Advances in Computer and Computational Sciences

With the aim of resolving the issue of cluster analysis more precisely and validly, a new approach was proposed based on biogeography-based optimization (abbreviated as BBO) algorithm. (Method) First, we reformulated the problem with an optimization model based on the variance ratio criterion (VARAC). Then, BBO was presented to search the optimal solution of the VARAC. There are 400 data of four groups in the experimental dataset, which have the degrees of overlapping of three distinct scales. The first one is nonoverlapping, the second one is partial overlapping, and the last is severely overlapping. BBO algorithm was compared with three different state-of-the-art approaches. We ran every algorithm 20 times. In this experiment, our results demonstrate the maximum VARAC values that can be found by BBO. The conclusion is that BBO is predominant which is extremely quick for the issue of clustering analysis.

This paper presents an interesting android-based application “BTpower” which turns our phone into a remote. The app lets us control PowerPoint presentation from across the room, so we can walk around freely during presentations. The ppt/pptx file will be stored on the mobile. Bluetooth is used for connectivity purpose. The application provides a user-friendly interactive interface by which we can interact with Microsoft Office PowerPoint on our PC. With BTpower, we can start our PowerPoint presentation, jump to the next or previous slides, resume, or exit the slide show with a touch of our finger—all from our phone.

Crowd monitoring on public places is very demanding endeavor to accomplish. Huge population and assortment of human actions enforces the crowded scenes to be more continual. Enormous challenges occur into crowd management including proper crowd analysis, identification, monitoring and anomalous activity detection. Due to severe clutter and occlusions, conventional methods for dealing with crowd are not very effective. This paper highlights the various issues involved in analyzing crowd behavior and its dynamics along with classification of crowd analysis techniques. This review summarizes the shortcomings, strength and applicability of existing methods in different environmental scenarios. Furthermore, it overlays the path to device a proficient method of crowd monitoring and classification which can deal with most of the challenges related to this area.

Component Based Software System (CBSS) is widely popular in the modern era because of the reduction of development cost, time, and effort. To increase the success rate and acceptability of CBSS among the users, it is important to increase the quality of CBSS. Usability is one of the important quality factors, but challenges exist in measurement of usability. Complexity plays important role in acceptance of usable software system. So, to measure the usability, it is important to measure its complexity by using complexity metrics. Various complexity metrics have been proposed in the literature. The main objective of this research paper is to identify the complexity metrics of traditional and object-oriented software system and to provide a roadmap for the requirement of complexity metrics for CBSS. Present paper may help system designers, developers, and analysts to select the appropriate complexity metrics for CBSS on the basis of provided analytical results. Based on the selected complexity metrics, usability can be measured in easier way.

In the past few years, the growing population in the smart city demands an efficient transportation sharing (bike sharing) system for its development. The bike sharing as we know is affordable, easily accessible and reliable mode of transportation. But an efficient bike sharing system should be capable not only of sharing bike but also of providing information regarding the availability of bike per station, route business, time/daywise bike schedule. The embedded sensors are able to opportunistically communicate through wireless communication with stations when available, providing real-time data about tours/minutes, speed, effort, rhythm, etc. Based on our study analysis data to predict regarding the bike’s available at stations, bike schedule, a location of the nearest hub where a bike is available, etc., reduces the user time and effort.

Object Oriented software design metrics has already proven capability in assessing the overall quality of any object oriented software system. At the design level it is very much desirable to estimate software reliability, which is one of the major indicators of software quality. The reliability can also be predicted with help of identifying useful patterns and applying that knowledge in constructing the system in a more specified and reliable manner. Prediction of software fault at design level will also be helpful in reducing the overall development and maintenance cost. Authors have classified data on the basis of fault occurrence and identified some of the classification algorithm performance up to 97%. The classification is carried out using different classification techniques available in Waikato Environment for Knowledge Analysis (WEKA). Classifiers were applied over defect dataset collected from NASA promise repository for different versions of four systems namely jedit, tomact, xalan, and lucene. The defect data set consist of six metrics of CK metric suite as input set and fault as class variable. Outputs of different classifiers are discussed using measures produced by data mining tool WEKA. Authors found Naive Bayes classifier as one of the best classifiers in terms of classification accuracy. Results show that if overall distribution of CK metrics is as per proposed Mean Metric Value (MMV), the probability of overall fault occurrence can be predicted under consideration of lower standard deviation values with respect to given metric values.

In the software project development software estimation and planning is a crucial process. This paper proposes a framework that outlines the estimation of a software development at the initial stages and consists of feedforward and feedbackward approaches in the whole development cycle. Improved software estimations are also elaborated on an agile project implementation using the proposed framework.

Drowsiness of a person is major cause for accidents and to avoid accidents alerting person at right time is very necessary. Yawning is one of the signs, which indicates whether the person is drowsy or not. Most of the algorithms in literature detect yawn state considering the region between the lips. Mouth localization is the fundamental step in yawn detection. Region between the lips is segmented using algorithms of different complexities. In this work, a simple segmentation algorithm like thresholding is investigated for its effectiveness. The segmented region with maximum area within the mouth region is considered to classify the frame as yawn frame or otherwise. Yawn video sequences from YawDD dataset are used to test and validate the algorithm. Yawn detection accuracy using the proposed algorithm is 76% which is bit higher than the accuracy obtained with more complex algorithm. Such simple algorithms might be more useful for real-time applications. The time consumption of the implementation is to be verified.

Many applications require high throughput for operating small files, such as picture server. File systems often use tree structure to manage data blocks, which can improve the efficiency of adding, deleting, and searching files under a directory. This article gives an introduction to design of the directory of several popular file systems and shows the influence of structure on the performance by several experiments.

seL4 is formally verified for its functional correctness and provides a trusted code base for ARINC 653 partitioning operating systems. ARINC 653 needs a two-level scheduler to enforce temporal isolation between partitions. We cannot modify the scheduler provided by seL4 to adapt ARINC 653, which may invalidate the formal correctness of seL4. Thus, we propose a user-mode scheduling mechanism, where several user threads serve as the partition scheduler and process schedulers. The execution trace result shows that the temporal partitioning can be enforced by this mechanism. We also elaborate the scheduling overheads.

