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STAGE framework: A stock dynamic anomaly detection and trend prediction model based on graph attention network and sparse spatiotemporal convolutional network

Shi M, Mohamad Rasli R, Wang SL

PLoS One, 2025;20(3):e0318939.
PMID: 40096646 DOI: 10.1371/journal.pone.0318939

As the financial market becomes increasingly complex, stock prediction and anomaly data detection have emerged as crucial tasks in financial risk management. However, existing methods exhibit significant limitations in handling the intricate relationships between stocks and addressing anomalous data. This paper proposes the STAGE framework, which integrates the Graph Attention Network (GAT), Variational Autoencoder (VAE), and Sparse Spatiotemporal Convolutional Network (STCN), to enhance the accuracy of stock prediction and the robustness of anomaly data detection. Experimental results show that the complete STAGE framework achieved an accuracy of 85% after 20 training epochs, which is 10% to 20% higher than models with key algorithms removed. In the anomaly detection task, the STAGE framework further improved the accuracy to 95%, demonstrating fast convergence and stability. This framework offers an innovative solution for stock prediction, adapting to the complex dynamics of real-world markets.
Fulltext A plain-image correlative semi-selective medical image encryption algorithm using enhanced 2D-logistic map

Zhang B, Rahmatullah B, Wang SL, Liu Z

Multimed Tools Appl, 2023;82(10):15735-15762.
PMID: 36185323 DOI: 10.1007/s11042-022-13744-9

Modern medical examinations have produced a large number of medical images. It is a great challenge to transmit and store them quickly and securely. Existing solutions mainly use medical image encryption algorithms, but these encryption algorithms, which were developed for ordinary images, are time-consuming and must cope with insufficient security considerations when encrypting medical images. Compared with ordinary images, medical images can be divided into the region of interest and the region of background. In this paper, based on this characteristic, a plain-image correlative semi-selective medical image encryption algorithm using the enhanced two dimensional Logistic map was proposed. First, the region of interest of a plain medical image is permuted at the pixel level, then for the whole medical image, substitution is performed pixel by pixel. An ideal compromise between encryption speed and security can be achieved by full-encrypting the region of interest and semi-encrypting the region of background. Several main types of medical images and some normal images were selected as the samples for simulation, and main image cryptanalysis methods were used to analyze the results. The results showed that the cipher-images have a good visual quality, high information entropy, low correlation between adjacent pixels, as well as uniformly distribute histogram. The algorithm is sensitive to the initial key and plain-image, and has a large keyspace and low time complexity. The time complexity is lower when compared with the current medical image full encryption algorithm, and the security performance is better when compared with the current medical image selective encryption algorithm.
A literature review of the state of the art of sustainable waste collection and vehicle routing problem

Li W, Ng TF, Ibrahim H, Wang SL

J Air Waste Manag Assoc, 2024 Nov 26.
PMID: 39401374 DOI: 10.1080/10962247.2024.2415298

Over the past decades, the amount of waste has dramatically increased worldwide due to rapid population growth and urbanization. Inefficient waste collection and transportation, known as the waste collection vehicle routing problem (WCVRP), negatively impacts economic, environmental, and social dimensions. This issue has drawn considerable attention from local and national governments. There is an urgent need for sustainable practices in waste collection and transportation. This paper conducts an exhaustive literature review on the WCVRP. The review covers various aspects, including waste types, common model characteristics, objective functions, solution methods, datasets and case studies. The analysis indicates a need for further research on underrepresented waste types, such as medical waste (MW). It also stresses the importance of incorporating more model characteristics to better capture the complexities of real-world scenarios. Moreover, there is a lack of multiple objectives optimization models that concurrently address economic, environmental, and social dimensions, in line with sustainable development goals. Additionally, there is insufficient research on hybrid algorithms, especially regarding their application to uncertainty management and advanced techniques. Finally, the use of hybrid testing is restricted, highlighting the need for diverse tests to validate solution methods under various real-world conditions. This study outlines a roadmap for decision-makers in the WCVRP domain, offering opportunities for the evolution of more efficient, adaptable, and sustainable waste collection and transportation systems.Implications: The discussion of WCVRP is an urgent global concern in waste management that requires immediate attention. Through a multi-dimensional evaluation of the research papers, this review paper provides recommendations for future research and practice in WCVRP. Initially, while urban solid waste has received significant attention, other categories remain insufficiently examined. Future research should focus on efficient collection and transportation strategies for these types. Then, although common characteristics are well-explored, this review emphasizes the need for further investigation into lesser-studied characteristics and vehicle types in WCVRP models. Next, current models predominantly prioritize cost and public health exposure risk minimization. There is a necessity for more holistic approaches that incorporate multiple objectives, particularly those crucial for achieving sustainable development goals. Moreover, hybrid algorithms have emerged as efficient solutions, yet advanced technologies coupled with uncertainty management strategies remain underutilized, presenting significant potential to address the evolving complexities of WCVRP. Finally, the study highlights the importance of datasets and case studies in validating WCVRP models. Hybrid tests enable researchers to comprehensively evaluate WCVRP solutions, providing insight into their performance under various conditions. In conclusion, these implications offer a roadmap for advancing WCVRP research and guiding practical strategies to contribute to the development of more efficient, adaptable, and sustainable waste collection and transportation systems.
A variable dimensional chaotic map-based medical image encryption algorithm with multi-mode

Zhang B, Rahmatullah B, Wang SL, Almutairi HM, Xiao Y, Liu X, et al.

