New Titles

Absolute Java

Savitch, Walter J., 1943-2021.

Includes index. Absolute Java offers complete coverage of the Java programming language. Written by Walt Savitch, one of the most widely read authors in the area of programming, it provides all the tools necessary for experienced and novice programmers to master Java, including thorough coverage of the Java Library, complete and fully executable code throughout, sections highlighting programming tips and common pitfalls, and a logical order of coverage of Java topics in order for students to better understand the language. The book is appropriate for introductory courses covering Java, intermediate programming courses introducing Java to students familiar with another language, or as a reference for students and industry professionals.

Date Published:2004

Management information systems : managing the digital firm.

Laudon, Kenneth C.,

A case-based approach to IS in business today Management Information Systems: Managing the Digital Firm provides a comprehensive introduction to Information Systems that draws connections between MIS and business performance. The authors present real-world case studies that explain how well-known companies use IT to solve problems and achieve their objectives. This real-world approach helps students develop sought-after skills, learn to lead IS-related management discussions, and use IT to meet bottom-line results. The 18th Edition has been updated to cover important contemporary topics including the latest on AI, sustainability and analytics.

Date Published:

Power electronics : devices, circuits, and applications

Rashid, Muhammad H. (Muhammad Harunur), 1945-

Chapter 1 Introduction PART I Power Diodes and Rectifiers Chapter 2 Power Diodes and Switched RLC Circuits Chapter 3 Diode Rectifiers PART II Power Transistors and DC–DC Converters Chapter 4 Power Transistors Chapter 5 DC–DC Converters PART III Inverters Chapter 6 DC–AC Converters Chapter 7 Resonant Pulse Inverters Chapter 8 Multilevel Inverters PART IV Thyristors and Thyristorized Converters Chapter 9 Thyristors Chapter 10 Controlled Rectifiers Chapter 11 AC Voltage Controllers PART V Power Electronics Applications and Protection Chapter 12 Flexible AC Transmission Systems Chapter 13 Power Supplies Chapter 14 DC Drives Chapter 15 AC Drives Chapter 16 Introduction to Renewable Energy Chapter 17 Protection of Devices and Circuits Appendix A Three-Phase Circuits Appendix B Magnetic Circuits Appendix C Switching Functions of Converters Appendix D DC Transient Analysis Appendix E Fourier Analysis Appendix F Reference Frame Transformation Bibliography Answers to Selected Problems Index. This text covers the basics of emerging areas in power electronics and a broad range of topics such as power switching devices, conversion methods, analysis and techniques, and applications. Its unique approach covers the characteristics of semiconductor devices first, then discusses the applications of these devices for power conversions. Four main applications are included: flexible ac transmissions (FACTs), static switches, power supplies, dc drives, and ac drives.

Date Published:2018

Computer science illuminated.

Dale, Nell.,

Designed for the introductory computing and computer science course, the student-friendly Computer Science Illuminated, Eighth Edition provides students with a solid foundation for further study, and offers non-majors a complete introduction to computing. Fully revised and updated, the eighth edition of this best-selling text retains the accessibility and in-depth coverage of previous editions, while incorporating all-new material on cutting-edge issues in computer science. Authored by the award-winning team Nell Dale and John Lewis, the text provides a unique and innovative layered approach, moving through the levels of computing from an organized, language-neutral perspective.

