Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Classification of Women Working in ICT based Education

Women segment considering the family need and passion towards work equally entered into all profession. Women members have multifaceted commitments. She has to take care of her family as well as official works without any hindrance, which lead to stress. Now-a-days, society expectation from education sector is keeping on increasing. Hence, teachers have to put more effort to develop new skills and knowledge for student’s growth. This often results in over whelming burdens and challenges for the faculty, which subsequently leads to struggle and stress. The aim of this research is to study the causes of stress among the women faculty membersworking in select arts and science colleges in Coimbatore district. The result of study disclosed that favouritismand discrimination, unexpected class adjustments etc. lead to stress
A Survey on Spectral and Energy Efficient Next-Generation Wireless Networks

The exponential increase in mobile data traffic and ever-increasing mobile users has led to a swift rise in the energy consumption and operational costs of wireless networks. Spectral Efficiency (SE) and Energy Efficiency (EE) are considered to be the most important metrics for any wireless network but both, SE and EE, usually conflict with each other. In this paper, we have done survey of different physical layer technologies like OFDMA, mm-wave, heterogeneous networks etc. and various radio resource allocation techniques to simultaneously optimize SE and EE in different wireless networks. The various network planning strategies such as reduction in cell size, cell zooming and dense deployment of base stations (BSs) are investigated to optimize EE besides SE of the wireless networks. The findings of survey paper also reveal different power control algorithms to optimize EE along with increasing the overall system capacity and number of users in the shared spectrum. The survey done in this work provides an essential understanding on the deployment of spectral and energy-efficient next-generation wireless networks.
Impact of Activation Functions and Number of Layers on the Classification of Fruits using CNN

The classification of fruits into various classes is becoming inherent to the food processing industry. This paper presents a simulation analysis of effect of activation functions: ReLu, Softmax, sigmoid, and Softplus on the accuracy and latency of the CNN algorithm for classification of fruits: apple, banana and orange. The paper presents the comparative increase of accuracy of different activation functions over the ReLu activation function. The algorithm is trained and tested over a database created by downloading fruit images from the online sources. Also, this paper presents the effect of increasing the number of convolutional layers of the CNN algorithm on the Accuracy and latency of the model. The software used for simulation of the model is Python implemented using Jupyter Notebook over the Anaconda platform.
Mangrove Information Extraction Based on Multi-source Remote Sensing Images

Mangrove is an important wetland ecosystem in coastal areas. It plays an important role in maintaining the coastal ecological environment, and also provides a guarantee for the survival of various organisms. In the past half century, mangrove has been seriously damaged, so it is very important to find an efficient and accurate method to monitor mangrove changes. The remote sensing technology is an important approach to monitor the dynamic changes of mangrove resources and health status. This paper takes the mangrove in Dandou Ocean of Guangxi province as the research area, uses a variety of optical satellite data sources, analyzes the key features of mangrove remote sensing information recognition, and studies the effective methods of mangrove extraction from different data sources. The experimental results show that the accuracy of mangrove extraction is mainly related to the amount of information in the spectrum feature space and the spatial resolution of the data source, and the difficulty of classification is mainly the distinction between terrestrial plants and mangroves. The accuracy of Sentinel-2A satellite using maximum likelihood classification(MLC) and support vector machine classification(SVM) is very close. Landsat8 satellite with medium and low resolution and large number of spectrum feature space and Gaofen-1 satellite with small number of high spatial resolution spectra are more suitable to be extracted by support vector machine classification. The accuracy of landsat8 satellite image extraction by support vector machine classification is the highest, the overall accuracy is 91.42%, and the kappa coefficient is 0.8819.
Presence-Aware Smart Office

The world is now more connected than it has ever been. Billions of smartphones, laptops, servers and other devices communicate with each other over the internet every second of every day. One place where IoT devices can make a huge difference is the workplace. Most office spaces use dozens of lights and ceiling fans. Since physically turning them on and off whenever an employee occupies a space is impractical and time consuming, we can make use of smart devices which can power up depending on whether people are occupying the room or not. This will also lead to reduced wasted energy which will in turn lead to a greener planet. Several methods of detecting if a person is present in a room can be implemented. Our project aims to find the best possible method and improve on it, ultimately leading to a completely autonomous smart office.
Detection of Insomnia using Electrocardiography and Electromyography

