RETRACTED: Minimum makespan task scheduling algorithm in cloud computing

RETRACTEDFollowing a rigorous, carefully concerns and considered review of the article published in International Journal of Advances in Intelligent Informatics to article entitled “Minimum makespan task scheduling algorithm in cloud computing” Vol 2, No 3, pp. 123-130, November 2016, DOI: http://dx.doi.org/10.26555/ijain.v2i3.59.This paper has been found to be in violation of the International Journal of Advances in Intelligent Informatics Publication principles and has been retracted.The article contained redundant material, the editor investigated and found that the paper published in International Journal of Grid and Distributed Computing, Vol. 9, No. 11, pp. 61-70, 2016, DOI: http://dx.doi.org/10.14257/ijgdc.2016.9.11.05.The document and its content has been removed from International Journal of Advances in Intelligent Informatics, and reasonable effort should be made to remove all references to this article

SLA based cloud service composition using genetic algorithm

Cloud computing tends to provide high quality on-demand services to the users. Numerous services are evolving today. Functionally similar services are having different non-functional properties such as reliability, availability, accessibility, response time and cost. A single service is inadequate for constructing the business process. Such business process is modeled as composite service. Composite service consists of several atomic services connected by workflow patterns. Selecting services for service composition with the constraints specified in Service Level Agreement is the NP-hard problem. Such a cloud service composition problem is modeled in this paper. Genetic based cloud service composition algorithm (GCSC) is proposed. Proposed algorithm is compared with the existing genetic based cloud service composition algorithm based on average utility rate and convergence time. It is proved that the proposed algorithm provides better performance as compared to the existing cloud service composition algorithm

A Morbidity study of Health Related Risk Factors of Bus Drivers of Metropolitan Transport Corporation Limited, Chennai 2014

BACKGROUND: The health of bus drivers is significant in public health, transport policy and their employment condition. Preventing health related risk factors of bus drivers enhances the value of human resources in the organization, enhances productivity and prevents under utilization of services and also loss of skills of driver. In this study, an attempt was made to assess the magnitude of morbidities among the bus drivers of Metropolitan Transport Corporation Limited. The study also explored the health related risk factors associated with these morbidities. OBJECTIVES: To assess the morbidity pattern among bus drivers of Metropolitan Transport Corporation, Chennai by conducting health examination 2014. To study the associated risk factors for the morbidities prevailing among the same bus drivers. MATERIALS AND METHODS: Cross sectional study was conducted in the bus drivers of Metropolitan Transport Corporation Limited using a multistage random sampling technique from January 2014 to June 2014. About 422 bus drivers participated in the study. A validated semi-structured questionnaire was used and health examination was done. RESULTS: The analysis revealed the fact that the mean age of bus drivers was 42.32 years, 85.3% of the respondents were not tobacco users, 76.8% were not smokers,54.5% have not used alcohol 56.2 have not suffered from any chronic disease in the past. However 41.1% were with overweight, 61.4% skipped the morning breakfast, 66.8% never did any exercise, 18.5% suffered diabetes, 12.8% suffered from hypertension and 9.2% acid peptic disease. 2.8% chest pain, 24.6% heart burn, 5.7% abdominal pain, 19.2% back pain, 18.3% joint pain, 24.2% pain in arms and legs, 10.9% neck pain, 18.2% fatigue, 25.6% visual impairment, 0.9% hearing defect, 18% loss of sleep,2.8% breathlessness and 5.7% other complaints. The association between age and other chronic diseases was examined by using chi square test. The association between health risk factors and chronic diseases were also examined by using chi square test. There was a significant association between age and obesity, age and back pain, age and joint pain, age and hypertension, age and visual defect. There was found to be a statistical significance between skipping of morning breakfast and acid peptic disease. There was also a statistical association between family history and hypertension, obesity and hypertension, tobacco use and hypertension. There was also a statistical association between age and diabetes mellitus, physical exercise and diabetes mellitus, family history and diabetes mellitus. DISCUSSION: The analysis and interpretation of the primary data collected revealed the fact that there are health risk factors associated with driving in general and bus driving in particular. The health risk factors are possibility for hypertension, diabetes mellitus, cardiovascular diseases, obesity, visual impairment, declining professional efficiency on account of diabetes mellitus, backache etc and these risk factors are preventable. Key words: bus drivers, morbidity, health risk factors, Chennai

Mapping the scarcity of data on antibiotics in natural and engineered water environments across India

Antimicrobial resistance is a growing public health concern, increasingly recognized as a silent pandemic across the globe. Therefore, it is important to monitor all factors that could contribute to the emergence, maintenance and spread of antimicrobial resistance. Environmental antibiotic pollution is thought to be one of the contributing factors. India is one of the world’s largest consumers and producers of antibiotics. Hence, antibiotics have been detected in different environments across India, sometimes at very high concentrations due to their extensive use in humans and agriculture or due to manufacturing. We summarize the current state of knowledge on the occurrence and transport pathways of antibiotics in Indian water environments, including sewage or wastewater and treatment plants, surface waters such as rivers, lakes, and reservoirs as well as groundwater and drinking water. The factors influencing the distribution of antibiotics in the water environment, such as rainfall, population density and variations in sewage treatment are discussed, followed by existing regulations and policies aimed at the mitigation of environmental antimicrobial resistance in India, which will have global benefits. Then, we recommend directions for future research, development of standardized methods for monitoring antibiotics in water, ecological risk assessment, and exploration of strategies to prevent antibiotics from entering the environment. Finally, we provide an evaluation of how scarce the data is, and how a systematic understanding of the occurrence and concentrations of antibiotics in the water environment in India could be achieved. Overall, we highlight the urgent need for sustainable solutions to monitor and mitigate the impact of antibiotics on environmental, animal, and public health