Date Published:2023

Computer architecture : a quantitative approach

Hennessy, John L.,

Computer Architecture: A Quantitative Approach, has been considered essential reading by instructors, students and practitioners of computer design for nearly 30 years. The seventh edition of this classic textbook from John Hennessy and David Patterson, winners of the 2017 ACM A.M. Turing Award recognizing contributions of lasting and major technical importance to the computing field, along with new author Christos Kozyrakis, is fully revised with the latest developments in processor and system architecture. True to its original mission of demystifying computer architecture, this edition continues the longstanding tradition of focusing on areas where the most exciting computing innovation is happening, while always keeping an emphasis on good engineering design. Winner of a 2019 Textbook Excellence Award (Texty) from the Textbook and Academic Authors Association Each chapter follows a consistent framework: explanation of the ideas in each chapter; a "crosscutting issues" section, which presents how the concepts covered in one chapter connect with those given in other chapters; a "putting it all together" section that links these concepts by discussing how they are applied in real machine; and detailed examples of misunderstandings and architectural traps commonly encountered by developers and architects Includes "Putting It All Together" sections near the end of every chapter, providing real-world technology examples that demonstrate the principles covered in each chapter Covers new developments in GPU and CPU architectures, as well as domain specific architectures Features more comprehensive coverage of systems on chip and heterogeneity.

Date Published:2026

Crash risk prediction in cryptocurrency markets using financial market and technical indicators : the moderating role of network maturity.

Hameed, Sarah Abdul,

This dissertation aimed to design an overall framework to predict crash risk in cryptocurency markets by integrating financial market variables, technical trading variables, and the moderating effect of the maturity of cryptocurrency networks. The research used a pane data approach to predict the effect of global financial market conditions and cryptocurrency market technical indicators like momentum patterns, volatility clustering, and downside asymmetry on downside risk in cryptocurrency market. Cryptocurrency network maturity index was constructed using on- chain activity metrics such as transaction volume and network efficiency. This study employec machine learning regression and classification algorithms to predict crash risk in the cryptocurrency market. The results highlighted the predictive strengths of different algorithms and enabled identification of variables with the highest predictive power. The findings are relevan to stakeholders by explaining why consideration of financial market fundamentals, as well as intrinsic cryptocurency market indicators is important in gauging crash risk. This dissertatior contributes to literature by conceptualizing cryptocurrency network maturity as a moderator ir crash-risk prediction models, and a multidimensional framework integrating traditional finance market microstructure, and blockchain analytics. Further directions include focus on deep-learning based prediction models for more sophisticated results, and exploration of different quantification of network maturity to further consolidate one of the main findings of this research, which is tha crash risk affects all cryptocurrencies synonymously, regardless of maturity levels.

Date Published:2026

Deep Iearning - based predictive models for forex market trends : practical implementation and profit performance evaluation.

Afaq, Muhammad,

Forex market is the biggest and the most volatile market of the world with a valuation of around 7.5 trillion USD. It is the most liquid financial market where currencies are traded twenty-four hours, five days a week. It is a decentralized market which operates through a network of banks, corporations and other institutions that trade directly or through brokers Researchers have long sought to develop methods and tools to predict the direction of a currency in the forex market. Initially these researchers developed statistical models to predict the direction of a currency in the forex market, which were replaced by AI models, nowadays. Forex history and macroeconomic data have been used to predict forex currency trends but no research so far incorporates such diverse data for forex prediction. Prior studies have been conducted on individual data groups in forex trend forecasting, this research study combines multiple sources across traditional, machine learning and deep learning models. We aim to combine multiple data sources to perform forex trend pre- diction. This study undertakes empirical evaluation of traditional, machine learning and deep learning models on multiple data sources and determines which models offer better economic value in market-based simulation constraints. This research is based on the time period from January 2018 to December 2024 for the currencies of USD, EUR, GBP, JPY, AUD, NZD, CHF and CAD from reputable global sources, including the IMF, World Bank, and ESG databases. Sentiment data has been obtained from the Global Knowledge Graph and oil volatility from the Energy Information Administration We find that the traditional models have higher accuracy (~70%) than machine learning and deep learning models whereas the Multi-Layer Perceptron (MLP) achieves higher economic performance (Sharpe ratio of 0.74 at ~62% accuracy), highlighting the need to align research motivation with practical implementation.

Date Published:2026

Predicting liquidity risk In SMEs through machine learning to improve financial stability.

