Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Deception Using Machine Learning

Today’s digital society creates an environment potentially conducive to the exchange of deceptive information. The dissemination of misleading information can have severe consequences on society. This research investigates the possibility of using shared characteristics among reviews, news articles, and emails to detect deception in text-based communication using machine learning techniques. The experiment discussed in this paper examines the use of Bag of Words and Part of Speech tag features to detect deception on the aforementioned types of communication using Neural Networks, Support Vector Machine, Naïve Bayesian, Random Forest, Logistic Regression, and Decision Tree. The contribution of this paper is two-fold. First, it provides initial insight into the identification of text communication cues useful in detecting deception across different types of text-based communication. Second, it provides a foundation for future research involving the application of machine learning algorithms to detect deception on different types of text communication.
Recommendation system: Online movie store

This research paper highlights the importance of recommendation system to suggest item for the customer such as which movie to watch or what music to listen. Recommendation system plays an important in increasing sale of the product, customer satisfaction, increase sale of diverse product etc. In order to increase sale of the product, every organization concern with increase the new customer and retain the existing customer with the organization. Traditional business had limitation of geographical location, but with the new era business spread all over the world. With the help of technological innovation e-business grows rapidly. Customer purchase the item using online store, the only limitation is to search the item in the store on its own; no helping hand available online, in this scenario product recommender system is very useful.
Molecular Docking Simulation and in vivo Assay on Anti-Nephrolitic Potency of Avocado Leaf and Cat’s Whiskers Extract

The aim of this study was to evaluate the activity of Avocado (Persea americana Mill) leaf extract and Cat whiskers (Orthosiphon aristatus (Blume)Miq) extract combination as calcium oxalate nephrolithiasis inhibitor through in vivo assay and molecular docking simulation. Commercial brand Batugin Eilixir® (BG) was used as positive control group. Thirty five rats were divided into 7 groups i.e normal (n=5), EG as inducer group (n=5), BG as positive control BG (n=5), avocado leaf extract with 300 mg/kgBW dose (AV) (n=5), whisker cats extract with 250 mg/kgBW dose(OR) (n=5), combination dose 1 containing AV 300 mg/kgBW and OR 250 mg/kgBW (Mix1) (n=5) and combination dose 2 containing AV 600 mg/kgBW and OR 500 mg/kgBW (Mix2) (n=5). Biochemical variables measured in rat serum were urea and creatinin. Urine Ca, Mg, pH and urine volume were also measured. Histopathology of kidney was analysed in order to evaluate protective effects of the treatment and crystal dissemination in the tissue. The result showed that group of Mix1 showed good activity performance by lowering urine calcium level (P<0.05) and increasing urine magnesium level (P<0.05), increasing urine volume, and modify urine pH. Increase of urea and creatinin from group Mix1 are suppressed compared to normal group. The other group result showed lower activity than group Mix1. Molecular docking simulation showed that active compounds such as quercetine and rosmanic acid occupied the active site of receptor glycolate oxidase (GO). The conclusion of this study was grup Mix1 had the best activity as inhibitor of nephrolithiasis.
Hybrid Approaches Based Emotion Detection in Memes Sentiment Analysis

A social network is a decent platform in which users share daily emotions and opinions. As a consequence, they have become an essential information source related to sentiment/opinion. The purpose of Sentiment Analysis (SA) techniques are used to extract sentiments, emotions, and opinions from texts. This data is available by different data sources, such as social media, e-blogs, e-sources, etc. The problem of previous research work has done only text or image analysis using different techniques. Most of the research work results have given low performance. This research work presents a hybrid approach that is needed for the computational processing of Internet memes. Internet Memes provide different behavior of human expressions and activities. The increasing prevalence of Internet memes on social media sites such as Facebook, Instagram, and Twitter also indicates that such multimodal content can no longer be ignored. The present work is analyzing internet memes using lexicon-based and machine learning approaches. In sentiment analysis, every meme is denoted as different polarities (positive or negative memes). The emotions contain six classes that are anger, fear, happiness, sadness, disgust, and neutral. The polarity contains positive (+1), neutral (0), and negative (-1) values. Additionally, the results obtained from the proposed work give better performance of sentiment analysis. The emotion and polarity classes are predicted in both text and image data
Linking Social and Emotional Learning Standards to the Social Emotional Competency Assessment (SECA)

