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This paper introduces a nature-inspired optimized algorithm called modified moth-flame optimization (MMFO) for usability feature selection. To determine quality of software usability plays a significant role. This model contains various usability factors that are divided into several features, which have some characteristics, thus making a hierarchical model. Here, the authors have introduced MMFO (Modified Moth-flame optimization algorithm) for the selection of usability features to get an optimal solution MMFO is an extension of moth-flame optimization algorithm (MFO), which is based on the navigation method of moths called transverse orientation and to the best of our knowledge; this algorithm is introduced in software engineering practices. The selected features and accuracy of proposed MMFO is compared with the original MFO and other related optimization techniques. The results shows that the proposed nature-inspired optimization algorithm outperforms the other related optimizers as it generates a fewer number of selected features and having low accuracy.

In web information retrieval, every single user has a unique contextual objective when searching for information on web blogs. Based on the given query, the task of the web search engine is to fetch the most related information from the collection of web blogs. In order to enhance the searching ability, efficient semantic matching models, richer training, and evaluation resources are required. The conventional keyword-based search algorithms have a minimum efficiency in knowing users’ intentions compared to machine learning algorithms. Recently, neural networks are well-recognized in information retrieval due to the ability of vector representation learning. This paper proposes an adaptive fuzzy feedback recurrent neural network-based web blog searching technique which follows inverse filtering (IF) algorithm using Word2Vec representation. Initially, the user query is pre-processed, and then given to the IF for accelerating the search process. Inside the IF, the web blog content is labelled according to their blog information and stored in the hash table, and then relevant contents are extracted by TF–IDF; this result is given to the similarity estimation to obtain the similarity score. Finally, the proposed technique considers the feedback got from users and performs re-ranking by fuzzy-based RNN to achieve rich user intention satisfaction. The implementation is performed in Python, and statistical measures such as accuracy, precision, and recall are utilized for performance evaluation. The results demonstrate that the proposed technique has enhanced accuracy (94%) compared to conventional techniques, namely deep auto-encoder, deep neural networks, and artificial neural networks.

Introduction: The need for efficient search engines has been identified with the everincreasing technological advancement and huge growing demand for data on the web. Method: Automating duplicate detection over a query results in identifying the records from multiple web databases that point to a similar real-world entity and return non-matching records to the end-users. The proposed algorithm in this paper is based on an unsupervised approach with classifiers over heterogeneous web databases that return more accurate results with high precision, Fmeasure, and recall. Different assessments have also been executed to analyze the efficacy of the proposed algorithm for the identification of duplicates. Result: Results show that the proposed algorithm has greater precision, F-score measure, and the same recall values as compared to standard UDD. Conclusion: This paper aims to introduce an algorithm that automates the process of duplicate detection for lexical heterogeneous web databases. Discussion: This paper concludes that the proposed algorithm outperforms the standard UDD.

The internet content increases exponentially day-by-day leading to the pop-up of irrelevant data while searching. Thus, the vast availability of web data requires curation to enhance the results of the search in relevance to searched topics. The proposed F-CapsNet deals with the content curation of web blog data through the novel integration of fuzzy logic with a machine learning algorithm. The input content to be curated is initially pre-processed and seven major features such as sentence position, bigrams, TF-IDF, cosine similarity, sentence length, proper noun score and numeric token are extracted. Then the fuzzy rules are applied to generate the extractive summary. After the extractive curation, the output is passed to the novel capsule network based deep auto-encoder where the abstractive summary is produced. The performance measures such as precision, recall, F1-score, accuracy and specificity are computed and the results are compared with the existing state-of-the-art methods. From the simulations performed, it has been proven that the proposed method for content curation is more efficient than any other method.

