检索结果-内蒙古大学图书馆

19th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2024

作者： Bagane, Pooja Amenu, Obsa Das, Lahitanshu Potdukhe, Omkar Gandhi, Manraj Singh Kothari, Sonali Symbiosis Institute of Technology Pune India Jimma Institute of Technology Faculty of Computing and Informatics Jimma Ethiopia

ISBN: (纸本)9798331504502

Crop forecasting constitutes an integral part of the modern farming practice, affecting the decisions of farmers and agricultural companies. This machine learning model is intended to produce more accurate yield forecast with multiple datasets including weather temperature, pesticides, location and other as well. The methodology that systematizes data acquirement, preprocessing, model selection, training and implementing is used by the project to overcome many obstacles in crop production. Accurate performance forecasting in this technology is therefore not only a matter of innovation, but of food security, economy and environment as well. The model training & testing is done with mean absolute error to determine its accuracy & generalization. After training the model is launched into the production environment or plenty into the existing agricultural platforms for real-time yield forecasting. This project will constantly be striving for improvement and filter knowledge down to the stakeholders, optimizes resources allocation and brings about change entrenching food security and sustainable development. © 2024 IEEE.

关键词： Crop forecasting Data preprocessing Food security Machine learning model Yield forecast

来源：评论

学校读者我要写书评

暂无评论

Leveraging Large Language Models for Smart Pharmacy Systems: Enhancing Drug Safety and Operational Efficiency 19

Leveraging Large Language Models for Smart Pharmacy Systems:...

引用

19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Osheba, Abdelrahman Abou-El-Ela, Ahmed Adel, Osama Maaod, Youssef Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. The system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. This transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. The proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications. © 2025 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Puppetry in Tangible Narratives: Interactive and Collaborative Storytelling in The Non-myth of the Noble Red 24

Puppetry in Tangible Narratives: Interactive and Collaborati...

引用

18th International Conference on Tangible, Embedded, and Embodied Interaction, TEI 2024

作者： Echeverri, Daniel Atelier of Graphic Design and Multimedia Department of Visual Computing Faculty of Informatics Masaryk University Czech Republic

ISBN: (纸本)9798400704024

The Non-myth of the Noble Red is a tangible narrative that combines cardboard puppets with digital storytelling in an integrated physical storyworld. This narrative employs networked microcontrollers within the puppets, RFID readers, and sensors to enable interactivity. The narrative unfolds within three physical, interactive environments. Each environment can track the puppets' positions and trigger audio fragments of the story. Performers use these puppets to navigate the narrative, interact with objects, and engage in a battle. This project explores the potential of puppets as virtual and physical avatars, emphasising interactive and collaborative storytelling. It seeks to look at the impact of these puppets and the potential emerging social dynamics during the narrative experience. The work presented here contributes to tangible narratives by emphasising an integrated physical storyworld (instead of a mapped one) and utilising puppets as storytelling artefacts that serve as both input devices and virtual/physical avatars, promoting immersive and collaborative storytelling. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Interactive Storytelling Puppetry Tangible Interaction Tangible Narrative

来源：评论

学校读者我要写书评

暂无评论

The Performance of Classification Method in Telco Customer Trouble Ticket Dataset

IAENG International Journal of Computer Science

引用

IAENG International Journal of Computer Science 2022年第2期49卷 325页

作者： Yayah, Fauzy Che Ghauth, Khairil Imran Ting, Choo-Yee Multimedia University Cyberjaya Malaysia Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

A customer trouble ticketing system (CTT) is an organization’s tool to track the detection, reporting, and resolution of tickets submitted by customers. It also comprises a summary of the issue reported, the status of the ticket, the incident information, and the approach that was previously utilized to resolve the problems. The technician’s skill set and experience rely solely on completing the task without the right direction on which area to focus on first. As a result of this manual classification of a trouble ticket, it will be necessary to build methodologies for predicting future resolution codes. The research for this report is mainly focused on one of the telco companies in Malaysia. This study result assists the telco engineer, and the specialists resolve each issue in a very short amount of time. Additionally, the classification of the trouble ticket resolution code method used in this study will indicate the characteristics of each issue that is being investigated. The relationship between events is feasible to discover by exploring the root cause. It is critical to establish a link between recent events and events in the previous. Because of current data mining limitations, the study needs to be more comprehensive. Data processing methods are being implemented within big data platforms to overcome the limitation of data scalability, enhance classification accuracy, and increase computation speed. The research work will continue to progress in the direction of big data centricity. Some of the most effective approaches for big data integration and machine learning will be discussed in this paper. Throughout the experiment, any problems will be explained, as well as the solutions to each situation. A wide range of research subjects will be discussed, including construction classification models for trouble tickets. To achieve reasonable accuracy, a few customized transformations are required. The data set’s custom parameter optimization process will furthe

