2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)最新文献

{"title":"Predictive Modeling of Cardiovascular Disease using Machine Learning Techniques","authors":"Shefali Bajaj, Lalatendu Behera","doi":"10.1109/ICSCCC58608.2023.10176425","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176425","url":null,"abstract":"Coronary artery disease (CAD), is consistently ranked among the leading causes of death around the globe. Over several decades, many non-invasive approaches for predicting and detecting coronary artery disease have been proposed. Despite the extensive study that has been conducted, the death rate due to CAD continues to be at an all-time high. It is possible that predictive models constructed with machine learning (ML) algorithms can help doctors discover CAD earlier, which in turn may improve patient outcomes. This study focuses on applying several machine learning algorithms to make predictions about coronary vascular disease. We rely on the Coronary Artery Disease Data Collection for our analysis. Python and the jupyter notebook environment are used to realize this project. Many machine learning techniques are utilized in this research to predict CAD results, including a random forest, a decision tree, a gradient-boosted tree, and a logistic regression. These algorithms are compared to each other in this paper, and the gradient-boosted tree algorithm obtained more accurate results than the other existing machine-learning methods.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126222314","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}

{"title":"Diabetic Retinopathy Severity Classification based on attention mechanism","authors":"Avinash Jha, A. V. S.","doi":"10.1109/ICSCCC58608.2023.10176443","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176443","url":null,"abstract":"One of the significant factors causing blindness is diabetic retinopathy, a typical microvascular side effect of diabetes. Highly qualified professionals often examine colored fundus photos to identify this catastrophic condition. It takes much time and effort for ophthalmologists to diagnose diabetic retinopathy (DR) manually. The number of diabetes patients has dramatically increased during the last several years, which has made automated DR diagnosis a research hotspot. This paper proposes a hybrid deep learning model using a pre-trained DenseNet architecture integrated with CBAM for feature refinement. The dataset provided by the Kaggle Asia Pacific Tele-Ophthalmology Society (APTOS), having 3662 fundus images, is used in this research. In the multiclass classification experiment, we achieved 86.22% accuracy and 91.44 Kappa score(QWK). The local interpretable model-agnostic explanations (LIME) framework is used to assess predictions further and produce visual explanations, which can assist in decreasing the drawback of black-box models in aiding medical decision-making.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127471210","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}

{"title":"CNN-SVM Model for Accurate Detection of Bacterial Diseases in Cucumber Leaves","authors":"D. Banerjee, V. Kukreja, S. Hariharan, Vishal Jain, S. Dutta","doi":"10.1109/ICSCCC58608.2023.10176783","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176783","url":null,"abstract":"This research paper presents a deep learning approach for detecting and classifying plant diseases in citrus crops. The proposed model uses a convolutional neural network (CNN) architecture with three convolutional layers, three pooling layers, and two fully connected layers, followed by support vector machine (SVM) classifiers. The model was trained and tested using a dataset of citrus crop images containing nine different classes of diseases. The performance of the model was evaluated based on precision, recall, F1-score, support, accuracy, and average metrics. The overall accuracy of the model was found to be 86.03%, with a weighted average F1 score of 86.10%. The model achieved the highest precision score of 86.96% for the Citrus nematode class and the lowest precision score of 84.00% for the Dothiorella blight class.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114824427","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}

{"title":"Cyber Attack Detection Techniques in Cyber Physical System for Pharmaceutical Care Services","authors":"Swati Devliyal, H. Goyal, Sachin Sharma","doi":"10.1109/ICSCCC58608.2023.10176986","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176986","url":null,"abstract":"Cyber-physical systems have proven to be widely used in the healthcare industry to provide specialized patient care in a range of clinical situations. These systems' diverse array of medical equipment offers enormous attack surfaces, necessitating advanced security solutions for these challenging circumstances. Since system resilience is a fundamental design element, preventative network security measures are insufficient to protect these systems, necessitating the use of intrusion detection and prevention technology. In order to identify various assault types and how to spot them, a review was done for this paper. Because the pharmaceutical sector also deals with a lot of intellectual property and sensitive data, including personal information, account information, research data, medical records, etc., this study can also be helpful in pharmaceutical care services. The pharmaceutical industry has a big responsibility to ensure the information should be secured.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117120533","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}

