Papers by Anto Gracious L. A.
The potential for machine learning and deep learning to revolutionize the healthcare industry is ... more The potential for machine learning and deep learning to revolutionize the healthcare industry is examined in this thorough survey study. These cutting-edge technologies have the potential to completely transform healthcare by providing accurate diagnoses, customizing drugs for each patient, and ultimately enhancing patient outcomes. The study offers a thorough investigation of a number of applications, such as clinical decision support systems, electronic health record analysis, illness diagnosis and prediction, personalized medicine, and drug development. In this article, we focus on the essential methodologies, obstacles, and opportunities related to the use of machine learning and deep learning in healthcare. This source aims to be a helpful resource for researchers, medical practitioners, and decision-makers who are looking to maximize the benefits of modern technologies to improve the provision of healthcare services. Additionally, we would like to contribute to a better and more effective healthcare environment by bridging the technology and healthcare barrier.
2023 4th IEEE Global Conference for Advancement in Technology (GCAT), 2023
The potential for machine learning and deep learning to revolutionize the healthcare industry is ... more The potential for machine learning and deep learning to revolutionize the healthcare industry is examined in this thorough survey study. These cutting-edge technologies have the potential to completely transform healthcare by providing accurate diagnoses, customizing drugs for each patient, and ultimately enhancing patient outcomes. The study offers a thorough investigation of a number of applications, such as clinical decision support systems, electronic health record analysis, illness diagnosis and prediction, personalized medicine, and drug development. In this article, we focus on the essential methodologies, obstacles, and opportunities related to the use of machine learning and deep learning in healthcare. This source aims to be a helpful resource for researchers, medical practitioners, and decision-makers who are looking to maximize the benefits of modern technologies to improve the provision of healthcare services. Additionally, we would like to contribute to a better and more effective healthcare environment by bridging the technology and healthcare barrier.
2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), 2023
The importance of green energy is growing in the modern world. That's why, in terms of environmen... more The importance of green energy is growing in the modern world. That's why, in terms of environmental impact, electric vehicles are the best option for commuters and car owners alike. Lithium-ion batteries are commonly utilized in EVs because of their high energy and current density. Lithium-ion batteries pose a risk if used outside of their Safe Operating Area (SOA). This necessitates the incorporation of BMS technology into all lithium-ion batteries, not just those used in EVs. It delves deeply into the nature, function, and structure of BMS. A literature review also covers early battery models as well as the hardware and system architecture for BMS. The simulation results are then used to show how well the updated model of the battery performs. Preprocessing, feature selection, and model training are all included in the research article. It employs a PCC for feature selection and an LSTM-ILA for model training. The proposed method achieves better results than both the LSTM and LSTM-LA models.
The importance of green energy is growing in the modern world. That's why, in terms of en... more The importance of green energy is growing in the modern world. That's why, in terms of environmental impact, electric vehicles are the best option for commuters and car owners alike. Lithium-ion batteries are commonly utilized in EVs because of their high energy and current density. Lithium-ion batteries pose a risk if used outside of their Safe Operating Area (SOA). This necessitates the incorporation of BMS technology into all lithium-ion batteries, not just those used in EVs. It delves deeply into the nature, function, and structure of BMS. A literature review also covers early battery models as well as the hardware and system architecture for BMS. The simulation results are then used to show how well the updated model of the battery performs. Preprocessing, feature selection, and model training are all included in the research article. It employs a PCC for feature selection and an LSTM-ILA for model training. The proposed method achieves better results than both the LSTM and LSTM-LA models.
