In advanced driver assistance system detection of road surfaces is an important task. Few algorithms have been proposed in past to detect the road surfaces based on intensities. However, problem arises in detection process is due to the presence of shadows or wet road surfaces. Here we have proposed a novel algorithm for detection of shadows with the help of machine learning approaches. Initially shadow is being detected with the help of a threshold-based approach followed by windowing-based method. The detected shadow region gets confirmed with the help of a set of features and classifier. The detected shadow or wet pixels are in painted to obtain set of pixels without shadow for road classification problems. The simplicity and accuracy of the algorithm makes it robust and can be used as a part of road surface detection algorithm.
{"title":"A Machine Learning Approach for Detection and Suppression of Shadow or Wet Road Surfaces","authors":"Pankaj Prusty, Bibhu Prasad Mohanty","doi":"10.37391/ijeer.110321","DOIUrl":"https://doi.org/10.37391/ijeer.110321","url":null,"abstract":"In advanced driver assistance system detection of road surfaces is an important task. Few algorithms have been proposed in past to detect the road surfaces based on intensities. However, problem arises in detection process is due to the presence of shadows or wet road surfaces. Here we have proposed a novel algorithm for detection of shadows with the help of machine learning approaches. Initially shadow is being detected with the help of a threshold-based approach followed by windowing-based method. The detected shadow region gets confirmed with the help of a set of features and classifier. The detected shadow or wet pixels are in painted to obtain set of pixels without shadow for road classification problems. The simplicity and accuracy of the algorithm makes it robust and can be used as a part of road surface detection algorithm.","PeriodicalId":491088,"journal":{"name":"International journal of electrical & electronics research","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136011466","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}
Prachi Goyal, P.K Singhal, Pooja Sahoo, Deep K. Parsediya
A modified E-shape dual bands rectangular microstrip patch antenna for wireless applications is presented in this paper. An E-slot Microstrip patch antenna with a defective ground structure method has been proposed and getting two bands at 1.9 GHz and 2.89 GHz with S11 -10dB. Defective ground structures provide a maximum gain and low insertion loss i.e., a gain of 3.16 dB, voltage standing wave ratio less than 2, and insertion loss less than -10 dB for both bands. The size of the antenna is 46.83mm x 38.41mm x 1.676mm, which is compact in term of size. The dual band microstrip patch antenna exhibits low cost. The simulation's outcome closely resembles the actual printed antenna and applicable for WiMAX application. The antenna was designed using the Computer Simulation Technology (CST) software and printed on FR-4 substrate.
{"title":"Modified E-Shape Rectangular Microstrip Patch Antenna with DGS for Wireless Communication","authors":"Prachi Goyal, P.K Singhal, Pooja Sahoo, Deep K. Parsediya","doi":"10.37391/ijeer.110327","DOIUrl":"https://doi.org/10.37391/ijeer.110327","url":null,"abstract":"A modified E-shape dual bands rectangular microstrip patch antenna for wireless applications is presented in this paper. An E-slot Microstrip patch antenna with a defective ground structure method has been proposed and getting two bands at 1.9 GHz and 2.89 GHz with S11 -10dB. Defective ground structures provide a maximum gain and low insertion loss i.e., a gain of 3.16 dB, voltage standing wave ratio less than 2, and insertion loss less than -10 dB for both bands. The size of the antenna is 46.83mm x 38.41mm x 1.676mm, which is compact in term of size. The dual band microstrip patch antenna exhibits low cost. The simulation's outcome closely resembles the actual printed antenna and applicable for WiMAX application. The antenna was designed using the Computer Simulation Technology (CST) software and printed on FR-4 substrate.","PeriodicalId":491088,"journal":{"name":"International journal of electrical & electronics research","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136011464","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}
The extensive review of the literature evaluation on predictive maintenance (PdM) in this work focuses on system designs, goals, and methodologies. In the business world, any equipment or system failures or unscheduled downtime would negatively affect or stop an organization's key operations, possibly incurring heavy fines and irreparable reputational damage. Traditional maintenance methods now in use are plagued by a variety of limitations and preconceptions, including expensive preventive maintenance costs, insufficient or incorrect mathematical deterioration procedures, and manual feature extraction. The PdM maintenance framework is suggested as a new method of maintenance framework to prevent any damage only after the analytical analysis shows specific malfunctions or breakdowns, which is in line with the growth of digital building and the advancement of the Internet of Things (IoT), and Artificial Intelligence (AI), and so on. We also present an overview of the three main types of fault diagnosis and prognosis methods used in PdM mechanisms: scientific, conventional Machine Learning (ML), and deep learning (DL). While offering a thorough assessment of DL-dependent techniques, we make a quick overview of the knowledge-based and conventional ML-dependent strategies used in various components or systems. Eventually, significant possibilities for further study are discussed.