Med Biol Eng Comput, 2023 Nov;61(11):2971-3002.
PMID: 37542682 DOI: 10.1007/s11517-023-02874-3

Since the COVID-19 pandemic, telemedicine or non-face-to-face medicine has increased significantly. In practice, various types of medical images are essential to achieve effective telemedicine. Medical image encryption algorithms play an irreplaceable role in the fast and secure transmission and storage of these medical images. However, most of the existing medical image encryption algorithms are full encryption algorithms, which are inefficient and time-consuming, so they are not suitable for emergency medical scenarios. To improve the efficiency of encryption, a small number of works have focused on partial or selective encryption algorithms for medical images, in which different levels of encryption strategies were adopted for different information content regions of medical images. However, these encryption algorithms have inadequate security more or less. In this paper, based on the Logistic map, we designed an improved variable dimension map. Then, an encryption algorithm for medical images was proposed based on it. This algorithm has two modes: (1) full encryption mode and (2) semi-full encryption mode, which can better adapt to different medical scenarios, respectively. In full encryption mode, all pixels of medical images are encrypted by using the confusion-diffusion structure. In semi-full encryption mode, the region of interest of medical images is extracted. The confusion was first adopted to encrypt the region of interest, and then, the diffusion was adopted to encrypt the entire image. In addition, no matter which encryption mode is used, the algorithm provides the function of medical image integrity verification. The proposed algorithm was simulated and analyzed to evaluate its effectiveness. The results show that in semi-full encryption mode, the algorithm has good security performance and lower time consumption; while in full encryption mode, the algorithm has better security performance and is acceptable in time.
Fulltext Convolutional neural network optimized by differential evolution for electrocardiogram classification

Chen SW, Wang SL, Qi X, Ng TF, Ibrahim H

Multimed Tools Appl, 2023 Apr 26.
PMID: 37362685 DOI: 10.1007/s11042-023-15407-9

The Coronavirus disease 2019, or COVID-19, has shifted the medical paradigm from face-to-face to telehealth. Telehealth has become a vital resource to contain the virus spread and ensure the continued care of patients. In terms of preventing cardiovascular diseases, automating electrocardiogram (ECG) classification is a promising telehealth intervention. The healthcare service ensures that patient care is appropriate, comfortable, and accessible. Convolutional neural networks (CNNs) have demonstrated promising results in ECG categorization, which require high accuracy and short training time to ensure healthcare quality. This study proposes a one-dimensional-CNN (1D-CNN) arrhythmia classification based on the differential evolution (DE) algorithm to optimize the accuracy of ECG classification and training time. The performance of 1D-CNNs of different activation functions are optimized based on the standard DE algorithm. Finally, based on MIT-BIH and SCDH arrhythmia databases, the performances of optimized and unoptimized 1D-CNN are compared and analysed. Results show that the 1D-CNN optimized by the DE has higher accuracy in heartbeats classification. The optimized 1D-CNN improves from 97.6% to 99.5% on MIT-BIH and from 80.2% to 88.5% on SCDH. Therefore, the optimized 1D-CNN shows improvements of 1.9% and 8.3% in the two datasets, respectively. In addition, compared with the unoptimized 1D-CNN based on the same parameter settings, the optimized 1D-CNN has less training time. Under the conditions of ReLU function and 10 epochs, the training takes 9.22 s on MIT-BIH and 10.35 s on SCDH, reducing training time by 67.2% and 64.2%, respectively.
Fulltext A bibliometric of publication trends in medical image segmentation: Quantitative and qualitative analysis

Zhang B, Rahmatullah B, Wang SL, Zhang G, Wang H, Ebrahim NA

J Appl Clin Med Phys, 2021 Oct;22(10):45-65.
PMID: 34453471 DOI: 10.1002/acm2.13394

