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The brain is the central nervous system of a human being. Brain tumor disease is considered the significant cause of death in many people. The core idea of deep learning is the comprehensive feature representations that will be learned efficiently along with the deep architectures, which are composed of trainable non-linear operations. Learning effective feature representations directly from the MRI becomes harder. Therefore, in the present study, a hybrid and optimal method are proposed. Grey Wolf Optimization algorithm is used for feature selection which reduces the more numbered features and then the classification of an image with the tumor type is done by the classifier Recurrent Neural Networks. The segmentation process is performed after the classification process, here segmentation is done by the MRG method with threshold optimization. The performance analysis is performed in terms of sensitivity, specificity, and accuracy which is done for the proposed techniques. Performance accuracy is obtained from this study is 98.16% using the proposed GWO technique.

This study aims to assess the retail formats in India have evolved vastly over a relatively short period of seven to eight years. Human resource management practices in Organized retailing outlet with special reference to selected retailers in Coimbatore city. Retailers today are focused on drawing customers into the Stores to drive sales while at the same time reducing cost overheads. This present study aims to focus on beyond the impact of globalization and mergers and acquisitions, retail organizations continue to struggle with the recruitment of service-oriented employees. Keywords: Human Resource Management, Organized, retailing, Growth and retailers .

Two hundred adult men between 36 and 55 years were selected for the assessment of their nutritional status. Information pertaining to socio economic status, anthropometric measurement, clinical profile and dietary pattern was collected using interview schedule. Energy costs of the activities were also collected. Information on the dietary nutrient intake was collected using 3-day dietary recall method. Anthropometric measurements namely height, weight, BMI and waist hip ratio were analyzed using standard procedures. Data was statistically analyzed. Results show that a greater percentage of the selected adult men were involved in moderate activity and belonged in middle income group. The educational qualification showed that 66% have completed school. The greater percentages of the selected subjects were from nuclear family. Most of the subjects were of normal height when compared with reference value. BMI revealed that forty nine percent were normal, 30% were overweight and 21% were obesity. Most of the selected subjects had WHR greater than 1. The food habits showed that majority of them were non vegetarians. Consumption of green leafy vegetables and fruits was less. On the contrary rice, wheat, red gram dhal were consumed daily. Most of the selected subjects mean nutrient intake was deficit when compared to RDA. The results of energy balance study showed that mean positive energy balance was 2098.2±90 kcal in the selected subjects. Nutrition education was given by power point, charts, pamphlets and a low energy snack was formulated and demonstrated used in the nutrition education programme. Results of paired t-test done shows that the nutrition education was effective (t = 3.84) and helped in improving the nutrition knowledge. The formulated product contains high amount of fibre. There is a need for educating these subjects on the right type and quantity of foods, higher intake of fibre rich foods, as well as the importance of regular physical activity to combat obesity.

In this paper, a new robust computational method that applies the geometrical theory of diffraction (GTD) in conjunction with the ray tracing (RT) technique is developed to evaluate the electromagnetic scattering pattern due to a plane wave represented as beam of parallel rays incident on a rough surface of quite arbitrary statistical parameters. The development of the proposed technique is explained in detail taking into consideration the generation of the geometrical model of the rough surface. The Fresnel reflection model is applied under the assumption of arbitrary electrical and optical properties of the rough surface material. Also the polarization of the plane wave primarily incident on the rough surface is taken into consideration. The algorithm developed in the present work accounts for multiple bounces of an incident ray and, hence, it can be considered arbitrary higher-order GTD-RT technique. The accuracy of the obtained results is verified through the comparison with the experimental measurements of the scattering pattern of a light beam incident on rough sheets with specific statistical properties. The numerical results of the present work are concerned with investigating the dependence of the scattering pattern on the surface roughness, refractive index, angle of incidence, and the resolution of the geometric model of the rough surface. Also, it is shown that, for limited resolution of the rough surface model, the accuracy of the calculated scattered field depends on the angle of incidence of the primary beam and the surface roughness.

