Pub Date : 2023-03-31DOI: 10.1109/CSCITA55725.2023.10104931
Pranav H. Panicker, Kashish Shah, S. Karamchandani
One in ten women of childbearing age experiences the health issue known as polycystic ovarian syndrome (PCOS). Hormonal dysregulation and metabolic issues in PCOS women might impact their general health and attractiveness. Infertility can also be caused by PCOS, which happens when the egg discharged each month as part of a normal menstrual cycle does not mature normally or may not be released during ovulation as it should be if PCOS is present. Hence detection of PCOS in its early stages is essential in many cases to help in ensuring swift treatment procedures. This detection may be tedious, especially if done by doctors and medical professionals using traditional ultrasound image analysis. Hence, automated ultrasound image detection techniques developed using deep learning methods like CNN architecture-based models are quite helpful. Studies in this area have yielded great detection results in recent years. This paper proposes a self-built CNN-based methodology for accurately detecting PCOS by classifying ultrasound ovary images into the PCO and non-PCO categories. The filters of the CNN are associated with the segmentation of the follicles while the fully connected layer of the CNN is responsible for the classification. A brief literature survey encapsulating previous works is also discussed. The findings substantiate our claim that segmentation of follicle blobs aids in isolating non-PCOS images. The CNN then proceeds to function as a confirmation test to classify the PCOS follicles with an accuracy of over 83%. The methodology and results are presented further in this study, and the discussion also involves the future scope & developments that this methodology can be improved.
{"title":"CNN Based Image Descriptor for Polycystic Ovarian Morphology from Transvaginal Ultrasound","authors":"Pranav H. Panicker, Kashish Shah, S. Karamchandani","doi":"10.1109/CSCITA55725.2023.10104931","DOIUrl":"https://doi.org/10.1109/CSCITA55725.2023.10104931","url":null,"abstract":"One in ten women of childbearing age experiences the health issue known as polycystic ovarian syndrome (PCOS). Hormonal dysregulation and metabolic issues in PCOS women might impact their general health and attractiveness. Infertility can also be caused by PCOS, which happens when the egg discharged each month as part of a normal menstrual cycle does not mature normally or may not be released during ovulation as it should be if PCOS is present. Hence detection of PCOS in its early stages is essential in many cases to help in ensuring swift treatment procedures. This detection may be tedious, especially if done by doctors and medical professionals using traditional ultrasound image analysis. Hence, automated ultrasound image detection techniques developed using deep learning methods like CNN architecture-based models are quite helpful. Studies in this area have yielded great detection results in recent years. This paper proposes a self-built CNN-based methodology for accurately detecting PCOS by classifying ultrasound ovary images into the PCO and non-PCO categories. The filters of the CNN are associated with the segmentation of the follicles while the fully connected layer of the CNN is responsible for the classification. A brief literature survey encapsulating previous works is also discussed. The findings substantiate our claim that segmentation of follicle blobs aids in isolating non-PCOS images. The CNN then proceeds to function as a confirmation test to classify the PCOS follicles with an accuracy of over 83%. The methodology and results are presented further in this study, and the discussion also involves the future scope & developments that this methodology can be improved.","PeriodicalId":224479,"journal":{"name":"2023 International Conference on Communication System, Computing and IT Applications (CSCITA)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115574462","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 : 2023-03-31DOI: 10.1109/CSCITA55725.2023.10104897
Thomas Schwarz, J. R. Santiago
As the industry slowly transitions to data centers made up of electronic instead of magnetic storage components, the rate of device failure and page corruption will decrease, but not vanish. In this emerging environment, redundant storage is still required to safeguard data. We argue that the use of two failure resilient linear codes with an exclusive-or (xor) based P-parity and a Q-parity calculated using a finite field operations is appropriate. We use the algebraic property of finite fields to show how to coagulate a number of small constituent’’ reliability stripes into larger coagulated stripes without recalculating parities. This allows to protect largely inactive data with more storage efficient larger reliability stripes. The procedure is reversible.
