Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific 'characteristics of facial expressions. To address such a chal...Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific 'characteristics of facial expressions. To address such a challenge, and inspired by the recent success of deep identity network (DeepID-Net) for face identification, this paper proposes a novel deep learning based framework for recognising human expressions with facial images. Compared to the existing deep learning methods, our proposed framework, which is based on multi-scale global images and local facial patches, can significantly achieve a better performance on facial expression recognition. Finally, we verify the effectiveness of our proposed framework through experiments on the public benchmarking datasets JAFFE and extended Cohn-Kanade (CK+).展开更多
Background: Antimicrobial resistance (AMR) is a global health challenge that has escalated due to the inappropriate use of antimicrobials in humans, animals, and the environment. Developing and implementing strategies...Background: Antimicrobial resistance (AMR) is a global health challenge that has escalated due to the inappropriate use of antimicrobials in humans, animals, and the environment. Developing and implementing strategies to reduce and combat AMR is critical. Purpose: This study aimed to highlight some global strategies that can be implemented to address AMR using a One Health approach. Methods: This study employed a narrative review design that included studies published from January 2002 to July 2023. The study searched for literature on AMR and antimicrobial stewardship (AMS) in PubMed and Google Scholar using the 2020 PRISMA guidelines. Results: This study reveals that AMR remains a significant global public health problem. Its severity has been markedly exacerbated by inappropriate use of antimicrobials in humans, animals, and the broader ecological environment. Several strategies have been developed to address AMR, including the Global Action Plan (GAP), National Action Plans (NAPs), AMS programs, and implementation of the AWaRe classification of antimicrobials. These strategies also involve strengthening surveillance of antimicrobial consumption and resistance, encouraging the development of new antimicrobials, and enhancing regulations around antimicrobial prescribing, dispensing, and usage. Additional measures include promoting global partnerships, combating substandard and falsified antimicrobials, advocating for vaccinations, sanitation, hygiene and biosecurity, as well as exploring alternatives to antimicrobials. However, the implementation of these strategies faces various challenges. These challenges include low awareness and knowledge of AMR, a shortage of human resources and capacity building for AMR and AMS, in adequate funding for AMR and AMS initiatives, limited laboratory capacities for surveillance, behavioural change issues, and ineffective leadership and multidisciplinary teams. Conclusion: In conclusion, this study established that AMR is prevalent among humans, animals, and the environment. S展开更多
Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone...Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone to attacks.Various management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic classification.Due to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural networks.Furthermore,machine learning-based models incline to misclassify internet traffic due to improper feature selection.In this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic.To examine the performance of the proposed technique,Moore-dataset is used for training the classifier.The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet traffic.The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.展开更多
Introduction: Indiscriminate prescribing and using of antibiotics have led to the development of antimicrobial resistance (AMR). To reduce this problem, the World Health Organization (WHO) developed the “Access”, “...Introduction: Indiscriminate prescribing and using of antibiotics have led to the development of antimicrobial resistance (AMR). To reduce this problem, the World Health Organization (WHO) developed the “Access”, “Watch”, and “Reserve” (AWaRe) classification of antibiotics that promotes antimicrobial stewardship (AMS). In Zambia, there are gaps in practice regarding prescribing of antibiotics based on the AWaRe protocol. This study assessed antibiotic prescribing patterns in adult in-patients in selected primary healthcare hospitals in Lusaka, Zambia. Materials and Methods: This retrospective cross-sectional study was conducted using 388 patient medical files from September 2021 to November 2021, five primary healthcare hospitals namely;Chawama, Matero, Chilenje, Kanyama, and Chipata. Data analysis was performed using the Statistical Package for Social Sciences version 23. Results: Of the selected medical files, 52.3% (n = 203) were for male patients. Overall, the prevalence of antibiotic use was 82.5% (n = 320) which was higher than the WHO recommendation of a less than 30% threshold. The most prescribed antibiotic was ceftriaxone (20.3%), a Watch group antibiotic, followed by metronidazole (17.8%) and sulfamethoxazole/trimethoprim (16.3%), both belonging to the Access group. Furthermore, of the total antibiotics prescribed, 41.9% were prescribed without adhering to the standard treatment guidelines. Conclusion: This study found a high prescription of antibiotics (82.5%) that can be linked to non-adherence to the standard treatment guidelines in primary healthcare hospitals. The most prescribed antibiotic was ceftriaxone which belongs to the Watch group, raising a lot of concerns. There is a need for rational prescribing of antibiotics and implementation of AMS programs in healthcare facilities in Zambia, and this may promote surveillance of irrational prescribing and help reduce AMR in the future.展开更多
