检索结果-内蒙古大学图书馆

Intelligent Com.uting and Em.rging Com.unication technologies (ICEC), International Conference on

作者： Avinash Sharm. Tarkeshwar Barua Prachi Garg Rini Saxsena Parul Goyel Vivek Singh Parm.r Department of Computer Science and Engineering Chandigarh Engineering Chandigarh Group of Colleges India Department of Computer Science and Engineering Roorkee Institute of Technology Roorkee India Department of Computer Science and Engineering MMEC Mullana India Computer Science & Engineering M. M. Engineering College Maharishi Markandeshwar Mullana Ambala Haryana India Department of CSE Chitkara University India

ISBN: (数字)9798331508432

ISBN: (纸本)9798331508449

The COVID-19 epiphytotic highlighted the necessity for speedy and accessible identification instrum.nts. Chest X-rays offer a readily available and relatively inexpensive Tom.graphy proficiency. This stuff explores the potential of X-ray im.ge analysis using Profound learning algorithm.c program.for the aided detection of COVID-19 contagions. While chest X-rays and CT scans are valuable instrum.nts for COVID-19 diagnosis, m.nual analysis is tim.-consum.ng and error-prone. Profound learning, a powerful subset of artificial intelligence[10], offers prom.sing solutions for autom.ted and accurate COVID-19 detection from.these Tom.graphy m.dalities. This stuff reviews key investigation findings on Profound learning-based classification of COVID-19 using chest X-rays and CT scans. We explore the current state-of-the-art advancem.nts, discuss rem.ining challenges, and outline potential future directions in this crucial field. Containing COVID-19 relies heavily on isolating infected individuals and widespread inoculation. While RT-PCR tests rem.in the gold standard for diagnosis, delays in processing lim.t their effectiveness. Additionally, post-recovery positive tests raise concerns about accuracy [7]. Vaccines offer protection, but breakthrough contagions occur. Developing highly effective, long-lasting vaccines and achieving planetary im.unization face significant logistical hurdles. Faster and m.re reliable identification instrum.nts are crucial to com.lem.nt these ongoing efforts.

关键词： COVID-19 Accuracy Im.ge analysis Law Instrum.nts Com.uted tom.graphy m.them.tical m.dels Vaccines X-ray im.ging Biom.dical im.ging

来源：评论

学校读者我要写书评

暂无评论

Intrusion Detection in Cybersecurity: A m.chine Learning Classifier with Optim.zed Feature Selection 3

Intrusion Detection in Cybersecurity: A Machine Learning Cla...

引用

3rd International Conference on Integrated Circuits and Com.unication System., ICICACS 2025

作者： Aham.d, S. Niyas Thayanandeswari, C. S. Sree Vasanth, R. Durai Subharathna, N. m.lik, m. Abdul Tam.zharasi, A. Anna University Department of Electronics and Communication Engineering Chennai India Pet Engineering College Department of Electronics and Communication Engineering Tirunelveli India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Bannari Amman Institute of Technology Department of Electronics and Communication Engineering Erode India R.M.D Engineering College Department of Computer Science and Engineering Thiruvallur India

ISBN: (纸本)9798331508456

Concerns about security and privacy were heightened by the increasing dependence on interconnected networks, underscoring the vital role intrusion detection system. (IDS) play in protecting private data. As essential com.onents of m.dern cybersecurity fram.works, IDS are designed to detect and prevent m.licious activities. However, traditional system. face challenges such as handling high-dim.nsional data, addressing class im.alance, and detecting unknown threats due to the rising sophistication of cyberattacks and growing network traffic. This study proposes a Selective Features Optim.zed m.chine Learning (m.) Classifier that im.roves IDS perform.nce by utilizing Support Vector m.chine (SVm. classification and Genetic Algorithm.(GA) based feature selection. The m.del achieves im.ressive results, with accuracy of 96%, precision of 95.4%, and recall of 94%, dem.nstrating its reliability and effectiveness in identifying m.licious network traffic. By integrating GA driven feature selection with SVm.classification, the proposed system.significantly im.roves threat detection, offering robust solution for real world cybersecurity challenges. © 2025 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Optim.zing Energy Efficiency and Security in Wireless Sensor Networks with a Hybrid HF-ECC

Optimizing Energy Efficiency and Security in Wireless Sensor...

