Pub Date : 2020-07-03DOI: 10.1109/ICSCAN49426.2020.9262355
R. Thendral, S. Revathi
The data can be missed in the image which are taken from the satellite due to covering of cloud in some places of image. This can reduce the usability of the image. Several methods can solve this accurately, but the method is not effective due to the requirement of multiple images to give the single clear image without cloud. Right now, propose a system called super pixel segmentation and neighbour embedding technique to remove the clouds placed in the images using the single image. This method works effectively using image processing and give the very accurate image. Experiment result can be obtained using satellite images.
{"title":"Deletion of Thick Clouds from Landsat images using Super Pixel Segmentation and Neighbour Embedding Techniques","authors":"R. Thendral, S. Revathi","doi":"10.1109/ICSCAN49426.2020.9262355","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262355","url":null,"abstract":"The data can be missed in the image which are taken from the satellite due to covering of cloud in some places of image. This can reduce the usability of the image. Several methods can solve this accurately, but the method is not effective due to the requirement of multiple images to give the single clear image without cloud. Right now, propose a system called super pixel segmentation and neighbour embedding technique to remove the clouds placed in the images using the single image. This method works effectively using image processing and give the very accurate image. Experiment result can be obtained using satellite images.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"51 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83672637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-03DOI: 10.1109/ICSCAN49426.2020.9262388
K. Jha, P. Das, H. Dutta
Background and Objective: Leukemia identification, detection, & classification has erupted an intriguing field in medical research. Several methodologies are convenient in theprevious work to detect five types WBCs (lymphocytes, eosinophils, monocytes, neutrophils, and basophils). Single cell Blood's smear images used for experiment. Propounded method is used for leukemia recognition, uncovering and distribution based on FAB classification. Methodology: This propounded task has developed French-American and British (FAB) classification-based detection module on blood smearimages (BSIs). Methods like pretreatment, segmentation, feature extraction, distribution are used in detection method. The Propounded algorithm-based propounded model is used for segmentation, which is combination of the segmented results of the Linde-Buzo-Gray (LBG) algorithm, Adaptive canny used for edge identification and Hysteresis and watershed algorithm used for thresholding. The shape, texture features, color of segmented image are picked by neural network and classification is performed by Support Vector Machine (SVM) and prediction by Naïve Bayes Classifier (NBC). Result: Dataset-master and Cellavison dataset is being used for the experimentation. The BSIs are considered for the Evaluation based on ROC curve analysis metrics like TPR, TNR and accuracy. Our propounded solution obtains superior classification performance in the given dataset. The suggested classifier enhanced the classification average accuracy to 99.06% and Mean Square Error (MSE) is 0.0407. Conclusion: The enhanced accuracy had achieved by enhancing performance and classification with comparison with some other methods.
{"title":"FAB Classification based Leukemia Identification and prediction using Machine Learning","authors":"K. Jha, P. Das, H. Dutta","doi":"10.1109/ICSCAN49426.2020.9262388","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262388","url":null,"abstract":"Background and Objective: Leukemia identification, detection, & classification has erupted an intriguing field in medical research. Several methodologies are convenient in theprevious work to detect five types WBCs (lymphocytes, eosinophils, monocytes, neutrophils, and basophils). Single cell Blood's smear images used for experiment. Propounded method is used for leukemia recognition, uncovering and distribution based on FAB classification. Methodology: This propounded task has developed French-American and British (FAB) classification-based detection module on blood smearimages (BSIs). Methods like pretreatment, segmentation, feature extraction, distribution are used in detection method. The Propounded algorithm-based propounded model is used for segmentation, which is combination of the segmented results of the Linde-Buzo-Gray (LBG) algorithm, Adaptive canny used for edge identification and Hysteresis and watershed algorithm used for thresholding. The shape, texture features, color of segmented image are picked by neural network and classification is performed by Support Vector Machine (SVM) and prediction by Naïve Bayes Classifier (NBC). Result: Dataset-master and Cellavison dataset is being used for the experimentation. The BSIs are considered for the Evaluation based on ROC curve analysis metrics like TPR, TNR and accuracy. Our propounded solution obtains superior classification performance in the given dataset. The suggested classifier enhanced the classification average accuracy to 99.06% and Mean Square Error (MSE) is 0.0407. Conclusion: The enhanced accuracy had achieved by enhancing performance and classification with comparison with some other methods.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"39 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89806965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-03DOI: 10.1109/ICSCAN49426.2020.9262359
K. Dhivya, G. Premalatha, M. Kayathri
An ophthalmic disease that affects the retinal blood vessels called diabetic retinopathy. The diabetic retinopathy results in vision loss. A diabetic retinopathy is not treated in primitive stages may lead to vision loss. The diabetic retinopathy has five different classes. They are normal, mild, moderate, secure, PDR. Generally, highly trained people process the colored fundus image to treat the fatal disease. The manual analysis, and detecting of diabetic retinopathy is complex and even error occurred in results. The manual detection takes long time to diagnose the DR. Using the different computer-based techniques have been used to detect the DR and it shows the retinal blood vessels but it does not differentiate the early stages and unable to process the tedious features. The results from computer vision based gives low accuracy. In this project, Artificial Neural Network (ANN) is used to classify various stages of Diabetic retinopathy. The results obtained from that shows better accuracy and performance.
