检索结果-内蒙古大学图书馆

2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Datta, Suseta Mondal, Rituparna Roy, Rajdeep Banerjee, Sourav Biswas, Utpal Narula Institute of Technology Department of Computer Application West Bengal Kolkata India Techno India University Department of Computer Applications West Bengal Kolkata India University of Kalyani Department of Computer Science and Engineering West Bengal Kalyani India Kalyani Government Engineering College Department of Computer Science and Engineering West Bengal Kalyani India

ISBN: (纸本)9798331540821

In the capricious digital dominion, conventional online lottery systems come across consequential impediment regarding transparency, fairness, and security. This paper presents an inventive solution by merging Blockchain technology and smart contracts into online lotteries to take forward these challenges. The preferred disperse implementation capitalizes on Blockchain's decentralized category to covenant the translucency and immutability of lottery transactions, thereby undoing the requisite for trusted interceders. Smart contracts are immersed to automate the entire lottery process-from ticket provisioning to winner selection and payout-certifying agreement to preconceived rules and reinforcing contender trust. Through meticulous perusal and enactment, illustration of how Blockchain technology can transform online lottery systems, providing users with a secure, transparent, and fair platform have been performed. Furthermore, the suggested system's resistance to recurrent deceit and its prospective impression on the online gambling industry are talked about, foregrounding the welfares of a Blockchain-drove technique to online lotteries. © 2024 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

MMDFD- A Multimodal Custom Dataset for Deepfake Detection 2023

MMDFD- A Multimodal Custom Dataset for Deepfake Detection

引用

15th International Conference on Contemporary Computing, IC3 2023

作者： Asha, S. Vinod, P. Menon, Varun G. Department of Computer Science & Engineering SCMS School of Engineering & Technology APJ Abdul Kalam Technological University Trivandrum India Department of Computer Applications Cochin University of Science & Technology Cochin India Department of Computer Science & Engineering SCMS School of Engineering & Technology Ernakulam India

ISBN: (纸本)9798400700224

A multi-modal deepfake dataset is relevant in addressing the growing concern of deepfake misuse, which poses a significant security and privacy threat. Deepfakes are becoming increasingly sophisticated, and their potential to deceive individuals and organizations is a significant issue. The ability to generate synthesized human voices using deep learning models and inserting fake subtitles has added to this problem, making it more challenging to detect deepfakes accurately. A superior quality dataset is essential to developing a competent deepfake detector. However, existing datasets are limited and often biased, making it difficult to detect deepfakes accurately. A multi-modal deepfake dataset, such as the proposed multi-modal Audio-Video-Text Deepfake dataset (MMDFD) addresses this gap by providing a more realistic and unbiased dataset. Such a dataset helps develop more accurate and effective deepfake detection methods, which can detect audio, video, and textual deepfakes simultaneously. The proposed dataset is more reflective of situations in the real world since it contains actual YouTube recordings of celebrities from four different racial origins. This helps to avoid the creation of deepfake detectors that are biased toward certain racial or ethnic groups. Overall, a multi-modal deepfake dataset is essential in addressing the growing concerns of deepfake misuse and developing effective detection methods that can detect deepfakes accurately, regardless of the medium. © 2023 ACM.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Credit Risk Customers Categorization with Random Forest Classifier using Various Searching Techniques

Credit Risk Customers Categorization with Random Forest Clas...

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Samprith, S. Rajesh, T.M. Babu, Tina Nair, Rekha R Kishore, S. Dayananda Sagar Univerity School of Engineering Department of Computer Science & Engineering Bengaluru India Dayananda Sagar University School of Engineering Department of Computer Science & Engineering Bengaluru India Alliance Univerity Department of Computer Science and Engineering Bengaluru India Nitte University Nitte Institute of Professional Education Department of Computer Applications Karnataka India

ISBN: (纸本)9798350313413

Effective credit risk assessment holds significant importance for financial institutions and businesses extending credit to customers. It is crucial to evaluate customers' creditworthiness to minimize default risks and manage financial exposure. This study focuses on enhancing credit risk customer classification through optimized feature selection and classification techniques. The dataset is preprocessed using Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance and enhance performance. Relevant features are then extracted utilizing Cfs Subset evaluation along with various search algorithms. The optimized features are subsequently employed for classification using the Random Forest (RF) classifier. Experimental results reveal that, in credit customer classification, Evolutionary, Tabu, and Scatter search algorithms combined with RF classifier demonstrate a commendable accuracy of 90.31%. The proposed framework also showcases comparable outcomes in terms of other performance metrics. This automated credit customer classification method proves to be highly effective in identifying fraudulent users. © 2023 IEEE.

关键词： classifier Credit risk Detection methods Search Techniques

来源：评论

学校读者我要写书评

暂无评论

Meme Expressive Classification in Multimodal State with Feature Extraction in Deep Learning 2

Meme Expressive Classification in Multimodal State with Feat...

