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Abstract The antinuclear antibody (ANA) test has high sensitivity in diagnosing and classifying systemic lupus erythematosus (SLE). Objectives To describe the immunological pattern of SLE patients through investigating specific antinuclear autoantibodies by enzyme dot immunoassay and studying their frequency in both positive and negative ANA indirect immunofluorescence assay (IIF) cases. Methods In a cross-sectional study, blood samples from 393 newly diagnosed SLE patients were analyzed using (IIF) on HEp-2 cells and ANA dot immunoassay by automated enzyme immunoassay (EIA) to detect 19 antibodies. Results Ninety-one percent of the patients are females; their mean age was 37 ± 12.28. Antinuclear antibody (ANA) was detected by IIF in 82.4% of cases, with 181 (46.1%) speckled and 167 (42.4%) homogeneous ANA patterns. The majority of patients (96%) demonstrated autoantibodies via EIA. Among the ANA-IIF-negative patients, 97.2% demonstrated autoantibodies. There was a significant difference in the frequency of certain autoantibodies between SLE patients with negative and positive ANA-IIF (1.44 0.73, 3.12 2.09, p = 0.00) respectively. Conclusion The results of analyzing 19 autoantibodies with the ANA staining pattern increased the significance of analyzing the immune profile even if IIF is negative when clinical symptoms strongly suggest SLE diagnosis. Certain autoantibodies may evade staining by the IFA approach while they are present in the patient’s serum, and they may not be detected by the ANA EIA profile if it does not contain that antigenic substrate. Key Points• Indirect immunofluorescence on Hep-2 is the conventional method for ANA detection and is regarded as the “gold standard” for testing in clinical practice for SLE.• In our study, ANA profile dot enzyme immunoassay (EIA)-based test was performed to evaluate 19 autoantibodies in SLE patients either positive or negative for ANA-IIF.• The presence of anti-dsDNA with ANA-IIF-negative serum in 32.4% of SLE patients provides evidence that not all anti-dsDNA antibodies are identified on standard HEp-2 substrates.• certain autoantibodies can evade staining by the ANA-IIF method despite being present in the SLE patient’s blood; this supports the ANA profile enzyme dot immunoassay as a more sensitive test.

Intelligent healthcare has made significant progress using artificial intelligence (AI) methods. Since the COVID-19 pandemic, healthcare service has gained more attention, particularly remote and automated healthcare consultation. Medical bots are becoming increasingly popular for obtaining medical advice and support. Medical bots offer numerous benefits, making them an attractive option for patients and healthcare providers. They provide 24/7 access to medical advice, reduce appointment wait times by providing quick answers to common questions or concerns, and cost savings associated with fewer visits or tests required for diagnosis and treatment plans. The outcome of the medical bot depends upon the learning quality. Thereby depends on the appropriate corpus within the domain of interest. Arabic is one of the most commonly used languages for sharing users' internet content. Developing a medical bot on Arabic faces many challenges: including the morphological composition of the language, the diversity of dialects, and the need for appropriate corpora in the domain. Thus, this paper's main contribution is introducing the largest Arabic Healthcare Q&A dataset, called (MAQA). The dataset consists of more than 430k questions distributed into 20 medical specializations. Also, the paper adopts three deep learning models, LSTM, Bi-LSTM, and transformers, for experimenting and benchmarking the proposed corpus MAQA. The experimental results indicate that the recent Transformer model, with an average cosine similarity of 80.81% and BLeU score of 58%, outperforms the traditional deep learning models.

This study aimed to report the dynamic profile of IgG-specific antibodies to SARS-CoV-2 infection for 6 months after infection. We conducted a prospective study, recruited 33 recently confirmed covid -19 patients and collected 6 samples from each patient. The first samples were collected one month from the start of symptoms and subsequent samples collected at 30 days interval. We measured the IgG by chemiluminescent immunoassay (CLIA). According to the disease severity, patients were categorized as asymptomatic 4 (12.1%), mild 14 (42,4%), moderate 9 (27.3%), and severe 6 (18.2%). Patients were 12 (35.3%) females and 21 (64.7%) males. The mean IgG levels maintained a high level till the second month (92.81 ± 110.15 AU/ml) from the onset of symptoms followed by a gradual decrease till the sixth month after infection (17.42 ± 22.61 AU/ml). The patients with severe symptoms significantly exhibited the highest IgG levels, reached the highest level (mean=237.44 ± 164.13 AU/ml) at the second month. While the lowest levels were detected among the asymptomatic patients (mean= 3.04 ± 2.94 AU/ml) at the second month. Older age correlated with higher IgG antibody level (r= 0.350 p=0.046); however, sex was not related to IgG level. In conclusion, Symptomatic COVID-19 disease is followed by protective immunity for more than 6 months. Immunity in asymptomatic patients is low and fades rapidly than symptomatic cases. Patients with severe disease had significantly higher IgG levels compared to mild, moderate, or asymptomatic patients.

