A Cluster-Based Energy-Efficient Secure Optimal Path-Routing Protocol for Wireless Body-Area Sensor Networks.

Recently, research into Wireless Body-Area Sensor Networks (WBASN) or Wireless Body-Area Networks (WBAN) has gained much importance in medical applications, and now plays a significant role in patient monitoring. Among the various operations, routing is still recognized as a resource-intensive activity. As a result, designing an energy-efficient routing system for WBAN is critical. The existing routing algorithms focus more on energy efficiency than security. However, security attacks will lead to more energy consumption, which will reduce overall network performance. To handle the issues of reliability, energy efficiency, and security in WBAN, a new cluster-based secure routing protocol called the Secure Optimal Path-Routing (SOPR) protocol has been proposed in this paper. This proposed algorithm provides security by identifying and avoiding black-hole attacks on one side, and by sending data packets in encrypted form on the other side to strengthen communication security in WBANs. The main advantages of implementing the proposed protocol include improved overall network performance by increasing the packet-delivery ratio and reducing attack-detection overheads, detection time, energy consumption, and delay.

A Comprehensive Survey on Certificate-Less Authentication Schemes for Vehicular Ad hoc Networks in Intelligent Transportation Systems.

Data transmission in intelligent transportation systems is being challenged by a variety of factors, such as open wireless communication channels, that pose problems related to security, anonymity, and privacy. To achieve secure data transmission, several authentication schemes are proposed by various researchers. The most predominant schemes are based on identity-based and public-key cryptography techniques. Due to limitations such as key escrow in identity-based cryptography and certificate management in public-key cryptography, certificate-less authentication schemes arrived to counter these challenges. This paper presents a comprehensive survey on the classification of various types of certificate-less authentication schemes and their features. The schemes are classified based on their type of authentication, the techniques used, the attacks they address, and their security requirements. This survey highlights the performance comparison of various authentication schemes and presents the gaps in them, thereby providing insights for the realization of intelligent transportation systems.

A Temporal Mining Framework for Classifying Un-Evenly Spaced Clinical Data: An Approach for Building Effective Clinical Decision-Making System.

BACKGROUND: Clinical time-series data acquired from electronic health records (EHR) are liable to temporal complexities such as irregular observations, missing values and time constrained attributes that make the knowledge discovery process challenging. OBJECTIVE: This paper presents a temporal rough set induced neuro-fuzzy (TRiNF) mining framework that handles these complexities and builds an effective clinical decision-making system. TRiNF provides two functionalities namely temporal data acquisition (TDA) and temporal classification. METHOD: In TDA, a time-series forecasting model is constructed by adopting an improved double exponential smoothing method. The forecasting model is used in missing value imputation and temporal pattern extraction. The relevant attributes are selected using a temporal pattern based rough set approach. In temporal classification, a classification model is built with the selected attributes using a temporal pattern induced neuro-fuzzy classifier. RESULT: For experimentation, this work uses two clinical time series dataset of hepatitis and thrombosis patients. The experimental result shows that with the proposed TRiNF framework, there is a significant reduction in the error rate, thereby obtaining the classification accuracy on an average of 92.59% for hepatitis and 91.69% for thrombosis dataset. CONCLUSION: The obtained classification results prove the efficiency of the proposed framework in terms of its improved classification accuracy.

A Q-backpropagated time delay neural network for diagnosing severity of gait disturbances in Parkinson's disease.

Parkinson's disease (PD) is a movement disorder that affects the patient's nervous system and health-care applications mostly uses wearable sensors to collect these data. Since these sensors generate time stamped data, analyzing gait disturbances in PD becomes challenging task. The objective of this paper is to develop an effective clinical decision-making system (CDMS) that aids the physician in diagnosing the severity of gait disturbances in PD affected patients. This paper presents a Q-backpropagated time delay neural network (Q-BTDNN) classifier that builds a temporal classification model, which performs the task of classification and prediction in CDMS. The proposed Q-learning induced backpropagation (Q-BP) training algorithm trains the Q-BTDNN by generating a reinforced error signal. The network's weights are adjusted through backpropagating the generated error signal. For experimentation, the proposed work uses a PD gait database, which contains gait measures collected through wearable sensors from three different PD research studies. The experimental result proves the efficiency of Q-BP in terms of its improved classification accuracy of 91.49%, 92.19% and 90.91% with three datasets accordingly compared to other neural network training algorithms.

Computer-assisted Medical Decision-making System for Diagnosis of Urticaria.