Shahid, Usama Bin,

Liquidity risk is the most imminent financial challenge for small and medium-sized enterprises SMEs) and the most manageable risk affecting firm-level financial stability. This study designs and tests an early warning system for SME liquidity risk using an interpretable machine- learing model that overcomes key drawbacks of traditional liquidity risk assessment. Traditional assessment relies on a handful of static ratios evaluated only on a single balance sheet date and assumes that the relationships among these ratios are linear and backward-looking. The empirical benchmark for the analysis is the Polish Companies Bankruptcy Dataset, the public version of which contains 43,405 firm- year observations from manufacturing firms across five forecastins periods in a transition economy (2002 to 2013). Bankruptcy is used as a proxy for liquidity failure among manufacturing firms. The feature space is intentionally limited to 17 firm-level liquidity, related ratios, including solvency ratios, cash-flow coverage ratios, days-of-liquidity-cover ratios and working-capital ratios, plus five macroeconomic conditioning ratios. Four supervised classifiers are trained and evaluated independently for one- and three-year time horizons under stratified five-fold cross-validation; SHAP attribution is calculated at the one- and three-year time horizons. The ROC-AUC is highest for gradient-boosted trees, at 0.867 and 0.771, respectively for one- and three-year-to-failure, outperforming linear baselines by 5 to 10 percentage points Debt-coverage time, quick ratio, cash-flow-to-debt coverage, eamed-equity buffer, and days-of liquidity-cover are the most dominant signals, while macroeconomic indicators do not play a significant role. The results reveal that managers, lenders, and policymakers have a clear, forward looking early-warning tool for SME liquidity risk: liquidity ratios, with a lead time of two to thred years. The benefits and potential of a liquidity-restricted feature design within this framework together with its horizon-conditional interpretability, are described, and its transferability to SME: in other emerging markets, such as South Asia, is discussed.

Date Published:2026

A I- powered evaluation of crisis communication in aviation using its financial impact.

Qamar, Maryam,

This research fills the gap in the absence of a quantitative framework that is structurec and quantitative in assessing the quality of crisis communication in the aviation industry. Existing Studies have mainly concentrated on stakeholder responses or qualitative evaluations of communication during measurement of the content of official crisis statements. This study develops an AI- a Crisis, leaving a gap in the systematic powered framework that quantifies three main elements of communication, including tone, transparency and timing, and integrates them into a composite Post-Crisis Communication Score (PCCS) to address the gaps present. These components are quantified using transformer-based natural language processing models and semantic analysis on official airline statements issued during the first three days of a CrisiS. The framework is empirically tested with an event study approach methodology, where cumulative abnormal returns are computed to determine the magnitude of investor reaction. The regression analysis is then used to test the relationship betweer communication quality and market response. The results suggest that timing and transparency have a strong effect on the containment of the investor reaction, whereas the tone does not have a direct significant impact. The composite PCCS has higher explanatory power and can therefore be used as an evaluative benchmark. The paper concludes that the quality of structured communication can be quantitatively evaluated and connected to financial outcomes, both of which provide a theoretical advancement and a practical crisis management tool in the aviation industry.

Date Published:2026

Real estate price prediction : a machine learning approach using online property market data.