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Intelligent and Secure Evolved Framework for Vaccine Supply Chain Management Using Machine Learning and Blockchain

引用

SN Computer Science 2025年第2期6卷 1-28页

作者： Abdel-salam, Mahmoud Elhoseny, Mohamed El-hasnony, Ibrahim M. Faculty of Computers and Information Science Mansoura University Mansoura Egypt College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

Supply chain management (SCM) ensures that fragile goods are delivered from their origin to their destination without suffering any damage. The COVID-19 pandemic’s widespread has exposed systemic weaknesses in SCM across a variety of sectors, particularly healthcare. This paper aims to tackle issues related to vaccine expiration and fraudulent vaccine records by facilitating vaccine traceability and innovative contract features. The proposed model suggests utilizing an intelligent and secure framework that merges machine learning intelligent techniques with blockchain technology. The proposed model utilizes the Ethereum blockchain’s characteristics to establish novel smart contracts between diverse participants in the supply chain. In addition, the proposed model presents two modules to assist in predicting the demand for vaccines and analyzing the sentiment of vaccine reviews to improve the quality of vaccines. Therefore, an Improved Honey Badger Algorithm (IHBA) is presented to enhance the performance of the Long Short-Term Memory (LSTM) algorithm in predicting vaccine demand besides the credibility evaluation of reviews. For the vaccine demand prediction module, a real dataset obtained from the centers for Disease Control and Prevention (CDC) is collected and the proposed model is applied obtaining the minimum RMSE of 1.3828 for the prediction results compared with other approaches. For the review analysis module, a publicly available dataset from the University of California, Irvine (UCI) is used, and the proposed model achieved a high accuracy of user reviews of 90.6% compared with other related approaches. The proposed model is compared with other related metaheuristic algorithms and the standard deep learning optimizers. The proposed new algorithm outperforms other competitors for tuning the hyperparameters of deep learning models. Finally, the proposed system presents implications for sustainable supply chain management, enterprise operations, and public pol

关键词： Blockchain HBA Intelligent LSTM Intelligent machine learning Intelligent model Machine learning Supply chain management Sustainability Traceability

来源：评论

学校读者我要写书评

暂无评论

Exploring Non-Functional Requirements in Requirements Engineering: A Systematic Review

Exploring Non-Functional Requirements in Requirements Engine...

引用

2024 IEEE International Conference on computing, ICOCO 2024

作者： Ramasamy, Rosaan Lim, Tek Yong Antdragon Sdn Bhd Negeri Sembilan Malaysia Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (纸本)9798331530303

Non-functional requirements (NFRs) are often poorly documented and inadequately integrated into the software development process resulting in discrepancies between expected and actual system quality. This gap leads to a number of issues like requirement ambiguity, ineffective stakeholder communication and inadequate validation which severely impact project outcomes. This systematic review addresses these challenges by investigating the state of NFRs management in requirements engineering. The review identifies predominant techniques such as automated tools and document analysis for eliciting NFRs while also highlighting current challenges and effective validation practices, including inspections, reviews and user feedback. Future research should develop comprehensive frameworks combining technology with human-centered approaches to address these challenges. ©2024 IEEE.

关键词： I. INTRODUCTION non-functional requirement requirements engineering systematic review

来源：评论

学校读者我要写书评

暂无评论

Web Service Prediction and Composition Based on Harris Hawks Optimization and Deep Learning

引用

SN Computer Science 2025年第5期6卷 1-25页

作者： Elhoseny, Mohamed Abdel-salam, Mahmoud El-hasnony, Ibrahim M. College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Faculty of Computers and Information Science Mansoura University Mansoura Egypt

QoS-aware service composition suffers from many problems due to the uncertainties of its working environment. Besides, the many candidate services for service composition consume more processing and execution time. This paper presents a four-phases framework for reliable dynamic Web service composition. The preprocessing phase helps to reduce the percentage of missing values of the dataset. Then, clustering phase applies self-organizing maps (SOM) algorithm to cluster the QoS data to reduce data sparsity and cold-start problems of web services. Then, the prediction phase predicts uncertain QoS values of services using an optimized LSTM deep learning model. In the composition module, an improved hybrid optimization algorithm IHHO-SWO is the proposed integrating the Spider Wasp Optimizer (SWO) and is applied to generate a composite service that fits the end user requirements. An exhaustive experimental and statistical study validates the superior performance of the suggested framework. The study reveals that the framework is able to successfully deal with service prediction and composition challenges such as outlier handling, cold start, and data sparsity, lowering these problems considerably. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Classification Data analysis Harris Hawk Optimization Prediction model Service composition Spider Wasp Optimization

来源：评论

学校读者我要写书评

暂无评论

Leveraging Internet of Things for Upstream and Downstream Real-Time Monitoring in Flood-Prone Areas: A Case Study in Brunei Darussalam 6

Leveraging Internet of Things for Upstream and Downstream Re...

