Automatika最新文献_第6页

An evolutionary approach for depression detection from Twitter big data using a novel deep learning model with attention based feature learning mechanism 利用基于注意力的特征学习机制的新型深度学习模型，从 Twitter 大数据中采用进化方法检测抑郁症

Automatika Pub Date : 2024-01-10 DOI: 10.1080/00051144.2023.2296793

Prabhakar K, K. V

引用次数: 0

Effective deep learning based grade prediction system using gated recurrent unit (GRU) with feature optimization using analysis of variance (ANOVA) 使用门控递归单元（GRU）和方差分析（ANOVA）进行特征优化的基于深度学习的有效成绩预测系统

Automatika Pub Date : 2024-01-10 DOI: 10.1080/00051144.2023.2296790

S. Lakshmi, C. Maheswaran

引用次数: 0

An improvised analysis of smart data for IoT-based railway system using RFID 使用 RFID 对基于物联网的铁路系统的智能数据进行简易分析

Automatika Pub Date : 2024-01-02 DOI: 10.1080/00051144.2023.2295141

Shirly Sudhakaran, R. Maheswari, V. Kanchana Devi

{"title":"An improvised analysis of smart data for IoT-based railway system using RFID","authors":"Shirly Sudhakaran, R. Maheswari, V. Kanchana Devi","doi":"10.1080/00051144.2023.2295141","DOIUrl":"https://doi.org/10.1080/00051144.2023.2295141","url":null,"abstract":"RFID (radio frequency identification) is a progressively adopted technology in today’s automated world. Wireless technologies have enabled contactless payments, tracking, identifying, and many more features in a system that can be introduced to build a smart environment. This work overviews the usage of the IoT (Internet of Things) platform for tracking passengers and enabling online payments through wireless sensors and RFID technology in Chennai Suburban Railways. The tracking system consists of an RFID reader that can locate and track passive as well as mobile objects attached with passive RFID tags. The proposed system incorporates the installation of RFID readers at every entrance and exit of the railway station, and every passenger carries their own RFID tags. This not only enables online payments for passengers but also helps the government in tracking the crowd for demand monitoring. The new methodology creates a digital workspace and enforces lawful safety regulations both for the administration and the consumers. A prototype of the proposed system is implemented in real-time to understand the workings of the system. Data collection is done through RFID tags that act as transit cards and an analysis for consumer demand is done using the DBSCAN (Density-Based Spatial Clustering of Application with Noise) algorithm with a Randomized KD-tree for the analysis of spatial and temporal patterns. A new algorithm, the iDBSCAN (improved Density-Based Spatial Clustering of Application with Noise) algorithm is proposed for faster performance on the datasets.","PeriodicalId":503352,"journal":{"name":"Automatika","volume":"138 41","pages":"361 - 372"},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139453069","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 comparative study of lung disease classification using fine-tuned CXR and chest CT images 使用微调 CXR 和胸部 CT 图像进行肺病分类的比较研究

Automatika Pub Date : 2024-01-02 DOI: 10.1080/00051144.2023.2293274

M. Shimja, K. Kartheeban

{"title":"A comparative study of lung disease classification using fine-tuned CXR and chest CT images","authors":"M. Shimja, K. Kartheeban","doi":"10.1080/00051144.2023.2293274","DOIUrl":"https://doi.org/10.1080/00051144.2023.2293274","url":null,"abstract":"The diagnosis of lung disease is a challenging process that frequently combines clinical information, such as patient symptoms, medical history and test findings, with medical imaging, like X-rays or CT scans. The classification of lung diseases is very important in healthcare since it helps with diagnosis and treatment of many different lung diseases. A precise classification of lung conditions can aid doctors in choosing the best course of action and enhancing patient outcomes. Additionally, accurate classification can aid in evaluating the effectiveness of therapies, forecasting results and tracking the development of diseases. It is extremely important to accurately classify lung conditions. A comparison of a novel model for lung disease classification from chest CT and CXR images was presented in this paper. A modified VGG-16 model was used as the classification model. To improve the performance, a fine-tuning mechanism was added to the proposed model. The effectiveness of the suggested method is analyzed and compared on two distinct datasets in terms of performance metrics. The experimental outcomes showed that the suggested model performs better on the CXR image dataset.","PeriodicalId":503352,"journal":{"name":"Automatika","volume":"132 11","pages":"312 - 322"},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139453313","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 security and privacy preserving approach based on social IoT evolving encoding using convolutional neural network 使用卷积神经网络的基于社交物联网演化编码的安全和隐私保护方法

Automatika Pub Date : 2024-01-02 DOI: 10.1080/00051144.2023.2295143

Maniveena C, R. Kalaiselvi

引用次数: 0

Overhead power line detection from aerial images using segmentation approaches 利用分割方法从航空图像中检测架空输电线

