Pub Date : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716703
M. K. Khaishagi, Praful Kumar, D. Naik
RAFT is a deep network architecture for the detection of optical flow in the images. The RAFT model relates the per pixel motion between images even for minor changes in the position of the objects. It also updates the flow of field through recurrent units that perform lookups on the performance of the model. RAFT also works well with different datatypes and also it has better efficiency, training speed and count of parameters. Experiments were performed by using different parameters and also by changing certain values in the model itself. One cycle learning was also used to find the best parameters for the model. We also found that the RAFT model performs better than most of the other existing models for optical flow calculation in to images.
{"title":"Dense Optical Flow using RAFT","authors":"M. K. Khaishagi, Praful Kumar, D. Naik","doi":"10.1109/ICAECC54045.2022.9716703","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716703","url":null,"abstract":"RAFT is a deep network architecture for the detection of optical flow in the images. The RAFT model relates the per pixel motion between images even for minor changes in the position of the objects. It also updates the flow of field through recurrent units that perform lookups on the performance of the model. RAFT also works well with different datatypes and also it has better efficiency, training speed and count of parameters. Experiments were performed by using different parameters and also by changing certain values in the model itself. One cycle learning was also used to find the best parameters for the model. We also found that the RAFT model performs better than most of the other existing models for optical flow calculation in to images.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128701388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716594
Ashwini Desai, Manasi Mathkar, Manav Nisar, G. Thampi
With the increasing number of customers willing to trade their data in exchange for lower premiums, harnessing customer and insurance data to generate a customized insurance product is a powerful concept. Personalization engines have very often been powered by machine learning algorithms and big data analytics. Customer data once collected, can be analyzed for insights and then utilized in decision making with the help of off-the-shelf or novel learning algorithms. One common barrier in this process is the problem of imbalanced insurance data which affects the accuracy of these algorithms. We have carried out a thorough analysis of the applications of various learning algorithms such as Random Forest, Bayesian Network, Neural Network, Apriori algorithm and so on. Through our paper, we aim to present a clear picture of the advancements in systems that deliver customized insurance products to customers while discussing how all of those approaches can be used to solve problems pervasive in the industry.
{"title":"Customizing Insurance Product Based On Customer Data Leveraging Learning Algorithms","authors":"Ashwini Desai, Manasi Mathkar, Manav Nisar, G. Thampi","doi":"10.1109/ICAECC54045.2022.9716594","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716594","url":null,"abstract":"With the increasing number of customers willing to trade their data in exchange for lower premiums, harnessing customer and insurance data to generate a customized insurance product is a powerful concept. Personalization engines have very often been powered by machine learning algorithms and big data analytics. Customer data once collected, can be analyzed for insights and then utilized in decision making with the help of off-the-shelf or novel learning algorithms. One common barrier in this process is the problem of imbalanced insurance data which affects the accuracy of these algorithms. We have carried out a thorough analysis of the applications of various learning algorithms such as Random Forest, Bayesian Network, Neural Network, Apriori algorithm and so on. Through our paper, we aim to present a clear picture of the advancements in systems that deliver customized insurance products to customers while discussing how all of those approaches can be used to solve problems pervasive in the industry.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129633927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716622
A. Patil, Harivind Premkumar, Kiran M H M, Pranav Hegde
With the current advances in networking and the usage of computer networks in different sectors of technology, network security plays a prime role in enabling the proper functioning of networks by detecting and preventing attacks. In this paper, we propose an architecture using the Snort IDS/IPS and machine learning to build an Intelligent Network Intrusion Detection and Prevention System with dynamic rule updation creating robust and secure system with reduced resource consumption which can be used in Domestic Networks. The objective of JARVIS, the proposed system, is to detect malicious patterns in real-time traffic data and take action by dynamically updating Snort rules. By deploying a machine learning model (Random Forest) in parallel and dynamically enabling rules, resource consumption of Snort can be reduced and optimized. The model detects any attacks and suggests rules that can be deployed on Snort to prevent the attack. The false-positive rate of the model was reduced by looking at DNS queries to analyze the intent behind the traffic data. JARVIS also provides a web interface where the User can view Network Traffic Data, Detected Attacks as well as take the necessary actions. The machine learning model successfully detected incoming attacks with considerable accuracy and suggested rules in the web interface which allowed the user to deploy them and prevent the attack from causing further damage.
