Muhammad Haleem

1. Entrepreneurial initiatives arise from the need to create conditions that encourage active participation in business ventures. Educational institutions play a crucial role in cultivating this en...

The advent of artificial intelligence (AI) models presents significant opportunities alongside inherent security risks, such as the exploitation by adversaries generating malicious data to compromise other AI-enabled systems. Despite the urgent need to address such threats, AI-based threat modelling remains largely underexplored in research, primarily constrained by three key challenges: (i) the lack of formal representation of security and AI-based data, (ii) the absence of inference rules for automated threat identification, and (iii) inconsistent risk and vulnerability assessment. As a result, these limitations, coupled with stakeholders’ insufficient security knowledge and AI expertise, lead to erroneous threat modelling of AI-enabled systems. This research aims to develop and implement OntoSecAI, an ontology-based approach to automate threat modelling and assessment for AI-enabled systems. In particular, we design 03 ontologies and 30 inference rules, followed by risk and CVSS-based vulnerability assessments to perform automated threat modelling and assessment comprehensively. In addition, the approach is validated through 10 case studies and verified using mathematical theorems to confirm its correctness and completeness. The research findings demonstrate that the developed ontologies effectively facilitate unified representation and comprehensive coverage of security and AI systems’ data. Furthermore, the inference rules implemented effectively map system assets to potential security threats. Crucially, the utilization of ontologies provides consistent risk and vulnerability assessments across AI-enabled systems. Consequently, a comprehensive security knowledge base is offered to stakeholders, regardless of their varying security and AI expertise, ensuring uniform threat modelling across diverse AI systems and adaptability to emerging security threats.

Industry 5.0 marks a transformative shift in industrial operations, emphasizing human-machine collaboration, sustainability, and intelligent automation. This study conducts a Systematic Literature Review (SLR) to analyze research contributions from leading digital libraries, including IEEE, ACM, Springer, and MDPI, focusing on publications between 1992 and 2024. The objective is to identify existing research gaps, challenges, and synergies across Federated Learning (FL), Collaborative Robotics (Cobots), and Autonomous Systems (AS). From an initial collection of 12,079 papers, a core set of 92 relevant publications was selected based on predefined research questions. This review provides a structured analysis to guide scholars and practitioners in optimizing the interplay between FL, cobots, and autonomous systems, aiming to enhance adaptability, operational resilience, and environmental sustainability. By addressing unresolved challenges and highlighting emerging opportunities, this SLR contributes to bridging research gaps and supports the development of strategic frameworks that balance efficiency with sustainability, advancing the goals of Industry 5.0.

In an era dominated by digital communication, the vast amounts of data generated from social media and financial markets present unique opportunities and challenges for forecasting stock market prices. This paper proposes an innovative approach that harnesses the power of social media sentiment analysis combined with stock market data to predict stock prices, directly addressing the critical challenges in this domain. A major challenge in sentiment analysis is the uneven distribution of data across different sentiment categories. Traditional models struggle to accurately identify fewer common sentiments (minority class) due to the overwhelming presence of more common sentiments (majority class). To tackle this, we introduce the Off-policy Proximal Policy Optimization (PPO) algorithm, specifically designed to handle class imbalance by adjusting the reward mechanism in the training phase, thus favoring the correct classification of minority class instances. Another challenge is effectively integrating the temporal dynamics of stock prices with sentiment analysis results. Our solution is implementing a Transductive Long Short-Term Memory (TLSTM) model that incorporates sentiment analysis findings with historical stock data. This model excels at recognizing temporal patterns and gives precedence to data points that are temporally closer to the prediction point, enhancing the prediction accuracy. Ablation studies confirm the effectiveness of the Off-policy PPO and TLSTM components on the overall model performance. The proposed approach advances the field of financial analytics by providing a more nuanced understanding of market dynamics but also offers actionable insights for investors and policymakers seeking to navigate the complexities of the stock market with greater precision and confidence.