Image segmentation is the prime factor to elicit the detailed investigation of an image. The desired information from an image can be easily obtained through the intuitive technique called thresholding. In this technique, detailed analysis of different classes of an image is realised through Multilevel Thresholding (MLT). Accurate and ideal threshold values achieved by non-parametric objective functions such as Tsallis and Renyi are briefed in this paper. The non-additive property of Tsallis and entropic threshold selection property of Renyi drive to search the global threshold value precisely. Higher the segmentation level more is the computational time for exploration of optimal threshold with Tsallis and Renyi. This challenge is countered by MLT based Tsallis and Renyi, aided with Exchange Market Algorithm (EMA). Several research on nature- inspired algorithms such as Bacterial Foraging (BF), Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA) are carried out. For the first time, this paper proposes a powerful metaheuristic EMA technique for image segmentation, which implements the strategies of shareholders in stable and unstable mode to earn profit. The cognizance of the shareholders is extracted to attain the desired goal to reach the global threshold avoiding premature convergence. Empirical outcome of the results indicate that outstanding tuning search is achieved by EMA compared to extensive search techniques such as PSO, BF and GA. Exploration and exploitation assessment by metrics such as stability, computational efficiency, Peak Signal to Noise Ratio (PSNR), uniformity measure and Wilcoxon rank sum test affirm the Tsallis and Renyi based EMA surpass the existing techniques to analyse the real-world images.

The main objective of multi-area economic/environmental dispatch (MAEED) problems is the effective management of electrical energy generation by minimizing the total fuel cost and emissions of a multi-area power generation units, while satisfying various system and operating constraints. This paper bestows a new swarm intelligence approach, multi-objective squirrel search algorithm (MOSSA) to solve the MAEED problem. In the suggested approach, an exterior elitist vault system, crowding distance and fuzzy clustering principles are acquainted to procure a uniformly spaced Pareto optimal front curve. In addition, the MAEED problem is unraveled by squirrel search algorithm based weighted sum approach (SSA-WSA) with price penalty factors, artificial bee colony (ABC) and exchange market algorithm (EMA). The adequacy of the suggested strategy is approved on 40 generating units with single and multi-area power systems. The numeric results exhibit that the suggested MOSSA strategy accomplished much preferred trade-off solutions over the SSA-WSA, ABC, EMA and different strategies surfaced in the literature. The result transmuted evinces the desirability of MOSSA to solve the MAEED problem.

The essential goal of multi-area economic environmental dispatch (MAEED) is to determine the optimum power generation schedule of each unit and power transfer between the areas in order to minimize fuel costs and pollutant emissions, when the generation, power balance and tie-line limits are satisfied. This paper focuses on developing multi-objective squirrel search algorithm (MOSSA) to solve the MAEED problem, of which the goal is to simultaneously minimize the total fuel cost and emission considering valve point effects and multi-fuel options. The proposed MOSSA combines squirrel search algorithm along with Pareto-dominance theory to generate non-dominated solutions. It uses an external elitist depository mechanism with crowding distance sorting to preserve the distribution diversity of Pareto-optimal solutions as the evolution continues. In addition, a fuzzy decision maker is used to select the best compromised solution from the obtained Pareto frontiers. Furthermore, the MAEED problem is unraveled by squirrel search algorithm based weighted sum approach with price penalty factors, artificial bee colony and exchange market algorithm. Different case studies are performed on 10-unit with three-area system, 40-unit with four-area system and 140-unit real Korean power system considering valve point effects and multi-fuel options which testify the supremacy of the suggested approach. The comparisons with state-of-the-art approaches suggest that MOSSA can generate more competitive trade-off solutions for solving the MAEED problems.

Machine learning is an approach of artificial intelligence (AI) where the machine can automatically learn and improve its performance on experience. It is not explicitly programmed; the data is fed into the generic algorithm and it builds logic based on the data provided. Traditional algorithms have to define new rules or massive rules when the pattern varies or the number of patterns increases, which reduces the accuracy or efficiency of the algorithms. But the machine learning algorithms learn new input patterns capable of handling complex situations while maintaining accuracy and efficiency. Due to its effectual benefits, machine learning algorithms are used in various domains like healthcare, industries, travel, game development, social media services, robotics, and surveillance and information security. In this chapter, the application of machine learning technique in healthcare is discussed in detail.

Multilevel thresholding (MLT) is one of the most widely used methods in image segmentation. However, the exhaustive search method is computationally time consuming for selecting the optimal thresholds. Consequently, heuristic algorithms are extensively used to reduce the complexity of the MLT problem. In this paper, an efficient Exchange Market Algorithm (EMA) is proposed to segment images using minimum cross entropy thresholding method. In the EMA, a market risk variable is used to balance the exploration and exploitation capabilities of the algorithm. Moreover, the local search capability is strengthened by the search and absorbent operators of EMA. Meanwhile, the most competent shareholders of EMA retain their best rank without undergoing any changes in their shares. These help in reducing the computational time. The proposed EMA based MLT is tested on benchmark and brain images with different threshold levels. Additionally, EMA approach is compared with other well-known algorithms such as, genetic algorithm, particle swarm optimization, bacterial foraging algorithm, firefly algorithm, honey bee mating optimization and teaching–learning based optimization. The experimental results show that the proposed EMA approach provides better outcomes than other algorithms.

