Pub Date : 2022-12-10DOI: 10.1109/STCR55312.2022.10009501
R. K, G. Flora, S. K, Lakshmi Priya. P, N. V
The rise of Covid-19 pandemic has exaggerated the necessity for safe, quick and sensitive diagnostic tools to confirm the protection of tending employees and patients. Although ML has shown success in medical imaging, existing studies concentrate on Covid-19 medicine victimization using Deep Learning (DL) with X-ray and computed axial Tomography (CT) scans. During this study we tend to aim to implement CNN model on Lung Ultrasound (LUS), to assist doctors with the designation of Covid-19 patients. We selected LUS since it's quicker, cheaper and additional out there in rural areas compared to CT and X- ray. We have used the biggest public dataset containing LUS pictures and videos of Covid, Pneumonia and healthy patients that has been collected from totally different resources. We tried out frame level approach that extracted 5 frames per patient video. We'll use this dataset to experiment with a CNN model that has hyper parameter calibration. We conjointly enclosed explainable AI using Grad-CAM that uses gradients of a selected target that flows through the convolutional network to localize and highlight regions of the target within the image. Moreover, we'll experiment with completely different data preprocessing techniques that may aid with pattern recognition and increasing the DL model’s accuracy like histogram equalization, standardization, Principle Component Analysis (PCA) and Synthetic Minority Oversampling Technique (SMOTE). Lastly, we tend to create a straightforward application that diagnoses LUS videos with our CNN model, and shows the frame results with visual illustration of why the model has taken certain prediction with the help of Gradient-Weighted category Activation Mapping (Grad-CAM).
{"title":"Viral Pneumonia and Covid Screening on Lung Ultrasound","authors":"R. K, G. Flora, S. K, Lakshmi Priya. P, N. V","doi":"10.1109/STCR55312.2022.10009501","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009501","url":null,"abstract":"The rise of Covid-19 pandemic has exaggerated the necessity for safe, quick and sensitive diagnostic tools to confirm the protection of tending employees and patients. Although ML has shown success in medical imaging, existing studies concentrate on Covid-19 medicine victimization using Deep Learning (DL) with X-ray and computed axial Tomography (CT) scans. During this study we tend to aim to implement CNN model on Lung Ultrasound (LUS), to assist doctors with the designation of Covid-19 patients. We selected LUS since it's quicker, cheaper and additional out there in rural areas compared to CT and X- ray. We have used the biggest public dataset containing LUS pictures and videos of Covid, Pneumonia and healthy patients that has been collected from totally different resources. We tried out frame level approach that extracted 5 frames per patient video. We'll use this dataset to experiment with a CNN model that has hyper parameter calibration. We conjointly enclosed explainable AI using Grad-CAM that uses gradients of a selected target that flows through the convolutional network to localize and highlight regions of the target within the image. Moreover, we'll experiment with completely different data preprocessing techniques that may aid with pattern recognition and increasing the DL model’s accuracy like histogram equalization, standardization, Principle Component Analysis (PCA) and Synthetic Minority Oversampling Technique (SMOTE). Lastly, we tend to create a straightforward application that diagnoses LUS videos with our CNN model, and shows the frame results with visual illustration of why the model has taken certain prediction with the help of Gradient-Weighted category Activation Mapping (Grad-CAM).","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"107 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121559820","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009618
Ajin R. Nair, K. S
Simultaneous Wireless Information and Power Transfer (SWIPT) system is a feasible technology for the next-generation wireless communication system. The crucial problem encountered by the SWIPT system during practical deployment is hardware impairment. So, this article analyses the impact of total harmonic distortion in SWIPT systems. Here we consider the harmonic distortion in a realistic SWIPT HPA transmitter model that follows M-ary modulation. The SWIPT receiver employs power-splitting architecture. We analyse the harvested energy and the Signal to Noise Ratio of the received signal with and without harmonic distortion. For the maximum applied amplifier input signal power of 1dBm, harvested energy of 13dBm and SNR of 20dB is obtained.