{"title":"Comprehensive Analysis of IoT with Artificial Intelligence to Predictive Maintenance Optimization for Indian Shipbuilding","authors":"PNV Srinivasa Rao, PVY Jayasree","doi":"10.37391/ijeer.110325","DOIUrl":"https://doi.org/10.37391/ijeer.110325","url":null,"abstract":"The extensive review of the literature evaluation on predictive maintenance (PdM) in this work focuses on system designs, goals, and methodologies. In the business world, any equipment or system failures or unscheduled downtime would negatively affect or stop an organization's key operations, possibly incurring heavy fines and irreparable reputational damage. Traditional maintenance methods now in use are plagued by a variety of limitations and preconceptions, including expensive preventive maintenance costs, insufficient or incorrect mathematical deterioration procedures, and manual feature extraction. The PdM maintenance framework is suggested as a new method of maintenance framework to prevent any damage only after the analytical analysis shows specific malfunctions or breakdowns, which is in line with the growth of digital building and the advancement of the Internet of Things (IoT), and Artificial Intelligence (AI), and so on. We also present an overview of the three main types of fault diagnosis and prognosis methods used in PdM mechanisms: scientific, conventional Machine Learning (ML), and deep learning (DL). While offering a thorough assessment of DL-dependent techniques, we make a quick overview of the knowledge-based and conventional ML-dependent strategies used in various components or systems. Eventually, significant possibilities for further study are discussed.","PeriodicalId":491088,"journal":{"name":"International journal of electrical & electronics research","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136011469","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}
A persistent brain's neurological state is epilepsy, characterised by recurring seizure. Brain electrical activity is measured using EEG signals, which can be used to detect and diagnose significant brain problems such as Epilepsy, Autism, Alzheimer’s etc. However, manual EEG data processing is time-consuming, requires highly skilled clinicians, and is associated with low inter-rater reliability (IRA). A computer-aided diagnosis approach for epileptic seizure detection from multichannel EEG recordings by fusing the time-frequency features and the deep learning features extracted from Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) model using canonical correlation analysis (CCA) method is provided in this study. Deep Learning features are extracted using CNN-GRU layers, motivated by recent advancements in image classification and optimised for use with EEG data. We have also extracted time-frequency features such as spectral entropies and Sub Band energies from Empirical mode decomposition (EMD) and Hilbert Marginal Spectrum (HMS). We used CHBMIT dataset to carry out the results and showed that the method proposed for fusing the time-frequency features and deep learning has given better performance.
{"title":"Feature Fusion of Time-frequency and Deep Learning Features for Epileptic Seizure Detection using EEG Signals","authors":"Seshasai Priya Sadam, Nalini NJ","doi":"10.37391/ijeer.110329","DOIUrl":"https://doi.org/10.37391/ijeer.110329","url":null,"abstract":"A persistent brain's neurological state is epilepsy, characterised by recurring seizure. Brain electrical activity is measured using EEG signals, which can be used to detect and diagnose significant brain problems such as Epilepsy, Autism, Alzheimer’s etc. However, manual EEG data processing is time-consuming, requires highly skilled clinicians, and is associated with low inter-rater reliability (IRA). A computer-aided diagnosis approach for epileptic seizure detection from multichannel EEG recordings by fusing the time-frequency features and the deep learning features extracted from Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) model using canonical correlation analysis (CCA) method is provided in this study. Deep Learning features are extracted using CNN-GRU layers, motivated by recent advancements in image classification and optimised for use with EEG data. We have also extracted time-frequency features such as spectral entropies and Sub Band energies from Empirical mode decomposition (EMD) and Hilbert Marginal Spectrum (HMS). We used CHBMIT dataset to carry out the results and showed that the method proposed for fusing the time-frequency features and deep learning has given better performance.","PeriodicalId":491088,"journal":{"name":"International journal of electrical & electronics research","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136011468","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}
Ishi Gupta, Manika Choudhury, G. Harish Gnanasambanthan, Debashis Maji
Recent advancements in flexible electronics and wearable sensors have given biomedical technology a new edge overcoming the limitations of traditional rigid silicon-based electronics. Furthermore, high flexibility of these wearable sensors enables it to conformally sit over any uneven surface helping in accurate determination of any physical, chemical, or physiological parameter associate with the surface. Conventionally expensive micro/nano photolithography techniques under strict clean room conditions are used for the development of these flexible and wearable biomedical sensors with high degree of accuracy and sensitivity. However, the developed wearable sensors need not only be extremely sensitive, but also cost effective for its successful usage. To address this, the present work discusses the use of a photo-patternable UV sheet for realization of micro patterns over flexible copper cladded surface eliminating the need of costly clean room facilities. It demonstrates the standardization of various design geometries using the photo-patternable UV sheet over the flexible surface similar to photolithography process and involves optimization of the exposure timing of the UV sheets and their development time towards various design patterns over different thick film metal surfaces. Finally, patterned micro devices like micro-electrodes were successfully realized using the above process to ascertain its efficacy.
