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共 161 条记录，以下是11-20 订阅

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Bug predictive models based on Data Analytics and Soft Computing Techniques: A Survey

Bug Predictive Models based on Data Analytics and Soft Compu...

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international conference on Computing for Sustainable Global Development (INDIACom)

作者： Veena Jadhav Prakash Devale Rohini Jadhav Mayuri Molawade Sagar Mohite Ranjeet Vasant Bidwe Department of Computer Engineering Bharati Vidyapeeth (Deemed to be University)College of Engineering Pune India Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed) University (SIU) Lavale Pune India

As software projects become larger and more complex, bugs pose a significant threat to their consistency and efficiency. During early phases of software development, bug prediction essentially enhances vital aspects such as software efficiency reliability quality, cost of development. Hence, various software bug prediction techniques have been presented previously but there is no research that focuses on surveying logical analysis and Just-In-Time (JIT) bug predictions with the consideration of data analytics and soft computing techniques. Hence this research investigates Soft computing and hybrid logistic model by determining imperfect range of data, class labels with heuristics prediction of bugs. Also, survey data analytics techniques such as clustering and oversampling techniques with JIT prediction for forecasting software bug in future. the study also compares various software bug prediction techniques to determine their effectiveness. then, mentioned the basic benefits and drawbacks of the various bug prediction techniques. Moreover, future directions are provided to overcome the existing impact of various techniques in software bug prediction.

关键词： Data analysis Costs Computational modeling Computer bugs predictive models software Data models

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Enhancing Sales Forecasting Accuracy in the Presence of Missing Data 17

Enhancing Sales Forecasting Accuracy in the Presence of Miss...

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IEEE Congress on Cybermatics: 17th IEEE international conference on Internet of things, ithings 2024, 20th IEEE international conference on Green Computing and Communications, GreenCom 2024, 17th IEEE international conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE international conference on Smart Data, SmartData 2024

作者： Tang, Jun Sun, Jingpeng Guo, Bing Shen, Yan Dai, Shengxin Wang, Peng College of Computer Science Sichuan University Chengdu China College of Computer Science University of Birmingham Dubai United Arab Emirates School of Computer Science Chengdu University of Information Technology Chengdu China College of Software Engineering Sichuan University Chengdu China

ISBN: (纸本)9798350351637

Accurate sales forecasting serves as a critical foundation for informed business decision-making and numerous methodologies have been advanced by scholars predominantly rely on complete datasets, however, in practice, uncontrollable variables often result in the presence of missing data, diminishing the applicability of established techniques, so the sales forecasting research based on missing data is of great significance. Some researchers have resorted to employing deletion or interpolation techniques to pre-process incomplete data before conducting sales forecasts, but such completion methods tend to introduce distributional biases, subsequently leading to forecast inaccuracies. In light of these challenges, this study introduces a novel computational framework combining Convolutional Neural Network (CNN), Self-Attention, and Sequence-to-Sequence (Seq2Seq) models, where global features are first extracted via the synergistic application of CNN and Self-Attention mechanisms, then the extracted features are seamlessly integrated with the original dataset, supplanting conventional interpolation strategies, and finally the amalgamated data are inputted into the Seq2Seq model for sales prediction. the proposed methodology is empirically evaluated using sales data from a real-world company, demonstrating commendable predictive performance and underscoring the efficacy of the proposed approach in enabling accurate sales forecasts within the context of missing data scenarios. © 2024 IEEE.

关键词： Data accuracy

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Another Look at Non-homogeneous Markovian software Reliability Modeling

Another Look at Non-homogeneous Markovian Software Reliabili...

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international conference on Dependable Systems and their Applications (DSA)

作者： Nanxiang Qiu Siqiao Li Tadashi Dohi Hiroyuki Okamura Graduate School of Advanced Sciences and Engineering Hiroshima University Higashi-Hiroshima Japan

this paper explores yet another software reliability modeling framework based on non-homogeneous Markov processes (NHMPs). For two subclasses of NHMPs; generalized binomial processes (GBPs) and generalized Polya processes (GPPs), we formulate 22 novel NHMP-based software reliability models (SRMs) with 11 kinds of baseline intensity functions, which are different from the existing NHMP-based SRMs by Li et al. (2023). Our evaluation of NHMP-based SRMs focuses on assessing their goodness-of-fit and predictive performances using 8 sets of software fault detection time-interval data (group data). the results are compared with the infinite-failure NHPP-based SRMs. through comprehensive numerical experiments, we show that our new modeling framework could provide the strengths in the goodness-of-fit performance but have some limitations on the predictive performance in the early testing phase.

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Intrusion Detection Explainability by Ensemble Learning with a Case Study

Intrusion Detection Explainability by Ensemble Learning with...

