Recommendation system for immunization coverage and monitoring

Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach

Individuals in any country are badly impacted both economically and physically whenever an epidemic of infectious illnesses breaks out. A novel coronavirus strain was responsible for the outbreak of the coronavirus sickness in 2019. Corona Virus Disease 2019 (COVID-19) is the name that the World Health Organization (WHO) officially gave to the pneumonia that was caused by the novel coronavirus on February 11, 2020. The use of models that are informed by machine learning is currently a major focus of study in the field of improved forecasting. By displaying annual trends, forecasting models can be of use in performing impact assessments of potential outcomes. In this paper, proposed forecast models consisting of time series models such as long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), generalized regression unit (GRU), and dense-LSTM have been evaluated for time series prediction of confirmed cases, deaths, and recoveries in 12 major countries that have been affected by COVID-19. Tensorflow1.0 was used for programming. Indices known as mean absolute error (MAE), root means square error (RMSE), Median Absolute Error (MEDAE) and r2 score are utilized in the process of evaluating the performance of models. We presented various ways to time-series forecasting by making use of LSTM models (LSTM, BiLSTM), and we compared these proposed methods to other machine learning models to evaluate the performance of the models. Our study suggests that LSTM based models are among the most advanced models to forecast time series data.

A GAN-based method for 3D lung tumor reconstruction boosted by a knowledge transfer approach

Three-dimensional (3D) image reconstruction of tumors has been one of the most effective techniques for accurately visualizing tumor structures and treatment with high resolution, which requires a set of two-dimensional medical images such as CT slices. In this paper we propose a novel method based on generative adversarial networks (GANs) for 3D lung tumor reconstruction by CT images. The proposed method consists of three stages: lung segmentation, tumor segmentation and 3D lung tumor reconstruction. Lung and tumor segmentation are performed using snake optimization and Gustafson-Kessel (GK) clustering. In the 3D reconstruction part first, features are extracted using the pre-trained VGG model from the tumors that detected in 2D CT slices. Then, a sequence of extracted features is fed into an LSTM to output compressed features. Finally, the compressed feature is used as input for GAN, where the generator is responsible for high-level reconstructing the 3D image of the lung tumor. The main novelty of this paper is the use of GAN to reconstruct a 3D lung tumor model for the first time, to the best of our knowledge. Also, we used knowledge transfer to extract features from 2D images to speed up the training process. The results obtained from the proposed model on the LUNA dataset showed better results than state of the art. According to HD and ED metrics, the proposed method has the lowest values ​​of 3.02 and 1.06, respectively, as compared to those of other methods. The experimental results show that the proposed method performs better than previous similar methods and it is useful to help practitioners in the treatment process.

Automatic lung disease classification from the chest X-ray images using hybrid deep learning algorithm

The chest X-ray images provide vital information about the congestion cost-effectively. We propose a novel Hybrid Deep Learning Algorithm (HDLA) framework for automatic lung disease classification from chest X-ray images. The model consists of steps including pre-processing of chest X-ray images, automatic feature extraction, and detection. In a pre-processing step, our goal is to improve the quality of raw chest X-ray images using the combination of optimal filtering without data loss. The robust Convolutional Neural Network (CNN) is proposed using the pre-trained model for automatic lung feature extraction. We employed the 2D CNN model for the optimum feature extraction in minimum time and space requirements. The proposed 2D CNN model ensures robust feature learning with highly efficient 1D feature estimation from the input pre-processed image. As the extracted 1D features have suffered from significant scale variations, we optimized them using min-max scaling. We classify the CNN features using the different machine learning classifiers such as AdaBoost, Support Vector Machine (SVM), Random Forest (RM), Backpropagation Neural Network (BNN), and Deep Neural Network (DNN). The experimental results claim that the proposed model improves the overall accuracy by 3.1% and reduces the computational complexity by 16.91% compared to state-of-the-art methods.

Detection of Diabetic Retinopathy using Convolutional Neural Networks for Feature Extraction and Classification (DRFEC)

Diabetic Retinopathy (DR) is caused as a result of Diabetes Mellitus which causes development of various retinal abrasions in the human retina. These lesions cause hindrance in vision and in severe cases, DR can lead to blindness. DR is observed amongst 80% of patients who have been diagnosed from prolonged diabetes for a period of 10-15 years. The manual process of periodic DR diagnosis and detection for necessary treatment, is time consuming and unreliable due to unavailability of resources and expert opinion. Therefore, computerized diagnostic systems which use Deep Learning (DL) Convolutional Neural Network (CNN) architectures, are proposed to learn DR patterns from fundus images and identify the severity of the disease. This paper proposes a comprehensive model using 26 state-of-the-art DL networks to assess and evaluate their performance, and which contribute for deep feature extraction and image classification of DR fundus images. In the proposed model, ResNet50 has shown highest overfitting in comparison to Inception V3, which has shown lowest overfitting when trained using the Kaggle's EyePACS fundus image dataset. EfficientNetB4 is the most optimal, efficient and reliable DL algorithm in detection of DR, followed by InceptionResNetV2, NasNetLarge and DenseNet169. EfficientNetB4 has achieved a training accuracy of 99.37% and the highest validation accuracy of 79.11%. DenseNet201 has achieved the highest training accuracy of 99.58% and a validation accuracy of 76.80% which is less than the top-4 best performing models.

