Imbalance Issue Research Articles

Bi-Level Game Strategy for Virtual Power Plants Based on an Improved Reinforcement Learning Algorithm

To address the issue of economic dispatch imbalance in virtual power plant (VPP) systems caused by the influence of operators and distribution networks, this study introduces an optimized economic dispatch method based on bi-level game theory. Firstly, a bi-level game model is formulated, which integrates the operational and environmental expenses of VPPs with the revenues of system operators. To avoid local optima during the search process, an enhanced reinforcement learning algorithm is developed to achieve rapid convergence and obtain the optimal solution. Finally, case analyses illustrate that the proposed method effectively accomplishes multi-objective optimization for various decision-making stakeholders, including VPP and system operators, while significantly reducing curtailment costs associated with the extensive integration of distributed renewable energy. Furthermore, the proposed algorithm achieves fast iteration and yields superior dispatch outcomes under the same modeling conditions.

Predicting financial distress in high-dimensional imbalanced datasets: a multi-heterogeneous self-paced ensemble learning framework

Financial distress prediction (FDP) is a critical area of study for researchers, industry stakeholders, and regulatory authorities. However, FDP tasks present several challenges, including high-dimensional datasets, class imbalances, and the complexity of parameter optimization. These issues often hinder the predictive model’s ability to accurately identify companies at high risk of financial distress. To mitigate these challenges, we introduce FinMHSPE—a novel multi-heterogeneous self-paced ensemble (MHSPE) FDP learning framework. The proposed model uses pairwise comparisons of data from multiple time frames combined with the maximum relevance and minimum redundancy method to select an optimal subset of features, effectively resolving the high dimensionality issue. Furthermore, the proposed framework incorporates the MHSPE model to iteratively identify the most informative majority class data samples, effectively addressing the class imbalance issue. To optimize the model’s parameters, we leverage the particle swarm optimization algorithm. The robustness of our proposed model is validated through extensive experiments performed on a financial dataset of Chinese listed companies. The empirical results demonstrate that the proposed model outperforms existing competing models in the field of FDP. Specifically, our FinMHSPE framework achieves the highest performance, achieving an area under the curve (AUC) value of 0.9574, considerably surpassing all existing methods. A comparative analysis of AUC values further reveals that FinMHSPE outperforms state-of-the-art approaches that rely on financial features as inputs. Furthermore, our investigation identifies several valuable features for enhancing FDP model performance, notably those associated with a company’s information and growth potential.

Deep learning and smart energy-based lightweight urban power load forecasting model for sustainable urban growth

IntroductionUrban power load forecasting is essential for smart grid planning but is hindered by data imbalance issues. Traditional single-model approaches fail to address this effectively, while multi-model methods mitigate imbalance by splitting datasets but incur high costs and risk losing shared power distribution characteristics.MethodsA lightweight urban power load forecasting model (DLUPLF) is proposed, enhancing LSTM networks with contrastive loss in short-term sampling, a difference compensation mechanism, and a shared feature extraction layer to reduce costs. The model adjusts predictions by learning distribution differences and employs dynamic class-center contrastive learning loss for regularization. Its performance was evaluated through parameter tuning and comparative analysis.ResultsThe DLUPLF model demonstrated improved accuracy in forecasting imbalanced datasets while reducing computational costs. It preserved shared power distribution characteristics and outperformed traditional and multi-model approaches in efficiency and prediction accuracy.DiscussionDLUPLF effectively addresses data imbalance and model complexity challenges, making it a promising solution for urban power load forecasting. Future work will focus on real-time applications and broader smart grid systems.

Steel surface defect detection based on improved YOLOv8

Abstract Addressing the issue of imbalance between detection accuracy and speed in current steel surface defect
detection methods, we propose an improved YOLOv8-based algorithm, named YOLO-SSD, for steel surface defect
detection. This algorithm replaces the downsampling module with a SPD-Conv module in the backbone network and
introduces the CBAM attention module to enhance feature extraction capability and small object detection ability.
Additionally, in the neck network, the C2f module is replaced with a C2f-Ghost module, which not only lightweightens
the neck module but also improves the utilization of computational resources. Experimental results on the NEU DET dataset demonstrate that compared to the original YOLOv8 algorithm, the average detection accuracy of the
YOLO-SSD algorithm for steel surface defect detection has been improved by 2.8%, with a frame per second (FPS)
reaching 96. Compared to current mainstream steel surface defect detection algorithms, the improved detection
algorithm can enhance the detection accuracy of the defect detection model without compromising detection speed.

