14 research outputs found
Deep transfer CNNs models performance evaluation using unbalanced histopathological breast cancer dataset
Cancer is one of the top deadly diseases. Of this disease, around about 9.8 million death cause annually. It has been recorded by the American Cancer Society that every eight women die due to breast cancer in the USA. In this paper, we have identified eight different lesion categories: Benign Tumor: Adenosis-Adenoma, Fibro-Adenoma, Phyllodes-Tumor, Tubular-Adenoma, and Malignant Tumor; Ductal-Carcinoma, Lobular-Carcinoma, Mucinous-Carcinoma, Papillary-Carcinoma. The main contribution of this paper is to examine the performance of five pre-trained CNN models on an unbalanced cancer dataset for cancer prediction. The identification of different cancer tumors has been recognized by using transfer learning models namely ResNet50, ResNet101, ResNet152, VGG16, and VGG19. BreakHis dataset has four different magnifications (40x-100x-200x-400x), and used for experiments setup in this study. The total number of images for all magnification levels is 7909. The experimental results state that the pre-trained model Residual Net with different variations has worked well 91%~96% as compared to other pre-trained models. The ResNet101 architecture model has gained a multiclass identification higher than 95%. In this paper, the proposed methodology has different evaluation parameters such as accuracy, recall, and f1-score of all pre-trained models that will help to build optimal, and automated breast lesion multiclass identification
A study on the Impacts of COVID-19 on health, Economy, Employment and Social Life of People in Indonesia
Background: The aftershocks of COVID-19 pandemic are still emanating in different regions of the world in term of increasing number of cases and deaths due to mutation in the virulence and pathogenicity of the virus. The pandemic affected almost every part of our lives including health, economy, employment, and social interactions. This study surveyed the Indonesian public to better understand their health, employment, and economic deterioration during the early stages of the COVID-19 outbreak.Methods: An online cross-sectional survey of 200 participants was conducted from eight different regions (Jawa Timur, DKI Jakarta, Kalimantan Tengah, Yogyakarta, Bali, Sulawesi Selatan, Jawa Tengah) of Indonesia who speak Bahasa. A standardized questionnaire was used to obtain information about COVID-19 impacts on health, employment, the economy, and social life from the respondents. Descriptive statistics and Chi-square tests were conducted to analyze the data.Results: According to the findings, out of 200 participants, 40% stated that the impact of COVID-19 did not affect their salary. People under the age of 20 with an intermediate education who worked in government sectors were more likely to lose their jobs (p-value 0.05), which would result in a loss in salary that would have an impact on the education of their children. Only the "use of hand sanitizers" indicated a statistically significant difference between the practices of male and female respondents (p-value = 0.038), which is one of the activities that helps to prevent fever and respiratory difficulties during the present pandemic.Conclusion: The finding of the study depicted that COVID-19 has no immediate collateral effects on the economy of the study participants. However, the pandemic has a negative impact on the employment, health, and social life of the people. To mitigate the negative effects of this pandemic on health, employment, economy, and social life, a complete evaluation of COVID-19 impacts, as well as public health interventions, should be conducted
Robust estimation based nonlinear higher order sliding mode control strategies for PMSG-WECS
The wind energy conversion systems (WECS) suffer from an intermittent nature of source (wind) and the resulting disparity between power generation and electricity demand. Thus, WECS are required to be operated at maximum power point (MPP). This research paper addresses a sophisticated MPP tracking (MPPT) strategy to ensure optimum (maximum) power out of the WECS despite environmental (wind) variations. This study considers a WECS (fixed pitch, 3KW, variable speed) coupled with a permanent magnet synchronous generator (PMSG) and proposes three sliding mode control (SMC) based MPPT schemes, a conventional first order SMC (FOSMC), an integral back-stepping-based SMC (IBSMC) and a super-twisting reachability-based SMC, for maximizing the power output. However, the efficacy of MPPT/control schemes rely on availability of system parameters especially, uncertain/nonlinear dynamics and aerodynamic terms, which are not commonly accessible in practice. As a remedy, an off-line artificial function-fitting neural network (ANN) based on Levenberg-Marquardt algorithm is employed to enhance the performance and robustness of MPPT/control scheme by effectively imitating the uncertain/nonlinear drift terms in the control input pathways. Furthermore, the speed and missing derivative of a generator shaft are determined using a high-gain observer (HGO). Finally, a comparison is made among the stated strategies subjected to stochastic and deterministic wind speed profiles. Extensive MATLAB/Simulink simulations assess the effectiveness of the suggested approaches
