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共 20250 条记录，以下是241-250 订阅

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Hotel's Price Prediction Based on Country Specific data 25th

Hotel's Price Prediction Based on Country Specific Data

引用

25th international conference on intelligent data engineering and automated learning

作者： Balan, Andrei Popescu, Paul Stefan Mihaescu, Marian Cristian Univ Craiova Craiova Romania

ISBN: (纸本)9783031777370;9783031777387

Online hotel booking became increasingly popular as time passed, and with its popularity, the data that can be collected based on customer actions has increased. This data can serve to build intelligent systems that can provide knowledge for both customers and hotel owners. In this paper, we focus on hotel owners who can benefit from the collected data by adjusting the prices to optimise the profit of their accommodations. To accomplish this, we built a system that collected the data from *** and gathered a helpful dataset for price prediction. We used five regression algorithms and an optimization technique to obtain the best results, leading us to a 9% error for price prediction. This result allows accommodation owners to predict the room price to keep the rooms fully occupied.

关键词： price prediction linear regression optimization

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Soft Alert Generation for student Dropout Mitigation and Proactive Management by Machine learning Algorithms 3

Soft Alert Generation for student Dropout Mitigation and Pro...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Geetha, S. Spandana, A.P. Vijay, D. Vishruth, M.V. B.N.M Institute of Technology Dept. of Cse Karnataka Bangalore India

ISBN: (纸本)9798331515911

Proactive dropout mitigation is crucial for enhancing student success and retention in schools. Dropout rates pose significant challenges in education systems globally, impacting students, communities, and societies. This paper emphasizes the necessity of proactive measures to address dropout rates and support student achievement by building an educational network. Through proactive approaches, schools can identify and address risk factors early, preventing student disengagement and dropout. Various strategies such as early interventions, personalized support systems, academic enrichment programs, and community partnerships are examined for their efficacy in promoting student engagement and retention. Additionally, the role of data-driven decision-making and predictive analytics in identifying at-risk students and tailoring interventions is explored. Implementing proactive dropout mitigation requires collaboration among educators, administrators, families, and community stakeholders. Prioritizing proactive measures can foster a supportive learning environment, empowering students to excel academically. This paper highlights the significance of proactive dropout mitigation that has been enabled using a seemingly enhanced automated full-stack student management system integrated with ML algorithms used in enhancing student success and promoting educational equity. By investing in proactive interventions, educational institutions can reduce dropout rates and foster a culture of academic excellence and lifelong learning. © 2025 IEEE.

关键词： Predictive analytics

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Generative Replay and Multi-steps Knowledge Distillation in Class-Incremental learning 24

Generative Replay and Multi-steps Knowledge Distillation in ...

引用

3rd international conference on Artificial Intelligence and intelligent Information Processing, AIIIP 2024

作者： Meng, Yicheng Ping, Jinming Shi, Jingye Zhi, Ruicong School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400707308

data-free generative modeling (GM) methods have gained much attention because pseudo data avoids security and privacy issues, and no amount limitation for storing real data in Class-Incremental learning (CIL). However, GM-based methods have three drawbacks: un stable quality of generated image, semantic sifting, and domain bias for knowledge and distillation. To tackle with these problems, we propose an effective two-stage training method based on generative replay distillation, namely GA-RCT in this paper. It mainly consists of three modules. The Generative Adaptive Replay (GAR) module in stage one obtainsa well-trained generator for every task, then freezes generator for generating stable images, and trains feature extractor with mixture of real data and generated data. The Global Average Class (GAC) module is designed to reserve class prototypes to reduce semantic drift in the training stage. And the Generative Auxiliary Teaching (GAT) module in stage two performs generative distillation in dual-teacher to student way, for solving domain bias. Experiments on four datasets have shown that GA-RCT performs better than other generative replay methods. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive learning

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AI-Powered Solutions for Safer Stock Market Investments: Trend Forecasting using LSTM and Sentiment Analysis 3

AI-Powered Solutions for Safer Stock Market Investments: Tre...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Singh, Navneet Pratap Prasad, Anoushka Navya, Avvagiri Ameetha, Balla Natarajan, Karthika SCOPE Vellore Institute of Technology-Amaravati Bharuch India SCOPE Vellore Institute of TechnologyAmaravati Jaipur India SCOPE Vellore Institute of TechnologyAmaravati Secundrabad India SCOPE Vellore Institute of TechnologyAmaravati Nizamabad India Department of Software and System Engineering Vellore Institute of Technology Amaravati India