{"title":"Reliability Stripe Coagulation in Two Failure Tolerant Storage Arrays","authors":"Thomas Schwarz, J. R. Santiago","doi":"10.1109/CSCITA55725.2023.10104897","DOIUrl":"https://doi.org/10.1109/CSCITA55725.2023.10104897","url":null,"abstract":"As the industry slowly transitions to data centers made up of electronic instead of magnetic storage components, the rate of device failure and page corruption will decrease, but not vanish. In this emerging environment, redundant storage is still required to safeguard data. We argue that the use of two failure resilient linear codes with an exclusive-or (xor) based P-parity and a Q-parity calculated using a finite field operations is appropriate. We use the algebraic property of finite fields to show how to coagulate a number of small constituent’’ reliability stripes into larger coagulated stripes without recalculating parities. This allows to protect largely inactive data with more storage efficient larger reliability stripes. The procedure is reversible.","PeriodicalId":224479,"journal":{"name":"2023 International Conference on Communication System, Computing and IT Applications (CSCITA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115414853","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 : 2023-03-31DOI: 10.1109/CSCITA55725.2023.10104858
Pallavi L. Patil, K. Noronha
An efficient segmentation module plays an important role in the complete OCR system as errors in the segmentation module hampers the recognition rate of OCR systems. Compared to basic characters, segmentation of modified and conjunct characters is a difficult task because of the presence of modifiers and half characters. In this paper, a novel technique based on projection profile, which also utilizes different inherent features possessed by these characters for finding an accurate segmentation path is proposed. The proposed system accurately segments basic as well as modified and conjunct characters with segmentation accuracy ranging from 91.84% to 99.11%.
{"title":"Character Segmentation of Devnagari Script in Printed Document Images using Projection Profiles","authors":"Pallavi L. Patil, K. Noronha","doi":"10.1109/CSCITA55725.2023.10104858","DOIUrl":"https://doi.org/10.1109/CSCITA55725.2023.10104858","url":null,"abstract":"An efficient segmentation module plays an important role in the complete OCR system as errors in the segmentation module hampers the recognition rate of OCR systems. Compared to basic characters, segmentation of modified and conjunct characters is a difficult task because of the presence of modifiers and half characters. In this paper, a novel technique based on projection profile, which also utilizes different inherent features possessed by these characters for finding an accurate segmentation path is proposed. The proposed system accurately segments basic as well as modified and conjunct characters with segmentation accuracy ranging from 91.84% to 99.11%.","PeriodicalId":224479,"journal":{"name":"2023 International Conference on Communication System, Computing and IT Applications (CSCITA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130570931","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 : 2023-03-31DOI: 10.1109/CSCITA55725.2023.10105050
Madhura Ranade
The paper aims to investigate and perform mortality analysis of different neonatal data trends present in the ‘‘hospital’’ module of MIMIC-IV dataset. MIMIC-IV is an openly available medical dataset consisting of around 60000 neonatal patients. The hospital module stores digital health records of patients like laboratory tests performed, procedures or services provided by the hospital etc. Google Big query is used to access and filter the MIMIC-IV database. The data visualization is done by using Google Looker Studio. The results show that 98.6% of the admitted neonates were advised for blood tests. 40% of neonates could not survive as anticipated in laboratory tests by ‘‘abnormal’’ flag. The topmost tested lab item in case of neonates was pH’’. 47% of the neonates belonged to group‘‘neonates with birth weight greater than 2.49 kg having other problems’’ followed by group ‘‘premature newborns with significant problems The highest microbiological specimen tested for neonates was ‘‘Blood Culture’’ accounting for 45% of all specimens. It was seen from the analysis that ESCHERICHIA COLI’’ is the microorganism affecting neonatal mortality highest out of all. It was interesting to acknowledge from the analysis that 80% of the antibiotics given to non-surviving neonates fall into sensitive category. Hence, this analysis has highly contributed in finding the correlative features with respect to mortality from hospital module of MIMIC-IV neonatal dataset and will be useful for AI and medical researchers. It will also be supportive in the process of building a machine learning model for neonatal mortality prediction.
