Composition Operators On De Branges-rovnyak Spaces Associated To A Rational (Not Inner) Function

In this paper, we characterize the boundedness, the compactness and the Hilbert-Schmidt property for composition operators acting from a de Branges-Rovnyak space $\mathcal H(b)$ into itself, when $b$ is a rational function in the closed unit ball of $H^\infty$ (but not a finite Blaschke product). In particular, we extend some of the results obtained by D. Sarason and J.N. Silva in the context of local Dirichlet spaces

A comparative study of different pre-trained deeplearning models and custom CNN for pancreatic tumor detection

Artificial Intelligence and its sub-branches like MachineLearning (ML) and Deep Learning (DL) applications have the potential to have positive effects that can directly affect human life. Medical imaging is briefly making the internal structure of the human body visible with various methods. With deep learning models, cancer detection, which is one of the most lethal diseases in the world, can be made possible with high accuracy. Pancreatic Tumor detection, which is one of the cancer types with the highest fatality rate, is one of the main targets of this project, together with the data set of computed tomography images,which is one of the medical imaging techniques and has an effective structure in Pancreatic Cancer imaging. In the field of image classification, which is a computer vision task, the transfer learning technique, which has gained popularity in recent years, has been applied quite frequently. Using pre-trained models werepreviously trained on a fairly large dataset and using them on medical images is common nowadays. The main objective of this article is to use this method, which is very popular inthe medical imaging field, in the detection of PDAC, one of the deadliest types of pancreatic cancer, and to investigate how it per-forms compared to the custom model created and trained from scratch. The pre-trained models which are used in this project areVGG-16 and ResNet, which are popular Convolutional Neutral Network models, for Pancreatic Tumor Detection task. With the use of these models, early diagnosis of pancreatic cancer, which progresses insidiously and therefore does not spread to neighboring tissues and organs when the treatment process is started, may be possible. Due to the abundance of medical images reviewed by medical professionals, which is one of the main causes for heavy workload of healthcare systems, this applicationcan assist radiologists and other specialists in Pancreatic Tumor detection by providing faster and more accurate method

Mobile Translator Guide for Tourism Destination in Langkawi (MTGTDL)

Mobile devices can be used anywhere and anytime. Relying on this characteristic, this search aims to introduce mobile electronic translator guide for tourism destination in Langkawi in order to ease the communication between the who are talking in different languages to have easy access to places of interest in Langkawi and facilitate the user to use this application without internet. Facilitating or enhancing the communication leads to convergence of cultures. As long as the research deals with Malaysians and tourists who talk Arabic language, the prototype created will be useful for both sides where. Learning common phrases will urge users to have information about a specific languag

Explainable artificial intelligence through graph theory by generalized social network analysis-based classifier

We propose a new type of supervised visual machine learning classifier, GSNAc, based on graph theory and social network analysis techniques. In a previous study, we employed social network analysis techniques and introduced a novel classification model (called Social Network Analysis-based Classifier-SNAc) which efficiently works with time-series numerical datasets. In this study, we have extended SNAc to work with any type of tabular data by showing its classification efficiency on a broader collection of datasets that may contain numerical and categorical features. This version of GSNAc simply works by transforming traditional tabular data into a network where samples of the tabular dataset are represented as nodes and similarities between the samples are reflected as edges connecting the corresponding nodes. The raw network graph is further simplified and enriched by its edge space to extract a visualizable 'graph classifier model-GCM'. The concept of the GSNAc classification model relies on the study of node similarities over network graphs. In the prediction step, the GSNAc model maps test nodes into GCM, and evaluates their average similarity to classes by employing vectorial and topological metrics. The novel side of this research lies in transforming multidimensional data into a 2D visualizable domain. This is realized by converting a conventional dataset into a network of 'samples' and predicting classes after a careful and detailed network analysis. We exhibit the classification performance of GSNAc as an effective classifier by comparing it with several well-established machine learning classifiers using some popular benchmark datasets. GSNAc has demonstrated superior or comparable performance compared to other classifiers. Additionally, it introduces a visually comprehensible process for the benefit of end-users. As a result, the spin-off contribution of GSNAc lies in the interpretability of the prediction task since the process is human-comprehensible; and it is highly visual

