Emotion Recognition as a Novel Indicator for Assessing Brain Health: A Machine learning Approach

Background: Emotion is being referred to as a person’s mental state, since it relates to their ideas, feelings, and actions. There is a lot of evidence that health affects the emotion. Therefore, the nature of emotions ought to reveal the health of a person. The emotions are represented by facial expressions controlled by muscular motor actions. Brain health may affect the working of the muscles leading to the emotional changes extracted from the facial images. Methods: A dataset of facial images annotated with matching emotion labels is the first step in using convolutional neural networks (CNNs) for facial expression emotion recognition. A CNN architecture is selected with particular layers intended for feature extraction, and a final layer for classification. Using the labelled images for training, the model\u27s hyper parameters are adjusted to maximize the performance. Further it is evaluated using an independent test set to check the accuracy. This helps in predicting a more sophisticated understanding of the emotions linked with underlying diseases, such as cancer. Results: Utilizing CNN, our initial examinations of facial images and the identified emotions suggest a clear connection between muscular actions and brain diseases, warranting further exploration for potential applications in detecting underlying brain-related illnesses. The utilization of facial muscles, beyond conveying a person\u27s emotions, may also contribute to early cancer detection, manifesting in various symptoms and functional limitations such as paralysis, facial weakness, and alterations in facial appearances. Conclusion: This study introduces an innovative emotion recognition using CNN, which has proved to be a potent and successful method for facial expression emotion recognition. CNNs are good at automatically deriving hierarchical features from photos, which makes them useful for identifying complex patterns in face expressions. The algorithm would also be useful for applications in clinical psychology and extracting the facial expressions linked with early detection of symptoms for various types of cancer

Modeling and Design of AlN Based SAW Device and Effect of Reflected Bulk Acoustic Wave Generated in the Device

Investigations of the effect of generation and reflection of bulk acoustic waves (BAWs) on the performance surface acoustic wave (SAW) device using finite element method (FEM) simulation is carried out. A SAW delay line structure using Aluminum Nitride (AlN) substrate is simulated. The dimension of the device is kept in the range of the 42 22.5 m in order to analyze the effect in MEMS devices. The propagation of the bulk wave in all the direction of the substrate is studied and analyzed. Since BAW reflect from the bottom of the SAW device and interfere with the receiving IDTs. The output of the SAW device is greatly affected by the interference of the BAW with SAWs in the device. Thus in SAW devices, BAW needed to be considered before designing the device. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3100

Microfluidic Mechanics and Applications: a Review

Microfluidics involves the transportation, splitting and mixing of minute fluids to perform several chemical and biological reactions including drug screening, heating, cooling or dissolution of reagents. Efforts have been made to develop different microfluidic devices, droplets and valves that can stop and resume flow of liquids inside a microchannel. This paper provides the review related to the theory and mechanics of microfluidic devices and fluid flow. Different materials and techniques for fabricating microfluidic devices are discussed. Subsequently, the microfluidic components that are responsible for successful micrfluidic device formation are presented. Finally, recent applications related to the microfluidics are highlighted. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3553

Scene based Classification of Aerial Images using Convolution Neural Networks

1087-1094The advent of computer vision and evolution of high-end computing in remote sensing images have embellish various researchers for unprecedented development in remotely sensed aerial images. The requirement to extract essential information stimulated anatomization of aerial images for its usefulness. Deep learning provides state of the art solutions for widely explored visual recognition system and has emerged as an evolutionary area, being applicable to large scale image processing applications. Convolutional Neural Networks (CNNs), an essential component of deep learning algorithms consists of increasing the depth and connections in the processing layers to learn various features of data at different abstract levels. In this paper, we present an outlook for classifying and extracting the features of aerial images using CNN. We propose a CNN architecture based on various parameters and layers for classification. CNN has been evaluated on two publicly available aerial data sets: UC Merced Land Use and RSSCN7. Experimental results show that the proposed CNN architecture is competent and efficient in terms of accuracy as performance evaluation parameter in comparison with conventional classifiers like Bag of Visual Words (BOVW)

EFFECT OF DYNAMIC TIME WARPING ON ALIGNMENT OF PHRASES AND PHONEMES

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Investigation of the Effect of Microwaves on Mustard Seeds Fertility

