检索结果-内蒙古大学图书馆

1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： More, Amruta Patil, Satchal Savale, Aditya Satle, Prafull Tilwane, Krishna Shinde, Sanket Ajeenkya DY Patil School of Engineering Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350355611

This Research aimed at the challenge of finding college for students, but this is complex task as students are unable to find best institutes based on entrance exam scores in India. To bounce on that, we focus on the development of a College Predictor Bot, which uses some key factors notably JEE and CET scores and predicts admission to different Indian colleges. You are trained on years of admissions data from hundreds of colleges, with students of all shapes and sizes and performance levels. In terms of methodology used in the study, a combination of random forest and decision tree models were used for analysing the exam scores and make findings. The ML-model is subject to a lot of statistics and testing, and the analysis results in highly valuable co-relations between performance on entrance exams and admitting results. Our results show that College Predictor Bot accurately predicts admissions, therefore helping students know what colleges are suitable. The larger implications [of this tool] are that this might make the college selection process easier and provide a slightly more transparent and informed way of getting deserved seat in the Eductaion system of Bharat. © 2025 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Road Traffic Forecasting Using Air Pollution and Atmospheric data 4

Road Traffic Forecasting Using Air Pollution and Atmospheric...

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Sivamurugan, S. Vedapriya, N. Vijayashree, K. Nithya, P. Sri Sairam Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Urban traffic congestion continues to be a serious problem that compromises environmental sustainability, economic productivity, and public health. Accurately predicting patterns of congestion and implementing appropriate actions are essential for managing traffic flow. However, the influence of environmental elements such as weather and air pollution on traffic patterns is largely overlooked by standard traffic prediction *** models that may better comprehend the intricate relationships between traffic patterns and environmental factors are developed by compiling historical data on air pollution levels and atmospheric conditions. Our trials show that this methodology outperforms previous methods in terms of traffic forecast accuracy. This study adds to our understanding of how traffic flow is influenced by environmental factors and provides useful information for improving urban transportation management techniques. © 2024 IEEE.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Cloud-Based AIOT Soil Monitoring System for Earthworms 9

Cloud-Based AIOT Soil Monitoring System for Earthworms

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Varma, S. Asha Mangamuri, Meghana Kalagara, Anusha Kurukanti, Kezia Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350377972

Determining the soil conditions that are favourable to earthworm species is vital for maintaining soil health and improving agricultural yield. Timely reactions are constrained by the work-intensive nature of conventional soil monitoring techniques and their lack of real-time insights into characteristics of the soil. In order to address these issues, we created an Internet of Things (IoT) cloud-based soil monitoring system that tracks pH and moisture levels, two factors that are essential for earthworm survival, while utilizing clustering algorithms for categorizing soil conditions and Random Forest machine learning algorithms for predicting optimal habitats. This state-of-the-device enables more informed soil management decisions by offering cloud-based trend analysis visuals and fast feedback via an LCD display. The method effectively improves the detection of suitable circumstances for earthworm habitats by combining innovative sensor technology with real-time data transmission and processing. Compared to conventional techniques, our system reveals significant gains in graphical representation and data collection as well as automated choices. Collectively, these features promote a more proactive approach to soil management, improving the fertility of the soil and creating optimal conditions for earthworms. With potential uses across various precision farming domains, our methodology provides a significant instrument for sustainable agriculture. © 2024 IEEE.

关键词： Soil surveys

来源：评论

学校读者我要写书评

暂无评论

StealthWatch: artificial intelligence based Shoplifting Detection [Retail Theft] 4

StealthWatch: Artificial Intelligence based Shoplifting Dete...

引用

4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Kanimozhi, T. Rajamurugan, A. Sudhesh Rajan, M.N. Harihareshwar, D.P. Vanaj Kalyan, S.R. Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

Retail industries face significant challenges in preventing shoplifting, which leads to substantial financial losses. 'Stealth Watch' is a comprehensive solution that leverages advanced computer vision techniques to combat this issue. Utilizing the power of YOLO (You Only Look Once) and OpenCV (Open Source Computer Vision Library), the system monitors live video footage in real-time to detect suspicious activities. When a potential shoplifting event is identified, 'Stealth Watch' sends immediate alerts to connected systems and triggers an automatic call to the store's owner or manager, ensuring prompt awareness and action. The system also incorporates facial recognition technology to log shoplifters' identities, storing this information along with the date and time of the incident in a database for future reference. 'Stealth Watch' exemplifies how cutting-edge technology can enhance security in retail environments. By automating the detection and alert process, this system reduces the need for constant human monitoring while increasing the speed and accuracy of responses to theft. Its ability to log repeat offenders also serves as a deterrent, making it a valuable tool for store owners and security personnel. In conclusion, 'Stealth Watch' is a recent advancement in the use of technology to address the persistent challenges of retail security. © 2024 IEEE.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

