Acoustic AI for Biodiversity: Deep Learning Applications in Insect Monitoring
The global insect biodiversity crisis demands scalable monitoring solutions. Passive Acoustic Monitoring (PAM) generates vast data through insect sounds, but analyzing this “data deluge” requires automated methods. This review outlines the deep learning (DL) pipeline for insect bioacoustics, including data acquisition, pre-processing (denoising, segmentation), and feature representation (e.g., spectrograms and adaptive frontends such as LEAF). […]
A Gender-Aware Support Vector Machine Classification Framework for Lung Cancer Diagnosis: A Bias Evaluation Study
The use of artificial intelligence in healthcare raises ethical concerns, particularly around bias toward protected characteristics such as gender, race, and ethnicity. Gender bias remains common, as unconscious beliefs held by patients, clinicians, researchers, and administrators can shape care delivery and influence health outcomes. This study examined 309 lung cancer patient records from a Kaggle […]
A High-Performance DNA Multiple Pattern Matching Algorithm Based on Index Binding and ASCII Hashing
Beyond health, the exponential trend in the volume of genomic data, combined with broad access to personal genome sequencing, has generated a pressing demand for fast and scalable algorithms to explore patterns in DNA sequences. Rapid and specific identification of DNA sub-sequences is of critical importance in various applications, such as personalized medicine, evolutionary biology, […]
Evaluating AI Performance in Academic Settings: A Comparative Study of ChatGPT-4 and Gemini
This study conducts a systematic comparison of ChatGPT-4 and Gemini in addressing academic queries across four disciplines: Python programming, financial accounting, business administration, and medical sciences. Through a mixed-methods analysis of 40 standardized questions (balanced between numerical and narrative formats), we evaluate the models’ accuracy, reasoning capabilities, and limitations. Results reveal ChatGPT-4’s superior performance with […]
Predicting employee retention using artificial intelligence and survival analysis approaches
Background: Employee retention is a critical concern for organizations seeking to maintain a stable and productive workforce. Understanding the factors driving turnover is essential for designing effective HR interventions. Methods: This study applies advanced survival analysis techniques, including the Kaplan–Meier estimator, Cox proportional hazards model, and Random Survival Forests (RSF), to predict employee retention and […]
Machine Learning-Based Quality of Service Monitoring and Anomaly Detection for Mobile Telecommunications Networks
Mobile telecommunication networks frequently experience Quality of Service (QoS) degradation and anoma-lous network conditions that traditional rule-based monitoring systems may struggle to detect. This study in-vestigates the effectiveness of machine learning techniques for accurate, reliable, and efficient QoS monitor-ing and anomaly detection using the CSE-CIC-IDS2018 dataset. The proposed system, termed NetSentinel, consists of two integrated […]
Enhancing grid dispatch reliability using hybrid CNN-LSTM solar power forecasting
Short-term solar power forecasting (STSPF) is critical for the integration of renewable energy into smart grids, yet its accuracy is often compromised by the stochastic nature of meteorological variables. This study proposes a hybrid deep learning framework that integrates 1D-Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks to capture both spatiotemporal features and […]
A Systematic Survey of Large Language Models: Architectures, Training Paradigms, Capabilities, and Limitations
LLMs represent an unprecedented breakthrough in the development of artificial intelligence. They exhibit extraordinary capabilities in both understanding and generating human language as well as reasoning using knowledge from diverse areas. By employing transformer architecture, which is scalable from a few million to hundreds of billions of parameters through self-supervised training on web corpus data, […]
Efficient Arrhythmia Classification on the MIT-BIH Database via Lightweight 1D-Convolutional Neural Networks for CPU-Constrained Environments
Objective: Deployable Electrocardiogram (ECG) arrhythmia detection on standard Central Processing Units (CPUs) remains challenging due to deep learning models’ computational demands. This paper proposes a lightweight One-Dimensional Convolutional Neural Network (1D-CNN) optimized for CPU execution. Methods: The model (two convolutional layers, <10,000 parameters) evaluated on the MIT-BIH Arrhythmia Database using single-split (80/20) and 10-fold cross-validation […]
Performance Evaluation of Embedding and Large Language Models for Procurement Retrieval Augmented Generation
This paper addresses the challenge of evaluating large language model and embedding architecture performance in procurement-specific retrieval augmented generation systems. While existing research focuses on general-purpose benchmarks that ignore domain-specific terminology, query complexity, and real-world decision-making needs, specialized procurement applications require tailored evaluation frameworks. We evaluate three large language models (LLM) architectures, Llama3.2-3B, Phi3.5-Mini-Instruct, and […]