检索结果-内蒙古大学图书馆

9th International Conference on Smart Computing and communications, ICSCC 2023

作者： Sasikumar, Reshma Vaman, Dhadesugoor R. Lal, Poulose Sojan Mbits Apjaktu Department of Computer Science and Engineering Trivandrum India Mbits Department of Electronics and Communication Engineering India Mbits Department of Computer Science and Engineering India

ISBN: (纸本)9798350314090

This paper presents random seed and data dependent dynamic key generation algorithm for secure end-to-end transport of data in a clustered Mobile Ad Hoc Network (MANET). Security is a major concern in MANETs since they are designed using wireless transport with Commercial-Off-The-Shelf Technologies (COTS) which facilitate many users to have similar radios and can cause security threats in terms of presence of malicious radios which can be used to pick the data and reroute the data to users with no allowability, thus compromising the data. In this paper, the key in each packet varies and it is generated partially with a seed, a portion of the data from a previous packet, which changes in each packet. When malicious radio pick up the packet, they will not be able to decode the data in the packet. The security algorithm using this principle has been applied to a MANET to support secure data transmission end-to-end. MANET is considered as a group of small clusters and every cluster will have a Cluster Head (CH) and a group of assigned nodes. End-to-end transport of data implies that the data is transported over multiple clusters between a source and a destination. Both intra-cluster and inter-cluster communications take place when packets are transmitted end-to-end. When a radio in a cluster sends data, it will be received by the local CH which forwards the data across multiple CHs before the data reaches the destination radio. Radios can move freely within and outside the cluster. The distributed network management ensures that these radios are assigned to appropriate CHs as they move from one cluster to another cluster. The focus of this paper is to provide a secure method to transport data from the source radio to a destination radio. The intra-cluster communication uses an access protocol to send data from a radio to the CH. The receiving data from other clusters are broadcasted to the destination radio. When data is transmitted across different clusters, the CHs of

关键词： Mobile ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

Detection of Distributed Denial of Service in the Application Layer of Iot Using Machine Learning 2

Detection of Distributed Denial of Service in the Applicatio...

引用

2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Nanthiya, D. Manjula Devi, R. Gopal, S.B. Madhumitha, C. Ramya Sree, N. Kowsika, D.V. Kongu Engineering College Department of Computer Technology Perundurai India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

Cybersecurity prevents harmful assaults on computers, networks, and mobile devices. Included in the category of cyber-attacks are threats like as malware, distributed denial of service assaults, phishing, spoofing, code injection, and supply chain attacks. Massive denial-of-service assaults are destructive. As part of a Distributed Denial-of-Service (DDoS) assault, hackers bypass botnets and go straight for the server. In order to temporarily block or suspend a website, these botnets send a flood of requests to the server. The server typically takes requests and returns responses. There will be a brief outage if the server gets more requests than it can handle. While this attack makes the service inaccessible, it prevents data theft. A machine-learning approach is used to detect server-side DDoS assaults. The term "machine learning"refers to software that can learn new tasks and solve old ones without any input from a human. Learning Vector Quantization, Support Vector Machine, Naive Bayes, K-Nearest Neighbour, Random Forest, and Decision Tree are the six ML classification models used since these ML procedures enhance accuracy. The main goal of this research is to use the CIDAD dataset to find ways to protect the application layer of the Internet of Things (IoT) against Distributed Denial of Service (DDoS) attacks. The lower layers of the IoT network architecture are disregarded by this dataset, which comprises information at the COAP level. © 2024 IEEE.

关键词： Denial-of-service attack

来源：评论

学校读者我要写书评

暂无评论

Integrated normal discriminant analysis in mapreduce for diabetic chronic disease prediction using bivariant deep neural networks

引用

International Journal of Information Technology (Singapore) 2024年第8期16卷 4915-4929页

作者： Ramani, R. Dhinakaran, D. Edwin Raja, S. Thiyagarajan, M. Selvaraj, D. Department of Computer Science and Engineering P.S.R Engineering College Sivakasi India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

This study presents the Normal Discriminant Feature Selection based Regressive Deep Neural MapReduce (NDFS-RDNMR) framework designed for efficient prediction of diabetic chronic diseases using input datasets. The primary aim of NDFS-RDNMR is to enhance accuracy and recall in handling large datasets for chronic disease prediction. The framework integrates the Normal Discriminative Preprocessing Model (NDPM) and bivariant regressive deep artificial neural network with MapReduce (BRDANNMR) classifier. Utilizing the Pima Indian diabetic dataset as input, NDFS-RDNMR conducts feature preprocessing through NDPM to extract relevant features for disease prediction. Non-traditional datasets are transformed into traditional formats via parameter rescaling to fit within predefined value ranges. Min–max normalization is applied to improve system accuracy while preserving data relationships. The BRDANNMR classifier utilizes bivariant regression analysis in the mapping phase to generate intermediary outcomes, which are then classified using a bipolar activation function in the reducer process. The framework achieves high accuracy and recall in early diabetes disease prediction, offering valuable insights for medical practitioners and researchers. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Artificial neural network Classifier Feature selection MapReduce Normalization Regression analysis

来源：评论

学校读者我要写书评

暂无评论

Low Power, High Speed Current Starved Ring Oscillator based ADC using 18nm FinFET Technology 15

Low Power, High Speed Current Starved Ring Oscillator based ...

