Pub Date : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073930
V. Srilakshmi, Anupama Anumolu, M. Safali, Vallabhaneni Siva Parvathi
Alzheimer’s disease (AD) is the most common disease that can cause a brain disorder in a human aged above 65. Detecting and diagnosing AD becomes a more complicated and complex task by using various manual processes. DL and ML algorithms are most widely used to analyze the complex features from the medical data used to detect AD from various samples. Several types of sample formats are used to detect AD. This paper mainly focused on detecting the AD from the retinal fundus images. Analyzing the early symptoms of AD can prevent the patient’s life from permanent eye loss. ML algorithms are having various drawbacks that use complex computations and more computation time for the processing of data. The AD prediction is done by using the fundus color images collected from the Kaggle dataset. ML follows various steps to complete the task such as training, pre-processing and algorithm implementation. In the existing approaches, a limited number of parameters are used. Another disadvantage of the traditional algorithms shows the low accuracy and unmatched results. This paper introduced the hybrid-layered framework is developed to detect the AD from the fundus images dataset. Several performance metrics such as precision, recall, F1-score, and accuracy are used to show the results.
{"title":"A Hybrid-Layered Framework for Detection and Diagnosis of Alzheimer’s Disease (AD) from Fundus Images","authors":"V. Srilakshmi, Anupama Anumolu, M. Safali, Vallabhaneni Siva Parvathi","doi":"10.1109/ICAIS56108.2023.10073930","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073930","url":null,"abstract":"Alzheimer’s disease (AD) is the most common disease that can cause a brain disorder in a human aged above 65. Detecting and diagnosing AD becomes a more complicated and complex task by using various manual processes. DL and ML algorithms are most widely used to analyze the complex features from the medical data used to detect AD from various samples. Several types of sample formats are used to detect AD. This paper mainly focused on detecting the AD from the retinal fundus images. Analyzing the early symptoms of AD can prevent the patient’s life from permanent eye loss. ML algorithms are having various drawbacks that use complex computations and more computation time for the processing of data. The AD prediction is done by using the fundus color images collected from the Kaggle dataset. ML follows various steps to complete the task such as training, pre-processing and algorithm implementation. In the existing approaches, a limited number of parameters are used. Another disadvantage of the traditional algorithms shows the low accuracy and unmatched results. This paper introduced the hybrid-layered framework is developed to detect the AD from the fundus images dataset. Several performance metrics such as precision, recall, F1-score, and accuracy are used to show the results.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121303484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073682
K. C, Vivek Karthick Perumal, M. Vivek Kumar, J. Muralidharan
A power, delay efficient error acquiescent adder is proposed. In recent VLSI expertise, the manifestation of all categories of faults has developed foreseeable. By embracing an emergent perception in VLSI strategy, fault-tolerant adder (FTA) is suggested. The FTA is talented to comfort the harsh constraint on exactitude, and at the identical period accomplish marvelous enhancements in together the power ingestion and speediness enactment. For any transportable uses anywhere the power ingestion and speed are the utmost significant limit, one must diminish the power feeding and upsurge the speed as ample as probable. In this technique certain amendments are suggested to predictable adders to significantly decrease its power feeding. The amendments to the conservative building comprise the elimination of carry generation from LSB to MSB. With this the adder works at high speed with low power consumption.
