A hybrid deep learning approach towards building an intelligent system for pneumonia detection in chest X-ray images

Pneumonia is a major cause for the death of children. In order to overcome the subjectivity and time consumption of the traditional detection of pneumonia from chest X-ray images; this work hypothesized that a hybrid deep learning system that consists of a convolutional neural network (CNN) model with another type of classifiers will improve the performance of the detection system. Three types of classifiers (support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) were used along with the traditional CNN classification system (Softmax) to automatically detect pneumonia from chest X-ray images. The performance of the hybrid systems was comparable to that of the traditional CNN model with Softmax in terms of accuracy, precision, and specificity; except for the RF hybrid system which had less performance than the others. On the other hand, KNN hybrid system had the best consumption time, followed by the SVM, Softmax, and lastly the RF system. However, this improvement in consumption time (up to 4 folds) was in the expense of the sensitivity. A new hybrid artificial intelligence methodology for pneumonia detection has been implemented using small-sized chest X-ray images. The novel system achieved a very efficient performance with a short classification consumption time

The Influence of E-Marketing Mix Strategy on Organizational Performance: An Empirical Analysis of Jordanian Smes

Purpose: The purpose of this empirical research article is to investigate the association between e-marketing mix strategy (EMMS) and organizational performance (OP) in Jordanian SMEs.   Theoretical framework: The study used a quantitative research methodology to explore the impact of EMMS factors on OP indicators, such as product development, pricing strategies, promotional activities, and online distribution channels.   Design/methodology/approach: The study's data is gathered using a survey questionnaire issued to 250 Jordanian SMEs. The dependent variables include OP indicators such as financial performance, customer happiness, market share, and innovation, whereas the independent variables are EMMS factors. Control factors including business size, firm age, and industry type are also taken into account. To evaluate the hypothesized correlations, the obtained data is analyzed using the PLS-SEM (PLS 4.0) technique.   Findings: The results of this study show that in the Jordanian context, all e-marketing techniques, including product development, price strategies, promotional activities, and online distribution channels, have a significant positive impact on OP metrics   Research implications: The findings of this study demonstrate the value of e-marketing and the necessity of sound e-marketing tactics for fostering the expansion and competitiveness of SMEs in the digital era.   Practical and social implications: The study underscores the significance of implementing e-marketing strategies for SMEs in Jordan, as they contribute to enhancing OP. Policymakers can utilize these findings to develop supportive measures for SMEs, while practitioners can gain insights into the value of e-marketing strategies in achieving growth and competitiveness.   Originality/value: This study adds to the current literature by investigating the association between EMMS and OP in Jordanian SMEs. The study adds to the field's knowledge base by providing empirical evidence and insights on the impact of various e-marketing methods on OP measures

Artificial Intelligence In Managing The Electronic Customer Relationship And Enhancing The Level Of Satisfaction With Electronic Services

The customer is the backbone of the process of using the applications in the government institution. In a way that ensures the creation of its information about the change of his desires and opinions about the products and applications that are developed by electronic governments, and even his reactions and complaints within a marketing strategy that artificial intelligence sought with its embodied tools for information technology to provide it, and the result was to manage the relationship with the customer using the technological developments that help to do so. Throughout this article, we try to find out the following questions: What The role of artificial intelligence in managing a government institution's customer relationship? For this, we proposed three objectives, how expert systems embody the mechanisms of artificial intelligence within the government institution, while the mechanism of customer relationship management within the government institution is represented, how artificial intelligence has contributed to the success of customer relationship management to the e-governmen

3,4-Dichlorobenzoic acid biodegradation by the Edwardsiella tarda: Effect of Some Growth Conditions

The biodegradation of 3,4-DiChlorobenzoic acid was investigated by using Edwardsiella tarda and it used 3,4-DCBA as sole carbon and energy source. Several concentrations of 3,4-D CBAs (1mM, 2mM ,3mM ,4mM and 5mM) were used. The highest rate of degradation of 3,4-D CBAs was obtained at a concentration (2mM). The experiments were included substrate concentration, temperature, pH, starvation, adaptation, carbon and nitrogen sources. The degradation ability was monitored through the release of chloride disappearance of the substrate and finally the growth of bacterial cells on that substrate. The optimal temperature and pH for the bacteria were 42ºC and 7.5, respectively. Adaptation of the cells on 3,4-DCBA for 48 hours and cells starvation for 24 hours and 48 hours increasing the initial degradation rate. The carbon sources affected the 3,4 –DCBA degradation differently from that on chloride and cell mass production. Nitrogen sources supplied (yeast extract, L-proline, casein, NH4, K-Nitrate, arginine, urea and glycine). Urea and casine caused a repression in 3,4-DCBA degradation. Catechol 1,2 dioxygenase activity was found to be present in cell free extracts suggesting that 3,4-DCBA is catabolized by ortho-ring cleavage pathway

