This paper presents a novel technique of document clustering based on frequent concepts. The proposed technique, FCDC (Frequent Concepts based document clustering), a clustering algorithm works with frequent concepts rather than frequent items used in traditional text mining techniques. Many well known clustering algorithms deal with documents as bag of words and ignore the important relationships between words like synonyms. the proposed FCDC algorithm utilizes the semantic relationship between words to create concepts. It exploits the WordNet ontology in turn to create low dimensional feature vector which allows us to develop a efficient clustering algorithm. It uses a hierarchical approach to cluster text documents having common concepts. FCDC found more accurate, scalable and effective when compared with existing clustering algorithms like Bisecting K-means , UPGMA and FIHC.

The coronaviruses are a deadly family of epidemic viruses that can spread from one individual to another very quickly, infecting masses. The literature on epidemics indicates that the early diagnosis of a coronavirus infection can lead to a reduction in mortality rates. To prevent coronavirus disease 2019 (COVID-19) from spreading, the regular identification and monitoring of infected patients are needed. In this regard, wireless body area networks (WBANs) can be used in conjunction with machine learning and the Internet of Things (IoT) to identify and monitor the human body for health-related information, which in turn can aid in the early diagnosis of diseases. This paper proposes a novel coronavirus-body area network (CoV-BAN) model based on IoT technology as a real-time health monitoring system for the detection of the early stages of coronavirus infection using a number of wearable biosensors to examine the health status of the patient. The proposed CoV-BAN model is tested with five machine learning-based classification methods, including random forest, logistic regression, Naive Bayes, support vector machine and multi-layer perceptron classifiers, to optimize the accuracy of the diagnosis of COVID-19. For the long-term sustainability of the sensor devices, the development of energy-efficient WBAN is critical. To address this issue, a long-range (LoRa)-based IoT program is used to receive biosensor signals from the patient and transmit them to the cloud directly for monitoring. The experimental results indicate that the proposed model using the random forest classifier outperforms models using the other classifiers, with an average accuracy of 88.6%. In addition, power consumption is reduced when LoRa technology is used as a relay node.

In the modern world, wireless body area networks (WBAN) is projected to play a vital role in biomedical and psychological applications. The practical implementation of WBAN technology surfer from various deployment issues that have to be dealt with. The more serious concern is associated with the energy consumption of these networks. Biosensor nodes continuously sense the signals and send the same to sink. Sending data to sink is an energy-consuming operation, so routing is done to optimize energy utilization in WBAN. The continuous data sensing and the transmission of information over long-distances result in huge energy consumption of these nodes. So, conservation of energy is the need of the hour. The main focus of the current study is to invise a routing mechanism that makes use of particle swarm optimization based on metaheuristic algorithm along with the relay node selection based on distances and residual energies. Experimental results show that the proposed protocol strikes a perfect balance between minimizing the number of relay nodes (to be positioned on subject) along with the energy efficient WBAN.

Recently there is an emerging trend in the research to recognize handwritten characters and numerals of many Indian languages and scripts. In this manuscript we have practiced the recognition of handwritten Gurmukhi numerals. We have used three different feature sets. First feature set is comprised of distance profiles having 128 features. Second feature set is comprised of different types of projection histograms having 190 features. Third feature set is comprised of zonal density and Background Directional Distribution (BDD) forming 144 features. The SVM classifier with RBF (Radial Basis Function) kernel is used for classification. We have obtained the 5-fold cross validation accuracy as 99.2% using second feature set consisting of 190 projection histogram features. On third and first feature sets recognition rates 99.13% and 98% are observed. To obtain better results pre-processing of noise removal and normalization processes before feature extraction are recommended, which are also practiced in our approach.

The karyotype is analyzed to detect the genetic abnormalities. It is generated by arranging the chromosomes after extracting them from the metaphase chromosome images. The chromosomes are non-rigid bodies that contain the genetic information of an individual. The metaphase chromosome image spread contains the chromosomes, but these chromosomes are not distinct bodies; they can either be individual chromosomes or be touching one another; they may be bent or even may be overlapping and thus forming a cluster of chromosomes. The extraction of chromosomes from these touching and overlapping chromosomes is a very tedious process. The segmentation of a random metaphase chromosome image may not give us correct and accurate results. Therefore, before taking up a metaphase chromosome image for analysis, it must be analyzed for the orientation of the chromosomes it contains. The various reported methods for metaphase chromosome image selection for automatic karyotype generation are compared in this paper. After analysis, it has been concluded that each metaphase chromosome image selection method has its advantages and disadvantages.

No abstract

No abstract

The medical health systems empowered by wireless body area networks (WBANs) are becoming a reliable technology with unmatched facilities for personalised health monitoring and managing real‐time health issues. A WBAN is primarily composed of a miniature, and smart devices called sensors that are worn in, on, or around the human body. In recent years, research has been mainly centred towards the reduction of the energy consumption and stability of the network. The selection of the relay node is one of the foremost issues for balancing the energy consumption in WBAN. To overcome this issue, the authors propose a hybrid analytic hierarchy process–neutrosophic method for making the decision of the relay node selection, in which the analytic hierarchy process (AHP) technique calculates the weights for different criteria and forwards them to the neutrosophic technique which finds out the best alternative solution or choice which is closest to the ideal solution. A detailed analysis is carried out on multi‐criteria decision‐making techniques by comparing the proposed AHP–neutrosophic and AHP techniques taking into account various criteria and alternatives. The experimental outcomes of this research indicate a significant reduction in terms of energy consumption and enhanced overall network‐stability.