Pattern Recognition and Event Detection on IoT Data-streams

Karras, Christos; Karras, Aristeidis; Sioutas, Spyros

doi:10.48550/arxiv.2203.01114

Cited by 8 publications

(5 citation statements)

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“…Similar works in the field of Machine Learning as in [13] highlight the significance of neural networks for pattern recognition while research as in [10] pave the wave for the integration of traditional MCMC methods with neural networks. Additionally, MCMC methods are of great importance when trying to gain useful insights from data or to approximate functions that are unable to be estimated with traditional methods [12], [11].…”

Section: Related Workmentioning

confidence: 88%

Integrating User and Item Reviews in Deep Cooperative Neural Networks for Movie Recommendation

Karras¹,

Karras²

2022

Preprint

Self Cite

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User evaluations include a significant quantity of information across online platforms. This information source has been neglected by the majority of existing recommendation systems, despite its potential to ease the sparsity issue and enhance the quality of suggestions. This work presents a deep model for concurrently learning item attributes and user behaviour from review text. Deep Cooperative Neural Network (DeepCoNN) is the suggested model consisting of two parallel neural networks connected in their final layers. One of the networks focuses on learning user behaviour from reviews submitted by the user, while the other network learns item attributes from user reviews. On top, a shared layer is added to connect these two networks. Similar to factorization machine approaches, the shared layer allows latent factors acquired for people and things to interact with each other. On a number of datasets, DeepCoNN surpasses all baseline recommendation systems, according to experimental findings.

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Section: Related Workmentioning

confidence: 88%

Integrating User and Item Reviews in Deep Cooperative Neural Networks for Movie Recommendation

Karras¹,

Karras²

2022

Preprint

Self Cite

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“…Apart from the integration of live data, different events detected from heterogenous sensors can also be added to the KG for increased perception. Different events detected within IoT streams were integrated into a KG for improved intelligence of the manufacturing system (Karras et al, 2022). On top of that, prediction results from deep learning-based models were attached to the generated KG (B.…”

Section: Knowledge Graph Generationmentioning

confidence: 99%

Cognitive manufacturing: definition and current trends

El Kalach,

Yousif,

Wuest

et al. 2024

J Intell Manuf

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Manufacturing systems have recently witnessed a shift from the widely adopted automated systems seen throughout industry. The evolution of Industry 4.0 or Smart Manufacturing has led to the introduction of more autonomous systems focused on fault tolerant and customized production. These systems are required to utilize multimodal data such as machine status, sensory data, and domain knowledge for complex decision making processes. This level of intelligence can allow manufacturing systems to keep up with the ever-changing markets and intricate supply chain. Current manufacturing lines lack these capabilities and fall short of utilizing all generated data. This paper delves into the literature aiming at achieving this level of complexity. Firstly, it introduces cognitive manufacturing as a distinct research domain and proposes a definition by drawing upon various preexisting themes. Secondly, it outlines the capabilities brought forth by cognitive manufacturing, accompanied by an exploration of the associated trends and technologies. This contributes to establishing the foundation for future research in this promising field.

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“…Pattern recognition is a crucial component of AI systems as it involves teaching machines to identify and categorize specific patterns within data. This capability has led to significant advancements in various fields, such as speech-to-text conversion, facial recognition, and object detection (Karras et al 2022). In speech-to-text conversion, AI systems analyze audio signals and employ pattern recognition algorithms to accurately transcribe spoken words into written text.…”

Section: Pattern Recognition and Language Processingmentioning

confidence: 99%

Silent no more: a comprehensive review of artificial intelligence, deep learning, and machine learning in facilitating deaf and mute communication

ZainEldin,

Gamel,

Talaat

et al. 2024

Artif Intell Rev

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People who often communicate via sign language are essential to our society and significantly contribute. They struggle with communication mostly because other people, who often do not understand sign language, cannot interact with them. It is necessary to develop a dependable system for automatic sign language recognition. This paper aims to provide a comprehensive review of the advancements in artificial intelligence (AI), deep learning (DL), and machine learning (ML) technologies that have been used to facilitate communication for individuals who are deaf and mute (D–M). This study explores various applications of these technologies, including sign language interpretation, speech recognition, and text-to-speech synthesis. By examining the current state of research and development in AI, ML, and DL for the D–M field, the survey sheds light on the potential and challenges faced in utilizing AI, deep learning, and ML to bridge the communication gap for the D–M community. The findings of this survey will contribute to a greater understanding of the potential impact of these technologies in improving access to communication for individuals who are D–M, thereby aiding in the development of more inclusive and accessible solutions.

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Pattern Recognition and Event Detection on IoT Data-streams

Cited by 8 publications

References 0 publications

Integrating User and Item Reviews in Deep Cooperative Neural Networks for Movie Recommendation

Integrating User and Item Reviews in Deep Cooperative Neural Networks for Movie Recommendation

Cognitive manufacturing: definition and current trends

Silent no more: a comprehensive review of artificial intelligence, deep learning, and machine learning in facilitating deaf and mute communication

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