Facial expressions constitute one of the most effective and instinctive methods that allow people to communicate their emotions and intentions. In this context, the both Machine Learning (ML) and Convolutional Neural Networks (CNNs) have been used for emotion recognition. Efficient recognition systems are required for good human-computer interaction. However, facial expression recognition is related to several methods that impact the performance of facial recognition systems. In this paper, we demonstrate a state-of-the-art of 65% accuracy on the FER2013 dataset by leveraging numerous techniques from recent research and we also proposed some new methods for improving accuracy by combining CNN architectures such as VGG-16 and Resnet-50 with auxiliary datasets such as JAFFE and CK. To predict emotions, we used a second approach based on geometric features and facial landmarks to calculate and transmit the feature vector to the SVM model. The results show that the ResNet50 model outperforms all other emotion prediction models in real time by maximizing.

Virtual machine (VM) placement in recent cloud systems remains a challenging and new task due to the challenge of finding the best way to use resources efficiently while also ensuring service reliability. The current study offers an in-depth comparative evaluation of six VM placement algorithms—three established heuristics (Best Fit Decreasing (BFD), First Fit Decreasing (FFD), and Worst Fit (WF)), a baseline RANDOM method, and two metaheuristics (Ant Colony Optimization (ACO) and NSGA-III)—conducted through 2,400 simulation trials. We meticulously evaluate each algorithm across four critical performance metrics: placement success rate, energy consumption, execution time, and service reliability. The statistical analysis confirmed significant differences in performance across all methods using the Kruskal-Wallis and Mann-Whitney tests. Classical heuristics achieve a placement success rate (PSR) of 87.38% with BFD and FFD operating without any service violations. These methods use more energy than other approaches, which is a drawback of their good performance. NSGA-III reduces energy usage by 11% while maintaining acceptable placement performance (71.62%). The execution time (ET) can really vary, from super-fast heuristics to more time-consuming optimization methods. The results indicate that classical heuristics provide the highest reliability. No SLA violations (SLAV) were observed in the scenarios evaluated. Classical heuristics, in particular BFD and FFD, are therefore the most reliable. The study’s findings also show that cloud providers can choose the algorithms that best match their operational priorities. These priorities may include maximizing performance, minimizing energy consumption, or ensuring service stability.