{"title":"Automated Identification of Diabetic Retinopathy Using Artificial Neutral Network","authors":"K. Dhivya, G. Premalatha, M. Kayathri","doi":"10.1109/ICSCAN49426.2020.9262359","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262359","url":null,"abstract":"An ophthalmic disease that affects the retinal blood vessels called diabetic retinopathy. The diabetic retinopathy results in vision loss. A diabetic retinopathy is not treated in primitive stages may lead to vision loss. The diabetic retinopathy has five different classes. They are normal, mild, moderate, secure, PDR. Generally, highly trained people process the colored fundus image to treat the fatal disease. The manual analysis, and detecting of diabetic retinopathy is complex and even error occurred in results. The manual detection takes long time to diagnose the DR. Using the different computer-based techniques have been used to detect the DR and it shows the retinal blood vessels but it does not differentiate the early stages and unable to process the tedious features. The results from computer vision based gives low accuracy. In this project, Artificial Neural Network (ANN) is used to classify various stages of Diabetic retinopathy. The results obtained from that shows better accuracy and performance.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"27 5","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91471506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-03DOI: 10.1109/ICSCAN49426.2020.9262286
M. Madhumitha, P. Dhivya
Vehicle Recognition from obtaining images in a motion platform is still challenging. The system would focus and capture attributes of vehicles like color, number plate and speed of the vehicle. The images are being captured from various CCTV systems through distributed intelligence along with time and location stamps. The database used to identify suspects from video clips of crime related CCTV footages. This can be achieved by optical character recognition (OCR) and algorithm based on regression YOLO (You Only Look Once). To recognize an vehicle features, Conda tool is used with Tensor flow and Keras framework.
在运动平台上获取图像进行车辆识别仍然具有挑战性。该系统将聚焦并捕捉车辆的颜色、车牌和速度等属性。这些图像是通过分布式智能从不同的闭路电视系统捕获的，并附有时间和地点戳。该数据库用于从与犯罪有关的闭路电视录像片段中识别嫌疑人。这可以通过光学字符识别(OCR)和基于YOLO (You Only Look Once)回归的算法来实现。为了识别车辆特征，将Conda工具与Tensor flow和Keras框架结合使用。
{"title":"Vehicle Recognition and Compilation in Database Software","authors":"M. Madhumitha, P. Dhivya","doi":"10.1109/ICSCAN49426.2020.9262286","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262286","url":null,"abstract":"Vehicle Recognition from obtaining images in a motion platform is still challenging. The system would focus and capture attributes of vehicles like color, number plate and speed of the vehicle. The images are being captured from various CCTV systems through distributed intelligence along with time and location stamps. The database used to identify suspects from video clips of crime related CCTV footages. This can be achieved by optical character recognition (OCR) and algorithm based on regression YOLO (You Only Look Once). To recognize an vehicle features, Conda tool is used with Tensor flow and Keras framework.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"22 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84774872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-03DOI: 10.1109/ICSCAN49426.2020.9262294
Vishnu H Lal, G. Varaprasad
This paper discusses the various student satisfaction studies carried out in the context of higher education and its meta- analysis. To achieve this objective, 172 studies linked to student satisfaction were identified from Web of Science, and bibliometric analysis was done on the same. The papers were examined to determine the nature of the study and the dimensions studied by the authors. Various analyses like keyword, citations, author, etc. were carried out, and conclusions were drawn from the same. This study will aid future researchers in having an idea about the nature of studies carried out in the past and how-to extent it further.
