Pub Date : 2021-11-10DOI: 10.1109/ICTAI53825.2021.9673195
Prabhjot Singh Manocha, Rajiv Kumar
Internet of Things is an uncontrollable innovation in numerous parts of our general public, shifting from interchanges to monetary exchanges to public safety (e.g., Internet of Battlefield/Military Things), and a lot more. Security and energy viewpoints assume significant parts in information transmission across IoT and edge organizations, on account of restricted energy and figuring (e.g., handling and capacity) assets of arranged gadgets. Whether we say malevolent or unintentional, impedance with information in an IoT network possibly has some genuine results, which has an enormous effect. In Internet of Things (IoT), we say there are billions of gadgets that are interconnected to worldwide organizations utilizing various advances and stages (cloud, edge, remote and so forth). This empowers IoT network clients, with the appearance of Software- characterized Networks (SDN), to get network assets during a common and convenient way or gives Always Best Connected organization between the shifted advancements. In any case, IoT faces many difficulties, similar to those connected with the deficiency of a focal regulator or a concentrated framework, heterogeneity of gadgets, different assaults, and equivalence Security and energy utilization are among the first squeezing difficulties not in violation of our spending plan space.
{"title":"A Review Paper: Improving Spider Monkey Optimization Algorithm SDN Routing for IOT Security","authors":"Prabhjot Singh Manocha, Rajiv Kumar","doi":"10.1109/ICTAI53825.2021.9673195","DOIUrl":"https://doi.org/10.1109/ICTAI53825.2021.9673195","url":null,"abstract":"Internet of Things is an uncontrollable innovation in numerous parts of our general public, shifting from interchanges to monetary exchanges to public safety (e.g., Internet of Battlefield/Military Things), and a lot more. Security and energy viewpoints assume significant parts in information transmission across IoT and edge organizations, on account of restricted energy and figuring (e.g., handling and capacity) assets of arranged gadgets. Whether we say malevolent or unintentional, impedance with information in an IoT network possibly has some genuine results, which has an enormous effect. In Internet of Things (IoT), we say there are billions of gadgets that are interconnected to worldwide organizations utilizing various advances and stages (cloud, edge, remote and so forth). This empowers IoT network clients, with the appearance of Software- characterized Networks (SDN), to get network assets during a common and convenient way or gives Always Best Connected organization between the shifted advancements. In any case, IoT faces many difficulties, similar to those connected with the deficiency of a focal regulator or a concentrated framework, heterogeneity of gadgets, different assaults, and equivalence Security and energy utilization are among the first squeezing difficulties not in violation of our spending plan space.","PeriodicalId":278263,"journal":{"name":"2021 International Conference on Technological Advancements and Innovations (ICTAI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127828820","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 : 2021-11-10DOI: 10.1109/ICTAI53825.2021.9673435
K. Kumar, Amanpreet Kaur, K. Ramkumar, Anurag Shrivastava, Vishal Moyal, Y. Kumar
With the development and growth in industries, the society and the environment are facing two huge problems. Advancement in technology have raised the problem of communication and data over safe channels. The power/energy deficiency can be reduced by the practice of Green Communication (GC) technologies and energy efficient comopnents. This paper focuses on the use of these technologies in one framework. In this article a power-efficient Advanced Encryption Standard (AES) algorithm is realized on hardware device. For hardware implementations, Field Programmable Gate Array (FPGA) devices are considered. The AES algorithm is designed on VIVADO tool and the results are analyzed on 28 nanometer (nm) Artix-7 FPGA. The power calculation of the AES algorithm is calculated for different clock speed of the device. And it is detected that the AES algorithm is energy efficient, when the clock speed is 2.0ns for Artix-7 FPGA.