{"title":"Investigative Analysis of Hospital Module In MIMIC-IV Database for Neonatal Patients","authors":"Madhura Ranade","doi":"10.1109/CSCITA55725.2023.10105050","DOIUrl":"https://doi.org/10.1109/CSCITA55725.2023.10105050","url":null,"abstract":"The paper aims to investigate and perform mortality analysis of different neonatal data trends present in the ‘‘hospital’’ module of MIMIC-IV dataset. MIMIC-IV is an openly available medical dataset consisting of around 60000 neonatal patients. The hospital module stores digital health records of patients like laboratory tests performed, procedures or services provided by the hospital etc. Google Big query is used to access and filter the MIMIC-IV database. The data visualization is done by using Google Looker Studio. The results show that 98.6% of the admitted neonates were advised for blood tests. 40% of neonates could not survive as anticipated in laboratory tests by ‘‘abnormal’’ flag. The topmost tested lab item in case of neonates was pH’’. 47% of the neonates belonged to group‘‘neonates with birth weight greater than 2.49 kg having other problems’’ followed by group ‘‘premature newborns with significant problems The highest microbiological specimen tested for neonates was ‘‘Blood Culture’’ accounting for 45% of all specimens. It was seen from the analysis that ESCHERICHIA COLI’’ is the microorganism affecting neonatal mortality highest out of all. It was interesting to acknowledge from the analysis that 80% of the antibiotics given to non-surviving neonates fall into sensitive category. Hence, this analysis has highly contributed in finding the correlative features with respect to mortality from hospital module of MIMIC-IV neonatal dataset and will be useful for AI and medical researchers. It will also be supportive in the process of building a machine learning model for neonatal mortality prediction.","PeriodicalId":224479,"journal":{"name":"2023 International Conference on Communication System, Computing and IT Applications (CSCITA)","volume":"414 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120897205","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}
Online catalogs on e-commerce websites are sometimes too overwhelming where customers have a choice of as much variety and richness to find what they need in one place. In e-commerce websites, recommendation systems are crucial since they enhance the user experience by assisting visitors in finding what they want by recommending products. These suggestions can be based on user traits, demographics, past purchases, or search history. In this paper, we focus on identifying a complementary relationship between products, we have made a content-based recommendation system for discovering complementary products using Siamese Neural Networks (SNN). Algorithms like this have a lot of potential to increase the average purchase amount on an e-commerce website by recommending comparable products. After implementing the network we propose an extension of the network of the SNN approach to handling more products and will improve the time for recommending products by the KNN algorithm.
{"title":"Complementary Product Recommendation using Siamese Neural Network","authors":"Roshan Rai, Monika Patel, Poonam Varma, Danish Parvaiz, Santosh V. Chapaneri, Deepak Jayaswal","doi":"10.1109/CSCITA55725.2023.10104621","DOIUrl":"https://doi.org/10.1109/CSCITA55725.2023.10104621","url":null,"abstract":"Online catalogs on e-commerce websites are sometimes too overwhelming where customers have a choice of as much variety and richness to find what they need in one place. In e-commerce websites, recommendation systems are crucial since they enhance the user experience by assisting visitors in finding what they want by recommending products. These suggestions can be based on user traits, demographics, past purchases, or search history. In this paper, we focus on identifying a complementary relationship between products, we have made a content-based recommendation system for discovering complementary products using Siamese Neural Networks (SNN). Algorithms like this have a lot of potential to increase the average purchase amount on an e-commerce website by recommending comparable products. After implementing the network we propose an extension of the network of the SNN approach to handling more products and will improve the time for recommending products by the KNN algorithm.","PeriodicalId":224479,"journal":{"name":"2023 International Conference on Communication System, Computing and IT Applications (CSCITA)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127695622","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 : 2023-03-31DOI: 10.1109/CSCITA55725.2023.10104717
A. Deshmukh, Siddharth Nagaokar, Shreyas Tawde, V. P. Chavali
Design of a circularly polarized corner truncated square microstrip antenna for GPS L5 band application is proposed. The corner truncation in the square patch, degenerate patch fundamental mode into dual diagonally directed orthogonal modes with optimum inter spacing between them yields circular-polarized characteristics. Initially the designs were presented on thinner FR4 and Arlon substrates. In these designs, antenna yields impedance bandwidth of 4--5% with an axial ratio bandwidth of 0. 7 --1%. Based on these optimum configurations, formulation of resonant length and design methodology is presented, using which a similar design is presented to cover global positioning system L5 frequency band. In this band, proposed design on substrate of thickness 0.023$lambda_{mathrm{A}mathrm{R}}$, results in impedance, and axial ratio bandwidth of 79 MHz (6.67%) and 12 MHz (1.02%), respectively, offering broadside radiation pattern and peak gain of 6.1 dBi. Lastly the comparison is presented for the similar design with slotted ground plane, exactly below the patch corners. Configuration with modified ground plane offers 17 MHz (1.44%) of axial ratio and 74 MHz (6.26%) of impedance bandwidth, with a reduced broadside peak gain of 5.7 dBi. With these antenna characteristics, both the proposed low profile designs satisfy the requirement of global positioning system L5 band application. To authenticate the simulated results, measurements have been carried out, which show, similar values.