Matrix-vector multiplication is one of the core computing of many algorithms calculation in scientific computing, the vectorization algorithm mapping is a difficult problem to vector processors. In this study, based on the background of BP algorithm for deep learning application, on the basis of in-depth analysis of the BP algorithm, according to the characteristics of vector processor architecture, we proposed an efficient vectorization method of matrix-vector multiplication. The L1D configured into SRAM mode, with double buffer “ping-pong” way to smooth data transmission of multistage storage structure, makes the calculation of the kernel and the DMA data moving overlap, let the kernel run at a peak speed, so as to achieve the best calculation efficiency. Through the way of transpose matrix transmission with DMA to avoid the inefficient access to column of matrix and summation reduction of floating-point calculation between the VPEs, Obtain the optimal kernel computing performance. Experimental result on MATRIX2 shows that the single-core performance of presented double precision matrix multiplication achieves 94.45 GFLOPS, and the efficiency of kernel computation achieves 99.39%.

In the verification and testing of electronic embedded controllers, one of the main difficulties is how to generate a collection of consistent and complete test-cases, and how to implement the test process in an automatic means. In this paper, we propose an approach to generating test-cases based on the timed automata–the formal models of controllers. The main contribution of this work is that we present a number of rules, which can be used to guide the generation of test-cases and to reduce their total number. Furthermore, we prove that the presented rules enjoy the completeness property in the sense that the set of the test-cases is able to cover all the functionalities of controllers which formally specified with timed automata.

In this paper, a novel fractal microstrip antenna is proposed for IEEE 802.11 wireless local area network (WLAN). The geometry of ‘X’ shape slots with dissimilar dimensions is used to design the proposed fractal antenna. The proposed fractal antenna is designed with FR4 Glass Epoxy material. The dielectric constant and thickness of antenna are ε _r = 4.4 and 1.6 mm. Radiating patch size of proposed antenna is of 35.4 mm × 27.82 mm with feed width and length 16.4 and 2.6 mm, respectively. Proposed fabricated antenna is analyzed for WLAN frequency band of 2.4 GHz. Ansoft HFSS simulator software is used to obtain and validate the simulation results of proposed antenna.

In this paper, an efficient structure of metamaterial is proposed to enhance the performance of an E-shaped micro strip antenna. E-shaped antenna has been considered for the research because of its advantages over other shapes. It is less vulnerable to interference when several other antenna elements are present in the vicinity. The frequency band selected for the design is 6–7 GHz, C-band. The E-shaped antenna proposed is intended to be applied for many satellite communications transmissions. The metamaterial structure is made up of two nested split octagonal rings located on a 10 × 10 FR4_epoxy with 1.6 mm thickness and dielectric constant of 4.4. The patch antenna substrate consists of a 5 × 4 array of such metamaterials. By using this metamaterial in the microstrip antenna, enhancement of performance parameters in terms of return loss is seen.

In this paper, a narrow channel multiple frequency microstrip patch antenna is proposed. This paper entrusts the trend for not using wider channels, which is even though an increasing trend now a days, but instead we advocate that the radio communication should take place over multiple channels for fair and efficient spectrum utilization. For this purpose, we present a narrow channel multiple frequency rectangular microstrip patch antenna having slits. The proposed design is applicable for commercial frequency bands of 3.20, 5.5, 6.25, and 7.96 GHz which makes it useful for the modern wireless communication purposes (2–8 GHz). HFSS version 13.0 is used for design, evaluation, and analysis of the proposed antenna design. The design is analyzed for radiation pattern, return loss, VSWR, and gain, where the simulated results infer that the planned antenna design shows appreciable performance in terms of VSWR, gain, return loss, and radiation pattern at resonant frequencies.

Mobile Agents are the set of processes that can move from one host to another host. The request granted by the client machine is executed by movement of mobile agents from a host machine to another host machine. The processes called by client are executed on a machine. The host machine must have all the resource needed to implement the service. This paper deals with allocation of resources through a proposed technique. In order to implement the tasks offered by the client, the mobile agents resume execution at the new machine (Nelson in Remote Procedure Call, PhD thesis, Computer Science, Carnegie Mellon University, 2002) [1]. The extension of client and server model is followed in the development of mobile agents. This paper also analyses the resolution of deadlock through different methods of study. The client only performs the operations provided by the server. There is scope of network scalable system when a particular server does not provide request desired by the client (Almes et al. in The Eden System: A Technical Review, IEEE Transactions on Software Engineering, 1998) [2].

Wireless Mesh Network arises as a promising innovation for providing quick and productive communication for which numerous algorithms have been proposed in networking infrastructure. For routing there are various performance parameters such as throughput, network congestion, resiliency, fairness, robustness, network jitter, delay, stability, optimality, simplicity, completeness, etc. Robustness provides the capability to deal with all the failures that come across during the connection in the network to increase the network performance. In this paper, we have shown and analyzed three algorithms namely robustness parameter Resilient multicasting, Resilient Opportunistic Mesh Routing for Wireless Mesh Network (ROMER) and Buffer-Based Routing (BBR) in Wireless Mesh Networks. We have shown that network performance of BBR is better than these approaches which is analyzed through various parameters such as network congestion, network throughput and resiliency.

A hybrid cloud becomes the preferred solution when enterprises deploy cloud service. Using new technology such as software-defined network (SDN) and network virtualization to form a network will be the trend of the future. This paper introduces the new safety protection opportunities brought by new network technologies, and analyzes the challenges and risks of hybrid cloud system using these technologies. Finally, it puts forward the corresponding solution.

Aspect-level sentiment analysis refers to sentiment polarity detection from unstructured text at a fine-grained feature or aspect level. This paper presents our experimental work on aspect-level sentiment analysis of movie reviews. Movie reviews generally contain user opinion about different aspects such as acting, direction, choreography, cinematography, etc. We have devised a linguistic rule-based approach which identifies the aspects from movie reviews, locates opinion about that aspect and computes the sentiment polarity of that opinion using linguistic approaches. The system generates an aspect-level opinion summary. The experimental design is evaluated on datasets of two movies. The results achieved good accuracy and shows promise for deployment in an integrated opinion profiling system.

