Performance Investigation of Different Boosting Algorithms in Predicting Chronic Kidney Disease

Nishat, Mirza Muntasir; Faisal, Fahim; Dip, Rezuanur Rahman; Shikder, Md. Fahim; Ahsan, Ragib; Asif, Md. Asfi-Ar-Raihan; Udoy, Mahmudul Hasan

doi:10.1109/sti50764.2020.9350440

Cited by 36 publications

(5 citation statements)

References 13 publications

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“…Using an unpredictably huge number of signals, AI and ML can determine the subject's current state [9][10] [11] [12]. In order to generate results equal to manual analysis, artificial intelligence (AI)-based solutions, which are constrained by the amount of the data, depend on the [15]. Due to the fact that remote operations and quarantine have developed into industry standards during in the pandemic, these solutions can afterwards be made accessible to telemedicinebased applications [16].…”

Section: Introductionmentioning

confidence: 99%

Mental State Prediction Using Machine Learning and EEG Signal

Datar

Khobragade

2023

IJRITCC

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One of the most exciting areas of computer science right now is brain-computer interface (BCI) research. A conduit for data flow between both the brain as well as an electronic device is the brain-computer interface (BCI). Researchers in several disciplines have benefited from the advancements made possible by brain-computer interfaces. Primary fields of study include healthcare and neuroergonomics. Brain signals could be used in a variety of ways to improve healthcare at every stage, from diagnosis to rehabilitation to eventual restoration. In this research, we demonstrate how to classify EEG signals of brain waves using machine learning algorithms for predicting mental health states. The XGBoost algorithm's results have an accuracy of 99.62%, which is higher than that of any other study of its kind and the best result to date for diagnosing people's mental states from their EEG signals. This discovery will aid in taking efforts [1] to predict mental state using EEG signals to the next level.

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Section: Introductionmentioning

confidence: 99%

Mental State Prediction Using Machine Learning and EEG Signal

Datar

Khobragade

2023

IJRITCC

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“…However, clinical assessment of mass undergraduate students would be unwieldy and resource-heavy, since there can be numerous factors involved in instigating depression. In this regard, Machine Learning (ML) models [16][17][18][19][20][21][22][23][24][25] can perhaps become valuable for detecting and predicting subsequent health issues [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] as well as depressive episodes. Furthermore, the result can be analyzed to identify depression-related trends revealed among young people which can aid higher education institutions to understand the factors better and develop effective strategies to mitigate these factors.…”

Section: Introductionmentioning

confidence: 99%

Impact of mobile connectivity on students’ wellbeing: Detecting learners’ depression using machine learning algorithms

Siraji,

Rahman,

Nishat

et al. 2023

PLoS ONE

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Depression is a psychological state of mind that often influences a person in an unfavorable manner. While it can occur in people of all ages, students are especially vulnerable to it throughout their academic careers. Beginning in 2020, the COVID-19 epidemic caused major problems in people’s lives by driving them into quarantine and forcing them to be connected continually with mobile devices, such that mobile connectivity became the new norm during the pandemic and beyond. This situation is further accelerated for students as universities move towards a blended learning mode. In these circumstances, monitoring student mental health in terms of mobile and Internet connectivity is crucial for their wellbeing. This study focuses on students attending an International University of Bangladesh to investigate their mental health due to their continual use of mobile devices (e.g., smartphones, tablets, laptops etc.). A cross-sectional survey method was employed to collect data from 444 participants. Following the exploratory data analysis, eight machine learning (ML) algorithms were used to develop an automated normal-to-extreme severe depression identification and classification system. When the automated detection was incorporated with feature selection such as Chi-square test and Recursive Feature Elimination (RFE), about 3 to 5% increase in accuracy was observed by the method. Similarly, a 5 to 15% increase in accuracy has been observed when a feature extraction method such as Principal Component Analysis (PCA) was performed. Also, the SparsePCA feature extraction technique in combination with the CatBoost classifier showed the best results in terms of accuracy, F1-score, and ROC-AUC. The data analysis revealed no sign of depression in about 44% of the total participants. About 25% of students showed mild-to-moderate and 31% of students showed severe-to-extreme signs of depression. The results suggest that ML models, incorporating a proper feature engineering method can serve adequately in multi-stage depression detection among the students. This model might be utilized in other disciplines for detecting early signs of depression among people.

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“…An ample amount of literature exists on the successful executions of the Bio-Inspired Algorithms in the field of power converters professed by many researchers [11][12][13][14][15][16][17][18][19][20]. One of such promising instances of work has been witnessed with Machine learning (ML) algorithms [21][22][23][24][25][26][27][28][29], artificial intelligence [30][31][32][33][34][35][36][37][38][39], and different applications of ML in healthcare sectors [40][41][42][43][44][45][46][47][48][49], and so many other cases [50][51][52][53][54][55][56][57][58][...…”

Section: Introductionmentioning

confidence: 99%

Implementation of Bio-inspired Algorithms in Designing Optimized PID controller for Cuk Converter for Enhanced Performance: A Software based Approach

Shagor,

Faisal,

Nishat

et al. 2023

EAI Endorsed Trans AI Robotics

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This paper represents the idea of implementing bio-inspired algorithms in designing an optimized PID controller to investigate the stability and improve the performance of the closed-loop CUK converter. Bio-inspired algorithms (BIA) such as Firefly Algorithm (FA), Particle Swarm Optimization (PSO) and, Artificial Bee Colony (ABC) are stochastic optimization techniques and have been increasingly applied to attain optimal solution for designing and optimizing power converters in current times. The closed-loop transfer function of the CUK converter has been developed by the State Space Average (SSA) technique. This paper assesses both the cases of converter stability when it is integrated with the conventional PID controller and the BIA-PID controllers (FA-PID, PSO-PID, ABC-PID) and eventually compares the outcomes from all the controllers. For examining the stability of the system, three objective functions (IAE, ITAE, and ISE) and various performance specifications such as percentage of overshoot, rise time, settling time, and peak amplitude are tabulated. MATLAB and Simulink are used to carry out the simulations meticulously. Hence, a comparative analysis is illustrated in this paper to state a clear-sighted evaluation of the performances.

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Performance Investigation of Different Boosting Algorithms in Predicting Chronic Kidney Disease

Cited by 36 publications

References 13 publications

Mental State Prediction Using Machine Learning and EEG Signal

Mental State Prediction Using Machine Learning and EEG Signal

Impact of mobile connectivity on students’ wellbeing: Detecting learners’ depression using machine learning algorithms

Implementation of Bio-inspired Algorithms in Designing Optimized PID controller for Cuk Converter for Enhanced Performance: A Software based Approach

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