Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 1

Java server pages (JSP) and Hypertext Preprocessor (PHP) are the most common scripting language which is used for web designing. Both are used with Hyper Text Markup Language (HTML) and Cascading Style Sheets (CSS) to make the website better look and feel. The websites make the communication easier in the real time scenario. So the need of security comes into picture in case of data sending and receiving. In this paper, we have applied RC6 encryption technique for securing the web pages for sending and receiving. For this we are using JS, HTML and CSS combination with the Apache Tomcat Server environment. We have also detected the attacked file if there is any attack will happened and compare eavesdrop time (ET) along with the alert time (AT).

This paper suggests mapping of application on course grain reconfigurable architecture. The Data flow graph (DFG) in form of Op Codes of an application is stored in Memory i.e. Configuration memory. The CGRA reconfigure itself according to DFG and does parallel multi-processing.

In this paper, a picture analysis technique by providing authentication to photographs is planned. In our approach, invisible watermark bits were taken in circular regular sequence in exceedingly quad-octagon patterns. Quad-octagon patterns are generating by considering thirty-two bits in at random continuation eight bit planes. In every bit plane, a regular equal quad image bits were extracted and interlinked with its beside the bit plane, quad image bits circularly, forming completely thirty-two bits. These circular pattern bits were entered stepwise in an exceedingly boxlike manner in embedded image bits. Many quad-octagon patterns were addressed in our planned technique. Recovered pictures from our technique shown higher survival technique beneath numerous attacks.

The concept behind Semantic Web of Things (SWoT) is to extend the IoT (Internet of Things) architectures and to provide advanced resource management and discovery. It also uses integration of knowledge representation and reasoning techniques which are basically devised from the Semantic Web. The combining of two technologies aim towards the association of semantic annotations to real world objects. This paper discusses such three frameworks which are SWoT framework based on Ubiquitous Knowledge Base, CoAP based framework and smart gateway framework that have been developed and proposed for the extended IoT (SWoT) along with the basic challenges faced in the IoT.

Mobile ad hoc networks (MANETs) are wireless, infrastructure-less and multi-hop networks consisting of mobile nodes. All the aspects of network initialization, operation and maintenance are performed by the host nodes. Host nodes also act as routers to send the information to other nodes in the network. Most of the research in ad hoc networks focused on routing, but another important issue in network layer is addressing. All the nodes participating in a communication, needs unique address. In literature, several addressing schemes have been proposed and each one has its own strengths and weaknesses. In this paper, performance analysis of variable length addressing scheme in terms of communication cost and latency is done by considering DSR, DSDV and ZRP routing protocols. The simulation results show that our addressing scheme works effectively and gives consistent results with the routing protocols considered.

Social networking sites are gaining popularity among Internet users. As users are enjoying this new style of networking, the privacy concerns are also attracting public attention due to privacy breaches in social networking sites. We propose a framework that protects user privacy on a social networking site by shielding a user’s personal post or messages, other service providers or third parties who are not explicitly authorized by the user to view the content. The architecture maintains the usability of sites services and stores sensitive information in encrypted form on a separate server. Our result shows that the proposed framework successfully conceals a user’s personal information, while allowing the user and his friends to explore Social networking site services as usual.

With wireless devices gaining greater prevalence, there is a growing need for energy conservation for these devices. We propose a routing algorithm that reduces energy consumption at these mobile devices by modifying Optimized Link State Routing Protocol (OLSR). The protocol we propose is energy aware and reduces traffic to those nodes in the network that are low on battery life by using a modified Dijkstra’s algorithm.

The RCM algorithm may lead to undesirable solutions in practice because the points close to data points being assigned are neglected which led to the development of the decision theoretic rough set (DTRS) model and the decision theoretic rough C-means algorithm (DTRCM). It was recently improved further with the introduction of decision theoretic rough fuzzy C-means (DTRFCM). Here, we present a further improved algorithm called the decision theoretic intuitionistic fuzzy rough C-means (DTRIFCM) and provide a comparative performance analysis of DTRCM, DTRFCM and DTRIFCM through experiments and efficiency measuring indices DB, D and Acc. According to DB and D indexes DTRIFCM is better than the other two, where as far as the accuracy is concerned DTRFCM is better. We have chosen the data sets Iris, Wine, WDBC and Glass from UCI repository as input for the experimental purpose.

