检索结果-内蒙古大学图书馆

7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Balaram, Pawar Ankush Jyoti, Baviskar Kashmira, Dalvi Computer Science and Engineering Vishwaniketan's iMEET Affiliated to Mumbai University Mumbai India SIES Nerul Affiliated to Mumbai University Computer Engineering Dept. Mumbai India

ISBN: (纸本)9798331534271

The concept of data auditing has been developed due to the increasing concern about the safety of data stored in the cloud which can be attacked externally or modified without authorization. The main objective of this work is to involves assessing data integrity through a Third-Party Auditor (TPA). The audit request triggers an Anomaly Detection module which analyzes patterns to identify any unusual or malicious behavior. This study focuses on identifying and mitigating malicious TPAs in cloud-based data storage using privacy-preserving public auditing. Simulation results carried out using Python tool. The improvement enables TPA to handle multiple authorization audits concurrently thus enhancing the overall efficiency of the auditing system. The suggested system is both secure and efficient, according to a thorough evaluation of its performance and security. © 2024 IEEE.

关键词： Cloud storage

来源：评论

学校读者我要写书评

暂无评论

Enhancing DNA Sequence Classification Through Machine Learning: A Comparative Study on Human and Chimpanzee Genomic Data

Enhancing DNA Sequence Classification Through Machine Learni...

引用

2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Pandya, Kushagra Hathi, Heli Ravikumar, R.N. Nadarajan, Sivakumar Praveen, Manoj Pal, Vinod Kumar Tss Consultancy Pvt. Ltd. Rajkot India Marwadi University Dept. of Computer Engineering Rajkot India Alliance University Dept. of Computer Science and Engineering Bangalore India Velalar College of Engineering and Technology Dept. of Ai & Ds Erode India Marwadi University Dept. of Computer Science Rajkot India

ISBN: (纸本)9798350379716

DNA sequence classification is a major challenge in biological processing of data. The classification of DNA sequences is an important study field in bioinformatics since it allows researchers to perform genomic analysis. Through this research, we tried to analyze the performance of various machine learning (ML) algorithms in identifying DNA genes in humans and chimpanzees to find the most effective algorithm for classification of DNA sequences. The results of this research outperformed earlier studies, which had an accuracy around 98%. The dataset was preprocessed using k-mer counting, which assisted in finding key characteristics for the model's training. Naïve Bayes stood out, with an outstanding accuracy of 98.4% for human DNA and 91.4% for chimpanzee DNA. After improving the parameters, Naïve Bayes continued to improve, with human DNA accuracy increasing to 99% and chimpanzee DNA accuracy to 92% in all categories followed by Logistic Regression at 93.9% for human DNA sequences and Ridge Classifier at 90.2% for chimpanzee DNA sequences. K-Nearest Neighbors, Ridge Classifier, Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Extra Trees, XGBoost, and Naïve Bayes were among the algorithms tested, with accuracy, F1 score, precision, and recall used in assessing their performance. These findings highlight the necessity of selecting the appropriate models and tweaking their parameters to improve performance. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Lightweight Authentication System Based on Zero Knowledge Proof for IoT Devices 2

Lightweight Authentication System Based on Zero Knowledge Pr...

引用

2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Khandait, Ashish Umesh Pattanshetti, Tanuja R. Coep Technological University Pune Dept. of Computer Science and Engineering Maharashtra Pune India

ISBN: (纸本)9798350388916

In the rapidly expanding field of the Internet of Things, ensuring secure and efficient communication between IoT devices and servers is crucial. This paper henceforth designs a lightweight authentication system under the Zero Knowledge Proof (ZKP) protocol, utilizing the HMAC hashing algorithm for the improvement of security measures put in place. This system is designed to enable the authentication of IoT devices without sending their actual password, thus avoiding risks due to password interception at the point of authentication. For instance, if using the phrase "God is great"as the password, one can get it hashed into HMAC with this method and still be assured that it is secure even if proof is intercepted. Simulations in the simulator shall evaluate the system's effectiveness and performance. Performance metrics include accuracy, response time, memory consumption, latency, and throughput. The presented results intend to serve lightweight device authentication mechanisms for IoT devices such that the trustworthiness of IoT devices is achieved without hampering their performance. The presented results are also shown to outperform conventional methods. © 2024 IEEE.

关键词： Authentication Protocol

来源：评论

学校读者我要写书评

暂无评论

Secure Sentinel Leveraging Machine Learning for Fraud Detection in Blockchain Transactions 5

Secure Sentinel Leveraging Machine Learning for Fraud Detect...

