Muhammad Shahbaz Khan

Muhammad Arslan, Muhammad Mubeen, Saadullah Farooq Abbasi et al.

Sleep plays a crucial role in neonatal development. Monitoring the sleep patterns in neonates in a Neonatal Intensive Care Unit (NICU) is imperative for understanding the maturation process. While polysomnography (PSG) is considered the best practice for sleep classification, its expense and reliance on human annotation pose challenges. Existing research often relies on multichannel EEG signals; however, concerns arise regarding the vulnerability of neonates and the potential impact on their sleep quality. This paper introduces a novel approach to neonatal sleep stage classification using a single-channel gradient boosting algorithm with Hjorth features. The gradient boosting parameters are fine-tuned using random search cross-validation (randomsearchCV), achieving an accuracy of 82.35% for neonatal sleep-wake classification. Validation is conducted through 5-fold cross-validation. The proposed algorithm not only enhances existing neonatal sleep algorithms but also opens avenues for broader applications.

Muhammad Hassan Jamal, Abdulwahab Alazeb, Shahid Allah Bakhsh et al.

Fire safety practices are important to reduce the extent of destruction caused by fire. While smoke alarms help save lives, firefighters struggle with the increasing number of false alarms. This paper presents a precise and efficient Weighted ensemble model for decreasing false alarms. It estimates the density, computes weights according to the high and low-density regions, forwards the high region weights to KNN and low region weights to XGBoost and combines the predictions. The proposed model is effective at reducing response time, increasing fire safety, and minimizing the damage that fires cause. A specifically designed dataset for smoke detection is utilized to test the proposed model. In addition, a variety of ML models, such as Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Nai:ve Bayes (NB), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (ADAB), have also been utilized. To maximize the use of the smoke detection dataset, all the algorithms utilize the SMOTE re-sampling technique. After evaluating the assessment criteria, this paper presents a concise summary of the comprehensive findings obtained by comparing the outcomes of all models.