This paper provides analysis through disparate detection techniques of edge like Prewitt, Sobel, and Robert to detect edges of the image with different analyses. Image segmentation and data extraction are considered process for edge detection. It is an image processing technique for detecting the boundaries within image. The process involves detecting discontinuities in brightness. In this paper, the proposed method shows the performance analysis of edge detection techniques as mentioned above.

Internet of Things (IoT) is a backboneless network. Because of the uni-directional link and mobility nature of the nodes, the network is dynamic. The nodes are self-organized and two nodes can transfer data directly when they are within the transmission range. The nodes in IoT are self-organized and dynamic so MANET routing plays a vital role. The Light weight Ad hoc On-demand Distance Vector (LOAD) is a reactive routing protocol. In LOAD, the routing involves three major processes namely route discovery, path establishment, and route maintenance. The route discovery is carried out by Route REQuest (RREQ), Route REPly (RREP), and Route ERRor (RERR) control packets. The cost of the route discovery is estimated by control packets propagation. The proposed technique Cost And Load overhead reDUction for routE discovery in LOAD ProtocoL (CALDUEL) is proposed to reduce the load overhead, by reducing the cost of route discovery.

Mobile Ad-Hoc Networks (MANETs) provides substantial services in the field of network. MANETs is an infrastructure-less network, containing more number of individual wireless mobile nodes (devices) that communicate with one another without any aid of centralized server or base station. Due to dynamic topology, MANETs faces many issues associated with mobile nodes such as link failure, battery power, delay, etc. The main aim of this paper is to reduce data transfer delay by finding optimum path. Ultimately, the outcomes of the proposed work AASOP increase the performance of MANETs by minimizing data transfer delay which in turn increases Packet Delivery Ratio (PDR).

A leader election is one of the fundamental problems in distributed systems. A node should have sufficient amount of resources to act as a leader. In this paper, we have proposed a leader election algorithm considering available resources of the nodes. All the nodes compute their resource factor (RF) value and form a process priority status queue (PPSQ) to transmit it to the next connected node. Finally, the node with highest RF value will be elected as a leader. Extensive simulations are performed, and it is shown that the proposed technique is better than the existing random leader election techniques in terms of available resources.

Call Admission Control (CAC) in wireless communication plays major role in deciding the admission of real-time and non real-time mobile users. For non real-time, it does not care for the Quality of Service (QoS) performance. But for real-time services, the CAC cares for the QoS by keeping the sufficient bandwidth throughout the transmission. Bandwidth determines the system capacity and speed. In this paper, a new Analytic Hierarchy Process (AHP)-based CAC algorithm is proposed for increasing the number of admission and reducing the admission of less compressed calls. For this task the various codecs such as G.711, G.729, G.723, G.726, AMR, EVRC, and iLBC have been taken. AHP is applied among them to take the right decision and it produces the ranking order. That rank helps to save more bandwidth and increases the throughput (Packets per second). This task is carried out by taking the criteria like bandwidth, packetization delay, and compression ratio of each individual codecs. The ultimate aim of this TACA is to give better QoS performance for real-time services and increase the system throughput for Voice over Internet Protocol.

Wireless sensor network (WSN) is an ad hoc low power wireless network in which sensor nodes cooperatively monitor and gather the information from environment then broadcast that information to other nodes. So security against denial of services (DoS) at routing levels is the most significant area of research. In this paper, denial of service at the routing level are discussed. DoS is cause by resource exhaustion at the network layer, which completely disable the network by consuming node’s battery power. This power draining attack is known as “vampire attack” which is not definitive to any routing protocol. A single Vampire can raise network energy usage by a factor of O(N) in worst case, here N is the number of nodes. This paper reviews some methods to detect and mitigate this attack that raise the energy consumption of network and concepts that bounds the damage from Vampires.

“Information available for anybody at anywhere and anytime.” Ranging from a domestic connection via personal computers toward a mobile access via smart devices using communications technologies, those devices can access to all kinds of information through mobile applications in the cloud. Mobile Cloud Computing (MCC) can be seen as a solution for limitations of cloud computing because all mobile devices are limited by memory capacity, screen, battery, and intermittent connectivity, the MCC exploits the user’s information, e.g., localization, memory, power, and bandwidth capacity while running these applications on the cloud. In the aim of addressing the problems of mobile environment which treat mobility of users and services, we propose in this paper a model of cloud-based on agent in mobile environment which ensuring high availability of services by their migration or replication, the aspect of decision-making between mobile devices and mobile applications using top-k algorithm which contribute to find the most appropriate service in the cloud while reducing energy consumption with respecting the SLA.

A fundamental challenge in underwater wireless sensor network (UWSN) is mobility of sensor nodes during the communication held in the network. There is no fixed location of the sensor nodes present under sea level in wireless sensor network. The nodes are mobile which results in improper communication. Although there are various issues in underwater wireless sensor network, some of them have been encountered in this paper. The paper focuses on node mobility in network during the communication among the nodes. An approach has been proposed in this paper so that communication in the network between the nodes can take place even if the node changes its location. The approach is based on the Euclidean distance. The approach is called as an Arc moment and is used to show how nodes placed in the underwater wireless sensor network can communicate with each other based on various assumptions and conditions.

In wireless communication, turbo and modified turbo codes are used as forward error correction (FEC) codes to improve the performance of the system in terms of bit error rate (BER). This work revisits the FEC using conventional turbo codes and proposes few modifications in them to obtain modified turbo codes. Trivial alterations in the frame length of the codes have been implemented along with puncturing scheme. The decoding complexity of the system is alleviated with the use of Viterbi algorithm for punctured codes. A comparative study between the performance of the system using turbo and modified turbo codes is presented in terms of their BER. Simulation results show that there is significant enhancement in the performance of the system when modified turbo codes are employed.

Strong designated verifier signature scheme (SDVS) is a unique kind of digital signature scheme that has numerous applications in the networking infrastructure of present and future generations. In recent years, Lee-Chang has put forward a strong designated verifier signature scheme and stated that their scheme is secure. In this paper we show that Lee-Chang’s scheme is not secure, and any combatant can forge their scheme unaware of the secret key of the original signer and suggest an improved scheme which solves the fraud attack problem by any adversary. We analyze our scheme and compared with the Lee-Chang’s scheme and show that our scheme is secure and meet the security feathers needed by SDVS.

