Sustainability Measures: An Experimental Analysis of AI and Big Data Insights in Industry 5.0

In the context of Industry 5.0, this empirical research investigates the concrete effects of artificial intelligence (AI) and big data insights on sustainability metrics. Real-world data analysis shows that during a two-year period, there was a 10% rise in the energy used by solar panels, a 6.7% increase in the energy consumed by wind turbines, and a 6.7% drop in the energy consumed by the grid. Paper trash output was reduced by 14% and plastic waste by 24% as a consequence of waste reduction initiatives. Product quality was maintained by AI-driven quality control, with quality ratings ranging from 89 to 94. Moreover, there was a 6% decrease in carbon emissions from industry, 3.1% from transportation, and 4.6% from energy production. These results highlight how AI and Big Data may revolutionize Industry 5.0 by promoting environmental responsibility, waste reduction, energy efficiency, sustainability, and high-quality products

Search Behaviour in Public Spaces: Insights from Urban Kiosks and the Search Behaviour Test

We investigated data acquired from varied people engaging with urban kiosks in this study on search Behaviour in public settings. The data shows a diverse variety of user demographics, such as age, gender, and educational level. The research found that interaction durations varied, with an average of 16 minutes, suggesting the fluid nature of user involvement. Furthermore, the Search Behaviour Test findings revealed varying success rates for different search categories, with "News" queries attaining the greatest success rate of 85%. These results highlight the need of user-centric design and strategic content optimization in urban kiosk interfaces, therefore improving user experience and information retrieval efficacy in the developing environment of smart cities

User Satisfaction and Technology Adoption in Smart Homes: A User Experience Test

Using a mixed-methods approach, we examine the complex link between user happiness and technology adoption in the context of smart homes. Our tests show that user happiness and adoption are highly influenced by the versions of smart home technologies, with Version A producing better user satisfaction (7.2) and adoption rates (68%) than Version B (6.8, 62%). Furthermore, consumers engaging with Features A and C reported the greatest adoption rates (80%) and satisfaction (8.1), indicating that certain features, particularly when paired, have a significant influence on user pleasure. Extended training times resulted in higher user satisfaction and adoption rates of the technology; the 6-hour training group had the greatest adoption rate (84%), and the highest satisfaction (8.3%). Furthermore, user age demographics have a substantial impact on adoption and happiness; young adults have the greatest adoption rate (70%) and contentment (7.6). These results highlight the necessity of developing smart home technologies that are appropriate for various age groups, as well as the significance of feature customization, thorough training, and user-centric design in improving user satisfaction and encouraging technology adoption. Introductio

IoT in Home Automation: A Data-Driven User Behaviour Analysis and User Adoption Test

This research carried out a thorough data-driven examination of user behaviour, adoption rates, satisfaction, and energy efficiency in the context of IoT in home automation, within the quickly changing environment of smart homes and Internet of Things (IoT) technologies. The study found that users interacted with various kinds of IoT devices in diverse ways. Smart security systems and thermostats, for example, were quickly adopted and received high levels of satisfaction. The potential for significant energy savings demonstrated the contribution of IoT devices to sustainability. These results highlight the significance of making well-informed decisions when using IoT technology to create smarter, more efficient, and greener living environments. They also provide useful insights for manufacturers, legislators, and homeowners

Performance Evaluation of IoT Sensors in Urban Air Quality Monitoring: Insights from the IoT Sensor Performance Test

In this paper, we report on extensive experiments conducted to evaluate Internet of Things (IoT) sensor performance in monitoring urban air quality. As certified sensors showed a considerably reduced air quality measurement error of 4.3% compared to uncalibrated sensors at 8.5%, our results highlight the crucial function of sensor calibration. The performance of sensors was impacted by environmental factors; higher temperatures produced better accuracy (3.6%), while high humidity levels caused sensors to react more quickly (2.3 seconds). The average air quality index (AQI) recorded by inside sensors was 45, but outside sensors reported an AQI of 60. This indicates that the positioning of the sensors had a substantial influence on the air quality data. Additionally, the methods of data transmission were examined, and it was found that Wi-Fi-transmitting sensors had lower latency (0.6 seconds) and data loss (1.8%) than cellular-transmitting sensors. These results emphasize the significance of environmental factors, sensor placement strategy, sensor calibration, and suitable data transmission techniques in maximizing IoT sensor performance for urban air quality monitoring, ultimately leading to more accurate and dependable air quality assessment

Sustainability Measures: An Experimental Analysis of AI and Big Data Insights in Industry 5.0

In the context of Industry 5.0, this empirical research investigates the concrete effects of artificial intelligence (AI) and big data insights on sustainability metrics. Real-world data analysis shows that during a two-year period, there was a 10% rise in the energy used by solar panels, a 6.7% increase in the energy consumed by wind turbines, and a 6.7% drop in the energy consumed by the grid. Paper trash output was reduced by 14% and plastic waste by 24% as a consequence of waste reduction initiatives. Product quality was maintained by AI-driven quality control, with quality ratings ranging from 89 to 94. Moreover, there was a 6% decrease in carbon emissions from industry, 3.1% from transportation, and 4.6% from energy production. These results highlight how AI and Big Data may revolutionize Industry 5.0 by promoting environmental responsibility, waste reduction, energy efficiency, sustainability, and high-quality products

User Satisfaction and Technology Adoption in Smart Homes: A User Experience Test

Using a mixed-methods approach, we examine the complex link between user happiness and technology adoption in the context of smart homes. Our tests show that user happiness and adoption are highly influenced by the versions of smart home technologies, with Version A producing better user satisfaction (7.2) and adoption rates (68%) than Version B (6.8, 62%). Furthermore, consumers engaging with Features A and C reported the greatest adoption rates (80%) and satisfaction (8.1), indicating that certain features, particularly when paired, have a significant influence on user pleasure. Extended training times resulted in higher user satisfaction and adoption rates of the technology; the 6-hour training group had the greatest adoption rate (84%), and the highest satisfaction (8.3%). Furthermore, user age demographics have a substantial impact on adoption and happiness; young adults have the greatest adoption rate (70%) and contentment (7.6). These results highlight the necessity of developing smart home technologies that are appropriate for various age groups, as well as the significance of feature customization, thorough training, and user-centric design in improving user satisfaction and encouraging technology adoption. Introductio

Performance Evaluation of IoT Sensors in Urban Air Quality Monitoring: Insights from the IoT Sensor Performance Test

In this paper, we report on extensive experiments conducted to evaluate Internet of Things (IoT) sensor performance in monitoring urban air quality. As certified sensors showed a considerably reduced air quality measurement error of 4.3% compared to uncalibrated sensors at 8.5%, our results highlight the crucial function of sensor calibration. The performance of sensors was impacted by environmental factors; higher temperatures produced better accuracy (3.6%), while high humidity levels caused sensors to react more quickly (2.3 seconds). The average air quality index (AQI) recorded by inside sensors was 45, but outside sensors reported an AQI of 60. This indicates that the positioning of the sensors had a substantial influence on the air quality data. Additionally, the methods of data transmission were examined, and it was found that Wi-Fi-transmitting sensors had lower latency (0.6 seconds) and data loss (1.8%) than cellular-transmitting sensors. These results emphasize the significance of environmental factors, sensor placement strategy, sensor calibration, and suitable data transmission techniques in maximizing IoT sensor performance for urban air quality monitoring, ultimately leading to more accurate and dependable air quality assessment