PURPOSE: Medical images are important in diagnosing disease and treatment planning. Computer algorithms that describe anatomical structures that highlight regions of interest and remove unnecessary information are collectively known as medical image segmentation algorithms. The quality of these algorithms will directly affect the performance of the following processing steps. There are many studies about the algorithms of medical image segmentation and their applications, but none involved a bibliometric of medical image segmentation.
METHODS: This bibliometric work investigated the academic publication trends in medical image segmentation technology. These data were collected from the Web of Science (WoS) Core Collection and the Scopus. In the quantitative analysis stage, important visual maps were produced to show publication trends from five different perspectives including annual publications, countries, top authors, publication sources, and keywords. In the qualitative analysis stage, the frequently used methods and research trends in the medical image segmentation field were analyzed from 49 publications with the top annual citation rates.
RESULTS: The analysis results showed that the number of publications had increased rapidly by year. The top related countries include the Chinese mainland, the United States, and India. Most of these publications were conference papers, besides there are also some top journals. The research hotspot in this field was deep learning-based medical image segmentation algorithms based on keyword analysis. These publications were divided into three categories: reviews, segmentation algorithm publications, and other relevant publications. Among these three categories, segmentation algorithm publications occupied the vast majority, and deep learning neural network-based algorithm was the research hotspots and frontiers.
CONCLUSIONS: Through this bibliometric research work, the research hotspot in the medical image segmentation field is uncovered and can point to future research in the field. It can be expected that more researchers will focus their work on deep learning neural network-based medical image segmentation.
Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector

Ooi AZH, Embong Z, Abd Hamid AI, Zainon R, Wang SL, Ng TF, et al.

Sensors (Basel), 2021 Sep 24;21(19).
PMID: 34640698 DOI: 10.3390/s21196380

Optometrists, ophthalmologists, orthoptists, and other trained medical professionals use fundus photography to monitor the progression of certain eye conditions or diseases. Segmentation of the vessel tree is an essential process of retinal analysis. In this paper, an interactive blood vessel segmentation from retinal fundus image based on Canny edge detection is proposed. Semi-automated segmentation of specific vessels can be done by simply moving the cursor across a particular vessel. The pre-processing stage includes the green color channel extraction, applying Contrast Limited Adaptive Histogram Equalization (CLAHE), and retinal outline removal. After that, the edge detection techniques, which are based on the Canny algorithm, will be applied. The vessels will be selected interactively on the developed graphical user interface (GUI). The program will draw out the vessel edges. After that, those vessel edges will be segmented to bring focus on its details or detect the abnormal vessel. This proposed approach is useful because different edge detection parameter settings can be applied to the same image to highlight particular vessels for analysis or presentation.
Birth Cohort Consortium of Asia: Current and Future Perspectives

Kishi R, Zhang JJ, Ha EH, Chen PC, Tian Y, Xia Y, et al.

Epidemiology, 2017 10;28 Suppl 1:S19-S34.
PMID: 29028672 DOI: 10.1097/EDE.0000000000000698

BACKGROUND: The environmental health of children is one of the great global health concerns. Exposures in utero and throughout development can have major consequences on later health. However, environmental risks or disease burdens vary from region to region. Birth cohort studies are ideal for investigating different environmental risks.
METHODS: The principal investigators of three birth cohorts in Asia including the Taiwan Birth Panel Study (TBPS), the Mothers and Children's Environmental Health Study (MOCEH), and the Hokkaido Study on Environment and Children' Health (Hokkaido Study) coestablished the Birth Cohort Consortium of Asia (BiCCA) in 2011. Through a series of five PI meetings, the enrolment criteria, aim of the consortium, and a first-phase inventory were confirmed.
RESULTS: To date, 23 birth cohorts have been established in 10 Asian countries, consisting of approximately 70,000 study subjects in the BiCCA. This article provides the study framework, environmental exposure and health outcome assessments, as well as maternal and infant characteristics of the participating cohorts.
CONCLUSIONS: The BiCCA provides a unique and reliable source of birth cohort information in Asian countries. Further scientific cooperation is ongoing to identify specific regional environmental threats and improve the health of children in Asia.
The genomic history and global migration of a windborne pest

Hu QL, Zhuo JC, Fang GQ, Lu JB, Ye YX, Li DT, et al.

Sci Adv, 2024 Apr 26;10(17):eadk3852.
PMID: 38657063 DOI: 10.1126/sciadv.adk3852

Many insect pests, including the brown planthopper (BPH), undergo windborne migration that is challenging to observe and track. It remains controversial about their migration patterns and largely unknown regarding the underlying genetic basis. By analyzing 360 whole genomes from around the globe, we clarify the genetic sources of worldwide BPHs and illuminate a landscape of BPH migration showing that East Asian populations perform closed-circuit journeys between Indochina and the Far East, while populations of Malay Archipelago and South Asia undergo one-way migration to Indochina. We further find round-trip migration accelerates population differentiation, with highly diverged regions enriching in a gene desert chromosome that is simultaneously the speciation hotspot between BPH and related species. This study not only shows the power of applying genomic approaches to demystify the migration in windborne migrants but also enhances our understanding of how seasonal movements affect speciation and evolution in insects.