Ahmad Ali

Dayananda Herurkar, Ahmad Ali, Andreas Dengel

Generative models have revolutionized multiple domains, yet their application to tabular data remains underexplored. Evaluating generative models for tabular data presents unique challenges due to structural complexity, large-scale variability, and mixed data types, making it difficult to intuitively capture intricate patterns. Existing evaluation metrics offer only partial insights, lacking a comprehensive measure of generative performance. To address this limitation, we propose three novel evaluation metrics: FAED, FPCAD, and RFIS. Our extensive experimental analysis, conducted on three standard network intrusion detection datasets, compares these metrics with established evaluation methods such as Fidelity, Utility, TSTR, and TRTS. Our results demonstrate that FAED effectively captures generative modeling issues overlooked by existing metrics. While FPCAD exhibits promising performance, further refinements are necessary to enhance its reliability. Our proposed framework provides a robust and practical approach for assessing generative models in tabular data applications.

Ahmad Ali

Density Functional Theory (DFT) is a widely used computational method for estimating the energy and behavior of molecules. Machine Learning Interatomic Potentials (MLIPs) are models trained to approximate DFT-level energies and forces at dramatically lower computational cost. Many modern MLIPs rely on a scalar regression formulation; given information about a molecule, they predict a single energy value and corresponding forces while minimizing absolute error with DFT's calculations. In this work, we explore a multi-class classification formulation that predicts a categorical distribution over energy/force values, providing richer supervision through multiple targets. Most importantly, this approach offers a principled way to quantify model uncertainty. In particular, our method predicts a histogram of the energy/force distribution, converts scalar targets into histograms, and trains the model using cross-entropy loss. Our results demonstrate that this categorical formulation can achieve absolute error performance comparable to regression baselines. Furthermore, this representation enables the quantification of epistemic uncertainty through the entropy of the predicted distribution, offering a measure of model confidence absent in scalar regression approaches.

Toqeer Ali Syed, Salman Jan, Gohar Ali et al.

In contemporary retail, the variety of products available (e.g. clothing, groceries, cosmetics, frozen goods) make it difficult to predict the demand, prevent stockouts, and find high-potential products. We suggest an agentic AI model that will be used to monitor the inventory, initiate purchase attempts to the appropriate suppliers, and scan for trending or high-margin products to incorporate. The system applies demand forecasting, supplier selection optimization, multi-agent negotiation and continuous learning. We apply a prototype to a setting in the store of a middle scale mart, test its performance on three conventional and artificial data tables, and compare the results to the base heuristics. Our findings indicate that there is a decrease in stockouts, a reduction of inventory holding costs, and an improvement in product mix turnover. We address constraints, scalability as well as improvement prospect.

Toqeer Ali Syed, Sohail Khan, Salman Jan et al.

The challenge is growing towards extreme and short-duration rainfall events like a cloudburst that are peculiar to the traditional forecasting systems, in which the predictions and the response are taken as two distinct processes. The paper outlines an agentic artificial intelligence system to study atmospheric water-cycle intelligence, which combines sensing, forecasting, downscaling, hydrological modeling and coordinated response into a single, interconnected, priceless, closed-loop system. The framework uses autonomous but cooperative agents that reason, sense, and act throughout the entire event lifecycle, and use the intelligence of weather prediction to become real-time decision intelligence. Comparison of multi-year radar, satellite, and ground-based evaluation of the northern part of Pakistan demonstrates that the multi-agent configuration enhances forecast reliability, critical success index and warning lead time compared to the baseline models. Population reach was maximised, and errors during evacuation were minimised through communication and routing agents, and adaptive recalibration and transparent auditability were provided by the embedded layer of learning. Collectively, this leads to the conclusion that collaborative AI agents are capable of transforming atmospheric data streams into practicable foresight and provide a platform of scalable adaptive and learning-based climate resilience.

Salman Jan, Toqeer Ali Syed, Gohar Ali et al.

The paper presents a detailed Agentic Artificial Intelligence (AI) model that would enable people with disabilities and neurodivergence to lead healthier lives and have more regular days. The system will use a multi-layer structure; it will include an Application and Interface Layer, an Agents Layer, and a Data Source Layer to provide adaptive, transparent, and inclusive support. Fundamentally, a hybrid reasoning engine will synchronize four special-purpose agents, which include: a personalized-nutrition-based, called a Meal Planner Agent; an adaptive-scheduling-based, called a Reminder Agent; interactive assistance during grocery shopping and cooking, called a Food Guidance Agent; and a continuous-intake-and-physiological-tracking, called a Monitoring Agent. All the agents interact through a central communicative system called the Blackboard/Event Bus, which allows autonomous interaction and real-time feedback loops with multimedia user interfaces. Privacy-sensitive data sources, including electronic health records (EHRs), nutritional databases, wearable sensors, and smart kitchen Internet of Things, are also included in the framework and placed into a policy-controlled layer, which ensures data safety and compliance with consent. Collaborative care and clinician dashboards allow common supervision, and discussable artificial intelligence (XAI) modules give brief explanations of why a decision was made, making users responsible and reliant. The proposed agentic AI framework is an extension beyond traditional assistive systems since it incorporates inclusiveness, personalization, and accessibility at all levels. It displays the intersection of multi-agent reasoning, multi-modal interfaces, and human-centered design that will enable the development of autonomy, health, and digital equity among people with disabilities and neurodivergence.

Faisal Hussain, Syed Ghazanfar Abbas, Ubaid U. Fayyaz et al.

The security pitfalls of IoT devices make it easy for the attackers to exploit the IoT devices and make them a part of a botnet. Once hundreds of thousands of IoT devices are compromised and become the part of a botnet, the attackers use this botnet to launch the large and complex distributed denial of service (DDoS) attacks which take down the target websites or services and make them unable to respond the legitimate users. So far, many botnet detection techniques have been proposed but their performance is limited to a specific dataset on which they are trained. This is because the features used to train a machine learning model on one botnet dataset, do not perform well on other datasets due to the diversity of attack patterns. Therefore, in this paper, we propose a universal features set to better detect the botnet attacks regardless of the underlying dataset. The proposed features set manifest preeminent results for detecting the botnet attacks when tested the trained machine learning models over three different botnet attack datasets.