检索结果-内蒙古大学图书馆

SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

Scalable Computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

来源：评论

学校读者我要写书评

暂无评论

SMART LAVATORY SOLUTION: INTEGRATING IOT AND DEEP LEARNING MODELS FOR ENHANCED HYGIENE

引用

Scalable Computing 2025年第3期26卷 1057-1070页

作者： Patel, Jigna Shah, Aeshwi Rushali, Chaudhari Patel, Jitali Ukani, Vijay Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahmedabad India

In the current era of smart technology, integrating the Internet of Things (IoT) with Artificial Intelligence has revolutionized several fields, including public health and sanitation. The smart lavatory solution proposed in this paper improves hygiene standards using deep learning models and IoT system. The proposed system collect real-time data from deployed sensors to monitor and assess hygiene conditions regularly. Proposed model consists of four consequent phases as hardware implementation, data preprocessing, application and user interface modules. Rasberry Pi based sensor integration at hardware layer, normalization based techniques at data preprocessing layer, LSTM and GRU based deep learning model at application development layer and mobile notification to the cleaning staff at user interface layer ensure efficiently cleaning and monitoring of lavatory systems. Prior to assessing the proposed model’s testing accuracy experiments on the activation functions, optimizer, learning rate, and number of epochs were selected to choose the best to prevent overfitting or underfitting problems. With an accuracy of 98.61%, the proposed system performs better than the conventional approaches. © 2025 SCPE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

E-voting system using cloud-based hybrid blockchain technology

引用

Journal of Safety science and Resilience 2024年第1期5卷 102-109页

作者： Beulah Jayakumari S Lilly Sheeba Maya Eapen Jani Anbarasi Vinayakumar Ravi A.Suganya Malathy Jawahar Department of Information Technology Tagore Engineering College Chennai India School of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram ChennaiIndia Department of Information Technology Jerusalem College of Engineering ChennaiIndia School of Computer Science and Engineering Vellore Institute of Technology Chennai India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Leather Process Technology Division CSIR-Central Leather Research Institute ChennaiIndia

With the invention of Internet-enabled devices,cloud and blockchain-based technologies,an online voting system can smoothly carry out election *** pandemic situations,citizens tend to develop panic about mass gatherings,which may influence the decrease in the number of *** urges a reliable,flexible,transparent,secure,and cost-effective voting *** proposed online voting system using cloud-based hybrid blockchain technology eradicates the flaws that persist in the existing voting system,and it is carried out in three phases:the registration phase,vote casting phase and vote counting phase.A timestamp-based authentication protocol with digital signature validates voters and candidates during the registration and vote casting *** smart contracts,third-party interventions are eliminated,and the transactions are secured in the blockchain ***,to provide accurate voting results,the practical Byzantine fault tolerance(PBFT)consensus mechanism is adopted to ensure that the vote has not been modified or ***,the overall performance of the proposed system is significantly better than that of the existing *** performance was analyzed based on authentication delay,vote alteration,response time,and latency.

关键词： E-voting Cloud Blockchain Smart contract Consensus mechanism

来源：评论

学校读者我要写书评

暂无评论

Analyzing Hierarchical Relationships and Quality of Embedding in Latent Space

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年第4期6卷 843-858页

作者： Chatterjee, Ankita Mukherjee, Jayanta Das, Partha Pratim Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur721302 India

Existing learning models partition the generated representations using hyperplanes which form well defined groups of similar embeddings that is uniquely mapped to a particular class. However, in practical applications, the embedding space does not form distinct boundaries to segregate the class representations. There exists interaction among similar classes which cannot be visually determined in high-dimensional space. Moreover, the structure of the latent space remains obscure. As learned representations are frequently reused to reduce the inference time, it is important to analyse how semantically related classes interact among themselves in the latent space. Therefore, we propose a boundary estimation algorithm that minimises the inclusion of other classes in the embedding space to form groups of similar representations and compare the quality of these class embeddings for various models in an already encoded space. These groups are overlapping to denote ambiguous embeddings that cannot be mapped to a particular class with high confidence. The algorithm determines which representations to be included or discarded to form well defined regions, separating discriminating, ambiguous and rejected embeddings to depict a particular class. Later, we construct relation trees to evaluate the hierarchical relationships formed among the classes, and compare it with the WordNet ontology using phylogenetic tree comparison methods. © 2020 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Enhancing Multicriteria-Based Recommendations by Alleviating Scalability and Sparsity Issues Using Collaborative Denoising Autoencoder

