Pub Date : 2022-11-10DOI: 10.1109/I-SMAC55078.2022.9987432
M. P. Suresh, V. R. Vedha Rhythesh, J. Dinesh, K. Deepak, J. Manikandan
A basic design of robot that can fight fires at an affordable cost could prove to be boon in fighting domestic fires, till help arrives. The robot developed consists of three elements which is the hardware, electronic interfacing circuits, and software program. The robot has four battery operated motor (BO motor). This firefighting robotic system is capable of detecting and extinguishing fire. These robots can be made to roll into places where it is not safe for humans to enter. Time is of essence when it comes to fighting fires as even a few minutes’ delay can turn small fires into raging inferno. This robot is designed as a first response unit so it can suppress the fire keeps it under control till help arrives. This firefighting robotic system is controlled by an Arduino Uno development board. It is also equipped with the fire flame sensor for detecting fires. It is equipped with a water tank and a pump. So, on detecting fires it sprays water extinguishing the fire. Water spraying nozzle is mounted on servo motor to cover maximum area. Although there is a lot of scope for improvement, this could be a first step in developing a complete fire-fighting robot that could also rescue victims. The main function of this robot is to become an unmanned support vehicle, developed to search and extinguish fire. By using such robots, fire identification and rescue activities can be done with greater accuracy and securely without exposing the fire fighters to dangerous conditions. In other words, robots can reduce the need to expose fire fighters to danger.
{"title":"An Arduino Uno Controlled Fire Fighting Robot for Fires in Enclosed Spaces","authors":"M. P. Suresh, V. R. Vedha Rhythesh, J. Dinesh, K. Deepak, J. Manikandan","doi":"10.1109/I-SMAC55078.2022.9987432","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987432","url":null,"abstract":"A basic design of robot that can fight fires at an affordable cost could prove to be boon in fighting domestic fires, till help arrives. The robot developed consists of three elements which is the hardware, electronic interfacing circuits, and software program. The robot has four battery operated motor (BO motor). This firefighting robotic system is capable of detecting and extinguishing fire. These robots can be made to roll into places where it is not safe for humans to enter. Time is of essence when it comes to fighting fires as even a few minutes’ delay can turn small fires into raging inferno. This robot is designed as a first response unit so it can suppress the fire keeps it under control till help arrives. This firefighting robotic system is controlled by an Arduino Uno development board. It is also equipped with the fire flame sensor for detecting fires. It is equipped with a water tank and a pump. So, on detecting fires it sprays water extinguishing the fire. Water spraying nozzle is mounted on servo motor to cover maximum area. Although there is a lot of scope for improvement, this could be a first step in developing a complete fire-fighting robot that could also rescue victims. The main function of this robot is to become an unmanned support vehicle, developed to search and extinguish fire. By using such robots, fire identification and rescue activities can be done with greater accuracy and securely without exposing the fire fighters to dangerous conditions. In other words, robots can reduce the need to expose fire fighters to danger.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130739807","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 : 2022-11-10DOI: 10.1109/I-SMAC55078.2022.9987254
R. Yugha, V. Vinodhini, J. Arunkumar, K. Varalakshmi, G. Karthikeyan, G. Ramkumar
A condition known as glaucoma, is an eye illness brought on by high intraocular pressure, may lead to total blindness. On the other hand, prompt glaucoma screening-based therapy may keep the individual from losing all vision. Professionals manually analyze retina to pinpoint the areas affected by glaucoma using precise testing procedures. However, because of complicated glaucoma testing methods and a lack of resources, delays in detection are often experienced that may raise the global rate of visual impairment. Moreover, the significant resemblance between the lesion and eye color also makes the manual categorization procedure more difficult. Hence, there exists an urgent need to develop an effective smart approach that can precisely detect the Optic Disc as well as Optic Cup lesions at the early stage in order to address the difficulties of manual methods. Therefore, a Deep Learning based strategy called EfficientDet-DO with EfficientNet-B0 serving as its foundation has been proposed in this paper. There are three phases in the conceptual methodology for the localization and categorization of glaucoma. First, the EfficientNet-B0 feature extractor computes the feature representations from the suspicious examples. Next, the top-down and bottom-up key points merging operations are repeatedly carried out by the Bi-Directional Feature Pyramid system modules of EfficientDet-DO using the calculated characteristics from EfficientNet-B0. The resulting localized areas of a glaucoma lesion and its accompanying classification are anticipated in the last stage.
