IoT-Based Kalman Filtering and Particle Swarm Optimization for Detecting Skin Lesion

Ramkumar, J.; Dinakaran, S. Samson; Lingaraj, M.; Boopalan, S.; Narasimhan, B.

doi:10.1007/978-981-19-8353-5_2

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Bio-inspired optimization, a computational approach inspired by biological systems, has the potential to address diverse research issues across multiple domains [11][12][13][14][15][16][17][18][19][20][21][22][23][24]. These algorithms offer versatile and practical solutions by emulating adaptive behaviors observed in nature.…”

Section: Motivationmentioning

confidence: 99%

Tenacious Fish Swarm Optimization Based Hidden Markov Model (TFSO-HMM) for Augmented Accurate Cotton Leaf Disease Identification and Yield Prediction

Govindasamy,

Jayaraj

2023

Preprint

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This research presents an innovative approach called Tenacious Fish Swarm Optimization based Hidden Markov Model (TFSO-HMM) for augmented accurate cotton leaf disease identification and yield prediction. Cotton leaf diseases significantly threaten crop productivity, requiring timely detection and precise prediction for effective disease management. The proposed TFSO-HMM framework combines the strengths of Tenacious Fish Swarm Optimization (TFSO) and the Hidden Markov Model (HMM) to address the challenges associated with disease identification and yield prediction in cotton plants. TFSO, a nature-inspired optimization algorithm, optimizes the classification process, enhancing the accuracy of disease identification. By harnessing the collective intelligence of fish swarms, TFSO intelligently explores the search space to identify the optimal solution. The selected information is then incorporated into the HMM framework, which captures the temporal dependencies in disease progression and yield prediction. HMM's sequential modelling approach facilitates understanding the dynamic behaviour of cotton leaf diseases over time, leading to more accurate predictions. Experimental results on a comprehensive dataset demonstrate the superior performance of the TFSO-HMM method over existing approaches in terms of accuracy and predictive capability. The augmented accuracy achieved through TFSO-HMM enables early detection and precise prediction of cotton leaf diseases, enabling timely interventions for disease management and maximizing crop yield.

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Section: Motivationmentioning

confidence: 99%

Tenacious Fish Swarm Optimization Based Hidden Markov Model (TFSO-HMM) for Augmented Accurate Cotton Leaf Disease Identification and Yield Prediction

Govindasamy,

Jayaraj

2023

Preprint

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Optimizing IoT-Based Quantum Wireless Sensor Networks Using NM-TEEN Fusion of Energy Efficiency and Systematic Governance

Ramkumar,

Karthikeyan,

Lingaraj

2024

Lecture Notes in Electrical Engineering

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Securing Library Data With Blockchain Advantage

Ramkumar,

Karthikeyan,

Nitish

2024

Advances in Knowledge Acquisition, Transfer, and Management

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Blockchain technology is poised to revolutionize library operations by offering solutions to enhance data security, streamline transactions, and ensure transparency. This chapter explores the multifaceted applications of blockchain in libraries, focusing on managing digital collections, implementing smart contracts for automated transactions, and optimizing interlibrary loan systems. By leveraging blockchain's decentralized ledger and smart contract capabilities, libraries can improve the integrity of cataloging and metadata management, protect patron privacy through secure authentication mechanisms, and automate complex workflows with increased efficiency. However, the adoption of blockchain faces challenges such as technical complexity, financial feasibility, and compliance with legal and ethical standards. Addressing these challenges requires collaboration, pilot testing, and continuous adaptation of policies to ensure responsible implementation.

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IoT-Based Kalman Filtering and Particle Swarm Optimization for Detecting Skin Lesion

Cited by 3 publications

References 27 publications

Tenacious Fish Swarm Optimization Based Hidden Markov Model (TFSO-HMM) for Augmented Accurate Cotton Leaf Disease Identification and Yield Prediction

Tenacious Fish Swarm Optimization Based Hidden Markov Model (TFSO-HMM) for Augmented Accurate Cotton Leaf Disease Identification and Yield Prediction

Optimizing IoT-Based Quantum Wireless Sensor Networks Using NM-TEEN Fusion of Energy Efficiency and Systematic Governance

Securing Library Data With Blockchain Advantage

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