{"title":"JARVIS: An Intelligent Network Intrusion Detection and Prevention System","authors":"A. Patil, Harivind Premkumar, Kiran M H M, Pranav Hegde","doi":"10.1109/ICAECC54045.2022.9716622","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716622","url":null,"abstract":"With the current advances in networking and the usage of computer networks in different sectors of technology, network security plays a prime role in enabling the proper functioning of networks by detecting and preventing attacks. In this paper, we propose an architecture using the Snort IDS/IPS and machine learning to build an Intelligent Network Intrusion Detection and Prevention System with dynamic rule updation creating robust and secure system with reduced resource consumption which can be used in Domestic Networks. The objective of JARVIS, the proposed system, is to detect malicious patterns in real-time traffic data and take action by dynamically updating Snort rules. By deploying a machine learning model (Random Forest) in parallel and dynamically enabling rules, resource consumption of Snort can be reduced and optimized. The model detects any attacks and suggests rules that can be deployed on Snort to prevent the attack. The false-positive rate of the model was reduced by looking at DNS queries to analyze the intent behind the traffic data. JARVIS also provides a web interface where the User can view Network Traffic Data, Detected Attacks as well as take the necessary actions. The machine learning model successfully detected incoming attacks with considerable accuracy and suggested rules in the web interface which allowed the user to deploy them and prevent the attack from causing further damage.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125428323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716664
B. Bairwa, K. Pareek, Mrinal Sarvagya, U. Yaragatti
In this paper, we evaluated the leakage current of super capacitor during self-discharge. A three branch electrical equivalent circuit model (ECM) is constructed to estimate voltage response and leakage current of commercially available 2.7 V 350F (BCAP350) double-layer capacitor (DLC). Experimental work carried out with two constant current charging segments 0.25 ampere, and 0.5 amperes. Simulated data shows good agreement with experimental results obtained at electro-chemical workstation CH760e with RMSE and MAE error up to 0.0633, 0.05715, and 0.0759, 0.04173 for 0.25 ampere and 0.5 amperes charging current, respectively. The results confirm that the ECM model is capable to simulate the complex terminal behavior of the super capacitor and provides the means to study its application as an energy storage device.
本文对超级电容器自放电时的漏电流进行了计算。建立了三支路等效电路模型(ECM)，对市售2.7 V 350F (BCAP350)双层电容器(DLC)的电压响应和漏电流进行了估计。实验工作采用0.25安培和0.5安培两个恒流充电段进行。仿真数据与电化学工作站CH760e的实验结果吻合较好，在0.25安培和0.5安培充电电流下，RMSE和MAE误差分别为0.0633、0.05715和0.0759、0.04173。结果证实了ECM模型能够模拟超级电容器的复杂终端行为，为研究其作为储能器件的应用提供了手段。
{"title":"Analysis of Leakage Current Mechanism in Supercapacitor with Experimental Approach","authors":"B. Bairwa, K. Pareek, Mrinal Sarvagya, U. Yaragatti","doi":"10.1109/ICAECC54045.2022.9716664","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716664","url":null,"abstract":"In this paper, we evaluated the leakage current of super capacitor during self-discharge. A three branch electrical equivalent circuit model (ECM) is constructed to estimate voltage response and leakage current of commercially available 2.7 V 350F (BCAP350) double-layer capacitor (DLC). Experimental work carried out with two constant current charging segments 0.25 ampere, and 0.5 amperes. Simulated data shows good agreement with experimental results obtained at electro-chemical workstation CH760e with RMSE and MAE error up to 0.0633, 0.05715, and 0.0759, 0.04173 for 0.25 ampere and 0.5 amperes charging current, respectively. The results confirm that the ECM model is capable to simulate the complex terminal behavior of the super capacitor and provides the means to study its application as an energy storage device.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125015042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716611
G. R. K. Dora, R. Biradar, M. Prakruthi
New age wireless applications demand sharp rejection of unwanted signals at MHz frequencies. This necessitates the design of low loss, high attenuation bandpass filters. This paper proposes a design of compact 0.5 dB Chebyshev interdigital bandpass filter (IDBPF) which operates at a frequency of F0=875MHz used for space applications. The IDBPF is designed for an order n=9. Advanced Design System (ADS) software is used to design the filter. The designed filter is fabricated on a RO4350B substrate which has a relative dielectric constant $varepsilon_{r}$ of 3.66. Filter has pass bandwidth of $F0 pm 125MHz$, and insertion loss of $5 pm 3dB$ at 875MHz with steep rejection of 40dB min at 1050MHz and 25dB min at 650MHz. In this design, rejection is considered at prime importance than the insertion loss.
