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A proper understanding of malware characteristics is necessary to protect massive data generated because of the advances in Internet of Things (IoT), big data and the cloud. Because of the encryption techniques used by the attackers, network security experts struggle to develop an efficient malware detection technique. Though few machine learning-based techniques are used by researchers for malware detection, large amounts of data must be processed and detection accuracy needs to be improved for efficient malware detection. Deep learning-based methods have gained significant momentum in recent years for the accurate detection of malware. The purpose of this paper is to create an efficient malware detection system for the IoT using Siamese deep neural networks.

In this work, a novel Siamese deep neural network system with an embedding vector is proposed. Siamese systems have generated significant interest because of their capacity to pick up a significant portion of the input. The proposed method is efficient in malware detection in the IoT because it learns from a few records to improve forecasts. The goal is to determine the evolution of malware similarity in emerging domains of technology.

The cloud platform is used to perform experiments on the Malimg data set. ResNet50 was pretrained as a component of the subsystem that established embedding. Each system reviews a set of input documents to determine whether they belong to the same family. The results of the experiments show that the proposed method outperforms existing techniques in terms of accuracy and efficiency.

The proposed work generates an embedding for each input. Each system examined a collection of data files to determine whether they belonged to the same family. Cosine proximity is also used to estimate the vector similarity in a high-dimensional area.

The paper aims to introduce an intelligent recognition system for viewpoint variations of gait and speech. It proposes a convolutional neural network-based capsule network (CNN-CapsNet) model and outlining the performance of the system in recognition of gait and speech variations. The proposed intelligent system mainly focuses on relative spatial hierarchies between gait features in the entities of the image due to translational invariances in sub-sampling and speech variations.

This proposed work CNN-CapsNet is mainly used for automatic learning of feature representations based on CNN and used capsule vectors as neurons to encode all the spatial information of an image by adapting equal variances to change in viewpoint. The proposed study will resolve the discrepancies caused by cofactors and gait recognition between opinions based on a model of CNN-CapsNet.

This research work provides recognition of signal, biometric-based gait recognition and sound/speech analysis. Empirical evaluations are conducted on three aspects of scenarios, namely fixed-view, cross-view and multi-view conditions. The main parameters for recognition of gait are speed, change in clothes, subjects walking with carrying object and intensity of light.

The proposed CNN-CapsNet has some limitations when considering for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor devices. It can also act as a pre-requisite tool to analyze, identify, detect and verify the malware practices.

This research work includes for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor devices. It can also act as a pre-requisite tool to analyze, identify, detect and verify the malware practices.

This proposed research work proves to be performing better for the recognition of gait and speech when compared with other techniques.

In the current exploration, the machinability of three different nickel-based super-alloy materials (Inconel 625, Inconel 718 and Nimonic 90) was experimentally investigated by using a die-sinking electrical discharge machining (EDM). The effect of changing important input process parameters such as pulse on time (T_on), off time (T_off), peak current (Ip) and tool rotation (TR) was investigated to get optimum machining characteristics such as material removal rate, roughness, electrode wear rate and overcut.

Experimentation has been performed by using Taguchi L9 orthogonal design. An integrated route of fuzzy and grey relational analysis approach with Taguchi’s philosophy has been intended for the simultaneous optimization of machining output parameters.

The most approbatory factors for machining setting have been attained as: (T_on = 100 µs, T_off = 25 µs, Ip = 14 A, TR = 725 rpm) for machining of Inconel 625 and Inconel 718; and (T_on = 100 µs, T_off = 75 µs, Ip = 14 A, TR = 925 rpm) for machining of the Nimonic 90 material. Peak current has been observed as an overall influencing factor to achieve better machining process. Microstructural study through SEM has also been carried out to figure out the surface morphology for the EDMed Ni-based super alloys.

The proposed machining variables and methodology has never been presented for Nimonic 90 alloy on die-sinking EDM.

Parents or primary caregivers of children with Autism Spectrum Disorder (ASD) have important role in early recognition of the disorder as well as in the management of the disorder. Knowledge, attitude and practice of primary caregivers towards children with ASD are important factors in promoting health and wellness of such children. The purpose of this paper is to evaluate the effectiveness of video-assisted teaching regarding care of children with ASD on knowledge, attitude and practice among primary caregivers.

Quasi-experimental research design with one group pre-test post-test was adopted. A total of 60 primary caregivers were selected through stratified random sampling technique. Video-assisted teaching was given to 60 primary caregivers. Data collection was done before and after the video-assisted teaching programme using structured questionnaire which consists of 57 questions.

The result of the study showed that the post-test level of knowledge attitude and practice among primary caregivers of children with ASD was significantly high (p<0.0001) when compared to pre-test level by using Wilcoxon Signed Rank Test. The study finding revealed that video-assisted teaching was effective in enhancing the knowledge, developing a positive attitude and good practice among primary caregivers regarding care of children with ASD.

