Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10151341
Shivam Pandey, Aditya, Seema Jain, Usha Dhankar
Images shared online have a high likelihood of being altered, and further global alterations like compression, resizing, or filtering mask the potential change. Many restrictions are placed on forgery detection systems by such manipulations. Image forgery detection is the fundamental solution to many issues, particularly social issues like those on Facebook and legal issues. The most frequent form of image fraud is called a copy-move forgery, where a portion of the original image is copied and pasted in a different spot within the same image. Because the duplicated portions' attributes are similar to those of the original image's components, this type of picture counterfeiting is simpler to carry out but more challenging to detect. The method for spotting copy-move forgeries described in this study is based on processing blocks into features and then extracting those features from the blocks' transforms. A Convolutional Neural Network (CNN) is another tool for detecting forgeries Serial pairings of convolution and pooling layers are employed to conduct feature extraction. Original and changed images are then categorised using transforms and without transformations. We use the CASIA2 dataset, which has 4795 images, of which 1701 are authentic and 3274 are forged. The accuracy of our proposed model is 97.7%. This improved the detection process's overall processing effectiveness and allowed it to fulfill real-time processing demands..
{"title":"Image Forgery Detection","authors":"Shivam Pandey, Aditya, Seema Jain, Usha Dhankar","doi":"10.1109/ICDT57929.2023.10151341","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151341","url":null,"abstract":"Images shared online have a high likelihood of being altered, and further global alterations like compression, resizing, or filtering mask the potential change. Many restrictions are placed on forgery detection systems by such manipulations. Image forgery detection is the fundamental solution to many issues, particularly social issues like those on Facebook and legal issues. The most frequent form of image fraud is called a copy-move forgery, where a portion of the original image is copied and pasted in a different spot within the same image. Because the duplicated portions' attributes are similar to those of the original image's components, this type of picture counterfeiting is simpler to carry out but more challenging to detect. The method for spotting copy-move forgeries described in this study is based on processing blocks into features and then extracting those features from the blocks' transforms. A Convolutional Neural Network (CNN) is another tool for detecting forgeries Serial pairings of convolution and pooling layers are employed to conduct feature extraction. Original and changed images are then categorised using transforms and without transformations. We use the CASIA2 dataset, which has 4795 images, of which 1701 are authentic and 3274 are forged. The accuracy of our proposed model is 97.7%. This improved the detection process's overall processing effectiveness and allowed it to fulfill real-time processing demands..","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":" 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132041328","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 : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150721
N. Garg, Radhika Gupta, M. Kaur, Suhaib Ahmed, H. Shankar
Orange is an important citrus fruit grown globally, and its consumption is encouraged by health-conscious individuals due to its nutritional value. Classifying oranges is important for quality control, sorting, and grading in the food industry. For the production of high-quality oranges, farm-based disease prediction is not utilizing technology to its full potential. A hybrid version is proposed in this research paper for the categorization of six common disorders of oranges, namely Penicillium, Scab, Anthracnose, Melanose, Phytophthora, and Citrus Canker, using a blend of the classifier - Support Vector Machine and ANN prototype - Convolutional Neural Network. With CNN being accustomed for feature derivation and SVM being utilized for classification, the suggested model leverages the best aspects of both algorithms. Using a dataset of 4,864 orange photos, the suggested hybrid model’s performance is assessed, and as a result, an accuracy of 88.13734% is achieved. Our sensitivity analysis indicates that the form, size, and texture of the lesions were the most crucial characteristics for categorizing orange-colored illnesses, followed by their texture and color. The effectiveness of utilizing a hybrid model for illness diagnosis in citrus fruits is shown by the postulated hybrid model’s superior performance over existing classification models like SVM, Random Forest, and K-Nearest Neighbor (KNN). The impeccable competence of the proposed hybrid model makes it suitable to be employed in automated disease detection systems to make prompt and well-informed decisions about disease management and prevention, thereby enhancing citrus crop productivity and quality.
