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Bug Prioritization Using Average One Dependence Estimator

Intelligent Automation & Soft computing 2023年第6期36卷 3517-3533页

作者： Kashif Saleem Rashid Naseem Khalil Khan Siraj Muhammad Ikram Syed Jaehyuk Choi Department of IT and Computer Science Institute of Applied Sciences and TechnologyPakAustriaFochhshuleHaripurPakistan Faculty of Computer Sciences and Information Technology Superior UniversityLahore54660Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper DirKhyber PakhtunkhwaPakitan School of Computing Gachon University1342Seongnam-daeroSujeong-guSeongnam-si13120Korea

Automation software need to be continuously updated by addressing software bugs contained in their ***,bugs have different levels of importance;hence,it is essential to prioritize bug reports based on their sever-ity and *** managing the deluge of incoming bug reports faces time and resource constraints from the development team and delays the resolu-tion of critical ***,bug report prioritization is *** study pro-poses a new model for bug prioritization based on average one dependence estimator;it prioritizes bug reports based on severity,which is determined by the number of *** more the number of attributes,the more the *** proposed model is evaluated using precision,recall,F1-Score,accuracy,G-Measure,and Matthew’s correlation *** of the proposed model are compared with those of the support vector machine(SVM)and Naive Bayes(NB)*** and Mozilla datasetswere used as the sources of bug *** proposed model improved the bug repository management and out-performed the SVM and NB ***,the proposed model used a weaker attribute independence supposition than the former models,thereby improving prediction accuracy with minimal computational cost.

关键词： Bug report triaging prioritization support vector machine Naive Bayes

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A New Image Watermarking Scheme Using Genetic Algorithm and Residual Numbers with Discrete Wavelet Transform

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Journal of Information Security 2023年第4期14卷 422-436页

作者： Peter Awonnatemi Agbedemnab Mohammed Akolgo Moses Apambila Agebure Department of Information Systems and Technology School of Computing and Information Sciences C.K. Tedam University of Technology and Applied Sciences Navrongo Ghana Department of Computer Science School of Computing and Information Sciences C.K. Tedam University of Technology and Applied Sciences Navrongo Ghana

Transmission of data over the internet has become a critical issue as a result of the advancement in technology, since it is possible for pirates to steal the intellectual property of content owners. This paper presents a new digital watermarking scheme that combines some operators of the Genetic Algorithm (GA) and the Residue Number (RN) System (RNS) to perform encryption on an image, which is embedded into a cover image for the purposes of watermarking. Thus, an image watermarking scheme uses an encrypted image. The secret image is embedded in decomposed frames of the cover image achieved by applying a three-level Discrete Wavelet Transform (DWT). This is to ensure that the secret information is not exposed even when there is a successful attack on the cover information. Content creators can prove ownership of the multimedia content by unveiling the secret information in a court of law. The proposed scheme was tested with sample data using MATLAB2022 and the results of the simulation show a great deal of imperceptibility and robustness as compared to similar existing schemes.

关键词： Discrete Wavelet Transform (DWT) Digital Watermarking Encryption Genetic Algorithm (GA) Residue Number System (RNS) GARN

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Evaporation Parameter-Ant Colony Optimization (EP-ACO) Algorithm With Elam Neural Network (ENN) for Sentiment Analysis 2

Evaporation Parameter-Ant Colony Optimization (EP-ACO) Algor...

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2nd International Conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Malliga, L. Malathy, V. Ramappa, Srikanth Alsalami, Zaid Bala Dhandayuthapani, V. Electronics and Communication Engineering Department Telangana Hyderabad India Sr University Department of Ece Warangal India o9 Solutions Bengaluru India The Islamic University Najaf Iraq College of Computing and Information Sciences University of Technology and Applied Sciences Department of It Shinas Branch Oman

ISBN: (纸本)9798350382952

In recent times, online shopping is major stream that refers to users for purchasing and also consume along development of internet technology. The user satisfaction is maximized efficiently through performing Sentiment Analysis (SA) of huge amount of user reviews on e-commerce platforms. That is still difficult for predicting a correct sentiment polarities of user reviews due to modifications in sequence length, order of textual with difficult logic. In this research, proposed an Evaporation Parameter-Ant Colony Optimization (EP-ACO) algorithm with Elam Neural Network (ENN) for sentiment analysis of amazon product reviews. The reviews from amazon platform are taken for sentiment analysis and then the pre-processing and word embedding process are performed. The features are selected by using EP-ACO algorithm which selects the relevant features for sentiment analysis. Then the sentiment analysis id performed by ENN network, that classifies the reviews as good or bad. Proposed technique obtained 98.26% accuracy, 97.73% precision, 97.03% recall and 97.41% f1-score that is better than previous methods like Convolutional Neural Network (CNN) - Long Short-Term Memory (LSTM). © 2024 IEEE.

