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Assessment for Learning is an approach to teaching and learning that creates feedback which is then used to improve students’ performance. Understudies become more associated with the growing experience and from this gain trust in what they are generally anticipated to learn and to what standard. Evaluation for Learning includes understudies turning out to be more dynamic in their learning and beginning to ‘take on a similar mindset as an educator’. They ponder where they are currently, where they are going and how to arrive appraisal for Learning. It tends to be adjusted to suit the age and capacity of the students in question. Evaluation for learning methodologies is straightforwardly connected to upgrades in understudy execution in summative tests and assessments. Researches show that these strategies particularly help low-achieving students to enhance their learning. &nbsp

In recent years, cardiovascular disease becomes a prominent source of death. The web services connect other medical equipments and the computers via internet for exchanging and combining the data in novel ways. The accurate prediction of heart disease is important to prevent cardiac patients prior to heart attack. The main drawback of heart disease is delay in identifying the disease in the early stage. This objective is obtained by using the machine learning method with rich healthcare information on heart diseases. In this paper, the smart healthcare method is proposed for the prediction of heart disease using Biogeography optimization algorithm and Mexican hat wavelet to enhance Dragonfly algorithm optimization with mixed kernel based extreme learning machine (BMDA-MKELM) approach. Here, data is gathered from the two devices such as sensor nodes as well as the electronic medical records. The android based design is utilized to gather the patient data and the reliable cloud-based scheme for the data storage. For further evaluation for the prediction of heart disease, data are gathered from cloud computing services. At last, BMDA-MKELM based prediction scheme is capable to classify cardiovascular diseases. In addition to this, the proposed prediction scheme is compared with another method with respect to measures such as accuracy, precision, specificity, and sensitivity. The experimental results depict that the proposed approach achieves better results for the prediction of heart disease when compared with other methods.

Wireless sensor networks (WSN) allude to gathering of spatially fragmented and committed sensors for observing and documenting various physical and climatic variables like temperature, moistness and, so on. WSN is quickly growing its work in different fields like clinical, enterprises, climate following and so on. However, the sensor nodes have restricted battery life and substitution or re-energizing of these batteries in the sensor nodes is exceptionally troublesome for the most parts. Energy effectiveness is the significant worry in the remote sensor networks as it is significant for keeping up its activity. In this paper, clustering algorithms based on sensor module energy states to strengthen the network longevity of wireless sensor networks is proposed (i.e. modified MPCT algorithm) in which cluster head determination depends on the every cluster power centroid as well as power of the sensor nodes. Correspondence between cluster leader and sink module employ a parameter distance edge for lessening energy utilization. The outcome got shows a normal increment of 60% in network lifetime compared to Low energy adaptive protocol, Energy efficient midpoint initialization algorithm (EECPK-means), Park K-means algorithm and Mobility path selection protocol.

Recently, a significant way to diagnose the disease is using the model of medical data mining. The most challenging task in the healthcare field is to face a large amount of data during disease analyzes and prediction. Once the data are transformed into valuable data by means of data mining models then the actual prediction and decision making is easier. The existing studies met few shortcomings because of higher execution time, more computational complexities, less scalability, slow convergence, and lack of providing the solution. In this article, we have proposed an ensemble SVM-based sample weighted random forests (eSVM-swRF) with novel improved colliding body optimization (NICBO) algorithm to predict liver diseases. The extraction, loading, transformation, and analysis (ELTA) are used to pre-process the patient data. The significant feature with a suitable model is generated depending upon the filter-based method. Based on eSVM-swRF, the parameter values such as penalty parameter (P), threshold (T), and mTry are optimized via a novel improved colliding boding optimization (NICBO) algorithm. The UCI dataset provides liver disease data for this study. The implementation platform of RapidMiner Studio version 7.6 with different evaluation measures is used to validate the performance of eSVM-swRF with the NICBO method. Anyway, the proposed method yields outstanding performance than other existing methods such as Particle Swarm Optimization-based Support Vector Machine (PSO-SVM), fuzzy adaptive, and neighbor weighted k-NN (FuzzyANWKNN), Naïve Bayes-based Support Vector Machine (NB-SVM), and Neural network.

Attack detection is the major issue in healthcare‐based wireless sensor networks (H‐WSNs). Due to their low processing speed, very low storage space, poor attack detection rate, longer dep...

A web service is a piece of software exposed over the internet, which can use XML messaging system to communicate with other systems to carry out some kind of operation. The fascination of the mathematical community when it comes to utilizing the Web to facilitate the use of mathematics has lead to the development of mathematical Web services. Since mathematics is a structured domain, it needs to be defined and registered by its provider incorporating semantics, and afterwards discovered by service requesters. In this research work, discussed about the development of an interface that handles registration for the service providers named web service registration interface. It provides the capability for semantic description and registration while making use of an ontology tree to catalogue the characteristics of the mathematical web services, functional as well as non-functional. An XML file and a sorted file are used to store the registration details. The usability of user interface is analyzed and compared.

Adopting an efficient and effective method to locate and select desired services among thousands of available web services is an important task in the service-oriented computing environment. If the services are registered properly, then it will be easy for the service requesters to discover the Web services. Here, we provide Web Service Registration Interface for the service providers along with an ontology tree to register the functional and non-functional properties of the mathematical Web services under a particular domain. The registration details are stored in an XML file and a sorted file. A user-centric client interface is also provided for the service requesters to search the Web services based on user’s preference. Finally, a Quality of Service based ranking algorithm is provided to rank the Web services and top-k Web services are returned. With the experimental evaluation, our approach not only alleviates the requesters from time consuming discovery tasks but also reduce the search space for discovery processes in the user-centric Web environment.