引用

computers, Materials & Continua 2024年第2期78卷 2269-2286页

作者： S.Abinaya K.Uttej Kumar School of Computer Science&Engineering Vellore Institute of TechnologyChennaiTamilnadu600127India

A Recommender System(RS)is a crucial part of several firms,particularly those involved in *** conventional RS,a user may only offer a single rating for an item-that is insufficient to perceive consumer ***,businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’*** the other hand,the collaborative filtering(CF)algorithm utilizing AutoEncoder(AE)is seen to be effective in identifying user-interested ***,the cost of these computations increases nonlinearly as the number of items and users *** triumph over the issues,a novel expanded stacked autoencoder(ESAE)with Kernel Fuzzy C-Means Clustering(KFCM)technique is proposed with two *** the first phase of offline,the sparse multicriteria rating matrix is smoothened to a complete matrix by predicting the users’intact rating by the ESAE approach and users are clustered using the KFCM *** the next phase of online,the top-N recommendation prediction is made by the ESAE approach involving only the most similar user from multiple *** the ESAE_KFCM model upgrades the prediction accuracy of 98.2%in Top-N recommendation with a minimized recommendation generation *** experimental check on the Yahoo!Movies(YM)movie dataset and TripAdvisor(TA)travel dataset confirmed that the ESAE_KFCM model constantly outperforms conventional RS algorithms on a variety of assessment measures.

关键词： Recommender systems multicriteria rating collaborative filtering sparsity issue scalability issue stacked-autoencoder Kernel Fuzzy C-Means Clustering

来源：评论

学校读者我要写书评

暂无评论

Network Security and Cryptography: Threats, Obstacles and Solutions-A Bibliometric Analysis

Recent Advances in Computer Science and Communications

引用

Recent Advances in computer science and Communications 2025年第2期18卷 83-101页

作者： Singh, Purushottam Dutta, Sandip Pranav, Prashant Department of Computer Science and Engineering Birla Institute of Technology Mesra Ranchi835215 India

Background: In the wake of escalating cyber threats and the indispensability of ro-bust network security mechanisms, it becomes crucial to understand the evolving landscape of cryptographic research. Recognizing the significant contributions and discerning emerging trends can guide future strategies and technological advancements. Our study endeavors to shed light on this through a bibliometric analysis of publications in the realms of Network Security and Cryptography. Method: To chronicle and synthesize the progression of research methodologies from their in-ception to the present day, we undertook a comprehensive Bibliometric Analysis of Network Security and Cryptography. Our data set was culled from the Clarivate Analytics Web of science Database, encompassing 3,897 papers, 603 sources, and 7,886 authors from across the globe. Results: Our analysis revealed a marked upsurge in cryptographic research since 1992, with China standing out as a dominant contributor in terms of publications. Notably, while 'security' and 'cryptography' emerged as recurrent research themes, there's an observable downtrend in international collaborations. Our study also highlights pivotal topics shaping the network security domain, offering insights into the trajectories of research source growth, structural variabil-ities in research relevance, and prospective intellectual and collaborative avenues as guided by authorship patterns. Conclusion: Cryptographic research is on an upward trajectory, both in volume and signifi-cance. However, the tapering of international collaborations and an evident need to concentrate on emergent challenges, such as data privacy and innovative network attacks, emerge as nota-ble insights. This bibliometric review serves as a compass, directing researchers and academi-cians towards areas warranting heightened attention, thereby informing the roadmap for future investigative pursuits. © 2025 Bentham science Publishers.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

IWM-LSTM encoder for abstractive text summarization

引用

Multimedia Tools and Applications 2025年第9期84卷 5883-5904页

作者： Gangundi, Ravindra Sridhar, Rajeswari Department of Computer Science and Engineering National Institute of Technology Tamilnadu Tiruchirappalli620015 India

Sequence-to-sequence models are fundamental building blocks for generating abstractive text summaries, which can produce precise and coherent summaries. Recently proposed, different text summarization models aimed to enhance summarization performance through the use of copying mechanisms, reinforcement learning, and multiple-level encoders. However, there has been limited research on improving the summarization output by modifying the structure of the long short-term memory (LSTM) cell. We introduced an improved version of LSTM called improved working memory LSTM (IWM-LSTM). IWM-LSTM removes the output gate and enhances the input and forget gates by incorporating cell state information into these gates. In our sequence-to-sequence model for text summarization, we replaced the LSTM encoder with a bi-directional IWM-LSTM, resulting in better summaries with minimal training time and less computational intensiveness. Additionally, we utilized Bidirectional encoder representations from transformers (BERT) embeddings to enhance the rouge score. The CNN/DailyMail dataset is used to train and test the model performance. The proposed model achieves better Recall-oriented understudy for gisting evaluation (ROUGE) scores than state-of-the-art models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Attention enabled viewport selection with graph convolution for omnidirectional visual quality assessment

