检索结果-内蒙古大学图书馆

A classification-based privacy-preserving decision-making for secure data sharing in Internet of Things assisted applications

引用

Digital Communications and Networks 2022年第4期8卷 436-445页

作者： Alaa Omran Almagrabi A.K.Bashir Department of Information Systems Faculty of Computing and Information Technology(FCIT)King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK

The introduction of the Internet of Things(IoT)paradigm serves as pervasive resource access and sharing platform for different real-time *** resource availability,access,and allocation provide a better quality of user experience regardless of the application type and ***,privacy remains an open issue in this ubiquitous sharing platform due to massive and replicated data *** this paper,privacy-preserving decision-making for the data-sharing scheme is *** scheme is responsible for improving the security in data sharing without the impact of replicated resources on communicating *** this scheme,classification learning is used for identifying replicas and accessing granted resources *** on the trust score of the available resources,this classification is recurrently performed to improve the reliability of information *** user-level decisions for information sharing and access are made using the classification of the resources at the time of *** proposed scheme is verified using the metrics access delay,success ratio,computation complexity,and sharing loss.

关键词： Classification learning Data mining IoT Privacy-preserving Resource replication

来源：评论

学校读者我要写书评

暂无评论

Child's Age Range Prediction Using Sinhala Speech Recognition System 5

Child's Age Range Prediction Using Sinhala Speech Recognitio...

引用

5th International Conference on Advanced Research in computing, ICARC 2025

作者： Kathriarachchi, Ashimi Pulasinghe, Koliya Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331530983

This study predicts the age range of a child speaking Sinhala by analyzing voice characteristics and acoustic features. Identifying speech impairments in children aged 6 to 72 months is critical for early intervention, mainly when using a system that recognizes their native language. The developed system generates accurate insights to assist speech pathologists in diagnosing speech disorders. A Multilayer Perceptron neural network is proposed for age group prediction, leveraging Mel Frequency Cepstral Coefficients (MFCC) and pitch features to enhance recognition accuracy. The system demonstrated an overall accuracy rate of 77% in age range identification, providing a valuable tool for healthcare professionals to evaluate and monitor speech development in Sinhala-speaking children. © 2025 IEEE.

关键词： Speech analysis

来源：评论

学校读者我要写书评

暂无评论

SafeChurn: Assessing Adversarial Attacks AgainstChurn Prediction Model and Defense

SafeChurn: Assessing Adversarial Attacks AgainstChurn Predic...

引用

2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2023

作者： Kaleem, Amani Mauroof, Aaisha Kalam, Hasna Rupasinghe, Lakmal Liyanapathirana, Chethana Sri Lanka Institute of Information Technology Faculty of Computing Colombo Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798350341072

Numerous fields including the telecom industry's ability to predict customer churn, have been transformed by neural network models. However, these models' vulnerability to adversarial attacks pose questions about their security and reliability. In this study, we test the robustness of a neural network model created for the classification of telecom churn data by measuring how well it performs under three different adversarial attacks: The Projected Gradient Descent (PGD), Boundary attack, and Carlini Wagner attacks. We first created a convolutional neural network model specifically designed for the telecom churn dataset. A large dataset containing pertinent customer attributes, past usage trends, and churn labels is used to train the model. The models functionality is enhanced to precisely classify customer churn by utilizing appropriate strategies. The trained neural network model undergoes three distinct adversarial attacks. The PGD attack looks for perturbations that maximize themodel's loss function within a given - bound. The Boundary attack crosses decision boundaries in order to provide adversarial cases that result in misclassification. The Carlini attack uses an optimization-based methodology to produce the fewest possible model-fooling perturbations. In this research compare the accuracy of the neural network model before and after each adversarial attack to determine the impact of these attacks. We investigate range of defense mechanisms designed especially for telecom churn data categorization in order to increase the model's resistance to these attacks. By implementing these strategies, our aim is to mitigate the impact of adversarial perturbations and increase the model's resistance to attacks from adversaries. © 2023 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble

引用

Big Data Mining and Analytics 2024年第2期7卷 247-270页

作者： Mona Ahamd Alghamdi Abdullah S.A.L-Malaise AL-Ghamdi Mahmoud Ragab Information Systems Department Faculty of Computing and Information Technology(FCIT)King Abdulaziz University(KAU)Jeddah 21589Kingdom of Saudi Arabia Information Systems Department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah 22246Kingdom of Saudi Arabia Information Technology Department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with Mathematics DepartmentFaculty of ScienceAl-Azhar UniversityNaser City 11884Egypt