Relative positions are recent solutions to overcome the limited accuracy of GPS in urban environment. Vehicle positions obtained using V2I communication are more accurate because the known roadside unit (RSU) locations help predict errors in measurements over time. The accuracy of vehicle positions depends more on the number of RSUs; however, the high installation cost limits the use of this approach. It also depends on nonlinear localization nature. They were neglected in several research papers. In these studies, the accumulated errors increased with time due to the linearity localization problem. In the present study, a cooperative localization method based on V2I communication and distance information in vehicular networks is proposed for improving the estimates of vehicles’ initial positions. This method assumes that the virtual RSUs based on mobility measurements help reduce installation costs and facilitate in handling fault environments. The extended Kalman filter algorithm is a well-known estimator in nonlinear problem, but it requires well initial vehicle position vector and adaptive noise in measurements. Using the proposed method, vehicles’ initial positions can be estimated accurately. The experimental results confirm that the proposed method has superior accuracy than existing methods, giving a root mean square error of approximately 1 m. In addition, it is shown that virtual RSUs can assist in estimating initial positions in fault environments.

Objective: The present study aims to evaluate coronavirus shedding in the tears of asymptomatic pediatric COVID-19 positive patients. Methods: A prospective interventional study that included a total of 145 pediatric asymptomatic COVID-19 patients hospitalized from 17th May 2020 to 16th July 2020 in Sohag Tropical Hospital. On admission, all of them were COVID-19 positive detected through nasopharyngeal swab. They were in intimate contact with positive symptomatic COVID-19 patients before testing and admission. Reverse Transcriptase Polymerase chain reaction (RT-PCR) was done for tears samples at an interval of 5 days after admission and twice before discharge. Results: Of the 145 asymptomatic pediatric COVID-19 positive patients, no one showed ocular or systemic manifestations. They were silent carriers. Ten were positive for tears sample on admission. They became negative for nasopharyngeal and tear samples before discharge. Conclusion: Pediatric positive COVID-19 patients can shed coronavirus through their tears. Even among asymptomatic patients, transmission through tears is possible.

The expansion of the Internet of Things has resulted in new creative solutions, such as smart cities, that have made our lives more productive, convenient, and intelligent. The core of smart cities is the Intelligent Transportation System (ITS) which has been integrated into several smart city applications that improve transportation and mobility. ITS aims to resolve traffic issues, especially traffic congestion. Recently, new traffic flow prediction models and frameworks have been rapidly developed in tandem with the introduction of Artificial Intelligence (AI) approaches to improve the accuracy of traffic flow prediction. Traffic forecasting is a crucial duty in the transportation industry. It can significantly affect the design of road constructions and projects in addition to its importance for route planning and traffic rules. Furthermore, traffic congestion is a critical issue in urban areas and overcrowded cities. Therefore, it must be accurately evaluated and forecasted. Hence, a reliable and efficient method for predicting traffic is essential. The main objectives of this study are: First, present a comprehensive review of the most popular machine learning and deep learning techniques applied in traffic prediction. Second, identifying inherent obstacles to applying machine learning and deep learning in the domain of traffic prediction.

Aim This paper aims to present a comprehensive survey of existing machine learning and deep learning approaches utilized in healthcare prediction, as well as identify inherent obstacles to applying these approaches in the healthcare prediction domain. Background Healthcare prediction has been a significant factor in saving human lives in recent years. In the domain of healthcare, there is a rapid development of intelligent systems for analyzing complicated relationships among data and transforming them into real information for use in the prediction process. Consequently, artificial intelligence is rapidly transforming the healthcare industry. Thus comes the role of systems depending on machine learning as well as deep learning in the creation of steps that diagnose and predict diseases, whether from clinical data or based on images, that provide tremendous clinical support by simulating human perception and can even diagnose diseases that are difficult to detect by human intelligence. Methods The studies discussed in this paper have been presented in journals published by IEEE, Springer, and Elsevier. Machine learning, deep learning, healthcare, surgery, cardiology, radiology, hepatology, and nephrology are some of the terms used to search for these studies. The studies chosen for this survey are concerned with the use of machine learning as well as deep learning algorithms in healthcare prediction. Results A total of 40 working papers were selected and the methodology for each paper was clarified. Conclusion This paper presents a comprehensive survey as well as the current challenges in healthcare prediction. studies have shown that artificial intelligence plays a significant role in diseases diagnosing.

Recently, most companies market their products on the web to recognize their customers’ requirements and to improve their services’ quality according to the customers’ feedback and opinions. A huge amount of reviews and opinions are posted daily on products. Obtaining and quickly analyzing these opinions become a difficult task. These opinions might lead to a tendency or disinclination to a specific point of view. To represent the products’ opinions from customers’ perspectives, opinion retrieval becomes a demanding and essential task for automatically extracting, analyzing, and summarizing customers’ reviews. Usually, online products are offered by several suppliers in e-commerce. Therefore, to keep up the competitiveness among suppliers, the need for innovative requirements is required. This paper proposed an enhanced opinion retrieval approach depending on the explicit feature based opinion mining. The proposed approach expands the initial products’ requirements using extended heuristics and linguistic patterns of the Arabic opinions. Besides the relevant score, several factors, like features’ weight, the opinion importance, and the sentiment polarity are used to rank the retrieved results. The experimental results show the capability of the proposed approach to automatically extract more innovative features compared to the conditional random field (CRF) results.

