This research presents a novel hybrid framework for heart disease prediction, integrating advanced data preprocessing with ensemble learning to enhance diagnostic accuracy. The methodology begins with rigorous data cleansing to ensure reliability, followed by Synthetic Minority Over-sampling Technique (SMOTE) to balance class distribution. Outliers are identified and mitigated using the Z-score method, preserving data integrity. A unique Recursive Hybrid Feature Extraction (RHFE) strategy, combining filter and wrapper techniques, optimizes feature selection by reducing multicollinearity and enhancing model efficiency. Key predictive markers include age, chest pain type, maximum heart rate, ST depression induced by exercise, and major vessel count via fluoroscopy. The refined dataset is used to train an CatBoost -based ensemble model, achieving remarkable performance with 94.2% accuracy, 93.5% precision, 94% recall, and an outstanding ROC AUC score of 0.98. These results highlight the model's robustness and its potential for real-world clinical implementation in early heart disease detection and risk assessment.

Introduction: Having a noble cause, our work intends enhancing the quality of life for individuals who are visually impaired by effectively combining aural feedback systems with object detection technologies. By addressing practical problems, this incorporation improves the community’s general quality of life and safety. Objectives: Our primary objective is to provide individuals with visual impairments the necessary tools and information to navigate their surroundings effectively. By doing this, we hope to encourage inclusivity and enable them full involvement in daily activities. Methods: We used the You Only Look Once (YOLO) approach, namely YOLOv4, which is well known for its remarkable mean Average Precision (mAP) score of 92% and realtime object identification capabilities, to accomplish our goals. We had applied the 80 class names as well as bounding boxes in the COCO Dataset for effective item identification. This technology uses the relative distances among objects in order to offer visually impaired people more timely and reliable access to essential distance information. Results: We were able to create a system that can quickly and accurately detect items in real time when there are multiple objects by utilizing the COCO Dataset and the YOLOv4 approach.Furthermore, relative distance drive improves the user’s vision when identifying numerous objects. Conclusion: Our research indicates an important breakthrough in addressing challenges faced by people who are visually impaired. We developed a key strategy that encourages greater inclusivity and involvement in day-to-day activities while also improving mobility and safety through the application of innovative and state-of-the-art techniques.

This article describes the architecture and system design for assisting blind people in navigating freely inside an enclosed environment, such as the home or the outdoors. Thus, the proposed technology uses IoT technology and emerging techniques for machine learning to provide high-tech cane functionality that allows visually impaired navigators to walk independently. It also includes mobile applications to safeguard visually impaired persons and allow guardians to observe them. The proposed in this study system is intended to identify and classify any obstacles within a defined distance using machine learning. In this connection, an indoor and outdoor architecture on YOLO v3 is implemented for its detection technique, and multi-layer perceptron (MLP) neural network technology supports this framework. Based on the detection and classification, YOLO v3 and MLP are crucial for their accuracy.

Dyslexia, a neurodevelopment condition impacting reading and language abilities, presents notable difficulties in promptly identifying and implementing effective interventions Traditional methods for diagnosing dyslexia often rely on subjective assessments and standardized tests, leading to delays in recognition and support. This paper offers an extensive examination of how deep learning techniques are applied in the domain of detecting and intervening in dyslexia. The integration of deep learning algorithms into dyslexia research offers promising avenues for more accurate and timely identification of individuals at risk. By leveraging neural networks and advanced machine learning models, researchers have begun to explore novel approaches that analyze linguistic patterns, eye-tracking data, brain imaging, and behavioral markers associated with dyslexia. Furthermore, this paper discusses the potential of deep learning in tailoring personalized interventions for individuals with dyslexia. These interventions aim to adapt to the specific learning needs of each individual, providing targeted support and enhancing the effectiveness of remediation strategies. While highlighting the advancements made in utilizing deep learning for dyslexia, this review also addresses challenges, including data scarcity, model interpretability, and ethical considerations. Additionally, it proposes future research directions that emphasize collaborative efforts among researchers, educators, and technology developers to foster the development of robust and accessible tools for dyslexia assessment and intervention.

Outliers are considered as unexpected things observed while analyzing the data. Investigators found that the prediction and identification of outliers are extremely complex. Generally, a stream is measured as an unbounded data source executed promptly, and this research provides a novel way of predicting the outliers over the incoming data. Here, the incoming data is acquired from the hospital to validate the patients' records. There are higher chances of outliers over the incoming data based on the density of the arrival data. The execution of outlier prediction is performed independently with the integration of LSTM (Long Short Term Memory) over the stacked CNN model. The outlier detection process constantly measures the incoming data from the emotional input as an outlier or inlier. Here, the data reconstruction is achieved with the auto-Regressive model, and the prediction model considers the outliers to construct the training data. Various hidden representation acquired from the stacked model is considered for outlier prediction, and the experimental results demonstrate that the anticipated model shows superior prediction accuracy, specificity, sensitivity and F1 score. The proposed model is best-suited for prediction as the deep learning approaches perform well over complex applications and acquire superior results. The simulation is done in MATLAB 2016b, and the performance metrics show a better trade-off than the other approaches.

Optical character recognition (OCR) technology is indispensable for converting and analyzing text from various sources into a format that is editable and searchable. Telugu handwriting presents notable challenges due to the resemblance of characters, the extensive character set, and the need to segment overlapping characters. To segment the overlapping characters, we assess the width of small characters within a word and segment the overlapping characters accordingly. This method is well suited for the segmentation of overlapping compound characters. To address the recognition of similar characters with less training periods we have used ResNet 18 and SqueezeNet models which have achieved character recognition rates of 95% and 94% respectively.

Recently, deep learning has been used in enormous successful applications, specifically considering medical applications. Especially, a huge number of data is captured through the Internet of Things (IoT) based devices related to healthcare systems. Moreover, the given captured data are real-time and unstructured. However, the existing approaches failed to reach a better accuracy rate, and the processing time needed to be lower. This work considers the medical database for accessing the patient's record to determine the outliers over the dataset. Based on this successful analysis, a novel approach is proposed where some feasible and robust features are extracted to acquire the emotional variations for various ways of expression. Here, a novel dense-Convolutional Neural Network (CNN) with ResNet (CNN-RN) extracts features from patients', while for establishing visual modality, deep residual network layers are used. The significance of feature extraction is less sensitive during outlier prediction while modeling the context. To handle these issues, this dense network model is used for training the network in an end-to-end manner by correlating the significance of CNN and RN of every stream and outperforming the overall approach. Here, MATLAB 2020b is used for simulation purposes, and the model outperforms various prevailing methods for consistent prediction. Some performance metrics include detection accuracy, F1-score, recall, MCC, p-value, etc. Based on this evaluation, the experimental results attained are superior to other approaches.

The explanation of the identified outliers is frequently provided to the users when other techniques are used to detect the outliers that are suggested in the study. This situation is quite challenging for the users to take the relevant actions that are regarded with the identified outliers as the outcome. If the outliers are detected, then the explanations need to be provided with outliers to reduce the challenge. Some surveys are available for the outlier interpretation, and some surveys are available to detect an outlier. The survey presents an outlier interpretation in the proposed system where useful knowledge is obtained from anomalous data to fill the gap and describe the data. The types of outlier explanations are defined, and the difficulties are discussed while creating every type. The existing outlier interpretation approaches are reviewed, and the processes of mentioning the difficulties are discussed. The applications of outlier interpretation are discussed, and the existing techniques are utilised for the determination of outlier explanations are reviewed. Further, the possible future research ways are discussed.