School districts across the United States are developing systemic, district-wide approaches to support students’ social and emotional learning (SEL), due to the increasingly recognized role that students’ social-emotional competencies (SECs) play in their academic success (Domitrovich et al., 2017; Kendziora & Osher, 2016; Mahoney et al., 2017). Such initiatives have increased the need for cost-effective measures of students’ SECs that are feasible for large-scale use, embedded in scholarly literature, and aligned to local needs (Stecher & Hamilton, 2014). This brief describes evidence for one such measure, the Social-Emotional Competency Assessment (SECA), which is a self-report assessment of students’ SECs that can be used from late elementary to high school (Crowder et al., 2018; Davidson et al., 2018; Schamberg et al., 2017). The measure was developed in the context of a researcher-practitioner partnership that provided the opportunity to align the measure not only with theory and literature about core SECs – and the widely-used Collaborative for Academic Social and Emotional Learning five broad domains (CASEL 5; Weissberg et al., 2015) – but also the school district’s SEL standards. This brief shows how such alignment informs us about how well the measure operates and also offers insights into the standards and underlying theory and practice (see Crowder et al., 2018 for the full paper on which this brief is based).
GPU Based Image Classification using Convolutional Neural Network Chicken Dishes Classification

The internet makes human life easier, one of them is in ordering food. By only using smartphones and opening food delivery features, in the application, consumers can order food from restaurants that collaborate with the e-commerce. The case study that we did was classifying the types of chicken dishes namely “ayam geprek”, “ayam goreng”, and “ayam bakar” using CNN method. CNN method is a popular method for image classification. This research is doing image classification using CNN method based on GPU. From several models built, obtained the best model with an accuracy of 99% and training speed about 233 seconds. While for comparison of CNN method based on CPU and GPU obtained the conclusion that the most rapid training process using GPU.
The Use of Dried Distillers Grains with Solubles in Feedlot Rations for Lambs

In the U.S., dried distillers grains with solubles (DDGS) is a highly available coproduct of ethanol and brewery industries utilized in ruminant diets that, due to the comparable nutritional quality, is capable of substituting (partially or totally) traditional sources of protein and energy with a possibility of reducing feed costs. With the use of this product, there is the potential to increase reproductive efficiency, survivability, growth performance, and meat production and quality when a well-designed supplementation program is implemented and monitored. However, the literature has scarce papers evaluating the effects of DDGS in sheep nutrition, notably in supplementation programs. The lack of DDGS utilization in sheep diets may be related to some challenges, such as nutritional composition variability, handling, storage, and feeding issues. In addition to addressing these challenges, an overview of the statistics and concepts applied to the ethanol and sheep industry will be presented here, as well as the criteria involved in the incorpo-ration of DDGS into sheep supplementation programs. These latter factors include the interaction between pasture and supplement type, along with feeding and herd characteristics. The objective was to determine the effects of leucine supplementation during the pre-weaning period on growth performance until slaughter. Nineteen commercial Dorset ram lambs (5.07 ± 0.15 kg) were used. Leucine was added to milk replacer at 0 (control) or 2.9% of DM and provided to lambs for ad libitum intake for 42 d using LAC-TEK automated milk feeders. Lambs were then fed a corn-based finishing diet and slaughtered in two blocks based on final body weight (BW). Data were analyzed using the MIXED and GLM procedure of SAS. the pre-weaning milk replacer the it is conclude effects of on pre-weaning in milk replacer the finishing by preweaning Leu supplementation. reticulorumen
Investigating the Factors Influence the Reuse Behavior of Group-Buying Website

With the proliferation of online group-buying shopping, website interaction will play a vital role in the rapidly growing shopping websites. To explore what factor drives online customers satisfied and reuse online group-buying website are important challenges. However, there is not enough accumulated knowledge to understand specifically the determinants of consumer satisfaction in online group-buying. In order to understand the issue more accurately, this study focus on customer’s perceived benefits and examines its relationships with the customer’s satisfaction and reuse intention. Prior studies suggested that price benefit make repurchase and reuse online group-buying website. Besides the price benefit, this study proposes convenient, risk-reducing, interpersonal, and experimental benefits to figure out perceived benefits more exactly. The results of this study show that risk-reducing, interpersonal and experimental benefits and website interaction contribute significantly to the attainment of user satisfaction. However, the convenient and price benefits have no significant positive impact on user satisfaction toward reuse intention. Moreover, the finding indicates that website interaction is an antecedent of perceived benefits, and influencing reuse intention through user satisfaction. Thus, user satisfaction is also a strong mediator on reuse intention. Explanations are presented along with the implications.
Nonlinear Solution of the Time Eigenvalue of a Fast Burst Reactor Using the Finite Volume Method