ISBN: (纸本)9798331527549

This paper presents a novel web application designed to mitigate the unpredictability of the stock market by providing accurate stock predictions and investment insights. Leveraging advanced machine learning models, including regression analysis and Long Short-Term Memory (LSTM) networks, our system processes historical stock data to forecast future trends. The application offers users customizable time periods for tailored predictions, interactive visualizations, and curated investment tips from reliable financial sources. Our goal is to empower investors of all experience levels to make informed decisions, navigating the complexities of the stock market with confidence. This research contributes to the development of a comprehensive platform that blends quantitative predictions with qualitative financial advice, optimizing the stock market investing experience. © 2025 IEEE.

关键词： Deep neural networks

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intelligent Waste Management System (IWMS): Deep learning Enabled Sorting with Bin-Fill Sensor Integration 3rd

Intelligent Waste Management System (IWMS): Deep Learning En...

引用

3rd international conference on Environmental Protection and Disaster Risks, EnviroRISKs 2024, and 12th Annual CMDR COE conference on Crisis management and Disaster Response, CMDR 2024

作者： Petrovski, Aleksandar Radovanović, Marko Behlić, Aner Ilievski, Kristijan Mustafovski, Rexhep ‘Goce Delcev University’ Stip Military Academy "General Mihailo Apostolski" Skopje Macedonia University of Defence Military Academy Belgrade Serbia

ISBN: (纸本)9783031747069

This research introduces an innovative waste management system leveraging deep learning algorithms in conjunction with Raspberry Pi 3 and intelligent bins, enhanced by a bin-fill sensor. The system offers a comprehensive solution for efficient waste sorting and bin management. Utilizing cameras connected to the Raspberry Pi, the deep learning model accurately identifies various types of garbage in real-time. Upon classification, the corresponding intelligent bin's lid is automatically opened through actuators controlled by the Raspberry Pi. Additionally, a bin-fill sensor integrated into each bin detects the level of waste accumulation, providing crucial data for optimizing waste collection schedules. This multi-faceted approach aims to revolutionize waste management processes, facilitating automated sorting, and timely waste collection, thereby contributing to sustainable environmental practices. The project underscores the synergy between deep learning, edge computing, and sensor technology in developing intelligent waste management solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Development of a Flexible Student Training System Using the Moodle LMS 3

Development of a Flexible Student Training System Using the ...

引用

3rd IEEE international conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Yusupova, Shohida B. Davletboyev, Sardorbek Z. Ishmetov, Bahrom Y. Urgench Branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Department of Information Technology Urgench Uzbekistan

ISBN: (纸本)9798331516321

In today's era of extensive information flow and the rise of distance education, there is a growing need for innovative educational technologies that can support personalized learning paths. This article explores the development of an adaptive learning system within the Moodle LMS, focusing on the implementation of individual educational trajectories based on automated testing and neural network models. The system enables the delivery of educational materials at varying levels of complexity, combined with adaptive tests that adjust based on student performance. Through machine learning, neural networks evaluate students' knowledge and generate customized training courses tailored to their needs. The proposed system addresses the limitations of traditional assessment methods, such as subjectivity in grading, by offering a more objective, data-driven approach. The use of adaptive testing and neural networks enhances the accuracy of knowledge assessment and fosters independent learning, ultimately contributing to improved educational outcomes in a distance learning environment. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on intelligent Agricultural Decision System Based on Machine learning Algorithm 3

Research on Intelligent Agricultural Decision System Based o...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Wang, Wenqian Jiang, Mengmeng Wuhan Donghu University Hubei Wuhan430070 China

ISBN: (纸本)9798350380989

Firstly, the application status and problems of machine learning in modern intelligent agricultural production were introduced. A smart agricultural monitoring system design based on Internet of Things technology and machine learning algorithm was proposed. A sensor node for collecting agricultural environment information is designed, and a wireless sensor network composed of ZigBee nodes is used to sense and detect agricultural environment information. The application software system developed by Qt software technology can manage and display the agricultural environment information. Secondly, a smart agricultural decision system based on machine learning is constructed by using decision tree algorithm to analyze the problem. The communication method based on blackboard model is adopted to avoid the complicated communication design of session layer. In the decision-making process of knowledge, ontology is combined with the current mature rule-based reasoning technology. It is proved that this system can make timely and accurate decision according to the change of agricultural environment, and carry out dynamic adaptive learning according to historical data. © 2024 IEEE.

