Pub Date : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744248
P. Sharmila, J. Nandhini, K. Anuratha, Soshya Joshi
Road safety is the major issue nowadays there are thousands of road fatalities and injuries due to drive fatigue and drunk and drive. To avoid and reduce these kind of road accidents simple sensors used within a vehicle to do different functions, such as horn control and speed control to manage and control the speed of the vehicle in different places such as flyovers, bridges, highways, schools. The vehicle is controlled on traffic signal when the signal is red, the vehicle is automatically stopped. The RF transmitter includes four buttons like no horn, speed control, green signal and no parking. This RF transmitter is placed on signal panels that sends the signals to the RF receiver which is connected with NodeMCU. The LCD screen displays the messages by pressing the buttons required by the transmitter.
{"title":"An IoT based Intelligent Transport and Road Safety System","authors":"P. Sharmila, J. Nandhini, K. Anuratha, Soshya Joshi","doi":"10.1109/ICITIIT54346.2022.9744248","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744248","url":null,"abstract":"Road safety is the major issue nowadays there are thousands of road fatalities and injuries due to drive fatigue and drunk and drive. To avoid and reduce these kind of road accidents simple sensors used within a vehicle to do different functions, such as horn control and speed control to manage and control the speed of the vehicle in different places such as flyovers, bridges, highways, schools. The vehicle is controlled on traffic signal when the signal is red, the vehicle is automatically stopped. The RF transmitter includes four buttons like no horn, speed control, green signal and no parking. This RF transmitter is placed on signal panels that sends the signals to the RF receiver which is connected with NodeMCU. The LCD screen displays the messages by pressing the buttons required by the transmitter.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134180396","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744228
Siddharth Gupta, A. Panwar, Akanksha Kapruwan, Nisha Chaube, Manav Chauhan
Diabetes is a rapidly spreading illness that has devastating consequences on human organs such as kidney, lungs, heart, eyes, etc. Diabetic Retinopathy (DR) is a condition caused by abiding diabetes that damages small vessels carrying blood and tissues in the eyes. The condition is characterized by the creation of inflated formations in the retinal region known as Micro-aneurysms, which if ignored can result in irreversible damage to the eye's blood vessels, eventually leading to blindness. In the early stages of the disease, such clinical manifestations do not appear. As a result, regular and timely checkups are foremost important. However, manual identification of diabetic retinopathy is time intensive and prone to human mistake. In the stated research, the color fundus dataset scans after processing are passed to multiple Deep Learning (DL) models employed to learn characteristics. These models trained on millions of different images from thousands of classes. Finally, several machine learning classifiers were used to classify lesions using the collected characteristics. The extracted result shows very eye catching performance. This enables experts to create architecture that fully address the problem of classifying unidentified scans into the right class or category.
{"title":"Real Time Analysis of Diabetic Retinopathy Lesions by Employing Deep Learning and Machine Learning Algorithms using Color Fundus Data","authors":"Siddharth Gupta, A. Panwar, Akanksha Kapruwan, Nisha Chaube, Manav Chauhan","doi":"10.1109/ICITIIT54346.2022.9744228","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744228","url":null,"abstract":"Diabetes is a rapidly spreading illness that has devastating consequences on human organs such as kidney, lungs, heart, eyes, etc. Diabetic Retinopathy (DR) is a condition caused by abiding diabetes that damages small vessels carrying blood and tissues in the eyes. The condition is characterized by the creation of inflated formations in the retinal region known as Micro-aneurysms, which if ignored can result in irreversible damage to the eye's blood vessels, eventually leading to blindness. In the early stages of the disease, such clinical manifestations do not appear. As a result, regular and timely checkups are foremost important. However, manual identification of diabetic retinopathy is time intensive and prone to human mistake. In the stated research, the color fundus dataset scans after processing are passed to multiple Deep Learning (DL) models employed to learn characteristics. These models trained on millions of different images from thousands of classes. Finally, several machine learning classifiers were used to classify lesions using the collected characteristics. The extracted result shows very eye catching performance. This enables experts to create architecture that fully address the problem of classifying unidentified scans into the right class or category.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132561707","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744144
B. Joseph, Sajimon Abraham
The advancement of Internet technology has expanded the horizon of face-to-face classroom learning environments to an open, borderless learning space that is no longer curbed to the walls of a classroom. E-Learning encompasses all forms of electronically supported teaching and learning. Asynchronous e-Learning has the potential to be customized to the unique needs of each learner. Despite the possible benefits of e-Learning, the experience of educators confirms that there are many students who have lower rates of learning and require special attention and assistance in digital learning. These slow learners, as with classroom learning, also constitute a noticeable part of the student community in the e-Learning environment. Over the past decade, rapid developments in the field of big data and data analytics have offered opportunities to discover useful insights from massive volumes of educational data. In this paper, the authors have explored the possibilities in identifying and supporting slow learners in e-Learning, which will bring learning satisfaction and academic improvement. Data mining of log files from a Learning Management System (LMS) can have the power to support, challenge, and reshape current educational practices in e-Learning. The potentials of Machine Learning (ML) and Educational Data mining techniques can be employed to classify these learners based on the rate of learning and assessments conducted. An intelligent personalized remedial instruction system that addresses each learner's learning necessities and preferences will help slow learners to reach their optimum levels in the e-Learning situation and will ensure the best quality of education.
