Fazal Qudus Khan

With the rapid expansion of the Internet of Medical Things (IoMT), the risks of cybersecurity have increased exponentially in healthcare settings, exposing patients’ safety. Three fundamental issues that existing intrusion detection systems (IDS) are challenged by are: (1) limited cross-domain generalization, (2) high computation requirements not suitable for edge deployment, and (3) absence of systematic comparison between classical machine learning (ML) and deep learning (DL) approaches on IoMT-specific data. In this paper, we propose a multi-dataset evaluation framework that covers six models (Random Forest, XGBoost, DNN, CNN, LSTM, and CNN-LSTM) across three different datasets: WUSTL-EHMS-2020, Edge-IIoTset, and UNSW-NB15. We show that there is a scale-dependent pattern: classical ensemble methods work best when the data is small (F1 = 0.914 ± 0.013 on WUSTL-EHMS-2020); the proposed hybrid CNN–Ensemble framework performs best when the data is large (F1 = 0.968 ± 0.002 on UNSW-NB15 with 62.8% fewer features). The proposed framework achieves a total model size of 2.11 MB and an inference latency of 111.6 ms, with seven out of the top 15 discriminative features being patient vital signs, giving the first quantitative evidence that physiological data systematically contributes to IoMT attack detection, which is demonstrated through an explainability analysis using the SHAP approach. Cross-dataset generalization experiments across six transfer scenarios expose fundamental limitations in domain transfer, establishing an important baseline for future research.

The development of a large-scale, Part-of-Speech (POS) tagged corpus for Urdu, a morphologically rich and low-resource language, remains a significant challenge due to the scarcity of annotated linguistic data. This paper presents a semi-automatic approach to creating a comprehensive Urdu POS-tagged dataset, addressing the limitations of existing resources. The dataset, named MM-POST (Mushtaq & Muzammil POS Tagged) corpus, comprises 119,276 words/tokens drawn from 2,871 sentences across 75 online BBC Urdu news articles. These articles span seven diverse domains: General, Finance, Entertainment, Politics, Health, Sports, and Science, ensuring broad coverage of linguistic patterns. The tagging process combines the automated tagging capabilities of the Center for Language Engineering (CLE) POS Tagger with rigorous manual annotation, resulting in a high-quality and domain-sensitive corpus with a vocabulary size of 9,395 unique tokens. The study provides a detailed analysis of the corpus construction process, including tagging methodology, tagset design, domain-wise distribution, and corpus characteristics. The MM-POST corpus supports various NLP tasks such as Named Entity Recognition, Information Retrieval, Text Classification, and serves as a benchmark resource for training and evaluating Urdu POS taggers. By preserving syntactic structure, the dataset enables both token- and sentence-level text processing. Additionally, the paper reviews the evolution of Urdu tagsets and highlights prior machine learning-based POS tagging efforts. This work not only contributes a valuable linguistic resource for Urdu but also establishes a scalable framework for developing corpora in other low-resource languages. Future extensions include automated tagger development and Named Entity annotation to enhance the corpus’s applicability across NLP applications.

Artificial Intelligence (AI) has emerged as one of the most sought-after academic disciplines worldwide, primarily due to its strong career prospects and expanding industrial applications. This study provides data-driven career guidance for prospective AI graduates by analysing recent labour market trends and examining the influence of geographic location on AI career opportunities. A structured dataset was analysed using a Random Forest regression model to forecast market trends and employment trajectories. Projections from the World Economic Forum’s 2025 report on emerging jobs towards 2030 were incorporated to provide a forward-looking perspective on AI employment dynamics. The findings indicate substantial growth in AI-related occupations, with net job growth for AI and Machine Learning roles projected to increase by approximately 82% by 2030. Among the most promising career paths are Data Scientists ([ ]113% projected growth), Machine Learning Specialists ([ ]82%), Software Engineers ([ ]57%), Cybersecurity Specialists ([ ]53%) and Robotics Engineers ([ ]40%). These results offer evidence-based insights to support informed academic and professional decision-making for students pursuing AI careers. These trends provide empirical support for strategic academic and workforce planning in AI-related fields. Students are encouraged to align their research and skill development with these domains, as AI is poised to become a central component of the future workforce.

Building the right software starts with understanding and answering key questions i.e. what is truly needed, checking the capability of the team on can they build, how they will build, will they be reimbursed correctly? These questions are summation of the requirement engineering or requirement analysis, an essential step of the software development methodology. As Natural Language Processing (NLP) grows more powerful, particularly with new Artificial Intelligence (AI) like Large Language Models (LLM), getting these requirements right is more necessary than ever. This paper proposes a new framework for requirement analysis formulated on structured methods like Delphi and Nominal Group Technique (NGT) gathering expert input and building a solid work. Our approach works step by step: it first checks a project's basic feasibility, then uses MEASUR’s Collateral Analysis to consider greater impact and risks. Next, it clarifies all aims using a KAOS goal model, and finally maps out all the parts and their connections in a Feature Diagram. A case study at the university of swat was carried out on NLP and LLMs based projects for requirement analysis. The approach was tested against traditional, radical, contemporary and agile techniques for requirement analysis. Our proposed framework performed 12% better than the traditional techniques, 17% better than agile and contemporary techniques and 14% better than Radical technique for Requirement analysis, proving its practical value for creating reliable and well planned NLP applications.

