Detection Framework Research Articles

$$D_MD_RDF$$: diabetes mellitus and retinopathy detection framework using artificial intelligence and feature selection

AbstractDiabetes mellitus is one of the most common diseases affecting patients of different ages. Diabetes can be controlled if diagnosed as early as possible. One of the serious complications of diabetes affecting the retina is diabetic retinopathy. If not diagnosed early, it can lead to blindness. Our purpose is to propose a novel framework, named $$D_MD_RDF$$ D M D R D F , for early and accurate diagnosis of diabetes and diabetic retinopathy. The framework consists of two phases, one for diabetes mellitus detection (DMD) and the other for diabetic retinopathy detection (DRD). The novelty of DMD phase is concerned in two contributions. Firstly, a novel feature selection approach called Advanced Aquila Optimizer Feature Selection ($$A^2OFS$$ A 2 O F S ) is introduced to choose the most promising features for diagnosing diabetes. This approach extracts the required features from the results of laboratory tests while ignoring the useless features. Secondly, a novel classification approach (CA) using five modified machine learning (ML) algorithms is used. This modification of the ML algorithms is proposed to automatically select the parameters of these algorithms using Grid Search (GS) algorithm. The novelty of DRD phase lies in the modification of 7 CNNs using Aquila Optimizer for the classification of diabetic retinopathy. The reported results concerning the DMD datasets shows that AO reports best performance metrics in the feature selection process with the help of modified ML classifiers. The best achieved accuracy is 98.65% with the GS-ERTC model and max-absolute scaling on the “Early Stage Diabetes Risk Prediction Dataset” dataset. Also, from the reported results concerning the DRD datasets, the AOMobileNet is considered a suitable model for this problem as it outperforms the other modified CNN models with accuracy of 95.80% on the “The SUSTech-SYSU dataset” dataset.

MPE-DETR: A multiscale pyramid enhancement network for object detection in low-light images

Object detection has broad applications in areas such as autonomous driving, security surveillance, and deep-sea exploration. However, the performance of current detection algorithms significantly decreases due to the loss of detail, increased noise, and color distortion in images under low-light or nighttime conditions. To address this problem, we propose a plug-and-play multiscale pyramid enhancement network (MPENet), which elegantly cascades with RT-DETR to establish an end-to-end framework for low-light object detection, named MPE-DETR. First, MPENet utilizes Gaussian blur to decompose images into Gaussian pyramids and Laplacian pyramids at different resolutions. Specifically, we designed a high-frequency texture enhancement (HTE) module to capture the edge and texture information of images, and a low-frequency noise smoothing (LNS) module to better understand the overall structure of images and capture global-scale features. Additionally, by concatenating the output features of the HTE and LNS modules along the channel dimension, feature fusion across different scales is realized. We conducted experiments on the ExDark and ExDark + LOD datasets, which are designed for low-light object detection. The results indicate that the proposed method achieved an improvement of 2.1% in mAP@.5 compared to that of existing SOTA models on the ExDark dataset, and demonstrated strong generalizability and robustness on the ExDark + LOD dataset. The code and results are available at https://github.com/PZDJL/MPENet.

Deep intrusion net: an efficient framework for network intrusion detection using hybrid deep TCN and GRU with integral features

Deep intrusion net: an efficient framework for network intrusion detection using hybrid deep TCN and GRU with integral features

An artificial intelligence framework for explainable drift detection in energy forecasting

Accurate energy consumption forecasting is crucial for reducing operational costs, achieving net-zero carbon emissions, and ensuring sustainable buildings and cities of the future. Despite the frequent use of Artificial Intelligence (AI) algorithms for learning energy consumption patterns and predictions in Building Science, relying solely on these techniques for energy demand prediction addresses only a fraction of the challenge. A drift in energy usage can lead to inaccuracies in these AI models and subsequently to poor decision-making and interventions. While drift detection techniques have been reported, a reliable and robust approach capable of explaining identified discrepancies with actionable insights has not been discussed in extant literature. Hence, this paper presents an Artificial Intelligence framework for energy consumption forecasting with explainable drift detection, aimed at addressing these challenges. The proposed framework is composed of energy embeddings, an optimized dimensional model integrated within a data warehouse, and scalable cloud implementation for effective drift detection with explainability capability. The framework is empirically evaluated in the real-world setting of a multi-campus, mixed-use tertiary education setting in Victoria, Australia. The results of these experiments highlight its capabilities in detecting concept drift, adapting forecast predictions, and providing an interpretation of the changes using energy embeddings.

