A Sequential Data Mining Technique for Identification of Fault Zone Using FACTS-Based Transmission

Rao, Koppula Srinivas; Reddy, B. Veerasekhar; Sarada, K.; Saikumar, K.

doi:10.4018/978-1-7998-6870-5.ch028

Cited by 11 publications

(4 citation statements)

References 18 publications

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“…The most and least amount of emotional leakage was seen with emotions of fear and delight, correspondingly. Real and fraudulent emotions could not be distinguished beyond the level of chance by untrained observers [27].…”

Section: Literature Surveymentioning

confidence: 99%

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Deep Learning-Based Micro Facial Expression Recognition Using an Adaptive Tiefes FCNN Model

Durga,

Rajesh,

Jagannadham

et al. 2023

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The scientific community and media have increasingly recognized the significance of microexpressions as indicators for detecting deception, as they reveal genuine emotions that individuals attempt to conceal. To capitalize on these subtle cues of deceit, researchers have developed applications capable of automatically detecting and recognizing microexpressions, which are typically imperceptible to the human eye. Facial expressions serve as fundamental ground truth determinants in multimedia applications. Earlier models, such as GA, RFO, X-Boosting, and Gradient Boosting, demonstrate greater efficiency in terms of time and accuracy. However, not all applications are capable of detecting micro facial expressions. In this study, a deep learning-based Tiefes FCNN model is designed specifically for micro facial expression recognition. Implemented using Python software, the proposed model consists of two stages: first, pre-processing is performed using image segmentation, followed by the application of a deep learning model employing Tiefes FCNN technology in the second stage. The experimental results exhibit significant performance improvements, including an accuracy of 99.02%, precision of 98.82%, F1-score of 97.8%, PSNR of 56.31, and CC of 96.31.

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Section: Literature Surveymentioning

confidence: 99%

“…Pre-processing is the start of overseeing facial image reading. This level is to enhance the character of the facial image in light of the fact that the image regardless of the pixels analysis [27].…”

Section: 𝑃 = [𝑇0 → 1(𝐶𝑠 + 𝐶𝑙) + 𝑇𝑐𝐶𝑐]𝑉2𝑑𝑑𝐹𝑐𝑘mentioning

confidence: 99%

Deep Learning-Based Micro Facial Expression Recognition Using an Adaptive Tiefes FCNN Model

Durga,

Rajesh,

Jagannadham

et al. 2023

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“…A three-factor user authentication mechanism was introduced in the domain of IoT-based healthcare systems [17]. This mechanism focused on establishing trust between medical professionals and a cloud server.…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Symmetric Cryptography based User Authentication Protocol for IoT based Applications

2024

SCPE

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The utilization of IoT is expanding across various domains, including telecare, intelligent home systems, and transportation networks. In these environments, IoT devices generate data gathered on remote servers, requiring external users to authenticate themselves to access the data. However, existing authentication protocols for IoT must meet the crucial requirements of speed, security against multiple attacks, and ensuring user anonymity and un-traceability. The main objective of this work is to find lightweight symmetric cryptography-based user authentication protocol tailored for IoT-based applications, focusing on MIM (Man-in-the-Middle) attack prevention, enhanced anonymity, and secure communication between IoT nodes and remote servers via IoT gateways. Existing protocols often lack sufficient defenses against MIM attacks and do not adequately address the need for enhanced user anonymity and secure communication channels within the IoT framework. Our research has identified that authentication techniques based on pairing are susceptible to attacks targeting temporary session-specific data, impersonation, privileged insiders, and offline password guessing. Furthermore, using bilinear pairing in these techniques requires significant computational and communication resources to address the security as mentioned above concerns. A new authentication mechanism must be proposed and designed explicitly for IoT scenarios. The proposed approach exclusively utilizes hash and exclusive-or operations to ensure suitability within the IoT context; thoroughly evaluated the recommended protocol against existing authentication protocols, employing both informal and formal analytical routines like BAN logic, ROR model, and AVISPA simulation. Our findings suggest protocol not only enhances performance but also enhances security. The proposed approach is a tried-and-true strategy for improving security rules in practical Internet of Things (IoT) settings addressing the inherent challenges posed by authentication requirements in IoT environments. The accuracy 98.93%, and Node detection rate 46.57% were improved which is a better outcome.

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“…The homogeneity principle depends on grey scale levels along with their thresholding. The threshold value can be determined using the local and global mean concepts [24]. The local mean is a particular grey-scale variance mechanism and the global mean is the complete image grey-scale variance.…”

Section: Segmentationmentioning

confidence: 99%

Cloud-Based LeNet-5 CNN for MRI Brain Tumor Diagnosis and Recognition

Srinivasarao,

Rajesh,

Saikumar

et al. 2023

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Early and accurate diagnosis of brain tumors is crucial in the medical field, as undetected or misdiagnosed tumors can lead to sudden death. Traditional models for diagnosing brain tumors suffer from low Time of Conversion (ToC) and low accuracy, contributing to a high mortality rate among the 5 million people affected by brain disease annually, as reported by the World Health Organization (WHO). Previous methods, such as Elastic Net Regression (ENR), Logistic Regression (LR), and other machine learning models, struggle to accurately locate and identify brain lesions. Moreover, these models are not suited for cloud-based platforms. To address this issue, we developed a sophisticated, cloud-based brain abnormality detection application using the LeNet-5 Convolutional Neural Network (CNN) on the DriveHQ platform. The pre-trained LeNet-5 model extracts features from ADNI-1, ADNI-2, and MIRIAD datasets. Real-time MRI brain images were collected from Manipal Hospital in Vijayawada, Andhra Pradesh, India. The LeNet-5 model employs hidden layers, flattened layers, max-pooling layers, dense layers, and ReLu layers for optimal performance. Our 2D-LeNet-5 CNN approach preprocesses images using split and merge techniques of binary mask segmentation. The Python 3.7 software tool was used to train and test datasets to identify abnormalities in MRI brain images. The proposed application achieved remarkable performance metrics, including 99.65% accuracy, 99.59% sensitivity, 99.72% F1-score, 99.25% recall, 59.32 PSNR, and 0.9929 MCC. These results demonstrate the superiority of our methodology in comparison to existing models, making it a promising solution for cloud-based brain tumor diagnosis and recognition.

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A Sequential Data Mining Technique for Identification of Fault Zone Using FACTS-Based Transmission

Cited by 11 publications

References 18 publications

Deep Learning-Based Micro Facial Expression Recognition Using an Adaptive Tiefes FCNN Model

Deep Learning-Based Micro Facial Expression Recognition Using an Adaptive Tiefes FCNN Model

A Lightweight Symmetric Cryptography based User Authentication Protocol for IoT based Applications

Cloud-Based LeNet-5 CNN for MRI Brain Tumor Diagnosis and Recognition

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