This paper proposes a computer-aided methodology for detecting and segmenting the tumor regions in ultrasound thyroid images using machine and deep learning algorithms. This proposed tumor detectio...

Copy move forgery (CMF) of digital images is the frequently adapted tampering by simply copying a region of the image and pasting it on to the same image using user-friendly image processing tools....

PurposeEconomic load dispatch (ELD) is one of the crucial optimization problems in power system planning and operation. The ELD problem with valve point loading (VPL) and multi-fuel options (MFO) is defined as a non-smooth and non-convex optimization problem with equality and inequality constraints, which obliges an efficient heuristic strategy to be addressed. The purpose of this study is to present a new and powerful heuristic optimization technique (HOT) named as squirrel search algorithm (SSA) to solve non-convex ELD problems of large-scale power plants.Design/methodology/approachThe suggested SSA approach is aimed to minimize the total fuel cost consumption of power plant considering their generation values as decision variables while satisfying the problem constraints. It confers a solution to the ELD issue by anchoring with foraging behavior of squirrels based on the dynamic jumping and gliding strategies. Furthermore, a heuristic approach and selection rules are used in SSA to handle the constraints appropriately.FindingsEmpirical results authenticate the superior performance of SSA technique by validating on four different large-scale systems. Comparing SSA with other HOTs, numerical results depict its proficiencies with high-qualitative solution and by its excellent computational efficiency to solve the ELD problems with non-smooth fuel cost function addressing the VPL and MFO. Moreover, the non-parametric tests prove the robustness and efficacy of the suggested SSA and demonstrate that it can be used as a competent optimizer for solving the real-world large-scale non-convex ELD problems.Practical implicationsThis study has compared various HOTs to determine optimal generation scheduling for large-scale ELD problems. Consequently, its comparative analysis will be beneficial to power engineers for accurate generation planning.Originality/valueTo the best of the authors’ knowledge, this manuscript is the first research work of using SSA approach for solving ELD problems. Consequently, the solution to this problem configures the key contribution of this paper.

This paper bestows a new swarm intelligence approach, Squirrel Search Algorithm (SSA) to solve Economic Load Dispatch (ELD) of the thermal unit by addressing the valve point loading effects and mul...

Five new multicomponent salts of perchloric acid with a series of substituted anilines and N-heterocyclic amines namely Diphenylaminium perchlorate (DPAPC) (1), 2, 5-dichloroanilinium perchlorate (25DAP) hydrate (2), 4-Methylanilinium perchlorate (4MAPC) (3), 4-diamino-6-methyl-1, 3, 5-triazin-1-ium hydrogen perchlorate (24DAMTHP) (4) and 8-hydroxyquinolinium hydrogen perchlorate (8HQP) (5) were prepared and structurally characterized. The entire complexes were subjected to FTIR and elemental analysis. A vast family of intermolecular contacts N-H…O, O-H…O, N-H…N and C-H…O were observed, which are key ingredient in the generation of privileged supramolecular self-assemblies appeared as one-dimensional chain, two-dimensional ladder and helix. Cambridge structural Database (CSD) analysis of 52 hits revealed the perchloric acid display higher propensity of ladder architectures. Molecular stability of the complexes were studied by quantum chemical calculations using DFT/B3LYP method with 6-31G(d,p) basis set. Further their relative charge distributions were identified using molecular electrostatic potential map. The use of Hirshfeld surfaces in combination with fingerprint plots was visualized in order to study the closer contacts within the molecule. The relative contribution of whole percentage of interactions associated is highlighted.

