Bladder Adenocarcinoma: A Case Report

Background: Bladder adenocarcinoma (AC) is a rare histological variant and research on the best ways to treat it is scant. Clinical Case: We present the case of a 70-year-old woman who has had hematuria for the past month with no history of serious illness. She visited a urologist, who performed a cystoscopy on her as a result. A urinary bladder adenocarcinoma was discovered in a biopsy. Complete investigations revealed no metastasis. The patient was considered for a partial cystectomy, according to the results of the MRI. She underwent the surgery, which was followed by concurrent chemo-radiotherapy. She underwent multiple reevaluations, and her case was stable after about a year of follow-up. Conclusions: With the best surgical outcomes, the choice to perform a partial cystectomy was appropriate given the tumor\u27s location. However, a lengthy follow-up is required

Treatment of Forty Adult Patients with Hodgkin Disease; Baghdad Teaching Hospital Experience

Background: Hodgkin disease was the first cancer in which the curative potential of combination chemotherapy was demonstrated. The affected patients are often young and there is a great potential for adding years of productive life by giving curative therapy even when the disease is advanced. Objective: to describe the experience of the hematology unit,Baghdad Teaching Hospital, in the management of 40 adult patients with Hodgkin disease. Patients and Methods: a retrospective cohort study of forty adult Iraqi patients with Hodgkin disease between 2005 and 2013 in the hematology unit. Patients were treated initially with 6-8 cycles of ABVD chemotherapy protocol (doxorubicine+ bleomycin+ vinblastin+ dacarbazine) , nine patients received additional involved field radiotherapy for residual masses or bulky disease. Overall survival and progression free survivals were estimated using Kaplan Meier survival plot. Results: The mean age was 28.6±12.88 years with females forming 61.5% of patients, mean duration of follow up was 27.9± 20.6 months. Staging showed that 55% and 27.5% had stage II and III respectively. B symptoms were found in 72.5% patients , bulky disease in 42.5% patients. Complete Response+ Complete Response undetermined was seen in 85% of cases. First Relapse occurred in 14%, and death in 7.5% of the patients. The 8 year overall survival and progression free survival were 82% and 50% respectively while the mean overall survival and progression free survival times were 84.7 and 59.9 months respectively. Conclusion: The results of the treatment of adult patients with Hodgkin disease in our unit is rather comparable to the results from other studies

SPARC 2018 Internationalisation and collaboration : Salford postgraduate annual research conference book of abstracts

Welcome to the Book of Abstracts for the 2018 SPARC conference. This year we not only celebrate the work of our PGRs but also the launch of our Doctoral School, which makes this year’s conference extra special. Once again we have received a tremendous contribution from our postgraduate research community; with over 100 presenters, the conference truly showcases a vibrant PGR community at Salford. These abstracts provide a taster of the research strengths of their works, and provide delegates with a reference point for networking and initiating critical debate. With such wide-ranging topics being showcased, we encourage you to take up this great opportunity to engage with researchers working in different subject areas from your own. To meet global challenges, high impact research inevitably requires interdisciplinary collaboration. This is recognised by all major research funders. Therefore engaging with the work of others and forging collaborations across subject areas is an essential skill for the next generation of researchers

Review on Deep Learning-Based Face Analysis

This paper reviews the development of face recognition based on deep learning in the field of biometrics. Firstly, the basic application of face recognition and the definition of the deep learning model is explained. In addition, the research overview and application are summarized, such as face recognition method based on convolution neural network (CNN), deep nonlinear face shape extraction method, face-based robustness modeling based on deep learning, fully automatic face recognition in constrained environments, face recognition based on deep learning video monitoring, low resolution face recognition based on deep learning, and other deep learning of the face information recognition; analysis of the current face recognition technology in the deep learning applications in the problems and development trends. Finally, it is concluded that the deep learning can learn to get more useful data and can build a more accurate model. However, there are some shortcomings in deep learning, such as the length of the training model, the need for continuous iteration to model optimization, being difficult to guarantee the optimal global solution, which also needs to continue to explore in the future

Review of deep convolution neural network in image classification

With the development of large data age, Convolutional neural networks (CNNs) with more hidden layers have more complex network structure and more powerful feature learning and feature expression abilities than traditional machine learning methods. The convolution neural network model trained by the deep learning algorithm has made remarkable achievements in many large-scale identification tasks in the field of computer vision since its introduction. This paper first introduces the rise and development of deep learning and convolution neural network, and summarizes the basic model structure, convolution feature extraction and pooling operation of convolution neural network. Then, the research status and development trend of convolution neural network model based on deep learning in image classification are reviewed, which is mainly introduced from the aspects of typical network structure construction, training method and performance. Finally, some problems in the current research are briefly summarized and discussed, and the new direction of future development is forecaste

Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm

Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites. In this paper, a Malay sentiment analysis classification model is proposed to improve classification performances based on the semantic orientation and machine learning approaches. First, a total of 2,478 Malay sentiment-lexicon phrases and words are assigned with a synonym and stored with the help of more than one Malay native speaker, and the polarity is manually allotted with a score. In addition, the supervised machine learning approaches and lexicon knowledge method are combined for Malay sentiment classification with evaluating thirteen features. Finally, three individual classifiers and a combined classifier are used to evaluate the classification accuracy. In experimental results, a wide-range of comparative experiments is conducted on a Malay Reviews Corpus (MRC), and it demonstrates that the feature extraction improves the performance of Malay sentiment analysis based on the combined classification. However, the results depend on three factors, the features, the number of features and the classification approach

Infinite-term memory classifier for wi-fi localization based on dynamic Wi-Fi Simulator

Wi-Fi localization is an active research topic, and various challenges are not yet resolved in this field. Researchers develop models and use benchmark datasets for Wi-Fi or fingerprinting to create a quantitative comparative evaluation. These benchmarking datasets are limited by their failure to support dynamical navigation. As a result, Wi-Fi models are only evaluated as usual classifiers without including actual navigation maneuvers in the evaluation, which makes the models incapable of handling the actual navigation behavior and its impact on the performance. One common navigation behavior is the cyclic dynamic behavior, which occurs frequently in the indoor environment when a person visits the same place or location multiple times or repeats the same trajectory or similar one more than once. For this purpose, we developed two models: a simulation model for generating time series data to support actual conducted navigation scenarios and a Wi-Fi classification model to handle dynamical scenarios generated by the simulator under cyclic dynamic behavior. Various testing scenarios were conducted for evaluation, and a comparison with benchmarks was performed. Results show the superiority of our developed model which is infinite-term memory online sequential extreme learning machine (OSELM) to the benchmarks with a percentage of 173% over feature adaptive OSELM and 1638% over OSELM

Word sense disambiguation using hybrid swarm intelligence approach

Word sense disambiguation (WSD) is the process of identifying an appropriate sense for an ambiguous word. With the complexity of human languages in which a single word could yield different meanings, WSD has been utilized by several domains of interests such as search engines and machine translations. The literature shows a vast number of techniques used for the process of WSD. Recently, researchers have focused on the use of meta-heuristic approaches to identify the best solutions that reflect the best sense. However, the application of meta-heuristic approaches remains limited and thus requires the efficient exploration and exploitation of the problem space. Hence, the current study aims to propose a hybrid meta-heuristic method that consists of particle swarm optimization (PSO) and simulated annealing to find the global best meaning of a given text. Different semantic measures have been utilized in this model as objective functions for the proposed hybrid PSO. These measures consist of JCN and extended Lesk methods, which are combined effectively in this work. The proposed method is tested using a three-benchmark dataset (SemCor 3.0, SensEval-2, and SensEval-3). Results show that the proposed method has superior performance in comparison with state-of-the-art approaches

MFA-OSELM algorithm for WiFi-based indoor positioning system

Indoor localization is a dynamic and exciting research area. WiFi has exhibited a tremendous capability for internal localization since it is extensively used and easily accessible. Facilitating the use of WiFi for this purpose requires fingerprint formation and the implementation of a learning algorithm with the aim of using the fingerprint to determine locations. The most difficult aspect of techniques based on fingerprints is the effect of dynamic environmental changes on fingerprint authentication. With the aim of dealing with this problem, many experts have adopted transfer-learning methods, even though in WiFi indoor localization the dynamic quality of the change in the fingerprint has some cyclic factors that necessitate the use of previous knowledge in various situations. Thus, this paper presents the maximum feature adaptive online sequential extreme learning machine (MFA-OSELM) technique, which uses previous knowledge to handle the cyclic dynamic factors that are brought about by the issue of mobility, which is present in internal environments. This research extends the earlier study of the feature adaptive online sequential extreme learning machine (FA-OSELM). The results of this research demonstrate that MFA-OSELM is superior to FA-OSELM given its capacity to preserve previous data when a person goes back to locations that he/she had visited earlier. Also, there is always a positive accuracy change when using MFA-OSELM, with the best change achieved being 27% (ranging from eight to 27% and six to 18% for the TampereU and UJIIndoorLoc datasets, respectively), which proves the efficiency of MFA-OSELM in restoring previous knowledge