FACIAL EXPRESSION RECOGNITION BASED ON CULTURAL PARTICLE SWAMP OPTIMIZATION AND SUPPORT VECTOR MACHINE

Facial expressions remain a significant component of human-to-human interface and have the potential to play a correspondingly essential part in human-computer interaction. Support Vector Machine (SVM) by the virtue of its application in a various domain such as bioinformatics, pattern recognition, and other nonlinear problems has a very good generalization capability. However, various studies have proven that its performance drops when applied to problems with large complexities. It consumes a large amount of memory and time when the number of dataset increases. Optimization of SVM parameter can influence and improve its performance.Therefore, a Culture Particle Swarm Optimization (CPSO) techniques is developed to improve the performance of SVM in the facial expression recognition system. CPSO is a hybrid of Cultural Algorithm (CA) and Particle Swarm Optimization (PSO). Six facial expression images each from forty individuals were locally acquired. One hundred and seventy five images were used for training while the remaining sixty five images were used for testing purpose. The results showed a training time of 16.32 seconds, false positive rate of 0%, precision of 100% and an overall accuracy of 92.31% at 250 by 250 pixel resolution. The results obtained establish that CPSO-SVM technique is computational efficient with better precision, accuracy, false positive rate and can construct efficient and realistic facial expression feature that would produce a more reliable security surveillance system in any security prone organization

Anti-malarial prescriptions in three health care facilities after the emergence of chloroquine resistance in Niakhar, Senegal (1992–2004)

<p>Abstract</p> <p>Background</p> <p>In the rural zone of Niakhar in Senegal, the first therapeutic failures for chloroquine (CQ) were observed in 1992. In 2003, the national policy regarding first-line treatment of uncomplicated malaria was modified, replacing CQ by a transitory bi-therapy amodiaquine/sulphadoxine-pyrimethamine (AQ/SP), before the implementation of artemisinin-based combination therapy (ACT) in 2006.</p> <p>The aims of the study were to assess the evolution of anti-malarial prescriptions in three health care facilities between 1992 and 2004, in parallel with increasing CQ resistance in the region.</p> <p>Methods</p> <p>The study was conducted in the area of Niakhar, a demographic surveillance site located in a sahelo-sudanese region of Senegal, with mesoendemic and seasonal malaria transmission. Health records of two public health centres and a private catholic dispensary were collected retrospectively to cover the period 1992–2004.</p> <p>Results</p> <p>Records included 110,093 consultations and 292,965 prescribed treatments. Twenty-five percent of treatments were anti-malarials, prescribed to 49% of patients. They were delivered all year long, but especially during the rainy season, and 20% of patients with no clinical malaria diagnosis received anti-malarials. Chloroquine and quinine represented respectively 55.7% and 34.6% of prescribed anti-malarials. Overall, chloroquine prescriptions rose from 1992 to 2000, in parallel with clinical malaria; then the CQ prescription rate decreased from 2000 and was concomitant with the rise of SP and the persistence of quinine use. AQ and SP were mainly used as bi-therapy after 2003, at the time of national treatment policy change.</p> <p>Conclusion</p> <p>The results show the overall level of anti-malarial prescription in the study area for a considerable number of patients over a large period of time. Even though resistance to CQ rapidly increased from 1992 to 2001, no change in CQ prescription was observed until the early 2000s, possibly due to the absence of an obvious decrease in CQ effectiveness, a lack of therapeutic options or a blind follow-up of national guidelines.</p

