Deposit subscribe Prediction using Data Mining Techniques based Real Marketing Dataset

Recently, economic depression, which scoured all over the world, affects business organizations and banking sectors. Such economic pose causes a severe attrition for banks and customer retention becomes impossible. Accordingly, marketing managers are in need to increase marketing campaigns, whereas organizations evade both expenses and business expansion. In order to solve such riddle, data mining techniques is used as an uttermost factor in data analysis, data summarizations, hidden pattern discovery, and data interpretation. In this paper, rough set theory and decision tree mining techniques have been implemented, using a real marketing data obtained from Portuguese marketing campaign related to bank deposit subscription [Moro et al., 2011]. The paper aims to improve the efficiency of the marketing campaigns and helping the decision makers by reducing the number of features, that describes the dataset and spotting on the most significant ones, and predict the deposit customer retention criteria based on potential predictive rules

An innovative IoT service for medical diagnosis

Due to the misdiagnose of diseases that increased recently in a scarily manner, many researchers devoted their efforts and deployed technologies to improve the medical diagnosis process and reducing the resulted risk. Accordingly, this paper proposed architecture of a cyber-medicine service for medical diagnosis, based internet of things (IoT) and cloud infrastructure (IaaS). This service offers a shared environment for medical data, and extracted knowledge and findings between patients and doctors in an interactive, secured, elastic and reliable way. It predicts the medical diagnosis and provides an appropriate treatment for the given symptoms and medical conditions based on multiple classifiers to assure high accuracy. Moreover, it entails different functionalities such as on-demand searching for scientific papers and diseases description for unrecognized combination of symptoms using web crawler to enrich the results. Where such searching results from crawler, are processed, analyzed and added to the resident knowledge base (KB) to achieve adaptability and subsidize the service predictive ability

DiaMe: IoMT deep predictive model based on threshold aware region growing technique

Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity

Deep Learning-aided Brain Tumor Detection: An Initial ‎Experience based Cloud Framework ‎

Lately, the uncertainty of diagnosing diseases increased and spread due to the huge intertwined and ambiguity of symptoms, that leads to overwhelming and hindering the reliability of the diagnosis ‎process. Since tumor detection from ‎MRI scans depends mainly on the specialist experience, ‎misdetection will result an inaccurate curing that might cause ‎critical harm consequent results. In this paper, detection service for brain tumors is introduced as ‎an aiding function for both patients and specialist. The ‎paper focuses on automatic MRI brain tumor detection under a cloud based framework for multi-medical diagnosed services. The proposed CNN-aided deep architecture contains two phases: the features extraction phase followed by a detection phase. The contour ‎detection and binary segmentation were applied to extract the region ‎of interest and reduce the unnecessary information before injecting the data into the model for training. The brain tumor ‎data was obtained from Kaggle datasets, it contains 2062 cases, ‎‎1083 tumorous and 979 non-tumorous after preprocessing and ‎augmentation phases. The training and validation phases have been ‎done using different images’ sizes varied between (16, 16) to ‎‎ (128,128). The experimental results show 97.3% for detection ‎accuracy, 96.9% for Sensitivity, and 96.1% specificity. Moreover, ‎using small filters with such type of images ensures better and faster ‎performance with more deep learning.

On Individual, Sex and Age Differentiation of Indian House Crow (\u3cem\u3eCorvus splendens\u3c/em\u3e) Call: A Preliminary Study in Potohar, Pakistan

Considering importance of acoustics studies in population biology, 500 calls of the Indian House Crow (Corvus splendens) were recorded in morning - mid-afternoon hours (January-February, 2009) from 23 sites of urban areas of Potahar (Punjab, Pakistan). Calls were recorded using Sony CFS 1030 S sound records (sampling rate = 48 KHz) and edited using Sound Analysis Pro (Version 1.02). software using FFT method rate 50%, data window 9.27 ms, advanced window 1.36 ms. Through editing of calls, we selected 60 (37 ♂♂, 17 ♀♀, 6 Juvenile ♂♂) good quality spectrograms for detailed analysis. Spectrograms were characterized by rapid frequency modulations using 6 (call pitch, mean pitch goodness, mean frequency of the calls, frequency of modulations, mean amplitude modulation, mean wiener entropy) acoustic parameters. Significance of difference was analysed using Multivariate and Discriminate Function Analysis. Calls could be assigned to correct individual in 10.8% males, 21.0% females, and 42.9% juveniles, which was significantly higher than percentage of correct classification per chance. Calls could be attributes to correct sex in 88.5% and to correct age group in 80.6% of cases

Minimal hepatic encephalopathy: Effect of H. pylori infection and small intestinal bacterial overgrowth treatment on clinical outcomes

The effect Helicobacter pylori (Hp) infection and small intestinal bacterial over growth (SIBO) in minimal hepatic encephalopathy (MHE) is not well understood. The aim of the study was to determine the effect of eradication of Hp infection and SIBO treatment on MHE in patients with cirrhosis. Patients with cirrhosis were enrolled and MHE was determined by psychometric tests and critical flicker frequency analysis. Hp infection and SIBO were assessed by urea breath and Hydrogen breath tests respectively in patients with cirrhosis and in healthy volunteers. Patients with Hp infection and SIBO were given appropriate treatment. At six weeks follow-up, presence of Hp infection, SIBO and MHE status was reassessed. Ninety patients with cirrhosis and equal number of healthy controls were included. 55 (61.1%) patients in the cirrhotic group were diagnosed to have underlying MHE. Among cirrhotic group, Hp infection was present in 28 with MHE (50.9%) vs. in 15 without MHE (42.8%) (p = 0.45). Similarly, SIBO was present in 17 (30.9%) vs. 11 (31.4%) (p = 0.95) in patients with and without MHE respectively. In comparison with healthy controls, patients with cirrhosis were more frequently harboring Hp and SIBO (47.7% vs. 17.7% (p \u3c 0.001) and 31.1% vs. 4.4% (p \u3c 0.001) respectively. On follow-up, all patients showed evidence of eradication of Hp and SIBO infection. Treatment of SIBO significantly improved the state of MHE in cirrhotics, however eradication of Hp infection did not improve MHE significantly. Additionally, patients with low Model for End-Stage Liver Disease (MELD) score and belonging to Child class B had significantly better improvement in MHE. A large number of patients with cirrhosis had either active Hp infection or SIBO with or without MHE, compared to healthy controls. Treatment of SIBO significantly improved MHE in patients with cirrhosis, whereas eradication of Hp did not affect the outcome of MHE in these patients