Background: A mobile Ad-hoc Network is a network in which each node is independent of all other nodes of the network. In MANETs, nodes do not have proper infrastructure. The power source of the Mobile Ad-hoc network is battery power. As we all know, battery power has a certain limit and problems. Objective: The main objective of this manuscript is to resolve the power problem of nodes of mobile ad hoc networks and increase the lifetime of the network. Methods: This paper implements the concept of wireless power transfer between the nodes of Adhoc network and transfers the power to these nodes wirelessly and uses the maximum lifetime power efficient routing algorithm (MLPER) to reduce the consumption of power used by the nodes. The wireless power transfer can be achieved with the help of one type of near-field techniques called magnetic resonance which works at the same resonance frequency. This technique is nonradiative in nature. Results: This paper reduces the problem of power of the ad hoc network nodes nearly 70 to 80 % from the traditional concept used for lifetime optimization of these nodes. Conclusion: In this manuscript, performance optimization can be analysed with the help of AODV (Ad hoc On-Demand Distance Vector Routing Protocol) on-demand routing protocol. This manuscript shows that the proposed algorithm gives significant better results than AODV protocol with some performance metrics. We revised some patents relating to Wireless power transfer.

Vitality effectiveness is the most testing theme in Wireless Sensor Networks (WSN). As a result of the expanding requests of different applications, and the requirements of vitality, memory and computational intensity of the WSN hubs, numerous thinks about have concentrated on these territories as of late. Specialists have proposed an assortment of conventions, for example, LEACH, PEGASIS and so forth. In this Manuscript, we will talk about how vitality proficiency is influenced by scaling, for example distinctive system sizes, and by various steering calculations. With the expanding utilizations of substantial scale WSNs, for example, savvy framework and ecological observing. This manuscript proposes another steering calculation to enhance the vitality proficiency by decreasing the number and absolute transmissions remove so as to spare vitality. Reproductions propose that the proposed calculation will be more vitality proficient in medium to vast scale remote sensor systems. Kinds of sensors which are utilized to send and get information through the remote system. Since sensors might be conveyed haphazardly, WSNs don't have a fixed framework and there is no very much characterized brought together sorting out system.

Software usability is usually used in reference to the hierarchical software usability model by researchers and is an important aspect of user experience and software quality. Thus, evaluation of software usability is an essential parameter for managing and regulating a software. However, it has been difficult to establish a precise evaluation method for this problem. A large number of usability factors have been suggested by many researchers, each covering a set of different factors to increase the degree of user friendliness of a software. Therefore, the selection of the correct determining features is of paramount importance. This paper proposes an innovative metaheuristic algorithm for the selection of most important features in a hierarchical software model. A hierarchy-based usability model is an exhaustive interpretation of the factors, attributes, and its characteristics in a software at different levels. This paper proposes a modified version of grey wolf optimisation algorithm (GWO) termed as modified grey wolf optimization (MGWO) algorithm. The mechanism of this algorithm is based on the hunting mechanism of wolves in nature. The algorithm chooses a number of features which are then applied to software development life cycle models for finding out the best among them. The outcome of this application is also compared with the conventional grey wolf optimization algorithm (GWO), modified binary bat algorithm (MBBAT), modified whale optimization algorithm (MWOA), and modified moth flame optimization (MMFO). The results show that MGWO surpasses all the other relevant optimizers in terms of accuracy and produces a lesser number of attributes equal to 8 as compared to 9 in MMFO and 12 in MBBAT and 19 in MWOA.

The aim of this study is to design and develop a blockchain-based web app called Health Stack to maintain accurate and complete medical records of patients, to help doctors to fetch previous medical history of the patients, to assist user to find out the disease he or she is suffering from and much more. For medical services, secure data storage is one of major concern for people. This problem can be resolved by developing an app using a blockchain technology having the features of decentralization and verifiability. Development of this app doesn’t involve any kind of dependency on third-party.

In today’s time each individual needs remote framework, yet at the same time control transmission for low power gadgets are wired in nature. Consistent power supply is one of the real issues in the motivation behind the utilization of remote sensor network. Yet, in the power arrangement of remote Sensor Network, the battery has an extremely constrained lifetime and is not supplanted yet by some other persistent power framework. There are separate techniques proposed for shorter and more separate power transmission: Inductive coupling, resonant inductive coupling and air ionization for short separations; microwave and Laser transmission for longer separations. The pioneer of the field, Tesla endeavoured to make a capable, remote electric transmitter more than a century back that has now observed an exponential development. This paper overall lights up all the effective strategies proposed for transmitting power without wires. This study is important for find out the future ways of power transmission. These methods are so important in today’s world because of drastic wastage of power. Common wireless power transmission is a point-to-point control transmission. It was demonstrated that the power transmission effectiveness could approach near 100%.

