Performance modelling of a multiple threshold RED mechanism for bursty and correlated Internet traffic with MMPP arrival process

Access to the large web content hosted all over the world by users of the Internet engage many hosts, routers/switches and faster links. They challenge the internet backbone to operate at its capacity to assure e±cient content access. This may result in congestion and raises concerns over various Quality of Service (QoS) issues like high delays, high packet loss and low throughput of the system for various Internet applications. Thus, there is a need to develop effective congestion control mechanisms in order to meet various Quality of Service (QoS) related performance parameters. In this paper, our emphasis is on the Active Queue Management (AQM) mechanisms, particularly Random Early Detection (RED). We propose a threshold based novel analytical model based on standard RED mechanism. Various numerical examples are presented for Internet traffic scenarios containing both the burstiness and correlation properties of the network traffic

Two-stream deep learning architecture-based human action recognition

Human action recognition (HAR) based on Artificial intelligence reasoning is the most important research area in computer vision. Big breakthroughs in this field have been observed in the last few years; additionally, the interest in research in this field is evolving, such as understanding of actions and scenes, studying human joints, and human posture recognition. Many HAR techniques are introduced in the literature. Nonetheless, the challenge of redundant and irrelevant features reduces recognition accuracy. They also faced a few other challenges, such as differing perspectives, environmental conditions, and temporal variations, among others. In this work, a deep learning and improved whale optimization algorithm based framework is proposed for HAR. The proposed framework consists of a few core stages i.e., frames initial preprocessing, fine-tuned pre-trained deep learning models through transfer learning (TL), features fusion using modified serial based approach, and improved whale optimization based best features selection for final classification. Two pre-trained deep learning models such as InceptionV3 and Resnet101 are fine-tuned and TL is employed to train on action recognition datasets. The fusion process increases the length of feature vectors; therefore, improved whale optimization algorithm is proposed and selects the best features. The best selected features are finally classified using machine learning (ML) classifiers. Four publicly accessible datasets such as Ut-interaction, Hollywood, Free Viewpoint Action Recognition using Motion History Volumes (IXMAS), and centre of computer vision (UCF) Sports, are employed and achieved the testing accuracy of 100%, 99.9%, 99.1%, and 100% respectively. Comparison with state of the art techniques (SOTA), the proposed method showed the improved accuracy