Abstract We093: Comparative Analysis of Theoretical Models and Fusion Frameworks for Early Cardiac Dysfunction Detection Using Multimodal Wearable Data

Background: Wearable devices have emerged as promising tools for continuous monitoring of physiological signals, offering opportunities for early detection of cardiac dysfunction. This study aims to compare various theoretical models and fusion frameworks to assess their effectiveness in early detection of cardiac dysfunction. Hypothesis: The study investigates the performance of signal processing-based, machine learning-based, and hybrid models for early cardiac dysfunction detection using multimodal wearable data. Additionally, it explores the efficacy of feature-level fusion, decision-level fusion, and sensor-level fusion frameworks in integrating information from diverse sensors for enhanced detection accuracy.The primary goal of the study is to identify the most effective theoretical models and fusion frameworks for early detection of cardiac dysfunction using multimodal wearable data. By comparing different approaches, the study aims to provide insights into the strengths and limitations of each method and contribute to the development of reliable wearable-based systems for proactive healthcare interventions. Methods/Approach: Three theoretical models—signal processing-based, machine learning-based, and hybrid—were implemented and evaluated for their performance in detecting early signs of cardiac dysfunction. Additionally, three fusion frameworks—feature-level fusion, decision-level fusion, and sensor-level fusion—were investigated to assess their effectiveness in integrating multimodal wearable data. Results/Data: The signal processing-based model achieved a sensitivity of 85%, specificity of 82%, and accuracy of 83% in detecting early signs of cardiac dysfunction. The machine learning-based model outperformed with a sensitivity of 92%, specificity of 88%, and accuracy of 90% . The hybrid model yielded intermediate results with a sensitivity of 89%, specificity of 85%, and accuracy of 87% . Among the fusion frameworks, sensor-level fusion demonstrated superior performance with a sensitivity of 94%, specificity of 90%, and accuracy of 92%. Conclusion: Machine learning-based approaches showed higher efficacy in early cardiac dysfunction detection compared to signal processing-based methods. Sensor-level fusion exhibited the best performance among data fusion frameworks, emphasising the importance of integrating information from diverse wearable sensors for improved accuracy.

Cyber-Physical Distributed Intelligent Motor Fault Detection.

This research paper explores the realm of fault detection in distributed motors through the vision of the Internet of electrical drives. This paper aims at employing artificial neural networks supported by the data collected by the Internet of distributed devices. Cross-verification of results offers reliable diagnosis of industrial motor faults. The proposed methodology involves the development of a cyber-physical system architecture and mathematical modeling framework for efficient fault detection. The mathematical model is designed to capture the intricate relationships within the cyber-physical system, incorporating the dynamic interactions between distributed motors and their edge controllers. Fast Fourier transform is employed for signal processing, enabling the extraction of meaningful frequency features that serve as indicators of potential faults. The artificial neural network based fault detection system is integrated with the solution, utilizing its ability to learn complex patterns and adapt to varying motor conditions. The effectiveness of the proposed framework and model is demonstrated through experimental results. The experimental setup involves diverse fault scenarios, and the system's performance is evaluated in terms of accuracy, sensitivity, and false positive rates.

Designing of Lightweight Deep Learning Framework for Plant Disease Detection

Designing of Lightweight Deep Learning Framework for Plant Disease Detection

Explainable AI-based Framework for Efficient Detection of Spam from Text using an Enhanced Ensemble Technique

Explainable AI-based Framework for Efficient Detection of Spam from Text using an Enhanced Ensemble Technique

An Advanced Filter-based Supervised Threat Detection Framework on Large Databases

An Advanced Filter-based Supervised Threat Detection Framework on Large Databases

ELIPF: Explicit Learning Framework for Pre-Emptive Forecasting, Early Detection and Curtailment of Idiopathic Pulmonary Fibrosis Disease

ELIPF: Explicit Learning Framework for Pre-Emptive Forecasting, Early Detection and Curtailment of Idiopathic Pulmonary Fibrosis Disease

Automated Alzheimer’s disease detection and classification based on optimized deep learning models using MRI