Enhanced Intrusion Detection based on Advanced Deep Scaled Multi Perceptron Neural Network with Enhanced Particle Swarm intelligence 8

Enhanced Intrusion Detection based on Advanced Deep Scaled M...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Nathiya, N. Mohanarengan, S. Diwakar, S. Chinnamannan, K. Pugazhraj, D. Elango, S.T. Paavai College Of Engineering Artificial Intelligence and Data Science Tamil Nadu Namakkal India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

Network communication is an incredible service for sharing the information among all over the world. Among the communication, the data breaches are occurring during transmission is increasing due to hackers plays to attack the service. By the intrusion created by the attackers during the commutation, through cyber-attack become more powerful to create various attacks like Distributed service attacks, Sybil attacks, vampire attack, defense data drop attacks, decoder attack and soon. So, the development of intrusion detection is important through artificial intelligence is crucial to prevent the attacks. Mort of exiting model failed to analyze the nature and behavior of feature properties to identify the interrupts due to non-relational communication feature creates more dimension. The data shift problems degrade the poor accuracy in precision and recall rate. To resolve this problem, to propose an advanced Deep scaled Multi perceptron neural network (DSMPNN) classifier based on Enhanced particle swarm intelligence (EPSI) is used for optimal feature selection to improve the classification IDS. Initially the communication logs are collected as to take DarkNet-IDS dataset and preprocessing is carried put to normalize the dataset based on Min-Max normalization. The Traffic intensive defect rate (TIDM) is estimated by observing the feature defect scaling measure. Then the feature section is carried out by EPSI to identify the relation mutual defect feature list and classified with Deep scaled multi perceptron neural network. Finally, the DSMPNN identifies the intrusion classed based on class by reference effectively. The proposed system improves the classification accuracy ass well in higher precision rate, Fl1 rate with higher positive feature limits related to class with redundant false rate compared to the other exiting system. © 2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Personalized Music Recommendation: A Comparative Study of Machine Learning and Deep Learning Approaches 11

Personalized Music Recommendation: A Comparative Study of Ma...

引用

11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Iqbal, Nafla Mohan, Aiswarya Syjo, Ann Rose Rajagiri School of Engineering and Technology Dept of Artificial Intelligence and Data Science Kakkanad India

ISBN: (纸本)9798350379136

The rapid advancement of music playback technology, particularly within mobile platforms, has fundamentally transformed user engagement with music. Despite significant improvements in music retrieval techniques over the past decade, the evolution of music recommendation systems remains in its early stages. With users having access to extensive online music libraries, the task of selecting preferred tracks from an overwhelming array of options presents a considerable challenge. Individual musical tastes vary widely, and the influx of new users and music necessitates a system that can adapt *** music recommendation systems face the dual challenge of managing vast amounts of data while delivering accurate results, making them resource-intensive. However, the high precision of these models drives their increasing application in commercial settings. This paper proposes a framework aimed at enhancing recommendation accuracy by tailoring suggestions to align more closely with user preferences.A comprehensive analysis of traditional and advanced music recommendation methodologies is presented, focusing on two user-centric approaches: user preference and emotion-based metrics. These methodologies are critically compared, leveraging the Million Song dataset (MSD) for training the recommendation system. Additionally, an Android application has been developed to collect empirical data on user experiences, facilitating a deeper understanding of user interactions and satisfaction with the recommendation framework. This research aims to improve user experience in music discovery by providing more relevant and personalized recommendations. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Text-Image Feature Fine-Grained Learning for Joint Multimodal Aspect-Based Sentiment Analysis

引用

Computers, Materials & Continua 2025年第1期82卷 279-305页

作者： Tianzhi Zhang Gang Zhou Shuang Zhang Shunhang Li Yepeng Sun Qiankun Pi Shuo Liu School of Data and Target Engineering Information Engineering UniversityZhengzhou450001China Information Engineering Department Liaoning Provincial College of CommunicationsShenyang110122China School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou450000China

Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal *** this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for ***,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect ***,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature ***,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and *** on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.

