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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

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Design of a prediction model based on improved BP neural network and particle swarm optimization for more accurate budget of biogas production 6

Design of a prediction model based on improved BP neural net...

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2022 6th international conference on Electrical, Mechanical and Computer engineering, ICEMCE 2022

作者： Wu, Yuchen Zhang, Guangming College of Electrical Engineering and Control Science Nanjing Tech University Nanjing211899 China

In order to accurately predict the daily gas production of large and medium-sized biogas projects, the improved BP neural network algorithm was used, and the PSO algorithm was introduced to optimize the parameters. According to the anaerobic fermentation mechanism and the actual engineering operation status, a prediction model was established with temperature, daily feed volume, NH3, TS concentration and pH value as input layer nodes, and daily biogas production as output layer nodes. the 116 sets of data obtained by remote data acquisition are used as training samples and test samples of the model, and the simulation is carried out by Matlab software. the results show that the PSO-LM-BP neural network has good predictive ability for the daily gas production of biogas. the established biogas daily gas production prediction model not only converges fast but also has high accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Biogas

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Comparative Analysis of Hybrid Long Short-Term Memory models for Fire Danger Index Forecasting with Weather Data 6

Comparative Analysis of Hybrid Long Short-Term Memory Models...

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6th international conference on Information and Communications Technology, ICOIACT 2023

作者： Dzulhijjah, Dwi Ahmad Majid, Muhammad Nurkholis Alwanda, Almi Yulistia Kusuma, Dimas Candra Zakaria, Fariz Kusrini, Kusrini Kusnawi, Kusnawi Universitas Amikom Magister of Informatics Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350315639

In East Kalimantan, the absence of predictive tools for time series problems using data from Automatic Weather Stations further exacerbates this issue. Forest fires are a critical concern in this region, with their frequency and severity closely tied to weather conditions. In this study, we address this challenge by conducting an analysis employing various Long Short Term Memory (LSTM) models to predict time series data, specifically focusing on forest fire risk prediction through weather variables. the data used in this research is sourced from Station 1's Automatic Weather Station system, encompassing 7778 rows of data from January to June 2023. Based on this data, we aim to develop an accurate predictive model for the Fire Hazard Index (FDI). the FDI is a crucial indicator of forest fire risk, and accurate predictions can help authorities take proactive measures to mitigate fire outbreaks. To achieve this goal, we compare several algorithms, including LSTM, GRU-LSTM, CNN-LSTM, BILSTM, and Stacked-LSTM, in forecasting the FDI for the Automatic Weather Station. Our performance evaluation involves essential metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Coefficient of Variation (CV) for Predicted values, and CV for Actual values. the findings of our study indicate that all tested algorithms exhibit promising performance in predicting the FDI variable, with negligible discernible differences among them. However, the GRU-LSTM algorithm demonstrates slightly superior performance, boasting an MAE of 20.762499, MSE of 1500.318800, and RMSE of 38.733949. this suggests that GRULSTM may be the preferred choice for FDI prediction in this context. Furthermore, it's noteworthy that all the algorithms generate predictions that closely mirror the actual values of the FDI variable. this is evident from the congruent ranges of CV for Predicted and CV for Actual values. © 2023 IEEE.

关键词： Long short-term memory

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Implementation of Speech to Text Conversion Using Hidden Markov Model 6

Implementation of Speech to Text Conversion Using Hidden Mar...

引用

6th international conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Elakkiya, A. Surya, K. Jaya Venkatesh, Konduru Aakash, S. Saveetha Engineering College Department of Ece Saveetha Nagar Sriperumbadur Taluk Chennai602105 India

ISBN: (纸本)9781665482714

Deep learning is revolutionary when used to transcribe spoken language into text that computers can read with the same intent as human readers. the fundamental idea is to give intelligent systems with human language as data that may be utilized in various domains. A speech-to-text synthesizer is a piece of software that can convert an audio file into text using Digital Signal Processing (DSP) algorithms that analyze and process the speech signal in the audio file. the objective of Speech To Text (STT) is to convert audio input from a user or computer into readable text. the STT is proposed to be transformed using the Hidden Markov Model (HMM) method. the development of a speech-to-text synthesizer will be a tremendous advantage for the visually handicapped and will make reading lengthy texts much easier. © 2022 IEEE.

关键词： Hidden Markov models

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Four-Function Fully Automatic Refrigerator Protection System Design 6

Four-Function Fully Automatic Refrigerator Protection System...