Khan, Muhammad Yaseen,

The purpose of this study is to develop a predictive model for real estate prices in Pakistan by integrating property characteristics that include structural features locational, and amenities in the property through using machine learning techniques The findings of this study aim to assist customers. investors. and policymakers ïnvestment and policy decisions by providing more accurate and transparent price predictions. in making informed property purchase, Real estate is one of the most important sectors contributing to the economy through its forward and backward linkages with around forty other industries. Therefore, building a machine learning based model that predicts house prices will also be very useful for the allied industries to plan their business activities and take their investment decisions. Moreover, this will also be useful for public departments such as federal board of revenue to make their revenue projections and policies. However, at present, the relevant data is mostly fragmented and investment activity is speculative. To cover this gap this study focuses to utilize a comprehensive dataset consisting of listings from zameen.com and predict house prices based on different features of houses like structural (number of rooms, bathrooms and amenities). geographical (location, city etc.) and property types houses flats etc.). For this, we utilized OLS model, Hedonic Model and different models of machine learning (ML) including Random Forest, XG Boost, Cat Boost and Gradient Boosting. ML models are applied nonlinearities and heterogeneity can be captured. In the methodological framework so that preprocessing, feature engineering, hyper parameter tuning and cross validation are performed so that the model is robust and performs wel. In findings the performance of machine leaming models showed better predictive performance in this study in terms of accuracy, predictive performance precision in which ensemble methods performs better t0 capture nonlinearities between different variables compared to the traditional econometric models. This study help: in providing key insights to consumer and stakeholders that are significantly important in decision making, pricing and investment planning it will also help in the formulation of policy for reducing speculation and improving the transparency in the real estate market. The future direction would involve by expanding the dataset to cover rental market and including images of property using deep learning models like LSTM for long term predictions and expanding sentiment model to social media by taking the indication from it in order to know about rea time market.

Date Published:2026

Leveraging machine learning models and explainable AI to enhance customer segmentation and marketing insights

Zafar, Musferah,

The rapid growth of e-commerce has led to an increase in customer data; however, traditional segmentation methods relying mainly on purchase behavior fail to capture deeper customer intent This study proposes a hybrid customer segmentation approach that integrates both browsıng behavior and purchase activity to generate more meaningful insights. A quantıtative and exploratory design is used in which several clustering algorithms appled and Integrated in an ensemble framework, such as K are Means, DBSCAN, Agglomerative, and Spectral Clustering o enhance the stability of segmentation. The dataset 1S analyzed on the OpenDataBay E- commerce dataset; the most important variables are the Ad CTR. Ad CPC. Ad Spend, Revenue, and Unıts Sold. SHAP is used to explain the model to increase the interpretability and aid in business decision-making The findings reveal a lear difterentiation of customer groups with different levels of engagement and purchasing behaviors, which is useful in targeting them, personalizing, and marketing effectively. The significance of this study lies in the fact that it offers a practical and interpretable framework of customer segmentation in e-commerce.

Date Published:2026

Predicting non- performing loans using machine and deep learning approaches : comparative evidence from Pakistan Indonesia and Malaysia

Asghar, Shayan,

This study aims to improve the prediction of non-performing loans (NPLs) in dua banking_systems where traditional statistical tools fail to capture the nonlinear high-dimensional relationships in modern banking data. It comparatively evaluates four models- Random Forest (RF), Artificial Neural Networks (ANN) Long Short-Term Memory (LSTM) and CNN-LSTM hybrid for Islamic and conventional banks in Pakistan, Indonesia, and Malaysia. Using bank financial data alongside macroeconomic variables, model performance was assessed or accuracy, precision, recall, and F1-score. Results show that Random Forest outperformed ANN for Islamic banks, achieving 96% accuracy, while ANN performed better for conventional banks (83-85%), reflecting the modeling challenges posed by risk-sharing structures and smaller samples in Islamic banking. Macroeconomic factors such as inflation and interest rates significantly influenced predictions, confirming their relevance to credit risk modeling. The findings imply that generic credit risk models applied uniformly across banking types are inadequate, and that institution- and region-specific predictive frameworks are required. The study adds value by delivering an interpretable framework-feature importance analysis identifies NPL ratios, capital adequacy, and Shariah compliance as key risk drivers- -that is directly actionable for credit managers, policymakers, and regulators, with future work directed toward synthetic data augmentation, hybrid RF-LSTM architectures. and SHAP-based explainability.

