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Novel Multi-objective Feature Selection Framework for 5-year Breast Cancer Risk Prediction
No full textThis thesis presents a novel multi-objective genetic algorithm framework for clinical feature
selection in breast cancer risk prediction, addressing the critical gap between predictive
performance and clinical interpretability in automated feature selection methods. Traditional
feature selection approaches optimize for statistical performance alone, often selecting
algorithmically convenient but clinically meaningless variables, limiting their real-world
applicability in medical decision support systems.
The proposed framework systematically integrates expert oncological knowledge into
evolutionary optimization through three innovative variants: Clinical Expert-Guided Geneatic
Algorithm (GA), Adaptive GA, and Multi-Population GA. These methods simultaneously
optimize predictive performance, clinical relevance, and model parsimony using the
comprehensive PLCO (Prostate, Lung, Colorectal, and Ovarian) cancer screening trial data
containing 78,209 participants and 176 related features.
Experimental results demonstrate distinct performance-interpretability trade-offs across
the three variants. The Clinical Expert-Guided GA achieves excellent clinical interpretability
(3.79/5 clinical relevance score, 79% clinically relevant features) while maintaining competitive
predictive performance (AUC = 0.757) using only 29 features. The Adaptive GA achieves
superior predictive performance (AUC = 0.948, F1 = 0.444) representing substantial
improvements over baseline methods, but with reduced clinical interpretability (2.09/5 clinical
score, 25.9% clinically relevant features). The Multi-Population GA provides specialized
solutions optimized for different clinical scenarios: an efficiency variant achieving 0.934 AUC
with 32 features, a clinical-focused variant achieving 54.3% clinical relevance, and a diversity
variant achieving 0.464 F1 score with balanced precision-recall performance. Cross-dataset
validation on the METABRIC dataset confirms framework generalizability, with the Adaptive
GA maintaining 99.3% of baseline performance while the Clinical GA achieves the highest
clinical interpretability across both datasets.
The framework\u27s primary contributions include: (1) first systematic integration of expert
medical knowledge into multi-objective genetic algorithms for breast cancer prediction, (2)
novel clinical-aware genetic operators and adaptive optimization strategies, (3) comprehensive
multi-objective optimization enabling deployment flexibility based on clinical requirements, and
(4) clinical validation demonstrating that domain knowledge integration enhances rather than
xicompromises model performance. This work advances the field of clinically-applicable
evolutionary computation and provides multiple deployment-ready solutions addressing
different clinical priorities in breast cancer risk assessment systems
FIA seeks details of all foreign travel tickets of IBA Karachi’s three officials by Oct 3
No full textThe Federal Investigation Agency (FIA) has launched an inquiry under Section 25 of the Anti-Money Laundering Act 2010 against IBA Karachi. It has requested details of all foreign travel tickets and financial records related to the executive director, registrar, and director HR by October 3. The investigation concerns alleged misconduct, corruption, and misuse of institutional funds. FIA has also asked for procurement records, payment details, employee recruitment data, and asset declarations of senior officials from FY 2019–20 to FY 2024–25. The proceedings aim to verify transparency and possible financial irregularities in IBA’s management and operations
Digital Twins in Industry 4.0
No full textDigital Twin (DT) technologies are central to the industry 4.0 revolution, offering realtime simulation, monitoring, and optimization of physical systems. However, their adoption is often hindered by high costs and proprietary constraints. This project proposes a cost-effective, open-source alternative by integrating Godot; a lightweight 3D game engine with CODESYS, a widely used PLC development environment. The resulting prototype models an articulated robotic arm capable of real-time motion control, sensor integration, and synchronized simulation with a virtual conveyor system. Importantly, the platform now operates as a bi-directional digital twin, where changes applied to the physical robotic arm are instantly mirrored in the simulation, and simulation-side adjustments are propagated back to the physical system in real time. Targeted toward SMEs, educational institutions, and research labs, the system enables safe experimentation, training, and procurement evaluation without reliance on physical hardware. This scalable, modular platform demonstrates that industrial-grade digital twins can be developed with minimal overhead, fostering broader access to Industry 4.0 innovation
Examining the relationship between perceived AI adoption and innovation performance, mediating role of absorptive capacity and moderating role of organizational size and culture
No full textIn this paper, we empirically investigate the moderating and mediating relationships between Perceived Artificial Intelligence (AI) adoption level, innovation performance, absorptive capacity, organisational size and corporate culture and organisational performance. As artificial intelligence (AI) continues to influence how business functions and competes in the digital age, it is essential to understand how it can be applied and what broader implications it has. The quantitative research design was employed using the questionnaire based survey research design which saw a sample population of 350 participants. The analysis was conducted by using Hayes Process Macro (Model 9) and Model 4. The result showed that there is a positive significant relationship between perceived AI adoption and innovation performance. However, the statistical significance of the mediation role played by absorptive capacity in this relationship was insignificant. Finally, the moderating effect of organisational size and corporate culture for the relationship between perceived AI adoption and absorptive capacity was also not significant. These results shed light on the complexity of the relationship between AI and innovation and suggest the need for further research to investigate other mediating and moderating variables that may influence this relationship
India must rethink
No full textThe April 22 Pahalgam incident has evoked a hysterical reaction from India, which has attacked Pakistani and Azad Kashmir cities killing innocent civilians, including children, and damaging the Neelum-Jhelum hydel works. Within five minutes of the Pahalgam attack, Indian media started pointing fingers at Pakistan alleging it had enabled the terrorist attack. Two weeks later, India attacked without providing a shred of evidence of Pakistan’s alleged involvement. The purpose of this article is to urge Indian policymakers to avoid prolonging their actions as the political, social and economic costs for their own country are substantial, and instead, examine their policy stance dispassionately
Performance Management of Employees in the Hospitality Industry
No full textPurpose: The purpose of this paper is to harmonize the scattered literature on performance management of these employees (PME) and the hospitality industry across numerous countries, thereby augmenting the literature breadth and fashioning a gap. Next, it takes care of information on how much scholarly work has concentrated on considering scholarly work in the interrelatedness of the concepts mentioned above.
