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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

共 3232 条记录，以下是251-260 订阅

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KGR-Rainfall: Temperature-Based Rainfall Prediction in Bangladesh with Novel KGR Stacking Ensemble

KGR-Rainfall: Temperature-Based Rainfall Prediction in Bangl...

引用

2023 international conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology conference, ATCON-1 2023

作者： Bitto, Abu Kowshir Rubi, Maksuda Akter Bijoy, Md. Hasan Imam Shuvo, Subrata Das Das, Aka Chowdhury, Amit Daffodil International University Department of Software Engineering Dhaka Bangladesh Development Daffodil International University Department of General Educational Dhaka Bangladesh Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Chittagong University of Engineering and Technology Department of Electronics and Telecommunication Engineering Chattogram Bangladesh Premier University Department of Computer Science and Engineering Chattogram Bangladesh

ISBN: (纸本)9781665456272

Climate change factors such as wet or dry, cold or warm seasons have a significant impact on both the economy and culture. Extreme rainfall events have historically posed a major threat to many parts of the world. In Bangladesh, during monsoon seasons, wet southern airflows from the Bay of Bengal collide with dry mainland air, causing heavy rainfall that negatively affects various socio-economic sectors. These include agriculture, food production, urban planning, energy, water resource management, fisheries, forest management, healthcare, disaster management, transportation, tourism, sports, and leisure. To address this issue, the paper proposes a machine-learning approach to forecast rainfall in Bangladesh using multiple regression models and a novel Stacked Ensemble Model (KGR Stacking). The study also investigates the relationship between rainfall and temperature. The KGR Stacking model outperforms the other 12 regression models, achieving an accuracy of 86.43% and lower error. © 2023 IEEE.

关键词： machine learning

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machine learning Algorithms for Automotive software Defect Prediction 1

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international conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2022

作者： Tsouli Fathi, Ramz Tsouli Fathi, Maroi Ammari, Mohammed Ben Allal, Laïla FSTT Abdelmalek Essaadi University Tetouan Morocco

ISBN: (数字)9783031263842

ISBN: (纸本)9783031263835

The use of machine learning algorithms has increased lately in multiple domains, this includes software engineering. Identifying a suitable methodology to recognize software defect, will help improvement the quality of the software and save costs, especially in an industry like cars manufacturing. It is important to find suitable and significant measures which are most relevant for finding the defects in a software. This paper contributes towards the decision of adoption of machine learning techniques for the purpose of software defect predictions. In our approach, we considered supervised machine learning algorithms to build our model that aims to predict the occurrence of the software defects. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Predictive analytics

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Fault Detection and Diagnosis in Automotive Control Systems using machine learning

Fault Detection and Diagnosis in Automotive Control Systems ...

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2023 international conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Jawalkar, Anil Pandurang Kumar, S Kiran Deshmukh, Ram Mounika, P. Manohara, H.T. Malla Reddy Engineering College for Women It Department Hyderabad India Cmr Engineering College Department of Computer Science and Engineering Hyderabad India Sr University Department of Electrical and Electronics Engineering Warangal India Cmr Technical Campus Department of Computer Science and Engineering Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

The software Fault detection and diagnosis approaches supports to find the fault vulnerable constituents in the software development in early stages. An effective diagnosis approach can support test administrators to locate defects and defect-vulnerable software modules. The Feature Extraction (FE) process is the applicable solution to solve high dimensionality and polynomial time complexity issue in the prediction of software defect. In this research, a new fault detection approach is proposed by utilizing the Stacked Auto Encoder (SAE) with Support Vector machine (SVM) and Artificial Neural Network (ANN). In this approach, the ANN is utilized for the diagnosis process to distribute the data named the fault type. Now, the input generates the Deep Neural Network as well as fuzzy depiction process. the outcome of these approaches is then integrated by the dense connected layers. The proposed SAE based SVM-ANN model attains better results by utilizing evaluation metrics like Accuracy, Precision, F1-score, and Area Under Curve (AUC) values about 99.87%, 99.52%, 99.82% and 0.99 respectively which is comparatively higher than existing techniques like Unsupervised learning, SVM, Generative Adversarial Neural Network. © 2023 IEEE.