关键词： agricultural system decision tree machine learning sensor Smart agriculture

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Traffic Incident Detection Algorithm based on Deep learning: A Statistical Optimization Approach 3

Traffic Incident Detection Algorithm based on Deep Learning:...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Jing, Zhao Heilongjiang Institute of Technology College of Automobile and Traffic Engineering Harbin150050 China

ISBN: (纸本)9798331509675

The continuous evolution of smart transportation has led to the vigorous development of research in the field of intelligent vehicle anomaly detection. This study proposes a new traffic incident detection algorithm based on deep learning and combines it with statistical optimization methods for real-time traffic incident monitoring. This model first uses the pre-trained YOLOv5m-c model to detect vehicles and outputs images with vehicle frames from the video stream. At the same time, from the data analysis level, it determines whether there is abnormal behavior between vehicles based on the characteristics of the vehicle frame. The proposed algorithm improves the effect of abnormal events by optimizing the network structure of the YOLOv5 model. Then, starting from the sensor level, the detection method based on the Internet of Vehicles collects and shares vehicle status information in real time. This analysis process fully considers the task offloading and migration strategy in the edge computing environment. Quantitative experimental results show that the proposed algorithm can effectively improve the detection accuracy of traffic incidents and reduce the false alarm rate in real-time traffic data. © 2025 IEEE.

关键词： intelligent systems

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Agricultural Question Answering dataset Creation and its Evaluation through BERT based Experimental Analysis 3

Agricultural Question Answering Dataset Creation and its Eva...

引用

3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Sarma, Dubari Dasgupta, Meghna Deka, Vaskar Das, Dipankar Rashed, Golam Gauhati University Department of Information Technology India Rajshahi University Department of Information and Communication Engineering Bangladesh

ISBN: (纸本)9798331523893

In the field of Information Retrieval (IR). Question Answering (QA) is considered as a contemporary area for providing relevant answers in response to questions that are proposed in natural language. The QA system consist of three core components and these are question classification, information retrieval and answer extraction. The goal of QA is to develop automated systems that can comprehend the meaning of questions and provide either accurate or relevant answers. Machine learning algorithms plays a vital role in solving automated question answering (QA) problems using natural language processing. These algorithms are designed to enable computers to understand, infer, and respond to human language queries. This work considered deep learning based LLM-BERT for implementing a QA system over the agricultural domain. BERT is used because it comprehends the context of words by observing at both their left and right environment, making it highly operative for complex question and answers. Its ability to adapt to specific domains through fine-tuning ensures accurate and relevant responses in tasks like QA. The dataset used here has been collected, generated and constructed by the authors. © 2025 IEEE.

关键词： Question answering

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Predictive Maintenance Analysis using Multivariate Machine Performance data for Industrial Downtime Reduction 3

Predictive Maintenance Analysis using Multivariate Machine P...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Saddala, Rajeswara Reddy Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita school of computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita school of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Tool downtime prediction requires efficient monitoring because it helps maximize industrial operation quality while maintaining uninterrupted plant operations. The analysis utilizes machine learning algorithms with Decision Trees to identify machine downtime events through operational measurements which include hydraulic pressure and coolant temperature and spindle speed and vibration levels. data normalization alongside Forward Selection feature selection methods were used on a balanced dataset to boost model performance for prediction purposes. The Decision Tree model functioned to differentiate machine defects into two discrete categories—failure and no failure—thus proving its competence in such predictive classification activities. The study conducted a comparative evaluation between Decision Trees and Random Forest along with other algorithms which included SVM and Logistic Regression and Adaboost in addition to CatBoost and XGBoost and Naive Bayes and KNN using KD-Tree and Ball-Tree variations. The experimental findings show that Decision Trees provide clear predictions coupled with high precision and fair runtimes to support predictive maintenance implementations. The research delivers fundamental findings that help improve downtime prediction methods to boost operational effectiveness. © 2025 IEEE.

关键词： Predictive maintenance

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