Wound dressings play a vital role in the wound healing process. Although a variety of wound dressings have been developed so far, most of them still have many drawbacks such as rigidity, non-porosity, low mechanical strength, an affinity to stick onto the injury surface and less antimicrobial activity. To overcome these issues, a novel type of porous three-dimensional (3D) film was fabricated using chitosan/carboxymethyl pullulan polyelectrolyte complex (PEC) loaded with 45S5 bioglass (CCMPBG) by the freeze-drying method for wound healing application. The developed films were analysed by FTIR, XRD, EDS and SEM to confirm their chemical nature, microstructure and surface morphologies. The CCMPBG films exhibited rough surface morphology and well-interconnected micropores with an average size range of 101–74 μm. Compared to the control chitosan/carboxymethyl pullulan (CCMP) film, the CCMPBG films showed an enhanced mechanical strength and controlled rate of swelling and biodegradation behaviours due to the interaction of polymer matrix and 45S5 bioglass (BG). Furthermore, CCMPBG films presented the improved biocompatibility, antimicrobial activity and wound closure ability because of the synergistic effects of chitosan, carboxymethyl pullulan (CMP) and BG. The results demonstrated that CCMPBG films can be an effective dressing material for wound therapy.

A very active expansion of data and convenience in modern period accept to motivate extremely significant automation tasks with the advanced algorithmic models with the technologies such as Artificial Intelligence, Deep Learning (Portion belonging to Machine Learning) to overcome the challenges in speech synthesis and brain tumor analysis. This research study explores different deep learnign algorithms and its applications. This review paper investigates the incoporation of Deep Learning techniques in signal processing, communications, medical image processing, particularly the models, which manage through effective activation of Artificial Intelligence. At this moment, many researchers attempt to obtain novel solutions to several issues that occur in different domains. This investigation focuses on anticipating threats that arise in different domains, which will be clarified through Deep Learning and it unquestionably be an upcoming generation of certain issues that are valuable and may help the analysts.

The present study investigated the adsorption of phenol from aqueous solution by adsorption onto Borassus flabellifer fruit husk activated carbon (BFAC). The activated carbon was produced by three activation processes like pyrolysis (BFAC), sulfuric acid (H2SO4-BFAC), and zinc chloride (ZnCl2-BFAC) method. BET, SEM, EDAX, XRD, and FTIR studies were examined to test the adsorption behavior of synthesized carbons. Characterization studies and batch studies reveal that among the three carbons, ZnCl2-BFAC was found to be the best one for the removal of phenol with high surface area (1389 m2/g) and maximum adsorption efficiency (95%). Isotherm and kinetics studies were explained that adsorption of phenol by all the three carbons follow multilayer adsorption process. Desorption studies indicated that spent carbon could be regenerated by using 0.1 M NaOH. 87.6% of phenol removal was achieved by ZnCl2-BFAC in real textile industry wastewater. Cost analysis suggested that the prepared activated carbons are economically cheaper than the commercial activated carbons.

Organic single crystal of Guanidine Acrylate (GuAcr) was grown by simple inexpensive slow evaporation technique. The grown crystal was subjected into various characterizations like single crystal XRD, UV-Vis, FTIR studies, electrical, mechanical properties and SHG technique. The XRD data shows that the crystal belongs to orthorhombic structure. The UV-Vis spectral studies observed that the cutoff wavelength of the grown crystal is 320nm. Optical band gap of the grown crystal was also calculated, and it was found to be 3.99 eV. Vibrational frequencies of GuAcr crystal were confirmed by FTIR spectra. Electrical properties of the GuAcr single crystals were studied using impedance spectroscopy. Decreasing impedance with increasing temperature is indicated that the negative temperature co-efficient of resistance behavior like an insulator. Dielectric response of the crystal with varying frequencies was also studied. Second harmonic generation of the title compound was studied using Kurtz Perry- powder SHG technique. It reveals that as grown crystal is suitable for Nonlinear Optical (NLO) device fabrications.

In this computer world, digital transformation is a thoughtful and unavoidable one. The impact of this digital transformation is reflected both in government and private sectors. Amongst these, financial and business sectors must face a lot of challenges to undergo this transformation. In financial sector, transaction between two parties has become a part of their life. A transaction normally happens with the help of few centralized authorities. In the centralized system, all the transaction needs to be verified and signed by a centralized authority. But there are many issues exist in executing this methodology. Among them two important issues concerned with financial sector are, large storage space required for processing and cost of committing a transaction. Blockchain, a blooming technology has overcome this drawback by changing the execution model. Blockchain makes use of distributed ledger and crypto currencies, thus converting the centralized processing into a distributed processing system. Here, all the ledgers needed for the transaction are maintained as distributed database. Block chain also minimizes the process by executing all the rules with the help of software. The software that acts as the execution unit of the blockchain is known as smart contract. Smart contracts facilitate the implementation of user-defined operations of uninformed complexity which are not probably plain cryptocurrency protocols. It facilitates user to realize the constraints, instructions, and policies of the domain applications. To explore the feature of the smart contracts, it should be properly designed and coded. Smart contracts work in the application layer of the blockchain. All the rules for the applications can be realized using smart contract. Data for the verification can also be obtained from external sources. i.e., usually, the data for the contract execution is supplied through the transactions. But it can also be obtained from external sources. Each smart contract in the blockchain is assigned a unique address of size 20 bytes. To run a contract, the transaction must be sent to the contract's address. The smart contract performs the necessary operation and when all the protocols are satisfied, the smart contract by itself executes the transaction. This transaction will be run by different miners whose output will be validated and the block of the miner who wins it will be added to the chain. Smart contracts run on Ethereum virtual machine and solidity is the programming language used for writing smart contracts. Ethereum smart contracts need gas to run. When the required gas is not found, then the requested transactions cannot be executed. In this chapter, the detailed explanation of the smart contract along with its architecture is discussed. Also, designing of the smart contract using the programming language solidity is discussed.

