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In recent years, Polycystic Ovary Syndrome (PCOS) becomes one of the most prominent research areas, where several researchers are concentrating to improve the accuracy of PCOS classification. It is much difficult to find the presence of PCOS in women with traditional techniques and various researchers are dealt with the problem that affects the accuracy in detecting such symptom. In this paper, we have proposed Integrated Transfer Learning-based Convolutional Neural Network (ITL-CNN) model to improve the classification accuracy for the detection of PCOS using ultrasound images. In this proposed model, we have used active contour with modified Otsu method and Multifactor Dimension Reduction-based GIST feature extractor for improving the performance of the ITL-CNN model. The performance of the proposed model is analyzed using various performance metrics such as accuracy, sensitivity, precision, recall, and F1 score. Furthermore, the results show that the proposed ITL-CNN model outperforms by achieving 98.9% of accuracy when compared with other existing techniques such as Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Gaussian Naïve Bayes (NB).

Purpose This paper has used the well-known machine learning (ML) computational algorithm with Internet of Things (IoT) devices to predict the COVID-19 disease and to analyze the peak rate of the disease in the world. ML is the best tool to analyze and predict the object in reasonable time with great level of accuracy. The Purpose of this paper is to develop a model to predict the coronavirus by considering majorly related symptoms, attributes and also to predict and analyze the peak rate of the disease. Design/methodology/approach COVID-19 or coronavirus disease threatens the human lives in various ways, which leads to deaths in most of the cases. It affects the respiratory organs slowly and this penetration leads to multiple organ failure, which causes death in some cases having poor immunity system. In recent times, it has drawn the international attention because of the pandemic threat that is harder to control the spreading of infection around the world. Findings This proposed model is implemented by support vector machine classifier and Bayesian network algorithm, which yields high accuracy. The K-means algorithm has been applied for clustering the data set models. For data collection, IoT devices and related sensors were used in the identified hotspots. The data sets were collected from the selected hotspots, which are placed on the regions selected by the government agencies. The proposed COVID-19 prediction models improve the accuracy of the prediction and peak accuracy ratio. This model is also tested with best, worst and average cases of data set to achieve the better prediction rate. Originality/value From that hotspots, the IoT devices were fixed and accessed through wireless sensors (802.11) to transfer the data to the authors’ database, which is dedicated in data collection server. The data set and the proposed model yield good results and perform well with expected accuracy rate in the analysis and monitoring of the recovery rate of COVID-19.

Water management is one of the crucial topics discussed in most of the international forums. Water harvesting and recycling are the major requirements to meet the global upcoming demand of the water crisis, which is prevalent. To achieve this, we need more emphasis on water management techniques that are applied across various categories of the applications. Keeping in mind the population density index, there is a dire need to implement intelligent water management mechanisms for effective distribution, conservation and to maintain the water quality standards for various purposes. The prescribed work discusses about few major areas of applications that are required for efficient water management. Those are recent trends in wastewater recycle, water distribution, rainwater harvesting and irrigation management using various Artificial Intelligence (AI) models. The data acquired for these applications are purely unique and also differs by type. Hence, there is a dire need to use a model or algorithm that can be applied to provide solutions across all these applications. Artificial Intelligence (AI) and Deep Learning (DL) techniques along with the Internet of things (IoT) framework can facilitate in designing a smart water management system for sustainable water usage from natural resources. This work surveys various water management techniques and the use of AI/DL along with the IoT network and case studies, sample statistical analysis to develop an efficient water management framework.

Polycystic ovary syndrome (PCOS) is hormone related health illness in women, commonly known as endocrine system disorder. It majorly affects during the child bearing periods in between the age of 15 to 44. This condition causes an imbalance in hormone production that leads to other problems such as irregular menstrual cycles, baldness, also linked with long term health issues such as heart disease and diabetes. Around 70 percent of the women all around the world with PCOS had not been diagnosed properly and most among them are unaware about the presence of the disease in their body. In this paper, an effective automated PCOS detection and classification system is proposed from the ultrasound images by analyzing the affected and unaffected cases. The pre-processing of input images were done with Gaussian low pass filter, multilevel thresholding for image segmentation, extraction of features through proposed GIST-MDR technique and PCOS classification with supervised machine learning algorithms. The proposed system attained better results based on the scores calculated from the aforementioned evaluation criteria and proves its efficiency. The proposed feature extraction GIST-MDR model produces accuracy of 93.82% for Support Vector Machine, 89.7% for Random Forest, 91.05% for Linear Discriminant Analysis, and 88.26% for Naive Bayes algorithm with its optimal features. This computer aided automated disease diagnosis system assists the medical practitioners on difficult situations to make accurate decisions over patient's condition.

