Pub Date : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297379
Purna Patel, J. Dave
This article focuses on the design and dynamic modeling of a VTOL aircraft with a quadrotor design. Contrary to other VTOL aircraft, quadrotor VTOL aircraft has no control surfaces and is controlled and maneuvered by varying the angular velocities of rotors. This aircraft can take off and land vertically, hover and glide, which makes the design useful in many fields of operations. Dynamic equations of the quadrotor VTOL aircraft in hovering and gliding modes are derived and discussed in detail in this paper. The aircraft based on the stated design was tested successfully in a controlled environment with minimal wind speed. The benefits of this design over other drone designs are also discussed.
{"title":"Design and Dynamic Modelling of Quadrotor VTOL aircraft","authors":"Purna Patel, J. Dave","doi":"10.1109/ICECA49313.2020.9297379","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297379","url":null,"abstract":"This article focuses on the design and dynamic modeling of a VTOL aircraft with a quadrotor design. Contrary to other VTOL aircraft, quadrotor VTOL aircraft has no control surfaces and is controlled and maneuvered by varying the angular velocities of rotors. This aircraft can take off and land vertically, hover and glide, which makes the design useful in many fields of operations. Dynamic equations of the quadrotor VTOL aircraft in hovering and gliding modes are derived and discussed in detail in this paper. The aircraft based on the stated design was tested successfully in a controlled environment with minimal wind speed. The benefits of this design over other drone designs are also discussed.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117346021","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297428
B. Dhanalakshmi, R. Ramesh, D. Raguraman, R. Menaka
Due to the increase in vehicle usage, itis a challenging task to monitor, analyze the vehicles by a human for security purposes. There is a need for an automatic vehicle recognition system since various places nowadays have checkpoints for vehicles, to track the stolen vehicles, and to monitor traffic violations. The problem exists when the vehicle number plate is encountered in different formats, different scales, and illumination to number-plates. In the case of an indeterminate situation, identifying vehicle number plates in poor lighting conditions and worse traffic situations can be analyzed using an automatic vehicle number plate recognition system. The vehicle name board edge finding techniques are used to easily identify the vehicle number in the name board. A dataset with 200 license plates has been collected as training datasets for recognition, estimation, and identification, thus improving system accuracy of recognition when compared to existing works. The training input samples include images of vehicle number plates taken from the traffic department. The automated vehicle number recognition system is improvised in terms of accuracy by estimating stability score and using the k-means clustering algorithm.
{"title":"Automated Vehicle Number Plate Recognition System using Stability Score and K-Means Clustering Algorithm","authors":"B. Dhanalakshmi, R. Ramesh, D. Raguraman, R. Menaka","doi":"10.1109/ICECA49313.2020.9297428","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297428","url":null,"abstract":"Due to the increase in vehicle usage, itis a challenging task to monitor, analyze the vehicles by a human for security purposes. There is a need for an automatic vehicle recognition system since various places nowadays have checkpoints for vehicles, to track the stolen vehicles, and to monitor traffic violations. The problem exists when the vehicle number plate is encountered in different formats, different scales, and illumination to number-plates. In the case of an indeterminate situation, identifying vehicle number plates in poor lighting conditions and worse traffic situations can be analyzed using an automatic vehicle number plate recognition system. The vehicle name board edge finding techniques are used to easily identify the vehicle number in the name board. A dataset with 200 license plates has been collected as training datasets for recognition, estimation, and identification, thus improving system accuracy of recognition when compared to existing works. The training input samples include images of vehicle number plates taken from the traffic department. The automated vehicle number recognition system is improvised in terms of accuracy by estimating stability score and using the k-means clustering algorithm.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127415646","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297535
M. Ram, Kuda Nageswara Rao, S. J. Basha, S. S. Reddy
Wireless Sensor Network (WSN) is a system with huge number of sensors connected to one another by placing them in a specific area. Different issues with WSN includes (but not limited to) the coverage, network lifetime and aggregation. The lifetime of a network can be improved by the clustering with the reduction of energy consumption. Clustering will group the related type of sensors into a single place with a head sensor node for message aggregation and transmission between other nodes and Base Station (BS). The cluster head (CH) consume more energy, when aggregating and transmitting the data. With the suitable identification of CH, there will be a reduction in the consumption of energy and improves the life of Wireless Sensor Network to be more. This paper modifies the meta-heuristic algorithms for improving the network lifetime by choosing appropriate cluster head and optimal path. K-Genetic Algorithm (K-GA) is proposed for efficient cluster head selection. Initially, the sensors are clustered using k-means clustering based on their location and Genetic Algorithm has been applied to detect the best cluster head. For secure optimal routing, Trust based Firefly (T-FA) path selection algorithm is used. Extensive simulations are conducted on various circumstances. The results obtained on the simulation indicates that the proposed K-GA helps in determining the optimized head of the cluster and T-FA discovers the optimal paths which enriches the life of the network by reducing end-to-end delay compared to other techniques.