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international conference on Control, Decision and Information Technologies (CoDIT)

作者： Elyes Manai Mohamed Mejri Jaouhar Fattahi Department of Computer Science and Software Engineering Laval University Québec Canada

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

this paper introduces a novel ensemble learning methodology that leverages the confusion matrix and statistical metrics, particularly the mean, to intelligently select the most suitable model from a pool of candidates for predictive tasks on test inputs. the approach has been tested on a well-known Network Intrusion Detection Dataset (UNSW-NB15) and compared against its underlying models and other ensembling methods where it yielded superior results in three performance metrics (accuracy, recall, and F1). Out approach is model-agnostic, easily adaptable, and scalable to accommodate any number of models. Additionnally, the use of interpretable metrics such as the confusion matrix and mean enhances explainability, providing a comprehensive understanding of the model’s decision-making processes and improvement directions.

关键词： Measurement Adaptation models Refining Decision making Network intrusion detection predictive models Ensemble learning Information technology Standards Genetic algorithms

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Validation of Adaptive Cruise Control based on Model predictive Control for Autonomous Vehicles in Real-Time System

Validation of Adaptive Cruise Control based on Model Predict...

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international conference on Control, Decision and Information Technologies (CoDIT)

作者： Duc-Tien Bui Hung Duy Nguyen Zhengguo Gu Arno Eichberger Institute of Automotive Engineering Graz University of Technology Graz Austria Automation and Control Institute (ACIN) Vienna University of Technology Austria

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Controller models are crucial in maintaining stability, safety, and efficiency in dynamic systems. Adaptive Cruise Control (ACC), a common feature in modern vehicles, adjusts the vehicle’s speed to maintain a safe following distance from the preceding vehicle. this paper presents a new approach to designing an ACC controller, utilizing a Model predictive Control (MPC) to obtain multiple objectives. the model is tested on the one hand with co-simulation between IPG CarMarker (i.e., a high-fidelity vehicle dynamics and testing software) and Matlab/Simulink on a Model-in-the-Loop (MiL) Level and on the other hand on a Hardware-in-the-Loop (HiL) to prove the real-time capability of the controller. the results show the performance of the MPC controller and the gap between MiL and HiL in the case the target car cuts in the lane of the ego car and the ego car approaches the target car.

关键词： Adaptation models Real-time systems Stability analysis software Safety Automobiles Vehicle dynamics Testing predictive control Cruise control

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Digital Twin-based software-defined UAV Networks Using Queuing Model

Digital Twin-based Software-defined UAV Networks Using Queui...

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international conference on Signal Processing and Integrated Networks (SPIN)

作者： Md. Abu Baker Siddiki Abir Mostafa Zaman Chowdhury Institute of Information and Communication Technology Khulna University of Engineering & Technology (KUET) Khulna Bangladesh Dept. of Electrical and Electronic Engineering Khulna University of Engineering & Technology (KUET) Khulna Bangladesh

In order to deploy unmanned aerial vehicles (UAVs) in a variety of scenarios and to ensure the performance, reliability, and safety of these systems, a flexible and adaptable core network infrastructure is required. In this regard, software-defined network (SDN), allows the centralized control and coordination using software rather than traditional hardware-based controllers for the operation of UAVs to provide a flexible and scalable platform. On the other hand, digital twin (DT) technology has the potential which can provide real-time monitoring, analysis, and virtualization of network performance with rapid prototyping and testing to revolutionize the way that software-defined UAV networks are designed, implemented, and maintained. therefore, in this paper, DT integrated approach for software-defined UAV networks is proposed with a routing flow adjustment algorithm. We also demonstrate its capability through the M/M/1 queueing model. Results have shown that our proposal outperforms typical queueing models without the integration of SDN and DT in terms of efficiency, reliability, and agility of UAV networks and provides visualized network analyzing platform with real-time monitoring feature.

关键词： Wireless communication Visualization Signal processing algorithms predictive models Autonomous aerial vehicles Routing Mathematical models

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Prediction of the Most Common Symptoms in Psychological Illnesses with Language Representation models

Prediction of the Most Common Symptoms in Psychological Illn...

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international conference on Smart Computing & Communications (ICSCC)

作者： İrfan Aygün Mehmet Kaya Department of Software Engineering Celal Bayar University Manisa Turkey Department of Computer Engineering Fırat University Elaziğ Turkey

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

It is a known fact as a result of researches that psychological disorders are seen more frequently in society day by day and early diagnosis of these disorders is very important. To detect psychological disorders, it is an important achievement to identify the symptoms in the sentences of potential patients. In the present study, the most frequently used symptoms in the sentences of past psychiatric patients were investigated. the deep learning supported BERT model was used to analyze the texts and the Named Entity Recognition (NER) method was used for symptom detection. thus, a model is proposed that enables the detection of symptoms even when they are expressed in different ways. the success of the proposed model in detecting the symptoms is between 83.6 and 86.2% and the most common symptoms are shortness of breath, loss of attention and loss of appetite.

关键词： Deep learning Computational modeling Semantics Psychology Medical services Named entity recognition predictive models Depression Proposals Diseases

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An Open-Source Framework for Sensitivity Analysis of predictive Neuromuscular Simulations: How Muscle-Tendon Stiffness and Tendon Slack Length Affect Push-Off

An Open-Source Framework for Sensitivity Analysis of Predict...

