Effect of routing flexibility on the performance of manufacturing system

[EN] This work presented in this paper is based on the simulation of the routing flexibility enabled manufacturing system. In this study four levels of each factor (i.e. routing flexibility, system load conditions, system capacity and four part sequencing rules) are considered for the investigation. The performance of the routing flexibility enabled manufacturing system (RFEMS) is evaluated using three performance measures like make-span time, resource utilization and work-in-process. The analysis of results shows that the performance of the manufacturing system may be improved by adding in routing flexibility at the initial level along with other factors. However, the benefit of this flexibility diminishes at higher levels of routing flexibilities.Khan, WU.; Ali, M. (2019). Effect of routing flexibility on the performance of manufacturing system. International Journal of Production Management and Engineering. 7(2):133-144. https://doi.org/10.4995/ijpme.2019.8726SWORD1331447

Infant birth weight estimation and low birth weight classification in United Arab Emirates using machine learning algorithms

Accurate prediction of a newborn’s birth weight (BW) is a crucial determinant to evaluate the newborn’s health and safety. Infants with low BW (LBW) are at a higher risk of serious short- and long-term health outcomes. Over the past decade, machine learning (ML) techniques have shown a successful breakthrough in the field of medical diagnostics. Various automated systems have been proposed that use maternal features for LBW prediction. However, each proposed system uses different maternal features for LBW classification and estimation. Therefore, this paper provides a detailed setup for BW estimation and LBW classification. Multiple subsets of features were combined to perform predictions with and without feature selection techniques. Furthermore, the synthetic minority oversampling technique was employed to oversample the minority class. The performance of 30 ML algorithms was evaluated for both infant BW estimation and LBW classification. Experiments were performed on a self-created dataset with 88 features. The dataset was obtained from 821 women from three hospitals in the United Arab Emirates. Different performance metrics, such as mean absolute error and mean absolute percent error, were used for BW estimation. Accuracy, precision, recall, F-scores, and confusion matrices were used for LBW classification. Extensive experiments performed using five-folds cross validation show that the best weight estimation was obtained using Random Forest algorithm with mean absolute error of 294.53 g while the best classification performance was obtained using Logistic Regression with SMOTE oversampling techniques that achieved accuracy, precision, recall and F1 score of 90.24%, 87.6%, 90.2% and 0.89, respectively. The results also suggest that features such as diabetes, hypertension, and gestational age, play a vital role in LBW classification.</p

China-Pak Economic Corridor (CPEC): Economic Transformation-Challenges and Opportunities for the Local Residents

China-Pakistan Economic corridor is a game-changing project between China andPakistan. This corridor provides opportunities for economic Development to Pakistan especially to the people of Balochistan. This research paper determined the concept of CPEC in the specific context of socio-economic life of local people. It will look the positive and negative aspects of the projects by taking the local resident into consideration. This study has tended to rely on qualitative methods in order to explain the use, values and interpretation of concepts. The study is qualitative therefore; in depth semi structure interviews and focus group discussion are selected as a tool of data collection. The findings of study are discussed in details. The practical implications of study are future directions are also discussed

Predictive Modeling for the Diagnosis of Gestational Diabetes Mellitus Using Epidemiological Data in the United Arab Emirates

Gestational diabetes mellitus (GDM) is a common condition with repercussions for both the mother and her child. Machine learning (ML) modeling techniques were proposed to predict the risk of several medical outcomes. A systematic evaluation of the predictive capacity of maternal factors resulting in GDM in the UAE is warranted. Data on a total of 3858 women who gave birth and had information on their GDM status in a birth cohort were used to fit the GDM risk prediction model. Information used for the predictive modeling were from self-reported epidemiological data collected at early gestation. Three different ML models, random forest (RF), gradient boosting model (GBM), and extreme gradient boosting (XGBoost), were used to predict GDM. Furthermore, to provide local interpretation of each feature in GDM diagnosis, features were studied using Shapley additive explanations (SHAP). Results obtained using ML models show that XGBoost, which achieved an AUC of 0.77, performed better compared to RF and GBM. Individual feature importance using SHAP value and the XGBoost model show that previous GDM diagnosis, maternal age, body mass index, and gravidity play a vital role in GDM diagnosis. ML models using self-reported epidemiological data are useful and feasible in prediction models for GDM diagnosis amongst pregnant women. Such data should be periodically collected at early pregnancy for health professionals to intervene at earlier stages to prevent adverse outcomes in pregnancy and delivery. The XGBoost algorithm was the optimal model for identifying the features that predict GDM diagnosis

