Integrating Information Theory Measures and a Novel Rule-Set-Reduction Tech-nique to Improve Fuzzy Decision Tree Induction Algorithms

Machine learning approaches have been successfully applied to many classification and prediction problems. One of the most popular machine learning approaches is decision trees. A main advantage of decision trees is the clarity of the decision model they produce. The ID3 algorithm proposed by Quinlan forms the basis for many of the decision trees’ application. Trees produced by ID3 are sensitive to small perturbations in training data. To overcome this problem and to handle data uncertainties and spurious precision in data, fuzzy ID3 integrated fuzzy set theory and ideas from fuzzy logic with ID3. Several fuzzy decision trees algorithms and tools exist. However, existing tools are slow, produce a large number of rules and/or lack the support for automatic fuzzification of input data. These limitations make those tools unsuitable for a variety of applications including those with many features and real time ones such as intrusion detection. In addition, the large number of rules produced by these tools renders the generated decision model un-interpretable. In this research work, we proposed an improved version of the fuzzy ID3 algorithm. We also introduced a new method for reducing the number of fuzzy rules generated by Fuzzy ID3. In addition we applied fuzzy decision trees to the classification of real and pseudo microRNA precursors. Our experimental results showed that our improved fuzzy ID3 can achieve better classification accuracy and is more efficient than the original fuzzy ID3 algorithm, and that fuzzy decision trees can outperform several existing machine learning algorithms on a wide variety of datasets. In addition our experiments showed that our developed fuzzy rule reduction method resulted in a significant reduction in the number of produced rules, consequently, improving the produced decision model comprehensibility and reducing the fuzzy decision tree execution time. This reduction in the number of rules was accompanied with a slight improvement in the classification accuracy of the resulting fuzzy decision tree. In addition, when applied to the microRNA prediction problem, fuzzy decision tree achieved better results than other machine learning approaches applied to the same problem including Random Forest, C4.5, SVM and Knn

A Taxonomy of Free Network Sniffers for Teaching and Research

Today\u27s networking environment has become very complex. Networks have been growing in size rapidly and have come to support more complex applications. As result, troubleshooting and maintaining networks has become cumbersome and has created the need for new specialized tools such as Network Protocol Analyzers, better known as Network Sniffers .Network Sniffers have become critical tools in today\u27s networking management and troubleshooting processes. They enable network managers to evaluate and examine the data running through their network by troubleshooting network performance problems and identifying certain network faults. Network Sniffers can help identify network attacks and detect security threats; they can be used in intrusion detection systems.Besides their usage in the technical environment, network sniffers can be used for educational and research purposes. They can be used to help understand packets\u27 architecture and traffic patterns generated by common network applications. Network Sniffers can also be used to evaluate protocol performance and assist in protocol development. Despite their usefulness, network sniffers can be harmful when used by hackers. With network sniffers, hackers can capture data and steal information from targeted networks.This study consists of two major efforts. The first major effort entails researching and determining a set of criteria to use in evaluating and comparing network sniffers. The second major effort involves using the criteria to evaluate and compare three free network sniffers, thus building a taxonomy. The three free network sniffers used in this study were Ethereal, EtherSnoop and Packetyzer. Each of these three sniffers was evaluated and tested. Then their features and capabilities were compared

Evaluation of Glycated Hemoglobin (HbA1c) for Diagnosing Type 2 Diabetes and Prediabetes among Palestinian Arab Population

The purpose of the study is to compare the potential of HbA1c to diagnose diabetes among Palestinian Arabs compared to fasting plasma glucose (FPG). A cross-sectional sample of 1370 Palestinian men (468) and women (902) without known diabetes and above the age of 30 years were recruited. Whole blood was used to estimate HbA1c and plasma for FPG and total lipid profile. Fasting plasma glucose was used as a reference to diagnose diabetes ($126 mg/dL) and prediabetes (100–125 mg/dL). The area under the receiver operating characteristic curve (AUC) for HbA1c was 81.9diabetes and 63.90.498) and low with prediabetes (K = 0.142). The optimal cut-off value for HbA1c to diagnose diabetes was$ 6.3% (45 mmol/mol). The sensitivity, specificity and the discriminant ability were 65.6% (53.1–76.3%), 94.5% (93.1–95.6%), 80.0% (72.8–87.3%), respectively. However, using cut-off value of $6.5the sensitivity, specificity and the discriminant ability were 57.4For diagnosing prediabetes with HbA1c between 5.7–6.4discriminant ability were 62.7value of$ 6.5% (48 mmol/mol) by itself diagnosed 5.3% and 48.3% as having diabetes and prediabetes compared to 4.5% and 24.2% using FPG, respectively. Mean HbA1c and FPG increase significantly with increasing body mass index. In conclusion, the ROC curves showed HbA1c could be used for diagnosing diabetes when compared to FPG but not for prediabetes in Palestinians Arabs even though only about 50% of the diabetic subjects were identified by the both HbA1c and FPG.This project was partially supported by United Nation Relief and Working Agency (UNRWA. No additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Prevalence of Giardia Assemblages Among Equines in Jordan