In smart homes, heavy reliance on appliance automation has increased, along with the energy demand in developing urban areas, making efficient energy management an important factor. To address the scheduling of appliances under Demand-Side Management, this article explores the use of heuristic-based optimization techniques (HOTs) in smart homes (SHs) equipped with renewable and sustainable energy resources (RSERs) and energy storage systems (ESSs). The optimal model for minimization of the peak-to-average ratio (PAR), considering user comfort constraints, is validated by using different techniques, such as the Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), Wind-Driven Optimization (WDO), Bacterial Foraging Optimization (BFO) and the Genetic Modified Particle Swarm Optimization (GmPSO) algorithm, to minimize electricity costs, the PAR, carbon emissions and delay discomfort. This research investigates the energy optimization results of three real-world scenarios. The three scenarios demonstrate the benefits of gradually assembling RSERs and ESSs and integrating them into SHs employing HOTs. The simulation results show substantial outcomes, as in the scenario of Condition 1, GmPSO decreased carbon emissions from 300 kg to 69.23 kg, reducing emissions by 76.9%; bill prices were also cut from an unplanned value of 400.00 cents to 150 cents, a 62.5% reduction. The PAR was decreased from an unscheduled value of 4.5 to 2.2 with the GmPSO algorithm, which reduced the value by 51.1%. The scenario of Condition 2 showed that GmPSO reduced the PAR from 0.5 (unscheduled) to 0.2, a 60% reduction; the costs were reduced from 500.00 cents to 200.00 cents, a 60% reduction; and carbon emissions were reduced from 250.00 kg to 150 kg, a 60% reduction by GmPSO. In the scenario of Condition 3, where batteries and RSERs were integrated, the GmPSO algorithm reduced the carbon emission value to 158.3 kg from an unscheduled value of 208.3 kg, a reduction of 24%. The energy cost was decreased from an unplanned value of 500 cents to 300 cents with GmPSO, decreasing the overall cost by 40%. The GmPSO algorithm achieved a 57.1% reduction in the PAR value from an unscheduled value of 2.8 to 1.2.

Procedural content generation (PCG) of terrains is one of the main building blocks for creating an automated and real-time virtual world. This study provides and in-depth review of the different tools, algorithms, and engines used for the PCG of terrains for the PCG of digital worlds; we focus especially on terrain generation but address also the main issues related to build structures and characters. It is important to know that the PCG of terrains can be implemented in a multidisciplinary scenario, for instance, modeling of games, simulating industrial maps, urban and rural planning of developments, government, and nongovernmental agency improvement plans. Many problems in current approaches were identified from the literature, where most of the researches studied were merely throwaway prototyping based, i.e., proof of concept-based systems, and were not tested in the real environment. Also, genetic algorithm-based PCG of terrains lack multiple features such as roads and buildings. The virtual cities generated through different engines were lacking a realistic look and feel. Terrain generation through multiobjective evolutionary algorithms (MOEAs) is investigated, and it is deemed the usage was restricted to the gaming domain and not extended to other fields. Findings suggest that the correctness of generated terrain is a big issue in the automatic generation of terrains. Thus, a focus on the automated correctness check is required. Other important content generation in video games such as structure generation and character generation has been extensively studied, and the techniques are analysed in the further sections of this research work.

Every so often, a confluence of novel technologies emerges that radically transforms every aspect of the industry, the global economy, and finally, the way we live. These sharp leaps of human ingenuity are known as industrial revolutions, and we are currently in the midst of the fourth such revolution, coined Industry 4.0 by the World Economic Forum. Building on their guideline set of technologies that encompass Industry 4.0, we present a full set of pillar technologies on which Industry 4.0 project portfolio management rests as well as the foundation technologies that support these pillars. A complete model of an Industry 4.0 factory which relies on these pillar technologies is presented. The full set of pillars encompasses cyberphysical systems and Internet of Things (IoT), artificial intelligence (AI), machine learning (ML) and big data, robots and drones, cloud computing, 5G and 6G networks, 3D printing, virtual and augmented reality, and blockchain technology. These technologies are based on a set of foundation technologies which include advances in computing, nanotechnology, biotechnology, materials, energy, and finally cube satellites. We illustrate the confluence of all these technologies in a single model factory. This new factory model succinctly demonstrates the advancements in manufacturing introduced by these modern technologies, which qualifies this as a seminal industrial revolutionary event in human history.

Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d’Alembert principle. Secondly, an adaptive robust controller, based on a sliding mode, is designed to manipulate the problem of uncertainties, including modeling errors. Last, a higher stability controller, based on the RBF neural network, is implemented with the adaptive robust controller to stabilize the ARAs, avoiding modeling errors and unknown payload issues. The novelty of the proposed design is that it takes into account high nonlinearities, coupling control loops, high modeling errors, and disturbances due to payloads and environmental conditions. The model was evaluated by the simulation of a case study that includes the two proposed controllers and ARA trajectory tracking. The simulation results show the validation and notability of the presented control algorithm.

In the past decades, a significant increase has been observed in cyber-attacks on the web-based systems used for financial purposes. Such individual systems often contain security weaknesses, called vulnerabilities that can be exploited for malicious purposes. The exploitation of such vulnerabilities can result in disclosure and manipulation of sensitive data as well as have destructive effects. To protect such systems, security testing is required on a periodic basis. Various detection and assessment techniques have been suggested by developers and researchers to address these security issues. In this paper, we survey the contributions of academia in the field of security testing for software applications and communication systems. A comprehensive review and in-depth analysis of the existing literature testing approaches has been performed to analyze their effectiveness and applicability under various scenarios. Further, we discuss various techniques used for conducting various security assessments. We follow the widely used method by Kitchenham and Charters for conducting a comprehensive systematic literature review process. Also, we propose a taxonomy for security testing techniques consisting of three main categories (Identification, Testing, and Reporting) and 17 subcategories consisting of specific security testing techniques (e.g., Black-box testing, risk assessment). Further, we assign a distinctive category from our taxonomy to each published paper in the security testing area, based on the material presented/discussed in the paper.