Ecology and Temporal Changes in Algal Composition and Spatial Distribution of Periphyton Community of a Drowned Tropical Forest Reservoir in Malaysia

A study on periphyton was carried out in Kenyir Reservoir in the tropical environmental perspectives. It included species abundance, species composition, diversity, standing crop, primary production and their vertico-temporal changes. The physico-chemical features of the lake littoral environment were also characterised. The lake physico-chemical features were influenced by monsoon and dry seasons. The lake water shifted to alkaline in dry period and weak acidic in monsoon. There occurred significant correlationship (at p<O.05) between water level and some important water parameters like conductivity, alkalinity, ammonium-nitrogen and nitrite-nitrogen. A total of 392 periphytic algae species under 132 genera was identified from the all sorts of substrates. Out of which the highest number of species belonged to diatoms (183) followed by cyanophytes (123), chlorophytes (81) and dinoflagellates (5). Although, diatoms possessed the highest number of species, cyanophytes maintained dominance in terms of standing crop.The substrate based periphyton species composition showed that 329 species were found on ,standing tree substrates. One hundred thirty-six epilithic species with predominance of diatoms were collected on rocks. Forty-two epiphytic periphyton species were encountered on macrophytes. One hundred twenty seven species were collected on plywoods and one hundred species were collected on glass slides. The cyanophytes and diatoms specIes exhibited groups of cluster in dendrogram which showed good indication regarding the occurrence of the concerned species, their environmental response and attachment between the species. Species diversity as indicated by the index (H') manifested clear seasonal trend, the lowest (H'= 2.87) in wet season and the highest (H'= 3.66) in dry season. The periphytic floral species number and standing crop manifested seasonal changes between monsoon and dry period (significant in Mann-Whitney U test at p<O.05). The cluster analysis on monthly species abundance data also showed conspicuous grouping between the two seasons. The dominance of species between the seasons varied in response to ambient environmental changes. Moreover, the periphytic floral monthly mean species number and cell counts (standing crop) data demonstrated significant temporal differences between the months at p<O.05 in oneway ANOV A. The spatial differences of the periphyton between the two stations were not significant except diatoms (p<O.05 in one-way ANOVA). The periphyton assemblage showed gradual decrease of species number and standing crop with the depths. However, the chlorophyll a was higher at the lower depths than that of the upper depth. The species composition was different with depths. The environmental factors influencing the vertical distribution were light, temperature, pH. and conductivity. The annual mean value of chlorophyll a was 283.32 mg/m2 substrate surface The mean chlorophyll a values varied two folds in dry season compared to monsoon (significant in t-test (p<O.05). The annual mean autotrophic index (AI) values were 153 and 110 at the Dam side and the Petang River stations respectively. The annual mean primary production at the littoral ranged 67. 15g/m2 to 93.33g/m2 of the lake surface at the aforementioned stations respectively. The correlation between chlorophyll a and ambient environmental parameters like pH, temperature, dissolved oxygen, temperature, solar radiation, alkalinity, water transparency, nitrate, silica, sunshine hour and lake water level were significant (p<O.05). It can be concluded that the reservoir supports a diverse and wide array of periphytic autotrophs. The limnological features of the water body exhibited differences between dry and monsoon seasons. The flora clearly demonstrated seasonal as well as depth profile variations. The autotrophs contributed substantially to the lake primary production which probably being utilised by higher trophic fauna. All these ecological indications and insights will be of immensely beneficial and contribute to the understanding of the tropical limnology as well as autotrophs ecolog

Studying the connection between SF3B1 and four types of cancer by analyzing networks constructed based on published research