The problem of finding relevant data while searching the internet represents a big challenge for web users due to the enormous amounts of available information on the web. These difficulties are related to the well-known problem of information overload. In this work, we propose an online web assistant called OWNA. We developed a fully integrated framework for making recommendations in real-time based on web usage mining techniques. Our work starts with preparing raw data, then extracting useful information that helps build a knowledge base as well as assigns a specific weight for certain factors. The experiments show the advantages of the proposed model against alternative approaches.

Determining the resource requirements at airports especially in-ground services companies is essential to successful planning in the future, which is represented in the resources demand curve according to the future flight schedule, through which staff schedules are created at the airport to cover the workload with ensuring the highest possible quality service provided. Given in the presence of variety service level agreements used on flight service vary according to many flight features, the resources assumption method makes planning difficult. For instance, flight position is not included in future flight schedule but it's efficacious in the identification of flight resources. In this regard, based on machine learning, we propose a model for building a resource demand curve for future flight schedules. It is divided into two phases, the first is the use of machine learning to predict resources of the service level agreement required on future flight schedules, and the second is the use of implement a resource allocation algorithm to build a demand curve based on predicted resources. This proposal could be applicable to airports that will provide efficient and realistic for the resources demand curve to ensure the resource planning does not deviate from the real-time resource requirements. the model has proven good accuracy when using one day of flights to measuring deviation between the proposed model predict demand curve when flights did not include the location feature and the actual demand curve when flights include location.

Background Lupus nephritis can be seen in up to 60% of all SLE patients with 10–15% of nephritis patients progressing to end-stage renal disease; late diagnosis of lupus nephritis is correlated with a higher frequency of renal insufficiency. The study aim is determination of the value of urinary human epidermal growth factor (urinary EGF) as an early biomarker of lupus nephritis in SLE patients and its relevance to disease activity and renal histopathology. Results The study included 58 SLE patients and 30 healthy controls; a significant difference was noticed between SLE and controls in urinary protein, creatinine, protein/creatinine ratio, and urinary EGF. The mean level of urinary EGF was less in classes IV and V renal nephritis than in classes I, II, and III. There is a significant difference in urinary EGF (33±29, 27±16, P = 0.04) between class II and class III lupus nephritis, with no significant differences in urinary protein, creatinine, protein/creatinine ratio, and SLEDAI. On the other hand, the comparison between classes II and IV showed significant difference not only in urinary EGF (33±29, 11.7±4.9 m, P=0.003), but also in SLEDAI (37.4±8, 70.5±27, P= 0.007), and protein/creatinine ratio (0.98±0.62, 3±1.8, P=0.006). Conclusion This study raises the attention to test the sensitivity of urinary EGF in detecting the early and the subsequent changes in renal pathology of SLE patients as an easy, non-invasive, accurate, cheap marker that could help in following up the nephritis progression and adjusting the plan of treatment; also, it can be used to guide the time of biopsy or as an alternative in cases where renal biopsy is contraindicated.

In artificial neural networks, activation functions play a significant role in the learning process. Choosing the proper activation function is a major factor in achieving a successful learning performance. Many activation functions are sufficient universal approximators, but their performance is lacking. Thus, many efforts have been directed toward activation functions to improve the learning performance of artificial neural networks. However, the learning process involves many challenges, such as saturation, dying, and exploding/vanishing the gradient problems. The contribution of this work resides in several axes. First, we introduce two novel activation functions: absolute linear units and inverse polynomial linear units. Both activation functions are augmented by an adjustable parameter that controls the slope of the gradient. Second, we present a comprehensive study and a taxonomy of various types of activation functions. Third, we conduct a broad range of experiments on several deep neural architecture models with consideration of network type and depth. Fourth, we evaluate the proposed activation functions’ performance in image and text classification tasks. For this purpose, several public benchmark datasets are utilized to evaluate and compare the performance of the proposed functions with that of a group of common activation functions. Finally, we deeply analyze the impact of several common activation functions on deep network architectures. Results reveal that the proposed functions outperform most of the popular activation functions in several benchmarks. The statistical study of the overall experiments on both classification categories indicates that the proposed activation functions are robust and superior among all the competitive activation functions in terms of average accuracy.

Background: Rheumatoid arthritis (RA) is a long-lasting autoimmune disorder that primarily affects joints. It typically results in warm, swollen, and painful joints. Pain and stiffness often worsen following rest. Most commonly, the wrist and hands are involved, with the same joints typically involved on both sides of the body. Often, symptoms come on gradually over weeks to months. The disease may also affect other parts of the body. This may result in a low red blood cell count, inflammation around the lungs, and inflammation around the heart. Fever and low energy may also be present. Objective: To study leptin levels in rheumatoid arthritis and relationship between these levels and disease activity. Conclusion: Blocking the key signal pathways of leptin and inhibiting leptin activity, such as with leptin antagonists, may be a promising way for the therapeutic potential of RA at risk of detrimental effects. Hence, further understanding of the mechanism of leptin would be advantageous in the future in RA treatment.

