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Smart cities and the IoT: an in-depth analysis of global research trends and future directions

Discover Internet of Things 2024年第1期4卷 19页

作者： Bhardwaj, Vivek Anooja, A. Vermani, Lovkesh Singh Sunita Dhaliwal, Balwinder Kaur School of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India School of Computer Application JECRC University Rajasthan Jaipur India Multani Mal Modi College Punjab Patiala India Department of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

Extensive scientific investigation is necessary because every government wants to construct smart cities. This is why examining how researchers approach this area of study is critical. This study investigates global research trends in smart cities and the Internet of Things (IoT) by analyzing 14,309 articles from the Scopus database (2010–2024). Using text mining and latent semantic analysis (LSA) via the KNIME tool, the research identifies key areas and trends in smart city development. The k-means clustering algorithm predicts future research directions, highlighting the most active countries, influential authors, and significant sources in the field. The results underscore the need for additional research in sectors where IoT applications for smart cities are still in their early stages. This analysis also offers insights into fostering international collaborations among institutions and researchers. The findings suggest that future research should focus on developing secure, scalable solutions to address challenges across various industries. Overall, this study provides a comprehensive overview of smart city research, offering valuable guidance for researchers and policymakers aiming to advance the integration of IoT technologies into urban environments. © The Author(s) 2024.

关键词： Latent semantic analysis

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Efficient net-based deep learning: A deep-fake image detection 3

Efficient net-based deep learning: A deep-fake image detecti...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Singh, Rohan Sharma, Dilip Kumar Kumar Jain, Praphula Gla University Department of Computer Science and Engineering Mathura India Gla University Department of Computer Engineering and Application Mathura India Mits Gwalior Department of Computer Science and Engineering MP India

ISBN: (纸本)9798331528201

With the substantial volume of social media data generated daily and the concurrent emergence of powerful multimedia modification techniques, detecting manipulations in photographs and videos has become an important task. This study examines the strength of a deep learning-based efficient net model for detecting manipulation in multimedia images. This study examines the publicly accessible benchmark dataset DFDC to prove its efficiency. We also highlighted the real-world challenges and emerging trends to combat deep-fakes. The research experiment resulted in and confirmed the efficiency of Efficient-Net as it performed well in both the training and testing stages, exhibiting more accuracy and reduced loss. The test accuracy varies between 50 and 95, with epochs spanning from 0 to 10. © 2024 IEEE.

关键词： Deep Learning Deepfakes Efficient Net Fake Image Video

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Exploring Deep Learning Techniques for Sentiment Analysis on IMDb Movie Reviews Dataset

Exploring Deep Learning Techniques for Sentiment Analysis on...

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2024 International Conference on Big Data Analytics in Bioinformatics, DABCon 2024

作者： Sharma, Madhav Saini, Hukum Chand Sikarwal, Pushpendra Kumar Jaipur India Department of Engineering and Technology Jaipur India Department of Computer Application Jaipur India

ISBN: (纸本)9798350378047

Deep learning methods have changed the way we deal with natural language processing (NLP), and now our models can generate Textual Data almost as well like humans, but it was recent advances that introduced these revolutionized mechanisms for understanding complex human languages. The task of sentiment analysis-identifying the sentiment in text like movie reviews- is a fundamental one for NLP. This paper focuses on utilizing deep learning techniques, more specifically recurrent neural networkswith Long Short-Term Memory (LSTM) cells for sentiment analysis in IMDb movie reviews dataset. The model proposed in this study uses word embeddings [1], LSTM layers which are integrated as the temporality and gives a way to understand temporal dependencies important for analyzing meaning representations of sequential text data for sentiment classification. We start by pre-processing the IMDb data - cleaning and tokenizing text-data, after which we convert words into dense vector representations using pre-trained word embeddings. These embeddings are then processed by the LSTM layers, which helps to understand the context and sequential information [2]. © 2024 IEEE.

关键词： Spatio-temporal data

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Principle research of word vector representation in natural language processing

Principle research of word vector representation in natural ...