{"title":"Estimation and Compensation of Total Harmonic Distortion in SWIPT","authors":"Ajin R. Nair, K. S","doi":"10.1109/STCR55312.2022.10009618","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009618","url":null,"abstract":"Simultaneous Wireless Information and Power Transfer (SWIPT) system is a feasible technology for the next-generation wireless communication system. The crucial problem encountered by the SWIPT system during practical deployment is hardware impairment. So, this article analyses the impact of total harmonic distortion in SWIPT systems. Here we consider the harmonic distortion in a realistic SWIPT HPA transmitter model that follows M-ary modulation. The SWIPT receiver employs power-splitting architecture. We analyse the harvested energy and the Signal to Noise Ratio of the received signal with and without harmonic distortion. For the maximum applied amplifier input signal power of 1dBm, harvested energy of 13dBm and SNR of 20dB is obtained.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131152634","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009137
S. P., S. S, Inthrash V
The primary source of power to electrify remote regions are generated using diesel generator which has high carbon emission. In order to minimize greenhouse gas emissions and utilize agricultural waste efficiently Biomass gasifier are a ideal choice for generating power. This paper proposed and analyzed a downdraft biomass gasifier with coconut shells as fuel to generate electricity. The producer gas produced by the gasifier is used to fuel the combustion engine which is coupled to a generator. To assess the biomass gasifier’s performance and to calculate the generator efficiency additional factors, including the reactor temperature, tar content, producer gas, mass balance of the system are taken into account. The use of coconut shells as fuel to generate power decreases carbon footprint by 60%, according to the results. Thus, biomass gasifiers can provide power in isolated locations where coconut shells are abundant and not currently being used commercially.
{"title":"Biomass Gasification using Coconut Shell for Small-Scale Electricity Generation","authors":"S. P., S. S, Inthrash V","doi":"10.1109/STCR55312.2022.10009137","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009137","url":null,"abstract":"The primary source of power to electrify remote regions are generated using diesel generator which has high carbon emission. In order to minimize greenhouse gas emissions and utilize agricultural waste efficiently Biomass gasifier are a ideal choice for generating power. This paper proposed and analyzed a downdraft biomass gasifier with coconut shells as fuel to generate electricity. The producer gas produced by the gasifier is used to fuel the combustion engine which is coupled to a generator. To assess the biomass gasifier’s performance and to calculate the generator efficiency additional factors, including the reactor temperature, tar content, producer gas, mass balance of the system are taken into account. The use of coconut shells as fuel to generate power decreases carbon footprint by 60%, according to the results. Thus, biomass gasifiers can provide power in isolated locations where coconut shells are abundant and not currently being used commercially.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131181167","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009251
Vairavel K S, Logasundari V
In this paper, to control the temperature of the Peltier element for DNA amplification. They are quick variation in temperature and temperature setback method required for DNA amplification. Temperature control at 95℃, 65℃ and 75℃. Peltier element was used for producing the temperature at different stage. Heating and cooling of peltier element was change the polarity of the power supply by using h-bridge. Peltier element temperature depends on current value, doesn’t voltage. Current value of the peltier element control by PWM signal. The PWM signal generated for the PIC microprocessor depends on the RTD temperature values. The PID control logic was programming by microprocessor controller.
{"title":"Development of DNA Amplification Instrument used in Disease Diagnosis","authors":"Vairavel K S, Logasundari V","doi":"10.1109/STCR55312.2022.10009251","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009251","url":null,"abstract":"In this paper, to control the temperature of the Peltier element for DNA amplification. They are quick variation in temperature and temperature setback method required for DNA amplification. Temperature control at 95℃, 65℃ and 75℃. Peltier element was used for producing the temperature at different stage. Heating and cooling of peltier element was change the polarity of the power supply by using h-bridge. Peltier element temperature depends on current value, doesn’t voltage. Current value of the peltier element control by PWM signal. The PWM signal generated for the PIC microprocessor depends on the RTD temperature values. The PID control logic was programming by microprocessor controller.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133798680","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009240
S. Subramanian, Chidamparam Ganesh Babu
The computation of the First-Order plus Time-Delay (FOPTD) model parameters are computed using different techniques. This type models are widely used in the industry to approximate the process models because it is easy to tune the controllers based on the model parameters. The controllers are derived from the models. The model derived from the time domain data. In this article, the closed loop system identification methods are examined. The closed loop / feedback controller is designed based on the step output data as to the original system. The closed loop identification method is executed using different set of inputs/excitation. The model obtained from the closed identification methods and it is validated using model performance metrics. This identification process is illustrated through the FOPTD bench mark system.
