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Many machine learning techniques have been used in past few decades for various medical applications. However, these techniques suffer from parameter tuning issue. Therefore, an efficient tuning of these parameters has an ability to improve the performance of existing machine learning techniques. Therefore, in this work, a novel multi-objective differential evolution based random forest technique is proposed. The proposed technique is able to tune the parameters of random forest in an efficient manner. Extensive experiments are carried out by considering the proposed and the existing competitive machine learning techniques on various medical applications. It is observed that the proposed technique outperforms existing techniques in terms of accuracy, f-measure, sensitivity and specificity.

The advanced technology Internet of Things (IoT) visualizes a worldwide, that is, internally connected, networks of smart physical entities. IoT is a promising technology used in several applications including disaster management. In disaster management, the role of IoT is so important and ubiquitous and could be life-saving. This article describes the role of IoT in disaster management. More precisely, it presents IoT-based disaster management for different kind of disasters with a comparison between some solutions that are available in the market. It shows an implementation of some examples of the application of IoT such as early-warning system for fire detection and earthquake and represents some approaches talking about the application, IoT architecture, and focusing of the study on different disasters. This study could be a good guide to stakeholder about the use of IoT technology to secure their smart cities’ infrastructure and to manage disaster and reduce risks.

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Colorectal Cancer (CRC) is the third most dangerous cancer in the world and also increasing day by day. So, timely and accurate diagnosis is required to save the life of patients. Cancer grows from polyps which can be either cancerous or noncancerous. So, if the cancerous polyps are detected accurately and removed on time, then the dangerous consequences of cancer can be reduced to a large extent. The colonoscopy is used to detect the presence of colorectal polyps. However, manual examinations performed by experts are prone to various errors. Therefore, some researchers have utilized machine and deep learning-based models to automate the diagnosis process. However, existing models suffer from overfitting and gradient vanishing problems. To overcome these problems, a convolutional neural network- (CNN-) based deep learning model is proposed. Initially, guided image filter and dynamic histogram equalization approaches are used to filter and enhance the colonoscopy images. Thereafter, Single Shot MultiBox Detector (SSD) is used to efficiently detect and classify colorectal polyps from colonoscopy images. Finally, fully connected layers with dropouts are used to classify the polyp classes. Extensive experimental results on benchmark dataset show that the proposed model achieves significantly better results than the competitive models. The proposed model can detect and classify colorectal polyps from the colonoscopy images with 92% accuracy.

The rationale of the research work is to suggest a multi-modal biometric authentication and secure transaction operation framework for E-Banking. The literature survey identifies the various types of E-Banking Channels available as on-date, the various types of biometric technologies available as on-date as well the significant metrics affecting their performance while deploying them in various different e-banking channels. The performance analysis of various types of biometric technologies based on significant metrics for Biometrics Implementation further identifies the currently implementable biometric technologies for the various different e-banking channels. Subsequently a requirement analysis of potential e-banking channels is followed by System Suitability Analysis to identify which multi-biometrics and support mechanisms are suitable for particular e-banking channels. The final conclusion suggests a viable multi-modal biometric authentication and secure transaction operation framework for various e-banking channels.

This paper presents an off-line writer-independent handwriting analysis system which utilizes both classical crisp and fuzzy methodologies to output possible personality traits of the writer. The design deploys an analytical handwriting analysis approach based on two primitives, the baseline and the slant angle of the characters. The objective of the design strategy is to present a group of parameters for handwriting analysis based on the text. These parameters allow for the classification of writing into different categories which could be used as a preliminary step for outputting the personality traits of the writer. Two parameters, the baseline and the slant-angle, are the inputs to a rule-base which outputs the personality trait category. The evaluation of the baseline is uon-fnzzy (crisp) whereas the evaluation of the slant-angle utilizes the fuzzy paradigm.The approach is based on a combination of classical geometric arithmetic evaluation and fuzzy control designs. For determination of the base line angle two methodologies are explored: the geometric-features based segmentation method and B method based on biologically inspired generation theories or the low pass fdtering method. We utilize the geometric features evaluation for the baseline extraction since it proves more robust with respect to the variations of the handwriting in an off-line environment.For determination of the slant type a fuzzy technique is adopted to determine the contributions of the slant-type angle to each of the five variations of the slant-type categories. The uncertainties in the system model are expressed by fuzzyvalued model parameters with their membership functions derived from experimental data. In total five variations of slant type are considered. These include extreme left, controlled left, vertical, controlled right and extreme right.Fifteen personality traits PTl -PT15 were identified and sets of rules formulation were created, (e.g., IfInputl is "level" and "Input2" is "Controlled Left" then Output is PTx.)The proposed approach takes advantage of two differing methodologies that have clear outputs to evaluate two attributes of handwriting. The outputs are utilized to determine a personality trait The system can he further enbaneed by including more parameters such as size of letters, spacing between letters and other attributes of bandwriting as part of the inputs for trait determination.

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