Child Prime Label Approaches to Evaluate XML Structured Queries

The adoption of the eXtensible Markup Language (XML) as the standard format to store and exchange semi-structure data has been gaining momentum. The growing number of XML documents leads to the need for appropriate XML querying algorithms which are able to retrieve XML data efficiently. Due to the importance of twig pattern matching in XML retrieval systems, finding all matching occurrences of a tree pattern query in an XML document is often considered as a specific task for XML databases as well as a core operation in XML query processing. This thesis presents a design and implementation of a new indexing technique, called the Child Prime Label (CPL) which exploits the property of prime numbers to identify Parent-Child (P-C) edges in twig pattern queries (TPQs) during query evaluation. The CPL approach can be incorporated efficiently within the existing labelling schemes. The major contributions of this thesis can be seen as a set of novel twig matching algorithms which apply the CPL approach and focus on reducing the overhead of storing useless elements and performing unnecessary computations during the output enumeration. The research presented here is the first to provide an efficient and general solution for TPQs containing ordering constraints and positional predicates specified by the XML query languages. To evaluate the CPL approaches, the holistic model was implemented as an experimental prototype in which the approaches proposed are compared against state-of-the-art holistic twig algorithms. Extensive performance studies on various real-world and artificial datasets were conducted to demonstrate the significant improvement of the CPL approaches over the previous indexing and querying methods. The experimental results demonstrate the validity and improvements of the new algorithms over other related methods on common various subclasses of TPQs. Moreover, the scalability tests reveal that the new algorithms are more suitable for processing large XML datasets

Empowering Foot Health: Harnessing the Adaptive Weighted Sub-Gradient Convolutional Neural Network for Diabetic Foot Ulcer Classification

In recent times, DFU (diabetic foot ulcer) has become a universal health problem that affects many diabetes patients severely. DFU requires immediate proper treatment to avert amputation. Clinical examination of DFU is a tedious process and complex in nature. Concurrently, DL (deep learning) methodologies can show prominent outcomes in the classification of DFU because of their efficient learning capacity. Though traditional systems have tried using DL-based models to procure better performance, there is room for enhancement in accuracy. Therefore, the present study uses the AWSg-CNN (Adaptive Weighted Sub-gradient Convolutional Neural Network) method to classify DFU. A DFUC dataset is considered, and several processes are involved in the present study. Initially, the proposed method starts with pre-processing, excluding inconsistent and missing data, to enhance dataset quality and accuracy. Further, for classification, the proposed method utilizes the process of RIW (random initialization of weights) and log softmax with the ASGO (Adaptive Sub-gradient Optimizer) for effective performance. In this process, RIW efficiently learns the shift of feature space between the convolutional layers. To evade the underflow of gradients, the log softmax function is used. When logging softmax with the ASGO is used for the activation function, the gradient steps are controlled. An adaptive modification of the proximal function simplifies the learning rate significantly, and optimal proximal functions are produced. Due to such merits, the proposed method can perform better classification. The predicted results are displayed on the webpage through the HTML, CSS, and Flask frameworks. The effectiveness of the proposed system is evaluated with accuracy, recall, F1-score, and precision to confirm its effectual performance

Ensemble deep learning for brain tumor detection

With the quick evolution of medical technology, the era of big data in medicine is quickly approaching. The analysis and mining of these data significantly influence the prediction, monitoring, diagnosis, and treatment of tumor disorders. Since it has a wide range of traits, a low survival rate, and an aggressive nature, brain tumor is regarded as the deadliest and most devastating disease. Misdiagnosed brain tumors lead to inadequate medical treatment, reducing the patient's life chances. Brain tumor detection is highly challenging due to the capacity to distinguish between aberrant and normal tissues. Effective therapy and long-term survival are made possible for the patient by a correct diagnosis. Despite extensive research, there are still certain limitations in detecting brain tumors because of the unusual distribution pattern of the lesions. Finding a region with a small number of lesions can be difficult because small areas tend to look healthy. It directly reduces the classification accuracy, and extracting and choosing informative features is challenging. A significant role is played by automatically classifying early-stage brain tumors utilizing deep and machine learning approaches. This paper proposes a hybrid deep learning model Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) for classifying and predicting brain tumors through Magnetic Resonance Images (MRI). We experiment on an MRI brain image dataset. First, the data is preprocessed efficiently, and then, the Convolutional Neural Network (CNN) is applied to extract the significant features from images. The proposed model predicts the brain tumor with a significant classification accuracy of 99.1%, a precision of 98.8%, recall of 98.9%, and F1-measure of 99.0%.Qatar National Library and Qatar university internal - grant No. IRCC-2021-01