Evaluation of software quality is an important aspect for controlling and managing the software. By such evaluation, improvements in software process can be made. The software quality is significantly dependent on software usability. Many researchers have proposed numbers of usability models. Each model considers a set of usability factors but do not cover all the usability aspects. Practical implementation of these models is still missing, as there is a lack of precise definition of usability. Also, it is very difficult to integrate these models into current software engineering practices.In order to overcome these challenges, this paper aims to define the term ‘usability’ using the proposed hierarchical usability model with its detailed taxonomy. The taxonomy considers generic evaluation criteria for identifying the quality components, which brings together factors, attributes and characteristics defined in various HCI and software models. For the first time, the usability model is also implemented to predict more accurate usability values. The proposed system is named as fuzzy hierarchical usability model that can be easily integrated into the current software engineering practices. In order to validate the work, a dataset of six software development life cycle models is created and employed. These models are ranked according to their predicted usability values. This research also focuses on the detailed comparison of proposed model with the existing usability models.

We are going towards the Web world and IoT with the speed of light. Personalization and Recommendation systems are the pillars of web world. Now a day’s users want to search the relevant content on web in minimum amount of time. It’s hard to make an efficient system which searches the relevant data to the user on a click. To show the curated results and recommends the best search results as per user’s interest is a typical task, and gets more complicated when we are going to apply on web data. In this paper, an approach and proposed model is discussed which is based on user’s personal interest and automatically recommends the relevant information as per his/her interest. Hybrid computing is used to make the algorithm and process the data to refine the results. Some existing personalized searching techniques and models are also compared as literature survey.

In todays world many numbers of people are affected with neurological disorder. Neurological disease is a brain abnormalities or brain dysfunction. It affects all age group of people. In past years, the detection of neurological disorder is a very difficult task but now, we are blessed with many medical technologies which are available such as MRI, CT scan and PET. Convolutional neural network is best classifier for image feature extraction without any image segment. In this paper, we detect neurological disorder for normal as well as abnormal brain using brain MRI dataset. To trace the MRI dataset, we select a convolutional neural network algorithm to classify the MRI data for normal and abnormal brain.

"Enormous Data" could be a term together with the employment of strategies to catch, process, break down and envision conceivably vast datasets during a wise time span not available to straightforward IT innovations. By augmentation, the stage, instruments and programming used for this reason for existing are by and enormous referred to as "Huge knowledge advancements". In this manuscript, we give the importance, attributes, models, advances, life cycle and diverse totally different components of Big data.

Wireless data broadcasting seems to be an efficient way for dissemination of data to a large number of mobile users. Because of its scalability and flexibility, the service providers use this technology to resolve request of thousands of users in one single response. Full text search is the latest area of research in wireless data broadcasting. Access time and tuning time are the two metrics for evaluation of an indexing technique. In this paper, we propose an air indexing technique based on the dictionary data structure namely Dict-Based Air Indexing Technique for full text search over wireless broadcast stream that utilizes hash-tables. We also propose algorithms for the implementation of the technique. We analyze, evaluate and compare its performance with existing schemes. The results from simulation experiments demonstrate that the proposed technique is the most latency and energy efficient air indexing technique for full text searches.

Since WSN having a limitation of the limited battery life time and if the battery will die soon i.e. Network has a finite lifespan. The most famous issue facing network designers in wireless networks is to maximize network life. In wireless communication devices, due to resource constraints sensors evidence and delivery of reliable data is a difficult task. The motivation behind this study is the some critical limitation of energy aware routing protocols. Such as; it’s not easy to communicate to Base Station (BS) for that Cluster Head (CH) which is at maximum distance from Base Station therefore to make a novel mechanism by which long distance/route CH can be communicate with fast time with minimum energy consumptions to the base station (BS), here introduced data fusion concept. In WSN can be deployed a fusion point for accurate decision. This study includes directing the efficient use of artificial intelligent protocol with data fusion concept. General Terms Wireless Sensor Network, Routing, Clustering, Data fusion, Artificial Neural Network, Kohonen Self Organising Map.