Ensemble-Based Machine Learning Algorithms Combined with Near Miss Method for Software Bug Prediction

Abstract Software bug prediction (SBP) involves identifying or categorizing software modules likely to contain defects, utilizing underlying system properties such as software metrics. SBP plays a crucial role in enhancing software project quality and mitigating maintenance risks. Numerous machine learning (ML) algorithms have been developed to predict software bugs. Class imbalance poses a significant challenge for these algorithms, significantly impeding their effectiveness and resulting in imbalanced false-positive and false-negative outcomes. However, limited research has been conducted to specifically tackle the issue of class imbalance in the context of SBP. This study investigates the prediction performance of a homogeneous ensemble: Bagging, boosting, and voting classifiers (VC) methods combined with the under-sampling methods to address the class imbalance problem and improve the accuracy of SBP. Two ensembles are classified as bagging ensembles: decision tree (DT) and random forest (RF); two ensembles are classified as boosting ensembles: AdaBoost (AB) and gradient boosting (GB), while the DT, RF, K-Nearest Neighbours (K-NN), and support vector machine (SVM) are considered as VC. To establish the effectiveness of the proposed models, the experiments were conducted on the available benchmark datasets, which comprise five public datasets based on both class and file-level metrics. We compared and evaluated the performance of the proposed models according to several performance measures, namely accuracy, precision, recall, f-measure, Matthew’s correlation coefficient (MCC), and the area under the receiver operating characteristic curve (AUROC). The experimental findings demonstrated that the proposed models exhibit superior efficiency in predicting software bugs on balanced datasets compared to the original datasets, with an improvement of up to 11% accuracy for the class-level metrics and 10% for the file-level metrics. The results indicate that the use of data sampling techniques had a positive impact on the prediction accuracy of the presented models. We compared our proposed method with existing SBP methods based on several standard performance measures. The comparison outcomes revealed a significant superiority of our method over the prevailing state-of-the-art SBP methods across most datasets.

PREmbed: Balancing Conditional Generative Models with Embedding Pretraining and Regularization

Recent advancements in conditional generative models, e.g., Conditional Variational AutoEncoder (CVAE) and Conditional Denoising Diffusion Probabilistic Model (CDDPM), which utilize class-specific embeddings for generating images in specific classes, have demonstrated exceptional capabilities in producing high-quality images on balanced datasets. However, their performance decreases with imbalanced real-world data, affecting the fidelity and diversity of the generated images. This paper discusses the reasons and solutions for the imbalance issue in CVAE and CDDPM. By selectively reweighting the gradients of the embedding layer and main network, we identify the embedding layer’s bias as a critical bottleneck in these models. Motivated by this, we propose Embedding Pretraining and Regularization (PREmbed), a training methodology for addressing biased learning in the embedding layer of these models. PREmbed consists of two key components: (i) Masked Autoencoder pretraining on the original imbalanced dataset to obtain a robust initial embedding, and (ii) an embedding regularization loss to preserve class-level distances during the training phase to improve imbalanced embedding learning. Implementing PREmbed leads to the enhanced convergence of a robust embedding layer. Our experiments on imbalanced CIFAR-10 and CUB-200 datasets demonstrate that PREmbed enhances the performance of CVAE and CDDPM; it outperforms the baseline CVAE by 22.3% (FID 18.63) and the CDDPM by 30.3% (FID 12.70) in imbalanced CIFAR-10; 33.7% (FID 24.08) and 26.5% (FID 13.90) in CUB-200; and 16.4% (FID 49.41) and 15.2% (FID 33.05) in ImageNet-LT (which is where it achieves the state-of-the-art performance in imbalanced conditional image generation task).

Research on the Individual Identification of Communication Radiation Sources in Complex Circumstances

Communication radiation source individual identification technology is an essential technique in electronic reconnaissance and a crucial link in electronic warfare support measures. Nevertheless, when the sample set encounters complex circumstances, such as class imbalance or a small sample, the classification network model, driven by big data, disrupts the symmetry between the recognition effect and the quantity of the datasets, leading to suboptimal recognition performance. Thus, it is requisite to optimize the existing models and algorithms to better propose more representative fingerprint features. This paper references the speech signal recognition model multivariate long short-term memory–fully convolutional network (MLSTM-FCN), and ameliorates the recognition algorithm and training strategy for the two scenarios of class imbalance and a small sample. It puts forward a communication radiation source individual identification method based on MLSTM-FCN incremental random feature concatenation and a communication radiation source individual identification method based on meta-learning. Proceeding from improving the class imbalance issue among features and small-sample learning, the experimental results under various signal-to-noise ratios demonstrate that the proposed methods have superior recognition effects and higher accuracy.

