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Plant Biomass to Bioenergy: Use and Impact on Environment and Development
(CRC Press, 2026) Akhtar Hussain, Irum, Gyanendra Tripathi, Alvina Farooqui, Mohammad Ashfaque
The exhaustion of fossil fuel reserves and increasing atmospheric environ-mental pollution and climate change have heightened the quest for sustain-able alternatives. Within these options, biofuels from plant biomass have emerged as a better alternative. This chapter provides the accessibility of plant biomass to substitute fossil fuels with biofuels. The initial part delin-eates the environmental advantages of biofuels, including their ability to lessen greenhouse gas emissions and reduce reliance on limited fossil fuel reserves. Subsequently, the conversation delves into the various origins of plant biomass, spanning from specialized energy crops. The quantity and dispersion of raw materials are assessed, considering geographical differences, land accessibility, and farming efficiency. The technological progressions in biomass transformation, encompassing biochemical and thermochemical responses, are discussed. These processes include fermentation, pyrolysis, and gasification, which convert lignocellulosic
Empowering Smart Education Through Computer Vision and Internet of Everything in School Transportation
(IGI Global, 2025) Farooq Ahmad, Shweta Dwivedi, Nupur Mittal
This chapter introduces an innovative smart school transportation system designed for a smart town within a broader smart city framework. By leveraging computer vision, the Internet of Everything (IoE), and advanced surveillance technologies, the system enhances safety and convenience for students, parents, schools, public transportation, drivers, law enforcement, and government agencies. The research approach uses qualitative methods to gather insights from key stakeholders. The case study focuses on Kota Baru Parahyangan City, with Cahaya Bangsa Classical School as a key example. Through the use of computer vision tools like facial recognition and object detection, the system allows for real-time monitoring and tracking of students throughout their journey. The IoE integration ensures smooth connectivity for communication and data exchange between surveillance cameras, central monitoring systems, and IoT-enabled vehicles. This intelligent school transportation system not only enhances safety but also improves traffic management and resource allocation.
Advancing Green Artificial Intelligence: Strategies for a Sustainable Future
(IGI Global, 2025) Farooq Ahmad, Shweta Dwivedi, Zohaib Hasan Khan, Nupur Mittal
Green artificial intelligence (AI) is designed to be more eco-friendly and accessible compared to traditional AI. It delivers precise results without the added computational costs and allows researchers with just a laptop to conduct high-quality work without the need for expensive cloud servers. This chapter explores green AI as a crucial method for improving the environmental sustainability of AI systems. It covers AI solutions that promote eco-friendly practices in various fields (referred to as “green-by AI”), methods for developing energy-efficient machine learning (ML) algorithms and models (known as “green-in AI”), and tools for accurately measuring and optimizing energy usage. The chapter also looks at how regulations can support green AI and discusses future directions for sustainable ML. It highlights the need to integrate environmental considerations into AI practices to promote a more eco-conscious and energy-efficient future for AI technologies.
Improving the Hardware Security of Wireless Sensor Network Systems by Using Soft Computing
(Bentham Science, 2025) Masood Ahmad, Mohd Waris Khan, Satish Kumar, Mohd Faizan, Mohd Faisal, Malik Shahzad Ahmad Iqbal, Raees Ahmad Khan
Hardware security is a critical concern for organizations that use information systems to protect their assets from unauthorized access and malicious attacks. Hardware security assessment involves evaluating the security of hardware components and systems to identify vulnerabilities and areas for improvement. This research paper proposes a framework for hardware security assessment using the Analytic Hierarchy Process (AHP) approach. The proposed framework is applied in a case study to evaluate the security of a wireless sensor network (WSN) system. Based on this calculation with respect to each alternative, the author finds that Hardware encryption obtained the highest final weighted score (0.555), which would be the preferred choice according to the AHP method for improving the security of the hardware of WSN. Based on the obtained weight, authors assign the ranks S1
Study of an Innovative Approach to IoT Based Human Activity Recognition
(BP International, 2025) Motashim Rasool , Rizwan Akhtar , Uvais Ahmad
Recognizing human activities is vital for numerous contemporary applications rooted in the Internet of Things (IoT) framework, spanning from the creation of intelligent video surveillance setups to the advancement of robotic assistants for the elderly. Recently, there has been significant exploration into machine learning algorithms to enhance the recognition of human activities. Despite these research endeavors, there remains a notable dearth of studies focusing on efficiently recognizing complex human activities, particularly those involving transitions, and no research has been conducted to assess the impact of noise in training data on algorithm performance. This paper addresses these gaps by presenting an innovative activity recognition system centered on a neural classifier with memory capabilities, designed to optimize the classification of both transitional and non-transitional human activities. Utilizing unobtrusive IoT devices such as accelerometers and gyroscopes integrated into widely-used smartphones, the system effectively identifies human activities [1,2]. The key feature of the proposed system lies in leveraging a neural network augmented with short-term memory to retain information about preceding activities' characteristics. Experimental validation demonstrates the reliability and accuracy of the proposed system compared to state-of-the-art classifiers, highlighting its robustness in handling noisy data. Human Activity Recognition (HAR) is essential for various modern applications within the Internet of Things (IoT) framework, from developing intelligent video surveillance systems to enhancing robotic assistants for the elderly. Despite significant advancements in machine learning algorithms for HAR, there is a notable lack of research on effectively recognizing complex human activities, particularly those involving transitions, and assessing the impact of noise in training data on algorithm performance. This paper addresses these gaps by presenting an innovative activity recognition system centered on a neural classifier with memory capabilities. Designed to optimize the classification of both transitional and non-transitional human activities, the system employs unobtrusive IoT devices such as accelerometers and gyroscopes integrated into widely used smartphones [1,3]. A key feature of the proposed system is the utilization of a neural network augmented with short-term memory to retain information about preceding activities' characteristics. Experimental validation demonstrates the system's reliability and accuracy compared to state-of-the-art classifiers, emphasizing its robustness in handling noisy data.