引用

Multimedia Tools and Applications 2025年第14期84卷 12925-12948页

作者： C, Nandhini M, Brindha Department of Computer Science and Engineering National Institute of Technology TamilNadu Tiruchirappalli620015 India

Omnidirectional images provide an immersive viewing experience in a Virtual Reality (VR) environment, surpassing the limitations of traditional 2D media beyond the conventional screen. This VR technology allows users to interact with visual information in an exciting and engaging manner. However, the storage and transmission requirements for 360-degree panoramic images are substantial, leading to the establishment of compression frameworks. Unfortunately, these frameworks introduce projection distortion and compression artifacts. With the rapid growth of VR applications, it becomes crucial to investigate the quality of the perceptible omnidirectional experience and evaluate the extent of visual degradation caused by compression. In this regard, viewport plays a significant role in omnidirectional image quality assessment (OIQA), as it directly affects the user’s perceived quality and overall viewing experience. Extracting viewports compatible with users viewing behavior plays a crucial role in OIQA. Different users may focus on different regions, and the model’s performance may be sensitive to the chosen viewport extraction strategy. Improper selection of viewports could lead to biased quality predictions. Instead of assessing the entire image, attention can be directed to areas that are more importance to the overall quality. Feature extraction is vital in OIQA as it plays a significant role in representing image content that aligns with human perception. Taking this into consideration, the proposed ATtention enabled VIewport Selection (ATVIS-OIQA) employs attention based view port selection with Vision Transformers(ViT) for feature extraction. Furthermore, the spatial relationship between the viewports is established using graph convolution, enabling intuitive prediction of the objective visual quality of omnidirectional images. The effectiveness of the proposed model is demonstrated by achieving state-of-the-art results on publicly available benchmark datasets, n

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Optimized Neural Network for Automated Cardiovascular Disease Classification: Meta-heuristic Enabled Model

引用

SN computer science 2025年第1期6卷 1-14页

作者： Kusuma, S. Jothi, K.R. Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Madanapalle India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Cardiovascular disease remains a major issue for mortality and morbidity, making accurate classification crucial. This paper introduces a novel heart disease classification model utilizing Electrocardiogram (ECG) signals. The model consists of two main steps: feature extraction using a refined Lion Algorithm with New Mutated evaluation (LA-NM) and classification through an ensemble approach combined with a Neural Network (NN). The proposed model significantly outperforms traditional classifiers. At 80% training data, it achieves accuracy improvements of 8.4% to 3.11% over Neural Network (NN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and ensemble models. At 90% training data, it shows precision improvements ranging from 1.03% to 0.31% better than the comparison models and specificity improvements ranging from 13.92% to 0.52%. These results underscore the effectiveness of the model and suggest its potential for further development in personalized treatment recommendations based on patient history. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ensemble models Feature extraction Heart disease classification Neural network Optimization

来源：评论

学校读者我要写书评

暂无评论

Advancing emotion recognition via EEG signals using a deep learning approach with ensemble model

引用

Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 143-154页

作者： Immanuel, Rajeswari Rajesh Sangeetha, S.K.B. Department of Computer Science and Engineering SRM Institute of Science and Technology Faculty of Engineering and Technology Vadapalani Campus Tamil Nadu Chennai India

Human emotions are the mind's responses to external stimuli, and due to their dynamic and unpredictable nature, research in this field has become increasingly important. There is a growing trend in utilizing deep learning and machine learning techniques for emotion recognition through EEG (electroencephalogram) signals. This paper presents an investigation based on a real-time dataset that comprises 15 subjects, consisting of 7 males and 8 females. The EEG signals of these subjects were recorded during exposure to video stimuli. The collected real-time data underwent preprocessing, followed by the extraction of features using various methods tailored for this purpose. The study includes an evaluation of model performance by comparing the accuracy and loss metrics between models applied to both raw and preprocessed data. The paper introduces the EEGEM (Electroencephalogram Ensemble Model), which represents an ensemble model combining LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) to achieve the desired outcomes. The results demonstrate the effectiveness of the EEGEM model, achieving an impressive accuracy rate of 95.56%. This model has proven to surpass the performance of other established machine learning and deep learning techniques in the field of emotion recognition, making it a promising and superior tool for this application. © 2024 - IOS Press. All rights reserved.

关键词： Convolutional neural networks

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：