{"title":"Open Loop Subspace Identification of a FOPTD System","authors":"S. Subramanian, Chidamparam Ganesh Babu","doi":"10.1109/STCR55312.2022.10009240","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009240","url":null,"abstract":"The computation of the First-Order plus Time-Delay (FOPTD) model parameters are computed using different techniques. This type models are widely used in the industry to approximate the process models because it is easy to tune the controllers based on the model parameters. The controllers are derived from the models. The model derived from the time domain data. In this article, the closed loop system identification methods are examined. The closed loop / feedback controller is designed based on the step output data as to the original system. The closed loop identification method is executed using different set of inputs/excitation. The model obtained from the closed identification methods and it is validated using model performance metrics. This identification process is illustrated through the FOPTD bench mark system.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114485065","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009635
Supriya Suryakant Ingale, S. Ram
In this study, an accurate estimation of elbow joint angle by measuring surface electromyography(sEMG) signal from human biceps muscle is done using machine learning regression methods such as linear and polynomial regression. The result of the regression technique is further validated using Kalman Filter Technique (KL) which gave better accurate results for the angle taken. As the first step of regression analysis, the sEMG signal measured from the human biceps muscle, using the Myoware IC has been preprocessed to make it suitable for further analysis. Then the second step was to extract the features from the measured sEMG signal and in this paper, four features of the time-domain method were extracted to estimate the elbow joint angle, namely integrated EMG (iEMG), LOG, RMS and Mean. The extracted features were then applied to the machine learning regression algorithm to predict the elbow joint angle. The predicted elbow joint angle using regression and the Kalman filter showed that the results found using the Kalman filter gave higher accuracy than polynomial regression.
{"title":"Analysis of Elbow Joint Angle for Prediction based on EMG using Kalman Filtering Technique","authors":"Supriya Suryakant Ingale, S. Ram","doi":"10.1109/STCR55312.2022.10009635","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009635","url":null,"abstract":"In this study, an accurate estimation of elbow joint angle by measuring surface electromyography(sEMG) signal from human biceps muscle is done using machine learning regression methods such as linear and polynomial regression. The result of the regression technique is further validated using Kalman Filter Technique (KL) which gave better accurate results for the angle taken. As the first step of regression analysis, the sEMG signal measured from the human biceps muscle, using the Myoware IC has been preprocessed to make it suitable for further analysis. Then the second step was to extract the features from the measured sEMG signal and in this paper, four features of the time-domain method were extracted to estimate the elbow joint angle, namely integrated EMG (iEMG), LOG, RMS and Mean. The extracted features were then applied to the machine learning regression algorithm to predict the elbow joint angle. The predicted elbow joint angle using regression and the Kalman filter showed that the results found using the Kalman filter gave higher accuracy than polynomial regression.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"1091 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122906880","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009190
Harreni V, Hinduja S N, V. S, A. S, Vanathi P T
Detecting defects in fabric is one of the most important steps in the process of quality control in manufacturing processes. The textile structure can deviate from the design due to improper mechanical motion or yarn breakage on a loom, producing a warp, weft, or point defect like harness misdraw, endout, mispick, and slub. Visual human inspection results in common mistakes and takes more time, both of which might reduce productivity. Therefore, automated fabric defect identification will save time and enable more accurate and rapid defect prediction. Due to the Convolution Neural Network's high level of image classification and recognition accuracy, it is utilised to detect fabric defects. It chooses just appropriate features for object identification from a vast number of created features. The proposed model works on the multipath CNN concept, where first path is CNN with tanh activation layer + GLCM and the second path is VGG – 16 + Gabor. The novel multipath CNN was evaluated using TILDA dataset with total of 2000 images and simulated for 20 epochs.
