检索结果-内蒙古大学图书馆

6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Nivedita, M. Pawar, Rohan Kathuria, Hardik Rateneshwar Siddiqui, Ibrahim Ahmad Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350394702

The increasing proliferation of surveillance cameras to keep an eye on human behavior has led to a rise in the need for an automated system that can identify violent incidents and suspicious activities. In smart buildings, this technology is mandatory. Violence Detection in real time will prevent any mishappenings from taking place by raising alerts to the security guards. Our model also has YOLO object detection inculcated in it in order to recognize any weapons or tools that an attacker may be using against people and the security guards will act *** this paper, we have utilized deep learning model and techniques to create a lightweight system for detecting violence that is fully automated and can act as an assistant for law enforcement officers, and can also be deployed in public areas as well as corporate buildings. When the system detects violence, it rings an alarm which can be used to alert security personals that some commotion may be happening in certain area. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Characteristics Removal of Tumefaction in Brain from Diagnosis Radiology Images Using Machine Learning 9

Characteristics Removal of Tumefaction in Brain from Diagnos...

引用

9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Suresh, C. Gokilapriya, S. Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397376

Brain tumefaction is the most dangerous disease which makes the physical survival days as countable. Tumefactions are detected with the help of MRI scans and manually detected by the radiologist. Manually detecting the details of tumefactions could be errors. This old method requires more time to detect, less preciseness and expertise radiologist. The Machine learning algorithm and Artificial Intelligence is used to clinically approve a profound learning framework for computerized spotting and grouping of segments the brain tumefaction tissues of publicly available dataset, using the algorithm KNN, SVM AND GRNN in which includes that are extricate of pictures, information pre-handling strategies, and the section examination. AI (ML) strategies are utilized to separate dangerous from harmless brain cancers. The socioeconomics, mean pixel values, pitched, elements of Dim Level Co-event Lattice, mean, difference, vitality, randomness, disparity, oneness, relationship, noticeable quality, and shade, were separated from MRI ADC pictures of every patient. At the component choice stage, the legitimacy of the extricated highlights was estimated utilizing ANOVA f-test. The exact locations are revealed with the help of Feature extraction includes recognizing the growth, grouping the cancer concerning grade, type, and distinguishing proof of growth area. © 2023 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Internet of Things: An Aggregated Proof based Secure Healthcare Monitoring System- Comparative Analysis 6

Internet of Things: An Aggregated Proof based Secure Healthc...

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Aswathy, R.H. Suresh, P. Jenifa, G. Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Thandalam Chennai India Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India

ISBN: (纸本)9798331522667

The healthcare monitoring system plays a crucial role in remote monitoring. A highly secure healthcare system utilizing advanced cryptographic methods to protect sensitive information. The system integrates a multifactor authentication framework combined with aggregated proof mechanisms to enhance security. A detailed security analysis has been conducted to assess its effectiveness against various cyber threats. Compared to existing solutions, the proposed model demonstrates exceptional resilience to numerous known attacks, including Session Key Computation, Password Guessing, User & Gateway Node Impersonation, Replay Attacks, Sensor Node Spoofing, and Privileged Insider Attacks. These features ensure robust protection for healthcare data, addressing critical security concerns in modern applications. By leveraging the encryption and authentication strategies, the system provides a dependable and secure environment for the healthcare industry. This approach not only strengthens data privacy but also ensures operational integrity, making it a comprehensive solution for addressing current and emerging security challenges. © 2025 IEEE.

关键词： Authentication Protocol

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning Model on Healthcare Based Chatbot and Appointment System 3

A Machine Learning Model on Healthcare Based Chatbot and App...

引用

3rd International Conference on Artificial Intelligence and Signal Processing, AISP 2023

作者： Srivastava, Kushagra Pandey, Trilok Nath Roy, Debaraj Sahoo, Sambit Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350320749

This paper discusses the clinical chatbot which could examine the contamination and deliver essential insights regarding the contamination previous to counseling a specialist. To lower the healthcare charges and similarly increase openness to clinical facts the clinical chatbot is constructed. Certain chatbots pass approximately as clinical reference books, which facilitates the affected person to discover approximately their contamination and assists with running on their fitness. The person can accomplish the real gain of a chatbot simply while it is able to examine all forms of infections and deliver essential data. In later sections of the paper, a doctor appointment system software is also discussed that is integrated with the chatbot so that the specialist which is recommended by medical chatbot can be easily visited at the mutually agreed time of both specialist and patient. Such an appointment system has its own advantages like it reduces the waiting time of the patient, patients can choose the appointment time in keeping with their desire additionally to be had and booked slots are proven in powerful graphical person interface. © 2023 IEEE.