{"title":"Design Of Corner Truncated Square Microstrip Antenna For Circular Polarized Response in Global Positioning System L5 Band Application","authors":"A. Deshmukh, Siddharth Nagaokar, Shreyas Tawde, V. P. Chavali","doi":"10.1109/CSCITA55725.2023.10104717","DOIUrl":"https://doi.org/10.1109/CSCITA55725.2023.10104717","url":null,"abstract":"Design of a circularly polarized corner truncated square microstrip antenna for GPS L5 band application is proposed. The corner truncation in the square patch, degenerate patch fundamental mode into dual diagonally directed orthogonal modes with optimum inter spacing between them yields circular-polarized characteristics. Initially the designs were presented on thinner FR4 and Arlon substrates. In these designs, antenna yields impedance bandwidth of 4--5% with an axial ratio bandwidth of 0. 7 --1%. Based on these optimum configurations, formulation of resonant length and design methodology is presented, using which a similar design is presented to cover global positioning system L5 frequency band. In this band, proposed design on substrate of thickness 0.023$lambda_{mathrm{A}mathrm{R}}$, results in impedance, and axial ratio bandwidth of 79 MHz (6.67%) and 12 MHz (1.02%), respectively, offering broadside radiation pattern and peak gain of 6.1 dBi. Lastly the comparison is presented for the similar design with slotted ground plane, exactly below the patch corners. Configuration with modified ground plane offers 17 MHz (1.44%) of axial ratio and 74 MHz (6.26%) of impedance bandwidth, with a reduced broadside peak gain of 5.7 dBi. With these antenna characteristics, both the proposed low profile designs satisfy the requirement of global positioning system L5 band application. To authenticate the simulated results, measurements have been carried out, which show, similar values.","PeriodicalId":224479,"journal":{"name":"2023 International Conference on Communication System, Computing and IT Applications (CSCITA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126680926","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 : 2023-03-31DOI: 10.1109/CSCITA55725.2023.10104969
Ansh Sharma, Rashmi Gupta
The YOLOv2 is one of the most prominent model used for object detection, it works on the concept of anchor boxes. However, this model is prone to some problems like double anchor boxes, missing small objects, and high time complexity. In this paper, we aim to solve the problem of double anchor boxes and undetected small objects by tuning the parameters like intersection over union (IoU) and customizing non-max suppression thresholds. Also, to reduce the time complexity of the model, we have proposed the use of depth wise convolution (DW-Conv2D) instead of fundamental convolution (Conv2D) in this paper. Once we applied the proposed model to datasets like PASCAL VOC07 and VOC12, we observed significant improvements like reduced floating-point operations per second by 9.5% and better accuracy than the existing state-of-the-art models.