{"title":"Optimization of Microstructure Patterning for Flexible Bioelectronics Application","authors":"Ishi Gupta, Manika Choudhury, G. Harish Gnanasambanthan, Debashis Maji","doi":"10.37391/ijeer.110315","DOIUrl":"https://doi.org/10.37391/ijeer.110315","url":null,"abstract":"Recent advancements in flexible electronics and wearable sensors have given biomedical technology a new edge overcoming the limitations of traditional rigid silicon-based electronics. Furthermore, high flexibility of these wearable sensors enables it to conformally sit over any uneven surface helping in accurate determination of any physical, chemical, or physiological parameter associate with the surface. Conventionally expensive micro/nano photolithography techniques under strict clean room conditions are used for the development of these flexible and wearable biomedical sensors with high degree of accuracy and sensitivity. However, the developed wearable sensors need not only be extremely sensitive, but also cost effective for its successful usage. To address this, the present work discusses the use of a photo-patternable UV sheet for realization of micro patterns over flexible copper cladded surface eliminating the need of costly clean room facilities. It demonstrates the standardization of various design geometries using the photo-patternable UV sheet over the flexible surface similar to photolithography process and involves optimization of the exposure timing of the UV sheets and their development time towards various design patterns over different thick film metal surfaces. Finally, patterned micro devices like micro-electrodes were successfully realized using the above process to ascertain its efficacy.","PeriodicalId":491088,"journal":{"name":"International journal of electrical & electronics research","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136379211","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}
In Indian railway, the smart monitoring system for the train also train track is a significant aspect to prevent accidents. Indian railway system is underdeveloped in terms of smart monitoring of the train when compared with the other developed countries. Using the smart monitoring system for train, the deterioration of the railway track could be identified and secondly, accident between two trains could be prevented, thirdly any obstacle present in railway track, could be find and removed, two coaches of the train getting disconnected during the movement of the train due to manufacturing mistakes could also be detected. It helps to detect fire in the particular coach of train. Smart monitoring of the train can be achieved by the help of some semiconductor devices such as laser, laser camera and photodiode is used. Smart monitoring system of the railway could help to monitor the train and its track in an efficient way it could be implemented in Indian railway to avoid accident and extricate people’s life.