引用

5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Shirley, C.P. Thanga Helina, S. Berin Jeba Jingle, I. Saran, P. Absin, S.J. Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India Kpr College of Arts Science and Research Department of Commerce with Computer Application Coimbatore India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Blockchain technology's decentralized and transparent transaction platforms have changed a number of sectors. However, harmful activities like fraud and money laundering are also drawn to blockchain due to its distributed and unchangeable nature. The distinctive features of blockchain transactions frequently prove to be too much for conventional fraud detection techniques to handle. In order to identify fraud in blockchain transactions, this work suggests "Secure Sentinel,"a machine learning-based method based on the Isolation Forest concept. By separating anomalies in a binary tree structure, the Isolation Forest algorithm is highly effective in detecting abnormalities inside datasets. By utilizing this approach, our system is able to identify transactions on the blockchain that are suspicious or stray from the norm. The Isolation Forest model will be trained using attributes such transaction amount, frequency, source/destination addresses, and transaction time. The project's goal is to improve blockchain networks' security and integrity by offering a real-time fraud detection system. Users, miners, and regulators are among the blockchain stakeholders who can take proactive steps to reduce risks and preserve system confidence by automatically identifying potentially fraudulent transactions. We will use past blockchain data to test our model's performance and determine how well it detects different kinds of fraudulent activity in terms of accuracy, precision, recall, and efficiency. By using machine learning to protect blockchain ecosystems from new dangers, Secure Sentinel is a step in the right direction toward advancing the acceptance and sustainability of blockchain technology. © 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

BIOPHYSICAL SIGNAL PROCESSING FOR AUTOMATIC ANXIETY CLASSIFICATION IN A VIRTUAL REALITY EXPOSURE THERAPY SYSTEM

UPB Scientific Bulletin, Series C: Electrical Engineering an...

引用

UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2024年第2期86卷 73-84页

作者： Jinga, Nicolae Petrescu, Cătălin-Dumitru Mitruț, Oana Moldoveanu, Alin Moldoveanu, Florica Petrescu, Livia Dept. of Computer Science National University of Science and Technology POLITEHNICA Bucharest Romania Dept. of Automatic Control and Systems Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Faculty of Biology University of Bucharest Romania

This paper presents a signal processing framework for automatic anxiety level classification in a virtual reality exposure therapy system. Two types of biophysical data (heart rate and electrodermal activity) were recorded, pre-processed and passed through a feature extraction procedure that provided input for the real-time anxiety level classification algorithm. We showcase the mathematical and engineering techniques behind the procedures and conclude with the challenges encountered in our research and future development ideas. The proposed method provides a good estimate of the level of anxiety while having at the same time a reduced level of complexity, allowing implementation on equipment with limited computing resources. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Fine-Tuning of Large Language Models for Solving DSA Problems Using QLoRA

Fine-Tuning of Large Language Models for Solving DSA Problem...

引用

2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Pavan Kalyan, S. Arun Kumar, B.R. Satish Kumar, T. Prem Raj, S. Patil, Pratik B. Harshini, Pothireddy Bms Institute of Technology and Management Dept. Computer Science and Engineering Karnataka Bangalore5600119 India

ISBN: (纸本)9798331538538

Fine-tuning large language models (LLMs) for domain specific tasks is often an expensive resource intensive procedure requiring large computing and memory resources. In this paper, We introduce finetuned-leetcode-CodeLlama-7b, a version of CodeLlama that was fine tuned for solving LeetCode problems, specifically. Using QLoRA (Quantized Low Rank Adaptation) techniques, we show that smaller models achieve high performance when solving Data Structure and Algorithm (DSA) problems, providing a cost effective alternative to larger models. The proposed incremental approach reduces memory and computational needs, keeping high accuracy while providing advanced problem-solving in resource constrained environments. © 2025 IEEE.

关键词： Digital differential analyzers

来源：评论

学校读者我要写书评

暂无评论

Understanding the Student Confidence in Viva Responses Through Audio Analysis 3

Understanding the Student Confidence in Viva Responses Throu...

引用

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

作者： Reddy, Kunuthuru Navya Keerthi, Medikonda Sree, R. Nithya Pati, Peeta Basa Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508456

Assessment of student responses during viva examinations presents challenges in terms of consistency, objectivity, and efficiency. Traditional methods rely on manual evaluation, which can be lengthy and prone to human bias. To address this, machine learning techniques are applied to automatically evaluate student confidence levels based on their oral responses. Student answers are transformed into numerical features using word embeddings such as TF-IDF, RoBERTa, BERT, GloVe, T5, GPT-2, FastText, and SBERT. A variety of machine learning classifiers, including MLP, AdaBoost, and Logistic Regression, were used to classify answers into confidence levels: weak, moderate, and strong. Among these, MLP with GloVe embedding models yielded the best results, achieving a test accuracy of 76%, an F1-score of 76%, and minimal overfitting with a training-test performance gap of 4%. These results demonstrate the strong potential of ML-based systems in improving automated grading and ensuring reliability and scalability in evaluating student responses. © 2025 IEEE.