{"title":"Efficient Detection of Small and Complex Objects for Autonomous Driving Using Deep Learning","authors":"Ansh Sharma, Rashmi Gupta","doi":"10.1109/CSCITA55725.2023.10104969","DOIUrl":"https://doi.org/10.1109/CSCITA55725.2023.10104969","url":null,"abstract":"The YOLOv2 is one of the most prominent model used for object detection, it works on the concept of anchor boxes. However, this model is prone to some problems like double anchor boxes, missing small objects, and high time complexity. In this paper, we aim to solve the problem of double anchor boxes and undetected small objects by tuning the parameters like intersection over union (IoU) and customizing non-max suppression thresholds. Also, to reduce the time complexity of the model, we have proposed the use of depth wise convolution (DW-Conv2D) instead of fundamental convolution (Conv2D) in this paper. Once we applied the proposed model to datasets like PASCAL VOC07 and VOC12, we observed significant improvements like reduced floating-point operations per second by 9.5% and better accuracy than the existing state-of-the-art models.","PeriodicalId":224479,"journal":{"name":"2023 International Conference on Communication System, Computing and IT Applications (CSCITA)","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116514007","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 COVID-19 pandemic highlighted a major flaw in the current medical oxygen supply chain and inventory management system. This shortcoming caused the deaths of several patients which could have been avoided by accurate prediction of the oxygen demand and the distribution of oxygen cylinders. To avoid such calamities in the future, this paper proposes an Internet of Everything (IoE) based solution which forecasts the demand for oxygen with 80-85 % accuracy. The predicted variable of expected patients enables the system to calculate the requirement of oxygen up to the next 30 days from the initiation of data collection. The system is scalable and if implemented on a city or district level, will help in the fair distribution of medical oxygen resources and will save human lives during extreme load on the supply chain.
{"title":"IoE-Based Predictive Oxygen Inventory Management","authors":"Arpan Dhamelia, Gideon Harpanhalli, Arya Doshi, Ashna Kabsuri, Minal Lopes, Garima Singh","doi":"10.1109/CSCITA55725.2023.10104693","DOIUrl":"https://doi.org/10.1109/CSCITA55725.2023.10104693","url":null,"abstract":"The COVID-19 pandemic highlighted a major flaw in the current medical oxygen supply chain and inventory management system. This shortcoming caused the deaths of several patients which could have been avoided by accurate prediction of the oxygen demand and the distribution of oxygen cylinders. To avoid such calamities in the future, this paper proposes an Internet of Everything (IoE) based solution which forecasts the demand for oxygen with 80-85 % accuracy. The predicted variable of expected patients enables the system to calculate the requirement of oxygen up to the next 30 days from the initiation of data collection. The system is scalable and if implemented on a city or district level, will help in the fair distribution of medical oxygen resources and will save human lives during extreme load on the supply chain.","PeriodicalId":224479,"journal":{"name":"2023 International Conference on Communication System, Computing and IT Applications (CSCITA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133418714","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 : 2023-03-31DOI: 10.1109/CSCITA55725.2023.10104960
Palak Jain, Taneesha Chaudhary, S. Gajjar
Waste management has now become a necessary practice in all countries around the world. India generates approximately 65 million tons of garbage every year. The main challenge is to manage this waste. Conventional systems are not efficient to manage this much amount of waste. IoT can play a dominant role in handling waste and making any country greener and more efficient. Time to Time disposal of waste is important and if ignored can be a health hazard. The current system for waste disposal in local areas for small and densely populated cities is inactive which causes garbage to spread all over the area. The rate of garbage generation is higher than garbage disposal. It is required to manage and dispose of the waste for a sustainable and clean country. This paper presents an IoT-enabled Waste management system for the collection of garbage. The system consists of smart garbage bins made using Ultrasonic sensors, NodeMCU, and a Global Positioning System (GPS) module. The ultrasonic sensor detects the level of garbage in the dustbin and notifies the garbage collection authorities when the dustbin is full. The SMS and email notification are sent using If This Then That (IFTTT) and ThingSpeak the online digital automation platforms. A mobile application is created using the Massachusetts Institute of Technology (MIT) app inventor where the authorities can see the status of the dustbin and an optimized shortest route to be followed by the garbage collector truck for garbage collection and disposal. The proposed system is economical, practical, easy to use, and requires minimal human interaction.
