Performance evaluation of transfer learning based deep convolutional neural network with limited fused spectro-temporal data for land cover classification

Deep learning (DL) techniques are effective in various applications, such as parameter estimation, image classification, recognition, and anomaly detection. They excel with abundant training data but struggle with limited data. To overcome this, transfer learning is commonly used, leveraging complex learning abilities, saving time, and handling limited labeled data. This study assesses a transfer learning (TL)-based pre-trained “deep convolutional neural network (DCNN)” for classifying land use land cover using a limited and imbalanced dataset of fused spectro-temporal data. It compares the performance of shallow artificial neural networks (ANNs) and deep convolutional neural networks, utilizing multi-spectral sentinel-2 and high-resolution planet scope data. Both machine learning and deep learning algorithms successfully classified the fused data, but the transfer learning-based deep convolutional neural network outperformed the artificial neural network. The evaluation considered a weighted average of F1-score and overall classification accuracy. The transfer learning-based convolutional neural network achieved a weighted average F1-score of 0.92 and a classification accuracy of 0.93, while the artificial neural network achieved a weighted average F1-score of 0.87 and a classification accuracy of 0.89. These results highlight the superior performance of the transfer learned convolutional neural network on a limited and imbalanced dataset compared to the traditional artificial neural network algorithm

EFFECT OF MAN-MADE STRUCTURES ON NATURAL WETLANDS IN PAKISTAN: A CASE STUDY OF CHOTIARI RESERVOIR

Chotiari was a natural wetland before the construction of the reservoir. In 1994 wetland was converted into a reservoir to develop a water source for the people. But due to lack of proper attention this reservoir promoted waterlogging and salinity problems instead. The goal of this study is to investigate the variation that occurred on the land cover of the Chotiari wetland and surrounding area, before and after the construction of the reservoir. Satellite images of Chotiari reservoir and its buffer up to 5 km area of 1990 and 2019 were taken. The study was conducted at USPCASW (United States Pakistan Center for Advanced Studies in Water) Mehran University of Engineering and Technology Jamshoro. In the study, compositing was done by GIS (Geographical Information Systems) to join the bands of images of 1990 and 2019 for observing changes. After extracting the required shapefile of the Chotiari reservoir and its buffer, unsupervised classification was done for three classes: water, vegetation, and barren land. The difference in areas of water, vegetation, and barren land was calculated by superimposing both the images and joining all the bands of Landsat 5 image and Landsat 9 image. By comparing results of both years, a 12% increase in water availability was found whereas 8% and 4% net reduction was found in both vegetation and barren land, respectively. The main reason behind the increment of water quantity might be the construction of a reservoir. Moreover, it also affected the 5 km buffer area around the reservoir which resulted in a 9% reduction in water, 11% increment in vegetation, and 2% drop in the barren land

Identification of Catalytic Active Sites for Durable Proton Exchange Membrane Fuel Cell: Catalytic Degradation and Poisoning Perspectives

Recent progress in synthetic strategies, analysis techniques, and computational modeling assist researchers to develop more active catalysts including metallic clusters to single-atom active sites (SACs). Metal coordinated N-doped carbons (M-N-C) are the most auspicious, with a large number of atomic sites, markedly performing for a series of electrochemical reactions. This perspective sums up the latest innovative and computational comprehension, while giving credit to earlier/pioneering work in carbonaceous assembly materials towards robust electrocatalytic activity for proton exchange membrane fuel cells via inclusive performance assessment of the oxygen reduction reaction (ORR). M-Nx-Cy are exclusively defined active sites for ORR, so there is a unique possibility to intellectually design the relatively new catalysts with much improved activity, selectivity, and durability. Moreover, some SACs structures provide better performance in fuel cells testing with long-term durability. The efforts to understand the connection in SACs based M-Nx-Cy moieties and how these relate to catalytic ORR performance are also conveyed. Owing to comprehensive practical application in the field, this study has covered very encouraging aspects to the current durability status of M-N-C based catalysts for fuel cells followed by degradation mechanisms such as macro-, microdegradation, catalytic poisoning, and future challenges

Knowledge and Awareness about Cervical Cancer and Its Prevention amongst Interns and Nursing Staff in Tertiary Care Hospitals in Karachi, Pakistan

