检索结果-内蒙古大学图书馆

3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Syed, Shakir Sondinti, Lakshminarayana Reddy Kothapalli Sapra, Puneet Joshi, Amit Shobana, M. Deepan Chakravarthi, A.V. Shivam hills India Us Bank LB. Nagar India Rayat Bahara University Department of Computer Science Engineering Mohali India Isma University Department of Information Technology Rīga Latvia M.Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India Velammal College of Engineering and Technology Department of Civil Engineering Madurai India

ISBN: (纸本)9798331508456

Researchers and financial entities have long been fascinated by the ability to accurately predict the success of start-ups. Modern start-ups, which are creative, tech-focused, and quickly growing companies, face this challenge more than most. This study introduces a deep learning Stacked Denoising Autoencoder (SDAE) model that uses important technology domains to forecast the likelihood of a startup's success. The three primary steps of the proposed approach are as follows: preprocessing, feature extraction, and training the model. To ensure statistical independence of signal components in multivariate datasets, preprocessing steps include min-max normalisation for data standardisation and Independent Component Analysis (ICA) for feature extraction. The predictive power of the SDAE model is then enhanced by training. The proposed model outperforms traditional methods like Decision Trees (DT) and Denoising Autoencoders (DAE) with an average accuracy of 94.28%, according to the experimental data. This makes it a strong contender for predicting startup success in changing technical contexts. © 2025 IEEE.

关键词： Independent component analysis

来源：评论

学校读者我要写书评

暂无评论

Evaluating the Impact on Usability and Acceptance of ECAs in m-Health Applications for Older Adults 11

Evaluating the Impact on Usability and Acceptance of ECAs in...

引用

11th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2025

作者： Gilaberte, Raquel Lacuesta Bagdasari, Eva Cerezo Navarro-Alamán, Javier EUPT Universidad de Zaragoza Spain EINA Universidad de Zaragoza Spain Department of Computer Science and Engineering of Systems EUPT Universidad de Zaragoza Spain

ISBN: (纸本)9789897587436

In the context of m-health applications, developing user-friendly interfaces to improve usability and acceptance by older adults has become a prominent research topic. The use of embodied conversational agents (ECAs) seems promising as they allow interacting through natural verbal and nonverbal communication. However, analyses about the design and acceptance of embodied conversational agents embedded in m-health applications for older adults are needed. In this paper, we present a study carried out to analyse the usability and acceptance of ECAs interfaces in m-health applications, compared to traditional tactile text interfaces. The study carried out has involved 23 users over 65 years old with promising results. The ECA interface was positively assessed and its acceptance increased compared to the traditional one, but although the feelings arisen are positive, users still claim for less complexity and a more careful design. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Avatars ECAs m-Health Older Adults

来源：评论

学校读者我要写书评

暂无评论

Water Quality Classification Using an Artificial Neural Network 1st

Water Quality Classification Using an Artificial Neural Netw...

引用

1st International Conference on Intelligent systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Mohsen, Saeed Abdel-Rehim, Wael M. F. Abdel-Aziz, M. Electronics and Communications Engineering Department Al-Madinah Higher Institute for Engineering and Technology Giza12947 Egypt South Sinai46511 Egypt Department of Computer Science Faculty of Computers and Information Suez University Suez43512 Egypt Department of Basic Sciences Faculty of Computer and Information Sciences Ain Shams University Cairo11566 Egypt

ISBN: (纸本)9783031823763

Water quality is vital for public health and ecosystem sustainability. However, approximately 2 billion people worldwide do not have access to drinking water that is appropriately managed, contributing to significant health risks and environmental issues. This paper proposes an efficient approach to classifying water quality as potable or impotable water using artificial neural networks (ANNs). This work highlights the perspective of ANNs in reducing water pollution and ensuring safe water access. The ANN is implemented with a dataset consisting of 2 water classes based on 3,276 water samples. The proposed ANN is tested via the Keras framework with a tuning technique to obtain the most significant water quality classification (WQC) precision. An error matrix is used to evaluate the ANN efficiency. The results show that the ANN is highly efficient in the classification of water quality. Moreover, The ANN provides a test accuracy of 68.38%, a mean precision of 69.64%, and the average area under the ROC curve for the implemented ANN is 51%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Artificial Neural Network Classification Deep Learning Water Quality

来源：评论

学校读者我要写书评

暂无评论

Electrohysterogram Data Augmentation Using Generative Adversarial Network for Pregnancy Outcome Prediction

