Liver biopsies for diagnosing cirrhosis, the last stage of chronic liver disease was analysed using imaging and it could able to determine the severity level of the disease. Liver lesions can be segmented for evaluation of tumor burden, therapeutic strategy, prognosis, and follow-up on the efficacy of therapy. Automatic technologies for malignancy identification and segmentation are preferable because manual segmentation is a laborious process that is prone to error. We thus propose an M-Net approach to cirrhosis of the liver segmentation. The liver datasets are collected initially as raw data, which, due to the existence of noise, must be normalized before the investigation can begin. With the help of the Bilateral Filter and the Wavelet Transform, we can get rid of the noise and improve the de-noised image. The Gabor Filter is used for feature extraction. Hybrid Genetic Algorithm (HGA) is used to pick the best feature subsets for classification. Ensemble Deep Convolutional Neural Network (EDCNN) method is applied to the classification process. MATLAB, a simulation program, is used to conduct the entire inquiry. In terms of accuracy, our system has outperformed the most sophisticated automatic techniques.

The persistent exhaustion of traditional energy sources with their impacts on the environment has been initiating a significant interest in a selection of renewable-energy sources (RES) based water-pumping system. Among several renewable sources, solar-PV is the most promising and practical source for water-pumping applications that can be easily installed on a building's roof. The available solar-PV energy is integrated to AC electric motor through front-end DC-DC boost converter topology. Among the various DC-DC converter topologies, a novel switched-inductor type modified LUO converter has been proposed for solar-PV powered water pumping system. The proposed switched-inductor type modified LUO (SI-MLUO) converter have simple structure which delivers high voltage gain with low leakage currents, low current ripples, reduced voltage spikes, low dv/dt stress and high efficiency over the several conventional DC-DC converters. The operating modes and performance of proposed SI-MLUO converter topology is verified by using MATLAB/Simulink tool, simulation results are validated with conventional topologies.

BACKGROUND:Fatty liver disease is a common condition caused by excess fat in the liver. It consists of two types: Alcoholic Fatty Liver Disease, also called alcoholic steatohepatitis, and Non-Alcoholic Fatty Liver Disease (NAFLD). As per epidemiological studies, fatty liver encompasses 9% to 32% of the general population in India and affects overweight people.OBJECTIVE:An Optimized Support Vector Machine with Support Vector Regression model is proposed to evaluate the volume of liver fat by image analysis (LFA-OSVM-SVR).METHOD:The input computed tomography (CT) liver images are collected from the Chennai liver foundation and Liver Segmentation (LiTS) datasets. Here, input datasets are pre-processed using Gaussian smoothing filter and bypass filter to reduce noise and improve image intensity. The proposed U-Net method is used to perform the liver segmentation. The Optimized Support Vector Machine is used to classify the liver images as fatty liver image and normal images. The support vector regression (SVR) is utilized for analyzing the fat in percentage.RESULTS:The LFA-OSVM-SVR model effectively analyzed the liver fat from CT scan images. The proposed approach is activated in python and its efficiency is analyzed under certain performance metrics.CONCLUSION:The proposed LFA-OSVM-SVR method attains 33.4%, 28.3%, 25.7% improved accuracy with 55%, 47.7%, 32.6% lower error rate for fatty image classification and 30%, 21%, 19.5% improved accuracy with 57.9%, 46.5%, 31.76% lower error rate for normal image classificationthan compared to existing methods such as Convolutional Neural Network (CNN) with Fractional Differential Enhancement (FDE) (CNN-FDE), Fully Convolutional Networks (FCN) and Non-negative Matrix Factorization (NMF) (FCN-NMF), and Deep Learning with Fully Convolutional Networks (FCN) (DL-FCN).