关键词： Ant colony optimization

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A Review of Smart Contract Blockchain Based on Multi-Criteria Analysis:Challenges and Motivations

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Computers, Materials & Continua 2023年第5期75卷 2833-2858页

作者： Norah M.Alshahrani M.L.Mat Kiah B.B.Zaidan A.H.Alamoodi Abdu Saif Department of Computer System&Technology Faculty of Computer Science&ITUniversity of MalayaLembah Pantai50603Kuala LumpurMalaysia Department of Computer Science Applied CollageKing Khalid UniversityP.O.Box 62569AbhaSaudi Arabia Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlin64002Taiwan Department of Computing Faculty of ArtsComputing and Creative Industry(FSKIK)Universiti Pendidikan Sultan IdrisTanjung MalimMalaysia Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur50603Malaysia

A smart contract is a digital program of transaction protocol(rules of contract)based on the consensus architecture of *** contracts with Blockchain are modern technologies that have gained enormous attention in scientific and practical applications.A smart contract is the central aspect of a blockchain that facilitates blockchain as a platformoutside the cryptocurrency *** development of blockchain technology,with a focus on smart contracts,has advanced significantly in recent ***,research on the smart contract idea has weaknesses in the implementation sectors based on a decentralized network that shares an identical *** paper extensively reviews smart contracts based on multi-criteria analysis,challenges and ***,implementing blockchain in multicriteria research is required to increase the efficiency of interaction between users via supporting information exchange with high *** blockchain in the multi-criteria analysis is necessary to increase the efficiency of interaction between users via supporting information exchange and with high confidence,detecting malfunctioning,helping users with performance issues,reaching a consensus,deploying distributed solutions and allocating plans,tasks and joint *** smart contract with decision-making performance,planning and execution improves the implementation based on efficiency,sustainability and ***,the uncertainty and supply chain performance lead to improved users’confidence in offering new solutions in exchange for problems in smart *** includes code analysis and performance,while development performance can be under development.

关键词： Blockchain smart contract MCDM MADM

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Modified Dai-Liao Spectral Conjugate Gradient Method with Application to Signal Processing

arXiv

引用

arXiv 2025年

作者： Sahu, D.R. Sharma, Shikher Gautam, Pankaj Department of Mathematics Banaras Hindu University India Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee India

In this article, we present a modified variant of the Dai-Liao spectral conjugate gradient method, developed through an analysis of eigenvalues and inspired by a modified secant condition. We show that the proposed method is globally convergent for general nonlinear functions under standard assumptions. By incorporating the new secant condition and a quasi-Newton direction, we introduce updated spectral parameters. These changes ensure that the resulting search direction satisfies the sufficient descent property without relying on any line search. Numerical experiments show that the proposed algorithm performs better than several existing methods in terms of convergence speed and computational efficiency. Its effectiveness is further demonstrated through an application in signal processing. © 2025, CC BY.

关键词： Conjugate gradient method

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Clustering the Various Categorical Data: An Exploration of Algorithms and Performance Analysis 4

Clustering the Various Categorical Data: An Exploration of A...

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4th International Conference for Emerging technology, INCET 2023

作者： Kumar, Raman Pati, Peeta Basa Deepa, K. Yanan, Suresh Amrita School of Computing Department of Computer Science and Engineering India Amrita School of Engineering Department of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham Bengaluru India University of Technology and Applied Sciences-Al Musannah Department of Engineering Oman

ISBN: (纸本)9798350335750

Clustering is a method of grouping data based on similarities, and is an unsupervised technique for discovering patterns in data. In this research paper, various clustering algorithms such as k-Means, DBSCAN, Spectral Clustering, Gaussian Mixture, and Agglomerative Clustering are compared and evaluated on Amazon Prime Video Movies and TV Shows, Netflix Movies and TV Shows, and Disney+ Movies and Tv Shows datasets. The results of the study indicate that the k-Means algorithm performed well in clustering the data for all datasets, with an overall high level of performance. Additionally, the study provides valuable insights into the genre distribution of the data, and highlights the advantages and limitations of each clustering algorithm. © 2023 IEEE.

关键词： K-means clustering

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Exploring the Dynamics of Exchange Rates: An Empirical Analysis and Predictive Modelling 2

Exploring the Dynamics of Exchange Rates: An Empirical Analy...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Anand, R.N. Pati, Peeta Basa Deepa, K. Konidhala, Jothibabu K Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering India Amrita School of Engineering Department of Electrical & Electronics Engineering Bangalore India University of Technology and Applied Sciences-Al Musannah Department of Engineering Oman

ISBN: (纸本)9798350397635

In India, globalization and reforms in the financial sector have brought about a major overhaul of the financial landscape. The connection between financial markets and the tangible economy is now highly valued in the current scenario. Exchange rate affects a country's economy in several ways. It has a huge effect on international trade, economic growth, capital flows, interest rates and also inflation. Here, the aim is to understand the underlying factors that affects the exchange rate of US dollar with respect to Indian rupee. To accomplish this, a Vector Auto Regression (VAR) model, as well as other machine learning techniques such as multi-variable linear regression, polynomial regression, and regression tree, are employed to forecast exchange rates. Our findings indicate that Trade Balance is a significant factor which cause fluctuations in exchange rate of USD and regression tree model performs better from the side of all machine learning model applied for predicting the same. © 2023 IEEE.

