Browse Articles

This manuscript explores the performance of five different machine learning models in classifying brain tumors from a dataset of MRI scans. The authors find that several of the models showed >90% accuracy. Thus, the authors suggest that machine learning models demonstrate potential for effective implementation in clinical settings, including as a diagnostic tool that can be used to complement the expertise of neuroradiologists.

This manuscript evaluates peak detection algorithms for feature extraction in EMG-based hand gesture recognition using a random forest classifier. The study demonstrates that wavelet-based peak detection features achieve the highest classification accuracy (96.5%), outperforming other methods. The results highlight the potential of peak features to improve EMG-based prosthetic control systems.

The authors test whether coating stainless steel orthopedic screws in silver will promote calcium precipitation to improve orthopedic implant integration into bone.

The authors looked at different factors that influence specific gravity in beverages, including sweetener used, caffeine, carbonation, and sodium.

The authors looked at genes that were differentially expressed in patients who had and had not had febrile seizures to determine what differences in gene expression existed.

The authors looked at the ability to use graphite from pencils in anodes of lithium-anode batteries.

The authors design and test an easy-to-use and cost-effective mobile app-based alert system to help senior citizens rapidly communicate with caregivers in emergencies or when in need of assistance.

Authors examine the impact of environmental noise on cognitive processes in teenagers, focusing on five different noise conditions: two types of noise (aircraft and construction) at two different decibel levels (30 dBA and 60 dBA) and a quiet condition.

The authors assessed the degree of women participation in India's economy as a way to estimate woman's participation in India's economic growth.

This study used an improved CMS-seq method to profile 5hmC in ormalin-fixed and paraffin-embedded (FFPE) samples from HNC tumors and adjacent normal tissues, identifying three genes (PRKD2, HADHA, and AIPL1) with promising potential as biomarkers for Head and neck cancer (HNC) diagnosis.