检索结果-内蒙古大学图书馆

2023 Applied Mathematics, Computational Science and Mechanics: Current Problems, AMCSM 2023

作者： Borisov, Dmitriy Chistyakov, Alexander Don State Technical University Department of Computer Engineering and Automated Systems Software Rostov-on-Don Russia

ISBN: (纸本)9798350348576

The paper presents an analysis of the problem of time series forecasting. The two most common forecasting methods, ARIMA and LSTM, are considered. Since these models independently predict linear and, accordingly, nonlinear dependencies in the best way, a hybrid model is considered using both initial methods in order to combine their strengths, improved by a genetic algorithm for automated model parameterization. A software implementation is built and advantages are described in relation to the original methods based on datasets with different trend, seasonal, non-linear components. In result, an integrative model was compared with ARIMA and LSTM models on 3 datasets and got better results by 6.33% and 8.36% respectively. © 2023 IEEE.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Predictive Classification Model of Crop Yield Data Using Artificial Neural Network 5

Predictive Classification Model of Crop Yield Data Using Art...

引用

5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Kanagaraj, R. Elakiya, E. Kumaresan, A. Selvakumar, J. Suganya, J. Shruthi, K. School of Computer Science and Engineering Vellore Institute of Technology Vellore India Vellore Institute of Technology Department of Computer Science and Engineering Chennai India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Mailam Engineering College Mailam India Bosch Global Software Technologies Coimbatore India

ISBN: (纸本)9798350321425

Agriculture is under tremendous pressure to produce more productivity with fewer resources as a result of the expanding global population. Decision-making along the entire supply chain needs to be improved considering competing economic, environmental, and societal demands. Data from satellites, drones, sensors, and other sources must be processed in order to make informed judgments. However, sophisticated machine learning and data analytics methods must be used due to the complexity and volume of the data. This initiative covers crucial duties in yield prediction and precision agriculture. Management zones are areas of a field that have high inter-regional variability and low intra-regional variability, dividing the field into uniform zones for which agronomic practices should be modified independently. For fertilizer optimization and post-harvest logistics, yield prediction is crucial. In order to make up for the nutrients that have been taken out of the soil, fertilizer type and quantity must be adjusted accordingly. The yield data is also essential for enhancing harvesting, shipping, storage, and sales. So, utilizing web scraping and convolution neural networks, the proposed model has been forecast yields from input satellite photos. The result of investigations is illustrated that the artificial neural network algorithm is used to predicting the agricultural data rules and powerfully decrease the error rate from crop yield data. © 2023 IEEE.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

SGD: Smart Gas Leakage Detection System for Home Safety 14

SGD: Smart Gas Leakage Detection System for Home Safety

引用

14th International Conference on Advanced computer Information Technologies, ACIT 2024

作者： Saleh, Eiman M. Imbarake, Tariq Elakeili, Salwa Faraj, Abdul Ghafar Benghazi University Department of Software Engineering Benghazi Libya Benghazi University Department of Computer Science Benghazi Libya Benghazi University Department of Statistics Benghazi Libya

ISBN: (纸本)9798350350036

Nowadays, gas leak detection equipment in the workplace, schools, and homes plays a vital role in protecting public safety. Liquified Petroleum Gas (LPG) leaks are one of the most serious issues in the kitchen because they raise the possibility of an explosion. LPG is the main cooking fuel in Libya. Multiple incidences of household fires, injuries, and deaths have been recorded in Libya as a result of LPG leakage and explosions, as the country's citizens do not have gas leak-detecting systems. This paper aims to present the design and implementation of a smart gas leak detection system based on Arduino. In this system, an Arduino Uno microcontroller with an MQ6 sensor was used to detect gas leakage, an air fan motor to reduce gas concentration by ventilating the air, and GSM as a wireless communication method to notify users via SMS. The proposed system identifies three gas leakage risk levels, each Including its unique scenario. Level one is low-risk, the user is notified Unhealthy status of the dwelling due to gas leakage. level two is medium-risk, the user's friends are informed about the unsafety status due to gas leakage in the user's home. Level three is high-risk;the nearest firefighting organizations are informed of the hazard status. The experimental findings demonstrate that the system is capable of efficiently detecting gas and protecting individuals from a range of undesired situations, as well as being useful in reducing resource and human life losses. © 2024 IEEE.

关键词： Liquefied petroleum gas

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Violence Detection in Surveillance: The Role of Transfer Learning and Pre-Trained Models 24

Deep Learning for Violence Detection in Surveillance: The Ro...