{"title":"Efficient Detection and Classification of Orange Diseases using Hybrid CNN-SVM Model","authors":"N. Garg, Radhika Gupta, M. Kaur, Suhaib Ahmed, H. Shankar","doi":"10.1109/ICDT57929.2023.10150721","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150721","url":null,"abstract":"Orange is an important citrus fruit grown globally, and its consumption is encouraged by health-conscious individuals due to its nutritional value. Classifying oranges is important for quality control, sorting, and grading in the food industry. For the production of high-quality oranges, farm-based disease prediction is not utilizing technology to its full potential. A hybrid version is proposed in this research paper for the categorization of six common disorders of oranges, namely Penicillium, Scab, Anthracnose, Melanose, Phytophthora, and Citrus Canker, using a blend of the classifier - Support Vector Machine and ANN prototype - Convolutional Neural Network. With CNN being accustomed for feature derivation and SVM being utilized for classification, the suggested model leverages the best aspects of both algorithms. Using a dataset of 4,864 orange photos, the suggested hybrid model’s performance is assessed, and as a result, an accuracy of 88.13734% is achieved. Our sensitivity analysis indicates that the form, size, and texture of the lesions were the most crucial characteristics for categorizing orange-colored illnesses, followed by their texture and color. The effectiveness of utilizing a hybrid model for illness diagnosis in citrus fruits is shown by the postulated hybrid model’s superior performance over existing classification models like SVM, Random Forest, and K-Nearest Neighbor (KNN). The impeccable competence of the proposed hybrid model makes it suitable to be employed in automated disease detection systems to make prompt and well-informed decisions about disease management and prevention, thereby enhancing citrus crop productivity and quality.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129373649","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 : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150457
Shiv Narain Gupta, Rahul Dev, Abdul Samad, A. Asadullah, R. Bhardwaj, Dhiraj Gupta
Technology is rapidly increasing these days, and the entire world is shifting toward home automation. Home automation is a technology of automating the operation of household appliances. More than 90% of the world's households do not have home automation or smart home appliances since this technology is expensive. As a result, it is important to have some technology that can make home automation affordable. This smart extension board can convert any electrical home appliance into a smart device that can be controlled from anywhere in the world using cell phones. This smart board is cost efficient so it is affordable to all household.
{"title":"IoT Based Smart Extension Board","authors":"Shiv Narain Gupta, Rahul Dev, Abdul Samad, A. Asadullah, R. Bhardwaj, Dhiraj Gupta","doi":"10.1109/ICDT57929.2023.10150457","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150457","url":null,"abstract":"Technology is rapidly increasing these days, and the entire world is shifting toward home automation. Home automation is a technology of automating the operation of household appliances. More than 90% of the world's households do not have home automation or smart home appliances since this technology is expensive. As a result, it is important to have some technology that can make home automation affordable. This smart extension board can convert any electrical home appliance into a smart device that can be controlled from anywhere in the world using cell phones. This smart board is cost efficient so it is affordable to all household.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131364768","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 : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150836
Sahote Tejinder Singh, Madhuri Dinesh Gabhane, C. Mahamuni
Spam is the act of sending unsolicited emails to a large number of users for phishing, spreading malware, etc. Internet Service Providers (ISPs) and email inbox providers (like Gmail, Yahoo Mail, AOL, etc.) rely on SPAM filters, firewalls, and blacklist directories to prevent "unsolicited" SPAM emails from entering your inbox. Spam mails are overrunning email inboxes, which significantly slows down internet performance. It is crucial to properly analyze the connections between these spammers and spam because the majority of us tend to provide them with crucial information, such as our contact information. Since the benefactor covers a large percentage of the costs related to spamming, it effectively serves as advertising for the cost of mailing. The study of existing work shows that machine learning and deep learning are frequently employed to effectively identify email spam. This research paper is secondary work in which we have studied, and implemented the various machine learning and deep learning approaches to identify email spam in Python. The four machine learning algorithms—KNN, Navies Bayes, BiLSTM, and Deep CNN—show that they can be utilized effectively to detect spam. Yet the Deep CNN outperforms the other three based on accuracy and the F1 score.
