检索结果-内蒙古大学图书馆

Leveraging deep learning with sentiment analysis for Online Book reviews polarity classification model

Multimedia Tools and Applications 2024年 1-20页

作者： Bharathi, R. Bhavani, R. Priya, R. Department of Computer Science & amp Engineering Annamalai University Annamalai Nagar Chidambaram TamilNadu Cuddalore India

Sentiment analysis (SA) identifies the opinions or feelings expressed as negative, positive or neutral. It exploits models namely natural language processing (NLP) and computational linguistics to detect personal data. Recently, SA gained significant interest and is highly related to text classification to determine the intent of the text posted by the user. On the other hand, online book reviews are considered as a recently developed customer feeder source. In the digital era, people can explore books and make choices based on online review sites. SA can be commonly utilized by methods based on lexicon, machine learning (ML), combined, or hybrid analysis. Deep learning (DL) models are presented to classify sentiments without requiring high-level attribute engineering. Therefore, this research develops an optimal deep learning-based sentiment analysis for the online book review classification technique. This research work aims to classify the sentiments of the online book reviews from Amazon Kindle. The data were pre-processed in different ways initially to transform the input book reviews into a meaningful format. Two approaches are used for feature extraction: N-grams and Global Vectors for Word Representation (Gloves). Besides, cascaded recurrent neural network (CRNN) and convolutional neural network (CNN) models are used for the review classification process. The experimental evaluation of this research work is validated by utilizing the Amazon-kindle-book-review dataset. The proposed Glove-CNN model illustrates superior accuracy, precision, recall, and F-score values of 98.00%, 96.95%, 96.13%, and 96.52% over other state-of-the-art techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Pseudo-random Number Generator Using Knight’s Travel Path and Brightness Response of the Eye

引用

SN computer science 2024年第7期5卷 943页

作者： Devara, Sandeep Reddy Azad, Chandrasekhar Kiran, S. Sandisk India Karnataka Bangalore 560103 India Department of Computer Science and Engineering NIT Jamshedpur Jharkhand 831014 India Department of Computer Science and Engineering YSR Engineering College of YVU Andhra Pradesh Proddatur 516360 India

Random number generation plays a pivotal role in various fields, including cryptography, simulations, and gaming. This research paper introduces a novel approach to pseudo-random number generation that utilizes the Knight’s Travel Path, a unique mathematical construct from chess, and the Brightness Response of The Eye. The proposed method combines elements of both algorithmic and human-in-the-loop systems, offering a hybrid approach that capitalizes on the strengths of each. The Knight’s Travel Path, known for its non-linear and unpredictable nature, serves as the foundation for generating pseudo-random sequences. By iteratively traversing the chessboard, our algorithm produces sequences when incorporated with the Brightness Response of The Eye formulae. The Brightness Response of The Eye formulae is sensitivity to variations in luminance is exploited to introduce a dynamic component to the pseudo-random number generation process. To evaluate the proposed method’s performance, extensive simulations and empirical studies were conducted. The results demonstrate that the Knight’s Travel Path with Brightness Response of The Eye based pseudo-random number generator achieves consistent random results in tests such as the Frequency Test (Monobit), Run Test, and Cumulative Sums Test. It significantly outperforms conventional methods in terms of randomness and unpredictability, evidenced by higher p-values in multiple tests. However, it also showed some non-random results in tests like the Binary Matrix Rank Test and Maurer’s Universal Statistical Test, indicating areas for further refinement. This hybrid approach bridges the gap between algorithmic and human-generated randomness, offering a significant opportunities for future research and practical implementations in diverse domains. This approach presents an intriguing alternative to traditional pseudo-random number generators based on linear congruential or cryptographic algorithms. © The Author(s), under exclusive licen

关键词： Brightness response of the eye Digital signature Eye perception Hashing Knight’s travel path Pseudo-random Randomness testing

来源：评论

学校读者我要写书评

暂无评论

Detecting Misinformation in COVID-19 Content: A Machine Learning and Deep Learning Approach with Word Embeddings