Attribute selection is primary for many machine learning applications. Attribute selection in labelled data is not a difficult issue, because of the presence of class labels. The correlation of the features with the class labels guides the feature selection process in supervised learning. It is a difficult issue in unlabelled data because of the absence of the labels. Most of the attribute selection algorithms are proposed for attribute selection in supervised and unsupervised learning. But in real time getting labels for all the data is much harder. So it is required to propose a feature selection technique for partially labelled data. It guides the selection of the most informative features for analysing the data. Since the data is partially labelled, the label information cannot be ignored. A semi supervised attribute selection technique known as Label Propagation based laplacian Score is proposed in this work. It works on the basis of one of the assumptions of Semi – supervised learning i.e cluster assumption. This assumption is utilized to evaluate the affinity between the data. The labels are propagated by using the label propagation technique. The features are ranked based on the laplacian score and the relevant attributes are selected based on this score.

Detection and segmentation of tumor regions in Ultra Sound (US) images is important for saving the human life. At present, computer aided methods are developed for detecting the tumor regions in US images. Hence, this paper develops computer aided tumor regions detection and segmentation in ultrasound thyroid images using Hidden Markov Model (HMM). This method computes the features from both benign and malignant thyroid images from the open access database. The computed features are trained and classified into benign and malignant using the improved hidden states with HMM approach. Then, region growing algorithm is applied on the malignant image to segment the tumor pixels in the classified malignant thyroid image. This proposed tumor detection and segmentation methodology is tested on both low resolution and high resolution thyroid image dataset for evaluating the proficiency of the developed model. The performance of the proposed method is well compared with other existing methods in terms of sensitivity, specificity and tumor segmentation accuracy.

The technology Block-chain is most emerging and widely used in almost all the applications like supply chain management systems, healthcare, banking industries, business, IT sectors, government organizations, agriculture etc. The food products are more sensitive, and it is very hard to trace the origin, buyers, sellers, farmers with the existing centralized method. The usage of Block-chain technology is proposed and experimented in this work along with the features of decentralization, increased security, immutability, and tamper proof for supply chains. The proposed work uses the smart contract on Ethereum for food supply chain management systems, which avoids falsifying information, corrupting database, external attacks. The analysis of food supply chains is shown in the experimental results.

In today’s internet era, people are in need to do all house hold activity wherever they are. They wish to control certain activities even in their work environment. They don’t want to go to the particular environment for a minor work. Moreover, with the use of mobile and its applications we can easily achieve this. This project focuses on the smart home application to make human work easy and efficient. This project is used to reduce the necessity of user work for productive of goods and then service automation. In this scope the project is developed, for automation for step further on mechanization. This type of automation provides good insights to the dare models of software and hardware. By the project deployment of automation, it becomes possible to expand a dissimilar diversity of engineering concepts that includes computer programming, printed circuit board models, Wi-Fi, TCP and IP protocols, Web Server logic design.

Aluminium alloy LM25 utilization is restricted in many requirements due to less wear resistance and frictional force resistance. In this paper, to enhance its properties for such applications, it is reinforced with nano Alumina (Al2O3). A series of LM25 Aluminium alloy with 0, 1, 1.5, 2% weight of nano Al2O3 particle were fabricated in liquid process technique using stir casting. The nanoparticles distributed uniformly in the metal by means of enhanced wettability of particles with the matrix LM25 Alloy as confirmed by doing Scanning Electron Microscopy. The tribological behaviour of LM25 Aluminium alloy with nano Al2O3 was investigated by pin on disc apparatus. The results show that nano Al2O3 particles addition with the LM25 alloy has improved the wear resistance of LM25 Aluminium alloy significantly. Also, friction coefficient can be made as an optimum. A comparison of wear performance and the friction of LM25 Aluminium alloy with metal matrix composite containing 1, 1.5 and 2% by weight was done. Increase in volume percentage of nano Alumina particles reinforcement has reduced the wear and frictional force.