{"title":"Design of Power and Delay Efficient Fault Tolerant Adder","authors":"K. C, Vivek Karthick Perumal, M. Vivek Kumar, J. Muralidharan","doi":"10.1109/ICAIS56108.2023.10073682","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073682","url":null,"abstract":"A power, delay efficient error acquiescent adder is proposed. In recent VLSI expertise, the manifestation of all categories of faults has developed foreseeable. By embracing an emergent perception in VLSI strategy, fault-tolerant adder (FTA) is suggested. The FTA is talented to comfort the harsh constraint on exactitude, and at the identical period accomplish marvelous enhancements in together the power ingestion and speediness enactment. For any transportable uses anywhere the power ingestion and speed are the utmost significant limit, one must diminish the power feeding and upsurge the speed as ample as probable. In this technique certain amendments are suggested to predictable adders to significantly decrease its power feeding. The amendments to the conservative building comprise the elimination of carry generation from LSB to MSB. With this the adder works at high speed with low power consumption.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127904105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073908
A. S, Sheshathri V M, Shaik Muhammad Aasif, Srikanta Yeswanth Adithya
The Internet of Things (IoT) will enable intelligent objects to interact and exchange data, facilitating the integration of the real world with computerized structures for greater comfort and control. These organizations are more than ordinary organizations and have a great deal of influence in the field of IoT, regardless of their dominant characteristics, they face some key challenges such as versatility, safety and limited power supply on board. The rise of Wireless Sensor Networks (WSNs) is one of the major advances that will bring other types of disruption, necessities, and better exhibitions in the coming years. However, the processing, energy, transmitting, and memory capabilities of sensors are constrained, which might have a negative effect on agricultural production. In addition to effectiveness, these IoT-based agricultural sensors need to be protected from hostile opponents. This article has presented an application to smart agriculture by using an IoT-based WSN framework with several design levels. First, agricultural sensors gather pertinent data and use a multi-criteria decision function to select a set of cluster heads. To ensure reliable and effective data transmissions, the Signal to Noise Ratio (SNR) is also used to monitor the signal strength on the transmission connections. Simulation results prove that the proposed framework significantly improves communication performance.
{"title":"A Low-Energy System for IoT-based Wireless Sensor Networks","authors":"A. S, Sheshathri V M, Shaik Muhammad Aasif, Srikanta Yeswanth Adithya","doi":"10.1109/ICAIS56108.2023.10073908","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073908","url":null,"abstract":"The Internet of Things (IoT) will enable intelligent objects to interact and exchange data, facilitating the integration of the real world with computerized structures for greater comfort and control. These organizations are more than ordinary organizations and have a great deal of influence in the field of IoT, regardless of their dominant characteristics, they face some key challenges such as versatility, safety and limited power supply on board. The rise of Wireless Sensor Networks (WSNs) is one of the major advances that will bring other types of disruption, necessities, and better exhibitions in the coming years. However, the processing, energy, transmitting, and memory capabilities of sensors are constrained, which might have a negative effect on agricultural production. In addition to effectiveness, these IoT-based agricultural sensors need to be protected from hostile opponents. This article has presented an application to smart agriculture by using an IoT-based WSN framework with several design levels. First, agricultural sensors gather pertinent data and use a multi-criteria decision function to select a set of cluster heads. To ensure reliable and effective data transmissions, the Signal to Noise Ratio (SNR) is also used to monitor the signal strength on the transmission connections. Simulation results prove that the proposed framework significantly improves communication performance.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127447806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073845
C. Kavitha, N. Sridevi, D. Dhivagar
Heart rate (HR) and Heart rate variability (HRV) have received a great deal of attention that promises to change the dimension of awareness of health and fitness while swimming. HRV is very useful to understand physiological and psychological status of an individual. The variation in HR, provides a reliable information about the role of Autonomic Nervous System (ANS). HRV is very convenient to understand the overall physiological status of an individual. Due to individuality of the HRV, regular monitoring HRV is useful to understand training adaptation, load, recovery, overtraining. The study provides a brief concept on HR and HRV in swimming individual. Although RR intervals are highly individual centric but due to same practice pattern or same type of physical activity, the swimmer group has very small quartile range. A significance difference in RR intervals between control group and swimmer group may come from two different effects of the nervous system. Either it indicates a significant increase in parasympathetic tone due to normal training adaptation or a sign of overtraining that has caused increase in parasympathetic tone. High HRV denotes good indication of positive adaptation, good cardiovascular efficiency. Low HRV score indicates deterioration in VO2max.