A semantic web approach to license agreements

Zsfassung in dt. SpracheMit dem Aufkommen digitaler Technologien wurde das Management von Information sowohl in ihrer herkömmlichen dinglichen Ausprägung als auch in ihrer elektronischen Ausprägung wesentlich revolutioniert.Lizenzvereinbarungen und allgemeine Geschäftsbedingungen stellen die eine besondere Form von Information dar, Die Administration von Lizenzvereinbarungen und allgemeinen Geschäftsbedingungen mit Zuhilfenahme der Techniken des Semantic Web stellen eine besondere technische und wissenschaftliche Herausforderung dar. Die Anwendung von Semantic Web Techniken sollte hier eine Kategorisierung der verschiedenen Charakteristika von Lizenzvereinbarungen für Endbenutzer und Konsumenten ermöglichen und damit eine Grundlage für Entscheidungs-unterstützung auf Grund der Benutzeranforderungen bieten.Aufgrund der Komplexität von Lizenzverträgen und der großen Vielfalt der Formulierung vermeidet der Endanwender sehr oft Lizenzvereinbarungen genau zu studieren und für gewöhnlich unterzeichnen Endanwender Lizenzvereinbarungen ohne die Details genau zu kennen. Dies führt in einigen Fällen dazu, dass Endbenutzer wegen Verletzung der Lizenzvereinbarungen bestraft werden, obwohl dies nur unwissentlich bzw.versehentlich verursacht worden ist. Die Motivation dieser Arbeit liegt in der Beseitigung dieser Mängel durch einen semantisch basierte Lösungsansatz. In dieser Arbeit werden in einem Semantic-Web-Modell die Benutzeranforderungen bezüglich Lizenzvereinbarungen konzeptionell spezifiziert. Diese Benutzer-anforderungen werden mit Hilfe von Fallbeispielen zur Bildung eines ontologischen Modells herangezogen.Dieses Modell wird sodann zu einer weitestgehend allgemeinen Lizenzontologie ausgebaut, Jede Lizenzontologie bildet die Grundlage für ein Modell einer Lizenzvereinbarung. Auf Grund dieser Lizenzontologien wurde die Applikation "Digital Licence Agreement"entwickelt.Diese Applikation ermöglicht ein viel besseres und tiefer gehendes Verständnis von Lizenzvereinbarungen und darüber hinaus eine Überprüfung und Abgleichung der Benutzeranforderungen bezüglich einer Vereinbarung.Ein vorrangiges Ziel dieser Dissertation ist die Schließung der (semantischen) Lücke, die zwischen dem Endbenutzer und dem Text einer (meist dem Laien schwer zugänglichen) Lizenzvereinbarung.durch die Einführung eines semantischen Modells. Dieses semantische Modell ist von generischer Natur und ist so geartet, dass geringfügige konzeptionelle Änderungen von Vereinbarungen leicht umzusetzen sind. Das hier vorgeschlagene Modell ist vielfältig anwendbar. Es ist geeignet für Abfragen bezüglich spezifischer Anforderungen an Lizenzvereinbarungen und für die Abgleichung von Benutzeranforderungen bezüglich einer bestimmten Lizenzvereinbarung. Zwei große Gebiete von Lizenzvereinbarungen werden in dieser Dissertation tiefergehend behandelt, nämlich Software und On-line-shopping.Emerging digital technologies, especially in information techniques, have revolutionized information management. Information management includes both electronic and physical information and is regardless of source or format. License agreements are also a form of information, which describes product's usage and its terms and conditions. Management of license agreements using Semantic Web is a multi-disciplinary challenge, involving categorization of common features and structuring the required information in such semantics that could be easily extendable and fulfilling the requirements of end users.Consequently, the solution proposed in this thesis is to find and match user requirements in license agreements using Semantic Web techniques.Generally, License Agreements are in documented form and are heterogeneously constructed. Documented license agreements are lengthy and each of the license agreement (of different organization), have very less resemblances, but the agreements that lay under same category in one organization still remain quiet similar. Due to the lengthiness of license agreements and its heterogeneous formation, end user very often avoids to read and study to understand it, and habitually end users sign the agreement without knowing and understanding, "what is written in it". Therefore, in some cases end user face penalty, if any section of license agreement is violated; whether violation is occurred willingly or mistakenly. To address the issues, a semantic based solution is proposed in this thesis.User requirements are defined, which are based on the license agreements to construct a Semantic Web model. These requirements are structured by use cases, which help to construct an ontological model. The ontological model is then extended to construct "real world license ontologies".Each license ontology is a model for an agreement. Using the license ontologies, an application named Digital License Agreement is developed.The application facilitates better understanding of agreements and also specifies for the user required information according to an agreement. The ultimate goal of this thesis is to bridge the gap between an end-user and a license agreement, by introducing a semantic license model. The introduced semantic model is a general model for each license agreement. Furthermore it is designed to be adaptable with minor changes according to specificities of an agreement. The model can be used for multiple purposes such as querying appropriate licenses for specific requirements or checking the license terms and conditions with the help of user requirements. The general semantic model, of license agreements, is divided into two sub-sections i.e. software license agreements and online shopping agreements. In the thesis, the extended models of software license and online shopping agreements are investigated.12

Using Semantic Web to Enhance User Understandability for Online Shopping License Agreement

Abstract. Normally, a common user sign license agreement without understanding the agreement. License agreements are a form of information, which describes product's usage and its terms and conditions. Habitually, users agree with it but without understanding. In the today’s information age, there is no integration of license agreements with any current technology. The contents of license agreements are out of scope for search engines. Management of license agreements using Semantic Web is a multi-disciplinary challenge, involving categorization of common features and structuring the required information in such semantics that is easily extendable and fulfilling the requirements of common user. In this paper construction of Semantic Web model for Online Shopping license agreement is discussed. The user requirements facilitate the construction of License Ontological model. Moreover, rules are used to capture the complex statements of “terms and conditions”. Finally, an explicit semantic model for agreements is constructed that facilitates users ’ queries

A Fair Contention Access Scheme for Low-Priority Traffic in Wireless Body Area Networks

Recently, wireless body area networks (WBANs) have attracted significant consideration in ubiquitous healthcare. A number of medium access control (MAC) protocols, primarily derived from the superframe structure of the IEEE 802.15.4, have been proposed in literature. These MAC protocols aim to provide quality of service (QoS) by prioritizing different traffic types in WBANs. A contention access period (CAP)with high contention in priority-based MAC protocols can result in higher number of collisions and retransmissions. During CAP, traffic classes with higher priority are dominant over low-priority traffic; this has led to starvation of low-priority traffic, thus adversely affecting WBAN throughput, delay, and energy consumption. Hence, this paper proposes a traffic-adaptive priority-based superframe structure that is able to reduce contention in the CAP period, and provides a fair chance for low-priority traffic. Simulation results in ns-3 demonstrate that the proposed MAC protocol, called traffic- adaptive priority-based MAC (TAP-MAC), achieves low energy consumption, high throughput, and low latency compared to the IEEE 802.15.4 standard, and the most recent priority-based MAC protocol, called priority-based MAC protocol (PA-MAC)

Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events

Abstract Drug-to-drug interaction (DDIs) occurs when a patient consumes multiple drugs. Therefore, it is possible that any medication can influence other drugs’ effectiveness. The drug-to-drug interactions are detected based on the interactions of chemical substructures, targets, pathways, and enzymes; therefore, machine learning (ML) and deep learning (DL) techniques are used to find the associated DDI events. The DL model, i.e., Convolutional Neural Network (CNN), is used to analyze the DDI. DDI is based on the 65 different drug-associated events, which is present in the drug bank database. Our model uses the inputs, which are chemical structures (i.e., smiles of drugs), enzymes, pathways, and the target of the drug. Therefore, for the multi-model CNN, we use several layers, activation functions, and features of drugs to achieve better accuracy as compared to traditional prediction algorithms. We perform different experiments on various hyperparameters. We have also carried out experiments on various iterations of drug features in different sets. Our Multi-Modal Convolutional Neural Network - Drug to Drug Interaction (MCNN-DDI) model achieved an accuracy of 90.00% and an AUPR of 94.78%. The results showed that a combination of the drug’s features (i.e., chemical substructure, target, and enzyme) performs better in DDIs-associated events prediction than other features

Outcome of patients with primary sclerosing cholangitis and ulcerative colitis undergoing colectomy

Background and Aim: Patients with ulcerative colitis (UC) are at risk of developing primary sclerosing cholangitis (PSC). As a form of continence preservation, ileal pouch-anal anastomosis or ileorectal anastomosis are used for patients undergoing colectomy. The present study aimed to determine the patient outcomes following colectomy for primary sclerosing cholangitis and ulcerative colitis. Patients and Methods: 64 patients with PSC and UC undergoing colectomy were enrolled and investigated in the Department of Gastroenterology, Lady Reading Hospital, Peshawar from January 2018 to June 2020. Study protocol was approved by the institutional research and ethical committee. Patient’s data regarding clinical information, preoperative liver tests, colectomy date, pathological findings, and follow-up liver tests were reviewed from medical records. The colectomy-leading indications such as colonic dysplasia, bowel perforation, and colonic inflammation etc. were recorded. Several preoperative tests were conducted, including total bilirubin, albumin levels, direct bilirubin and ALP. SPSS version 26 was used for data analysis.  Results: The overall mean age was 40.42±3.6 years. Of the total 64 patients, there were 42 (65.6%) male and 22 (34.4%) females. Colectomy was most commonly indicated by severe intestinal inflammation (52%), bowel perforations (4%), and dysplastic colons (38%). The incidence of postoperative complications were 43.8% (n=28)

Frequency of celiac disease in patient with irritable bowel syndrome

Background: One of the most prevalent functional gastrointestinal illnesses is irritable bowel syndrome (IBS), which causes a variety of gastrointestinal symptoms, including changed bowel habits and stomach pain or discomfort, without an underlying basis. Objective: To assess the frequency of celiac disease in patient with irritable bowel syndrome. Methodology: This cross sectional study was carried out at the department of gastroenterology, Lady Reading Hospital Peshawar. The duration of study was two years from January 2018 to December 2019.  The serological tests for celiac disease like IgA anti TTG and IgG anti TTG was determined.  Biopsies were taken on upper gastrointestinal endoscopy. Data was collected in a specialized proforma for our research. All the data collected was analyzed by employing IBM SPSS version 23. Results: In our study, a total of 240 patients with irritable bowel syndrome were enrolled. Gender wise distribution shows that there were 132 (55%) male patients while the female patients were 108 (45%). The mean age of patients (SD) was 26.72 (± 2.27) years. The frequency of celiac disease based on serological testing was 20 (8.33%). The frequency of celiac disease based on histological findings was 15 (6.25%).&nbsp

Gastric varices amongst patients with upper gastrointestinal bleeding: A single centre cross sectional study

Background: The term "upper gastrointestinal bleeding" refers to bleeding from the gastrointestinal system that occurs above the Treitz ligament. It is one of the gastroenterological problems that is seen most often in clinical settings by gastroenterologists. Objective: To assess the frequency of gastric Varices amongst patients with upper gastrointestinal bleeding. Methodology: Our study was descriptive-cross sectional study, carried out at the Gastroenterology Department…….hospital Peshawar for duration of six months from August 2022 to January 2023. A careful upper-gastrointestinal endoscopy was carried out. Endoscopic findings of gastric varices in patients were recorded on a designed proforma for our research. All the documented data was analyzed by using 23. Results: In our current study a total of 120 patients were included. The male were 67 (55.83%) whereas female patients were 53 (44.17%). The mean age (SD) was 36 (9.11) years. The overall frequency of gastric varices amongst patients with gastrointestinal bleeding was 24 (20%). Conclusion: Our study concludes that frequency of gastric varices amongst patients with upper-gastrointestinal bleeding is very high. Multiple centre studies with large sample size should be conducted to get better results