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To read the news, most smartphone users prefer social media over the internet. The news is posted on news websites, and the source of the verification is cited. The problem is determining how to verify the news and publications shared on social media platforms such as Twitter, Facebook Pages, WhatsApp Groups, and other microblogs and social media platforms. It is damaging to society to hold on to rumors masquerading as news. The request for an end to speculations, particularly in developing countries such as India, with a focus on authenticated, accurate news reports. This essay demonstrates a model and process for detecting false news. The internet is a significant invention, as well as a substantial number of individuals use it. These people use it for a variety of purposes. These users have access to a variety of social media platforms. Through these online platforms, any user can make a post or spread news. FAKE NEWS has spread to a larger audience than ever before in this digital era, owing primarily to the rise of social media and direct messaging platforms. Fake news detection requires significant research, but it also presents some challenges. Some difficulties may arise as a result of a limited number of resources, such as a dataset. In this project, we propose a machine learning technique for detecting fake news and implementing a novel automatic fake news credibility inference model with Natural language processing steps that include text mining. Machine learning algorithms construct a deep diffusive network model based on a set of explicit and latent features extracted from textual information to simultaneously learn the representations of news articles, creators, and subjects. The "Fake News Challenge" is a Kaggle competition, and the social network is using AI to sift fake news articles out of users' feeds. In the comparison study, three algorithms—Random Forest, Navy Bayes, and Passive Aggressive classifier—are used to determine the text accuracy value for the precision, recall, and f1 score using these methods. Finally, Passive Aggressive Classifier approach provides greater accuracy compared to others. Combating fake news is a traditional text categorization project with a simple proposition.

Gaucher disease (GD) is a rare autosomal recessive metabolic disorder. It is characterized by a deficiency of lysosomal glucocerebrosidase, which results in the accumulation of glycosphingolipid substrates, primarily glucosylceramide, in the phagocyte system. In GD Type 1, the liver, spleen, and bone marrow are typically affected. We report the case of a 7-year-old female with GD Type 1 who presented with hepatosplenomegaly detected incidentally following a motor vehicle accident. She was found to have concomitant thrombocytopenia and Erlenmeyer flask deformities of her lower limbs. Diagnosis was made on the basis of very low leukocyte β-glucocerebrosidase activity and elevated plasma chitotriosidase. DNA mutation studies revealed both c.1226A>G and c.116_1505 deletion (exons 3-11). The patient is currently managed with biweekly intravenous imiglucerase (Cerezyme) replacement therapy. She demonstrated resolution of thrombocytopenia and hepatosplenomegaly at 2-year follow-up. Physicians must consider this rare diagnosis in children presenting with hepatosplenomegaly to prompt timely management.

Syzygium alternifolium (Wight) Walp. is a medium-sized tree economically exploited locally for its medicinal uses. Its seeds, fruit pulp, leaves and bark are used locally in ayurvedic medicines for the treatment of various ailments. It is an endemic and endangered species that is narrowly confined to the southern parts of Eastern Ghats, India. To understand its ecological and geographic distribution and to identify potential habitats for its introduction/reintroduction to assist its recovery from the endangered category, we used MaxEnt for ecological niche modeling (ENM), taking one hundred sixty-six species occurrences and twenty-six climatic and topographical datasets as input. The MaxEnt model has identified five variables having maximum bearing on S. alternifolium distribution and has successfully reconstructed the species’ geographic distribution in the Seshachalam, Veligonda and Lankamalla hill ranges of Eastern Ghats. The model predicts 95% of the study area to be potentially least suitable for S. alternifolium, whereas only 5% of the study area is predicted to be moderate to highly potentially suitable for the species. Furthermore, the model identified novel, potential habitats with similar ecological conditions that can be used as a guide for the introduction/reintroduction of S. alternifolium to expand its range and assist in species recovery from its endangered category.