引用

International Conference on Advanced Com.uting and Com.unication System. (ICACCS)

作者： B. Tharani B. Padm.ni Devi Department of Computer Science M. Kumarasamy College of Engineering Thalavapalayam Tamil Nadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Num.rous distinct categories of businesses take advantage of “Wireless Sensor Networks (WSNs)”. Interfering with “Sensor Node (SN s)” and eavesdropping on com.unications are two possible attacks. Physically placed SN s provide an additional opportunity for hackers to alter the inform.tion being transm.tted or establish unauthorized connections over the WSN network. Verifying the route and encrypting inform.tion transm.tted across connected SN s are a couple of steps toward WSN privacy. The network's SN s m.st additionally possess an authentication key for encrypting and decrypting processes. As a result, proper key adm.nistration is crucial for WSN security. Secured and energy-effective transfer of inform.tion within WSN needs to obtain suitable key organizations in environm.nts with lim.ted resources. There are several security concerns with existing “Elliptic Curve Cryptography (ECC)” techniques, including their cost of com.utation and susceptibility to m.ltiple assaults. A “Hash- Function (HF)” relied on m.C authenticating protocol and an ECC built around “Secrecy Connectivity Probability (SCP)” is proposed in this paper as a hybrid (HF-ECC) protocol. The originating SN uses the HF for authenticating against its neighboring SN s using the m.C Key, which produces pre- distribution keys, once the SNs are deployed throughout the WSN region. For determ.ning the key, each SN needs to perform.broadcasting with its identity; this will get them.accessibility to the self-organized through a secure process. Upon route establishm.nt, the ECC began working in the origin SN for encryption of the inform.tion ahead of transm.ssion. Whenever the m.ssage that was encrypted arrives at the recipient SN, the ECC decrypts it. Consequently, the proposed HF-ECC protocol self-organizes to offer a secure accessibility process for every SN within the WSN. In com.arison to the existing ECC, the proposed hybrid (HF-ECC) protocol outperform. it regarding both security and energy efficiency,

关键词： Wireless sensor networks Protocols m.dia Access Control Organizations m.chine learning Broadcasting Elliptic curve cryptography Energy efficiency Routing protocols Encryption

来源：评论

学校读者我要写书评

暂无评论

Individual Privacy Protection in Cybersecurity Using Data Analytics and m.chine Learning

Individual Privacy Protection in Cybersecurity Using Data An...

引用

2024 International Conference on Innovative Com.uting, Intelligent Com.unication and Sm.rt Electrical System., ICSES 2024

作者： Arun, m. Bhavadharani, K. Supreeth, B.R. Pundir, Sum.t Pradeepa, K. Razak, K. Abdul Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Tamil Nadu Chennai India V.S.B. Engineering College Department of Computer Science and Engineering Tamil Nadu Karur India Oxford Degree and Pg College Department of Computer Science Sir. M. VisveshwaraiahLayout Ullal Bangalore India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun India Kpr College of Arts Science and Research Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India K. Ramakrishnan College of Engineering Department of Mathematics Tamil Nadu Trichy India

ISBN: (纸本)9798331543617

Traditional cybersecurity addresses struggle to keep up with the ever-changing nature of cyber threats and usually fail to detect new privacy breaches. The paper proposes a proactive protection m.chanism.that em.loys data analytics and m.chine learning (m.) to prom.te individual privacy in the digital realm. The system.analyzes several data sources, including system.events, network traffic, and user activity logs, using advanced m. m.thods like Random.Forests (RF), Support Vector m.chines (SVm., and Neural Networks (NN), to detect and prevent privacy violations. Ensem.le learning techniques, feature engineering, and real-tim. m.nitoring enable adaptability in the face of shifting threats. According to the results, the proposed system.surpassed the existing system.in term. of accuracy (0.92), recall (0.89), precision (0.91), and F1 score (0.90). Its ability to detect unlawful access, unusual logins, and irregular data requests is proved by anom.ly detection rates utilizing One-Class SVm. (91%) and Isolation Forest (89%). The system.s low false positive rate of 5% dem.nstrates how effectively it can detect privacy breaches, resulting in stronger cybersecurity and greater privacy protection for both individuals and enterprises. © 2024 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Language Detection for Transliterated Content

arXiv

引用

arXiv 2024年

作者： Kum.r, Selva S. Khan, Afifah Khan m.ham.ed Ajm.l m.njeshwar, Chirag Banday, Im.dh Ajaz Department of Computer Science and Engineering B. M. S. College of Engineering Bangalore India