{"title":"Analyzing the Cognitive Process Dimension and Rate of Learning to Identify the Slow Learners in e-Learning","authors":"B. Joseph, Sajimon Abraham","doi":"10.1109/ICITIIT54346.2022.9744144","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744144","url":null,"abstract":"The advancement of Internet technology has expanded the horizon of face-to-face classroom learning environments to an open, borderless learning space that is no longer curbed to the walls of a classroom. E-Learning encompasses all forms of electronically supported teaching and learning. Asynchronous e-Learning has the potential to be customized to the unique needs of each learner. Despite the possible benefits of e-Learning, the experience of educators confirms that there are many students who have lower rates of learning and require special attention and assistance in digital learning. These slow learners, as with classroom learning, also constitute a noticeable part of the student community in the e-Learning environment. Over the past decade, rapid developments in the field of big data and data analytics have offered opportunities to discover useful insights from massive volumes of educational data. In this paper, the authors have explored the possibilities in identifying and supporting slow learners in e-Learning, which will bring learning satisfaction and academic improvement. Data mining of log files from a Learning Management System (LMS) can have the power to support, challenge, and reshape current educational practices in e-Learning. The potentials of Machine Learning (ML) and Educational Data mining techniques can be employed to classify these learners based on the rate of learning and assessments conducted. An intelligent personalized remedial instruction system that addresses each learner's learning necessities and preferences will help slow learners to reach their optimum levels in the e-Learning situation and will ensure the best quality of education.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125028015","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744215
Revathi Ganesan, Dilip Kothari
This paper proposes a novel dynamic multi-party key agreement method. The algorithm proposed is used for an individual or a group of receivers. The algorithm imbibes a unique method of storing the bits resulting in reduced retransmission. The algorithm uses pattern-based private key generation to encode the message. The private key is mutually decided and agreed upon by the sender and the intended recipient. The message is represented in a block cipher while the transmission occurs row-wise. Providing an opportunity to jumble the message according to the pattern before the transmission. Due to row-based transmission, the bandwidth requirement and channel utilization are efficient. The algorithm reduces the probability of interception such that all the channels should be inferred correctly to identify the key hence decoding the message. The algorithm is implemented, the results are simulated and verified.
{"title":"Quantum Key Agreement simulation using pattern-based encoding","authors":"Revathi Ganesan, Dilip Kothari","doi":"10.1109/ICITIIT54346.2022.9744215","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744215","url":null,"abstract":"This paper proposes a novel dynamic multi-party key agreement method. The algorithm proposed is used for an individual or a group of receivers. The algorithm imbibes a unique method of storing the bits resulting in reduced retransmission. The algorithm uses pattern-based private key generation to encode the message. The private key is mutually decided and agreed upon by the sender and the intended recipient. The message is represented in a block cipher while the transmission occurs row-wise. Providing an opportunity to jumble the message according to the pattern before the transmission. Due to row-based transmission, the bandwidth requirement and channel utilization are efficient. The algorithm reduces the probability of interception such that all the channels should be inferred correctly to identify the key hence decoding the message. The algorithm is implemented, the results are simulated and verified.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125296364","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 : 2022-02-12DOI: 10.1109/icitiit54346.2022.9744190
{"title":"[ICITIIT 2022 Front cover]","authors":"","doi":"10.1109/icitiit54346.2022.9744190","DOIUrl":"https://doi.org/10.1109/icitiit54346.2022.9744190","url":null,"abstract":"","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122042218","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744194
G. S. Madhan Kumar, S. P. Shiva Prakash, K. Krinkin
Artificial Intelligence(AI) has become a global plat-form that allows objects in IoT to Interact and perform computations. The wide range of application areas of IoT are Smart Cities, Smart grids, Smart Supply chain and Ambient Assisted Living(AAL). These applications have challenges like tolerance to uncertainty,adaptiveness to the changing environment and improved trust among users. Thus, machine learning algorithms improve the performance of smart objects in various environment. The AAL environment deploys heterogeneous devices and sensors to capture various activities carried out through the daily by the individuals who resides in the smart home. In this work, an ensemble method using k-Nearest Neighbor(KNN), Decision Tree(DT) and Logistic Regression(LR)is proposed by investigating the performance of existing conventional supervised machine learning algorithms and selecting best model by considering the sensors features and improves the performance metrics. The work is evaluated using the benchmark ARAS (Activity Recognition with Ambient Sensing) dataset. The results are analysed using different parameters. The comparative analysis show that the proposed ensemble method gives accuracy of 76.28%.
