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| 1 | Impact of methods for uterine incision closure on repeat Caesarean sec- tion scar of lower uterine segment 显示文摘 | Shakila Yasmin Joveria Sadaf Naheed Fatima | 2011 | Journal of the College of Physicians and Surgeons Pakistan2011，21，9： | 1 |
| 2 | Impact of methods for uterine incision closure on repeat caesarean section scar of lower uterine segment显示文摘 | Yasmin S Sadaf J Fatima N | 2011 | J Coll Physicians Surg Pak2011，21，9： | 1 |
| 3 | Impact of methods for uterine incision closure on repeat caesarean section scar of lower uterinesegment 显示文摘 | Yasmin S Sadaf J Fatima N | 2011 | J Coil Physicians Surg Pak2011，21，9： | 1 |
| 4 | An LSTM Based Forecasting for Major Stock Sectors Using COVID Sentiment显示文摘Stock market forecasting is an important research area,especially for better business decision making.Efficient stock predictions continue to be significant for business intelligence.Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices,moving averages,or daily returns.However,major events’news also contains significant information regarding market drivers.An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market.This research proposes an efficient model for stock market prediction.The current proposed study explores the positive and negative effects of coronavirus events on major stock sectors like the airline,pharmaceutical,e-commerce,technology,and hospitality.We use the Twitter dataset for calculating the coronavirus sentiment with a Long Short-Term Memory(LSTM)model to improve stock prediction.The LSTM has the advantage of analyzing relationship between time-series data through memory functions.The performance of the system is evaluated by Mean Absolute Error(MAE),Mean Squared Error(MSE),and Root Mean Squared Error(RMSE).The results show that performance improves by using coronavirus event sentiments along with the LSTM prediction model. | Ayesha Jabeen Sitara Afzal Muazzam Maqsood Irfan Mehmood Sadaf Yasmin Muhammad Tabish Niaz Yunyoung Nam | 2021 | Computers, Materials & Continua2021，，4： | 1 |
| 5 | Impact of methods for uterine in- cision closure on repeat caesarean section scar of lower uterine segment显示文摘 | Yasmin S Sadaf J Fatima N | 2011 | J Coil Physicians Surg Pak2011，21，9： | 1 |
| 6 | An efficient deep learning-assisted person re-identification solution for intelligent video surveillance in smart cities显示文摘Innovations on the Internet of Everything(IoE)enabled systems are driving a change in the settings where we interact in smart units,recognized globally as smart city environments.However,intelligent video-surveillance systems are critical to increasing the security of these smart cities.More precisely,in today’s world of smart video surveillance,person re-identification(Re-ID)has gained increased consideration by researchers.Various researchers have designed deep learningbased algorithms for person Re-ID because they have achieved substantial breakthroughs in computer vision problems.In this line of research,we designed an adaptive feature refinementbased deep learning architecture to conduct person Re-ID.In the proposed architecture,the inter-channel and inter-spatial relationship of features between the images of the same individual taken from nonidentical camera viewpoints are focused on learning spatial and channel attention.In addition,the spatial pyramid pooling layer is inserted to extract the multiscale and fixed-dimension feature vectors irrespective of the size of the feature maps.Furthermore,the model’s effectiveness is validated on the CUHK01 and CUHK02 datasets.When compared with existing approaches,the approach presented in this paper achieves encouraging Rank 1 and 5 scores of 24.6% and 54.8%,respectively. | Muazzam MAQSOOD Sadaf YASMIN Saira GILLANI Maryam BUKHARI Seungmin RHO Sang-Soo YEO | 2023 | Frontiers of Computer Science2023，17，4： | 0 |
| 7 | A Hybrid System for Customer Churn Prediction and Retention Analysis via Supervised Learning显示文摘Telecom industry relies on churn prediction models to retain their customers.These prediction models help in precise and right time recognition of future switching by a group of customers to other service providers.Retention not only contributes to the profit of an organization,but it is also important for upholding a position in the competitive market.In the past,numerous churn prediction models have been proposed,but the current models have a number of flaws that prevent them from being used in real-world largescale telecom datasets.These schemes,fail to incorporate frequently changing requirements.Data sparsity,noisy data,and the imbalanced nature of the dataset are the other main challenges for an accurate prediction.In this paper,we propose a hybrid model,name as“A Hybrid System for Customer Churn Prediction and Retention Analysis via Supervised Learning(HCPRs)”that used Synthetic Minority Over-Sampling Technique(SMOTE)and Particle Swarm Optimization(PSO)to address the issue of imbalance class data and feature selection.Data cleaning and normalization has been done on big Orange dataset contains 15000 features along with 50000 entities.Substantial experiments are performed to test and validate the model on Random Forest(RF),Linear Regression(LR),Naïve Bayes(NB)and XG-Boost.Results show that the proposed model when used with XGBoost classifier,has greater Accuracy Under Curve(AUC)of 98%as compared with other methods. | Soban Arshad Khalid Iqbal Sheneela Naz Sadaf Yasmin Zobia Rehman | 2022 | Computers, Materials & Continua2022，，9： | 0 |
| 8 | A Smart Heart Disease Diagnostic System Using Deep Vanilla LSTM显示文摘Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics,which aid in the prevention of several diseases including heart-related abnormalities.In this context,regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram(ECG)signals has the potential to save many lives.In existing studies,several heart disease diagnostic systems are proposed by employing different state-of-the-art methods,however,improving such methods is always an intriguing area of research.Hence,in this research,a smart healthcare system is proposed for the diagnosis of heart disease using ECG signals.The proposed framework extracts both linear and time-series information on the ECG signals and fuses them into a single framework concurrently.The linear characteristics of ECG signals are extracted by convolution layers followed by Gaussian Error Linear Units(GeLu)and time series characteristics of ECG beats are extracted by Vanilla Long Short-Term Memory Networks(LSTM).Following on,the feature reduction of linear information is done with the help of ID Generalized Gated Pooling(GGP).In addition,data misbalancing issues are also addressed with the help of the Synthetic Minority Oversampling Technique(SMOTE).The performance assessment of the proposed model is done over the two publicly available datasets named MIT-BIH arrhythmia database(MITDB)and PTB Diagnostic ECG database(PTBDB).The proposed framework achieves an average accuracy performance of 99.14%along with a 95%recall value. | Maryam Bukhari Sadaf Yasmin Sheneela Naz Mehr Yahya Durrani Mubashir Javaid Jihoon Moon Seungmin Rho | 2023 | Computers, Materials & Continua2023，77，10： | 0 |
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