KES College

Early detection of genetic defects in newborns is required for prompt medical intervention leading to improved health outcomes. A new model framework is introduced in this paper that aims to increase the detection accuracy of potential early risks with Cuckoo type Search Optimization (CSO) and Recurrent based Neural Networks (RNNs) linking.The Cuckoo type Search Algorithm is used for optimizing the selection of features and keeping the key genes with less computational complexity. The RNN on its part, because of its ability to remember the crucial parts of genetic data in a certain period, makes it possible for the model to execute the most precise classification of the severe cases. The developed method is a combination of CSO and RNN-learning where the process of CSO improves the set of extracted features thus making the Rnn function faster and better. The testing of a new system has concluded that, in terms of accuracy, sensitivity, as well as early detection rate, higher quality has reached than that of previous methods. The tight thereof of CSO and RNN development not only makes predictions more reliable but also the system becomes scalable to newborn genetic screening that gives better opportunities for healthcare planning.

Hand gesture recognition has emerged as a vital aspect of human-computer interaction, offering intuitive, touchless interfaces for diverse applications. This study presents a novel approach to virtual mouse operations using intelligent hand gesture recognition, enabling seamless human-computer interaction. The system employs a hybrid Vision Transformer (ViT) and Gated Recurrent Unit (GRU) model to achieve robust gesture recognition by combining spatial and temporal feature extraction. A diverse dataset of hand gestures was used, ensuring adaptability across different lighting conditions, user preferences, and gestures. Preprocessing techniques such as noise reduction, normalization, and data augmentation improved the system's reliability. The proposed model achieved an accuracy of 96.8%, outperforming existing models like YOLO-BiLSTM and CapsNet-TCN, with F1-scores of 94.7% and 95%, respectively. Gesture-wise accuracy showed high performance for cursor movement (97.5%) and drag-and-drop (96.7%), while scrolling gestures maintained consistent accuracy above 94.8%. Additionally, the system demonstrated a low latency of 78 ms, enabling real-time performance. User testing reported an average satisfaction score of 4.7 out of 5, highlighting its practicality. This innovative approach has significant applications in accessibility, gaming, and smart environments, making it a promising tool for intuitive human-machine interaction.

Aim/Purpose: Traditional hyperparameter tuning methods’ inefficiency and low accuracy in diabetes prediction. Application and evaluation of Bayesian optimization aim to increase the accuracy of diabetes prediction models. Background: Bayesian optimization (BO) has emerged as a powerful technique for hyperparameter tuning in machine learning models with its methodical approach for optimizing complex functions with expensive evaluations. Selecting the optimal hyperparameters for models can significantly increase prediction accuracy in the healthcare sector, particularly for diabetes prediction, hence improving patient outcomes and resource management. Methodology: Among the well-known diabetes databases used in the study is the Pima Indian Diabetes Database. Among the machine learning models developed and whose hyperparameters are modified via Bayesian optimization are Support Vector Machines (SVM), Random Forest, and Gradient Boosting Machines (GBM). The optimization process is compared with traditional tuning methods to assess improvements in model performance. Contribution: Problems include the increasing prevalence of diabetes worldwide and the role vital function prediction models play in early diagnosis and therapy. Especially when dealing with enormous datasets common in healthcare, grid search, and random search are sometimes computationally taxing and inefficient. However, Bayesian optimization promises a more practical and economical approach by selecting hyperparameters iteratively based on earlier evaluations. Findings: Bayesian optimization not only yields faster calculation times but also outperforms traditional methods. More precisely, models adapted by Bayesian optimization show greater sensitivity and specificity, which are crucial for accurate and prompt diabetes diagnosis. Recommendation for Researchers: By highlighting the specific elements and network characteristics that are behind the anomalies it discovers, on a real-time or large scale dataset. Future Research: This work can be improvised using several recent artificial intelligence algorithms with the integration of IoT on real-time datasets.