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2022 International Conference on Electronic Information engineering and computer Science, EIECS 2022

作者： Kang, Shijia Kong, Linggang Luo, Bin Zheng, Cuifang Wu, Jiaju Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (纸本)9781510663312

Natural language processing is a research direction in many fields such as linguistics, computer science, and data fusion of study. The representation of word vector is a method to map words into the real vector space, which is the core technology of many current natural language processing tasks. This paper summarizes and studies some typical expression methods of word vector as well as research the word vectors of linguistics and mathematical principle. We first elaborate the process of mapping the words to the vector, namely, encoding natural language information to word vector according to semantics. Secondly, we analyze several typical methods such as co-occurrence matrix, Word2Vec, GloVe, ELMo on information carrying capacity. Thirdly, on the basis of analyzing the principles of these methods, this paper also uses SVD decomposition, neural network, and other methods respectively to reproduce the specific process of generating word vectors. Finally, combining word similarity calculation and text sentiment classification task, we compare the performance of word vectors trained by various methods in different tasks. Experiments verify the conclusion that different word vector generation methods have different emphases in carrying linguistic ability and perform differently in different tasks. © 2023 SPIE.

关键词： Data fusion

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Traffic Verification Based on API Function Control in IIoT Service Cluster 11

Traffic Verification Based on API Function Control in IIoT S...

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2023 IEEE 11th International Conference on Information, Communication and Networks, ICICN 2023

作者： Xu, Sibo Yin, Mingyong Hu, Teng Li, Junhao Li, Hang Deng, Kai Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (纸本)9798350314014

Typically, traffic within a cluster of IIoT (Industrial Internet of Things) services is secure by default. However, accidental access by privileged users inside the cluster, compromised services, and malicious requests carefully constructed by attackers can cause sensitive data leakage and threaten the data security of IIoT services and even the security of other applications themselves. In order to defend against possible threats from inside the IIoT service cluster, we propose an approach to implement traffic verification inside the IIoT service cluster based on API function control. The API function of the service, when handling requests from IIoT devices or other services, will asynchronously request the internal verification service to verify whether the request comes from a reliable intracluster service or IIoT device, and decide whether to return a response to the requester based on the result of the verification. © 2023 IEEE.

关键词： Internet of things

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Sentiment Analysis for Movie Reviews on Social Media based on Sentiment Lexicon and Deep Learning 15

Sentiment Analysis for Movie Reviews on Social Media based o...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Jakhar, Yogesh Kumar Mishra, Nidhi Department of Computer Application University of Engineering & Management Jaipur India Amity University Noida India

ISBN: (纸本)9798331300579

Now days, with the fast improvement in internet technology and social media network, user give his/her feedback on many things and companies also asked to give feedback about their services as product or service delivered to the customers in the form of review. User's review about product or service playing major role in improving services as user pleasure. In this paper we present a sentiment analysis model, which is constructed with the help of sentiment lexicon with association of attention-based Bidirectional Gated Recurrent Unit (BiGRU) and CNN (Convolutional Neural Network). For experimental work we used movie review dataset and matched the sentiment analysis results of the proposed method with the mutual sentiment analysis models (SVM, Naïve Bayes, Decision Tree, Random Forest, CNN, and BiGRU) on the dataset. The Experiment result shows that proposed model perform better than other classifiers. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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Enterprise Intranet Threat Intelligence Processing Framework Based on Open Source Community 7

Enterprise Intranet Threat Intelligence Processing Framework...