{"title":"Framework for Bank Loan Re-Payment Prediction and Income Prediction","authors":"Chudi Dhruv, Deva Paul, M. H. Kumar, M A., M. S. Reddy","doi":"10.1109/ICSCCC58608.2023.10176363","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176363","url":null,"abstract":"This research study aims to develop a predictive framework for income and bank loan repayment prediction. The primary objective is to accurately predict an individual's income and their ability to repay a loan to help them make informed financial decisions. Using a data-driven approach, we collected and analyzed data on various factors that impact income and loan repayment, such as employment history, education, credit score, and demographic information. This data will be used to build predictive models that can provide accurate estimates for both income and loan repayment. The models will be validated using historical data and refined to improve accuracy. The study will focus on developing two separate predictive models: one for income prediction and another for bank loan repayment prediction. The income prediction model will provide individuals with an estimate of their future income based on their individual financial circumstances. The bank loan repayment prediction model will help financial institutions predict the likelihood of loan repayment based on the borrower's financial history and current financial circumstances. This predictive framework will provide valuable insights into the financial stability of individuals and the creditworthiness of borrowers. It will help individuals plan for their financial future, such as saving for retirement or investing in the stock market. It will also assist financial institutions in making informed lending decisions, reducing the risk of loan defaults, and improving the overall health of the financial industry. The development of a predictive framework for income and bank loan repayment prediction will provide valuable insights and tools for both individuals and financial institutions. Accurate predictions of income and loan repayment will enable informed financial decisions, improving the financial stability and well-being of all parties involved. We have created a user-friendly financial bot that can provide basic definitions of financial terms based on user queries.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129101225","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}

{"title":"Hybrid Deep Neural Networks for Improved Sentiment Analysis in Social Media","authors":"Sabah Auda Abdul Ameer, Raed Khalid, Ali H. O. Al Mansor, Pardeep Singh","doi":"10.1109/ICSCCC58608.2023.10176880","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176880","url":null,"abstract":"Based on S-BERT pre-trained embeddings, this body of work suggests an approach to sentiment analysis using a convolutional neural network (CNN). GloVe and Word2Vec, utilizing the IMDB dataset. The results of our testing showed that the CNN algorithm we built had the highest accuracy at 89.8 percent, outperforming the GloVe and Word2Vec models, which were considered gold standards at the time. During our research on ablation, we noticed that replacing bigrams or trigrams with N-grams can result in improved model performance. In addition, we used a sentiment lexicon to provide context to the text data, which helped improve the model's accuracy. Our study has demonstrated that sentiment analysis can be performed using S-BERT pre-trained embeddings in combination with a CNN model. This strategy has the potential to outperform both standard machine learning approaches and commonly used word embedding models. When these factors are considered, our suggested strategy of using S BERT pre-trained embeddings shows significant potential in real-world applications where sentiment analysis is critical.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126945830","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}

{"title":"Analysis of Tweets for Cyberbullying Detection","authors":"Shipra Mathur, Shivam Isarka, Bhuvaneswar Dharmasivam, J. C. D.","doi":"10.1109/ICSCCC58608.2023.10176416","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176416","url":null,"abstract":"Cyberbullying takes place online on gadgets like smartphones and computers. Cyberbullying can occur through social media platforms. This paper presents a real-time cyber-bullying detection system for Twitter using Natural Language Processing (NLP) and Machine Learning (ML). The system is trained on a dataset of cyberbullying tweets using several ML algorithms and their performance is compared. Random Forest was found to provide the best results after tuning. To achieve real-time analysis, Selenium was used to scrape tweets from a given Twitter account and store the timestamp of the already checked tweets. Additionally, an image captioning model was employed to generate descriptions for images posted on the account and compare them with user-written captions to filter out spam tweets. The proposed work aims to prevent cyberbullying and provides a valuable tool for online platforms to detect and remove harmful content. The results of this study have shown that the selection of appropriate ML algorithms and preprocessing techniques significantly impact the performance of cyberbullying detection on Twitter. Our model sheds light on the appropriateness of different ML algorithms for the detection of cyberbullying.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129250628","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}

