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This paper conducts a thorough comparative analysis of optimization algorithms for an unconstrained convex optimization problem. It contrasts traditional methods like Gradient Descent (GD) and Nesterov Accelerated Gradient (NAG) with modern techniques such as Adaptive Moment Estimation (Adam), Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP). Through empirical experiments, convergence speed, solution accuracy and robustness, is evaluated providing insights to aid algorithm selection. The convergence dynamics of convex optimization, is explored analyzing classical algorithms and contemporary neural network (NN) methodologies. The study concludes with a comparative assessment of these algorithms performance metrics and their respective strengths and weaknesses.

The emergence of online enterprises spread across continents has given rise to the need for managing the tacit knowledge and expertise of employees. Scenarios that includes the intention of the employer to find tacit expertise and knowledge of an employee that is not documented or self-disclosed have been addressed in this article. In today’s world management of tacit knowledge has become important for the organizations. Recent studies have also proposed hosting tacit knowledge management module over cloud for large global enterprises. There are many conceptual frameworks for the acquisition and processing of the tacit knowledge. This article has proposed a reputation based approach utilizing social interactions of employees to identify the expert based on the tacit knowledge. The existing reputation-based approaches towards expertise ranking in enterprises utilize PageRank, Normal distribution, and the Hidden Markov model for expertise ranking. These models suffer negative referral, collusion, reputation inflation, and dynamism. However, the authors have proposed a Bayesian-based approach utilizing the beta probability distribution reputation model for employee ranking in enterprises that can be hosted as a cloud service for the employees of the enterprise. The experimental results reveal improved performance compared to previous techniques in terms of mean average error (MAE) for the three data sets. The proposed scheme is able to differentiate categories of interactions in a dynamic context. The results reveal that the technique is independent of the rating pattern and density of data.

Smart devices in smart cities face the dual challenge of requiring real-time data processing and storing that data permanently on the cloud for future use. This creates a significant conflict between the need for immediate responsiveness and the demands of long-term storage. While Cloud Computing (CC) offers a viable platform for processing and storing this data, it introduces delays, longer response times, and network congestion issues. Internet of Things (IoT) devices require real-time responses, but their data must also be available for subsequent analysis and decision-making. Although edge and fog computing paradigms have been developed to address these issues, they suffer from limited resources and scalability challenges, making them inadequate for the above needs. A hybrid paradigm is often proposed to address the limitations of both cloud and edge computing. However, synchronization between cloud and edge can introduce delays, negatively affecting overall performance. The proposed framework, Regional Computing (RC), addresses big data challenges in smart cities by providing an intermediate solution between edge (limited power) and cloud (distant) computing. RC manages data during peak hours, alleviating congestion on the public network. The collected data is subsequently offloaded to cloud servers for long-term storage and detailed analysis. Preliminary findings are very encouraging, indicating substantial enhancements in reducing network congestion, minimizing delays, and lowering costs.