Kandala N. V. P. S. Rajesh

This study presents a novel fragile watermarking technique to detect and restore image tampering, enhancing security in digital image transmission. The proposed method integrates Schur decomposition and discrete wavelet transform (DWT) for watermark embedding, ensuring robustness against attacks compared to existing methods. Schur decomposition provides numerical stability in matrix factorization, while DWT enhances resilience through multi-resolution analysis. A semi-blind extraction algorithm, relying only on a secret key, enables active tampering detection without requiring the original image. Upon detection of distortions, the proposed recovery mechanism restores the tampered regions of the image. The effectiveness of the proposed scheme is validated through structural similarity, peak signal-to-noise ratio, and normalized cross-correlation metrics, demonstrating superior performance compared to existing methods. This approach is applicable to secure medical imaging, forensic investigations, and copyright protection, ensuring image integrity in real-world scenarios.

This systematic review comprehensively examines the application and impacts of Educational Data Mining (EDM) over the past decade. It explores the use of various data mining tools and techniques, statistics, and machine learning algorithms in education. The review discusses how EDM helps understand and improve the learning experience, educational strategies, and institutional efficiency. It highlights the iterative process of EDM, its applications, and the benefits it offers to different stakeholders, including students, teachers, and educational institutions. The paper also discusses the challenges related to data ethics, privacy, and security in EDM. Key sections include a methodology for conducting the systematic review, exploring different data mining techniques and learning styles, and using Artificial Intelligence in EDM. The review concludes with a discussion of findings, future research directions, and a summary of the study’s contributions and limitations.

Parkinson's disease is a progressive neurological disorder that significantly affects individuals worldwide. Early and accurate classification of the disease is crucial for timely intervention and improved patient outcomes. This study aims to develop an effective classification system using drawings of spirals and waves to discriminate between healthy individuals and those with Parkinson's disease, aiming to provide an early diagnostic method, leading to improved patient lifespan. The study utilizes two sets of drawings: spirals and waves. Data augmentation techniques are employed to increase the dataset size and enhance training data for deep neural networks. The Pyramid Histogram of Oriented Gradients (PHoG) algorithm is applied to compute shape descriptors from healthy and Parkinson's drawings. A Visual Geometry Group (VGG)-based deep learning model is used to extract significant features from the modified drawings, particularly from the fc6 and fc7 layers. Supervised classifiers, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), are employed individually and in combination to classify the extracted features. The results demonstrate that the fused features achieved the highest accuracy values: 98.6% for spiral drawings using SVM and 96.57% for wave drawings using KNN. These accuracy rates highlight the effectiveness of the proposed method in accurately classifying Parkinson's disease based on drawings of spirals and waves. The findings suggest that the proposed method has the potential to serve as a non-invasive and reliable tool for early diagnosis of Parkinson's disease. It can enable timely interventions and improved patient care.

Attention deficit hyperactivity disorder (ADHD) is one of the common neurodevelopmental disorders in children. This paper presents an automated approach for ADHD detection using the proposed entropy difference (EnD)-based encephalogram (EEG) channel selection approach. In the proposed approach, we selected the most significant EEG channels for the accurate identification of ADHD using an EnD-based channel selection approach. Secondly, a set of features is extracted from the selected channels and fed to a classifier. To verify the effectiveness of the channels selected, we explored three sets of features and classifiers. More specifically, we explored discrete wavelet transform (DWT), empirical mode decomposition (EMD) and symmetrically-weighted local binary pattern (SLBP)-based features. To perform automated classification, we have used k-nearest neighbor (k-NN), Ensemble classifier, and support vectors machine (SVM) classifiers. Our proposed approach yielded the highest accuracy of 99.29% using the public database. In addition, the proposed EnD-based channel selection has consistently provided better classification accuracies than the entropy-based channel selection approach. Also, the developed method has outperformed the existing approaches in automated ADHD detection.

Facial expression analysis is one of the most essential tools for behavior interpretation and emotion modeling in Intelligent Human-Computer Interaction (HCI). Although humans can easily interpret facial emotions, computers have great difficulty doing so. Analyzing changes and deformations in the face is one of the methods through which machines can interpret facial expressions. However, maintaining great precision while being accurate, stable, and quick is still challenging in this field. To address this issue, this research presents an innovative and novel method to fully automatically extract critical features from a face during a facial expression. Various machine learning models are used on these features to analyze emotions. We used the optical flow algorithm to extract motion vectors divided into sections on the subject’s face. Finally, each section and its symmetric section were used to calculate a new vector. The final features produce a state-of-the-art accuracy of over 98% in emotion classification in the Extended Cohen-Kanade (CK+) facial expression dataset. Furthermore, we proposed an algorithm to filter the most important features with an SVM classifier and achieved an accuracy of over 97 % by only looking at 15% of the face area.

Magnetic resonance imaging (MRI) is a powerful medical imaging technique widely used for diagnosing various conditions due to its ability to provide detailed images of internal structures within the body. However, like any imaging modality, MRI images can be susceptible to artifacts, which may arise from a variety of sources including hardware imperfections, patient motion, and image acquisition techniques. Detecting and mitigating these artifacts are crucial steps in ensuring the reliability and clinical utility of MRI scans. In this paper, we present algorithms specifically designed to address the challenges of undersampling and motion artifacts inMRimages. Our approach involves leveraging advanced image processing techniques, including line detection algorithms for undersampling detection and blur parameter estimation for motion artifact analysis. By accurately identifying and quantifying these artifacts, our algorithms aim to improve the overall quality and completeness of MRI data, ultimately enhancing diagnostic accuracy and patient care.