无线传感器网络(WSN)是一个由大量传感器组成的系统，通过将它们放置在特定区域而相互连接。WSN的不同问题包括(但不限于)覆盖范围、网络生命周期和聚合。通过聚类可以提高网络的生命周期，同时降低能耗。集群将相关类型的传感器分组到一个具有头部传感器节点的地方，用于其他节点和基站(BS)之间的消息聚合和传输。在聚合和传输数据时，簇头(CH)消耗更多的能量。通过对CH的适当识别，将大大降低无线传感器网络的能耗，提高无线传感器网络的使用寿命。本文改进了元启发式算法，通过选择合适的簇头和最优路径来提高网络生存时间。为了有效地选择簇头，提出了k -遗传算法(K-GA)。首先，根据传感器的位置采用k-means聚类方法对其进行聚类，并应用遗传算法检测最佳簇头。为了实现安全最优路由，采用了基于信任的萤火虫(Trust based Firefly, T-FA)选路算法。在各种情况下进行了大量的模拟。仿真结果表明，与其他技术相比，K-GA有助于确定最优簇头，T-FA发现最优路径，通过减少端到端延迟，丰富了网络的寿命。
{"title":"Cluster Head and Optimal Path Slection Using K-GA and T-FA Algorithms for Wireless Sensor Networks","authors":"M. Ram, Kuda Nageswara Rao, S. J. Basha, S. S. Reddy","doi":"10.1109/ICECA49313.2020.9297535","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297535","url":null,"abstract":"Wireless Sensor Network (WSN) is a system with huge number of sensors connected to one another by placing them in a specific area. Different issues with WSN includes (but not limited to) the coverage, network lifetime and aggregation. The lifetime of a network can be improved by the clustering with the reduction of energy consumption. Clustering will group the related type of sensors into a single place with a head sensor node for message aggregation and transmission between other nodes and Base Station (BS). The cluster head (CH) consume more energy, when aggregating and transmitting the data. With the suitable identification of CH, there will be a reduction in the consumption of energy and improves the life of Wireless Sensor Network to be more. This paper modifies the meta-heuristic algorithms for improving the network lifetime by choosing appropriate cluster head and optimal path. K-Genetic Algorithm (K-GA) is proposed for efficient cluster head selection. Initially, the sensors are clustered using k-means clustering based on their location and Genetic Algorithm has been applied to detect the best cluster head. For secure optimal routing, Trust based Firefly (T-FA) path selection algorithm is used. Extensive simulations are conducted on various circumstances. The results obtained on the simulation indicates that the proposed K-GA helps in determining the optimized head of the cluster and T-FA discovers the optimal paths which enriches the life of the network by reducing end-to-end delay compared to other techniques.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"1310 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127437727","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297404
Sining Hua
City brand image building strategy based on the interactive data mining and visual saliency is discussed in this paper. In the big data environment, through the general interface and interaction design, the operation and management and also scheduling capabilities of big data can be improved. By using this feature, this paper proposes the listed novelties. (1) The GSSL method mainly relies on the Euclidean distance between point pairs to construct a graph model composed of multiple overlapping local blocks. This feature is used to estimate the distance of the visual information. (2) The simplest form of the region matching is to divide the whole image into many sub-regions, and then measure the similarity of photometric information. This has been used to construct the analytic framework. The verifications have proven the better performance.
