{"title":"Empirical Analysis of Heart Disease Prediction Using Deep Learning","authors":"Arunima Jaiswal, Monika Singh, Nitin Sachdeva","doi":"10.1109/ACCAI58221.2023.10201235","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10201235","url":null,"abstract":"In this current world, we keep hearing about heart disease problems every day and about the deaths due to them. and heart disease is also the reason for the crucial mortality rate around the world. According to the WHO, according to estimates, 17.9 million individuals die from cardiovascular diseases (CVD) each year. Detecting cardiovascular conditions, such as cardiac arrest and coronary heart disease, using regular clinical data analysis is a vital challenge. If cardiac disease is identified early, many lives can be spared. The use of machine learning (ML) algorithms enables intelligent decisions and exact predictions. In this study, a number of patient-provided factors decide whether or not heart disease exists. Our goal is to improve diagnostic precision and safeguard human resources in the medical industry. Some of the approaches used in this study to identify cardiac disease include the Long-Term Memory Network Model (LSTM), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Densenet, and Bi- LSTM. Of all the techniques utilized, CNN has the highest accuracy rate of 94.5%.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114444443","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":"Detection and classification of tumor cells from bone x-ray imagery using SVM classifier with Naïve Bayes classifier","authors":"Tanya Kumar, P. Jagadeesh","doi":"10.1109/ACCAI58221.2023.10199612","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10199612","url":null,"abstract":"The primary objective of this research article is to employ detection and classification of tumour cells from bone x-ray imagery by utilising the Support Vector Machine (SVM) classifier in comparison with the Naive Bayes (NB) classifier. This comparison will be made between the two classification methods. Components and Techniques: The dataset that is used in this paper makes use of the database that is housed in the computer vision lab at National Tsing Hua University (NTHU), which is open to the public. The detection and classification of tumour cells using bone x-ray images required a sample size of 280 (Group 1 = 140 and Group 2 =140), and the calculation was carried out using G-power 0.8, with alpha and beta qualities of 0.05 and 0.2, and a confidence interval of 95%. The sample size was determined by the number of tumour cells in each of the two groups. The Support Vector Machine (SVM) classifier and the Naive Bayes (NB) classifier were used to perform the detection and classification of tumour cells extracted from bone x-ray images. The number of samples used for each classification method was ten. The results show that the accuracy rate of the Support Vector Machine (SVM) classifier is 95.9034 times higher than the accuracy rate of the Naive Bayes (NB) classifier, which is 92.0934 times higher. The significance level of the study is determined to be p = 0.021. When it comes to the detection and classification of tumour cells using bone x-ray images, the Support Vector Machine (SVM) classifier yields superior results in terms of its accuracy rate when compared to the Naive Bayes (NB) classifier.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114827510","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":"A Robust Breast Cancer Classification Model using Extra-Trees Classifier for Histopathological Image","authors":"S. G, G. Ramkumar","doi":"10.1109/ACCAI58221.2023.10199852","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10199852","url":null,"abstract":"Thousands of people die every quarter from breast cancer. Diagnosis and treatment at an early stage can drastically lower mortality rates. Traditional manual diagnosis, on the other hand, necessitates a large amount of labor by pathologists and is prone to diagnostic mistakes the longer they work. Rapid and accurate diagnosis are greatly aided by automatic histopathological image recognition. The biomedical industry has been drawn to Artificial Intelligence and its innovative methodologies because of its familiarity with the field's successes. Recent research has shown that AI can grasp details better than humans, leading to more accurate findings that aid professionals in making more informed judgments. This study presents the Extra-Tree classifier (ETC) for breast cancer image categorization. These findings demonstrate that ETC outperformed the other algorithms we examined for this data in terms of accuracy. Future researchers in the field of breast cancer will be able to use the findings of this study to guide their investigations and inform their efforts to boost the efficiency of certain algorithms.