检索结果-内蒙古大学图书馆

A reinforcement learning based backup queue strategy for reliable and fault tolerant scheduling

International Journal of Information technology (Singapore) 2025年 1-6页

作者： Archana, R. Kumar, K. Pradeep Mohan Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

Fog computing has the capability to perform tasks in the local distributed environment within the expected time period. The approaches used for managing the faults in a fog computing environment are not competent to resolve the issues of latency and energy consumption in the network. Allocating and executing all the tasks regardless of how they scale up and completing them within the allotted time frame is crucial for achieving a higher throughput and a lower drop rate. In order to achieve higher scalability, timely execution, and dependability, the given paper introduces a reinforcement learning (RL) backup queue strategy (SEEDBACK_RL) that ensures every task is executed irrespective of its size. The proposed strategy is enhanced to manage the scalability and reliability by identifying the best nodes depending on the type of task. With the use of a simulative environment, the algorithm's efficacy is assessed, and the results show a notable improvement in load balancing, reliability, and network efficiency. © Bharati Vidyapeeth's institute of Computer Applications and Management 2025.

关键词： Asynchronous advantage actor critic (A3C) Distributed reinforcement learning Fog computing Internet of things (IoT) Load balancing

来源：评论

学校读者我要写书评

暂无评论

Detecting a suitable path method for assisted localization techniques in transmission of data over wireless sensor networks

引用

International Journal of Information technology (Singapore) 2025年第4期17卷 2235-2242页

作者： Navaneethan, S. Arun, A. Department of Networking & Communications School of Computing SRM Institute of Science and Technology SRM Nagar Tamilnadu KattanKulathur 603203 India Department of Networking & Communications School of Computing Faculty of Engineering & Technology SRM Institute of Science and Technology Tamilnadu KattanKulathur 603203 India

In many Wireless Sensor Networks (WSNs) applications, the relevant sensor node’s location information is essential in determining where the event or situation occurs. Therefore, localization is one of the critical challenges in WSNs. Mobile Anchor Node Assisted Localization (MANAL) is one of the promising solutions for the localization of statically deployed sensors. The main problem in MANAL is that the path planning of the Mobile Anchor Node (MAN) should be done in a way that the localization error in the network will be minimal and that all unknown nodes in the network are covered. In this paper, a new path planning approach called Nested Hexagons Curves (NH-exCurves) is proposed for MANAL. NH-exCurves guarantees that it will receive messages from at least three non-collinear anchors to locate all unknown nodes in the network. The proposed model is compared to six different path planning schemes using Accuracy Priority Trilateration (APT) under different evaluation criteria. In these comparisons, first of all, localization errors of the models are compared using some statistical concepts. Secondly, the variation of the localization error according to parameters such as resolution (R) and the standard deviation of noise (σ) are observed. The simulation results present the superiority of the proposed work over existing path planning models used in this study. © Bharati Vidyapeeth's institute of Computer Applications and Management 2025.

关键词： Accuracy priority trilateration (APT) Localization Mobile anchor node assisted localization (MANAL) Static path planning Wireless sensor networks (WSNs)

来源：评论

学校读者我要写书评

暂无评论

Adaptive grouped balanced binary tree based multi user secure data transmission on cloud

引用

International Journal of Information technology (Singapore) 2025年第2期17卷 941-949页

作者： Pavithra, P. Hariharan, B. Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur India

Secure multi-keyword search for outsourced cloud data has gained popularity, especially for scenarios involving multiple data owners. This work proposes a method for secure multi-keyword searches across encrypted cloud data from various owners, utilizing an administrator or trustworthy third party for key management. The approach employs the TF-IDF model and Vector Space Model (VSM) to generate index and query vectors, and uses an advanced Attribute-Based Encryption (ABE) algorithm for data privacy. The Improved Dingo Optimization Algorithm (IDOA) optimally selects the key value. Encrypted emails are sent to a cloud server, and a Grouped Balanced Binary (GBB) tree structure is used for index construction. The Greedy Depth-First Search (GDFS) method efficiently searches the index, grouping relevant documents to exclude unnecessary ones. Performance is evaluated based on memory usage, execution time, encryption and decryption times, and search times, implemented in Java and tested on a cloud simulator. © Bharati Vidyapeeth's institute of Computer Applications and Management 2024.