{"title":"An Automated Glaucoma Detection from Fundus Images based on Deep Learning Network","authors":"R. Yugha, V. Vinodhini, J. Arunkumar, K. Varalakshmi, G. Karthikeyan, G. Ramkumar","doi":"10.1109/I-SMAC55078.2022.9987254","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987254","url":null,"abstract":"A condition known as glaucoma, is an eye illness brought on by high intraocular pressure, may lead to total blindness. On the other hand, prompt glaucoma screening-based therapy may keep the individual from losing all vision. Professionals manually analyze retina to pinpoint the areas affected by glaucoma using precise testing procedures. However, because of complicated glaucoma testing methods and a lack of resources, delays in detection are often experienced that may raise the global rate of visual impairment. Moreover, the significant resemblance between the lesion and eye color also makes the manual categorization procedure more difficult. Hence, there exists an urgent need to develop an effective smart approach that can precisely detect the Optic Disc as well as Optic Cup lesions at the early stage in order to address the difficulties of manual methods. Therefore, a Deep Learning based strategy called EfficientDet-DO with EfficientNet-B0 serving as its foundation has been proposed in this paper. There are three phases in the conceptual methodology for the localization and categorization of glaucoma. First, the EfficientNet-B0 feature extractor computes the feature representations from the suspicious examples. Next, the top-down and bottom-up key points merging operations are repeatedly carried out by the Bi-Directional Feature Pyramid system modules of EfficientDet-DO using the calculated characteristics from EfficientNet-B0. The resulting localized areas of a glaucoma lesion and its accompanying classification are anticipated in the last stage.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128373497","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 : 2022-11-10DOI: 10.1109/I-SMAC55078.2022.9987345
S. Sruthi, S. Sridhar
Main goal of the research is to employ Music genre prediction-based classification of audio files with low level feature of frequency domain and time domain using K-Means Clustering (K-Means) and Support Vector Machine (SVM). Materials and Methods: SVM and K-Means are implemented in this research work. Sample size is calculated using G power software and determined as 10 per group with pretest power 80%, threshold 0.05% and CI 95%. Result: SVM provides a higher of 95.35% compared to K-Means algorithm with 75.20% in predicting classification of Audio files with low level feature of frequency domain. There is a noteworthy difference between two groups with a significance value of 0.28 (p>0.05). Conclusion: NovelSupport Vector Machine algorithm predicts audio files with low level frequency better than K-Means algorithm.
{"title":"Music Genre Predictor based Classification of Audio Files with Low Level Feature of Frequency and Time Domain using Support Vector Machine Over K-Means Clustering Algorithm","authors":"S. Sruthi, S. Sridhar","doi":"10.1109/I-SMAC55078.2022.9987345","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987345","url":null,"abstract":"Main goal of the research is to employ Music genre prediction-based classification of audio files with low level feature of frequency domain and time domain using K-Means Clustering (K-Means) and Support Vector Machine (SVM). Materials and Methods: SVM and K-Means are implemented in this research work. Sample size is calculated using G power software and determined as 10 per group with pretest power 80%, threshold 0.05% and CI 95%. Result: SVM provides a higher of 95.35% compared to K-Means algorithm with 75.20% in predicting classification of Audio files with low level feature of frequency domain. There is a noteworthy difference between two groups with a significance value of 0.28 (p>0.05). Conclusion: NovelSupport Vector Machine algorithm predicts audio files with low level frequency better than K-Means algorithm.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"75 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134254989","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 : 2022-11-10DOI: 10.1109/I-SMAC55078.2022.9987321
S. Sivasubramanian, N. K. Sundaram, S. Padhi, Dipesh Uike, B. Maheswari, V. Banupriya
An automatic vending machine is designed to supply people with a variety of items, such as snacks, beverages, newspapers, and tickets without any human intervention. According to the money that is deposited into a vending machine as well as the product that has been selected by the user, the machine will determine the item and will distribute it to the user. In the proposed work, the vending machine has been designed to distribute fruits to the user as per their requirement. Classification algorithms have been used to predict the type of fruits required by the user with the help of the input provided by camera. The load cell is used to measure the kilogram or the quantity of the fruits as per the requirement by using some input peripherals like keyboard. The proposed system is also a user interactive based once. Here, there is a display device that has interfaced with the system and the display device will provide information such as the fruit which has been chosen and the quantity of the fruit that the user has entered and also shares the information on the status of the requirements. So, it will be useful for the user to know the process going in the vending machine. The raspberry pi microprocessor has employed here as a processor along the required input and output peripherals like LCD, Keypad, Load cell, camera, and motors. The machine learning algorithm like a support vector machine has been employed to predict the type of fruit as per the requirements of the user. The insertion of intelligence like machine learning algorithms in the vending machine is comparatively providing better performance. The long-term objective is to equip a vending machine solution that is both affordable and efficient, therefore boosting the shopping experience of customers and increasing the need for widespread deployment of intelligence in smart vending machines.