引用

IEEE Access 2025年 13卷 92468-92482页

作者： Cheema, Muhammad Omar Din, Zia Mohy Ud Imran, Azhar Khan, Fawad Salam Al-Rawi, Mahmood Basil A. El-Meligy, Mohammed Gul, Jahan Zeb Air University Department of Biomedical Engineering Islamabad44000 Pakistan Air University Faculty of Computing & AI Department of Creative Technologies Islamabad44000 Pakistan King Saud University College of Applied Medical Sciences Department of Optometry Riyadh Saudi Arabia Amman Arab University College of Computer Science and Informatics Amman Jordan Maynooth University Department of Electronic Engineering Ireland

There are many difficulties in managing and detecting preterm pregnancies, especially in the early stages. Analyzing electrohysterogram data, which show the electrical activity of uterine muscles, is a promising non-invasive method for classifying term and preterm pregnancies. However, the effectiveness of machine learning classifiers is hampered by the inherent class imbalance in datasets. In order to overcome this constraint, this study uses a Generative Adversarial Network to produce high-quality synthetic EHG data while maintaining the statistical properties of actual signals. By successfully reducing class imbalance, the enhanced dataset makes it possible for machine learning models to be trained with resilience. Real and synthetic features did not differ significantly, according to statistical validation using a t-test (p > 0.05). With this method, the Random Forest classifier obtained a ROC-AUC of 0.99 and a 10-fold cross-validation accuracy of 98%. The suggested GAN-based method’s simplicity and efficacy demonstrate its promise for resolving class imbalance and developing reliable classification systems for diagnostic applications. © 2013 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Scanning the Issue

引用

IEEE Transactions on Intelligent Transportation systems 2025年第1期26卷 3-21页

作者： Simona Sacone Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genova Genova Italy

“Scanning the Issue.“

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing Data Aggregation in Cluster-Based Wireless Sensor Networks with LCSS: Longest Common Subsequence Empowerment 8th

Enhancing Data Aggregation in Cluster-Based Wireless Sensor ...

引用

8th World Conference on Smart Trends in systems Security and Sustainability, WorldS4 2024

作者： Jamil, Muhammad Farooq, Umer Tariq, Noshina Alwakid, Ghadah Humayun, Mamoona Khan, Nadia Department of Computer Science Abdul Wali Khan University Mardan Pakistan Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Department of Avionics Engineering Air University Islamabad Pakistan Department of Computer Science College of Computer and Information Sciences Jouf University Sakaka Al-Jouf Saudi Arabia Department of Information Systems College of Computer and Information Sciences Jouf University Sakaka Al-Jouf Saudi Arabia Institute of Computer Sciences and Information Technology Faculty of Management and Computer Sciences The University of Agriculture Peshawar Pakistan

ISBN: (纸本)9789819793266

A wireless sensor network (WSN) is a system of interconnected sensors that can gather environmental information. On the other hand, data redundancy is a common source of problems with WSNs. The literature presents a plethora of approaches. Despite these mechanisms’ best efforts, most current systems still need help with difficulties like noise, redundancy, or application specificity. Thus, we proposed an algorithm to overcome all the problems with redundant and highly correlated data. The longest common subsequence (LCSS)-enabled sliding window algorithm can resolve issues in constrained networks related to energy conservation and congestion control. The proposed method exhibits remarkable performance on multiple benchmark datasets. According to the result analysis, the suggested technique can outperform its equivalents for a range of data sets by about 32%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Congestion control (communication)

来源：评论

学校读者我要写书评

暂无评论

Anomaly-Based Detection of Log4j Attack in SDN

Anomaly-Based Detection of Log4j Attack in SDN

引用

International Conference on Smart systems and Wireless Communication, SSWC 2024

作者： Deshmukh, Pruthviraj Nitin Harish, R. Sangeetha, V. Praveen, K. TIFAC-CORE in Cyber Security Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Information Systems BITS Pilani India

ISBN: (纸本)9789819613472

The widely used Java library Log4j logging framework has lately come under attack from a major vulnerability known as CVE-2021–44228, also known as Log4Shell. Through the use of malicious log messages, the vulnerability enables attackers to compromise vulnerable systems by executing remote code. A key component of SDN systems is the RYU controller, an open-source software-defined networking (SDN) controller that provides a wide range of functionalities. It can communicate and interact with SDN-capable switches and other network elements. It is used in SDN installations for managing, controlling, and supporting network automation and orchestration. This paper proposes a new anomaly-based Log4j detection approach in SDN networks. We have used multiple machine-learning classifiers to analyze and detect anomalies in network traffic generated by Log4j attacks to get the best achievable accuracy for real-time model development. The conducted experiment results that the proposed approach can give high accuracy up to 98% in detecting Log4j attack in SDN environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： E-learning

来源：评论

学校读者我要写书评

暂无评论

Optimizing Inventory Management using Demand Forecasting based Ensemble Stacking Model with Hyperparameter Tuning 1

Optimizing Inventory Management using Demand Forecasting bas...