Date Published:2026

A neuro-symbolic fraud prevention framework for e-commerce transactions using spending profiles and rule-based purchase verification

Hira, Adina,

The dramatic growth in e-commerce has greatly enhanced market efficiency, market participation and consumer experience. But with this rise considerable financial risks have come to global online platforms, especially through ever-evolving fraud schemes. With the growth of digital economies, the methods of fraudsters are constantly evolving, making traditional fraud dctection approaches increasingly ineffective. Existing methods of fraud detection are broadly classified into rule-based and machine learning-based methods. Although rule-based approaches are straightforward and easy to understand, they are not adaptable to dynamic fraudulent patterns. In contrast, machine learning techniques enhance prediction accuracy but are often less explainable, less scalable, and struggle to effectively model complex relations in transaction data. Such shortcomings often lead to high false alarms and a lack of transparency in machine decision-making. This impacts botk efficiency and user confidence and poses regulatory challenges in the financial sector. This paper proposes a neuro-symbolic approach to fraud prevention systems, combining neural pattern recognition with symbolic reasoning, to ensure both performance and explainability. Our approach also integrates graph neural networks (GNNs) to capture relationships between users, devices accounts, and transactions. This allows fraudulent transactions to be detected effectively, including complex fraud schemes such as collusion and mule account networks, which are not often detected using traditional methods. Furthermore, behavioral spending patterns are correlated with symboli verification rules to collectively evaluate the authenticity of transactions, with fraud detectior decisions being both data-based and explainable. The system is evaluated on three public datasets showing its effectiveness. The proposed system achieves a fraud detection accuracy of96.8%, witt an improvement of 32% in false alarms over conventional machine learning models. Additionally it enhances the recall of fraud networks by 27%, demonstrating its effectiveness in detecting complex fraud networks. It is also robust to skewed transaction data distributions, which is typica. in real-world financial transactions. All in all, the proposed system offers a scalable anc explainable approach to fraud detection and can be further integrated with real-time biometric behavior monitoring systems and online learning for improved performance in large-scale onling transaction systems.

Date Published:2026

Machine learning-based credit scoring for microfinance institutions in Pakistan : enhancing loan decisions and financial inclusion

Ali, Muhammad Ghulam,

in developing countries, microfinance institutions (MFIs) face challenges in credit risk assessmen lue to lack of information on borrowers, quality of information, and scarce resources. In Pakistan his is further complicated by a credit risk assessment and management process that is largely manual and subjective, resulting in inconsistent loan approvals, increased default risk and altimately financial exclusion of the marginalised population. Even though machine learning (ML has been shown to significantly decrease credit risks in the global setting, empirical results on the end-to-end operationalization of machine learning in the Pakistani MFI market have been limited The thesis fills this gap by developing and analysing an extensive ML-based loan repayment prediction model in collaboration with Agahe Pakistan, using information. The paper presents a stringent data refinement flow that both standardizes actual historical loan-leve] heterogeneous MIS data and manages missingness, fixes inconsistencies, pehavioural, time, and contextual variables for local lending behaviour. It has a multi-tie and develops modelling approach, in which logistic regression is benchmarked against ensemble models, such as Random Forests, XGBoost, and LightGBM, and, where possible, shallow neural networks Based on institutional risk thresholds, model performance is evaluated using discrimination (AUC: ROC, PR-AUC), calibration (Brier Score, Expected Calibration Error), as well as operational lecision metrics (precision, recall, F1-score, false negative rate). Robustness checks determine the effects of class imbalance treatment, strategy of imputation, period validation, and stability of features across branches and loan products, as well as borrower segments. In order to promote responsible adoption of AI, such tools as fairness diagnostics and interpretability, such as SHAP- based explanations, comparison of subgroup performance, and monotonicity validation, are also included in the study. The results demonstrate that the ensemble models are superior to the linear baselines in terms of discriminatory ability and risk ranking as well, and they maintain a reasonable palibration under the condition of being assisted by well-organized pre-processing. Featurc stability analysis clearly shows that the demographic-only scoring is not appropriate approach; i reveals the predictive significance of behavioural and repayment-pattern indicator. The fairness analysis uncovers subgroup disparities and enables proactive risk management, in line witt responsible lending guidelines. This thesis provides Pakistan-specific empirical evidence, links calibration with fairness - a gap in previous studies - and presents a scalable ML credit scoring model for resource-limited microfinance institutions. Practically, it offers MFIs a practical guide o enhance portfolio quality, mitigate default risks and promote financial inclusion with a transparent, auditable, and contextual model.