The ability to make accurate energy predictions while considering all related energy factors allows production plants,regulatory bodies,and governments to meet energy demand and assess the effects of energy-saving *** energy consumption falls within normal parameters,it will be possible to use the developed model to predict energy consumption and develop improvements and mitigating measures for energy *** objective of this model is to accurately predict energy consumption without data limitations and provide results that are easily *** proposed model is an implementation of the stacked Long Short-Term Memory(LSTM)snapshot ensemble combined with the Fast Fourier Transform(FFT)and *** and Berard’s Individual Household Electric-Power Consumption(IHEPC)dataset incorporated with weather data are used to analyse the model’s accuracy with predicting energy *** model is trained,and the results measured using Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),and coefficient of determination(R^(2))metrics are 0.020,0.013,0.017,and 0.999,*** stacked LSTM snapshot ensemble performs better than the compared models based on prediction accuracy and minimized *** results of this study show that prediction accuracy is high,and the model’s stability is high as *** model shows that high levels of accuracy prove accurate predictive ability,and together with high levels of stability,the model has good interpretability,which is not typically accounted for in ***,this study shows that it can be inferred.

关键词： energy consumption prediction Artificial Intelligence(AI) Deep Learning(DL) snapshot ensemble

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Jellyfish Classification: A Study Using YOLOv8 and Pre-trained Models

Comparative Analysis of Jellyfish Classification: A Study Us...

引用

2024 International Research Conference on Smart computing and Systems Engineering, SCSE 2024

作者： Nawarathne, U.M.M.P.K. Kumari, H.M.L.S. Kumari, H.M.N.S. Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka University of Peradeniya Computing Centre Faculty of Engineering Peradeniya Sri Lanka

ISBN: (纸本)9798350375688

Jellyfish classification holds significant importance due to its implications in environmental monitoring, marine conservation, and public safety. Understanding the distribution and characteristics of jellyfish populations can provide valuable insights into the health of marine ecosystems, helping to manage fisheries, protect biodiversity, and mitigate potential ecological imbalances. Therefore, this study experimented with deep learning models for jellyfish classification, ranging from traditional convolutional neural networks (CNNs) such as DenseNet, InceptionV3, MobileNet, MobileNetV2, and NASNet Mobile and as well as the newly introduced YOLO architecture, YOLOv8. The dataset was comprised of six different types of jellyfish images. Notably, YOLOv8 stands out with an impressive accuracy of 99.5%, demonstrating its efficacy in accurately detecting and classifying jellyfish instances. Other models, such as DenseNet and MobileNet, also yield high accuracy scores, emphasizing the versatility of deep learning models in handling complex image classification tasks. The robust methodologies employed in training these models, combined with the achieved high accuracies, pave the way for enhanced understanding and monitoring of jellyfish populations, contributing to the sustainable management of marine ecosystems. In conclusion, jellyfish classification using YOLOv8 architecture is a crucial tool for marine researchers, environmentalists, and policymakers © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

KidSwatch: Children Development Observation System 5

KidSwatch: Children Development Observation System

引用

5th International Conference on Advancements in computing, ICAC 2023

作者： Oshadhini, P.A.L.N. Siriwardana, K.A.D.H.O. Peshala Ranmini, H.K. Sandeepa, S.R. Weerasinghe, Lokesha Kelegama, Thamali Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

The kidSwatch is a mobile application designed specifically for grade 1 children aged six years. It serves as a comprehensive framework for observing and evaluating children's developmental progress. The application focuses on the main components of children's development: Cognitive development, Emotional development, Language development, and Learning development. By analyzing the results obtained from observing these four components, parents can gain valuable insights into their children's development within a matter of minutes. The kidSwatch employs a range of observational techniques, including direct observation, structured interviews, and standardized tests, to gather information on a child's talents, skills, and behaviors. Trained observers proficient in the kidSwatch framework carefully record and analyze the child's interactions, activities, problem-solving abilities, emotional expressions, and social interactions. Ultimately, the kidSwatch empowers parents with a quick and effective means of understanding and assessing their child's development. To satisfy the above features there are a Hand number detection algorithm, letter recognition algorithm, Emotion recognition algorithm, and voice-based answering algorithm They perform an accuracy between 75%-90% on CNN. © 2023 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning Approach for Context-Aware Input/Output Validation in Mobile Applications 6

A Machine Learning Approach for Context-Aware Input/Output V...

引用

6th International Conference on Advancements in computing, ICAC 2024

作者： De Silva, Kushantha Thilakarthna, Thusithanjana Sri Lanka Institute of Information Technology Faculty of Graduate Studies Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331517878

The vulnerability of mobile applications to security risks due to inadequate input/output validation has been high-lighted. Conventional rule-based methods have found it difficult to adjust to changing attack surfaces and various environments. A unique method for context-aware input/output validation in mobile applications has been suggested, utilizing machine learning. The creation of a machine learning model that can instantly detect abnormal patterns by examining past data, app context, and user input sequences has been investigated. An adaptive validation system has been implemented based on the model's output, dynamically modifying the validation criteria. The efficacy and user experience of the system has been assessed through penetration testing and user research. It is anticipated that the findings will aid in the creation of mobile applications that are both safer and easier to use. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Predicting Crop Yields: Harnessing IoT Sensor Data for Accurate Forecasting and Sustainable Agricultural Planning 16

Predicting Crop Yields: Harnessing IoT Sensor Data for Accur...