引用

24th International Arab Conference on Information Technology, ACIT 2023

作者： Ilyas, Anum Obaid, Surayya Bawany, Narmeen Zakaria Center for Computing Research Jinah University for Women Department of Computer Science and Software Engineering Karachi Pakistan

ISBN: (纸本)9798350384307

In recent years, the use of CCTV footage for proactive crime prevention has surged, particularly in public places like airports, train stations, and malls. However, the efficacy of these surveillance systems becomes questionable for their substantial reliance on human resources which may lead to erroneous or delayed responses. This research takes a comprehensive approach for violence detection which was previously limited to binary classification, by categorizing violence into four distinct classes: abuse, arson, assault, and fight. It employs transfer learning combined with computer vision techniques for violence detection and classification in video footages. The study compares the performance of four pre-trained neural networks, namely DenseNet121, VGG16, MobileNet, and Xception. The dataset created for this research is compiled from three datasets available on Kaggle. The results reveal that the Xception model performed comparatively better achieving the highest AUC score of 98.39%, while the VGG16 model attained the lowest AUC score of 96%. In addition to the AUC score, precision, recall, and fl-score are employed as performance metrics. Transfer learning with convolutional neural networks (CNN) significantly reduced computational requirements and time. Automating the detection and categorization of violent behavior through the employed approach has the potential to reduce the risk of fatalities and injuries in public areas. It also improves the speed and accuracy of threat detection, enabling swift preventive actions by authorities. © 2023 IEEE.

关键词： computer Vision Deep Learning Pre-trained Models Transfer Learning Violence Categorization

来源：评论

学校读者我要写书评

暂无评论

Blockchain Assisted Intrusion Detection System Using Differential Flower Pollination Model

引用

computers, Materials & Continua 2022年第12期73卷 4695-4711页

作者： Mohammed Altaf Ahmed Sara A Althubiti Dronamraju Nageswara Rao ELaxmi Lydia Woong Cho Gyanendra Prasad Joshi Sung Won Kim Department of Computer Engineering College of Computer Engineering&SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science and Engineering Chitkara University Institute of Engineering and TechnologyChitkara UniversityPunjab140401India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

Cyberattacks are developing gradually sophisticated,requiring effective intrusion detection systems(IDSs)for monitoring computer resources and creating reports on anomalous or suspicious *** the popularity of Internet of Things(IoT)technology,the security of IoT networks is developing a vital *** of the huge number and varied kinds of IoT devices,it can be challenging task for protecting the IoT framework utilizing a typical *** typical IDSs have their restrictions once executed to IoT networks because of resource constraints and ***,this paper presents a new Blockchain Assisted Intrusion Detection System using Differential Flower Pollination with Deep Learning(BAIDS-DFPDL)model in IoT *** presented BAIDS-DFPDLmodelmainly focuses on the identification and classification of intrusions in the IoT *** accomplish this,the presented BAIDS-DFPDL model follows blockchain(BC)technology for effective and secure data transmission among the ***,the presented BAIDSDFPDLmodel designs Differential Flower Pollination based feature selection(DFPFS)technique to elect ***,sailfish optimization(SFO)with Restricted Boltzmann Machine(RBM)model is applied for effectual recognition of *** simulation results on benchmark dataset exhibit the enhanced performance of the BAIDS-DFPDL model over other models on the recognition of intrusions.

关键词： Internet of things feature selection intrusion detection blockchain security deep learning

来源：评论

学校读者我要写书评

暂无评论

A Blockchain based Framework for Verifying Drug Authenticity 24

A Blockchain based Framework for Verifying Drug Authenticity

引用

24th International Arab Conference on Information Technology, ACIT 2023

作者： Hamid, Soomaiya Bawany, Narmeen Zakaria Fatima, Kiran Jinnah University for Women Center for Computing Research Department of Computer Science and Software Engineering Karachi Pakistan

ISBN: (纸本)9798350384307

Counterfeit drugs are fake medicines that are potentially harmful for health. Safeguarding the integrity of pharmaceutical distribution plays a crucial role in preventing the circulation of counterfeit drugs. Traditional drug supply chain systems have no effective mechanism to ensure the traceability and integrity of the drug supply chain process. Various ICT-based solutions have been adopted previously and numerous regulations were passed by drug regulatory authorities to ensure interoperable and traceable drug distribution process. However, these solutions could not guarantee a tamper-proof history of supply chain process. Blockchain technology has gained traction in many diverse fields including real-estate, identity management, digital voting, healthcare, etc. Therefore, we have proposed a decentralized framework to insure the authenticity and traceability of the drugs. Moreover, this study presents an overview of how blockchain technology can be employed to counter the fake medicines circulation by embedding the traceability in drug supply chain process. Comparison of various blockchain-based solutions and open research challenges in this regard are also presented. © 2023 IEEE.

关键词： Blockchain Technology Counterfeit Drugs decentralized framework Drug Supply Chain Traceability of Drugs Traditional Pharmaceutical Supply Chain

来源：评论

学校读者我要写书评

暂无评论

A COMPARATIVE STUDY OF DEEP LEARNING MODELS FOR EARLY DIAGNOSIS OF MONKEYPOX AND CHICKENPOX

A COMPARATIVE STUDY OF DEEP LEARNING MODELS FOR EARLY DIAGNO...