Date Published:2026

Quantum - ensemble learning for proactive business network security and real - time threat mitigation.

Kazmi, Syeda Mahnoor,

The purpose of this research is to develop an improved, fast and proactive networking security solution for business networks that are increasingly ulnerable to cyberattacks, resulting in significant financial, operational and reputational losses Classical machine leaming (ML) models provide high accuracy and fast inference time but are limited by scalability in high- dimensional datasets. Quantum machine learning (QML) models provide rich representational diversity but are relatively less robust and accurate. To address this limitation, a hybrid quantum- classical ensemble approach is proposed that combines the accuracy and speed of classical ML with the discriminative capability of QML to maximize threat detection while ensuring real-time inference. This approach employs the CIC-IDS-2017 dataset preprocessed with cleaning, normalization, class balancing and principal components analysis PCA) dimensionality reduction to four principal components. Quantum models are trained on quantum- encoded features using angle encoding (Ry rotation gates). Three quantum classifiers Quantum Support Vector Machine (QSVM), Quantum Neura Network (ONN), Quantum Random Forest (QRF are trained on quantum features while six classical classifiers - Random Forest (F), XGBoost, LightGBM, CatBoost Support Vector Machine (SVM), Multilayer Perceptron (MLP) are trained on classical features. Soft-voting with weighted averaging is used to create Quantum. Classical and Hybrid Ensembles. The Hybrid Ensemble has the highest accuracy 97.0%) and the highest recall (97.2%) of all the models, compared with the Classica Ensemble (96.4% accuracy, 96.0% recall) and the best classical single model CatBoost (96.4% accuracy, 96.8% recall). It has an inference time per sample of 0.00866 milliseconds (8.66 us) or more than 115,000 flows per second enough sufficient for real-time use. Classical models have lower inference times (0.013-0.025 ms), but hybrid ensemble offers better threat coverage with diversity across paradigms. The approach mitigates financial loss, downtime, and fatigue, while providing tangible value in finance, healthcare, supply chain, education and food. The key innovation is to set up a hybrid quantum- classical ensemble for business network threat detection, as inference time enables proactive defence. Future works of this research model could be applied to large datasets and real quantum hardware and improved by incorporating automated response features for organizations to have self- defending security systems.

Date Published:2026

Identifying the Best Predictors of Market Returns : A Machine Learning Analysis of Macrocconomic Indicators, Investor Sentimcnt Dynamics, and Herding Behavior across Short and Long - Term Horizons.

Ajmal, Muhammad Haris Bin,

Efficient Market Hypothesis GMH) asserts that market prices are the most accurate since they reflect all available information and therefore, can hardly be predicted in systematic ways in the future. A growing body of behavioral-finance research, however, has cast doubt on this view by showing that retumns are also shaped by psychological forces and macroeconomic conditions that traditional asset-pricing models tend to overlook. This study examines whether macroeconomic indicators (inflation, interest rates and GDP growth), investor sentiment (proxied by detrended trading volume) and herding behavior (measured through the Cross-Sectional Standard Deviation, CSSD, and Cross-Sectional Absolute Deviation, CSAD, frameworks) help explain stock-market retumns across the seven developed G7 economies and Pakistan as a representative emerging market. Daily data from January 2000 to December 2024 are used, allowing the analysis to span the 2008 Global Financial Crisis, the COVID-19 shock and the post-pandemic recovery. The empirical strategy compares Ordinary Least Squares (OLS) regressions with two non-linear machine-learning estimators, Random Forest and XGBoost, both of which are well suited to high- dimensional structured data and can capture interactions among predictors without imposing a fixed functional form. The results indicate that herding (CSAD) is the dominant predictor in six of the eight markets, while investor sentiment is the leading predictor in France and Pakistan, and that interest rates contribute the smallest signal everywhere. Pakistan shows a positive sentiment- return relationship that is the structural opposite of the negative pattern observed in most G7 markets, consistent with the dominance of retail traders and momentum-style behavior in less mature markets. Out-of-sample comparisons show that the simple constant benchmark is hard to beat at daily horizons: Random Forest out-of-sample R2 is small and frequently negative across markets, and the clearest positive out-of-sample result is XGBoost for Italy at the six-month horizon. The machine-learning models are therefore most useful for ranking which variables carry predictive signal at each horizon rather than for directional forecasting. The findings reinforce the case for combining behavioral and macroeconomic information when modelling returns and for treating G7 and emerging-market dynamics as structurally different rather than as variants of a common process.