Recently, there has been an enormous increase in the number of reviews of popular products. Therefore, opinion analysis has become a tedious task for customers when making decisions. As a result, opinion retrieval systems have emerged as an effective tool to analyze and represent customers’ feelings toward offered services. Conventional opinion retrieval systems retrieve and rank products according to both relevance and the overall polarity scores of the opinions. However, customer reviews are usually more detailed, including multiple features with different polarities. Consequently, feature-based opinion retrieval is necessary to extract and analyze each feature separately. Customers’ opinions are usually written with a short and unclear structure and contain many implicit linguistic features that cannot be identified by retrieval systems. As a result, the recall results are negatively affected. Few studies have focused on implicit features, as most examined explicit features. Also, implicit features extraction is a challenging task in some languages like Arabic due to difficulties with morphology. This paper proposes an enhanced retrieval approach based on feature-based opinion mining to enhance retrieval performance. In addition to explicit feature extraction, a metaheuristic optimization method with several similarity measures is utilized to identify implicit features and measure its effect on the retrieval results. The experimental results on Arabic and English datasets revealed the effectiveness of the proposed approach, whereby more features were extracted compared to the explicit feature results. Furthermore, the ranking results were improved by identifying both implicit and explicit features compared to the results obtained by the conditional random field method and association rule mining.

PURPOSE: To assess SARS-CoV-2 virus in conjunctival tears and secretions of positive confirmed COVID-19 patients METHODS: A case series study that included 28 positive COVID-19 patients confirmed with nasopharyngeal swab in the period 18-28 May 2020 at Sohag Tropical Medicine Hospital Tears and conjunctival secretions of these confirmed positive cases were collected with disposable sampling swabs at interval of 3 days after admission due to respiratory symptoms They were examined for the presence of SARS-CoV-2 by reverse transcription-polymerase chain reaction (RT-PCR) assay RESULTS: Thirteen (46 43%) patients were stable, 4 (14 28%) patients suffered from dyspnea, 3 (10 72%) patients suffered from high fever, 5 (17 85%) patients suffered from cough, and 3 (10 72%) patients were on mechanical ventilation Ten (35 71%) patients suffered from conjunctivitis Tear and conjunctival swabs were positive in 8 (28 57%) patients, while other patients' swabs were negative (71 43%) Out of 10 patients with conjunctival manifestations, 3 patients had SARS-CoV-2 in their conjunctiva using (RT-PCR) test Out of the 18 patients with no conjunctival manifestations, 5 patients had positive SARS-CoV-2 in their conjunctiva using (RT-PCR) test CONCLUSION: The SARS-CoV-2 virus could be found in tears and conjunctival secretions in SARS-CoV-2 patients with or without conjunctivitis

The airport ground handling has a global trend to meet the Service Level Agreement (SLA) requirementsthat represents resource allocation with more restrictions according to flights. That can be achieved by predictingfuture resources demands. this research presents a comparison between the most used machine learning techniquesimplemented in many different fields for demand prediction and resource allocation. The prediction model nomi-nated and used in this research is the Support Vector Machine (SVM) to predict the required resources for eachflight, despite the restrictions imposed by airlines when contracting their services in the Service Level Agreement.The approach has been trained and tested using real data from Cairo International Airport. the proposed (SVM)technique implemented and explained with a varying accuracy of resource allocation prediction, showing thateven for variations accuracy in resource prediction in different scenarios; the Support Vector Machine techniquecan produce a good performance as resource allocation in the airport.

Background: Cerebrovascular disease is a great health burden. Hepatitis C Virus (HCV) infection has a role in the development of carotid atherosclerosis and recently associated to poor outcome in patients with stroke.Aim of Study: The aim of this work was to investigate the prognostic value of HCV viral load on acute first-ever ischemic stroke outcome.Patient and Methods: Sixty patients diagnosed with acute stroke were enrolled and divided into 41 patients with and 19 without chronic HCV. Stroke severity was assessed and correlated with HCV viral load which was determined By RT-PCR. The morphological and functional status of the liver was evaluated by ultrasonography and laboratory investigations including liver function tests.Results: The outcome was favorable in 35% and unfavo-rable in 65%. The high level of HCV RNA in stroke patients was found to be an independent predictor of stroke outcome after controlling for age, hypertension, DM and stroke severity. Patients who died had significantly higher levels of HCV RNA than survivors.Conclusion: High viremia is an independent predictor of short term outcome of first ever stroke.

Neurexin1 gene is essential for formulating synaptic cell adhesion to establish synapses. In a previous work, 38 SNPs in Neurexin1 recoded in mental disorder patients have been collected. Five computational prediction tools have been used to predict the effect of SNPs on protein function and stability. Only four SNPs in Neurexin1α have deleterious prediction results from at least four tools. The current work aims to use molecular dynamic simulation (MD) to study the effects of the four mutations on Neurexin1α both on the whole protein as well as identifying affected domains by mutations. A protein model that consists of five domains out of six domains in the real protein was used; missing residues were added, and model was tested for quality. The MD experiment has last for 1.5 μs where four parameters have been used for studying the whole protein in addition to three more parameters for the domain analysis. The whole protein study has shown that two mutations E427I for Autism and R525C for non-syndromic intellectual disability (NSID) have distinctive behavior across the four used parameters. Domain study has confirmed the previous results where the five domains of R525C have acted differently from wild type (WT), while E427I has acted differently for four domains from wild type. The other two mutations D104H and G379E have three domains that only acted differently from wild type. The fourth domain of all mutations has an obvious distinctive behavior from wild type. Further study of E427I and R525C mutations can lead to better understanding of autism and NSID.

