检索结果-内蒙古大学图书馆

5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Rao, Gouravelli Akshith Prakash, L.N.C.K. Suryanarayana, G. Joshua, Pathi Varun Reddy, Katta Nithin Kumar Karnati, Ramesh Computer Science and Engineering Vardhaman College of Engineering Hyderabad India CVR College of Engineering Hyderabad India Information Technology Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9789819780426

Sentiment analysis is part of text mining, which means extracting data from comments on specific products and people’s opinions or sentiments. It involves considering opinions, analysis, and suggestions to customers about Amazon Alexa products by classifying a review as positive, negative, or neutral. This work is to explore different learning algorithms to solve sentiment analysis on Amazon products. The proposed work is a comparative analysis of existing learning algorithms that can help businesses in real-time decision-making by providing insights into customer preferences, needs, and choices which in turn results in the growth of the company and enhancement of customer satisfaction. The existing works provided the accuracy of classification to a level of 80%. Algorithms used in previous projects are SVM, Naïve Bayes, Random Forest, and other learning algorithms. In this work, logistic regression, Gradient boosting, Multinominal NB, and Gaussian NB are used along with previously implemented algorithms for sentiment analysis. Deep learning algorithms such as Long Short-Term Memory (LSTM) and ANN with dropout and without dropout layers are also implemented and compared with machine learning algorithms. Word2Vec is employed with LSTM to increase the accuracy of the representation. Based on the evaluation of their metrics, accuracies are compared. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Comprehensive analysis of multiple machine learning techniques for rock slope failure prediction

引用

Journal of Rock Mechanics and Geotechnical engineering 2024年第11期16卷 4386-4398页

作者： Arsalan Mahmoodzadeh Abed Alanazi Adil Hussein Mohammed Hawkar Hashim Ibrahim Abdullah Alqahtani Shtwai Alsubai Ahmed Babeker Elhag Department of Civil Engineering University of HalabjaHalabjaKurdistan Region46018Iraq Department of Computer Science College of Computer Engineering and Sciences in Al-KharjPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Department of Communication and Computer Engineering Faculty of EngineeringCihan University-ErbilKurdistan RegionIraq Department of Civil Engineering College of EngineeringSalahaddin University-Erbil44002ErbilKurdistan RegionIraq Software Engineering Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Department of Civil Engineering College of EngineeringKing Khalid UniversityAbha61413Saudi Arabia

In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit mines around Iran,evenly distributed between the training(80%)and testing(20%)*** models are evaluated for accuracy using Janbu's limit equilibrium method(LEM)and commercial tool GeoStudio *** assessment metrics show that the random forest model is the most accurate in estimating the SFRS(MSE=0.0182,R2=0.8319)and shows high agreement with the results from the LEM *** results from the long-short-term memory(LSTM)model are the least accurate(MSE=0.037,R2=0.6618)of all the models ***,only the null space support vector regression(NuSVR)model performs accurately compared to the practice mode by altering the value of one parameter while maintaining the other parameters *** is suggested that this model would be the best one to use to calculate the SFRS.A graphical user interface for the proposed models is developed to further assist in the calculation of the SFRS for engineering *** this study,we attempt to bridge the gap between modern slope stability evaluation techniques and more conventional analysis methods.

关键词： Rock slope stability Open-pit mines Machine learning(ML) Limit equilibrium method(LEM)

来源：评论

学校读者我要写书评

暂无评论

Exploring the potential of cloud computing in revolutionizing healthcare 4th

Exploring the potential of cloud computing in revolutionizin...

引用

4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Saini, Renu Paras Tayal, Parag Thakur, Pallavi Computer Science and Engineering Department Chandigarh Engineering College Mohali Punjab Landran India

ISBN: (纸本)9781032911571

Cloud computing, which moves assets from desktop setting to cloud infra-structure—where apps are kept in a "cloud" with massive processing power and nearly infinite database storage—has fundamentally altered traditional computing over the past few years. Modern healthcare can benefit greatly from telemedicine when it is combined with cloud computing. Strategic thinking is necessary before moving healthcare assets and services to the cloud in order to weigh the advantages and disadvantages of the fresh approach, determine whether it may be implemented, and analyze its capabilities. The study illustrated how various technologies could be applied to big data analysis in healthcare using a cloud computing. In this research, we investigate the way cloud computing has changed the way in which healthcare organizations keep their data. © 2025 the Author(s).

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Smart Agriculture: A Crop Monitoring System to Empower Agricultural Field Using IOT

Smart Agriculture: A Crop Monitoring System to Empower Agric...