本文讨论了在高等教育背景下开展的各种学生满意度研究及其元分析。为了实现这一目标，我们从Web of Science中找出了172项与学生满意度相关的研究，并对其进行了文献计量学分析。对论文进行了检查，以确定研究的性质和作者研究的维度。对关键词、引文、作者等进行了各种分析，并从中得出结论。这项研究将帮助未来的研究人员了解过去开展的研究的性质，以及如何进一步扩大研究范围。
{"title":"A meta-analytic review of student satisfaction studies in higher education","authors":"Vishnu H Lal, G. Varaprasad","doi":"10.1109/ICSCAN49426.2020.9262294","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262294","url":null,"abstract":"This paper discusses the various student satisfaction studies carried out in the context of higher education and its meta- analysis. To achieve this objective, 172 studies linked to student satisfaction were identified from Web of Science, and bibliometric analysis was done on the same. The papers were examined to determine the nature of the study and the dimensions studied by the authors. Various analyses like keyword, citations, author, etc. were carried out, and conclusions were drawn from the same. This study will aid future researchers in having an idea about the nature of studies carried out in the past and how-to extent it further.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"14 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73050617","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}
According to reports, an astounding 69% of adult drivers report driving while drowsy at least once a month in the previous year according to The National Sleep Foundation. In today's fast-moving world people are usually stressed and sleep-deprived due to their demanding career. As a result of this such people fall asleep behind the wheel. Visual fatigue and drowsiness cause many accidents due to which many deaths and injuries are taking place around the world. To increase vehicle security, we propose an advanced driver assistance system (ADAS). This system aims to locate and estimate the driver's eye condition and head position using a camera that will be an indication of his drowsiness level. We also propose a speed control system to detect signboards on the way and instruct the driver either to continue with the same speed or to decelerate the vehicle based on machine learning. This system also calculates the distance between two vehicles, based on the distance it instructs the driver either to continue with the same speed or to slow down. With the system on board of multiple vehicles the safety of the travel increases and the rate of accidents caused due to driver negligence will be reduced.
{"title":"Neural Network Based Driver Warning System","authors":"Ishan Jain, Snehangsu Biswas, Hrishita Singh, Prakriti Aggarwal","doi":"10.1109/ICSCAN49426.2020.9262325","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262325","url":null,"abstract":"According to reports, an astounding 69% of adult drivers report driving while drowsy at least once a month in the previous year according to The National Sleep Foundation. In today's fast-moving world people are usually stressed and sleep-deprived due to their demanding career. As a result of this such people fall asleep behind the wheel. Visual fatigue and drowsiness cause many accidents due to which many deaths and injuries are taking place around the world. To increase vehicle security, we propose an advanced driver assistance system (ADAS). This system aims to locate and estimate the driver's eye condition and head position using a camera that will be an indication of his drowsiness level. We also propose a speed control system to detect signboards on the way and instruct the driver either to continue with the same speed or to decelerate the vehicle based on machine learning. This system also calculates the distance between two vehicles, based on the distance it instructs the driver either to continue with the same speed or to slow down. With the system on board of multiple vehicles the safety of the travel increases and the rate of accidents caused due to driver negligence will be reduced.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"41 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82022050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-03DOI: 10.1109/ICSCAN49426.2020.9262289
Akshay Prasad, Akshay Kurup, J. K, G. Abhisek, A. Samanta, G. Varaprasad
The level of service quality offered to the patients is drastically declining over the years. The main purpose of this paper is to show a systematic analysis of the literature review based on lean six sigma in the healthcare process. This review aims at to improve service quality by identifying problems faced in the healthcare process and providing reliable solutions. A descriptive review focusing on lean six sigma in the healthcare process, followed by bibliometric analysis aligned with consistent literature review. The literature review related to healthcare process identifies the problems faced in hospitals. Reliable solutions for the problems are identified from literature and summarized. Primary problems are identified through literature review, while the results might not be accurate due to lack of diversity of papers reviewed. Hospital management can utilize the literature classification and the notable references provided in this review for in-process quality improvement. The procedure adopted in this paper is an integrated bibliometric and systematic literature review. The main contribution of this paper includes providing reliable solutions for problems faced in the healthcare sector as derived from the review.