{"title":"Study of Machine Learning and Deep Learning Algorithms for the Detection of Email Spam based on Python Implementation","authors":"Sahote Tejinder Singh, Madhuri Dinesh Gabhane, C. Mahamuni","doi":"10.1109/ICDT57929.2023.10150836","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150836","url":null,"abstract":"Spam is the act of sending unsolicited emails to a large number of users for phishing, spreading malware, etc. Internet Service Providers (ISPs) and email inbox providers (like Gmail, Yahoo Mail, AOL, etc.) rely on SPAM filters, firewalls, and blacklist directories to prevent \"unsolicited\" SPAM emails from entering your inbox. Spam mails are overrunning email inboxes, which significantly slows down internet performance. It is crucial to properly analyze the connections between these spammers and spam because the majority of us tend to provide them with crucial information, such as our contact information. Since the benefactor covers a large percentage of the costs related to spamming, it effectively serves as advertising for the cost of mailing. The study of existing work shows that machine learning and deep learning are frequently employed to effectively identify email spam. This research paper is secondary work in which we have studied, and implemented the various machine learning and deep learning approaches to identify email spam in Python. The four machine learning algorithms—KNN, Navies Bayes, BiLSTM, and Deep CNN—show that they can be utilized effectively to detect spam. Yet the Deep CNN outperforms the other three based on accuracy and the F1 score.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"275 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124436118","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 : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150886
Lav Soni, Amanpreet Kaur
The purpose of the metaverse, a term that refers to the next generation of the internet, is to develop a fully immersive, identity virtual world in which individuals may work, learn, and engage. The purpose of this research is to focus on education in the metaverse. A comparison between learning in the metaverse and traditional learning is explored, as well as the use of structure and technology in the development of the educational system.
{"title":"Strategies for Implementing Metaverse in Education","authors":"Lav Soni, Amanpreet Kaur","doi":"10.1109/ICDT57929.2023.10150886","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150886","url":null,"abstract":"The purpose of the metaverse, a term that refers to the next generation of the internet, is to develop a fully immersive, identity virtual world in which individuals may work, learn, and engage. The purpose of this research is to focus on education in the metaverse. A comparison between learning in the metaverse and traditional learning is explored, as well as the use of structure and technology in the development of the educational system.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122183083","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 : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150611
Naman Goel, Nikhil Prabhat Yadav, Prakarti Prakarti, Anukul Pandey
The current focus of research is on using machine learning (ML) algorithms to predict heart disease. Using the UC Irvine (UCI) Cleveland Heart Disease dataset, this study investigates the effectiveness of various types of classifiers, including K-Nearest Neighbours (KNN), AdaBoost, Gaussian Naïve Bayes (GNB), support vector machines (SVM), multilayer perceptron (MLP) and random forests. The objective of this study is to assess the precision and speed of each classifier and gauge their effectiveness by utilizing measures like accuracy and F1 score for comparison. The study also looks into the potential benefits of fusion methods for improving the accuracy of heart disease prediction. The study concludes that combining various models could lead to improving the metrics. Our study contributes to the ongoing research on heart disease prediction using ML algorithms. The findings of our study can be used to develop more precise models for predicting heart disease, which can aid in improving clinical decision-making for heart disease prevention and treatment.
{"title":"Comparative Analysis of Single Classifier Models against Aggregated Fusion Models for Heart Disease Prediction","authors":"Naman Goel, Nikhil Prabhat Yadav, Prakarti Prakarti, Anukul Pandey","doi":"10.1109/ICDT57929.2023.10150611","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150611","url":null,"abstract":"The current focus of research is on using machine learning (ML) algorithms to predict heart disease. Using the UC Irvine (UCI) Cleveland Heart Disease dataset, this study investigates the effectiveness of various types of classifiers, including K-Nearest Neighbours (KNN), AdaBoost, Gaussian Naïve Bayes (GNB), support vector machines (SVM), multilayer perceptron (MLP) and random forests. The objective of this study is to assess the precision and speed of each classifier and gauge their effectiveness by utilizing measures like accuracy and F1 score for comparison. The study also looks into the potential benefits of fusion methods for improving the accuracy of heart disease prediction. The study concludes that combining various models could lead to improving the metrics. Our study contributes to the ongoing research on heart disease prediction using ML algorithms. The findings of our study can be used to develop more precise models for predicting heart disease, which can aid in improving clinical decision-making for heart disease prevention and treatment.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115502973","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 : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150962
Asif Khan, Dev Paliwal, Ritank Jaikar, S. Attri
An animal's normal state is altered by sickness which can stop or change critical processes. Concerns over animal diseases have existed as animal lovers interacted with animals and this concern is reflected in the first ideas about religion and magic. Animal illnesses still pose a threat, primarily due to the potential financial costs and risk of human transmission. The study, prevention, and treatment of diseases in animals including wild animals and those utilized in scientific research are the focus of the medical specialty known as veterinary medicine. This research examines recent developments in image-based animal illness detection and predicting the best deep learning model to detect the animal diseases. People now have a better grasp of machine learning and its potential uses in treating animal diseases as a result of the discussion of this paper. Regarding accuracy, DenseNet169 has performed remarkably better than other models whereas ResNet50V2 has least accuracy. These models are trained on the dataset which is built using images collected by the Authors.