Insomnia is one of the serious sleep disorders. In this disease, a patient feels trouble falling asleep and stay awake for the whole night. Insomnia also co-morbid with other diseases like cancer, cardiovascular disease, asthma, Alzheimer's, Parkinson's disease. This research aims at a method for identifying insomnia using both electrocardiogram (ECG) and electromyogram (EMG) signals. The Cyclic Alternating Pattern (CAP) database from Physionet is used. Preprocessing by empirical mode decomposition (EMD) is carried out in this research. Features with high discriminative ability are extracted from signals to classify via logistic regression (LR), support vector machines (SVM), K-nearest neighbor (KNN), decision tree (DT), ensemble classifier (EC), and Naïve Bayes (NB) classification methods. we have achieved the highest accuracy of 100% on both ECG and EMG signals from our proposed methodology.
Simulation Of Spurious Modes Suppression In Saw Resonator On Lnosic Substrate

In this paper, the spurious modes in SAW resonator based on LNOSiC substrate are compared with those in conventional SAW resonator in COMSOL Multiphysics. By changing the electrode material, electrode thickness and Euler angle $\beta$ of Piezoelectric crystal of the resonator using LNOSiC substrate, the spurious modes can be suppressed after optimization Finally, when the electrode material is aluminum, whose thickness is $0.15 \mu \mathrm{m}$, and the Euler angle $\beta$ is 85°, the spurious modes which appear in a certain frequency range higher than the designed frequency have been successfully suppressed. Through admittance curve and the cloud picture of displacement at resonance, it can be observed that Rayleigh wave and other spurious modes are effectively suppressed, and a relatively pure shear horizontal wave mode resonator is obtained. Moreover, electromechanical coupling coefficient and Bode-Q value are calculated, they reached 25.3 % and 1710 after optimization, respectively
High-Resolution Multi-Spectral Imaging With Diffractive Lenses and Learned Reconstruction

Spectral imaging is a fundamental diagnostic technique with widespread application. Conventional spectral imaging approaches have intrinsic limitations on spatial and spectral resolutions due to the physical components they rely on. To overcome these physical limitations, in this paper, we develop a novel multi-spectral imaging modality that enables higher spatial and spectral resolutions. In the developed computational imaging modality, we exploit a diffractive lens, such as a photon sieve, for both dispersing and focusing the optical field, and achieve measurement diversity by changing the focusing behavior of this lens. Because the focal length of a diffractive lens is wavelength-dependent, each measurement is a superposition of differently blurred spectral components. To reconstruct the individual spectral images from these superimposed and blurred measurements, model-based fast reconstruction algorithms are developed with deep and analytical priors using alternating minimization and unrolling. Finally, the effectiveness and performance of the developed technique is illustrated for an application in astrophysical imaging under various observation scenarios in the extreme ultraviolet (EUV) regime. The results demonstrate that the technique provides not only diffraction-limited high spatial resolution, as enabled by diffractive lenses, but also the capability of resolving close-by spectral sources that would not otherwise be possible with the existing techniques. This work enables high resolution multi-spectral imaging with low cost designs for a variety of applications and spectral regimes.
Predicting the Risk of Psychological Distress among Lung Cancer Patients: Development and Validation of a Predictive Algorithm Based on Sociodemographic and Clinical Factors

Objective: Lung cancer patients reported the highest incidence of psychological distress. It is extremely important to identify which patients at high risk for psychological distress. The study aims to develop and validate a predictive algorithm to identify lung cancer patients at high risk for psychological distress. Methods: This cross-sectional study identified the risk factors of psychological distress in lung cancer patients. Data on sociodemographic and clinical variables were collected from September 2018 to August 2019. Structural equation model (SEM) was conducted to determine the associations between all factors and psychological distress, and then construct a predictive algorithm. Coincidence rate was also calculated to validate this predictive algorithm. Results: Total 441 participants sent back validated questionnaires. After performing SEM analysis, educational level (β = 0.151, P = 0.004), residence (β = 0.146, P = 0.016), metastasis (β = 0.136, P = 0.023), pain degree (β = 0.133, P = 0.005), family history (β = −0.107, P = 0.021), and tumor, node, and metastasis stage (β = −0.236, P < 0.001) were independent predictors for psychological distress. The model built with these predictors showed an area under the curve of 0.693. A cutoff of 66 predicted clinically significant psychological distress with a sensitivity, specificity, positive predictive value, and negative predictive value of 65.41%, 66.90%, 28.33%, and 89.67%, respectively. The coincidence rate between predictive algorithm and distress thermometer was 64.63%. Conclusions: A validated, easy-to-use predictive algorithm was developed in this study, which can be used to identify patients at high risk of psychological distress with moderate accuracy.
Vision-based Human Posture Classification and Fall Detection using Convolutional Neural Network