The most fundamental step in image processing is image segmentation and it results in revealing enormous information embedded in most widely used RGB color space image. Excellent result is obtained for bi-level thresholding and the exhaustive search for optimal threshold values, to analyze complex images in multilevel thresholding (MLT), is reduced by promising objective functions such as Otsu and minimum cross entropy MCE aided with teaching-learning based optimization metaheuristic algorithm (TLBO). In TLBO, a teacher shares cognizance to a student. The use of only common control parameters and less-specific control parameters in TLBO achieves exploration and exploitation. The efficiency of TLBO is compared with cuckoo search algorithm (CS) at 4,5,6 and 7 threshold levels. Experimental results reveal that optimal output of TLBO is more successful in precise image segmentation and aids in various real time applications.

With the heightened interest over the preservation of environment, economic/ecological emissions dispatch (EELD) has devoted a great deal of notice. It remains virtually a multiobjective (MO) optimization issue that disparages twain the fuelling cost and ecological emissions concurrently in addition to the system constraints gratifications. This article bestows electromagnetic field (EMF) theories inspired metaheuristic algorithm to solve the MO-EELD problems with different generation units. EMF optimization (EMFO) is one of the modern metaheuristic algorithm global approaches which simulates the Coulomb's and Franklin's theories. In EMFO, the population comprises of objects made of point charges corresponding to generation units of EELD problem. In every generation, a new point charge is generated based on the attraction and repulsion between these EMFs. The MO-EELD concern is transferred to a sole-objective concern with weighted sum approach. Moreover, the fuzzy clustering mechanism is utilized to decide the best comprising solution from Pareto front solutions set. The competence of the suggested fuzzified EMFO (FEMFO) is ordained in comparison to the existing heuristic algorithms in the literature. This study resolutely exhibited that the FEMFO approach is adroit to discover more accurate solutions although it utilizes outnumbered function evaluation calls.

Type 2 diabetes mellitus (T2DM) is a metabolic disorder caused due to varied genetic and lifestyle factors. The search for a potential natural compound to enhance the treatment of diabetes is the need of the hour. Butein, a flavonoid, found sufficiently in Faba bean, is said to possess an anti-diabetic property. In-silico analysis, Butein is predicted as a potential anti-diabetic compound, due to its regulatory action on PPAR-Gamma. Based on this evidence, the Butein's anti-diabetic action is studied in diabetic induced rat models. The drug property of Butein is studied through in-silico analysis to determine the metabolic properties. In animal models, the biochemical analysis, histopathological and gene expression against PPAR-Gamma were studied comparatively. Butein being a hydrophobic compound, the bioavailability is said to be minimum. Hence, Butein formulation was made using biopolymer Chitosan for the synergistic anti-diabetic action. The Butein Chitosan formulation was optimized and characterized using analytical techniques. Further, the anti-diabetic activity of Butein and Butein Chitosan formulation was studied in diabetic induced rats. The obtained in-silico analysis results showed that Butein is the most favorable drug. Apparently, in the rat model, Butein and Butein Chitosan formulation effectively controlled the blood glucose levels without any side effects. The histopathological observations of the tissue samples showed nontoxic activity. Additionally, the gene expression analysis predicted the possible mechanism of anti-diabetic action exhibited through the down regulation of PPAR-Gamma. Whereas, the Butein Chitosan formulation failed, to show synergetic anti-diabetic activity as expected. This study is vital in introducing Butein as a safe anti-diabetic compound, which can be used in the treatment of T2DM.

In this paper, the tumor regions in source thyroid image are detected and segmented using machine learning approach. The noises in the source thyroid image are detected and removed using mean filter, and then, dual-tree complex wavelet (DTCW) transform is applied on the noise-reduced image for obtaining the coefficients. The features are computed from these transformed coefficients of transform, and then, principal component analysis (PCA) is used to select the optimum set of features. The principal component features are now classified by random forest (RF) approach.