引用

2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Barveen, A. Geetha, S. Faizal, M.K.Mohamed M.I.E.T. Engineering College Department of Computer Science and Engineering Trichy India University College of Engineering Department of Computer Applications Tamilnadu Trichy India

ISBN: (纸本)9798350397635

Memes are a socially interactive way to communicate online. Memes are used by users to communicate with one another on social networking sites and other forums. Memes essentially focus on speech recognition and image macros. While a meme is being created, it focuses on the semiotic type of resources that the internet community interprets with other resources, which facilitates the interaction among the internet and meme creators. Memes recreate based on various approaches, which fall under various acts such as existing speech acts. Based on the expressive face with captioned short texts, even the short text is exaggerated. Every year, meme mimicking applications are created that allow users to use the imitated meme expressions. Memes represent the shared texts of the younger generations on various social platforms. The classifications of sentiment based on the various memetic expressions are the most efficient way to analyse those feelings and emotions. HOG feature extraction allows the images to be segmented into blocks of smaller size by using a single feature vector for dimension, which characterizes the local object appearances to characterize the meme classification. The existence of specific characteristics, including such edges, angles, or patterns, is then analyzed by combining HOG features using multi-feature analysis on patches. Based upon the classification methodology, it classifies the sentiments, which tend to improve the learning process in an efficient manner. By combining a deep learning approach with a recurrent neural network, the extended LSTM-RNN can identify subtle nuances in memes, allowing for more accurate and detailed meme classification. This proposed method effectively evaluates several classification techniques, including CNN and Extended LSTM-RNN for meme image characterization. Through training and validation, Extended LSTM-RNN achieved 0.98% accuracy with better performance than CNN. © 2023 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Stacked Bi-LSTM for Advanced Toxicity Detection in Comment Classification 6

Stacked Bi-LSTM for Advanced Toxicity Detection in Comment C...

引用

6th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2024

作者： Kumar, N. Naveen Abraham, Prabhakaran Kaliba, Syed Ibrahim Diniesh, V.C. Maruthamuthu, R. Balamurugan Seshu Kumar, P. Vekkudu, Naveen Madanapalle Institute of Technology & Science Department of Computer Applications Andhra Pradesh Madanapalle India Mai-nefhi College of Engineering and Technology Department of Computer Science and Engineering Mainefhi Eritrea Kongu Engineering College Department of Electronics & Communication Engineering Tamil Nadu Erode India Jain University Department of Information Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350389692

The surge in Internet usage has revolutionized online forums, providing dynamic forums for active participation and meaningful debates. However, this has also exposed users to the risk of harassment. Efforts to address toxic comments in online forums have faced challenges, with current solutions unveiling unreliability. Advances in hardware, cloud computing, and natural language processing (NLP) enable the development of more robust approaches, predominantly NLP-based deep learning models. Pre-trained transformer models are gradually utilized for accurate toxic comment classification, bigger traditional methods. Stacked LSTM models, emphasizing hierarchical feature learning and improved contextual understanding, outperform single layer counterparts, offering an effective solution for online content moderation and sentiment analysis. This research indicates a significant stride towards more dependable and sophisticated toxic comment detection, addressing the challenges of social media effectively. © 2024 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

A robust method for malware analysis using stacking classifiers and dendrogram visualization

引用

International Journal of Information Technology (Singapore) 2024年 1-16页

作者： Naveen Kumar, N. Balamurugan, S. Maruthamuthu, R. Devi, P.P. Seshu Kumar, P. Department of Computer Applications Madanapalle Institute of Technology & amp Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Avadi Tamil Nadu Chennai India

Malware analysis is a vital and challenging task in the ever-changing cyber threat landscape. Traditional signature-based methods cannot keep up with the fast-paced evolution of malware variants. This underscores the need for developing a more effective malware classification tool helping in exploring new research directions with solutions. In this research work firstly, we present a comprehensive assessment on malware classification using dendrogram clustering techniques. This work proposes, a robust approach to malware analysis by constructing dendrogram that groups malware samples which works based on their ancestral relationships. Semantic analysis of the code also provides deeper insights into the malware’s behavior and functionality, enabling more precise identification. These methods help to detect new variants as well as enhance the recognition of existing malware families. Our proposed model results shows that, the robustness in accuracy and False Positive Rate when compared with other existing models. © Bharati Vidyapeeth's Institute of computer applications and Management 2024.

关键词： Autoencoder decoder Dendrogram Malware classification Semantic analysis

来源：评论

学校读者我要写书评

暂无评论

NL2Code: A Hybrid NLP and Model-Driven Framework for Automated Code Generation from Natural Language and UML

NL2Code: A Hybrid NLP and Model-Driven Framework for Automat...

