Pub Date : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053559
Venkata Ravi Teja Jaladanki, Rajeswara Rao Duvvada, Hari Venkata Samba Siva Rao Badugu
The extent of videos being produced per day across the world is enormous, and in the past few years it has increased to an unprecedented level. Information extraction from a video, however, is more difficult than information extraction from an image. A viewer must see the entire video in order to understand its context. Aside from context awareness, it is nearly impossible to make a universally applicable summary video because each person has a different preferred keyframe. A number of approaches came into existence for tackling this problem which include supervised and unsupervised learning techniques, and some associated with Deep Learning techniques. However, it would require a significant amount of individualized data labelling if we attempted to approach problem video summarizing via a supervised learning method. In this paper, we developed an algorithm based on Dynamic Clustering of projected frame histograms approach to address the challenge of video summarization using unsupervised learning. We have tested the performance of the approach on the VSUMM, a benchmark dataset and showcased that using dynamic clustering algorithm has been proven to perform competitively with some existing approaches.
{"title":"Dynamic Clustering Algorithm for Video Summarization on VSUMM Dataset","authors":"Venkata Ravi Teja Jaladanki, Rajeswara Rao Duvvada, Hari Venkata Samba Siva Rao Badugu","doi":"10.1109/IDCIoT56793.2023.10053559","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053559","url":null,"abstract":"The extent of videos being produced per day across the world is enormous, and in the past few years it has increased to an unprecedented level. Information extraction from a video, however, is more difficult than information extraction from an image. A viewer must see the entire video in order to understand its context. Aside from context awareness, it is nearly impossible to make a universally applicable summary video because each person has a different preferred keyframe. A number of approaches came into existence for tackling this problem which include supervised and unsupervised learning techniques, and some associated with Deep Learning techniques. However, it would require a significant amount of individualized data labelling if we attempted to approach problem video summarizing via a supervised learning method. In this paper, we developed an algorithm based on Dynamic Clustering of projected frame histograms approach to address the challenge of video summarization using unsupervised learning. We have tested the performance of the approach on the VSUMM, a benchmark dataset and showcased that using dynamic clustering algorithm has been proven to perform competitively with some existing approaches.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"60 1","pages":"831-837"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90631786","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053478
Bhanu Prasanna Koppolu
Skin Lesion also termed Skin Cancer has continuously recorded a high rate of mortality due to the ever-growing population, global warming, and various gases or pollution present in the atmosphere. Skin Lesions or Skin Cancer can be a horrifying way to die if not diagnosed early. Mainly Skin Lesion like Melanoma has been proven to be lethal. The mortality rate can be reduced if the skin disease is diagnosed at an early stage. The advancements in the Deep Learning community have been able to provide a way to diagnose skin diseases early. In this paper, the usage of pre-trained image classification model EfficientNetB0 is the proposed model which is used to classify 7 types of skin disease derived from the HAM10000 skin lesion dataset with Data Augmentation to increase the accuracy and help Dermatologists to classify and diagnose Skin Cancer early so it can be treated and can also be a way to cut down the cost of diagnosis. This project’s training accuracy and validation accuracy came out to be 97.61% and 93.50%. The weighted average and macro average precision, recall, and f1-score were 95%, 94%, and 95%. This paper proposes 90.5% accuracy to detect the most invasive skin cancer which is Melanoma and can help Dermatologists as a Decision Support System in the diagnosis process and create an application for ease of use.