BACKGROUND The overuse and misuse of antimicrobials contribute significantly to antimicrobial resistance(AMR),which is a global public health concern.India has particularly high rates of AMR,posing a threat to effecti...BACKGROUND The overuse and misuse of antimicrobials contribute significantly to antimicrobial resistance(AMR),which is a global public health concern.India has particularly high rates of AMR,posing a threat to effective treatment.The World Health Or-ganization(WHO)Access,Watch,Reserve(AWaRe)classification system was introduced to address this issue and guide appropriate antibiotic prescribing.However,there is a lack of studies examining the prescribing patterns of antimi-crobials using the AWaRe classification,especially in North India.Therefore,this study aimed to assess the prescribing patterns of antimicrobials using the WHO AWaRe classification in a tertiary care centre in North India.Ophthalmology,Obstetrics and Gynecology).Metronidazole and ceftriaxone were the most prescribed antibiotics.According to the AWaRe classification,57.61%of antibiotics fell under the Access category,38.27%in Watch,and 4.11%in Reserve.Most Access antibiotics were prescribed within the Medicine department,and the same department also exhibited a higher frequency of Watch antibiotics prescriptions.The questionnaire survey showed that only a third of participants were aware of the AWaRe classification,and there was a lack of knowledge regarding AMR and the potential impact of AWaRe usage.RESULTS The research was carried out in accordance with the methodology presented in Figure 1.A total of n=123 patients were enrolled in this study,with each of them receiving antibiotic prescriptions.The majority of these prescriptions were issued to inpatients(75.4%),and both the Medicine and Surgical departments were equally represented,accounting for 49.6%and 50.4%,respectively.Among the healthcare providers responsible for prescribing antibiotics,72%were Junior Residents,18.7%were Senior Residents,and 9.3%were Consultants.These findings have been summarized in Table 1.The prescriptions included 27 different antibiotics,with metronidazole being the most prescribed(19%)followed by ceftriaxone(17%).The mean number of antibiotics used per patient wa展开更多
A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial l...A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial logistic regression （ MLR ） and sparse representation （SR） based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating spectral.spatial context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased.展开更多
基金supported by the Academy of Finland(267581)the D2I SHOK Project from Digile Oy as well as Nokia Technologies(Tampere,Finland)
文摘Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific 'characteristics of facial expressions. To address such a challenge, and inspired by the recent success of deep identity network (DeepID-Net) for face identification, this paper proposes a novel deep learning based framework for recognising human expressions with facial images. Compared to the existing deep learning methods, our proposed framework, which is based on multi-scale global images and local facial patches, can significantly achieve a better performance on facial expression recognition. Finally, we verify the effectiveness of our proposed framework through experiments on the public benchmarking datasets JAFFE and extended Cohn-Kanade (CK+).
文摘Background: Antimicrobial resistance (AMR) is a global health challenge that has escalated due to the inappropriate use of antimicrobials in humans, animals, and the environment. Developing and implementing strategies to reduce and combat AMR is critical. Purpose: This study aimed to highlight some global strategies that can be implemented to address AMR using a One Health approach. Methods: This study employed a narrative review design that included studies published from January 2002 to July 2023. The study searched for literature on AMR and antimicrobial stewardship (AMS) in PubMed and Google Scholar using the 2020 PRISMA guidelines. Results: This study reveals that AMR remains a significant global public health problem. Its severity has been markedly exacerbated by inappropriate use of antimicrobials in humans, animals, and the broader ecological environment. Several strategies have been developed to address AMR, including the Global Action Plan (GAP), National Action Plans (NAPs), AMS programs, and implementation of the AWaRe classification of antimicrobials. These strategies also involve strengthening surveillance of antimicrobial consumption and resistance, encouraging the development of new antimicrobials, and enhancing regulations around antimicrobial prescribing, dispensing, and usage. Additional measures include promoting global partnerships, combating substandard and falsified antimicrobials, advocating for vaccinations, sanitation, hygiene and biosecurity, as well as exploring alternatives to antimicrobials. However, the implementation of these strategies faces various challenges. These challenges include low awareness and knowledge of AMR, a shortage of human resources and capacity building for AMR and AMS, in adequate funding for AMR and AMS initiatives, limited laboratory capacities for surveillance, behavioural change issues, and ineffective leadership and multidisciplinary teams. Conclusion: In conclusion, this study established that AMR is prevalent among humans, animals, and the environment. S
基金This work has supported by the Xiamen University Malaysia Research Fund(XMUMRF)(Grant No:XMUMRF/2019-C3/IECE/0007)。
文摘Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone to attacks.Various management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic classification.Due to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural networks.Furthermore,machine learning-based models incline to misclassify internet traffic due to improper feature selection.In this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic.To examine the performance of the proposed technique,Moore-dataset is used for training the classifier.The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet traffic.The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.
