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Articles 1 - 4 of 4
Full-Text Articles in Computer Engineering
Characterization Of Time-Variant And Time-Invariant Assessment Of Suicidality On Reddit Using C-Ssrs, Manas Gaur, Vamsi Aribandi, Amanuel Alambo, Ugur Kursuncu, Krishnaprasad Thirunarayan, Jonathan Beich, Jyotishman Pathak, Amit Sheth
Characterization Of Time-Variant And Time-Invariant Assessment Of Suicidality On Reddit Using C-Ssrs, Manas Gaur, Vamsi Aribandi, Amanuel Alambo, Ugur Kursuncu, Krishnaprasad Thirunarayan, Jonathan Beich, Jyotishman Pathak, Amit Sheth
Publications
Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt suicide has been nearly impossible. In the modern world, many individuals suffering from mental illness seek emotional support and advice on well-known and easily-accessible social media platforms such as Reddit. While prior artificial intelligence research has demonstrated the ability to extract valuable information from social media on suicidal thoughts and behaviors, these efforts have not considered both severity and temporality of risk. The insights made possible by access to such data have enormous clinical potential - most dramatically envisioned as a trigger …
Semantics Of The Black-Box: Can Knowledge Graphs Help Make Deep Learning Systems More Interpretable And Explainable?, Manas Gaur, Keyur Faldu, Amit Sheth
Semantics Of The Black-Box: Can Knowledge Graphs Help Make Deep Learning Systems More Interpretable And Explainable?, Manas Gaur, Keyur Faldu, Amit Sheth
Publications
The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively. The DL models utilizing massive computing power and enormous datasets have significantly outperformed prior historical benchmarks on increasingly difficult, well-defined research tasks across technology domains such as computer vision, natural language processing, signal processing, and human-computer interactions. However, the Black-Box nature of DL models and their over-reliance on massive amounts of data condensed into labels and dense representations poses challenges for interpretability and explainability of the system. Furthermore, DLs have not yet been proven in their ability to …
Explainable Ai Using Knowledge Graphs, Manas Gaur, Ankit Desai, Keyur Faldu, Amit Sheth
Explainable Ai Using Knowledge Graphs, Manas Gaur, Ankit Desai, Keyur Faldu, Amit Sheth
Publications
During the last decade, traditional data-driven deep learning (DL) has shown remarkable success in essential natural language processing tasks, such as relation extraction. Yet, challenges remain in developing artificial intelligence (AI) methods in real-world cases that require explainability through human interpretable and traceable outcomes. The scarcity of labeled data for downstream supervised tasks and entangled embeddings produced as an outcome of self-supervised pre-training objectives also hinders interpretability and explainability. Additionally, data labeling in multiple unstructured domains, particularly healthcare and education, is computationally expensive as it requires a pool of human expertise. Consider Education Technology, where AI systems fall along a …
Surgical And Medical Applications Of Drones: A Comprehensive Review, Brent Terwilliger, James C. Rosser Jr., Vudatha Vignesh, Brett C. Parker
Surgical And Medical Applications Of Drones: A Comprehensive Review, Brent Terwilliger, James C. Rosser Jr., Vudatha Vignesh, Brett C. Parker
Publications
Drones have the ability to gather real time data cost effectively, to deliver payloads and have initiated the rapid evolution of many industrial, commercial, and recreational applications. Unfortunately, there has been a slower expansion in the field of medicine. This article provides a comprehensive review of current and future drone applications in medicine, in hopes of empowering and inspiring more aggressive investigation.