{"title":"Skin Lesion Classification using Transfer Learning","authors":"Bhanu Prasanna Koppolu","doi":"10.1109/IDCIoT56793.2023.10053478","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053478","url":null,"abstract":"Skin Lesion also termed Skin Cancer has continuously recorded a high rate of mortality due to the ever-growing population, global warming, and various gases or pollution present in the atmosphere. Skin Lesions or Skin Cancer can be a horrifying way to die if not diagnosed early. Mainly Skin Lesion like Melanoma has been proven to be lethal. The mortality rate can be reduced if the skin disease is diagnosed at an early stage. The advancements in the Deep Learning community have been able to provide a way to diagnose skin diseases early. In this paper, the usage of pre-trained image classification model EfficientNetB0 is the proposed model which is used to classify 7 types of skin disease derived from the HAM10000 skin lesion dataset with Data Augmentation to increase the accuracy and help Dermatologists to classify and diagnose Skin Cancer early so it can be treated and can also be a way to cut down the cost of diagnosis. This project’s training accuracy and validation accuracy came out to be 97.61% and 93.50%. The weighted average and macro average precision, recall, and f1-score were 95%, 94%, and 95%. This paper proposes 90.5% accuracy to detect the most invasive skin cancer which is Melanoma and can help Dermatologists as a Decision Support System in the diagnosis process and create an application for ease of use.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"55 1","pages":"875-879"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90893006","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053476
Atul Lal Shrivastava, Rajendra Kumar Dwivedi
Given the current situation, A great number of reports about forged titles to real estate, fake land registries, undue transferring of delayed ownership, and government officials' involvement in deceptive practices are regularly being reported. On the other hand, this suggests that the current system for registering land deeds is inefficient and cannot reliably guarantee the safety of transactions between buyers and sellers or ensure that they are settled in a timely manner. To find a solution to this issue. In this paper, we suggested utilizing blockchain technology to create a land register system. The uniqueness and appeal of blockchain technology are its transparency and security. Persistence, immutability, and decentralization are qualities that blockchain is inculcating. its ascension to new opportunities for efficiency and cost savings. A decentralized application was suggested in this article. We used the Ethereum network specifically to build and deploy the smart contract. Through frontend web pages, interactions with the deployed contracts are possible. When creating websites, React is employed. Next.js is utilized for the server and routing. The analysis and findings demonstrate the viability and effectiveness of the suggested methodology.
{"title":"Blockchain-based Secure Land Registry System using Efficient Smart Contract","authors":"Atul Lal Shrivastava, Rajendra Kumar Dwivedi","doi":"10.1109/IDCIoT56793.2023.10053476","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053476","url":null,"abstract":"Given the current situation, A great number of reports about forged titles to real estate, fake land registries, undue transferring of delayed ownership, and government officials' involvement in deceptive practices are regularly being reported. On the other hand, this suggests that the current system for registering land deeds is inefficient and cannot reliably guarantee the safety of transactions between buyers and sellers or ensure that they are settled in a timely manner. To find a solution to this issue. In this paper, we suggested utilizing blockchain technology to create a land register system. The uniqueness and appeal of blockchain technology are its transparency and security. Persistence, immutability, and decentralization are qualities that blockchain is inculcating. its ascension to new opportunities for efficiency and cost savings. A decentralized application was suggested in this article. We used the Ethereum network specifically to build and deploy the smart contract. Through frontend web pages, interactions with the deployed contracts are possible. When creating websites, React is employed. Next.js is utilized for the server and routing. The analysis and findings demonstrate the viability and effectiveness of the suggested methodology.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"39 2 1","pages":"165-170"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89574531","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053396
R. Rathna
Due to India’s very high population and its direct proportional increase in the quantity of garbage and lack of new land for landfill, there is a very big need for bringing a smart system for garbage collection and management. Using IOT and a smart garbage processor at the ward level will increase the efficiency of the existing system. IoT usage in Waste Management is still in research level. Using Internet and spending a major portion of budget for this infrastructure change may seems to be a luxury for a developing country like India. But to avoid dangerous health hazards for the future generation, bringing this infrastructure change with additional budget amount for waste management is must and inevitable. Any infrastructure can be converted into IoT enabled by making small changes in the equipment used in the existing system. The existing bins kept in the major streets of the city can be easily converted into IoT by implanting sensors and internet connectivity unit. The proposal can be divided into two major levels. In the first level, the aim is to design a smart bin with three sections. One for bio degradable domestic waste, the second for inert debris (soiled diapers and sanitary napkins) and the third for non-bio degradable wastes. If the IoT is used for waste management, it will be easy to monitor all the smart bins and hence the number of trips of heavy vehicles and small vehicles can be reduced. By using the proposed model, the health problems faced by the corporation workers and scavengers can be reduced.