{"title":"Demonstration of an Intelligent and Efficient Smart Monitoring System for Train Track By using Arduino","authors":"Puspendu Roy, S. Rajalakshmi, N. Sangeetha","doi":"10.37391/ijeer.110316","DOIUrl":"https://doi.org/10.37391/ijeer.110316","url":null,"abstract":"In Indian railway, the smart monitoring system for the train also train track is a significant aspect to prevent accidents. Indian railway system is underdeveloped in terms of smart monitoring of the train when compared with the other developed countries. Using the smart monitoring system for train, the deterioration of the railway track could be identified and secondly, accident between two trains could be prevented, thirdly any obstacle present in railway track, could be find and removed, two coaches of the train getting disconnected during the movement of the train due to manufacturing mistakes could also be detected. It helps to detect fire in the particular coach of train. Smart monitoring of the train can be achieved by the help of some semiconductor devices such as laser, laser camera and photodiode is used. Smart monitoring system of the railway could help to monitor the train and its track in an efficient way it could be implemented in Indian railway to avoid accident and extricate people’s life.","PeriodicalId":491088,"journal":{"name":"International journal of electrical & electronics research","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136378606","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}
Debashish Dash, Shaik Abdul Rahiman, C. Pavitra Chowdary, Sagar Deo Singh
In this paper, a FinFET and Tunnel FET (TFET) are designed and implemented using Sentaurus TCAD. Due to numerous advantages, the TFET and FinFET have been proposed as a possible alternative to the conventional metal oxide semiconductor FET (MOSFET). A phenomenal performance-has been achieved using FinFET technology up to a 7 nm feature size. A detailed observation is made on FinFET and TFET regarding various effects such as short channel effects, quantum tunneling effect and characteristics like electric field, voltage and current, on-current, doping concentrations, energy band diagrams etc. FinFET technology can be used for designing different low power CMOS digital circuits and memory-based circuits. On the contrary, TFET based synthesized circuits are known for their high sensitivity, for which they are suitable for sensing applications, especially biosensors.
{"title":"Designing of Tunnel FET and FinFET using Sentaurus TCAD and Finding their Characteristics","authors":"Debashish Dash, Shaik Abdul Rahiman, C. Pavitra Chowdary, Sagar Deo Singh","doi":"10.37391/ijeer.110318","DOIUrl":"https://doi.org/10.37391/ijeer.110318","url":null,"abstract":"In this paper, a FinFET and Tunnel FET (TFET) are designed and implemented using Sentaurus TCAD. Due to numerous advantages, the TFET and FinFET have been proposed as a possible alternative to the conventional metal oxide semiconductor FET (MOSFET). A phenomenal performance-has been achieved using FinFET technology up to a 7 nm feature size. A detailed observation is made on FinFET and TFET regarding various effects such as short channel effects, quantum tunneling effect and characteristics like electric field, voltage and current, on-current, doping concentrations, energy band diagrams etc. FinFET technology can be used for designing different low power CMOS digital circuits and memory-based circuits. On the contrary, TFET based synthesized circuits are known for their high sensitivity, for which they are suitable for sensing applications, especially biosensors.","PeriodicalId":491088,"journal":{"name":"International journal of electrical & electronics research","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136378915","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}
Practical memristors have gained attention from researchers and scientists due to their potential use in a variety of electronic circuits and devices. In our paper, a hybrid Memristor-CMOS (MeMOS) logic circuit was designed and its transient response was analyzed. This circuit, which uses a N-type metal oxide semiconductor (NMOS), and P-type metal oxide semiconductor (PMOS) transistors, Operational amplifiers (OPAMPs), resistors, capacitors and multipliers replicate memristor characteristics. To facilitate the development of real memristor circuit applications, a memristor emulator is utilized for breadboard experiments. This emulator can be connected in a variety of configurations, including serial, parallel, or a combination of both, with identical or opposite polarities. By simply changing the connection, the emulator can be switched between decremental and incremental configurations. In our paper, we implemented AND logic using MeMOS. PSpice simulation of the proposed emulator have been demonstrated for TiO2 memristor model.
