Artificial intelligence (AI) and machine learning (ML) have come a long way from the earlier days of conceptual theories, to being an integral part today's technological society. Rapid growth AI/ML their penetration within plethora civilian military applications, while successful, has also opened new challenges obstacles. With almost no human involvement required for some decision-making systems, there is now pressing need gain better insights into how these decisions are made. This given...

Advances in artificial intelligence (AI) and wireless technology are driving forward the large deployment of interconnected smart technologies that constitute cyber–physical systems (CPSs) Internet Things (IoT) for many commercial military applications. CPS is characterized by communication, computing, control engineering based on a volume data originating from various devices, plants, sensors, etc. Wireless have enabled ease networking communications both IoT, providing massive critical...

Unmanned aerial vehicles (UAVs) have increasingly shown to be useful in civilian applications (such as agriculture, public safety, surveillance) and mission critical military applications. Despite the growth popularity applications, UAVs also been used for malicious purposes. In such instances, their timely detection identification has garnished rising interest from government, industry academia. While much work done detecting UAVs, there still exist limitations related impact of extreme...

Abstract Direct oral anticoagulants (DOACs) targeting activated factor Xa (FXa) are used to prevent or treat thromboembolic disorders. DOACs reversibly bind FXa and inhibit its enzymatic activity. However, DOAC treatment carries the risk of anticoagulant-associated bleeding. Currently, only one specific agent, andexanet alfa, is approved reverse anticoagulant effects FXa-targeting (FXaDOACs) control life-threatening because mechanism action, alfa requires a cumbersome dosing schedule, use...

Artificial intelligence (AI) and machine learning (ML) have made tremendous advancements in the past decades. From simple recommendation systems to more complex tumor identification systems, AI/ML been utilized a plethora of applications. This rapid growth its proliferation numerous private public sector applications, while successful, has also opened new challenges obstacles for regulators. With almost little no human involvement required some decision-making there is now pressing need...

The recent advances in machine learning (ML) and Artificial Intelligence (AI) have resulted widespread application of data-driven algorithms. Rapid growth AI/ML their penetration within a plethora civilian military applications, while successful, has also opened new vulnerabilities. It is now clear that ML algorithms for AI systems are viable targets malicious attacks. Therefore, there pressing need better understanding adversarial attacks against models, order to secure them such In this...

A key unmet need in the management of hemophilia (HA) is lack clinically validated markers that are associated with development neutralizing antibodies to Factor VIII (FVIII) (commonly referred as inhibitors). This study aimed identify relevant biomarkers for FVIII inhibition using Machine Learning (ML) and Explainable AI (XAI) My Life Our Future (MLOF) research repository. The dataset includes biologically variables such age, race, sex, ethnicity, variants F8 gene. In addition, we...

This is a survey paper on Explainable Artificial Intelligence (XAI).

Unmanned aerial vehicles (UAVs) are a growing threat to public safety if used maliciously. In this study, we present our multimodal data set containing image, audio, and radio frequency (RF) data, which can serve as valuable resource for researchers developers in the field of UAV detection. We multiclass ensemble approach address need improve identification Our is novel integrated multiple deep-learning classifiers into single classifier. evaluate performance proposed solution with...

Advancements in computer science, especially artificial intelligence (AI) and machine learning(ML) have brought about a scientific revolution plethora of military commercial applications. One such area has been data where the sheer astronomical amount available spurred sub-fields research involving its storage, analysis, use. focus recent years fusion coming from multiple modalities, called multi-modal fusion, their use analysis for practical employable Because differences within types,...

Artificial intelligence (AI) and machine learning (ML) systems have seen tremendous growth within the last few decades. Even with unprecedented new levels of autonomy for artificial reasoning systems, there are still challenges that remain. Challenges related to causal act as a roadblock AI/ML achieve human-like intelligence. For these they must be able gather information from given information. While causality has made progress past years, is lack ability generate relations image datasets....

Artificial Intelligence (AI) and Machine Learning (ML) based systems have seen tremendous progress in the past years. This unprecedent growth has also opened new challenges vulnerabilities for keeping AI/ML safe secure. With a multitude of studies investigating adversarial machine learning (AML) cyber security systems, there is need novel techniques methodologies securing these systems. Cyber often used as blanket term meaning all defenses context cyber. leaves out techniques, being more...

Artificial reasoning systems via Intelligence (AI) and Machine Learning (ML) have made tremendous progress within the past decade. AI/ML been able to reach unprecedented new levels of autonomy for a multitude applications ranging from autonomous vehicles biomedical imaging. This level intelligence freedom requires them degree human-like in terms causation beyond correlation. This, however, has remained major challenge investigators when combining causality with systems. that are capable...

The recent push for fair, trustworthy, and responsible Artificial Intelligence (AI) Machine Learning (ML) systems have pushed more explainable that are capable of explaining their predictions/decisions inner workings. This led to the field Explainable AI (XAI) going through an exponential growth in past few years. XAI has been crucial making AI/ML comprehensible. However, is limited model it being applied to, both post-hoc or transparent models. Even though can explain decisions made by ML...

Artificial intelligence (AI) and machine learning (ML) systems are required to be fair trustworthy. They must capable of bias detection mitigation achieve robustness. To this end, a plethora research fields have seen growth in related making AI/ML more Causal Explainable AI (XAI) two such that been used extensively the past few years explainability fairness. However, they as separate methodologies, not together. This paper provides new perspective using causal XAI together create robust...

This is a survey paper on Explainable Artificial Intelligence (XAI).