{"title":"Design and development of Interdigital Band pass filter for L-Band Wireless Communication Applications","authors":"G. R. K. Dora, R. Biradar, M. Prakruthi","doi":"10.1109/ICAECC54045.2022.9716611","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716611","url":null,"abstract":"New age wireless applications demand sharp rejection of unwanted signals at MHz frequencies. This necessitates the design of low loss, high attenuation bandpass filters. This paper proposes a design of compact 0.5 dB Chebyshev interdigital bandpass filter (IDBPF) which operates at a frequency of F0=875MHz used for space applications. The IDBPF is designed for an order n=9. Advanced Design System (ADS) software is used to design the filter. The designed filter is fabricated on a RO4350B substrate which has a relative dielectric constant $varepsilon_{r}$ of 3.66. Filter has pass bandwidth of $F0 pm 125MHz$, and insertion loss of $5 pm 3dB$ at 875MHz with steep rejection of 40dB min at 1050MHz and 25dB min at 650MHz. In this design, rejection is considered at prime importance than the insertion loss.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123476170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716645
K. M. Saifuddin, G. D. Devanagavi
Sensors have a strong connection to the real world, which sets them apart from conventional networks in a major way. Through the use of a variety of sensing devices as well as the processing of raw data, sensor networks may detect & monitor physical phenomena occurring in the locations/regions where even the sensors are located or deployed. Physical phenomena linked with geographic locations/regions are much more important to users of wireless sensor networks over raw data from individual sensor nodes. Geospatial information is becoming more important in sensor networks and applications, and sensor nodes having GPS signal receivers become more widely available. Sensor nodes may also be located without GPS using a variety of localization methods. A location-aware sensor network is now possible thanks to these recent technological advancements. To address the issue of estimating the location and position among wireless sensor nodes, new approaches, techniques, and algorithms must be created. In order to achieve this goal, In this Paper, we are proposing a Distance Vector Hop (DV-Hop) algorithm that Significantly decreases the average of localization error of sensor nodes and which is a solution for localizing nodes having few adjacent anchors. simulation results prove that the proposed algorithm substantially reduces the average of localization error of sensor nodes
{"title":"DV-Hop Propagation Based Localization in Wireless Sensor Networks","authors":"K. M. Saifuddin, G. D. Devanagavi","doi":"10.1109/ICAECC54045.2022.9716645","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716645","url":null,"abstract":"Sensors have a strong connection to the real world, which sets them apart from conventional networks in a major way. Through the use of a variety of sensing devices as well as the processing of raw data, sensor networks may detect & monitor physical phenomena occurring in the locations/regions where even the sensors are located or deployed. Physical phenomena linked with geographic locations/regions are much more important to users of wireless sensor networks over raw data from individual sensor nodes. Geospatial information is becoming more important in sensor networks and applications, and sensor nodes having GPS signal receivers become more widely available. Sensor nodes may also be located without GPS using a variety of localization methods. A location-aware sensor network is now possible thanks to these recent technological advancements. To address the issue of estimating the location and position among wireless sensor nodes, new approaches, techniques, and algorithms must be created. In order to achieve this goal, In this Paper, we are proposing a Distance Vector Hop (DV-Hop) algorithm that Significantly decreases the average of localization error of sensor nodes and which is a solution for localizing nodes having few adjacent anchors. simulation results prove that the proposed algorithm substantially reduces the average of localization error of sensor nodes","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115036695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716623
Venu Yarlagadda, R. Kapoor, K. Veeresham, O. Sobhana
The exponential growth of everlasting power demand, the management of power grid is easy with the increased usage of EHV and UHV Transmission systems. The performance of Power Transmission and distribution can be improved with the usage of FACTS Devices. The GTO Thyristor Turn off Series Capacitor (GCSC) is a powerful device among all FACTS Devices to control power flow and stability improvement. This article realized the fact of effectiveness of the GCSC in improving the power flow and stability of EHV, UHV and distribution systems. The EHV, UHV Transmission systems as well as distribution systems have been simulated with operating voltages of 1500KV, 1200KV, 800KV, 400KV, 220KV, 132KV and 11KV. All these systems have been simulated without and with GCSC and results have been presented in this article. The simulation results prove the power transfer and stability have been improved substantially with the open loop as well as closed loop controls of GCSC.
