检索结果-内蒙古大学图书馆

9th International Conference on Applying New technology in Green Buildings, ATiGB 2024

作者： Kim, Viet Nguyen Ngoc, Huyen Nguyen Thi Minh, Hieu Dam Khoi, Nguyen Do Hoang Do, Tien-Dung Dung, Cao Truong Posts and Telecommunication Institute of Technology Department of Information Technology Ha Noi Viet Nam Swinburne University Department of Computer Science Ha Noi Viet Nam Posts and Telecommunication Institute of Technology Department of Computer Science Ha Noi Viet Nam Posts and Telecommunication Institute of Technology Faculty of Information Ha Noi Viet Nam Posts and Telecommunication Institute of Technology Department of Electronic Engineering Ha Noi Viet Nam

ISBN: (纸本)9798331505042

Automatic speech recognition (ASR) systems have seen significant advancements in recent years, driven by deep learning and large-scale pre-training methods. However, achieving high accuracy across multiple accents remains a challenging task, especially for languages with rich phonetic diversity like Vietnamese. The Vietnamese language encompasses a wide range of regional accents, each with unique phonetic characteristics that can hinder the performance of conventional ASR. To address this issue, we present a Vietnamese language processing model called Wav2Vn as a novel approach to scalable unsupervised pretraining and adaptation for ASR targeting multiple Vietnamese accents. Our method leverages the power of the Mixture of Experts (MoE) to effectively handle the diverse phonetic variations across different Vietnamese accents. Our results demonstrate a significant performance improvement in terms of word error rate (WER) and robustness to accent variability compared to the current state-of-the-art PhoWhisper. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Intelligent Energy Utilization Analysis Using IUA-SMD Model Based Optimization Technique for Smart Metering Data

引用

Journal of Harbin Institute of technology(New Series) 2024年第1期31卷 90-98页

作者： K.Rama Devi V.Srinivasan G.Clara Barathi Priyadharshini J.Gokulapriya Department of Information Technology Panimalar Engineering CollegeChennaiTamil Nadu 600029India Department of Computer Applications Dayananda Sagar College of EngineeringBengaluru 560078KarnatakaIndia Department of Computer Appliations Karpagam Academy of Higher EducationCoimbatoreTamil Nadu 641021India Department of Computer Science Rathinam College of Arts and ScienceCoimbatoreTamil Nadu 641021India

Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on data management,rather than emphasizing efficiency. Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demandsupply balancing. Smart metering solutions offer users the benefits of effectively interpreting their energy utilization and optimizing costs. Motivated by this,this paper presents an Intelligent Energy Utilization Analysis using Smart Metering Data(IUA-SMD)model to determine energy consumption patterns. The proposed IUA-SMD model comprises three major processes:data Pre-processing,feature extraction,and classification,with parameter optimization. We employ the extreme learning machine(ELM)based classification approach within the IUA-SMD model to derive optimal energy utilization labels. Additionally,we apply the shell game optimization(SGO)algorithm to enhance the classification efficiency of the ELM by optimizing its parameters. The effectiveness of the IUA-SMD model is evaluated using an extensive dataset of smart metering data,and the results are analyzed in terms of accuracy and mean square error(MSE). The proposed model demonstrates superior performance,achieving a maximum accuracy of65.917% and a minimum MSE of0.096. These results highlight the potential of the IUA-SMD model for enabling efficient energy utilization through intelligent analysis of smart metering data.

关键词： electricity consumption predictive model data analytics smart metering machine learning

来源：评论

学校读者我要写书评

暂无评论

Examining the Effects of Applying AI to Enhance Big Data Retrieval Efficiency

Examining the Effects of Applying AI to Enhance Big Data Ret...

引用

2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Sharma, Nakul S. Yadav, Dhananjay Shrivastava, Manish Mandal, Sanjeev Kumar Ravivarman, G. Jana, D Little Femilin Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai India

ISBN: (纸本)9798350329773

This studies paper explores how synthetic Intelligence (AI) technologies may be used to improve the efficiency of information retrieval from huge databases, as applied in a diffusion of industries. The paper will discuss the concept of AI as a tool to reduce information complexity, at the same time as inspecting current studies projects inside the subject and unique programs for the enterprise and patron worlds. It'll then discover the demanding situations that get up from applying AI and massive records technology, and description a suggested framework for first-rate practices. The paper will recall how AI may be utilized to enhance information seek outcomes in real-time, and provide an explanation for the diverse ways wherein this might be carried out. Subsequently, the paper will discuss the capability implications of the usage of AI for large statistics Retrieval efficiency, as well as its destiny applications. © 2023 IEEE.