Accurate automated system for glaucoma diagnosis is very important since glaucoma is the second disease that leads to vision loss. Therefore, it is necessary to utilize all computational intelligence approaches to detect glaucoma accurately at its early stages in retinal images. The most significant sign of glaucoma in retinal fundus images, which helps doctors to diagnose if the patient has normal or glaucomatous image, is the ratio of vertical optic cup diameter to vertical optic disc diameter. Therefore, it is important to accurately segment both the optic disc and optic cup regions in the retinal fundus image. In this paper, our contribution proposed an approach that utilizes a combination of significant large-scale features to perform supervised superpixel classification by linear support vector machine for segmenting the optic disc and optic cup regions. We got an accuracy of 98.6% and 99.2% for disc and cup segmentation, respectively, and glaucoma diagnosis accuracy of 99%.

Plant diseases cause unprecedented crop loss across the world. Predictions of crop disease give the decision makers the warning that one or more diseases may occur at a particular location at a particular time in the future. This prediction ensures that control measures, especially the treatments, are used more effectively to avoid these diseases by tacking the actions to be ready, e.g., the crop will not plant in a particular area. This paper provides a fuzzy prediction model of plant diseases with climate changes. The proposed model consists of two components; one for prediction climate for next year using a climate dataset. The second one is fuzzy-based disease detection based on a set of disease cause’s fuzzy membership functions. These functions are defined based on knowledge acquired from agricultural experts. The result of this research proved that predicting the suitable time for crops diseases helps decision making to avoid them in suitable ways.

Some studies reported a high prevalence of ischemic stroke in hepatitis C virus patients, other several studies have suggested that hepatitis C virus (HCV) may act as a trigger for autoimmune diseases and autoantibodies including Anti-Neutrophil Cytoplasmic Antibody (ANCA) which predispose to vasculitis. Because vasculitis is a risk factor for ischemic stroke, we investigated the association of the hepatitis C virus with ANCA in first-ever ischemic stroke patients. This study included 67 Egyptian patients with first-ever ischemic stroke. These patients were clinically examined and investigated for HCV infection by chemiluminescenceReal Time-PCR, and ANCA antibodies by ELISA. Forty-two patients (62.7%) had HCV infection. Twenty-nine (43.2%) of them were cytoplasmic- Antineutrophil Cytoplasmic Antibodies (c-ANCA) positive, while none was perinuclear- Antineutrophil Cytoplasmic Antibodies (p-ANCA) positive. Comparison between c-ANCA positive and ANCA negative patients showed that 82.8% and 47.4% had anti-HCV antibody, respectively, with P-value 0.003. The c-ANCA level correlated significantly with age, and HCV antibody level. No statistically significant difference was found in both the consciousness and stroke severity between the negative and positive c- ANCA patients. However, patients with positive c-ANCA had smaller and multiple cerebral infarctions with P-value 0.002 and 0.01 respectively. Multiple regression analysis showed that the number and size of cerebral infarctions were independent predictors of c-ANCA positivity with P value 0.02, and 0.03 respectively. In conclusion, c-ANCA level correlates with HCV antibody and may predispose to ischemic stroke by a possible ANCA associated vasculitis.

Hany Mahmoud,1 Ahmed Hamody,2 Hesham M Hefny,3 Dalia Tohamy,4 Islam Awny5 1Department of Ophthalmology, Faculty of Medicine, Sohag University, Sohag, Egypt; 2Anesthesia Department, Sohag University, Sohag, Egypt; 3Clinical Pathology Department, Sohag University, Sohag, Egypt; 4Ophthalmology Department, Assiut University, Assiut, Egypt; 5Department of Ophthalmology, Sohag University, Sohag, EgyptCorrespondence: Hany MahmoudDepartment of Ophthalmology, Faculty of Medicine, Sohag University, EgyptTel +20 1024368111Email drhanymahmoud@gmail.comPurpose: To assess the presence of anti-SARS-CoV-2 IgA in the conjunctival secretions of confirmed COVID-19 patients by nasopharyngeal swabs and correlate its presence with the severity of the disease, patient’s age, sex and ocular symptoms.Methods: This study included 44 positive COVID-19 patients confirmed with nasopharyngeal swabs during the period 17– 28 February 2021 at Sohag Tropical Medicine Hospital. Tears and conjunctival secretions were examined for the presence of anti-SARS-CoV-2 IgA.Results: While non-reactive results are strongly correlated to low titre and vice versa, severity showed significant correlation with neither IgA reactivity nor titre. Meanwhile, IgA reactivity did not show significant correlation with either age or sex. The reactivity and IgA titre are correlated with ocular symptoms.Conclusion: The anti-SARS-CoV-2 IgA could be found in ocular secretions in SARS-CoV-2 patients. There is no correlation with age or sex or severity of the disease; however, they are correlated with ocular symptoms.Keywords: anti-SARS-CoV-2 IgA, ocular, secretions, swab

