检索结果-内蒙古大学图书馆

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

作者： Aravinda, N.L. Reddy, R. Archana Bala Dhandayuthapani, V. Nayak, Jithendra P. R. Husseen, Ahmad Electronics and Communication Engineering Department Telangana Hyderabad India School of Computer Science and Engineering Sr University Telangana Warangal India University of Technology and Applied Sciences Shinas Branch Department of It College of Computing and Information Sciences Oman Srinivas Institute of Technology Department of Cse Valachil Mangalore India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

A crucial aspect of maintaining a customer-oriented business in the telecommunications sector with machine learning (ML) is understanding the reasons and factors that lead to customer churn. However, the dataset is difficult by noise, misclassifications, duplicated data, and imbalanced customer information complicating the process of identifying ways to split the dataset based on events. The under-sampling method address the imbalance data by decreasing the data quantity of majority class, thereby achieving a balanced dataset. This method, utilizing Adaptive k-means clustering, directly determines the reduction based on that data quality in each class. The datasets are combined with class labels to generate a new balanced dataset used for classification in the SVM algorithm, which distinguishes between customer churn and non-churn in the prediction for the telecom sector. The SVM algorithm can perform linear and non-linear classification with a kernel function to control overfitting improve generalization and handle high-dimensional data. The obtained results demonstrate that the proposed method achieves better accuracy of 95.70%, precision of 56.01%, Recall of 55.05%, and F1-score of 96.05% on the telecom churn dataset. These results ensure superior detection and classification performance compared to other existing methods, such as Random Forest (RF), Decision Tree (DT) and K-Nearest Neighbour (KNN). © 2024 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Models for Brain Signal Classification: A Focus on EEG Analysis in Epilepsy Cases 2

Machine Learning Models for Brain Signal Classification: A F...

引用

2nd International Conference on Cyber Resilience, ICCR 2024

作者： Ali, Ali M Nashwan, Shadi Al-Qerem, Ahmad Almomani, Ammar Sakhnini, Mahmoud Al Aldweesh, Amjad AlAhliyya Amman University Communications And Computer Engineering Department Faculty Of Engineering Amman19328 Jordan Cybersecurity Department Faculty Of Information Technology Amman11831 Jordan Zarqa University Computer Science Department Faculty Of Information Technology Zarqa13110 Jordan School Of Computing Skyline University College Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I.T. Department Irbid Jordan Shaqra University College Of Computing And It Shaqra Saudi Arabia

ISBN: (纸本)9798350394962

Electroencephalography (EEG) provides critical insights into brain function and neurological disorders. However, analyzing and classifying EEG signals remains challenging. Manual review is time-consuming, prone to bias, and lacks scalability. Hence, developing automated EEG classification has become vital for clinical assessment and treatment of epilepsy. This study presents a machine learning framework for classifying normal and epileptic EEG recordings. The approach involves feature extraction using discrete wavelet transform (DWT) and statistical methods, feature selection to identify the most discriminative attributes, followed by classification across multiple algorithms. Specifically, DWT decomposes non-stationary signals for time-frequency representation. Principal component analysis and cosine similarity assist in selecting robust features. Supervised classifiers including naive Bayes, decision trees, neural networks, k-nearest neighbors, random forest, and support vector machines categorize the signals. Results demonstrate 100% accuracy with neural networks, indicating highly reliable automated classification is achievable. By comparing multiple techniques, the optimal machine learning pipeline emerges. This epilepsy EEG classification framework demonstrates the potential for AI to significantly improve screening, diagnosis, treatment, and management of neurological disorders. Ongoing research aims to further enhance efficiency, scalability, and real-time capabilities. © 2024 IEEE.

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting

Forte: An Interactive Visual Analytic Tool for Trust-Augment...