{"title":"Ensemble Method for User Activity classification in Ambient Assisted Living","authors":"G. S. Madhan Kumar, S. P. Shiva Prakash, K. Krinkin","doi":"10.1109/ICITIIT54346.2022.9744194","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744194","url":null,"abstract":"Artificial Intelligence(AI) has become a global plat-form that allows objects in IoT to Interact and perform computations. The wide range of application areas of IoT are Smart Cities, Smart grids, Smart Supply chain and Ambient Assisted Living(AAL). These applications have challenges like tolerance to uncertainty,adaptiveness to the changing environment and improved trust among users. Thus, machine learning algorithms improve the performance of smart objects in various environment. The AAL environment deploys heterogeneous devices and sensors to capture various activities carried out through the daily by the individuals who resides in the smart home. In this work, an ensemble method using k-Nearest Neighbor(KNN), Decision Tree(DT) and Logistic Regression(LR)is proposed by investigating the performance of existing conventional supervised machine learning algorithms and selecting best model by considering the sensors features and improves the performance metrics. The work is evaluated using the benchmark ARAS (Activity Recognition with Ambient Sensing) dataset. The results are analysed using different parameters. The comparative analysis show that the proposed ensemble method gives accuracy of 76.28%.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"26 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130632608","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744208
Amanda Sara Philip, Sreekala K.S.
The evolution of modern Complementary Metal Oxide Semiconductor technology has led to the scaling of the transistor size to nanometers. This has resulted in significant advantages for integrated circuits such as higher speed, smaller circuit dimension, and lower operating voltage. However, this smaller dimension and lower operating voltage are highly susceptible to operational disturbances such as signal coupling, substrate noise, and single event effects caused by ionizing particles. Single event transient occurs whilst a excessive power particle hits a time independent logic circuit. The charge unloaded by these particles root a temporary voltage disturbance to load incorrect data. In this work, the impact of Single Event Transient on different parameters associated with Efficient Charge Recovery Logic circuit was analyzed. The technology node used for this analysis is 180 nanometers and 90 nanometers using Cadence Virtuoso.The result shows that on scaling the effect of Single Event Transient increases and the power dissipation is also increased by 32.4% .
{"title":"The Ramification of Single Event Transient effect on Efficient Charge Recovery Logic circuit","authors":"Amanda Sara Philip, Sreekala K.S.","doi":"10.1109/ICITIIT54346.2022.9744208","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744208","url":null,"abstract":"The evolution of modern Complementary Metal Oxide Semiconductor technology has led to the scaling of the transistor size to nanometers. This has resulted in significant advantages for integrated circuits such as higher speed, smaller circuit dimension, and lower operating voltage. However, this smaller dimension and lower operating voltage are highly susceptible to operational disturbances such as signal coupling, substrate noise, and single event effects caused by ionizing particles. Single event transient occurs whilst a excessive power particle hits a time independent logic circuit. The charge unloaded by these particles root a temporary voltage disturbance to load incorrect data. In this work, the impact of Single Event Transient on different parameters associated with Efficient Charge Recovery Logic circuit was analyzed. The technology node used for this analysis is 180 nanometers and 90 nanometers using Cadence Virtuoso.The result shows that on scaling the effect of Single Event Transient increases and the power dissipation is also increased by 32.4% .","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116758044","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744176
M. Himansh, V. Manikandan
Open-Source Software has picked up pace in the past decade with support from Multinational conglomerates and huge Open-Source communities. We hear a lot about the success of many open-source projects, but we fail to understand how many do not make it. In this paper, we understand the dynamics behind open-source software. We start with the need for Open-Source Alternatives. Then look at a few concerns faced by Open-Source Software developers and maintainers. Next, we would understand the various requirements of Open-Source Software. Later, we would touch upon the various attributes that affect the selection of Open-Source Software and the decisions to be taken while building general-purpose Open-Source Software. Then we would analyze the 5-determinants of Open-Source Software success. Finally, we would look at the data collected from 482 datapoints from 24 countries and then analyze the data by forming graphs and charts.