Enhancing Smart Grid Security Using BLS Privacy Blockchain With Siamese Bi‐LSTM for Electricity Theft Detection

ABSTRACTEnergy management inside a blockchain framework developed for smart grids is primarily concerned with improving intrusion detection to protect data privacy. The emphasis is on real‐time detection of cyberattacks and preemptive forecasting of possible risks, especially in the realm of electricity theft within smart grid systems. Existing Electricity Theft Detection techniques for smart grids have obstacles such as class imbalance, which leads to poor generalization, increased complexity due to large EC data aspects, and a high false positive rate in supervised models, resulting in incorrect classification of regular customers as abnormal. To provide security in the smart grid, a novel BLS Privacy Blockchain with Siamese Bi‐LSTM is proposed. Initially, the privacy‐preserving Boneh‐Lynn‐Shacham blockchain technique is built on BLS Short signature and hash algorithms, which mitigate misclassification rates and false positives in the detection of smart grid attacks. Then, a hybrid framework employs an intrusion detection algorithm based on Siamese Bidirectional Long Short‐Term Memory to semantically distinguish between harmful and authentic behaviors, thereby improving data quality and predictive capabilities. Furthermore, a Recurrent Neural Network‐Generative Adversarial Network is presented for detecting electricity fraud, which addresses the issue of class imbalance. This uses both supervised and unsupervised loss functions to produce synthetic theft samples that closely resemble actual theft incidents. From the experiment, it is showing that the proposed models perform with high accuracy and low error rates. The proposed model from the outcomes when compared to other existing models achieves high accuracy, detection rate, recall, and low computation time.

EfficientDense-ViT: APT Detection via Hybrid Deep Learning Framework with Hybrid Dipper Throated Sine Cosine Optimization Algorithm (HDT-SCO)

Advanced Persistent Threats (APT) are intelligent, sophisticated cyberattacks that frequently evade detection by gradually interfering with vital systems or focusing on sensitive data. It is proposed herein the new approach of the Hybrid Dipper Throated Sine Cosine Optimization Algorithm (HDT-SCO) for APT detection in association with the EfficientDense-ViT model. It handles the class imbalance issue with advanced processing Adaptive Synthetic Minority Oversampling Technique (ADASYN), including min-max scaling for normalization, and median imputation for missing values. In terms of feature engineering, ResNet-152 and Symbolic Aggregate Approximation (SAX) are adopted for statistical, deep, and time series feature extraction. HDT-SCO optimizes the selection of relevant features to refine by integrating into it the three approaches: PCA, RFE, RF Feature Importance, and L1 Regularization (Lasso). Compared to current detection techniques, the best detection model shows high performance and efficiency through the hybrid deep learning model known as EfficientDense-ViT, which is a combination of EfficientNet, DenseNet, and Vision Transformers (ViT) that can detect APTs reliably. This method shows considerable improvement in both accuracy (0.98741 for the 7030 split and 0.99143 for the 8020 split) and efficiency as compared to existing models in the detection of APTs in cybersecurity.

Evaluating Ensemble Learning Techniques for Class Imbalance in Machine Learning: A Comparative Analysis of Balanced Random Forest, SMOTE-RF, SMOTEBoost, and RUSBoost

