Neda Abdelhamid

We introduce path-sampled integrated gradients (PS-IG), a framework that generalizes feature attribution by computing the expected value over baselines sampled along the linear interpolation path. We prove that PS-IG is mathematically equivalent to path-weighted integrated gradients, provided the weighting function matches the cumulative distribution function of the sampling density. This equivalence allows the stochastic expectation to be evaluated via a deterministic Riemann sum, improving the error convergence rate from O(m-1/2) to O(m-1) for smooth models. Furthermore, we demonstrate analytically that PS-IG functions as a variance-reducing filter against gradient noise---strictly lowering attribution variance by a factor of 1/3 under uniform sampling - while preserving key axiomatic properties such as linearity and implementation invariance.

In the contemporary business environment, the assimilation of artificial intelligence (AI) and machine learning (ML) is pivotal for fostering innovation and ensuring long-term growth. This paper examines the strategic aspects of AI/ML adoption, emphasizing that its success rests not just on technology but also on strategic alignment, collaboration, and robust leadership. Highlighting the indispensable role of senior leaders, the paper offers a managerial framework for AI/ML integration, ensuring its alignment with organizational goals. Using real-world examples, the paper presents how AI/ML can be strategically embedded to enhance customer interactions, streamline operations, and unveil new revenue streams. The objective is to provide senior leaders with an understanding, enabling them to harness AI/ML effectively, ensuring their organizations remain at the innovation forefront in a digital age dominated by disruptive AI/ML technologies.

In the contemporary business environment, the assimilation of artificial intelligence (AI) and machine learning (ML) is pivotal for fostering innovation and ensuring long-term growth. This paper examines the strategic aspects of AI/ML adoption, emphasizing that its success rests not just on technology but also on strategic alignment, collaboration, and robust leadership. Highlighting the indispensable role of senior leaders, the paper offers a managerial framework for AI/ML integration, ensuring its alignment with organizational goals. Using real-world examples, the paper presents how AI/ML can be strategically embedded to enhance customer interactions, streamline operations, and unveil new revenue avenues. The objective is to provide senior leaders with an understanding, enabling them to harness AI/ML effectively, ensuring their organizations remain at the innovation forefront in a digital age dominated by disruptive AI/ML technologies.

Autistic spectrum disorder (ASD) is a neurodevelopmental condition that characterises a range of people, from individuals who are not able to speak to others who have good verbal communications. The disorder affects the way people see, think, and behave, including their communications and social interactions. Identifying autistic traits, preferably in the early stages, is fundamental for clinicians in expediting referrals, and hence enabling patients to access to required healthcare services. This article investigates various ASD behavioral features in toddlers and proposes a data process using machine-learning techniques. The aims of this study were to identify early behavioral features that can help detect ASD in toddlers and to map these features to the neurodevelopment behavioral areas of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). To achieve these aims, the proposed data process assesses several behavioral features using feature selection techniques, then constructs a classification model based on the chosen features. The empirical results show that during the screening process of toddlers, cognitive features related to communications, social interactions, and repetitive behaviors were most relevant to ASD. For the machine-learning algorithms, the predictive accuracy of Bayesian network (Bayes Net) and logistic regression (LR) models derived from ASD behavioral data subsets were consistent pinpointing to the suitability of ML techniques in predicting ASD.

Early screening of autism spectrum disorders (ASD) is a key area of research in healthcare. Currently artificial intelligence (AI)-driven approaches are used to improve the process of autism diagnosis using computer-aided diagnosis (CAD) systems. One of the issues related to autism diagnosis and screening data is the reliance of the predictions primarily on scores provided by medical screening methods which can be biased depending on how the scores are calculated. We attempt to reduce this bias by assessing the performance of the predictions related to the screening process using a new model that consists of a Self-Organizing Map (SOM) with classification algorithms. The SOM is employed prior to the diagnostic process to derive a new class label using clusters learnt from the independent features; these clusters are related to communication, repetitive traits, and social traits in the input dataset. Then, the new clusters are compared with existing class labels in the dataset to refine and eliminate any inconsistencies. Lastly, the refined dataset is utilised to derive classification systems for autism diagnosis. The new model was evaluated against a real-life autism screening dataset that consists of over 2000 instances of cases and controls. The results based on the refined dataset show that the proposed method achieves significantly higher accuracy, precision, and recall for the classification models derived when compared to models derived from the original dataset.