Due to many applications need the management of spatial data; clustering large spatial databases is an important problem which tries to find the densely populated regions in the feature space to be used in data mining, knowledge discovery, or efficient information retrieval. A good clustering approach should be efficient and detect clusters of arbitrary shapes. It must be insensitive to the outliers (noise) and the order of input data. In this paper Cosine Cluster is proposed based on cosine transformation, which satisfies all the above requirements. Using multi-resolution property of cosine transforms, arbitrary shape clusters can be effectively identified at different degrees of accuracy. Cosine Cluster is also approved to be highly efficient in terms of time complexity. Experimental results on very large data sets are presented, which show the efficiency and effectiveness of the proposed approach compared to other recent clustering methods.

This paper introduces the use of Wave atom transformation as an efficient speech noise filter with Gaussian mixture models (GMM) for robust text-independent speaker identification. The individual Gaussian components of a GMM are shown to represent some general speaker identity. The focus of this work is on applications which require high robustness of noise and high identification rates using short utterance from noisy (Natural Noise) numerical speech and alphabetical words speech. A Full experimental evaluation of the Gaussian mixture speaker model is conducted on a 10 speakers. The experiments examine algorithmic issues (Preprocessing (Denoising by Wave Atom), Feature Extraction (MFCC), Training using GMM, Pattern Matching (Maximum likelihood estimation ML), Decision Rule (Expectation Maximization EM)). The Proposed algorithm attains 95% identification accuracy using 5 seconds noisy speech utterances without Wave atom preprocessing it attains 90% identification accuracy using 5 seconds noisy speech utterances. Proposed denoisy algorithm increases the identification ratio by 5% for noisy speech signals, this ratio is interesting enough.

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Background Ongoing protests in Gaza have led to numerous injuries, including long-bone fractures. We investigated assessment of pain severity and strategies for pain management in the emergency department. As no local guidelines exist, delivered care was compared with the guidance of the UK National Institute for Health and Care Excellence (NICE). Methods A clinical audit was conducted at the emergency department in Dar Al Shifa Medical Complex, Gaza, among patients who attended the emergency department with acute long-bone fractures between April 15 and July 15, 2018. Data were collected on pain assessment methods and strategies for pain management and analgesia administration. Ethics approval was obtained from the Palestinian Ministry of Health. The purpose of the audit was explained to patients and their written consent was obtained before inclusion. Findings Of 79 patients invited to participate, 50 gave consent. 25 patients (50%) were aged 16-24 years, 20 (40%) 25-64 years, and five (10%) 65 years and older. Structured pain assessments were performed in only three patients (6%). No analgesia was administered to patients in the emergency department, except for two patients (4%) received infiltration of lidocaine as analgesia for haematoma.Interpretation No local guidelines for acute pain management in emergency departments exist in the Gaza Strip. Clinical practice showed no adherence to international standards, such as the NICE guidelines for pain management. Factors contributing to such poor management might be large numbers of patients presenting at the time during conflict and protests and that only one room was available in the Shifa' Medical Complex emergency department for examining, assessment, prescribing, and cast application. A limitation of the study is the small sample size is relatively small, but strengths were the 3-month period and prospective enrolment in the emergency department.

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