Date Published:2026

Supply chain event-driven profit margin volatility forecasting with inventory buffer optimization.

Junaid, Muhammad,

the volatility of the operational margin proxy creates a practical decision problem to supply chain managers since operational delays, freight charges, insurance costs, choice of shipment mode anc conditions in the vendor or country can alter the economic value of delivered goods before the managers see the impact in the summary financial reports. This study proposes a business analytics method that forecasts the volatility in operational margin proxy next event through the compilation of a collection of past event deliveries based on the SCMS delivery history dataset. The stud) constructs shipment margin based on the line item value, the logistics cost proxy and defines supply lanes by product group, country, vendor and manufacturing site, and estimates event-basec margin volatility using a five-event rolling measure combining the standard deviation and mear absolute deviation. It is analyzed in comparison with persistence, tabular machine learning. neural sequence models, a Temporal Fusion Transformer style model, and a Ridge stacked ensemble. The final analytical sample has 5,389 shipment events that can be used and 262 supply lanes. The stacked ensemble has the lowest test RMSE of 0.0772 as compared to 0.0802 of persistence but he persistence is better on MAE, WMAPE, MASE and directional accuracy. The analysis of inventory buffer indicates that smaller RMSE does not necessarily generate smaller operation cost In the default case, persistence will result in a low total inventory cost compared to the ensemble. The research also adds to the business analytics by demonstrating how the events of shipments car be transformed into decision-relevant volatility data and also by showing why managers need to compare the predictive accuracy with decision outcomes before adopting complex models. Future studies will involve external disruption signals, firm-level margin data, demand elasticity and validated cost parameters. The study evaluates operational shipment-margin volatility rather than fill accounting profitability.

Date Published:2026

Impact of cryptocurrency crash risk predictor on equity market moderating role of economic policy uncertainty

Zaeem, Abdus Samad,

This paper examines the relationship between cryptocurrency crash risk and equity markets nd the moderating effect of Economic Policy Uncertainty (BPU) on the connection between the two variables among ten leading global economies. The objective of the study is to estimate the level of cross-market contagion caused by cryptocurrency crashes as well as to check whether changes in policy uncertainty intensify and diminish the impacts of such spillovers. The paper utilizes a quantitative and empirical design based on the contagion and uncertainty theory by employing a comprehensive dataset comprising eight major cryptocurrencies, macroeconomic factors and EPU between 2017 and 2025. This methodology uses higher- moment crash-risk measures and implements the linear and nonlinear machine learning-based methods to estimate spillovers and predict crash dynamics in different uncertainty regimes such as the use of OLS, Random Forest, and XGBoost. The results show a significant two-way spillover between the cryptocurrency markets and the equity indices and transmission in the crashes is pronounced when the EPU is elevated. It has also been found that selected cryptocurrencies, including Bitcoin and Cardano, are main sources of contagion, and some give impulses of hedging. These lessons have significant implications to policymakers, investors and regulators who are struggling to deal with systemic risk in more interconnected financial environments. The study contributes to the literature by proving the relevance of EPU to the cryptocurrency crash-risk modeling which has been underutilized in the literature and proving the superiority of machine learning models to represent nonlinear, regime-specific market dynamics. Future studies can develop on this framework by increasing or adding new variables for crypto-specific uncertainty indices, creating real-time predictive models using different methods, and the study of contagion mechanisms in the case of extreme geopolitical or technological or social shocks.

