Natural Language Processing Characterization of Recurring Calls in Public Security Services

Oliveira, Nicollas R. de; Reis, H. A. Lucio; Fernandes, Natália C.; Bastos, Anson; Medeiros, S. V. Dianne; Mattos, Diogo M. F.

doi:10.1109/icnc47757.2020.9049821

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…In the task of lemmatization, we try to eliminate the possible variants or plurals of the same word, reducing them to the same lemma, known as the dictionary form. On the other hand, in stemization this reduction is made by transforming each word into its radical [40][41][42].…”

Section: Natural Language Processingmentioning

confidence: 99%

“…The representation by the TF-IDF model, compared to the others, is the one that carries the greatest correlation between the semantics of the term and its weight in the vector space. This representation is very useful in problems that aim to extract knowledge from the datasets according to the semantics of the documents [40]. However, this representation is sensitive to the use of synonyms of common words.…”

Section: Vector Space Model Term Frequency-inverse Document Frequencymentioning

confidence: 99%

“…The process ends when a stop criterion is reached, often the number k of clusters. At the end of the algorithm, a clustering dendrogram is created, which is a binary tree hierarchy [40,67,72,73]. A possible hierarchical clustering considering the spatial arrangement between samples 1-6 of Figure 6a is illustrated in Figure 6b.…”

Section: Hierarchical Algorithmsmentioning

confidence: 99%

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Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges

Oliveira

Pisa²,

Lopez

et al. 2021

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The epidemic spread of fake news is a side effect of the expansion of social networks to circulate news, in contrast to traditional mass media such as newspapers, magazines, radio, and television. Human inefficiency to distinguish between true and false facts exposes fake news as a threat to logical truth, democracy, journalism, and credibility in government institutions. In this paper, we survey methods for preprocessing data in natural language, vectorization, dimensionality reduction, machine learning, and quality assessment of information retrieval. We also contextualize the identification of fake news, and we discuss research initiatives and opportunities.

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Section: Natural Language Processingmentioning

confidence: 99%

Section: Vector Space Model Term Frequency-inverse Document Frequencymentioning

confidence: 99%

See 1 more Smart Citation

Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges

Oliveira

Pisa²,

Lopez

et al. 2021

Information

Self Cite

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show abstract

“…NLP has multiple applications in smart cities. For instance, it can be used to power chatbots and virtual assistants to handle customer service, analyze social media data and other text-based sources to identify potential safety threats, analyze public opinion on various urban planning initiatives, provide multilingual support, and analyze social media data to identify traffic issues and provide real-time updates to drivers [123,124]. NLP has the potential to make smart cities more efficient, effective, and responsive to the needs of residents by reducing the workload of human customer service representatives, improving the overall customer experience, improving traffic flow, and helping city planners make more informed decisions [125,126].…”

Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends

Alahi

Sukkuea

Tina

et al. 2023

Sensors

140

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As the global population grows, and urbanization becomes more prevalent, cities often struggle to provide convenient, secure, and sustainable lifestyles due to the lack of necessary smart technologies. Fortunately, the Internet of Things (IoT) has emerged as a solution to this challenge by connecting physical objects using electronics, sensors, software, and communication networks. This has transformed smart city infrastructures, introducing various technologies that enhance sustainability, productivity, and comfort for urban dwellers. By leveraging Artificial Intelligence (AI) to analyze the vast amount of IoT data available, new opportunities are emerging to design and manage futuristic smart cities. In this review article, we provide an overview of smart cities, defining their characteristics and exploring the architecture of IoT. A detailed analysis of various wireless communication technologies employed in smart city applications is presented, with extensive research conducted to determine the most appropriate communication technologies for specific use cases. The article also sheds light on different AI algorithms and their suitability for smart city applications. Furthermore, the integration of IoT and AI in smart city scenarios is discussed, emphasizing the potential contributions of 5G networks coupled with AI in advancing modern urban environments. This article contributes to the existing literature by highlighting the tremendous opportunities presented by integrating IoT and AI, paving the way for the development of smart cities that significantly enhance the quality of life for urban dwellers while promoting sustainability and productivity. By exploring the potential of IoT, AI, and their integration, this review article provides valuable insights into the future of smart cities, demonstrating how these technologies can positively impact urban environments and the well-being of their inhabitants.

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“…• Oliveira et al [196] proposed an NLP-based clustering method to analyze calls that are recurrent in public safety services to determine the major causes of these calls. The unstructured data from the government's records are used to extract this information and then group it under major categories and identify the patterns in these types of calls.…”

Section: Smart Securitymentioning

confidence: 99%

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

Tyagi

Bhushan

2023

Wireless Pers Commun

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Smart cities provide an efficient infrastructure for the enhancement of the quality of life of the people by aiding in fast urbanization and resource management through sustainable and scalable innovative solutions. The penetration of Information and Communication Technology (ICT) in smart cities has been a major contributor to keeping up with the agility and pace of their development. In this paper, we have explored Natural Language Processing (NLP) which is one such technical discipline that has great potential in optimizing ICT processes and has so far been kept away from the limelight. Through this study, we have established the various roles that NLP plays in building smart cities after thoroughly analyzing its architecture, background, and scope. Subsequently, we present a detailed description of NLP's recent applications in the domain of smart healthcare, smart business, and industry, smart community, smart media, smart research, and development as well as smart education accompanied by NLP's open challenges at the very end. This work aims to throw light on the potential of NLP as one of the pillars in assisting the technical advancement and realization of smart cities.

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Natural Language Processing Characterization of Recurring Calls in Public Security Services

Cited by 9 publications

References 6 publications

Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges

Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges

Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

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