Dragonfly Political Optimizer Algorithm-Based Rider Deep Long Short-Term Memory for Soil Moisture and Heat Level Prediction in IoT

Muppidi, Satish; G, Om Prakash P; Bhamidipati, Kishore

doi:10.1093/comjnl/bxab215

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…LSTM algorithms are highly proficient in delivering exceptional performance in providing long-term stability and accuracy to diverse applications that rely on data aggregation, particularly in machine learning. Furthermore, the ability of these algorithms to operate with minimal electricity consumption makes them suitable for a wide range of applications, including distributed sensor networks and traditional big data applications [10]. Therefore, investigating the efficacy of these algorithms in this particular situation is expected to produce remarkable results that may be further examined.…”

Section: Introductionmentioning

confidence: 99%

Exploring Long Short Term Memory Algorithms for Low Energy Data Aggregation

2024

JMC

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Long short-term memory methods are employed for data consolidation in intricate low-energy devices. It has enabled accurate and efficient aggregation of statistics in limited electricity settings, facilitating the review and retrieval of data while minimizing electricity wastage. The LSTM rules analyze, organize, and consolidate vast datasets inside weakly connected structures. It has employed a recurrent neural network to handle data processing, particularly nonlinear interactions. The machine's capabilities are subsequently examined and stored utilizing memory blocks. Memory blocks retain extended temporal connections within the data, facilitating adaptive and precise information aggregation. These blocks facilitate the system's ability to shop and utilize relevant capabilities for quick retrieval. The proposed algorithm offers realistic tuning capabilities such as learning rate scheduling and total regularization based on dropout like green information aggregation. These enable systems to reduce over fitting while permitting precise adjustment of the settings. It allows for optimizing the algorithm to provide highly dependable performance within weak structures, enhancing data aggregation techniques' energy efficiency. Standard algorithms provide an efficient, accurate solution for aggregating information in low-power systems. It facilitates evaluating, retrieving, and aggregating accurate and reliable information using memory blocks, adaptive tuning, and efficient learning rate scheduling.

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Section: Introductionmentioning

confidence: 99%

Exploring Long Short Term Memory Algorithms for Low Energy Data Aggregation

2024

JMC

View full text Add to dashboard Cite

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Socio-inspired evolutionary algorithms: a unified framework and survey

2023

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Evolutionary computation has gone through vast and diverse research endeavors in the past few decades. Although the initial inspiration came from Darwin's ideas of biological evolution, the field has moved thereon to ideas from the collective intelligence of insects, birds, and fish, to name a few. A variety of algorithms have been proposed in the literature based on these ideas and have been shown to perform well in various applications. More recently, inspiration from human behavior and knowledge exchange and transformation has given rise to a new evolutionary computing paradigm. It is well recognized that human societies and problem-solving capabilities have evolved much faster than biological evolution. Many research endeavors have been reported in the literature inspired by diverse aspects of human societies with corresponding terminologies to describe the algorithm. These endeavors have resulted in a plethora of algorithms worded differently from each other, but the underlying mechanisms could be more or less similar, causing immense confusion for a new reader. This paper presents a generalized framework for these Socio-inspired Evolutionary Algorithms (SIEAs) or Socio-inspired Metaheuristic Algorithms. A survey of various SIEAs is provided to highlight the working of these algorithms on a common framework, their variations and improved versions proposed in the literature, and their applications in various fields of search and optimization. The algorithmic description of each SIEA enables a clearer understanding of the similarities and differences between these methodologies. Efforts have been made to provide an extensive list of references with due context. In that sense, this paper could become an excellent reference as a starting point for anyone interested in this fascinating field of research.

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Dragonfly Political Optimizer Algorithm-Based Rider Deep Long Short-Term Memory for Soil Moisture and Heat Level Prediction in IoT

Cited by 2 publications

References 20 publications

Exploring Long Short Term Memory Algorithms for Low Energy Data Aggregation

Exploring Long Short Term Memory Algorithms for Low Energy Data Aggregation

Socio-inspired evolutionary algorithms: a unified framework and survey

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