In this paper, a modified four-legged element frequency selective surface (FSS) with wide and dual band stop behaviour in two wireless local area network frequencies of 2.4 and 5.8 GHz have been proposed. The proposed design possesses 1050 and 1200 MHz bandwidths with insertion loss −41.45 and −35 dB around the centre operating frequencies 2.4 and 5.8 GHz, respectively. To obtain best characteristics, FSS unit cell has been designed using FR-4 substrate material having dielectric constant 4 and dissipation factor of 0.025. The structure exhibits a dual band from 1.8 to 2.85 GHz for GSM 1800/1900 MHz and Bluetooth and another band is from 5.25 to 6.45 GHz for WLAN for wideband application. It found applications into a number of vertical markets such as retail, warehousing, healthcare, manufacturing, retail and academic.

Power optimization in routing Protocol is an important factor in a mobile ad hoc network. In all of the existing routing protocols when a packet travels from source to destination, then source and all the intermediate nodes in the path assign a constant energy to the packet irrespective of the distance packet has to travel and hence there is wastage of power. In this paper, a new power control mechanism has been proposed which find the distance between two nodes, calculate the power required to cover the distance and assign only that amount of power to the packet. This mechanism works at physical layer, so this can be used with any of the existing routing protocols. After deployment of the scenario in a simulator, simulation result will show that energy consumption by using the proposed technique is less than the traditional mechanism used in the protocols.

This paper proposed a compact planar ultra wideband (UWB) antenna with single band-notched behavior. An inverted pie-shaped slot is etched in a circular patch for getting the notch behavior. The presented antenna is successfully designed, simulated and measured showing single notch nature in UWB band as this band interfere in it. This antenna is designed on FR-4 substrate with dielectric constant 4.4 and thickness of 1.6 mm. The antenna parameters such as return loss, VSWR, gain and directivity are simulated and optimized using commercial computer simulation technology microwave studio (CST MWS). The designed antenna is showing band notch property at 7 GHz (6.4–7.5 GHz for satellite communication) as this band interfere with the UWB. The main advantage of this antenna is that the designed structure is very simple and the cost for making this antenna is also low.

Vehicular Adhoc Networks (VANETs) are now famous for increasing safety on the road as well as providing value added services (i.e. entertainment and internet access). The intention to write this paper is to provide information for avoiding accidents using vehicle-to-vehicle (V2V) communication. We have provided a pioneering architecture for broadcasting the messages which contains information such as position of the vehicle with the help of latitude and longitude and the information regarding road conditioning as well as parking space. We have also defined some new parameters to avoid injuries, provide convenience to park vehicles and also provide infotainment applications for the drivers and passengers. With this paper, we propose a solution for ultimate usage of bandwidth and provide minimum delay in various situations in VANETs. We have developed a new protocol to take care of the quick and reliable delivery of information among all the users.

As the health care industry grows at a rapid pace, it is generating large volumes of data that needs to be stored, analyzed and acted upon by various organizations. India is a very diverse country with a large population that is having increased access to centrally managed healthcare systems. This is generating huge volumes of data, whose systematic storage and analysis for organized decision-making will be critical to the success of the industry in the coming years. This data can be classified into the realm of ‘big data’ for obvious reasons and appropriate technology will be required to handle it effectively. In this paper, we propose a model for analyzing historical healthcare data. Both temporal and spatial parameters have been used in this model to allow the healthcare professional different views into the information and thereby make informed judgments. Common constraints like quality, authenticity and security of the big data have also been addressed for complete effectiveness.

Automatic focusing has become essential part of imaging system as the image quality matters. There have been many researches carried out for autofocusing. Autofocusing gives the benefit of high-contrast image capturing even while the scene or imaging system is moving. The mechanism adjusts focal point of imaging system so that it gives the high contrast image. Need of different autofocusing technique arises as a single autofocusing mechanism cannot serve all the application. Autofocusing is divided into (i) Active and (ii) Passive autofocusing. Active autofocusing is good choice for SLRs, but it cannot be used when using an independent light source is not possible. Passive autofocusing is the best solution in such cases which captures the scene and analyzes to determine focus and is fractioned into two sub categories (i) Contrast (ii) Phase. This paper concludes the different autofocusing techniques and its applications.

