Pub Date : 2023-07-06DOI: 10.1109/ICESC57686.2023.10193094
Kapit, Neelima Singh, H. Shankar, Yogesh
The error rate of M-ary signaling schemes for reconfigurable intelligent surface (RIS) are analyzed in this paper. The potential for novel use cases and the demanding specifications of the upcoming RIS assisted $6^{th}$ generation (6G) wireless communication makes the cellular communication technology more advanced. In this paper, the RIS is working as a reflector that lies between transmitter (Tx) and receiver (Rx) with single antenna system. In this context, the error rate expressions for different signaling schemes are derived. The expressions are given in closed form which i s based on moment generating function (MGF) of Chi square distribution. The link between Tx and RIS reflector and, between RIS reflector and Receiver is assumed to be Rayleigh distribution. The results are presented for different modulation level and number of reflecting meta surface. The results are compared with existing upper bound error rate. The large number of reflecting meta surface show better error performance.
{"title":"Error Rate Analysis of M-ary Signaling for Reconfigurable Intelligent Surface","authors":"Kapit, Neelima Singh, H. Shankar, Yogesh","doi":"10.1109/ICESC57686.2023.10193094","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193094","url":null,"abstract":"The error rate of M-ary signaling schemes for reconfigurable intelligent surface (RIS) are analyzed in this paper. The potential for novel use cases and the demanding specifications of the upcoming RIS assisted $6^{th}$ generation (6G) wireless communication makes the cellular communication technology more advanced. In this paper, the RIS is working as a reflector that lies between transmitter (Tx) and receiver (Rx) with single antenna system. In this context, the error rate expressions for different signaling schemes are derived. The expressions are given in closed form which i s based on moment generating function (MGF) of Chi square distribution. The link between Tx and RIS reflector and, between RIS reflector and Receiver is assumed to be Rayleigh distribution. The results are presented for different modulation level and number of reflecting meta surface. The results are compared with existing upper bound error rate. The large number of reflecting meta surface show better error performance.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128170130","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 : 2023-07-06DOI: 10.1109/ICESC57686.2023.10193204
V. Nandhakumar, B. Jyothsna, S.GNANAPRIYA GP
With the increasing demand for fresh and healthy food options, more and more customers are turning to purchase fruits and vegetables in supermarkets. However, the current system for purchase can be time-consuming and cumbersome, involving long queues and delays, which can be frustrating for customers. In addition to this stocking fruits, and vegetables can be a challenging task as they are prone to spoilage, requiring constant manual monitoring to keep track of the remaining quantity. This paper proposes an innovative approach using deep learning to develop a self-checkout system and also streamline the stocking process by implementing an automated system that tracks the purchases and inventory in real-time. This not only improves the overall shopping experience but also benefits the supermarkets in several ways. This helps enhance the supermarkets’ efficiency, optimizes inventory management, minimizes waste, provides data-driven insights, and improves customer satisfaction. The proposed method involves training a deep learning model to recognize and classify fruits and vegetables and automate the billing process. The produce to be purchased is automatically scanned, weighed and billed thus significantly saving not only time but also manpower involved in the traditional manual process. By utilizing the current stock details as input, the system employs deep learning algorithms to provide real-time notifications, ensuring timely restocking and minimizing stock shortages in an automated and efficient manner. The quality and variety of the dataset used to train the deep learning model is a crucial step in ensuring its accuracy, precision, and recall. The model’s performance is evaluated on set metrics to determine its effectiveness and work on its improvement. Overall, the proposed use of deep learning to improve the purchase of fruits and vegetables in supermarkets has the potential to revolutionize the way customers shop for fresh produce. This innovative approach has the potential to transform the shopping experience by reducing checkout times and meeting customer needs, giving supermarkets a competitive edge. The potential limitations of the proposed method, such as the potential for errors in recognition and classification are to be factored in for the further development of this idea.