Purpose: This research aims to identify the optimal ensemble learning method for mitigating class imbalance in datasets utilizing various advanced techniques which include balanced random forest (BRF), SMOTE-random forest (SMOTE-RF), RUSBoost, and SMOTEBoost. The methods were systematically evaluated against conventional algorithms, including random forest and AdaBoost, across heterogeneous datasets with varying class imbalance ratios. Methods: This study utilized 13 secondary datasets from diverse sources, each with binary class outputs. The datasets exhibited varying degrees of class imbalance, offering scenarios to assess the effectiveness of ensemble learning techniques and traditional machine learning approaches in managing class imbalance issues. Study data were split into training (80%) and testing (20%), with stratified sampling applied to maintain consistent class proportions across both sets. Each method underwent hyperparameter optimization with distinct settings with repetition over 10 iterations. The optimal method was evaluated based on balanced accuracy, recall, and computation time. Result: Based on the evaluation, the BRF method exhibited the highest performance in balanced accuracy and recall when compared to SMOTE-RF, RUSBoost, SMOTEBoost, random forest, and AdaBoost. Conversely, the classical random forest method outperformed other techniques in terms of computational efficiency. Novelty: This study presents an innovative analysis of advanced ensemble learning techniques, including BRF, SMOTE-random forest, SMOTEBoost, and RUSBoost, which demonstrate significant effectiveness in addressing class imbalance across various datasets. By systematically optimizing hyperparameters and applying stratified sampling, this research produces findings that redefine the benchmarks of balanced accuracy, recall and computational efficiency in machine learning.

Reliable Data Delivery and Load Balancing in Improved Energy Efficient RPL Routing Protocol for Low Power and Lossy Networks

ABSTRACTThe energy‐constrained nodes and tree‐like topology of Low‐Power and Lossy Networks (LLNs) frequently result in serious problems when energy bottlenecks occur. This occurrence affects the network performance in terms of node failure, lower connectivity, and higher energy consumption. This paper proposes a novel solution to these problems by integrating an Energy Efficient Multipath Routing Protocol (EM‐RPL) with a Load‐Based Energy Efficient RPL (LBEE‐RPL) protocol. To address energy bottlenecks, the proposed EM‐RPL protocol initiates the crucial mechanisms as subnode switching mechanism and an improved Trickle timer. The network's energy load is balanced by the subnode switching mechanism, which redistributes traffic away from overloaded nodes. The enhanced Trickle timer guarantees prompt identification and response to energy shortage conditions. This method enhances overall network performance while addressing the issue of data imbalance in energy consumption. An extensive simulation shows the proposed EM‐RPL significantly increases network lifetime and energy efficiency. Specifically, integrating these protocols leads to a notable reduction in power consumption and a rise in the network's operational lifespan. The important problem of energy management in LLNs is effectively resolved by this work, which offers improved performance and sustainability for a range of Internet of Things (IoT) and related fields applications.

EDCLoc: a prediction model for mRNA subcellular localization using improved focal loss to address multi-label class imbalance

BackgroundThe subcellular localization of mRNA plays a crucial role in gene expression regulation and various cellular processes. However, existing wet lab techniques like RNA-FISH are usually time-consuming, labor-intensive, and limited to specific tissue types. Researchers have developed several computational methods to predict mRNA subcellular localization to address this. These methods face the problem of class imbalance in multi-label classification, causing models to favor majority classes and overlook minority classes during training. Additionally, traditional feature extraction methods have high computational costs, incomplete features, and may lead to the loss of critical information. On the other hand, deep learning methods face challenges related to hardware performance and training time when handling complex sequences. They may suffer from the curse of dimensionality and overfitting problems. Therefore, there is an urgent need for more efficient and accurate prediction models.ResultsTo address these issues, we propose a multi-label classifier, EDCLoc, for predicting mRNA subcellular localization. EDCLoc reduces training pressure through a stepwise pooling strategy and applies grouped convolution blocks of varying sizes at different levels, combined with residual connections, to achieve efficient feature extraction and gradient propagation. The model employs global max pooling at the end to further reduce feature dimensions and highlight key features. To tackle class imbalance, we improved the focal loss function to enhance the model’s focus on minority classes. Evaluation results show that EDCLoc outperforms existing methods in most subcellular regions. Additionally, the position weight matrix extracted by multi-scale CNN filters can match known RNA-binding protein motifs, demonstrating EDCLoc’s effectiveness in capturing key sequence features.ConclusionsEDCLoc outperforms existing prediction tools in most subcellular regions and effectively mitigates class imbalance issues in multi-label classification. These advantages make EDCLoc a reliable choice for multi-label mRNA subcellular localization. The dataset and source code used in this study are available at https://github.com/DellCode233/EDCLoc.

ელექტროსისტემის გადაცემის ქსელში უეცრად წარმოქმნილი რეაქტიული სიმძლავრის უბალანსობა

The paper discusses the issue of sudden imbalance of reactive power in transmission network. The article provides a formula based on which it is possible to estimate change of nodal voltages in transmission network during a sudden imbalance of reactive power. The mentioned problem is discussed on the example of transmission network of Georgian power system, where source of sudden imbalance of reactive power is emergency shutdown of shunt reactors. In the article there is calculated own and mutual impedances of nodes of Georgian transmission network and is evaluated change of nodal voltages during the sudden imbalance of reactive power.

