检索结果-内蒙古大学图书馆

9th International Conference and Exhibition on Sustainable Energy and Advanced Materials, ICE-SEAM 2023

作者： Mitan, Nona Merry M. Amanda, Tasya Efiyanti, Lisna Saputra, Nur Adi Nugroho, Agung Chemistry Department Faculty of Science and Computer Universitas Pertamina Kebayoran Lama Jakarta12220 Indonesia National Research and Innovation Agency Jl. Raya Bogor KM 46 Bogor16911 Indonesia Chemical Engineering Department Faculty of Industrial Technology Universitas Pertamina Kebayoran Lama Jakarta12220 Indonesia

ISBN: (纸本)9789819701056

BambooBamboo is one of the local biomass products in Indonesia that can be modified into activated carbonCarbon because it is rich in carbonCarbon elements. This study aims to observe the potential of using steam in the pyrolysisPyrolysis of andong bambooBamboo which can increase its porosityPorosity as a candidate for an adsorbent material. PyrolysisPyrolysis of andong bambooBamboo is performed at a temperature of 500 °C and 800 °C in the presence of steam activation for 2 h. The analysis of pristine bambooBamboo and its activated carbonCarbon products was performed, namely proximate analysis, functional group analysis by Fourier Transform Infra-Red spectroscopy, iodine number, surface area, and morphology. The surface area analysis reveals a significant result from applying steam during the pyrolysisPyrolysis generated surface area of 404.084 m2/g from the activated andong bambooBamboo and iodine number as 833.38 mg/g, respectively. The current findings highlight the potential use of steam during pyrolysisPyrolysis of bambooBamboo as a method that can potentially make bambooBamboo an adsorbent in dyes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Activated carbon

来源：评论

学校读者我要写书评

暂无评论

Designing Robust Deep Learning Models for RealTime Data Analysis in Machine Learning 3

Designing Robust Deep Learning Models for RealTime Data Anal...

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Borade, Monali R. Gupta, Mukur Faiz, Ayaan Acharjya, Kalyan Rajesh, A. Nunciya, P.M. Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Faculty of Engineering and Technology Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India

ISBN: (纸本)9798350319125

The developing call for real-time statistics analysis and selection-making has been a primary driver of improving deep studying models for the device getting to know. These models permit the evaluation of massive statistics sets, the generation of predictions from the statistics, and the speedy studying of new behaviors and patterns. Deep mastering fashions have been efficaciously used for various tasks, including natural language processing, computer imagination and prescient, and reinforcement studying. However, because of their complexity and a massive variety of parameters, they can be unreliable and volatile when uncovered to real-global records. This paper outlines strategies and techniques to lay out deep mastering fashions that can be robust to actual-world records. We also provide high-quality practices for deploying and retaining deep learning models in manufacturing packages. Those strategies include regularization techniques, model architectures, facts pre-processing techniques, and model validation and monitoring techniques. We additionally speak of the numerous strategies for assessing and improving the reliability of deep getting-to-know fashions. Subsequently, we provide a case study of a product that uses deep studying fashions to stumble on and classify objects in near-actual time in a manufacturing place. © 2023 IEEE.

关键词： improving maintaining production regularization techniques

来源：评论

学校读者我要写书评

暂无评论

American Sign Language Recognition Based on Machine Learning and Neural Network 2

American Sign Language Recognition Based on Machine Learning...

引用

2nd International Conference on Machine Learning and Intelligent Systems engineering, MLISE 2022

作者： Li, Lanxi Liu, Da Shen, Chenlin Sun, Jing The Ohio State University Computer Science And Engineering Columbus43201 United States Sichuan University Department Of Computer Science Chengdu610207 China Jilin University Faculty Of Software Engineering Changchun130012 China Institute Of Information And Software Engineering University Of Electronic Science And Technology Of China Chengdu610066 China