While typesetting the article, the surname of the second author has been incorrectly published.

Medicinal plants are of great interest in the field of biotechnology as most of the drug industries depend in part on plants for production of pharmaceutical compounds. Biological activities of the phyoconstituents in plants could be enhanced by manipulation of the culture conditions. In the present investigation leaves and nodal segments of Alhagi maurorum were cultured on MS medium with BAP, Kinetin, NAA and other adjuvants. MS medium with BAP (1.0 and 2.0 mg/l) alone and in combination with NAA (0.1 mg/l was good for initiation of calli and bud formation. BAP (2.0 and 5.0mg/l) with NAA(0.1mg/l) plus kinetin 0.25mg/l with ascorbic acid 50mg/l and adenine sulphate, citric acid and arginine 25mg/l each was found to be the best for bud proliferation and shoot multiplication. Best rooting was found on MS medium supplemented with IAA and IBA (0.5mg/ leach). Cultured materials at different growth phase were evaluated for their biochemical estimation of primary metabolites quantitatively. Maximum content of carbohydrates, total proteins and amino acids were noticed in leaves of field grown plants and shoots of regenerated plantlets. It is further concluded that Alhagi maurorum serve as a rich source of primary metabolites which can be used as raw materials in industry.

Manuscript presents the idea of the vitality effective data gathering calculations to enhance the lifespan of WSNs and remotely revives sensor hubs. Here we make an assumption that the sensor hubs and main-station are not versatile. To make the system heterogeneity three sorts of hubs: Traditional, advance, super hub as far as their underlying vitality has been considered. The manuscript proposed new circulated vitality effective calculations PPDPSH and DLAPSH, in light of the separation from the base station and sensor lingering vitality and also booking of sensor hubs to substitute amongst rest and dynamic mode. This manuscript also introduces another concept for resolving the power problem of WSN is Wireless Power Transfer with Magnetic Resonance concept. The recreation comes about demonstrates that the proposed calculations PPDPSH and DLAPSH adjust the vitality scattering over the entire system and enhance the system lifespan.

Information indexing is an extremely useful solution for wireless broadcast channels, because of its energy efficiency. Full text search on the wireless broadcast stream is another popular type of information dissemination access. This research paper discusses indexing schemes for full text search information, using two levels of structure B + tree and inverted list. The partial replication in the B + tree indexing scheme is proposed to extend the existing full text search indexing scheme. In the proposed work, the index information of B + tree has been cut to reduce access time and tuning time: metrics for evaluating indexing schemes. The replication in B + tree may be done up to a certain level of the indexed informat+ion (Data). Evaluation and analysis of the proposed indexing scheme show an excellent improvement over existing indexing schemes.

In self-organizing map, the neurons in the 1-neighborhood of winner neuron are called community of winner neurons. The neuron which turns out to be the winner for the least number of times after a specified number of iterations has been named here as the weakest neuron. The neurons which are either the weakest or the farthest in the community of winner neurons are not getting enough exposure, which decreases their learning efficiency a lot. A community self-organizing map has been proposed here, which facilitates the learning of the weakest and farthest neuron in the community of winner neurons using a different process, thereby increasing the overall learning.

The Self-Organizing Map (SOM) has applications like dimension reduction, data clustering, image analysis, and many others. In conventional SOM, the weights of the winner and its neighboring neurons are updated regardless of their distance from the input vector. In the proposed SOM, the farthest and nearest neurons from among the 1-neighborhood of the winner neuron, and also the winning frequency of each neuron are found out and taken into account while updating the weight. This new SOM is applied to various input data sets and the learning performance is evaluated using three standard measurements. It is confirmed that modified SOM obtained a far better result and better effective mapping as compared to the conventional SOM, which reflects the input data distribution.

In this paper, a simple non-linear analytical charge control model for the DC and microwave characteristics of AlGaN/GaN MODFET is presented. The effect of parasitic resistances R is also incorporated. The model has also been extended to obtain the expressions for transconductance, drain conductance and cut-off frequency of the device. The model predicts a high transconductance of 502.6mA/mm at 1Vof gate bias and a maximum cut-off frequency of 22.5GHz for a 50nm device gate length, which is important in realizing the device for microwave applications. The extrinsic and intrinsic characteristics show close agreement with the published results proving the validity of the model.