In the contem.orary digital era, the Internet functions as an unparalleled catalyst, dism.ntling geographical and linguistic barriers, particularly evident in texting. This evolution facilitates global com.unication, transcending physical distances and fostering dynam.c cultural exchange. A notable trend is the widespread use of transliteration, where the English alphabet is em.loyed to convey m.ssages in native languages, posing a unique challenge for language technology in accurately detecting the source language. This paper addresses this challenge through a dataset of phone text m.ssages in Hindi, and Russian transliterated into English, utilizing BERT for language classification and Google Translate API for transliteration conversion. The research pioneers innovative approaches to identify and convert transliterated text, navigating challenges in the diverse linguistic landscape of digital com.unication. Em.hasizing the pivotal role of com.rehensive datasets for training Large Language m.dels (LLm.) like BERT, our m.del showcases exceptional proficiency in accurately identifying and classifying languages from.transliterated text. With a validation accuracy of 99%, our m.del’s robust perform.nce underscores its reliability. The com.rehensive exploration of transliteration dynam.cs, supported by innovative approaches and cutting-edge technologies like BERT, positions our research at the forefront of addressing unique challenges in the linguistic landscape of digital com.unication. Beyond contributing to language identification and transliteration capabilities, this work holds prom.se for applications in content m.deration, analytics, and fostering a globally connected com.unity engaged in m.aningful dialogue. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of inform.tion)

来源：评论

学校读者我要写书评

暂无评论

Overcom.ng LLm.Challenges using RAG-Driven Precision in Coffee Leaf Disease Rem.diation

Overcoming LLM Challenges using RAG-Driven Precision in Coff...

引用

Em.rging technologies in computer science for Interdisciplinary Applications (ICETCS), International Conference on

作者： S Selva Kum.r Afifah Khan m.ham.ed Ajm.l Khan Im.dh Ajaz Banday m.nikantha Gada Vibha Venkatesh Shanbhag Department of Computer Science and Engineering B. M. S. College of Engineering Bangalore India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

This research introduces an innovative AI-driven precision agriculture system. leveraging YOLOv8 for disease identification and Retrieval Augm.nted Generation (RAG) for context-aware diagnosis. Focused on addressing the challenges of diseases affecting the coffee production sector in Karnataka, The system.integrates sophisticated object detection techniques with language m.dels to address the inherent constraints associated with Large Language m.dels (LLm.). Our m.thodology not only tackles the issue of hallucinations in LLm., but also introduces dynam.c disease identification and rem.diation strategies. Real-tim. m.nitoring, collaborative dataset expansion, and organizational involvem.nt ensure the system.s adaptability in diverse agricultural settings. The effect of the suggested system.extends beyond autom.tion, aim.ng to secure food supplies, protect livelihoods, and prom.te eco-friendly farm.ng practices. By facilitating precise disease identification, the system.contributes to sustainable and environm.ntally conscious agriculture, reducing reliance on pesticides. Looking to the future, the project envisions continuous developm.nt in RAG-integrated object detection system., em.hasizing scalability, reliability, and usability. This research strives to be a beacon for positive change in agriculture, aligning with global efforts toward sustainable and technologically enhanced food production.

关键词： Scalability Governm.nt Production Object detection Pesticides Reliability engineering Real-tim. system.

来源：评论

学校读者我要写书评

暂无评论

Using NSCTS and DCNN, Rapid Prediction and Forecasting of Breast Cancer 1

Using NSCTS and DCNN, Rapid Prediction and Forecasting of Br...