Primary stroke prevention worldwide : translating evidence into action

Funding Information: The stroke services survey reported in this publication was partly supported by World Stroke Organization and Auckland University of Technology. VLF was partly supported by the grants received from the Health Research Council of New Zealand. MOO was supported by the US National Institutes of Health (SIREN U54 HG007479) under the H3Africa initiative and SIBS Genomics (R01NS107900, R01NS107900-02S1, R01NS115944-01, 3U24HG009780-03S5, and 1R01NS114045-01), Sub-Saharan Africa Conference on Stroke Conference (1R13NS115395-01A1), and Training Africans to Lead and Execute Neurological Trials & Studies (D43TW012030). AGT was supported by the Australian National Health and Medical Research Council. SLG was supported by a National Heart Foundation of Australia Future Leader Fellowship and an Australian National Health and Medical Research Council synergy grant. We thank Anita Arsovska (University Clinic of Neurology, Skopje, North Macedonia), Manoj Bohara (HAMS Hospital, Kathmandu, Nepal), Denis ?erimagi? (Poliklinika Glavi?, Dubrovnik, Croatia), Manuel Correia (Hospital de Santo Ant?nio, Porto, Portugal), Daissy Liliana Mora Cuervo (Hospital Moinhos de Vento, Porto Alegre, Brazil), Anna Cz?onkowska (Institute of Psychiatry and Neurology, Warsaw, Poland), Gloria Ekeng (Stroke Care International, Dartford, UK), Jo?o Sargento-Freitas (Centro Hospitalar e Universit?rio de Coimbra, Coimbra, Portugal), Yuriy Flomin (MC Universal Clinic Oberig, Kyiv, Ukraine), Mehari Gebreyohanns (UT Southwestern Medical Centre, Dallas, TX, USA), Ivete Pillo Gon?alves (Hospital S?o Jos? do Avai, Itaperuna, Brazil), Claiborne Johnston (Dell Medical School, University of Texas, Austin, TX, USA), Kristaps Jurj?ns (P Stradins Clinical University Hospital, Riga, Latvia), Rizwan Kalani (University of Washington, Seattle, WA, USA), Grzegorz Kozera (Medical University of Gda?sk, Gda?sk, Poland), Kursad Kutluk (Dokuz Eylul University, ?zmir, Turkey), Branko Malojcic (University Hospital Centre Zagreb, Zagreb, Croatia), Micha? Maluchnik (Ministry of Health, Warsaw, Poland), Evija Migl?ne (P Stradins Clinical University Hospital, Riga, Latvia), Cassandra Ocampo (University of Botswana, Princess Marina Hospital, Botswana), Louise Shaw (Royal United Hospitals Bath NHS Foundation Trust, Bath, UK), Lekhjung Thapa (Upendra Devkota Memorial-National Institute of Neurological and Allied Sciences, Kathmandu, Nepal), Bogdan Wojtyniak (National Institute of Public Health, Warsaw, Poland), Jie Yang (First Affiliated Hospital of Chengdu Medical College, Chengdu, China), and Tomasz Zdrojewski (Medical University of Gda?sk, Gda?sk, Poland) for their comments on early draft of the manuscript. The views expressed in this article are solely the responsibility of the authors and they do not necessarily reflect the views, decisions, or policies of the institution with which they are affiliated. We thank WSO for funding. The funder had no role in the design, data collection, analysis and interpretation of the study results, writing of the report, or the decision to submit the study results for publication. Funding Information: The stroke services survey reported in this publication was partly supported by World Stroke Organization and Auckland University of Technology. VLF was partly supported by the grants received from the Health Research Council of New Zealand. MOO was supported by the US National Institutes of Health (SIREN U54 HG007479) under the H3Africa initiative and SIBS Genomics (R01NS107900, R01NS107900-02S1, R01NS115944-01, 3U24HG009780-03S5, and 1R01NS114045-01), Sub-Saharan Africa Conference on Stroke Conference (1R13NS115395-01A1), and Training Africans to Lead and Execute Neurological Trials & Studies (D43TW012030). AGT was supported by the Australian National Health and Medical Research Council. SLG was supported by a National Heart Foundation of Australia Future Leader Fellowship and an Australian National Health and Medical Research Council synergy grant. We thank Anita Arsovska (University Clinic of Neurology, Skopje, North Macedonia), Manoj Bohara (HAMS Hospital, Kathmandu, Nepal), Denis Čerimagić (Poliklinika Glavić, Dubrovnik, Croatia), Manuel Correia (Hospital de Santo António, Porto, Portugal), Daissy Liliana Mora Cuervo (Hospital Moinhos de Vento, Porto Alegre, Brazil), Anna Członkowska (Institute of Psychiatry and Neurology, Warsaw, Poland), Gloria Ekeng (Stroke Care International, Dartford, UK), João Sargento-Freitas (Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal), Yuriy Flomin (MC Universal Clinic Oberig, Kyiv, Ukraine), Mehari Gebreyohanns (UT Southwestern Medical Centre, Dallas, TX, USA), Ivete Pillo Gonçalves (Hospital São José do Avai, Itaperuna, Brazil), Claiborne Johnston (Dell Medical School, University of Texas, Austin, TX, USA), Kristaps Jurjāns (P Stradins Clinical University Hospital, Riga, Latvia), Rizwan Kalani (University of Washington, Seattle, WA, USA), Grzegorz Kozera (Medical University of Gdańsk, Gdańsk, Poland), Kursad Kutluk (Dokuz Eylul University, İzmir, Turkey), Branko Malojcic (University Hospital Centre Zagreb, Zagreb, Croatia), Michał Maluchnik (Ministry of Health, Warsaw, Poland), Evija Miglāne (P Stradins Clinical University Hospital, Riga, Latvia), Cassandra Ocampo (University of Botswana, Princess Marina Hospital, Botswana), Louise Shaw (Royal United Hospitals Bath NHS Foundation Trust, Bath, UK), Lekhjung Thapa (Upendra Devkota Memorial-National Institute of Neurological and Allied Sciences, Kathmandu, Nepal), Bogdan Wojtyniak (National Institute of Public Health, Warsaw, Poland), Jie Yang (First Affiliated Hospital of Chengdu Medical College, Chengdu, China), and Tomasz Zdrojewski (Medical University of Gdańsk, Gdańsk, Poland) for their comments on early draft of the manuscript. The views expressed in this article are solely the responsibility of the authors and they do not necessarily reflect the views, decisions, or policies of the institution with which they are affiliated. We thank WSO for funding. The funder had no role in the design, data collection, analysis and interpretation of the study results, writing of the report, or the decision to submit the study results for publication. Funding Information: VLF declares that the PreventS web app and Stroke Riskometer app are owned and copyrighted by Auckland University of Technology; has received grants from the Brain Research New Zealand Centre of Research Excellence (16/STH/36), Australian National Health and Medical Research Council (NHMRC; APP1182071), and World Stroke Organization (WSO); is an executive committee member of WSO, honorary medical director of Stroke Central New Zealand, and CEO of New Zealand Stroke Education charitable Trust. AGT declares funding from NHMRC (GNT1042600, GNT1122455, GNT1171966, GNT1143155, and GNT1182017), Stroke Foundation Australia (SG1807), and Heart Foundation Australia (VG102282); and board membership of the Stroke Foundation (Australia). SLG is funded by the National Health Foundation of Australia (Future Leader Fellowship 102061) and NHMRC (GNT1182071, GNT1143155, and GNT1128373). RM is supported by the Implementation Research Network in Stroke Care Quality of the European Cooperation in Science and Technology (project CA18118) and by the IRIS-TEPUS project from the inter-excellence inter-cost programme of the Ministry of Education, Youth and Sports of the Czech Republic (project LTC20051). BN declares receiving fees for data management committee work for SOCRATES and THALES trials for AstraZeneca and fees for data management committee work for NAVIGATE-ESUS trial from Bayer. All other authors declare no competing interests. Publisher Copyright: © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseStroke is the second leading cause of death and the third leading cause of disability worldwide and its burden is increasing rapidly in low-income and middle-income countries, many of which are unable to face the challenges it imposes. In this Health Policy paper on primary stroke prevention, we provide an overview of the current situation regarding primary prevention services, estimate the cost of stroke and stroke prevention, and identify deficiencies in existing guidelines and gaps in primary prevention. We also offer a set of pragmatic solutions for implementation of primary stroke prevention, with an emphasis on the role of governments and population-wide strategies, including task-shifting and sharing and health system re-engineering. Implementation of primary stroke prevention involves patients, health professionals, funders, policy makers, implementation partners, and the entire population along the life course.publishersversionPeer reviewe

