2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)最新文献

A Novel Framework for Building Vulnerability Assessment for the 2015 Nepal Earthquake 2015年尼泊尔地震脆弱性评估新框架

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) Pub Date : 2023-05-25 DOI: 10.1109/ACCAI58221.2023.10201114

R. Ranjan, S. Pasari, Sonu Devi, H. Verma

{"title":"A Novel Framework for Building Vulnerability Assessment for the 2015 Nepal Earthquake","authors":"R. Ranjan, S. Pasari, Sonu Devi, H. Verma","doi":"10.1109/ACCAI58221.2023.10201114","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10201114","url":null,"abstract":"On April 25, 2015, a devastating earthquake of magnitude Mw 7.8 hit Nepal, killing around 9000 people and injuring 22000 more. Following the disaster, extensive field research and inspections were conducted in Nepal to determine the extent of damage to the earthquake-affected structures. The post-earthquake investigation procedure becomes extremely difficult due to the vast number of structures and types of buildings in the area. However, knowing a building’s description beforehand can assist in determining the extent of possible damages due to a large event. In light of this, the present study aims to provide an effective formulation for building vulnerability assessment using several parameters, such as number of floors, construction materials, house type (public or private), and age of building. A huge dataset comprising building information of around 3,50,000 buildings on 39 variables is used for this purpose. Six machine learning methods, namely logistic regression, decision-tree classifier, k-nearest neighbor, linear discriminant analysis, random forest, and extreme gradient boosting algorithms are implemented. Based on the score, the grading boosting algorithm is found to be the most suitable algorithm. The findings are helpful for better urban planning, social policymaking, suitable material identification for building construction, and moreover, to set up a national level disaster risk reduction (DRR) strategy to minimize earthquake losses in Nepal..","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"57 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":"127093356","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

An Appraisal in to the Effects of Partial Shading on an Urban Photovoltaic System Using the Internet of Things 物联网城市光伏系统局部遮阳效果评价

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) Pub Date : 2023-05-25 DOI: 10.1109/ACCAI58221.2023.10201066

L. Maguluri, S. Farook, S. R., Ashutosh Dixit, K.P. Aishwarya, J. Dhanraj

引用次数: 0

An AI Enabled Framework for MRI-based Data Analytics for Efficient Brain Stroke Detection 基于核磁共振数据分析的人工智能框架，用于有效的脑卒中检测

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) Pub Date : 2023-05-25 DOI: 10.1109/ACCAI58221.2023.10201136

Anitha Patil, S. Govindaraj

{"title":"An AI Enabled Framework for MRI-based Data Analytics for Efficient Brain Stroke Detection","authors":"Anitha Patil, S. Govindaraj","doi":"10.1109/ACCAI58221.2023.10201136","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10201136","url":null,"abstract":"These days, it seems that medical image processing is a good fit for deep learning-based models. There is a potential basis for exploiting healthcare services thanks to advancements in deep learning and pre-trained models in computer vision applications. According to the World Health Organization (WHO), a clinical decision support system (CDSS) based on deep learning has the potential to advance the state of the art in medical image analysis. Timely management is essential due to the high death and disability rates associated with stroke. Timely study of brain imaging allows for rapid medical action. Existing research on MRI-based brain stroke analysis requires a refined and enhanced strategy to reap the potential advantages of deep learning, and MRI is discovered to provide additional possibilities for medical picture analysis. As may be seen in the material, a lot of focus has been placed here. In this study, we present the Deep Automated Brain Stroke Detection Framework (DABSDF), a deep learning-based system for detecting strokes in brain MRI. The Deep Convolutional Neural Network-based Pipeline for Brain Stroke Detection is a method we proposed (DCNNP-BSD). To test the effectiveness of the proposed framework and its algorithms, a prototype application has been developed in the Python data science environment. We evaluate our model against current deep learning models. The effectiveness of the various models on the MRI dataset varies widely. In terms of performance, VGG16 fares the worst while the suggested model, DCNNP-BSD, fares the best. With a dice similarity coefficient of 0.8822979, 0.8554022 sensitivity, 0.99595785 specificity, and 0.97774774 accuracy, the suggested CNN-based deep learning model beat the state-of-the-art.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"4 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":"124988924","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