引用

2025 International Conference on Energy, Power, Environment, Control and Computing, ICEPECC 2025

作者： Khalid, Maliha Azam, Muhammad Asif, Zia Muhammad Department of Computer Science University of Agriculture Faisalabad Pakistan Department of Computer and Software Engineering Information Technology University (ITU) Lahore Pakistan

ISBN: (纸本)9781837243235

Image processing enhances digital images by extracting valuable information and improving clarity through edge recognition, color modification, and filtering techniques. These methods facilitate the creation of training images that enhance the robustness and generalization of neural networks, improving diagnostic efficiency and accuracy. The similar symptoms of chickenpox and monkeypox make early clinical diagnosis difficult, necessitating rapid and precise diagnostic techniques. This study uses deep learning models to tackle the problem of correctly diagnosing chickenpox and monkeypox, facilitating early diagnosis and quick medical intervention for improving public health and epidemic management. Models such as DenseNetl21, DenseNetl69, DenseNet201, InceptionResNetV2, InceptionV3, MobileNet, MobileNetV2, ResNet50, ResNetlOl, VGG16, VGG19, and Xception have been used to classify and distinguish conditions. Advanced image processing techniques, including object detection and data normalization, improve model generalization and resilience. Among the others, MobileNet and DenseNetl21 achieved highest accuracy rates which is 0.9943 and 0.9914 respectively, while MobileNet offered superior speed. The DenseNetl69 model has highest precision and recall which is 0.9958 and fl-score is 0.9956 with an accuracy of 0.9857. The study also demonstrates that DL models are critical in developing robust and effective systems for detecting infectious diseases, aiding in precise and efficient healthcare solutions. © The Institution of engineering & Technology 2025.

关键词： CLASSIFICATION DEEP-LEARNING IMAGE PROCESSING MONKEYPOX DATASET TRANSFER LEARNING

来源：评论

学校读者我要写书评

暂无评论

MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

引用

computers, Materials & Continua 2023年第6期75卷 5573-5595页

作者： Zhanyang Xu Jianchun Cheng Luofei Cheng Xiaolong Xu Muhammad Bilal School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory Novel Software Technology Nanjing UniversityNanjing210023China Department of Computer and Electronics Systems Engineering Hankuk University of Foreign StudiesYongin-siGyeonggi-do17035Korea

Federated learning has been used extensively in business inno-vation scenarios in various *** research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment ***,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise *** proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model ***,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and *** addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global *** results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify ***,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.

关键词： Federated learning feature selection credit risk assessment MSEs

来源：评论

学校读者我要写书评

暂无评论

Real-Time Road Signs Detection and Recognition for Enhanced Road Safety 15

Real-Time Road Signs Detection and Recognition for Enhanced ...

引用

15th International Conference on Innovations in Information Technology, IIT 2023

作者： Teklesenbet, Hermon B. Demoz, Nahom H. Jabiro, Igor H. Tesfay, Yonatan R. Badidi, Elarbi UAE University CIT Department of Computer Science and Software Engineering Al-Ain United Arab Emirates

ISBN: (纸本)9798350382396

Road sign detection and recognition play a critical role in improving driver safety and awareness in the modern traffic era. This paper describes the development and evaluation of a Road Sign Detection and Recognition System (RSDRS). Our system leverages computer vision techniques and mobile application technology to provide drivers with real-Time visual and auditory feedback based on detected traffic signs. The implementation of RSDRS involves two basic phases: developing a robust recognition model and creating an Android application. The model, trained with the German Traffic Sign Recognition Benchmark (GTSRB) dataset and the YOLOv5s object detection algorithm, serves as the core component for accurate traffic sign recognition. The Android application captures live video frames, integrates seamlessly with the recognition model, and provides intuitive feedback to the driver. The performance evaluation reveals the exceptional capabilities of our system, with an average accuracy of 99.3% and a fast response time of 0.73 milliseconds. Metrics such as recall, precision, and F1 score highlight the model's ability to maintain accuracy while minimizing false positives. Real-world applicability is paramount, and our system excels in a variety of environments and lighting conditions, as evidenced by rigorous testing. © 2023 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

MA-MFIF: When misaligned multi-focus Image fusion meets deep homography estimation

引用

Multimedia Tools and Applications 2025年第12期84卷 10877-10898页

作者： Zhao, Baojun Luo, Fei Fuentes, Joel Ding, Weichao Gu, Chunhua School of Information Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Department of Computer Science and Information Technologies Universidad del Bio-Bio Chillán3780000 Chile

Multi-focus image fusion is a technique that combines multiple out-of-focus images to enhance the overall image quality. It has gained significant attention in recent years, thanks to the advancements in deep learning. However, one of the persistent challenges in this field is the processing of misaligned data, which can negatively impact the fusion results. To overcome this problem, a novel fusion framework with pre-registration is proposed for the fusion of misaligned multi-focus images. For pre-registration, content-aware deep homography estimation is used, which performs transfer learning on a real multi-focus image dataset to adapt to registration under defocused conditions. For fusion, a fusion module with dual-branch feature interaction is utilized to avoid invalid feature fusion and trained on real light field dataset to achieve better fusion performance. Qualitative and quantitative experimental results show that the proposed method has a 2-3 percentage point improvement in multiple evaluation metrics compared to existing advanced registration and fusion methods, and a maximum improvement of 4.83 percentage points in fusion performance when tested independently on the Lytro dataset. Additionally, We find that the value of the Qcv metric is greatly influenced by the alignment status of the input images, leading to its inability to reflect the fusion quality of aligned images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image enhancement

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：