A Lightweight Chaos-Based Medical Image Encryption Scheme Using Random Shuffling and XOR Operations

Medical images possess significant importance in diagnostics when it comes to healthcare systems. These images contain confidential and sensitive information such as patients’ X-rays, ultrasounds, computed tomography scans, brain images, and magnetic resonance imaging. However, the low security of communication channels and the loopholes in storage systems of hospitals or medical centres put these images at risk of being accessed by unauthorized users who illegally exploit them for non-diagnostic purposes. In addition to improving the security of communication channels and storage systems, image encryption is a popular strategy adopted to ensure the safety of medical images against unauthorized access. In this work, we propose a lightweight cryptosystem based on Henon chaotic map, Brownian motion, and Chen’s chaotic system to encrypt medical images with elevated security. The efficiency of the proposed system is proved in terms of histogram analysis, adjacent pixels correlation analysis, contrast analysis, homogeneity analysis, energy analysis, NIST analysis, mean square error, information entropy, number of pixels changing rate, unified average changing intensity, peak to signal noise ratio and time complexity. The experimental results show that the proposed cryptosystem is a lightweight approach that can achieve the desired security level for encrypting confidential image-based patients’ information

Early diagnosis and meta-agnostic model visualization of tuberculosis based on radiography images

Abstract Despite being treatable and preventable, tuberculosis (TB) affected one-fourth of the world population in 2019, and it took the lives of 1.4 million people in 2019. It affected 1.2 million children around the world in the same year. As it is an infectious bacterial disease, the early diagnosis of TB prevents further transmission and increases the survival rate of the affected person. One of the standard diagnosis methods is the sputum culture test. Diagnosing and rapid sputum test results usually take one to eight weeks in 24 h. Using posterior-anterior chest radiographs (CXR) facilitates a rapid and more cost-effective early diagnosis of tuberculosis. Due to intraclass variations and interclass similarities in the images, TB prognosis from CXR is difficult. We proposed an early TB diagnosis system (tbXpert) based on deep learning methods. Deep Fused Linear Triangulation (FLT) is considered for CXR images to reconcile intraclass variation and interclass similarities. To improve the robustness of the prognosis approach, deep information must be obtained from the minimal radiation and uneven quality CXR images. The advanced FLT method accurately visualizes the infected region in the CXR without segmentation. Deep fused images are trained by the Deep learning network (DLN) with residual connections. The largest standard database, comprised of 3500 TB CXR images and 3500 normal CXR images, is utilized for training and validating the recommended model. Specificity, sensitivity, Accuracy, and AUC are estimated to determine the performance of the proposed systems. The proposed system demonstrates a maximum testing accuracy of 99.2%, a sensitivity of 98.9%, a specificity of 99.6%, a precision of 99.6%, and an AUC of 99.4%, all of which are pretty high when compared to current state-of-the-art deep learning approaches for the prognosis of tuberculosis. To lessen the radiologist’s time, effort, and reliance on the level of competence of the specialist, the suggested system named tbXpert can be deployed as a computer-aided diagnosis technique for tuberculosis

DataSheet_1_Mapping the scarcity of data on antibiotics in natural and engineered water environments across India.zip

Antimicrobial resistance is a growing public health concern, increasingly recognized as a silent pandemic across the globe. Therefore, it is important to monitor all factors that could contribute to the emergence, maintenance and spread of antimicrobial resistance. Environmental antibiotic pollution is thought to be one of the contributing factors. India is one of the world’s largest consumers and producers of antibiotics. Hence, antibiotics have been detected in different environments across India, sometimes at very high concentrations due to their extensive use in humans and agriculture or due to manufacturing. We summarize the current state of knowledge on the occurrence and transport pathways of antibiotics in Indian water environments, including sewage or wastewater and treatment plants, surface waters such as rivers, lakes, and reservoirs as well as groundwater and drinking water. The factors influencing the distribution of antibiotics in the water environment, such as rainfall, population density and variations in sewage treatment are discussed, followed by existing regulations and policies aimed at the mitigation of environmental antimicrobial resistance in India, which will have global benefits. Then, we recommend directions for future research, development of standardized methods for monitoring antibiotics in water, ecological risk assessment, and exploration of strategies to prevent antibiotics from entering the environment. Finally, we provide an evaluation of how scarce the data is, and how a systematic understanding of the occurrence and concentrations of antibiotics in the water environment in India could be achieved. Overall, we highlight the urgent need for sustainable solutions to monitor and mitigate the impact of antibiotics on environmental, animal, and public health.</p