digital signatures generated using the Elliptic Curve Digital Signature Algorithm (ECDSA) are employed to authenticate the integrity of each card. The system allows users to easily issue library cards by providing their personal information and a photo. The generated library card includes user details and a unique identifier, along with a digital signature to validate its authenticity. Additionally, users can verify the validity of library cards by querying the blockchain and verifying the digital signatures. By employing blockchain technology, our system enhances the security and trustworthiness of library card management, reducing the risk of fraud and ensuring the integrity of issued cards. It provides a robust and efficient solution for libraries to manage their card issuance process whilemaintaining data integrity and security.

Customer Satisfaction with Service Quality, Pricing and Promotion Approaches

This research aims to determine and analyze the influence of service quality, price and promotion on consumer satisfaction, both simultaneously and partially. This research design uses descriptive, verification and path analysis research methods. The sampling technique uses the saturated sampling method. The number of respondents was 100 people. The subjects of this research are Bogor Punya Kopi consumers, and the research objects are service quality, price, promotion and consumer satisfaction. The tools used in this analysis are path analysis, multiple correlation analysis, coefficient of determination analysis, and hypothesis testing (F test and t test). The results of the analysis show that partially or simultaneously service quality, price and promotion have a direct and positive influence on consumer satisfaction at the Bogor Punya Kopi cafe.

Pengaruh Kompetensi Pramuwisata Arung Jeram Terhadap Kepuasan Wisatawan di Sungai Citarik Kabupaten Sukabumi

Rafting is one of the sport challenge and it has high rish. It is necessary need guide security an safety procedures before doing rafting. Rafting guide is needed in his activity and it must has acompetency. This research uses a simple regression analysis techniques, the variabels tha is used are competency assembling influences positively and significantly toward the tourist satisfaction as hypothesis through verificative and descriptive quantitative method. The total sample of this research is 98 respomdemts through simple random sample method. The instrument that is used to collect the data is questionnaires. The result shows that the all questions are valid above 0,30 and reliable because it is above 0,70. As descriptively, the competency is generally “good” with average3,64 of 1-5 scale and the tourist satisfaction visit shows “good anough” result with average 3,15 of scale 1-5 scale. Mean while. The result of verificative analysis show determination coefficient (R2) in the amount of 0,370 or 37%, the rest (epsilon) is 63% which is other variabel that has not been researched. The result of the hypothesis examine shows that there are positive and significant influence among the variabels of competence toward the variabel satisfaction of tourist, which the significancy either partially (t test) and simultaneous (F test) is 0,000 less than 0,05.

Smart Attendance System (Multi-Factor Authentication)

Abstract: The "SMART Attendance System using Facial and OTP Verification" project aims to revolutionize traditional attendance management processes by integrating advanced biometric technologies. In this project, a sophisticated system is developed to automate attendance tracking using facial recognition and OTP verification methods. The system utilizes computer vision algorithms to accurately identify individuals based on their facial features, ensuring reliable attendance recording. Additionally, OTP verification adds an extra layer of security, enhancing the integrity of the system. The project encompasses the design and implementation of the software and hardware components required for seamless operation. Through this innovative solution, organizations can streamline attendance monitoring, improve efficiency, and mitigate identity fraud. The project not only offers a practical application of emerging technologies but also provides valuable insights into biometric authentication systems' development and deployment.

Face Recognition Based Attendance System

Abstract: In the field of image analysis and computer vision, one of the most arduous tasks presently considered is Face recognition. The biometric system which basically works on the principle of face recognition is used for the identification or verification of a person from a digitalized image preferably used in surveillance, security and attendance purpose. Face Recognition is becoming more popular than other biometric verification methods due to its simplicity, non-invasiveness, and lack of touch. The system’s major goal is to identify and recognize faces in a real-time environment, match them with data in the database, and record their attendance. This is intended to make the time-consuming manual attendance process more efficient. The main building block on which all automated systems concerned with human faces are designed is face detection. In many person-system user interfaces, precise face identification is important; the real implication is the precise approach to face identification.