{"title":"City Brand Image Building Strategy Based on Interactive Data Mining and Visual Saliency","authors":"Sining Hua","doi":"10.1109/ICECA49313.2020.9297404","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297404","url":null,"abstract":"City brand image building strategy based on the interactive data mining and visual saliency is discussed in this paper. In the big data environment, through the general interface and interaction design, the operation and management and also scheduling capabilities of big data can be improved. By using this feature, this paper proposes the listed novelties. (1) The GSSL method mainly relies on the Euclidean distance between point pairs to construct a graph model composed of multiple overlapping local blocks. This feature is used to estimate the distance of the visual information. (2) The simplest form of the region matching is to divide the whole image into many sub-regions, and then measure the similarity of photometric information. This has been used to construct the analytic framework. The verifications have proven the better performance.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129164113","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297467
A. Gowtham, L. Anirudh, B. Sreeja, BA Aakash, S. Adittya
If there is an availability of technological medical electronic devices to classify heart disease, it would absolutely change the future in terms of making it more economical and qualitative for all the people suffering from heart-related ailments. With the increasing medical expenses and non-affordability of the poor families, it becomes logical to design a system that can detect heart disease in particular Arrhythmia, without higher expense. Recently, the Cardiovascular systems are evaluated more reliably by using Electrocardiogram (ECG) waves. This project in particular is designed to check for any irregularities in heart beats, which is represented in the variations of an ECG wave, and then compared it with normal beats to detect Arrhythmia. The electronics behind this project is Raspberry Pi and ADS1115, an ADC, which converts the real-time, analog ECG wave signal into a digital wave with the help of heart rate sensor-AD8232, and a three-lead system. A normalized wave is fed into the deep convolutional neural network to predict the output into one of the 5 different categories. Furthermore, the ADASYN – Adaptive Synthetic Sampling - algorithm is used to effectively classify the disease in accordance with the MIT-BIH dataset.
{"title":"Detection of Arrhythmia using ECG waves with Deep Convolutional Neural Networks","authors":"A. Gowtham, L. Anirudh, B. Sreeja, BA Aakash, S. Adittya","doi":"10.1109/ICECA49313.2020.9297467","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297467","url":null,"abstract":"If there is an availability of technological medical electronic devices to classify heart disease, it would absolutely change the future in terms of making it more economical and qualitative for all the people suffering from heart-related ailments. With the increasing medical expenses and non-affordability of the poor families, it becomes logical to design a system that can detect heart disease in particular Arrhythmia, without higher expense. Recently, the Cardiovascular systems are evaluated more reliably by using Electrocardiogram (ECG) waves. This project in particular is designed to check for any irregularities in heart beats, which is represented in the variations of an ECG wave, and then compared it with normal beats to detect Arrhythmia. The electronics behind this project is Raspberry Pi and ADS1115, an ADC, which converts the real-time, analog ECG wave signal into a digital wave with the help of heart rate sensor-AD8232, and a three-lead system. A normalized wave is fed into the deep convolutional neural network to predict the output into one of the 5 different categories. Furthermore, the ADASYN – Adaptive Synthetic Sampling - algorithm is used to effectively classify the disease in accordance with the MIT-BIH dataset.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129016170","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297473
Harshita kushwah, R. Gamad, R. Gurjar
Low power and high-speed comparator design are presented in this article. Design is intended for the implementation of SAR ADC. The advantage of the proposed design can minimize power dissipation and maximize speed in SAR ADC. Simulation results are obtained in 0.18um Technology in the cadence tool. This design exhibit improved accuracy and less power consumption about 129.8$mu mathrm{W}$ with sampling frequency 100MHz and 1.8V supply. Prior work done is compared with simulated results and progress is also marked in present work.