引用

2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

作者： Bhattacharjee, Kaustav Kundu, Soumya Chakraborty, Indrasis Dasgupta, Aritra New Jersey Institute of Technology Department of Data Science United States Pacific Northwest National Laboratory Optimization and Control Group United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States

ISBN: (纸本)9798350313604

Accurate net load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature and humidity, remains challenging, particularly for eliciting a high degree of trust in the model outcomes. In this context, there is a growing need for data-driven technological interventions to aid scientists in comprehending how models react to both noisy and clean input variables, thus shedding light on complex behaviors and fostering confidence in the outcomes. In this paper, we present Forte, a visual analytics-based application to explore deep probabilistic net load forecasting models across various input variables and understand the error rates for different scenarios. With carefully designed visual interventions, this web-based interface empowers scientists to derive insights about model performance by simulating diverse scenarios, facilitating an informed decision-making process. We discuss observations made using Forte and demonstrate the effectiveness of visualization techniques to provide valuable insights into the correlation between weather inputs and net load forecasts, ultimately advancing grid capabilities by improving trust in forecasting models. © 2024 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Design of Intelligent Home Care System based on Internet of Things technology 2

Design of Intelligent Home Care System based on Internet of ...

引用

2nd International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2022

作者： Wang, Liangliang Jia, Lianqin Liu, Bingzhan College of Cloud Computing Industry Shandong Institute of Commerce and Technology Jinan China Institute of Commerce and Technology Department of Applied Electronics Shandong Jinan China

ISBN: (数字)9781665454766

ISBN: (纸本)9781665454766

In recent years, China's population is aging seriously, and the elderly population is increasing year by year. The pension issue is a major social issue related to the national economy and people's livelihood. At present, influenced by traditional Chinese concepts, about 70% of the elderly are willing to take care of themselves at home. During the epidemic, the elderly at home were unable to meet their daily needs, unable to monitor their own health status, and unable to adjust their mental state due to physical aging, absence of children, and other reasons. The AIOT-based home care system proposed in this paper uses AIOT technology to monitor the living environment and health status of the elderly at home in real time through various sensors and intelligent electronic devices, and transmits early warning information to children or communities. Human-machine dialogue realizes spiritual companionship and health promotion, realizes online shopping, medical treatment, entertainment and learning through intelligent APP application software, and effectively collects and processes information in combination with the intelligent cloud platform of the Internet of Things to realize health file management. © 2022 IEEE.

关键词： Application programs

来源：评论

学校读者我要写书评

暂无评论

An Optimal Cluster Head and Secure Route Path Selection Using Multi Objective—GWO Approach in Wireless Sensor Networks

An Optimal Cluster Head and Secure Route Path Selection Usin...

引用

International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Muvva, Vijaya Bhaskar Reddy Almoussawi, Zainab abed Naik, Narayan Madhavan, Sowmya Praveena, Hirald Dwaraka Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences -Shinas Shinas Oman College of Islamic Science Ahl Al Bayt University Karbala Iraq Department of IS&E Canara Engineering College Visvesvaraya Technological University Karnataka BantwalBelagavi India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Andhra Pradesh Tirupati India

ISBN: (纸本)9789819778751

Wireless Sensor Networks (WSNs) play a crucial role ranging from environmental control to queue tracking. These networks are established by deploying numerous tiny sensor nodes that communicate with each other wirelessly. However, the routing paths in WSNs often suffer from delays in timely information delivery. The proposed Multi Objective Grey Wolf Optimization (MOGWO) aims to address these challenges where information transmission may converge slowly and routing paths may struggle in large-dimensional spaces. The objective of MOGWO is to efficiently send information by selecting the shortest path based on the regions. The MOGWO focuses on achieving high efficiency by choosing the shortest path to explore information. In the context of network routing MOGWO optimizes the path to the destination considering the energy levels of the sensor nodes. The proposed MOGWO method demonstrates superior performance compared to existing methods such as PSO, Black Widow Optimization (BWO) and Ant Colony Optimization (ACO). Specifically, the MOGWO method yields better results, achieving an energy consumption of 43 J, Packet Delivery Ratio (PDR) of 90%, and End-To-End Delay (ETED) of 33 s. This outperforms existing methods, highlighting the effectiveness of the MOGWO approach in enhancing network routing paths in WSN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

来源：评论

学校读者我要写书评

暂无评论

Empirical Analysis of Machine Learning Methods for Predicting Student Performance

Empirical Analysis of Machine Learning Methods for Predictin...