{"title":"Demystifying the Transfer Learning based Detection of Animal Diseases from Images","authors":"Asif Khan, Dev Paliwal, Ritank Jaikar, S. Attri","doi":"10.1109/ICDT57929.2023.10150962","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150962","url":null,"abstract":"An animal's normal state is altered by sickness which can stop or change critical processes. Concerns over animal diseases have existed as animal lovers interacted with animals and this concern is reflected in the first ideas about religion and magic. Animal illnesses still pose a threat, primarily due to the potential financial costs and risk of human transmission. The study, prevention, and treatment of diseases in animals including wild animals and those utilized in scientific research are the focus of the medical specialty known as veterinary medicine. This research examines recent developments in image-based animal illness detection and predicting the best deep learning model to detect the animal diseases. People now have a better grasp of machine learning and its potential uses in treating animal diseases as a result of the discussion of this paper. Regarding accuracy, DenseNet169 has performed remarkably better than other models whereas ResNet50V2 has least accuracy. These models are trained on the dataset which is built using images collected by the Authors.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116191703","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 : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150712
I. Jeya Daisy, G. Diyaneshwaran, K. Ravivarmaa, S. Shobana, M. Sneha, N. S. Monessha
Modern obstetrics places a high priority on foetal health monitoring. Although foetal movement is frequently used as a proxy for foetal health, it is difficult to accurately monitor foetal movement over an extended period of time without causing any harm. In high-risk pregnancies and in high-risk moms who have previously experienced miscarriages, it is highly helpful to determine the foetus position because, in the majority of cases, an incorrect foetal position results in both foetal and maternal mortality. Pregnant women may benefit from the design and construction of a device that can accurately identify the location of the foetus. Recent years have seen the development of a few accelerometer-based systems to address frequent problems with ultrasound measurement and allow for remote, self-managed monitoring of foetal movement throughout pregnancy. The optimum design for body-worn accelerometers, data processing, and deep learning methods used to identify foetal movement. This study will explore four alternative techniques for determining the location of the foetus. Ultrasonograms are the most popular methods for foetal position detection. The wearable ambulatory device known as Femom, which has been made available to women on home prescription, can also be used to determine the location of the foetus. Deep learning techniques and thermal imaging cameras are also utilised to determine the position of the foetus.
