| 1 | DLMNN Based Heart Disease Prediction with PD-SS Optimization Algorithm显示文摘In contemporary medicine,cardiovascular disease is a major public health concern.Cardiovascular diseases are one of the leading causes of death worldwide.They are classified as vascular,ischemic,or hypertensive.Clinical information contained in patients’Electronic Health Records(EHR)enables clin-icians to identify and monitor heart illness.Heart failure rates have risen drama-tically in recent years as a result of changes in modern lifestyles.Heart diseases are becoming more prevalent in today’s medical setting.Each year,a substantial number of people die as a result of cardiac pain.The primary cause of these deaths is the improper use of pharmaceuticals without the supervision of a physician and the late detection of diseases.To improve the efficiency of the classification algo-rithms,we construct a data pre-processing stage using feature selection.Experi-ments using unidirectional and bidirectional neural network models found that a Deep Learning Modified Neural Network(DLMNN)model combined with the Pet Dog-Smell Sensing(PD-SS)algorithm predicted the highest classification performance on the UCI Machine Learning Heart Disease dataset.The DLMNN-based PDSS achieved an accuracy of 94.21%,an F-score of 92.38%,a recall of 94.62%,and a precision of 93.86%.These results are competitive and promising for a heart disease dataset.We demonstrated that a DLMNN framework based on deep models may be used to solve the categorization problem for an unbalanced heart disease dataset.Our proposed approach can result in exceptionally accurate models that can be utilized to analyze and diagnose clinical real-world data. | S.Raghavendra Vasudev Parvati R.Manjula Ashok Kumar Nanda Ruby Singh D.Lakshmi S.Velmurugan | 2023 | Intelligent Automation & Soft Computing2023，，2： | 0 |
| 2 | Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems显示文摘The Internet of Things(IoT)technologies has gained significant interest in the design of smart grids(SGs).The increasing amount of distributed generations,maturity of existing grid infrastructures,and demand network transformation have received maximum attention.An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling.The dynamic electrical energy stored model using Electric Vehicles(EVs)is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids.This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder(HBFOA-SAE)model for IoT Enabled energy systems.The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge(SOC)values in the IoT based energy system.To accomplish this,the SAE technique was executed to proper determination of the SOC values in the energy systems.Next,for improving the performance of the SOC estimation process,the HBFOA is employed.In addition,the HBFOA technique is derived by the integration of the hill climbing(HC)concepts with the BFOA to improve the overall efficiency.For ensuring better outcomes for the HBFOA-SAE model,a comprehensive set of simulations were performed and the outcomes are inspected under several aspects.The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches. | Helen Josephine V L Ramchand Vedaiyan V.M.Arul Xavier Joy Winston J A.Jegatheesan D.Lakshmi Joshua Samuel Raj | 2023 | Computer Systems Science & Engineering2023，45，4： | 0 |
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