关键词： Attribute-based encryption and vector space model Cloud computing Dingo optimization algorithm Multi keyword search scheme Multiple data owner

来源：评论

学校读者我要写书评

暂无评论

Hybrid Product Recommendation System Using Popularity Based and Content-Based Filtering 3

Hybrid Product Recommendation System Using Popularity Based ...

引用

3rd International Conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Anchalia, Chayan Sinha, Utkarsh Rajalakshmi, M. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India

ISBN: (纸本)9798331515911

The paper introduces a hybrid product recommendation system that combines popularity-based and content-based filtering methods. The aim is to enhance the accuracy and relevance of product suggestions by utilizing both the intrinsic features of products (content-based filtering) and their popularity among users (popularity-based filtering). We assess the system's performance using standard metrics for recommendations, including precision and recall showcasing its effectiveness in delivering improved results. © 2025 IEEE.

关键词： Wiener filtering

来源：评论

学校读者我要写书评

暂无评论

Privacy-enhanced secure framework for educational data protection and analysis

引用

International Journal of Information technology (Singapore) 2025年 1-18页

作者： Balachandar, V. Venkatesh, K. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Kattankulathur India

The need for real-time processing of educational data versus the sensitivity of student data makes the balancing between privacy and accessibility a vital challenge in data analysis. We present the PSEDA model (Privacy-Secure Educational Data Analysis) that integrates homomorphic encryption, differential privacy and role-based access control for strong privacy protection while permitting analysis. The primary breakthrough of the model is its ability to process large volumes of information without incurring huge costs due to encryption and decryption. We consistently outperform the state-of-the-art over the tasks within PSEDA. It obtains the highest accuracy of 96.7% at 30 features, but also provides the lowest time cost for encryption 1009 ms and decryption 704 ms at 5 features. Furthermore, it achieves an impressive 90.8% for access restriction and shows a very high throughput of 90.4% at 5 features. Based on assessment on the EdNet dataset, these findings demonstrate that the PSEDA model can securely and efficiently operate on large-scale educational data, reflecting a significant step forward in privacy-preserving data analysis. © Bharati Vidyapeeth's institute of Computer Applications and Management 2025.

关键词： Access control Confidentiality Data security Differential privacy Homomorphic encryption Privacy

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence algorithms for object detection and recognition in video and images

引用

Multimedia Tools and Applications 2025年 1-18页

作者： Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Department of Computer Science Engineering Saveetha School Of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai6021055 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India

The usage of machine learning and deep learning algorithms have necessitated Artificial Intelligence'. AI is aimed at automating things by limiting human interference. It is widely used in IT, healthcare, finance, and agriculture. It is achieved through several deep learning algorithms that reflect the human brain's intelligence. These AI algorithms can be manipulated according to changing needs and improved efficiency. This paper tries to utilize the developments made in AI technology to classify the images and recognize the objects present in them. One widely used AI algorithm is CNN (Convolutional Neural Networks). The CNN is a deep learning-based algorithm that consists of various layers that extract and filters the parameters present in the images. Some additional layers of ResNet50 and the CNN algorithm are used to extract the parameters to improve image recognition accuracy. The image dataset taken for training and testing the proposed model is imageNet. The images are initially processed before sending them to the proposed model. The proposed model is trained, validated, and tested through the images obtained after the initial processing. The same process is repeated several times until getting the maximum accuracy. The accuracy of the proposed model in terms of image recognition is recorded. The obtained results are compared with other image classification algorithms like VGG16 and VGG19. It is concluded that the proposed model outperforms other traditional methods in terms of accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A novel approach on heart disease prediction using optimized hybrid deep learning approach

引用

Multimedia Tools and Applications 2025年 1-26页

作者： C, Adline Sherifa Kanisha, B. Department of Computing Technologies Faculty of Engg and Technology SRM Institute of Science and Technology Kattankulathur Chennai603 203 India