引用

2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Madhu, Surya Sujith Kumar, P.S. Supriya, L.P. Sree Buddha College of Engineering Dept. of Computer Science and Engineering Kerala India

ISBN: (纸本)9798331507541

Agriculture has long been the cornerstone of India's economy and way of life, contributing significantly to the nation's GDP and employment. With a diverse range of crops and climatic conditions, India's agricultural sector is both vast and complex, demanding innovative approaches to meet the growing needs of its population. The sector faces numerous challenges, including climate variability, soil degradation, water scarcity, and pest infestations, which impact productivity and sustainability. In response to these challenges, modern agricultural system in India increasingly focuses on integrating technology to enhance efficacy and eco-friendly. One such initiative is the development of smart agricultural systems that leverage advanced technologies. This paper presents an advanced Internet of Things (IoT) based crop monitoring system designed to enhance agricultural efficacy and conciliatory. The system integrates multiple sensors to monitor critical environmental parameters, including pH levels, soil properties, and temperature. By continuously measuring these variables, the system provides real-time data to optimize crop health and growth conditions. Additionally, the paper incorporates pest control mechanisms, leveraging IoT technology to detect and manage pest populations effectively. This comprehensive approach not only ensures the optimal condition for crops but also minimizes the need for manual intervention and reduces the reliance on chemical treatments. The data collected through this system is analysed and visualized through a user-friendly interface, offering farmers actionable insights and enabling timely decision-making. The integration of these technologies aims to promote sustainable agricultural practices, improve crop yield, and reduce environmental impact, demonstrating the potential of IoT in modernizing agricultural operations. © 2025 IEEE.

关键词： Insect control

来源：评论

学校读者我要写书评

暂无评论

A-DSCNet: Implementing Attention-based Deep Serial Cascaded Network for Predicting Insider Threats using Human Behaviors 3

A-DSCNet: Implementing Attention-based Deep Serial Cascaded ...

引用

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

作者： Ilampiray, P. Ramya, S. Thanga R.M.K. Engineering College Computer Science and Engineering Thiruvallur India

ISBN: (纸本)9798331509675

Insider threats are considered a major issue in the field of system engineering. For modern applications, which demand a seamless integration among humans and machines, human interactions along with the applications are assumed as a significant phase of operation. It generates security issues within the network while safeguarding the network from insider threats. Due to the development of technologies, vulnerabilities within the networks and third-party access from the external environment remain the primary keys for security issues. Among other security threats, insider attacks are similarly challenging as exterior threats. However, insider attacks are unrecognized for a long period, and currently no effective measures to address the security issues as well as to safeguard the network from insider threats. The primary issue faced by the current technology is to detect insider attacks within the cloud. If the information in the system gets lost, then negotiating with the cloud consumers becomes hard. Moreover, cloud networks are less trusted as they lack to guarantee of security and privacy to the data. Currently, various solutions are presented to ensure external privacy for cloud systems. Yet, the challenges due to internal or insider threats must be tackled. Thus, it is crucial to address various problems produced by the traditional insider threat detection model including human actions. So, a novel deep learning-based insider threat prediction model using human behavior is suggested here. Initially, insider thread prediction data utilized for the validation are collected from benchmark resources. Next, the collected data is provided for the insider threats prediction phase. In the insider threats prediction phase, a developed Attention-based Deep Serial Cascaded Network (A-DSCNet) is used to identify the insider threats. The developed A-DSCNet is the integrated version of Deep Belief Network (DBN) and Capsule Network (CapsNet). Finally, various analyses are exec

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Supervised Learning-Driven Emotion Recognition through Facial Landmark Trajectories Enhanced by SMOTE Methodology

Supervised Learning-Driven Emotion Recognition through Facia...

引用

2025 AI-Driven Smart Healthcare for Society 5.0, AdSoc5.0 2025

作者： Kar, Ishita Chatterjee, Mehuli Manna, Sukanya Das, Akash Dutta, Stobak Department of Computer Science and Engineering St Thomas’ College of Engineering and Technology Kolkata India

ISBN: (纸本)9798331536336

The study highlights the importance of facial landmark trajectories in understanding mental health by investigating the application of supervised learning systems to identify emotions. Diagnoses and treatment plans are impacted by the difficulty in recognizing and controlling emotions, which are intrinsically tied to mental health. Although physiological signals like ECG and EEG are accurate, they have drawbacks including expense and user discomfort. Because they are noninvasive, facial landmarks present a viable substitute for practical uses. Using information from the ASCERTAIN dataset, this study divided emotions into four valence-arousal plane quadrants: High Arousal High Valence (HAHV), High Arousal Low Valence (HALV), Low Arousal High Valence (LAHV), and Low Arousal Low Valence (LALV). Algorithms including Random Forest, K-Nearest Neighbors (KNN), Decision Tree, and Support Vector Machine (SVM) were used to solve a 4-class classification problem. A common problem, class imbalance, was lessened by employing the Synthetic Minority Oversampling Technique (SMOTE). The study showed that SMOTE greatly enhanced predictions for minority classes, particularly for LALV, whereas classifiers strongly preferred the majority class in the absence of balance. Despite possible overfitting, Decision Tree has the greatest true positive rate among classifiers for LALV after SMOTE application. The findings demonstrate developments in emotional computing and provide information for applications in mental health support and human-computer interaction. © 2025 IEEE.

关键词： k-nearest neighbors

来源：评论

学校读者我要写书评

暂无评论

EDUVERSE: Inclusive and Immersive Metaverse Learning Environment for Education

EDUVERSE: Inclusive and Immersive Metaverse Learning Environ...