Customer Satisfactions Based on Service Quality for VVIV Inpatient Patient of PMI Bogor Hospital

The success of health development plays an important role in improving the quality of human resources in Indonesia. The goal of health development itself can be achieved through various forms of implementing health efforts in a comprehensive, tiered and integrated manner. This research aims to analyze service quality on customer satisfaction simultaneously and partially for VVIP patients at PMI Bogor Hospital. The sample size was 100 respondentsnon-probability sampling technique. The questionnaire was tested using validity tests, reliability tests and also classical assumption tests. The test is valid, reliable, has classic assumptions and can be used for multiple linear regression data. The analytical method used in this research is a descriptive and verification method with a quantitative approach. The results of the research show that there is a positive and significant simultaneous and partial influence of service quality on consumer satisfaction.

Mechanizing the CMP Abstraction for Parameterized Verification

Parameterized verification is a challenging problem that is known to be undecidable in the general case. ‍is a widely-used method for parameterized verification, originally proposed by Chou, Mannava and Park in 2004. It involves abstracting the protocol to a small fixed number of nodes, and strengthening by auxiliary invariants to refine the abstraction. In most of the existing applications of CMP, the abstraction and strengthening procedures are carried out manually, which can be tedious and error-prone. Existing theoretical justification of the ‍method is also done at a high level, without detailed descriptions of abstraction and strengthening rules. In this paper, we present a formally verified theory of ‍in Isabelle/HOL, with detailed, syntax-directed procedure for abstraction and strengthening that is proven correct. The formalization also includes correctness of symmetry reduction and assume-guarantee reasoning. We also describe a tool AutoCMP for automatically carrying out abstraction and strengthening in , as well as generating Isabelle proof scripts showing their correctness. We applied the tool to a number of parameterized protocols, and discovered some inaccuracies in previous manual applications of ‍to the FLASH cache coherence protocol.

Research on the Internal Talent Training Path and Performance Evaluation Mechanism of New R&D Institutions in Zhejiang Province

New research and development (R&D) institutions are an important part of the national innovation system, playing an important role in promoting the transformation of scientific and technological achievements. In recent years, new R&D institutions have gradually become the driving force of innovation-driven development in China. Taking new R&D institutions in Zhejiang Province as the research object, this paper studies the internal talent training path and performance evaluation mechanism of new R&D institutions in Zhejiang Province by using the literature research method, comparison method, case verification method, and other methods. The investigation results show that there are problems such as lack of material and spiritual support and neglect of the absorption of local talents in the internal talent training, and there are problems such as unclear standards, insufficient data, and opaque processes in the performance evaluation mechanism, which greatly affect the establishment and improvement of the performance evaluation mechanism. Given the above problems, this paper puts forward a forward-looking, oriented, flexible, and compatible talent training path and performance evaluation mechanism, hoping to optimize the effective internal talent training path of new R&D institutions, improve the evaluation performance, and promote healthy development of new R&D institutions in Zhejiang Province.

Signature Recognition and Verification Using Machine Learning

This project focuses on the development and implementation of a robust signature recognition and verification system leveraging machine learning techniques. Handwritten signatures serve as essential personal identifiers in numerous applications, such as financial transactions, legal documents, and access control. Traditional methods of signature verification often lack efficiency and accuracy, prompting the need for automated and intelligent systems. The proposed project aims to address this challenge by employing state-of-the-art machine learning algorithms for signature analysis. The project involves the creation of a comprehensive dataset consisting of diverse signature samples. Through the utilization of image processing and deep learning techniques, the system will extract relevant features such as stroke dynamics, pressure, and spatial characteristics from the signatures. The core of the project lies in training a machine learning model on the dataset, enabling it to learn the distinctive patterns inherent in individual signatures. During the verification phase, the developed system will assess the input signature's similarity against the stored templates, providing a confidence score to indicate the level of authenticity. The outcome of this project will be a reliable and efficient signature recognition and verification system that can be applied in real-world scenarios, enhancing security and reducing the risk of fraudulent activities. The project not only contributes to the advancement of biometric authentication systems but also provides valuable insights into the integration of machine learning in document security applications.

Writer verification using feature selection based on genetic algorithm: A case study on handwritten Bangla dataset

AbstractAuthor verification is challenging because of the diversity in writing styles. We propose an enhanced handwriting verification method that combines handcrafted and automatically extracted features. The method uses a genetic algorithm to reduce the dimensionality of the feature set. We consider offline Bangla handwriting content and evaluate the proposed method using handcrafted features with a simple logistic regression, radial basis function network, and sequential minimal optimization as well as automatically extracted features using a convolutional neural network. The handcrafted features outperform the automatically extracted ones, achieving an average verification accuracy of 94.54% for 100 writers. The handcrafted features include Radon transform, histogram of oriented gradients, local phase quantization, and local binary patterns from interwriter and intrawriter content. The genetic algorithm reduces the feature dimensionality and selects salient features using a support vector machine. The top five experimental results are obtained from the optimal feature set selected using a consensus strategy. Comparisons with other methods and features confirm the satisfactory results.