{"title":"Novel Multipath Convolutional Neural Network Based Fabric Defect Detection System","authors":"Harreni V, Hinduja S N, V. S, A. S, Vanathi P T","doi":"10.1109/STCR55312.2022.10009190","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009190","url":null,"abstract":"Detecting defects in fabric is one of the most important steps in the process of quality control in manufacturing processes. The textile structure can deviate from the design due to improper mechanical motion or yarn breakage on a loom, producing a warp, weft, or point defect like harness misdraw, endout, mispick, and slub. Visual human inspection results in common mistakes and takes more time, both of which might reduce productivity. Therefore, automated fabric defect identification will save time and enable more accurate and rapid defect prediction. Due to the Convolution Neural Network's high level of image classification and recognition accuracy, it is utilised to detect fabric defects. It chooses just appropriate features for object identification from a vast number of created features. The proposed model works on the multipath CNN concept, where first path is CNN with tanh activation layer + GLCM and the second path is VGG – 16 + Gabor. The novel multipath CNN was evaluated using TILDA dataset with total of 2000 images and simulated for 20 epochs.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121930241","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009543
U. S, K. Kausalya, K. S
Object recognition plays a vital role in many applications in computer vision. In sports video recognition an action is a major part, in that object recognition is a key requirement. In the video, many challenges are faced which includes fast motion, occluded objects, different sizes of objects, difficult illumination, and continuous change in the background in identifying the object is a major task. The proposed system’s main aim is to deliver a summary of the existing system’s object detection approaches that belongs to CNN and debug their performance on soccer sport video and their training videos and match with the input soccer video. The performance of object recognition is discussed in various situations.
{"title":"Object Recognition in Soccer Sports Videos","authors":"U. S, K. Kausalya, K. S","doi":"10.1109/STCR55312.2022.10009543","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009543","url":null,"abstract":"Object recognition plays a vital role in many applications in computer vision. In sports video recognition an action is a major part, in that object recognition is a key requirement. In the video, many challenges are faced which includes fast motion, occluded objects, different sizes of objects, difficult illumination, and continuous change in the background in identifying the object is a major task. The proposed system’s main aim is to deliver a summary of the existing system’s object detection approaches that belongs to CNN and debug their performance on soccer sport video and their training videos and match with the input soccer video. The performance of object recognition is discussed in various situations.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122122148","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009185
M. Nirmal, Pramod E Jadhav, Santoshi A. Pawar, Manoj Kharde, Pravara
The goal of this research is to detect a pomegranate plant leaf disease that will identify the diseases by making use of a deep convolutional neural network. Plant diseases are a serious problem in India and other Asian Countries that rely heavily on agriculture. Throughout the course of the year, several diseases can be found causing havoc on the harvest by attacking crops. Plant diseases can be difficult to identify with the naked eye alone. As a consequence of this, the development of a system that is capable of recognizing diseases is of the utmost importance. This paper proposes a deep learning technique to an image of a plant leaf, the disease detection model that has been suggested makes use of a deep convolutional neural network to locate and identify the disease. 447, 56, 56 pictures representing 14 unique species and 26 distinct diseases were utilized throughout the training process of the model. A CNN + LSTM is further developed with the help of a trained model. This proposed technique not only diagnoses a health problem, but it also suggests courses of treatment based on the information that it has gathered. In the vast majority of cases, farmers and other specialists in the sector keep a close eye on plants in order to detect and identify diseases. The proposed framework was developed with the assistance of deep learning technique. According to the findings of the tests, the framework that has been proposed is accurate to the degree of 90.546percent when it comes to differentiating between good and unhealthy leaves. The framework allows for the classification of diseases that affect pomegranate leaf to an accuracy of 97.246 %. The data sets are from Mendeley Data Total: 559 images. In which healthy 287 images were identified and 272 diseases images were identified. Originally data were split in 8:1:1 ratio.