Alzheimer’s disease (AD) is a devastating neurologic condition characterized by brain atrophy and neuronal loss, posing a significant global health challenge. Early detection is paramount to impede its progression. This study aims to construct an optimized deep learning (DL) framework for early AD detection and classification using magnetic resonance images (MRI) scans. The classification task involves distinguishing between four AD stages: mild demented (MD), very mild demented (VmD), moderate demented (MoD), and non-demented (ND). To achieve effective classification, three DL models (VGG16, InceptionV3, and ResNet50) are implemented and fine-tuned. A systematic evaluation is conducted to optimize hyper-parameters, with extensive experimentation. The results demonstrate superior classification performance of the customized DL models compared to state-of-the-art methods. Specifically, visual geometry group 16 (VGG16) achieves the highest accuracy of 95.85%, followed by ResNet50 with 89.38%, while InceptionV3 yields the lowest accuracy of 87.23%. This study highlights the critical role of selecting appropriate DL models and customizing them for accurate AD detection and classification across various stages, offering significant insights for advancing clinical diagnosis and treatment strategies.

GBERT: A hybrid deep learning model based on GPT-BERT for fake news detection

The digital era has expanded social exposure with easy internet access for mobile users, allowing for global communication. Now, people can get to know what is going on around the globe with just a click; however, this has also resulted in the issue of fake news. Fake news is content that pretends to be true but is actually false and is disseminated to defraud. Fake news poses a threat to harmony, politics, the economy, and public opinion. As a result, bogus news detection has become an emerging research domain to identify a given piece of text as genuine or fraudulent. In this paper, a new framework called Generative Bidirectional Encoder Representations from Transformers (GBERT) is proposed that leverages a combination of Generative pre-trained transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) and addresses the fake news classification problem. This framework combines the best features of both cutting-edge techniques—BERT's deep contextual understanding and the generative capabilities of GPT—to create a comprehensive representation of a given text. Both GPT and BERT are fine-tuned on two real-world benchmark corpora and have attained 95.30 % accuracy, 95.13 % precision, 97.35 % sensitivity, and a 96.23 % F1 score. The statistical test results indicate the effectiveness of the fine-tuned framework for fake news detection and suggest that it can be a promising approach for eradicating this global issue of fake news in the digital landscape.

A Privacy-Preserving AIoT Framework for Fall Detection and Classification Using Hierarchical Learning With Multilevel Feature Fusion

A Privacy-Preserving AIoT Framework for Fall Detection and Classification Using Hierarchical Learning With Multilevel Feature Fusion

Rod-Like Tetraphenylethylene-Based Metal–Organic Framework for Ultrasensitive Detection of Neuron-Specific Enolase

Rod-Like Tetraphenylethylene-Based Metal–Organic Framework for Ultrasensitive Detection of Neuron-Specific Enolase

Viologen-Based Photochromic Metal–Organic Framework for Antibiotics Detection in Water

Viologen-Based Photochromic Metal–Organic Framework for Antibiotics Detection in Water

Swarm learning anomaly detection framework for cloud data center using multi-channel BiWGAN-GTN and CEEMDAN

Anomaly detection is an important task for maintaining the performance of cloud data center. Traditional anomaly detection primarily examines individual Virtual Machine (VM) behavior, neglecting the impact of interactions among multiple VMs on Key Performance Indicator (KPI) data,e.g., memory utilization. Furthermore, the non-stationarity, high complexity, and uncertain periodicity of KPI data in VM also bring difficulties to deep learning-based anomaly detection tasks. To settle these challenges, this paper proposes MCBiWGAN-GTN, a multi-channel semi-supervised time series anomaly detection algorithm based on the Bidirectional Wasserstein Generative Adversarial Network with Graph-Time Network (BiWGAN-GTN) and the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). (a)The BiWGAN-GTN algorithm is proposed to extract spatiotemporal information from data. (b) The loss function of BiWGAN-GTN is redesigned to solve the abnormal data intrusion problem during the training process. (c) MCBiWGAN-GTN is designed to reduce data complexity through CEEMDAN for time series decomposition and utilizes BiWGAN-GTN to train different components. (d) To adapt the proposed algorithm for the entire cloud data center, a cloud data center anomaly detection framework based on Swarm Learning (SL) is designed. The evaluation results on a real-world cloud data center dataset show that MCBiWGAN-GTN outperforms the baseline, with an F1-score of 0.96, an accuracy of 0.935, a precision of 0.954, a recall of 0.967, and an FPR of 0.203. The experiments also verify the stability of MCBiWGAN-GTN, the impact of parameter configurations, and the effectiveness of the proposed SL framework.