{"title":"AI-Driven Produce Management and Self-Checkout System for Supermarkets","authors":"V. Nandhakumar, B. Jyothsna, S.GNANAPRIYA GP","doi":"10.1109/ICESC57686.2023.10193204","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193204","url":null,"abstract":"With the increasing demand for fresh and healthy food options, more and more customers are turning to purchase fruits and vegetables in supermarkets. However, the current system for purchase can be time-consuming and cumbersome, involving long queues and delays, which can be frustrating for customers. In addition to this stocking fruits, and vegetables can be a challenging task as they are prone to spoilage, requiring constant manual monitoring to keep track of the remaining quantity. This paper proposes an innovative approach using deep learning to develop a self-checkout system and also streamline the stocking process by implementing an automated system that tracks the purchases and inventory in real-time. This not only improves the overall shopping experience but also benefits the supermarkets in several ways. This helps enhance the supermarkets’ efficiency, optimizes inventory management, minimizes waste, provides data-driven insights, and improves customer satisfaction. The proposed method involves training a deep learning model to recognize and classify fruits and vegetables and automate the billing process. The produce to be purchased is automatically scanned, weighed and billed thus significantly saving not only time but also manpower involved in the traditional manual process. By utilizing the current stock details as input, the system employs deep learning algorithms to provide real-time notifications, ensuring timely restocking and minimizing stock shortages in an automated and efficient manner. The quality and variety of the dataset used to train the deep learning model is a crucial step in ensuring its accuracy, precision, and recall. The model’s performance is evaluated on set metrics to determine its effectiveness and work on its improvement. Overall, the proposed use of deep learning to improve the purchase of fruits and vegetables in supermarkets has the potential to revolutionize the way customers shop for fresh produce. This innovative approach has the potential to transform the shopping experience by reducing checkout times and meeting customer needs, giving supermarkets a competitive edge. The potential limitations of the proposed method, such as the potential for errors in recognition and classification are to be factored in for the further development of this idea.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127051226","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 : 2023-07-06DOI: 10.1109/ICESC57686.2023.10193233
Kamini Lamba, Shalli Rani
Reproduction of quick and indefinite cells within brain cause a tissue which is generally known as a brain tumor. A number of individuals remain untreated as it does not show any hard symptoms at an initial stage. For identification of such disease, many neurologists suggest Computer Tomography Scan, Magnetic Resonance Imaging etc. which can be time consuming process and expensive too. To avoid so, various computer assisted methods have been suggested by researchers to overcome the drawbacks of traditional approaches. Deep learning has been considered as one of the reliable approaches for identification and classification of brain tumor disease that can prevent an individual from death due to its strong features capability for providing quick and better results at an early stage as compared to the traditional approaches. This research study has considered 3264 images from kaggle having 2764 tumor images and 500 with healthy ones and proposed a model that comprises of Visual Geometry Group (VGG) having 16 layers in collaboration with the concept of transfer learning to perform the diagnosis and classification of brain tumor disease. The proposed model has delivered an accuracy of 98.16%, precision of 99.09%, recall of 98.73% and F1-score of 98.91% which is far better when compared to the existing approaches.