Binarized spiking neural networks optimized with a color harmony algorithm for liver cancer classification (BSNN-CHA-LCC) are proposed to classify liver cancer as normal and abnormal. Initially, fusion of an MRI dataset and CT-scan datasets of a liver cancer dataset were taken, and the input images were given to CWF-based preprocessing for removing noise and increasing the quality of input computed tomography (CT) and magnetic resonance imaging (MRI). The preprocessed images of CT and MRI are given to improve the non-sub sampled Shearlet transform (INSST) method-based feature extraction for extracting features. The extracted features were given BSNN to classify liver cancer as normal and abnormal. The proposed method was implemented, and the efficiency of the proposed BSNN-CHA-LCC method was evaluated under performance metrics, such as precision, sensitivity, F-scores, specificity, accuracy, error rate, and computational time. The proposed technique achieved23.03%, 11.56%, and 21.22% higher accuracy and 36.12%, 15.23%, and 27.11% lower error rates than the existing models, such as hybrid-feature analysis depending on machine-learning for liver cancer categorization utilizing fused images (MLP-LCC), Deep learning-based classification of liver cancer histopathology images utilizing only global labels (mask-RCNN-LCC), and deep learning based liver cancer identification utilizing watershed transform and Gaussian mixture method (DNN-GMM-LCC), respectively.

The uninterrupted power supply (UPS) plays a dominant role in domestic appliances and also the growth rate of solar roof top system implementation is massively increasing day by day. To minimize the cost on the converters, solar PV power is directly connected with existing UPS with separate MPPT controller. However, due to the presence of nominal frequency transformer, the size and cost of UPS becomes more. In addition, it consumes the power with poor power quality to charge the battery. Apart this, the current supplied from the battery is much higher due to the lower voltage level of battery, which makes voltage dip during peak load condition and resulting to shorten the battery life and degrades its performances. In order to overcome all these drawbacks, this particular paper proposes a transformer-less novel hybrid converter based solar PV fed UPS system. In the hybrid structure, the gain can be achieved higher with minimum components counts. The novelty of the proposed transformer-less hybrid converter is that the regeneration of switched capacitor voltage is applied to input of switched inductor network in DC–DC converter, due to this hybrid structure the gain has been extended drastically with minimum number of devices. Also the coordinated control algorithm is implemented in solar PV fed UPS to take the decision according to the situation of generation, load and battery back-up. The detailed operation of the proposed coordinated control algorithm has been addressed in this paper. Moreover, the detailed simulation study is carried out for each mode of operation and measured results are obtained and presented in this paper.

Switched-mode DC-DC converters with high-voltage capability is comprehensively used in several energy conversion applications with a voltage levels from milli-volts to thousand-volts and power levels from milli-watts to megawatts. A unique framework of DC-DC boost converters with efficient operation, reliable performance, high boost competency, continuous input current is regarded as modified DC-DC boost converters for solar-PV fed water-pumping system. In this work, a comprehensive review on basic SEPIC, CUK, LUO and modified SEPIC, modified CUK, modified LUO DC-DC boost converters are presented with operating features and voltage-boosting methods. Finally, the comprehensive summary of basic and modified dc-dc boost converters and its operating features are presented.

In-this-work, different-control-strategies such as 'fractional-order-PID(FOPID)controller', 'Hysteresiscontroller (HC) and Fuzzy Logic Controller(FLC)' is exploited to sustain the constant yield-speed of the Zeta-cum Triple Lift Converter Inverter(ZTLCI)- PMBLDC drive. In the present work, ZTLCI proposed for PMBLDC drive. Also, the line disturbance is introduced to analyze the performance of ZTLCI-PMBLDC driven by different-controllers. "-The-simulation has been done utilizing –MATLAB/-Simulink-software". The-objective of this-effort is to enhance the time-response of ZTLCI-PMBLDC-drive using FOPIDC/HC and FLC. Outcomes reveal that the ZTLCI-PMBLDC –driven by the FL controller has better-performance such as transient-response etc. when compared to the ZTLCI-PMBLDC driven by FOPID/Hysteresis controller.

The concept of new Hilbert sequence space was introduced by Harun Polat[8]. The initial works on double sequences are found in Bromwich[15]. In this paper, we study some new Hilbert double sequence space defined by Orlicz function and also study some topological properties of the resulting sequence spaces were examined.

In this paper, we have proved some characterization theorems of best approximation in linear 2-normed spaces, Some of the results in inner product spaces have been extended to 2-inner product space set up and also provided some results in 2-inner product spaces and also established some equivalent conditions for orthogonality in the context of linear 2-normed spaces.