Economic load dispatch (ELD) is one of the most imperative problems to be solved for the economic operation of a power system. In this context, a new meta-heuristic swarm intelligence algorithm named squirrel swarm optimizer (SSO) for solving the ELD problems is proposed. SSO mimics the foraging behavior of squirrels which is based on the dynamic jumping and gliding strategies. The proposed SSO approach is implemented for two-test power systems encompassing 6 and 15 units systems and compared with genetic algorithm (GA), particle swarm optimization (PSO), artificial immune system (AIS), chaotic PSO (CPSO), bacterial foraging algorithm (BFA), biogeography based optimization (BBO), firefly algorithm (FA), glowworm swarm optimization (GSO), and exchange market algorithm (EMA). Results reveal the supremacy of the proposed SSO approach in terms of solution quality and convergence speed.

PurposeThis study aims to provide a secured data aggregation with reduced energy consumption in WSN. Data aggregation is the process of reducing communication overhead in wireless sensor networks (WSNs). Presently, securing data aggregation is an important research issue in WSNs due to two facts: sensor nodes deployed in the sensitive and open environment are easily targeted by adversaries, and the leakage of aggregated data causes damage in the networks, and these data cannot be retrieved in a short span of time. Most of the traditional cryptographic algorithms provide security for data aggregation, but they do not reduce energy consumption.Design/methodology/approachNowadays, the homomorphic cryptosystem is used widely to provide security with low energy consumption, as the aggregation is performed on the ciphertext without decryption at the cluster head. In the present paper, the Paillier additive homomorphic cryptosystem and Bonehet al.’s aggregate signature method are used to encrypt and to verify aggregate data at the base station.FindingsThe combination of the two algorithms reduces computation time and energy consumption when compared with the state-of-the-art techniques.Practical implicationsThe secured data aggregation is useful in health-related applications, military applications, etc.Originality/valueThe new combination of encryption and signature methods provides confidentiality and integrity. In addition, it consumes less computation time and energy consumption than existing methods.

Self compacting concrete SCC is relatively a recent development in the construction world. SCC is defined basically by two properties Deformability and segregation resistance. It flows under its own weight while remaining homogeneous in composition. This research is aimed at examining the feasibility of using Rice Husk Ash as supplementary cementations material. In this research, the main variables are the proportion of rice husk ash 0 , 10 , 15 , 20 and cement content.All the results were in range as per code specified. Mix 20RHA showed minimum workability.. The increase of about 25 strength at 7 days, 33 strength at 28days and 36 strength at 56 days were observed with increase of RHA content from control mix 0RHA to 15RHA. Maximum splitting tensile strength was also obtained bythe mix 15RHA. Greatest increase in mechanical properties was observed for the mix containing 15 rice husk ash. The inclusion of rice husk ash as replacement of cement does not affect the strength properties negatively as the strength remains within limits up to 20 replacement. Inclusion of RHA showed great improvement in durability Properties of concrete. D Sathya Narayana | A Pavani | N Venkata Hussain Reddy "Mechanical Properties of Self Compacting Concrete with Partial Replacement of Rice Husk Ash" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: https://www.ijtsrd.com/papers/ijtsrd2192.pdf

Two new organic dicarboxylate salts, namely Benzimidazolium hydrogen maleate (BHM) (1) and Hexadecylaminium hydrogen maleate (HDHM) (2) have been prepared and characterized by single crystal X-ray diffraction, FT-IR and TG/DTA analysis. The crystal structures of both the compounds are stabilized by intramolecular O H⋯O and intermolecular N H⋯O,C H⋯O hydrogen bonds. The supramolecular structure of the salts consists of various ring motifs generating diverse 2D and 3D architectures. The structural parameters were correlated with computed geometrical parameters obtained from DFT/B3LYP quantum chemical calculations using 6–31++g(d,p) basis set. The experimentally determined vibrational frequencies were matched with theoretically achieved FTIR modes and the complete vibrational assignments were done based on PED calculations. The TG/DTA studies reveal the thermal stability of the title compounds. Molecular electrostatic potential mapping were drawn to understand the chemical reactivity based on their charge distribution. The Frontier Molecular orbitals and other related molecular energies were evaluated using the same theoretical calculations. Hirshfeld surface analysis and its associated fingerprint plots were visualised to make clear signs on entity of intermolecular contacts and their impact on crystal packing. The intermolecular and lattice energies of the compounds were studied using PIXELC method to elucidate the quantitative information on interactions appeared between the molecules.