{"title":"Deep Learning based Analysis for Automated Detection and Classification of Brain Tumor","authors":"Kamini Lamba, Shalli Rani","doi":"10.1109/ICESC57686.2023.10193233","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193233","url":null,"abstract":"Reproduction of quick and indefinite cells within brain cause a tissue which is generally known as a brain tumor. A number of individuals remain untreated as it does not show any hard symptoms at an initial stage. For identification of such disease, many neurologists suggest Computer Tomography Scan, Magnetic Resonance Imaging etc. which can be time consuming process and expensive too. To avoid so, various computer assisted methods have been suggested by researchers to overcome the drawbacks of traditional approaches. Deep learning has been considered as one of the reliable approaches for identification and classification of brain tumor disease that can prevent an individual from death due to its strong features capability for providing quick and better results at an early stage as compared to the traditional approaches. This research study has considered 3264 images from kaggle having 2764 tumor images and 500 with healthy ones and proposed a model that comprises of Visual Geometry Group (VGG) having 16 layers in collaboration with the concept of transfer learning to perform the diagnosis and classification of brain tumor disease. The proposed model has delivered an accuracy of 98.16%, precision of 99.09%, recall of 98.73% and F1-score of 98.91% which is far better when compared to the existing approaches.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129169271","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 : 2023-07-06DOI: 10.1109/ICESC57686.2023.10192956
L. Sowmiya, R. A. Sekar, Gnanasaravanan Subramaniam, A. Purushothaman, M. Tiwari, Tripti Tiwari
Due to the recent emerging trends in technology, Vehicle to Grid (V2G) networks plays a major role in smart grid system. Now a days using of electric vehicles has increased than the usage of fuel-based automobiles under transportation. Electric vehicles mainly use electricity instead of fuels that is distributed by the charging terminal. Generally, the charging terminal will be situated in gasoline stations or on road sides and electric vehicles get charged at the stations. In V2G communication networks, the communication takes place between the charging terminal and the electric vehicle will be affected by several cyber-attacks. In addition to that, the electric vehicle owner’s personal information should be kept secure. In this proposed system, charging terminal and electric vehicles initially register their identities at the control authority. Using of Anonymous mutual authentication scheme, the charging terminal and the electric vehicle authenticates with each other and establish protected communication between them. During Session Key Exchange protocol, the information passed from the charging terminal will be transferred to the electric vehicle with the help of session keys. Duplicate identity of the charging terminal and electric vehicle is created in order to protect the original identity of the users who are present in V2G networks. Proposed scheme gives strength against various security attacks and cost of the system is also reduced. The main aim of this proposed system is to maintain confidentiality and integrity among the V2G communication networks.
{"title":"A Secure Authenticated Message Transfer in Vehicle to Grid Networks","authors":"L. Sowmiya, R. A. Sekar, Gnanasaravanan Subramaniam, A. Purushothaman, M. Tiwari, Tripti Tiwari","doi":"10.1109/ICESC57686.2023.10192956","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10192956","url":null,"abstract":"Due to the recent emerging trends in technology, Vehicle to Grid (V2G) networks plays a major role in smart grid system. Now a days using of electric vehicles has increased than the usage of fuel-based automobiles under transportation. Electric vehicles mainly use electricity instead of fuels that is distributed by the charging terminal. Generally, the charging terminal will be situated in gasoline stations or on road sides and electric vehicles get charged at the stations. In V2G communication networks, the communication takes place between the charging terminal and the electric vehicle will be affected by several cyber-attacks. In addition to that, the electric vehicle owner’s personal information should be kept secure. In this proposed system, charging terminal and electric vehicles initially register their identities at the control authority. Using of Anonymous mutual authentication scheme, the charging terminal and the electric vehicle authenticates with each other and establish protected communication between them. During Session Key Exchange protocol, the information passed from the charging terminal will be transferred to the electric vehicle with the help of session keys. Duplicate identity of the charging terminal and electric vehicle is created in order to protect the original identity of the users who are present in V2G networks. Proposed scheme gives strength against various security attacks and cost of the system is also reduced. The main aim of this proposed system is to maintain confidentiality and integrity among the V2G communication networks.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132849005","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}
Music streaming services have become an essential part of our daily life. These platforms' recommendation systems are essential because they let consumers receive tailored music recommendations. Similar songs can be found using content-based recommendation systems that make use of audio attributes and lyrics. Major music streaming services, however, mostly rely on audio characteristics. This study proposes a novel approach for constructing a Siamese network-based content-based music recommendation system that integrates audio features and lyrics. Using a dataset accessible on Kaggle, audio attributes are extracted from the Spotify API and lyrics from the Genius API. In terms of accuracy and user happiness, the suggested solution exceeds already-existing content-based recommendation systems. Unlike collaborative filtering techniques, which tends to propose more mainstream and popular music, this strategy can support up-and-coming and lesser-known musicians by recognizing their distinctive work. Our findings have implications for the creation of more precise and reliable music recommendation systems that consider users' distinct preferences and musical inclinations.