A Novel Technique to Minimising Mean Square Error in Stock Price Index Prediction Utilising Logistics Regression and LSTM Model 利用logistic回归和LSTM模型最小化股价指数预测均方误差的新技术

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) Pub Date : 2023-05-25 DOI: 10.1109/ACCAI58221.2023.10201122

C. Ebenesh, R. S. Kumar, Ezhil Grace. A

{"title":"A Novel Technique to Minimising Mean Square Error in Stock Price Index Prediction Utilising Logistics Regression and LSTM Model","authors":"C. Ebenesh, R. S. Kumar, Ezhil Grace. A","doi":"10.1109/ACCAI58221.2023.10201122","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10201122","url":null,"abstract":"The methodology that is recommended makes an attempt to anticipate and forecast changes in the price indices of the stock market for three specific equities that are traded on the stock market. The stock market is comprised of all of the many types of equities that are now being discussed. This paradigm makes an attempt to classify two unique types of classification algorithms, namely Long-Term Memory (LSTM) and Logistics Regression (LR). Long-Term Memory is an acronym for \"Long-Term Memory,\" while Logistics Regression is an acronym for \"LR.\" (LSTM). One of the criteria that is used to assess the performance of the models is the ability of both models to accurately anticipate the movement of an index that is traded on the Bombay Stock Exchange. (BSE). For the purposes of performing a study of the suggested structure for the projection of three stocks, an estimated total of thirty different participants were used. (AAPL, MSFT, and AMZN). When comparing the two models' levels of performance, it was found that the LR model (99.8%) performed substantially better than the LTSM model (72.3%) on average. This was noticed while conducting the comparison. (p0.05). When it comes to predicting stock indices by making use of the various parameters, the LR model performed noticeably better than the LTSM model.","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":"125049643","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

Intellectual Design of Bomb Identification and Defusing Robot based on Logical Gesturing Mechanism 基于逻辑手势机构的拆弹机器人智能设计

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) Pub Date : 2023-05-25 DOI: 10.1109/ACCAI58221.2023.10201034

M. Shyam, M. Amalasweena, S. S., K. Balasaranya, R. Renugadevi, K. P. Chandran

{"title":"Intellectual Design of Bomb Identification and Defusing Robot based on Logical Gesturing Mechanism","authors":"M. Shyam, M. Amalasweena, S. S., K. Balasaranya, R. Renugadevi, K. P. Chandran","doi":"10.1109/ACCAI58221.2023.10201034","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10201034","url":null,"abstract":"Expanding human wants coincide with the development of ever more sophisticated technologies. The majority of this study focuses on robotic arms, which are used daily in the job done to satisfy these objectives and make life simpler. Mechanical limbs can interact with humans or be programmed to follow specific instructions. Law enforcement officers face a high-stakes, high-precision challenge while neutralizing explosives, since any misstep might result in serious injury or death. An idea derived from wireless bomb disposal robots is offered as a solution to the issue. Through using wireless control module, the robot will dispose of the explosive. A transmitter makes up the control module, while a receiver makes up the Robot itself. An ARDUINO UNO R3 board connects to the receiver module. Input from the transmitter controls the Robot's actions. An LCD screen is connected to a wireless video receiver in the control module, so you can watch what the camera captures on the screen. The robot may be operated with a wave of the hand thanks to a sensor module linked to an Arduino Nano microcontroller. The hand gesture based robot is better option to pick up the explosive items and diffusing","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"22 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":"125921307","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

Osteoarthritis Disease Detection using Efficient Hyper-Tuning Parameters 基于高效超调参数的骨关节炎疾病检测

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) Pub Date : 2023-05-25 DOI: 10.1109/ACCAI58221.2023.10200102

Nagendra Panini Challa, Beebi Naseeba, Gudigntla Vyshnavi, Thanneeru Priyanka, Nagaraju Jajam, K. Prasanna

引用次数: 2

Improved Accuracy in Early Identification of Ischaemic Stroke using K- Nearest Neighbors with Support Vector Machine 基于支持向量机的K近邻模型在缺血性脑卒中早期识别中的应用

