Towards Industrial Revolution 5.0 and Explainable Artificial Intelligence: Challenges and Opportunities

Taj, Imran; Jhanjhi, N. Z.

doi:10.12785/ijcds/120124

Cited by 32 publications

(10 citation statements)

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“…By processing data closer to the source, edge computing reduces latency and minimizes the need for reliance on cloud-based infrastructure, making drones more responsive and efficient in real-time applications. With edge computing capabilities, drones can analyze sensor data, imagery, and telemetry streams in real-time, extracting actionable insights without the need for continuous communication with ground-based servers (Taj and Zaman, 2022). This localized data processing enhances the autonomy and agility of drones, enabling them to adapt to dynamic environments and make informed decisions without significant delays.…”

Section: Emerging Trends In Ai and Drone Technologymentioning

confidence: 99%

Reviewing the Role of Ai in Drone Technology and Applications

Nwankwo Constance Obiuto,

Igberaese Clinton Festus-Ikhuoria,

Oladiran Kayode Olajiga

et al. 2024

Comput. sci. IT res. j.

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This comprehensive review delves into the transformative impact of artificial intelligence (AI) on drone technology, examining its pivotal role in revolutionizing various applications. As drones continue to evolve from recreational gadgets to indispensable tools across industries, the integration of AI enhances their capabilities, enabling advanced functionalities and expanding their potential use cases. The convergence of AI and drone technology has given rise to a myriad of applications, transforming industries ranging from agriculture to surveillance. Machine learning algorithms empower drones with autonomous navigation capabilities, allowing them to navigate complex environments and adapt to dynamic scenarios. Computer vision technologies enable drones to perceive and analyze visual information, facilitating tasks such as object recognition, tracking, and environmental monitoring. These advancements significantly contribute to enhanced aerial surveying, precision agriculture, and disaster response efforts. In the realm of precision agriculture, AI-equipped drones aid in crop monitoring, disease detection, and yield estimation, optimizing resource allocation and boosting agricultural productivity. Drones with AI-driven capabilities are increasingly employed in environmental monitoring, wildlife conservation, and disaster response, providing real-time data for efficient decision-making. Recent trends in AI-infused drone technology highlight its dynamic evolution. Edge computing solutions empower drones to process data locally, reducing latency and enhancing real-time responsiveness. Reinforcement learning algorithms enable drones to learn from their experiences, adapting and optimizing their performance over time. Swarm intelligence, an emerging field in drone technology, leverages AI to enable coordinated and synchronized actions among multiple drones, expanding their capabilities for collaborative tasks. In conclusion, this review sheds light on the pivotal role of AI in transforming drone technology and expanding its applications. The synergy between AI and drones has unlocked new possibilities across various industries, ranging from agriculture to disaster response. As technology continues to advance, the collaborative integration of AI and drones promises to redefine the future of aerial technology, introducing unprecedented efficiencies and capabilities across diverse sectors. Keywords: Role, AI, Drone, Applications, Technology.

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Section: Emerging Trends In Ai and Drone Technologymentioning

confidence: 99%

Reviewing the Role of Ai in Drone Technology and Applications

Nwankwo Constance Obiuto,

Igberaese Clinton Festus-Ikhuoria,

Oladiran Kayode Olajiga

et al. 2024

Comput. sci. IT res. j.

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“…Furthermore, the article discusses the issues of Industry 5.0, such as the need for a qualified labor, cybersecurity measures, and impediments to technology adoption. This thorough examination of both potential and difficulties provides significant insights for scholars and practitioners alike, directing future efforts to handle the changing landscape of Industry 5.0 [ 34 ].…”

Section: Related Workmentioning

confidence: 99%

Fuzzy ensemble of fined tuned BERT models for domain-specific sentiment analysis of software engineering dataset

Anwar,

Afzal,

Altaf

et al. 2024

PLoS ONE

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Software engineers post their opinions about various topics on social media that can be collectively mined using Sentiment Analysis. Analyzing this opinion is useful because it can provide insight into developers’ feedback about various tools and topics. General-purpose sentiment analysis tools do not work well in the software domain because most of these tools are trained on movies and review datasets. Therefore, efforts are underway to develop domain-specific sentiment analysis tools for the Software Engineering (SE) domain. However, existing domain-specific tools for SE struggle to compute negative and neutral sentiments and can not be used on all SE datasets. This work uses a hybrid technique based on deep learning and a fine-tuned BERT model, i.e., Bert-Base, Bert-Large, Bert-LSTM, Bert-GRU, and Bert-CNN presented that is adapted as a domain-specific sentiment analysis tool for Community Question Answering datasets (named as Fuzzy Ensemble). Five different variants of fine-tuned BERT on the SE dataset are developed, and an ensemble of these fine-tuned models is taken using fuzzy logic. The trained model is evaluated on four publicly available benchmark datasets, i.e., Stack Overflow, JavaLib, Jira, and Code Review, using various evaluation metrics. The fuzzy Ensemble model is also compared with the state-of-the-art sentiment analysis tools for the software engineering domain, i.e., SentiStrength-SE, Senti4SD, SentiCR, and Generative Pre-Training Transformer (GPT). GPT mode is fine-tuned by the authors for domain-specific sentiment analysis. The Fuzzy Ensemble model covers the limitation of existing tools and improve accuracy to predict neutral sentiments even on diverse dataset. The fuzzy Ensemble model performs superior to state-of-the-art tools by achieving a maximum F1-score of 0.883.

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“…The system employs the use of a Node MCU, which is a low-cost, open-source microcontroller board, as the primary device for monitoring the irrigation system. The system also incorporates several sensors, such as soil moisture, temperature, and humidity sensors, to gather important data about the conditions of the soil and the environment (Taj et al, 2022). This data is then processed by the Node MCU to determine whether the water pump should be turned on or off.…”

Section: Literature Reviewmentioning

confidence: 99%

IoT Irrigation System Using Arduino

Basheer,

Hanass,

Sheikh

et al. 2024

Preprint

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The increasing demand for water in agriculture and the need for more efficient irrigation systems has led to the development of smart irrigation systems based on the Internet of Things. In this system, an Arduino microcontroller is used to control and monitor the watering of crops. The system is equipped with sensors that measure soil moisture which are used to optimize the irrigation process. The data collected by the sensors is transmitted to the Arduino, which then adjusts the watering schedule based on the information received. The use of IoT in smart irrigation systems provides several benefits, including reduced water waste, increased crop yields, and improved plant health. The implementation of this system using an Arduino platform is relatively simple, cost-effective, and can be easily integrated into existing irrigation systems. Overall, the use of IoT in smart irrigation using an Arduino platform has the potential to revolutionize the way we manage water resources in agriculture.

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Towards Industrial Revolution 5.0 and Explainable Artificial Intelligence: Challenges and Opportunities

Cited by 32 publications

References 0 publications

Reviewing the Role of Ai in Drone Technology and Applications

Reviewing the Role of Ai in Drone Technology and Applications

Fuzzy ensemble of fined tuned BERT models for domain-specific sentiment analysis of software engineering dataset

IoT Irrigation System Using Arduino

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