High Resolution (HR) image is obtained utilizing set of Low Resolution images using the Super Resolution processes. For motion vector within the frames in Super resolution, the motion estimation algorithms are used. Motion estimation gives a fully designed algorithm in programmable platforms. Highest spatial accuracy is used by the algorithm to adapt the resolution of the image content where it is necessary, that is the border of the moving objects. Binocular vision is the most focused work in reconstruction of image and helps in collecting motion and depth image from defocused images. Shape from Focus (SFF) method is a sequence of images, which is used in the application where high resolution of focused images is given priority. It helps in removing blurred frames and helps in reconstruction. A framework model is proposed in this paper to derive HR images from LR images using motion estimation algorithms and further reconstruction algorithms are implemented using SFF. In this paper, we discuss how to increase the resolution of any image and to reconstruct image and then transferring data to WiMAX network and through WiMAX network this data can be viewed in mobile area as well. The paper proposes with a framework where the model deals with the sequence where we can recreate high resolution image using set of low resolution images and the transferring of data to WiMAX network.

Nowadays, Vehicular Ad Hoc networks is one of the most emerging and favorable technology which succor in governing day-to-day road traffic in roads. The main disquiet of VANETs is focused on providing security to moving nodes in the vehicular network, so that possibility of accidents, traffic jams or any other hindrance in communication among different vehicles will get reduced. Black hole is most popular security attack that sends false reply message to the source by advertising itself as having optimal route toward destination. This paper focused on black hole problem. Here, a reliable mechanism is proposed for averting black hole attack in VANET by unicasting data packet to vehicles. To enhance security, trust factor technique is used which detect routing misbehavior and gives surety of relaying data packets to the destination. This elucidation defends against black hole attack and simulation on Ns-2 will sustain its efficiency and reliability.

Vehicle Routing Problem (VRP) is among the intensively studied problem in the field of operations research. The literature of VRP has spread to dozens of variants that are studied till now, which makes the problem more complex. Due to its complexity and several real-time constraints, it is difficult to find optimal solutions for VRP models. In recent decades, swarm optimization techniques have emerged as promising solution to solve these problems optimally. The purpose of this research is to develop structural classification of different domains and attributes of VRP solved using swarm techniques. The findings of the study show the most studied attributes, capacitated VRP, time windows VRP, objective function with cost minimization and the least studied attributes, maximization objective function. The VRP literature is summarized in a manner that provides a clear view to identify future research directions.

Around the world governments have made many policies and laws in order to avoid accidents that occur on road, however, they seem to be unavoidable. Therefore, it becomes important to take care of road safety. This is where VANETs have helped in introducing various applications primarily based on V2V and V2I communication which have helped in increasing safety of commuters on road. Although to deploy VANET successfully the following things have to be kept in mind: First range of wireless networks, second secure communication and lastly stable network performance. When we look into VANETs we find that there are primarily two unique forms of communication available. The environments where VANETs are deployed are mainly the highways and cities/towns. VANET communication on highways is fairly simple to implement but when it is to be deployed in cities communication becomes complex. This is because cities have congested outlook mainly because of buildings, trees and houses, etc. And it is not possible to always have direct communication between nodes in the intended direction. This paper analyses the different protocols in VANET and make a comparison on the basis of communication. Also this paper shows a scenario based implementation of Wireless Access in Vehicular Environments (WAVE) 802.11p/1609.4.

The Internet-based businesses are increasing day by day and even the new concept of digital India is developing, through which every government and private companies are switching to Internet and cloud services. Internet users are increasing day by day at State Bank of India and about 69% of daily transactions happen through alternative channels, including Internet, ATM, and mobile banking. This figure is rising every year and more young generation is using online services. But on the other hand, there are many security concerns as well; presently we have many secured transaction channels. In this paper, we have proposed the new algorithm to prevent frauds and track the transactions location if any fraud occurs in the Internet. Using Online Hybrid Model algorithm, we will generate unique Internet id of the user. This algorithm also supports in the prevention of fraudulent activities, for example, if terrorist do any transaction online, we can track easily.

The precise prediction of the popular trend in music can contribute to the exploration of the potential entertainment market. According to surveys, the technical difficulties of such prediction contain the difference between computer simulation and the real human emotions, as well as the comprehensive factors and data that are processed. Therefore, this thesis will present SANet which can forecast the popular trend in songs by self-accommodating and nonlinear mapping. It will be demonstrated by focusing on the discussion in the areas on data preprocessing, model constructing, and accommodating of hidden columns, as well as the test of partial data by random sampling and the analysis of the experiment result.

Although two-dimensional probability random multiple access protocol can have a better control effect on the system, it features a single agreement, the practical application of the network is not strong. To solve the above problem, the model of multichannel dual clocks two-dimensional probability random access protocol with three-way handshake mechanism (DMTPTH protocol) is proposed. DMTPTH is based on the two-dimensional probability random access model, adding dual-clock, multichannel and three-way handshake mechanism, respectively, to achieve lower system idle rate, confirm the multiservice access and information transmission states. Theoretical analysis and simulation experiments show that the model can not only improve system throughput, but also achieve throughput by changing the two-dimensional probability adapt to different network environments, enhanced agreement practicality, and reliability.

Hierarchical addressing and locator/ID separation solutions have been proposed to address the scalability issue of the Internet. However, how to combine the two addressing schemes has not been received much attention. In this paper, we present an address encoding method to integrate hierarchical addressing and locator/ID separation. Our analysis and evaluation results show that the proposed encoding method could guarantee the scalability property, and alleviate the inefficiency of address space.

This paper presents a link utilization performance analysis of the hybrid WiMAX-Wi-Fi and WiMAX video surveillance systems. Link utilization is ratio of throughput to capacity expressed as a percentage. Therefore, Link utilization measurement must take into account throughput, packet losses and signal-to-noise ratio. A WiMAX-Wi-Fi video surveillance system consists of a Base Station (BS) which is connected in a point-to-point configuration with the Customer Premises Equipment (CPE). The base station then connects to the Internet, routers and video servers. The CPE has a Wi-Fi and WiMAX wireless interfaces. Results show that the hybrid WiMAX-Wi-Fi video surveillance system outperforms the WiMAX in throughput, packet loss and signal-to-noise ratio terms. The hybrid WiMAX-Wi-Fi is 4.5 times better in utilizing the WiMAX link than the WiMAX system.