{"title":"Lean Six Sigma solutions for quality improvement in healthcare sector: a systematic review","authors":"Akshay Prasad, Akshay Kurup, J. K, G. Abhisek, A. Samanta, G. Varaprasad","doi":"10.1109/ICSCAN49426.2020.9262289","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262289","url":null,"abstract":"The level of service quality offered to the patients is drastically declining over the years. The main purpose of this paper is to show a systematic analysis of the literature review based on lean six sigma in the healthcare process. This review aims at to improve service quality by identifying problems faced in the healthcare process and providing reliable solutions. A descriptive review focusing on lean six sigma in the healthcare process, followed by bibliometric analysis aligned with consistent literature review. The literature review related to healthcare process identifies the problems faced in hospitals. Reliable solutions for the problems are identified from literature and summarized. Primary problems are identified through literature review, while the results might not be accurate due to lack of diversity of papers reviewed. Hospital management can utilize the literature classification and the notable references provided in this review for in-process quality improvement. The procedure adopted in this paper is an integrated bibliometric and systematic literature review. The main contribution of this paper includes providing reliable solutions for problems faced in the healthcare sector as derived from the review.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"33 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78546936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-03DOI: 10.1109/ICSCAN49426.2020.9262420
Maheeja Maddegalla, A. B. Bazil Raj, Gurugubelli Syamala Rao
With the advent of Active Electronically Scanned Array (AESA) technology in the design and development of advanced multi-target handling Radar and Electronic Warfare (EW) systems, a new EW system with a Phased Array of a uniform spacing was developed, whose beam can be controlled using adaptive software programs. The critical EW system is recognized with miniaturized Planar Arrays using Transmit/Receive modules or T-R modules. The T-R modules use a novel core technology for the development of AESA technology. The planar arrays are miniaturized using multifunctional Monolithic Microwave Integrated Circuits (MMIC) with an inbuilt digital circuitry for beam steering, which requires high quality and different levels of programming using Field Programmable Gate Arrays (FPGA's). The AESA generally consists of thousands of T-R modules which can individually spread their signal emissions out across a band of the frequencies and sensitively receive the echoes from target objects, allowing it to broadcast transmitting signals while remaining stealthy and greatly increasing the detection and tracking abilities. Implementation of beam steering and control algorithms has to be designed in the frequency of 5–18 GHz for T-R module based planar arrays.