A cross-sectional study was carried out on 400 equine holding (326 horses and 74 donkeys) samples to determine the prevalence of Giardia assemblages A, B, and E in Jordan. Identifying the Giardia assemblages was carried out using enzyme-linked immunosorbent assay (ELISA) as a screening test and PCR-RFLP targeting β-giardin loci. In addition, polymerase chain reaction targeting triose phosphate isomerase gene specific for assemblages A and B were used as confirmatory. Thirty-four samples tested positive by ELISA for Giardia with an apparent prevalence of 8.5%. The PCR-RFLP test confirmed Giardia assemblages in 30 of the 34 ELISA-positive samples giving a true prevalence of 7.7% (95% confidence interval: 4.8–10.1). Of the 30 positive animals/holdings, 18, 4, and 8 had assemblages A, B, and E. Assemblage A was significantly (P < .05) more prevalent when compared to assemblages B and E. The total infection rates of Giardia, assemblages B and E were significantly (P < .05, chi-square) higher in donkeys 14.8%, 2.7%, and 5.5% compared to horses 5.8%, 0.6%, and 1.2%, respectively. Analysis of risk factors revealed that only season was significantly associated with the different Giardia assemblages. Autumn (odds ratio [OR] = 0.09) was associated with Giardia infection regardless of the assemblage type as reducing factor. The odds of infection of assemblages A and E increased in winter (OR = 6.8) and spring (OR = 4.5), respectively. Giardia assemblages A, B, and E infect both horses and donkeys in Jordan with potential impact on human and animal health, and the odds of infections is significantly associated with season

Evaluation of Glycated Hemoglobin (HbA1c) for Diagnosing Type 2 Diabetes and Prediabetes among Palestinian Arab Population

The purpose of the study is to compare the potential of HbA1c to diagnose diabetes among Palestinian Arabs compared to fasting plasma glucose (FPG). A cross-sectional sample of 1370 Palestinian men (468) and women (902) without known diabetes and above the age of 30 years were recruited. Whole blood was used to estimate HbA1c and plasma for FPG and total lipid profile. Fasting plasma glucose was used as a reference to diagnose diabetes ($126 mg/dL) and prediabetes (100–125 mg/dL). The area under the receiver operating characteristic curve (AUC) for HbA1c was 81.9diabetes and 63.90.498) and low with prediabetes (K = 0.142). The optimal cut-off value for HbA1c to diagnose diabetes was$ 6.3% (45 mmol/mol). The sensitivity, specificity and the discriminant ability were 65.6% (53.1–76.3%), 94.5% (93.1–95.6%), 80.0% (72.8–87.3%), respectively. However, using cut-off value of $6.5the sensitivity, specificity and the discriminant ability were 57.4For diagnosing prediabetes with HbA1c between 5.7–6.4discriminant ability were 62.7value of$ 6.5% (48 mmol/mol) by itself diagnosed 5.3% and 48.3% as having diabetes and prediabetes compared to 4.5% and 24.2% using FPG, respectively. Mean HbA1c and FPG increase significantly with increasing body mass index. In conclusion, the ROC curves showed HbA1c could be used for diagnosing diabetes when compared to FPG but not for prediabetes in Palestinians Arabs even though only about 50% of the diabetic subjects were identified by the both HbA1c and FPG.This project was partially supported by United Nation Relief and Working Agency (UNRWA. No additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors thank Fida Zeidan from UNRWA for organizing the teams at different UNRWA clinics. Also, the authors thank the staff of UNRWA clinics for their cooperation in the study. Thanks to Dr. Khaldoun Bader from Al-Quds University for his assistance in statistical analysis.Guarantor: Akram T. Kharroubi

The effect of Instagram on millennials consumer’s purchase intentions in the fashion industry