Nowadays all the University and Organization have a dedicated website of their own. The most challenging part in maintaining the website is to effectively handle the user doubts and queries. Often, users wish to interact with the authoritative person. Answering every customer is tiresome and leads to loss of time and human resource. It is impossible to handle lot of queries simultaneously. In order to make the work of the organization simple, Chatbots can be used. A Chatbot simulates the conversation with user in mobile applications, web applications and messaging applications. The proposed system will be able to read a piece of text, that is being asked as a question by the user and it will provide the appropriate response by text categorization and keyword matching. The screenshots illustrate the successful implementation of chatbot application.

Mobile application is a software program designed to use in mobile phones or tablets for various purposes. The proposed GATE Capsule is an android application (app) used by the students for the preparation of GATE Exams. Previously, there are many mobile applications available for GATE Preparation. This app improves our knowledge by focusing on anyone entire subject, in the form of Lectures and PDF. The lectures will be of both kind documents and video. The incorporation of practice tests will be helpful to understand the strengths and weakness in the particular subject. Provisions are also given to the question patterns from previous GATE question papers to gain an extensive knowledge. The rating and review about an app through play store can also be given. This app is lesser in size and this app is a user friendly app with an advantage to use it in an offline state. GATE Capsule is completely free of cost, so anybody can use the app for the preparation of Exams.

The human body metabolism is maintained by proper segregation of thyroid hormones from thyroid gland. The abnormal cells are formed in this thyroid gland due to improper food habits and genes from forefathers. These abnormal cells lead to the development of tumor regions in the thyroid gland. In this paper, a tumor region in thyroid ultra sound image is detected and segmented using Co-Active Adaptive Neuro Fuzzy Expert System (CANFES) classification framework with its improved performance. The proposed framework is split into three main modules as Enhancement, Gabor transform, CANFES classification trained by feature extraction process with tumor segmentation method. The extracted features are optimized using Genetic Algorithm (GA). The CANFES classification with GA improves approximately 2.5% of classification rate when compared with CANFES classification without GA This proposed system achieves 97.7% of sensitivity, 99.8% of specificity and 99.1% of accuracy.

Color image segmentation is the primary step to elicit detailed information of the image with RGB color space. The key to acquire the region of interest is the straightforward, instinctual technique called thresholding. Bi-level thresholding can be assessed concretely for simple images, whereas revealing various classes from a complex image is achieved only through multiple threshold values. Hence, multilevel thresholding with the most propitious maximizing objective functions such as Kapur, Otsu and Minimum Cross Entropy (MCE) to extract the precise threshold are employed in this paper. As the segmentation level increases, execution of non-parametric objective functions leads to exponential increase in computational time. Several researches to enhance the speed of objective functions along with various metaheuristics such as Krill Herd Algorithm (KHA), Teaching-Learning Based Optimization (TLBO) and Cuckoo Search Algorithm (CSA) are performed. The need to unlock the computational complexity drifted our view towards the most powerful, robust, recently introduced metaheuristic Exchange Market Algorithm (EMA). EMA involves exchange of shares among investors to gain profit. The strategic approach of share members in stable and unstable modes of the market results in precise exploration and exploitation. Furthermore, low fitness shareholders gaining experience from their high and moderate counterparts, stands out as an exceptional step to reduce the time consumption. This concept is utilized in our paper for the first time to obtain the exact details from the image without any delay. Empirical outcome of the results indicates that exceptional image segmentation is achieved by EMA in less time compared to extensive search techniques such as KHA, TLBO and CSA. Quantitative and qualitative validation furnished by metrics such as Structural Similarity Index (SSIM), computational time, Peak Signal to Noise Ratio (PSNR) and statistical Wilcoxon test affirm that Otsu, Kapur and MCE based EMA outperforms the existing techniques to analyze the real-world images.

Color image segmentation is the primary factor to provide the intended information from the input image. The straightforward method called multilevel thresholding (MLT) is used to analyse the various classes of complex images. But, when the level of threshold increases, computational difficulty increases. Hence, MLT with most promising objective functions such as Kapur, Otsu and minimum cross entropy aided with cuckoo search algorithm (CSA) is used. The efficient metaheuristic cuckoo search algorithm’s controlling parameter balances the local and global search. In this paper, the efficacy of CSA at 4,5,6 and 7 threshold levels with various fitness functions are utilized for precise image segmentation. It is seen from experimental results, the Otsu based cuckoo search algorithm outperform than Kapur and MCE. Quality metrices such as computational time, PSNR (peak signal to noise ratio) and SSIM (structural similarity index) authenticate the exploration and exploitation capability of CSA algorithm for real-world applications.