Saeed Akbar

Alzheimer’s disease (AD) is the most common form of dementia marked by cognitive decline and memory loss. Early detection is essential for timely intervention; however, traditional biomarkers, including cerebrospinal fluid (CSF) assays, neuroimaging, and cognitive assessments, are limited by cost, invasiveness, and accessibility. Digital biomarkers, obtained from wearable sensors, smartphone applications, speech analysis, and other passive monitoring technologies, represent a promising alternative for scalable, non-invasive, and continuous assessment of early cognitive decline. This paper provides a comprehensive review of the current landscape of digital biomarkers for AD diagnosis, emphasizing their potential application in the preclinical and prodromal stages of the disease. In addition, a bibliometric analysis demonstrates the rapid expansion of digital biomarker research, highlighting key trends in publication volume, influential authors, institutions, and interdisciplinary collaborations. Despite the significant promise of digital biomarkers, challenges remain regarding validation, regulatory approval, data privacy, and integration into clinical practice. The results indicate that future research should prioritize standardization, multimodal biomarker integration, and large-scale longitudinal studies to fully realize the potential of digital technologies in AD detection.

Fog and edge computing require adaptive control schemes that can handle partial observability, severe latency requirements, and dynamically changing workloads. Recent research on Agentic AI (AAI) increasingly integrates reasoning systems powered by Large Language Models; however, these tools are not applicable to infrastructure-level systems due to their high computational cost, stochastic nature, and poor formal analyzability. In this paper, a generic model, Agentic Fog (AF), is presented, in which fog nodes are represented as policy-driven autonomous agents that communicate via p2p interactions based on shared memory and localized coordination. The suggested architecture decomposes a system's goals into abstract policy guidance and formalizes decentralized fog coordination as an exact potential game. The framework is guaranteed to converge and remain stable under asynchronous updates, bounded-rational best-response dynamics, and node failures. Simulations demonstrate that the AF system achieves lower average latency and adapts more efficiently to varying demand than greedy heuristics and integer linear programming under dynamic conditions. The sensitivity analysis also demonstrates the capability to perform optimally under different memory and coordination conditions.

Software development projects are highly susceptible to risks during the design phase, which plays a crucial role in shaping the architecture, functionality, and quality of the final product. Decisions made during the design stage significantly affect the outcomes of the subsequent phases, including coding, testing, deployment, and maintenance. However, the complexities and uncertainties inherent in the design phase are often inadequately addressed by traditional risk management tools as they rely on deterministic models that oversimplify interdependent risks. This research introduces a fuzzy logic-based risk assessment model tailored specifically for the design phase of software development projects. The proposed fuzzy model, unlike the existing state-of-the-art models, regards the iterative nature of the design phase, the interaction between diverse stakeholders, and the potential inconsistencies that may arise between the initial and final version of the software design. More specifically, it develops a customized fuzzy model that incorporates design-specific risk factors such as evolving architectural requirements, technical feasibility concerns, and stakeholder misalignment. Finally, it integrates expert-driven rule definitions to enhance model accuracy and real-world applicability, ensuring that risk assessments reflect actual challenges faced by software design teams. Simulations conducted across diverse real-world scenarios demonstrate the model’s robustness in predicting risk levels and supporting mitigation strategies. The simulation results confirm that the proposed fuzzy logic model outperforms conventional approaches by offering greater flexibility and adaptability in managing design-phase risks, assisting project managers in prioritizing mitigation efforts more effectively to improve project outcomes.

As the global population ages, robust technological solutions are increasingly necessary to support and enhance elderly autonomy in-home or care settings. This paper presents a novel computer vision-based activity monitoring system that uses cameras and infrared sensors to detect and analyze daily activities of elderly individuals in care environments. The system integrates a frame differencing algorithm with adjustable sensitivity parameters and an anomaly detection model tailored to identify deviations from individual behavior patterns without relying on large volumes of labeled data. The system was validated through real-world deployments across multiple care home rooms, demonstrating significant improvements in emergency response times and ensuring resident privacy through anonymized frame differencing views. Upon detecting anomalies in daily routines, the system promptly alerts caregivers and family members, facilitating immediate intervention. The experimental results confirm the system’s capability for unobtrusive, continuous monitoring, laying a strong foundation for scalable remote elderly care services and enhancing the safety and independence of vulnerable older individuals.