{"title":"Recommender System using Audio and Lyrics","authors":"Shaik Faizan, Roshan Ali, Daggumati Siva, S. Kiran, Tuluva Prem Sai, Durga Thanuj","doi":"10.1109/ICESC57686.2023.10193474","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193474","url":null,"abstract":"Music streaming services have become an essential part of our daily life. These platforms' recommendation systems are essential because they let consumers receive tailored music recommendations. Similar songs can be found using content-based recommendation systems that make use of audio attributes and lyrics. Major music streaming services, however, mostly rely on audio characteristics. This study proposes a novel approach for constructing a Siamese network-based content-based music recommendation system that integrates audio features and lyrics. Using a dataset accessible on Kaggle, audio attributes are extracted from the Spotify API and lyrics from the Genius API. In terms of accuracy and user happiness, the suggested solution exceeds already-existing content-based recommendation systems. Unlike collaborative filtering techniques, which tends to propose more mainstream and popular music, this strategy can support up-and-coming and lesser-known musicians by recognizing their distinctive work. Our findings have implications for the creation of more precise and reliable music recommendation systems that consider users' distinct preferences and musical inclinations.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130959731","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 : 2023-07-06DOI: 10.1109/ICESC57686.2023.10193160
P. Aravindan, V. Ravindranath, T. Midhun
Electric vehicles significantly contribute to the creation of a pollution-free environment because of their extremely low to zero carbon emissions, minimum noise, and high efflciency. They are also a viable technology for creating a sustainable transportation sector in the future. Electric vehicles (EVs) are aggressively being promoted worldwide as a means of reducing the effects of fossil fuel emissions and addressing environmental issues. With the help of incentives, many governments are luring people to switch to electric cars. Li-ion battery technologies have been researched and developed in recent years for use in electric vehicle applications due to their increased power density and lighter weight. Even the world’s best Electric car company tesla use this battery in their car for better performance, high speed and fast charging, it lasts only two to three years after manufacturer, sensitive to high temperatures. The “separator” has the potential to catch fire if it sustains damage. So along with all this advantage the lithium-ion battery contains some dangerous drawbacks. Due to its higher power density the temperature is getting higher, so a better replacement for the battery is required in evehicles. This study uses IoT for monitoring and ensuring the security of the vehicle. Vehicle can be monitored through mobile application or web page from anywhere in the world.
{"title":"IoT based Regenerative Battery Charging for E-Cycle","authors":"P. Aravindan, V. Ravindranath, T. Midhun","doi":"10.1109/ICESC57686.2023.10193160","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193160","url":null,"abstract":"Electric vehicles significantly contribute to the creation of a pollution-free environment because of their extremely low to zero carbon emissions, minimum noise, and high efflciency. They are also a viable technology for creating a sustainable transportation sector in the future. Electric vehicles (EVs) are aggressively being promoted worldwide as a means of reducing the effects of fossil fuel emissions and addressing environmental issues. With the help of incentives, many governments are luring people to switch to electric cars. Li-ion battery technologies have been researched and developed in recent years for use in electric vehicle applications due to their increased power density and lighter weight. Even the world’s best Electric car company tesla use this battery in their car for better performance, high speed and fast charging, it lasts only two to three years after manufacturer, sensitive to high temperatures. The “separator” has the potential to catch fire if it sustains damage. So along with all this advantage the lithium-ion battery contains some dangerous drawbacks. Due to its higher power density the temperature is getting higher, so a better replacement for the battery is required in evehicles. This study uses IoT for monitoring and ensuring the security of the vehicle. Vehicle can be monitored through mobile application or web page from anywhere in the world.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127873157","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 : 2023-07-06DOI: 10.1109/ICESC57686.2023.10193567
Dr. P.Nandhini, Dr .P.Chellammal, J.S.Jaslin, S. Harthy, R. Priya, Dr. M. Uma
Robotic systems that can be operated remotely are becoming more and more common in a variety of industries, including construction, disaster relief, space exploration, and industrial applications. Teleoperation, however, necessitates skilled operators who can handle sophisticated data processing and programming. This research introduces a unique teleoperation method that makes use of Virtual Reality (VR) and the Robot Operating System (ROS) to overcome this problem. By creating a 3D representation of the robot’s workspace in Unity and connecting it to the actual robot with ROS, this technique optimizes control. Programming is not necessary because the robot’s vision serves as the foundation for the user interface. Through ROS integration, a target point is established in Unity and sent to the robot as the end effector position. By simply setting up a new environment, reducing the requirement for knowledgeable operators, and improving situational awareness, this method may be used to any sort of robot. For even greater situational awareness, this technique may also be used with augmented reality (AR). AR can give the operator real-time information about the robot’s surroundings by superimposing digital information over the physical world. This can be achieved by including AR markers in the physical surroundings that the VR system can detect. In the operator’s field of view, the system can then superimpose information such as sensor data or robot status onto the real-world environment.