本研究的目的是检测石榴植物叶片病害，并利用深度卷积神经网络对病害进行识别。在印度和其他严重依赖农业的亚洲国家，植物病害是一个严重的问题。在一年中，可以发现几种疾病通过攻击作物对收成造成严重破坏。植物病害很难单凭肉眼识别。因此，开发一种能够识别疾病的系统是至关重要的。本文提出了一种植物叶片图像的深度学习技术，提出的病害检测模型利用深度卷积神经网络对病害进行定位和识别。在整个模型的训练过程中，使用了代表14个独特物种和26种不同疾病的447、56、56张图片。在训练好的模型的帮助下，进一步开发了CNN + LSTM。这项拟议中的技术不仅可以诊断健康问题，还可以根据收集到的信息提出治疗方案。在绝大多数情况下，农民和该部门的其他专家密切关注植物，以便发现和识别疾病。该框架是在深度学习技术的帮助下开发的。根据测试结果，提出的框架在区分好叶和不健康叶方面的准确率为90.546%。该框架允许对影响石榴叶的疾病进行分类，准确率为97.246%。数据集来自Mendeley data Total: 559张图像。其中健康图像287张，疾病图像272张。最初数据是按8:1:1的比例分割的。
{"title":"Deep Learning-based Disease Detection using Pomegranate Leaf Image","authors":"M. Nirmal, Pramod E Jadhav, Santoshi A. Pawar, Manoj Kharde, Pravara","doi":"10.1109/STCR55312.2022.10009185","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009185","url":null,"abstract":"The goal of this research is to detect a pomegranate plant leaf disease that will identify the diseases by making use of a deep convolutional neural network. Plant diseases are a serious problem in India and other Asian Countries that rely heavily on agriculture. Throughout the course of the year, several diseases can be found causing havoc on the harvest by attacking crops. Plant diseases can be difficult to identify with the naked eye alone. As a consequence of this, the development of a system that is capable of recognizing diseases is of the utmost importance. This paper proposes a deep learning technique to an image of a plant leaf, the disease detection model that has been suggested makes use of a deep convolutional neural network to locate and identify the disease. 447, 56, 56 pictures representing 14 unique species and 26 distinct diseases were utilized throughout the training process of the model. A CNN + LSTM is further developed with the help of a trained model. This proposed technique not only diagnoses a health problem, but it also suggests courses of treatment based on the information that it has gathered. In the vast majority of cases, farmers and other specialists in the sector keep a close eye on plants in order to detect and identify diseases. The proposed framework was developed with the assistance of deep learning technique. According to the findings of the tests, the framework that has been proposed is accurate to the degree of 90.546percent when it comes to differentiating between good and unhealthy leaves. The framework allows for the classification of diseases that affect pomegranate leaf to an accuracy of 97.246 %. The data sets are from Mendeley Data Total: 559 images. In which healthy 287 images were identified and 272 diseases images were identified. Originally data were split in 8:1:1 ratio.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130571997","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009205
P. K, B. Nataraj
The Internet of Things (IoT) and Blockchain Technology are more trending which are gaining popularity all over the world in recent times. IoT is a collection of several things that consists of sensors, actuators, and many other devices to perform identification and sensing by aggregation of data, interacting with other devices, and advanced processes in a real-time environment. However, at the same time, Blockchain comprises small blocks where each block can have an identity and hash value. Due to the huge sector of applications that can be achieved with IoT devices, threats and huge attacks can occur and make it an insecure state. Therefore, security and privacy have become an integral part of the internet of things. To the best of our knowledge, extensive studies are made on the security threats and different types of attacks in every layer of the Internet of Things. Moreover, the article focuses on the integration of blockchain technology with the Internet of Things (IoT) and the classification of Blockchain IoT (BIoT) attacks. The threat levels and the prevention techniques of attacks in IoT and Blockchain are incorporated respectively.
{"title":"Certain Investigation of Attacks in the Field of Internet of Things and Blockchain Technology","authors":"P. K, B. Nataraj","doi":"10.1109/STCR55312.2022.10009205","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009205","url":null,"abstract":"The Internet of Things (IoT) and Blockchain Technology are more trending which are gaining popularity all over the world in recent times. IoT is a collection of several things that consists of sensors, actuators, and many other devices to perform identification and sensing by aggregation of data, interacting with other devices, and advanced processes in a real-time environment. However, at the same time, Blockchain comprises small blocks where each block can have an identity and hash value. Due to the huge sector of applications that can be achieved with IoT devices, threats and huge attacks can occur and make it an insecure state. Therefore, security and privacy have become an integral part of the internet of things. To the best of our knowledge, extensive studies are made on the security threats and different types of attacks in every layer of the Internet of Things. Moreover, the article focuses on the integration of blockchain technology with the Internet of Things (IoT) and the classification of Blockchain IoT (BIoT) attacks. The threat levels and the prevention techniques of attacks in IoT and Blockchain are incorporated respectively.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131928612","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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