{"title":"Design of Low Power & High Speed Comparator of SAR ADC using 180nm Technology","authors":"Harshita kushwah, R. Gamad, R. Gurjar","doi":"10.1109/ICECA49313.2020.9297473","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297473","url":null,"abstract":"Low power and high-speed comparator design are presented in this article. Design is intended for the implementation of SAR ADC. The advantage of the proposed design can minimize power dissipation and maximize speed in SAR ADC. Simulation results are obtained in 0.18um Technology in the cadence tool. This design exhibit improved accuracy and less power consumption about 129.8$mu mathrm{W}$ with sampling frequency 100MHz and 1.8V supply. Prior work done is compared with simulated results and progress is also marked in present work.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130517678","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297442
Syed Faiazuddin, M. Lakshmaiah, K. Alam, M. Ravikiran
Poor quality is a major concern in urbanized areas. With more than 85% of people exposed to high levels of a particular matter. According to the World Health Organization, people are more cautious to look up the quality of air, their health by focusing on the spaces where they spend most of their time at home, school etc., and in their car. In this concept, a system with low power and data consumption is introduced. In this article, the air quality using a Raspberry Pi4 with Grove - Air Quality Sensor v1.3, CCS811 CO2 Air Quality Sensor, DHT 11 Temperature and Humidity Sensor were discussed. The communication between the sensor and Raspberry Pi4 will be through a serial port communication protocol and the code is implemented on the Python interface. Air pollution is a global environmental health problem many people’s are dying every year due to some of the visible and invisible parameters like small particles, gases and so on. Most of the parameters of the environment to be monitored such as volume of CO, CO2, Temp, Humidity, Gas Leakage, Smoke, temperature sensor, and etc. These parameters information can received by Rasp Pi4, Arduino Uno and process the information and transmitted to clouds where they are being continuously monitored and information will be stored in the cloud database.
{"title":"IoT based Indoor Air Quality Monitoring system using Raspberry Pi4","authors":"Syed Faiazuddin, M. Lakshmaiah, K. Alam, M. Ravikiran","doi":"10.1109/ICECA49313.2020.9297442","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297442","url":null,"abstract":"Poor quality is a major concern in urbanized areas. With more than 85% of people exposed to high levels of a particular matter. According to the World Health Organization, people are more cautious to look up the quality of air, their health by focusing on the spaces where they spend most of their time at home, school etc., and in their car. In this concept, a system with low power and data consumption is introduced. In this article, the air quality using a Raspberry Pi4 with Grove - Air Quality Sensor v1.3, CCS811 CO2 Air Quality Sensor, DHT 11 Temperature and Humidity Sensor were discussed. The communication between the sensor and Raspberry Pi4 will be through a serial port communication protocol and the code is implemented on the Python interface. Air pollution is a global environmental health problem many people’s are dying every year due to some of the visible and invisible parameters like small particles, gases and so on. Most of the parameters of the environment to be monitored such as volume of CO, CO2, Temp, Humidity, Gas Leakage, Smoke, temperature sensor, and etc. These parameters information can received by Rasp Pi4, Arduino Uno and process the information and transmitted to clouds where they are being continuously monitored and information will be stored in the cloud database.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116703122","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297548
R. Prasad, T. Jaya
Cognitive Radio (CR) is a mode of wireless communication, where a transceiver has been used to automatically detect the communication channel that are in use and not used, where it will switch immediately into the vacant space. To avoid the causing interference with primary user, CR needs to change the transmission and receiver parameter. The adaptive framework used for range handoff is driven by a decision technique i.e. Additive weighting method (AW), Technique for Order Preference (ETOP) etc. Decision Method (DM) technique utilize video, voice and data organizations by depending on CR tendencies. The reenactment shows that, ETOP strategy is incredible than AW technique for picking the ideal system for range handoff to significantly increase the play administration.