In the e-technology world, the growth of online system usage is very high. Hence it is also necessary that security and privacy of users must secure. In the telecare medical system, a remote patient and the medical server requires to authenticate each other over an insecure channel. In 2014, Li et al. proposed an enhanced scheme for telecare medicine system and claimed that it’s secure against various attacks. However, in this paper, we identified that Li et al.’s scheme cannot resist against Temporary Information, Forgery, Stolen Smart Card attack. Therefore, in this paper proposed scheme to be secure against the mentioned attacks and also it requires less number of operations. Thus we have achieved more security with less computation overhead.

The Web is a vast pool of resources which comprises of a lot of web pages covering all aspects of life. Understanding a user’s interests is one of the major research areas towards understanding the web today. Identifying the relevance of the surfed web pages for the user is a tedious job. Many systems and approaches have been proposed in literature, to try and get information about the user’s interests by user profiling. This paper proposes an improvement in determining the relevance of the webpage to the user, which is an extension to the relevance formula that was proposed earlier. The current work aims to create user profiles automatically and implicitly depending on the various web pages a user browses over a period of time and the user’s interaction with them. This automatically generated user profile assigns weights to web pages proportional to the user interactions on the webpage and thus indicates relevancy of web pages to the user based on these weights.

Habitual typing rhythm or keystroke dynamics is a behavioural biometric characteristic in Biometric Science relates the issue of human identification/ authentication. In 30 years of on-going research, many keystroke dynamics databases have been created on various pattern of strings (“greyc laboratory”, “.tie5Roanl”, “the brown fox”, …) taking various combination of keystroke features (flight time, dwell time) and many features-mining classification algorithms have been proposed. Many have obtained impressive results. But in evaluation process, a classifier’s average Equal Error Rates (EERs) are widely varied from 0 to 37 % on different datasets ignoring typographical errors. The question may arise, which classifier is best on which pattern of keystroke databases? To get the answer, we have started our experiment and created our own five rhythmic keystroke databases on different daily used common pattern of strings (“kolkata123”, “facebook”, “gmail.com”, “yahoo.com”, “123456”) and executed various classification algorithms in R statistical programming language, so, we can compare the performance of all the classification algorithms soundly on different datasets. We have executed 22 different classification algorithms on collected data considering various keystroke features separately. In the observation, obtained best average EER of the classifier Lorentzian is 1.86 %, where 2.33 % for Outlier Count, 3.69 % for Canberra, 5.3 % for Naïve Baysian and 8.87 % for Scaled Manhattan by taking all five patterns of strings and all combination of features in the consideration. So the adaptation of keystroke dynamics technique in any existing system increases the security level up to 98.14 %.

The modern portable devices demand ultra-low power consumption due to the limited battery size. With each new generation, the need of more transistors on the same chip is increasing due to the increased functionality. The leakage causes static power consumption is exceeding the dynamic power in the sub-nanometer designs. Therefore, effective leakage reduction technique is required to minimize the power consumption. In this paper, we have explored the existing leakage reduction techniques and propose a new leakage reduction technique that provides significant reduction in the leakage without significant area/power overhead. The simulation results on Synopsys HSPIC shows that that proposed leakage reduction technique provides 10 % reduction in leakage over the existing leakage reduction technique in the literature.

Different Design Techniques for implementing the Analog Baseband filter are presented in this paper. The baseband filter is classified on the basis of approximation techniques, type of component, technology and type of signal used. These techniques are compared on the basis of CMOS technology, minimum cutoff frequency, filter order, power consumption, supply voltage and integrating capacitance. The filters compared in this paper are active continuous time analog filters.

The Intrusion detection system is a network security application which detects anomalies and attackers. Therefore, there is a need of devising and developing a robust and reliable intrusion detection system. Different techniques of machine learning have been used to implement intrusion detection systems. Recently, ensemble of different classifiers is widely used to implement it. In ensemble method, the appropriate selection of base classifiers is a very important process. In this paper, the issues of base classifiers selection are discussed. The main goal of this experimental work is to find out the appropriate base classifiers for ensemble classifier. The best set of base classifier and the best combination rules are identified to build ensemble classifier. A new architecture, DAREnsemble, have proposed for intrusion detection system that consists of unstable base classifiers. DAREnsemble is formulated by combining the advantages of rule learners and decision trees. The performance of the proposed ensemble based classifier for intrusion detection system has evaluated in terms of false positives, root mean squared error and classification accuracy. The experimental results show that the proposed ensemble classifier for intrusion detection system exhibits lowest false positive rate with higher classification accuracy at the expense of model building time and increased complexity.

