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Found 27 papers, 0 papers with code
Review for Handling Missing Data with special missing mechanism
Understanding what missing data is, how it occurs, and why it is crucial to handle it appropriately is paramount when working with real-world data, especially in tabular data, one of the most commonly used data types in the real world.
The Pursuit of Fairness in Artificial Intelligence Models: A Survey
This survey offers a synopsis of the different ways researchers have promoted fairness in AI systems.
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats
Within this second tier, we also embed a multi-classification mechanism coupled with a clustering algorithm.
Hierarchical Classification for Intrusion Detection System: Effective Design and Empirical Analysis
With the increased use of network technologies like Internet of Things (IoT) in many real-world applications, new types of cyberattacks have been emerging.
usfAD Based Effective Unknown Attack Detection Focused IDS Framework
To address this challenge, we put forth two strategies for semi-supervised learning based IDS where training samples of attacks are not required: 1) training a supervised machine learning model using randomly and uniformly dispersed synthetic attack samples; 2) building a One Class Classification (OCC) model that is trained exclusively on benign network traffic.
Training Machine Learning models at the Edge: A Survey
This survey delves into Edge Learning (EL), specifically the optimization of ML model training at the edge.
Exploring Gene Regulatory Interaction Networks and predicting therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung adenocarcinoma
Our research findings have suggested common therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC).
MLSTL-WSN: Machine Learning-based Intrusion Detection using SMOTETomek in WSNs
This blend synthesizes minority instances and eliminates Tomek links, resulting in a balanced dataset that significantly enhances detection accuracy in WSNs.
Enabling clustering algorithms to detect clusters of varying densities through scale-invariant data preprocessing
In this paper, we show that preprocessing data using a variant of rank transformation called 'Average Rank over an Ensemble of Sub-samples (ARES)' makes clustering algorithms robust to data representation and enable them to detect varying density clusters.
Empowering COVID-19 Detection: Optimizing Performance Through Fine-Tuned EfficientNet Deep Learning Architecture
Furthermore, EfficientNetB4 excelled in identifying Lung disease using Chest X-ray dataset containing 4, 350 Images, achieving remarkable performance with an accuracy of 99. 17%, precision of 99. 13%, recall of 99. 16%, and f1-score of 99. 14%.
HUMS2023 Data Challenge Result Submission
We implemented a simple method for early detection in this research.
An efficient deep learning model to categorize brain tumor using reconstruction and fine-tuning
In this study, we propose a novel DL approach based on transfer learning to effectively classify brain tumors.
SHINE: Deep Learning-Based Accessible Parking Management System
However, this gradual increment in the number of vehicles has inevitably led to parking-related issues, including the abuse of disabled parking spaces (hereafter referred to as accessible parking spaces) designated for individuals with disabilities.
Elastic Step DQN: A novel multi-step algorithm to alleviate overestimation in Deep QNetworks
However, divergent and unstable behaviour have been long standing issues in DQNs.
A Novel Data Pre-processing Technique: Making Data Mining Robust to Different Units and Scales of Measurement
In this paper, we present a better and effective alternative based on ranks over multiple sub-samples of data.
Levels of explainable artificial intelligence for human-aligned conversational explanations
Over the last few years there has been rapid research growth into eXplainable Artificial Intelligence (XAI) and the closely aligned Interpretable Machine Learning (IML).
New Bag of Deep Visual Words based features to classify chest x-ray images for COVID-19 diagnosis
General deep features may not be appropriate to represent chest x-rays as they have a few semantic regions.
Content and Context Features for Scene Image Representation
Existing research in scene image classification has focused on either content features (e. g., visual information) or context features (e. g., annotations).
Scene Image Representation by Foreground, Background and Hybrid Features
In this paper, we propose to use hybrid features in addition to foreground and background features to represent scene images.
A Comprehensive Survey on Outlying Aspect Mining Methods
For each category, we have provided existing work that falls in that category and then provided their strengths and weaknesses in those categories.
A new effective and efficient measure for outlying aspect mining
In this paper, we introduce a new score called SiNNE, which is independent of the dimensionality of subspaces.
HDF: Hybrid Deep Features for Scene Image Representation
In this paper, we propose a novel type of features -- hybrid deep features, for scene images.
Improved histogram-based anomaly detector with the extended principal component features
In this era of big data, databases are growing rapidly in terms of the number of records.
Unsupervised Deep Features for Privacy Image Classification
Sharing images online poses security threats to a wide range of users due to the unawareness of privacy information.
Tag-based Semantic Features for Scene Image Classification
In this paper, we introduce novel semantic features of an image based on the annotations and descriptions of its similar images available on the web.
Indoor image representation by high-level semantic features
Nevertheless, most of the existing feature extraction methods, which extract features based on pixels, color, shape/object parts or objects on images, suffer from limited capabilities in describing semantic information (e. g., object association).
A new simple and effective measure for bag-of-word inter-document similarity measurement
To measure the similarity of two documents in the bag-of-words (BoW) vector representation, different term weighting schemes are used to improve the performance of cosine similarity---the most widely used inter-document similarity measure in text mining.