Dress-up: deep neural framework for image-based human appearance transfer

The fashion industry is at the brink of radical transformation. The emergence of Artificial Intelligence (AI) in fashion applications creates many opportunities for this industry and make fashion a better space for everyone. Interesting to this matter, we proposed a virtual try-on interface to stimulate consumers purchase intentions and facilitate their online buying decision process. Thus, we present, in this paper, our flexible person generation system for virtual try-on that aiming to treat the task of human appearance transfer across images while preserving texture details and structural coherence of the generated outfit. This challenging task has drawn increasing attention and made huge development of intelligent fashion applications. However, it requires different challenges, especially in the case of a wide divergences between the source and target images. To solve this problem, we proposed a flexible person generation framework called Dress-up to treat the 2D virtual try-on task. Dress-up is an end-to-end generation pipeline with three modules based on the task of image-to-image translation aiming to sequentially interchange garments between images, and produce dressing effects not achievable by existing works. The core idea of our solution is to explicitly encode the body pose and the target clothes by a pre-processing module based on the semantic segmentation process. Then, a conditional adversarial network is implemented to generate target segmentation feeding respectively, to the alignment and translation networks to generate the final output results. The novelty of this work lies in realizing the appearance transfer across images with high quality by reconstructing garments on a person in different orders and looks from simlpy semantic maps and 2D images without using 3D modeling. Our system can produce dressing effects and provide significant results over the state-of-the-art methods on the widely used DeepFashion dataset. Extensive evaluations show that Dress-up outperforms other recent methods in terms of output quality, and handles a wide range of editing functions for which there is no direct supervision. Different types of results were computed to verify the performance of our proposed framework and show that the robustness and effectiveness are high by utilizing our method.

Double chain system for online and offline medical data sharing private and consortium blockchain: A system design study

With the informatization development and digital construction in the healthcare industry, electronic medical records and Internet medicine facilitate people's medical treatment. However, the current data storage method has the risk of data loss, leakage, and tampering, and can't support extensive and secure sharing of medical data. To realize effective and secure medical data storage and sharing among offline medical institutions and Internet medicine platforms, this study used a combined private blockchain and consortium blockchain to design a medical blockchain double-chain system (MBDS). This system can store encrypted medical data in distributed storage mode and systematically integrate the medical data of patients in offline medical institutions and Internet medicine platforms, to achieve equality, credibility, and data sharing among participating nodes. The MBDS system constructed in this study incorporated Internet medicine care services into the current healthcare system and provided new solutions and practical guidance for the future development of collaborative medical care. This study helped to solve the problems of medical data interconnection and resource sharing, improve the efficiency and effect of disease diagnosis, alleviate the contradiction between doctors and patients, and facilitate personal health management. This study has substantial theoretical and practical implications for the research and application of medical data storage and sharing.

Geographical variations and associated factors of defaulting from immunization among children aged 12 to 23 months in Ethiopia: using spatial and multilevel analysis of 2016 Ethiopian Demographic and Health Survey

BACKGROUND

In Ethiopia, despite the considerable improvement in immunization coverage, the burden of defaulting from immunization among children is still high with marked variation among regions. However, the geographical variation and contextual factors of defaulting from immunization were poorly understood. Hence, this study aimed to identify the spatial pattern and associated factors of defaulting from immunization.

METHODS

An in-depth analysis of the 2016 Ethiopian Demographic and Health Survey (EDHS 2016) data was used. A total of 1638 children nested in 552 enumeration areas (EAs) were included in the analysis. Global Moran's I statistic and Bernoulli purely spatial scan statistics were employed to identify geographical patterns and detect spatial clusters of defaulting immunization, respectively. Multilevel logistic regression models were fitted to identify factors associated with defaulting immunization. A p value < 0.05 was used to identify significantly associated factors with defaulting of child immunization.