Kidney disease is a significant global health concern, requiring accurate and early diagnosis to prevent progression and ensure timely treatment. This study proposes an improved neural classification logic combining an AutoEncoder and an Artificial Neural Network (ANN) to predict kidney disease using medical image processing techniques. A dataset comprising kidney images from CT, MRI, and ultrasound was utilized to train and test the model. The AutoEncoder was employed for unsupervised feature extraction, reducing dimensionality while retaining key information related to kidney abnormalities. The ANN then classified these features into healthy or diseased categories. Image preprocessing techniques such as noise reduction, resizing, normalization, and contrast enhancement were applied to ensure data consistency. Additionally, advanced segmentation algorithms like U-Net were used to isolate the kidney regions from the background, focusing on the Regions of Interest (ROI). Data augmentation was applied to improve generalization. The proposed model achieved a classification accuracy of 94.8%, outperforming baseline models such as CNN (92.1 %) and SVM (88.7<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>). The AutoEncoder + ANN combination demonstrated superior precision, recall, and Fl-score, with an AUC-ROC of 97.4%. The results highlight the model's robustness and potential for practical applications in the early detection of kidney disease.

The identification and detection of animal footprints are essential for the conservation of wildlife, ecological studies, and wildlife monitoring. This paper introduces a novel method for detecting animal footprints that combines deep learning and image processing. The approach, known as the Enhanced Neural Optimization and Classification Scheme (ENOCS), is cross-validated with the conventional model, the Support Vector Machine (SVM), to assess the efficacy of the proposed scheme. Our approach employs a learning model that has been fined-tuned on a dataset of various types of animal print to extract and classify features with high accuracy. The quality of the input data is enhanced through the image-processing component by eliminating noise, normalizing illumination, and performing morphological operations to draw emphasis to footprint patterns. Performance metrics are tested on a benchmark dataset containing a wide range of animal traces and evaluated with conventional image recognition methods. The results indicate that our method attains significant improvement in accuracy relative to the state-of-the-art technique, known as SVM. Furthermore, our approach is invariant to variations in environmental conditions and image quality. The proposed scheme thus provides a reliable and effective tool to identify animal imprints, thereby increasing the effectiveness of measures for ecosystems and conservation.

Clinicians, staff, patients, and visitors use the healthcare environment, and a deeper understanding of spatial elements that promote their well-being is necessary. However, clinical and academic nursing does not naturally fit within the field of architectural design and planning. A design charrette, an interactive and innovative approach for faculty, staff, and clinicians, offers participants a chance to be creative in merging evidence-based practice with evidence-based design principles to aid in well-being. This educational brief outlines the process and outcomes of spaces that promote well-being and restoration. The design example presented is a restorative space in a clinical unit. We describe the methods of this teaching experience and highlight the application of a design charrette for a clinical unit.

Abstract Antibacterial creams come in a wide variety, with varying concentrations, prices, levels of activity, active ingredients, and excipients. Based on their ability to suppress the chosen bacterial strains (S. aureus, E. coli, B. subtilis, P. vulgaris) four different commercial antibacterial creams were evaluated as the most popular ones. There were two types of creams: one with gentamicin sulfate and the other with framycetin sulphate. The combination medicine creams included silver sulfadiazine, miconazole nitrate, neomycin sulfate, and chlorhexidine gluconate. In addition to being commonly utilized due to their antibacterial properties, the chosen bacterial strains are principally responsible for the majority of human illnesses and skin disorders. The study explains the four creams that were utilized, how economical they were, how well they worked against various bacterial strains, and how efficient they were whether applied singly or in combination. It also aids in determining which cream performs the best overall. In the antibacterial experiment, the disk diffusion method was used to use all four of the creams. Out of the four antibacterial creams, the study indicates that gentamicin had the highest efficacy. Among the three drugs that make up cream, gentamicin is the most economical and has the biggest zone of inhibition of all four creams combined. This study improves our knowledge of the best cream while also educating pharma providers regarding the advantages of creams.

For over a decade, the accounting profession and regulators have recognized the importance of maintaining public trust in US financial markets. This study uses a bad news context to examine the source of financial reporting restatements and audit tenure on public trust. We predict and find a positive association between auditor-initiated restatements and the public's trust in both the management of the firm and the auditor. Surprisingly, it was observed that the public does not penalize management for implementing an independent auditor-proposed restatement despite the negative market reactions associated with financial restatements. The results indicate that efforts to promote transparency and reliability in financial reporting are important. The public's trust in the management of corporations and their auditors remains intact, regardless of how the restatements are initiated.