Comparison of the virulence markers of helicobacter pylori and their associated diseases in patients from Pakistan and Afghanistan

BACKGROUND/AIM: Helicobacter pylori is a Gram-negative bacteria, which is associated with development of gastroduodenal diseases. The prevalence of H. pylori and the virulence markers cytotoxin-associated gene A and E (cagA, cagE) and vacuolating-associated cytotoxin gene (vacA) alleles varies in different parts of the world. H. pylori virulence markers cagA, cagE, and vacA alleles in local and Afghan nationals with H. pylori-associated gastroduodenal diseases were studied. PATIENTS AND METHODS:Two hundred and ten patients with upper gastrointestinal symptoms and positive for H. pylori by the urease test and histology were included. One hundred and nineteen were local nationals and 91 were Afghans. The cagA, cagE, and vacA allelic status was determined by polymerase chain reaction. RESULTS:The nonulcer dyspepsia (NUD) was common in the Afghan patients (P = 0.025). In Afghan H. pylori strains, cagA was positive in 14 (82%) with gastric carcinoma (GC) compared with 29 (45%) with NUD (P = 0.006), whereas cagE was positive in 11 (65%) with GC and 4 (67%) with duodenal ulcer (DU) compared with 12 (18%) with NUD (P \u3c 0.001 and 0.021, respectively). The vacA s1a/b1was positive in 10 (59%) of GC compared with 20 (31%) in NUD (P = 0.033). In Pakistani strains, cagE was positive in 12 (60%) with GC, 7 (58%) with GU, 12 (60%) with DU compared with 11 (16%) with NUD (P \u3c 0.001, 0.004, and \u3c 0.001, respectively). In Pakistani strains, cagA/s1a/m1 was 39 (33%) compared with Afghans in 17 (19%) (P = 0.022). Moderate to severe mucosal inflammation was present in 51 (43%) Pakistani patients compared with 26 (28%) (P = 0.033) in Afghans. It was also associated with grade 1 lymphoid aggregate development in Pakistani patients 67 (56%) compared with 36 (40%) (P = 0.016) in Afghans. CONCLUSION: Distribution of H. pylori virulence marker cagE with DU was similar in Afghan and Pakistan H. pylori strains. Chronic active inflammation was significantly associated with Pakistani H. pylori strains

Evolution of an Emerging Symmetric Quantum Cryptographic Algorithm

With the rapid evolution of data exchange in network environments, information security has been the most important process for data storage and communication. In order to provide such information security, the confidentiality, data integrity, and data origin authentication must be verified based on cryptographic encryption algorithms. This paper presents a new emerging trend of modern symmetric encryption algorithm by development of the advanced encryption standard (AES) algorithm. The new development focuses on the integration between Quantum Key Distribution (QKD) and an enhanced version of AES. A new quantum symmetric encryption algorithm, which is abbreviated as Quantum-AES (QAES), is the output of such integration. QAES depends on generation of dynamic quantum S-Boxes (DQS-Boxes) based quantum cipher key, instead of the ordinary used static S-Boxes. Furthermore, QAES exploits the specific selected secret key generated from the QKD cipher using two different modes (online and off-line)

Itopride for gastric volume, gastric emptying and drinking capacity in functional dyspepsia

AIM: To study the effect of itopride on gastric accommodation, gastric emptying and drinking capacity in functional dyspepsia (FD). Methods: Randomized controlled trial was conducted to check the effect of itopride on gastric accommodation, gastric emptying, capacityof tolerating nutrient liquid and symptoms of FD. We recruited a total of 31 patients having FD on the basis of ROME III criteria. After randomization, itopride was received by 15 patients while 16 patients received placebo. Gastric accommodation was determined using Gastric Scintigraphy. 13C labeled octanoic breadth test was performed to assess gastric emptying. Capacity of tolerating nutrient liquid drink was checked using satiety drinking capacity test. The intervention group comprised of 150 mg itopride. Patients in both arms were followed for 4 wk. Results: Mean age of the recruited participant 33 years (SD = 7.6) and most of the recruited individuals, i.e., 21 (67.7%) were males. We found that there was no effect of itopride on gastric accommodation as measured at different in volumes in the itopride and control group with the empty stomach (P = 0.14), at 20 min (P = 0.38), 30 min (P = 0.30), 40 min (P = 0.43), 50 min (P = 0.50), 60 min (P = 0.81), 90 min (P =0.25) and 120 min (P = 0.67). Gastric emptying done on a sub sample (n = 11) showed no significant difference (P = 0.58) between itoprideand placebo group. There was no significant improvement in the capacity to tolerate liquid in the itopride group as compared to placebo (P = 0.51). Similarly there was no significant improvement of symptoms as assessed through a composite symptom score (P = 0.74). The change in QT interval in itopride group was not significantly different from placebo (0.10). Conclusion: Our study found no effect of itopride on gastric accommodation, gastric emptying and maximum tolerated volume in patients with FD