Wireless sensor networks have recently gained a lot of attention from the researches. Wireless sensor networks are often partitioned into clusters, each managed by a cluster head (gateway). This paper reviews medium access control (MAC), an enabling technology in wireless sensor networks. MAC protocols control how sensors access a shared radio channel to communicate with neighbors in small area coverage. It classifies traditional (IEEE 802.11) and existing MAC protocols and a power efficient gathering protocol, known as PEGASIS, as examples of MAC protocol designed specifically for a sensor network. SENSOR MAC sets the radio to sleep during transmissions of other nodes unlike PEGASIS which involves non sleeping cycles. Finally this paper compares the performance of both protocols and displays the results. General Terms Small Sensor Network, Sensor – MAC, PEGASIS

The self-organizing map (SOM) is applied to data clustering, image analysis, dimension reduction and so forth. During the study of conventional SOM, we identified active and inactive neurons on the map. But the conventional SOM treats all the neurons on the map as active neurons and does learning accordingly. Also, the conventional SOM does not consider the relative winning frequency of active neurons. In this paper, a modified SOM is being proposed, which divides the neurons into active and inactive groups and different learning processes are used for active and inactive neurons. The modified SOM considers the relative winning frequency of active neurons also. The learning efficiency is measured using three well-known parameters. The modified SOM is applied on various standard input data sets. The learning results of the modified SOM are better than that of the conventional SOM. It is observed that the modified SOM is able to obtain the effective map reflecting the distribution of the input data.

Mobile device has limited battery power, the minimization of the access time and tuning time for the broadcast data is an important problem. There have been many research efforts that focus on minimizing access time and tuning time by providing indexes on the broadcast data. This paper proposes two level energy efficient signatures indexing techniques over the multichannel to reduce power consumption for wireless broadcast. The two-level signature scheme is used for indexing the information frames. At the first level, the integrated signature is used to index the indexes about the information. At the second level a simple signature is used to index the indexes about the information. KeywordsWireless Environment, Multiple Broadcast Channels, Data Allocation, Broadcast Program, Index Information, integrated signature, simple signature.

Future efficient wireless communication systems require, for high quality performance a Broadband Amplifier in the frequency range under consideration. This could be plugged into the measuring path so that it enables the system to perceive even the weakest signals. For this a new Scattering-parameter model for the microwave analysis of a PHEMT (Pseudomorphic High Electron Mobility Transistor) has been developed, which is valid for a wide frequency range. The developed neural network model is used in designing the PHEMT power amplifier. The calculated S-parameters, gain and minimum noise figure from the ANN (Artificial Neural Networks) model are the parameters which are used to design the low noise PHEMT power amplifier. The various gains so obtained from the S-parameters are plotted with the frequency and yield a closer fit to the simulated model. The neural network training has been done using Levenberg-Marqaurdt back propagation algorithm implemented in ANN toolbox of MATLAB software. All the results have been compared with the experimental data and show a close agreement and the validity of our model.

This paper gives an overview of some classical Growing Neural Networks (GNN) using soft competitive learning. In soft competitive learning each input signal is characterized by adapting in addition to the winner also some other neurons of the network. The GNN is also called the ANN with incremental learning. The artificial neural networks (ANN) mapping capability depends on the number of layers and the number of hidden layers in the structure of ANN. There is no formal way of computing network structure. Network structure is usually selected by trial-and-error method but it is time consuming process. Basically, we make use of two mechanisms that may modify the structure of the network: growth and pruning. In this paper, the competitive learning is firstly introduced; secondly the SOM topology and limitations of SOM are illustrated. Thirdly, a class of classical GNN with soft competitive learning is reviewed, such as Neural Gas Network (NGN), Growing Neural Gas (GNG), Self-Organizing Surfaces (SOS), Incremental Grid Growing (lGG), Evolve Self-Organizing Maps (ESOM), Growing Hierarchical Self-Organizing Map (GHSOM), and Growing Cell Structures (GCS).

