Pub Date : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073711
S. Gopi, Hari Kishan Kondaveeti
To feed the world’s population of 7.9 billion people, preventing crop failure through early disease detection is essential. Various bacterial, viral, or fungal diseases affect the rice leaf and these diseases drastically lower rice yield. Therefore, identifying rice leaf diseases is essential to meeting the demand for rice from an extensive worldwide population. However, the ability to identify rice leaf disease is constrained by the image backgrounds and the circumstances under which the images were captured. When tested on independent rice leaf diseased data, the performance of deep learning models for automated detection of rice leaf diseases suffers substantially. This stusy examines the results of well-known and widely used transfer learning models to detect the rice leaf disease. This can be done in two ways: frozen layers and fine-tuning. It was observed that the results of the freeze layers, the DenseNet169, achieved a good testing accuracy of 99.66%, and when the results of the fine-tuned transfer learning models were examined, Xception performed well and achieved 99.99% of testing accuracy.
{"title":"Transfer Learning for Rice Leaf Disease Detection","authors":"S. Gopi, Hari Kishan Kondaveeti","doi":"10.1109/ICAIS56108.2023.10073711","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073711","url":null,"abstract":"To feed the world’s population of 7.9 billion people, preventing crop failure through early disease detection is essential. Various bacterial, viral, or fungal diseases affect the rice leaf and these diseases drastically lower rice yield. Therefore, identifying rice leaf diseases is essential to meeting the demand for rice from an extensive worldwide population. However, the ability to identify rice leaf disease is constrained by the image backgrounds and the circumstances under which the images were captured. When tested on independent rice leaf diseased data, the performance of deep learning models for automated detection of rice leaf diseases suffers substantially. This stusy examines the results of well-known and widely used transfer learning models to detect the rice leaf disease. This can be done in two ways: frozen layers and fine-tuning. It was observed that the results of the freeze layers, the DenseNet169, achieved a good testing accuracy of 99.66%, and when the results of the fine-tuned transfer learning models were examined, Xception performed well and achieved 99.99% of testing accuracy.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"38 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":"124865631","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/ICAIS56108.2023.10073903
Amanoollah Khurwolah, V. Oree
This paper presents the design and implementation of a dual-axis solar tracker that allows the PV panel on which it is mounted to capture maximum solar energy throughout the day. The device tracks the azimuth and elevation angles of the Sun as it moves across the sky to maintain the PV panel perpendicular to sunlight at all times. Four light sensors are used for this purpose and an Arduino microcontroller processes their signals to actuate driving mechanisms that maintain the orthogonal position of the PV panel with respect to sunlight. The mechanical structure of the solar tracker is designed in such a way as to minimize its inherent energy consumption so that the overall energy performance is optimized. The prototype is tested in the tropical island of Mauritius. Previous studies have shown that the energy performance of solar tracking systems is highly dependent on the climate, with negligible energy gain achieved in very hot regions. Results indicate that improvements of 30.5% and 28.5% in the total energy output of the PV panel are obtained compared to a fixed PV panel on a cloudy and sunny day respectively.
{"title":"Investigating the Performance of a Dual-Axis Solar Tracking System in a Tropical Climate","authors":"Amanoollah Khurwolah, V. Oree","doi":"10.1109/ICAIS56108.2023.10073903","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073903","url":null,"abstract":"This paper presents the design and implementation of a dual-axis solar tracker that allows the PV panel on which it is mounted to capture maximum solar energy throughout the day. The device tracks the azimuth and elevation angles of the Sun as it moves across the sky to maintain the PV panel perpendicular to sunlight at all times. Four light sensors are used for this purpose and an Arduino microcontroller processes their signals to actuate driving mechanisms that maintain the orthogonal position of the PV panel with respect to sunlight. The mechanical structure of the solar tracker is designed in such a way as to minimize its inherent energy consumption so that the overall energy performance is optimized. The prototype is tested in the tropical island of Mauritius. Previous studies have shown that the energy performance of solar tracking systems is highly dependent on the climate, with negligible energy gain achieved in very hot regions. Results indicate that improvements of 30.5% and 28.5% in the total energy output of the PV panel are obtained compared to a fixed PV panel on a cloudy and sunny day respectively.","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":"122760461","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/ICAIS56108.2023.10073708
K. M, Jones Nirmal L, Jenin Prabhu R, S. P
The MPPT controller of a photovoltaic (PV) system fluctuates, increasing solar irradiance, and has complex voltage-current properties. To balance the solar PV system towards the load and design the solar energy system on MPPT, a two-cell interleaved DC-DC boost connected with an inverter is utilized. Using just load voltage statistics, a voltage control technique is created, excluding array current monitoring. When compared to non-coupled pair interleaved conversions, the current conversion design has lower ripple contents from the loads and supply sides, better efficiency, as well as lower switch stress. Consequently, a decreased array capacitance value is enough to stabilize the array voltage output. For maximum power point functioning, analytical formulas for the solar supply and interleaved boost converter are constructed. The power interleaved converter is functioning using the control technique of MPPT, the Perturb and Observe (P&O) algorithm is employed, and the power is supplied to the alternating voltage conversion, and it is connected to the voltage source converter. The modeling and experimental findings are presented here to demonstrate how well suited that particular kind of power converter is for the application. In addition, a comparison of coupled and non-coupled interleaved boost converters for solar applications is performed.