Predictive Modeling for the Diagnosis of Gestational Diabetes Mellitus Using Epidemiological Data in the United Arab Emirates

Gestational diabetes mellitus (GDM) is a common condition with repercussions for both the mother and her child. Machine learning (ML) modeling techniques were proposed to predict the risk of several medical outcomes. A systematic evaluation of the predictive capacity of maternal factors resulting in GDM in the UAE is warranted. Data on a total of 3858 women who gave birth and had information on their GDM status in a birth cohort were used to fit the GDM risk prediction model. Information used for the predictive modeling were from self-reported epidemiological data collected at early gestation. Three different ML models, random forest (RF), gradient boosting model (GBM), and extreme gradient boosting (XGBoost), were used to predict GDM. Furthermore, to provide local interpretation of each feature in GDM diagnosis, features were studied using Shapley additive explanations (SHAP). Results obtained using ML models show that XGBoost, which achieved an AUC of 0.77, performed better compared to RF and GBM. Individual feature importance using SHAP value and the XGBoost model show that previous GDM diagnosis, maternal age, body mass index, and gravidity play a vital role in GDM diagnosis. ML models using self-reported epidemiological data are useful and feasible in prediction models for GDM diagnosis amongst pregnant women. Such data should be periodically collected at early pregnancy for health professionals to intervene at earlier stages to prevent adverse outcomes in pregnancy and delivery. The XGBoost algorithm was the optimal model for identifying the features that predict GDM diagnosis

A Robust Color Image Watermarking Scheme using Chaos for Copyright Protection

An exponential growth in multimedia applications has led to fast adoption of digital watermarking phenomena to protect the copyright information and authentication of digital contents. A novel spatial domain symmetric color image robust watermarking scheme based on chaos is presented in this research. The watermark is generated using chaotic logistic map and optimized to improve inherent properties and to achieve robustness. The embedding is performed at 3 LSBs (Least Significant Bits) of all the threecolor components of the host image. The sensitivity of the chaotic watermark along with redundant embedding approach makes the entire watermarking scheme highly robust, secure and imperceptible. In this paper, various image quality analysis metrics such as homogeneity, contrast, entropy, PSNR (Peak Signal to Noise Ratio), UIQI (Universal Image Quality Index) and SSIM (Structural Similarity Index Measures) are measures to analyze proposed scheme. The proposed technique shows superior results against UIQI. Further, the watermark image with proposed scheme is tested against various image-processing attacks. The robustness of watermarked image against attacks such as cropping, filtering, adding random noises and JPEG compression, rotation, blurring, darken etc. is analyzed. The Proposed scheme shows strong results that are justified in this paper. The proposed scheme is symmetric; therefore, reversible process at extraction entails successful extraction of embedded watermark