Polycystic ovary syndrome (PCOS) disorder is identified by the presence of a number of follicles present in the ovary of female reproductive system. Ultrasound imaging of the ovary contains essential information about the size, number of follicles and its position. In real time, the detection of PCOS is a difficult task for radiologists due to the various sizes of follicles and is highly connected with blood vessels and tissues. This often results in error diagnosis. For preprocessing various standard filtering techniques are applied on ovary image. Based on the performance, appropriate filter is chosen to remove the noise from the image. This paper presents an effectual active contour with modified Otsu threshold value to automated discovery of follicles from the ultrasound images. The performances of the proposed method illustrate the betterments of the proposed approach over other techniques.

The recent advancement of computing paradigm named “Fog Computing” helps to satisfy the real-time latency sensitive, geo-distributed applications that requires high computational demand. Fog computing function as a middle layer in-between sensors and IoT that brings computation, storage and network functionality of cloud. This paper presents a comprehensive analysis and challenges in fog computing and provides a taxonomy of these challenges and properties with their research challenges and future research directions.

Power management has turned into a basic plan parameter as more transistors are coordinated on a solitary chip. Bringing down the supply voltage is one of the alluring ways to deal with spare intensity of the variable outstanding task at hand framework, and to accomplish long battery life. DVFS is one of the effective methods to lessen the vitality utilization. The principle thought behind DVFS plan is to powerfully scale the supply voltage of CPU, to give enough circuit speed to process the framework remaining burden so as to meet the time and execution, along these lines lessening power. The problem faced by users in cloud computing is energy consumption. Some resources in cloud stay idle for some time. This idleness of resources consume energy thereby causing more consumption of energy. Both the hardware and software reduces the energy consumptions. The results shows that proposed model reduces the consumption of energy by using DVFS.

The period of Social Computing has lit immense development of suppositions and audits on the online, together with surveys on organizations, item and sentiments concerning people permit us to just mull over audits of movies. Since a client is typically curious about selecting associate in assessments on the element, positioning substances amid this way gives an extra mission to a client's basic leadership errand. We should dismantle a question to get inclinations on totally unique perspectives. Amid this paper, tends to work in discovering viability of different positioning systems to expect that the various viewpoints amid a client's question have as of now been keeping in mind the end goal to the impact of question division on recovery precision. As novelty clients query inclination was assessed and optimized using nature inspired meta-heuristic technique in cloud environments.

Diabetic retinopathy (DR) is a serious eye infection which cause visual impairment. Henceforth, early discovery of DR is an absolute necessity. Exudates is the essential indication of Diabetics in retina. At the initial treatment of DR is conceivable in the event that we recognize Exudates at the most punctual arrange. The principle centralization of paper is to speak about strategies for productive discovery of Exudates. The first technique talks about Exudate discovery utilizing numerical morphology. The next procedure, proposes a half approach for detection of Exudates. These approach comprises of some phases pre-processing technique, grouping, and post processing. At the pre-processing stage, resizing the picture and morphological enlargement done. At grouping stage Linde Buzo-Grey is applied, k-implied calculation to recognize Exudates. In post preparing step, we expel every undesirable component segments in the picture to receive exact outcomes. The ideal outcomes will be got when the quantity of groups took is 8 in both of the grouping calculations.

In today’s quandary, the effort required to prevent the disease occurrence should be more as compared to the methodologies followed for identifying it. In this paper list down the different number of diseases are affected in distinct regions are extracted and by fixing disease based coupling and cohesion range metric the severity level of each disease and its dependence level will be confirmed. By preparing the coupling and cohesion metrics, additional tests are performed to measure its updating as the value is ‘1’ then the disease severity level will be very high otherwise consider as low.

Software development has nowadays evolved into a extreme change that uses the best modules being run in various closed and open source software. Extracting the best component and fit them into an ongoing component based development software poses great challenges to run the software error free with desired outcomes. In this proposal we have introduced an `Intelligent Risk Analysis Model (IRAM) for reusability in developing component service oriented software, which are developed using eminent object oriented programming language paradigm Java. This model performs surfing operation into Java object oriented programming modules warehouse, which consists of several availability modules of various projects, and gives a list of suitable module. Those modules are tested on cohesion /coupling testing analysis to determine the individual strength and binding capacity of modules before performing Regression test. Final desirable outcomes are tested on performing regression test while integrating successful level module with domain being developed and proper deployment is made on successful expected outcome of the product. Risk analysis of software project is analyzed for transformation of reusability components risk before transformation Analysis, adaptable risk Analysis, and reusability risk Analysis for package in Java programming. In this paper IRAM model to focus Java package the domain and size of package, integration and dependency relationship of package, measuring the coupling and cohesion in risk metric for development and implementation of reusability package without any risk factor.

[This corrects the article DOI: 10.1039/D4RA00711E.]., (This journal is © The Royal Society of Chemistry.)