2023 International Conference on Disruptive Technologies (ICDT)最新文献_第10页

Image Forgery Detection 图像伪造检测

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10151341

Shivam Pandey, Aditya, Seema Jain, Usha Dhankar

{"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}

引用次数: 0

Efficient Detection and Classification of Orange Diseases using Hybrid CNN-SVM Model 基于CNN-SVM混合模型的柑橘病害高效检测与分类

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150721

N. Garg, Radhika Gupta, M. Kaur, Suhaib Ahmed, H. Shankar

{"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}

引用次数: 1

IoT Based Smart Extension Board 基于IoT的智能扩展板

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150457

Shiv Narain Gupta, Rahul Dev, Abdul Samad, A. Asadullah, R. Bhardwaj, Dhiraj Gupta

引用次数: 0

Best Ways Using AI in Impacting Success on MBA Graduates 利用人工智能影响MBA毕业生成功的最佳方法

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10151211

D. Praveenadevi, S. Kowsalyadevi, B. Girimurugan, Penugonda. Sreemai, Kolli. Nandini, Sumit Pareek

{"title":"Best Ways Using AI in Impacting Success on MBA Graduates","authors":"D. Praveenadevi, S. Kowsalyadevi, B. Girimurugan, Penugonda. Sreemai, Kolli. Nandini, Sumit Pareek","doi":"10.1109/ICDT57929.2023.10151211","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151211","url":null,"abstract":"It is not an easy decision to make when deciding whether or not to let a student continue their studies in a graduate program. There are several factors to take into consideration. An application is analyzed based on a variety of different criteria, and the results of this examination are utilized to provide a prediction of the applicant's likelihood of being successful. Through the course of human history, regression analysis has been used as a methodology for the development of many kinds of prediction systems. On the other hand, it has been demonstrated that the models that were presented in this research had a very limited capacity for predictive ability. An empirical examination of these relationships was carried out by these authors using survey data acquired from MBA students attending a private university. The structural equation models that were generated using this information were used in the investigation. It was found that the content of the courses themselves was the single most critical factor in correctly predicting all learning, satisfaction, and quality.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":" 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113951783","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}

引用次数: 0

Comparative Analysis of Single Classifier Models against Aggregated Fusion Models for Heart Disease Prediction 单一分类器模型与聚合融合模型在心脏病预测中的比较分析

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150611

Naman Goel, Nikhil Prabhat Yadav, Prakarti Prakarti, Anukul Pandey

{"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}

引用次数: 0

Study of Machine Learning and Deep Learning Algorithms for the Detection of Email Spam based on Python Implementation 基于Python实现的垃圾邮件检测的机器学习和深度学习算法研究

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150836

Sahote Tejinder Singh, Madhuri Dinesh Gabhane, C. Mahamuni

{"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}

引用次数: 0

Review On Foetal Position Detection Using Different Techniques 胎儿体位检测技术综述

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150712

I. Jeya Daisy, G. Diyaneshwaran, K. Ravivarmaa, S. Shobana, M. Sneha, N. S. Monessha

{"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}

引用次数: 0

Demystifying the Transfer Learning based Detection of Animal Diseases from Images 揭示基于迁移学习的动物疾病图像检测方法

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150962

Asif Khan, Dev Paliwal, Ritank Jaikar, S. Attri

引用次数: 1

Machine Learmusht for Brain Stroke Prediction 脑卒中预测的机器学习

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10151148

S. Mushtaq, K. S. Saini, Saimul Bashir

{"title":"Machine Learmusht for Brain Stroke Prediction","authors":"S. Mushtaq, K. S. Saini, Saimul Bashir","doi":"10.1109/ICDT57929.2023.10151148","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151148","url":null,"abstract":"Brain stroke is a serious medical condition that needs timely diagnosis and action to avoid irretrievable harm to the brain. Machine learmusht (ML) techniques have been extensively used in the healthcare industry to build predictive models for various medical conditions, including brain stroke, heart stroke and diabetes disease. In this paper, we present an advanced stroke detection algorithm for predicting the occurrence of stroke. We used a dataset contaimusht detail of important parameters which are responsible for the brain stroke like: Age: Body Mass Index (BMI): Gender: Heart Disease: Smoking Status etc, to develop a predictive model. The dataset was preprocessed to handle missing values, handle categorical features and to balance the dataset. We used different classification algorithms such as Naïve Bayes, logistic regression, XgBoost, decision trees, AdaBoost, K-Nearest Neighbor, random forests, Voting classifier and support vector machines to develop our predictive model. The evaluation of the models was conducted using several metrics such as accuracy, F1-score, recall, precision. Moreover an additional metrics parameter is calculated in this paper known as Specificity which was not calculated in earlier studies. Our results showed that the Support Vector Machine algorithm outperformed other models, achieving an accuracy of 99.5%, precision of 99.9% , recall of 99.1%, F1-score of 99.5% and specificity of 99%.","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":"128757677","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}

引用次数: 0

Blind Spot Monitoring System Using Ultrasonic Sensor 基于超声波传感器的盲点监测系统

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/icdt57929.2023.10150838

Ajay Kumar, J. Jaiswal, Naman Tiwari

{"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}

引用次数: 0