Improved Ant Colony Optimization and Machine Learning Based Ensemble Intrusion Detection Model

Vanitha, S.; Balasubramanie, P.

doi:10.32604/iasc.2023.032324

Cited by 10 publications

(4 citation statements)

References 31 publications

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“…Pre-trained cells can be utilised to initialise the network in sequential models such as LSTMs before retinopathy-specific recurrent layers are added. enables improving performance on downstream prediction issues with little data by utilising knowledge from related tasks and domains [51] [52]. In the context of predicting hospitalisation risks related to diabetic retinopathy, transfer learning enhances model generalisation, data efficiency, and performance overall.…”

Section: Transfer Learningmentioning

confidence: 99%

Predictions of Early Hospitalization of Diabetes Patients Based on Deep Learning: A Review

Al-Atroshi,

Adnan Mohsin Abdulazeez

2024

ijcs

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Unmanaged diabetes can result in a number of complications that need to be hospitalised. Diabetes is a chronic disorder. With preventive treatment, outcomes may be improved through early prediction of diabetes-related hospitalisation using data-driven algorithms. Here, we examine recent advances in deep learning methods for anticipating readmissions and unexpected hospital stays in adult patients with diabetes. Firstly, we present an overview of the main factors that indicate the need for hospitalisation due to diabetic complications. The research on hospitalisation risk prediction using structured health data, such as demographics, prescriptions, test results, etc., using conventional machine learning techniques is then summarised. Using data from insurance claims and electronic health records, we then examine current research that has used deep learning models. It is emphasised that longitudinal data can be included using recurrent neural networks. Model architectures, training methods, and important data modalities are covered. The assessment also addresses deployment difficulty and the model's performance assessment on real-world datasets. Ultimately, potential paths forward include hybrid models that integrate data diversity, explainable predictions, and clinical knowledge. In order to provide evidence-based predictions of the risk of hospitalisation and readmission for diabetes patients, we examine the potential and constraints of recently developed deep learning algorithms in this review.

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Section: Transfer Learningmentioning

confidence: 99%

Predictions of Early Hospitalization of Diabetes Patients Based on Deep Learning: A Review

Al-Atroshi,

Adnan Mohsin Abdulazeez

2024

ijcs

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“…The dataset has been used extensively and has been introduced into a different class of network traffic classification system based on multiple artificial intelligence techniques to study network traffic to ensure network security using artificial intelligence [37] and into a Machine Learning Based Ensemble Intrusion Detection (MLEID) methodology to minimize malicious behaviors in botnet attacks related to the Message Queuing Telemetry Transport (MQTT) and Hyper-Text Transfer Protocol (HTTP) protocols by minimizing the use of artificial intelligence in the detection of network intrusions. Protocol (HTTP) functions by minimizing malicious behavior in botnet attacks related to message queue telemetry transfer (MQTT) and the Hyper-Text Transfer Protocol (HTTP) [38], all of which demonstrate the dataset's ability to improve and increase the accuracy and reliability of network intrusion detection systems.…”

Section: Experimental Setup (1) Experimental Environment Settingmentioning

confidence: 99%

Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm

Lu,

Cao,

Wang

2024

Applied Sciences

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To address the challenges posed by high data dimensionality and class imbalance during intrusion detection, which result in increased computational complexity, resource consumption, and reduced classification accuracy, this paper presents an intrusion-detection algorithm based on an improved Random Forest approach. The algorithm employs the Bald Eagle Search (BES) optimization technique to fine-tune the Kernel Principal Component Analysis (KPCA) algorithm, enabling optimized dimensionality reduction. The processed data are then fed into a cost-sensitive Random Forest classifier for training, with subsequent model validation conducted on the reduced-dimension data. Experimental results demonstrate that compared to traditional Random Forest algorithms, the proposed method reduces the training time by 11.32 s and achieves a 5.59% increase in classification accuracy, an 11.7% improvement in specificity, and a 0.0558 increase in the G-mean value. These findings underscore the promising application potential and performance of this approach in the field of network intrusion detection.

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“…The quality of the value will directly affect the performance of the model [19]. The commonly used hyperparameter selection methods include trial and error method [20], expert experience method [21], and meta-heuristic optimization algorithm [22]. The trial and error method takes a finite number of hyperparameter values, uses each value for training, and finally takes the value corresponding to the best training result as the hyperparameter value of the model.…”

Section: Introductionmentioning

confidence: 99%

Application of GA-WELM Model Based on Stratified Cross-Validation in Intrusion Detection

Chen,

Guo,

Zhang

et al. 2023

Symmetry

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Aiming at the problem of poor detection performance under the environment of imbalanced type distribution, an intrusion detection model of genetic algorithm to optimize weighted extreme learning machine based on stratified cross-validation (SCV-GA-WELM) is proposed. In order to solve the problem of imbalanced data types in cross-validation subsets, SCV is used to ensure that the data distribution in all subsets is consistent, thus avoiding model over-fitting. The traditional fitness function cannot solve the problem of small sample classification well. By designing a weighted fitness function and giving high weight to small sample data, the performance of the model can be effectively improved in the environment of imbalanced type distribution. The experimental results show that this model is superior to other intrusion detection models in recall and McNemar hypothesis test. In addition, the recall of the model for small sample data is higher, reaching 91.5% and 95.1%, respectively. This shows that it can effectively detect intrusions in an environment with imbalanced type distribution. Therefore, the model has practical application value in the field of intrusion detection, and can be used to improve the performance of intrusion detection systems in the actual environment. This method has a wide application prospect, such as network security, industrial control system, and power system.

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Improved Ant Colony Optimization and Machine Learning Based Ensemble Intrusion Detection Model

Cited by 10 publications

References 31 publications

Predictions of Early Hospitalization of Diabetes Patients Based on Deep Learning: A Review

Predictions of Early Hospitalization of Diabetes Patients Based on Deep Learning: A Review

Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm

Application of GA-WELM Model Based on Stratified Cross-Validation in Intrusion Detection

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