Anomalous Node Detection in Blockchain Networks Based on Graph Neural Networks

With the rapid development of blockchain technology, fraudulent activities have significantly increased, posing a major threat to the personal assets of blockchain users. The blockchain transaction network formed during user transactions can be represented as a graph consisting of nodes and edges, making it suitable for a graph data structure. Fraudulent nodes in the transaction network are referred to as anomalous nodes. In recent years, the mainstream method for detecting anomalous nodes in graphs has been the use of graph data mining techniques. However, anomalous nodes typically constitute only a small portion of the transaction network, known as the minority class, while the majority of nodes are normal nodes, referred to as the majority class. This discrepancy in sample sizes results in class imbalance data, where models tend to overfit the features of the majority class and neglect those of the minority class. This issue presents significant challenges for traditional graph data mining techniques. In this paper, we propose a novel graph neural network method to overcome class imbalance issues by improving the Graph Attention Network (GAT) and incorporating ensemble learning concepts. Our method combines GAT with a subtree attention mechanism and two ensemble learning methods: Bootstrap Aggregating (Bagging) and Categorical Boosting (CAT), called SGAT-BC. We conducted experiments on four real-world blockchain transaction datasets, and the results demonstrate that SGAT-BC outperforms existing baseline models.

White Blood Cell Classification Using SMOTE-SVM Method with Hybrid Feature Extraction and Image Segmentation Using Gaussian Mixture Model

White blood cells are crucial to the immune system. The irregular structure of white blood cells, along with the fact that each type has its unique structure, makes manual identification challenging. Manual identification is prone to errors due to medical personnel's subjectivity and fatigue from time and effort demands. A fast and accurate method for classifying white blood cells is needed, but challenges remain regarding the quality and quantity of samples for each cell type. This study proposes the use of SMOTE and SVMSMOTE to address the issue of data imbalance, as well as a combination of shape features (size, circularity, convexity, solidity) and convolutional autoencoder (CAE) for feature extraction, along with a Gaussian mixture model for nucleus segmentation. The study finds that, without using SMOTE or SVMSMOTE for data balancing, the proposed features are already sufficient to represent each cell type except eosinophils, achieving an accuracy of 92.4%, precision of 91.9%, recall of 92.3%, F1-Score of 92%, MCC of 0.862, and CEN of 0.1376 using a polynomial kernel. The worst results were obtained with the sigmoid kernel. The combined feature extraction (shape and CAE) outperformed individual methods. Shape alone achieved 86.8% accuracy, CAE alone 87.8%. Recall for eosinophil cells improved using SMOTE and SVMSMOTE.

Accurate Arrhythmia Classification with Multi-Branch, Multi-Head Attention Temporal Convolutional Networks.

Electrocardiogram (ECG) signals contain complex and diverse features, serving as a crucial basis for arrhythmia diagnosis. The subtle differences in characteristics among various types of arrhythmias, coupled with class imbalance issues in datasets, often hinder existing models from effectively capturing key information within these complex signals, leading to a bias towards normal classes. To address these challenges, this paper proposes a method for arrhythmia classification based on a multi-branch, multi-head attention temporal convolutional network (MB-MHA-TCN). The model integrates three convolutional branch layers with different kernel sizes and dilation rates to capture features across varying temporal scales. A multi-head self-attention mechanism dynamically allocates weights, integrating features and correlations from different branches to enhance the recognition capability for difficult-to-classify samples. Additionally, the temporal convolutional network employs multi-layer dilated convolutions to progressively expand the receptive field for extracting long-term dependencies. To tackle data imbalance, a novel data augmentation strategy is implemented, and focal loss is utilized to increase the weight of minority classes, while Bayesian optimization is employed to fine-tune the model's hyperparameters. The results from five-fold cross-validation on the MIT-BIH Arrhythmia Database demonstrate that the proposed method achieves an overall accuracy of 98.75%, precision of 96.60%, sensitivity of 97.21%, and F1 score of 96.89% across five categories of ECG signals. Compared to other studies, this method exhibits superior performance in arrhythmia classification, significantly improving the recognition rate of minority classes.

