Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Close, Liam; Kashef, Rasha

doi:10.4236/jilsa.2020.124005

Cited by 21 publications

(4 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So far, a large number of theories and methods have been proposed, and remarkable research results have been achieved. After a long period of research and development, clustering analysis can be divided as follows [ 14 , 15 ]:…”

Section: Analysis and Resultsmentioning

confidence: 99%

RFID Data Analysis and Evaluation Based on Big Data and Data Clustering

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The era people live in is the era of big data, and massive data carry a large amount of information. This study aims to analyze RFID data based on big data and clustering algorithms. In this study, a RFID data extraction technology based on joint Kalman filter fusion is proposed. In the system, the proposed data extraction technology can effectively read RFID tags. The data are recorded, and the KM-KL clustering algorithm is proposed for RFID data, which combines the advantages of the K-means algorithm. The improved KM-KL clustering algorithm can effectively analyze and evaluate RFID data. The experimental results of this study prove that the recognition error rate of the RFID data extraction technology based on the joint Kalman filter fusion is only 2.7%. The improved KM-KL clustering algorithm also has better performance than the traditional algorithm.

show abstract

Section: Analysis and Resultsmentioning

confidence: 99%

RFID Data Analysis and Evaluation Based on Big Data and Data Clustering

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Here, t is the iteration number, η(t) is the learning rate at iteration t , and h(c i , t) is the neighbourhood function that determines the influence of the input vector x i on the weight vector w i based on the distance between c i and the winning node, and c i is the index of the winning node in the grid for the input vector x i [ 68 , 69 , 70 ].…”

Section: Clustering Analysismentioning

confidence: 99%

Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets

Razfar

Kashef

Mohammadi

2023

Sensors

Self Cite

View full text Add to dashboard Cite

Stroke survivors often suffer from movement impairments that significantly affect their daily activities. The advancements in sensor technology and IoT have provided opportunities to automate the assessment and rehabilitation process for stroke survivors. This paper aims to provide a smart post-stroke severity assessment using AI-driven models. With the absence of labelled data and expert assessment, there is a research gap in providing virtual assessment, especially for unlabeled data. Inspired by the advances in consensus learning, in this paper, we propose a consensus clustering algorithm, PSA-NMF, that combines various clusterings into one united clustering, i.e., cluster consensus, to produce more stable and robust results compared to individual clustering. This paper is the first to investigate severity level using unsupervised learning and trunk displacement features in the frequency domain for post-stroke smart assessment. Two different methods of data collection from the U-limb datasets—the camera-based method (Vicon) and wearable sensor-based technology (Xsens)—were used. The trunk displacement method labelled each cluster based on the compensatory movements that stroke survivors employed for their daily activities. The proposed method uses the position and acceleration data in the frequency domain. Experimental results have demonstrated that the proposed clustering method that uses the post-stroke assessment approach increased the evaluation metrics such as accuracy and F-score. These findings can lead to a more effective and automated stroke rehabilitation process that is suitable for clinical settings, thus improving the quality of life for stroke survivors.

show abstract

“…Precision, Recall, F1 score, and Accuracy [49][50][51][52][53][54] the well-known evaluation metrics to assess the performance of a classifier. Precision finds pertinent instances among the gathered instances.…”

Section: Evaluation Metricsmentioning

confidence: 99%

E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model

Aljasim

Kashef

2022

Sensors

Self Cite

View full text Add to dashboard Cite

The increasing number of car accidents is a significant issue in current transportation systems. According to the World Health Organization (WHO), road accidents are the eighth highest top cause of death around the world. More than 80% of road accidents are caused by distracted driving, such as using a mobile phone, talking to passengers, and smoking. A lot of efforts have been made to tackle the problem of driver distraction; however, no optimal solution is provided. A practical approach to solving this problem is implementing quantitative measures for driver activities and designing a classification system that detects distracting actions. In this paper, we have implemented a portfolio of various ensemble deep learning models that have been proven to efficiently classify driver distracted actions and provide an in-car recommendation to minimize the level of distractions and increase in-car awareness for improved safety. This paper proposes E2DR, a new scalable model that uses stacking ensemble methods to combine two or more deep learning models to improve accuracy, enhance generalization, and reduce overfitting, with real-time recommendations. The highest performing E2DR variant, which included the ResNet50 and VGG16 models, achieved a test accuracy of 92% as applied to state-of-the-art datasets, including the State Farm Distracted Drivers dataset, using novel data splitting strategies.

show abstract

Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Cited by 21 publications

References 38 publications

RFID Data Analysis and Evaluation Based on Big Data and Data Clustering

RFID Data Analysis and Evaluation Based on Big Data and Data Clustering

Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets

E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model

Contact Info

Product

Resources

About