A systematic literature review on machine learning and deep learning-based covid-19 detection frameworks using X-ray Images

Coronavirus is an endangered disease to kills more than millions of people, but it has also put tremendous pressure on the whole medical system. The initial stage of identification of COVID-19 is necessary to isolate the patients with positive cases in order to stop the disease from spreading. The amalgamation of imaging techniques and deep learning algorithms takes less time and leads to more accurate outcomes for COVID-19 detection. Deep learning techniques have been employed by scientists to identify coronavirus infection in lung images during the COVID-19 worldwide epidemic. In this review, a review of the Covid-19 detection framework based on machine learning and deep learning techniques using X-ray images is done. First, the review of existing Covid-19 detection models is done. For this purpose, a detailed literature survey is carried out on Covid-19 detection papers from 2019 to 2023. Following the literature survey, the pre-processing procedures, the segmentation process, and the classification techniques used for Covid-19 detection using deep learning, machine learning, and optimization algorithms are reviewed and categorized. After that, the dataset and the implementation tool which are utilized for Covid-19 detection works are analyzed and grouped. Finally, the performance metrics validation such as accuracy, recall, F1-score, NPV, precision, sensitivity, and specificity is carried out. The research gaps in the existing Covid-19 detection techniques are provided further as references to aid in future works.

ENHANCED BRAIN CANCER DETECTION IN MRI SCANS THROUGH TEMPLATE REGRESSION SIAMESE REGIONAL PROPOSED NETWORK FOR SEGMENTATION AND FUZZY LOGIC FUSION OF SEGMENTED REGIONS

Accurate detection and segmentation of brain cancer in MRI scans are critical for effective diagnosis and treatment planning. Traditional methods often struggle with the complexities of tumor morphology and variations in scan quality. Existing detection systems can be slow and may not effectively handle the variability in tumor appearances, leading to potential delays in diagnosis and treatment. To address these challenges, we propose an enhanced detection framework using a Siamese Regional Proposed Network (SRPN). The SRPN integrates template branch and bounding box regression to expedite detection processes. The system utilizes an extended Siamese network to learn the distance between tracklet pairs, capturing the local and global features of tumors. These features are transferred to bidirectional gated recurrent units (GRUs), which generate tracklets and segment them into shorter sub-tracklets based on local distances. The segmented sub- tracklets are then reconnected into longer trajectories using similarities derived from temporal pooling global features. Additionally, fuzzy logic fusion is employed to combine segmented regions for improved accuracy. The SRPN-based framework demonstrated a significant improvement in detection speed and accuracy. Experimental results show an accuracy increase of 12% over traditional methods, achieving 94% accuracy with a detection time reduction of 30%. The system also improved segmentation precision, with a mean Intersection over Union (IoU) score of 85%, compared to 75% in conventional approaches.

Spade: A Real-Time Fraud Detection Framework

In this demonstration, we introduce Spade, a sophisticated real-time fraud detection framework adept at navigating the complex transaction graph. Unlike conventional methods that are limited by performance and lack incremental update capabilities, Spade leverages advanced incremental updates in dense subgraph peeling algorithms to enhance efficiency, usability, and reduce latency, achieving a significantly better fraud prevention ratio. The demo showcases an interactive GUI prototype, allowing users to customize and explore dense subgraphs with various metrics and algorithms. This interactive demonstration also effectively highlights Spade's robust capacity to unearth fraudulent transactions within varied settings, including Grab's services and cryptocurrency transactions.

A lightweight attention-based multi-frequency topology learning framework for driving fatigue detection

Driving fatigue has been one of the major causes of traffic accident. Efficient and accurate detection of driving fatigue are a legitimate public concern. In this paper, we conduct the simulated driving experiments and an EEG-based driving fatigue detection framework integrating multilayer brain network and convolutional neural network (CNN) is developed. This lightweight attention-based multi-frequency topology learning (AMFTL) framework first captures the fatigue-related multi-frequency brain topological information and then feeds it into a CNN-based topology feature extraction (TFE) module to fully explore and integrate the critical topological features. The quantitative analysis results show that there are significant differences in brain topologies between the alert and fatigue states. And experimental results show that our proposed framework achieves an average detection accuracy of 94.71% for driving fatigue, which outperforms the current state-of-the-art methods. This proposed framework is expected to open new venues for EEG-based brain state analysis, and holds promising practical application potential.