Splicing factor 3B subunit 1 (SF3B1) is the largest component of SF3b protein complex which is involved in the pre-mRNA splicing mechanism. Somatic mutations of SF3B1 were shown to be associated with aberrant splicing, producing abnormal transcripts that drive cancer development and/or prognosis. In this study, we focus on the relationship between SF3B1 and four types of cancer, namely myelodysplastic syndrome (MDS), acute myeloid leukemia (AML), and chronic lymphocytic leukemia (CLL) and breast cancer (BC). For this purpose, we identified from the Pubmed library only articles which mentioned SF3B1 in connection with the investigated types of cancer for the period 2007 to 2018 to reveal how the connection has developed over time. We left out all published articles which mentioned SF3B1 in other contexts. We retrieved the target articles and investigated the association between SF3B1 and the mentioned four types of cancer. For this we utilized some of the publicly available databases to retrieve gene/variant/disease information related to SF3B1. We used the outcome to derive and analyze a variety of complex networks that reflect the correlation between the considered diseases and variants associated with SF3B1. The results achieved based on the analyzed articles and reported in this article illustrated that SF3B1 is associated with hematologic malignancies, such as MDS, AML, and CLL more than BC. We found that different gene networks may be required for investigating the impact of mutant splicing factors on cancer development based on the target cancer type. Additionally, based on the literature analyzed in this study, we highlighted and summarized what other researchers have reported as the set of genes and cellular pathways that are affected by aberrant splicing in cancerous cells

الصورة البيانية عند الملك الأمجد

قَامت هذهِ الدراسة بتسليط الضوء على أحد الشُّعراء الأيوبيين، وقد قُمنا في هذا البحثِ بدراسة الصورة البيانية للشاعر الملك الأمجد، هو شاعر بليغٌ و جاء البحث لإجلاء الصورة البيانية ومنها: التَّشبيه، والاستعارة، والكناية, وفي البداية تَطرقنا لِسيرتهِ الشخصية, وبعد ذلك ذكرنا أهم أقسام صوره البيانية التي ظَهرت لنا بشكل واسع في شعرهِ, ويُعَدُ الملك الأمجد من الشُعراء الأيوبيين, وهو أحد شُعراء العصر العباسي الثاني, وسُمي بأبي المظفر بهرام شاه بن فروخ شاه بن شاهنشاه بن أيوب بن شادي, لم تَذَكُر المصادر تأريخ ولادتَه ولكنهُ تمَلَكَ بعلبك سنة (٥٧٨ ه) التي يمثل عصر الازدِهار والتقدم الحضاري، إذ ازدهرت الحركة الأدبية ذلك لحب الملوك الأيوبيين للعلم والأدب وتشجيعهم لِدارسيها، وقد نال هؤلاء اهتمامًا كبيرًا من نقاد الأدب ومؤرخي الشعر على مر السنين، وعلى الرغم من وصول الشاعر الملك الأمجد المرتبة الشعرية العالية والجهد الكبير الذي بدلهُ في الشعر والأدب، إِلّا انه لم يُدوّن له شيء من شعره في أول حَياتِه، لقد برزت المواقف الحربية للملك الأمجد في الحروب الصليبية ضد الأفرنج حتى أنَّ كثيرًا من الشُعراء خلّدوا مواقفه الحربية في شِعرهم وذَلك لأهمية وعَظمة ما قام به الملك في الحروب الصليبية. وبالرغم من غزارة الدراسات والبحوث التي تناولت أعلام تلك الحقبة، إلا أنه لم ينل نصيبًا وافرًا من هذه الدراسات مثل غيره ممن هم في مرتبته الشعرية وربما أقل منه شأنًا, وتوفي الشاعر الملك الأمجد في ليلة الأربعاء الثاني عشر من شوال سنة ( ٦٢٨ للهجرة), وتناول ايضا دراسة الشاعر وصوره الشعرية و البيانية التي نظم فيها أوزانه الشعرية, والذي يُعد الحجر الأساس في ديوانه, ومن خلال أبياته وأوزانه وقوافيه, وإيقاعاته الشَّعرية, فقد قَسّمنا الصورة البيانية الى ثلاثة أقسام اعتمادًا على كثرة ورودها في الديوان، بدءً من: ( التَّشبيه, الاستعارة, الكناية) التي تشكل أهم الوسائل اللغوية والحسية في منظومة الصورة البيانية. التشبيه تَعتبر مَصدر اساسي لِلنابع اللغوي عند الشُعراء وخاصة عند ديوان الملك الأمجد, والاستعارة أيضا لا تقلُّ أهمية عن التشبيه ولم يكن مجهولاً عند الشعر العربي, وتأتي في المرتبة الثانية في الصورة البيانية, والكناية هي الطرف الثالث الذي يلجأ إليه الشاعر مع التشبيه والاستعارة, وتعتبر الكناية عند الشعراء فن من فنون البلاغة, ولم يستغنِ عنه الشعراء في شعرهم, ولكونه له أثر كبير في نفس المتلقي, وتأثيراً واضحاً في النص الشعري, نظراً لما يحققه من دلالات معنوية في النصوص الشعرية. واعتَمد البحثُ وفقًا للمنهج التحليلي للأبيات الشعرية لتحقيق الرؤية الشاملة للنّصّ و الكشف عن معانيه المختلفة, وخَتمتُ هذه الدراسة بأهم ّالنتائج التي وصلتُ إليها