引用

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

作者： Nair, Reshma P Thushara, M.G. Amrita School of Computing Amrita Department of Computer Science & Engineering Kerala Vishwa Vidyapeetham Amritapuri Kollam India Amrita School of Computing Amrita Department of Computer Science & Applications Kerala Vishwa Vidyapeetham Amritapuri Kollam India

ISBN: (纸本)9798331529833

The process of converting natural language requirements and visual models into executable software code remains an ongoing challenge in software engineering. We developed an intelligent system that adopts Natural Language Processing (NLP) and Model-Driven engineering (MDE) to generate code from natural language requirements and UML diagrams. It then applies advanced NLP techniques for natural language specification, such as tokenization, POS tagging, semantic analysis, and information extraction. This information can be transformed into computer language construct and design patterns with the help of MDE concepts. It generates code templates, skeleton code, and real code and incorporates key principles of software engineering and technological specializations. Case studies explain how the system can improve the software development lifecycle for a development company by automatically translating the natural language and the visual models into code. The system synthesized code with 92-95 % accuracy and 92-93 % completeness in three particular cases of use, proving the efficiency of the translation process of natural language requirements and UML diagrams into executable code. The present work enriches software engineering, NLP, as well as the integration of MDE, and code generation, reducing time consumption, manual efforts, and fast communication in the Software Development Process. © 2025 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Future Database Technologies for Big Data Analytics

Future Database Technologies for Big Data Analytics

引用

2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Rani, Geeta Sharma, Taruna Sharma, Avinash Chitkara University Institute of Engineering and Technology Dept of Computer Applications Punjab India Maharishi Markandeshwar Engineering College Department of Computer Science and Engineering Mullana India

ISBN: (纸本)9798350335835

Data is valuable asset for organizations but it cannot be used directly for business decision making. It needs to be processed in a form that is easily understandable and can to be used by decision makers. The database technology is continuously changing as the data is growing at a rapid date and business requirements change with time. With the advent of IOT, social media and other technology advancements, data become more complex in terms of volume, variety and veracity. Traditional technology can't be used for handling this data due to its complex and dynamic nature of Big Data. So more advanced and sophisticated tools are essential to handle complex nature of data. In this paper the various database tools are discussed and analysed by different vendors which are suitable for big data Analytics. © 2023 IEEE.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

The Prediction of Diabetes Using Machine Learning in the Healthcare System 5th

The Prediction of Diabetes Using Machine Learning in the Hea...

引用

5th International Conference on Trends in Computational and Cognitive engineering, TCCE 2023

作者： Deep, Ishaan Singh, Payaswini Senapati, Biswa Ranjan Swain, Rakesh Ranjan Nayak, Subrat Kumar Patra, Binaya Kumar University Odisha Bhubaneswar751030 India Department of Computer Science Engineering and Applications Indira Gandhi Institute of Technology Sarang Dhenkanal India

ISBN: (纸本)9789819719228

Diabetes has been increasing at an exponential rate in India in recent years, and it is expected to reach 537 million by 2030. Diabetes will also cause a large rise in a variety of other health issues. Many machine learning techniques are being employed to solve the problem of diabetes prediction. In this paper, the authors have considered two machine learning techniques, such as support vector machine (SVM) and logistic regression (LR) for diabetes prediction. The SVM accuracy was found to be 87% when eight independent parameters were used to predict it, whereas the logistic regression accuracy was 87.01%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Hybrid Approaches for Stocks Price Prediction: A New Institutive Way of Neural Architecture Design

Hybrid Approaches for Stocks Price Prediction: A New Institu...

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Sharma, Vikram Rakhra, Manik Sharma, Vikrant Sarkar, Tiyas Singh, Amanpreet Jairath, Kapil Lovely Professional University Department of Computer Science and Engineering Phagwara India Graphic Era Deemed to be University Department of Computer Science and Engineering Graphic Era Hill University Phagwara India Lovely Professional University School of Computer Science and Engineering Phagwara India Lovely Professional University Department of Computer Scienceand Applications Phagwara India Trinity College Department of Sciences Jalandhar India

ISBN: (纸本)9798350378092

Stock price prediction is a critical aspect of financial decision-making for both individual investors and institutions. Traditional methods such as ARIMA and deep learning techniques like ANNs, LSTM, and GRU have demonstrated effectiveness in forecasting stock prices but suffer from inherent limitations. In recent years, hybrid models have emerged as a promising approach to address these shortcomings. This research paper introduces and evaluates three novel hybrid models for stock price prediction. First model integrates GRU, LSTM, and attention mechanisms to leverage the strengths of each component. By employing attention mechanisms, the model can focus on relevant information, thereby enhancing prediction accuracy. The second model combines GRU and LSTM networks with attention mechanisms, incorporating additional architectural elements to further refine predictive capabilities. The third proposed model is a fusion of the first two, augmented with additional features and enhancements to achieve superior performance in stock price prediction tasks. Evaluation of these proposed models utilizes commonly used metrics to assess predictive accuracy and generalizability. The results demonstrate the effectiveness of the proposed hybrid approaches in improving stock price prediction performance. This research contributes to the advancement of stock price prediction methodologies by proposing innovative hybrid models, offering valuable tools for researchers and practitioners in the finance industry seeking enhanced forecasting accuracy and decision-making capabilities. © 2024 IEEE.

关键词： Prediction models

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：