{"title":"A Design of Power-Efficient AES Algorithm on Artix-7 FPGA for Green Communication","authors":"K. Kumar, Amanpreet Kaur, K. Ramkumar, Anurag Shrivastava, Vishal Moyal, Y. Kumar","doi":"10.1109/ICTAI53825.2021.9673435","DOIUrl":"https://doi.org/10.1109/ICTAI53825.2021.9673435","url":null,"abstract":"With the development and growth in industries, the society and the environment are facing two huge problems. Advancement in technology have raised the problem of communication and data over safe channels. The power/energy deficiency can be reduced by the practice of Green Communication (GC) technologies and energy efficient comopnents. This paper focuses on the use of these technologies in one framework. In this article a power-efficient Advanced Encryption Standard (AES) algorithm is realized on hardware device. For hardware implementations, Field Programmable Gate Array (FPGA) devices are considered. The AES algorithm is designed on VIVADO tool and the results are analyzed on 28 nanometer (nm) Artix-7 FPGA. The power calculation of the AES algorithm is calculated for different clock speed of the device. And it is detected that the AES algorithm is energy efficient, when the clock speed is 2.0ns for Artix-7 FPGA.","PeriodicalId":278263,"journal":{"name":"2021 International Conference on Technological Advancements and Innovations (ICTAI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117086046","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 : 2021-11-10DOI: 10.1109/ICTAI53825.2021.9673337
N. N. Das, Neharika Srivastav, S. Verma
This paper proposes a methodology to predict Alzheimer’s disease patient using their brain MRI scans. Alzheimer’s disease is an irrecoverable one. It is a prolonged degenerative disorder and listed as one of the most frequent dementia threats in individuals over 65 years of age. The suggested solution will be tested on the Alzheimer’s disease Neuroimaging Initiative (ADNI) standard MRI datasets. We obtained MRI scans from two Alzheimer stages that are moderately demented and non-demented. Live Neuron Estimation, Gray-Level Co-occurrence Matrix (GLCM), and Random Forest Mapping are the techniques used to extract features. In the MRI images, Live Neurons known as white pixels. The features like homogeneity, contrast, and correlation determined using the Gray Level Co-Occurrence Matrix (GLCM) and Random Forest mapping helps us to identify the shape and size of other essential parts of the brain like temporal Lobe, occipital Lobe, frontal Lobe, insular. Features that contribute to the prediction identified using the correlation matrix. Distinct machine learning models were employed to predict the presence of disease. The accuracy is 96.4% by Random Forest Classifier, having an area of 82.1% under ROC-AUC. Furthermore, it has the best result obtained over PR Curve. We used a cross-validation score to fine-tune our Random Forest Classifier and configured 100 trees, predicting the best outcome of 95.
{"title":"Magnetic Resonance Imaging based Feature Extraction and Selection Methods for Alzheimer Disease Prediction","authors":"N. N. Das, Neharika Srivastav, S. Verma","doi":"10.1109/ICTAI53825.2021.9673337","DOIUrl":"https://doi.org/10.1109/ICTAI53825.2021.9673337","url":null,"abstract":"This paper proposes a methodology to predict Alzheimer’s disease patient using their brain MRI scans. Alzheimer’s disease is an irrecoverable one. It is a prolonged degenerative disorder and listed as one of the most frequent dementia threats in individuals over 65 years of age. The suggested solution will be tested on the Alzheimer’s disease Neuroimaging Initiative (ADNI) standard MRI datasets. We obtained MRI scans from two Alzheimer stages that are moderately demented and non-demented. Live Neuron Estimation, Gray-Level Co-occurrence Matrix (GLCM), and Random Forest Mapping are the techniques used to extract features. In the MRI images, Live Neurons known as white pixels. The features like homogeneity, contrast, and correlation determined using the Gray Level Co-Occurrence Matrix (GLCM) and Random Forest mapping helps us to identify the shape and size of other essential parts of the brain like temporal Lobe, occipital Lobe, frontal Lobe, insular. Features that contribute to the prediction identified using the correlation matrix. Distinct machine learning models were employed to predict the presence of disease. The accuracy is 96.4% by Random Forest Classifier, having an area of 82.1% under ROC-AUC. Furthermore, it has the best result obtained over PR Curve. We used a cross-validation score to fine-tune our Random Forest Classifier and configured 100 trees, predicting the best outcome of 95.","PeriodicalId":278263,"journal":{"name":"2021 International Conference on Technological Advancements and Innovations (ICTAI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121312999","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}
Cloud computing is the on-request supply of computing resources through the Web with pay-as-you-use billing. Instead of purchasing, operating, and maintaining physical computers, hardware, and servers, cloud solutions providers such as Microsoft Azure of Microsoft, Amazon Web Services (AWS) of Amazon, and Google Cloud Platform (GCP) by Google offer cloud solutions such as processing power, memory, and databases on an as-needed basis. This research paper discusses the architecture and types of cloud computing services, as well as comparison of the performance and service among three main Cloud Computing platforms: Microsoft Azure, Amazon AWS, and Google Cloud Platform. All three systems have been evaluated in identical virtual environments, specifically micro instance of Ubuntu 16.04. The benchmark application Phoronix Test Suite 10.4 is used to assess performance, and the results for the Apache, Dbench, and RAM speed benchmarks are evaluated in this paper.