An updated review of human monkeypox disease: A new potential global hazard
Monkeypox, a recently developed viral infectious disease, has become a concern for the general public. This predicament has emerged because of the increased incidence of human monkeypox infections. It was previously a serious zoonotic virus native to just sections of Central and Western Africa and was never recorded outside endemic areas. In this review, the author presented considerable data on this disease and offered a detailed summary of MPXV. More recently, as of October 27, 2022, Monkeypox cases spread quickly across the globe, infecting 76713 people globally, with the majority of cases from Europe, the United Kingdom, North and South America, Asia, and the Middle East. By October 27, 2022, the disease had spread to almost 109 countries. In the United States, there were 28244 (36.82%) cases of Monkeypox; in Brazil, 9045 (11.79%); in Spain, 7317 (9.53%); in the United Kingdom, 3698 (4.82%); in France, 4094 (5.34%); in Germany, 3662 (4.77%); in Colombia, 3298 (4.29%); in Peru, 3048 (3.92%); in Canada, 1436 (1.87%); in Belgium, 786 (1.02%) and Portugal, 944 (1.23%). In terms of the overall number of deaths, 36 deaths were reported, with the maximum eight deaths being from Brazil (22.22%), seven from Nigeria (19.44%), and six deaths from the United States (16.67%). However, human Monkeypox epidemiological trends are rapidly changing, leaving endemic areas and moving to non-endemic countries. Therefore, international health authorities must implement priority-based preventive measures to prevent the spread of Monkeypox infection worldwide
The regulation and function of the complement regulatory protein decay-accelerating factor on murine endothelium
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Organization of Islamic Co-operation (OIC) and Prospects of Yemeni Conflict Resolution: Delusion or Plausible Reality
Abstract The Romans named Yemen ‘An Arabian Flex’ (Happy Arabia) because of its blossoming rain-fed mountain scenery but the epithet sounds tragically extraneous today. Internal power struggle between various ethno-religious groups and tribes has destabilized the state. This tumultuous situation afforded political and physical space to non-state actors for gaining foot-hold and conducting terrorist activities within and without with impunity. Export of terrorism in the world in general and neighboring states, especially in Kingdom of Saudi Arabia, in particular, has long term political, social and economic implications for the region. Iran is blamed to be patronizing few terror outfits on sectarian grounds thus adding another dimension to regional instability. Military action initiated by coalition of GCC countries (spearheaded by Saudi Arabia) against sovereign state of Yemen is yet another test for OIC being an inter-governmental organization for resolution of conflicts in Muslim world. This article argues that illusive efforts undertaken by OIC so far in resolving this intra-Muslim world conflict are apocryphal and may pose serious threat to the wanting organization. There is a dire need for adopting a multipronged strategy by OIC for ensuring peace in Arab peninsula as blood never washes the blood. Keywords: Yemen Conflict, OIC’s Involvements, Conflict Resolution, Challenges for OI
Efficient and Accurate Image Classification via Spatial Pyramid Matching and SURF Sparse Coding: Efficient and Accurate Image Classification via Spatial Pyramid Matching and SURF Sparse Coding
Sparsely coded signal space representations do well in feature quantization. Instead of using standard vector quantization, the suggested method uses selective sparse coding to assemble the most important features of the appearance descriptors of nearby image patches. Inadequate coding also enables adjacent max pooling on some spatial scales, which, unlike the setup of average pooling in a histogram, links interpretation with scale invariance. The acquired visual illustration is the key contribution of this research; it performs outperform with linear-SVM, improves the model training's, which in turn speeds up testing with improves accuracy. The efficacy of the method we have employed has been substantiated through a series of experiments conducted on diverse datasets. Since top-performing image classification systems heavily rely on nonlinear SPM in mean of vector quantization, the trustworthy recommended linear SPM greatly increases the use of larger sets of training data. The method given herein deduces that the sparse coding of SURF feature’s function hampered a more comprehensive local appearance descriptor for general-purpose image processing. Experiments and comparisons are conducted on standard datasets such as Caltech-101, FTVL, and Corel-1000, using state-of-the-art techniques and descriptors. When compared over several other image categories and descriptors, the method provided here comes out on top
