Miracle-dz/UniFAR-FTUs huggingface dataset:
An Investigation of Methods for CT Synthesis in MR-only Radiotherapy

A D. Andreasen, K. Van Leemput, R.H. Hansen, J.A. L. Andersen, and J.M. Edmund: ”Predicting a Pseudo-CT scan from T1-weighted MR Images using Patches – towards MRI-only based radiotherapy”. Accepted for e-poster at ESTRO 33, Vienna, Austria, 2014 Abstract B D. Andreasen, K. Van Leemput, and J.M. Edmund: ”Patchbased Generation of a pseudo-CT Scan for MRI-only based Radiotherapy in the Pelvic Region”. Accepted for poster presentation at 3rd ESTRO Forum, Barcelona, Spain, 2015.B D. Andreasen, K. Van Leemput, and J.M. Edmund: ”Patchbased Generation of a pseudo-CT Scan for MRI-only based Radiotherapy in the Pelvic Region”. Accepted for poster presentation at 3rd ESTRO Forum, Barcelona, Spain, 2015. 1.4 Overview of the thesis The main part of this thesis focuses on the three papers that I am first author of (papers B-D). Paper A has no dedicated chapter, but is referenced throughout the thesis and serves as the basis for much of Chapter 2. This thesis is structured as follows: • Chapter 2 provides an overview of the development in approaches to sCT prediction as well as a description of the a priori most promising solutions. I highlight the advantages and limitations of these methods in order to set the stage for our choices of sCT strategies. • Chapter 3 provides an introduction to a patch-based approach for sCT prediction as well as a summary and discussion of the results presented in paper B. 1.4 Overview of the thesis 5 • Chapter 4 provides an introduction to a speeded up version of the patchbased approach applied in the pelvic region as well as a summary and discussion of the results presented in paper C. • Chapter 5 highlights issues with intensity normalization in MR scans which can affect the patch-based approach. • Chapter 6 provides an introduction to a voxel-based sCT method as well as a summary and discussion of the results presented in paper D. • Chapter 7 summarizes the main findings of this thesis and points out future directions.
The Role of Ghrelin in Blood Pressure Reduction Induced by Exercise in Patients with CABG

The effect of exercise on cardiovascular function was one of the main interested topics of researchers in the field of exercise physiology in the last decade and it also is on the focus of many researchers right now. The researchers in the present study are looking for examining the potential role of exerciseinduced ghrelin leakage on cardiovascular efficacy in the CABG patients. 16 CABG patients were chosen by the Available sampling method and randomly assigned in two groups of cardiac rehabilitation and control groups in the javad al-aemeh hospital. Ghrelin gene expression in lymphocytes, arterial stiffness, brachialankle index and the mean blood pressure were measured. The results showed that cardiac rehabilitation was the reason of a slight increase in ghrelin gene expression, but it was not significant (p=0.185 and F=1.68). Also Brachial-ankle index that reflects the efficacy of peripheral arterial function, as a result of rehabilitation exercises showed a significant improvement (p=0.041 and F=3.44), and the mean blood pressure in the rehabilitation group decreased significantly (p=0.0008 and F=7.64). Researchers clearly showed improvement in blood pressure and peripheral vascular resistance as a result of rehabilitation training in CABG patients in present study. Even though several mechanisms are proposed for ghrelin dilating effect, but changes in the blood pressure were independent of ghrelin in this study.
A Novel Approach for Web Usage Mining Using Improved FP-Tree With Map-Reduce

— The Web is an huge source of data mining which is fast growing and dynamic that provides ample opportunities which are often not used. Due to its huge amount and the unstructured nature web data represent a real challenge to traditional data mining techniques. The constantly increasing demand of finding pattern from large data enhances the association rule mining. The traditional algorithm for association rule discovery is Apriori. Scanning the database many times is the drawback of Apriori algorithm, so that it doesn’t work well with the large database. Researchers developed a plenty of algorithms and techniques for finding association rules. The generation of candidate set is the main problem. Among the existing techniques, the most efficient and scalable approach is frequent pattern growth (FP-growth) method. Generation of a massive number of conditional FP tree is the main obstacle of FP growth. In this research paper, we proposed an algorithm improved FP tree with a table for mining association rules. This algorithm mines all possible frequent item set without conditional FP tree generation. Our proposed method implemented the improved FP-Tree based on Map-Reduce framework which has high achieving performance compared with the basic FP-Growth. Moreover, use of Map-Reduce to parallelize FP-Growth algorithm, thereby improving the overall performance of frequent item sets mining. It also gives the frequency of frequent items to evaluate the desired association rule and enhance the time efficiency of mining association rule.
Location Management Strategies in Mobile Networks

