Learning in Big Data: Introduction to Machine Learning

Bouchefry, Khadija El; Souza, Rafael S. de

doi:10.1016/b978-0-12-819154-5.00023-0

Cited by 83 publications

(35 citation statements)

References 105 publications

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“…Unsupervised learning (i.e., without a pre-existing library) is intended to uncover hidden patterns in unlabeled data. 399 It shows clustering of data but does not provide predictive classification. This approach has been extensively involved in the analysis of VOC sensing data, using different methods including principal component analysis (PCA) and hierarchical cluster analysis (HCA).…”

Section: Main Limitations In the Fieldmentioning

confidence: 99%

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Sensors for Volatile Organic Compounds

2022

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This paper provides an overview of recent developments in the field of volatile organic compound (VOC) sensors, which are finding uses in healthcare, safety, environmental monitoring, food and agriculture, oil industry, and other fields. It starts by briefly explaining the basics of VOC sensing and reviewing the currently available and quickly progressing VOC sensing approaches. It then discusses the main trends in materials’ design with special attention to nanostructuring and nanohybridization. Emerging sensing materials and strategies are highlighted and their involvement in the different types of sensing technologies is discussed, including optical, electrical, and gravimetric sensors. The review also provides detailed discussions about the main limitations of the field and offers potential solutions. The status of the field and suggestions of promising directions for future development are summarized.

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Section: Main Limitations In the Fieldmentioning

confidence: 99%

“…ML can be divided into two categories: unsupervised and supervised. Unsupervised learning (i.e., without a pre-existing library) is intended to uncover hidden patterns in unlabeled data . It shows clustering of data but does not provide predictive classification.…”

Section: Advanced and Emerging Trendsmentioning

confidence: 99%

Sensors for Volatile Organic Compounds

2022

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“…The latter is an effective data-driven approach that allows for more rigorous handling of sparse and heterogeneous data. AI methods vary greatly and are related to wide fields, prominent examples of these techniques are heuristic methods, swarm intelligence, expert systems, evolutionary algorithms, inference, fuzzy logic, machine learning, etc [136]- [138].…”

Section: ) Aimentioning

confidence: 99%

On 5G-V2X Use Cases and Enabling Technologies: A Comprehensive Survey

2021

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5G technologies promise faster connections, lower latency, higher reliability, more capacity and wider coverage. We are looking to rely on these technologies to achieve Vehicle-to-Everything (V2X) communications, which increase the safety and autonomy of vehicles in addition to road safety, saving energy and costs. The integration of vehicular communication systems and 5G is the subject of many research. Nowadays, researchers address challenges such as automated and intelligent networks, cloud and edge data processing, network management, virtualization, security, privacy and finally interoperability. This paper provides a survey of the latest V2X use cases including requirements, and various 5G enabling technologies under consideration for vehicular communications. Subsequently, we first provide an interesting mapping between the three 5G pillars and V2X use case groups. Then, we present a summary of potential applications of enabling technologies for V2X use case groups. Finally, the open directions of research are discussed, and the challenges that await to be met are pointed out.

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“…The hierarchical clustering algorithm visualizes data as a hierarchical tree that illustrates the fusion or division in each stage because the tree contains nested clusters. As mentioned, the hierarchical clustering approach is classified into agglomerative and divisive methods [22]. The agglomerative method begins by merging distinct clusters (items) based on similarity until a single cluster that contains all members is obtained.…”

Section: Introductionmentioning

confidence: 99%

Grey wolf optimization algorithm for hierarchical document clustering

Jabbar

Ku-Mahamud²

2021

IJEECS

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In data mining, the application of grey wolf optimization (GWO) algorithm has been used in several learning approaches because of its simplicity in adapting to different application domains. Most recent works that concern unsupervised learning have focused on text clustering, where the GWO algorithm shows promising results. Although GWO has great potential in performing text clustering, it has limitations in dealing with outlier documents and noise data. This research introduces medoid GWO (M-GWO) algorithm, which incorporates a medoid recalculation process to share the information of medoids among the three best wolves and the rest of the population. This improvement aims to find the best set of medoids during the algorithm run and increases the exploitation search to find more local regions in the search space. Experimental results obtained from using well-known algorithms, such as genetic, firefly, GWO, and k-means algorithms, in four benchmarks. The results of external evaluation metrics, such as rand, purity, F-measure, and entropy, indicates that the proposed M-GWO algorithm achieves better document clustering than all other algorithms (i.e., 75% better when using Rand metric, 50% better than all algorithm based on purity metric, 75% better than all algorithms using F-measure metric, and 100% based on entropy metric).

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Learning in Big Data: Introduction to Machine Learning

Cited by 83 publications

References 105 publications

Sensors for Volatile Organic Compounds

Sensors for Volatile Organic Compounds

On 5G-V2X Use Cases and Enabling Technologies: A Comprehensive Survey

Grey wolf optimization algorithm for hierarchical document clustering

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