Region-of-interest (ROI) segmentation is an important critical step and challenging task in the evolution of computer-aided detection (CAD) system for breast cancer. The discovery of breast cancer in early stages can save many women lives. However, most of the early detection systems are costly in terms of complexity, price and processing time; that make it unsuited for developing countries. The digital mammography is proven to be one of the most important diagnostic techniques for breast cancer tumors. Therefore, this work proposes a CAD system for breast cancer detection from digital mammography based on Gaussian Mixture Model (GMM) followed by Support Vector Machine (SVM). The best contribution of our proposed system is the usage of GMM for the first time in the literature for mammogram images segmentation into ROI areas. Besides, the discrimination between the three classes of tissues as normal, benign or malignant, is used without previous knowledge of mammogram images’ type. Moreover, the proposed system is fully automated in all of its stages with reduced computation compared with recent used methods. Hence, it offers a suitable early detection system to our country regarding moneywise, timewise, and reduced complexity. A non-linear multi-class SVM is used for classifying the ROI into three classes: normal, benign or malignant tissue. The experiments show overall average classification accuracy of 90% for detecting normal, malignant or benign on randomly chosen 90 cases from the benchmark mini-MIAS dataset. On the other hand, the proposed method achieves 92.5% accuracy when classifying the benign from malignant cases.

Background Activation of the platelet plays an important role in the process of atherosclerosis. Mean platelet volume (MPV) is significantly associated with the poor outcome of acute ischemic stroke while the results of studies about the relationship between plateletcrit (PCT) and stroke outcome were inconsistent. The aim of this work is to determine whether an association exists between MPV and plateletcrit (PCT) and outcome of acute ischemic stroke. Methods We examined 157 patients with ischemic stroke, admitted to the Sohag University Hospital. The diagnosis of stroke was performed clinically according to The World Health Organization and confirmed by brain CT and MRI when needed. Platelet indices including MPV and PCT were assessed immediately (within 2 h) after admission. After 3 months, the functional outcome was assessed using the modified Rankin Scale (mRS) with assessment of the relationship between platelet indices and stroke outcome. Results About 50% of the participants have favorable outcome. MPV was significantly higher in the unfavorable group (10.4 ± 2.3 fL) than in the favorable one (8.7 ± 1.3 fL) (P

When forecasting time series, it is important to classify them according to linearity behavior; the linear time series remains at the forefront of academic and applied research. It has often been found that simple linear time series models usually leave certain aspects of economic and financial data unexplained. The dynamic behavior of most of the time series in our real life, with its autoregressive and inherited moving average terms, pose the challenge to forecast nonlinear times series that contain inherited moving average terms using computational intelligence methodologies such as neural networks. It is rare to find studies that concentrate on forecasting nonlinear times series that contain moving average terms. In this study, we demonstrate that the common neural networks are not efficient for recognizing the behavior of nonlinear or dynamic time series which has moving average terms and hence low forecasting capability. This leads to the importance of formulating new models of neural networks such as Deep Learning neural networks with or without hybrid methodologies such as Fuzzy Logic.

Numerous endeavors have been made to improve the retrieval procedure in Textual Case-Based Reasoning (TCBR) utilizing clustering and feature selection strategies. SOPHisticated Information Analysis (SOPHIA) approach is one of the most successful efforts which is characterized by its ability to work without the domain of knowledge or language dependency. SOPHIA is based on the conditional probability, which facilitates an advanced Knowledge Discovery (KD) framework for case-based retrieval. SOPHIA attracts clusters by themes which contain only one word in each. However, using one word is not sufficient to construct cluster attractors because the exclusion of the other words associated with that word in the same context could not give a full picture of the theme. The main contribution of this chapter is to introduce an enhanced clustering approach called GloSOPHIA (GloVe SOPHIA) that extends SOPHIA by integrating word embedding technique to enhance KD in TCBR. A new algorithm is proposed to feed SOPHIA with similar terms vector space gained from Global Vector (GloVe) embedding technique. The proposed approach is evaluated on two different language corpora and the results are compared with SOPHIA, K-means, and Self- Organizing Map (SOM) in several evaluation criteria. The results indicate that GloSOPHIA outperforms the other clustering methods in most of the evaluation criteria.

Extracting useful and relevant information from the massive amount of data by web users becomes a challenging task. This work regarding applications based on the use of Web Usage Mining (WUM). Clickstream, transaction data, and user profile data represent various sources of web usage data. Preprocessing is an essential process in WUM for understanding the user's life as a whole within a session on a website. It signifies the aggregation of consequent series of page views executed by a singular user traversing through a website. Therefore, it represents a critical task that involves converting raw web log data to obtain a suitable pattern for efficient analysis. The importance of these processes relies on the complex nature of web architecture, and it takes almost 80% of the mining process. This work introduces a complete framework for data preprocessing with a comprehensive demonstration of a real dataset.

