Development of a Smart Instrumentation for Analyzing Railway Track Health Monitoring Using Forced Vibration

Chowdhry, BS; Shah, Ali Akbar; Harris, Nick; Hussain, Tanweer; Nisar, Kashif

doi:10.1109/aict50176.2020.9368670

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…The proposed methodology was compared with other state of the art machine learning classifiers like SVM, RF, NN, and auto-encoders, and was shown to provide more accurate prediction results. Other research has discussed the application of technologies such as the Internet of Things (IoT), networks, software-defined networking (SDN), and wireless sensor networks (WSN) [32][33][34][35][36][37][38][39] Boyu Lyu et al [22] proposed an approach converting RNA-Seq data into 2D images which were then classified by CNN. This technique was applied to 32 types of tumors for classification.…”

Section: Related Workmentioning

confidence: 99%

Analyzing RNA-Seq Gene Expression Data Using Deep Learning Approaches for Cancer Classification

et al. 2022

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Ribonucleic acid Sequencing (RNA-Seq) analysis is particularly useful for obtaining insights into differentially expressed genes. However, it is challenging because of its high-dimensional data. Such analysis is a tool with which to find underlying patterns in data, e.g., for cancer specific biomarkers. In the past, analyses were performed on RNA-Seq data pertaining to the same cancer class as positive and negative samples, i.e., without samples of other cancer types. To perform multiple cancer type classification and to find differentially expressed genes, data for multiple cancer types need to be analyzed. Several repositories offer RNA-Seq data for various cancer types. In this paper, data from the Mendeley data repository for five cancer types are analyzed. As a first step, RNA-Seq values are converted to 2D images using normalization and zero padding. In the next step, relevant features are extracted and selected using Deep Learning (DL). In the last phase, classification is performed, and eight DL algorithms are used. Results and discussion are based on four different splitting strategies and k-fold cross validation for each DL classifier. Furthermore, a comparative analysis is performed with state of the art techniques discussed in literature. The results demonstrated that classifiers performed best at 70–30 split, and that Convolutional Neural Network (CNN) achieved the best overall results. Hence, CNN is the best DL model for classification among the eight studied DL models, and is easy to implement and simple to understand.

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Section: Related Workmentioning

confidence: 99%

Analyzing RNA-Seq Gene Expression Data Using Deep Learning Approaches for Cancer Classification

et al. 2022

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“…Data security was also supported by the absence of intermediaries, because the involvement of intermediaries also leads to data security breaches. By utilizing distributed ledger technology, intermediaries may become obsolete, significantly boosting the user's safety [35].…”

Section: Literature Studymentioning

confidence: 99%

Blockchain Technology and Artificial Intelligence Based Decentralized Access Control Model to Enable Secure Interoperability for Healthcare

Rana

Nisar

et al. 2022

Sustainability

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Healthcare, one of the most important industries, is data-oriented, but most of the research in this industry focuses on incorporating the internet of things (IoT) or connecting medical equipment. Very few researchers are looking at the data generated in the healthcare industry. Data are very important tools in this competitive world, as they can be integrated with artificial intelligence (AI) to promote sustainability. Healthcare data include the health records of patients, drug-related data, clinical trials data, data from various medical equipment, etc. Most of the data management processes are manual, time-consuming, and error-prone. Even then, different healthcare industries do not trust each other to share and collaborate on data. Distributed ledger technology is being used for innovations in different sectors including healthcare. This technology can be incorporated to maintain and exchange data between different healthcare organizations, such as hospitals, insurance companies, laboratories, pharmacies, etc. Various attributes of this technology, such as its immutability, transparency, provenance etc., can bring trust and security to the domain of the healthcare sector. In this paper, a decentralized access control model is proposed to enable the secure interoperability of different healthcare organizations. This model uses the Ethereum blockchain for its implementation. This model interfaces patients, doctors, chemists, and insurance companies, empowering the consistent and secure exchange of data. The major concerns are maintaining a history of the transactions and avoiding unauthorized updates in health records. Any transaction that changes the state of the data is reflected in the distributed ledger and can be easily traced with this model. Only authorized entities can access their respective data. Even the administrator will not be able to modify any medical records.

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“…In this way, security can be ensured to mitigate attacks caused by human carelessness, lack of awareness, and other bot attacks to protect sensitive data from possible security attacks [1][2][3][4][5][6] like distributed denial of service (DDoS) attacks [7][8][9][10] as the first line of defense. Furthermore, information security is big factor to implement internet of things (IoT) for smart cities [11][12][13][14][15][16][17][18][19] using software-defined networking [SDN] [20][21][22][23][24][25][26], named data networking (NDN) [27][28][29] and cloud computing network [30] with voice over IP (VoIP) [31][32][33][34] fiber optic [35][36][37], worldwide interoperability for microwave access (WiMAX) [37][38][39][40], swarm intelligence (SI) [41], artificial intelligence (AI), machine learning (ML) [42], deep learning (DL) [42][43]…”

Section: Introductionmentioning

confidence: 99%

Analysis and Intellectual Structure of the Multi-Factor Authentication in Information Security

Saqib¹,

Khan²,

Javed³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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This study presents the current state of research on multi-factor authentication. Authentication is one of the important traits in the security domain as it ensures that legitimate users have access to the secure resource. Attacks on authentication occur even before digital access is given, but it becomes quite challenging with remote access to secure resources. With increasing threats to single authentication schemes, 2Factor and later multi-factor authentication approaches came into practice. Several studies have been done in the multi-factor authentication discipline, and most of them proposed the best possible approaches, but there are very limited studies in the area that can comprehend all these innovative and effective approaches. Using Web of Science data of the research publications on the topic, the study adopted the bibliometric approach to find the evolution of authentication in the security domain, especially multi-factor authentication. This study finds the impact of the research in the selected domain using bibliometric analysis. This research also identifies the key research trends that most of the researchers are paying attention to. The highest number of publications on multi-factor authentication were published in 2019 while the highest number of citations were received in 2014. United States, India, and China are the leading countries publishing the most on multi-factor authentication.

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Development of a Smart Instrumentation for Analyzing Railway Track Health Monitoring Using Forced Vibration

Cited by 5 publications

References 20 publications

Analyzing RNA-Seq Gene Expression Data Using Deep Learning Approaches for Cancer Classification

Analyzing RNA-Seq Gene Expression Data Using Deep Learning Approaches for Cancer Classification

Blockchain Technology and Artificial Intelligence Based Decentralized Access Control Model to Enable Secure Interoperability for Healthcare

Analysis and Intellectual Structure of the Multi-Factor Authentication in Information Security

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