{"title":"Next generation Fruit Vending Machine using Artificial Intelligence","authors":"S. Sivasubramanian, N. K. Sundaram, S. Padhi, Dipesh Uike, B. Maheswari, V. Banupriya","doi":"10.1109/I-SMAC55078.2022.9987321","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987321","url":null,"abstract":"An automatic vending machine is designed to supply people with a variety of items, such as snacks, beverages, newspapers, and tickets without any human intervention. According to the money that is deposited into a vending machine as well as the product that has been selected by the user, the machine will determine the item and will distribute it to the user. In the proposed work, the vending machine has been designed to distribute fruits to the user as per their requirement. Classification algorithms have been used to predict the type of fruits required by the user with the help of the input provided by camera. The load cell is used to measure the kilogram or the quantity of the fruits as per the requirement by using some input peripherals like keyboard. The proposed system is also a user interactive based once. Here, there is a display device that has interfaced with the system and the display device will provide information such as the fruit which has been chosen and the quantity of the fruit that the user has entered and also shares the information on the status of the requirements. So, it will be useful for the user to know the process going in the vending machine. The raspberry pi microprocessor has employed here as a processor along the required input and output peripherals like LCD, Keypad, Load cell, camera, and motors. The machine learning algorithm like a support vector machine has been employed to predict the type of fruit as per the requirements of the user. The insertion of intelligence like machine learning algorithms in the vending machine is comparatively providing better performance. The long-term objective is to equip a vending machine solution that is both affordable and efficient, therefore boosting the shopping experience of customers and increasing the need for widespread deployment of intelligence in smart vending machines.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133713862","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 : 2022-11-10DOI: 10.1109/I-SMAC55078.2022.9987277
S. Shete, Pranjal Jog, R. Kamalakannan, J. T. A. Raghesh, S. Manikandan, R. Kumawat
Developed nations have focused more on environmental degradation and climate change in response to rising concerns about meeting the needs of their citizens. The market for emission-free Electric Vehicles (EVs) is now a key area of international rivalry and progress. Rising concerns over high voltage hazards in EVs are a direct result of their increasing popularity. It is crucial to examine the problem diagnosis method of lithium-ion batteries (LIB) because the battery system is responsible for more than 30% of EV accidents. EV’s LIB has complicated fault types that are difficult to treat. Timely and efficient battery pack problem diagnosis is crucial for ensuring the real-time safety of EV function. With the help of neural network models like Multilayer Perceptron (MLP) and Radial Basis Function (RBF), this research demonstrates a technique for detecting and fixing EV battery problems. MATLAB is used to simulate the battery and generate the necessary data for the battery failure detection system. Accuracy is improved through pre-processing the data after it has been generated. Both models are trained and then put through tests to determine how well the models are performing. By contrasting the positive and negative metrics, the best model can be determined.
{"title":"Fault Diagnosis of Electric Vehicle’s Battery by Deploying Neural Network","authors":"S. Shete, Pranjal Jog, R. Kamalakannan, J. T. A. Raghesh, S. Manikandan, R. Kumawat","doi":"10.1109/I-SMAC55078.2022.9987277","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987277","url":null,"abstract":"Developed nations have focused more on environmental degradation and climate change in response to rising concerns about meeting the needs of their citizens. The market for emission-free Electric Vehicles (EVs) is now a key area of international rivalry and progress. Rising concerns over high voltage hazards in EVs are a direct result of their increasing popularity. It is crucial to examine the problem diagnosis method of lithium-ion batteries (LIB) because the battery system is responsible for more than 30% of EV accidents. EV’s LIB has complicated fault types that are difficult to treat. Timely and efficient battery pack problem diagnosis is crucial for ensuring the real-time safety of EV function. With the help of neural network models like Multilayer Perceptron (MLP) and Radial Basis Function (RBF), this research demonstrates a technique for detecting and fixing EV battery problems. MATLAB is used to simulate the battery and generate the necessary data for the battery failure detection system. Accuracy is improved through pre-processing the data after it has been generated. Both models are trained and then put through tests to determine how well the models are performing. By contrasting the positive and negative metrics, the best model can be determined.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"220 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122396770","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 : 2022-11-10DOI: 10.1109/I-SMAC55078.2022.9987279
M. Vanitha, C. S. Joice, M. Selvi, T. Archana, S. Kavitha
This paper proposes an Android mobile application that gives information about the real time location of the buses under the organization. ESP8266 Node MCU and GPS Module is used to get geographic coordinates and the vehicle location is updated to the application through the internet which would give the exact location of buses may help the users to plan their way to reach their destination on time. The RFID (Radio Frequency Identification)-based access control system can only be unlocked by those who have been authenticated. The service will then activate and authenticate the person as a result of this action. The RFID reads an ID number from an RFID tag and transfers the information to a database that can be accessed via an Android app. The Android platform necessitates open-source development, making it the most practical and user-friendly option. Human evolution has included the development of transportation systems. It is impossible to imagine life without automobiles. To accommodate the large population, the number of automobiles has been significantly increasing. This resulted in a rise in the number of accidents. The accident-prevention methods in use today are all static and outdated. Furthermore, no adequate accident detection mechanism exists.