{"title":"Photovoltaic System based Interleaved Converter for Grid System","authors":"K. M, Jones Nirmal L, Jenin Prabhu R, S. P","doi":"10.1109/ICAIS56108.2023.10073708","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073708","url":null,"abstract":"The MPPT controller of a photovoltaic (PV) system fluctuates, increasing solar irradiance, and has complex voltage-current properties. To balance the solar PV system towards the load and design the solar energy system on MPPT, a two-cell interleaved DC-DC boost connected with an inverter is utilized. Using just load voltage statistics, a voltage control technique is created, excluding array current monitoring. When compared to non-coupled pair interleaved conversions, the current conversion design has lower ripple contents from the loads and supply sides, better efficiency, as well as lower switch stress. Consequently, a decreased array capacitance value is enough to stabilize the array voltage output. For maximum power point functioning, analytical formulas for the solar supply and interleaved boost converter are constructed. The power interleaved converter is functioning using the control technique of MPPT, the Perturb and Observe (P&O) algorithm is employed, and the power is supplied to the alternating voltage conversion, and it is connected to the voltage source converter. The modeling and experimental findings are presented here to demonstrate how well suited that particular kind of power converter is for the application. In addition, a comparison of coupled and non-coupled interleaved boost converters for solar applications is performed.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"58 30","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114000136","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/ICAIS56108.2023.10073810
J. Manju, R. Manjula, Ritesh Dash
In order to maintain a balance between demand and supply, the Internet of Things (IoT) enabled Smart Grid (SG) plays a critical role in establishing a Demand Response (DR) program. It is all about Demand Side Management (DSM) in SG’s system. When IoT gadgets are programmed to turn on and off according to supply and demand, they become an essential part of the smart grid load prediction system and help to balance energy use. This research use Artificial Bee Colony (ABC) optimization model for load prediction in the smart grid environment. To effectively predict the load in the SG, an Efficient Artificial Bee Colony Optimized Model for Load Prediction in Smart Grid (EABCOM-LPSG) model is proposed in this research. The Artificial Bee Colony (ABC) algorithm is as warm-based meta-heuristic technique used for numerical problem optimization. It was inspired by honey bees’ clever foraging behavior. The proposed method’s two-step prediction system, specifically developed to improve forecasting precision as one of its major advantages. A major benefit of the suggested method is that it can statistically examine the effects of several major aspects, which is extremely useful when selecting attribute combinations and deploying on-board sensors for smart grids with large areas, diverse climates, and different social conventions. The proposed model when contrasted with traditional model exhibits better performance levels.
{"title":"An Efficient Artificial Bee Colony based Optimized Model for Load Prediction in IoT Enabled Smart Grid","authors":"J. Manju, R. Manjula, Ritesh Dash","doi":"10.1109/ICAIS56108.2023.10073810","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073810","url":null,"abstract":"In order to maintain a balance between demand and supply, the Internet of Things (IoT) enabled Smart Grid (SG) plays a critical role in establishing a Demand Response (DR) program. It is all about Demand Side Management (DSM) in SG’s system. When IoT gadgets are programmed to turn on and off according to supply and demand, they become an essential part of the smart grid load prediction system and help to balance energy use. This research use Artificial Bee Colony (ABC) optimization model for load prediction in the smart grid environment. To effectively predict the load in the SG, an Efficient Artificial Bee Colony Optimized Model for Load Prediction in Smart Grid (EABCOM-LPSG) model is proposed in this research. The Artificial Bee Colony (ABC) algorithm is as warm-based meta-heuristic technique used for numerical problem optimization. It was inspired by honey bees’ clever foraging behavior. The proposed method’s two-step prediction system, specifically developed to improve forecasting precision as one of its major advantages. A major benefit of the suggested method is that it can statistically examine the effects of several major aspects, which is extremely useful when selecting attribute combinations and deploying on-board sensors for smart grids with large areas, diverse climates, and different social conventions. The proposed model when contrasted with traditional model exhibits better performance levels.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"18 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":"114468079","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/ICAIS56108.2023.10073812
A. Lakshmi, V. Krishnaveni, G. Vinuthna, A. L. Goud
Many developments have happened in the recent times by applying new technologies in various fields. Precising to agriculture, use of these technologies not only save time and energy but also bring advancements to various processes. Using agricultural technologies for irrigation and fertilizer sensing eases work to farmers one of which including use of Solar Power for automatic water pumping to conserve energy. Fertilizers content in soil causing soil and water pollution cannot be neglected. Hence, this system has been proposed to know if fertilizers are being used in required amounts. A Solar based water pumping is also present additionally to pump water based on soil moisture. A RTC is used to keep track of soil moisture thus pumping water over a fixed interval of time.
{"title":"Fertilizer Sensing and Solar based RTC Water Pumping","authors":"A. Lakshmi, V. Krishnaveni, G. Vinuthna, A. L. Goud","doi":"10.1109/ICAIS56108.2023.10073812","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073812","url":null,"abstract":"Many developments have happened in the recent times by applying new technologies in various fields. Precising to agriculture, use of these technologies not only save time and energy but also bring advancements to various processes. Using agricultural technologies for irrigation and fertilizer sensing eases work to farmers one of which including use of Solar Power for automatic water pumping to conserve energy. Fertilizers content in soil causing soil and water pollution cannot be neglected. Hence, this system has been proposed to know if fertilizers are being used in required amounts. A Solar based water pumping is also present additionally to pump water based on soil moisture. A RTC is used to keep track of soil moisture thus pumping water over a fixed interval of time.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"206 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":"122428996","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/ICAIS56108.2023.10073930
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/ICAIS56108.2023.10073872
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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