Network Science and Graph Analytics

Signal Processing for AI: Quick Guide! Signal processing plays a crucial role in AI, especially in fields like speech recognition, computer vision, biomedical analysis, and sensor data interpretation. It involves techniques to manipulate, analyze, and transform signals (e.g., audio, images, sensor readings) to extract useful features for AI models. Why Signal Processing Matters in AI? Noise Reduction : Removes unwanted interference from data (e.g., background noise in speech). Feature Extraction : Converts raw signals into meaningful features for machine learning models. Data Transformation : Transforms signals into a more useful format (e.g., frequency domain). Compression : Reduces the data size while preserving essential information. Key Techniques in Signal Processing for AI Filtering : Low-pass, high-pass, and band-pass filters to remove unwanted frequencies. Example: Noise removal in speech recognition. Fourier Transform (FT) & Short-Time Fourier Transform (STFT) : Converts s...

NASA Monitors Asteroid 2024 YR4: Low but Rising Impact Probability for 2032 NASA is closely tracking an asteroid, 2024 YR4 , which has a small but increasing probability of impacting Earth on December 22, 2032 . While the chances of collision remain extremely low , scientists have noted that the probability has nearly doubled since its initial detection. The asteroid was first observed on December 27, 2024 , by the Asteroid Terrestrial-impact Last Alert System (ATLAS) station in Chile . Since then, researchers have been analyzing its orbital path and characteristics using ground-based telescopes. According to NASA, the risk of impact, initially estimated at 1.2% , has now risen to 2.3% (or 1 in 43 chance) . Despite this increase, the likelihood of 2024 YR4 colliding with Earth remains very low , and NASA scientists emphasize that further observations will help refine its trajectory. The space agency also noted that, as more data becomes available, the probability may either decrease s...

NASA's Mars Rover Finds Something Unbelievable! NASA’s Mars rover has discovered unexpected organic compounds in ancient Martian rocks, hinting at possible past life. Scientists believe these findings suggest Mars once had habitable conditions. The discovery raises exciting questions about the planet’s history and potential for microbial life, fueling future exploration and astrobiology research. Key Discovery: Organic Molecules in Jezero Crater Perseverance is currently exploring Jezero Crater, a region believed to have once contained a river delta and a vast lake billions of years ago. The rover’s SHERLOC (Scanning Habitable Environments with Raman & Luminescence for Organics & Chemicals) instrument detected organic molecules in sedimentary rock samples. These rocks are thought to have formed in an environment rich in water—a crucial factor for life. What Makes This Discovery Unbelievable? Ancient Martian Habitability: The presence of organics suggests that Mars had cond...

 Computer-Aided Design of Imatinib Derivatives to Overcome Drug Resistance in CML Chronic Myeloid Leukemia (CML) often develops resistance to imatinib due to mutations in the BCR-ABL kinase domain, limiting treatment efficacy. Computer-aided drug design (CADD) enables the development of imatinib derivatives with enhanced binding affinity and efficacy against resistant mutations. Molecular docking, molecular dynamics simulations, and ADMET analysis aid in optimizing novel derivatives to overcome resistance. By targeting key resistant variants, these computational approaches help identify promising drug candidates with improved potency and selectivity. This strategy accelerates drug discovery, offering potential new therapies for CML patients who no longer respond to first-line treatments like imatinib. Role of Computer-Aided Drug Design (CADD) Computer-aided drug design (CADD) has emerged as a powerful tool for developing new therapeutic compounds. By leveraging computational techni...

  AI-Driven Neurodevelopmental Disorder Detection Using rs-fMRI Neurodevelopmental disorders (NDDs), such as Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), and other cognitive impairments, significantly impact brain function and development. Early and accurate diagnosis is crucial for effective intervention, yet traditional diagnostic methods rely on subjective assessments, making them prone to variability and delays. Recent advances in Artificial Intelligence (AI) and computational intelligence have introduced new opportunities to enhance the detection of NDDs using advanced neuroimaging techniques like resting-state functional Magnetic Resonance Imaging (rs-fMRI). This article explores how AI-enabled computational intelligence can be used to detect neurodevelopmental disorders by analyzing rs-fMRI data, leveraging machine learning (ML), deep learning (DL), and graph-based approaches. Understanding rs-fMRI and Its Role in NDD Detection Resting-sta...

 "Graph vs. Metrics: A Comparison of Predictive Game Analytics" This study compares graph-based representation learning using game provenance graphs with traditional metrics-based machine learning for predictive game analytics. It evaluates their effectiveness in forecasting player behavior, performance, and outcomes. The research highlights the strengths and limitations of both approaches, offering insights into their applicability in game data analysis. Key Objectives of the Study Compare Predictive Accuracy – Assess how well each approach predicts player behavior, game performance, and outcomes. Evaluate Interpretability – Examine how easily insights can be extracted from the models. Analyze Computational Efficiency – Measure the computational costs and feasibility of each method in real-world applications. Methodology Graph-Based Representation Learning : Uses game provenance graphs, where nodes represent events, actions, or players, and edges capture their relationshi...