引用

SN computer science 2025年第1期6卷 1-18页

作者： Chabukswar, Arati Shenoy, P. Deepa Venugopal, K.R. Department of Computer Science & Engineering University Visvesvaraya College of Engineering Bangalore University Karnataka Bengaluru 560001 India

Inaccurate information that is purposefully spread for a certain goal known as fake news related to COVID-19 tweets posing threats to public health, harm to the society. But creating a reliable method to identify news of this kind is difficult, particularly when the news is released combining factual and fictitious material. The identification of fake news with COVID-19 is challenging task in Natural Language Processing (NLP). This study uses Machine Learning (ML) models such as Support Vector Machine (SVM), Random Forest (RF), Gaussian Naıve Bayes (NB), Logistic Regression (LR) and Deep Learning (DL) models with several architectures such as Long-short Term memory (LSTM), Bidirectional LSTM with one-hot encoding representation and pre-trained word embeddings such as Word2Vec, GloVe and FastText, trained using 14,571 records on dataCOVID19 dataset obtained by combining datasets from three different sources available in Kaggle and GitHub repository. The tests on a real-world COVID-19 news dataset show that Bi-LSTM model improves performance when built over various embedding techniques such as Word2Vec, GloVe, FastText and One-hot encoding representation achieving highest accuracy score. The findings emphasise the potential of deep learning methods for identifying false news and have important ramifications for curbing the dissemination of false information about COVID-19 tweets known as Fake News Detection (FND). © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19 Deep learning Fake news Machine learning Natural language processing One-hot encoding Social media Word embeddings (Word2Vec, GloVe, FastText)

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective Teaching-Learning-Based Optimizer for a Multi-Weeding Robot Task Assignment Problem

引用

Tsinghua science and Technology 2024年第5期29卷 1249-1265页

作者： Nianbo Kang Zhonghua Miao Quan-Ke Pan Weimin Li M.Fatih Tasgetiren School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200444China School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Industrial Engineering Department Baskent UniversityAnkara 06790Türkiye

With the emergence of the artificial intelligence era,all kinds of robots are traditionally used in agricultural ***,studies concerning the robot task assignment problem in the agriculture field,which is closely related to the cost and efficiency of a smart farm,are ***,a Multi-Weeding Robot Task Assignment(MWRTA)problem is addressed in this paper to minimize the maximum completion time and residual herbicide.A mathematical model is set up,and a Multi-Objective Teaching-Learning-Based Optimization(MOTLBO)algorithm is presented to solve the *** the MOTLBO algorithm,a heuristicbased initialization comprising an improved Nawaz Enscore,and Ham(NEH)heuristic and maximum loadbased heuristic is used to generate an initial population with a high level of quality and *** effective teaching-learning-based optimization process is designed with a dynamic grouping mechanism and a redefined individual updating rule.A multi-neighborhood-based local search strategy is provided to balance the exploitation and exploration of the ***,a comprehensive experiment is conducted to compare the proposed algorithm with several state-of-the-art algorithms in the *** results demonstrate the significant superiority of the proposed algorithm for solving the problem under consideration.

关键词： genetic algorithm heuristic algorithm Multi-Weeding Robot Task Assignment(MWRTA) teaching optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Embedded Deep NN with Convolution NN-ID to Binominal Sentiment Prediction 9

Embedded Deep NN with Convolution NN-ID to Binominal Sentime...

引用

9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Gokila, S. Rajeswari, S. Helen, D. Lakshna, A. Hindustan Institute of Technology And Science Department of Computer Applications Chennai India Shrimathi Devkunvar Nanalal Bhatt Vaishnav College For Women Pg Department of Computer Science Chennai India Srm Institute of Science And Technology Department of Computer Applications Chennai Kattankulathur India Jeppiaar Engineering Collge Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798350365092