关键词： Artificial Neural Network Fault detection Fault diagnosis Stacked Auto Encoder and Support Vector machine

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Proceedings - 2023 IEEE/ACM 45th international conference on software engineering, ICSE 2023

Proceedings - 2023 IEEE/ACM 45th International Conference on...

引用

45th IEEE/ACM international conference on software engineering, ICSE 2023

ISBN: (纸本)9781665457019

The proceedings contain 208 papers. The topics discussed include: source code recommender systems: the practitioners’ perspective;GameRTS: a regression testing framework for video games;keeping pace with ever-increasing data: towards continual learning of code intelligence models;fill in the blank: context-aware automated text input generation for mobile GUI testing;DIVER: Oracle-guided SMT solver testing with unrestricted random mutations;reachable coverage: estimating saturation in fuzzing;learning graph-based code representations for source-level functional similarity detection;SmallRace: static race detection for dynamic languages - a case on SmallTalk;towards understanding fairness and its composition in ensemble machine learning;CCRep: learning code change representations via pre-trained code model and query back;Sibyl: improving software engineering tools with SMT selection;and generating realistic and diverse tests for LiDAR-based perception systems.

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A Comparative Study for Temperature Prediction by machine learning and Deep learning

A Comparative Study for Temperature Prediction by Machine Le...

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2023 international conference on Intelligent Computing and Control, IC and C 2023

作者： Zhao, Heng Chen, Yixing Shenzhen Technology University College of Big Data and Internet Shenzhen China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9781665457095

Time series prediction has been a hot research topic for decades. Temperature data is typical time series data, so temperature prediction is an important applications. In recent years, machine learning has been applied to time series prediction in many fields. Besides, the development of Deep learning has made many new breakthroughs in these predictions. Generally, Deep learning is considered to be superior to traditional machine learning in temperature prediction. But in fact, the traditional methods can also perform well for short-Term forecast in this regard. This research compares the performance of Linear Regression, SVR, CART, KNN and LSTM models in short-Term temperature prediction through three evaluation indicators: MAE, MSE and R2-score. The results explicate that the traditional model can also be better than the depth model in short-Term temperature prediction. © 2023 IEEE.

关键词： Time series

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Runtime Monitoring of Human-centric Requirements in machine learning Components: A Model-driven engineering Approach

Runtime Monitoring of Human-centric Requirements in Machine ...

引用

ACM/IEEE international conference on Model Driven engineering Languages and Systems (MODELS)

作者： Naveed, Hira Monash Univ Fac IT Dept Software Syst & Cybersecur HumaniSE Lab Clayton Vic Australia

ISBN: (纸本)9798350324983

As machine learning (ML) components become increasingly integrated into software systems, the emphasis on the ethical or responsible aspects of their use has grown significantly. This includes building ML-based systems that adhere to human-centric requirements, such as fairness, privacy, explainability, well-being, transparency and human values. Meeting these human-centric requirements is not only essential for maintaining public trust but also a key factor determining the success of ML-based systems. However, as these requirements are dynamic in nature and continually evolve, pre-deployment monitoring of these models often proves insufficient to establish and sustain trust in ML components. Runtime monitoring approaches for ML are potentially valuable solutions to this problem. Existing state-of-the-art techniques often fall short as they seldom consider more than one human-centric requirement, typically focusing on fairness, safety, and trust. The technical expertise and effort required to set up a monitoring system are also challenging. In my PhD research, I propose a novel approach for the runtime monitoring of multiple human-centric requirements. This approach leverages model-driven engineering to more comprehensively monitor ML components. This doctoral symposium paper outlines the motivation for my PhD work, a potential solution, progress so far and future plans.

关键词： Runtime monitoring Human-centric requirements machine learning components Model-driven engineering

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software Vulnerability Prediction Knowledge Transferring Between Programming Languages 18

Software Vulnerability Prediction Knowledge Transferring Bet...

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18th international conference on Evaluation of Novel Approaches to software engineering (ENASE)

作者： Hanifi, Khadija Fouladi, Ramin F. Unsalver, Basak Gencer Karadag, Goksu Ericsson Secur Res Istanbul Turkiye Vodafone Istanbul Turkiye

ISBN: (纸本)9789897586477

Developing automated and smart software vulnerability detection models has been receiving great attention from both research and development communities. One of the biggest challenges in this area is the lack of code samples for all different programming languages. In this study, we address this issue by proposing a transfer learning technique to leverage available datasets and generate a model to detect common vulnerabilities in different programming languages. We use C source code samples to train a Convolutional Neural Network (CNN) model, then, we use Java source code samples to adopt and evaluate the learned model. We use code samples from two benchmark datasets: NIST software Assurance Reference Dataset (SARD) and Draper VDISC dataset. The results show that proposed model detects vulnerabilities in both C and Java codes with average recall of 72%. Additionally, we employ explainable AI to investigate how much each feature contributes to the knowledge transfer mechanisms between C and Java in the proposed model.