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2022 6th international conference on Electrical, Mechanical and Computer engineering, ICEMCE 2022

作者： Shi, Lulu Zhang, Bo He, Ruilong Zhao, Yuge Xi'An University of Science and Technology School of Electrical and Control Engineering Xi'an China Shanghai Maierzi Engineering Technology Co. LTD Electrical Engineering and the Automatization Specialty Xi'an China

In the automatic refrigerator protection system hardware design, the MCU is selected as the core control of the system, the specific design content includes the selection of the automatic refrigerator protection system design scheme, MCU, and sensor types and models, in addition, it is necessary to combine the components of the display module design;In the system software design, the most core content is the system program design. the system collects the undervoltage, overvoltage, power failure, leakage, and other conditions of the power supply refrigerator, and the control switch is automatically disconnected to protect the refrigerator. C language was chosen to design the system software code, and Proteus software was used to realize the system function simulation, and the functions of the system were verified. © Published under licence by IOP Publishing Ltd.

关键词： Refrigerators

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Identifying Security-Related Requirements in Regulatory Documents Based on Cross-Project Classification 18

Identifying Security-Related Requirements in Regulatory Docu...

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18th ACM international conference on predictive models and Data Analytics in software engineering (PROMISE)

作者： Mohamad, Mazen Steghoefer, Jan-Philipp Astrom, Alexander Scandariato, Riccardo Chalmers Univ Gothenburg Gothenburg Sweden Xitaso GmbH Augsburg Germany Comentor AB Gothenburg Sweden Hamburg Univ Technol Hamburg Germany

ISBN: (纸本)9781450398602

Security is getting substantial focus in many industries, especially safety-critical ones. When new regulations and standards which can run to hundreds of pages are introduced, it is necessary to identify the requirements in those documents which have an impact on security. Additionally, it is necessary to revisit the requirements of existing systems and identify the security related ones. We investigate the feasibility of using a classifier for security-related requirements trained on requirement specifications available online. We base our investigation on 15 requirement documents, randomly selected and partially pre-labelled, with a total of 3,880 requirements. To validate the model, we run a cross-project prediction on the data where each specification constitutes a group. We also test the model on three different United Nations (UN) regulations from the automotive domain with different magnitudes of security relevance. Our results indicate the feasibility of training a model from a heterogeneous data set including specifications from multiple domains and in different styles. Additionally, we show the ability of such a classifier to identify security requirements in real-life regulations and discuss scenarios in which such a classification becomes useful to practitioners.

关键词： Security Requirements Requirements Classification Machine Learning Automated Requirements engineering

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Heart Disease Prediction Using Eye Retinal Images 6

Heart Disease Prediction Using Eye Retinal Images

引用

6th IEEE international conference on Advances in Science and Technology, ICAST 2023

作者： Shaikh, Sameer Wagh, Ishan Zaveri, Vrushab Mujawar, Mohammad Bazil Surve, Divya Vidyalankar Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350359817

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide. Early detection and risk assessment of cardiovascular diseases are important for effective prevention and timely intervention. An analysis of deep learning methods used for prediction of CVR based on RFI. Since it is an extension of central nervous system, the retina gives an exceptional chance for safe medical diagnosis. Using multiple types of retinal fundus images together with the patient and clinical data, we developed a new deep learning model for prediction of several vital parameters for cardiovascular diseases. With CNN, it is possible to extract relevant features from retinal images without involving any manual procedures. the main aim of our study is to predict hypertension, diabetes and hyperlipidemia, which are risk factors for cardiovascular diseases. Our deep learning model achieves incredible accuracy in identifying high-risk individuals by identifying changes in brain vessels, microaneurysms, and other signs of pathology. Additionally, the model provides good information about the relationship between the visibility of the eye and the severity of these problems. We also consider ways of interpreting predictive models which help us understand how retinal changes relate to CV risk based on pathophysiological mechanism. Such research can lead the doctors on how to improve themselves if possible and intervene earlier for the sake of preventing this health condition. © 2023 IEEE.

关键词： Forecasting

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A Machine Learning Based predictive Model to Diagnose Heart Failure Patients using Imbalanced Classification Problem 4

A Machine Learning Based Predictive Model to Diagnose Heart ...

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4th international conference on Advancements in Computational Sciences, ICACS 2023

作者： Mudassar, Muhammad Afzal, Mehtab Muhammad, Tufail Univeristy of Lahore Department of Software Engineering Lahore Pakistan Air Univeristy Islamabad Aerospace and Aviation Campus Department of Computer Science Kamra Pakistan

ISBN: (纸本)9781665463775

Heart failure (HF) is now one of the most common diseases, causing approximately seventeen million death cases every year all over the world. HF occurs due to less pumping ratio of blood by the heart that a normal human being needs to survive. In this regard, research studies have been proposed to predict the causes behind the heart failure of a patient using the 'Heart failure clinical records (HFCR's) dataset. Although, much research has been performed on this dataset, however, there is a lack of construction of a more reliable predictive model that helps to predict HF patients with better prediction results. We aimed to apply imbalance learning to handle the imbalance dataset as a very few researchers applied it. We trained the models using six ensemble and three non-ensemble classifiers with the help of multiple experiments. In the end, we performed an evaluation measure to compare our prediction results with the previous research work. Our proposed model gives a significant increase in accuracy value as well as in precision, recall, and f1-score. © 2023 IEEE.