In this paper, we propose a novel computer vision based system for detecting falls on the basis of a monocular camera that can be applied for assisting independent living of an older adult living alone at home. From the recorded video data, mask regional convolutional neural network (mask R-CNN) is first applied to detect and extract the human silhouettes. The low-dimensionality representative features of extracted silhouettes are then extracted by convolutional neural network (CNN), which is applied for classification of different human postures (e.g., sit, bend, stand and lie) and a detection of a fall event (i.e., lying posture detected on the ground). For performance comparison, several machine learning algorithms such as support vector machine (SVM), random forest (RF), k-means clustering and artificial neural network (ANN) are trained with the same dataset. The proposed framework proved to be stable in human posture detection and achieved an overall accuracy of 97.5% and F-measure of 97% with small false alarms. On the same dataset the accuracies of other competing models, i.e., ANN, SVM, RF, and k-means are; 96.56%, 86.95%, 91.92% and 40.06%, respectively.
Optical Analysis of the UPQC using PI Controller in Power flow System

This examination depends on a three-phase unified control quality conditioner (UPQC) to extend control quality in a three-phase distribution framework. It is nice for day-to-day trade and domestic sectors. Researchers have endeavored and executed different advantageous advances to induce freed of both voltage and current consonant issues, making strides the quality of control given by the control framework. UPQC was made up of a arrangement and a shunt converter that were associated back-to-back employing a common dc-link capacitor. This paper works by actualizing UPQC control quality to compensate for voltage and current sounds issues. UPQC uses the PI controller, which calculates error value differently between the measured and desired set points. The conventional UPQC tool is limited to the PI controller’s DC link voltage law. Also, it presents a three-phase transformation using a special multi-winding transformer connection scheme. A MATLAB simulation is utilized in this paper to demonstrate how the UPQC demonstrate can decrease the percent THD in source voltage, source current, and stack voltage waveforms actuated by nonlinear loads. The execution of UPQC is analyzed with a nonlinear load, and simulation studies utilizing MATLAB/Simulink prove the suitable execution of UPQC.
Probabilistic Data Allocation in Pervasive Computing Applications

Pervasive Computing (PC) deals with the placement of services and applications around end users for facilitating their everyday activities. Current advances on the Internet of Things (IoT) and the Edge Computing (EC) provide the room for adopting their infrastructures and hosting the desired services for supporting PC applications. Numerous devices present in IoT and EC infrastructures give the opportunity to record and process data through the interaction with users and their environment. Upon these data, the appropriate processing should be realized as requested by end users or applications. It is efficient to process such requests as close as possible to end users to limit the latency in the provision of responses. The research community, identifying this need, proposes the use of the EC as the appropriate place to perform the discussed processing which has the form of tasks or queries. Tasks/queries set specific conditions for data they desire imposing a number of requirements for the dataset upon which the desired processing should be executed. It is wise to pre-process the data and detect their statistics to know beforehand if it is profitable to have any dataset as part of the requested processing. This paper focuses on a model that is responsible to efficiently distribute the collected data to the appropriate datasets. We store similar data to the same datasets and keep their statistics solid (i.e., we meet a low deviation) through the use of a probabilistic approach. The second part of the proposed approach is related to an aggregation scheme upon multiple outlier detection methods. We decide to transfer outliers to Cloud avoiding to store them locally as they will jeopardize the solidity of datasets. If data are going to be locally stored, we provide a mechanism for selecting the most appropriate dataset to host them while we perform a controlled replication to support a fault tolerant system. The performance of the proposed models is evaluated by a high number of experiments for different scenarios.
Building Occupancy Behavior and Prediction Methods: A Critical Review and Challenging Locks

Energy use in buildings is increasing to provide optimal comfort for the occupants. People spend 90% of their lifetime in buildings. Therefore, indoor environment quality and comfort management have a crucial role in maintaining occupants’ health and productivity. Reducing energy consumption for optimal comfort management is important to minimize CO2 emission and global warming by the building sector. According to the literature, it is possible to control and reduce energy consumption by monitoring occupants’ behavior and estimating the number of occupants. A critical review is carried out in this paper to analyze the existing methodologies for modeling occupant behavior and prediction with respect to comfort and energy management. A comprehensive analysis is also performed on recent developments and challenges in modeling, along with recommendations and future perspectives.
Depression Detection on Social Media with the Aid of Machine Learning Platform: A Comprehensive Survey

Depression is a group of mental disorders associated with certain factors which can affect the mood, feelings, negativity, losing interest, and sadness in human participants. To maintain the quality of life, people tend to experience fewer mental health issues. Today social media is a major part of our daily life and these social media sites offer an important platform to share their emotions, feelings in day-to-day routine and life events. In recent years, automatic depression detection on social media-related studies has improved. The objective of this paper is to identify the different machine learning algorithm methods, techniques, and approaches used by various studies related to depression detection on social media platforms by conducting a comprehensive review. Various studies of from year 2013 to 2020 are reviewed to explore the research gaps and future directions.
In vitro Studies of Polycaprolactone Nanofibrous Scaffolds Containing Novel Gehlenite Nanoparticles