关键词： Semi-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Hybrid Deep Learning Model for Open-Set Seed Classification Using Hyperspectral and RGB Imaging 7

Hybrid Deep Learning Model for Open-Set Seed Classification ...

引用

7th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2025

作者： Jawahar, Kishore Muthupandi, G. Shopika, S. Pavithra, S. Dept of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai 600069 India Dept of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai 600069 India Dept of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai 600069 India

ISBN: (纸本)9798331515683

The classification of seed varieties is important to maintain the productivity of agriculture and check fraudulence and the control of quality in seed businesses. The earlier approaches for the seed classification conventional techniques are tedious and rudimentary, besides being to an extent, arbitrary and though permissible they have low scalability, especially to big data or even as real-time solutions. This paper is a suggestion of using hybrid deep learning framework open to hyperspectral imaging with HSI and RGB Imaging. To this end, the newly proposed Dual Deep Support Vector Data Description model and a vision transformer-based architecture are used for our classification task of known seed types along with the rejection of unknown seeds. The framework uses incremental learning techniques, and spatial-spectral features as well as attention-based bands to enhance the scalability of the technique and its performance. Comparing the results of the present work with previous techniques, it can be concluded that the proposed methods of hybrid imaging and improved deep learning model may help to solve the problems of classic, as well as open-set recognition in agriculture. Future studies could apply this approach to other crops with the goal to pursue precision agriculture and improve world's food safety. © 2025 IEEE.

关键词： Deep learning Hyperspectral learning Incremental learning Open-Set Seed classification Spatial-spectral feature fusion

来源：评论

学校读者我要写书评

暂无评论

Advantages of MIMO Radar for Contactless Pulse Waveform Monitoring using End-to-End Learning

Advantages of MIMO Radar for Contactless Pulse Waveform Moni...

引用

2025 IEEE Radio and Wireless Symposium, RWS 2025

作者： Bauder, Chandler J. Fathy, Aly E. University of Tennessee Knoxville Dept. of Electrical Engineering & Computer Science KnoxvilleTN United States

ISBN: (纸本)9798350377743

Radar-based methods for contactless pulse monitoring typically use heart rate estimation accuracy as the primary performance evaluation metric. Recently, more in-depth metrics such as heart rate variability (HRV) have been of interest, requiring precise pulse waveform reconstructions for reliable estimations. Traditional phase variation-based vital signs signal processing techniques have proven to be susceptible to sources of noise, impacting the shape of the extracted pulse waveform. In this work, the advantages of using raw multi-channel radar data and deep learning in an end-to-end manner for precise pulse waveform reconstruction are explored. Preliminary results show electrocardiogram (ECG) waveforms can be reconstructed from raw in-phase and quadrature 60 GHz multiple-input-multiple-output (MIMO) frequency modulated continuous wave (FMCW) radar data using an end-to-end neural network. Additionally, an ablation study is performed, revealing that waveform reconstruction similarity and estimation accuracy of several HRV metrics generally improve as the number of utilized MIMO channels increases. © 2025 IEEE.

关键词： Electrocardiography

来源：评论

学校读者我要写书评

暂无评论

Convolutional Neural Network-based image tamper detection with Error Level Analysis 2

Convolutional Neural Network-based image tamper detection wi...

引用

2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Manjunatha, S. Swetha, M.D. Rashmi, S. Subramanian, Ananda Kumar Vinoth Kumar, V. Mallikarjuna Swamy, S. Global Academy of Technology Department of Information Science and Engineering Bengaluru India BNM Institute of Technology Department of Computer Science and Engineering Bengaluru India Bengaluru India Vit - University School of Computer Science Engineering Dept of Computational Intelligence Vellore India Vit - University School of Computer Science Engineering and Information Systems Vellore India Jss Academy of Technical Education Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350306415

Photography is the most important, powerful, and reliable means of expression. Today, digital images not only provide disinformation but also act as agents for secret communication. Users and editing professionals work with digital images for a variety of purposes. Images are often regarded as facts or proof of reality, so they are misleading and fake news or publications of any form that use images manipulated in a highly misleading way. To recognize image tampering needs multiple image data and a model that can handle all the pixels in the image. Furthermore, training the data more efficiently and needed flexibility support everyday use. Models based on Deep learning such as Convolutional Neural Networks with error level analysis (ELA) are the perfect solution. © 2024 IEEE.

关键词： Convolutional Neural Network Deep Learning Efficiency Error Level Analysis Image Forgery Detection

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：