Deregulation in the electricity industry has made price forecasting the basis for maximizing profit of the different market players in the competitive market. The profit of market player depends on the bidding strategy and the successful bidding strategy requires accurate price forecasting of electricity price. The existing methods of price forecasting can be broadly classified into (i) statistical methods (ii) simulation-based methods and (iii) soft computing methods. The conventional neural networks were used for price forecasting due to their ability to find an accurate relation between the historical data and the forecasted price without any system knowledge. They suffer from major drawbacks like training time dependency on complexity of the system, huge data requirement, ANN structure is not fixed, hidden neurons requirement is large relatively, local minima. In the proposed work, the problems associated with conventional ANN trained using back-propagation are solved using improved generalized neuron model. The genetic algorithm along with fuzzy tuning is used for training the free parameters of the proposed forecasting model.

This paper attempts to compare ternary and binary logic gate design using CMOS and carbon nanotube (CNT)-FETs technology. Ternary logic is an effective approach over the default binary logic design technique because it allows to define one more voltage level which is VDD/2 and it also allows a circuit to be simple in design and energy efficient due to its property of reduction in circuit overhead such as interconnects and chip area. A CMOS and CNTFET-based ternary logic gates and arithmetic circuit design has been proposed to implement and compare binary and ternary logic design based on CMOS and CNTFET. The main objective is to compare the CMOS and CNTFET results and verify the advantages of CNTFET technology. The proposed CNTFET technique combined with ternary logic provides an usable performance, improved speed and reduces propagation delay characteristics in circuit such as inverter and decoder. Simulation results of proposed designs using H-SPICE are observed and shown that the proposed ternary logic gates consume significant less delay than the CMOS gates implementations. In realistic digital application, the proposed design of ternary logic compared with binary logic results in over 95% reductions in terms of the consumption of propagation delay.

Delay-Tolerant Networks (DTNs) are intermittently connected mobile wireless networks in which the connectivity between nodes changes frequently due to nodes movement. A delay-tolerant network suffers from nodes energy efficiency issues due to frequent mobility and reliable transmission of data packet from source to destination. Due to the mobility of the node there are frequent changes in the path. Due to which enormous amount of energy is wasted. So to conserve the energy of the node energy efficient transmission from one node to another node can be achieved by using the proposed algorithm EADBNHS. Connectivity of the network can be determined by Synchronism, Simultaneousness and Discontinuity using EADBNHS. EADBNHS is the algorithm devised for energy efficiency in this article. To achieve energy efficiency of a node the algorithm is designed in a way, which includes transmission energy, reception energy and the health of the node. This algorithm supports energy efficient transfer of data packet; it selects a node on the basis of minimum energy available on the node and on the basis of its connectivity to other nodes. With the help of Synchronicity, Simultaneousness and Discontinuity we can easily judge a node’s condition to become the participant node in the path from source to destination.

This paper introduces detailed study of remote interaction of vehicle with user so that user can have value parameters of parameters like fuel tire pressure, etc., while sitting at remote location. Here we have focused specifically on vehicle fuel measurement and its feasibility. This study is about achieving wireless measurement of vehicle’s fuel using wireless protocols, and add the comfort level for user for any kind of automobile like Car, Bike. This kind of feature will not only help private vehicle owners but will help public vehicle owner to know the state of vehicle with parameters like fuel, Tire pressure while they are sitting remotely.

“Reassurance Broiler Project” is one of the “Ten Secure Food Projects” in Tianjin, China. The platform, a third-party authentication and management platform, contains eight subsystems and a website for query and information release. It applies object-oriented system analysis and design on the basis of use cases under RUP.“.NET” framework is applied, SQL Server is used as DBMS while a mode combining C/S and B/S structure is adopted. A 25-digit unique identification is used as industry chain transmission identification, with RFID as the carrier. Besides, 10-digit one-dimensional tracing code and two-dimensional QR code are used on tracing label. The platform can trace information for processes from broiler production, inspection and quarantine, slaughtering, processing, as well as packaging and selling, which can help improve food safety, reduce management cost, increase brand advantages and help government authorities provide early warnings against animal epidemic and problems related to production quality and safety.

Electronics industry in present-day scenario is facing the major problem of standby leakage current in most of the electronic devices. As the speed of processor is increasing, the demand for high-speed cache memory is ever increasing. SRAM being mainly used for cache memory design, several low-power techniques are being used to reduce its leakage current. Full CMOS 6T SRAM cell is the most preferred choice for most of the digital circuits. This paper implements 6T CMOS SRAM cell using MTCMOS technique and simulation results show significant reduction in leakage during standby mode. The simulations are done on Cadence Virtuoso Tool using 45 nm technology.

With the advent of deregulation, electricity has become a commodity which is capable of being traded in the deregulated electricity market. In the deregulated environment, accurate electricity price forecasting has become necessity for the generating companies in order to maximize their profits. The existing forecasting models can be broadly classified into statistical models, simulation models, and soft computing models. The soft computing based models have gained popularity among other existing models because of their nonlinear mapping capabilities and ease of implementation. In the presented work, a generalized neuron based electricity price forecasting model has been proposed to forecast the electricity price of New South Wales electricity market. The de-noising capability of the wavelet transform is explored for decomposing the ill-behaved price signal into low- and high-frequency signals for better representation. The low- and high-frequency signals were given as input to the generalized neuron model individually for improving the forecasting accuracy of the model.

Each input object in multiple instance learning (MIL) is represented by a set of instances, referred to as ‘bag.’ Therefore, in MIL, class labels are associated with each bag instead of individual instance. This study proposes a classifier for multiple instance learning based on Twin Support Vector Machine, termed as MIL-TWSVM. The proposed approach is trained at bag level, where each bag is represented by a vector of its dissimilarities to other bags in the training set. A comparative analysis of MIL-TWSVM approach is performed with the instance-level and noisy-or (NOR) learning approaches based on TWSVM. The performance of the proposed MIL-TWSVM approach has also been compared with several existing approaches of multiple instance learning. The experiments on eight multiple instance benchmark datasets have shown the superiority of the proposed approach. The significance of experimental results has been tested via statistical analysis conducted by using Friedman’s statistic and Nemenyi post hoc tests.