{"title":"Detection and Security in Falls with IoT Server","authors":"C. Kavitha, N. Sridevi, D. Dhivagar","doi":"10.1109/ICAIS56108.2023.10073845","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073845","url":null,"abstract":"Heart rate (HR) and Heart rate variability (HRV) have received a great deal of attention that promises to change the dimension of awareness of health and fitness while swimming. HRV is very useful to understand physiological and psychological status of an individual. The variation in HR, provides a reliable information about the role of Autonomic Nervous System (ANS). HRV is very convenient to understand the overall physiological status of an individual. Due to individuality of the HRV, regular monitoring HRV is useful to understand training adaptation, load, recovery, overtraining. The study provides a brief concept on HR and HRV in swimming individual. Although RR intervals are highly individual centric but due to same practice pattern or same type of physical activity, the swimmer group has very small quartile range. A significance difference in RR intervals between control group and swimmer group may come from two different effects of the nervous system. Either it indicates a significant increase in parasympathetic tone due to normal training adaptation or a sign of overtraining that has caused increase in parasympathetic tone. High HRV denotes good indication of positive adaptation, good cardiovascular efficiency. Low HRV score indicates deterioration in VO2max.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117231723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073675
R. Balamurugan, A. A. Kumar, A. Kalaimaran, V. Sathish
The most plentiful form of renewable energy is solar energy. Windstorms and constant soling significantly impair effectiveness. Consequently, it is crucial to clean the panel on a regular basis and properly. The majority of the components require hand cleaning. This kind of cleaning is inconsistent and might harm the workers' health. Solar panel cleaning systems that are permanently installed and fully automated with or without water can address this issue. It contains a brush to remove the dust and water/ chemical solution in addition to have gentle cleaning on the solar panels. In solar power plants, business buildings, and homes, the proposed technique may be installed directly onto the panels. This technique allows a multiple row cleaning. By eliminating any type of dust, this approach aims to boost the efficiency of solar panels. The proposed work comprises a cloud server powered by the internet of things (IoT) to enable online status tracking from anywhere in the world.
{"title":"Integrated IoT System for Automatic Dust Cleaning of Solar Panels","authors":"R. Balamurugan, A. A. Kumar, A. Kalaimaran, V. Sathish","doi":"10.1109/ICAIS56108.2023.10073675","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073675","url":null,"abstract":"The most plentiful form of renewable energy is solar energy. Windstorms and constant soling significantly impair effectiveness. Consequently, it is crucial to clean the panel on a regular basis and properly. The majority of the components require hand cleaning. This kind of cleaning is inconsistent and might harm the workers' health. Solar panel cleaning systems that are permanently installed and fully automated with or without water can address this issue. It contains a brush to remove the dust and water/ chemical solution in addition to have gentle cleaning on the solar panels. In solar power plants, business buildings, and homes, the proposed technique may be installed directly onto the panels. This technique allows a multiple row cleaning. By eliminating any type of dust, this approach aims to boost the efficiency of solar panels. The proposed work comprises a cloud server powered by the internet of things (IoT) to enable online status tracking from anywhere in the world.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131083253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073722
Malathi Janapati, Dr. Shaheda Akhtar
Today, tumours are the second leading cause of cancer deaths. Cancer poses a significant threat to a large population of patients. The medical community needs a quick, automated, efficient, and trustworthy method for detecting tumours like brain tumours. Detection is crucial to effective treatment. If doctors are able to catch a tumour in its earliest stages, they have a better chance of preserving the patient's health. To do this, several distinct image processing methods are used. Through this method, doctors have been able to effectively treat tumours and save the lives of many patients. Tumors are simply abnormal growths of cells that cannot be stopped. As brain tumour cells multiply, they eventually deplete the brain's supply of nutrients. Clinicians currently use MR images (MRI) of the patient's brain to manually pinpoint the location and extent of a brain tumour. Brain tumours can develop at any age in both children and adults. However, this is not the case if detection is timely and accurate. This investigation focuses on three subtypes of brain cancer: gliomas, meningiomas, and pituitary tumours. While there have been numerous publications on the topic of brain tumour classification and prediction, very few have focused on the importance of feature extraction. Manual diagnosis and conventional feature extraction methods have their limitations, and new approaches are needed to overcome them. An automated diagnostic system is necessary for extracting features and making an accurate diagnosis of brain cancer. Although advancements are being made, automatic brain tumour diagnosis continues to struggle with issues like low accuracy and a high proportion of false-positive findings. In this research work, a brief survey is provided on feature extraction for brain tumor detection using machine learning and deep learning techniques.