{"title":"Teleoperation in the Age of Mixed Reality: VR, AR, and ROS Integration for Human-Robot Direct Interaction","authors":"Dr. P.Nandhini, Dr .P.Chellammal, J.S.Jaslin, S. Harthy, R. Priya, Dr. M. Uma","doi":"10.1109/ICESC57686.2023.10193567","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193567","url":null,"abstract":"Robotic systems that can be operated remotely are becoming more and more common in a variety of industries, including construction, disaster relief, space exploration, and industrial applications. Teleoperation, however, necessitates skilled operators who can handle sophisticated data processing and programming. This research introduces a unique teleoperation method that makes use of Virtual Reality (VR) and the Robot Operating System (ROS) to overcome this problem. By creating a 3D representation of the robot’s workspace in Unity and connecting it to the actual robot with ROS, this technique optimizes control. Programming is not necessary because the robot’s vision serves as the foundation for the user interface. Through ROS integration, a target point is established in Unity and sent to the robot as the end effector position. By simply setting up a new environment, reducing the requirement for knowledgeable operators, and improving situational awareness, this method may be used to any sort of robot. For even greater situational awareness, this technique may also be used with augmented reality (AR). AR can give the operator real-time information about the robot’s surroundings by superimposing digital information over the physical world. This can be achieved by including AR markers in the physical surroundings that the VR system can detect. In the operator’s field of view, the system can then superimpose information such as sensor data or robot status onto the real-world environment.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127888466","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 : 2023-07-06DOI: 10.1109/ICESC57686.2023.10193244
S. Arora, Saurabh Pargaien, F. Khan, Isha Tewari, D. Nainwal, Akansha Mer, Amit Mittal, A. Misra
This study records the general perception of local residents on tourism industry at Nainital in Uttarakhand and highlights the major concerns of the local residents on the influx of tourists. The researchers conducted a perception analysis of the local residents and addressed issues like environmental threats, safety risks, traffic congestion, increased cost and inconvenience etc. and proposed a sensor model as a solution to the existing problem. It reviews the existing traditional method of data collection and management of the tourist inflow and suggests sensor technologies to maintain accuracy in footfall data collection for the regulation of the arrival and exit of tourists at Nainital. The current study highlights the need for footfall monitoring and management via footfall sensors. The researchers in the study recommend the selection and installation of sensor technological solution discussed in the study over the current system of footfall data collection done manually. The readily available footfall sensors will facilitate visitor analytics and enable an effective management of tourist inflow at Nainital. The data will help in framing policies by the local authorities to control the arrival of visitors at the destination. In future, it can also be implemented across other tourist places of Uttarakhand state.