Task scheduling plays a key role for efficiently assigning resources to tasks and performing multitasking. In heterogeneous environments, hard computing task scheduling does not give optimal solution. There are many soft computing techniques used for task scheduling such as evolutionary algorithm which includes genetic algorithm, Differential Evolution (DE), metaheuristic, and swarm intelligence like particle swarm intelligence and ant colony optimization. Genetic Algorithms give locally optimum solution but get stuck in nonoptimal conditions and suffers from quick convergence. DE does not get stuck in local minima and gives a globally optimum solution. Rate of convergence of DE is also slower than GAs and increases with problem size. We have implemented DE for solving task scheduling problem and results demonstrated significant improvement in the fitness of solution with varying parameters as mutation factor, crossover probability, number of iterations, and population. The main aim of this paper is to visualize the effect of variation in various parameters of DE algorithm on the solution of task allocation problem.

A recent bio-inspired optimization algorithm, that is, based on the Lagrangian and evolutionary behavior of krill individuals in nature is called the Krill Herd (KH) Algorithm. Randomization has a key role in both exploration and exploitation of a problem using KH algorithm. A new randomization technique termed adaptive technique is integrated with Krill Herd algorithm and tested on several global numerical functions. The KH uses Lagrangian movement which includes induced movement, random diffusion, and foraging motion, and therefore, it covers a vast area in the exploration phase. And then adding the powerful adaptive randomization technique potent the adaptive KH (AKH) algorithm to attain global optimal solution with faster convergence as well as less parameter dependency. The proposed AKH outperforms the standard KH in terms of both statistical results and best solution.

This paper investigates a Chebyshev Neural Network (CNN) sliding mode controller for stabilization of time-delayed version of system with uncertainty and nonlinearity. The nonlinearity in the system is unknown but bounded and has been approximated with the help of CNN. The input delay has been balanced and further converted into regular form and the original system is converted into a delayed free version with the help of Smith Predictor. Now, the predicted states of the system and “Gao’s reaching law” are used to derive the robust control law. Further, to prove the stability analysis Lyapunov–Krasovskii candidates has been chosen according to the proposed system. A numerical example is provided to illustrate the stability of the system in the presence of uncertainty, time delay and nonlinearity.

Artificial Bee Colony (ABC) algorithm is now a long-familiar example of Swarm Intelligence. It has been consistently drawing the attention of research scholars since last decade. The adept performance of ABC algorithm has already been proved in various researches. Hence this algorithm has been used in wide variety of applications, spanning almost all aspects of engineering optimization. This manuscript details out some of the application areas of ABC algorithm in a concise way and it aims to provide a bird eye view of various application areas for the beginner researchers.

India has always been an attraction seeker to tourist from all over the world. India indeed stands through its tittle “INCREDIBLE INDIA” because of its diversity in culture and religion. Tourism in India is economically important and is growing rapidly. About 22.57 million tourist arrived in India in 2014, compared to 19.80 million in 2013. In terms of foreign tourist arrivals, India ranked as the 38th country in the world. With the help of Fuzzy-AHP technique, i.e. Fuzzy Analytical Hierarchy Process is the best method decided for finding the most influential tourist place from the tourist point of view. The purpose of this work is to present a multi criteria decision making (MCDM) model for management of tourists across various tourist places in India. In this work five (5) criteria from various literature reviews and practical investigations has been taken. Fuzzy-AHP techniques is used to ample decision makers assesments about criteria weightings. Finally, a factual study is done for identifying the best tourist place across India. In this work, about 30 states are taken and various survey are conducted among different groups of people and then final decision is made by the computational process and effectiveness of Fuzzy-AHP.

In this paper we propose PATCOMP—a PARTICIA-based novel approach for Network motif search. The algorithm takes advantage of compression and speed of PATRICIA data structure to store the collection of subgraphs in memory and search for classification and census of network. Paper also describes the structure of PATRICIA nodes and how data structure is developed for using it for counting of subgraphs. The main benefit of this approach is significant reduction in memory space requirement particularly for larger network motifs with acceptable time performance. To assess the effectiveness of PATRICIA-based approach we compared the performance (memory and time) of this proposed approach with QuateXelero. The experiments with different networks like ecoli and yeast validate the advantage of PATRICIA-based approach in terms of reduction in memory usage by 4.4–20% for E. coli and 5.8–23.2% for yeast networks.

Recent trend of research is to hybridize two and more algorithms to obtain superior solution in the field of optimization problems. In this context, a new method hybrid PSO (Particle Swarm Optimization)—MFO (Moth-Flame Optimizer) is exercised on some unconstraint benchmark test functions and overcurrent relay coordination optimization problems in contrast to test results on constrained/complex design problem. Hybrid PSO-MFO is combination of PSO used for exploitation phase and MFO for exploration phase in uncertain environment. Position and Velocity of particle is updated according to Moth and flame position in each iteration. Analysis of competitive results obtained from PSO-MFO validates its effectiveness compare to standard PSO and MFO algorithm.

For the last two decades, the differential evolution is considered as one of the powerful nature inspired algorithm which is used to solve real-world problems. DE takes minimum number of function evaluations to reach close to global optimum solution. The performance is very good, but it suffers from the problem of stagnation when tested on multi-modal functions. In this paper, the population-based homeostasis difference vector strategy has been used to improve the performance of differential evolution algorithms. Here we propose two independent difference random vectors named as best difference vector and random difference vector which helps in avoiding stagnation problem of multi-modal functions. The performance of proposed algorithm is compared with other state-of-the-art algorithms on COCO (Comparing Continuous Optimizers) framework. The result verifies that our proposed population-based homeostasis difference vector strategy outperform most of the state-of-the-art DE variants.

Cost effective and reliable scheduling of tasks on real-time systems has always been a challenge and with multiprocessor platforms penetrating into the real-time systems environment, the job of efficient scheduling has become even more problematic. Over the years several techniques on the lines of Rate Monotonic Scheduling algorithm have been suggested in the literature to optimize the scheduling process on multicore systems. This paper presents the comparison of three such optimal techniques called Compatibility Aware Task Partition (CATP), Group-wise Compatibility Aware Task Partition (GCATP) and Rate Monotonic Least Splitting (RMLS) algorithms in an attempt to identify the most efficient approach of scheduling. The efficiency of the algorithms has been evaluated in terms of CPU utilization, Throughput, Average Turnaround time, Acceptance Ratio, Average Waiting time and Deadline Miss Ratio. The results of the comparative analysis have shown RMLS to be more effective then CATP and GCATP. Therefore, on this basis, RMLS algorithm has been chosen to be worked upon in future for further optimization to guarantee even higher task scheduling reliability.