引用

Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Prashanth Kumar Bolisetty Satyanarayana Bora Department of Information Technology College of Computing and Information Sciences University of Technology And Applied Sciences Shinas Sultanate of Oman Preparatory Studies Center University of Technology and Applied Applied Sciences Musandam Sultanate of Oman

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Performance prediction for students is relatively a new topic of research that aims to apply quantitative and qualitative approaches to evaluate student performance. This paper reviews existing approaches in predicting student performance across various learning environments. It explores fundamental statistical models, Machine Learning (ML) algorithms, and Deep Learning (DL) models, highlighting their pertinent characteristics and suitability for analyzing academic, behavioral and demographic data. The paper focuses on analyzing the existing methodologies for student performance prediction. The datasets used consist of student grades, attendance records, socioeconomic status, and interaction rates, along with an assessment of their efficiency in feature selection and data preprocessing. Lastly, the explainability and interpretability of the proposed predictive models are explored to enable reliability for educational stakeholders on these predictions for timely interventions. This survey concludes with a discussion of future scopes, focusing on enhancing prediction accuracy, ensuring fairness in models, and integrating multiple data modalities into performance analysis.

关键词： Wireless communication Surveys Analytical models Reviews Predictive models Prediction algorithms Data models Stakeholders Reliability Socioeconomics

来源：评论

学校读者我要写书评

暂无评论

Optimal Deep Belief Network Enabled Cybersecurity Phishing Email Classification

引用

Computer Systems Science & Engineering 2023年第3期44卷 2701-2713页

作者： Ashit Kumar Dutta T.Meyyappan Basit Qureshi Majed Alsanea Anas Waleed Abulfaraj Manal M.Al Faraj Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Science Alagappa UniversityKaraikudi630003India Department of Computer Science Prince Sultan UniversityRiyadh11586Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah23613Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Recently,developments of Internet and cloud technologies have resulted in a considerable rise in utilization of online media for day to day *** results in illegal access to users’private data and compromises *** is a popular attack which tricked the user into accessing malicious data and gaining the *** identification of phishing emails can be treated as an essential process in the domain of *** article focuses on the design of bio-geography based optimization with deep learning for Phishing Email detection and classification(BBODL-PEDC)*** major intention of the BBODL-PEDC model is to distinguish emails between legitimate and *** BBODL-PEDC model initially performs data pre-processing in three levels namely email cleaning,tokenization,and stop word ***,TF-IDF model is applied for the extraction of useful feature ***,optimal deep belief network(DBN)model is used for the email classification and its efficacy can be boosted by the BBO based hyperparameter tuning *** performance validation of the BBODL-PEDC model can be performed using benchmark dataset and the results are assessed under several *** comparative studies reported the superior outcomes of the BBODL-PEDC model over the recent approaches.

关键词： Cybersecurity phishing email data classification deep learning biogeography based optimization hyperparameter tuning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Battery Voltage Prediction with Deep Learning: A Comparative Analysis of LSTM and Traditional Models 6

Enhancing Battery Voltage Prediction with Deep Learning: A C...

引用

6th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Niakan, Masoumeh Rostam Hajihosseini, Mojtaba Madani, Seyed Saeed Ziebert, Carlos Nri Department of Electricity and Energy Economics Tehran Iran University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Renewable Energy Systems Zagreb Croatia Institute of Applied Materials-Applied Materials Physics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798350305623

The growing demand for efficient energy storage solutions has sparked increased interest in precise battery voltage prediction. In this study the Long Short-Term Memory networks, are applied in the time series forecasting, to improve battery voltage prediction compared to conventional models. The developed forecasting system includes three main parts: Pre-processing, modeling, and evaluation. In the pre-processing, the voltage and current time series are normalized, and divided to the test and train subsets. Then the windows of consecutive samples are generated for both train and test subsets. The forecasting LSTM models are learnt from training data set, in the modeling phase. Finally, the Mean Absolute Error (MAE) is used as the evaluation criterion. This model is compared to two other neural network models. The study concludes that LSTM outperforms the other models, highlighted by a significantly lower MAE. With a comprehensive methodology and successful experimental framework, this paper demonstrates the efficacy of LSTM in predicting battery voltage, indicating its superiority over simple neural networks for time series forecasting. © 2023 IEEE.

关键词： Battery voltage prediction Deep learning Long Short-Term Memory neural networks

来源：评论

学校读者我要写书评

暂无评论

Leveraging Internet of Things (IoT) Sensors and Deep Learning Techniques for Precision Agriculture 1

Leveraging Internet of Things (IoT) Sensors and Deep Learnin...

