IoT and Machine Learning for Traffic Monitoring, Headlight Automation, and Self-parking: Application of AI in Transportation

Sinha, Anurag; Singh, Rajesh; Shubham, Prince; Raj, Vivek; Ramish, Md.

doi:10.1007/978-981-19-4606-6_56

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…Stronger associations are shown by peaks in the lines, whereas transitions or reassignments may be represented by dips. Insights into the temporal patterns and trends that the clustering algorithms have been able to capture may be gained by analysing this graph, which demonstrates dynamically how data points change in their cluster membership across successive time intervals [ 44 ].…”

Section: Simulation and Resultsmentioning

confidence: 99%

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

Sinha,

Aljrees,

Pandey

et al. 2023

Sensors

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Wireless sensor networks (WSNs) have emerged as a promising technology in healthcare, enabling continuous patient monitoring and early disease detection. This study introduces an innovative approach to WSN data collection tailored for disease detection through signal processing in healthcare scenarios. The proposed strategy leverages the DANA (data aggregation using neighborhood analysis) algorithm and a semi-supervised clustering-based model to enhance the precision and effectiveness of data collection in healthcare WSNs. The DANA algorithm optimizes energy consumption and prolongs sensor node lifetimes by dynamically adjusting communication routes based on the network’s real-time conditions. Additionally, the semi-supervised clustering model utilizes both labeled and unlabeled data to create a more robust and adaptable clustering technique. Through extensive simulations and practical deployments, our experimental assessments demonstrate the remarkable efficacy of the proposed method and model. We conducted a comparative analysis of data collection efficiency, energy utilization, and disease detection accuracy against conventional techniques, revealing significant improvements in data quality, energy efficiency, and rapid disease diagnosis. This combined approach of the DANA algorithm and the semi-supervised clustering-based model offers healthcare WSNs a compelling solution to enhance responsiveness and reliability in disease diagnosis through signal processing. This research contributes to the advancement of healthcare monitoring systems by offering a promising avenue for early diagnosis and improved patient care, ultimately transforming the landscape of healthcare through enhanced signal processing capabilities.

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Section: Simulation and Resultsmentioning

confidence: 99%

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

Sinha,

Aljrees,

Pandey

et al. 2023

Sensors

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“…80 Similarly, in 2021, of the eight (8) sampled authors who conducted a study on intelligent headlight beam intensity control and design of intelligent headlight, five (5) authors representing 62.5% used the sensor-based headlight beam intensity control approach, 5,43,81,82 and the remaining three (3) authors representing 37.5% used the machinelearning-based intensity control approach in the design of the intelligent headlight. 31,83,84 In 2022 out of the ten (10) sampled authors who conducted a study into the design of intelligent headlights, seven (7) authors representing 70% adopted the machine-learning-based headlight beam intensity control approach, 75,83,[85][86][87][88] , two (2) authors representing 20% utilized the sensor-based headlight beam intensity control approach, 40 and the remaining one (1) author representing 10% used the pulse width modulation approach for the design of the intelligent headlight. 89 Figure 4 illustrates that the predominant approaches for controlling intelligent headlight beams are machinelearning-based and sensor-based intensity control methods.…”

Section: The Utilization Rate Surveymentioning

confidence: 99%

A review of automotive intelligent and adaptive headlight beams intensity control approaches

Nkrumah,

Cai,

Jafaripournimchahi

2024

Advances in Mechanical Engineering

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The automotive headlight stands out as a critical vehicle component, particularly emphasized during nighttime driving. The high beam, designed for optimal driver visibility on long-distance roads, traditionally relies on manual control by the driver. However, this manual control poses challenges, particularly when the high beam light temporarily blinds oncoming drivers. The resultant dazzle for drivers of opposing vehicles is a significant concern. In response to these issues, there is a growing demand for adaptive and intelligent headlights that can autonomously adjust beam intensity. The intelligent headlight system takes on the responsibility of modifying the beam intensities without requiring explicit input from the drivers. This study aims to systematically review various approaches to controlling intelligent headlight beam intensity. The paper identifies four prominent approaches to intelligent headlight beam intensity control, recognized as widely used techniques. Furthermore, the study uncovers intriguing connections between some of these intensity control approaches. A survey on utilization rates indicates that sensor-based and machine learning (ML)-based intensity control approaches are the most commonly employed methods by automotive headlight designers. The paper concludes by providing insights into the future prospects of intelligent headlight technology, offering guidance for future researchers in this field.

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FL-XGBTC: federated learning inspired with XG-boost tuned classifier for YouTube spam content detection

Sharma,

Sinha,

Alkhayyat

et al. 2024

Int J Syst Assur Eng Manag

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IoT and Machine Learning for Traffic Monitoring, Headlight Automation, and Self-parking: Application of AI in Transportation

Cited by 5 publications

References 16 publications

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

A review of automotive intelligent and adaptive headlight beams intensity control approaches

FL-XGBTC: federated learning inspired with XG-boost tuned classifier for YouTube spam content detection

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