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117259376","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":"Interference-Aware Channel Allocation using Cournot Game Comparing with Potential Game Algorithm to Maximize the Transmission Data Rate of Secondary Users in Cognitive radio Network","authors":"K. Kumar, P. Bharathi","doi":"10.1109/ACCAI58221.2023.10199339","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10199339","url":null,"abstract":"The objective of the proposed study is to improve the transmission data rate during interference-aware channel allocation for secondary users by substituting the Novel Cournot Game for other game algorithms. Resources and techniques Potential algorithms are compared, and the Novel Cournot game algorithm is suggested. The sample size was calculated using G power (80%), which resulted in 20 samples (N = 10 each). There is a statistically significant difference of 0.014 between the two groups (p 0.05). The mean transmission data rate of the Cournot game is 0.3520, which is significantly higher than the mean data rate of the potential game, which is 0.2460. The Novel Cournot Game outperforms the prospective game in terms of InterferenceAware Channel Allocation for Secondary transmission data rate.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115789200","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}
BukyaTejesh Naik, N. Bhavani, C.Gnanaprakasam, M. Swarna, R. Geetha, G.Saranya, S. Murugan, N. Duraimutharasan, Hannah Rose, P.N.Periyasamy, K. Rajeshkumar, P. G. Senthilvel, D. Manivarma, A.Akilandeswari, S. Radhika, Guna Sekhar, Reddy Thummala, R. Baskar, Rimlon Shibi, R. Vignesh, R. Sankar, A. Balaji, K. S. Kumar, Sharmila Bhargavi, R. Anusuya, P. N. Reddy, P. Bharathi, Krishna Kumar, A. Veeramuthu, R. Anandhi, Ata Kishore Kumar, R. Lakshmi, D. M. Sohan, Y.Harshavardhan, M. Shyam, K. Karthikeyan, K. K. Veni, S. Ramanaathan, B.T.Geetha, R. Pavaiyarkarasi, S.Ben, Paul D. Richard, R. P. Kumar, Tapan Kumar, P. Jagadeesh, K. Chandran, V. Chinnammal, M. Venkatanaresh, Nettyam Manideep, J.Mohana, P. C. Kumar, K. Jeevitha, J. Venkatesh, V. Indhumathi, Normamatova Mahsuda, C. Ananth, Zakirov Farukh, Khaydarov Fakhriddin, Makhmudov Iskandar, T. Kumar, S. Kavitha, N. Darwin, Anita Titus, V. Kishore, M. Babu, K. Rajesh, P.Ganesh, Josiah Samuel, Vishnu P Parandhaman, Pawan Rajesh Kumar, K. Rakesh, D. Sreehari, Dr S
{"title":"Decision Making on Deep Learning Algorithm in Claustrophobic Health Care Image Retrieval","authors":"A. Lavanya, Dr. B.Sheela","doi":"10.1109/ACCAI58221.2023.10200267","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200267","url":null,"abstract":"Content of medical Images, which has become a major topic in today's society. Several strategies have emerged and are being improved on a regular basis by developers in order to efficiently recognise images. During the revolution in technology, image retrieval becomes a major issue for computer society. This paper focused on the Claustrophobia images. Claustrophobia is the fear of enclosed spaces. These images will be incorporated using CNN technique and hashing techniques ,became the attention of programmers in order to improve the efficacy of calculating similarities between images. In fact, convolutional neural networks (CNNs) and deep learning have been regarded the foundation of image analysis over the past few years (CNN). Image retrieval research is the subject of this study, which provides an overview of the most recent developments. This field has seen a slew of new approaches emerge that provides well-coordinated care of the patient and enhance patient outcomes. However, neural network-based hash encoding is the most often used approach, and it may be divided into three primary categories: supervised, unsupervised, and semisupervised. It has been evaluated and compared to the most relevant literature to highlight the strengths and weaknesses of various strategies. Finally, the prediction of Claustrophobia will be incorporated.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115471061","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}
S. R, Ata Kishore Kumar, A. Titus, S. Hemajothi, J. Venkatesh, Lavanya A
{"title":"Design and Development of an AI based Intelligent Door for Home Security System","authors":"S. R, Ata Kishore Kumar, A. Titus, S. Hemajothi, J. Venkatesh, Lavanya A","doi":"10.1109/ACCAI58221.2023.10200307","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200307","url":null,"abstract":"These days, safety concerns are universal. A house, a factory, a bank, and so on are all things that virtually everyone desires. In today's world, when more and more people have easier access to various resources, it is increasingly vital to ensure that one has a safe place to live. There must be a way to keep us secure since the daily rise in crime rates demands it. We're all aware that there are sophisticated security systems out there, but unfortunately, they're out of reach for a lot of people. Trespassing, intrusion, and other forms of illegal entry are commonplace, with many common targets including banks, business offices, financial organizations, jewellery stores, and government organizations. Our goal is to alleviate this problem by creating a low-priced electronic system that can detect intruders' movements and trigger an alert. To this end, we offer a new Android app called Door Security System that makes use of IoT technology to keep tabs on the door's state, manage the door, and boost home safety. The smart lock system in this instance is an android-based one, and it can function in both single-mode and multi-mode settings. Financial and commercial institutions have an urgent need for this kind of technology. The system also provides features for the average user, who does not need to be the only one with access to the lock's controls. The system's high efficiency, low price, and cutting-edge features and user-friendly design all contribute to its value. In the event that the door is opened without permission, the alarm will sound and a message will be sent to the homeowners.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124814234","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}
