AneRBC dataset: a benchmark dataset for computer-aided anemia diagnosis using RBC images.

Visual analysis of peripheral blood smear slides using medical image analysis is required to diagnose red blood cell (RBC) morphological deformities caused by anemia. The absence of a complete anaemic RBC dataset has hindered the training and testing of deep convolutional neural networks (CNNs) for computer-aided analysis of RBC morphology. We introduce a benchmark RBC image dataset named Anemic RBC (AneRBC) to overcome this problem.

Awareness of Melanoma and Skin Self-Examination at a Tertiary Care Hospital in Pakistan: A Study of Knowledge, Attitudes, and Practices.

Background Melanoma, the most lethal form of skin cancer, contributes to a majority of skin cancer fatalities globally. It carries a grim prognosis with a high propensity for early metastasis. People are not aware of moles, their natural course of progression, and skin self-examination.

Exploring nonlinear chaotic systems with applications in stochastic processes.

This manuscript explores the stability theory of several stochastic/random models. It delves into analyzing the stability of equilibrium states in systems influenced by standard Brownian motion and exhibit random variable coefficients. By constructing appropriate Lyapunov functions, various types of stability are identified, each associated with distinct stability conditions.

Salinity Stress Responses and Adaptation Mechanisms of : A Comprehensive Study on Growth, Water Relations, Ion Balance, Photosynthesis, and Antioxidant Defense.

(Decne.) is a leaf succulent C perennial found in arid saline areas of southern Pakistan and neighboring countries, where it is utilized as herbal medicine. This study investigated how growth, water relations, ion content, chlorophyll fluorescence, and antioxidant system of change as salinity levels increase (0, 150, 300, 600, and 900 mM NaCl).

Enhancing software defect prediction: a framework with improved feature selection and ensemble machine learning.

Effective software defect prediction is a crucial aspect of software quality assurance, enabling the identification of defective modules before the testing phase. This study aims to propose a comprehensive five-stage framework for software defect prediction, addressing the current challenges in the field. The first stage involves selecting a cleaned version of NASA's defect datasets, including CM1, JM1, MC2, MW1, PC1, PC3, and PC4, ensuring the data's integrity.