An efficient deep learning technique for facial emotion recognition

Emotion recognition from facial images is considered as a challenging task due to the varying nature of facial expressions. The prior studies on emotion classification from facial images using deep learning models have focused on emotion recognition from facial images but face the issue of performance degradation due to poor selection of layers in the convolutional neural network model.To address this issue, we propose an efficient deep learning technique using a convolutional neural network model for classifying emotions from facial images and detecting age and gender from the facial expressions efficiently. Experimental results show that the proposed model outperformed baseline works by achieving an accuracy of 95.65% for emotion recognition, 98.5% for age recognition, and 99.14% for gender recognition

Comparison of efficacy and safety of intramuscular magnesium sulphate with low dose intravenous regimen in treatment of eclampsia

INTRODUCTION: Eclampsia contributes to maternal mortality in developing, underdeveloped world. Various drugs have been tried to treat eclampsia. Magnesium sulphate has become the drug of choice due to various advantages and is associated with adverse outcome for both mother and fetus if not used correctly. OBJECTIVE: To compare the efficacy and safety of intramuscular magnesium sulphate with low dose intravenous regimen in treatment of eclampsia STUDY SETTING: The study was conducted at Gynecology and Obstetrics Department unit II, Holy Family Hospital, Rawalpindi, from June 20, 2020 to December 20, 2020. Study design was Randomized Controlled Trial. SUBJECTS & METHODS: Patients were randomly distributed into two groups, group-A (IM Group) and group-B (IV Group). Group-A patients received a loading dose of 4 gm IV MgSO4 over 5-10 minutes+10 gm MgSO4 deep intra-muscular injection (5 gm in each buttock) and a maintenance dose of 5 gm MgSO4 deep intramuscular injection in alternate buttock every 4 hourly. Group-B patients received MgSO4 4 gm slow IV over 5-10 minutes as loading dose and 1 gm MgSO4 per hour as continuous intravenous maintenance infusion. Clinical response to therapy for both drugs was calculated in terms of efficacy and safety. All the data were entered & analyzed by using SPSS v25.0. Both the groups were compared in terms of efficacy and safety by using Chi-Square test. A p-value less than 0.05  was taken as significant. RESULTS: A total of 160 patients with eclampsia were enrolled for this study. Patients were divided into two groups i.e. Group-A (IM MgSO4) and Group-B (IV MgSO4). In group-A, there were 45(56.3%) in 18-30 years age group and 35(43.8%) in 31-40 years age group, while in group-B, there were 48(60.0%) in 18-30 years age group and 32(40.0%) in 31-40 years age group. In IM MgSO4 group, prevention from recurrence of seizure was noted in 74(92.5%) and 78(97.5%) in IV MgSO4 group, which is statistically insignificant with a p-value of 0.147. CONCLUSION: Both IM and IV regimen are equally effective in controlling the recurrence of convulsions. IM Magnesium Sulphate is associated with a higher incidence of toxicity as evidenced by significantly higher incidence of loss of knee jerk reflex. Both IM and IV regimen are equally effective but IM Magnesium Sulphate is associated with a higher incidence of toxicity. KEY WORDS: Eclampsia, Intramuscular MgSO4, Intravenous MgSO4

Rumor Detection in Business Reviews Using Supervised Machine Learning

© 2018 IEEE. Currently, a high volume of business data is generating with a high velocity in different forms like unstructured, structured or semi-structured. Due to social media arrival, there is a deluge of business rumors and their manual screening is time-consuming and difficult. In the current social computing era, it is necessary to move towards an automated process for the detection of business rumors. This work aims at developing an automated system for detecting business rumors from online business reviews using supervised machine learning classifiers, namely Logistic Regression, Support Vector Classifier (SVC), Naïve Bayesian (NB), K-Nearest Neighbors (KNN) to classify the business reviews into rumor and nonrumor. Experimental results show that Naïve Bayesian (NB), achieved efficient results with respect to other classifiers with an accuracy of 72.43 %

Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model

Citrus fruit diseases are the major cause of extreme citrus fruit yield declines. As a result, designing an automated detection system for citrus plant diseases is important. Deep learning methods have recently obtained promising results in a number of artificial intelligence issues, leading us to apply them to the challenge of recognizing citrus fruit and leaf diseases. In this paper, an integrated approach is used to suggest a convolutional neural networks (CNNs) model. The proposed CNN model is intended to differentiate healthy fruits and leaves from fruits/leaves with common citrus diseases such as Black spot, canker, scab, greening, and Melanose. The proposed CNN model extracts complementary discriminative features by integrating multiple layers. The CNN model was checked against many state-of-the-art deep learning models on the Citrus and PlantVillage datasets. The experimental results indicate that the CNN Model outperforms the competitors on a number of measurement metrics. The CNN Model has a test accuracy of 94.55 percent, making it a valuable decision support tool for farmers looking to classify citrus fruit/leaf diseases