The purpose of the current research is to explore the impact of Instagram pages on consumers’ purchasing intentions among millennials in the fashion industry in Jordan. This study uses a quantitative, cause-effect, and cross-sectional approach. Online surveys were used to collect data from 212 respondents through different social media tools. The collected data was analyzed by SPSS software and Smart PLS to test the research hypothesis. Results show that bloggers’ recommendations significantly affect eWOM and engagement; usefulness information significantly affects eWOM and engagement; while trust insignificantly affects eWOM and engagement; brand familiarity insignificantly affects eWOM and engagement; participation and socialization insignificantly affect eWOM and engagement. Finally, useful information, eWOM, and engagement significantly affect consumers buying intention on Instagram. The study gives new information about the influence of Instagram pages on consumers' intentions. Therefore, this research expands the knowledge about factors that affect customers’ buying intentions. Since the study is a quantitative cross-sectional conducted on fashion industry Instagram users through an online survey in Jordan, which may limit its generalization to other industries and countries, therefore, the study suggests applying similar studies to online users of different ages, industries, and countries. Marketers can use Instagram to contact, promote, advertise, and sell their products by developing strong relationships with their customers through different social media tools. Using social media tools for marketing and selling reduces paperwork, printed advertisement, and transportation, which positively affects corporate social responsibility and reduces the consumption of energy and pollution

Rapid detection and differentiation of pathogenic Campylobacter jejuni and Campylobacter coli by real-time PCR

A two-tube real-time assay, developed in a LightCyclerTM, was used to detect, identify and differentiate Campylobacter jejuni and Campylobacter coli from all other pathogenic members of the family Campylobacteriaceae. In the first assay, continuous monitoring of the fluorescence resonance energy transfer (FRET) signal acquired from the hybridisation of two adjacent fluoroprobes, a specific FITC probe 5′-GTGCTAGCTTGCTAGAACTTAGAGA-FITC-3′) and a universal downstream probe Cy5 (5′-Cy5-AGGTGITGCATGGITGTCGTTGTCG-PO4-3′), to the 681-base pair 16S rRNA gene amplicon target (Escherichia coli position 1024–1048 and 1050–1075, respectively) produced by the primer pair, F2 (ATCTAATGGCTTAACCATTAAAC, E. coli position 783) and Cam-Rev (AATACTAAACTAGTTACCGTC, E. coli position 1464), detected C. coli, C. lari and C. jejuni. As expected, a Tm of 65 °C was derived from the temperature-dependent probe DNA strand disassociation. In the second assay, an increase in fluorescence due to binding of the intercalating dye SYBR Green I to the DNA amplicons of the hippuricase gene (hipO) (produced by the primer pair hip2214F and hip2474R) was observed for C. jejuni but not for C. coli which lacks the hipO gene. A Tm of and 56 °C determined from temperature-dependent dye–DNA disassociation identified C. jejuni and the non-specific PCR products, respectively, in line with our expectation. The two-tube assay was subsequently used to identify and differentiate the 169 Campylobacteriaceae isolates of animal, human, plant and bird origin held in our culture collection into C. coli (74 isolates), C. jejuni (86 isolates) and non-C. coli–C. jejuni (9 isolates). In addition, the method successfully detected C. jejuni, C. coli and C. lari from 24-h enrichment cultures initiated from 30 commercial chicken samples

Cellulose Acetate Membranes: Fouling Types and Antifouling Strategies—A Brief Review

Cellulose acetate (CA) is a semisynthetic, biodegradable polymer. Due to its characteristics, CA has several applications, including water membranes, filament-forming matrices, biomedical nanocomposites, household tools, and photographic films. This review deals with topics related to the CA membranes, which are prepared using different techniques, such as the phase inversion technique. CA membranes are considered very important since they can be used as microfiltration membranes (MF), ultrafiltration membranes (UF), nanofiltration membranes (NF), reverse osmosis (RO) membranes, and forward osmosis (FO) membranes. Membrane fouling results from the accumulation of materials that the membrane rejects on the surface or in the membrane’s pores, lowering the membrane’s flux and rejection rates. There are various forms of CA membrane fouling, for instance, organic, inorganic, particulate fouling, and biofouling. In this review, strategies used for CA membrane antifouling are discussed and summarized into four main techniques: feed solution pretreatment, cleaning of the membrane surface, membrane surface modification, which can be applied using either nanoparticles, polymer reactions, surface grafting, or surface topography, and surface coating