引用

15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Chandrasekaran, Gokul Pavithara, P. Dinesh, P.M. Arun Kumar, V. Sharanyanivasini, J.S. Kavin Kumar, K. Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Kongu Engineering College Department of Electronics and Communication Engineering Erode Perundurai638060 India Sona College of Technology Department of Electronics and Communication Engineering Salem636005 India Nandha Engineering College Department of Electrical and Electronics Engineering Erode India Erode Sengunthar Engineering College Department of Computer Science and Engineering Thuduppathi Perundurai638057 India

ISBN: (纸本)9798350370249

The goal of achieving high performance, low power consumption, and small area footprint is still central to the field of analog-to-digital converters (ADCs). This work describes a new methodology that enables the design of Current Starved Ring Oscillator (CSRO) architecture using 18nm FinFET technology. The CSRO based ADC meets the growing needs of contemporary electronic systems with promised improvements in speed, power efficiency, and portability. The intrinsic qualities of FinFET transistors are utilized by the CSRO architecture to produce high performance metrics such as improved sub threshold behavior, lower leakage currents, and superior gate control, all of which are essential for low power consumption and high speed operation. In addition, compared to conventional ADC designs, the 18 nm production node enables tighter component integration, resulting in a compact ADC design. The precise analog-to- digital conversion is made possible by controlling the oscillation frequency through the use of current starving techniques. In order to reduce power consumption, mitigate electromagnetic interference and extend the battery life, starving ring oscillators are employed. The ability of the GDI (Gate Diffusion Input) technique to lower space overhead and power consumption provides an effective solution for low-power and high-density applications in contemporary integrated circuits. The proposed CSRO based ADC with GDI technique experiences a delay of 1.208 ns and power consumption of 356.08 mW while the ADC without GDI technique involves a delay of 1.44 ns and 391 mW of power consumption. This shows that the proposed ADC exhibits 14% improvement inspeed and 9% improvement in power compared to the ADCwithout GDI technique. © 2024 IEEE.

关键词： ADC Current starved ring oscillator FinFET technology GDI technique

来源：评论

学校读者我要写书评

暂无评论

Synergistic applications of nanostructured boron nitride in photocatalysis and targeted drug delivery

引用

Nanotechnology for Environmental engineering 2024年第2期9卷 179-188页

作者： Mali, Priyadarshani Shivakumar Jagtap, Bhagyashree Krishnarao Gowda V, Dankan Ashreetha, B. Vasanthakumar, G.U. kaur, Mandeep Hariram, Venkatesan Department of Electronics and Telecommunications Engineering Bharati Vidyapeeth’s college of Engineering Kolhapur Maharashtra416013 India Department of Electronics and Computer Engineering Sharad Institute of Technology college of Engineering Yadrav Maharashtra Ichalkaranji Kolhapur416115 India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka560064 India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Andhra Pradesh Tirupati India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Yelahanka Bengaluru560064 India Department of Electronics and Communication Engineering Punjabi University Punjab Patiala147002 India Department of Practice of Medicine Vinayaka Mission’s Homoeopathic Medical College and Hospital A Constituent College of Vinayaka Mission’s Research Foundation Deemed to be University Tamilnadu Salem636 308 India

This study explores the applications of nanostructured boron nitride (BN) in photocatalysis and targeted drug delivery, highlighting its notable thermal conductivity, chemical stability, and wide bandgap. Through the synthesis and detailed characterization of BN nanoparticles, employing Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM), the material’s efficacy in light-induced chemical reactions and potential as a nanocarrier for drug delivery systems are investigated. Photocatalytic experiments reveal an enhanced ability of BN to absorb light and facilitate charge separation, suggesting a promising approach for environmental detoxification and sustainable energy generation. In parallel, investigations into BN’s drug loading capacity, release kinetics, and targeting efficiency, particularly in the context of cancer therapy, are conducted. Findings indicate that BN nanoparticles significantly enhance photocatalytic performance and offer a safe, effective platform for controlled drug release. This research underscores the dual utility of BN in addressing critical challenges in environmental engineering and medical treatment, pointing towards novel interdisciplinary applications of this versatile nanomaterial. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Photocatalysis