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IEEE/RAS-EMBS international conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Alexandra Buchmann Daniel Renjewski Department of Mechanical Engineering Chair of Applied Mechanics TUM School of Engineering and Design Technical University of Munich Garching Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

the impulsive ankle push-off is crucial for natural leg dynamics and efficiency in human walking. To study the biomechanics of the ankle push-off, we use 2D predictive neuromuscular simulations. Assessing the sensitivity of model outputs to input variations is necessary to evaluate the reliability of model predictions. Sensitivity analysis (SA) can quantify how uncertainty in model outputs is related to different sources of uncertainty in model inputs. this work presents a new open-source software framework for global SA of predictive neuromuscular simulations using SCONE-Hyfydy and the SAFE toolbox. With the framework, we investigate how sensitive the ankle push-off and related gait characteristics are to variations in muscle-tendon unit (MTU) stiffness and tendon slack length (TSL) of Soleus (SOL), Gastrocnemius (GAS), and hip flexor muscles (HFL), the muscles active during push-off. To characterize the quality of the ankle push-off, we evaluate the models' maximum positive ankle power, maximum normalized cross-correlation of simulated ankle kinematics with human data to assess the signal similarity, cost of transport (CoT), and walking speed. Four different SA methods are compared: EET, FAST, PAWN, and VBSA. We found GAS MTU stiffness and TSL to have a significant impact on the models' CoT, walking speed, and ankle kinematics. Variations of SOL and HFL properties had limited to no effect on the assessed outputs. the four SA methods showed similar trends, but the computational costs and detailed results varied. With this work, we provide the biomechanics community with a software framework for SA of predictive neuromuscular simulations. Our results will improve the functional understanding of the ankle push-off, aiding in developing robotic devices and prostheses.

关键词： Ankle Legged locomotion Biomechanics Analytical models Adaptation models Neuromuscular Biological system modeling Kinematics predictive models Robot sensing systems

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User Study on the Effects Explainable AI Visualizations on Non-experts 10th

User Study on the Effects Explainable AI Visualizations on N...

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10th European-Alliance-for-Innovation (EAI) international conference on ArtsIT, Interactivity and Game Creation (EAI ArtsIT)

作者： Schulze-Weddige, Sophia Zylowski, thorsten CAS Software AG Future Labs CAS Weg 1-5 D-76131 Karlsruhe Germany

ISBN: (纸本)9783030955311;9783030955304

Artificial intelligence is drastically changing the process of creating art. However, in art, as in many other domains, algorithms and models are not immune from generating discriminatory and unfair artifacts or decisions. Explainable Artificial Intelligence (XAI) makes it possible to look into the "black box" and to identify biases and discriminatory behaviour. One of the main problems of XAI is that state-of-the-art explanation tools are usually tailored to AI experts. this paper evaluates how intuitively understandable the same tools are to laypeople. By using the prototypical use case of predictive sales, and testing the results with users, the abstract ideas of XAI are transferred to a real-world setting to study its understandability. Based on our analysis, it can be concluded that explanations are easier to understand if they are presented in a way that is familiar to the users. A presentation in natural language is favorable because it presents facts unambiguously. All relevant information should be accessible in an intuitive manner that avoids sources of misinterpretations. It is desirable to design the system in an interactive way that allows the user to request further details on demand. this makes the system more flexible and adjustable to the use case. the results presented in this paper can guide the development of explainability tools that are adapted to a non-expert audience.

关键词： Explainable AI Human-centric AI User study

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Detection and Classification of Brain Tumor and 3D Reconstruction of MRI

Detection and Classification of Brain Tumor and 3D Reconstru...

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Image Processing and Capsule Networks (ICIPCN), international conference on

作者： Maheshwari Kongalla Rhutuja Desai Benson Vangepuram T Padma Department of ECE Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Brain tumors pose a significant global health concern, with millions affected worldwide, often resulting in death. In the United States, approximately 70,000 people have primary brain tumors, making brain cancer the 10th most common tumor in India. Accurate diagnosis is crucial for initiating appropriate treatment, and improving patient survival. Treatment varies based on tumor type, size, location, and aggressiveness. Deep learning methods, like transfer learning-based techniques using traditional classifiers, are increasingly used for tumor detection in MRI images. Our study explores VGG-16 and VGG-19 transfer learning methods, leveraging 3D Slicer’s adaptable algorithm for reconstructing MRI scans of brain tumors. By utilizing robust software components like Tcl/Tk, VTK, and ITK, alongside an intuitive application layer, we enable advanced functionalities such as image-guided surgery and brain mapping. Our work highlights 3D Slicer’s alignment with research goals, despite challenges like prolonged diagnosis times due to workload pressures, potentially leading to errors. this predictive models enhance patient outcomes in this critical medical domain, with improved ROC-AUC score >0.9 and classification accuracy of $\sim{99\%}$.

关键词： three-dimensional displays Accuracy Magnetic resonance imaging Transfer learning software algorithms Surgery predictive models

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