云计算是通过Web按需提供计算资源，并提供按需付费的计费方式。微软的Microsoft Azure、亚马逊的Amazon Web Services (AWS)、谷歌的谷歌cloud Platform (GCP)等云解决方案提供商不再需要购买、运营和维护物理计算机、硬件和服务器，而是按需提供处理能力、内存和数据库等云解决方案。本文讨论了云计算服务的架构和类型，以及三个主要的云计算平台:Microsoft Azure、Amazon AWS和谷歌cloud Platform之间的性能和服务的比较。这三个系统都在相同的虚拟环境中进行了评估，特别是Ubuntu 16.04的微实例。本文使用基准测试应用程序Phoronix Test Suite 10.4来评估性能，并对Apache、Dbench和RAM速度基准测试的结果进行了评估。
{"title":"Cloud Computing and Comparison based on Service and Performance between Amazon AWS, Microsoft Azure, and Google Cloud","authors":"P. Kaushik, Ashwin Murali Rao, Devang Pratap Singh, Swati Vashisht, Shubhi Gupta","doi":"10.1109/ICTAI53825.2021.9673425","DOIUrl":"https://doi.org/10.1109/ICTAI53825.2021.9673425","url":null,"abstract":"Cloud computing is the on-request supply of computing resources through the Web with pay-as-you-use billing. Instead of purchasing, operating, and maintaining physical computers, hardware, and servers, cloud solutions providers such as Microsoft Azure of Microsoft, Amazon Web Services (AWS) of Amazon, and Google Cloud Platform (GCP) by Google offer cloud solutions such as processing power, memory, and databases on an as-needed basis. This research paper discusses the architecture and types of cloud computing services, as well as comparison of the performance and service among three main Cloud Computing platforms: Microsoft Azure, Amazon AWS, and Google Cloud Platform. All three systems have been evaluated in identical virtual environments, specifically micro instance of Ubuntu 16.04. The benchmark application Phoronix Test Suite 10.4 is used to assess performance, and the results for the Apache, Dbench, and RAM speed benchmarks are evaluated in this paper.","PeriodicalId":278263,"journal":{"name":"2021 International Conference on Technological Advancements and Innovations (ICTAI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116570752","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 : 2021-11-10DOI: 10.1109/ICTAI53825.2021.9673328
{"title":"Copyright Page","authors":"","doi":"10.1109/ICTAI53825.2021.9673328","DOIUrl":"https://doi.org/10.1109/ICTAI53825.2021.9673328","url":null,"abstract":"","PeriodicalId":278263,"journal":{"name":"2021 International Conference on Technological Advancements and Innovations (ICTAI)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124160246","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 : 2021-11-10DOI: 10.1109/ICTAI53825.2021.9673437
Yogesh Kumar, Surbhi Gupta, Anish Gupta
The Internet of Things (IoT) is bringing a new revolution in academia and research. It has penetrating roots, which are bringing remarkable changes in various domains, especially healthcare. The advancements in other technologies like wearable devices, sensors, cloud-based computing have led to its proliferation. IoT has led the transition from traditional center-based systems to personalized healthcare systems (PHS). It is no wonder that such advanced and robust technology has its associated challenges and leggings like increased cost, the increased storage requirement for data storage, maintenance of heterogeneity of operable devices, and many more. This presented work deals with studying such a robust technique, IoT, and its applications in the healthcare domain along with machine learning and deep learning techniques. It describes the framework of an IoT-enabled system, its benefits, and present applications. This article also apprises its challenges and, most importantly, the study of various researchers to design IoT-enabled healthcare systems using various machine learning and deep learning algorithms. The study reveals that IoT is successful in establishing better relationships between healthcare professionals and patients, diagnosing the forthcoming medically critical conditions, and helps manage the medical resources effectively.