{"title":"Smart Waste Management Scheme using IoT for Metropolitan Cities","authors":"R. Rathna","doi":"10.1109/IDCIoT56793.2023.10053396","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053396","url":null,"abstract":"Due to India’s very high population and its direct proportional increase in the quantity of garbage and lack of new land for landfill, there is a very big need for bringing a smart system for garbage collection and management. Using IOT and a smart garbage processor at the ward level will increase the efficiency of the existing system. IoT usage in Waste Management is still in research level. Using Internet and spending a major portion of budget for this infrastructure change may seems to be a luxury for a developing country like India. But to avoid dangerous health hazards for the future generation, bringing this infrastructure change with additional budget amount for waste management is must and inevitable. Any infrastructure can be converted into IoT enabled by making small changes in the equipment used in the existing system. The existing bins kept in the major streets of the city can be easily converted into IoT by implanting sensors and internet connectivity unit. The proposal can be divided into two major levels. In the first level, the aim is to design a smart bin with three sections. One for bio degradable domestic waste, the second for inert debris (soiled diapers and sanitary napkins) and the third for non-bio degradable wastes. If the IoT is used for waste management, it will be easy to monitor all the smart bins and hence the number of trips of heavy vehicles and small vehicles can be reduced. By using the proposed model, the health problems faced by the corporation workers and scavengers can be reduced.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"1 1","pages":"7-10"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89580443","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053394
Megha Vamsi Kiran Choda, Sri Vardhan Perla, Brahmender Shaik, Yuva Teja Anirudh Yelchuru, P. Yalla
Real-Time Traffic Sign Recognition System (RTTSRS) is used for recognizing the traffic signboards (Take left, take right, speed limit 60 kmph… etc.), it plays a crucial role in the domains of driverless vehicles etc. By using Real-Time Traffic Sign Recognition, Traffic related problems can be reduced. It is categorized into two types- localization and recognition. Localization deals with identifying and locating traffic sign regions within the radius. Real-Time Traffic Sign Recognition is used to identify the traffic sign region within the space (rectangular) provided. This study describes an approach for a traffic sign recognition system, many machine learning algorithms like Support Vector Machine (SVM) and Convolutional Neural Networks (CNN) have been studied for recognizing traffic signs. This study has conducted a critical investigation on various machine learning algorithms which gives high accuracy to predict, recognize real-time traffic signs.
{"title":"A Critical Survey on Real-Time Traffic Sign Recognition by using CNN Machine Learning Algorithm","authors":"Megha Vamsi Kiran Choda, Sri Vardhan Perla, Brahmender Shaik, Yuva Teja Anirudh Yelchuru, P. Yalla","doi":"10.1109/IDCIoT56793.2023.10053394","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053394","url":null,"abstract":"Real-Time Traffic Sign Recognition System (RTTSRS) is used for recognizing the traffic signboards (Take left, take right, speed limit 60 kmph… etc.), it plays a crucial role in the domains of driverless vehicles etc. By using Real-Time Traffic Sign Recognition, Traffic related problems can be reduced. It is categorized into two types- localization and recognition. Localization deals with identifying and locating traffic sign regions within the radius. Real-Time Traffic Sign Recognition is used to identify the traffic sign region within the space (rectangular) provided. This study describes an approach for a traffic sign recognition system, many machine learning algorithms like Support Vector Machine (SVM) and Convolutional Neural Networks (CNN) have been studied for recognizing traffic signs. This study has conducted a critical investigation on various machine learning algorithms which gives high accuracy to predict, recognize real-time traffic signs.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"20 1","pages":"445-450"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75110397","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053387
N. Behera, M. Umaselvi, Devikanniga Devarajan, B. Komathi, Pragnesh B. Parmar, Raj kumar Gupta
Lymphatic system reinforces immune system by degrading as well as eliminating the cancer cells, and pathogens, rejecting unwanted sources, debris, and dead blood cells. It assists in assimilating the fat vitamins and fat-soluble from digestive system and delivers them to body tissues. Furthermore, the interstitial spaces amongst cells eradicate the extra fluids and redundant substances from body. Automatic diagnosis of cancer metastases in lymph nodes has the prospective to increase calculation of prognoses for patients. Machine learning¬based classification methods offer provision for the decision¬making method in various regions of healthcare, involving screening, diagnosis, prognosis, and so on. This study introduces an Optimal Feed Forward Deep Neural Network for Lymph Disease Detection and Classification (OFFDNN-LDC) model. The presented OFFDNN-LDC model intends to apply the classification model to determine the presence of lymph diseases in medical data. For attaining this, the presented OFFDNN-LDC model exploits the FFDNN model as a classifier to assign effective class labels. Besides, the presented OFFDNN-LDC model executes root mean square propagation (RMSProp) optimizer to properly elect the hyperparameter values of the FFDNN model. A series of simulations are performed for demonstrating the improved outcome of the OFFDNN-LDC model. The experimental values referred that the OFFDNN-LDC model is superior to other models.