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7th IEEE Information Technology and Mechatronics engineering Conference, ITOEC 2023

作者： Zeng, Wenjing Lu, Peng Wang, Hao Lou, Fang China Academy of Engineering Physics Institute of Computer Application Mianyang China

ISBN: (纸本)9798350334197

Network attack tactics, techniques, procedures, and IOC are being updated more and more rapidly. Defenders need to obtain such threat intelligence in a timely manner, to identify whether the enterprise intranet is under attack, and take corresponding protective measures. Before the official disclosure of the details of the attack, the open source community often already has users in the discussion of the attack, if we can extract threat intelligence that strong related to the enterprise from the open source community in a timely manner. It will be a great help in monitoring the enterprise intranet. However, new posts are published every day in the open source community, and some of the posts may have nothing to do with network security, and the threat intelligence disclosed in the posts may not be useful to the enterprise intranet. To bridge the gap, we propose EITIP, a framework for automated enterprise intranet threat intelligence processing. EITIP automatically collects new posts from open source community, classifies posts related to cyber security, obtains effective threat intelligence, and keep it updated. To address multiple challenges, EITIP provides: (1) effective threat entities, which can help obtain richer threat intelligence. (2) a method for obtaining strongly correlated threat intelligence on an enterprise intranet. (3) a complete threat intelligence processing framework. © 2023 IEEE.

关键词： Intranets

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8th International Conference on Communication, Image and Signal Processing, CCISP 2023

作者： Duan, Yubing Wang, Hejun Deng, Kai Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (纸本)9798350305838

Text similarity calculation is an important component of many text mining techniques. Most of the existing studies only consider model fusion of textual features or semantic features, which are not suitable for long text processing scenarios due to the time consumption of topic extraction for words of the whole document. This article introduces related work of text similarity calculation, including TF-IDF algorithm, lexical features and LDA(Latent Dirichlet Allocation) model. Then a text similarity calculation method that combines text word features and text implicit topic features is proposed, making full use of the surface and hidden information of text to calculate text similarity. The proposed method first improves traditional word mover's distance (WMD) algorithm by introducing lexical features of text, and then calculates text similarity by linear weighting of the improved WMD algorithm and LDA model algorithm. Experimental results indicate that compared with traditional WMD algorithm and LDA model algorithm, the proposed method obtains better text similarity calculation results. © 2023 IEEE.

关键词： Text processing

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The Design of Postgresql's Security Protection Scheme 7

The Design of Postgresql's Security Protection Scheme

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7th IEEE Information Technology and Mechatronics engineering Conference, ITOEC 2023

作者： Lu, Peng Lou, Fang China Academy of Engineering Physics Institute of Computer Application Mianyang China

ISBN: (纸本)9798350334197

Postgresql is a widely used open source database, but it pays more attention to its general functions and performance, and the data security protection measures are seriously insufficient. Aiming at the problems of database security protection such as authentication password leakage and malicious copy of data, this paper studies postgresql database security protection mechanism and the data encryption, including database security protection model, authentication password leakage protection mechanism and underlying data malicious copy protection mechanism. Postgresql's security protection scheme designed in this paper can break through the key problems such as abnormal data identification in high confusion mode, homomorphic migration of data operation and performance balance, and focus on solving two sensitive data leakage hidden dangers, such as fake login and copy & restore datas, so as to provide security for data storage. © 2023 IEEE.

关键词： Authentication

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Virtual Assistant Using Deep Learning Techniques 7

Virtual Assistant Using Deep Learning Techniques

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7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Nithin Anupama Kumar, S. Rv College of Engineering Department of Master of Computer Application Bangalore India

ISBN: (纸本)9798350343144

An application programme known as a virtual assistant, is as an AI based digital assistant, that can recognize natural language voice commands and carry out the user's requests. The goal of this work is to use deep learning to build a thorough virtual assistant for the Core Windows platform. The purpose is to solve the lack of an appropriate virtual assistant for Windows users, especially in scenarios with slow internet or server problems. The suggested virtual assistant seeks to provide offline capability, improve natural language interpretation, and optimize performance on Windows devices by utilizing recurrent neural networks, transformer topologies, and pre-trained language models. This study helps the Windows ecosystem's virtual assistants reach more audiences and enhance user experience. solution for the lack of an appropriate virtual assistant for Windows users. By utilizing recurrent neural networks, transformer topologies, and pre-trained language models, the proposed virtual assistant offers offline capability, enhanced natural language interpretation, and optimized performance on Windows devices. These advancements aim to reach a broader audience and improve the user experience within the Windows ecosystem. © 2023 IEEE.

关键词： Recurrent neural networks

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