Background: Urticaria is a common allergic disease that affects all age groups. Allergic disorders are diagnosed at allergy testing centers using skin tests. Though skin tests are the gold standard tests for allergy diagnosis, specialists are required to interpret the observations and test results. Hence, a computer-assisted medical decision-making (CMD) system can be used as an aid for decision support, by junior clinicians, in order to diagnose the presence of urticaria. Methods: The data from intradermal skin test results of 778 patients, who exhibited allergic symptoms, are considered for this study. Based on food habits and the history of a patient, 40 relevant allergens are tested. Allergen extracts are used for skin test. Ten independent runs of 10-fold cross-validation are used to train the system. The performance of the CMD system is evaluated using a set of test samples. The test samples were also presented to the junior clinicians at the allergy testing center to diagnose the presence or absence of urticaria. Results: From a set of 91 features, a subset of 41 relevant features is chosen based on the relevance score of the feature selection algorithm. The Bayes classification approach achieves a classification accuracy of 96.92% over the test samples. The junior clinicians were able to classify the test samples with an average accuracy of 75.68%. Conclusion: A probabilistic classification approach is used for identifying the presence or absence of urticaria based on intradermal skin test results. In the absence of an allergy specialist, the CDM system assists junior clinicians in clinical decision making.

Knowledge mining from clinical datasets using rough sets and backpropagation neural network.

The availability of clinical datasets and knowledge mining methodologies encourages the researchers to pursue research in extracting knowledge from clinical datasets. Different data mining techniques have been used for mining rules, and mathematical models have been developed to assist the clinician in decision making. The objective of this research is to build a classifier that will predict the presence or absence of a disease by learning from the minimal set of attributes that has been extracted from the clinical dataset. In this work rough set indiscernibility relation method with backpropagation neural network (RS-BPNN) is used. This work has two stages. The first stage is handling of missing values to obtain a smooth data set and selection of appropriate attributes from the clinical dataset by indiscernibility relation method. The second stage is classification using backpropagation neural network on the selected reducts of the dataset. The classifier has been tested with hepatitis, Wisconsin breast cancer, and Statlog heart disease datasets obtained from the University of California at Irvine (UCI) machine learning repository. The accuracy obtained from the proposed method is 97.3%, 98.6%, and 90.4% for hepatitis, breast cancer, and heart disease, respectively. The proposed system provides an effective classification model for clinical datasets.

A discrete wavelet based feature extraction and hybrid classification technique for microarray data analysis.

Cancer classification by doctors and radiologists was based on morphological and clinical features and had limited diagnostic ability in olden days. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in cancer classification. In this paper, we have proposed a hybrid approach for microarray data classification based on nearest neighbor (KNN), naive Bayes, and support vector machine (SVM). Feature selection prior to classification plays a vital role and a feature selection technique which combines discrete wavelet transform (DWT) and moving window technique (MWT) is used. The performance of the proposed method is compared with the conventional classifiers like support vector machine, nearest neighbor, and naive Bayes. Experiments have been conducted on both real and benchmark datasets and the results indicate that the ensemble approach produces higher classification accuracy than conventional classifiers. This paper serves as an automated system for the classification of cancer and can be applied by doctors in real cases which serve as a boon to the medical community. This work further reduces the misclassification of cancers which is highly not allowed in cancer detection.

A novel supervised approach for segmentation of lung parenchyma from chest CT for computer-aided diagnosis.

Segmentation of lung parenchyma from the chest computed tomography is an important task in analysis of chest computed tomography for diagnosis of lung disorders. It is a challenging task especially in the presence of peripherally placed pathology bearing regions. In this work, we propose a segmentation approach to segment lung parenchyma from chest. The first step is to segment the lungs using iterative thresholding followed by morphological operations. If the two lungs are not separated, the lung junction and its neighborhood are identified and local thresholding is applied. The second step is to extract shape features of the two lungs. The third step is to use a multilayer feed forward neural network to determine if the segmented lung parenchyma is complete, based on the extracted features. The final step is to reconstruct the two lungs in case of incomplete segmentation, by exploiting the fact that in majority of the cases, at least one of the two lungs would have been segmented correctly by the first step. Hence, the complete lung is determined based on the shape and region properties and the incomplete lung is reconstructed by applying graphical methods, namely, reflection and translation. The proposed approach has been tested in a computer-aided diagnosis system for diagnosis of lung disorders, namely, bronchiectasis, tuberculosis, and pneumonia. An accuracy of 97.37 % has been achieved by the proposed approach whereas the conventional thresholding approach was unable to detect peripheral pathology-bearing regions. The results obtained prove to be better than that achieved using conventional thresholding and morphological operations.