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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

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Design Flaws Prediction for Impact on Software Maintainability using Extreme learning machine 5

Design Flaws Prediction for Impact on Software Maintainabili...

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5th Joint international conference on Digital Arts, Media and Technology with 3rd ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2020

作者： Thongkum, Patcharapan Mekruksavanich, Sakorn University of Phayao Department of Software Engineering Phayao Thailand University of Phayao Department of Computer Engineering Phayao Thailand

ISBN: (纸本)9781728163987

The software that contains flaws in its design is an indication that the design and coding standards have been violated by the developer during the software system's development. It has been observed in recent empirical studies that classes with flaws in the design flaws have a higher probability of change proneness or fault proneness when compared to classes without flaws in the design. There is an exponential increase in terms of the effort required to remove bugs due to design flaws in cases where the flaws are not detected in the early stages of the development of the software. The use of source code metrics for the prediction of design flaws can be implemented in the initial stages of the life cycle of the software development for the reduction of the testing effort and the maintenance of the software as well as the improvement of its quality. This empirical research study examines and assesses a variety of techniques for classification, feature selection, and data sampling in order to deal with the imbalance data for prediction of several categories of design flaws. The assessment of more than 20 application packages is the basis of the conclusions of this study. The results of the experiments indicate that the estimating capability of various classifiers, feature selection, and data imbalance techniques for the development of prediction models for design flaws can be confirmed. In addition, it was also revealed that the models that were developed through the use of one particular technique were found to be superior to the models that were developed with the use of other techniques, according to our analysis. © 2020 IEEE.

关键词： Forecasting

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Privacy-Preserving Classification of Personal Data with Fully Homomorphic Encryption: An application to High-Quality Ionospheric Data Prediction 3rd

Privacy-Preserving Classification of Personal Data with Full...

引用

3rd international conference on machine learning for Cyber Security, ML4CS 2020

作者： Li, Zheng Sun, Maohua School of Management and Engineering Capital University of Economics and Business Beijing100070 China

ISBN: (纸本)9783030622220

In recent years, the problem of data leakage is really common, so designing a safe and efficient privacy-preserving machine learning protocol has become an urgent demand to many researchers. The fully homomorphic encryption algorithm has attracted the interest of many researchers. This algorithm can directly operate on the ciphertext without decryption, and the decrypting result is the same as that obtained by performing the same processing on the plaintext. At present, many scholars have designed privacy-preserving machine learning protocols, and have achieved good results. However, as one of the most frequent used algorithms, there are relatively few studies concentrating on the privacy-preserving protocols for logistic regression and fewer focusing on the prediction process. Therefore, we propose a privacy-preserving protocol to solve the data leakage problem during the logistic regression process. Based on the semi-honest assumption and BGV fully homomorphic encryption algorithm, our protocol is mainly focused on the prediction process of logistic regression. In this protocol, there are two parties (client and server), the client holds the test set data and label data, the server holds the model coefficients obtained from the training process. The two parties execute the protocol to finish the logistic regression prediction process in ciphertext. Finally, the client side obtains the prediction category of the dependent variable and calculates the accuracy of the prediction result. During this process, the server will not learn anything about the test set, nor will the client know any information about the model used by the server. © 2020, Springer Nature Switzerland AG.

关键词： machine learning

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LIDS: Enhancing Industrial IoT Network Security through Lightweight machine learning-Powered Intrusion Detection System 3rd

LIDS: Enhancing Industrial IoT Network Security through Ligh...

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3rd international conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： Peng, Silin Han, Yu Zhou, Nan Liang, Xiaojun Deng, Wenfeng Gui, Weihua Pengcheng Laboratory Shenzhen China Sun Yat-Sen University Shenzhen China Central South University Changsha China

ISBN: (纸本)9789819645022

In recent years, security landscape of Industrial Internet of Things (IIoT) has emerged as a pivotal area of concern within the realms of cyberwarfare and engineering research, particularly after the rise of advanced and sophisticated malware (e.g., Stuxnet) deliberately crafted to target such systems. Although artificial intelligence has achieved a great success in the field of intrusion detection, the limited computing capabilities and storage of IIoT devices hinder the actual deployment of high-complexity models. In this paper, a lightweight intrusion detection system (LIDS) empowered by machine learning technologies is proposed to detect intrusions in an effective and efficient manner to enhance network security of IIoT. Firstly, we apply a wrapper-based approach via LightGBM model to identify the most important features. Features with the highest importance scores are effectively selected and constructed as the optimal feature subset. Then, we develop a advanced model of extreme gradient boosting (XGBoost) with Bayesian optimization (BO) for intrusion detection, where the important parameters of XGBoost are optimized and fine-tuned using BO algorithm to improve the detection accuracy, computational efficiency, and stability of the model. Experimental results on NSL-KDD, a benchmark dataset to demonstrate that the proposed method achieves an accuracy of 99.79% in binary classification and 99.84% in multi-classification, showcasing its potential as a reliable security solution for IIoT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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A Novel Strategy for Gender Identification from Hand Dorsal Images using computer Vision 3

A Novel Strategy for Gender Identification from Hand Dorsal ...