{"title":"VLSI Implementation of Hybrid Memristor Based Logic Gates","authors":"Ritesh Samanta, Namburi VamsiKrishna, Poongundran Selvaprabhu, Rajeshkumar V, Vetriveeran Rajamani","doi":"10.37391/ijeer.110314","DOIUrl":"https://doi.org/10.37391/ijeer.110314","url":null,"abstract":"Practical memristors have gained attention from researchers and scientists due to their potential use in a variety of electronic circuits and devices. In our paper, a hybrid Memristor-CMOS (MeMOS) logic circuit was designed and its transient response was analyzed. This circuit, which uses a N-type metal oxide semiconductor (NMOS), and P-type metal oxide semiconductor (PMOS) transistors, Operational amplifiers (OPAMPs), resistors, capacitors and multipliers replicate memristor characteristics. To facilitate the development of real memristor circuit applications, a memristor emulator is utilized for breadboard experiments. This emulator can be connected in a variety of configurations, including serial, parallel, or a combination of both, with identical or opposite polarities. By simply changing the connection, the emulator can be switched between decremental and incremental configurations. In our paper, we implemented AND logic using MeMOS. PSpice simulation of the proposed emulator have been demonstrated for TiO2 memristor model.","PeriodicalId":491088,"journal":{"name":"International journal of electrical & electronics research","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136379062","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}
The proposed study provides a novel technique for recognizing hand gestures that use a combination of Deep Convolutional Neural Networks (DCNN) and 60 GHz Frequency Modulated Continuous Wave (FMCW) radar. The motion of a Human's hand is detected using the FMCW radar, and the various gestures are classified using the DCNN. Motion detection and frequency analysis are two techniques that the suggested system combines. The basis of the capability of motion detection in FMCW radars' is to recognize the Doppler shift in the received signal brought on by the target's motion. To properly identify the hand motions, the presented technique combines these two techniques. The system is analyzed using a collection of hand gesture photos, and the outcomes are analyzed with those of other hand gesture recognition systems which are already in use. A dataset of five different hand gestures is used to examine the proposed system. According to the experimental data, the suggested system can recognize gestures with an accuracy of 96.5%, showing its potential as a productive gesture recognition system. Additionally, the suggested system has a processing time of 100 ms and can run in real time. The outcomes also demonstrate the proposed system's resistance to noise and its ability to recognize gestures in a variety of configurations. For gesture detection applications in virtual reality and augmented reality systems, this research offers a promising approach.
{"title":"Hand Gesture Recognition System based on 60 GHz FMCW Radar and Deep Neural Network","authors":"Daswini Nadar, Saista Anjum, K.C. Sriharipriya","doi":"10.37391/ijeer.110319","DOIUrl":"https://doi.org/10.37391/ijeer.110319","url":null,"abstract":"The proposed study provides a novel technique for recognizing hand gestures that use a combination of Deep Convolutional Neural Networks (DCNN) and 60 GHz Frequency Modulated Continuous Wave (FMCW) radar. The motion of a Human's hand is detected using the FMCW radar, and the various gestures are classified using the DCNN. Motion detection and frequency analysis are two techniques that the suggested system combines. The basis of the capability of motion detection in FMCW radars' is to recognize the Doppler shift in the received signal brought on by the target's motion. To properly identify the hand motions, the presented technique combines these two techniques. The system is analyzed using a collection of hand gesture photos, and the outcomes are analyzed with those of other hand gesture recognition systems which are already in use. A dataset of five different hand gestures is used to examine the proposed system. According to the experimental data, the suggested system can recognize gestures with an accuracy of 96.5%, showing its potential as a productive gesture recognition system. Additionally, the suggested system has a processing time of 100 ms and can run in real time. The outcomes also demonstrate the proposed system's resistance to noise and its ability to recognize gestures in a variety of configurations. For gesture detection applications in virtual reality and augmented reality systems, this research offers a promising approach.","PeriodicalId":491088,"journal":{"name":"International journal of electrical & electronics research","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136379212","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}
Aadhitya S V, Ashwin Hariharan R, Sriharipriya K C
Agriculture and allied activities still continue to be one of the major occupations in world. Various modern methods and inventions have been incorporated to make it more efficient and successful. One of the main problems the farmers are facing are plant diseases. This can affect the entire yield of a season, so to tackle that problem we are proposing a ResNet based Convolutional neural network model which can detect the various disease in plants in early stage itself. For this purpose, ‘New plant village’ dataset to train and test the model. The proposed Resnet based approach has achieved high accuracy in detecting diseases as well as suggesting a proper solution and possible causes for a plant disease.
{"title":"Disease Detection and Diagnosis of Agricultural Plant Leaf Using Machine Learning","authors":"Aadhitya S V, Ashwin Hariharan R, Sriharipriya K C","doi":"10.37391/ijeer.110317","DOIUrl":"https://doi.org/10.37391/ijeer.110317","url":null,"abstract":"Agriculture and allied activities still continue to be one of the major occupations in world. Various modern methods and inventions have been incorporated to make it more efficient and successful. One of the main problems the farmers are facing are plant diseases. This can affect the entire yield of a season, so to tackle that problem we are proposing a ResNet based Convolutional neural network model which can detect the various disease in plants in early stage itself. For this purpose, ‘New plant village’ dataset to train and test the model. The proposed Resnet based approach has achieved high accuracy in detecting diseases as well as suggesting a proper solution and possible causes for a plant disease.","PeriodicalId":491088,"journal":{"name":"International journal of electrical & electronics research","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136379066","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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