Objective: The objective is to develop a generalized deep learning system for sentiment analysis in a feedback system. This implies an intention to create a model that can effectively analyze sentiment across different domains or datasets. Methodology: It is proven that the deep learning systems are playing a major role in NLP, but still there is a need of enhancement to make the prediction system a perfect one. The Convolution Neural Network (CNN) could emphasize its ability to capture spatial dependencies in textual data and the Deep Neural Network(DNN) fetches the feature in text with varying semantics. In this context the proposed work Embed the Deep Neural Network with Convolution Neural Network in a three-layered ID architecture(EDNN with CNN-ID.) The model is enhanced in generalizing input-shape, kernel, channel, and filter parameters of the convolution layer. It produces remarkable results in sentimental analysis. The work is experimented with a feedback system from three different domains like Movie Review, Doctor Treatment Review, Product Review. Result: The EDNN with CNN-ID produces accuracy 93%, 91% and 91% for the three datasets © 2024 IEEE.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Transforming Agriculture with Cloud Computing: data Processing and Analysis Innovations 3

Transforming Agriculture with Cloud Computing: Data Processi...

引用

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Kaur, Harpreet Lovely Professional University Department of Computer Application Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Lovely Professional University School of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798331515911

Cloud computing is a game-changer in modern agriculture, providing new forms to access enormous data analysis. In this paper, we investigate how cloud computing solutions are being integrated with agricultural practices to improve the efficiency, scalability and availability in managing data pertaining to agriculture. Farming operations generate massive amounts of data from a multitude of sources like sensors, drones, and satellite imagery., the performance bottleneck becomes the traditional methods with which the data are being processed. So the common factor in all these problems lies with Cloud computing itself, it has a scalable infrastructure that provides reliable analysis and Real-time processing of large data sets throwing light on an array of questions people have about various aspects. In this paper, we explore the major innovations in cloud computing that power precise agriculture and examine how machine learning algorithms, data fusion methods, and distributed processing platforms are utilized. It also discusses cloud-based solutions, which can reduce costs of the use of resources, crop yield prediction, and risk management to weather/climate and pests. Cloud computing opens up a world of monitoring, analysis, and optimization to help provide higher yields more efficiently while maintaining a commitment to sustainable agriculture. Challenges and considerations in deploying cloud computing in agriculture, e.g., data security risks, privacy concerns, and the need for stable internet connection for rural environments are also articulated in the paper. The results of this project underscore the vital importance of cloud technology in redefining the agri-future and providing a robust platform for smarter and more sustainable agriculture. © 2025 IEEE.

关键词： agricultural data processing Cloud computing data analysis machine learning precision agriculture sustainable farming

来源：评论

学校读者我要写书评

暂无评论

A Comparative Exploration of Time Series Models for Wild Fire Prediction 4

A Comparative Exploration of Time Series Models for Wild Fir...

引用

4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Sri Sowmya, V. Sasikala, D. Theetchenya, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350343670

Wildfires pose a great threat to human safety and property arising from both natural and human causes. According to a technical assessment by the Forest Survey of India more than 95% of fires are of anthropogenic origin. Wildfires erupt due to burning of fossils by local communities for crop rotation, camp fires without proper supervision etc. Climate change further elevates the risk by fostering dry conditions. Traditionally, wildfire prediction relied on statistical models and expert judgment. However, the emergence of Machine Learning (ML) and Deep Learning (DL) techniques has significantly improved the accuracy of forest fire prediction. The objective of this work is to prevent wildfires and save the ecosystem. In this work, LightGBM(Light Gradient Boosting Machine) and LSTM(Long Short-Term Memory) machine learning models are utilized to predict the forest fire. Both models exhibit high F1 scores of 97% and 95% accuracy in forest fire prediction, enabling the development of reliable prediction systems. The results of these ML-based models may aid in identifying highrisk areas, optimizing prevention measures, refining evacuation plans, and guiding firefighting efforts. © 2024 IEEE.

关键词： Deep Learning(DL) LightGBM LSTM Machine learning(ML) Wildfires

来源：评论

学校读者我要写书评

暂无评论

A Natural Language Processing Based Risk Prediction Framework for Pathological Gambling 24

A Natural Language Processing Based Risk Prediction Framewor...