2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), Aug 2023
One of the most life-threatening diseases that causes human beings to suffer is heart disease. Ac... more One of the most life-threatening diseases that causes human beings to suffer is heart disease. According to WHO report, more than 17.9 million people are affected and raised to death due to cardiovascular diseases around the globe. Many factors that influence the disease are high blood pressure, LDL cholesterol, obesity, physical inactivity, diabetes, etc. An earlier diagnosis of the disease helps to change
the lifestyle and the start of treatment helps to save the human being life. Machine learning techniques are used in this work to analyze heart disease effectively. The purpose of the research is to perform efficient heart disease analysis using ML models. Machine learning is a sub-category of AI that makes decisions based on past and historical data. ML can be sub-classified into 3 types. In this research, supervised ML models are considered for analysis. In supervised ML models: K-NN, SVM, and RF are applied. The experimental approach is conducted with the heart disease dataset with three different ML models. The results demonstrate that the RF approach has the best performance across all metrics, subsequent to the SVM model and the KNN model. Among the three models, the RF model
has the greatest sensitivity (0.8654), precision (0.8182), specificity (0.7674), and accuracy (0.82105) compared to other approaches in this particular heart disease dataset.
2023 7th International Conference on Computing Methodologies and Communication (ICCMC), 2023
This research study shows how IoT technology can screen local meteorological conditions and share... more This research study shows how IoT technology can screen local meteorological conditions and share that information globally. Weather shifts cause extreme rainfall. A flood monitoring system uses NODEMCU ESP8266 to store and retrieve data and inform authorities of rising water levels using ultrasonic sensors and LEDs. Soil moisture affects crop growth. Its microprocessor and sensor improve soil moisture monitoring. An earthquake warning system can detect the slightest vibration before a major earthquake. Due to industry and autos, air quality is getting worse. Air quality and chemical content must be assessed using IoT since it has changed so much. Connected devices and enhanced sensor technology have transformed traditional environmental monitoring into a cutting-edge Smart
Environment Monitoring System (SEMS). This paper evaluates SEM aids and research investigations, including air quality, weather, soil, and seismic monitoring systems. SEM applications segment the examination, with a deeper dig into each section's sensors. Discussion findings and analyzed research patterns form the basis for the in-depth analysis that follows the comprehensive review and suggests key SEMS research implications. The authors studied how IoT, machine learning, and other sensorbased advancements have made environmental monitoring smart.
Abstract:
A wireless sensor network consists of spatially distributed autonomous sensors to coope... more Abstract:
A wireless sensor network consists of spatially distributed autonomous sensors to cooperatively monitor physical or environmental conditions. In addition to one or more sensors, each node in a sensor network is typically equipped with a radio transceiver or other wireless communications device, a small microcontroller, and an energy source, usually a battery. A sensor network normally constitutes a wireless ad-hoc network, meaning that each sensor supports a multi-hop routing algorithm several nodes may forward data packets to the base station. Location and internodes distance estimation is of profound importance for various WSN applications. Similarly, estimation of the hop distance between two network locations is equivalent to estimating the minimum number of hops, which leads to maximization of the distance covered in multihop paths. Furthermore, hop distance estimation is closely related with transmission delay estimation and minimization of multihop energy consumption. Determination of the maximum multihop Euclidean distance corresponding to a given hop distance in a 2D network is a complex problem. The accuracy of the Gaussian pdf model depends on the number of hops and the chosen parameters, which affect the obtained error ranges. A Greedy distance maximization model is proposed, which approximates the maximum multihop Euclidean distance and evaluates the distribution of the obtained multihop distance in planar networks.
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Papers by Anto Gracious L. A.
the lifestyle and the start of treatment helps to save the human being life. Machine learning techniques are used in this work to analyze heart disease effectively. The purpose of the research is to perform efficient heart disease analysis using ML models. Machine learning is a sub-category of AI that makes decisions based on past and historical data. ML can be sub-classified into 3 types. In this research, supervised ML models are considered for analysis. In supervised ML models: K-NN, SVM, and RF are applied. The experimental approach is conducted with the heart disease dataset with three different ML models. The results demonstrate that the RF approach has the best performance across all metrics, subsequent to the SVM model and the KNN model. Among the three models, the RF model
has the greatest sensitivity (0.8654), precision (0.8182), specificity (0.7674), and accuracy (0.82105) compared to other approaches in this particular heart disease dataset.