Automatika Pub Date : 2024-01-02 DOI: 10.1080/00051144.2023.2296798

Satheeswari Damodaran, Leninisha Shanmugam, N. M. J. Swaroopan

{"title":"Overhead power line detection from aerial images using segmentation approaches","authors":"Satheeswari Damodaran, Leninisha Shanmugam, N. M. J. Swaroopan","doi":"10.1080/00051144.2023.2296798","DOIUrl":"https://doi.org/10.1080/00051144.2023.2296798","url":null,"abstract":"Ensuring the optimal efficiency of electrical networks requires vigilant surveillance and preventive maintenance. While traditional methods, such as human patrols and helicopter inspections, have been longstanding practices for grid control by electrical power distribution companies, the emergence of Unmanned Aerial Vehicles (UAV) technology offers a more efficient and technologically advanced alternative. The proposed comprehensive pipeline integrates various elements, including preprocessing techniques, deep learning (DL) models, classification algorithms (CA), and the Hough transform, to effectively detect powerlines in intricate aerial images characterized by complex backgrounds. The pipeline begins with Canny edge detection, progresses through morphological reconstruction using Otsu thresholding, and concludes with the development of the RsurgeNet model. This versatile model performs binary classification and feature extraction for power line identification. The Hough transform is employed to extract semantic powerlines from intricate backgrounds. Comparative assessments against three existing architectures and classification algorithms highlight the superior performance of RsurgeNet. Experimental results on the VL-IR dataset, encompassing both visible light (VL) and infrared light (IR) images validate the effectiveness of the proposed approach. RsurgeNet demonstrates reduced computational requirements, achieving heightened accuracy and precision. This contribution significantly enhances the field of electrical network maintenance and surveillance, providing an efficient and precise solution for power line detection.","PeriodicalId":503352,"journal":{"name":"Automatika","volume":"18 11","pages":"261 - 288"},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139452095","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

Breast cancer recurrence prediction with deep neural network and feature optimization 利用深度神经网络和特征优化预测乳腺癌复发

Automatika Pub Date : 2024-01-02 DOI: 10.1080/00051144.2023.2293280

Arathi Chandran, V. Mary, Amala Bai

{"title":"Breast cancer recurrence prediction with deep neural network and feature optimization","authors":"Arathi Chandran, V. Mary, Amala Bai","doi":"10.1080/00051144.2023.2293280","DOIUrl":"https://doi.org/10.1080/00051144.2023.2293280","url":null,"abstract":"Breast cancer remains a pervasive global health concern, necessitating continuous efforts to attain effectiveness of recurrence prediction schemes. This work focuses on breast cancer recurrence prediction using two advanced architectures such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), integrated with feature selection techniques utilizing Logistic Regression (LR) and Analysis of Variance (ANOVA). The well-known Wisconsin cancer registry dataset, which contains vital diagnostic data from breast mass fine-needle aspiration biopsies, was employed in this study. The mean values of accuracy, precision, recall and F1-score for the proposed LR-CNN-LSTM model were calculated as 98.24%, 99.14%, 98.30% and 98.14% respectively. The mean values of accuracy, precision, recall and F1-score for the proposed ANOVA-GRU model were calculated as 96.49%, 97.04%, 96.67% and 96.67% respectively. The comparison with traditional methods showcases the superiority of our proposed approach. Moreover, the insights gained from feature selection contribute to a deeper understanding of the critical factors influencing breast cancer recurrence. The combination of LSTM and GRU models with feature selection methods not only enhances prediction accuracy but also provides valuable insights for medical practitioners. This research holds the potential to aid in early diagnosis and personalized treatment strategies.","PeriodicalId":503352,"journal":{"name":"Automatika","volume":"11 6","pages":"343 - 360"},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139452302","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 cryptographic method to have a secure communication of health care digital data into the cloud 将医疗数字数据安全传输到云端的加密方法

Automatika Pub Date : 2024-01-02 DOI: 10.1080/00051144.2023.2301240

K. Selvakumar, S. Lokesh

{"title":"A cryptographic method to have a secure communication of health care digital data into the cloud","authors":"K. Selvakumar, S. Lokesh","doi":"10.1080/00051144.2023.2301240","DOIUrl":"https://doi.org/10.1080/00051144.2023.2301240","url":null,"abstract":"Cloud computing is a technology that holds great promise and has potential to revolutionize the healthcare sector. Many security and privacy issues are brought up by the cloud’s centralization of data for both patients and healthcare professionals. There is a need for maintaining secrecy in communication in exchanging medical data between the sender and the receiver, which can be done by cryptography. This article presents a cryptographic algorithm (encryption and decryption) to have a secure communication of digital health care confidential data using DNA cryptography and Huffman coding. The interesting property is the cipher size obtained from our algorithm is equal to the size of the cipher obtained from the character set of given data. Security analysis is provided to show the security of data when stored and transmitted to the cloud. The cryptographic requirements, key space analysis, key and plain text sensitivity, sensitive score analysis, sensitivity and specificity, optimal threshold, randomness analysis, uniqueness of implementation, entropies of binary bits, DNA bases, DNA bases with Huffman code, Huffman encoded binary bits and cloud service provider’s risk are analyzed. The method proposed is compared with other cryptographic methods and results that it is more secure and stronger than other methods.","PeriodicalId":503352,"journal":{"name":"Automatika","volume":"141 35","pages":"373 - 386"},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139453068","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

Finite-time kinematic path-following control of underactuated ASV with disturbance observer 带扰动观测器的欠驱动自动变速器有限时运动学路径跟踪控制

Automatika Pub Date : 2024-01-02 DOI: 10.1080/00051144.2023.2298099

Lina Jin, Shuanghe Yu, Guoyou Shi, Xiaohong Wang

引用次数: 0

Study and evaluation of automatic offloading for function blocks of applications 研究和评估应用程序功能块的自动卸载

Automatika Pub Date : 2024-01-02 DOI: 10.1080/00051144.2024.2301888

Yoji Yamato

引用次数: 0