Design/methodology/approach: A congruent literature search was undertaken using key search items: “employee performance” and” hospitality sector.” Additionally, this paper is unique in that it focuses on the concept of performance management for employees in the hospitality sector (536) or hospitality industry (1000).
Findings: Based on the analysis of fifteen different articles related to the theme of performance management in the hospitality industry, a clear insight will help academicians and practitioners to get a deeper view of performance management through this review paper.
Originality: This is a systematic review that suggests the evolution of developmental feedback with the passage of time and research gaps. This paper is unique in that it focuses on the concept of performance management for employees in the hospitality sector or hospitality industry. As there is less literature review is present on this topic so this paper will provide a comprehensive insight from present literature in summarized form.
Research Limitations/Implications: This paper will assist researchers and managers to understand how performance management is taking place in the hospitality industry. Further, how the tools can better be used in future for measuring the performance of employees in the best possible manner.
Practical Implications: This paper offers deep insights into literature of performance management which is useful for both academicians and practitioners in thinking about the factors that can help in performance management.
Social Implications: This study contributes for the guidance of managers in terms of enhancing performance by analyzing performance management in hospitality industry and exalt performance of employees in hospitality industry
A Multi-Objective Heuristic Approach to Enhancing Perishable Supply Chain Efficiency via Progressive Dataset Partitioning
No full textEfficient transport systems are crucial to reducing logistics costs and improving the performance of supply chains, particularly in the dairy sector, where the decomposition of milk requires timely and cost-effective collection. This study can bridge the gap between cost control and the transportation of perishable goods by optimizing a milk collection network for dairy processors. The proposed algorithm reduces the network, dividing the problem into subproblems, and applying MST iteratively for each truck and passing through the next truck until the last truck exhausts the entire network. The results show that the optimized route significantly reduces operational costs, travel distances and collection time while preserving the vulnerability constraints. The proposed algorithm is applied to the Sahiwal district of Punjab, Pakistan. Comparative analysis reveals that our proposed MST-based iterative heuristic, approach exceeds many of the methods mentioned in the literature on the data set. These findings provide logistical planners and dairy companies with flexible and scalable decision support tools to ensure sustainable and cost-effective milk transportation
Impact of Social Support on Task Performance through the mediating role of Reward Responsiveness and Affective Commitment as moderator
No full textThe success of present day organizations, primarily, depend on the people working for it. The well-being and contentment of employees is, therefore, prioritized immensely. The present study investigates one of the key factor reflecting a proficient workforce of the organization. In this study, task performance of the employees is measured as an outcome of social support with the mediating role of reward responsiveness. The moderating role of affective commitment has been investigated between reward responsiveness and task performance
Value Premium and Time-Varying Market Volatility
No full textWe examine whether the value premium depends on ex‑ante market volatility. Using U.S. equity data from 1963 to 2023, we sort stocks into Size–B/M portfolios and classify each month as low or high volatility according to the quartile of the rolling 60‑month market variance. The value–growth (H–L) spread averages 0.48 % per month after low‑volatility months but is economically trivial after high‑volatility months. The effect is driven by the underperformance of growth stocks, is concentrated in micro‑ and small‑capitalization stocks, and remains partially unexplained by the Fama and French (2025) five‑factor and Hou, Xue, and Zhang (2015) q‑factor models. We also show that institutions systematically sell growth stocks and buy value stocks following low-volatility markets, reversing these trades after high-volatility markets. This volatility‑contingent rebalancing helps reconcile the conditional return pattern with duration risk theory
Leveraging Fintech for Business Sustainability: Insights From Pakistani MSMEs
No full textMicro, Small, and Medium enterprises worldwide are considered pillars of the economy, despite their significant contribution to economic development and growth. MSMEs are confronted with numerous challenges, including access to formal finance. Fintech emerges as a feasible enabler of sustainable growth and is restructuring the way these firms operate, manage funding, and achieve sustainability in the long run.
This paper intends to scrutinize the impact of the TOE factors on financial technology adoption and its subsequent impact on MSMEs\u27 business sustainability in Pakistan. A Structured survey will be distributed to the selected MSMEs to collect cross-sectional data. For data analysis, PLS- SEM technique is adopted, and the hypothesis will be tested using SmartPLS Software. The results of the study will provide insights for the government, fintech companies, and MSMEs