{"title":"Optimal Feed Forward Deep Neural Network for Lymph Disease Detection and Classification","authors":"N. Behera, M. Umaselvi, Devikanniga Devarajan, B. Komathi, Pragnesh B. Parmar, Raj kumar Gupta","doi":"10.1109/IDCIoT56793.2023.10053387","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053387","url":null,"abstract":"Lymphatic system reinforces immune system by degrading as well as eliminating the cancer cells, and pathogens, rejecting unwanted sources, debris, and dead blood cells. It assists in assimilating the fat vitamins and fat-soluble from digestive system and delivers them to body tissues. Furthermore, the interstitial spaces amongst cells eradicate the extra fluids and redundant substances from body. Automatic diagnosis of cancer metastases in lymph nodes has the prospective to increase calculation of prognoses for patients. Machine learning¬based classification methods offer provision for the decision¬making method in various regions of healthcare, involving screening, diagnosis, prognosis, and so on. This study introduces an Optimal Feed Forward Deep Neural Network for Lymph Disease Detection and Classification (OFFDNN-LDC) model. The presented OFFDNN-LDC model intends to apply the classification model to determine the presence of lymph diseases in medical data. For attaining this, the presented OFFDNN-LDC model exploits the FFDNN model as a classifier to assign effective class labels. Besides, the presented OFFDNN-LDC model executes root mean square propagation (RMSProp) optimizer to properly elect the hyperparameter values of the FFDNN model. A series of simulations are performed for demonstrating the improved outcome of the OFFDNN-LDC model. The experimental values referred that the OFFDNN-LDC model is superior to other models.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"3 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78422752","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053453
B. Bhavana, J. Karthik, P. L. Kumari
Sentiment analysis is the most trending research area. Generally, most purchase decisions and price predictions are made based on product reviews. Sentiment analysis helps in understanding the product better. The sentiment analysis of a product summarizes whether the product has a positive, negative or neutral rating. Existing machine learning algorithms like logistic Regression, Decision Tree are used to determine sentiment for product reviews. This work includes XGBOOST and a hybrid model XGBOOST - RF used to observe sentiment on product reviews. The model that gives best performance is used to build a system that recommends products to users.
{"title":"A Novel Approach for Product Recommendation using XGBOOST","authors":"B. Bhavana, J. Karthik, P. L. Kumari","doi":"10.1109/IDCIoT56793.2023.10053453","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053453","url":null,"abstract":"Sentiment analysis is the most trending research area. Generally, most purchase decisions and price predictions are made based on product reviews. Sentiment analysis helps in understanding the product better. The sentiment analysis of a product summarizes whether the product has a positive, negative or neutral rating. Existing machine learning algorithms like logistic Regression, Decision Tree are used to determine sentiment for product reviews. This work includes XGBOOST and a hybrid model XGBOOST - RF used to observe sentiment on product reviews. The model that gives best performance is used to build a system that recommends products to users.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"240 1","pages":"256-261"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74983243","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10052785
Dan Pang, Yueling Bian, Yunhe Wang, Xiaofeng Zhang, Hong Qu
This article analyzes the current situation of scientific research management informatization construction in universities based on the knowledge gained from the research literature and Java language, where the utilization rate of university scientific research management informatization is not high, and it can only be limited to traditional Java and word forms for simple data statistics and scientific research. A comprehensive analysis of management methods and other shortcomings was carried out. Further, the design and implementation of the university scientific research management information platform were explored, and the establishment of a networked database of information resources was proposed, and the university scientific research management information system was developed, which increased the efficiency by 6.5%.
{"title":"The Java Framework Construction of the Intelligent Information System of University Scientific Research","authors":"Dan Pang, Yueling Bian, Yunhe Wang, Xiaofeng Zhang, Hong Qu","doi":"10.1109/IDCIoT56793.2023.10052785","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10052785","url":null,"abstract":"This article analyzes the current situation of scientific research management informatization construction in universities based on the knowledge gained from the research literature and Java language, where the utilization rate of university scientific research management informatization is not high, and it can only be limited to traditional Java and word forms for simple data statistics and scientific research. A comprehensive analysis of management methods and other shortcomings was carried out. Further, the design and implementation of the university scientific research management information platform were explored, and the establishment of a networked database of information resources was proposed, and the university scientific research management information system was developed, which increased the efficiency by 6.5%.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"22 1","pages":"542-545"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75888277","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053415
S. Shubhang, Sudhanshu Kumar, Uttkarsha Jindal, Ashutosh Kumar, N. Roy
The propagation of hateful speech on social media has increased in past few years, creating an urgent need for strong counter-measures. Governments, corporations, and scholars have all made considerable investments in these measurements. Hate speech on social media platforms can lead to cyber-conflict that can impact social life at the individual and national levels. It can make people feel isolated, anxious and fearful. It can also lead to hate crimes. However, social media platforms are not able to monitor all content posted by users. This is why there is a need for automated identification of hate speech. The English text is notorious for its difficulty, complexity and lack of resources. When examining each class individually, it should be noticed that a many hateful tweets have been misclassified. As a result, it is advised to further examine the forecasts and mistakes to obtain additional understanding on the misclassification. To automatically detect hate speech in social media data, we propose a NLP model that blends convolutional and recurrent layers. Using the proposed model, we were able to identify occurrences of hate on the test dataset. According to our research, doing so could considerably raise test scores. Proposed model uses a deep learning technique based on the Bi-GRU-LSTM-CNN classifier with an accuracy of 77.16%.