Background Endoscopy is an important tool in assessing the severity of gastrointestinal diseases including Eosinophilic Esophagitis (EoE). Agreement regarding endoscopy outcomes is important when using tools such as the Endoscopic Reference Score for EoE (EREFS). Purpose Our goal was to determine interrater and intrarater agreement of EREFS among Canadian pediatric gastroenterologists. Method Survey-based study of interrater and intrarater reliability amongst pediatric gastroenterologists with interest in pediatric EoE. Participants were sourced from the Canadian Pediatric EoE Network. Participants were asked how many years of training they’ve had with endoscopy for pediatric EoE and their comfort in disease scoring for pediatric EoE. Pediatric EoE cases were identified from the pediatric EoE registry at the Stollery Children’s Hospital with an endoscopic video associated with each case. Participants were asked to score each video using the EREFS questionnaire for the proximal, middle and distal segments of the esophagus. 15 endoscopic videos were evaluated, with 3 cases provided each week over a period of 5 weeks. Additional data included ratings of the video quality and endoscopy quality. Of 15 cases, 12 were unique cases, distributed evenly in severity between no active disease to severe disease. 3 cases were repeated to assess intrarater reliability. The maximum grade of the proximal, middle and distal segments of the esophagus for each component endoscopic finding (edema, rings, exudates, furrows, strictures) were used for reliability calculations. Fleiss Kappa was calculated for all EREFS items and for each component endoscopic finding. Cohen’s Kappa was calculated to assess intrarater reliability. Result(s) Fifteen participants were recruited for the study. The participants had a median of 12 years (IQR: 7, 19) of clinical experience in endoscopy for pediatric EoE. The majority of participants were “comfortable” (i.e., 4 on 5-point scale) with EREFS scoring for pediatric EoE. Fleiss Kappa for all EREFS items was 0.481. For each component endoscopic finding (edema, rings, exudates, furrows, strictures), Fleiss Kappa was 0.365, 0.293, 0.548, 0.263, 0.445 respectively. Cohen’s Kappa had a median of 0.620 (IQR: 0.593, 0.704). The majority of raters rated video quality and endoscopy quality as “good” (i.e., 4 on 5-point scale). Conclusion(s) There is moderate interrater reliability in EREFS scoring for pediatric EoE. Interrater reliability was between fair to moderate for each component endoscopic finding. Intrarater reliability was good. This study shows there is room for improvement in disease scoring for pediatric EoE. This could be in the form of additional training, expert-defined conventions, or centralized reading which have reduced variability in endoscopic reporting for adult GI disease in past studies and could be used in a follow-up study to attempt to improve agreement. Additionally, incorporating EREFS into routine clinical practice may increase agreement amongst endoscopists. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared

BACKGROUND: Less than two-thirds of children with abdominal pain in the emergency department receive analgesia. We sought to determine whether hyoscine butylbromide was superior to acetaminophen for children with nonspecific colicky abdominal pain. METHODS: We randomly allocated children aged 8–17 years with nonspecific colicky abdominal pain who presented to the pediatric emergency department of London Health Sciences Centre, London, Ontario to receive hyoscine butylbromide, 10 mg given orally, or acetaminophen, 15 mg/kg given orally (maximum 975 mg). We considered the minimal clinically important difference for the primary outcome (self-reported pain at 80 min) to be 13 mm on a 100 mm visual analogue scale. Secondary outcomes included administration of rescue analgesia, adverse effects and pain score less than 30 mm at 80 minutes. RESULTS: A total of 236 participants (120 in the hyoscine butylbromide group and 116 in the acetaminophen group) were included in the trial. The mean visual analogue scale scores at 80 minutes were 29 mm (standard deviation [SD] 26 mm) and 30 mm (SD 29 mm) with hyoscine butylbromide and acetaminophen, respectively (adjusted difference 1, 95% confidence interval −7 to 7). Rescue analgesia was administered to 4 participants (3.3%) in the hyoscine butylbromide group and 1 participant (0.9%) in the acetaminophen groups (p = 0.2). We found no significant differences in rates of adverse effects between hyoscine butylbromide (32/116 [27.6%]) and acetaminophen (28/115 [24.3]) (p = 0.5); no serious adverse effects were observed. The proportion with a pain score less than 30 mm at 80 minutes was 66 (55.0%) with hyoscine butylbromide and 63 (54.3%) with acetaminophen (p = 0.9). INTERPRETATION: Hyoscine butylbromide was not superior to acetaminophen in this setting. Both agents were associated with clinically important pain reduction, and either can be considered for children presenting to the emergency department with nonspecific colicky abdominal pain. Trial registration:Clinicaltrials.gov, no. NCT02582307