Artificial Intelligence Using a Neural Network System to Support Human Resources in the Workplace

Artificial neural networks mirror the behaviour of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of artificial intelligence, machine learning, and deep learning and from it an attempt to represent human behaviour according to learning algorithms; These artificial networks target cognitive and cognitive activities associated with brain function. The current study aims mainly to reveal the nature of the relationship between human resources and the smart organization through the neural network system and the level of its practices, the neural network resembles the sources of human mental activity as an effective resource in the functional environment, and to identify the most important what these super-intelligent applications can add to organizational behaviour. The data was analyzed using The simulation system for neural networks through mean clustering tests and to indicate the level of influence of inputs and outputs to reach the proposed model, as well as tests of accuracy and performance of the model, the level of error in training neural networks, then the strength of the relationship between layer nodes, the weights of the effect of hidden neurons (layer hidden) and the relative importance The dimensions of the inputs over the outputs of the final model. The results of the study showed that the practice of smart organizations has a greater impact on the human resources elements The study recommended that decision centres should realize the importance of the simulation system in neural networks in providing solutions for to administrative problems because of the time, effort, and money it saves, in addition to the accuracy of the results, in addition to the need to continue to Disseminate and adopt human resources because it is the basis on which to achieve an important strategic competitive advantage and The necessity of compatibility of the objectives set by the Bank with the economies of knowledge, electronic commerce, and mutual communication and innovative knowledge

Occluded iris classification and segmentation using self-customized artificial intelligence models and iterative randomized Hough transform

A fast and accurate iris recognition system is presented for noisy iris images, mainly the noises due to eye occlusion and from specular reflection. The proposed recognition system will adopt a self-customized support vector machine (SVM) and convolution neural network (CNN) classification models, where the models are built according to the iris texture GLCM and automated deep features datasets that are extracted exclusively from each subject individually. The image processing techniques used were optimized, whether the processing of iris region segmentation using iterative randomized Hough transform (IRHT), or the processing of the classification, where few significant features are considered, based on singular value decomposition (SVD) analysis, for testing the moving window matrix class if it is iris or non-iris. The iris segments matching techniques are optimized by extracting, first, the largest parallel-axis rectangle inscribed in the classified occluded-iris binary image, where its corresponding iris region is crosscorrelated with the same subject’s iris reference image for obtaining the most correlated iris segments in the two eye images. Finally, calculating the iriscode Hamming distance of the two most correlated segments to identify the subject’s unique iris pattern with high accuracy, security, and reliability

Fatty acids as biological markers for symbiotic bacteria in Phyllidia varicosa and Phyllidiella pustulosa

The fatty acid (FA) composition of Phyllidia varicosa and Phyllidiella pustulosa (notum and viscera) was investigated. Samples were collected from coastal water of Balok - Pahang - Malaysia. This study was conducted to test the hypothesis that nudibranchs species host symbiotic bacteria by using fatty acids as biological markers. A high level of fatty acids group specific to the bacteria were detected in the selected species that called odd- branched chain fatty acids. Among them, high levels of iso- anteiso-C15:0, C15:0, iso-C16:0, C17:0, iso-C17:0, iso C17:1(n-5), iso C18:0, 14-methyl-C18:0 and iso-C18:0) were found and their percentages in the notum are significantly different compared to viscera. The total odd- branched chain fatty acids were 29.64% in P. varicosa and 30.66% in P. pustulosa compared to another group of fatty acids such as saturated FA, monounsaturated FA and polyunsaturated FA. The present study deals with the identification of cyclopropane FA in the nudibranch tissue for the first time which cyclopropaneoctanoic acid 2-hexyl and cyclopropaneoctanoic acid 2-octyl were detected. We suggest that symbiotic bacteria associated with the nudibranchs tissue originate these fatty acids

The Relationship between the Academic Procrastination and Self-Efficacy among Sample of King Saud University Students

The purpose of this study is to explore the relationship between the academic procrastination and self-efficacy among students of King Saud University. It also aims to identify whether the level of Academic procrastination varies depending on variables such as type of the college, academic level, or the level of students’ achievement. Two instruments were developed: academic procrastination instrument, and self-efficacy instrument. The questionnaires were distributed to random sample of 195 students from Science and Arts colleges at King Saud University. The Findings indicate that the highest percentage of the distribution of the sample on of procrastination Academic scale is (83.6%), followed by the low percentage (9.7%) of procrastination while the lowest percentage of procrastination is (6.7%). The findings also showed that there were statistically significant differences at the level of academic procrastination due to level of achievement for favour of group who get (acceptable) in their achievement. It also found that there were no statistically significant differences due to the type of college and the academic achievement. In addition, the findings revealed that there were statistically significant differences between of the academic procrastination scale and the self-efficacy scale. Keywords: Academic procrastination, self-efficacy, university student