activity
20232026
most citedExplainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction

222 citations · 284 across the 13 of their papers we have counts for

collaborators

13 papers

cs.CL2026★ 2 cited

Explainable AI: Context-Aware Layer-Wise Integrated Gradients for Explaining Transformer Models

Melkamu Abay Mersha, Jugal Kalita

Transformer models achieve state-of-the-art performance across domains and tasks, yet their deeply layered representations make their predictions difficult to interpret. Existing e…

cs.CY2025★ 5 cited

Toxicity in Online Platforms and AI Systems: A Survey of Needs, Challenges, Mitigations, and Future Directions

Smita Khapre, Melkamu Abay Mersha, Hassan Shakil +2

The evolution of digital communication systems and the designs of online platforms have inadvertently facilitated the subconscious propagation of toxic behavior. Giving rise to rea…

cs.CL2025

Semantic-Driven Topic Modeling for Analyzing Creativity in Virtual Brainstorming

Melkamu Abay Mersha, Jugal Kalita

Virtual brainstorming sessions have become a central component of collaborative problem solving, yet the large volume and uneven distribution of ideas often make it difficult to ex…

cs.CL2025★ 15 cited

Explainable AI: XAI-Guided Context-Aware Data Augmentation

Melkamu Abay Mersha, Mesay Gemeda Yigezu, Atnafu Lambebo Tonja +4

Explainable AI (XAI) has emerged as a powerful tool for improving the performance of AI models, going beyond providing model transparency and interpretability. The scarcity of labe…

cs.CL2025

A Unified Framework with Novel Metrics for Evaluating the Effectiveness of XAI Techniques in LLMs

Melkamu Abay Mersha, Mesay Gemeda Yigezu, Hassan Shakil +3

The increasing complexity of LLMs presents significant challenges to their transparency and interpretability, necessitating the use of eXplainable AI (XAI) techniques to enhance tr…

cs.CL2025★ 19 cited

Evaluating the Effectiveness of XAI Techniques for Encoder-Based Language Models

Melkamu Abay Mersha, Mesay Gemeda Yigezu, Jugal Kalita

The black-box nature of large language models (LLMs) necessitates the development of eXplainable AI (XAI) techniques for transparency and trustworthiness. However, evaluating these…

cs.CV2025★ 17 cited

SMART-Vision: Survey of Modern Action Recognition Techniques in Vision

Ali K. AlShami, Ryan Rabinowitz, Khang Lam +4

Human Action Recognition (HAR) is a challenging domain in computer vision, involving recognizing complex patterns by analyzing the spatiotemporal dynamics of individuals' movements…

cs.CL2025★ 1 cited

Explainability in Neural Networks for Natural Language Processing Tasks

Melkamu Mersha, Mingiziem Bitewa, Tsion Abay +1

Neural networks are widely regarded as black-box models, creating significant challenges in understanding their inner workings, especially in natural language processing (NLP) appl…

cs.AR2024

Agile TLB Prefetching and Prediction Replacement Policy

Melkamu Mersha, Tsion Abay, Mingziem Bitewa +1

Virtual-to-physical address translation is a critical performance bottleneck in paging-based virtual memory systems. The Translation Lookaside Buffer (TLB) accelerates address tran…

cs.CL2024★ 3 cited

Ethio-Fake: Cutting-Edge Approaches to Combat Fake News in Under-Resourced Languages Using Explainable AI

Mesay Gemeda Yigezu, Melkamu Abay Mersha, Girma Yohannis Bade +3

The proliferation of fake news has emerged as a significant threat to the integrity of information dissemination, particularly on social media platforms. Misinformation can spread…

cs.CL2024

Semantic-Driven Topic Modeling Using Transformer-Based Embeddings and Clustering Algorithms

Melkamu Abay Mersha, Mesay Gemeda yigezu, Jugal Kalita

Topic modeling is a powerful technique to discover hidden topics and patterns within a collection of documents without prior knowledge. Traditional topic modeling and clustering-ba…

cs.AI2024★ 222 cited

Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction

Melkamu Mersha, Khang Lam, Joseph Wood +2

Artificial intelligence models encounter significant challenges due to their black-box nature, particularly in safety-critical domains such as healthcare, finance, and autonomous v…

cs.CL2023

First Attempt at Building Parallel Corpora for Machine Translation of Northeast India's Very Low-Resource Languages

Atnafu Lambebo Tonja, Melkamu Mersha, Ananya Kalita +2

This paper presents the creation of initial bilingual corpora for thirteen very low-resource languages of India, all from Northeast India. It also presents the results of initial t…