引用

16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Abhayasundara, Kulesh Jayakody, Anuradha Sri Lanka Institute of Information Technology Faculty of Graduate Studies Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331505264

This research addresses the gap in precision agriculture by leveraging IoT technology to enhance crop yield prediction and health monitoring. Agro-Sense integrates real-time data from in-field sensors measuring key parameters such as soil moisture, nitrogen, pH, temperature, and humidity, providing a data-driven solution for rice, maize, mango, and watermelon. This approach overcomes the limitations of traditional models that often fail to consider diverse environmental factors. Machine learning algorithms, specifically XGBoost and RandomForestClassifier, were employed to forecast crop yield and health status. XGBoost outperformed RandomForestClassifier in accuracy, demonstrating superior prediction performance, and served as a benchmark for model robustness. The research highlights the importance of continuous data collection and quality-focused preprocessing to support reliable and robust predictive modeling. By incorporating advanced machine learning techniques, Agro-Sense offers specific health assessments for each crop, enabling real-time, data-driven decision-making. This system is adaptable to varying environmental conditions and can be easily integrated into existing farming practices. The tool empowers farmers by providing efficient, sustainable agricultural practices, improving resource use, and enhancing crop health. Agro-Sense bridges the gap in current crop prediction methods and creates a scalable, IoT-driven framework for agriculture. Future advancements should further optimize resource management, enhance crop health prediction, and support sustainable farming in diverse environments globally. © 2024 IEEE.

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

Influence of different form of multimedia on motivation and topic perception in augmented reality learning experiences

引用

Multimedia Tools and Applications 2025年 1-28页

作者： Yap, Shook Chin Rahmat, Rahmita Wirza Ali, Siti Khadijah School of Computing and Data Science Xiamen University Malaysia Selangor Sepang Malaysia Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang Malaysia

It has been widely proven that Augmented Reality (AR) brings numerous benefits in learning experiences, including enhancing learning outcomes and motivation. However, not many studies investigate how different forms of multimedia help in motivating students in AR learning experiences. Instead of examining whether AR can be used in education to motivate students, our research focused on investigating how and when AR learning experiences influence motivation when integrated with multimedia learning. A between-subjects 2 × 3 factorial design was employed in this study. The first factor was the type of AR (static-AR or animated-AR), and the second factor was the modality of the description (concurrent narration-text, text only, or narration only). Six experimental conditions were designed, each equipped with different versions of AR learning applications with various combinations of multimedia formats. One hundred and forty-four computer science and information technology undergraduate students (70 males and 74 females) were randomly assigned to one of the six experimental conditions. Data collection was conducted with questionnaires, and data were analyzed using univariate analysis of variance (ANOVA). Results show that attention and satisfaction subscales of the ARCS motivational model can be enhanced with 3D animation implemented in AR. Descriptions delivered with concurrent narration and written text in AR are effective in enhancing motivation as well. Besides, participants’ perceived understanding and interest in learning the topic increased after AR learning experiences. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Based Face Mask Detection in Religious Mass Gathering During COVID-19 Pandemic

引用

Computer Systems Science & Engineering 2023年第8期46卷 1863-1877页

作者： Abdullah S AL-Malaise AL-Ghamdi Sultanah MAlshammari Mahmoud Ragab Information Systems Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia Center of Excellence in Smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt

Notwithstanding the religious intention of billions of devotees,the religious mass gathering increased major public health concerns since it likely became a huge super spreading event for the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Most attendees ignored preventive measures,namely maintaining physical distance,practising hand hygiene,and wearing *** a face mask in public areas protects people from spreading *** intelligence(AI)based on deep learning(DL)and machine learning(ML)could assist in fighting covid-19 in several *** study introduces a new deep learning-based Face Mask Detection in Religious Mass Gathering(DLFMD-RMG)technique during the COVID-19 *** DLFMD-RMG technique focuses mainly on detecting face masks in a religious mass *** accomplish this,the presented DLFMD-RMG technique undergoes two pre-processing levels:Bilateral Filtering(BF)and Contrast *** face detection,the DLFMD-RMG technique uses YOLOv5 with a ResNet-50 *** addition,the face detection performance can be improved by the seeker optimization algorithm(SOA)for tuning the hyperparameter of the ResNet-50 module,showing the novelty of the *** last,the faces with and without masks are classified using the Fuzzy Neural Network(FNN)*** stimulation study of the DLFMD-RMG algorithm is examined on a benchmark *** results highlighted the remarkable performance of the DLFMD-RMG model algorithm in other recent approaches.

关键词： Religious mass gathering Hajj and Umrah covid-19 pandemic face mask computer vision deep learning

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：