来源：评论

学校读者我要写书评

暂无评论

Sign Language Recognition using BiLSTM model 9

Sign Language Recognition using BiLSTM model

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Renjith, S. Manazhy, Rashmi Suresh, M.S. Sumi Amrita Vishwa Vidyapeetham Computer Science And Engineering Amritapuri India Amrita Vishwa Vidyapeetham Electronics And Communication Engineering Amritapuri India University At Buffalo Computer Science And Engineering New York City United States

ISBN: (纸本)9798350394474

Sign Language Recognition (SLR) is a crucial tool that facilitates efficient communication for people with hearing disabilities. This research investigates the use of Bidirectional Long Short-Term Memory (BiLSTM) neural networks to recognize sign language. Two distinct datasets, namely the German Sign Language dataset and the MNIST Sign Language dataset, are employed. Both datasets consist of 26 classes, each representing different alphabets. The goal is to develop a dependable and accurate model for comprehending the movements of sign language. The BiLSTM design's capacity to retain sequential information from both the past and future makes it well-suited for the dynamic and temporal characteristics of sign language. The MNIST collection incorporates signs from American Sign Language (ASL), whereas the German Sign Language dataset is used to recognize German finger-spelled alphabets. These datasets provide a comprehensive assessment of the model's capabilities due to the variety of signals and movements they include. The BiLSTM model exhibited impressive accuracy rates of 92.2 percent and 96.6 percent on the German and MNIST datasets, respectively, as seen by the experimental findings. This work contributes to the development of advanced sign language detecting systems, which have the capacity to enhance accessibility and communication for individuals with hearing impairments. The findings illustrate the versatility of BiLSTM models in sign language recognition and their potential use in real-world scenarios. © 2024 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Convolutional Neural Network with Bidirectional Gated Recurrent Unit for Classification of Psoriasis using Dermatological Images 1

Convolutional Neural Network with Bidirectional Gated Recurr...

引用

1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Jagannathan, Veeraraghavan Sindhuja, Sarikonda Sasirekha, N. Jasmin, M. Al-Fatlawy, Ramy Riad Department of Computer Science and Engineering Bhimavaram India School of Computer Science and Engineering Maisammaguda Hyderabad India Sona College of Technology Department of Electronics and Communication Engineering Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India The Islamic University Department of Computers Techniques Engineering College of Technical Engineering Najaf Iraq

ISBN: (纸本)9798350352931

Psoriasis is a common chronic inflammatory skin disease that causes physical and psychological burden to patients. However, various type of dermatological image blend in the original skin due to overfitting and unwanted noise occur in the image classify. This paper, proposed Convolutional Neural Network (CNN) with a Bidirectional Gated Recurrent Unit (Bi-GRU) combined to performed in the dermatological image classification efficiently capture the disease achieve better accuracy. Initially data obtained from the Dermnet NZ dataset and pre-processing utilized the min-max normalization is efficiently handle the uniformed feature vector and remove unwanted noise. The segmentation involved Otsu thresholding technique is utilized to create a mask image then applied it grayscale image and handle the overfitting issue then lesion segmented form normal skin region. the feature extraction using VGG 16 technique is efficiently extracted the high level feature. The classification utilized CNN with Bi-GRU process the feature sequences capturing dependencies across the feature dimensions, learning the pattern efficiently and achieve better performance. performance. The performance of DL model evaluated with various metrics such as accuracy, precision, recall and f1-score metrics on Dermnet NZ dataset. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Optimizing User Recommendations with Variational Autoencoders: Insights from MovieLens-1M and BookCrossing Datasets

Optimizing User Recommendations with Variational Autoencoder...

引用

2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Samridhi, Kumari Sinha, Bam Bahadur Das, Trinanjan Indian Institute of Information Technology Electronics and Communication Engineering Ranchi India National Institute of Technology Computer Science and Engineering Sikkim India Indian Institute of Information Technology Computer Science and Engineering Ranchi India

ISBN: (纸本)9798350376876

Recommender systems utilize algorithms and data to predict user preferences based on their past choices. While these systems can be highly accurate, this increased accuracy can sometimes lead to predictability and monotony. In the realm of big data, recommender systems are both prevalent and valuable. Research on these systems has intensified in recent years, accompanied by a growing number of datasets. This paper examines the application of Variational Autoencoder (VAE)-based recommender systems using the MovieLens 1M and BookCrossing datasets. The study primarily evaluates the performance of the VAE model using Root Mean Square Error (RMSE) as the key metric. The VAE model was trained on these datasets to predict user preferences and generate personalized recommendations. Experimental results revealed an RMSE of less than 1 for both datasets, indicating the model's proficiency in accurately estimating user ratings for items. These findings suggest that VAE-based recommender systems are promising for enhancing recommendation accuracy and personalization. This research contributes to the understanding of VAE models within the context of recommender systems and provides insights for future research and potential improvements in this field. © 2024 IEEE.