{"title":"A Brief Survey on Feature Extraction Models for Brain Tumor Detection","authors":"Malathi Janapati, Dr. Shaheda Akhtar","doi":"10.1109/ICAIS56108.2023.10073722","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073722","url":null,"abstract":"Today, tumours are the second leading cause of cancer deaths. Cancer poses a significant threat to a large population of patients. The medical community needs a quick, automated, efficient, and trustworthy method for detecting tumours like brain tumours. Detection is crucial to effective treatment. If doctors are able to catch a tumour in its earliest stages, they have a better chance of preserving the patient's health. To do this, several distinct image processing methods are used. Through this method, doctors have been able to effectively treat tumours and save the lives of many patients. Tumors are simply abnormal growths of cells that cannot be stopped. As brain tumour cells multiply, they eventually deplete the brain's supply of nutrients. Clinicians currently use MR images (MRI) of the patient's brain to manually pinpoint the location and extent of a brain tumour. Brain tumours can develop at any age in both children and adults. However, this is not the case if detection is timely and accurate. This investigation focuses on three subtypes of brain cancer: gliomas, meningiomas, and pituitary tumours. While there have been numerous publications on the topic of brain tumour classification and prediction, very few have focused on the importance of feature extraction. Manual diagnosis and conventional feature extraction methods have their limitations, and new approaches are needed to overcome them. An automated diagnostic system is necessary for extracting features and making an accurate diagnosis of brain cancer. Although advancements are being made, automatic brain tumour diagnosis continues to struggle with issues like low accuracy and a high proportion of false-positive findings. In this research work, a brief survey is provided on feature extraction for brain tumor detection using machine learning and deep learning techniques.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128080077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073702
Ramanamma P, M. Jayanthi, Anuj M A, A. Dharmik Sai Reddy, Devu Maheswar Reddy, G. Pavan Kalyan Reddy
The interest for reasonable Energy age and utilization is expanding step by step as the human populace is relying more upon electronic gadgets for their everyday life. Hence, the need of a full-evidence and monetarily practical power age and circulation framework requests a specific attention. This task proposes usage of human loco motion energy, which albeit extractible goes principally to squander. This demo offers a model that utilizes human strolling, hopping and running as a wellspring of energy and stores it for fundamental use. Such a model is able in a demography that of a nation like India which has such a colossal walker populace. This framework represents a technique for collecting this human headway energy with the utilization of piezoelectric sensor and exhibits a request with the put away energy i.e., to charge a cell phone safely utilizing RFID. The ground response force (GRF) applied from the foot, when switched over completely to voltage by piezoelectric sensors is sufficiently able to control up a gadget. Advanced effort prompts aperiodic voltage develop which with legitimate hardware can be utilized to charge a capacity battery. The power delivered by this method can likewise be used in fundamental application, for example, road lighting, notice sheets, rec centres and different areas of public space. It likewise advances efficient power energy and climate cordial methodology towards energy age. In this undertaking we will give the essential idea and configuration restraints of this model and a fundamental execution of the equal.