Tamil people living in the countryside. They are living their lives by putting their thoughts on natural products. Folklore rewards the good and evil of the stars in the sky. The Sun or Moon appears after the disappearance of the people and is considered a new beginning. People believe that appealing to the deity can convey rain through rituals. The folk believe that appeals to the deity can be communicated by laws such as showers of rain. People believe that the gods and the evil spirits stay in the plants so that some plants are worshiped and some are taken away from their lives. People believe that animals are related to their body organs and that they are beneficial. Thus the confidence in the thoughts of the people is developed. Such beliefs are the cause of people who think that the benefits are good. They have established themselves in the community. That emerges as a community-based culture.

Background Eosinophilic esophagitis (EoE) is a chronic disorder treated by food elimination diet (FED), topical steroids and/or proton-pump inhibitors (PPI). Serial endoscopies and biopsies assess response to therapy. EoE management has evolved as guidelines are updated. Aims To identify practice variation among Canadian paediatric gastroenterologists (PG) who care for children with EoE. Methods An online survey using REDCap about decision-making in children with EoE was distributed to PG in Canada in November 2020. Results 62 PG completed the survey (response rate 69%, 62/94). The majority work in academic centres (92%). 3 centers indicated an accrual of >50 new patients per year; 9/16 centres have >100 patients in follow-up. An EoE Clinic is present in 5 centres. Diagnosis: Familiarity with the 2018 AGREE and 2020 AGA EoE guidelines was found to be 57% and 67% respectively. Criteria required to diagnose EoE according to current guidelines were correctly indicated by 42% of PG. (Figure 1). Endoscopy: The majority of PG (95%) adhere to guidelines in terms of required number and location of biopsies for the initial diagnosis. Ideal timing of repeat endoscopy after change in therapy in patients who are not in histological remission was 8–12 weeks by 67% of PG, timing in stable patients on maintenance therapy varied (33% only if patient is symptomatic). 25% used the EREFS Score in reporting endoscopic findings. Therapy: Improvement of symptoms was the highest ranked goal (64%), followed by remission of histologic findings (30%). A treatment algorithm was in place in 4 centers. The majority routinely assess adherence to therapy (73%) and consult a dietitian for FED (77%). Most (87%) do not consult an allergist for initial management. Preferred choices of 1st-line therapy varied among PG (Figure 2). When FED was selected, 32% of PG started with 1 food, 32% started with 2 foods, most frequently excluding dairy, followed by wheat. 14 (26%) start with ≥6 FED. Prescription of budesonide slurry was consistent among PG with doses of 1 and 2 mg/day in children 10 years, respectively. Conclusions The is the first Canadian study to assess the variation in management of children with EoE by PG. Overall, PG demonstrated good adherence to the guidelines in terms of initial diagnosis, but differences in maintenance therapy choice and timing of endoscopies. The results highlight a need for standardized management algorithms to deliver uniform care to this growing group. Grounding these guidelines in evidence will warrant a significant investment in further paediatric EoE research. Funding Agencies None