Measurement and analysis of regional water-energy-food nexus resilience with an improved hybrid kernel extreme learning machine model based on a dung beetle optimization algorithm

CONTEXTIn traditional water-energy-food nexus (WEFN) system analysis, the impact of resilience is insufficiently considered, and the accuracy of resilience assessment is low, which can easily lead to system imbalance. Modeling measures can yield optimal evaluation results and provide decision makers with a scientific and efficient basis for management. OBJECTIVEThe objective of this study was to provide a new research paradigm for exploring the sustainable development of WEFN systems by quantitatively and accurately assessing WEFN resilience and identifying its key driving factors. METHODSThe connotation of WEFN resilience was established, and a human-centered conceptual framework for WEFN resilience was constructed. A WEFN resilience evaluation system was scientifically and completely constructed using qualitative and quantitative index screening methods. The hybrid kernel extreme learning machine (HKELM) model was improved based on the dung beetle optimization (DBO) algorithm to measure and analyze the WEFN resilience of the China Beidahuang Group. The CRiteria Importance Through Intercriteria Correlation (CRITIC) method was adopted to calculate the evaluation index weight of the WEFN system of the Beidahuang Group. Model performance verification methods, such as application of the theory of serial number summation, were used to evaluate the performance of the DBO-HKELM model. RESULTS AND CONCLUSIONSThe results showed that over time, the Beidahuang Group's WEFN resilience exhibited a fluctuating upward trend. From 2006 to 2008, WEFN resilience declined to level II because of predatory agricultural production and management practices. From 2008 to 2018, the WEFN resilience increased to level IV due to the protection of uncultivated land, the optimization of irrigation methods, and the increasing maturity of the agricultural modernization development model. Likewise, from 2018 to 2020, the WEFN resilience stabilized at level IV because of the optimization of agricultural planting structures and the reduction in coal consumption. Spatially, the WEFN resilience of various branches of the Beidahuang Group exhibited a trend of “gradually decreasing from north to south.” Differences in certain variables, such as the proportion of area irrigated with electromechanical wells, were the most important factors related to changes in WEFN resilience over time. The DBO-HKELM model has notable advantages over the FA-HKELM and NGO-HKELM models in terms of fitting accuracy, optimization rate, reliability, rationality, and robustness. SIGNIFICANCEThese research results could enrich quantitative assessment methods for determining WEFN system recovery strength and provide scientific support for the safe operation and sustainable development of agricultural economies and societies.

Fingerprint Recognition and Verification using Fourier Domain Filtering and Histogram Equalization Techniques

Fingerprint Recognition is a vital method in biometric identification and verification of human beings in various domains like Security, Digital Forensics, Internet of Things (IoT), and many more. Each individual human is having distinct fingerprint pattern than others, hence it is one of the most prominent and widely used method to distinguish individuals. Many research studies and solutions have been developed in biometric domain since a decade, which influences now in making the process of fingerprint recognition more optimized, faster and efficient. However, present fingerprint acquisition/recognition systems have some limitations, mainly longer computation time for fingerprint matching and evaluating the results. This paper presents a procedure for fingerprint matching that takes into account minutiae features in finger print images and the process of creating an OpenCV structure for minutiae extraction and matching of fingerprints.

Landslide susceptibility assessment based on frequency ratio and semi-supervised heterogeneous ensemble learning model.