{"title":"Secured IoT based Smart Vehicle Tracking System","authors":"M. Vanitha, C. S. Joice, M. Selvi, T. Archana, S. Kavitha","doi":"10.1109/I-SMAC55078.2022.9987279","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987279","url":null,"abstract":"This paper proposes an Android mobile application that gives information about the real time location of the buses under the organization. ESP8266 Node MCU and GPS Module is used to get geographic coordinates and the vehicle location is updated to the application through the internet which would give the exact location of buses may help the users to plan their way to reach their destination on time. The RFID (Radio Frequency Identification)-based access control system can only be unlocked by those who have been authenticated. The service will then activate and authenticate the person as a result of this action. The RFID reads an ID number from an RFID tag and transfers the information to a database that can be accessed via an Android app. The Android platform necessitates open-source development, making it the most practical and user-friendly option. Human evolution has included the development of transportation systems. It is impossible to imagine life without automobiles. To accommodate the large population, the number of automobiles has been significantly increasing. This resulted in a rise in the number of accidents. The accident-prevention methods in use today are all static and outdated. Furthermore, no adequate accident detection mechanism exists.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127842727","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 : 2022-11-10DOI: 10.1109/I-SMAC55078.2022.9987264
Uday Kulkarni, Sitanshu S Hallad, A. Patil, Tanvi Bhujannavar, Satwik Kulkarni, S. Meena
Deep Neural Networks (DNNs) have been an important and fast-developing tool used for computer vision, and artificial intelligence. Since these algorithms are widely used for image classification, they are bound to a few issues, creating a need for the DNN models to be optimized. The need for optimization is created due to computational complexity, the number of parameters and model size. Pruning techniques have been employed to mitigate this issue in DNNs, one of these techniques is Filter pruning. There are huge numbers of methods under Filter pruning that have been proposed and each one of them is based on specific sub-objectives. In this paper, we aim to represent different types of pruning methods in a summarized way and conclude on a method that is most efficient in delivering pruned model. The conclusion is stated after trying the methods in a common environment of data set and computational system.
{"title":"A Survey on Filter Pruning Techniques for Optimization of Deep Neural Networks","authors":"Uday Kulkarni, Sitanshu S Hallad, A. Patil, Tanvi Bhujannavar, Satwik Kulkarni, S. Meena","doi":"10.1109/I-SMAC55078.2022.9987264","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987264","url":null,"abstract":"Deep Neural Networks (DNNs) have been an important and fast-developing tool used for computer vision, and artificial intelligence. Since these algorithms are widely used for image classification, they are bound to a few issues, creating a need for the DNN models to be optimized. The need for optimization is created due to computational complexity, the number of parameters and model size. Pruning techniques have been employed to mitigate this issue in DNNs, one of these techniques is Filter pruning. There are huge numbers of methods under Filter pruning that have been proposed and each one of them is based on specific sub-objectives. In this paper, we aim to represent different types of pruning methods in a summarized way and conclude on a method that is most efficient in delivering pruned model. The conclusion is stated after trying the methods in a common environment of data set and computational system.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128025154","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 : 2022-11-10DOI: 10.1109/I-SMAC55078.2022.9987293
B Surendiran, K. Dhanasekaran, A. Tamizhselvi
Recently Quantum Computing has gained much attention in the field of data science and computational problem solving. It is expected that the quantum machine learning will help researchers to find solutions for many complex problems in areas such as weather forecasting, data science, computational biology, energy management, secure communication, and many others. This paper presents a study on quantum machine learning techniques, challenges and applications of these techniques in climate change prediction, and weather forecasting towards future research in Quantum Machine Learning and Quantum Computing. It also discusses the latest developments and trends in Quantum machine Learning and presents practical examples to understand how Quantum Machine Learning considerably improves the performances of existing machine learning approaches.