引用

1st International Conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Pavithra, V. Abbas, Haayder M. Manjula, B.M. Chidambaram, P.K. Jothilakshmi, S. Srm Institute of Science and Technology Faculty of Science and Humanities Department of Computer Science and Applications Ramapuram Chennai India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Information Technology Mamallapuram India

ISBN: (纸本)9798331529246

In recent days, the effective inventory management relies on accurate demand forecasting for effective management of business balance stocks. However, the traditional forecasting methods often struggled with different complexities such as variability, overstocking and wasted resources. In this research, the Ensemble Stacking Model with Hyperparameter Tuning (En-Stacking-HT) is proposed for effective optimized inventory management. Initially, the data is collected from M5 accuracy completion dataset and further preprocessed with data imputation, z-score normalization. These preprocessing techniques handles the missing values in data and standardizes the data in significant range. Then, Seasonal and Trend Decomposition (STL) is employed for extracting seasonality and trend features from preprocessed data by using Locally Estimated Scatterplot Smoothing (LOESS). Finally, the proposed En-Stacking-HT is utilized for inventory management prediction and optimized through hyperparameter tuning for predicting effective optimized inventory management for demand forecasting. From the results, the proposed En-Stacking-HT attained better results in terms of Mean Absolute Error (MAE) (16.82), Root Mean Square Error (RMSE) (22.85) and Mean Absolute Percentage Error (MAPE) (4.97%) when compared to existing Random Forest (RF). © 2025 IEEE.

关键词： demand forecasting ensemble stacking model hyperparameter tuning optimized inventory management seasonality and trend decomposition

来源：评论

学校读者我要写书评

暂无评论

Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions

引用

Discover Internet of Things 2025年第1期5卷 1-23页

作者： Mazhar, Tehseen khan, Sunawar Shahzad, Tariq khan, Muhammad Amir Saeed, Mamoon M. Awotunde, Joseph Bamidele Hamam, Habib School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science International Islamic University Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Science and Information Technology School Education Department Government of Punjab 31200 Layyah Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

This article discusses Blockchain and Generative AI in healthcare, including their uses, difficulties, and solutions. Blockchain technology improves EHR security, privacy, and interoperability, while smart contracts streamline supply chain management and administrative procedures. Blockchain verifies and secures IoT data, improving medical care and treatment, according to case studies. Generative AI systems like ChatGPT have transformed healthcare by personalizing therapy, diagnostics, and predictive analytics. AI systems can examine massive databases to diagnose diseases early, anticipate dangers, and personalize therapies. By providing timely information, boosting treatment adherence, and giving continuous support, AI-powered virtual health assistants have enhanced patient involvement. Generative AI has additionally enhanced medical research and drug development, cutting the time and expense of introducing new medicines. Generative AI and Blockchain provide safe patient data storage, high-quality AI training datasets, and efficient healthcare operations. Scalability, energy usage, and interoperability issues remain. Scalable Blockchain designs and standardized data integration and exchange protocols are suggested by this study. These technologies could improve medical research and therapy by making them safer, more effective, and more individualized. © The Author(s) 2025.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Enhanced Security Protocol for VLSI systems: Modified AES Algorithm for Robust Data Transmission 3

Enhanced Security Protocol for VLSI Systems: Modified AES Al...

引用

3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Lawrence, Jinsha Budagam, Dinesh Kumar Mukilan, P. Veerappan, Kavitha Chandrasekhar, E. Barathi, K. Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Visa Department of Ms in Software Engineering CA United States Dhanalakshmi Srinivasan College of Engineering Department of Electronics and Communication Engineering Coimbatore India Kongu Engineering College Department of Electronics and Communication Engineering Erode India Department of Electronics and Communication Engineering Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331508456

The development of compact and efficient devices has been made possible by the growth of Very Large Scale Integration (VLSI) technologies, which has transformed modern electronics. However, there are cautions regarding the security of data, especially when it comes to transmission due to the extensive usage of technology. To address these, this study proposes a modified Advanced Encryption Standard (AES) algorithm as a solution for improved security protocol in VLSI systems. The proposed algorithm are intended to support the encryption procedure, increasing stronger production against cryptographic attacks while preserving the system's effectiveness and speed factors that are critical aspects for VLSI applications. Through extensive simulations and testing, the modified AES algorithm demonstrated significant security enhancements the operational efficiency of the VLSI system. The attained outcomes of the proposed work shows that the Modified AES strategy provides a workable technique to protect data in VLSI-based devices while maintaining data communication integrity and confidentiality. The proposed modified AES algorithm demonstrates a significant improvement in performance with a propagation delay of 7 ns, power consumption of 29 mW, and a computational overhead of 62 bits, leading to enhanced efficiency in cryptographic operations. This study highlights that the modified AES algorithm improve security in modern electronic systems and maintain performance. © 2025 IEEE.

关键词： Network security

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：