Lung cancer remains a critical health concern in the entire world, which has been a major cause of high rates of cancer-related mortalities that affect individuals in every part of the world. The findings emphasize the notable potential of deep learning procedures to assist radiologists in diagnosing cases of lung-related abnormalities appropriately. Such methods are also leading to the improvement of AI-based healthcare products. The enhancements to the suggested model [16, 17, 18, 21] in the future will be aimed at tuning hyperparameters, 3D CNN [16, 17, 18] architectures, and the integration of patient clinical data, with the aim of further increasing the accuracy [16, 17, 19] of diagnosis as well as system performance. This paper uses the IQ-OTHNCCD dataset, a publicly available and highly annotated set of CT imaging that has been annotated by experts in the medical field. The preprocessing techniques applied will involve changing the images to Grayscale, normalizing the pixel values, ensuring consistency in the images, and converting them to a standard size of 128x128 pixels, which is the ideal size to feed the images into the CNN [16, 17, 18]. In the proposed work, the model [16, 17, 18, 21] integrates multi-scale convolutional layers with adaptive dropout (rate=0.5) and ReLU activations, yielding 95% accuracy [16, 17, 19] and 0.95 F1-score (95% CI: 93.8–96.2%) on a 70/15/15 train/validation/test split— a 4% improvement in F1-score. Preprocessing includes grayscale conversion, pixel normalization to [0,1], and resizing to 128x128 pixels. The architecture comprises three convolutional blocks (32/64/128 filters, 3x3 kernels), max-pooling (2x2), flattening, a 512-unit dense layer, and a 3-unit softmax output. Future enhancements include hyperparameter tuning, 3D CNN [16, 17, 18] integration, and clinical data fusion to exceed 97% accuracy [16, 17, 19].

This paper presents a novel hybrid control strategy for a specific class of nonlinear systems based on interval Type 3 fuzzy logic systems (IT3‐FLSs), and its effectiveness in the management of Type 1 diabetes is demonstrated. The main achievements of this work can be summarized as the following main points. First, an online system identification framework whose inputs are only from the measurable signals of the system, including glucose concentration and administered insulin, is introduced using a newly developed IT3‐FLS. Second, the system identification method is independent of the type of specific model or a predefined model and aims to estimate the measurable output of the system to predict the system behavior. Third, a novel hybrid controller consisting of a final control signal steady‐state controller, a proportional controller, a step‐forward predictive controller, and an adaptive compensator is developed. Fourth, through Lyapunov‐based stability analysis, adaptive learning rules are derived for both IT3‐FLS identifier and adaptive compensator, and finally the asymptotic stability of the closed‐loop system will be guaranteed. The proposed method is validated on a large range of virtual Type 1 diabetes patients modeled by the modified Bergman model (MBM) under significant uncertainties, noisy glucose measurements, and perturbations such as food intake. Comparative simulation results confirm the superior performance and robustness of the proposed hybrid strategy over existing insulin–glucose control approaches.

Viral conjunctivitis, also known as “Eye Flu,” presents significant public health challenges worldwide. India has recently witnessed a surge in cases, affecting numerous people and causing widespread concerns. This research delves into the realm of medical image processing and deep learning to address the pressing need for accurate and efficient detection of viral conjunctivitis, a highly contagious ocular infection. Leveraging advancements in computer vision and convolutional neural networks (CNNs), the study focuses on the development and evaluation of a robust diagnostic system capable of discerning viral conjunctivitis from other common forms of conjunctivitis, namely allergic and bacterial. Challenges in using image processing for disease detection include the need for large amounts of descriptive data to train machine learning models, ensuring the accuracy and reliability of image analysis algorithms, and addressing data privacy and security concerns. Future directions in this field may include developing more advanced deep learning models that can handle complex medical imaging data, integration of imaging technology and other diagnostic tools to diagnose diseases and learn how to use it in real-time for fast processing. and more efficient diagnosis. Additionally, efforts should be made to standardize image processing protocols in different healthcare settings to facilitate sharing and comparison of medical imaging data for research and clinical purposes. Overall, more research and collaboration between medical, informatics, and image processing experts is essential in developing future image processing applications for disease detection. The methodology encompasses the acquisition of a diverse dataset comprising annotated images of ocular conditions, rigorous preprocessing techniques to standardize image quality, and the implementation of three distinct CNN architectures: ResNet, VGG, and GoogleNet. These architectures were selected for their proven efficacy in medical image analysis and classification tasks. Through extensive experimentation and rigorous validation, the research elucidates the efficacy of each architecture in accurately classifying conjunctival diseases, with a particular emphasis on delivering actionable diagnostic outcomes.

In this article, in a bigeneralized topological space, we introduce an interesting tool namely, $(s, v)$-dense set, and examine the significance of this set. Also, we give the relations between nowhere-dense sets defined in generalized and bigeneralized topological space and give some of their properties by using functions. Finally, we give some applications for $(s, v)$-dense and $(s, v)$-nowhere dense sets in a soft set theory.

Heart disease remains among the leading causes of death worldwide, and its early detection ability can be the difference between life and death. In this research, we investigate the capability of machine learning—namely Support Vector Machines (SVM)—to predict the occurrence of heart disease based on regular clinical information. We used the Cleveland Heart Disease dataset, which contains critical patient data like age, gender, blood pressure, cholesterol level, type of chest pain, and other crucial health factors. Prior to creating our model, we pre-processed and cleaned the data by dealing with missing values, changing categorical variables into numerical form, and scaling the features for uniformity. We then optimized the SVM model using grid search and cross-validation to make it run at its optimal level. The resulting model had an accuracy of 86.41% in the test set and performed better than other popular models such as logistic regression and random forest. The significant about this work is the potential for applying it in practical situations. An SVM-based program such as this could be a second opinion for physicians or integrated into early diagnostic tools—most helpful in clinics with limited access to specialists. It's progress toward smarter, data-driven healthcare that enables faster and more precise diagnoses. There's still potential for expansion, using bigger, more varied datasets or incorporating real-time patient information could further enhance the model. But this research demonstrates that with the proper data and methodology, machine learning can be a useful tool in the early diagnosis of heart disease.

In this article, we introduce a new form of compound distribution by combining Gamma and Laplace components. This new distribution is specifically designed to address the limitations of traditional models when dealing with skewed and heavy tailed data. Its flexible parameters enable precise control over tail heaviness and data concentration, making it adaptable to assorted data behavior common in finance, insurance, and engineering. Additionally its balanced decay mechanism ensures symmetrical treatment of extreme values, enhancing accuracy in modeling risks and uncertainties, ultimately supporting more robust decision-making in high-stakes environments. A detailed analysis of the unique structural properties of this newly proposed model is carried out, including probability density function (PDF), cumulative distribution function(CDF), survival function, hazard function, moments, parameter estimation, tail behavior analysis, ordered statistics, and risk measures.