ISBN: (数字)9781665492461

ISBN: (纸本)9781665492461

Numerous disabilities such as deaf and mute are suffered from not being capable of communicating with normal people, it is necessary to find a way to solve this problem. A feasible method is Sign Language Recognition (SLR) which is a sort of pattern recognition technique. In this paper, machine learning and deep learning methods are applied to recognize and classify American Sign Language (ASL), and only 24 English letters are classified because letter J and Z require fingers to move. First, Principal Component Analysis (PCA) and manifold algorithms are used to do dimension reduction to accelerate the training of machine learning and visualize it. Second, various machine learning methods such as Random Forest Classification (RFC), K-Nearest Neighbor (KNN), Gaussian Naïve Bayes (GNB), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD) are applied to classify the pattern. Since the SVM algorithm has several hyperparameters, this study uses the Grid Search method to find the best combination of hyperparameter which lead to predicting more accurately. It is found that different dimensionality reduction algorithms have unequal effects on the accuracy of each prediction model, and it can be concluded that the manifold algorithm is the best dimension reduction algorithm only for KNN but not for other prediction models, and PCA is much more feasible than KNN applied in such machine learning algorithms except KNN. Two deep learning algorithms such as Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) are also used in classification and their accuracy is highest among such algorithms mentioned above. © 2022 IEEE.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Sundararamaiah, Munikrishnaiah Rao Katru, Chandrasekhar Kolli, Chandra Sekhar Sutaria, Kruti Rao, K.B. Venkata Brahma Maranan, Ramya Indian Land SC29707 United States Aditya University Department of Computer Science and Engineering Andhra Pradesh Surampalem533437 India Parul institute of Engineering and Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology Gujarat India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

People are making daily purchases and sales through the internet, and most of them require them to use their credit cards. Besides making things easier for the customer, this heightens the probability of online credit card frauds. Credit card fraud detection has increased in several nations in the recent past due to the increased incidence of fraud that requires it to become even more of a security issue than before. In this paper, a new deep-learning-based approach for credit card fraud detection is proposed. It is based on the preprocessing stage, hybrid feature selection, and classification stage. In the first instance, the proposed method is used on the Credit Card Fraud Detection (CCF) data set. A prescreening step that uses Z-score Min-Max normalization is applied to ensure that the ranges of each data set are the same. The Crayfish Optimization Algorithm (COA) and the Emperor Penguin Optimizer (EPO) are integrated and used to identify the better attributes. Next, a similarity-navigated graph neural network (SNGNN) is utilized for classification so that the fraudulent transactions and the transactions that are not fraudulent can be differentiated easily. The overall system is also enhanced with Greylag Goose Optimization (GGO) that maximizes the chances of detection. The proposed method achieves a high accuracy in detecting fraudulent transactions, which means the proposed process has virtually limitless potential to speed up the entire process. Thus, high effectiveness is proved by the 99% accuracy rate of the suggested method. This exceptional outcome proves the robustness of the approach and its ability to adapt to various patterns of dependence and data changes. © 2025 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Transcription for Thai Traditional Medicine Texts with Wav2Vec2

Transcription for Thai Traditional Medicine Texts with Wav2V...

引用

13th International Electrical engineering Congress, iEECON 2025

作者： Popun, Jettasic Lee, Wilaiporn Prayote, Akara Srisomboon, Kanabadee Pipanmekaporn, Luepol King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand

ISBN: (纸本)9798331543952

Recently, Automatic Speech Recognition (ASR) technology is widely used for communication convenience in converting speech to text. Initial surveys reveal that existing studies on ASR, both in Thai and foreign languages, focus on contemporary language communication, lacking support for specialized vocabulary. Thai Traditional Medicine (TTM) contain ancient medical terminology recorded in various media such as palm leaves, parchment, notebooks, and inscriptions. Centuries of textual preservation have led to text degradation. Given the suitability of ASR study to accommodate medical terminology and ancient languages, it is proposed to apply it to convert original Thai Traditional Medicine texts into contemporary language, aiding experts in reading and interpreting them. ASR involves converting speech signals into text. In this paper, we propose an end-to-end speech recognition approach for Thai Traditional Medical texts leveraging self-supervised learning (SSL) to overcome the limitations of limited labeled data. We utilize a transformer-based model pre-trained (XLSR-Wav2Vec) on unlabeled TTM texts speech data using SSL techniques and fine-tune it with a small dataset of labeled TTM texts audio-text pairs. Due to the absence of a Thai Traditional Medicine vocabulary repository, a dataset comprising text and speech is developed, consisting of 1,800 training sentences and 200 test sentences randomly selected from recordings of 10 Thai Traditional Medicine experts. Offline evaluation methods are employed, resulting in improved accuracy rates. Future efforts will focus on expanding the speech dataset to enhance recognition accuracy further. This study lays the groundwork for efficiently extracting knowledge from Thai Traditional Medicine prescriptions, ensuring the preservation of this valuable heritage for future generations. © 2025 IEEE.