{"title":"A Statistical Study and Analysis to Identify the Importance of Open-source Software","authors":"M. Himansh, V. Manikandan","doi":"10.1109/ICITIIT54346.2022.9744176","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744176","url":null,"abstract":"Open-Source Software has picked up pace in the past decade with support from Multinational conglomerates and huge Open-Source communities. We hear a lot about the success of many open-source projects, but we fail to understand how many do not make it. In this paper, we understand the dynamics behind open-source software. We start with the need for Open-Source Alternatives. Then look at a few concerns faced by Open-Source Software developers and maintainers. Next, we would understand the various requirements of Open-Source Software. Later, we would touch upon the various attributes that affect the selection of Open-Source Software and the decisions to be taken while building general-purpose Open-Source Software. Then we would analyze the 5-determinants of Open-Source Software success. Finally, we would look at the data collected from 482 datapoints from 24 countries and then analyze the data by forming graphs and charts.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115592015","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744231
Vimal W
Mel Frequency Cepstral Coefficient or simply MFCC is a feature extracting algorithm can be applied on the real time signals. The Algorithm involves various steps and each step can be optimized mathematically, one of the stages is to apply a window to the signal for the signal processing proposes. There is list of windows which are actually can optimize the algorithm to get optimized. This paper notices each one of the window applications of the algorithm and its behaviours and based on the response of the windows to the signal input, particular segment of the algorithm can be modified. The modification of the small segment can lead us to the overall improvement of the MFCC algorithm.
{"title":"Study on the Behaviour of Mel Frequency Cepstral Coffecient Algorithm for Different Windows","authors":"Vimal W","doi":"10.1109/ICITIIT54346.2022.9744231","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744231","url":null,"abstract":"Mel Frequency Cepstral Coefficient or simply MFCC is a feature extracting algorithm can be applied on the real time signals. The Algorithm involves various steps and each step can be optimized mathematically, one of the stages is to apply a window to the signal for the signal processing proposes. There is list of windows which are actually can optimize the algorithm to get optimized. This paper notices each one of the window applications of the algorithm and its behaviours and based on the response of the windows to the signal input, particular segment of the algorithm can be modified. The modification of the small segment can lead us to the overall improvement of the MFCC algorithm.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129923159","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 : 2022-02-12DOI: 10.1109/ICITIIT54346.2022.9744150
Md. Tarek Habib, D. Raza, Md. Mohaiminul Islam, Debasish Bhattacharjee Victor, Md. Ariful Islam Arif
AI branches many areas including computer vision and machine learning which are growing in a variety of application sectors. In this perspective, the agriculture sector is a promising application space for these two areas. Many efforts have been undertaken to address various agricultural challenges using computer vision and machine learning. Some prominent problem domains are fruit, vegetable, and crop disease diagnosis, recognition of distinct fruits, vegetables, and crops, and quality grading of fruits, vegetables, and crops which we attempt to delineate from state of-the-art perspective.
{"title":"Applications of Computer Vision and Machine Learning in Agriculture: A State-of-the-Art Glimpse","authors":"Md. Tarek Habib, D. Raza, Md. Mohaiminul Islam, Debasish Bhattacharjee Victor, Md. Ariful Islam Arif","doi":"10.1109/ICITIIT54346.2022.9744150","DOIUrl":"https://doi.org/10.1109/ICITIIT54346.2022.9744150","url":null,"abstract":"AI branches many areas including computer vision and machine learning which are growing in a variety of application sectors. In this perspective, the agriculture sector is a promising application space for these two areas. Many efforts have been undertaken to address various agricultural challenges using computer vision and machine learning. Some prominent problem domains are fruit, vegetable, and crop disease diagnosis, recognition of distinct fruits, vegetables, and crops, and quality grading of fruits, vegetables, and crops which we attempt to delineate from state of-the-art perspective.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125393096","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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