The remarkable developments in wireless communication technologies in recent years along with the anticipation of further advances in these technologies have stimulated the efforts to investigate vehicle to everything (V2X) communication to increase road safety, improve traffic management, and facilitate infotainment applications. V2X communication is based either on cellular infrastructures such as 4G and 5G, or wireless LAN technologies such as the IEEE 802 protocol family. Several challenges, however, face the deployment of V2X. Examples of these challenges are routing, security, and analysis of collected data. Several research efforts have investigated computational intelligence methods to tackle these issues. In this paper, we review recent advances in computational intelligence in V2X communications. First, we provide preliminaries of V2X communication. Second, we discuss classical and computational intelligence solutions in V2X communication. Last, we present open problems and research challenges that need to be addressed to realize the full potential of V2X systems.

Mobile Ad hoc Network (MANET) is the cornerstone for the Internet of Things (IoT) and Vehicle to Everything (V2X) networks, its devices are remarkable of lightweight to be portable and free to join or disjoined the network. Therefore, one of the MANET routing types is reactive routing protocols. Reactive routing protocols set up the connection between devices either on-demand such as Ad Hoc On-demand Distance Vector (AODV) protocol or source routing such as Dynamic Source Routing (DSR) protocol. Reactive routing protocols increase routing overhead on discovering new routes. Thus routing overhead, and delay will increase. In this paper, the method of generating a fuzzy model is presented. Also, a tuned fuzzy stability model is introduced to handle the imprecision of routing decisions by the Fuzzy Stability model for Ad Hoc On-demand Distance Vector (FSAODV) and Fuzzy Stability model for Dynamic Source Routing (FSDSR). The results showed that FSAODV and FSDSR have outperformed the state of art protocols AODV and DSR respectively.

The reliability of the recommender system is highly essential for the continuity of any system. Fake and malicious users may be spoiling system predictions reliability by inserting and injecting fake profiles called “shilling attacks” into the target recommender system. Thus, the detection of these attacks is necessary for any recommender system. Therefore, several shilling attacks detection approaches have proposed. In this work, we propose a survey for the recent detection methods, which pick up famous shilling attack models against the collaborative filtering recommender systems.

This paper proposes a new swarm intelligence algorithm called vortex swarm optimization, inspired from vortex collective behavior where particles move around a common known center, they cover all the directions so they play as alarm system where any individual at any direction see the target (resources, predator…etc.) it informs other individuals to take an action. This type of collective behavior was seen in many creatures like (Bats and fishes). They use this type of collective behavior for feeding, access to resources, moving, information transmission, decision making, and protection from predators. This type is different from straight-ahead collective behavior (polarized collective behavior) where individuals move forward. The majority of swarm intelligence algorithms inspired their techniques from straight-ahead collective behavior, but vortex collective behavior had a few pieces of research so the importance of this research appears. The algorithm performs the two basics functions that any searching algorithm had (exploitation and exploration) by circling phase the algorithm will know the direction of the promising region, By swimming phase the algorithm will go to this region (exploration)and by attacking phase individuals will reduce their interaction range and reduce their velocity to get much closer to each other and best region found so far (exploitation), Algorithm skip local minimum by eliminating some individuals to a new regions to explore them and to change vortex direction to explore new directions. The algorithm was tested over ten benchmark functions and it competes with some other powerful algorithms.

Position is a vital element for ITS applications. Its accuracy helps to deliver services quickly to drivers to increase their satisfaction. GPS is a well-known position system but it suffers from multipath effect and non-line of sight in tunnel environments. Relative Position or sometimes called cooperative localization is an alternative position estimation. It utilizes different forms of v2x communication to exchange position, distance, direction, and velocity parameters. It will benefit in collecting a large amount of data to increase the accuracy of position estimation. However, the dependency of radio range communication methods has drawbacks such as poor-received signal, multipath, lose packets, delay, and overhead communication that will have inverse impact on position accuracy. In addition, safety applications require fewer seconds to make quick response. This chapter provides the latest related papers, the state art of radio range, the well-known localization algorithms, and current challenges and future direction.

Social Network Analysis measures the interconnection between humans, entities or communities and the streaming of messages between them. This kind of Analysis studies the relationship between different people in a very deep way; it shows how one node (subscriber) in the network can affect the others. This research studies the connections between the customers in many different ways to help any telecom operator increase the cross and up-selling of its products and services as follows: detect communities of subscribers which are a group of nodes collected together to form a community, identify the connection types and label the links between the customers as (business, friends , family and others), as well as identifying the top influencers in the network who can spread positive or negative messages about products and services provided by the company through communities in the network and determine off-net customers who can be acquired to be targeted by specific marketing campaigns. A real cell phone dataset of 116 Million call detailed records of SMS and Voice Calls of an Egyptian Communication Service Provider (CSP) is used.