Environment Monitoring System (SEMS). This paper evaluates SEM aids and research investigations, including air quality, weather, soil, and seismic monitoring systems. SEM applications segment the examination, with a deeper dig into each section's sensors. Discussion findings and analyzed research patterns form the basis for the in-depth analysis that follows the comprehensive review and suggests key SEMS research implications. The authors studied how IoT, machine learning, and other sensorbased advancements have made environmental monitoring smart.
A wireless sensor network consists of spatially distributed autonomous sensors to cooperatively monitor physical or environmental conditions. In addition to one or more sensors, each node in a sensor network is typically equipped with a radio transceiver or other wireless communications device, a small microcontroller, and an energy source, usually a battery. A sensor network normally constitutes a wireless ad-hoc network, meaning that each sensor supports a multi-hop routing algorithm several nodes may forward data packets to the base station. Location and internodes distance estimation is of profound importance for various WSN applications. Similarly, estimation of the hop distance between two network locations is equivalent to estimating the minimum number of hops, which leads to maximization of the distance covered in multihop paths. Furthermore, hop distance estimation is closely related with transmission delay estimation and minimization of multihop energy consumption. Determination of the maximum multihop Euclidean distance corresponding to a given hop distance in a 2D network is a complex problem. The accuracy of the Gaussian pdf model depends on the number of hops and the chosen parameters, which affect the obtained error ranges. A Greedy distance maximization model is proposed, which approximates the maximum multihop Euclidean distance and evaluates the distribution of the obtained multihop distance in planar networks.
the lifestyle and the start of treatment helps to save the human being life. Machine learning techniques are used in this work to analyze heart disease effectively. The purpose of the research is to perform efficient heart disease analysis using ML models. Machine learning is a sub-category of AI that makes decisions based on past and historical data. ML can be sub-classified into 3 types. In this research, supervised ML models are considered for analysis. In supervised ML models: K-NN, SVM, and RF are applied. The experimental approach is conducted with the heart disease dataset with three different ML models. The results demonstrate that the RF approach has the best performance across all metrics, subsequent to the SVM model and the KNN model. Among the three models, the RF model
has the greatest sensitivity (0.8654), precision (0.8182), specificity (0.7674), and accuracy (0.82105) compared to other approaches in this particular heart disease dataset.
Environment Monitoring System (SEMS). This paper evaluates SEM aids and research investigations, including air quality, weather, soil, and seismic monitoring systems. SEM applications segment the examination, with a deeper dig into each section's sensors. Discussion findings and analyzed research patterns form the basis for the in-depth analysis that follows the comprehensive review and suggests key SEMS research implications. The authors studied how IoT, machine learning, and other sensorbased advancements have made environmental monitoring smart.
A wireless sensor network consists of spatially distributed autonomous sensors to cooperatively monitor physical or environmental conditions. In addition to one or more sensors, each node in a sensor network is typically equipped with a radio transceiver or other wireless communications device, a small microcontroller, and an energy source, usually a battery. A sensor network normally constitutes a wireless ad-hoc network, meaning that each sensor supports a multi-hop routing algorithm several nodes may forward data packets to the base station. Location and internodes distance estimation is of profound importance for various WSN applications. Similarly, estimation of the hop distance between two network locations is equivalent to estimating the minimum number of hops, which leads to maximization of the distance covered in multihop paths. Furthermore, hop distance estimation is closely related with transmission delay estimation and minimization of multihop energy consumption. Determination of the maximum multihop Euclidean distance corresponding to a given hop distance in a 2D network is a complex problem. The accuracy of the Gaussian pdf model depends on the number of hops and the chosen parameters, which affect the obtained error ranges. A Greedy distance maximization model is proposed, which approximates the maximum multihop Euclidean distance and evaluates the distribution of the obtained multihop distance in planar networks.