{"title":"Review On Foetal Position Detection Using Different Techniques","authors":"I. Jeya Daisy, G. Diyaneshwaran, K. Ravivarmaa, S. Shobana, M. Sneha, N. S. Monessha","doi":"10.1109/ICDT57929.2023.10150712","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150712","url":null,"abstract":"Modern obstetrics places a high priority on foetal health monitoring. Although foetal movement is frequently used as a proxy for foetal health, it is difficult to accurately monitor foetal movement over an extended period of time without causing any harm. In high-risk pregnancies and in high-risk moms who have previously experienced miscarriages, it is highly helpful to determine the foetus position because, in the majority of cases, an incorrect foetal position results in both foetal and maternal mortality. Pregnant women may benefit from the design and construction of a device that can accurately identify the location of the foetus. Recent years have seen the development of a few accelerometer-based systems to address frequent problems with ultrasound measurement and allow for remote, self-managed monitoring of foetal movement throughout pregnancy. The optimum design for body-worn accelerometers, data processing, and deep learning methods used to identify foetal movement. This study will explore four alternative techniques for determining the location of the foetus. Ultrasonograms are the most popular methods for foetal position detection. The wearable ambulatory device known as Femom, which has been made available to women on home prescription, can also be used to determine the location of the foetus. Deep learning techniques and thermal imaging cameras are also utilised to determine the position of the foetus.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115958939","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 : 2023-05-11DOI: 10.1109/icdt57929.2023.10150838
Ajay Kumar, J. Jaiswal, Naman Tiwari
Blind spot is the region that is not visible to the driver while driving car via side or rear mirrors. The blind spot is usually located at the rear of the vehicle, but may also be found on both sides. It is caused due to obstruction from other vehicles, objects or pedestrians. Other names for blind areas include "blind zones," "fatal zones," and "dead spots.". This blind spot can be dangerous for drivers, especially when they are driving at night or in bad weather conditions. When drivers neglect to examine their blind areas before changing lanes or making a right turn, this can result in accidents and injuries. Our proposed model will be able to identify the objects that lies in the vehicle's blind spot area using an Arduino and an ultrasonic sensor. The use of a BSMS while driving can help you stay safe. It can be installed on the car’s rear fender and if there are any objects in the vicinity of the model then an alarm will be generated and the driver will have enough time to react before he gets into an accident We have suggested the idea of implementing machine learning algorithms for better accuracy and reliability.
{"title":"Blind Spot Monitoring System Using Ultrasonic Sensor","authors":"Ajay Kumar, J. Jaiswal, Naman Tiwari","doi":"10.1109/icdt57929.2023.10150838","DOIUrl":"https://doi.org/10.1109/icdt57929.2023.10150838","url":null,"abstract":"Blind spot is the region that is not visible to the driver while driving car via side or rear mirrors. The blind spot is usually located at the rear of the vehicle, but may also be found on both sides. It is caused due to obstruction from other vehicles, objects or pedestrians. Other names for blind areas include \"blind zones,\" \"fatal zones,\" and \"dead spots.\". This blind spot can be dangerous for drivers, especially when they are driving at night or in bad weather conditions. When drivers neglect to examine their blind areas before changing lanes or making a right turn, this can result in accidents and injuries. Our proposed model will be able to identify the objects that lies in the vehicle's blind spot area using an Arduino and an ultrasonic sensor. The use of a BSMS while driving can help you stay safe. It can be installed on the car’s rear fender and if there are any objects in the vicinity of the model then an alarm will be generated and the driver will have enough time to react before he gets into an accident We have suggested the idea of implementing machine learning algorithms for better accuracy and reliability.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121770701","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 : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150618
A. Dwivedi, Ayush Mohan, Shivani Singh, Ayushi Goel, Eklavya Singh
In the field of medical science, the ability to diagnose diseases from medical images has gained significant importance. Detecting abnormalities in retinal images is particularly challenging and requires specialized expertise.The following paper proposes a Modified Hopfield Neural Network (MHNN) for accurately diagnosing Diabetic Retinopathy from retinal images. The MHNN adjusts both weight and output values simultaneously, unlike conventional neural networks, leading to improved accuracy. The proposed method was tested on 540 images and achieved an average sensitivity and specificity of 0.99 and accuracy of 99.25%. Compared to other neural network approaches, the proposed MHNN is superior in diagnosing Diabetic Retinopathy from Retinal Images.
{"title":"Advancements in Medical Imaging for Sugar Diagnosis Using Modified Hopfield Neural Network","authors":"A. Dwivedi, Ayush Mohan, Shivani Singh, Ayushi Goel, Eklavya Singh","doi":"10.1109/ICDT57929.2023.10150618","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150618","url":null,"abstract":"In the field of medical science, the ability to diagnose diseases from medical images has gained significant importance. Detecting abnormalities in retinal images is particularly challenging and requires specialized expertise.The following paper proposes a Modified Hopfield Neural Network (MHNN) for accurately diagnosing Diabetic Retinopathy from retinal images. The MHNN adjusts both weight and output values simultaneously, unlike conventional neural networks, leading to improved accuracy. The proposed method was tested on 540 images and achieved an average sensitivity and specificity of 0.99 and accuracy of 99.25%. Compared to other neural network approaches, the proposed MHNN is superior in diagnosing Diabetic Retinopathy from Retinal Images.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125702582","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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