引用

1st International Conference on Innovative Engineering Sciences and Technological Research, ICIESTR 2024

作者： Wilfred Blessing, N.R. Pyingkodi, M. Wasim Haidar, S.K. Palarimath, Suresh Paravathavarthini, S. Balakumar, C. Sheeja Kumari, V. Sutherlin Subitha, G. Seldev Christopher, S. University of Technology and Applied Sciences-Ibri College of Computing and Information Sciences Ibri Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences-Salalah College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Technology Erode India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Saveetha Institute of Medical and Technical Sciences Department of Computational Intelligence Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Aruthenganvilai India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Nagercoil India

ISBN: (纸本)9798350348637

Precision agriculture, driven by advancements in technology, aims to optimize farming practices by utilizing data and technology to enhance efficiency, productivity, and sustainability This research introduces an innovative pilot investigation centered on the convergence of two revolutionary technologies are the Internet of Things (IoT) and Deep Learning. With a specific focus on advancing precision agriculture, the primary objective of this study is to evaluate the combined effects of data collection facilitated by IoT and analytical capabilities of deep learning in enhancing and optimizing various agricultural processes. The integration of IoT in agriculture has revolutionized data acquisition, employing an array of sensors to monitor critical parameters such as soil moisture, temperature, and crop health. Concurrently, Deep Learning, a subset of artificial intelligence, exhibits the potential to glean actionable insights from voluminous datasets, offering advanced analytics and predictive capabilities. This study investigates the practical implementation and efficacy of this integration in a controlled agricultural setting. Sensors strategically positioned in the pilot study capture real-time data, while deep learning algorithms process and analyze this information. The primary objectives include evaluating the effectiveness of this combined technology in optimizing irrigation schedules, predicting crop yields, and identifying anomalies in crop health. Preliminary findings underscore the transformative potential of IoT and Deep Learning, empowering farmers with real-time data for informed decision-making. Key considerations encompassed in the study include IoT Sensors, Deep learning algorithms, and user adoption. The research not only sheds light on the technical intricacies of the integration but also delves into the challenges and opportunities inherent in merging these technologies within the agricultural landscape. As agriculture transitions towards the next

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

Mathematical model of COVID-19 dynamics in the presence of multiple controls

引用

Quality and Quantity 2024年第1期59卷 261-290页

作者： Akanni, J.O. Fatmawati Ajao, S. Asamoah, J.K.K. Abimbade, S.F. Department of Mathematical and Computing Sciences Koladaisi University Oyo State Ibadan Nigeria Department of Mathematics Faculty of Science and Technology Universitas Airlangga Surabaya 60115 Indonesia Department of Mathematics and Computer Science Elizade University Ondo State Ilara-Mokin Nigeria Department of Mathematics Saveetha School of Engineering SIMATS Chennai India Department of Mathematics Kwame Nkrumah University of Science and Technology Kumasi Ghana Department of Pure and Applied Mathematics Ladoke Akintola University of Technology Oyo State Ogbomoso Nigeria

The coronavirus disease 19 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), is an infectious disease constituting the most significant challenge to human and socio-economic development across the globe. Many studies have been conducted to gain more knowledge on the dynamic spread of SARS-COV-2 in the community of people. In this study, a new mathematical model made up of a system of non-linear ODEs is designed and meticulously analyzed to understand the dynamics of transmission of COVID-19. Specific necessary properties exhibited by the COVID-19 model are examined through the theory of non-negativity and boundedness of solutions. The threshold parameter measuring the invasion potential of COVID-19 in the population is worked out by employing the Next Generation Matrix approach. The model is shown to have two endemic equilibria whenever basic reproduction number R₀<1. The analysis of the endemic equilibrium stability is explored using a suitably constructed Lyapunov function. It is shown that the COVID-19 model is globally asymptotically stable whenever the associated basic reproduction number is beyond unity. The Latin Hypercube Sampling or Partial Rank Correlation Coefficient is utilized to examine how parameter changes affect the COVID-19 spread in the population. Furthermore, the study considers six optimal control interventions to flatten the curve of the coronavirus pandemic in society. Numerical experiments of the COVID-19 model are executed in MatLab to corroborate the qualitative analysis of the model. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： 49J15 cost-effectiveness analysis COVID-19 Dynamical system LHS/PRCC Mathematical modelling Optimal control analysis Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：