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) Pub Date : 2023-05-25 DOI: 10.1109/ACCAI58221.2023.10200194

S. Manikandan, A. G, Josiah Samuel Raj. J

{"title":"Improved Accuracy in Early Identification of Ischaemic Stroke using K- Nearest Neighbors with Support Vector Machine","authors":"S. Manikandan, A. G, Josiah Samuel Raj. J","doi":"10.1109/ACCAI58221.2023.10200194","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200194","url":null,"abstract":"The major aim of this study was to use MRI scans as a diagnostic tool for identifying strokes in the brain. K-Nearest Neighbors, an innovative alternative to the Support Vector Machine, was used to improve accuracy and specificity beyond what had been achieved before. K-Nearest Neighbors (with a total of 20 participants) and Support Vector Machines (with a total of 10 participants) are compared and contrasted here. (which had a total of 10 participants). Alpha = 0.05, the enrollment ratio = 0.1, 95% confidence interval, and pre-test power = 98% were used in conjunction with the G Power software to arrive at the final sample size. In comparison to the Support Vector Machine algorithm's 89% accuracy and 76% specificity, the unique K-Nearest Neighbors algorithm achieves 97% accuracy and 89% specificity using the proposed method. According to the data, the level of statistical significance attained for accuracy is p = 0.005, while the level of significance attained for specificity is p = 0.045. These results are provided in light of the research's conclusions. When comparing K-Nearest Neighbors to Support Vector Machine classifiers, the state-of-the-art K-Nearest Neighbors method outperformed its predecessors.","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":"122398842","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

Detection of Holes on Indian Roads Using Information and Communication Technologies 利用信息和通信技术检测印度道路上的孔洞

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) Pub Date : 2023-05-25 DOI: 10.1109/ACCAI58221.2023.10199826

Dr. A. V. Sriharsha

引用次数: 0

An Innovative Application for Employee Attendance using Near Field Communication to Reduce the Time Complexity using IP and Geo Tracking Comparing with Biometrics 与生物识别技术相比，利用IP和地理跟踪技术降低员工考勤时间复杂度的近场通信创新应用

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) Pub Date : 2023-05-25 DOI: 10.1109/ACCAI58221.2023.10199812

K. Shriraam, N. Deepa, Ezhil Grace. A

{"title":"An Innovative Application for Employee Attendance using Near Field Communication to Reduce the Time Complexity using IP and Geo Tracking Comparing with Biometrics","authors":"K. Shriraam, N. Deepa, Ezhil Grace. A","doi":"10.1109/ACCAI58221.2023.10199812","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10199812","url":null,"abstract":"A manually tracking thus every employee's attendance usually produces issues including precision and employee productivity. The current study focuses on the research to develop an IP and geo tracking-based attendance system software. This suggested effort seeks to lessen Time complexity of an employee attendance when compared to an existing system like Biometrics. Materials and Methods: The study setup is in our University. Since it is an Attendance Application Since there are no human samples involved, there is no ethical approval. The current study focuses on the research to design and develop a software of IP and Geo Tracking based attendance system. Accuracy of the Employee attendance application is performed with two groups: IP and Geo tracking, and Biometrics of sample size (N=10), and G power is 80% threshold 0.05% , CI 95%. Results:- Its objective of the suggested article includes several characteristics and features of tracking employees, data management and monitoring and maintaining their records and providing information services. Independent sample T-Test was carried out using IP and Geo Tracking and Biometrics. IP and Geo Tracking (81.25%) performs better than Biometrics (79%). A statistically significant disparity exists between Geo Tracking and (p <0.01) 2- tailed. Conclusion: This type of attendance system has several components such that an employee’s mobile IP is monitored and GPS is tracked which reads the employee’s information and marks their attendance automatically.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"64 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":"122522069","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

Body Fat Prediction using Various Regression Techniques 使用各种回归技术预测体脂

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) Pub Date : 2023-05-25 DOI: 10.1109/ACCAI58221.2023.10200647

Nikhil Mahesh, P. Pati, K. Deepa, Suresh Yanan

引用次数: 1