2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)最新文献

{"title":"Phishing Detection in E-mails using Machine Learning","authors":"Pankaj Saraswat, Madhav Singh Solanki","doi":"10.1109/ICTACS56270.2022.9987839","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987839","url":null,"abstract":"Emails are extensively utilized for both personal and business purposes as a form of communication. Information such as financial information, credit reports, log in details, and other sensitive and personal information is frequently shared over email. The transition time of email from sender to a receiver allows cybercriminals to exploit or breach the data shared, hence causing harm to the integrity of the data. Phishing remains a technique rummage-sale through con artists to gain subtle data from persons through impersonating well-known entities. The sender of a phished email strength persuades users to submit personal information based on fake data. The identification of a phished electronic mail is treated by way of an arrangement issue in this experiment, this tabloid explains the usage of mechanism knowledge techniques to categorize electronic mail as phished or ham. SVM and Random Forest classifiers reach a maximum accuracy of 99.87 percent in email categorization.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126540389","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":"Partial Shaded Solar Photovoltaic System Using Particle Swarm Optimization (PSO) Algorithm and Comparing with Novel Flower Pollination Algorithm (FPA) to Enhance Power Output","authors":"V. R. Krishna Reddy, M. V. Priya","doi":"10.1109/ICTACS56270.2022.9987993","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987993","url":null,"abstract":"The aim of the study is to design Flower pollination MPPT algorithm for partial shaded solar Photovoltaic System (PV). Comparison of FPA and PSO algorithm is carried out under varied irradiation condition to obtain maximum output power. Particle Swarm Optimization (PSO) and Flower pollination MPPT algorithm with 10 samples per group and a G power of 0.80 are implemented to analyze partial shaded solar Photovoltaic System (PV) under varying partial shading conditions. Considering the findings, it can be deduced that the Flower pollination MPPT algorithm produces 86.03 W power output when compared to the power output in Particle Swarm Optimization (PSO) of 85.5 W under varying partial shading conditions in the PV panel. The study has a significance value of 0.02 which is less than 0.05, hence it holds hypothesis good. Conclusion: FPA based MPPT produce more output power appears to be superior in relation to the PSO method on the given dataset.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131348265","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":"Enhancing the Detection of Fake News in Social Media using Support Vector Machine Algorithms Comparing over Apriori Algorithms","authors":"M. Renuka, T.P. Anithaashri","doi":"10.1109/ICTACS56270.2022.9988701","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988701","url":null,"abstract":"To enhance the detection of fake news in social media using artificial intelligence techniques and its performance is compared with apriori algorithm. Materials and Methods: The performance analysis has been done with the sample of (N=10) and compared with apriori (N=10), results were compared based on accuracy of both the algorithms. The significance level for support vector machine algorithms and Apriori algorithms having the values (p<0.05) with better performance. Result: The fake news in social media is detected by using the SVM algorithm and have a better accuracy of 91.87% than apriori algorithm with an accuracy of 31.76%. Conclusion: The implementation of this project shows the enhanced detection with support vector machine algorithm which is significantly higher than the apriori algorithm.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132809183","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":"Information Leakage Techniques in Cloud Computing: A Review","authors":"Geetinder Saini, Navdeep Kaur","doi":"10.1109/ICTACS56270.2022.9988405","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988405","url":null,"abstract":"Cloud computing is referred as a model that facilitates universal, suitable and on-demand network access for commonly used configurable computing resources having the ability of quick provisioning and releasing with minimum management effort from the client side and least service provider communication. Cloud computing is also regraded as the development of various technological advancements that work cooperatively for changing the approach an organization to construct their IT set-up. Cloud computing does not make use of any new technology. The cloud computing has various issues which include security, load balancing, information leakage etc. In this paper, various schemes for the information leakage are reviewed in terms of methodologies and results.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126596346","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":"TabNet to Identify Risks in Chronic Kidney Disease Using GAN's Synthetic Data","authors":"P. Kiran Rao, Subarna Chatterjee","doi":"10.1109/ICTACS56270.2022.9988284","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988284","url":null,"abstract":"The objective of this study was to develop a system for chronic kidney disease (CKD) and to identify relevant prognostic features using a clinical dataset. Accurate classification and major risk factors in chronic kidney disease lead to better prognosis and assist nephrologists. Due to privacy and other factors, the data source is not balanced to trail any models. Therefore, it is difficult to achieve consistent accuracy with an imbalanced dataset, and there will be a variance in results with different machine learning models. In the proposed study, GAN's generated synthesised dataset, which is very close to the original dataset, is used. A hybrid synthesised dataset consists of the original dataset along with the synthesised data generated with the GAN model. The proposed model also includes the most important risk variables for CKD. The metrics used in the study include F1-score, accuracy, and from the plot, it shows that the TabNet with GAN's synthetic data is more consistent and more accurate than the traditional machine learning techniques with imbalanced dataset. The proposed model iterated for 150 times to get the variance, which is much less than in proposed techniques with hybrid preprocessed datasets. The proposed work significantly increased the classification accuracy of chronic kidney disease. These models and parameters show how important health status data is for predicting the risk of and development of kidney disease.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130895282","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":"Federated Learning Approach towards Sentiment Analysis","authors":"Shefali Bansal, Medha Singh, Madhulika Bhadauria, Richa Adalakha","doi":"10.1109/ICTACS56270.2022.9987996","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987996","url":null,"abstract":"Smartphones have access to a tremendous quantity of data relevant to models, henceforth enhancing the consumer experience. General ML algorithms require centralization of data and models but pose difficulty in GDPR compliance, lack of trust from users' end, and limited transparency. FL is a collective and decentralized approach to Machine Learning that enhances privacy and security of data, complies with GDPR. It also advocates low power consumption and latency. This paper delivers the application of Federated Learning in sentiment analysis, in comparison to the Deep Learning algorithms - RNN, CNN used. The sentiments of the text are segmented in accordance with the spectrum of emotions they articulate - positive, negative, and neutral using Deep Learning algorithms. We have also added a web component (a dynamic web-app) to the sentiment analysis model to automate the prediction process.