An analytical 2-dimensional model to explain the small signal and noise properties of an AlGaN/GaN modulation doped field effect transistor has been developed. The model is based on the solution of twodimensional Poisson’s equation. The developed model explains the influence of Noise in ohmic region (Johnson noise or Thermal noise) as well as in saturated region (spontaneous generation of dipole layers in the saturated region). Small signal parameters are obtained and are used to calculate the different noise parameters. All the results have been compared with the experimental data and show an excellent agreement and the validity of our model.

A clustering problem occurs when an object needs to be assigned into a predefined group or class based on a number of observed attributes related to that object. The existing Competitive Learning (CL) algorithm and its variants (Frequency Sensitive Competitive Learning (FSCL), Rival Penalized Competitive Learning (RPCL), and Rival Penalized Controlled Competitive Learning (RPCCL)) have provided an appealing way to perform data clustering without knowing the exact number of clusters prior to clustering. This paper studies and analyzes the performance of these algorithms. The experimental results have been analyzed on the 2-D Gaussian data with the learning rate parameter kept same for all algorithms. The result showed that if number of output clusters is chosen equal to the number of clusters present in the input data then the performance for all the algorithms remains almost equal but when this number is chosen larger than the clusters present, then the RPCCL outperforms the other algorithms. Thus RPCCL gives the best performance in automatic cluster selection and we can use this feature of RPCCL algorithm in various useful applications like cluster analysis, curve detection, image segmentation, medical data analysis etc.

A new charge-control model for the microwave characteristics of an AlGaAs/InGaAs/InP HEMT of 70 nm gate length has been developed. The threshold voltage has been modified by the inclusion of mole fraction and thermal effects. The current voltage characteristics have been obtained and compared with the 70 nm gate length device. In order to avoid short-channel effects, an aspect ratio (gate length to gate to channel distance) larger than 5 is maintained in the analysis. The small-signal microwave parameters (gm, gd) have also been evaluated. 2-D analysis has been carried out in the saturation region. The unity gain cut-off frequency of the order of 220 GHz is obtained at a gate length of 70 nm. Device parameters so obtained were used to calculate the hemt Y-parameters. The Y-parameters hold from the threshold region to the edge of saturation. The unilateral gain so obtained from the Y-parameters yield a closer fit to the numerical model. All the results have been compared with the experimental data and show a close agreement and the validity of our model. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 2462–2470, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22779

For any software, its evaluation is significant for managing, controlling so that we can improve a software development process. For such evaluation of software, many factors have been recognized in literature surveys. Quality is one of most important factor which cannot be measured easily, because of its dependency on various other factors. Usability is such important factor on which quality of software depends. But as far as real world is concerned, we face a major obstacle in implementation of any usability model because there is a lack in precise definition and concept of globally accepted usability. Literature tends to define usability in overly brief and with ambiguous terms and describe its application in informal terms. Many software Usability Models which has developed so far, do not include all usability aspects and it is difficult to integrate these model into current software engineering practices. This paper aims to define and explain the term ‘usability’ using proposed detailed taxonomy. This paper also presents a critical analysis and evaluation of hierarchy based usability of software systems. Our proposed hierarchy based usability model presents 7 major attributes and 23 sub-attributes and these sub-attributes are further decomposed into 42 characteristics which collectively test the usability of a software product.

In the learning process of the conventional SOM, the neuron which is closer to the winner neuron learns more than the neuron which is farther away from the winner neuron. The neurons farther away from input are not able to learn properly and some dead units are left on the map. To decrease dead unit problem and improve the learning efficiency, an efficient Self-organzing map algorithm using group of neurons has been proposed. In this paper, we have divided the neurons on the map into two groups according to distance from input: normal and distant. The neurons which are far away from the input have been named distant neurons. We have done some changes in the kernel function for the distant neurons and then compared the learning efficiency of the algorithms by applying on standard input dataset. The results have been compared using three well known parameters, which are widely accepted for checking the learning efficiency of machine learning algorithms. It has been observed from the experimental results that proposed SOM successfully decrease dead units,while still preserving the topology of input data with lesser errors. The maps achieved by the proposed SOM have a lower error measure than the maps formed by SOM and false neighbor degree SOM (FN-SOM).