Diagnosis of Pneumonia in Children with Dehydrating Diarrhoea

The World Health Organization (WHO) guidelines for diagnosis of pneumonia are based on the history of cough or difficult breathing and age-adjusted respiration rates. Metabolic acidosis associated with dehydrating diarrhoea also influences the respiration rate. Two hundred and four children, aged 2 to 59 months, with dehydrating diarrhoea and a history of cough and/or fast breathing, were enrolled in a prospective study. Pneumonia diagnoses were made on enrollment and again 6 hours post-enrollment (after initial rehydration), using the WHO guidelines. These were compared with investigators\u2019 clinical diagnosis based on history and findings of physical examination and a chest x-ray at the same time points. Using the WHO guidelines, 149/152 (98%) infants in the 2-11 months age-group and 38/40 (95%) children in the 12-59 months age-group were diagnosed to have pneumonia on enrollment, which dropped to 107 (70%) and 30 (75%) respectively at 6 hours post-enrollment. The specificity of the WHO guidelines for diagnosis of pneumonia was very low (6.9%) at enrollment but increased to 65.5% at 6 hours post-enrollment, after initial rehydration. The specificity of the WHO guidelines for diagnosis of pneumonia in young children is significantly reduced in dehydrating diarrhoea. For young children with dehydrating diarrhoea, rehydration, clinical and radiological assessments are useful in identifying those with true pneumonia

Neighbourhood oriented TDMA scheme for the internet of things-enabled remote sensing

Throughout the world, Internet of Things (IoT) have been used in different application areas to assist human beings in numerous activities such as smart buildings and cities via remote sensing-enabled techniques. However, simultaneous transmission of packet(s) by multiple devices Ci, which are interested to start a communication session with a common receiver device, is one of the challenging issues associated with these networks. In the literature, various mechanisms have been presented to resolve the aforementioned issue without changing the technological infrastructures; however, neighbourhood information of sensor nodes is not considered yet. In IoT-enabled remote sensing, neighbourhood information of various devices plays a vital role in developing a reliable communication mechanism specifically for scenarios where multiple devices Ci are interested to start communication with a common destination module. In this paper, a neighbourhood-enabled TDMA scheme is presented for the IoT to ensure the concurrent communication of multiple devices Ci with a common destination device Sj preferably with a minimum possible packet collision ratio (if avoidance is not possible). The proposed scheme bounds each and every member device Ci to assign a dedicated time slot to its neighbouring devices in the operational IoT network. Furthermore, neighbouring devices Ci are forced to communicate within the assigned time slot. Simulation results have verified that the proposed scheme is ideal solution compared to the existing schemes for the IoT and other resource-limited networks particularly in scenarios where the deployment process is random

Predicting preterm birth using explainable machine learning in a prospective cohort of nulliparous and multiparous pregnant women

Preterm birth (PTB) presents a complex challenge in pregnancy, often leading to significant perinatal and long-term morbidities. “While machine learning (ML) algorithms have shown promise in PTB prediction, the lack of interpretability in existing models hinders their clinical utility. This study aimed to predict PTB in a pregnant population using ML models, identify the key risk factors associated with PTB through the SHapley Additive exPlanations (SHAP) algorithm, and provide comprehensive explanations for these predictions to assist clinicians in providing appropriate care. This study analyzed a dataset of 3509 pregnant women in the United Arab Emirates and selected 35 risk factors associated with PTB based on the existing medical and artificial intelligence literature. Six ML algorithms were tested, wherein the XGBoost model exhibited the best performance, with an area under the operator receiving curves of 0.735 and 0.723 for parous and nulliparous women, respectively. The SHAP feature attribution framework was employed to identify the most significant risk factors linked to PTB. Additionally, individual patient analysis was performed using the SHAP and the local interpretable model-agnostic explanation algorithms (LIME). The overall incidence of PTB was 11.23% (11 and 12.1% in parous and nulliparous women, respectively). The main risk factors associated with PTB in parous women are previous PTB, previous cesarean section, preeclampsia during pregnancy, and maternal age. In nulliparous women, body mass index at delivery, maternal age, and the presence of amniotic infection were the most relevant risk factors. The trained ML prediction model developed in this study holds promise as a valuable screening tool for predicting PTB within this specific population. Furthermore, SHAP and LIME analyses can assist clinicians in understanding the individualized impact of each risk factor on their patients and provide appropriate care to reduce morbidity and mortality related to PTB.</p