关键词： Forecasting

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Distributed Attacks Classification Based on Radical Basis Function and Particle Swarm Optimization In Hypervisor Layer 6

Distributed Attacks Classification Based on Radical Basis Fu...

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6th international conference on Information Systems and Computer Networks, ISCON 2023

作者： Shukla, Aditya Kumar Sharma, Ashish GLA University Department of Computer Engineering and Applications UP Mathura India

ISBN: (纸本)9798350346961

the term 'cloud computing' refers to a concept for on-demand provisioning of computer resources (including hardware and software) through the Internet. Cloud computing's primary benefits lie in the time and money savings it may provide to IT departments. While cloud computing offers many benefits It also raises many safety considerations. these include data privacy, data availability, data placement, data security credential, license, access management, network security, web data protection, virtual devices protection, etc. Recently, machine learning has shown to be an effective tool for safeguarding the cloud. In order to automate the process of identifying cloud assaults with better accuracy than any other technology, machine learning algorithms are trained on numerous legitimate datasets to develop models. To mitigate the effects of DDoS invasions, our research suggests implementing a detection method at the hypervisor layer. As a means of detecting and categorizing DDoS attacks in the communication between virtual computers, the suggested detection method combines the particle swarm optimization (PSO) with radial basis function (RBF). High classification accuracy against DDoS attacks is the focus of our investigation and recommended method.. © 2023 IEEE.

关键词： Data privacy

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Application of Machine Learning in Stroke Prediction: A Systematic Review 6

Application of Machine Learning in Stroke Prediction: A Syst...

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6th Parul international conference on engineering and Technology, PICET 2024

作者： Khushbu, Shah Ganatra, Amit thacker, Chintan Department of Computer Scienceand Engineering Parul Institute of Engineering and Technology Parul University Vadodara India Parul University Vadodara India

ISBN: (纸本)9798350369748

A brain stroke is a dangerous condition in which there is insufficient blood flow to a part of the brain, frequently as a result of brain haemorrhage or clogged arteries. Long-term oxygen deprivation results from this, which damages the brain irreversibly and kills brain cells. Stroke patients should receive treatment as soon as possible since restoring blood flow might lessen the extent of the damage. In the world, stroke ranks as the second most common cause of death. Algorithms for machine learning can assist in both patient rehabilitation and the detection of brain *** datasets are being analysed using ML techniques to spot trends that might suggest a person’s risk of having a stroke. this is a growing area of use in stroke prediction. Key methods include NB, SVM, GB, RF, DT, KNN, Clustering, and Ensemble Learning. By combining predictions from several trees, these methods can help pinpoint important risk factors, understand how variables interact, and increase model accuracy. Subgroups and patterns in the data are also made easier with their assistance. Using the stack ensemble model, which employedthree 1D convolutional models using neural networks from the survey done on the ECG dataset, the maximum accuracy of 99.70% was achieved.. © 2024 IEEE.

关键词： Convolutional neural networks

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A Prediction Model for Minimization of Flood Effects using Machine Learning Algorithms 6

A Prediction Model for Minimization of Flood Effects using M...

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6th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Uttam Reddy, N. Venkatesh Kumar, P. Kapileswar, N. Simon, Judy Phani Kumar, P. University of Texas at Dallas Department of Electrical Engineering TX United States Srm Institute of Science and Technology Department of Electronics and Communication Engineering Ramapuram Campus Chennai India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

the most devastating hazard occurred by the disturbances of the environment are floods. these are considered as the ruinous natural disasters and their prediction is very complicated. Several researches are being conducted on advances in flood prediction models that reduce the calamity, mitigate loss of life, and reduce flood-related property damage. To reduce the complexity of physical processes in the floods, a vast predictive system, neural network techniques have made significant contributions by stipulating accurate performance and user economical solutions. this paper is depicting a clear path for the flood prediction using machine learning algorithms by Random Forest classifier and Decision Tree techniques. the observation from the report analyses that the performance of Flood prediction identifies the calamity and descripted in the confusion matrix with effective positive rates using python framework. © 2022 IEEE.

关键词： Decision trees

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