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3rd international conference on Computing Methodologies and Communication (ICCMC)

作者： Chakrabarty, Navoneel Jalpaiguri Govt Engn Coll Dept CSE Jalpaiguri 735121 W Bengal India

ISBN: (纸本)9781538678084

Criminal Investigations and Archaeological Studies often involve Gender Determination from different body parts when dismembered human remains are encountered. The body parts may include hair or hand in criminal investigations and hand bone length or hand to index finger length ratio in archaeological research. Often in crimes or even in accidents, Forensic Science comes into play. Forensic Science, by definition, is the scientific application of criminal and civil laws for collection of scientific evidences for prosecution or defense or both. So, in Forensic Research, Gender Determination is a very integral part especially from hair or hand, which becomes difficult for anyone other than Forensic Scientists, to conclude. This study aims at classification of the gender of an individual into male and female from the image of his or her dorsal side of hand. A Deep learning-machine learning Hybrid Model is developed, yielding a Validation Accuracy of 100% and Recall of 1.0. Such an Automated System is highly essential in making Forensic Research, easy and hence swift analysis of scientific evidences.

关键词： Forensic Science classification dorsal Deep learning machine learning

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Detecting anomalies in application performance management system with machine learning algorihms 3

Detecting anomalies in application performance management sy...

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3rd IEEE international conference on Electronic Information Technology and computer Engineering, EITCE 2019

作者： Shi, Yujun Miao, Kehua Xiamen University Department of Automation Xiamen China

ISBN: (纸本)9781728135847

application performance management which is short for APM is a real-time monitoring of enterprise systems to implement a systematic solution to application performance management and fault management. APM is a relatively new network management direction. It mainly refers to monitoring and optimizing key business applications of enterprises, improving the reliability and quality of enterprise applications, ensuring users to get good services and reducing total cost of ownership (TCO). If a company's business-critical applications are powerful, they can increase competitiveness and achieve business success. Therefore, enhancing application performance management (APM) can generate huge business benefits. This paper explores the application of machine learning-based anomaly detection algorithm in APM system by analyzing continuous time series data from actual network systems. © 2019 IEEE.

关键词： Anomaly detection

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Physics-Driven machine learning for Time-Optimal Path Planning in Stochastic Dynamic Flows 3rd

Physics-Driven Machine Learning for Time-Optimal Path Planni...

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3rd international conference on Dynamic Data Driven application Systems, DDDAS 2020

作者： Chowdhury, Rohit Subramani, Deepak N. Department of Computational and Data Sciences Indian Institute of Science Bangalore India

ISBN: (纸本)9783030617240

Optimal path planning of autonomous marine agents is important to minimize operational costs of ocean observation systems. Within the context of DDDAS, we present a Reinforcement learning (RL) framework for computing a dynamically adaptable policy that minimizes expected travel time of autonomous vehicles between two points in stochastic dynamic flows. To forecast the stochastic dynamic environment, we utilize the reduced order data-driven dynamically orthogonal (DO) equations. For planning, a novel physics-driven online Q-learning is developed. First, the distribution of exact time optimal paths predicted by stochastic DO Hamilton-Jacobi level set partial differential equations are utilized to initialize the action value function (Q-value) in a transfer learning approach. Next, the flow data collected by onboard sensors are utilized in a feedback loop to adaptively refine the optimal policy. For the adaptation, a simple Bayesian estimate of the environment is performed (the DDDAS data assimilation loop) and the inferred environment is used to update the Q-values in an greedy exploration approach (the RL step). To validate our Q-learning solution, we compare it with a fully offline, dynamic programming solution of the Markov Decision Problem corresponding to the RL framework. For this, novel numerical schemes to efficiently utilize the DO forecasts are derived and computationally efficient GPU-implementation is completed. We showcase the new RL algorithm and elucidate its computational advantages by planning paths in a stochastic quasi-geostrophic double gyre circulation. © 2020, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Food Image Recognition for Price Calculation using Convolutional Neural Network 2019

Food Image Recognition for Price Calculation using Convoluti...

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3rd international conference on Digital Signal Processing (ICDSP) / 2nd international conference on computer Graphics and Virtuality (ICCGV)

作者： Nordin, Md Jan Aziz, Norshakirah Xin, Ooi Wei Univ Kebangsaan Malaysia Fac Informat Sci & Technol CAIT Bangi Malaysia Univ Teknol PETRONAS High Performance Cloud Comp Ctr HPC3 Comp Informat Sci Dept Seri Iskandar Malaysia Univ Teknol PETRONAS Comp Informat Sci Dept Seri Iskandar Malaysia

ISBN: (纸本)9781450362047

This project is attempting to solve the issue of unfair and inconsistent food price being charged in economy rice or mixed rice that widely seen in the cafe of hawker stall in Malaysia. The main cause of the problem is the absence of standardized price list of the food which causes the pricing of the mixed rice remains unknown. Hence, the authors had decided to propose this project by utilizing convolutional neural network (CNN) algorithm and develop a web application to ease the vendor as well as to provide transparency to the buyer on the food price being charged. CNN model is trained to classify the different types of food. The food price will be stored in a database of the web application in order to calculate the food price with the recognized food in the machine learning model. The outcome of this project is a customized web application for Village 3 Cafe, UTP with a trained CNN classification model at the backend.