{"title":"Identification of Hate Speech and Offensive Content using BI-GRU-LSTM-CNN Model","authors":"S. Shubhang, Sudhanshu Kumar, Uttkarsha Jindal, Ashutosh Kumar, N. Roy","doi":"10.1109/IDCIoT56793.2023.10053415","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053415","url":null,"abstract":"The propagation of hateful speech on social media has increased in past few years, creating an urgent need for strong counter-measures. Governments, corporations, and scholars have all made considerable investments in these measurements. Hate speech on social media platforms can lead to cyber-conflict that can impact social life at the individual and national levels. It can make people feel isolated, anxious and fearful. It can also lead to hate crimes. However, social media platforms are not able to monitor all content posted by users. This is why there is a need for automated identification of hate speech. The English text is notorious for its difficulty, complexity and lack of resources. When examining each class individually, it should be noticed that a many hateful tweets have been misclassified. As a result, it is advised to further examine the forecasts and mistakes to obtain additional understanding on the misclassification. To automatically detect hate speech in social media data, we propose a NLP model that blends convolutional and recurrent layers. Using the proposed model, we were able to identify occurrences of hate on the test dataset. According to our research, doing so could considerably raise test scores. Proposed model uses a deep learning technique based on the Bi-GRU-LSTM-CNN classifier with an accuracy of 77.16%.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"74 1","pages":"536-541"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74565421","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053531
T. A. S. Srinivas, Monika M, N. Aparna, K. K., Narasimha Rao C, Ramprabhu J
Using symptoms as a basis for diagnosing lung cancer. Lung cancer detection is accomplished by using different machine learning techniques and regression algorithms. By comparing the efficacy of different regression algorithms for predicting lung cancer, various factors including age, gender, chest discomfort, shortness of breath, alcohol intake, chronic illness, trouble swallowing, anxiety, and peer pressure are taken into consideration. Lung cancer prediction and evaluation are accomplished by using different regression methods such as linear algorithm, polynomial regression, logistic regression, logarithmic regression and multiple regression. With a predictive accuracy of 96%, multiple regression remains superior to other regression techniques when it comes to lung cancer prediction. The R-squared value can be calculated by using a number of regression approaches, which may also be used to evaluate the association between various symptoms and lung cancer. Lung cancer is diagnosed by using the R squared value, which is calculated by using several algorithms and considers symptoms including chronic illness.
{"title":"A Methodology to Predict the Lung Cancer and its Adverse Effects on Patients from an Advanced Correlation Analysis Method","authors":"T. A. S. Srinivas, Monika M, N. Aparna, K. K., Narasimha Rao C, Ramprabhu J","doi":"10.1109/IDCIoT56793.2023.10053531","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053531","url":null,"abstract":"Using symptoms as a basis for diagnosing lung cancer. Lung cancer detection is accomplished by using different machine learning techniques and regression algorithms. By comparing the efficacy of different regression algorithms for predicting lung cancer, various factors including age, gender, chest discomfort, shortness of breath, alcohol intake, chronic illness, trouble swallowing, anxiety, and peer pressure are taken into consideration. Lung cancer prediction and evaluation are accomplished by using different regression methods such as linear algorithm, polynomial regression, logistic regression, logarithmic regression and multiple regression. With a predictive accuracy of 96%, multiple regression remains superior to other regression techniques when it comes to lung cancer prediction. The R-squared value can be calculated by using a number of regression approaches, which may also be used to evaluate the association between various symptoms and lung cancer. Lung cancer is diagnosed by using the R squared value, which is calculated by using several algorithms and considers symptoms including chronic illness.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"5 1","pages":"964-970"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74612369","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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