引用

2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Madhubala, R. Gomathi, V. Jothikumar, Karthikeyan National Engineering College Department of Computer Science and Engineering Tamil Nadu Kovilpatti India

ISBN: (纸本)9798331529833

Eduverse is an advanced Virtual Reality (VR) platform developed to transform traditional education by creating interactive and immersive virtual learning environments. Designed to support both students and teachers, Eduverse simulates real-world classrooms, discussion rooms, and conference halls, enabling real-time communication and collaboration. One of its standout features is the accessibility tools, such as text-to-speech (TTS) and speech-to-text (STT) systems, which make the platform highly beneficial for students with disabilities. The TTS system helps students with speech impairments by converting their typed messages into spoken words, while the STT system transcribes spoken communication into text for students who have difficulty *** addition to accessibility, Eduverse offers customizable avatars, allowing students to represent themselves digitally, which helps reduce social anxiety and encourages participation. The platform also provides teachers with speech management tools, enabling them to moderate discussions, assign speaking privileges, and manage class activities. This ensures that every student has the opportunity to contribute, even in larger classes. Eduverse has been tested within a set of students, showing significant improvements in student engagement, participation, and inclusivity compared to traditional online learning platforms. Students with disabilities reported that the integrated accessibility tools allowed them to engage more fully in class discussions, making Eduverse a valuable solution for inclusive education. By combining a collaborative and supportive learning space, Eduverse demonstrates the potential to improve the way students learn and interact in the virtual world. © 2025 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Implementing Machine Learning Techniques for Hair Health Prediction in Follicle Futures 2

Implementing Machine Learning Techniques for Hair Health Pre...

引用

2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Duraisamy, Premkumar Vidhya, V. Preetha, T. Niranjani, V. Kpr Institute Of Engineering And Technology Department Of Computer Science And Engineering Coimbatore India Sri Eshwar College Of Engineering Department Of Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350381665

This study explores the effectiveness of various machine learning algorithms in forecasting hair health using a comprehensive dataset incorporating individual traits and lifestyle elements. Logistic regression, random forest, and AdaBoost were assessed, with random forest notably achieving a remarkable 94.6% accuracy. These results emphasize machine learning's potential for precise, non-invasive personalized hair health predictions. Key factors such as genetics, diet, and stress were pinpointed, informing targeted care approaches. The research underscores machine learning's promise in accurately anticipating hair loss and suggests future avenues for expanding datasets, integrating diverse data sources, and developing user-friendly tools for improved hair care management. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Empowering patients: Evaluating user experience with the MED-LIST platform 4th

Empowering patients: Evaluating user experience with the MED...

引用

4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Agarwal, Paras Choudhary, Vishal Agarwal, Swati Tyagi, Shivika Chaurasiya, Nitish Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9781032911571

In today’s fast-paced world, people have hectic lives. Almost everyone faces the challenge of taking their medicines on time, enduring lengthy waits for lab reports, people face challenges in managing their medical records, misplacing their medical prescriptions, navigating cumbersome appointment booking systems in the clinics, etc. This paper introduces a mobile and a web application designed to provide a complete healthcare system and resolve all the mentioned problems and many more. It will help people to connect to their respective healthcare provider and provide a notification system for medication reminders, streamlined appointment booking with integrated payment functionality, instant lab reports on the phone, disease prediction by symptoms, medicines history and reports history, an intelligent queue management system that predicts appointment times bases on a regression ML model with 85% accuracy, etc. © 2025 the Author(s).

关键词： Reservation systems

来源：评论

学校读者我要写书评

暂无评论

Mental Health Well-Being Assessment and Surveillance Mobile App 3

Mental Health Well-Being Assessment and Surveillance Mobile ...

引用

3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Deshmukh, Revati Kanojiya, Roshan Jivtode, Prajwal Deshmukh, Prithviraj Khan, Atiya G H Raisoni College of Engineering Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798331542375

The proposed software, for monitoring children's health and well-being is a platform that aims to evaluate and support their emotional state using various features and tools. Firstly our software will collect the data from users through assessment and private journals. Our software includes games and activities that captivate children while discreetly gathering data about their well-being. These games serve as an assessment tool. After the assessment there will be Analysis of the data collected with the help of games by utilizing machine learning techniques like sentiment analysis and tracking response times the software examines the collected data from the games to identify signs of depression in children. It provides a score or range indicating their health condition. According to the range there will be tailored recommendations and solutions based on the analysis of the data then our software offers solutions to enhance children's well-being. These suggestions may involve self-help exercises, relaxation techniques or other helpful resources. According to the seriousness the therapist helps the child for their well-being. Accordingly the therapist can see the progress of the mental health of the child through Dashboard. Our software includes separate login portals for parents and therapists. Parents can monitor their child's progress, view assessment results, and access resources to support their child's mental health. Therapists can access relevant data, communicate with their young clients, and provide professional guidance. Our software also includes a journal feature where children can document their thoughts and feelings, which they may not be comfortable sharing with parents or friends. This journal can be a valuable tool for both assessment and emotional expression. © 2025 IEEE.

关键词： mHealth

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：