Fast burst reactors are used in research environments to study super critical systems and as a source of fast neutron pulses for use in other experiments. These reactors fill an important role in both civilian and defense research. However, due to costs associated with securing the highly enriched uranium fuel used by these reactors, many have been shut down in recent years. Replacing these reactors with less expensive low enriched uranium fuel has been proposed as a solution to this problem. However, before a fast burst reactor using this new fuel type can be constructed, it is desirable to first determine the feasibility of such a design using numerical modeling. Previous attempts have been made to develop these predictive models, but these methods are currently lacking in verification data. Therefore, this work builds off of one such numerical model in order to enhance the verification of this predictive method. This effort models fast burst reactors using the one dimensional, one group neutron diffusion equation to solve for the time eigenvalue. For this problem there exist an analytical solution against which the numerical results can be verified. The existing solution method is enhanced by the addition of a second order accurate finite volume discretization, which is then used to model two separate fast burst reactors. The results of these models are then compared to the results of previous work, the analytical solution, and existing experimental burst width data for each of the two reactors.
Condition limite absorbante d'ordre élevé, décomposition de domaines et modèle hydrodynamique de vagues

La condition limite absorbante d’ordre élevé de Hagstrom et Warburton [1] de filiation Engquist-Majda (1977), Higdon (1986), Givoli-Neta (2003) a été appliquée sur différents modèles basés sur l’équation des ondes dispersives, convectées ainsi que pour des matériaux stratifiés ou avec une vitesse d’onde variable (Hagstrom, Mass-or, Givoli (2008), Bécache, Givoli, Hagstrom (2010)). Nous l’utilisons ici (voir figure 1) sur un modèle hydrodynamique de vagues composé d’un modèle de surface Γs couplé à un modèle dans le bassin Ω, faisant suite à [2], ici utilisé sous sa forme non convectée. La condition limite absorbante permet d’une part de borner le domaine Ω mais aussi de le décomposer dans un premier temps sans recouvrement de domaine (utile pour paralléliser la résolution numérique). Ce travail est effectué en utilisant de manière optimale l’algorithme de Schwarz grâce à la condition limite absorbante permettant d’approcher l’opérateur transparent de type Dirichlet to Neumann pour dimensionner les conditions aux interfaces comme proposé par Gander, Halpern et Nataf [3]. Du fait du couplage de modèles, nous avons besoin d’assurer la continuité de coefficients, associés aux vitesses d’ondes, apparaissant au sein de la condition limite entre les deux modèles (surface/domaine). L’application numérique pour un bassin rempli d’eau (voir figure 1) utilise la vitesse d’onde minimale en surface obtenue analytiquement à l’aide d’une onde plane pour dimensionner les conditions limites transparentes et les conditions d’interfaces.
Endometrial Stromal Nodule: A Case Report

DOI: 10.46998/IJCMCR.2021. 10.000229 Abstract The Endometrial Stromal Nodule (ESN) is the rarest subtype of endometrial stromal tumors (ESTs) that’s why experience with ESN is limited. ESN could mimic highly cellular leiomyomas, usually seen in the premenopausal age. Patients present with nonspecific symptoms such as abnormal bleeding, lower abdominal pain, or maybe asymptomatic. Our patient was 46 years old women suffering from abnormal uterine bleeding and lower abdominal pain and had no desire to protect fertility. The preoperative diagnosis of ESN is very difficult. Also, no specific imaging is defined for the ESN in ultrasound or MRI examination. ESN is usually misdiagnosed as uterine fibroids, interstitial ectopic pregnancy, or stromal hyperplasia. Macroscopically, these tumors are classically well-circumscribed, solitary, encapsulated. In a curettage specimen, the distinction between an ESN and a lowgrade endometrial stromal sarcoma is almost impossible, unless the tumor is too small, and the margins can be fully evaluated. In conclusion, ESN is a rare subtype of endometrial stromal sarcomas, but there is nothing to afraid of if you do the hysterectomy. So, it is important to diagnose preoperatively especially in young nulliparous women who desire to preserve fertility. Clinical presentation is nonspecific and there is no preoperative imaging that can completely rule out malignancy. It is necessary to develop new diagnostic methods or new treatment options to protect the uterus.
A Macroeconomic Model of Federal and Commercial Spectrum Usage

A Macroeconomic Model of Federal and Commercial Spectrum Usage introduces a methodology to assess the macroeconomic impact of electromagnetic spectrum decisions and provides general results on a case study of the economic impact of shifting from fourth generation (4G) to fifth generation (5G) wireless technology. This article provides a summary introduction to this new research tool.
Hydroxyapatite-decorated Fmoc-hydrogel as a bone-mimicking substrate for osteoclast differentiation and culture