Managing power and its subsequent thermal implications is of paramount concern in modern Data Centers (DCs) management. Failure to adequately address the escalating energy use can result in excessive heat dissipation, leading to thermal imbalances and hotspots. In addition, the prolonged execution of CPU-intensive user jobs on servers operating at higher temperatures can significantly aggravate the DCs cooling efforts. Researchers advocate Thermal-aware (TA) scheduling as a promising tool to counter the said issue. However, existing stateof-the-art overlooks user jobs runtime heterogeneity, potentially causing aggravated heat dissipation when CPU-intensive tasks run on servers at elevated temperatures for longer duration. Moreover, existing works do not provide any mechanism to detect overloaded computing nodes at runtime in a TA context. Finally, existing strategies do not adapt according to the DCs dynamic thermal conditions. This paper offers a Coalitional Game-based Thermal-aware Adaptive Scheduling (CGTAS) tailored for heterogeneous DCs to minimize the cooling cost stemming from excessive heat generated during compute-intensive job execution. CGTAS intelligently differentiates incoming jobs based on their thermal profiles and CPU-time for optimal thermal outcomes. In addition, it dynamically allocates user jobs to computing nodes based on their real-time marginal thermal performance using the Core solution concept from game theory. Finally, unlike existing TA strategies, the proposed design identifies thermally overloaded computing elements using the Core and performs task migrations to optimize thermal-efficiency. Extensive simulations confirm substantial energy savings (up to 26.08%) compared to its TA substitutes, promoting sustainable and high-performance computing infrastructure in large-scale cloud DCs.

Scratch is an innovative and the most popular block-based Visual Programming Language designed for beginners to learn programming effectively. However, it lacks the capacity to allow users to learn and solve larger real-world problems such as the Travelling Salesman Problem (TSP). Hence, there is a need for an accessible and effective tool to assist beginners and novice programmers in learning and tackling the TSP. This paper introduces ScratchTSP, an extension of the Scratch programming Framework, specifically designed for the TSP. It offers an opportunity for beginners and novice programmers to grasp the TSP and the various algorithms used to solve it. For evaluation, we conduct a survey among undergraduate students to assess the usability and usefulness of the proposed ScratchTSP extension. Survey findings indicate that ScratchTSP offers a user-friendly and effective tool for beginners or novice programmers, aiding them in learning the TSP while developing their critical thinking and problem-solving skills.

Project management tools are critical for managing software projects, decreasing costs, accelerating timelines, and using available resources effectively. However, project managers find it very tedious to choose a suitable software project management (SPM) tool from available tools in the market. The appropriateness of the SPM tool depends on the nature of the software project being developed – an SPM tool suitable for a large-scale software project may not be appropriate for small-scale projects. However, due to the complexities of the software projects and the development conditions, the selection of an SPM tool is often biased by personal preferences or marketing publicities which may lead to inefficiencies in SPM and consequently, lead to higher cost and lower quality of end product. In this work, we present a decision support framework for SPM tool selection. The proposed system develops a set of selection criteria based on an analytic network process (ANP). To demonstrate the internal working of the proposed framework, we put forward an example of the tool selection problem using the proposed system. Furthermore, the proposed system can be used as is for SPM tool selection for other than software projects.

One of the primary jobs of a software project manager is to assign available resources to software development tasks in such a way that results in a high-quality product at a low cost. Software Project Scheduling (SPS) allocates the most appropriate human resource to project activities at the right time to reduce software project failure risks and minimise project makespan. In literature, the SPS problem is referred to as the Multiple Resource-Constrained Project Scheduling Problem (MRCPSP). The MRCPSP assigns human resources with multiple skills and proficiency levels to various project activities. Human abilities can be distinguished into technical/hard and non-technical/soft skills. The former describes the skills related to technology, tools, etc. While the latter deals with the skills related to the personality, such as being introvert, extrovert, sensing, etc. Recent studies have shown that some tasks may require specific soft skills. Moreover, the efficiency and productivity of the assigned resource significantly reduce if the soft skill requirements are ignored during task allocation. Ultimately, the development process might end up in lower-quality software products with higher development costs; worst case, the project may even fail. Several MRCPSP-based SPS approaches have been designed to reduce the development costs of software projects. These mechanisms consider the hard skills of a human resource with different proficiency levels. However, they overlook the soft skills required leading to the inefficiency of the allocated human resources. This will increase the project makespan and may cause higher development costs or even project failures. Therefore, to fill this gap, we propose Multi-Skill Resource Constrained and Personality Traits based Project Scheduling (MSRCPPS) considering the soft skills as well as the technical skills of a human resource during SPS. The main objective is to minimize software project makespan. Finally, the effectiveness of our proposed approach is evaluated against existing state-of-the-art using extensive simulations.