A survey of machine learning-based methods for COVID-19 medical image analysis

The ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus has already resulted in 6.6 million deaths with more than 637 million people infected after only 30 months since the first occurrences of the disease in December 2019. Hence, rapid and accurate detection and diagnosis of the disease is the first priority all over the world. Researchers have been working on various methods for COVID-19 detection and as the disease infects lungs, lung image analysis has become a popular research area for detecting the presence of the disease. Medical images from chest X-rays (CXR), computed tomography (CT) images, and lung ultrasound images have been used by automated image analysis systems in artificial intelligence (AI)- and machine learning (ML)-based approaches. Various existing and novel ML, deep learning (DL), transfer learning (TL), and hybrid models have been applied for detecting and classifying COVID-19, segmentation of infected regions, assessing the severity, and tracking patient progress from medical images of COVID-19 patients. In this paper, a comprehensive review of some recent approaches on COVID-19-based image analyses is provided surveying the contributions of existing research efforts, the available image datasets, and the performance metrics used in recent works. The challenges and future research scopes to address the progress of the fight against COVID-19 from the AI perspective are also discussed. The main objective of this paper is therefore to provide a summary of the research works done in COVID detection and analysis from medical image datasets using ML, DL, and TL models by analyzing their novelty and efficiency while mentioning other COVID-19-based review/survey researches to deliver a brief overview on the maximum amount of information on COVID-19-based existing researches. [Figure not available: see fulltext.

REVERSE ENGINEERING BASED APPROACH FOR TRANSFERRING LEGACY RELATIONAL DATABASES INTO XML

XML (extensible Markup Language) has emerged, and. is being gradually accepted as the standard for data interchange over the Internet. Since most data is currently stored in relational database systems, the problem of converting relational data into XML assumes special significance. Many researchers have already done some accomplishments in this direction. They mainly focus on finding XML schema (e.g., DTD, XML-Schema, and RELAX) that best describes a given relational database with a corresponding well-defined database catalog that contains all information about tables, keys and constraints. However, not all existing databases can provide the required catalog information. Therefore, these applications do not work well for legacy relational database systems that were developed following the logical relational database design methodology, without being based on any commercial DBMS, and hence do not provide well-defined metadata files describing the database structure and constraints. In this paper, we address this issue by first applying the reverse engineering approach described in [2] to extract the ER (Extended Entity Relationship) model from a legacy relational database, then convert the ER to XML Schema. The proposed approach is capable of reflecting the relational schema flexibility into XML schema by considering the mapping of binary and nary relationships. We have implemented a first prototype and the initial experimental results are very encouraging, demonstrating the applicability and effectiveness of the proposed approach

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. This paper is intended to provide experts (medical or otherwise) and technicians with new insights into the ways deep learning techniques are used in this regard and how they potentially further works in combatting the outbreak of COVID-19.Comment: 18 pages, 2 figures, 4 Table