Increasing the size of memory in network devices leads to the problem of a persistently full buffer (a.k.a, bufferbloat). The objective of this study is to compare the recently introduced Controlled Delay (CoDel) scheme with the traditional method of active queue management, such as Random Early Detection (RED) algorithms over TCP variants. To explore the potential of CoDel over RED, TCP variants have been assessed at three settings: variable congestion and fixed payload (VCFP), variable payload and fixed congestion (VPFC), and high congestion and high payload (HCHP). We assessed the CoDel and RED schemes for active queue management (AQM) using three performance metrics: link utilization, drop rate, and queuing delay. The analytical results show that CoDel outperformed RED in most aspects over variants of TCP because of its auto‐tuning and auto‐adjustment features. However, RED outperformed CoDel in a few cases. In the VCFP setting, RED recorded a lower drop rate overall TCP variants. Moreover, in the VPFC setting, RED with a payload of 500–1000 bytes performed better in terms of drop rate. Finally, in the HPHC setting, there were two cases where RED, over TCP NewReno and Vegas, performed well in terms of drop rate.
When it comes to conveying sentiments and thoughts, facial expressions are quite effective. For human-computer collaboration, data-driven animation, and communication between humans and robots to be successful, the capacity to recognize emotional states in facial expressions must be developed and implemented. Recently published studies have found that deep learning is becoming increasingly popular in the field of image categorization. As a result, to resolve the problem of facial expression recognition (FER) using convolutional neural networks (CNN), increasingly substantial efforts have been made in recent years. Facial expressions may be acquired from databases like CK+ and JAFFE using this novel FER technique based on activations, optimizations, and regularization parameters. The model recognized emotions such as happiness, sadness, surprise, fear, anger, disgust, and neutrality. The performance of the model was evaluated using a variety of methodologies, including activation, optimization, and regularization, as well as other hyperparameters, as detailed in this study. In experiments, the FER technique may be used to recognize emotions with an Adam, Softmax, and Dropout Ratio of 0.1 to 0.2 when combined with other techniques. It also outperforms current FER techniques that rely on handcrafted features and only one channel, as well as has superior network performance compared to the present state-of-the-art techniques.
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