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132898698","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 Original Approach to Identify the Better Accuracy in Credit Card Fraud Transaction by Comparing Logistic Regression with K-Nearest Neighbours Algorithm","authors":"S. Poojitha, K. Malathi","doi":"10.1109/ICTACS56270.2022.9987804","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987804","url":null,"abstract":"This work was extracted from the raw data by suggesting machine learning techniques like the clustering model K-Nearest Neural Network (KNN) to determine whether the input data classifies legitimate or fraudulent transactions and to obtain greater accuracy. Materials and Methods: Detect the fraud techniques utilizing the F1-Measure score is the mean between precision and recall. The range of F1 scores is [0,1]. Implementing Machine learning algorithms, the sample size for Logistic Regression (N = 20), proposing the under-sampling technique is K-NN (N = 20) and G power (value = 0.8). Results: K-Nearest Neural Network method classifies any new incoming transaction calculating the K-Nearest point to achieve this accuracy. The independent sample T-Test (=.272) result (p0.05) with a 95% confidence level does not statistically support the two algorithms KNN and LR. Conclusion: K Nearest Neural Algorithm proves (99.4%) to detect fraud, false alert rate with accuracy it appears to be better than Logistic Regression (99.1%).","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134456160","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":"Automated Multiclass Classification Using Deep Convolution Neural Network on Dermoscopy Images","authors":"Shaik Rasool, U. N. Dulhare, Mohammed Naseer Khan, Durgaprasad Gangodkar, A. Rana, Ravi Kalra","doi":"10.1109/ICTACS56270.2022.9988394","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988394","url":null,"abstract":"Due to the nature of the surgery, treating skin tumours manually takes a long time and can only be done on one individual at a time. As a result, it is evident that computational and analytical methodologies are required for meaningful classification of skin lesions at various stages. We have demonstrated a fully automated method of classifying the wide variety of skin lesions that exist. The automatic dissection of skin lesions and their isolation are two of the most critical and interconnected difficulties in computer-assisted skin cancer detection. Although deep learning models see widespread use, they are typically developed to address only one problem, when it could be more efficient to address both simultaneously. In this research, we propose a model for detecting and labelling skin lesions that makes use of Bootstrapping Ensembles and Convolutional Neural Networks (BE-CNN). This theory was developed by the authors of the study. The CI-SN (Compute-Intensive Segmentation Network) is the backbone of this approach (improved-SN). However, the Compute-Intensive Segmentation Network can detect and categorise skin lesions by creating pre-bootstrapping uneven lesion coverings. The strategy's objective is for the arrangement and division networks to cooperate and learn from one another. To do this, a “bootstrapping” process is used. However, we suggest a novel use of segmentation networks to address issues stemming from both class and pixel variation. On the ISIC-HAM 10000 datasets, we find that the proposed BE-CNN model outperforms the function of separating skin lesions based on the current condition and stages techniques, with a mean accuracy of 92.67%. We reached this result after observing that the suggested model more effectively classified skin lesions into their respective stages than the prevalent condition and stages-based methods. The findings demonstrate that a continuous bootstrapping strategy can be used to partition and classify skin lesions in a connected model. Doing both at once like this would prove the point.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"164 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115696552","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":"Autism Spectrum Disorder Detection using theDeep Learning Approaches","authors":"Ramanjot, Dalwinder Singh, Manik Rakhra, S. Aggarwal","doi":"10.1109/ICTACS56270.2022.9988442","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988442","url":null,"abstract":"This Autism Disorder is a developmental impairmentthat affects how a person perceives, communicates, and behaves.It is brought on by changes in the brain. Before the age of three,ASD develops, and it can persist up to death. The self-harm attempts made by those who have this disorder are significantly higher than people without have it. For diagnosing this disorder at an early stage, early detection is necessary. Various machine learning (ML) techniques namely- Random Forest, SVM, NaiveBayes, Decision Tree, etc., and deep learning (DL) approaches such as VGG16, DenseNet, AlexNet, etc., can be utilized on the questionnaires filled during the conducted survey based on behavior whereas, images can also be provided as input to these approaches for identifying of ASD. The proposed methodology suggests the detection of autism using deep learning algorithms based on transfer learning. ASD identification model consists of various stages namely- data collection/acquisition, pre- processing, data augmentation, feature extraction, and the last stage is classification.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"302 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114586083","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":"Design of Circular Microstrip Patch Antenna with Improved Return Loss at 35.2 GHz and Compare with the Existing 5G Antenna","authors":"B. S. P. Kumar, M. Raja","doi":"10.1109/ICTACS56270.2022.9988345","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988345","url":null,"abstract":"In this work, a novel circular microstrip patch antenna with reduced return loss at 35.2 GHz is compared with 5G antenna. With this research, the designs are presented based on the novel circular microstrip patch antenna which is made up of a circular structure that is built on a commercially available Rogers TMM 10i substrate material having a 1.6 mm thickness and compared with FR4. There were 20 samples in each group. The structure was simulated using HFSS 13.0. G power and significance value is calculated as 0.8 and p < 0.05. Results: According to simulation and experiment results, antenna return loss is decreased to -14.21 dB at 35.2 GHz. The Ansoft HFSS version 15.0 software was used to design this antenna and analyse its performance. Significant RF performance value p < 0.05 is obtained. Conclusion: The result shows that the antenna return loss has been minimized. It is more significant since the p value is less than 0.05.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116937997","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}