In the present digital age and with the huge volume of data floating around, the information security has become utmost importance. Intrusion detection is the process of analysing various events in a system/network for possible presence of intrusion. The aim of an intrusion detection system is to protect the system from unauthorized access. Design of intrusion detection systems (IDS) has gained lots of importance in the recent years and has become the standard component of in network security. Intrusion detection systems operate either using anomaly based or signature based and in some cases IDS operate in a hybrid way. The data growth rate and the higher bandwidth and network speed makes it very difficult to process the data in real-time. Many researchers have focused in this area and have used data mining techniques for detecting the intrusions. Classification is a data mining technique used to predict group membership for each data instance. Classification is being used by various researchers for detection intrusions. Lot of classification algorithms have been developed for intrusion detection with respective strengths and weaknesses. This paper presents a novel classification algorithm based on distance measure and Relief-F feature weighting. The performance measures of intrusion detection are compared with the commonly used classification algorithms such as Naive Bayes, Decision Tree and Support Vector Machine (SVM) and the proposed algorithm outperforms the above mentioned algorithms in terms of Detection Rate, Accuracy, False Alarm Rate, F-Score and Mathews Correlation Coefficient. The proposed algorithm is tested using a benchmark dataset (KDDcup99 dataset) and a real traces dataset (Kyoto 2006 + dataset). This study also intend to compare the execution time for various classifiers and the parallel performance of NADA since NADA outperforms all the other classifiers in terms of serial execution time. The algorithm is parallelized and the results are presented in terms of execution time with various data size, speed up and efficiency.

The aim of this study is to examine the awareness, accessibility and use of e-databases at Market research organizations because huge money is invested for the subscription, growth, and management of e-databases. The main aim is to determine the awareness of e-databases; and to find out their extent of use, level of satisfaction, problems, and instructions/help in accessing of e-databases.

Large scale Neural Network (NN) accelerators typically have multiple processing nodes that can be implemented as a multi-core chip, and can be organized on a network of chips (noise) corresponding to neurons with heavy traffic. Portions of several NoC-based NN chip-to-chip interconnect networks are linked to further enhance overall nerve amplification capacity. Large volumes of multicast on-chip or cross-chip can further complicate the construction of a cross-link network and create a NN barrier of device capacity and resources. In this paper, this refer to inter-chip and inter-chip communication strategies known as neuron connection for NN accelerators. Interconnect for powerful fault-tolerant routing system neural NoC is implemented in this paper. Regarding intra-chip communication, this recommend crossbar arbitrage placement, virtual interrupt and path-based parallelization strategies for virtual channel routing, leading to higher NoC output with lower hardware costs. For multicast-based traffic. Regarding inter-chip communication, this propose a lightweight NoC compatible chip-to-chip interconnection scheme to allow efficient interconnection for NoC-based NN chips. In addition, this will test the proposed methods with four Field Programmable Gate Arrays (FPGAs) on four hardwired Deep Neural Network (DNN) chips. The experimental results show that the proposed interconnection network can effectively handle data traffic with high thr

Sentiment and Emotion detection in social media conversations remains a challenge and analyzing the people emotion emerged as an important task in this unprecedented time of COVID-19. People sentiment and emotions are affected by lockdowns, social distancing, travel, work-from-home, wearing mask, reading social media posting. Most of them are feeling sad, anger, depressed, and some of them are neutral and happy. The most recent Sentiment Analysis (SA) researches are done using Twitter dataset (short-text) and rule-based (sentiment lexicon) approach, the outcome of these SA models' results is not showing the consistent prediction of people sentiment about COVID-19. To mitigate and overcome limitations of lexicon approach, processing unstructured social media long text posting, getting context based sentiment score, model overfitting, performance problems of sentiment models, authors' proposed and built a novel multi-class SA model using extension of Bidirectional LSTM (SAB-LSTM) with additional layers. In this experiment SAB-LSTM model has been used to process long text of social media posting, news articles text dataset. Experiment result showed, SAB-LSTM model performance is better than traditional LSTM and BLSTM. Compared SAB-LSTM performance metric of Precision, Recall, F1 Score and sentiment score with traditional LSTM and BLSTM. For this experiment collected COVID-19 related dataset from various social media sources such as Twitter, Facebook, YouTube, News articles blogs and collected data from friends and family.