关键词： Health care

来源：评论

学校读者我要写书评

暂无评论

An analysis on Keylogger Attack and Detection based on Machine Learning

An analysis on Keylogger Attack and Detection based on Machi...

引用

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Balakrishnan, Yeshaswini Renjith, P.N. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350334357

A keylogger attack is a type of cyberattack that involves the use of a software program to record keystrokes on a target device. Attacks of this kind can be used to steal sensitive data, including credit card numbers and login credentials. Keylogger attacks are often targeted at specific individuals or organizations, and the attackers may have prior knowledge of the target's systems and configuration. There are a variety of ways to carry out a keylogger attack, and the attacker's choice of method will depend on the type of information they are trying to steal. For example, an attacker may install a hardware keylogger on the target's computer in order to record every keystroke made. As an alternative, the attacker might develop malicious software that captures keystrokes and transmits them to a distant server. Keylogger attacks are difficult to detect, as the keylogger software can be disguised as a legitimate program or run in the background without the user's knowledge. However, there are some signs that a keylogger attack may be taking place, such as unexpected activity on the computer or unusual network traffic. The best way to protect against keylogger attacks is to use a reputable antivirus program and to keep all software up to date. Furthermore, users need to be cautious while opening attachments or clicking on hyperlinks that come from unknown sources. This paper focuses on the evolution of technology over time, as well as the implementation and observation of keylogger attacks. © 2023 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Optical sorting:past,present and future

引用

光：科学与应用（英文版） 2025年第5期14卷 1188-1234页

作者： Meng Yang Yuzhi Shi Qinghua Song Zeyong Wei Xiong Dun Zhiming Wang Zhanshan Wang Cheng-Wei Qiu Hui Zhang Xinbin Cheng Institute of Precision Optical Engineering School of Physics Science and EngineeringTongji UniversityShanghai 200092China MOE Key Laboratory of Advanced Micro-Structured Materials Shanghai 200092China Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 200092China Shanghai Frontiers Science Center of Digital Optics Shanghai 200092China Tsinghua Shenzhen International Graduate School Tsinghua UniversityShenzhen 518055China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

Optical sorting combines optical tweezers with diverse techniques,including optical spectrum,artificial intelligence(AI)and immunoassay,to endow unprecedented capabilities in particle *** comparison to other methods such as microfluidics,acoustics and electrophoresis,optical sorting offers appreciable advantages in nanoscale precision,high resolution,non-invasiveness,and is becoming increasingly indispensable in fields of biophysics,chemistry,and materials *** review aims to offer a comprehensive overview of the history,development,and perspectives of various optical sorting techniques,categorised as passive and active sorting *** begin,we elucidate the fundamental physics and attributes of both conventional and exotic optical *** then explore sorting capabilities of active optical sorting,which fuses optical tweezers with a diversity of techniques,including Raman spectroscopy and machine ***,we reveal the essential roles played by deterministic light fields,configured with lens systems or metasurfaces,in the passive sorting of particles based on their varying sizes and shapes,sorting resolutions and *** conclude with our vision of the most promising and futuristic directions,including Al-facilitated ultrafast and bio-morphology-selective *** can be envisioned that optical sorting will inevitably become a revolutionary tool in scientific research and practical biomedical applications.

关键词： present future past optical sorting

来源：评论

学校读者我要写书评

暂无评论

Fake News Detection Using Logistic Regression and LightGBM 2

Fake News Detection Using Logistic Regression and LightGBM

引用

2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Carmel Mary Belinda, M.J. Alex David, S. Kannan, E. Ruth Naveena, N. Rajathi, K. Mahesh, C. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of computer science and engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of computer science and engineering Chennai India Hindustan Institute of Technology & Science Department of mathematics Chennai India

ISBN: (纸本)9798350368413

The spread of false information via fake news in recent years has raised serious issues and caused social discontent and division. It is critical to create efficient techniques for quickly and reliably recognizing bogus news. In the context of identifying fake news, this paper compares two popular machine learning techniques: LightGBM and Logistic Regression. Our analysis makes use of a dataset that includes both fake and real news pieces, with 23,481 fraudulent news stories and 21,417 real news stories. Important performance parameters including recall, accuracy, precision, F1-score, and Matthews correlation coefficient are taken into account while evaluating these algorithms. Our goal in doing this research is to clarify how effective these strategies are at solving the urgent problem of the spread of false information. © 2024 IEEE.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Improvising the Stock Prediction by Integrating with roBERTa and LSTM

Improvising the Stock Prediction by Integrating with roBERTa...