{"title":"An Efficient Rainfall Prediction Model Using Deep Learning Method","authors":"Vishal Kumar Verma, Hima Sagar Janagama, Nagamma Patil","doi":"10.1109/ICSCCC58608.2023.10176598","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176598","url":null,"abstract":"Rainfall is a crucial aspect of the Earth's natural cycle and it is necessary for various activities such as agriculture, water supply and hydroelectric power generation. However excessive rainfall can lead to floods, landslides and other destructive consequences, while insufficient rainfall can cause droughts and water shortages. Therefore accurate estimation of rainfall is essential to manage and mitigate the impacts of rainfall. In this study, the dataset is collected from the NASA Power database [22] to predict the annual rainfall in Mangalore(Karnataka), India. The data is collected from January 1, 2003 to February 04, 2023 using NASA POWER API. The study used four models MLP[15], LSTM, BiLSTM, CNN to predict the daily average precipitation that contributes to the annual rainfall. The input parameters considered for the prediction are maximum monthly temperature, minimum monthly temperature, humidity, atmospheric pressure and wind speed[9]. The model's performance is measured using mean squared error (MSE) and mean absolute error (MAE) of the predicted values on training and testing ratio 80:20. CNN(Convolutional Neural Network) model outperforms and gives the MSE and MAE for the CNN(Convolutional Neural Network) model are 0.0041 and 0.0456 respectively.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133518327","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}

{"title":"Energy and Delay Efficient Partial Offloading for UAV-assisted MEC Systems using Differential Evolution Algorithm","authors":"Deepak Dhingan, Santanu Ghosh, B. B. Naik, P. Kuila","doi":"10.1109/ICSCCC58608.2023.10176657","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176657","url":null,"abstract":"The research describes a technique that enables an Unmanned Aerial Vehicle (UAV) to delegate a part of a task to mobile devices. By outsourcing the computationally costly sections of a work to the more capable UAV and using the mobile devices for tasks that can be completed locally, the system makes good use of the capabilities of both the UAV and mobile devices. In this paper, Differential Evolution (DE) based algorithm is proposed for energy and delay efficient partial offloading in UAV-assisted MEC system. An extensive Simulations have been done to measure the performance of the proposed algorithm. The results reveal a considerable reduction in latency. Overall, our method shows how partial task offloading may be used to enhance the performance of UAV-assisted systems.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133578692","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}

{"title":"Multifactor Authentication Schemes for Multiserver Based Wireless Application: A Review","authors":"Yashwant Nogia, Samayveer Singh, V. Tyagi","doi":"10.1109/ICSCCC58608.2023.10177011","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10177011","url":null,"abstract":"Multifactor Authentication (MFA) schemes for securing communication in wireless applications have gained popularity in Research academia and enterprises. MFA schemes are able to provide adequate security for Multiserver-based wireless applications and they are less vulnerable to attacks like privileged-insider attacks, replay attacks, and some similar attacks. This paper reviews several MFA schemes for wireless applications based on multiple servers. The authentication schemes employ numerous factors, including the things that the user knows (password), things that the user has (smartphone), and things that the user is (fingerprint or biometric data). The aimof comparing the authentication schemes is to analyze how MFA across multiple servers provide secure communication accross multiple servers. we also compares the performance of reviewed MFA scheme based on total execution time of registration phase, login & authentication phase and password changing phase and computational cost. finally the paper is summarized with future work.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131801204","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}