关键词： Speech enhancement

来源：评论

学校读者我要写书评

暂无评论

MAD-ANET:Malware Detection Using Attention-Based Deep Neural Networks

引用

computer Modeling in engineering & sciences 2025年第4期143卷 1009-1027页

作者： Waleed Khalid Al-Ghanem Emad Ul Haq Qazi Tanveer Zia Muhammad Hamza Faheem Muhammad Imran Iftikhar Ahmad Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11362Saudi Arabia Centre of Artificial Intelligence Naif Arab University for Security SciencesRiyadh14812Saudi Arabia School of Arts and Sciences The University of Notre DameSydneyNSW 2007Australia Center for Smart Analytics Institute of InnovationScience and SustainabilityFederation University AustraliaBerwickVIC 3806Australia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

In the current digital era,new technologies are becoming an essential part of our ***,the number ofmalicious software ormalware attacks is rapidly *** is no doubt,themajority ofmalware attacks can be detected by most antivirus ***,such types of antivirus programs are one step behind malicious *** to these dilemmas,deep learning become popular in the detection and classification of malicious ***,researchers have significantly focused on finding solutions for malware attacks by analyzing malicious samples with the help of different techniques and *** this research,we presented a lightweight attention-based novel deep Convolutional Neural Network(DNN-CNN)model for binary and multi-class malware classification,including benign,trojan horse,ransomware,and *** applied the Principal Component Analysis(PCA)technique for feature extraction for binary *** used the Synthetic Minority Oversampling Technique(SMOTE)to handle the imbalanced data during multi-class *** proposed attention-based malware detectionmodel is trained on the benchmarkmalware memory dataset named *** indicate that our model obtained high accuracy for binary and multi-class classification,99.5% and 97.9%,respectively.

关键词： Attention-based CNN malware detection machine learning deep learning classification

来源：评论

学校读者我要写书评

暂无评论

Impact of Mechanical Storage System Technologies: A Powerful Combination to Empowered the Electrical Grids Application 2

Impact of Mechanical Storage System Technologies: A Powerful...

引用

2nd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2022

作者： Khaleel, Mohamed Mohamed Mohamed Ghandoori, Tahir Ali Ahmed, Abdussalam Alsharif, Abdulgader Ahmed Alnagrat, Ahmed Jamah Ali Abulifa, Adel College of Civil Aviation Aeronautical Engineering Department Misrata Libya Higher Institute of Science and Technology Haraba Mechanical Engineering Department Haraba Libya Bani Waleed University Mechanical Engineering Department Bani Waleed Libya Faculty of Engineering Johor Malaysia Higher Institute of Science and Technology Department of Computer Science and Information Technology Wadi Al-Shati Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

Mechanical Energy Storage Systems (MESS) Technologies continue to pose huge challenges to electrical grids. The MESS model is intended to provide an extremely flexible facility to the electrical grids that engaged in harmonizing energy resources and demand loads in order economic impact, secure electric-power supplies at effectively delivering sustainable and high power quality. Moreover, the electrical grids can begin to derive full benefits from a powerful combination of distributed renewable energy resources (RER). The contribution of this paper aims to involve implementing innovative MESS technologies that work hand in hand with greater efficiency, energy-efficient, and rapid response to integrate electrical grids cope with intelligent techniques such as particle swarm optimization (PSO), artificial neural network (ANN), and fuzzy logic controller (FLC). These intelligent controllers are being actively considered to regulate the power from MESS technologies to integrate within the electrical grids. As result, the main concern of this paper is to provide critical analysis of main technologies in electrical MESS that are particularly suitable for evolving the electrical grid and their applications. © 2022 IEEE.

关键词： Renewable energy resources

来源：评论

学校读者我要写书评

暂无评论

Fuzzy-Based Weighted Federated Machine Learning Approach for Sustainable Energy Management with IoE Integration

Fuzzy-Based Weighted Federated Machine Learning Approach for...

引用

2024 Systems and Information engineering Design Symposium, SIEDS 2024

作者： Ghazal, Taher M. Iqbal Janjua, Jamshaid Abbas, Sagheer Fatima, Areej Saleem, Muhammad Khan, Muhammad Adnan Alqarafi, Abdulrahman Khalifa University Centre for Cyber Physical Systems Computer Science Department Abu Dhabi United Arab Emirates Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Selangor Malaysia National College of Business Administration and Economics School of Computer Sciences Lahore Pakistan Al-Khawarizmi Institute of Computer Science Lahore Pakistan Bahria University Department of Computer Science Lahore Pakistan LGU Department of Computer Science Lahore Pakistan Minhaj University School of Computer Science Lahore Pakistan Skyline University College School of Computing Sharjah United Arab Emirates Riphah International University Riphah School of Computing and Innovation Lahore Pakistan Taibah University College of Computer Science and Engineering Medina42353 Saudi Arabia

