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Server-Side Adaptive Trimming Policy to Defend Against Data Poisoning Attacks in Federated Learning

Ijaz Muhammad Fazal*, Sen Uddalok, Datta Debaleena, Hafez Mohamed, Amer Ayman, Islam Mohammad Tahidul

Source: 2025 Asian Conference on Communication and Networks
Performance Analysis of Hybrid Parallel Programming Models for Deep Learning in Heterogeneous HPC Environments

Tivari Gaurav*

Source: 2026 International Conference on Networks Computers and Communications
Comparative Evaluation of TF-IDF Machine-Learning Models and LSTM for Fake News Detection

Bhushan Yashmita*

Source: 2026 Indian Conference on Advances in Electrical, Computers, and Communications
Scheduling for Fully Connected Hybrid Precoding Under Power and QoS Constraints

Pham hung*

Source: 2026 ASIAN Conference on Communication and Networks
AI-Based Early Warning System for Stress-Induced Cardiac Risk in Cancer Patients: A Comparative Study of XGBoost and LSTM

M Akshaya*, R S Harney Jenifar, A Tamizhselvi

Source: International Conference on Intelligent Networks and Data-driven Intelligent Applications
Transformer-Based Federated Intrusion Detection for UAV Networks with Adaptive Aggregation

Zhang Lizhe, Yang Lihan, Wu Zhijun*, Zhang Pengyu, Wang Kenian, Li Ruiqi

Source: 2026 ASIAN Conference on Communication and Networks
CardioXen FusionBoost: An Adaptive Ensemble Machine Learning Framework for Explainable and Reliable Early Heart Disease Prediction

S Harinishri*, K Haripriya

Source: International Conference on Intelligent Networks and Data-driven Intelligent Applications
Driving Change: How Indonesian Taxi Company Utilize Mobile Applications

Yunus Ulani*, Parijata Amalia Yaksa, Adriani Vanny

Source: 2024 Asian Conference on Communication and Networks
A Hybrid Technique for Breast Cancer Detection with Efficient Imbalance Removal and Classification using ShuffleNetV2 Architecture

Ijaz Muhammad Fazal*, Datta Debaleena, Sen Uddalok, Attar Hani, Muda Zakaria Che, Jose Abey

Source: 2025 Asian Conference on Communication and Networks
TAM-U-Net: A Tumor Attention-Based U-Net for Liver Tumor Segmentation in CT Images

Vedant PS*

Source: 2026 International Conference on Networks Computers and Communications

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