A single firewall becomes traffic bottleneck depending on network expansion, number of connections and throughput required. The present paper describes a method of interconnecting the firewalls at multiple geographic locations through Open source network. The placement of firewall and routing device along with protocols will form the proposed optimized system to improve overall network security and scalability. To evaluate the performance of the approach, authors carried out performance testing under laboratory setup. OpenBSD PF firewalls processed 360 kpps of traffic with 80 % CPU load. Further, design for site requiring higher capacity is proposed and forwarding performance of firewalls is tested with different packet sizes under laboratory traffic by changing maximum transmission unit (MTU). Dynamic extension of design is proposed to connect to other networks using dynamically routed interconnections.

This paper presents a simple, yet accurate method for fault location in HVDC transmission lines using Artificial Neural Networks. The ±500 kV HVDC system has been modelled using PSCAD/EMTDC and further analysis has been done using MATLAB. Single-end AC RMS voltage and DC voltage and current have been used to identify the location of the fault. This method is relatively simple because the standard deviation of the fault data alone gives acceptably accurate results while using ANN.

This paper includes the various past and present researches which involve the study of the cellular radiations on the human cells. The effect of the weak electromagnetic fields from various sources like cell phones, cordless phones and Wi-Fi can be hazardous over long term exposures and can cause various health problems. The technique of superimposition of incoherent noise field can prove advantageous in suppressing the biological effects of radiofrequency (RF) electromagnetic fields.

Software developer’s pattern of activities, level of understanding of the source code and work practices are important factors that impact the defects introduced in software during development and its post-release quality. In very recent previous research (Lee et al. in Micro interaction metrics for defect prediction, pp 311–321, 2011), process metrics and micro-interaction metrics (Lee et al. in Micro interaction metrics for defect prediction, pp 311–321, 2011) that capture developer’s interaction with the source code have been shown to be influential on software defects introduced during development. Evaluation and selection of suitable classifiers in an unbiased manner is another conspicuous research issue in metrics based software defect prediction This study investigates software defect prediction models where micro-interactions metrics (Lee et al. in Micro interaction metrics for defect prediction, pp 311–321, 2011) are used as predictors for ten Machine Leaning (ML), fifteen Evolutionary Computation (EC) and eight Artificial Immune recognition system (AIRS) classifiers to predict defective files of three sub-projects of Java project Eclipse. They are -etc, mylyn and team. While no single best classifier could be obtained with respect to various accuracy measures on all datasets, we recommend a list of learning classifiers with respect to different goals of software defect prediction (SDP). For overall better quality of classification of defective and non-defective files, measured by F-measure, ensemble methods-Random Forests, Rotation Forests, a decision tree classifier J48 and UCS an evolutionary learning classifier system are recommended. For risk-averse and mission critical software projects defect prediction, we recommend logistic, J48, UCS and Immunos-1, an artificial immune recognition system classifier. For minimizing testing of non-defective files, we recommend Random Forests, Rotation Forests, MPLCS (Memetic Pittsburgh Learning Classifier) and Generational Genetic Algorithm (GGA) classifier.

In the field of agriculture, accurate and timely forecast of soil moisture has great influence on crop growth and cultivation. The soil water status of an irrigated crop needs to be monitored regularly to make effective irrigation decisions. The challenge is to develop a feasible method to collect and examine large volume of soil moisture data on continuous base. The developments in wireless technologies have made practical deployment of reliable sensor nodes possible for various agricultural monitoring operations, which facilitate to meet the goal. The historical status of the soil moisture needs to be known advance in order to predict future readings. This work introduces a Soil Moisture Forecasting Ensemble Model (SMFEM) by combining the features of various machine learning approaches. The experimental results confirm that the prediction accuracy of the proposed approach is better when compared to the individual classifiers.

Now-a-days power management plays a vital role in reducing the consumption and efficient utilization of the resource. In traditional system, manual operation of electrical devices in university gets unnoticed, that leads to maximum wastage of power i.e., with different device operating even when the classroom is abandoned. These extended hours of operation leads to maximum power wastage. In order to overcome this problem, we have designed a “smart and intelligent power saving system for Indian Universities”, where every classroom is equipped with passive infrared sensor (PIR) which responds to occupancy, and corresponding devices are switched ON/OFF automatically. The entire system is monitored and controlled by the central base station.