{"title":"Beam Steering and Control Algorithm for 5-18GHz Transmit/Receive Module Based Active Planar Array","authors":"Maheeja Maddegalla, A. B. Bazil Raj, Gurugubelli Syamala Rao","doi":"10.1109/ICSCAN49426.2020.9262420","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262420","url":null,"abstract":"With the advent of Active Electronically Scanned Array (AESA) technology in the design and development of advanced multi-target handling Radar and Electronic Warfare (EW) systems, a new EW system with a Phased Array of a uniform spacing was developed, whose beam can be controlled using adaptive software programs. The critical EW system is recognized with miniaturized Planar Arrays using Transmit/Receive modules or T-R modules. The T-R modules use a novel core technology for the development of AESA technology. The planar arrays are miniaturized using multifunctional Monolithic Microwave Integrated Circuits (MMIC) with an inbuilt digital circuitry for beam steering, which requires high quality and different levels of programming using Field Programmable Gate Arrays (FPGA's). The AESA generally consists of thousands of T-R modules which can individually spread their signal emissions out across a band of the frequencies and sensitively receive the echoes from target objects, allowing it to broadcast transmitting signals while remaining stealthy and greatly increasing the detection and tracking abilities. Implementation of beam steering and control algorithms has to be designed in the frequency of 5–18 GHz for T-R module based planar arrays.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"46 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78201202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-03DOI: 10.1109/ICSCAN49426.2020.9262364
R. Kalyani, P. Sathya, V. Sakthivel, J. Ravikumar
The most fundamental step in image processing is image segmentation and it results in revealing enormous information embedded in most widely used RGB color space image. Excellent result is obtained for bi-level thresholding and the exhaustive search for optimal threshold values, to analyze complex images in multilevel thresholding (MLT), is reduced by promising objective functions such as Otsu and minimum cross entropy MCE aided with teaching-learning based optimization metaheuristic algorithm (TLBO). In TLBO, a teacher shares cognizance to a student. The use of only common control parameters and less-specific control parameters in TLBO achieves exploration and exploitation. The efficiency of TLBO is compared with cuckoo search algorithm (CS) at 4,5,6 and 7 threshold levels. Experimental results reveal that optimal output of TLBO is more successful in precise image segmentation and aids in various real time applications.
{"title":"Teaching Tactics for Color Image Segmentation Using Otsu and Minimum Cross Entropy","authors":"R. Kalyani, P. Sathya, V. Sakthivel, J. Ravikumar","doi":"10.1109/ICSCAN49426.2020.9262364","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262364","url":null,"abstract":"The most fundamental step in image processing is image segmentation and it results in revealing enormous information embedded in most widely used RGB color space image. Excellent result is obtained for bi-level thresholding and the exhaustive search for optimal threshold values, to analyze complex images in multilevel thresholding (MLT), is reduced by promising objective functions such as Otsu and minimum cross entropy MCE aided with teaching-learning based optimization metaheuristic algorithm (TLBO). In TLBO, a teacher shares cognizance to a student. The use of only common control parameters and less-specific control parameters in TLBO achieves exploration and exploitation. The efficiency of TLBO is compared with cuckoo search algorithm (CS) at 4,5,6 and 7 threshold levels. Experimental results reveal that optimal output of TLBO is more successful in precise image segmentation and aids in various real time applications.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"27 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79426778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-03DOI: 10.1109/ICSCAN49426.2020.9262350
T. K. Das, A. Tripathy, Kathiravan Srinivasan
Shopping is really fascinating and alluring; at the same time, it involves getting tired due to standing in a long queue for the bill and payment process. Hence, it is proposed to design a smart trolley which can take care of shopping and billing. By this, the customer can walk straightaway into the shop, purchase products using the smart trolley and walk out of the shop. He gets the e-bill through the mail, and he can view his purchase details using the shop's website. In order to realize this, we need an Arduino board, Radio-Frequency Identification (RFID) reader, RFID tag, LCD display, ESP8266 Wi-Fi module, database manager and a website to maintain product and customer details, which can be accessed by the admin anywhere in the world. This is an IOT based system where the trolley can interact with the network spread worldwide.
{"title":"A Smart Trolley for Smart Shopping","authors":"T. K. Das, A. Tripathy, Kathiravan Srinivasan","doi":"10.1109/ICSCAN49426.2020.9262350","DOIUrl":"https://doi.org/10.1109/ICSCAN49426.2020.9262350","url":null,"abstract":"Shopping is really fascinating and alluring; at the same time, it involves getting tired due to standing in a long queue for the bill and payment process. Hence, it is proposed to design a smart trolley which can take care of shopping and billing. By this, the customer can walk straightaway into the shop, purchase products using the smart trolley and walk out of the shop. He gets the e-bill through the mail, and he can view his purchase details using the shop's website. In order to realize this, we need an Arduino board, Radio-Frequency Identification (RFID) reader, RFID tag, LCD display, ESP8266 Wi-Fi module, database manager and a website to maintain product and customer details, which can be accessed by the admin anywhere in the world. This is an IOT based system where the trolley can interact with the network spread worldwide.","PeriodicalId":6744,"journal":{"name":"2020 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73402120","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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