Cervical cancer is one of the leading causes of morbidity and mortality amongst the gynecological cancers worldwide, especially in developing countries. It is imperative for at least health professionals in developing countries like Pakistan to have a sound knowledge about the disease. This study was carried out to assess the knowledge and awareness about cervical cancer and its prevention amongst health professionals in tertiary care hospitals in Karachi, Pakistan.A cross-sectional, interview based survey was conducted in June, 2009. Sample of 400 was divided between the three tertiary care centers. Convenience sampling was applied as no definitive data was available regarding the number of registered interns and nurses at each center.Of all the interviews conducted, 1.8% did not know cervical cancer as a disease. Only 23.3% of the respondents were aware that cervical cancer is the most common cause of gynecological cancers and 26% knew it is second in rank in mortality. Seventy-eight percent were aware that infection is the most common cause of cervical cancer, of these 62% said that virus is the cause and 61% of the respondents knew that the virus is Human Papilloma Virus (HPV). Majority recognized that it is sexually transmitted but only a minority (41%) knew that it can be detected by PCR. Only 26% of the study population was aware of one or more risk factors. Thirty seven percent recognized Pap smear as a screening test. In total only 37 out of 400 respondents were aware of the HPV vaccine.This study serves to highlight that the majority of working health professionals are not adequately equipped with knowledge concerning cervical cancer. Continuing Medical Education program should be started at the hospital level along with conferences to spread knowledge about this disease

The Customer Reviews Analysis Platform by Correlating Sentiment Analysis and Text Clustering

Customer reviews and feedback are of paramount importance in the improvement cycle of any industry, product, or service. Formerly, product ratings were the basis for performance evaluation and key drivers of improvements. However, ratings were unable to depict the complete picture and were not adequate for an in-depth analysis of any product or service. Hence, customer reviews become the ultimate source of providing feedback for a specific detailed analysis as well as contributing to performance metrics. Although, customer reviews provide a very essential measure for performance evaluation, extracting important features and topics from customer reviews has been challenging due to its unlabeled and variant nature. This paper focuses on extracting topics from customer review data and bringing in use the of implicit knowledge for analytics. To extract topics and clusters from review data, unsupervised machine learning algorithms such as K-Means and Latent Dirichlet Allocation (LDA) are used. These topics are then correlated with sentiment analysis - score of positive or negative feedback - of each customer review. The products or services are then categorized with the help of the topics or domains they belong to alongside the sentiments. This provides a valuable analysis such as the score of positive, neutral, and negative feedback for each customer review input to new customers as well as product managers. This research work aims to use the hotel reviews dataset to categorize and rank hotels based on the different services captured in the text from customer reviews. The research work makes use of the hotel reviews dataset for categorizing and ranking hotels based on the different services discussed in the customer\u27s reviews text. Moreover, this paper also provides a visualization of both text clustering algorithms depicting the topics in each cluster for an insightful analysis

The Customer Reviews Analysis Platform by Correlating Sentiment Analysis and Text Clustering

Customer reviews and feedback are of paramount importance in the improvement cycle of any industry, product, or service. Formerly, product ratings were the basis for performance evaluation and key drivers of improvements. However, ratings were unable to depict the complete picture and were not adequate for an in-depth analysis of any product or service. Hence, customer reviews become the ultimate source of providing feedback for a specific detailed analysis as well as contributing to performance metrics. Although, customer reviews provide a very essential measure for performance evaluation, extracting important features and topics from customer reviews has been challenging due to its unlabeled and variant nature. This paper focuses on extracting topics from customer review data and bringing in use the of implicit knowledge for analytics. To extract topics and clusters from review data, unsupervised machine learning algorithms such as K-Means and Latent Dirichlet Allocation (LDA) are used. These topics are then correlated with sentiment analysis - score of positive or negative feedback - of each customer review. The products or services are then categorized with the help of the topics or domains they belong to alongside the sentiments. This provides a valuable analysis such as the score of positive, neutral, and negative feedback for each customer review input to new customers as well as product managers. This research work aims to use the hotel reviews dataset to categorize and rank hotels based on the different services captured in the text from customer reviews. The research work makes use of the hotel reviews dataset for categorizing and ranking hotels based on the different services discussed in the customer\u27s reviews text. Moreover, this paper also provides a visualization of both text clustering algorithms depicting the topics in each cluster for an insightful analysis