Thirunavukkarasu. J, Sanjanaa. J, Sivarakshana. M, Yuvashree. R
{"title":"Retail Business Convenience Segmentation using Clustering and Data Visualization","authors":"Thirunavukkarasu. J, Sanjanaa. J, Sivarakshana. M, Yuvashree. R","doi":"10.1109/ACCAI58221.2023.10200947","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200947","url":null,"abstract":"The conventional approach to launching a business is to research and gather data regarding the past performance of rival businesses unless they were profitable or unsuccessful. Innovation is the ethos of the modern day, as everyone is engaged in a struggle to outperform one another. The objective of our suggested research is to create knowledge that will be helpful to aspiring business owners and small companies that are losing money. Our main aim is to assist small-scale manufacturers in becoming successful marketers. In return for the dataset, which must be provided as input, we will provide them with clear instructions on how to start a profitable business and recover from their loss. In order to analyse data more effectively, our planned work will segment clients based on stock input, weekly updates of stocks sold, and waste products. In this work, two different clustering techniques (k-Means and hierarchical) are used to classify the products into subsets, and their respective results are compared. Data will be segmented using clustering algorithms, allowing for much more focused production of the final result.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"200 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122844014","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 Retrieval using OSINT and GHDB","authors":"Manohari D, Dafni Rose J, Adithya E S, V. K.","doi":"10.1109/ACCAI58221.2023.10200057","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200057","url":null,"abstract":"Data in the 21st Century is like Oil in the 18th Century. The amount of data available publicly on the web repository is indeterminable. By gathering, processing, filtering, analysing, and correlating data from every corner of cyberspace, Open-Source Intelligence (OSINT) actually benefits from that open nature in this aspect. The first step to any successful attack is Reconnaissance, which is about identifying and gathering information about the target and OSINT is the tool that helps in proactively finding out valuable information, which can be considered while trying to attack the target. Proactive measures seek to predict and prevent cyberattacks from occurring in the first place. New measures against Cyberthreats and Cybercrime can be made with improvement in OSINT.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123848360","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}
Mano Ashish Tripathi, Elizabeth Chacko, J. V., Aditi Srivastava, Shaik Rehana Banu, V. Dwivedi
{"title":"Human Resource Management in the Power Industry Using Fuzzy Data Mining Algorithm","authors":"Mano Ashish Tripathi, Elizabeth Chacko, J. V., Aditi Srivastava, Shaik Rehana Banu, V. Dwivedi","doi":"10.1109/ACCAI58221.2023.10200599","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200599","url":null,"abstract":"Currently, database and information technology's frontier study area is data mining. It is acknowledged as one of the essential technologies with the greatest potential. Numerous technologies with a comparatively high level of technical substance are used in data mining, including artificial intelligence, neural networks, fuzzy theory, and mathematical statistics. The realization is challenging as well. Job satisfaction is one of several factors that cause employees to leave or switch jobs, and it is also closely tied to the organization's human resource management (HRM) procedures. It is continuously difficult and at times beyond the HR office's control to keep their profoundly qualified and talented specialists, yet data mining can assume a part in recognizing those labourers who are probably going to leave an association, permitting the HR division to plan a mediation methodology or search for options. We have analysed the major thoughts, techniques, and calculations of affiliation rule mining innovation in this article. They effectively finished affiliation broadcasting, acknowledged perception, and eventually revealed valuable data when they were coordinated into the human resource management arrangement of schools and colleges.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125128529","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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