Hydrogels are water-swollen networks with great potential for tissue engineering applications. However, their use in bone regeneration is often hampered due to a lack of materials’ mineralization and poor mechanical properties. Moreover, most studies are focused on osteoblasts (OBs) for bone formation, while osteoclasts (OCs), cells involved in bone resorption, are often overlooked. Yet, the role of OCs is pivotal for bone homeostasis and aberrant OC activity has been reported in several pathological diseases, such as osteoporosis and bone cancer. For these reasons, the aim of this work is to develop customised, reinforced hydrogels to be used as material platform to study cell function, cell-material interactions and ultimately to provide a substrate for OC differentiation and culture. Here, Fmoc-based RGD-functionalised peptide hydrogels have been modified with hydroxyapatite nanopowder (Hap) as nanofiller, to create nanocomposite hydrogels. Atomic force microscopy showed that Hap nanoparticles decorate the peptide nanofibres with a repeating pattern, resulting in stiffer hydrogels with improved mechanical properties compared to Hap- and RGD-free controls. Furthermore, these nanocomposites supported adhesion of Raw 264.7 macrophages and their differentiation in 2D to mature OCs, as defined by the adoption of a typical OC morphology (presence of an actin ring, multinucleation, and ruffled plasma membrane). Finally, after 7 days of culture OCs showed an increased expression of TRAP, a typical OC differentiation marker. Collectively, the results suggest that the Hap/Fmoc-RGD hydrogel has a potential for bone tissue engineering, as a 2D model to study impairment or upregulation of OC differentiation. Statement of significance Altered osteoclasts (OC) function is one of the major cause of bone fracture in the most commonly skeletal disorders (e.g. osteoporosis). Peptide hydrogels can be used as a platform to mimic the bone microenvironment and provide a tool to assess OC differentiation and function. Moreover, hydrogels can incorporate different nanofillers to yield hybrid biomaterials with enhanced mechanical properties and improved cytocompatibility. Herein, Fmoc-based RGD-functionalised peptide hydrogels were decorated with hydroxyapatite (Hap) nanoparticles to generate a hydrogel with improved rheological properties. Furthermore, they are able to support osteoclastogenesis of Raw264.7 cells in vitro as confirmed by morphology changes and expression of OC-markers. Therefore, this Hap-decorated hydrogel can be used as a template to successfully differentiate OC and potentially study OC dysfunction.
Design of A Novel Power Boost Converter With Dual Coupled Inductors

The conventional DC-DC boost converters are widely used in several applications. They have the shortcomings of low voltage gain ratio and discontinuous output current. Reverse recovery problem may arise in the output diode which can harm the power switch. DC-DC converters having high gain are fast developing switching power converters in the field of like that Photo Voltaic (PV) applications, fuel cell and wind energy and others low constant energy producing systems, dc back-up energy systems, high-intensity discharge lamp, electric vehicles. Research is going on to develop different topologies for achieving higher voltage gain, low ripple, reduced switch stress, and cost effective converters. In this paper various High Gain cells are being studied. This paper also proposed a boost converter with dual coupled inductors with uninterrupted input current. Moreover, this makes it a appropriate choice for fuel cells. Simulations are done in MATLAB Simulink to verify the theoretical equations from the planned converter configuration. Keywords— High gain cell, Boost converters, Coupled inductor, Continuous current
I T N ET : ITERATIVE NEURAL NETWORKS FOR FAST AND EFFICIENT ANYTIME PREDICTION

Deep neural networks have usually to be compressed and accelerated for their usage in low-power, e.g. mobile, devices. Common requirements are high accuracy, high throughput, low latency, and a small memory footprint. A good trade-off between accuracy and latency has been shown by networks comprising multiple intermediate outputs. In this study, we introduce a multi-output network that has a tiny memory footprint in terms of its computational graph, which allows its execution on novel, massively-parallel hardware accelerators designed for extremely high throughput. To this end, the graph is designed to contain loops by iteratively executing a single network building block. These so-called iterative neural networks enable stateof-the-art results for semantic segmentation on the CamVid and Cityscapes datasets that are especially demanding in terms of computational resources. In ablation studies, the improvement of network training by intermediate network outputs as well as the trade-off between weight sharing over iterations and the network size are investigated.
REAL-TIME EMOTION RECOGNITION OF TWITTER POSTS USING A HYBRID APPROACH

The analysis of social media posts is a challenging task, particularly the recognition of user emotions. Text is one of the most common mediums used by humans to express emotion, particularly on social media platforms. As emotions play a pivotal role in human interaction, the ability to recognize them by analyzing textual content has various applications in human-computer interaction (HCI) and natural language processing (NLP). Previous studies on emotion classification used bag-of-words classifiers or deep learning on static Twitter data. Our proposed model is a hybrid approach that uses a combination of keyword-based and learning-based models to perform textual emotion recognition on Twitter posts obtained in real-time. Textual feature extraction is carried out by standard Natural Language Processing (NLP) techniques such as Part-of-Speech (PoS) tagging and topic modeling along with classification done using the random forest algorithm. Results show that our proposed model performs better in comparison to the traditional Unison model with an average accuracy that approximates to 88.39%.