RESULTS

A spatial heterogeneity of defaulting from immunization was observed (Global Moran's I = 0.386379, p value < 0.001), and four significant SaTScan clusters of areas with high defaulting from immunization were detected. The most likely primary SaTScan cluster was seen in the Somali region, and secondary clusters were detected in (Afar, South Nation Nationality of people (SNNP), Oromiya, Amhara, and Gambella) regions. In the final model of the multilevel analysis, individual and community level factors accounted for 56.4% of the variance in the odds of defaulting immunization. Children from mothers who had no formal education (AOR = 4.23; 95% CI: 117, 15.78), and children living in Afar, Oromiya, Somali, SNNP, Gambella, and Harari regions had higher odds of having defaulted immunization from community level.

CONCLUSIONS

A clustered pattern of areas with high default of immunization was observed in Ethiopia. Both the individual and community-level characteristics were statistically significant factors of defaulting immunization. Therefore, the Federal Ethiopian Ministry of Health should prioritize the areas with defaulting of immunization and consider the identified factors for immunization interventions.

Hybrid LSTM Self-Attention Mechanism Model for Forecasting the Reform of Scientific Research in Morocco

Education is the cultivation of people to promote and guarantee the development of society. Education reforms can play a vital role in the development of a country. However, it is crucial to continually monitor the educational model's performance by forecasting the outcome's progress. Machine learning-based models are currently a hot topic in improving the forecasting research area. Forecasting models can help to analyse the impact of future outcomes by showing yearly trends. For this study, we developed a hybrid, forecasting time-series model by long short-term memory (LSTM) network and self-attention mechanism (SAM) to monitor Morocco's educational reform. We analysed six universities' performance and provided a prediction model to evaluate the best-performing university's performance after implementing the latest reform, i.e., from 2015-2030. We forecasted the six universities' research outcomes and tested our proposed methodology's accuracy against other time-series models. Results show that our model performs better for predicting research outcomes. The percentage increase in university performance after nine years is discussed to help predict the best-performing university. Our proposed algorithm accuracy and performance are better than other algorithms like LSTM and RNN.

Critical Review of Data Analytics Techniques used in the Expanded Program on Immunization (EPI)

BACKGROUND

Immunization is a significant public health intervention to reduce child mortality and morbidity. However, its coverage, in spite of free accessibility, is still very low in developing countries. One of the primary reasons for this low coverage is the lack of analysis and proper utilization of immunization data at various healthcare facilities.

PURPOSE

In this paper, the existing machine learning-based data analytics techniques have been reviewed critically to highlight the gaps where this high potential data could be exploited in a meaningful manner.

RESULTS

It has been revealed from our review that the existing approaches use data analytics techniques without considering the complete complexity of Expanded Program on Immunization which includes the maintenance of cold chain systems, proper distribution of vaccine and quality of data captured at various healthcare facilities. Moreover, in developing countries, there is no centralized data repository where all data related to immunization is being gathered to perform analytics at various levels of granularities.

CONCLUSION

We believe that the existing non-centralized immunization data with the right set of machine learning and Artificial Intelligence-based techniques will not only improve the vaccination coverage but will also help in predicting the future trends and patterns of its coverage in different geographical locations.

Using data to improve outcomes of supplemental immunisation activities: 2017/2018 Nigeria measles vaccination campaign

BACKGROUND

Despite the enormous resources committed to the implementation of supplemental immunisation activities in Nigeria, achieving the required coverage (post-campaign survey) to halt the transmission of vaccine-preventable diseases has continued to seem like an impossibility. A vast volume of data is generated and transmitted during mass vaccination campaigns, but this administrative data does not always culminate into improved coverage. The absence of data-informed guidance from stakeholders with long years of experience in planning and implementing mass vaccination campaigns has impeded achieving 95% coverage in measles campaigns in Nigeria. This study reviews the use of data to guide the implementation of the 2017/2018 measles vaccination campaign in Nigeria.

METHODS

A central coordinating body was formed at the national level with the same replicated in every state. Tools were developed to measure the performance of the different phases and activities required for the implementation of a mass vaccination campaign as recommended in the international guidelines. Stakeholders were engaged to help ensure that feedback provided by the national measles technical coordinating committee was implemented at the lower level.

RESULTS

Monitoring and analysis of daily data submission caused a proper spread of senior supervisors, vaccination posts location during the campaign and helped identify areas targeted for mop-up. Although the verification of states' microplan increased the operational target population by 11.2%, the process aided the distribution of resources as appropriate. Maps showing the likely areas that needed additional effort to achieve required coverage with recommendation on the necessary approach to be deployed were transmitted to the states implementing the campaign.

CONCLUSION

The improvement in the use of data to guide implementation of the Nigeria 2017/2018 measles vaccination campaign caused an increase in the number of states that achieved higher coverage in the post-campaign coverage survey.