关键词： Computational complexity

来源：评论

学校读者我要写书评

暂无评论

A Secure NFT-Based System for Eliminating Counterfeit Certificates 5

A Secure NFT-Based System for Eliminating Counterfeit Certif...

引用

5th IEEE PhD Colloquium on Emerging Domain Innovation and technology for Society, PhD EDITS 2023

作者： Aparna, N. Kesavamoorthy, R. Cmr Institute of Technology Department of Information Science & Engineering Bengaluru India Visvesveraya Technological University Belagavi590018 India Cmr Institute of Technology Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350316278

In recent times, the proliferation of fake certificates has become a growing concern, undermining the credibility of educational and professional qualifications. This paper presents a system based on blockchain designed to avoid the issuance and circulation of counterfeit certificates using Non-Fungible Tokens (NFTs). Using the unchangeable and see-through nature of blockchain technology, the proposed system ensures secure verification and authentication of certificates. By employing NFTs, each certificate is tokenized, making it unique, indivisible, and tamper-resistant. The system provides a robust and trustworthy platform for institutions, employers, and individuals to eradicate the menace of fake certificates, promoting trust and integrity in the certification ecosystem. © 2023 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Evaluation of Biometric Classification and Authentication Using Machine Learning Techniques

Evaluation of Biometric Classification and Authentication Us...

引用

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Umasankari, N. Muthukumar, B. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India School of Computing Department of Cse Sathyabama Institute of Science and Technology Chennai India Department of Information Technology Dmi College of Engineering Chennai India

ISBN: (纸本)9798350334357

This research article proposed the performance measurement of biometric image with computational methodology. This research adopts the following procedures: pre-processing, Feature extraction and Classification. Designing and building the algorithm and simulation programs have been done in a MATLAB environment. My SQL has been utilized for the maintenance of overall dataset. By employing the classification technique comparative analysis was carried out by inspecting three data mining techniques which were: Random Tree, The multilayer Perceptron neural network (MPNN), and the C4.5 decision tree (DT) algorithms. As per the final conclusion, the Random Forest Classifier algorithm exhibits greater performance in contrast to the other techniques. It has been found that the 93.5 % accuracy exhibited by Random Forest Classifier is much greater and enhanced. © 2023 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Exploring the Benefits of AI for Content Retrieval 3

Exploring the Benefits of AI for Content Retrieval

引用

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

作者： Begum, Ahmadi Karthikeyan, Kavitha Takale, Dattatray G. Bhambu, Pawan Yadav, Dhananjay Das, Sidhant Faculty of Engineering and Technology Department of Basic Science Karnataka Ramnagar India Prince Shri Venkateshwara Padmavathy Engineering College Department of Civil Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

The latest emergence of synthetic Intelligence (AI) and its packages in content retrieval have made it an increasing hobby for educational and technical networks. AI has made the information and utilization of content material retrieval gear less complicated and less complicated, offering a more advantageous consumer revel. This newsletter explores the advantages of AI for content retrieval and affords examples of standard AI-based content material retrieval packages. First, AI algorithms help improve the accuracy of content retrieval, allowing more excellent relevant content material to be retrieved from the web. Furthermore, AI can be used to perceive and advocate content that can be of hobby to the person primarily based on the consumer's possibilities and behaviors. AI can examine textual content files, organizing them into topics and sub-topics to facilitate green content material retrieval. Moreover, online entities such as engines like Google or content recommendation systems can leverage AI-primarily based predictive analytics to tailor content material hints to character users, allowing more powerful content retrieval. In conclusion, AI can help streamline and enhance content retrieval by enhancing the accuracy of seeking outcomes, offering more excellent applicable content pointers, and imparting better organization of documents and topics. © 2023 IEEE.

关键词： documents organizing recommendation streamline understanding

来源：评论

学校读者我要写书评

暂无评论

Predicting Vocal Pathologies: A Multi-Dataset Machine Learning Framework for Healthcare Applications 3

Predicting Vocal Pathologies: A Multi-Dataset Machine Learni...