引用

6th IEEE Symposium on Computers and informatics, ISCI 2024

作者： Ismail, Rasyidah Suhaili, Wida Patchmuthu, Ravi Shams, Shahriar Universiti Teknologi Brunei School of Computing and Informatics Brunei Universiti Teknologi Brunei Faculty of Engineering Brunei

ISBN: (纸本)9798350353853

This study focuses and addresses the critical need for effective flood early detection and water quality assessment, particularly in regions prone to flooding in Brunei Darussalam. This is due to the alteration in weather patterns, commonly known as climate change. The research emphasizes the comprehensive understanding of the significance of pre-deployment characterization studies, which delve into various aspects such as hydrological patterns, water flow dynamics, sediment transport, and ecological conditions. This enables the development and optimal deployment of robust sensor networks and operation of Internet of Things (IoT) sensors capable of withstanding the environmental stressors by understanding the specific challenges posed by the river environment, such as variations in water levels, weather patterns and sediment concentrations. The implementation of IoT sensors in the river allows the collection and monitoring of real-time data on water quality parameters, water level, water flow dynamics and weather conditions. The use of mobile networks aims to transmit data through the gateway to display the data on a dashboard for visualization and generate an alert triggering an alarm for prompt evacuation and mitigation efforts. The integration of solar-powered IoT sensors with mobile networks offers a viable and sustainable approach for early flood detection and anomalies in water quality in river ecosystems leading to the improvement for community resilience and minimization of negative impacts floods have to infrastructure for efficient disaster management strategies around flood-prone regions in Brunei Darussalam. © 2024 IEEE.

关键词： Rivers

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation Study of Color Space Selection In Video Based Facial Expression Recognition Using Deep Neural Networks For Sentiment Analysis 13

Performance Evaluation Study of Color Space Selection In Vid...

引用

13th International Conference on Computer and Knowledge Engineering, ICCKE 2023

作者： Qin, Phee Wei Moung, Ervin Gubin Farzamnia, Ali Yahya, Farashazillah Khoo, John Julius Danker Sufian, Maisarah Mohd University Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia

ISBN: (纸本)9798350330151

Automated facial expression recognition is popular for diagnosing mental diseases and interpreting human emotions. Image sentiment analysis helps incorporate feedback for improved product development. However, there's a lack of research on the effect of different color spaces on facial expression recognition performance. Thus, there are three objectives in this project, which are to develop a deep learningbased facial expression recognition system for image sentiment analysis. Second, to investigate the effect of different color spaces on facial expression recognition and select the best one. Lastly, to evaluate the accuracy of the system on the AffectNet dataset. The color spaces to be tested are RGB, YCBCR, HSV, XYZ, and YIQ. The methodology involves selecting the AffectNet dataset, pre-processing the images, building a Convolutional Neural Network model, and evaluating performance using metrics like accuracy, precision, recall, and F1-score. © 2023 IEEE.

关键词： Color

来源：评论

学校读者我要写书评

暂无评论

Leth-Gait: A Fusion Formula for Gait-Based Gender Classification Using Depth Images

Leth-Gait: A Fusion Formula for Gait-Based Gender Classifica...

引用

2022 International Conference on Computer Technologies, ICCTech 2022

作者： Bin Mohd Sabilan, Muhammad Shazmil Aziz, Azim Zaliha Abd Mohamad, Fatma Susilawati Mansor, Nur Farraliza Hamid, Nazirah Abd Faculty of Informatics & Computing Terengganu Malaysia

ISBN: (纸本)9781665499187

Gender can be easily identified by humans by looking at a person's appearance. As the human brain is able to identify and classify gender, this study will describe a method for classifying gender using depth images. OU-ISIR Gait Database Large Population Dataset was used to perform this study. This classification process was performed by machines such as computers and software. The proposed method used Gait Energy Image (GEI) to normalize the depth image. After that, they were projected into their PCA plane, gaining a very strong cyclical picture of a change of perspective. Then the feature was extracted by measuring some body parts from the reconstructed image. Fine Gaussian Support Vector Machine (Fine Gaussian SVM), Decision Tree, and Weight K-Nearest Neighbor Classifier (KNN) was used separately as the classification method. The experimental results showed that the proposed method produced significant results, obtaining 96% accuracy using Fine Gaussian SVM, 98% accuracy using Weight KNN, and 78% accuracy using Fine Decision Tree classifiers. © 2022 IEEE.

关键词： Support vector machines

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：