A Critical Review on the 3D Cephalometric Analysis Using Machine Learning

Machine learning applications have momentously enhanced the quality of human life. The past few decades have seen the progression and application of machine learning in diverse medical fields. With the rapid advancement in technology, machine learning has secured prominence in the prediction and classification of diseases through medical images. This technological expansion in medical imaging has enabled the automated recognition of anatomical landmarks in radiographs. In this context, it is decisive that machine learning is capable of supporting clinical decision support systems with image processing and whose scope is found in the cephalometric analysis. Though the application of machine learning has been seen in dentistry and medicine, its progression in orthodontics has grown slowly despite promising outcomes. Therefore, the present study has performed a critical review of recent studies that have focused on the application of machine learning in 3D cephalometric analysis consisting of landmark identification, decision making, and diagnosis. The study also focused on the reliability and accuracy of existing methods that have employed machine learning in 3D cephalometry. In addition, the study also contributed by outlining the integration of deep learning approaches in cephalometric analysis. Finally, the applications and challenges faced are briefly explained in the review. The final section of the study comprises a critical analysis from which the most recent scope will be comprehended

Emotion Detection Using Deep Normalized Attention-Based Neural Network and Modified-Random Forest

In the contemporary world, emotion detection of humans is procuring huge scope in extensive dimensions such as bio-metric security, HCI (human–computer interaction), etc. Such emotions could be detected from various means, such as information integration from facial expressions, gestures, speech, etc. Though such physical depictions contribute to emotion detection, EEG (electroencephalogram) signals have gained significant focus in emotion detection due to their sensitivity to alterations in emotional states. Hence, such signals could explore significant emotional state features. However, manual detection from EEG signals is a time-consuming process. With the evolution of artificial intelligence, researchers have attempted to use different data mining algorithms for emotion detection from EEG signals. Nevertheless, they have shown ineffective accuracy. To resolve this, the present study proposes a DNA-RCNN (Deep Normalized Attention-based Residual Convolutional Neural Network) to extract the appropriate features based on the discriminative representation of features. The proposed NN also explores alluring features with the proposed attention modules leading to consistent performance. Finally, classification is performed by the proposed M-RF (modified-random forest) with an empirical loss function. In this process, the learning weights on the data subset alleviate loss amongst the predicted value and ground truth, which assists in precise classification. Performance and comparative analysis are considered to explore the better performance of the proposed system in detecting emotions from EEG signals that confirms its effectiveness

Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features

Minutiae feature extraction and matching are not only two crucial tasks for identifying fingerprints, but also play an eminent role as core components of automated fingerprint recognition (AFR) systems, which first focus primarily on the identification and description of the salient minutiae points that impart individuality to each fingerprint and differentiate one fingerprint from another, and then matching their relative placement in a candidate fingerprint and previously stored fingerprint templates. In this paper, an automated minutiae extraction and matching framework is presented for identification and verification purposes, in which an adaptive scale-invariant feature transform (SIFT) detector is applied to high-contrast fingerprints preprocessed by means of denoising, binarization, thinning, dilation and enhancement to improve the quality of latent fingerprints. As a result, an optimized set of highly-reliable salient points discriminating fingerprint minutiae is identified and described accurately and quickly. Then, the SIFT descriptors of the local key-points in a given fingerprint are matched with those of the stored templates using a brute force algorithm, by assigning a score for each match based on the Euclidean distance between the SIFT descriptors of the two matched keypoints. Finally, a postprocessing dual-threshold filter is adaptively applied, which can potentially eliminate almost all the false matches, while discarding very few correct matches (less than 4%). The experimental evaluations on publicly available low-quality FVC2004 fingerprint datasets demonstrate that the proposed framework delivers comparable or superior performance to several state-of-the-art methods, achieving an average equal error rate (EER) value of 2.01%

A Multi-Feature Fusion Framework for Automatic Skin Cancer Diagnostics

Malignant melanoma is the most invasive skin cancer and is currently regarded as one of the deadliest disorders; however, it can be cured more successfully if detected and treated early. Recently, CAD (computer-aided diagnosis) systems have emerged as a powerful alternative tool for the automatic detection and categorization of skin lesions, such as malignant melanoma or benign nevus, in given dermoscopy images. In this paper, we propose an integrated CAD framework for rapid and accurate melanoma detection in dermoscopy images. Initially, an input dermoscopy image is pre-processed by using a median filter and bottom-hat filtering for noise reduction, artifact removal, and, thus, enhancing the image quality. After this, each skin lesion is described by an effective skin lesion descriptor with high discrimination and descriptiveness capabilities, which is constructed by calculating the HOG (Histogram of Oriented Gradient) and LBP (Local Binary Patterns) and their extensions. After feature selection, the lesion descriptors are fed into three supervised machine learning classification models, namely SVM (Support Vector Machine), kNN (k-Nearest Neighbors), and GAB (Gentle AdaBoost), to diagnostically classify melanocytic skin lesions into one of two diagnostic categories, melanoma or nevus. Experimental results achieved using 10-fold cross-validation on the publicly available MED-NODEE dermoscopy image dataset demonstrate that the proposed CAD framework performs either competitively or superiorly to several state-of-the-art methods with stronger training settings in relation to various diagnostic metrics, such as accuracy (94%), specificity (92%), and sensitivity (100%)