Date Published:2026

What moves the needle? temporal neuromarketing signals fused with analytics to forecast ad performance and business outcomes

Musarat, Omaima,

This study develops a neuromarketing analytics framework to the predict vicwers' interest in video advertisements before the launch of an advertising campaign The research addresses the practical challenge faced by advertising managers, who often commit creative and media budgets without sufficient evidence of how the different audience segments are likely to respond Using the NeuroBioSense dataset, the study analyzes the advertisement viewing observations from 5S participants across 35 advertisements in three major categories 1.€. cosmetics and fashion, food and market, and car and technology. The proposed framework predicts advertisement interest by using the seven facial emotion profiles, namely joy sadness, anger, disgust, surprise, fear, and neutral expression, along with demographic Scores with confidence ranges, making eatures. These predicted probabilities are then converted into the interpretable Engagement compare. To reduce the risk of inflated performance caused by same-ad leakage, the mair the results easier for managers to understand and evaluation is based on the leave-one-ad-out validation This approach tests whether the model can generalize to advertisements that were not included during training. Among the models used in the study, Gradient Boosting achieved the strongest overall performance, with an accuracy of 0.8164, balanced accuracy of 0.7741, macro F1-score of 0.7824, ROC-AUC of 0.8477, and PR-AUC of 0.9130. The feature importance results show that the interest prediction is not driven by joy alone. Instead, several factors contribute to the model's predictions, including disgust rat1o, joy, negative emotion ratio, total emotion intensity, maximum emotion intensity, age, and anger ratio. These findings suggest that viewer engagement is shaped by a combination of positive, negative, and demographic signals. The framework is further tested on new advertisements, including Avon, Cadbury Fence, Ponds. and Toyota, to demonstrate how managers can compare creative options, examine audience segments, and interpret frame-level evidence before campaign launch. Overall, this study contributes a transparent, risk-aware, and managerially useful analytics workflow for pre- launch advertisement interest prediction.

Date Published:2026

An explainable AI- based resume ranking system for supporting human decision making.

Jilani, Muhammad Ibrar Ul Haq,

Organizations increasingly receive large numbers of resumes for individual vacancies making manual screening time-consuming, resource intensive, and prone to human bias. Although Al-based resume ranking systems as black boxes and provide 1 limited transparency about how candidates are assessed This study can improve screening efficicncy, many operate develops and evaluates an explainable, HR-aligned resume ranking system that semantically matches job descriptions with resumes, incorporates organization-specific hiring rules. and provides human-understandable explanations to support HR decision-making. Three Transformer-based models, JobBERT, ModernBERT, and E5-base, were evaluated and fine- tuned using labelled data. T wo ranking approaches were compared Whole-document semantic matching showed only moderate agreement with HR expert rankings, with Spearmar correlations below 0.5 across all. models. In contrast, competency-wise matching, based or technical skills, education, experience, certifications, and project or summary evidence produced substantially stronger alignment with HR judgement. Fine-tuned ModemnBERT achieved the best performance, with a Spearman correlation of 0.8326, Kendall Tau of 0.7880. NDCG@5 of 0.9487, and Precision@5 and Recall@s of 0.8333. The final system combines competency-level scoring with retrieval-augmented generation to verify generic and job specific HR rules and an explainable AI layer that presents evidence behind each ranking These components were integrated into a prototype dashboard that allows HR users to review ranked candidates, inspect strengths and concerns, and compare rule-aware and non-rule-aware results. Overall, the findings suggest that resume ranking is more usef ul and better aligned witt HR judgement when semantic matching is combined with structured competency assessment rule-aware verification, and clear explanations rather than relying on broad semantic similarit,y alone. The system is intended to support, not replace, human recruitment decisions.

Date Published:2026