{"title":"Decision Making Method ETOP for Handoff in Cognitive Radio Network","authors":"R. Prasad, T. Jaya","doi":"10.1109/ICECA49313.2020.9297548","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297548","url":null,"abstract":"Cognitive Radio (CR) is a mode of wireless communication, where a transceiver has been used to automatically detect the communication channel that are in use and not used, where it will switch immediately into the vacant space. To avoid the causing interference with primary user, CR needs to change the transmission and receiver parameter. The adaptive framework used for range handoff is driven by a decision technique i.e. Additive weighting method (AW), Technique for Order Preference (ETOP) etc. Decision Method (DM) technique utilize video, voice and data organizations by depending on CR tendencies. The reenactment shows that, ETOP strategy is incredible than AW technique for picking the ideal system for range handoff to significantly increase the play administration.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131431304","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297499
L. Rao, Coneri Harshitha, C. Z. Basha, Nazia Parveen
Nowadays in a situation like the Covid19 pandemic it is very sensitive to use biometric systems for attendance monitoring of employees. The reason is covid19 spreads from one person to another easily with a biometric system. It has become necessary for any organization to maintain an attendance monitoring system without taking fingerprints of any employee or a student. The automatic Face recognition system is best to alternate for the biometric system. An advanced automatic face recognition technique is proposed in this paper with the classification technique using Bag of Visual Words (BOVW) and Multi-Layer Perceptron (MLP) based Back Propagation Neural Network (BPNN). An Accuracy of 91% is achieved with the proposed methodology.
{"title":"Hybrid Computerized Face Recognition System Using Bag of Visual Words and MLP-Based BPNN","authors":"L. Rao, Coneri Harshitha, C. Z. Basha, Nazia Parveen","doi":"10.1109/ICECA49313.2020.9297499","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297499","url":null,"abstract":"Nowadays in a situation like the Covid19 pandemic it is very sensitive to use biometric systems for attendance monitoring of employees. The reason is covid19 spreads from one person to another easily with a biometric system. It has become necessary for any organization to maintain an attendance monitoring system without taking fingerprints of any employee or a student. The automatic Face recognition system is best to alternate for the biometric system. An advanced automatic face recognition technique is proposed in this paper with the classification technique using Bag of Visual Words (BOVW) and Multi-Layer Perceptron (MLP) based Back Propagation Neural Network (BPNN). An Accuracy of 91% is achieved with the proposed methodology.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133139793","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 : 2020-11-05DOI: 10.1109/ICECA49313.2020.9297570
M. Islam, Md. Shahriar Islam, M. Hossen, Minhaz Uddin Emon, Maria Sultana Keya, Ahsan Habib
To help farmers and rural people of Bangladesh, many research works are proposed in the recent years to recognize the papaya diseases that takes a great deal of advantage in machine learning fields. This research is mainly required to support agriculture to make it highly effective and helpful particularly for papaya cultivation. The primary objective of this paper is to compare some algorithms for papaya disease recognition and identify the ailment by capturing image and classify them based on their diseases with an intelligent system. To overcome this advantage, the recognition of papaya diseases will mainly involve two challenges and those are detecting the disease and classifying the diseases based on their symptoms. The proposed system is presenting an online machine learning based papaya disease in which a person captures an image via mobile app and sends it to the system for disease detection and also compare some algorithms accuracy those are random forest, k-means clustering, SVC and CNN. The system process the images and will give feedback. This intelligent system can easily detect the diseases with a high accuracy of about 98.4% to predict the papaya diseases.
{"title":"Machine Learning based Image Classification of Papaya Disease Recognition","authors":"M. Islam, Md. Shahriar Islam, M. Hossen, Minhaz Uddin Emon, Maria Sultana Keya, Ahsan Habib","doi":"10.1109/ICECA49313.2020.9297570","DOIUrl":"https://doi.org/10.1109/ICECA49313.2020.9297570","url":null,"abstract":"To help farmers and rural people of Bangladesh, many research works are proposed in the recent years to recognize the papaya diseases that takes a great deal of advantage in machine learning fields. This research is mainly required to support agriculture to make it highly effective and helpful particularly for papaya cultivation. The primary objective of this paper is to compare some algorithms for papaya disease recognition and identify the ailment by capturing image and classify them based on their diseases with an intelligent system. To overcome this advantage, the recognition of papaya diseases will mainly involve two challenges and those are detecting the disease and classifying the diseases based on their symptoms. The proposed system is presenting an online machine learning based papaya disease in which a person captures an image via mobile app and sends it to the system for disease detection and also compare some algorithms accuracy those are random forest, k-means clustering, SVC and CNN. The system process the images and will give feedback. This intelligent system can easily detect the diseases with a high accuracy of about 98.4% to predict the papaya diseases.","PeriodicalId":297285,"journal":{"name":"2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132127888","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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