关键词： Food image recognition convolutional neural network (CNN) machine learning

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Research on TSV Void Defects Based on machine learning 19

Research on TSV Void Defects Based on Machine Learning

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3rd international conference on computer Science and application Engineering (CSAE)

作者： Chen, Shouhong Kang, Huaiqiang Ma, Jun Guo, Ling Hou, Xingna Guilin Univ Elect Technol Guangxi Key Lab Automat Detecting Technol & Instr Sch Elect Engn & Automat Guilin Peoples R China

ISBN: (纸本)9781450362948

With the rapid development of 3D TSV (through silicon via) technology, it is particularly important to improve the yield for TSV fault detection. Aiming at TSV void defects, the paper adopts supervised machine learning method to train S parameters in TSV model with void faults, and carries out classification processing, then predicts the size of void faults through stimulus signal and S parameters. The results show that for spherical void defects detection, the classification accuracy of ELM (Extreme learning machine) algorithm and KNN (K-Nearest Neighbor) algorithm is above 85%, while for TSV cylindrical void defects detection, the classification accuracy of ELM algorithm is 96%.

关键词： machine learning TSV fault detection Classification algorithm

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Mutual Q-learning

Mutual Q-learning

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international conference on Control and Robots (ICCR)

作者： Cameron Reid Snehasis Mukhopadhyay Computer and Information Science Indiana University Purdue University Indianapolis Indianapolis Indiana USA

ISBN: (数字)9781728175621

ISBN: (纸本)9781728175638

Mutual learning is an emerging technique for improving performance in machine learning models by allowing functions to learn from each other. In this paper, we present an updated version of Q-learning, improved with the application of mutual learning techniques. We apply this algorithm to a traditional reinforcement learning control problem and compare our performance with that of a standard Q-learning approach. We show that these approaches lead to a significant improvement in performance.

关键词： Training machine learning algorithms Reinforcement learning Computational efficiency Task analysis Standards Robots

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1st Workshop on Conceptual Modeling Meets Artificial Intelligence and Data-Driven Decision Making, CMAI 2020, 1st international Workshop on Conceptual Modeling for Life Sciences, CMLS 2020, 2nd Workshop on Conceptual Modeling, Ontologies and (Meta)data Management for Findable, Accessible, Interoperable and Reusable (FAIR) Data, CMOMM4FAIR 2020, 1st Workshop on Conceptual Modeling for NoSQL Data Stores, CoMoNoS 2020 and 3rd international Workshop on Empirical Methods in Conceptual Modeling, EmpER 2020 held at 39th international conference on Conceptual Modeling, ER 2020

1st Workshop on Conceptual Modeling Meets Artificial Intelli...

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1st Workshop on Conceptual Modeling Meets Artificial Intelligence and Data-Driven Decision Making, CMAI 2020, 1st international Workshop on Conceptual Modeling for Life Sciences, CMLS 2020, 2nd Workshop on Conceptual Modeling, Ontologies and (Meta)data Management for Findable, Accessible, Interoperable and Reusable (FAIR) Data, CMOMM4FAIR 2020, 1st Workshop on Conceptual Modeling for NoSQL Data Stores, CoMoNoS 2020 and 3rd international Workshop on Empirical Methods in Conceptual Modeling, EmpER 2020 held at 39th international conference on Conceptual Modeling, ER 2020

ISBN: (纸本)9783030658465

The proceedings contain 21 papers. The special focus in this conference is on Conceptual Modeling Meets Artificial Intelligence and Data-Driven Decision Making. The topics include: Mapping the Web Ontology Language to the OpenAPI Specification;Evaluating FAIRness of Genomic Databases;Reusable FAIR Implementation Profiles as Accelerators of FAIR Convergence;Deimos: A Model-Based NoSQL Data Generation Language;Managing Physical Schemas in MongoDB Stores;JSON Schema Inference Approaches;Empirical Evaluation of a New DEMO Modelling Tool that Facilitates Model Transformations;acquiring and Sharing the Monopoly of Legitimate Naming in Organizations, an application in Conceptual Modeling;replicability and Reproducibility of a Schema Evolution Study in Embedded Databases;towards Automated Support for Conceptual Model Diagnosis and Repair;Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas;experimental Practices for Measuring the Intuitive Comprehensibility of Modeling Constructs: An Example Design;Superimposition: Augmenting machine learning Outputs with Conceptual Models for Explainable AI;Evaluating Tree Explanation Methods for Anomaly Reasoning: A Case Study of SHAP TreeExplainer and TreeInterpreter;the Importance of the Temporal Dimension in Identifying Relevant Genomic Variants: A Case Study;towards the Generation of a Species-Independent Conceptual Schema of the Genome;Conceptual Human Emotion Modeling (HEM);towards an Ontology for Tertiary Bioinformatics Research Process;Using BioPAX-Parser (BiP) to Annotate Lists of Biological Entities with Pathway Data.

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