{"title":"Footsteps Based Sustainable Energy Generation and Consumption System","authors":"Ramanamma P, M. Jayanthi, Anuj M A, A. Dharmik Sai Reddy, Devu Maheswar Reddy, G. Pavan Kalyan Reddy","doi":"10.1109/ICAIS56108.2023.10073702","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073702","url":null,"abstract":"The interest for reasonable Energy age and utilization is expanding step by step as the human populace is relying more upon electronic gadgets for their everyday life. Hence, the need of a full-evidence and monetarily practical power age and circulation framework requests a specific attention. This task proposes usage of human loco motion energy, which albeit extractible goes principally to squander. This demo offers a model that utilizes human strolling, hopping and running as a wellspring of energy and stores it for fundamental use. Such a model is able in a demography that of a nation like India which has such a colossal walker populace. This framework represents a technique for collecting this human headway energy with the utilization of piezoelectric sensor and exhibits a request with the put away energy i.e., to charge a cell phone safely utilizing RFID. The ground response force (GRF) applied from the foot, when switched over completely to voltage by piezoelectric sensors is sufficiently able to control up a gadget. Advanced effort prompts aperiodic voltage develop which with legitimate hardware can be utilized to charge a capacity battery. The power delivered by this method can likewise be used in fundamental application, for example, road lighting, notice sheets, rec centres and different areas of public space. It likewise advances efficient power energy and climate cordial methodology towards energy age. In this undertaking we will give the essential idea and configuration restraints of this model and a fundamental execution of the equal.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128470456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073692
K. Ganesh, K. Parimala, P. Raveesha, A. Samal, M. Ln, Ashish Verma
A lot has happened in the healthcare industry in recent years thanks to the Internet of Things (IoT). The smart gadgets attached to the body make it easy to measure medical parameters, resulting in a huge amount of individualized medical data for each patient. A wide range of security concerns can arise from this data. Things-as-internet is a new technology that has exploded in popularity in the last few of years. Revolutionary technologies like smart homes, grids, and cities are transforming our daily lives thanks to the Internet of Things (IoT). Internet of Things (IoT) principles are being employed to connect medical resources and provide patients with intelligent, dependable, and effective healthcare. The Internet of Things (IoT) can be used to improve the patient's lifestyle by monitoring their health in the context of active and supported living. For the purpose of introducing smart healthcare, this study first lists the major technologies that support it and introduce its current status in numerous vital fields. One of the most important contributions of this study is the discussion of security measures that can be used for both current and future IoT healthcare systems. Current IoT implementations have been thoroughly analysed and investigated in order to provide clear, exact solutions to the problems now faced by IoT implementations.
{"title":"Internet of Smart Things for Smart Healthcare and Safety Management","authors":"K. Ganesh, K. Parimala, P. Raveesha, A. Samal, M. Ln, Ashish Verma","doi":"10.1109/ICAIS56108.2023.10073692","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073692","url":null,"abstract":"A lot has happened in the healthcare industry in recent years thanks to the Internet of Things (IoT). The smart gadgets attached to the body make it easy to measure medical parameters, resulting in a huge amount of individualized medical data for each patient. A wide range of security concerns can arise from this data. Things-as-internet is a new technology that has exploded in popularity in the last few of years. Revolutionary technologies like smart homes, grids, and cities are transforming our daily lives thanks to the Internet of Things (IoT). Internet of Things (IoT) principles are being employed to connect medical resources and provide patients with intelligent, dependable, and effective healthcare. The Internet of Things (IoT) can be used to improve the patient's lifestyle by monitoring their health in the context of active and supported living. For the purpose of introducing smart healthcare, this study first lists the major technologies that support it and introduce its current status in numerous vital fields. One of the most important contributions of this study is the discussion of security measures that can be used for both current and future IoT healthcare systems. Current IoT implementations have been thoroughly analysed and investigated in order to provide clear, exact solutions to the problems now faced by IoT implementations.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133418241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073872
Syed Hauider Abbas, M. Guru Vimal Kumar, Lekha D, Geethamahalakshmi G, S. S, A. Deepak
There are a variety of civilian, public, and military applications that might be developed for drones. Because they come equipped with their own communications infrastructure, they may be remotely controlled from a distance. Unmanned Aerial Vehicles (UAVs) are gaining popularity for its utilization in a range of activities due to their low cost, versatility, ease of deployment, and the ability to replace manually-operated aircraft in many situations. These vehicles are capable of performing a wide range of activities, such as monitoring, managing crowds, providing wireless coverage, and surveillance. Unmanned Aerial Vehicles (UAVs), often known as drones have the ability to offer solutions that are not only trustworthy but also economical for addressing a wide range of real-time challenges. With the inherent characteristics such as mobility, flexibility, and compatibility in terms of communications, UAVs are able to provide a wide range of services. The ability to monitor a particular area and the flexibility to react to changing demands for services proves the effectiveness of deploying Unmanned Aerial Vehicles (UAVs). As a result, deep learning, also known as DL, is utilized in an increasingly broad manner to overcome the challenges that UAVs face in terms of connectivity and resource utilization.