PREDICTING COVID-19 FROM CHEST X-RAY IMAGES USING OPTIMIZED CONVOLUTION NEURAL NETWORK

Machine learning is emerging as a unique powerful method to improve the diagnosis and prognosis of several multifactorial diseases, including COVID-19. The COVID-19 pandemic is a major threat, and it has severe impact on the health and life of many people worldwide. The recent advances in computer vision made possible by various computational method has paved the way for computer assisted diagnosis in fighting COVID-19.&nbsp;Early detection of the COVID-19 through accurate diagnosis, may decrease the patient’s mortality rate. Chest X-ray images are crucial and mostly used for the diagnosis of this disease. Thus, this study used optimized Convolution Neural Network (OCNN) to support the diagnosis of COVID-19 using chest x-ray. Particle Swarm Optimization (PSO) was applied to optimize the network of CNN for improved performance. The dataset used in this study was acquired from Kaggle repository. The dataset contains the Chest X-Ray images of COVID-19 patients and normal patients. The model is created, and the results have been evaluated by using the various evaluation metrics, i.e., sensitivity, false positive rate, precision, accuracy, and prediction time. The approach adopted in this study enhances CNN by making it free from iterative adjustment of weights which increases the computational speed to a higher extent. The experimental results reveal that the proposed technique achieved an improved performance which indicates the very high accuracy of the proposed model