关键词： Data accuracy

来源：评论

学校读者我要写书评

暂无评论

Deep convolutional spiking neural network fostered automatic detection and classification of breast cancer from mammography images

引用

Research on Biomedical engineering 2023年第4期39卷 833-841页

作者： SenthilPrakash, T. Kannan, G. Prabhakaran, Salini Prajapati, Bhagirath Parshuram Department of Computer Science and Engineering Shree Venkateshwara Hi-Tech Engineering College Tamil Nadu Gobichettipalayam India Department of Electronics and Communication Engineering Amrita College of Engineering and Technology Nagercoil India Department of Computer Science and Engineering Puducherry Technological University Puducherry India Department of Computer Engineering A. D. Patel Institute of Technology Gujarat New Vallabh Vidyanagar India

Purpose: Breast cancer (BC) is a leading cause of death among women. Several existing methods on breast cancer classification were suggested with machine learning, but none of them accurately classify the breast cancer and requires high computational time. To overcome these problems, deep convolutional spiking neural network fostered automatic detection and classification of breast cancer from mammography images (DCSNN-BCDC-MI) is proposed in this manuscript. Methods: Initially, input breast mammography pictures are taken from Digital Database for Screening Mammography (DDSM) data set. The input breast mammography images are pre-processed and feature extracted. The DCSNN is employed to categorize pictures using retrieved characteristics, such as benign and malignant. Results: The proposed DCSNN-BCDC-MI technique is implemented at PYTHON. The performance of the proposed DCSNN-BCDC-MI technique attains 31.13%, 12.78%, 14.38%, 19.33%, and 19.11% high accuracy and 61.08%, 66.42%, 66.54%, 50.26%, and 53.03% low computation time comparing to the existing techniques, like deep learning fed capsule neural network for breast cancer detection utilizing mammogram images (OMLTS-DLCN-BCDC-MI), extended model for tumor location detection based on deep learning (VGG-CNN-BCDC-MI), deep learning algorithm for automated identification with breast cancer classification depending on transfer-learning strategy (VGG16-BCDC-MI), deep learning method for breast cancer identification on mammography by learning certain cancerous mass representations (CNN-Deep AE-BCDC-MI), and automatic identification with mammograms classification dependent on increased extreme learning machine along deep learning (ICS-ELM-BCDC-MI), respectively. Conclusion: The simulation result exemplifies that the proposed method can be capable to determine the optimum global solutions efficiently with the exact existing methods. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Enginee

关键词： Mammography

来源：评论

学校读者我要写书评

暂无评论

ENERGY-EFFICIENT ANT ROUTING ALGORITHM FOR OPTIMIZED PATH SELECTION NETWORK LONGEVITY IN WIRELESS SENSOR NETWORKS

引用

Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第2期84卷 21-42页

作者： Zelani, Shaik Khader Babu, D. Nagabhushana Surendra, Doddaga Rahman, Mohammad Zia Ur Department of Electronics and Communication Engineering Sri Vasavi Institute of Engineering and Technology-Autonomous Pedana India Department of Data Science Malla Reddy University Telangana Hyderabad India Department of Computer Science and Applications Koneru Lakshmaiah Education Foundation K L Deemed to be University Vaddeswaram Andhra Pradesh India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation K L Deemed to be University Vaddeswaram Andhra Pradesh India

The exponential growth of wireless sensor networks (WSNs) in various applications underscores the critical need for energy-efficient routing protocols to extend network lifespan. This paper introduces the Energy-Efficient Ant Routing Algorithm, a novel bioinspired routing strategy derived from ant colony optimization (ACO), which integrates traditional pheromone mechanisms with dynamic energy-aware adaptations to prioritize routes through nodes with higher residual energy levels, thereby balancing load and prolonging network functionality. The model’s adaptive pheromone updating mechanism, which considers both the cumulative path energy cost and the minimum residual energy of nodes along the path, encourages the use of energy-efficient routes and mitigates rapid energy depletion in critical network areas. This approach not only extends operational network lifetime but also supports higher data reliability and throughput in challenging WSN scenarios. The results of the proposed model demonstrated significant improvements over traditional ACO and other common energy-aware routing protocols. The network lifespan is extended by ~ 25% compared to standard ACO and by 15% compared to the best-performing conventional energy-aware routing protocol under the same conditions. Furthermore, EEARA achieved a 20% reduction in average energy consumption per node and enhanced the data delivery ratio by 10% due to more stable routing paths. The results underline the model’s potential as a scalable, efficient routing solution for energy-limited networks, promising substantial improvements in sustainable network operations. © 2025 by Begell House, Inc.

关键词： Ant colony optimization

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：