Frequency of Congenital Heart Defects Detected on Fetal Echocardiography in High-Risk Mothers

BACKGROUND: Early detection of congenital heart defects provides better postnatal treatment strategy and prognosis. Therefore, we aimed to determine the frequency of congenital heart defects on fetal echocardiography in high-risk mothers referred to the Children hospital, Lahore. METHODS: A cross-sectional observational study was conducted in the Cardiology department, of Children Hospital, Lahore from July to December 2015. The data were collected from 138 high-risk pregnant mothers. Both maternal and fetal risk factors associated with congenital heart defects were considered for indication of fetal echocardiography. RESULTS: Of the 138 high-risk pregnancies, 131 had maternal and 7 had fetal risk factors. Of the fetuses with maternal risk factors, the prevalence of congenital heart defects was 6%. We did not find any congenital heart defects in fetuses with fetal risk factors. 2.17% congenital heart defects presented with a history of gestational diabetes mellitus and 3.62% presented with poor obstetric history (p ≤ 0.05). Atrial septal defects and ventricular septal defects each were present in 2 (1.4%) fetuses while 1 (0.72%) fetus had complete atrioventricular septal defect, 2(1.4%) had septal hypertrophies and 1 (0.72%) had pericardial effusion. CONCLUSION: We observed a 6% frequency of congenital heart defects in high-risk mothers. With this high frequency of congenital heart defects, fetal echocardiography should be included as part of second-trimester anomaly scan in all high-risk mothers

Social Determinants of Rural Household Food Insecurity under the Taliban Regime

Despite the severity of food insecurity in Afghanistan, little is known about the factors contributing to household food insecurity (HFI) under the Taliban regime. Therefore, this paper investigated the social determinants of severe HFI in rural areas of Afghanistan. We used the fifth-round survey of 6019 rural households from 25 provinces, collected between July and August 2022 by the Food and Agriculture Organization. We used binary logistic regression to examine the association between household characteristics and HFI. The majority of household heads were male (97.8%) with no education (62.8%). The findings showed that female-headed households had significantly higher odds of severe HFI. Household heads with any level of formal education had significantly reduced odds of severe HFI, while the odds of severe HFI was not different among those with religious/informal household-head education compared to those with no education. Likewise, engagement in any type of agricultural activity decreased the odds of severe HFI. Additionally, household income per member was negatively, while household size was positively associated with severe HFI. In summary, interventions to alleviate HFI among rural households should prioritize income-generating opportunities and skills targeting households with female heads, low levels of household-head education, larger size, no agricultural activities, and low income

Performance Analysis of Artificial Neural Network Based Land Cover Classification

Landcover classification using automated classification techniques, while employing remotely sensed multi-spectral imagery, is one of the promising areas of research. Different land conditions at different time are captured through satellite and monitored by applying different classification algorithms in specific environment. In this paper, a SPOT-5 image provided by SUPARCO has been studied and classified in Environment for Visual Interpretation (ENVI), a tool widely used in remote sensing. Then, Artificial Neural Network (ANN) classification technique is used to detect the land cover changes in Abbottabad district. Obtained results are compared with a pixel based Distance classifier. The results show that ANN gives the better overall accuracy of 99.20% and Kappa coefficient value of 0.98 over the Mahalanobis Distance Classifier

NEUROSCAN: Revolutionizing Brain Tumor Detection Using Vision-Transformer

 Brain tumor detection is a pivotal component of neuroimaging, with significant implications for clinical diagnosis and patient care. In this study, we introduce an innovative deep-learning approach that leverages the cutting-edge Vision Transformer model, renowned for its ability to capture complex patterns and dependencies in images. Our dataset, consisting of 3000 images evenly split between tumor and non-tumor classes, serves as the foundation for our methodology. Employing Vision Transformer architecture, we processed high-resolution brain scans through patching and self-attention mechanisms. The model is trained through supervised learning to perform binary classification tasks. Our employed model achieved a high of 98.37% in tumor detection. While interpretability analysis was not explicitly performed, the inherent use of attention mechanisms in the Vision Transformer model suggests a focus on important brain regions and enhances its potential for prioritizing crucial information in brain tumor detection