{"title":"Study of Machine and Deep Learning Classifications for IOT Enabled Healthcare Devices","authors":"Yogesh Kumar, Surbhi Gupta, Anish Gupta","doi":"10.1109/ICTAI53825.2021.9673437","DOIUrl":"https://doi.org/10.1109/ICTAI53825.2021.9673437","url":null,"abstract":"The Internet of Things (IoT) is bringing a new revolution in academia and research. It has penetrating roots, which are bringing remarkable changes in various domains, especially healthcare. The advancements in other technologies like wearable devices, sensors, cloud-based computing have led to its proliferation. IoT has led the transition from traditional center-based systems to personalized healthcare systems (PHS). It is no wonder that such advanced and robust technology has its associated challenges and leggings like increased cost, the increased storage requirement for data storage, maintenance of heterogeneity of operable devices, and many more. This presented work deals with studying such a robust technique, IoT, and its applications in the healthcare domain along with machine learning and deep learning techniques. It describes the framework of an IoT-enabled system, its benefits, and present applications. This article also apprises its challenges and, most importantly, the study of various researchers to design IoT-enabled healthcare systems using various machine learning and deep learning algorithms. The study reveals that IoT is successful in establishing better relationships between healthcare professionals and patients, diagnosing the forthcoming medically critical conditions, and helps manage the medical resources effectively.","PeriodicalId":278263,"journal":{"name":"2021 International Conference on Technological Advancements and Innovations (ICTAI)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126263965","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 : 2021-11-10DOI: 10.1109/ICTAI53825.2021.9673162
Saurabh Singh, S. Soni
In general, cryptography is concerned with using complex mathematical operations to encrypt and decode a communication. Most of the time, complex mathematical computations are required. This may need a lot of processing time, hardware, and electricity. The main difficulty here is to provide optimum security while using little resources. This necessitates the use of sophisticated algorithms. Ancient Rigvedic mathematics, rediscovered by Bharathi Krishna Tirtaji, offers a wealth of mathematical shortcuts that may be used to the creation of sophisticated cryptographic algorithms. This paper looks at the work that has been done in this area so far. As the need for safe financial transactions and related sectors grows, cryptographic encryption and decryption play an increasingly essential role. Nowadays, the majority of safe transactions take place on smartphones and other portable devices; thus, an algorithm that uses less space while maintaining overall speed becomes essential. Several algorithms have been developed and implemented in the past to fulfil this need, but each of these algorithms has its own limitations in terms of ASIC or FPGA implementation. This paper discusses the development of an Advanced Encryption System suited for regions needing maximum area reduction, such as mobile phones. The design is created using the Verilog hardware description language, which allows for rapid hardware implementation. When compared to traditional designs, the system’s hardware implementation is quicker. To do this, we use methods from Vedic mathematics. To demonstrate the benefits of the suggested design, comparisons are made with traditional designs.
{"title":"Report on Cryptographic Hardware Design using Vedic Mathematics","authors":"Saurabh Singh, S. Soni","doi":"10.1109/ICTAI53825.2021.9673162","DOIUrl":"https://doi.org/10.1109/ICTAI53825.2021.9673162","url":null,"abstract":"In general, cryptography is concerned with using complex mathematical operations to encrypt and decode a communication. Most of the time, complex mathematical computations are required. This may need a lot of processing time, hardware, and electricity. The main difficulty here is to provide optimum security while using little resources. This necessitates the use of sophisticated algorithms. Ancient Rigvedic mathematics, rediscovered by Bharathi Krishna Tirtaji, offers a wealth of mathematical shortcuts that may be used to the creation of sophisticated cryptographic algorithms. This paper looks at the work that has been done in this area so far. As the need for safe financial transactions and related sectors grows, cryptographic encryption and decryption play an increasingly essential role. Nowadays, the majority of safe transactions take place on smartphones and other portable devices; thus, an algorithm that uses less space while maintaining overall speed becomes essential. Several algorithms have been developed and implemented in the past to fulfil this need, but each of these algorithms has its own limitations in terms of ASIC or FPGA implementation. This paper discusses the development of an Advanced Encryption System suited for regions needing maximum area reduction, such as mobile phones. The design is created using the Verilog hardware description language, which allows for rapid hardware implementation. When compared to traditional designs, the system’s hardware implementation is quicker. To do this, we use methods from Vedic mathematics. To demonstrate the benefits of the suggested design, comparisons are made with traditional designs.","PeriodicalId":278263,"journal":{"name":"2021 International Conference on Technological Advancements and Innovations (ICTAI)","volume":"24 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128456622","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 : 2021-11-10DOI: 10.1109/ICTAI53825.2021.9673336
Gaurav Soni, Ashim Sharma
The need for Internet application development is now extremely strong. As a result, the Internet of Things (IoT) is a significant technology that allows us to create a variety of valuable internet applications. The Internet of Things (IoT) is an excellent and clever method for reducing human effort and providing simple access to physical objects. With the assistance of different current technologies, these gadgets collect valuable data and exchange it with other devices. Home Automation Systems, for example, utilize Wi-Fi or Bluetooth to transmit data between different home gadgets. In this paper we discuss the implementation of Heart beat using LABVIEW2015 and with a set of tools like DAQmx USB 6008 and Heart Beat sensor. We use heart beat sensor MAX 30100 which is being interfaced with LabVIEW through DAQ USB 6008.
