Skip to content
Log in
Sponsored Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt

Author

N. Sani

View on Semantic Scholar

4 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

#small language model Open access Oct 2026

A new latent Dirichlet allocation for enhancing sentiment analysis in social network

The volume of sentiment-rich text data produced on a daily basis by social network sites such as Twitter is very large. However, traditional approaches to sentiment analysis, especially approaches based on the Bag-of-Words (BoW) model have a difficult time modeling more subtle thematic content structure and interrelati...

Fazidah Wahit, N. Sani, Salwani Abdullah · 0 citations
Open access 2026

AlphaOCR: Integrated Deep Learning and Optical Character Recognition for Receipt Extraction

AlphaOCR is introduced, a web-based application that integrates the PICK model with advanced Optical Character Recognition (OCR) technology and provides a practical integrated workflow for receipt information extraction, although broader claims regarding robustness, scalability, and generalisability require evaluation...

Muhammad Haikal Iman Osman, N. Sani, Lu Liu et al. · 0 citations
Open access Oct 2026

Two-layer long short-term memory with a hybrid feature selection method for intrusion detection in internet of things networks

Recently, the intrusion detection systems (IDSs) gained a lot of popularity for detecting intrusions in the Internet of Things (IoT) environments. However, the IoT has many challenges; the most important is the weak security. Accordingly, the IDS is applied. The Long Short-Term Memory (LSTM) is a powerful type of rec...

Mudatheer M. Al-Slivani, Ibrahim O. Alrubaye, Mayameen S. Kadhim et al. · 0 citations
Open access Aug 2026

Breast Cancer Risk Prediction and IDC Tumor Classification in Different Stages Based on Machine Learning Models

A multi-modal, data-driven intelligent framework especially designed to breast cancer analysis the Multi-Dimensional Feature Refinement Cancer Network (MDFR-Cancer-Net) that can be used to analyse breast cancer at various clinical stages based on multi-modal data is proposed.

Yun-Ze Li, N. Sani · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.