Hybrid CNN-LSTM and modified wild horse herd Model-based prediction of genome sequences for genetic disorders

Nandhini, K.; Tamilpavai, G.

doi:10.1016/j.bspc.2022.103840

Cited by 10 publications

(8 citation statements)

References 28 publications

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“…Horses behave differently depending on their age. A horse's lifespan can reach 25 to 30 years [19]. In this context, δ indicates horses that are between 0 and 5 years old, γ denotes that are between 5 and 10 years old, β reveals horses that are 10 and 15 old years, and also α shows above 15 years horses.…”

Section: Hho Methodsmentioning

confidence: 99%

“…When they are of the 5 to 15 old years, the horses adhere to the rules of hierarchy [19]. It is characterized by Equations ( 6) and (7).…”

Section: Hho Methodsmentioning

confidence: 99%

“…These algorithms can adapt to dynamic weather conditions and maximize energy production from solar panels. Recently metaheuristic optimization methods have been addressed in many works to solve complex problems [11][12][13][14][15][16][17][18][19][20]. The novel one is called the Horse herd optimization (HHO) algorithm inspired by horses' behavior.…”

Section: Introductionmentioning

confidence: 99%

“…Thermodynamic equilibrium problems are solved by applying Honey Badger and HHO algorithms [18]. To enhance the weight function of the neural network in genetic disorder prediction [19], wild HHO with modification is employed. The above-stated research works show that HHO is applied in many optimization problems but not in MPPT.…”

Section: Introductionmentioning

confidence: 99%

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An Optimization Algorithm for Embedded Raspberry Pi Pico Controllers for Solar Tree Systems

Punitha,

Rahman,

Radhamani

et al. 2024

Sustainability

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Solar photovoltaic (PV) systems stand out as a promising solution for generating clean, carbon-free energy. However, traditional solar panel installations often require extensive land resources, which could become scarce as the population grows. To address this challenge, innovative approaches are needed to maximize solar power generation within limited spaces. One promising concept involves the development of biological tree-like structures housing solar panels. These “solar trees” mimic the arrangement of branches and leaves found in natural trees, following patterns akin to phyllotaxy, which correlates with the Fibonacci sequence and golden ratio. By adopting an alternative 1:3 phyllotaxy pattern, three solar panels can be efficiently arranged along the stem of the solar tree structure, each rotated at a 120-degree displacement. Optimizing the performance of solar trees requires effective maximum power point tracking (MPPT), a crucial process for extracting the maximum available power from solar panels to enhance the overall efficiency. In this study, a novel metaheuristic algorithm called horse herd optimization (HHO) is employed for MPPT in solar tree applications. Moreover, to efficiently manage the generated power, a cascaded buck–boost converter is utilized. This converter is capable of adjusting the DC voltage levels to match the system requirements within a single topology. The algorithm is implemented using MATLAB and embedded within a Raspberry Pi Pico controller, which facilitates the generation of pulse-width modulation (PWM) signals to control the cascaded buck–boost converter. Through extensive validation, this study confirms the effectiveness of the proposed HHO algorithm integrated into the Raspberry Pi Pico controller for optimizing solar trees under various shading conditions. In essence, this research highlights the potential of solar tree structures coupled with advanced MPPT algorithms and power management systems to maximize solar energy utilization, offering a sustainable solution for clean energy generation within limited land resources.

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Section: Hho Methodsmentioning

confidence: 99%

“…When they are of the 5 to 15 old years, the horses adhere to the rules of hierarchy [19]. It is characterized by Equations ( 6) and (7).…”

Section: Hho Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Optimization Algorithm for Embedded Raspberry Pi Pico Controllers for Solar Tree Systems

Punitha,

Rahman,

Radhamani

et al. 2024

Sustainability

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show abstract

“…Therefore, the application of the neural network to early warning of the EV charging process has practical technical support. Since there are few applied cases of neural networks in the field of EV early warning at this stage, we can learn from other fields such as CNN [11][12][13], LSTM [14][15][16], BiLSTM [17][18][19], CNN-LSTM [20][21][22], and ConvLSTM [23][24][25] methods for research. In [11], a framework of automatically designed classifiers is proposed for the early detection of COVID-19 from chest X-ray images, minimizing redundant layers and improving prediction accuracy, which verifies the feasibility of CNN in the field of medication safety.…”

Section: Introductionmentioning

confidence: 99%

An Early Warning Protection Method for Electric Vehicle Charging Based on the Hybrid Neural Network Model

Zheng

Gao

Zhu

et al. 2022

WEVJ

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During the charging process of the electric vehicle (EV), a spontaneous combustion accident may occur due to overheating of the battery, causing personal danger and property damage. To address the charging safety of EVs, this paper proposes a new hybrid EV charging process early warning protection method by combining Convolutional Long-Short Term Memory (ConvLSTM), the sliding window method, and the residual analysis method. The method is fully trained by extracting the deep features of EV charging data through ConvLSTM, eliminating the influence of erroneous transmission data through the sliding window method, and setting a reasonable warning threshold through the residual analysis method. The cross-validation results showed that among the four training sets, the ConvLSTM model of training, set three, had the highest prediction accuracy compared with the CNN, LSTM, BiLSTM and CNN-LSTM models, with RMSE reaching 0.029, MAPE reaching 11.37, and r2 reaching 0.89. Training set one had the worst prediction in the four training sets, and after using it to set the warning threshold, the alarm task was completed five sampling points earlier. Therefore, the hybrid model can quickly complete the safety warning task, thereby ensuring the safety of EV charging.

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An intelligent deep feature based metabolism syndrome prediction system for sleep disorder diseases

Anisha,

Kishor Kumar Reddy,

Hanafiah

et al. 2023

Multimed Tools Appl

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Hybrid CNN-LSTM and modified wild horse herd Model-based prediction of genome sequences for genetic disorders

Cited by 10 publications

References 28 publications

An Optimization Algorithm for Embedded Raspberry Pi Pico Controllers for Solar Tree Systems

An Optimization Algorithm for Embedded Raspberry Pi Pico Controllers for Solar Tree Systems

An Early Warning Protection Method for Electric Vehicle Charging Based on the Hybrid Neural Network Model

An intelligent deep feature based metabolism syndrome prediction system for sleep disorder diseases

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