Professionally curated articles for both experts and aspiring learners.
Personal Experiences in the Digital Era
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Mohamed Sami, an experienced Industry Advisor, has been key in driving various digital transformations. His roles range from project management to designing enterprise solutions. His advisory skills have significantly influenced the digital landscape. Discover more about Mohamed.
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Agile AI Analysis Application security Architecture Architecture Decisions Artificial Intelligence Blackbox Testing Change Management Design Digital Transformation Incremental Model Iterative and Incremental Development Requirements Requirements Elicitation Requirements Engineering Ruby on Rails SDLC SDLC Phases SDLC Process SDLC Process Steps SDLC Stages SDLC Steps Security Security SDLC Security Testing Security Testing Techniques Software Analysis Software Architecture Software Design Software Development Software Development Life Cycle Software Development Methodologies Software Development Process Software Engineering Software Life Cycle Software Testing STLC Systems Development Life Cycle System Software Development Testing The Best SDLC model The Right SDLC model Waterfall Model WhiteBox Testing
Latest Articles
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Beyond the Hype: AI Risks and Best Practices for Smarter Responsible Software Development
AI is transforming how we build software—but it also brings unique risks. This follow-up explores real challenges companies face when integrating AI across the SDLC, with phase-by-phase best practices for responsible, secure, and ethical adoption. Discover how to balance innovation and caution to build smarter, safer software.
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Intelligent Development: How AI is Reshaping the SDLC
Discover how Artificial Intelligence is transforming the entire software development lifecycle (SDLC) — from requirements gathering to deployment and maintenance. In this article, we explore how AI tools boost productivity, enhance quality, and reshape the way teams build modern software. Learn how developers and organizations can harness AI as a powerful collaborator in creating faster,…
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AI is Not Only Generative, Here’s Why!
Artificial Intelligence (AI) is a multifaceted field impacting various industries, including healthcare and finance. It encompasses diverse methods such as Supervised Learning, Unsupervised Learning, and Reinforcement Learning, each designed to address specific challenges. Beyond generative models like ChatGPT, AI’s versatility lies in its ability to optimize processes and improve decision-making.
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Bayesian Methods
Bayesian Methods are statistical techniques that use Bayes’ Theorem to update the probability of a hypothesis as more evidence becomes available. These methods are particularly powerful in situations where data is incomplete or uncertain, as they allow for the incorporation of prior knowledge and real-time adjustments based on new information. Bayesian models are commonly used…
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Evolutionary Algorithms
Evolutionary Algorithms (EAs) are a subset of optimization algorithms inspired by the process of natural evolution. They are used to solve complex optimization problems by mimicking the process of natural selection. EAs operate by generating a population of possible solutions, evolving them through selection, mutation, and crossover processes, and then selecting the best solutions over…
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Symbolic AI
Symbolic AI, also known as classical AI, is a paradigm of artificial intelligence that uses symbols, logic, and predefined rules to represent knowledge and perform reasoning tasks. Unlike data-driven AI models, such as machine learning, symbolic AI focuses on encoding human-like knowledge explicitly through rules and logical statements. It operates by manipulating symbols based on…
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Neural Networks
Neural Networks (NN) are a subset of machine learning models inspired by the structure and functioning of the human brain. They consist of layers of interconnected nodes (also called neurons) that process input data through a series of transformations. Neural networks excel at modeling complex, non-linear relationships in data and are the foundation of many…
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Reinforcement Learning
Reinforcement Learning (RL) is a type of machine learning where an agent learns how to make decisions by performing actions in an environment to maximize cumulative reward over time. Unlike supervised learning, which learns from labeled data, RL relies on feedback in the form of rewards or penalties from the environment. The agent continuously interacts…
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Unsupervised Learning
Unsupervised Learning is a type of machine learning where the model is trained on data without labeled outputs. The goal is to uncover hidden patterns or structures in the data. In contrast to supervised learning, there is no target variable to predict. Instead, the model tries to identify inherent structures within the data, such as…
