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Dr. Jaidev Kumar is an Assistant Professor in the Department of Chemistry at Hariom Saraswati P.G. College, Dhanauri, Haridwar, Uttarakhand, India. He is involved in academic teaching and chemistry education. His contribution explores the application of data-driven intelligent approaches in industrial monitoring and predictive maintenance.
Immediate meteorological evaluation and drought appraisal are crucial for sustainable farming, water resource administration, disaster readiness, and climate adaptability. Traditional meteorological forecasting and drought assessment methods frequently depend on singular data sources, including weather observations, satellite images, or past precipitation records. This section introduces a comprehensive AI system that amalgamates meteorological sensor data, satellite and remote-sensing visuals, soil moisture metrics, hydrological parameters, and past climate archives. Deep learning architectures, such as convolutional neural networks, recurrent neural networks, Transformers, and multimodal fusion frameworks, are analysed for their contributions to feature extraction and the production of weather predictions and drought intensity assessments. The suggested methodology lays the groundwork for astute weather observation and anticipatory drought control, highlighting the necessity for thorough validation across various geographical areas, seasons, and climatic circumstances.
Air pollution constitutes a significant environmental issue that impacts public health, ecological viability, urban growth, and overall quality of life. Traditional air quality monitoring systems frequently rely on stationary monitoring stations and singular pollutant assessments, localised pollution incidents, or the intricate interactions between meteorological factors, vehicular emissions, industrial operations, and atmospheric dynamics. Through the understanding of interconnections among various supplementary sources, multimodal AI can facilitate pollutant concentration assessment, short-term pollution prediction, hotspot detection, and proactive alerts. This chapter delineates an extensive framework for the monitoring of air quality across many modalities and the forecasting of pollution, encompassing data collection, and implementation. The section additionally presents mathematical equations for multimodal representation learning and pollutant prediction, as well as an experimental approach employing regression, and spatial assessment measures. The suggested framework establishes a basis for creating intelligent and data-centric air quality control systems.
Industrial machinery is essential in manufacturing, energy generation, transportation, processing sectors, and infrastructural activities. Unforeseen machinery malfunctions may lead to production delays, heightened maintenance expenses, safety hazards, and diminished operational effectiveness. Multimodal AI offers a sophisticated methodology by amalgamating diverse industrial data sources, such as vibration signals, acoustic measurements, thermal imagery, electrical parameters, pressure readings, operating logs, and maintenance documentation. Through the acquisition of complimentary patterns across many modalities, multimodal AI can enhance defect detection, equipment state evaluation, failure forecasting, and maintenance strategising. This section offers an extensive structure for the oversight of industrial machinery and anticipatory upkeep through the application of multimodal artificial intelligence. The section also examines real-time implementation, edge artificial intelligence, digital replicas, interpretable AI, cybersecurity, and data dependability. The suggested paradigm establishes a basis for creating intelligent, dependable, and context-sensitive industrial maintenance systems.