废物管理现在已成为世界各国的必要做法。印度每年产生大约6500万吨垃圾。主要的挑战是如何管理这些废物。传统的系统无法有效地处理这么多的废物。物联网可以在处理废物和使任何国家更环保、更高效方面发挥主导作用。定期处理废物很重要，如果忽视，可能会危害健康。对于人口密集的小城市，目前当地的垃圾处理系统不活跃，导致垃圾到处都是。垃圾产生率高于垃圾处理率。管理和处理废物是可持续发展和清洁国家的要求。本文介绍了一种用于收集垃圾的物联网废物管理系统。该系统由使用超声波传感器、NodeMCU和全球定位系统(GPS)模块制成的智能垃圾箱组成。超声波传感器检测到垃圾桶中的垃圾水平，当垃圾桶满时通知垃圾收集部门。短信和电子邮件通知是通过If This Then That (IFTTT)和ThingSpeak在线数字自动化平台发送的。麻省理工学院(MIT)的应用程序发明人创建了一个移动应用程序，当局可以看到垃圾箱的状态，以及垃圾收集车收集和处理垃圾的优化最短路线。所提出的系统经济、实用、易于使用，并且需要最少的人工交互。
{"title":"Design and Development of Smart Waste Management System","authors":"Palak Jain, Taneesha Chaudhary, S. Gajjar","doi":"10.1109/CSCITA55725.2023.10104960","DOIUrl":"https://doi.org/10.1109/CSCITA55725.2023.10104960","url":null,"abstract":"Waste management has now become a necessary practice in all countries around the world. India generates approximately 65 million tons of garbage every year. The main challenge is to manage this waste. Conventional systems are not efficient to manage this much amount of waste. IoT can play a dominant role in handling waste and making any country greener and more efficient. Time to Time disposal of waste is important and if ignored can be a health hazard. The current system for waste disposal in local areas for small and densely populated cities is inactive which causes garbage to spread all over the area. The rate of garbage generation is higher than garbage disposal. It is required to manage and dispose of the waste for a sustainable and clean country. This paper presents an IoT-enabled Waste management system for the collection of garbage. The system consists of smart garbage bins made using Ultrasonic sensors, NodeMCU, and a Global Positioning System (GPS) module. The ultrasonic sensor detects the level of garbage in the dustbin and notifies the garbage collection authorities when the dustbin is full. The SMS and email notification are sent using If This Then That (IFTTT) and ThingSpeak the online digital automation platforms. A mobile application is created using the Massachusetts Institute of Technology (MIT) app inventor where the authorities can see the status of the dustbin and an optimized shortest route to be followed by the garbage collector truck for garbage collection and disposal. The proposed system is economical, practical, easy to use, and requires minimal human interaction.","PeriodicalId":224479,"journal":{"name":"2023 International Conference on Communication System, Computing and IT Applications (CSCITA)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127564123","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 : 2023-03-31DOI: 10.1109/CSCITA55725.2023.10104979
Mrityunjay Joshi, Amol Deshpande, D. Ambawade
Portfolio optimization is selecting the best set of possible weights for a group of assets where the objective is to maximize the returns and risk-return ratio and minimize the risks and volatility. This research aims to develop and test ARIMA and LSTM as forecasting techniques and subsequently perform portfolio optimization using a custom optimization methodology leveraging the forecasted returns from the models mentioned earlier. The intention is to develop a portfolio that dynamically allocates weights to the assets for the optimum investment strategy. The portfolio considers an initial investment of 100 units of currency, allowing uncomplicated interpretation of results and data.
{"title":"Situational Portfolio Forecasting and Allocation with Deep-Learning Approach","authors":"Mrityunjay Joshi, Amol Deshpande, D. Ambawade","doi":"10.1109/CSCITA55725.2023.10104979","DOIUrl":"https://doi.org/10.1109/CSCITA55725.2023.10104979","url":null,"abstract":"Portfolio optimization is selecting the best set of possible weights for a group of assets where the objective is to maximize the returns and risk-return ratio and minimize the risks and volatility. This research aims to develop and test ARIMA and LSTM as forecasting techniques and subsequently perform portfolio optimization using a custom optimization methodology leveraging the forecasted returns from the models mentioned earlier. The intention is to develop a portfolio that dynamically allocates weights to the assets for the optimum investment strategy. The portfolio considers an initial investment of 100 units of currency, allowing uncomplicated interpretation of results and data.","PeriodicalId":224479,"journal":{"name":"2023 International Conference on Communication System, Computing and IT Applications (CSCITA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117287634","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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