关键词： Multimodal sentiment analysis aspect-based sentiment analysis feature fine-grained learning graph convolutional network adjective-noun pairs

来源：评论

学校读者我要写书评

暂无评论

SourcePlag - Source Code Plagiarism Detection Based on Abstract Syntax Trees 3

SourcePlag - Source Code Plagiarism Detection Based on Abstr...

引用

3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Sharma, Nakul Shinde, Siddharth Bhosale, Swarup Patil, Suyog Vishwakarma Institute of Information Technology Faculty of Engineering Department of Artificial Intelligence and Data Science Pune India Vishwakarma Institute of Information Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350354348

Code plagiarism poses a significant challenge in programming communities, necessitating effective detection mechanisms. This paper introduces a novel system that employs Abstract Syntax Trees (ASTs) for code representation and comparison. The system utilizes ASTs to capture the structural essence of code, facilitating a comprehensive analysis of code similarity, it offers flexibility for application across multiple programming languages. In addition to leveraging the Levenshtein Distance Algorithm for Python code comparison, the system incorporates node counting for other languages such as Java and C/C++. By integrating AST-based representation and a combination of comparison techniques, the system offers a robust solution for identifying plagiarized code instances accurately across various programming environments. Through a detailed exploration of the system's methodology, this paper underscores its potential to address the pervasive issue of code plagiarism in programming communities. © 2024 IEEE.

关键词： C++ (programming language)

来源：评论

学校读者我要写书评

暂无评论

Enhancing Interpretability: The Role of Explainable AI in Healthcare Diagnostics 3

Enhancing Interpretability: The Role of Explainable AI in He...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Zade, Nikita Langote, Meher Verma, Prateek Faculty of Engineering & Technology Department of Artificial Intelligence & Data Science Maharashtra Sawangi442001 India Faculty of Engineering & Technology Department of Artificial Intelligence & Machine Learning Maharashtra Sawangi442001 India

ISBN: (纸本)9798331509675

XAI is now transforming the use of AI in diagnosing diseases by overcoming some of the problems inherent in most black-box approaches. In time-sensitive speciality areas like computer-aided diagnosis, image analysis, and predictive modelling, SHAP, LIME, and attention-based XAI techniques explain how AI decides to help doctors and other healthcare professionals trust AI. Therefore, this paper aims to clarify why XAI is needed in healthcare, introduce techniques, and show their examples in cancer diagnosis and disease prognosis. XAI improves diagnostic performance and satisfies regulatory and ethical requirements for increased usage in clinical practice. Because of XAI, clinicians can use the best of artificial intelligence to complement their knowledge in enhancing the treatment of patients. That is why the outlook of XAI in healthcare includes the next steps in achieving higher scalability, managing bias, and working with diverse regulatory frameworks, which will define the future of AI solutions in healthcare. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Mediguide:A Machine Learning based Smart Prescription System for Personalized Healthcare

Mediguide:A Machine Learning based Smart Prescription System...

引用

2025 International Conference on artificial intelligence and data engineering, AIDE 2025

作者： Kumar K, Jeyaganesh Mithilesh, G. Monisha, K.M. Nandhini, S. Nithish Kumar, M. M.Kumarasamy College of Engineering Department of Artificial Intelligence and Data Science Karur India M.Kumarasamy College of Engineering Department of Artificial Intelligence and Machine Learning Karur India

ISBN: (纸本)9798331527518

This project uses a Decision Tree Classifier model which gives accurate health predictions based on the symptoms provided by users. The model has been trained at thousands of symptoms against diseases and thus predicts very accurately for the inputted data provided by the user. The system has an easy-to-use interface that helps users input their symptoms with ease and get instant predictions of potential diseases. In addition to predicting ailments, the application provides extensive information on the detected disease. Users are given a comprehensive breakdown of the disease, recommended medications, precautions, and personalized advice on diet and exercise regimes. It provides health insights that are meant to help users better manage their health and make better lifestyle decisions. With feedback loops, prediction accuracy and user experience can always be improved directly without worrying much about each setup. It seeks to use advanced technology to make practical health advice possible. Combining disease prediction in real time with meaningful health advice creates added value for users and encourages informed health choices. It enables users to own their health by providing them with a complete picture of their possible health problems and possible solutions to address them. This Project provides a powerful tool for more proactive users, with its combo of predictive analytics and health education resources.(And it helps us recognise the sport itself the user-friendly and detailed output that makes it easy and helpful for everyone to understand their health and what they can do about it.) It upskill traditionally trained AI learners to be perpendicular over ruinous health data towards an accessibly governed future, furnished with health facts and outcomes to improvement on the go. © 2025 IEEE.

关键词： Personalized medicine

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：