Differential evolution is a nature-inspired optimization technique. It has achieved best solutions on large area of test suits. DE algorithm is efficient in programming and it has broad applicability in engineering. This paper presents modified mutation vector generation strategy of basic DE for solving stagnation problem. A new variant of differential evolution that is DE_New has been proposed and the performance of DE_New is tested on Comparing Continuous Optimisers (COCO) framework composed of 24 benchmark functions and found DE_New has better exploration capability inside the given search space in comparison to GA, DE-PSO, DE-AUTO on Black-Box Optimization Benchmarking (BBOB) 2015 devised by COCO.

Scientific visualization is the research era for vision measurement and formation of object as per the higher interaction. Various television shows, movies, etc., are designed to improve the better visualization by using key frames, frame rate, layering and compute their vision perspective in real-time systems. Most of the movies are using visualization for better attraction to the users and increase their usability for the rating as well as cost. I appreciate the previous survey and find out that the eye visualization and photo metrics are a necessity of people to show various pictures. Much of the research is done in this field and much more remains to be explored. Domains of interactive visualization are focused and the problems in derived parameters of visualization are found out. Novel parameters have been introduced to improve the quality of interactive visualization for better results.

The OCR generation systems are most sophisticated active field and interesting conversional discovery for digitalization of handwritten and typed imprecise data into machine detectable characters. The fuzzy logic system processes the data with help of primary-based bunch set of knowledge. Fuzzy logic closed-loop system having very good successful rate of frame work for decision-based functions, can derive the fuzzy rules to build the decision-making procedure and detect the letters human to system to human. Artificial neural networks are compatible and the best area to solve the pattern cum text recognition tasks. The innovative combinational based characteristic of neural fuzzy-based closed-loop hybrid system proposing a five-layered approach with technical ideas, solutions solve the critical problems in the field of character, face, symbol recognition procedure, and estimating the density ratio. Recognition of single text, numbers is easy than the recognition of mixed connective conjunct consonants. Because of their variations, various handwritten pen-stroke pulses, tuning the initial and end position of each conjunct consonant, some consonants are connected and mixed with their left-cum right-side placed conjuncts, numerals, and symbols. Many languages such as Arabic, Hindi, Urdu, Telugu, and Tamil represent syllabic, symbol scripted form, and most of words formed with the mixed conjuncts, mixed cum touched consonants, mixed conjuncts with numerals in their representation. This research approach has proposed the five-layered neural fuzzy closed-loop hybrid system with compound Bayesian decision-making process holding good outcome for classification cum identification of mixed connective conjunct consonants with their numerals. The recognition process can start with categorizing total text into two forms; normal conjunct consonants and mixed connective conjunct consonants. The permutation futures of five-layered neural fuzzy closed-loop hybrid system represent inputs as neurons, convert it into fuzzy set inputs, and then apply the fuzzification process with desirable fuzzy knowledge based rules to produce the required output through responses. The compound Bayesian decision-making process is used to perform the operations of probability of sum to unity to reduce the recognition problems in the mixed connective conjunct consonants.

Viciousness against women has become an alarming topic and a serious concern for the government of India in recent years. Pending cases related to domestic violence in different courts need to be resolved faster to give proper justice to the victims and their families. In this paper a fuzzy logic-based legal expert system, ‘UMEED’, is proposed that emulates the decision-making ability of a legal expert, to address different legal issues regarding domestic violence. The system is proposed in such a way that it produces the expert opinion on this subject within the legal bounds. Being a rule-based system it acts as a legal advisor for the lawyers and ensures faster delivery of the legal decisions, and hence reduces overall decision-making time and effort. The model is tested on few historical cases regarding domestic violence and its performance proved to be satisfactory when it is compared with the actual legal decisions.

Defect is an important issue in medical image; we have investigated the defects in medical images. The missing data in an image is computed through error detection mechanism. Defect is creating a major problem in the medical science. When a patient with disease has any internal infections like cancer, kidney stone and external infections like fungal infection, skin disease, etc., patient follows the doctor’s advice. Doctor checks his patient and his disease and he suggests many types of tests like X-ray. Theses tests are done by patients, but some defects are caused in the machine internal or external, which makes the result improper, hence the problem cannot be identified by the doctorclearly. In this paper calculate the percentage of defected image with healthy image and comparison of healthy image.

Association rule analysis, as one of the significant means of data mining, plays an important role in discovering the implicit knowledge in massive transaction data. Aiming at the inherent defects of the classic Apriori algorithm, this paper proposes IAPS (Improved Apriori with Prejudging and Screening) algorithm. IAPS algorithm adds a prejudging and screening procedure on the basis of the self-join and pruning progress in Apriori algorithm which can reduce and optimize the k-frequent item sets using prior probability. IAPS algorithm simplifies the operation process of mining frequent item sets. Experimental results show that the improved algorithm can effectively reduce the number of scanning databases and reduce the running time of the algorithm.

Automatic localization of neurons is the foundation of tracing and reconstructing the neuronal connections from the brain image stacks. With rapid development of fluorescence labeling and imaging at submicron resolution, a huge amount of data were generated, making it challenging to efficiently locate neurons from massive multidimensional images. In this manuscript, we present the implementation of an efficient parallel neuronal localization algorithm that is based on NeuroGPS. We split the image stack with a space overlapping scheme to eliminate the communication overhead among computing nodes. On this basis, we develop a hybrid parallel automated neuronal localization algorithm. We evaluate this implementation on the TianHe-2 supercomputer. The preliminary results on a terabyte-sized image stack indicate that it is capable of handle large data sets and obtains good scalability and computing performance.

With the development of optical fiber communication based on Synchronous Digital Hierarchy (SDH), network communication and network data processing become increasingly prominent, which brings serious challenges to network management. Cross-connect technology plays an important role in network management. This paper proposes the scheme of cross-connect system which is used for decreasing the number of routes and solving the problem about network management. Both high-order (VC-4) and low-order (VC-12) cross-connect system are designed, from the aspect of hardware and software. Test results show that the proposed scheme can meet the demand of cross-connect for large-capacity and high-speed SDH signal.