关键词： software Security Vulnerability Prediction Source Code machine learning Transfer learning

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Forecasting Crop Price using various approaches of machine learning

Forecasting Crop Price using various approaches of Machine L...

引用

2023 international conference on Innovations in engineering and Technology, ICIET 2023

作者： Chaitra, B. Meena, K. School of Technology GITAM Deemed to Be University Computer Science and Engineering GITAM Bengaluru India

ISBN: (纸本)9798350309980

Agriculture is the Backbone of India. India has major portion of cultivational Land. Many Crops have lot of demand in abroad. India is one of the major exporters of Food products other than software. But due to erratic climatic conditions, lack of human Resource and improper decision making in choosing crops, farmers are not able to make profits. Also due to urbanization, Agriculture Profession is losing its importance slowly. This problem has invited many Researchers to identify the issues and apply several machine learning approaches like Autoregressive Integrated Moving Average (ARIMA), Decision trees, Long Short-Term Memory (LSTM), K Nearest Neighbor (KNN) etc. for accurate Yield and Crop Price Prediction. This helps the Farmers to select suitable crops and obtain maximum yield and Profit. © 2023 IEEE.

关键词： Crops

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A Method of Identifying Causes of Prediction Errors to Accelerate MLOps

A Method of Identifying Causes of Prediction Errors to Accel...

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4th IEEE/ACM international Workshop on Deep learning for Testing and Testing for Deep learning (DeepTest) / international conference on software engineering (ICSE)

作者： Sakuma, Keita Matsuno, Ryuta Kameda, Yoshio NEC Corp Ltd Tokyo Japan

ISBN: (纸本)9798350338126

MLOps, techniques for solving operational problems in machine learning, have attracted attention in recent years. In MLOps, understanding why mispredictions occur is essential to improving operational models. However, misprediction analysis is currently a time-consuming process performed manually by data scientists. In this paper, we propose a flowchart-structured analysis method (called AIEDF) that automatically identifies the causes of mispredictions during model operation. Thanks to the flowchart structure, AIEDF's analyses are comprehensive and explainable. In addition, AIEDF is flexible in implementation and can be model agnostic. Through experiments with synthetic and real data, we have demonstrated that AIEDF accurately identifies root causes and provides valuable insights for model improvement.

关键词： MLOps machine learning Explainability

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Towards Accurate Stress Classification: Combining Advanced Feature Selection and Deep learning 3

Towards Accurate Stress Classification: Combining Advanced F...

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3rd IEEE international conference on software engineering and Artificial Intelligence, SEAI 2023

作者： Arya, Lovish Chowdhary, Harshal Agrawal, Ishita Sreedevi, Indu Delhi Technological University Department of Electronics and Communication Engineering New Delhi India

ISBN: (纸本)9798350337952

Stress is a significant health issue that affect both physical and mental health. The application of deep learning and machine learning techniques for physiological signal processing has become popular in detecting and classifying stress. In this research, we developed a fresh approach to classify stress on multiple levels using advanced machine and deep learning models. The PhysioNet SRA database was used to collect physiological signals which were pre-processed using a pipeline of filters and normalisation techniques to remove noise and artefacts. These signals were subjected to various feature extraction algorithms to determine the most pertinent characteristics for categorisation. On the acquired features, dimensionality reduction techniques were applied in order to further improve classification performance. The evaluation of Random Forest model outscored all the other classic ML models, achieving an accuracy of 93.77% and 82.42% in classifying stress on two levels and three levels respectively. Among the DL models, the LSTM model is most effective, achieving an accuracy of 95.67% and 88.70% in classifying stress on two levels and three levels respectively. The proposed approach provides a more comprehensive analysis of stress levels, which can help individuals manage stress more effectively. © 2023 IEEE.

关键词： Physiology

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