Epistemic uncertainty in data-driven landslide susceptibility assessment often tends to be increased by the limited accuracy of an individual model, as well as uncertainties associated with the selection of non-landslide samples. To address these issues, this paper centers on the landslide disaster in Ji'an City, China, and proposes a heterogeneous ensemble learning method incorporating frequency ratio (FR) and semi-supervised sample expansion. Based on the superimposed results of 12 environmental factor frequency ratios (FFR), non-landslide samples were selected and input into light gradient boosting machine (LightGBM), random forest (RF), and convolutional neural network (CNN) models for prediction along with historical landslide samples. The predicted probability values are integrated by four heterogeneous ensemble strategies to expand samples from high-confidence results. The model's performance is evaluated using the area under the receiver operating characteristic curve (AUC), partition frequency ratio (PFR), and other verification methods. The results demonstrate that the negative sample based on FFR sampling is more accurate than the random sampling method, and the FR-SSELR model based on frequency ratio sampling and semi-supervised ensemble strategy exhibits the highest performance (AUC = 0.971, ACC = 0.941). A more reasonable landslide susceptibility map was drawn based on this model, with the lowest percentage of landslides in the low and very low susceptibility zones (sum of PFR = 0.194), as well as the highest percentage of landslides in the high and very high susceptibility zones (sum of PFR = 6.800). Furthermore, the FR-SSELR model improved economic benefits by 3.82-14.2%, offering valuable guidance for decision-making regarding landslide management and the sustainability of Ji'an City.

Application of Neural Network in Seismic Risk Survey

The accuracy of inversion data is determined by the reliability of linear inversion method. Therefore, the study of nonlinear seismic inversion method is a meaningful work. This paper mainly aims at the application of nonlinear neural network inversion method in Fujian Risk Survey, the following work has been accomplished: (1) The theory and algorithm of the conventional multilayer feedforward neural network and some problems in the design of the model are analyzed and summarized. Aiming at the gradient descent function search algorithm in the BP algorithm, a method for global optimization is proposed by combining simulated annealing (global search algorithm) with Conjugate gradient method (local search algorithm) , the principle and implementation of simulated annealing and conjugate gradient algorithm are analyzed in detail. (2) Based on the analysis of conventional multilayer feedforward neural networks, the model structure and basic principle of probabilistic neural networks are studied. The object function search algorithm and its realization are analyzed. The method is applied to the risk elimination prediction for the first time, and the results are satisfactory. (3) The method and principle of inversion of reservoir parameters by using various seismic attribute information and neural network technology are discussed. In order to make full use of the effective information contained in seismic signal, the method of seismic attribute extraction and optimization is studied, the principle and implementation method of attribute optimal ranking by stepwise recursive method and selecting the most important attribute combination by interactive verification method are presented.

Evaluation of the consistency of the MRI- based AI segmentation cartilage model using the natural tibial plateau cartilage.

The study aims to evaluate the accuracy of an MRI-based artificial intelligence (AI) segmentation cartilage model by comparing it to the natural tibial plateau cartilage. This study included 33 patients (41 knees) with severe knee osteoarthritis scheduled to undergo total knee arthroplasty (TKA). All patients had a thin-section MRI before TKA. Our study is mainly divided into two parts: (i) In order to evaluate the MRI-based AI segmentation cartilage model's 2D accuracy, the natural tibial plateau was used as gold standard. The MRI-based AI segmentation cartilage model and the natural tibial plateau were represented in binary visualization (black and white) simulated photographed images by the application of Simulation Photography Technology. Both simulated photographed images were compared to evaluate the 2D Dice similarity coefficients (DSC). (ii) In order to evaluate the MRI-based AI segmentation cartilage model's 3D accuracy. Hand-crafted cartilage model based on knee CT was established. We used these hand-crafted CT-based knee cartilage model as gold standard to evaluate 2D and 3D consistency of between the MRI-based AI segmentation cartilage model and hand-crafted CT-based cartilage model. 3D registration technology was used for both models. Correlations between the MRI-based AI knee cartilage model and CT-based knee cartilage model were also assessed with the Pearson correlation coefficient. The AI segmentation cartilage model produced reasonably high two-dimensional DSC. The average 2D DSC between MRI-based AI cartilage model and the tibial plateau cartilage is 0.83. The average 2D DSC between the AI segmentation cartilage model and the CT-based cartilage model is 0.82. As for 3D consistency, the average 3D DSC between MRI-based AI cartilage model and CT-based cartilage model is 0.52. However, the quantification of cartilage segmentation with the AI and CT-based models showed excellent correlation (r = 0.725; P values < 0.05). Our study demonstrated that our MRI-based AI cartilage model can reliably extract morphologic features such as cartilage shape and defect location of the tibial plateau cartilage. This approach could potentially benefit clinical practices such as diagnosing osteoarthritis. However, in terms of cartilage thickness and three-dimensional accuracy, MRI-based AI cartilage model underestimate the actual cartilage volume. The previous AI verification methods may not be completely accurate and should be verified with natural cartilage images. Combining multiple verification methods will improve the accuracy of the AI model.