13 citations · 25 across the 28 of their papers we have counts for
32 papers
Tensor-based Fusion of Multi-modal Data for Predicting Cancer Risk Levels
Ali Anaissi, Yuanzhe Jia, Qishun Peng +3
Cancer diagnosis and treatment increasingly rely on multi-modal diagnostic technologies that generate high-dimensional datasets. Although these datasets are rich in pathological in…
Rethinking the Information Bottleneck: Structured Decomposition under Label-Induced Partitions
Jingyao Zhang, Yuxuan Li, Lu Han +2
Standard information bottleneck (IB) regularization constrains representations via a single scalar I(Z;X), implicitlytreating all information as homogeneous. However, a single glob…
Semantic Layer Induction from Raw Telemetry via Hierarchical LLM and RAG Abstraction
Yuanzhe Jia, Ali Anaissi
Modern applications generate massive volumes of raw telemetry data, but translating those noisy, heterogeneous event streams into actionable business insights remains a fundamental…
EvLIR: Learning Illumination Residuals from Ordered Events for Low-Light Image Enhancement
Haoxian Zhou, Chuanzhi Xu, Langyi Chen +5
Low-light image enhancement is severely ill-posed when the input frame contains missing structure, saturated noise, and weak local contrast. Event cameras provide asynchronous brig…
Towards Automated Differential Diagnosis of Skin Diseases Using Deep Learning and Imbalance-Aware Strategies
Ali Anaissi, Ali Braytee, Weidong Huang +3
As dermatological conditions become increasingly common and the availability of dermatologists remains limited, there is a growing need for intelligent tools to support both patien…
Benchmarking Preprocessing and Integration Methods in Single-Cell Genomics
Ali Anaissi, Seid Miad Zandavi, Weidong Huang +4
Single-cell data analysis has the potential to revolutionize personalized medicine by characterizing disease-associated molecular changes at the single-cell level. Advanced single-…
A Hybrid Computational Intelligence Framework for scRNA-seq Imputation: Integrating scRecover and Random Forests
Ali Anaissi, Deshao Liu, Yuanzhe Jia +3
Single-cell RNA sequencing (scRNA-seq) enables transcriptomic profiling at cellular resolution but suffers from pervasive dropout events that obscure biological signals. We present…
Capability-Based Multi-Tenant Access Management in Crowdsourced Drone Services
Junaid Akram, Ali Anaissi, Awais Akram +3
We propose a capability-based access control method that leverages OAuth 2.0 and Verifiable Credentials (VCs) to share resources in crowdsourced drone services. VCs securely encode…
Simplified Swarm Learning Framework for Robust and Scalable Diagnostic Services in Cancer Histopathology
Yanjie Wu, Yuhao Ji, Saiho Lee +3
The complexities of healthcare data, including privacy concerns, imbalanced datasets, and interoperability issues, necessitate innovative machine learning solutions. Swarm Learning…
Detecting and Understanding Hateful Contents in Memes Through Captioning and Visual Question-Answering
Ali Anaissi, Junaid Akram, Kunal Chaturvedi +1
Memes are widely used for humor and cultural commentary, but they are increasingly exploited to spread hateful content. Due to their multimodal nature, hateful memes often evade tr…
FedSAF: A Federated Learning Framework for Enhanced Gastric Cancer Detection and Privacy Preservation
Yuxin Miao, Xinyuan Yang, Hongda Fan +6
Gastric cancer is one of the most commonly diagnosed cancers and has a high mortality rate. Due to limited medical resources, developing machine learning models for gastric cancer…
Enhancing Sentiment Analysis through Multimodal Fusion: A BERT-DINOv2 Approach
Taoxu Zhao, Meisi Li, Kehao Chen +6
Multimodal sentiment analysis enhances conventional sentiment analysis, which traditionally relies solely on text, by incorporating information from different modalities such as im…
Fine-Tuning LLMs for Reliable Medical Question-Answering Services
Ali Anaissi, Ali Braytee, Junaid Akram
We present an advanced approach to medical question-answering (QA) services, using fine-tuned Large Language Models (LLMs) to improve the accuracy and reliability of healthcare inf…
Optimized Biomedical Question-Answering Services with LLM and Multi-BERT Integration
Cheng Qian, Xianglong Shi, Shanshan Yao +6
We present a refined approach to biomedical question-answering (QA) services by integrating large language models (LLMs) with Multi-BERT configurations. By enhancing the ability to…
A Blockchain-Enhanced Framework for Privacy and Data Integrity in Crowdsourced Drone Services
Junaid Akram, Ali Anaissi
We present an innovative framework that integrates consumer-grade drones into bushfire management, addressing both service improvement and data privacy concerns under Australia's P…
Decentralized PKI Framework for Data Integrity in Spatial Crowdsourcing Drone Services
Junaid Akram, Ali Anaissi
In the domain of spatial crowdsourcing drone services, which includes tasks like delivery, surveillance, and data collection, secure communication is paramount. The Public Key Infr…
Privacy-First Crowdsourcing: Blockchain and Local Differential Privacy in Crowdsourced Drone Services
Junaid Akram, Ali Anaissi
We introduce a privacy-preserving framework for integrating consumer-grade drones into bushfire management. This system creates a marketplace where bushfire management authorities…
DDRM: Distributed Drone Reputation Management for Trust and Reliability in Crowdsourced Drone Services
Junaid Akram, Ali Anaissi
This study introduces the Distributed Drone Reputation Management (DDRM) framework, designed to fortify trust and authenticity within the Internet of Drone Things (IoDT) ecosystem.…
Optimisation of federated learning settings under statistical heterogeneity variations
Basem Suleiman, Muhammad Johan Alibasa, Rizka Widyarini Purwanto +3
Federated Learning (FL) enables local devices to collaboratively learn a shared predictive model by only periodically sharing model parameters with a central aggregator. However, F…
Integration of Self-Supervised BYOL in Semi-Supervised Medical Image Recognition
Hao Feng, Yuanzhe Jia, Ruijia Xu +3
Image recognition techniques heavily rely on abundant labeled data, particularly in medical contexts. Addressing the challenges associated with obtaining labeled data has led to th…
Damage GAN: A Generative Model for Imbalanced Data
Ali Anaissi, Yuanzhe Jia, Ali Braytee +2
This study delves into the application of Generative Adversarial Networks (GANs) within the context of imbalanced datasets. Our primary aim is to enhance the performance and stabil…
ResNLS: An Improved Model for Stock Price Forecasting
Yuanzhe Jia, Ali Anaissi, Basem Suleiman
Stock prices forecasting has always been a challenging task. Although many research projects try to address the problem, few of them pay attention to the varying degrees of depende…
Privacy-Preserving Personalized Fitness Recommender System (P3FitRec): A Multi-level Deep Learning Approach
Xiao Liu, Bonan Gao, Basem Suleiman +4
Recommender systems have been successfully used in many domains with the help of machine learning algorithms. However, such applications tend to use multi-dimensional user data, wh…
Conditional Variational Autoencoder with Balanced Pre-training for Generative Adversarial Networks
Yuchong Yao, Xiaohui Wangr, Yuanbang Ma +5
Class imbalance occurs in many real-world applications, including image classification, where the number of images in each class differs significantly. With imbalanced data, the ge…