Abstract: With the growth of technology and increase in demand of cellular services day by day; mostly operated at 945 MHz, the presence of microwaves in environment is also increasing. Microwaves are electromagnetic waves with wavelengths ranging from as long as one meter to as short as one millimetre, or equivalently, with frequencies between 300 MHz and 300 GHz. Microwaves may have both positive and negative effects on crops. This paper proposes a technique to enhance the growth rate of crops; particularly mustard plants or sarsu (Brassica seeds). The investigations were carried out with mustard seeds exposed to microwaves for different durations and power levels. The growth of the plants was studied for ten days. The other control variables such as temperature, humidity, sun light and level of gases (CO 2 , N 2 , and O 2 ) were maintained almost constant for all the observations. The analysis of the results shows that seeds exposed for proper duration and power level show better growth rate in comparison to the natural growth procedure

Delays in hospital admissions in patients with fractures across 18 low-income and middle-income countries (INORMUS): a prospective observational study

© 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license Background: The Lancet Commission on Global Surgery established the Three Delays framework, categorising delays in accessing timely surgical care into delays in seeking care (First Delay), reaching care (Second Delay), and receiving care (Third Delay). Globally, knowledge gaps regarding delays for fracture care, and the lack of large prospective studies informed the rationale for our international observational study. We investigated delays in hospital admission as a surrogate for accessing timely fracture care and explored factors associated with delayed hospital admission. Methods: In this prospective observational substudy of the ongoing International Orthopaedic Multicenter Study in Fracture Care (INORMUS), we enrolled patients with fracture across 49 hospitals in 18 low-income and middle-income countries, categorised into the regions of China, Africa, India, south and east Asia, and Latin America. Eligible patients were aged 18 years or older and had been admitted to a hospital within 3 months of sustaining an orthopaedic trauma. We collected demographic injury data and time to hospital admission. Our primary outcome was the number of patients with open and closed fractures who were delayed in their admission to a treating hospital. Delays for patients with open fractures were defined as being more than 2 h from the time of injury (in accordance with the Lancet Commission on Global Surgery) and for those with closed fractures as being a delay of more than 24 h. Secondary outcomes were reasons for delay for all patients with either open or closed fractures who were delayed for more than 24 h. We did logistic regression analyses to identify risk factors of delays of more than 2 h in patients with open fractures and delays of more than 24 h in patients with closed fractures. Logistic regressions were adjusted for region, age, employment, urban living, health insurance, interfacility referral, method of transportation, number of fractures, mechanism of injury, and fracture location. We further calculated adjusted relative risk (RR) from adjusted odds ratios, adjusted for the same variables. This study was registered with ClinicalTrials.gov, NCT02150980, and is ongoing. Findings: Between April 3, 2014, and May 10, 2019, we enrolled 31 255 patients with fractures, with a median age of 45 years (IQR 31–62), of whom 19 937 (63·8%) were men, and 14 524 (46·5%) had lower limb fractures, making them the most common fractures. Of 5256 patients with open fractures, 3778 (71·9%) were not admitted to hospital within 2 h. Of 25 999 patients with closed fractures, 7141 (27·5%) were delayed by more than 24 h. Of all regions, Latin America had the greatest proportions of patients with delays (173 [88·7%] of 195 patients with open fractures; 426 [44·7%] of 952 with closed fractures). Among patients delayed by more than 24 h, the most common reason for delays were interfacility referrals (3755 [47·7%] of 7875) and Third Delays (cumulatively interfacility referral and delay in emergency department: 3974 [50·5%]), while Second Delays (delays in reaching care) were the least common (423 [5·4%]). Compared with other methods of transportation (eg, walking, rickshaw), ambulances led to delay in transporting patients with open fractures to a treating hospital (adjusted RR 0·66, 99% CI 0·46–0·93). Compared with patients with closed lower limb fractures, patients with closed spine (adjusted RR 2·47, 99% CI 2·17–2·81) and pelvic (1·35, 1·10–1·66) fractures were most likely to have delays of more than 24 h before admission to hospital. Interpretation: In low-income and middle-income countries, timely hospital admission remains largely inaccessible, especially among patients with open fractures. Reducing hospital-based delays in receiving care, and, in particular, improving interfacility referral systems are the most substantial tools for reducing delays in admissions to hospital. Funding: National Health and Medical Research Council of Australia, Canadian Institutes of Health Research, McMaster Surgical Associates, and Hamilton Health Sciences

Synthesis of Copper-Telluride (CuTe) Nanowires and its Characterization using He-Ne Laser

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