The condition known as Cardio Vascular Disease can result in heart attacks, Angina, and brain assaults due to the restriction of blood flow to the myocardium. Among the most important causes of death and mortality worldwide, heart disease is also one of the main sources of disability for both young and old people. Moreover, it is one of the reasons behind weariness and mortality on Earth. Interruption can prompt benevolence, adjustments in lifestyle, and tailored therapy interventions, which ultimately lead to peaceful outcomes and reduced healthcare expenses. The data gathered in this question about work is processed beforehand using information cleaning and information standardization techniques. Measurable highlights, higher-order measurable highlights (for example, why measure weights are not measured), correlation-based IDM, weight standard deviation-related entropy, and the degree of relationship between the data and homogeneity, which in turn are removed. From the extricated features, the foremost ideal highlights are chosen via the relief-f demonstration. By selecting the most suitable features, one can prepare the crossover classifier that generates the heart illness prediction. The crossover classifier could be a combination of MLP and O-RBM. To advance the expectation exactness of the anticipated heart malady location show, the weight of Limited Boltzmann Machines is fine-tuned through the Self-Adaptive TLBO (SA- TLBO) demonstration. The result from the crossover classifier is the identified result. This SA-TLBO show is an extended version of the standard TLBO show. By analyzing the "Heart Malady Dataset" of the Kaggle store, it is established that the proposed show has 95% precision and 96% accuracy with an FNR of 0.05. This demonstration is then tested against the real models to show whether a model matches those of the proposed one. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Demonstrations

来源：评论

学校读者我要写书评

暂无评论

Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

引用

Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory Data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid Data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based Computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Adaptive sail fish optimizer based optimized region selection for robust video steganography

引用

International Journal of Information technology (Singapore) 2025年第4期17卷 2417-2427页

作者： Babu, J. Suresh Niranjana, G. Ramana, Kadiyala Department of Computing Science & Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chengalpattu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chengalpattu India Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology (Autonomous) Hyderabad India

Securing data transmission in a digital era is a difficult one due to the broad application of the Internet, personal computers, and mobile phones for communication. Traditional video steganography techniques sometimes fail to identify the best pixels for encoding hidden data, resulting in lowerquality and robustness. To solve this issue, this article proposes an innovative steganography technique that improves the security and quality of data hiding. The proposed method takes cover video and secret images as input. The Structural Similarity Index Measurement (SSIM) first collects key frames from the input video. The adaptive sailfish optimizer (ASFO) is then used to find the best regions within these keyframes to insert the secret data in. To improve security, the discrete wavelet transform (DWT) is applied to the specified region, and the LL band is used for the hiding process. The secret image is encrypted by Adaptive elliptical curve cryptography (AECC) and transformed to binary bits, which then get embedded in the chosen band. This method leads to the construction of a stego-video. The extraction procedure is repeated using the same approach, which ensures secure and robust data transmission. Performance metrics, including PSNR, BER, NAE, TAF, and NC are analyzed to compare each method's efficacy. © Bharati Vidyapeeth's institute of Computer Applications and Management 2025.

关键词： Adaptive elliptical curve cryptography And stego-video Discrete wavelet transform Structural similarity index measurement Video steganography

来源：评论

学校读者我要写书评

暂无评论

AI-based Pneumonia Detection using a Deep Context Axial Reverse Capsule Neural Network with Sea-Horse Optimizer 3

AI-based Pneumonia Detection using a Deep Context Axial Reve...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mohideen AbdulKader, M. Tuteja, Ankush Joshi, Darsh Tholath, Joshua Bobby Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu India

ISBN: (纸本)9798331527549

Pneumonia detection with AI uses advanced deep learning algorithms for identifying the patterns in the chest X-ray images which makes diagnosis faster and efficient for physicians. AI-powered radiography improved the diagnostic approaches, even then existing methods face some limitations such as reduced accuracy and high computational costs. The Deep Context Axial Reverse Capsule Neural Network with Sea-Horse Optimizer (ADpCARCNNet+SHO) for pneumonia detection is proposed to overcome these challenges. This framework processes chest X-ray images from the Chest X-ray 14 dataset, beginning with Color Wiener Filtering (CWF) for noise reduction. Key features are then extracted using the Dynamic Range Quantized Discrete Haar Wavelet Transform (DRQDHWT), followed by classification using the ADpCARCNNet, optimized with the Sea-Horse Optimizer (SHO) to ensure high precision. Proposed method achieves a remarkable 99.9% accuracy on the Chest X-ray 14 dataset, outperforming existing techniques. The results demonstrate improved diagnostic accuracy, faster image analysis, enhanced reliability, and reduced computational cost, highlighting the potential of the ADpCARCNNet+SHO framework for efficient and reliable AI-driven pneumonia detection. © 2025 IEEE.

关键词： Diagnostic radiography

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：