关键词： Multiple linear regression

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Visual Object Tracking Using Machine Learning 1st

Visual Object Tracking Using Machine Learning

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Science, Engineering Management and Information technology First International Conference, SEMIT 2022

作者： Odeh, Ammar Keshta, Ismail Al-Fayoumi, Mustafa Department of Computer Science King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Amman Jordan Department of Computer Science College of Applied Sciences AlMaarefa University Riyadh Riyadh Saudi Arabia

ISBN: (纸本)9783031403972

Visual object tracking has become a very active research area in recent years. Each year, a growing number of tracking algorithms are proposed. Object detection and tracking is a critical and challenging task in many critical computer vision applications, including automated video surveillance, traffic monitoring, autonomous robot navigation, and intelligent environments. Object tracking is segmenting an object of interest and tracking its velocity, orientation, and occlusion in a video scene to extract useful information. Over the last two decades, several object tracking approaches have been developed to design a robust object tracker that covers all practical obstacles in real-world operations. This paper reviews recent trends and advances in tracking and assesses the reliability of various trackers based on feature extraction techniques. In video processing, visual tracking has a wide range of applications. When a target is identified in one video frame, it is frequently advantageous to track that object in subsequent frames. Every successful frame in which the target is tracked yields more information about the target’s identity and activity. Because tracking is more straightforward than detection, tracking algorithms can require fewer computational resources than object detectors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object recognition

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New Approaches to the Prognosis and Diagnosis of Breast Cancer Using Fuzzy Expert Systems

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Journal of Computer and Communications 2024年第5期12卷 151-169页

作者： Elias Ayinbila Apasiya Abdul-Mumin Salifu Peter Awon-Natemi Agbedemnab Department of Computer Science School of Computing and Information Sciences C. K. Tedam University of Technology and Applied Sciences Navrongo Ghana

Breast cancer remains a significant global health challenge, necessitating effective early detection and prognosis to enhance patient outcomes. Current diagnostic methods, including mammography and MRI, suffer from limitations such as uncertainty and imprecise data, leading to late-stage diagnoses. To address this, various expert systems have been developed, but many rely on type-1 fuzzy logic and lack mobile-based applications for data collection and feedback to healthcare practitioners. This research investigates the development of an Enhanced Mobile-based Fuzzy Expert system (EMFES) for breast cancer pre-growth prognosis. The study explores the use of type-2 fuzzy logic to enhance accuracy and model uncertainty effectively. Additionally, it evaluates the advantages of employing the python programming language over java for implementation and considers specific risk factors for data collection. The research aims to dynamically generate fuzzy rules, adapting to evolving breast cancer research and patient data. Key research questions focus on the comparative effectiveness of type-2 fuzzy logic, the handling of uncertainty and imprecise data, the integration of mobile-based features, the choice of programming language, and the creation of dynamic fuzzy rules. Furthermore, the study examines the differences between the Mamdani Inference System and the Sugeno Fuzzy Inference method and explores challenges and opportunities in deploying the EMFES on mobile devices. The research identifies a critical gap in existing breast cancer diagnostic systems, emphasizing the need for a comprehensive, mobile-enabled, and adaptable solution by developing an EMFES that leverages Type-2 fuzzy logic, the Sugeno Inference Algorithm, Python Programming, and dynamic fuzzy rule generation. This study seeks to enhance early breast cancer detection and ultimately reduce breast cancer-related mortality.

关键词： EMFES Breast Cancer Type-2 Fl Soft computing Membership Functions Fuzzy Set Fuzzy Rules Risk Factors.

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FAST ORTHOGONAL MATCHING PURSUIT FOR EXACT RECOVERY AND SPARSE APPROXIMATION

arXiv

引用

arXiv 2024年

作者： Yu, Huiyuan He, Jia Cheng, Maggie X. Department of Applied Mathematics College of Computing Illinois Institute of Technology ChicagoIL60616 United States

Orthogonal Matching Pursuit (OMP) has been a powerful method in sparse signal recovery and approximation. However OMP suffers computational issue when the signal has large number of non-zeros. This paper advances OMP in two fronts: it offers a fast algorithm for the orthogonal projection of the input signal at each iteration, and a new selection criterion for making the greedy choice, which reduces the number of iterations it takes to recover the signal. The proposed modifications to OMP directly reduce the computational complexity. Experiment results show significant improvement over the classical OMP in computation time. The paper also provided a sufficient condition for exact recovery under the new greedy choice criterion. For general signals that may not have sparse representations, the paper provides a bound for the approximation error. The approximation error is at the same order as OMP but is obtained within fewer iterations and less time. © 2024, CC BY.

关键词： Compressed sensing

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