—Global system for mobile communication (GSM) is globally accepted standard for digital cellular communication. GSM user uses a GSM based phone which is mobile. To identify the exact location of GSM user is a complex problem as user frequently moves between the cells. Location management deals with how to keep track of an active mobile station within cellular network. There are two basic operations involved with location management location update and paging. Through operation we find the location of mobile station. This paper analyze the location management techniques for GSM
DOM Tree based Approach for Extraction of Content from Web News Page

— With the huge amount of data on the World Wide Web, information extraction has become an increasingly important technology to help users locate desired Web information. Due to the heterogeneous nature of Web content, it is difficult to design a general Web information extracting approach that fits all application domains. When building a system for searching or mining Web content, a first task is extracting the main content and removing extraneous data such as navigation menus, functional and design elements, and commercial advertisements. It is very important to filter out such noise from web pages like web new pages. Also, when showing Web news pages on small screens like mobile phones or sending text to screen readers that translate the text to a more appropriate format like text-to-speech for visually impaired people, the content extraction operation is very valuable. Content extraction is defined as the process of determining those parts of an HTML document that represent the main textual content. The problem, however, is to find a solution that is generic which is portable to many types of Web news pages, accurate that finds all important content in a precise way and efficient where often a large number of Web pages are processed . An approach is designed which searches for relevant web pages using a web crawler then using a DOM tree based approach extracts the content from web news page by filtering out noise and the information retrieval agent extracts the key paragraph from the extracted content.
A Decentralized Accountability Framework For Enhancing Secure Data Sharing Through Icm In Cloud

The Cloud substitutes a computing criterion where shared configurable resources are afforded as an on-demand service over the Internet. Moreover, the cloud environment provides resources to the users on the basis of services like SaaS, PaaS and IaaS. Generally, a cloud can be referred as private cloud or public cloud. When a Cloud Service Provider (CSP) imposes upon public cloud resources to compile their private cloud, the result is demonstrated as a virtual private cloud. Private or public, the imperious intent of cloud computing is to provide simplistic, reliable usage of various computing resources. One of the significant features of cloud is that the outsourced data are accessed through any anonymous machines over the Internet. On the other hand, it creates an issue that user’s fear of unknown access of data, which can become a major difficulty to the wide implementation of cloud. In this paper, a decentralized accountability framework is developed to monitor the actual usage and access of the data that is shared on cloud. For that, a logging mechanism that includes authentication for each user to access the data has also been provided. Moreover, some procedures for providing the data under the control of data owner includes Integrity Checking Mechanism (ICM) have also been developed. The overall process strengthens the security constraints over cloud. And the experimental results reveal that the approach affords secure and scalable data sharing with reduced memory utilization and processing time
Spam Detection in Online Social Networks using Integrated Approach

: Online Social Networks (OSNs) are considered to be the much in demand societal tool used by the masses world over to communicate and transmit information. On these platforms for seeking opinions, news, updates, etc. is increasing day by day. While it is true that OSNs have become a new medium for dissemination of information, at the same time, they are also fast becoming a playground for the spread of misinformation, fake news, unsolicited messages, etc. Consequently, OSN platform comprises of two kinds of users namely, spammers and non-spammers. Spammers, out of harmful intent, post either unwanted (or irrelevant) information or spread misinformation on OSN platforms. The proposed mechanisms to detect such users (Spammers) in Facebook social network (a popular OSN). The work is based on a number of features at post-level and user-level like Spam Words, Replies and Post. Two learning algorithms namely Naive Bayes and Rule Based are used. Furthermore, to improve detection of spammers, integrated approach is proposed which “combines” the advantages of the two learning algorithms mentioned above. On the basis of total accuracy, spammers detection accuracy and non-spammers detection accuracy the improvement of spam detection is measured. Results, thus obtained, show that integrated approach that combines all algorithms performs better than other classical approaches in terms of overall accuracy and detect non-spammers with 99% accuracy with an overall accuracy of 92.3%.
Multi-Level Authentication Technique for Accessing Cloud Services

: Cloud computing is a new model to distribute services over the Internet. Data S e cur i t y is the most dangerous issues in a cloud computing environment. Authentication is a key technology for information security, which is a mechanism to establish proof of identities to get access of information in the system. Conventional p asswo rd authentication d o e s not provide sufficient security for information in cloud computing location to the most modern means of attacks. In this paper, we propose a new multi-factor authentication framework for cloud computing. The proposed framework provides a feasible and a most efficient mechanism which can closely integrate with the traditional authentication system. The proposed framework is verified by developing Cloud Access Management system which authenticates the user based on multiple factors. Prototype model for cloud computing own cloud server is implemented using open sources technology. The proposed framework shows the close agreement with the standard criteria for security.
Secure Protocol for Leader Election and Intrusion Detection in MANET