In this paper, we have proved some characterization theorems of best approximation in linear 2-normed spaces, Some of the results in inner product spaces have been extended to 2-inner product space set up and also provided some results in 2-inner product spaces and also established some equivalent conditions for orthogonality in the context of linear 2-normed spaces.

Voltage lifting techniques are employed invariably in high step-up applications to provide an enhanced voltage transfer and power gain, as it alleviates the problems encountered in operating the converter at an extreme duty ratio. Ripple content present in output of power converter would tend to affect the load performance. Hence soft-switched single ended primary inductor converter (SSSEPIC) is proposed between solar system and load to curtail the ripple content. This work deals with the simulation of SEPIC triple-lift converter system (SEPIC-TLCS) in closed loop mode of operation. The voltage ripple in output is minimized by connecting a T-filter. The prime objective of this work is to obtain a good regulated dc output voltage. The simulation studies are performed using Matlab Simulink models developed for proportional integral controlled closed loop SEPIC triple-lift converter system (PI-CLSTLCS) and fuzzy controlled closed loop SEPIC triple-lift converter system (FC-CLSTLCS). Time domain parameters are compared and comparative results illustrate the superior performance of FC-CLSTLCS. Therefore it can be used as a substitute for the existing dc-dc converters.

In this paper, we study certain new difference sequence spaces by using Hilbert sequence space defined by Orlicz function. We characterize some topological properties and inclusion relations involving these sequence spaces.

Zeta-converter finds a way between DC-source and DC-Motor to step-up and match the motor-voltage. This work proposes QBC between the DC source and DCM. The DC output is boosted by ZCS. The DC yield of ZC is provided to the DC-Motor. In-this-work, different-control-strategies for Zeta-Converter fed DC-Motor (ZC--DCM) such as ‘fractional-order-PID(FO--PID) controller’, ‘Hysteresis-controlle r(HC)’ & ‘-Fuzzy-logic- controller(F-LC)’ is exploited to sustain the constant yield-speed of the ZC--DCM. Also, the load disturbance is introduced to analyze the performance of ZC--DCM driven by different controllers. “-The-simulation of ZC--DCM has been done utilizing -MATLAB/-Simulink-software”. The objective of the present work is to enhance the closed-loop response of ZC--DCM using suitable-controller. Outcome reveals that the F-L-based ZC--DCM has good-performance, when compared to the FO--PID &Hysteresis-controller based ZC--DCM.

In this paper, we solve the following quartic functional equation f(x+y2−z)+f(y+z2−x)+f(z+x2−y)=916(f(x−y)+f(y−z)+f(z−x)) originating from the sum of the medians of a triangle, and prove the Ulam-Hyers stability of the quartic functional equation in fuzzy normed space by the direct and fixed point methods. An application of this functional equation is also provided.

Due to the impact of network technology, all information are transmitted in digital mode and the security of information is ever more important. In order to ensure the secret messages are not been stolen when transmitting, it will be a good countermeasure to encrypt the secret message before transmitting. The level of security of information is based on the number of participants. Security is definite when a single person is involved. In the scenario, if many people are involved, security may be ensured if secrets are kept in. This lead the direction for researchers to develop new cryptographic scheme in the past two decades. This paper proposes techniques for transmission of secret messages between two parties and also sharing of messages between multiple parties. These methods uses well known public key cryptography algorithm RSA and Hilbert matrix for authentication and encryption. The proposed method overcome the issues addressed by the existing scheme and ensures secure transmission of text messages with less computational Complexity and no additional code book. The proposed (N, N) Secret sharing Scheme also reduces the overhead of generation of keys for each pair of parties.

Remote satellite imaging provides vital information for observing number of applications such as, land region detection and urban area classification. This paper proposed a novel approach for Classification and extraction of texture features from high resolution satellite images dataset. Preprocessing is done for satellite sensing image using Hilbert matrix filter and Modified Hilbert matrix filter. Then the texture features are extracted from the Hilbert image and Modified Hilbert image using Gray Level Co-occurrence Matrix (GLCM). Finally, obtained features are classified using Decision tree and Random Forest and the accuracy, precision, recall and F-measure is analyzed for performance evaluation