Researchers are turning to nanofluids in PV/T hybrid systems for enhanced efficiency due to nanoparticle dispersion, improving thermal and optical properties over conventional fluids. Three different concentrations of formulated soybean oil based MXene nanofluids are considered 0.025, 0.075 and 0.125 wt.%. Maximum specific heat capacity nanofluids ( $c_{pNF}$ ) augmentation is 24.49% at 0.125 wt.% loading of Ti3C2 in the base oil. The calculation of the $c_{pNF}$ based on the temperature and nanoflakes concentration is very expensive and time-consuming as it should be calculated via the practical test investigation. This study employs a long short-term memory (LSTM) as an efficient machine learning method to extract the surrogate model for calculating the $c_{pNF}$ based on the temperature and nanoflakes concentration. In addition, a couple of other machines learning methods, including support vector regression (SVR), group method of data handling (GMDH), and multi-layer perceptron (MLP), are developed to prove the higher efficiency of the recently proposed LSTM model in the calculation of the $c_{pNF}$ . In addition, the Bayesian optimization technique is employed to calculate the optimal hyperparameters of the developed SVR, GMDH, MLP and LSTM to reach the highest efficiency of the system in predicting the $c_{pNF}$ based on temperature and nanoflakes concentration. Notably, 95% of the recorded data via differential scanning calorimetry (DSC) is used for training machine learning techniques. In comparison, 5% is used for testing and validation purposes of the developed algorithm. The newly proposed optimized SVR, GMDH, MLP, and LSTM are modelled in MATLAB software. The results show that the newly proposed optimized LSTM model can reduce the mean square error in calculating the $c_{pNF}$ by 99%, 99% and 91% compared with SVM, GMDH and MLP, respectively. The proposed methodology can be used to calculate other thermophysical properties of nanofluids.

With an increase in the number of processing cores or systems, the high-performance edge-computing system’s power consumption along with its computational speed will increase, essentially. However, this comes at the expense of high-energy utilization. One notable solution to reduce the energy consumption of these systems is to execute these systems at the slowest feasible speed so that the job’s deadline times are met. Unfortunately, this method is at the expense of more response time and performance loss. To resolve this issue, in this paper, we propose a scheduling approach that associates the genetic algorithm (GA) with the first feasible speed (FiFeS) technique i.e. GA-FiFeS algorithm. This does not jeopardize real-time tasks’ deadlines. The GA-FiFeS algorithm proposes an energy-efficient schedule while still ensuring high response times. The results of the proposed approach, using plausible assumptions and experimental parameters, are compared with currently in-practice approaches, i.e. FiFeS and LeFeS (least feasible speed) approaches. Using numerical simulations and plausible assumptions, our investigation suggests that the proposed GA-FiFeS technique outperforms the FiFeS technique in terms of energy consumption (~18.56%) and response times (~2.78%). Furthermore, the GA-FiFeS has comparable outcomes with the LeFeS method while taking the expected time of execution as an assessment feature for analysis.

This work applies the recently developed n th state Markovian jumping particle swarm optimisation algorithm with local search (NS-MJPSOloc) awareness method to address the economic/environmental dispatch (EED) problem. The proposed approach, known as the Non-dominated Sorting Multi-objective Particle Swarm Optimisation with Local Best (NS-MJPSOloc), aims to enhance the performance of the Particle Swarm Optimisation (PSO) algorithm in multi-objective optimisation problems. This is achieved by redefining the concept of best local candidates within the search space of the multi-objective optimisation. Economic dispatch refers to the systematic allocation of available power resources in order to fulfil all relevant limitations and effectively meet the demand for electricity at the lowest possible operating cost. As a result of heightened public consciousness regarding environmental pollution and the implementation of clean air amendments, nations worldwide have compelled utilities to adapt their operational practises in order to comply with environmental regulations. The (NS-MJPSOloc) approach has been utilised for the resolution of the EED problem, including cost and emission objectives that are not commensurable. Multiple optimisation iterations of the suggested methodology have been conducted on a conventional test setup. The objective of economic dispatch is to minimise the overall expenditure on fuel, without taking into account any limitations related to emissions. The emission dispatch prioritises the reduction of emissions without taking into account economic considerations. The findings illustrate the efficacy of the suggested (NS-MJPSOloc) approach in producing a collection of Pareto-optimal solutions that are evenly dispersed within a single iteration. The comparison of several approaches reveals the higher performance of the suggested (NS-MJPSOloc) in terms of the diversity of the Pareto-optimal solutions achieved. In addition, a measure of solution quality based on Pareto optimality has been incorporated. The findings validate the effectiveness of the proposed (NS-MJPSOloc) approach in addressing the multi-objective EED issue and generating a trade-off solution that is both optimal and of high quality.