Authors - Ali Raheman, Asad Khan, Tejas Bhagat, Fazal Raheman Abstract - The emergence of quantum computing challenges digital infrastructure to achieve both quantumresilient security and energy-efficient performance. This is particularly critical for decentralized finance (DeFi), where layered software stacks and exposed authentication mechanisms increase complexity, overhead, and attack-surface exposure. This paper proposes Quantum Ledger Technology (QLT), a blockchain-agnostic architectural framework that integrates Zero Vulnerability Computing (ZVC), Solid-State Software-on-a-Chip (3SoC), and Quantum-Resilient User-Evasive Cryptographic Authentication (QRUECA). Together, these mechanisms reduce software-mediated trust, minimize exposed authentication surfaces, and shift trust enforcement toward hardware-rooted execution environments. A hypothesis-driven evaluation framework is introduced to assess whether architectural simplification can reduce authentication latency, memory usage, energy overhead, and attack-surface complexity while preserving functional equivalence with existing blockchain systems. Preliminary results indicate that hardware-rooted authentication and reduced trusted software complexity can provide a promising foundation for scalable, energy-aware, and quantum-resilient digital infrastructure. The proposed framework aligns with the goals of secure, sustainable, and intelligent future computing systems.
Authors - Shamsa AlNasri, Muna Ali AlShamsi, Mariam AlNuaimi, Hanae Ouahhabi, Gurdal Ertek Abstract - This study presents an analytics framework for analyzing and benchmarking sales transactions data of e-commerce products across multiple countries. The framework consists of an integrated multi-faceted application of a carefully selected portfolio of data analytics techniques. Specifically, the framework combines (a) statistical distribution fitting to well-known probability distributions (Gamma, Normal, Weibull, Lognormal), (b) box plot analysis followed by statistical hypothesis testing (Kruskal-Wallis and Dunn tests) and visualization of pairwise comparison results, and (c) text mining (Latent Dirichlet Allocation (LDA) and word clouds). Although many studies in the literature report on the analysis of e-commerce product sales, this is the first study that combines the mentioned techniques within a multi-faceted yet also unified approach. The results obtained for a case study on the Gulf Cooperation Council (GCC) countries reveal regional differences in consumer behavior, pricing, and preferences. The insights obtained can be used to improve the marketing and engagement of the selected case with the selected products and countries. However, more importantly, the primary contribution of the study is the generalizable analytics framework presented that can be adopted and applied to any product set and country selection with similar data attributes.
Authors - Deepak Mane, Ashwanth Nair, Nihar Gundale, Om Khamkar, Tanmay Kulkarni, Ranjeet Bidwe, Amol Kamble, Suraj Sawant Abstract - There many areas in which the organization can make use of technologies that will make decision making easy. ai (artificial intelligence) is one of the most useful and innovative technologies that is used in many fields of organization to help in business management and decision making. in the recent years, HR department has became very important in the organization, since the quality and skills of the worker in directly proportional to the performance of the organization. After ai is being used in many ways in the organization like in marketing and sales department, now its starting to guide HR department for employee related decision. The purpose of using ai in HR department in to support decision that are based on objective data analysis, not on subjective aspects. The goal of this work is to analyse influence on employee attrition and objective factors. In order to identify the main causes that contribute to a workers decision to leave a company, and identify the employee that about to leave a company. After training, the obtained model for the prediction of employs attrition is tested on real dataset provided by IBM analytics, which has 35 features and about 1500 samples. Results are obtained in terms of classical metrics and the algorithm that produced the best results of the dataset is the gaussian naïve bayes classifier. It has best recall rate of 0.54, since it measures the ability of classifier to achieves an overall false negative rate equal to 4.5% of the total observations.
Authors - Deepak Mane, Ashwanth Nair, Nihar Gundale, Om Khamkar, Tanmay Kulkarni, Ranjeet Bidwe, Amol Kamble, Suraj Sawant Abstract - Accurate emotion recognition remains a significant challenge in affective computing, particularly when relying on unimodal approaches such as facial expression analysis. These systems are inherently limited because individuals can deliberately mask their emotions, and visually similar expressions such as fear and surprise often lead to misclassification. Such limitations highlight the need for more robust methods that incorporate complementary sources of information. The proposed system uses a multimodal framework which combines the Circumplex Model of Affect through its visual and physiological cues to achieve better reliability. The FER-2013 dataset provides data for a Convolutional Neural Network which estimates emotional valence based on facial expressions captured through standard camera systems. The MAX30102 photoplethysmography sensor measures heart rate and heart rate variability through its connection with an Arduino to determine emotional arousal. The rule-based fusion engine combines these modalities to determine the final emotional state which it then categorizes into joy, stress, anxiety, and calmness. The system uses physiological data to clarify between emotional states which appear similar and it also identifies hidden emotional states which facial expressions cannot express. The system offers health monitoring, human computer interaction, and psychological assessment fields a dependable and efficient solution.
Authors - Caio Tertuliano Ribeiro, Lilian Berton Abstract - This paper studies a Disparate Impact (DI)-oriented variant of fairness-aware hyperparameter optimization for XGBoost in banking and credit settings. The method combines Design of Experiments (DoE), Response Surface Methodology (RSM), and NBI-style sampling to jointly tune XGBoost hyperparameters, the decision threshold, and the positive-class weight under a fixed evaluation budget. Unlike the preliminary composite-fairness draft, the final experiment optimizes a DI-only objective while keeping Statistical Parity Difference (SPD), Equal Opportunity Difference (EOD), and Average Odds Difference (AOD) as audit metrics. Experiments on Bank Marketing, German Credit, and Default of Credit Card Clients with 30 replicas per dataset show that the proposed utopia selector is competitive with evaluation-matched random search and consistently superior to the XGBoost default configuration. Relative to the default baseline, it improves Balanced Accuracy by +0.059, +0.020, and +0.019, while reducing the DI gap by -0.375, -0.174, and -0.141, respectively. The main takeaway is that DI-only optimization provides a finance-oriented and reproducible way to navigate fairness–performance trade-offs rather than a universal domination claim over random search.
Authors - Syeda Fatima Rafique, Mohammed Abobaker Baobaid, Majid Shaher Ebrahim Tayfour, Hamed Marhoun Khamis Alsaedi, Ibrahim Alfaki, Gurdal Ertek Abstract - With the invention of Large Language Models (LLMs) and the development and increased usage of generative AI platforms, “vibe coding” (AI-assisted coding, coding with AI assistants) has become an integral part of software development, documentation, and maintenance. Although there are multiple detailed studies on the experiences of developers with vibe coding for software development, no earlier work was encountered on the vibe coding of analytics dashboards in particular. However, in an era of exponential growth in data volume, variety, and velocity, data analytics, and in particular, analytics dashboards, are highly relevant and can serve as competitive leverage for every organization. This paper is the first attempt in the literature to answer the following research question: “What are the practical project experiences of developers during vibe coding of analytics dashboards, especially in terms of challenges faced?” In this paper, experiences in two case study projects on analytics dashboard development are shared as lessons learned to guide developers, product managers, and project managers.