Selective Menin Deletion in the Hippocampal CA1 Region Leads to Disruption of Contextual Memory in the MEN1 Conditional Knockout Mouse: Behavioral Restoration and Gain of Function following the Reintroduction of MEN1 Gene

Cholinergic neuronal networks in the hippocampus play a key role in the regulation of learning and memory in mammals. Perturbations of these networks, in turn, underlie neurodegenerative diseases. However, the mechanisms remain largely undefined. We have recently demonstrated that an in vitro MEN1 gene deletion perturbs nicotinic cholinergic plasticity at the hippocampal glutamatergic synapses. Furthermore, MEN1 neuronal conditional knockout in freely behaving animals has also been shown to result in learning and memory deficits, though the evidence remains equivocal. In this study, using an AVV viral vector transcription approach, we provide direct evidence that MEN1 gene deletion in the CA1 region of the hippocampus indeed leads to contextual fear conditioning deficits in conditional knockout animals. This loss of function was, however, recovered when the same animals were re-injected to overexpress MEN1. This study provides the first direct evidence for the sufficiency and necessity of MEN1 in fear conditioning, and further endorses the role of menin in the regulation of cholinergic synaptic machinery in the hippocampus. These data underscore the importance of further exploring and revisiting the cholinergic hypothesis that underlies neurodegenerative diseases that affect learning and memory

Financial Studio: Android Based Application for Computing Tax, Pension,Zakat and Loan

<div>This work deals with the development of android-based financial studio, an integrated application for</div><div>calculating tax, pension, zakat, and loan. Financial studio can facilitate employers of any department and other</div><div>individuals. The application is developed using MIT app inventor-based android platform. The financial studio</div><div>has four computational modules, namely: (i) tax, (ii) pension, (iii) zakat, and (iv) loan. The system provides an</div><div>integrated environment for performing aforementioned distinct calculations by integrating different financial</div><div>modules into a single application in a user-friendly way. The statistical analysis shows that the application is</div><div>effective to deal with different financial calculations.</div

Financial Studio: Android Based Application for Computing Tax, Pension,Zakat and Loan

<div>This work deals with the development of android-based financial studio, an integrated application for</div><div>calculating tax, pension, zakat, and loan. Financial studio can facilitate employers of any department and other</div><div>individuals. The application is developed using MIT app inventor-based android platform. The financial studio</div><div>has four computational modules, namely: (i) tax, (ii) pension, (iii) zakat, and (iv) loan. The system provides an</div><div>integrated environment for performing aforementioned distinct calculations by integrating different financial</div><div>modules into a single application in a user-friendly way. The statistical analysis shows that the application is</div><div>effective to deal with different financial calculations.</div

Spatiotemporal Patterns of Menin Localization in Developing Murine Brain: Co-Expression with the Elements of Cholinergic Synaptic Machinery

Menin, a product of MEN1 (multiple endocrine neoplasia type 1) gene is an important regulator of tissue development and maintenance; its perturbation results in multiple tumors—primarily of the endocrine tissue. Despite its abundance in the developing central nervous system (CNS), our understanding of menin’s role remains limited. Recently, we discovered menin to play an important role in cholinergic synaptogenesis in the CNS, whereas others have shown its involvement in learning, memory, depression and apoptosis. For menin to play these important roles in the CNS, its expression patterns must be corroborated with other components of the synaptic machinery imbedded in the learning and memory centers; this, however, remains to be established. Here, we report on the spatio-temporal expression patterns of menin, which we found to exhibit dynamic distribution in the murine brain from early development, postnatal period to a fully-grown adult mouse brain. We demonstrate here that menin expression is initially widespread in the brain during early embryonic stages, albeit with lower intensity, as determined by immunohistochemistry and gene expression. With the progression of development, however, menin expression became highly localized to learning, memory and cognition centers in the CNS. In addition to menin expression patterns throughout development, we provide the first direct evidence for its co-expression with nicotinic acetylcholine, glutamate and GABA (gamma aminobutyric acid) receptors—concomitant with the expression of both postsynaptic (postsynaptic density protein PSD-95) and presynaptic (synaptotagamin) proteins. This study is thus the first to provide detailed analysis of spatio-temporal patterns of menin expression from initial CNS development to adulthood. When taken together with previously published studies, our data underscore menin’s importance in the cholinergic neuronal network assembly underlying learning, memory and cognition