引用

24th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF-WN 2023

作者： Talha, Abu Basu, Tanmay Department of Data Science and Engineering Indian Institute of Science Education and Research Bhopal India

Task 2 of eRisk 2023 shared-tasks challenge at Conference and Labs of the Evaluation Forum (CLEF) was to focus on the early detection of pathological gambling via sequential text processing over social media conversations. The challenge organizers have released different datasets, consisting of social media posts and questionnaires, for all three tasks. The BioNLP research group at the Indian Institute of science Education and Research Bhopal (IISERB) participated in task 2 of the challenge and submitted five runs that for five different text mining frameworks. The performance of different text classification frameworks and their effectiveness in detecting signs of pathological gambling are demonstrated in this paper. Several classifiers and feature engineering schemes are combined to build individual frameworks. The features from free text are generated following the bag of words model and transformer based embeddings methods. Subsequently, adaptive boosting, logistic regression, support vector machine, and transformer based classifiers were used to identify the signs of pathological gambling from the social media posts. The experimental analysis demonstrates that the support vector machine and adaptive boosting classifier respectively using the entropy and TF-IDF weighting scheme of the bag of words outperforms the other methods on the training set. Furthermore, the adaptive boosting classifier following TF-IDF-based weighting scheme achieves the best precision score among all other submissions of task 2 of erisk 2023. However, the rest of the frameworks could not achieve reasonable performance which needs to be introspected in future. © 2023 Copyright for this paper by its authors.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Image Caption Generation using CNN and Audio Conversion 2

Image Caption Generation using CNN and Audio Conversion

引用

2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Priya, K. Babu, R.Vijay Reddy, M.Muralidhar Mohan Reddy, T.H. Maanesh, M. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Artificial Intelligence & Data Science Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Arizona state university Department of Data Science Tempe United States

ISBN: (纸本)9798350368413

This research focuses on developing a system that can generate descriptive captions for images and convert them into audio output. The system employs a Convolutional Neural Network (CNN) to extract visual features from images, which are then fed into a Gated Recurrent Unit (GRU) to generate textual descriptions. The generated captions are subsequently converted into audio using text-to-speech techniques. By training the model on a large dataset of image-caption pairs, the system learns to associate visual information with textual descriptions, enabling accurate and coherent caption generation. This research contributes to the advancement of image understanding and generation, with potential applications in various fields such as image search, accessibility, and content creation. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Deep CNN-Based Approach for Revolutionizing Bengali Handwritten Numeral Recognition 2nd

A Deep CNN-Based Approach for Revolutionizing Bengali Handwr...

引用

2nd International Conference on Big data, IoT and Machine Learning, BIM 2023

作者： Islam, Sudipta Progga Parvin, Farjana Department of Computer Science and Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh

ISBN: (纸本)9789819989362

Recognition of Bengali handwritten digits is a fascinating and demanding research problem that has garnered significant interest from researchers in the fields of pattern recognition. In this paper, a task-oriented deep convolutional architecture for recognizing handwritten Bengali digits is proposed. The main goal is to get a high level of accuracy while using a small number of parameters. The proposed architecture is designed to address the challenges posed by the complex and diverse nature of handwritten numerals in Bengali script, while also being computationally efficient with only 1.08 million trainable parameters. The performance of the model was evaluated by conducting experiments on two commonly used benchmark datasets of handwritten numerals in Bengali script, CMATERdb-3.1.1 and BanglaLekha-isolated-numerals. Different augmentation techniques were utilized to enhance the diversity and size of the training set, which led to improved robustness and generalization of the model. On the CMATERdb-3.1.1 dataset, the proposed model achieved an accuracy of 99.28%, and on the BanglaLekha-isolated-numerals dataset, it achieved an accuracy of 99.12%, outperforming several state-of-the-art models with comparable or larger numbers of parameters. The results suggest that this task-oriented model can be an efficient and effective solution for the recognition of Bengali handwritten numerals, with potential applications in document analysis, digitization, and text recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer vision

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：