{"title":"Control of Software-Defined Networks of Unmanned Aerial Vehicles using Distributed Deep Learning","authors":"Syed Hauider Abbas, M. Guru Vimal Kumar, Lekha D, Geethamahalakshmi G, S. S, A. Deepak","doi":"10.1109/ICAIS56108.2023.10073872","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073872","url":null,"abstract":"There are a variety of civilian, public, and military applications that might be developed for drones. Because they come equipped with their own communications infrastructure, they may be remotely controlled from a distance. Unmanned Aerial Vehicles (UAVs) are gaining popularity for its utilization in a range of activities due to their low cost, versatility, ease of deployment, and the ability to replace manually-operated aircraft in many situations. These vehicles are capable of performing a wide range of activities, such as monitoring, managing crowds, providing wireless coverage, and surveillance. Unmanned Aerial Vehicles (UAVs), often known as drones have the ability to offer solutions that are not only trustworthy but also economical for addressing a wide range of real-time challenges. With the inherent characteristics such as mobility, flexibility, and compatibility in terms of communications, UAVs are able to provide a wide range of services. The ability to monitor a particular area and the flexibility to react to changing demands for services proves the effectiveness of deploying Unmanned Aerial Vehicles (UAVs). As a result, deep learning, also known as DL, is utilized in an increasingly broad manner to overcome the challenges that UAVs face in terms of connectivity and resource utilization.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121244430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073821
Pandithurai O, B. N, Pradeepa K, Meenakshi D, Kathiravan M, Vinoth Kumar M
Toxins in the air pose a threat to human health and the environment worldwide, a problem known as air pollution. Predicting air quality from pollution using machine learning techniques might be an effective step in mitigating this issue in the transportation sector. Statistical analysis, multiple analyses, variations, missing value treatment, validation, and cleaning/correction of air quality data have all been previously considered. Then, supervised machine learning methods like Logistic Regression, Random Forest, Decision Tree, and Naive Byes are used to make predictions about the air quality. Precision, Recall, and F1 Score are used to evaluate the effectiveness of various machine learning methods. Predictions of air quality using the Decision Tree method are accurate. The Bureau of Meteorology can use this app to improve their forecasts of air quality. The use of Artificial Intelligence methods to enhance this work is a possibility for the future.
{"title":"Air Pollution Prediction using Supervised Machine Learning Technique","authors":"Pandithurai O, B. N, Pradeepa K, Meenakshi D, Kathiravan M, Vinoth Kumar M","doi":"10.1109/ICAIS56108.2023.10073821","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073821","url":null,"abstract":"Toxins in the air pose a threat to human health and the environment worldwide, a problem known as air pollution. Predicting air quality from pollution using machine learning techniques might be an effective step in mitigating this issue in the transportation sector. Statistical analysis, multiple analyses, variations, missing value treatment, validation, and cleaning/correction of air quality data have all been previously considered. Then, supervised machine learning methods like Logistic Regression, Random Forest, Decision Tree, and Naive Byes are used to make predictions about the air quality. Precision, Recall, and F1 Score are used to evaluate the effectiveness of various machine learning methods. Predictions of air quality using the Decision Tree method are accurate. The Bureau of Meteorology can use this app to improve their forecasts of air quality. The use of Artificial Intelligence methods to enhance this work is a possibility for the future.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129182347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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