SliceNet-AD: slice selection-based convolution neural network model for classification of Alzheimer's disease

In recent days, a rapid advancement in imaging technologies has tremendously increased the collection of images in the medical field. These emerging technologies have also led the researchers to focus on computer aided diagnosis (CAD) using efficient machine learning and deep learning techniques. In this paper, we have proposed a framework for binary and multiclass classification of Alzheimer's disease (AD) using three-dimensional structural magnetic resonance images (sMRI) and clinical scores from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The collected images are subjected to pre-processing using FMRIB Software Library. After pre-processing, the three dimensional grey matter tissues are obtained as an output from tissue segmentation comprises of many two dimensional slices. But, processing and training all the slices requires a lot of computational time. Therefore our aim is to

Logistic and Tent Map Encrypted Image Steganography in Transformation Domain using DWT-LSB Technique

The data confidentiality in an image is predominant in digital-world and outstretching the importance. Steganography and Cryptography are generally used for securing information. The security level is enhanced by encrypting the secret image using dual chaotic system models to generate random encryption sequences using Logistic and Tent map in proposed algorithm. The encrypted image is inserted into another image in transformation domain using DWT-LSB steganography techniques. Proposed method is robust and providing high information security with a good SSIM and PSNR for encryption process and embedding process respectively

The new operational matrix of integration for the numerical solution of integro-differential equations via Hermite wavelet

Hermite wavelet method (HWM) is applied to approximate the solution of the integro-differential equations. The Hermite wavelet and operational matrix of integration are used to reduce the integro-differential equations into algebraic equations. The method is computationally attractive, and applications are demonstrated through illustrative examples. © 2021, The Author(s), under exclusive licence to Sociedad Española de Matemática Aplicada

Variability of ozone and oxides of nitrogen in the tropical city, Bengaluru, India

Bengaluru, also considered India's Silicon Valley, has seen steady growth in population over the years. Bengaluru's rapid development has resulted in dwindling reservoirs, increased traffic congestion, high levels of air pollution, and, to some measure, a rise in summer temperatures. As a result of these changes in urban form over the last decade, anthropogenic heat fluxes for ozone production have increased. However, an observational study on the effects of growing urbanisation on trace gases in Bengaluru for various seasons and periods of the day is missing. Hence, in situ measurements of O3, NO, NO2, and NOX concentrations were carried out at Bengaluru, India, from January 2015 to December 2018. The data were examined for diurnal and interannual variations in trace gas mixing concentrations. The diurnal trend in O3 exhibits unimodal behaviour. Changes in photochemistry, local meteorology, and the planetary boundary layer's distinctive features cause a rise in the value of concentrations and lead to a peak. In contrast, the diurnal trend in NO, NO2, and NOX displayed a bimodal peak due to the combined effect of vehicular emissions and the planetary boundary layer. The link involving the oxidant OX (O3 + NO2) and NOx levels were investigated to determine the NOx-independent regional and NOx-dependent local contributions to OX in the atmosphere. Daytime contributions are higher than night-time contributions, according to the present study. The observed anomalies could be the consequence of photochemical processes that produce OX

Orientational Dynamics of Magnetotactic Bacteria in Earth's Magnetic Field—a Simulation Study

We investigate through simulations the phenomena of magnetoreception to enable an understanding of the minimum requirements of a fail-safe mechanism, operational at the cellular level, to sense a weak magnetic field at ambient temperature in a biologically active environment. To do this, we use magnetotactic bacteria (MTB) as our model system. The magnetic field sensing ability of these bacteria is due to the presence of magnetosomes, which are internal membrane-bound organelles that contain an iron-based magnetic mineral crystal. These magnetosomes are usually found arranged in a chain aligned with the long axis of the bacterial body. This arrangement yields an overall magnetic dipole moment to the bacterial cell. To simulate this orientation process, we set up a rotational Langevin stochastic differential equation and solve it repeatedly over appropriate time steps for isolated spherical shaped MTB as well as for a more realistic model of spheroidal MTB with flagella. The orientation process appears to depend on shape parameters with spheroidal MTB showing a slower response time compared to spherical MTB. Further, our simulation also reveals that the alignment to the external magnetic field is more robust for an MTB when compared to single magnetosome. For the simulation involving magnetosomes, we include an extra torque that arises from the twisting of an attachment tether and enhance the viscosity of the surrounding medium to mimic intracellular conditions in the governing Langevin equation. The response time of alignment is found to be substantially reduced when one includes a dipole interaction term with a neighboring magnetosome and the alignment becomes less robust with increase in inter dipole distance. The alignment process can thereby be said to be very sensitively dependent on the distance between magnetosomes. Simulating the process of alignment between two neighboring magnetosomes, both in the absence and presence of an ambient magnetic field, we conclude that alignment between these dipoles at the distances typical in an MTB is highly probable and it would be the locked unit that responds to changes in the external magnetic field

ETPAC: ECC based trauma plight access control for healthcare Internet of Things

Healthcare Industry experiences a rapid change in the traditional way of providing Medical Services to Patients with the revolution of the Internet of Things (IoT) and the Digitization of services. The Healthcare IoT (HIoT) provides Hospitals to render excellent services to Patients at an optimized cost. The Healthcare IoT must ensure not to disrupt or tamper with sensitive Medical Information of the Patient in addition to the continuous availability of data and services. The proposed framework is designed for secure emergency access to Patient data over Healthcare IoT. Normally, the Doctor obtains the Shared Key directly from the Patient to access the Medical data. In Trauma Plight, a Patient pre-shares an Attribute based Key with a set of Secondary Authority in Trauma Plight (SATP) and access permission to the Doctor for utilizing Emergency Key from SATP. The Doctor retrieves the medical record by using the Attribute

Numerical solution for the (2+1) dimensional Sobolev and regularized long wave equations arise in fluid mechanics via wavelet technique

This article proposed an efficient numerical technique for the solution of (2+1) dimensional Sobolev and regularized long wave equations that arise in fluid mechanics using the Laguerre wavelet collocation method. Five examples are illustrated to inspect the proposed technique efficiency, and convergence analysis is discussed in terms of a theorem. Here, the Sobolev and regularized long wave equations are converted into a system of algebraic equations using the properties of Laguerre wavelet, and solutions obtained by the proposed scheme are more accurate, and they are compared with the analytical solution and other methods in the literature by calculating L2 and L∞ Errors. © 2020 The Author(s

SDAFPS: Secure Data Aggregation using Fuzzy Judgement, Pattern Category and SHAP Contribution

Secure data aggregation intends to reduce redundant data transmission and malicious node interference in the network. Therefore, designing secure data aggregation protocol is a crucial task in WSNs. In this paper, we have proposed a Secure Data Aggregation using Fuzzy Judgement, Pattern Category and SHAP Contribution (SDAFPS) protocol. The SDAFPS protocol involves three main phases. In the first phase, the protocol controls the topology with the selection of efficient aggregator node in every interval. The second phase uses category pattern code generation and utilization concept to reduce data size and to aggregate data. Finally, in third phase, the aggregated data are encrypted using partial equation of SHAP contribution and decrypted with SHAP contribution equation. The decrypted data are verified with dataset preserved at the sink node. The SDAFPS protocol is implemented using NS2 Simulator