{"title":"Power Flow Control and Stability Improvement of EHV and UHV Transmission and Distribution Systems with Closed Loop Control of GCSC","authors":"Venu Yarlagadda, R. Kapoor, K. Veeresham, O. Sobhana","doi":"10.1109/ICAECC54045.2022.9716623","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716623","url":null,"abstract":"The exponential growth of everlasting power demand, the management of power grid is easy with the increased usage of EHV and UHV Transmission systems. The performance of Power Transmission and distribution can be improved with the usage of FACTS Devices. The GTO Thyristor Turn off Series Capacitor (GCSC) is a powerful device among all FACTS Devices to control power flow and stability improvement. This article realized the fact of effectiveness of the GCSC in improving the power flow and stability of EHV, UHV and distribution systems. The EHV, UHV Transmission systems as well as distribution systems have been simulated with operating voltages of 1500KV, 1200KV, 800KV, 400KV, 220KV, 132KV and 11KV. All these systems have been simulated without and with GCSC and results have been presented in this article. The simulation results prove the power transfer and stability have been improved substantially with the open loop as well as closed loop controls of GCSC.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132419630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716630
Thati Sethu Sumanth, G.VenkataMallikarjjuna Reddy, A. Reddy
This paper presents the design of 1.8V/3.3V 100 Mbps General Purpose Input/Output (GPIO) transmitter for an Intel Max 10 FPGA. This transmitter works for both 1.8V and 3.3V IO supplies. The building blocks of this transmitter are level shifter and driver circuits. The level shifter is designed to level up the data levels from 0.8V to 1.8V/3.3V. A progressive sized driver circuit is designed to drive 50$Omega$ termination resistance and the load capacitance of 5pF as per the requirement of Intel Max10. The overall design is carried out with 22nm technology node on cadence virtuoso platform and is simulated across PVT. The simulation results shows that the proposed design supports up to a data rate of 100Mbps with a power consumption of 1.59mW at 1.8V supply and with a power consumption of 2.93mW at 3.3V supply.
本文介绍了基于Intel Max 10 FPGA的1.8V/3.3V 100 Mbps通用输入/输出(GPIO)变送器的设计。此发射器适用于1.8V和3.3V IO电源。该发射机的组成部分是电平移位器和驱动电路。电平移位器设计用于将数据电平从0.8V调至1.8V/3.3V。根据Intel Max10的要求，设计了一种递进式驱动电路，以驱动50$Omega$的终端电阻和5pF的负载电容。总体设计采用22nm技术节点在cadence virtuoso平台上进行，并在pvm上进行了仿真。仿真结果表明，所提出的设计在1.8V供电时支持高达100Mbps的数据速率，功耗为1.59mW，在3.3V供电时功耗为2.93mW。
{"title":"Design of a 1.8V/3.3V 100Mbps GPIO Transmitter for Intel Max 10 FPGA","authors":"Thati Sethu Sumanth, G.VenkataMallikarjjuna Reddy, A. Reddy","doi":"10.1109/ICAECC54045.2022.9716630","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716630","url":null,"abstract":"This paper presents the design of 1.8V/3.3V 100 Mbps General Purpose Input/Output (GPIO) transmitter for an Intel Max 10 FPGA. This transmitter works for both 1.8V and 3.3V IO supplies. The building blocks of this transmitter are level shifter and driver circuits. The level shifter is designed to level up the data levels from 0.8V to 1.8V/3.3V. A progressive sized driver circuit is designed to drive 50$Omega$ termination resistance and the load capacitance of 5pF as per the requirement of Intel Max10. The overall design is carried out with 22nm technology node on cadence virtuoso platform and is simulated across PVT. The simulation results shows that the proposed design supports up to a data rate of 100Mbps with a power consumption of 1.59mW at 1.8V supply and with a power consumption of 2.93mW at 3.3V supply.