This research involved designing and building an interactive generative AI application to conduct a comparative analysis of two advanced Large Language Models (LLMs), GPT-4 and Claude 2, using Langsmith evaluation tools. The project was developed to explore the potential of LLMs in facilitating postgraduate course recommendations within a simulated environment at Munster Technological University (MTU). Designed for comparative analysis, the application enables testing of GPT-4 and Claude 2 and can be hosted flexibly on either AWS (Amazon Web Services) or Azure. It utilizes advanced natural language processing and retrieval-augmented generation (RAG) techniques to process proprietary data tailored to postgraduate needs. A key component of this research was the rigorous assessment of the LLMs using the Langsmith evaluation tool against both customized and standard benchmarks. The evaluation focused on metrics such as bias, safety, accuracy, cost, robustness, and latency. Additionally, adaptability covering critical features like language translation and internet access, was independently researched since the Langsmith tool does not evaluate this metric. This ensures a holistic assessment of the LLM’s capabilities.

ChatGPT is another large language model (LLM) inline but due to its performance and ability to converse effectively, it has gained a huge popularity amongst research as well as industrial community. Recently, many studies have been published to show the effectiveness, efficiency, integration, and sentiments of chatGPT and other LLMs. In contrast, this study focuses on the important aspects that are mostly overlooked, i.e. sustainability, privacy, digital divide, and ethics and suggests that not only chatGPT but every subsequent entry in the category of conversational bots should undergo Sustainability, PrivAcy, Digital divide, and Ethics (SPADE) evaluation. This paper discusses in detail about the issues and concerns raised over chatGPT in line with aforementioned characteristics. We support our hypothesis by some preliminary data collection and visualizations along with hypothesized facts. We also suggest mitigations and recommendations for each of the concerns. Furthermore, we also suggest some policies and recommendations for AI policy act, if designed by the governments.Â

Intelligent reflecting surfaces (IRSs) are anticipated to provide reconfigurable propagation environment for next generation communication systems. In this paper, we investigate a downlink IRS-aided multi-carrier (MC) non-orthogonal multiple access (NOMA) system, where the IRS is deployed to especially assist the blocked users to establish communication with the base station (BS). To maximize the system sum rate under network quality-of-service (QoS), rate fairness and successive interference cancellation (SIC) constraints, we formulate a problem for joint optimization of IRS elements, sub-channel assignment and power allocation. The formulated problem is mixed non-convex. Therefore, a novel three stage algorithm is proposed for the optimization of IRS elements, sub-channel assignment and power allocation. First, the IRS elements are optimized using the bisection method based iterative algorithm. Then, the sub-channel assignment problem is solved using one-to-one stable matching algorithm. Finally, the power allocation problem is solved under the given sub-channel and optimal number of IRS elements using Lagrangian dual-decomposition method based on Lagrangian multipliers. Moreover, in an effort to demonstrate the low-complexity of the proposed resource allocation scheme, we provide the complexity analysis of the proposed algorithms. The simulated results illustrate the various factors that impact the optimal number of IRS elements and the superiority of the proposed resource allocation approach in terms of network sum rate and user fairness. Furthermore, we analyse the proposed approach against a new performance metric called computational efficiency (CE).

This article proposes a new resource allocation framework that uses the dual theory approach. Specifically, the sum-rate of the multi-cell network having backscatter tags and NOMA user equipments is maximized by formulating a joint optimization problem. To find the efficient base station transmit power and backscatter reflection coefficient in each cell, the original problem is first divided into two subproblems and then derived the closed-form solutions. A comparison with the orthogonal multiple access (OMA) ambient BackCom and pure NOMA transmission has been provided.

This work proposes a hybrid intelligent Deep Learning (DL)-enabled mechanism to secure IIoT infrastructure from lethal and sophisticated multi-variant botnet attacks. The proposed mechanism has been rigorously evaluated with latest available dataset, standard and extended performance evaluation metrics, and current DL benchmark algorithms. Besides, cross validation of our results are also performed to clearly show overall performance.