引用

2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2023

作者： Poornima, N. Abilash, D. Theodaniel, M. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350312126

In the Stock market, the volatility of leading MNCs'(Multi-National Corporations) shares is a major matter of concern and comes under the limelight nowadays. Unlike the 1920s sudden surge and dot-com crash, the contemporary world has never seen such a biggest bull or bear particularly in the past few decades. The stock market is majorly influenced by the credibility opinion of the general public on the firm. In the 21st century, the emergence of research LLC (Limited Liability Company) which gains profit from short selling of the shares by manipulating the share of a certain firm by exposing the legality of trespassing norms has made the researchers include a current public sentiment on the firm since short selling is a matter of one day. The first and foremost impact of such exposure would be instantly taken to Twitter, a credible social media. In order to infer the associativity of sentiment analysis on the stock market analysis we have taken time-series data of a recently exposed firm which faces the biggest bear in the market from Yahoo Finance for the timeline of 07-02-22 to 03-02-2023 and the Twitter data for the same timeline had been accessed by is a scraper for Social Networking Services (SNS). The extracted tweet data with almost 1000 tweets each day has been analyzed by Meta's roBERTa, an NLP(Natural Language Processing)-based framework for sentiment analysis. It is used to predict whether the market will be bearish or bullish on the day. Then the sentiment flag attribute and the market data attribute have been used to build a 3-layered Long Short Term Memory (LSTM), an ANN(Artificial Neural Network) where the data will be predicted for the same day's stock movement. The results show that the sentiment reflects on the stock's movement and the accuracy of the proposed work is about 96.14%. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Inspired Detection Model For Clustering Based Surveillance System: A Non-Conventional Review

Inspired Detection Model For Clustering Based Surveillance S...

引用

2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Gupta, Anil Kumar Sharma, Rupak Ojha, Rudra Pratap Srm Institute of Science and Technology Department of Computer Science and Engineering NCR Campus Ghaziabad Uttar Pradesh Modinagar India Srm Institute of Science and Technology Department of Computer Applications NCR Campus Ghaziabad Uttar Pradesh Modinagar India Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350382723

in current years, there has been a growing hobby in growing superior surveillance systems for detecting and monitoring anomalous activities. Traditional surveillance systems depend upon handmade policies and features, which are frequently limited of their capacity to accurately become aware of complex anomalies. To conquer this problem, we suggest a better visible anomaly detection version for clustering-based surveillance structures the use of long brief-time period reminiscence (LSTM) Diffusion manner. The proposed version incorporates the temporal context of the CCTV photos with the aid of making use of the LSTM network, which has been proven to deal with sequential data effectively. The LSTM community is used to extract movement features from the video frames, which are then fed into the clustering set of rules. This allows for the identification of comparable styles of sports and grouping them into clusters. Furthermore, we combine the concept of the Diffusion technique into the LSTM version to decorate the clustering method. The Diffusion procedure entails the gradual propagation of information among nodes in a network, which is particularly beneficial in figuring out abrupt changes in activities. This permits the version to locate anomalies with better accuracy and decrease fake alarms. To assess the performance of our proposed version, we behavior experiments on a real-global surveillance dataset. The consequences display that our model outperforms current techniques in terms of accuracy and detection price. © 2024 IEEE.

关键词： Diffusion

来源：评论

学校读者我要写书评

暂无评论

An approach for automatically solving handwritten equations using deep learning 2

An approach for automatically solving handwritten equations ...

引用

2nd International Conference on Trends in Electrical, Electronics and computer engineering, TEECCON 2023

作者： Khanduri, Arjun Mukherjee, Sourish Das, Sagnik Kartheek, G.C.R. CMR Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350339949

The mathematical description of two things that are equal-one on each side of an "equals"sign-is called an equation. Equations help us address problems in our daily lives. Other fields that employ mathematical equations include medical, aerospace, traffic control, etc. In this paper, we have proposed a deep learning-based approach to solving handwritten equations in a digital system. We have only attempted to solve algebraic or arithmetic equations. The accuracy achieved while testing the application with expressions and equations was observed to be 90%. To solve equations received from the image scanned, we have proposed the use of a string extraction and manipulation method to convert the handwritten tedious equations into an expression that can be solved easily using a computer or digital system. © 2023 IEEE.

关键词： 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：