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"10 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125730533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In today’s day and time, video streaming has become an integral part of one’s life and it won’t be long enough before videos become the most consumed entity in the world. Due to such consumption standards, it is also important to determine if the video being streamed is appropriate for the audience. The project assesses the sentiments of the video and provides users with categorical sentiment scores to effectively judge the appropriateness of the video. Contemporarily, people often find the need to get relevant information being targeted rather than wasting time on unnecessary video sections. Perennially, YouTube chrome extensions primarily focus on YouTube channels and provide insights to the channel creator. In contrast to this, the proposed system intends to target the video genre, feasibility, relevance and at the same time generates entities to help navigate the user to relevant video sections. People fall back to YouTube video reviews to find feasibility of investing in a particular product they are interested in. With such a high user base for YouTube streaming, it becomes a hotspot for business opportunities to grow. Keeping this in mind the project aims to provide business insights in form of reddit discussion forums scanning the genre and central idea of the video being streamed, thus providing them with better public reviews and insights. To tackle this, it is viable to create a Chrome Extension for the user to leverage an unobstructive YouTube streaming experience.
{"title":"YouTube Assistant for Quick Navigation and Content Moderation","authors":"Jash Shah, Mihir Shah, Heth Gala, Jenish Hirpara, Prof. Aruna Gawade","doi":"10.1109/ICAECC54045.2022.9716634","DOIUrl":"https://doi.org/10.1109/ICAECC54045.2022.9716634","url":null,"abstract":"In today’s day and time, video streaming has become an integral part of one’s life and it won’t be long enough before videos become the most consumed entity in the world. Due to such consumption standards, it is also important to determine if the video being streamed is appropriate for the audience. The project assesses the sentiments of the video and provides users with categorical sentiment scores to effectively judge the appropriateness of the video. Contemporarily, people often find the need to get relevant information being targeted rather than wasting time on unnecessary video sections. Perennially, YouTube chrome extensions primarily focus on YouTube channels and provide insights to the channel creator. In contrast to this, the proposed system intends to target the video genre, feasibility, relevance and at the same time generates entities to help navigate the user to relevant video sections. People fall back to YouTube video reviews to find feasibility of investing in a particular product they are interested in. With such a high user base for YouTube streaming, it becomes a hotspot for business opportunities to grow. Keeping this in mind the project aims to provide business insights in form of reddit discussion forums scanning the genre and central idea of the video being streamed, thus providing them with better public reviews and insights. To tackle this, it is viable to create a Chrome Extension for the user to leverage an unobstructive YouTube streaming experience.","PeriodicalId":199351,"journal":{"name":"2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124762675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-10DOI: 10.1109/ICAECC54045.2022.9716654
G. Selvamathiseelan, N. V. S. Pradyumna, N. N. V. Sai Prakash, V. Sudharsan Reddy, Sudha Yadav
The presented work proposes an interaction between human brain and laptop to control the devices used in home without having any touch of muscular body part. Brain Computer Interface (BCI) is one of the communication channels to make such a direct neural interface. The electronic sensor built into BCI headset captures and filters the raw brainwave signal which could be as Attention level (similar to concentration) and Meditation level (similar to relaxation), aided in control of home appliances (like bulb, fan), which is categorized under non-invasive method of brain signal measurement. This is being measured by NeuroSky Mindwave Headset. The Headset consists of EEG sensor (eSense meters) to capture the brain signal, which will be processed by using ThinkGear module in MATLAB. The devices have even access to Eye Blink, are akin to a standard on/off binary system and therefore are valuable for controls that require definitive responses. As a prototype, we have used one bulb and one fan.
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