文摘Introduction: Indiscriminate prescribing and using of antibiotics have led to the development of antimicrobial resistance (AMR). To reduce this problem, the World Health Organization (WHO) developed the “Access”, “Watch”, and “Reserve” (AWaRe) classification of antibiotics that promotes antimicrobial stewardship (AMS). In Zambia, there are gaps in practice regarding prescribing of antibiotics based on the AWaRe protocol. This study assessed antibiotic prescribing patterns in adult in-patients in selected primary healthcare hospitals in Lusaka, Zambia. Materials and Methods: This retrospective cross-sectional study was conducted using 388 patient medical files from September 2021 to November 2021, five primary healthcare hospitals namely;Chawama, Matero, Chilenje, Kanyama, and Chipata. Data analysis was performed using the Statistical Package for Social Sciences version 23. Results: Of the selected medical files, 52.3% (n = 203) were for male patients. Overall, the prevalence of antibiotic use was 82.5% (n = 320) which was higher than the WHO recommendation of a less than 30% threshold. The most prescribed antibiotic was ceftriaxone (20.3%), a Watch group antibiotic, followed by metronidazole (17.8%) and sulfamethoxazole/trimethoprim (16.3%), both belonging to the Access group. Furthermore, of the total antibiotics prescribed, 41.9% were prescribed without adhering to the standard treatment guidelines. Conclusion: This study found a high prescription of antibiotics (82.5%) that can be linked to non-adherence to the standard treatment guidelines in primary healthcare hospitals. The most prescribed antibiotic was ceftriaxone which belongs to the Watch group, raising a lot of concerns. There is a need for rational prescribing of antibiotics and implementation of AMS programs in healthcare facilities in Zambia, and this may promote surveillance of irrational prescribing and help reduce AMR in the future.
文摘BACKGROUND The overuse and misuse of antimicrobials contribute significantly to antimicrobial resistance(AMR),which is a global public health concern.India has particularly high rates of AMR,posing a threat to effective treatment.The World Health Or-ganization(WHO)Access,Watch,Reserve(AWaRe)classification system was introduced to address this issue and guide appropriate antibiotic prescribing.However,there is a lack of studies examining the prescribing patterns of antimi-crobials using the AWaRe classification,especially in North India.Therefore,this study aimed to assess the prescribing patterns of antimicrobials using the WHO AWaRe classification in a tertiary care centre in North India.Ophthalmology,Obstetrics and Gynecology).Metronidazole and ceftriaxone were the most prescribed antibiotics.According to the AWaRe classification,57.61%of antibiotics fell under the Access category,38.27%in Watch,and 4.11%in Reserve.Most Access antibiotics were prescribed within the Medicine department,and the same department also exhibited a higher frequency of Watch antibiotics prescriptions.The questionnaire survey showed that only a third of participants were aware of the AWaRe classification,and there was a lack of knowledge regarding AMR and the potential impact of AWaRe usage.RESULTS The research was carried out in accordance with the methodology presented in Figure 1.A total of n=123 patients were enrolled in this study,with each of them receiving antibiotic prescriptions.The majority of these prescriptions were issued to inpatients(75.4%),and both the Medicine and Surgical departments were equally represented,accounting for 49.6%and 50.4%,respectively.Among the healthcare providers responsible for prescribing antibiotics,72%were Junior Residents,18.7%were Senior Residents,and 9.3%were Consultants.These findings have been summarized in Table 1.The prescriptions included 27 different antibiotics,with metronidazole being the most prescribed(19%)followed by ceftriaxone(17%).The mean number of antibiotics used per patient wa
基金National Key Research and Development Program of China(No.2016YFF0103604)National Natural Science Foundations of China(Nos.61171165,11431015,61571230)+1 种基金National Scientific Equipment Developing Project of China(No.2012YQ050250)Natural Science Foundation of Jiangsu Province,China(No.BK20161500)
文摘A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial logistic regression （ MLR ） and sparse representation （SR） based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating spectral.spatial context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased.