{"title":"A Study on Quantum Machine Learning for Accurate and Efficient Weather Prediction","authors":"B Surendiran, K. Dhanasekaran, A. Tamizhselvi","doi":"10.1109/I-SMAC55078.2022.9987293","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987293","url":null,"abstract":"Recently Quantum Computing has gained much attention in the field of data science and computational problem solving. It is expected that the quantum machine learning will help researchers to find solutions for many complex problems in areas such as weather forecasting, data science, computational biology, energy management, secure communication, and many others. This paper presents a study on quantum machine learning techniques, challenges and applications of these techniques in climate change prediction, and weather forecasting towards future research in Quantum Machine Learning and Quantum Computing. It also discusses the latest developments and trends in Quantum machine Learning and presents practical examples to understand how Quantum Machine Learning considerably improves the performances of existing machine learning approaches.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128697339","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 : 2022-11-10DOI: 10.1109/I-SMAC55078.2022.9987366
J. Mahale, S. Degadwala, Dhairya Vyas
India is mostly a farming country. Agriculture is vital to the Indian economy and humanity’s destiny. Agriculture also employs a sizable portion of the workforce. 70% of India’s rural population relies on agricultural activity for their livelihood. Crop output forecasting is one of the most sought-after and difficult tasks that any government can do. Any farmer wants to know how much crop production they might expect in the near future. Traditionally, while calculating yields, the farmer’s expertise of the crop and land was taken into account. Machine Learning algorithms can be used to extract accuracy as well as previously unknown patterns or information from massive datasets. As a result, crop output projections will help farmers choose the best crop for their farms. They could also generate a larger profit as a result of this. Multiple attribute selection techniques for crop prediction, as well as the Machine Learning methodology, are discussed in this work. This research study will discuss about the future path of agricultural output prediction systems near the end of the programme.
{"title":"Crop Prediction System based on Soil and Weather Characteristics","authors":"J. Mahale, S. Degadwala, Dhairya Vyas","doi":"10.1109/I-SMAC55078.2022.9987366","DOIUrl":"https://doi.org/10.1109/I-SMAC55078.2022.9987366","url":null,"abstract":"India is mostly a farming country. Agriculture is vital to the Indian economy and humanity’s destiny. Agriculture also employs a sizable portion of the workforce. 70% of India’s rural population relies on agricultural activity for their livelihood. Crop output forecasting is one of the most sought-after and difficult tasks that any government can do. Any farmer wants to know how much crop production they might expect in the near future. Traditionally, while calculating yields, the farmer’s expertise of the crop and land was taken into account. Machine Learning algorithms can be used to extract accuracy as well as previously unknown patterns or information from massive datasets. As a result, crop output projections will help farmers choose the best crop for their farms. They could also generate a larger profit as a result of this. Multiple attribute selection techniques for crop prediction, as well as the Machine Learning methodology, are discussed in this work. This research study will discuss about the future path of agricultural output prediction systems near the end of the programme.","PeriodicalId":306129,"journal":{"name":"2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117093713","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 : 2022-11-10DOI: 10.1109/I-SMAC55078.2022.9987356
Suganya T, V. Rajendran, P. Mangaiyarkarasi
Medical electronic implants can basically work on the well-being and personal satisfaction of individuals. These plugs are usually fueled by batteries, which as a rule have a limited lifespan and as a result need to be replaced occasionally using surgery. In the latter, subcutaneous sun-based cells, which can generate energy by retaining the light transmitted by the skin, can be developed as an economic force to control medical electronic insertions in the body. This paper is to develop an Improved Maximum Power Point Tracking (IMPPT) controller aimed at an equivalent skin model with battery-less cardiac pacemaker. In the proposed methodology, the equivalent skin model with battery-less cardiac pacemaker is designed and analyzed. The Photovoltaic cellis utilized to power the cardiac pacemaker for design a battery-less cardiac pacemaker. After that, the PV is connected with the equivalent circuit model. The PV may be affected due to environmental conditions which will be solved by the MPPT controller. Artificial Intelligence (AI) technique is developed to maintain the stability operation by avoiding environmental conditions. Here, the Arithmetic Optimization Algorithm (AOA) can be utilized towards manage the MPPT controller. The proposed battery-less cardiac pacemaker is designed and executed in MATLAB/Simulink, and its performance is evaluated in terms of maximum power, maximum voltage, maximum current, irradiance, input power of pacemaker, and output power of pacemaker.
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