the majority cancer mortality among women is due to breast cancer over the world wide. Recent researches have shown the effectiveness of x-ray mammography in early detection of breast cancer. Unfortunately, the present systems for early detection are expensive and needs extremely complex algorithms. The crucial challenge in designing a computer-aided detection (CAD) systems for breast cancer are the segmentation phase, which requires highly complex computation. Hence, this paper proposes a CAD system to be utilized for breast cancer detection in mammographic datasets. The segmentation step is performed by a Particle Swarm Optimization Algorithm (PSO). Statistical, textural and shape feature are calculated over the segmented region. A non linear support vector machine (SVM) is exploited in the next phase in order to analyze the extracted features and classify the mammograms into normal, benign or malignant. For the sack of evaluating the performance, the experiment is performed on Mini-MIAS database. The obtained accuracy rates based on 10-folds cross validation are 85.4% for classifying normal from abnormal, 89.5% for classifying malignant from benign. The experiment shows that the classification accuracy is 81% when classifying normal, malignant or benign. The result compromises with recent researches concurs that the proposed algorithm compromises between the achieved accuracy to complexity cost.

A computer aided diagnosis and detection system (CADx) enhances the early detection accuracy for breast cancer which in turn save women life all over the world. The existing early detection CADx systems are costly and demands highly complex algorithms with complicated computation thus unsuitable for developing countries. The segmentation process is a key issue in designing CADx system for breast cancer and the most computationally costing. Hence, the contribution of this paper is proposing two CADx systems for breast cancer in mammographic datasets that compromise between efficiency, speed, and cost. The segmentation step in the first system is performed by a Particle Swarm Optimization (PSO) Algorithm while it is performed by a Gaussian Mixture Model (GMM) in the second proposed system. Textural, statistical and shape features are extracted from segmented ROIs and input for the classifier to distinguish the mammograms if normal, benign or malignant. The proposed method utilizes the nonlinear support vector machines for classification. Experiments are conducted on mini MIAS database. The resulted accuracy rates based on 10-folds cross validation for classifying normal from abnormal are 85.4% and 82.4% for PSO and GMM respectively. Besides, the ratios become 89.5%, 87.5% for classifying malignant from benign for PSO and GMM respectively. On the other hand, the experimental results show the overall average classification accuracy of 81% and 85% when classifying normal, malignant or benign. Confusion matrix is additionally evaluation metric that was also used to check and assess the process comprehensive balanced performances for both PSO and GMM based methods.

Climate change has noticeable significant impacts on development of most countries because of its direct negative effect on the production and revenue of most crops plantation process. In reality, the ongoing changes in climate variables affect the suitability of planting some crops in their traditional places at their traditional dates. Furthermore, the availability of huge volumes of agro-climatic data that almost incorporates uncertainty increases the complexity of managing and discovering the crops suitable plantation patterns from such data. Accordingly, a need appeared to an efficient approach to handle such uncertainty and to exploit such huge data volume to manage the crops plantation process accurately. This paper presents a fuzzy approach based on Hadoop for discovering crops plantation knowledge from the agro-climatic historical database of the years from 1983 to 2016 of Egypt. Commonly, the proposed approach provides a set of scenarios for plantation dates of each crop with a suitability degree for each scenario. Also, it helps managing crops plantation process from some other aspects such as harvesting dates, candidate diseases and follow up for crops water requirements respecting the data streaming of the prevailing weather data. The proposed approach has been tested on a set of crops with cooperation of researchers from Cairo University and Agricultural Research Center. The results show the added value of the proposed approach against other works respecting the more suitable crops plantation dates, harvesting dates, expected diseases and follow up for crops water requirements. Furthermore, the proposed approach benefits from Hadoop framework capabilities of handling huge amounts of data streamed from weather stations.

Objectives: This paper proposes an enhanced data access security approach to allow virtual private database security mechanism in heterogeneous multi-tier applications regardless of the data access security features provided by each database management system. Methods/Statistical Analysis: An implementation of Data Access Layer has been done respecting the proposed approach. This implementation enhances Microsoft’s Entity Framework that is widely used in commercial multi-tier database applications as a Data Access Layer. Accordingly, it’s overloaded by the required functionality including query modification and data validation. The output assembly then is tested in a typical HR database application that targets three different DBMS’s (SQL Server, MySQL, Oracle) with exactly same database state. A time measurement takes place to evaluate the processing cost of issuing CRUD operations compared with the same application architecture without using the proposed approach (e.g. relying on the row-level security provided by Oracle on the DBMS level). Findings: An illustrated case study respecting the proposed approach shows its scalability, reliability and efficiency. It allows data access security in both homogenous and heterogeneous database applications. On the other hand, the results show that the cost of processing both of data retrieval and data manipulation operations respecting predefined data access security policies of the proposed approach compared with Oracle VPD are reduced by around 59% and 57% respectively. Application/Improvements: As presented in the illustrative case study, the proposed approach can be easily applied and reused in any modern heterogeneous multi-tier database application. It allows defining data access security policies regardless of the target database management system type. Also, the results show an improvement in the processing cost of the proposed approach compared with the Oracle virtual private database with both data retrieval and data manipulation operations. Keywords: Data Privacy; Data Access; Database; Heterogeneous Database Applications