ISBN: (纸本)9798350385144

The considerable surge in energy consumption due to population growth and the use of new technology has posed significant challenges to energy security and the environment. Managing energy consumption on the consumer side is crucial as the demand for energy continues to increase. Consumers need to monitor their daily usage and understand consumption standards to prepare themselves for energy efficiency and cost savings. To address these challenges, a Smart Energy Management System (SEMS) based on the Internet of Energy (IoE) has been developed. This system uses day-Ahead planning and accurate energy availability forecasting to manage energy demand in a smart environment with high sustainable energy penetration. The Fuzzy based Weighted Federated Learning (FWFML) approach is integrated into the system to optimize energy management. The SEMS system monitors energy usage at the user level, and a review of management plans has been introduced to improve energy efficiency in terms of 93.5% accuracy, and 6.5% miss-rate. This research work also highlights future challenges in developing SEMS to increase energy efficiency in a more robust manner. © 2024 IEEE.

关键词： Energy management systems

来源：评论

学校读者我要写书评

暂无评论

Analyzing User Satisfaction with Mobile Internet Service Providers through Technical Performance and User Experience Evaluation

Analyzing User Satisfaction with Mobile Internet Service Pro...

引用

International Research Conference on Smart Computing and Systems engineering (SCSE)

作者： Prasanga Hewa Pothuwilage Pansujee Dissanayaka Uditha Prabhath Liyanage Department of Mathematics Faculty of Engineering University of Moratuwa Moratuwa Sri Lanka Department of Statistics & Computer Science Faculty of Science University of Kelaniya Kelaniya Sri Lanka

ISBN: (数字)9798350375688

ISBN: (纸本)9798350375695

In today's digital era, the Internet holds a fundamental position in daily life, particularly in Sri Lanka. This study addresses two primary objectives: first, to quantify the technical performance of internet services, and second, to assess customer satisfaction and its alignment with service provider performance. Conducted in the Narthanagala Grama Niladhari Division, specifically the 616C division in Horana, the study utilizes a structured questionnaire to gather customer experience data. Technical data are collected using the "Open Signal" and "Network Cell Info" mobile applications. Statistical techniques are employed to achieve the objectives. Principal Component Analysis reduces the initial 9 dimensions of internet service quality to a singular dimension, with the primary principal component explaining 84.40% of variance. This reduction is validated through the composite index of "Application Based Technical Performance of Mobile Data Service (AppBased_TecPerf)". Factor Analysis and simple linear regression unveil two influential latent factors: Customer-Perceived Technical Performance of Mobile Data Service (Customer_TecPerf) and Customer Service Satisfaction (Customer_Service), collectively accounting for 60.66% of variability across six dimensions. These dimensions include Video Quality Experience, Gaming Experience, Voice & Video Quality Experience on Social Media Apps, Client Rating for Internet Speed at Residence, Client Rating for Satisfaction with Price Fairness, and Client Rating for Customer Service Quality. Reliability analysis confirms the effectiveness of these factors in measuring the Application Based Technical Performance of Mobile Data Service. Regression analysis confirms that the index, AppBased_TecPerf, effectively captures Customer_TecPerf.

关键词： Customer services Web and internet services Linear regression Customer satisfaction Streaming media Quality assessment Internet

来源：评论

学校读者我要写书评

暂无评论

Optimized Pap Smear Image Enhancement: Hybrid PMD Filter-CLAHE Using Spider Monkey Optimization

arXiv

引用

arXiv 2025年

作者： Khozaimi, Ach Darti, Isnani Anam, Syaiful Kusumawinahyu, Wuryansari Muharini Department of Mathematics Faculty of Mathematics and Natural Sciences Brawijaya University Malang Indonesia Department of Computer Science Faculty of Engineering Trunojoyo University of Madura Indonesia

Pap smear image quality is crucial for cervical cancer detection. This study introduces an optimized hybrid approach that combines the Perona-Malik Diffusion (PMD) filter with contrast-limited adaptive histogram equalization (CLAHE) to enhance Pap smear image quality. The PMD filter reduces the image noise, whereas CLAHE improves the image contrast. The hybrid method was optimized using spider monkey optimization (SMO PMD-CLAHE). BRISQUE and CEIQ are the new objective functions for the PMD filter and CLAHE optimization, respectively. The simulations were conducted using the SIPaKMeD dataset. The results indicate that SMO outperforms state-of-the-art methods in optimizing the PMD filter and CLAHE. The proposed method achieved an average effective measure of enhancement (EME) of 5.45, root mean square (RMS) contrast of 60.45, Michelson's contrast (MC) of 0.995, and entropy of 6.80. This approach offers a new perspective for improving Pap smear image quality. © 2025, CC BY.

关键词： Image quality

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：