CAN bus and Modbus protocol is commonly used in intelligent building designed. This paper analyzes the different characteristics between them. Based on the analysis, this article proposes an overall concept design of intelligent evacuation and light system and power management solution, and further optimizes the communication topology and software design. The experience in intelligent building designed shows that the system is reliable and meets the design requirements.

The past 40 years since the introduction of enzyme field-effect transistor (ENFET) in 1976 has been invaluable towards the development of biological sensors. Many devices came up with its own merits and demerits which made this area of research very popular worldwide. When the biological materials such as living organisms, cells, enzymes, DNA, etc., were combined with ISFET, BioFETs came up. By detailed study of the BioFETs one finds that most of the devices were formed of Si-based ISFETs. Though these devices have many advantages but when it came to sub 22 nm range, scaling problems arose which led to power dissipation, leakage, short channel effects, etc. To overcome these problems researchers opted for the use of carbon nanotubes (CNT) as channel material which gave better scalability, reduced power dissipation, better control over channel formation, etc. The complex fabrication process of the traditional Si-based devices was also simplified by introduction of junctionless CNTFETs. This paper puts forward a study of advances and developments of various BioFETs starting with ENFET and continuing with junctionless CNTFET till date.

A search engine plays a pivotal role in finding information for the purpose of education, business, and entertainment. A users’ quest for any topic or browsing any website starts from a search engine, revolves around the search engine and in most of the cases ends with their preferred choice of search engine. The dependency of users upon search engine for their professional, personal, academic tasks appears to be ever increasing. This research work aims to determine users’ dependency on search engine. The authors present a method to analyze and evaluate users’ dependency on search engines based on an experiment which was conducted on working professionals employed in various domains like software companies, law firms, banks, educational institutes, government, etc.

The present study is focusing on the characteristics of knowledge harvesting continuity management, and knowledge management. This begins from reviewing the previous research papers on knowledge management and information systems following with some recommendations to an ideal knowledge management system. This paper worked on an organized analysis of 32 empirical refereed articles on knowledge harvesting, continuity management, and knowledge management. Findings of the study state that an organization must integrate its knowledge management system, knowledge harvesting, and management functioning to decrease the loss of knowledge at the time of knowledge worker leaving the organization.

There is always an increasing demand for the development of new soft computing technologies for medical diagnosis in regular clinical use. With the advent of soft computing technologies, the use of intelligent methods and algorithms provides a viable alternative for vague, uncertain and complex real life problems such as diagnosis of diseases, for which mathematical model is not available. In this work, a hybrid artificial intelligence system namely Adaptive Neuro-Fuzzy Inference System (ANFIS) based model is developed for early diagnosis of dengue disease. Dengue fever, caused by the dengue virus is an infectious tropical disease. Dengue disease has been considered as a fatal disease and delay in diagnosis may increase its severity as well as life risk of the patients. The signs and symptoms of early dengue disease are nonspecific and overlap with the other infectious diseases. So, the principal aim of this study was to develop an acceptable diagnostic system for early diagnosis of dengue disease.

The System Engineering Methods (SEM) can consider as standardization for the development of information systems often demands a methodical approach to knowing which steps have to be taken in which order and at which time in the development process. These methods usually designed for delimited parts of software development life cycle, leaving other phases of SDLC in an ad hoc environment. This results in a need to extend SEMethods and empowered them with the potential to support other phases of software development as well. The paper addresses this issue and shows how popular agile method Feature-Driven Development (FDD) can be extended to satisfy case organizational requirements.

Everybody is now addicted to the online social media. Social media sites have been used by millions of people globally. Each individual expresses his thoughts, daily life events, and opinions on social media. The individual’s expressions on social media are mostly in the text form. The text contains sentiments, opinions, attitudes and emotions of the individuals, which are largely related to the happiness in the personal life of individuals. Extensive usage of social media affects the happiness, which can be either on the positive or negative level. Happiness level is normally measured by self-report and often been indirectly characterized by more readily quantifiable economic indicators such as gross development product (GDP) or genuine progress indicator (GPI). However, the growing importance of linguistic text analysis of social media gives a direction to predict the happiness of individuals and is termed as subjective well-being (SWB). SWB is the scientific term used to describe happiness and quality of life of individuals. It includes emotional reactions and cognitive judgments and is of great use to public policy-makers as well as economic, sociological, and psychological research. The richness and availability of social media make it an ideal platform to conduct psychological research in the topic SWB. In this paper, at last, the evidence of the importance of the social media analytics has been provided followed by identification of major factors involved in SWB. Further, the effects of social media usage on the SWB of individuals have been elaborated.

This paper proposes a novel chattering free, finite-time convergent, robust high order super-twisting sliding mode controller for trajectory tracking of a robotic manipulator in presence of unknown structured uncertainties, parametric uncertainties and time varying external disturbances. The control method is designed using homogeneous sliding manifold and super-twisting sliding mode control (STC). Next, unmeasured states are estimated by a robust exact differentiator. The stability of the proposed controller is analyzed by Lyapunov stability theory and its efficacy is examined by performing simulations on 2-DoF planar robot manipulator system in presence of inertial uncertainty and external disturbances. The proposed controller judiciously eliminates the chattering and successfully overcomes the effect of external disturbances and inertia uncertainty.

Detection of objects for relocation in a video is a vital as well as initial step for many computer vision-based applications like moving object extraction, video surveillance, pattern classification, etc. The traditional methods used for detection of foreground objects include background subtraction, optical flow and frame differencing techniques. These methods are found to be advantageous only if the extraction of the moving object is precise and clearly visible that it is, the object must be of good quality. This paper emphasizes on the detection as well as the enhancement of the foreground objects. The proposed method uses the amalgam of two traditional techniques background subtraction and motion vector-based optical flow method along with morphological operators to extricate the nonstationary objects from the videos followed by the enhancement of the extracted object to be of better quality in terms of visibility. The proposed algorithm is executed over the videos having frame dimension of 640 × 360 along with the frame rate of 30 frames/second using MATLAB R2013.