Findings of this study will help mental health nurses, psychologists, intellectual disability nurses, teachers, public health, social workers, etc. to know the importance of video-assisted teaching programme regarding care of children with ASD and to motivate the primary caregivers to participate in such teaching programme. The community mental health nurse can plan video-assisted teaching programme in a community regarding care of children with ASD.

Results of this study indicate that the video-assisted teaching is effective and helps the primary caregivers to enhance the knowledge, attitude and practice regarding care of children with ASD. So continuous awareness in primary health centre and community area is necessary to improve the knowledge, attitude and practice of primary caregivers.

Doppler-Bearing Tracking (DBT) is commonly used in target tracking applications for the underwater environment using the Hull-Mounted Sensor (HMS). It is an important and challenging problem in an underwater environment.

The system nonlinearity in an underwater environment increases due to several reasons such as the type of measurements taken, the speeds of target and observer, environmental conditions, number of sensors considered for measurements and so on. Degrees of nonlinearity (DoNL) for these problems are analyzed using a proposed measure of nonlinearity (MoNL) for state estimation.

In this research, the authors analyzed MoNL for state estimation and computed the conditional MoNL (normalized) using different filtering algorithms where measurements are obtained from a single sensor array (i.e. HMS). MoNL is implemented to find out the system nonlinearity for different filtering algorithms and identified how much nonlinear the system is, that is, to measure nonlinearity of a problem.

Algorithms are evaluated for various scenarios with different angles on the target bow (ATB) in Monte-Carlo simulation. Computation of root mean squared (RMS) errors in position and velocity is carried out to assess the state estimation accuracy using MATLAB.

The purpose of this paper is to develop a new composite model using structural equation modelling (SEM) and analytic hierarchy process (AHP) for the selection of suppliers.

In this paper the authors have made an attempt to arrive at the supplier selection score using SEM and AHP. An attempt has been made to develop a new composite model using SEM and AHP technique, based on the survey of 151 respondents. Attributes' weightage are found out using cluster analysis.

Based on the output from the composite model, cluster analysis has been carried out to find out the strengths and weakness of each supplier on the influencing factors. Based on these findings, the supplier can improve on factors where they lag and can maintain the factors where they excel.

In this paper the authors have made an attempt to arrive at the supplier selection score using SEM and AHP.

Nowadays advancement in acoustic technology can be explored with marine assets. The purpose of the paper is pervasive computing underwater target tracking has aroused military and civilian interest as a key component of ocean exploration. While many pervasive techniques are currently found in the literature, there is little published research on the effectiveness of these paradigms in the target tracking context.

The unscented Kalman filter (UKF) provides good results for bearing and elevation angles only tracking. Detailed methodology and mathematical modeling are carried out and used to analyze the performance of the filter based on the Monte Carlo simulation.

Due to the intricacy of maritime surroundings, tracking underwater targets using acoustic signals, without knowing the range parameter is difficult. The intention is to find out the solution in terms of standard deviation in a three-dimensional (3D) space.

A new method is found for the acceptance criteria for range, course, speed and pitch based on the standard deviation for bearing and elevation 3D target tracking using the unscented Kalman filter covariance matrix. In the Monte Carlo simulation, several scenarios are used and the results are shown.

Outlines the problems faced by women with disabilities, stating that previous research has not usually singled out this category. States that this category faces double discrimination because of disability and gender, with few role models to follow. Discusses the early education of this group and the use of residential schools. Concludes that much more research is needed in this area.

Outlines the discoveries and developments in the treatment of leprosy over the last century. Looks at the progress towards the elimination of the disease. Profiles the problems in India and the factors which hinder elimination. Looks at the position of women with this disease including age, caste, educational level, occupation, marital status, mate selection, nature of marriage and precedence of leprosy among relatives before considering knowledge and type of treatment and the present role of government. Covers the role of vaccination.

Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this study is to find brain tumor diagnosis using Machine learning (ML) and Deep Learning(DL) techniques. The brain diagnosis process is an important task to medical research which is the most prominent step for providing the treatment to patient. Therefore, it is important to have high accuracy of diagnosis rate so that patients easily get treatment from medical consult. There are many earlier investigations on this research work to diagnose brain diseases. Moreover, it is necessary to improve the performance measures using deep and ML approaches.

In this paper, various brain disorders diagnosis applications are differentiated through following implemented techniques. These techniques are computed through segment and classify the brain magnetic resonance imaging or computerized tomography images clearly. The adaptive median, convolution neural network, gradient boosting machine learning (GBML) and improved support vector machine health-care applications are the advance methods used to extract the hidden features and providing the medical information for diagnosis. The proposed design is implemented on Python 3.7.8 software for simulation analysis.

This research is getting more help for investigators, diagnosis centers and doctors. In each and every model, performance measures are to be taken for estimating the application performance. The measures such as accuracy, sensitivity, recall, F1 score, peak-to-signal noise ratio and correlation coefficient have been estimated using proposed methodology. moreover these metrics are providing high improvement compared to earlier models.

The implemented deep and ML designs get outperformance the methodologies and proving good application successive score.