A Machine Learning Approach for Earthquake Prediction in Various Zones Based on Solar Activity
open access articleThis paper examines relationships between solar activity and earthquakes, it applied machine learning techniques: Knearest neighbour, support vector regression, random forest regression, and long short-term memory network. Data from the SILSO World Data Center, the NOAA National Center, the GOES satellite, NASA OMNIWeb, and the United States Geological Survey were used for the experiment. The 23rd and 24th solar cycles, daily sunspot number, solar wind velocity, proton density, and proton temperature were all included in the dataset. The study also examined sunspots, solar wind, and solar flares, which all reflect solar activity, and earthquake frequency distribution by magnitude and depth. The findings showed that the long short-term memory network model predicts earthquakes more correctly than the other models applied in the study, and solar activity is more likely to effect earthquakes of lower magnitude and shallow depth than earthquakes of magnitude 5.5 or larger with intermediate depth and deep depth
Washing off intensification of cotton and wool fabrics by ultrasound
Wet textile washing processes were set up for wool and cotton fabrics to evaluate the potential of ultrasound transducers (US) in improving dirt removal. The samples were contaminated with an emulsion of carbon soot in vegetable oil and aged for three hours in fan oven. Before washing, the fabrics were soaked for 3 min in a standard detergent solution and subsequently washed in a water bath. The dirt removal was evaluated through colorimetric measurements. The total color differences DE of the samples were measured with respect to an uncontaminated fabric, before and after each washing cycle. The percentage of DE variation obtained was calculated and correlated to the dirt removal. The results showed that the US transducers enhanced the dirt removal and temperature was the parameter most influencing the US efficiency on the cleaning process. Better results were obtained at a lower process temperatur
Knowledge, Attitudes and Practices Associated with Avian Influenza among Undergraduate University Students of East Java Indonesia : A Cross – Sectional Survey
Background: Several public health strategic actions are required for effective avian influenza (AI) prevention and control, as well as the development of a communication plan to keep undergraduate students sufficiently informed on how to avoid or reduce exposure. The aim of the survey was to measure the level of knowledge, attitudes and practices (KAPs) toward AI among undergraduate university students in East Java, Indonesia, and observe the correlation between KAPs and the factors associated with the control and prevention of AI.
Methods: A cross-sectional survey was conducted among undergraduate students to collect information about AI-related KAPs. Students were selected from three faculties of Universitas Airlangga Surabaya Indonesia (Faculty of Veterinary Medicine, Faculty of Fisheries and Marine, and Faculty of Science and Technology). Students voluntarily responded to a pre-designed questionnaire.
Results: A total of 425 students (222 female; and 203 male), of ages ranging from 18 years (n=240) to 20-30 years (n=185), responded to the survey. This cohort consisted of 157 students from the Faculty of Fisheries and Marine, 149 from the Faculty of Veterinary Medicine, and 119 from the Faculty of Science and Technology. The results indicated that appropriate knowledge was obtained by 76.94% of students; significantly higher levels were seen in Faculty of Veterinary Medicine students as compared to the other two faculties (p<0.05). 72.89% of students documented positive attitudes; veterinary medicine students had significantly more positive attitudes than other faculties (p<0.05). Proactive behaviors were observed in 56.90% of students. The aggregate scores for KAPs were 6.93 ± 0.77 (range: 0-9) for knowledge, 7.6 ± 1.25 (range: 0-10) for attitude, and 9.1 ± 1.5 (range: 0-12) for practice.
Keywords: Avian Influenza, Knowledge, Attitude, Practices, Public Health, Undergraduate