现在对Internet应用程序开发的需求非常强烈。因此，物联网(IoT)是一项重要的技术，它使我们能够创建各种有价值的互联网应用。物联网(IoT)是一种出色而聪明的方法，可以减少人力并提供对物理对象的简单访问。在各种现有技术的帮助下，这些小工具收集有价值的数据并与其他设备交换。例如，家庭自动化系统利用Wi-Fi或蓝牙在不同的家庭设备之间传输数据。在本文中，我们讨论了使用LABVIEW2015和一套工具，如DAQmx USB 6008和心跳传感器实现心跳。我们使用心跳传感器MAX 30100，通过DAQ USB 6008与LabVIEW接口。
{"title":"Implementation of Heart Beat Sensor using DAQmx USB 6008","authors":"Gaurav Soni, Ashim Sharma","doi":"10.1109/ICTAI53825.2021.9673336","DOIUrl":"https://doi.org/10.1109/ICTAI53825.2021.9673336","url":null,"abstract":"The need for Internet application development is now extremely strong. As a result, the Internet of Things (IoT) is a significant technology that allows us to create a variety of valuable internet applications. The Internet of Things (IoT) is an excellent and clever method for reducing human effort and providing simple access to physical objects. With the assistance of different current technologies, these gadgets collect valuable data and exchange it with other devices. Home Automation Systems, for example, utilize Wi-Fi or Bluetooth to transmit data between different home gadgets. In this paper we discuss the implementation of Heart beat using LABVIEW2015 and with a set of tools like DAQmx USB 6008 and Heart Beat sensor. We use heart beat sensor MAX 30100 which is being interfaced with LabVIEW through DAQ USB 6008.","PeriodicalId":278263,"journal":{"name":"2021 International Conference on Technological Advancements and Innovations (ICTAI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128478242","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 : 2021-11-10DOI: 10.1109/ICTAI53825.2021.9673214
T. Tawde, Kunal Deshmukh, Lobhas Verekar, A. Reddy, S. Aswale, Pratiksha R. Shetgaonkar
India is known for its rice cultivation. Rice being the major crop has it’s huge impact on people’s life as majority people cultivate it for their livelihood, it generate employment too and also many small scale industries directly or indirectly depend in its cultivation. This cultivation is affected by various disease and pets which may result in huge loss. With the help of modern science and technologies many research work and methods are proposed for better yielding of rice crop. Through this research paper a method is proposed which helps early detection of 8 major rice disease namely; Brown spots, Leaf blast, Leaf smut, Sheath rot, Tungro, Sheath blight and Gudi rotten. Proposed model was designed using Raspberry pi3b+ and various sensor’s like camera, temperature and moisture. CNN classifier was employed to train the proposed model. This model was proficient to identify the disease with an overall accuracy of 99.7%. Image captured were pushed to the cloud along with details like disease name, temperature, moisture, humidity and the image timestamp.
{"title":"Identification of Rice Plant Disease Using Image Processing and Machine Learning Techniques","authors":"T. Tawde, Kunal Deshmukh, Lobhas Verekar, A. Reddy, S. Aswale, Pratiksha R. Shetgaonkar","doi":"10.1109/ICTAI53825.2021.9673214","DOIUrl":"https://doi.org/10.1109/ICTAI53825.2021.9673214","url":null,"abstract":"India is known for its rice cultivation. Rice being the major crop has it’s huge impact on people’s life as majority people cultivate it for their livelihood, it generate employment too and also many small scale industries directly or indirectly depend in its cultivation. This cultivation is affected by various disease and pets which may result in huge loss. With the help of modern science and technologies many research work and methods are proposed for better yielding of rice crop. Through this research paper a method is proposed which helps early detection of 8 major rice disease namely; Brown spots, Leaf blast, Leaf smut, Sheath rot, Tungro, Sheath blight and Gudi rotten. Proposed model was designed using Raspberry pi3b+ and various sensor’s like camera, temperature and moisture. CNN classifier was employed to train the proposed model. This model was proficient to identify the disease with an overall accuracy of 99.7%. Image captured were pushed to the cloud along with details like disease name, temperature, moisture, humidity and the image timestamp.","PeriodicalId":278263,"journal":{"name":"2021 International Conference on Technological Advancements and Innovations (ICTAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130698087","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 : 2021-11-10DOI: 10.1109/ICTAI53825.2021.9673359
{"title":"ICTAI 2021 Cover Page","authors":"","doi":"10.1109/ICTAI53825.2021.9673359","DOIUrl":"https://doi.org/10.1109/ICTAI53825.2021.9673359","url":null,"abstract":"","PeriodicalId":278263,"journal":{"name":"2021 International Conference on Technological Advancements and Innovations (ICTAI)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131493412","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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