Multi Agents Classification System with Reliable Measure of Generalization

In this paper an efficient classification system using hierarchal multi_agent's technology based on neural network is introduced, where each agent implements as a neuraogy effort to o do thisl network (trained using back propagation learning algorithm). The system consist of two layers of agents, the top layer contain one agent works as control agent, its responsibility is to select the right agent from the agents in the lower layer to classify the related pattern depending on data’s features. Two techniques were used (regularization and earlier stopping criteria) to select the best one for each data set depending . The system provides a degree of generalization with the ability to improve it if there is a need for more generalization. The developed system was tested using different standard datasets obtained from the (University of California, Irvine) UCI Machine Learning Repository for the empirical analysis of machine learning algorithms. These are (User Knowledge Level, iris, and Banknote Authentication Dataset)

An Effective Hybrid Deep Neural Network for Arabic Fake News Detection

Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural Network (Text-CNN) and Long Short-Term Memory (LSTM) architecture to produce efficient hybrid model. Text-CNN is used to identify the relevant features, whereas the LSTM is applied to deal with the long-term dependency of sequence. The results showed that when trained individually, the proposed model outperformed both the Text-CNN and the LSTM. Accuracy was used as a measure of model quality, whereby the accuracy of the Hybrid Deep Neural Network is (0.914), while the accuracy of both Text-CNN and LSTM is (0.859) and (0.878), respectively. Moreover, the results of our proposed model are better compared to previous work that used the same dataset (AraNews dataset)

PANC Study (Pancreatitis: A National Cohort Study): national cohort study examining the first 30 days from presentation of acute pancreatitis in the UK

Background: Acute pancreatitis is a common, yet complex, emergency surgical presentation. Multiple guidelines exist and management can vary significantly. The aim of this first UK, multicentre, prospective cohort study was to assess the variation in management of acute pancreatitis to guide resource planning and optimize treatment. Methods: All patients aged greater than or equal to 18 years presenting with acute pancreatitis, as per the Atlanta criteria, from March to April 2021 were eligible for inclusion and followed up for 30 days. Anonymized data were uploaded to a secure electronic database in line with local governance approvals. Results: A total of 113 hospitals contributed data on 2580 patients, with an equal sex distribution and a mean age of 57 years. The aetiology was gallstones in 50.6 per cent, with idiopathic the next most common (22.4 per cent). In addition to the 7.6 per cent with a diagnosis of chronic pancreatitis, 20.1 per cent of patients had a previous episode of acute pancreatitis. One in 20 patients were classed as having severe pancreatitis, as per the Atlanta criteria. The overall mortality rate was 2.3 per cent at 30 days, but rose to one in three in the severe group. Predictors of death included male sex, increased age, and frailty; previous acute pancreatitis and gallstones as aetiologies were protective. Smoking status and body mass index did not affect death. Conclusion: Most patients presenting with acute pancreatitis have a mild, self-limiting disease. Rates of patients with idiopathic pancreatitis are high. Recurrent attacks of pancreatitis are common, but are likely to have reduced risk of death on subsequent admissions

Developed Models Based on Transfer Learning for Improving Fake News Predictions

In conjunction with the global concern regarding the spread of fake news on social media, there is a large flow of research to address this phenomenon. The wide growth in social media and online forums has made it easy for legitimate news to merge with comprehensive misleading news, negatively affecting people’s perceptions and misleading them. As such, this study aims to use deep learning, pre-trained models, and machine learning to predict Arabic and English fake news based on three public and available datasets: the Fake-or-Real dataset, the AraNews dataset, and the Sentimental LIAR dataset. Based on GloVe (Global Vectors) and FastText pre-trained models, A hybrid network has been proposed to improve the prediction of fake news. In this proposed network, CNN (Convolution Neural Network) was used to identify the most important features. In contrast, BiGRU (Bidirectional Gated Recurrent Unit) was used to measure the long-term dependency of sequences. Finally, multi-layer perceptron (MLP) is applied to classify the article news as fake or real. On the other hand, an Improved Random Forest Model is built based on the embedding values extracted from BERT (Bidirectional Encoder Representations from Transformers) pre-trained model and the relevant speaker-based features. These relevant features are identified by a fuzzy model based on feature selection methods. Accuracy was used as a measure of the quality of our proposed models, whereby the prediction accuracy reached 0.9935, 0.9473, and 0.7481 for the Fake-or-Real dataset, AraNews dataset, and Sentimental LAIR dataset respectively. The proposed models showed a significant improvement in the accuracy of predicting Arabic and English fake news compared to previous studies that used the same datasets.

Disconnected Pancreatic Duct Syndrome: A Multidisciplinary Management Dilemma

Disconnected pancreatic duct syndrome is a circumferential interruption of the pancreatic duct. It usually occurs secondary to pancreatitis and carries significant diagnostic and management challenges. We present a case of disconnected pancreatic duct syndrome that represented a diagnostic and management dilemma for both medical and surgical teams. The aim of this article is to share a successful management experience of disconnected pancreatic duct syndrome with other physicians and to perform a brief but focused literature review on this challenging condition

Renal Cell Carcinoma With Isolated Breast Metastasis

Renal cell carcinoma (RCC) is a highly prevalent disease worldwide with many cases being metastasised to various organs during the time of initial presentation. Metastatic RCC to the breast is a rare entity and can mimic primary breast carcinoma. In this article, we present a 63-year-old Caucasian woman presented with a breast mass that was detected by screening mammography and found to have a biopsy proven grade-II clear RCC in the breast tissue. Despite the high incidence and prevalence of primary breast cancer, metastasis from extramammary should be suspected in patients with a prior history of other cancers. In this brief literature review, we also highlight the survival benefit from surgery and close follow-up in selected group of patients with metastatic, metachronous and solitary RCC

Rare Allergic Reaction Of The Kidney: Sitagliptin-Induced Acute Tubulointerstitial Nephritis

A 56-year-old man with a history of diabetes mellitus type-2 and stage-2 chronic kidney disease secondary to diabetic nephropathy presented with an acute deterioration of kidney function. Non-invasive work-up failed to reveal the underlying aetiology for the acute kidney failure. Kidney biopsy revealed acute tubulointerstitial nephritis (ATIN) which was attributed to sitagliptin use. Only few case reports have shown this correlation. Our aim is to alert physicians and other providers of the potential effect of sitagliptin to cause ATIN with this biopsy-proven case