Software Defined Networking enables centralized control and dynamic programmability, but achieving efficient load balancing across distributed controllers remains challenging because of traffic variability and scalability constraints. Despite the significant potential of reinforcement learning for multicontroller load balancing, existing studies primarily focus on developing new learning architectures or switch migration mechanisms, with limited attention given to systematically comparing exploration strategies under identical network conditions. The experimental setup consists of twelve switches for data forwarding and three distributed controllers following east-west communication. This study compares five exploration strategies of Q-learning using throughput, latency, controller response time, load balancing efficiency, fairness, scalability, and congestion-related metrics. Existing studies also often neglect fault tolerance, energy efficiency, and real-world validation. Under congested conditions, SOFT Q-learning records the lowest throughput reduction of 0.92, followed by upper confidence bound with 1.56, epsilon-greedy strategy with 2.34, prioritized experience replay with 3.29, and Boltzmann exploration with 6.06. The epsilon-greedy strategy achieves the highest throughput of 7.88 and fairness of 0.9999, soft reinforcement learning records the lowest latency of 0.0115 seconds, upper confidence bound achieves the fastest controller response time of 0.0285 seconds, and Boltzmann exploration attains the highest load balance ratio of 0.9865.

This research focuses on object detection using Convolutional Neural Networks (CNN) applied to underwater image datasets. Underwater images often suffer from issues such as low clarity and quality, which pose challenges for accurate object identification. To address this, the research employs image enhancement techniques, including image illumination methods, to improve image quality and facilitate object detection algorithms. Subsequently, the study developed algorithms capable of detecting objects and accurately predicting their categories. The primary objective is to achieve optimal accuracy and efficiency in underwater recognition. This research utilizes Machine Learning techniques through Tensor Flow and Image Processing to accomplish underwater object detection. Deep learning techniques, particularly feature learning, object classification, and detection, have gained significant attention and momentum. In this research we implemented different image enhancement techniques on dataset and evaluated their performance. While one metric, IQI (Image Quality Index), slightly favoured histogram equalization (HE), the other three metrics strongly favoured the enhanced version of HE known as Contrast Limited Adaptive Histogram Equalization (CLAHE).