Search Results for author: Feras A. Batarseh

Found 14 papers, 2 papers with code

A Review of Cybersecurity Incidents in the Food and Agriculture Sector

no code implementations12 Mar 2024 Ajay Kulkarni, Yingjie Wang, Munisamy Gopinath, Dan Sobien, Abdul Rahman, Feras A. Batarseh

The increasing utilization of emerging technologies in the Food & Agriculture (FA) sector has heightened the need for security to minimize cyber risks.

Decision Making

ACWA: An AI-driven Cyber-Physical Testbed for Intelligent Water Systems

1 code implementation27 Sep 2023 Feras A. Batarseh, Ajay Kulkarni, Chhayly Sreng, Justice Lin, Siam Maksud

In this paper, the system is introduced in detail and compared with existing water testbeds; additionally, example use-cases are described along with novel outcomes such as datasets, software, and AI-related scenarios.

Decision Making Management

Rationalization for Explainable NLP: A Survey

no code implementations21 Jan 2023 Sai Gurrapu, Ajay Kulkarni, Lifu Huang, Ismini Lourentzou, Laura Freeman, Feras A. Batarseh

Recent improvements in natural language generation have made rationalization an attractive technique because it is intuitive, human-comprehensible, and accessible to non-technical users.

Explainable Artificial Intelligence (XAI) Question Answering +4

ExClaim: Explainable Neural Claim Verification Using Rationalization

1 code implementation21 Jan 2023 Sai Gurrapu, Lifu Huang, Feras A. Batarseh

We introduce a novel claim verification approach, namely: ExClaim, that attempts to provide an explainable claim verification system with foundational evidence.

Claim Verification Decision Making +3

AI Assurance using Causal Inference: Application to Public Policy

no code implementations1 Dec 2021 Andrei Svetovidov, Abdul Rahman, Feras A. Batarseh

Developing and implementing AI-based solutions help state and federal government agencies, research institutions, and commercial companies enhance decision-making processes, automate chain operations, and reduce the consumption of natural and human resources.

Causal Inference Decision Making

Outlier Detection using AI: A Survey

no code implementations1 Dec 2021 Md Nazmul Kabir Sikder, Feras A. Batarseh

An outlier is an event or observation that is defined as an unusual activity, intrusion, or a suspicious data point that lies at an irregular distance from a population.

Outlier Detection Survey

Public Policymaking for International Agricultural Trade using Association Rules and Ensemble Machine Learning

no code implementations15 Nov 2021 Feras A. Batarseh, Munisamy Gopinath, Anderson Monken, Zhengrong Gu

The recent shocks to the free trade regime, especially trade disputes among major economies, as well as black swan events, such as trade wars and pandemics, raise the need for improved predictions to inform policy decisions.

BIG-bench Machine Learning

A Survey on AI Assurance

no code implementations15 Nov 2021 Feras A. Batarseh, Laura Freeman

Artificial Intelligence (AI) algorithms are increasingly providing decision making and operational support across multiple domains.

Decision Making Survey

Measuring Outcomes in Healthcare Economics using Artificial Intelligence: with Application to Resource Management

no code implementations15 Nov 2021 Chih-Hao Huang, Feras A. Batarseh, Adel Boueiz, Ajay Kulkarni, Po-Hsuan Su, Jahan Aman

In most cases, such events lead to critical uncertainties in decision making, as well as in multiple medical and economic aspects at a hospital.

Decision Making Management

DeepAg: Deep Learning Approach for Measuring the Effects of Outlier Events on Agricultural Production and Policy

no code implementations22 Oct 2021 Sai Gurrapu, Feras A. Batarseh, Pei Wang, Md Nazmul Kabir Sikder, Nitish Gorentala, Gopinath Munisamy

Quantitative metrics that measure the global economy's equilibrium have strong and interdependent relationships with the agricultural supply chain and international trade flows.

Econometrics

Foundations of data imbalance and solutions for a data democracy

no code implementations30 Jul 2021 Ajay Kulkarni, Deri Chong, Feras A. Batarseh

Dealing with imbalanced data is a prevalent problem while performing classification on the datasets.

Context-Driven Data Mining through Bias Removal and Data Incompleteness Mitigation

no code implementations19 Oct 2019 Feras A. Batarseh, Ajay Kulkarni

Context-driven Data Science Lifecycle (C-DSL); the main contribution of this paper, is developed to address these challenges.

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