Reversible Logic-Based Hexel Value Differencing—A Spatial Domain Steganography Method for Hexagonal Image Processing

The field of steganography has witnessed considerable advancements in square-pixel-based image processing (SIP). However, the application of steganography in Hexel (Hexagonal pixel)-based Image Processing (HIP) is still underexplored. This study introduces a pioneering spatial steganography method called the Reversible Logic-Based Hexel Value Differencing (RLBHVD) method in the HIP domain. Our approach draws inspiration from Pixel-Value-Differencing (PVD), a SIP fundamental spatial-domain (S-D) steganography method. Initially, the image is transformed into the HIP domain using the custom software infrastructure developed for this project. Due to the absence of commercial equipment capable of producing HIP-domain images, traditional digital imaging systems are employed with their sensor components, analog-to-digital conversion units, and square-pixel-based displays. Once the image is converted, it is partitioned into standardized heptads, each comprising seven hexels. Simultaneously, the secret message is segmented for embedding into the hexels within each heptad. Unlike SIP-domain PVD, which embeds segments into independent pixel pairs, our method performs iterative embedding within each heptad. Additionally, we leverage Feynman gates, a core element of reversible logic, to achieve retrieval of both the cover image and the secret message. Unlike PVD in SIP, our approach enables reversibility in the recovery process. Experimental results demonstrate that our proposed method, RLBHVD, outperforms its SIP counterpart, PVD, by achieving a low Mean Squared Error (MSE), high Peak Signal-to-Noise Ratio (PSNR), and significant similarity between the stego-image and cover image histograms. These findings highlight the efficacy and superiority of our HIP-based steganography approach in comparison to existing SIP methods

Optimizing Kidney Stone Prediction through Urinary Analysis with Improved Binary Particle Swarm Optimization and eXtreme Gradient Boosting

Globally, the incidence of kidney stones (urolithiasis) has increased over time. Without better treatment, stones in the kidneys could result in blockage of the ureters, repetitive infections in the urinary tract, painful urination, and permanent deterioration of the kidneys. Hence, detecting kidney stones is crucial to improving an individual’s life. Concurrently, ML (Machine Learning) has gained extensive attention in this area due to its innate benefits in continuous enhancement, its ability to deal with multi-dimensional data, and its automated learning. Researchers have employed various ML-based approaches to better predict kidney stones. However, there is a scope for further enhancement regarding accuracy. Moreover, studies seem to be lacking in this area. This study proposes a smart toilet model in an IoT-fog (Internet of Things-fog) environment with suitable ML-based algorithms for kidney stone detection from real-time urinary data to rectify this issue. Significant features are selected using the proposed Improved MBPSO (Improved Modified Binary Particle Swarm Optimization) to attain better classification. In this case, sigmoid functions are used for better prediction with binary values. Finally, classification is performed using the proposed Improved Modified XGBoost (Modified eXtreme Gradient Boosting) to prognosticate kidney stones. In this case, the loss functions are updated to make the model learn effectively and classify accordingly. The overall proposed system is assessed by internal comparison with DT (Decision Tree) and NB (Naïve Bayes), which reveals the efficient performance of the proposed system in kidney stone prognostication

An automated hyperparameter tuned deep learning model enabled facial emotion recognition for autonomous vehicle drivers

The progress of autonomous driving cars is a difficult movement that causes problems regarding safety, ethics, social acceptance, and cybersecurity. Currently, the automotive industry is utilizing these technologies to assist drivers with advanced driver assistance systems. This system helps different functions to careful driving and predict drivers' ability of stable driving behavior and road safety. A great number of researches have shown that the driver's emotion is the major factor that handles the emotions, resulting in serious vehicle collisions. As a result, continuous monitoring of drivers' behavior could assist to evaluate their behavior to prevent accidents. The study proposes a new Squirrel Search Optimization with Deep Learning Enabled Facial Emotion Recognition (SSO-DLFER) technique for Autonomous Vehicle Drivers. The proposed SSO-DLFER technique focuses mainly on the identification of driver facial emotions in the AVs. The proposed SSO-DLFER technique follows two major processes namely face detection and emotion recognition. The RetinaNet model is employed at the initial phase of the face detection process. For emotion recognition, the SSO-DLFER technique applied the Neural Architectural Search (NASNet) Large feature extractor with a gated recurrent unit (GRU) model as a classifier. For improving the emotion recognition performance, the SSO-based hyperparameter tuning procedure is performed. The simulation analysis of the SSO-DLFER technique is tested under benchmark datasets and the experimental outcome was investigated under various aspects. The comparative analysis reported the enhanced performance of the SSO-DLFER algorithm on recent approaches