{"title":"Monitoring Tourist Footfall at Nainital in Uttarakhand using Sensor Technology","authors":"S. Arora, Saurabh Pargaien, F. Khan, Isha Tewari, D. Nainwal, Akansha Mer, Amit Mittal, A. Misra","doi":"10.1109/ICESC57686.2023.10193244","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193244","url":null,"abstract":"This study records the general perception of local residents on tourism industry at Nainital in Uttarakhand and highlights the major concerns of the local residents on the influx of tourists. The researchers conducted a perception analysis of the local residents and addressed issues like environmental threats, safety risks, traffic congestion, increased cost and inconvenience etc. and proposed a sensor model as a solution to the existing problem. It reviews the existing traditional method of data collection and management of the tourist inflow and suggests sensor technologies to maintain accuracy in footfall data collection for the regulation of the arrival and exit of tourists at Nainital. The current study highlights the need for footfall monitoring and management via footfall sensors. The researchers in the study recommend the selection and installation of sensor technological solution discussed in the study over the current system of footfall data collection done manually. The readily available footfall sensors will facilitate visitor analytics and enable an effective management of tourist inflow at Nainital. The data will help in framing policies by the local authorities to control the arrival of visitors at the destination. In future, it can also be implemented across other tourist places of Uttarakhand state.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128998332","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 : 2023-07-06DOI: 10.1109/ICESC57686.2023.10193043
Thilagaraj, Dr.M. Sivaramkrishnan, Dr. G. Venkatesan, A. Mohanraj, Dr. M. Siva, Ramkumar, Kottaimalai Ramaraj
Now days, the population is very high. Due to this high population the needs for the people also increasing rapidly. This ultimately leads to produce huge wastes in the country. In lot of places the people dumb the waste inside the garbage already filled. So, because of this, wastes are spilled outside the garbage. This causes to many dangerous diseases like Dengue, Malaria, Chicken Pox, etc. Not only spread of diseases, also animals can eat the wastes spilled around the garbage and causes to death. To avoid these things, the garbage is monitored smartly and attentive system that keep on watching the garbage level inside the bin continuously using the ultrasonic sensor. Once the level reaches the particular level, then the Arduino uno receives the bins’ site placed through the help of GPS. The garbage bins are shown in the map by means of Internet of Things (IoT) device with how much percentage waste are filled in the dustbin. Through this the waste collector collects the waste easily.
{"title":"Smart Garbage Monitorıng and Vigilant System","authors":"Thilagaraj, Dr.M. Sivaramkrishnan, Dr. G. Venkatesan, A. Mohanraj, Dr. M. Siva, Ramkumar, Kottaimalai Ramaraj","doi":"10.1109/ICESC57686.2023.10193043","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193043","url":null,"abstract":"Now days, the population is very high. Due to this high population the needs for the people also increasing rapidly. This ultimately leads to produce huge wastes in the country. In lot of places the people dumb the waste inside the garbage already filled. So, because of this, wastes are spilled outside the garbage. This causes to many dangerous diseases like Dengue, Malaria, Chicken Pox, etc. Not only spread of diseases, also animals can eat the wastes spilled around the garbage and causes to death. To avoid these things, the garbage is monitored smartly and attentive system that keep on watching the garbage level inside the bin continuously using the ultrasonic sensor. Once the level reaches the particular level, then the Arduino uno receives the bins’ site placed through the help of GPS. The garbage bins are shown in the map by means of Internet of Things (IoT) device with how much percentage waste are filled in the dustbin. Through this the waste collector collects the waste easily.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126433991","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 : 2023-07-06DOI: 10.1109/ICESC57686.2023.10192988
M. Kathiravan, M. Ramya, S. Jayanthi, Vangala Vamseedhar Reddy, Lokesh Ponguru, N. Bharathiraja
Car price forecasting is a popular study topic because it requires a lot of work and knowledge. Used car pricing forecasting is a major auto industry concern. Machine learning can accurately predict used automobile prices based on many characteristics. Many distinct qualities are considered for accurate predictions. The suggested model uses a dataset that contains vehicle brand and model, year of production, mileage, condition, and other factors that affect used car prices. This study used linear regression, GBT regression, and random forest regression to estimate secondhand car prices. Then, algorithm performance was compared to find which method better fit the data set. Thus, these methods outperform others.
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