— As the nodes in MANET are battery limited, one node per cluster is elected as leader to run IDS for all the nodes in the cluster. Leader is elected based on the residual energy of each node that is sufficient to run IDS. Too much of resources are wasted for the implementation of intrusion detection scheme for every node. Hence nodes are grouped into cluster and cluster head is elect to serve other node in network, where as selfish node with maximum resources are not nominated for cluster head selection, because of self interest to save its own power. Nodes are provided incentives in the leader election process by VCG mechanism for preventing the nodes from exhibiting the selfish behaviour.1) To ensure security and to detect the intrusion in Mobile Ad hoc networks select a leader from the 1hop cluster as cluster head contains most resource.2)To avoid the issues arise due to optimal collection of leader and performance overhead, a solution is Mechanism based design theory.3)The solution provides nodes with incentives in the form of reputations to encourage nodes in honestly participating in the election process.
Ultra-Widefield Retina Imaging: Principles of Technology and Clinical Applications

The peripheral retina is frequently affected by various diseases and ultra-widefield (UWF) imaging has become an essential tool for evaluation of peripheral retinal pathology. The wide angle and high resolution of this imaging device were recently updated along with multimodal capabilities, including pseudocolor fundus photographs, fluorescein and indocyanine green angiography, and autofluorescence images. Using this UWF technology, important clinical observations have been made in the normal population and patients with certain diseases including diabetic retinopathy, retinal vascular occlusion, retinal detachment, uveitis, age-related macular degeneration, and choroidal tumors. Although peripheral distortion and the need for clinical validation are obstacles to overcome for the accurate quantification and more generalized use, UWF imaging has provided new insights into a variety of disorders and has become the standard-ofcare imaging modality for many diseases. Therefore, use of UWF retinal imaging for the daily retina practice in the near future is inevitable. This review will describe the principles of UWF technologies with several modalities, and include its clinical applications and then suggest future directions.
Content Acquisition Using Hidden Web Mining

- Web mining is a heavily researched area in the field of data mining and web mining with wide range of applications. This paper contains the three different categories of web mining – web content mining, web structure mining and web usage mining. Some specific tools are available for extracting useful knowledge from web. Further a brief description on web mining with region extraction algorithms and hidden web retrieval seen as future research area for hidden web mining.
Performance Analysis of Wireless OFDM System Using Raptor Codes with different Modulation

: - From last few decades, wireless communications industry is gaining momentum in both fixed and mobile applications. The continued increase in demand for all types of wireless services (voice, data, and multimedia) is fuelling the need for higher capacity and data rates not only in fixed but also in mobile applications. There are many techniques that fulfil these requirements. One of the most important techniques is Orthogonal Frequency Division Multiplexing (OFDM). In this paper, we analysis the performance of wireless OFDM system using Rapter codes. The time-varying features of wireless communication channels adversely affect the performance of the system. Raptor code is new emerging rate less code which has shown amazing performance over variety of channels. There is a constraint on the interleaving depth of OFDM-based system due to delay and maximum packet size. This non-ideal interleaving affects the maximum achievable diversity from the channel. We investigated the effect of correlation between fading blocks, which relates to the limited interleaving possible between carriers in an OFDM system, based on Raptor code. The simulation result based on different modulation technique using Raptor codes for BER calculation.
APPLICATION OF TOPSIS: A MULTIPLE CRITERIA DECISION MAKING APPROACH IN SUPPLIER SELECTION

Selection of suppliers is not based on cost only. It involves many criteria. Techniques to deal with multicriteria have been proposed by many researchers. This paper demonstrates the application of one of the multicriteria decision making (MCDM) technique for supplier selection in a semi-conductor industry. TOPSIS, the MCDM approach, is very effective and gives better result. Future research areas for decision making have been suggested.
Secure And Energy-Efficient Clustering Scheme (SAEECS) with data aggregation in mobile wireless sensor networks

- Wireless Sensor Networks (WSNs) consists of small nodes having limited computations, memory, sensing and communication capabilities. The distributed nature and dynamic topology of WSN introduces very special requirements in conserving energy and providing security. To address these challenges current technique called secure and energy efficient clustering scheme with data aggregation has been developed. The comparison between LEACH and the proposed scheme with result analysis is presented in this paper. This paper presents an energy efficient clustering scheme with aggregation of information to save the bandwidth requirement which in turn prologs the network lifetime. The proposed scheme is processed in two-stages. In the first stage of clustering, all nodes calculate their potential score based on the similarity of movement, residual energy and density in distributed manner. Each node decides whether it should become a cluster head or not, by employing a potential score. In the second stages, every node choose its cluster head among those cluster head candidates. A node with higher potential score is chosen as a cluster head. When a cluster member wants to transmit the data to aggregator(cluster head), and security is provided using RSA algorithm. The simulation results proved that the proposed technique has improved throughput, packet delivery ratio with reduced packet drop, less energy consumption and ensures data security compared with LEACH
International Journal of Innovative Technology and Exploring Engineering (IJITEE)