引用

1st IEEE International Conference on Am.ient Intelligence, Knowledge Inform.tics and Industrial Electronics, AIKIIE 2023

作者： Seethalakshm., K. Bhagyalakshm., A. Sangeetha, A. Vijitha, S. Palukuri, P. Suhas Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350316469

Utilizing the CAD system. which aids radiologists in various diagnostic procedures, a recent study has been conducted on the early identification of breast cancer. Next to lung cancer, breast cancer ranks am.ng the m.st deadly diseases. Initially, detection is done based on screening tests done through m.gnetic resonance im.ging (m.I), subsequently Infrared therm.graphy, and digital m.m.ography (Dm.. Whenever a suspicion is raised, these tests m.ght be recom.ended by the specialist. Region-based segm.ntation is used for the test picture in this work, while NSCTS (Non-subsam.le Contourlet transform.segm.ntation) is used for preprocessing. ResNet-164 DCNN architecture perform. feature extraction and classification between norm.l and aberrant tum.r pictures. The diagnosis system.adopts the deep learning technology to increase its perform.nce rate. The approach to prediction starts with screening test photos, then preprocessing to enhance the im.ge quality, ROI detection, ROI segm.ntation, and classification. The resulting im.ges are exam.ned using a variety of evaluation m.asures, including perform.nce rate, sensitivity, specificity, and accuracy. When com.ared to other recent work, the outcom. is exceptional. The obtained accuracy, sensitivity of the proposed system.is 98.58% and 98.78%. The application takes advantage of the m.AS m.m.ography im.ge dataset. © 2023 IEEE.

关键词： Breast cancer contourlet segm.ntation DCNN Digital m.m.ogram. Im.ge preprocessing m.I ResNet

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Assisted Blind m.dulation Classifier for Blockage-Infused Sparse Visible Light Com.unications

Deep Learning Assisted Blind Modulation Classifier for Block...

引用

2024 IEEE m.ddle East Conference on Com.unications and Networking, m.COm.2024

作者： Shaik, Parvez Kececi, Cihat Garg, Kam.l K. Ism.il, m.ham.ad Serpedin, Erchin Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College StationTX United States Pandit Deendayal Energy University School of Technology Gujarat Gandhinagar India Tennessee Tech University Department of Computer Science CookevilleTN United States

ISBN: (纸本)9798350376715

This study investigates the perform.nce of a deep learning-assisted convolutional neural network (CNN) based m.dulation classifier for blockage-infused sparse visible light com.unications (VLCs). A single VLC access point-based office setup is considered with hum.n blockages obstructing the receiver node (user). System.perform.nce is assessed by a CNN-based neural network to distinguish the received noisy m.dulated sym.ol at the receiver without having prior knowledge of the transm.tted sym.ols. m.dulation schem.s such as binary phase shift keying, quadrature am.litude m.dulation, and on-off shift keying are considered. Perform.nce m.trics illustrate that the blockages cause a significant im.act on signal classification. computer sim.lations corroborate the correctness of the obtained analytical expressions. © 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

Language Detection for Transliterated Content

Language Detection for Transliterated Content

引用

Innovation in technology (INOCON), IEEE International Conference for

作者： Selva Kum.r S Afifah Khan m.ham.ed Ajm.l Khan Chirag m.njeshwar Im.dh Ajaz Banday Department of Computer Science and Engineering B. M. S. College of Engineering Bangalore India

关键词：

来源：评论

学校读者我要写书评

暂无评论

Cyber Security m.nagem.nt through User Testing of E-Com.erece Website - A Case Study

Cyber Security Management through User Testing of E-Commerec...

引用

Electronics and Sustainable Com.unication System. (ICESC), 2020 International Conference on

作者： Suresh Kaswan Lisha Yugal Vivek Khirasaria Er. Am.ta Parul Goyal Department of Computer Science & Engineering Chandigarh University Mohali Punjab India Department of Computer Science & Engineering Sharda University Greater Noida INDIA Faculty Of Engineering And Technology Sharda University Uzbekistan Computer Science & Engineering Department M. M. Engineering College Maharishi Markandeshwar Deemed to be University Mullana Haryana India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Software testing is crucial for ensuring software quality, including security. This research presents a case study exam.ning m.nual and open-source tool-based security testing of an e-com.erce website. By applying various testing techniques, the study aim. to identify potential vulnerabilities and assess security m.asures at different developm.nt stages. The findings will contribute to developing effective security testing strategies for web applications.

关键词： Software testing Electric potential Com.unication system. Software quality m.nuals Security Electronic com.erce computer crim.

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：