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Unlocking the Potential: A Comprehensive Guide to Collaborative Testing of the Pinterest App

Have you ever wondered how leading apps such as Pinterest ensure that they are robust, bug-free, and user-friendly? Collaborative testing is a dynamic process that harnesses the power of teamwork to elevate the quality of apps. An organization with diverse members collaborating to identify and optimize hidden glitches while keeping the user experience high. You’ll […] ....

Steam Achievements , Importance In App Development , Team Members , B Team Composition , Diverse Team Members , Collaborative Testing , Centric Evaluation , Centric Focus , Key Features , Feature Selection , Set Diversity , Functional Expertise , Resource Allocation , Time Connectivity , Friendly Interfaces , Centric Approach , Shared Testing , Testing Capability , Platform Compatibility , Control Measures , Meeting Slots , World Time Buddy , Meeting Times , Google Docs , Decision Paths , Driven Decision Making ,

A Python Data Analysis Project to Understand Hotel Cancellations

Data Cleaning, Analysis, Visualization, Feature Selection, Predictive Modeling The hospitality industry flourishes by providing outstanding guest experiences;… ....

Data Cleaning , Feature Selection , Reaking News ,

Exploring Exciting New Features in Java 17

In this blog, we will learn about 5 new java features: 1. Sealed Classes 2. Pattern Matching for Switch 3. Foreign Function Interface (FFI) 4. Memory API 5. Text Block ....

Java Program , Function Interface , Java Native Interface , Java Virtual Machine , Text Blocks , Data Type , Feature Selection , Function Type , Java Ee , User Defined Function ,

"Predicting lung cancer survival based on clinical data using machine l" by Fatimah Abdulazim Altuhaifa, Khin Than Win et al.

Machine learning has gained popularity in predicting survival time in the medical field. This review examines studies utilizing machine learning and data-mining techniques to predict lung cancer survival using clinical data. A systematic literature review searched MEDLINE, Scopus, and Google Scholar databases, following reporting guidelines and using the COVIDENCE system. Studies published from 2000 to 2023 employing machine learning for lung cancer survival prediction were included. Risk of bias assessment used the prediction model risk of bias assessment tool. Thirty studies were reviewed, with 13 (43.3%) using the surveillance, epidemiology, and end results database. Missing data handling was addressed in 12 (40%) studies, primarily through data transformation and conversion. Feature selection algorithms were used in 19 (63.3%) studies, with age, sex, and N stage being the most chosen features. Random forest was the predominant machine learning model, used in 17 (56.6%) studies. Whi ....

Google Scholar , Artificial Intelligence , Data Mining , Feature Selection , Lung Cancer , Machine Learning , Survival Prediction ,