Plants have been used for medicinal purposes long before recorded history. It plays a major role in medicines, food, perfumes and cosmetics industries. By knowing the herbal plants and its usage it can be used for above applications. In this digital era, people don’t have adequate knowledge to identify various herbal plants which are used by our ancestors for long time. Presently, the identification of herbal plants is purely based on the human perception or knowledge. There may be probability of human error occurring. In order to have an efficient herb species classification, there must be a complete model which should be automatic and convenient recognition system. This paper is reviewing the different leaf classification methodologies based on deep learning algorithms. The main aim of this research paper is to conclude the advanced technique for the leaf identification.
Observed and Future Climate Variability and Extremes Over East Shoa Zone, Ethiopia

This study has been conducted with the aim to analyze variability and extremes of daily values of maximum and minimum temperatures, and precipitation. The future data are downscaled using delta method based on outputs from four global climate models (GCMs). The data are simulated for three future 30 year periods, centered at 2030’s (2010-2039) and 2050’s (2040-2069) and for the two scenarios (A2 and B1). Analysis of the 27 core set of extreme weather indices, which are defined by ETCCDI, is carried out on six selected sites and all the results were reported in detail. In addition comparisons in variability has made between models and values of these indices observed in the base climate period (1981-2010) and values of projected periods. Among precipitation indicators, significant increasing trends in annual total wet-day precipitation, number of heavy and very heavy precipitation days and decreasing trends in consecutive wet days, simple daily intensity index and precipitation on extremely wet days were found. Yet, the trends show no spatial coverage. In contrast to precipitation indices, the results indicate that most temperature indices showed significant changes in majority of stations. Nevertheless, the trends show less spatial coherence except cold days and summer days indicators. Regionally, summer days and tropical nights showed significant increases while warm nights, cold days and warmest day showed significant decreases during the base period. On the other hand, for the projected climate, while summer days and warm days would significantly increases but coldest day and cold days would decrease on regional level. The observed decreasing changes in warm extremes (i.e., warm night and warmest night) which are inconsistent with a warming planet and the heterogeneous behavior identified in most temperature extreme indicators suggested that local factors may play a major role in the study area.
The performance research on solvingTSPby four typicalAI algorithms

Travelling Salesmen Problem(VRP) has an important theoretical value and practical significance inmathematical and logistics field. ItÂÂÂs a typical NPHard problem, and artificial intelligent (AI) Algorithm has been already proven to be a very effect way in solving this problem. This paper carried out the performance research on solving TSP by four typicalAI algorithms after in-depth analyzed the TSP and these four algorithms (genetic algorithm, particle swarmoptimization algorithm, simulated annealing algorithmand ant colony algorithm). This paper verified the TSP solving performance by China travelling salesmen problem experiments and MATLAB programming. The results showed that: considering the average iteration time, SA < PSO< ACA
PCA Based Face Detection and Recognition system

: This paper presents face detection and recognition system using PCA (Principal Component Analysis) based technique with eigennface approach for detection and tracking to enhance the results of face recognition in different head position, face profile and head scale. This will be implemented with a data set that have only five images for each individual, images will have unconstrained background for different environments for any given image to find the face and recognize the person in the image, where unconstrained background increase the complexity of the detection and recognition process, that requires these condition must be solved for real time implementation. Results have been showed that increasing number of training images can improve accuracy of the system. The number of training images per each individual, which is five for each sub-set group found as the optimal choice training for this approach. Results were fair and approving for developed work with good accuracy of detection and recognition.
Automatic Text Summarization With Statistical, Linguistic and Cohesion Features

— In recent days, the large amount of information getting increased on internet; it is difficult for the user to go through all the information available on web. Automatic summarization system is used to reduce the user’s time in reading the whole information available on web. Text summarization system is to identify the most important information from the given text and provided it to the end users without changing source text idea. The automatic text summarization with statistics and linguistics feature uses sentence scoring method for selecting important sentence according to their level of importance. The total number of importance sentences are equal to total number of paragraphs that time importance sentences are added in summery so meaningful information is not extracted effectively, it have text overloading problem. The automatic text summarization with cohesion features is grammatical and lexical linking within the text or sentences that hold together a sentence and provide meaningful sentences to end user without changing source text idea therefore it increase the effectiveness of summery and also solve text overloading problem.