引用

3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Pal, Nitin Shrivas, Avinash Roy, Rachit Shetty, Sachet Gidaye, Girish Deshpande, Shrinivas Vidyalankar Institute of Technology Department of Electronics and Computer Science Mumbai India Thakur College of Engineering & Technology Department of Information Technology Mumbai India DCPE HVPM P. G. Department of Computer Science and Technology Amravati India

ISBN: (纸本)9798331523893

Vocal pathologies affect vocal fold dynamics, altering pitch, loudness, and other voice traits. Early detection requires accurate prediction methods, but limited datasets and class imbalances pose challenges. To address these, we use data augmentation and synthetic minority oversampling to enhance dataset diversity and balance, ensuring model generalizability across demographics. Our study employs a machine learning paradigm for diagnosing vocal disorders using sustained vowel /a/ samples from four datasets in German, Spanish, English, and Arabic. Extracted features include mel-frequency cepstral coefficients, delta, delta-delta, and log-energy. Multiple classifiers were tested, with support vector machine, stochastic gradient descent, and long short-term memory models achieving accuracy over 97% and area under the curve scores near 1. These findings highlight the method's ability to uncover clinical voice patterns, offering a reliable and efficient tool for assessing vocal disorders. © 2025 IEEE.

关键词： Speech enhancement

来源：评论

学校读者我要写书评

暂无评论

An Enhanced Support Vector Machine Based Pattern Classification Method for Text Classification in English Texts 7

An Enhanced Support Vector Machine Based Pattern Classificat...

引用

7th IEEE International Conference on Computational Systems and information technology for Sustainable Solutions, CSITSS 2023

作者： Thatha, Venkata Nagaraju Veerasekharreddy, B. Venugopal, G. Ashok, K. Maddu, Sundarababu Srilakshmi, V. Mlr Institute of Technology Department of Information Technology Hyderabad India Kl Education Foundation Department of Computer Science and Engineering Vaddeswaram India Hyderabad India St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India Medak Narsapur India

ISBN: (纸本)9798350343144

Text mining is a growing in a unique field that attempts to convey information from natural language text. It is too strength be uncertainty characterized as the development of analysing text as well as to take out the information that is constructive for particular purposes. Compared with other kind of data stored in databases, text is unstructured, shapeless as well as difficult to deal with algorithmically. Traditionally the patterns of the text are analyzed by using PTM and FTM algorithms but this algorithms produces less accuracy. To improve the performance of pattern classification, in this paper an Enhanced Support Vector Machine based Pattern classification method(ESVMPCM) is proposed. Then ESVMPCM is applied to Reuters dataset and measure the performance using different metrics as precision, recall, F-measure and accuracy. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Spotted Hyena Optimizer with Deep Learning Driven Cybersecurity for Social Networks

引用

computer Systems Science & engineering 2023年第5期45卷 2033-2047页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Lubna A.Alharbi Mohamed K.Nour Abdullah Mohamed Ahmed S.Almasoud Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversityTabuk47512Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia

Recent developments on Internet and social networking have led to the growth of aggressive language and hate *** provocation,abuses,and attacks are widely termed cyberbullying(CB).The massive quantity of user generated content makes it difficult to recognize *** advancements in machine learning(ML),deep learning(DL),and natural language processing(NLP)tools enable to detect and classify CB in social *** this view,this study introduces a spotted hyena optimizer with deep learning driven cybersecurity(SHODLCS)model for *** presented SHODLCS model intends to accomplish cybersecurity from the identification of CB in the *** achieving this,the SHODLCS model involves data pre-processing and TF-IDF based feature *** addition,the cascaded recurrent neural network(CRNN)model is applied for the identification and classification of ***,the SHO algorithm is exploited to optimally tune the hyperparameters involved in the CRNN model and thereby results in enhanced classifier *** experimental validation of the SHODLCS model on the benchmark dataset portrayed the better outcomes of the SHODLCS model over the recent approaches.

关键词： Cybersecurity cyberbullying online social network deep learning spotted hyena optimizer

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

引用

computers, Materials & Continua 2022年第4期71卷 1473-1487页

作者： Pudi Sekhar T.J.Benedict Jose Velmurugan Subbiah Parvathy E.Laxmi Lydia Seifedine Kadry Kuntha Pin Yunyoung Nam Department of Electrical and Electronics Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Applications Government Arts&Science CollegeKanyakumari629401India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the *** classical grid can be updated to the smart grid by the integration of information and Communication technology(ICT)over the *** TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among *** the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)*** some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,*** *** this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in *** proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence ***,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading *** order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive *** simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.

关键词： Energy trading distributed systems power generation load forecasting deep learning peer-to-peer

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：