Background: Recently, many studies have been done on the physicochemical properties and biocompatibility of polycaprolactone (PCL) scaffolds containing ceramic reinforcers in the field of bone tissue engineering. In this study, the physical, mechanical and biological properties of electrospined-fabricated PCL scaffolds containing gehlenite (GLN) nanoparticles (NPs) as a novel bioceramic were investigated. Methods: To obtain the appropriate mechanical properties, the solution contains 3%, 5%, 7%, and 10% wt. of GLN NPs were prepared. Fiber morphology was investigated by scanning electron microscopy. In order to evaluate the NPs distribution, Energy Dispersive X-Ray Spectroscopy, X-ray diffraction, and Fourier Transform Infrared Spectroscopy spectroscopy were used. The scaffold hydrophilicity was measured by the water contact angle test. The tensile test was used to check the mechanical strength of the scaffold. The proliferation of MG-63 cells was evaluated by the MTT test. Alkaline phosphatase (ALP) activity of MG-63 cells was also examined. Results: Average fibers' diameters and porosity of PCL/GLN7% were obtained 150–500 nm and 80%, respectively. An increase in the scaffold hydrophilicity was observed by the addition of GLN NPs. The strength of PCL/GLN7% was higher than the blank PCL scaffold. Cell proliferation of scaffolds containing GLN was higher than the blank PCL scaffold. A significant increase in the secretion of ALP for GLN-loaded scaffolds was seen. Discussion: The results showed that PCL/GLN7% composite scaffold could be a good candidate for bone tissue engineering. Conclusion: The overall results indicate that the scaffold (PCL /GLN7%) has suitable mechanical properties, a great cell compatibility for bone tissue regeneration.
Anticipatory Grief among Chinese Family Caregivers of Patients with Advanced Cancer: A Cross-Sectional Study

Objective: The objective of this study was to explore the interrelationship between anticipatory grief (AG), caregiver burden, communication, preparation for death, and coping style. Methods: A convenience sample of 256 Chinese family caregivers of patients with advanced cancer were recruited from an academic cancer hospital between April 2018 and May 2019. This cross-sectional survey included the AG Scale, caregiver burden (Caregiver Reaction Assessment), communication (Caregivers' Communication with Patients about Illness and Death Scale), preparation for death, and coping style (Simplified Coping Style Questionnaire). Structural equation modeling tested the interrelation between them. Results: The final model fitted the data acceptably (χ2 = 25.79, degrees of freedom = 17, P = 0.08, root mean square error of approximation = 0.05, goodness-of-fit index [GFI] = 0.98, adjusted GFI [AGFI] = 0.95, parsimony GFI [PGFI] = 0.46, normed fit index = 0.94, comparative fit index = 0.98). Poor communication contributed to less preparation for death and caregiver burden, which further aggravate AG. Communication was positively associated with AG. In addition, communication and positive coping style interacted to further influence caregiver burden. Conclusions: Preliminary results supported the model and showed that poor communication, less preparation for death, and caregiver burden contributed to AG while positive coping alleviated AG. Findings suggest the need for further studies to explore effective intervention for communication, preparation for death, burden, and coping style of caregivers to ultimately alleviate AG.
Spectrum-toxicity correlation study revealed the influence of the nine-time steaming and sun drying method on hepatotoxic components of Polygoni Multiflori Radix

Objective: Polygoni Multiflori Radix (PM) is a traditional herbal medicine with repeated reports of liver injury events in recent years. We wondered whether the classical processing method, namely, nine-time steaming and sun drying (NSSD), had toxicity-attenuating effects on PM and the relationships between toxicity and times of processing, as well as with the alteration trends of its compounds. Materials and Methods: The chemical fingerprints of different PM extracts were developed using ultra-high-performance liquid chromatography. The spectrum-toxicity correlation between the chemical fingerprints and hepatocellular toxicity was analyzed with multiple correlation analysis. Results: The results suggested that the hepatotoxicity of NSSD processing products markedly decreased with the repeated steaming and sun drying, which was obviously superior to the product processed by the modern method. Comprehensive analysis revealed that the contents of cis-stilbene glycoside and emodin-8-O-β-D-glucoside related to liver injury susceptibility were reduced with the times of NSSD processing, which was consistent with the decreased trend of hepatocellular toxicity. After the five times of NSSD, the contents of them as well as the hepatotoxicity of PM were steady. Moreover, we found that the contents of catechin and physcion declined rapidly after the one time of NSSD and then remained stable until the nine times of NSSD. Based on the fact, they could be utilized to indicate whether PM products were processed by steaming and sun drying. Conclusions: This paper confirmed that the NSSD had a good influence on the toxicity attenuating to PM and found four compounds which could apply for the quality control of PM.