Novel variant transformer-based method for aluminum profile surface defect detection

Abstract The detection of surface defects on metal plays a pivotal role in the evaluation of product quality for aluminum profiles. So far, the most prevalent approach for detecting metal surface defects is still through the utilization of traditional conventional neural networks (CNNs). However, transformer-based methods have made notable improvements in computer vision in recent years, exhibiting superiority over traditional CNNs in the majority of tasks. In regard to aluminum surface defect image characteristics, such as intricate textures, few defect samples, and large-scale differences in various defects, traditional CNNs have difficulty further improving performance. This paper proposes a novel method based on a variant transformer for surface defect detection of aluminum profiles, which combines a traditional CNN and a transformer architecture with a deformable attention module (DAM). The DAM better focuses on part of critical sampling points around the reference point, which alleviates the unacceptable complexity in high-resolution feature maps and enhances the recognition effect of small target defects. The image stitching method proposed uses defect-free samples to generate new samples, which partially mitigates the issue of class imbalance. Moreover, we carried out supplementary experiments to confirm the effectiveness of transfer learning in training small-scale datasets. Compared with the baseline, the mean average precision (mAP) was improved by 3.32%. Extensive experimental results demonstrate the efficacy of our method in accurately detecting surface defects of aluminum profiles and significantly improving the detection of small target defects.

Res-UNet Ensemble Learning for Semantic Segmentation of Mineral Optical Microscopy Images

In geology and mineralogy, optical microscopic images have become a primary research focus for intelligent mineral recognition due to their low equipment cost, ease of use, and distinct mineral characteristics in imaging. However, due to their close reflectivity or transparency, some minerals are not easily distinguished from other minerals or background. Secondly, the number of background pixels often vastly exceeds the number of pixels for individual mineral particles, and the number of pixels of different mineral particles in the image also varies significantly. These have led to the issue of data imbalance. This imbalance results in lower recognition accuracy for categories with fewer samples. To address these issues, a flexible ensemble learning for semantic segmentation based on multiple optimized Res-UNet models is proposed, introducing dice loss and focal loss functions and incorporating a pre-positioned spatial transformer networks block. Twelve optimized Res-UNet models were used to construct multiple Res-UNet ensemble learnings using heterogeneous ensemble strategies. The results demonstrate that the system integrated with five learners using the weighted voting fusion method (RUEL-5-WV) achieved the best performance with a mean Intersection over Union (mIOU) of 91.65 across all nine categories and an IOU of 84.33 for the transparent mineral (gangue). The results indicate that this ensemble learning scheme outperforms individual optimized Res-UNet models. Compared to the classical Deeplabv3 and PSPNet, this scheme also exhibits significant advantages.

Underwater-MLA: lightweight aggregated underwater object detection network based on multi-branches for embedded deployment

Abstract In response to the limited memory and computing capabilities of underwater devices, as well as the various target leakage issues caused by complex underwater environments, we propose a lightweight aggregation underwater target detection algorithm called multi-branch lightweight aggregate networks underwater (underwater-MLA). The algorithm designs a new mixed convolutional fusion with contextual attention module. It constructs a multi-branch structure that integrates mixed convolutions and contextual attention to enhance the feature extraction capability for complex underwater targets. Additionally, to address the shortcomings of information loss and significant target localization errors during detection, we propose the SPPELAN efficient aggregation module to improve the model’s perception of target localization and background discrimination ability. Furthermore, targeting the issues of blurred boundaries and sample imbalance in underwater targets, we introduce the Focaler-IoU module to focus more on the distribution of sample boundaries. Finally, we deploy the underwater-MLA algorithm on a Raspberry Pi 4B development board and utilize a camera for real-time underwater target detection. Simulation results on the URPC dataset demonstrate that this method achieves an 85.86% detection accuracy, a 2.94% improvement over the original architecture. The computational complexity and parameter count are reduced by 32.94% and 11.31%, respectively. Moreover, it achieves satisfactory target detection performance on embedded terminals. Link to open-source code: https://github.com/fanchenye/fanchenye.

Novel Based Approach towards Diabetic’s Classification Using Artificial Intelligence and Internet of Things Environment

The work is focused on designing effective technique employing AI such as machine learning and Internet of things environment for diabetic classification and management. The model is focused in reliably and energy efficient manner in collecting data using IoT and edge-computing paradigm. The work is focused on designing a novel ML model that can classify diabetics and should address class imbalance issues. The model should also be robust considering different kinds of data/attributes related to diabetics.