Human brain mosaics the split images of a very large object which have been captured through eyes and each eye functions as a camera lens. But, it is not possible to cover very large area with the help of single eye than a pair of eyes. Similarly, multi view registration is essential because it may not be possible to capture a large object with a given camera in a single exposure. The field of view (FOV) of the commercial camera is much smaller than that of humans. Multiview image registration is an extremely challenging problem because of large degree of variability of the input data such as the images that are to be registered may contain visual information belonging to very different domains and can undergo many geometric distortions such as scaling, rotations, projective transformations, non rigid perturbations of the scene structure, temporal variations, and photometric changes due to different acquisition modalities and lighting conditions [1]. In proposed algorithm, transformation parameters are estimated using affine warp for corresponding matching points for the images to be registered. Here LM(Levenberg-Marquardt) optimization algorithm is used to optimize transformation matrix, which gives the minimum of a multivariate function that is specified as the summation of squares of nonlinear and real-valued functions, so some error can be tolerated in selection for control points. Final registered image is formed using backward mapping for sampling and distance based image blending. This proposed algorithm is compared with Euclidean warp algorithm and limitation of Euclidean warp is overcome and results are compared.

The new era begin to understand these beyond semiconductor CMOS devices, and circuit capabilities, to test and analysis the performance of CNTFET based structures compared to conventional silicon processes. These new devices can replace silicon in logic, analog, memory and data converters applications. The most close to the design of CNTFET based include the carbon-based options of graphene and carbon nanotube technologies, and also compound semiconductor-based Carbon nanotubes FET (CNTFETs). The study of high performance nine transistor static random access memory arrays and its optimization in 9-nm CNTFET technology are presented and comparative study done with the conventional six-transistors (6T) and previously issued eight-transistor (8T) Static RAM cell. The 9T CNTFET Static RAM cell provides same read speed comparatively 6T and 8T but has read data stability is enhanced by 1.56×. The proposed new memory cell consumes 53.40–19.17 % low leakage power comparatively the 6T and 8T Static RAM cell, respectively.

WSNs has verity of applications as it has feature of collecting sensed data from different sources and redirect it to the sink. Through this paper authors have made an effort to find the best data gathering protocol among latest two protocols EEDG and IAR by comparing performance metrics of both in the perspective of improvising existing protocol in future. Both protocols performance is compared by taking statistics based on performance metrics. Based on this Authors have came up with the conclusion that EEDG is efficient than IAR protocol but at the same time this protocol has few drawbacks like Redundancy removal and Idle listening of sensor nodes issues are not taken care. At the same time Mobile Sink’s does not contain feature like pause/wait state at node when node has not sensed any data. In future work authors have mentioned their focus towards minimizing mentioned drawbacks so that authors may come up with the new energy efficient protocol whose moto will be on improving life time of the WSN.

Irrigation in agricultural lands plays a crucial role in water and soil conservation. Real-time prediction of soil moisture content using wireless sensor network (WSN) based soil and environmental parameters sensing may provide an efficient platform to meet the irrigation requirement of agriculture land. In this research article, we have proposed Resilient Back-propagation optimization technique to train neural network pattern classification algorithm for the prediction of soil moisture content. Finally, the predicted soil moisture content is used by fuzzy weather model for generating adequate suggestions regarding irrigation requirement. The fuzzy model is developed by considering different weather parameters like sun light intensity, wind speed, environment humidity and environment temperature. Different weather conditions like cloudy situation, low pressure, cyclone and storm conditions are simulated in the fuzzy model. The soil moisture content prediction algorithm is tested with soil moisture content in each 1 h advance by considering eleven different soil and environmental parameters collected during a field test. The prediction errors are analysed using MSE (Mean Square Error), RMSE (Root Mean Square Error), and R-squared error.

A wireless sensor network (WSN) is typically comprised of a large number of nodes spread over a large area sensors. Sensor nodes are small, low battery, limited storage and processing power. Each node is usually equipped with a wireless radio transceiver, a small micro-controller, a source of power sensors and multiple types such as temperature, light, pressure, sound, vibration nodes etc. These ways to communicate directly with network among themselves or through other nodes. The first objective is to collect data on WSN sensor nodes. If the data on the web passes through the one or more sensor nodes, power consumption in the first node differs from the second node. Therefore, the loaded nodes too quickly lose battery power and shut down. Imagine this situation happens to a group of neighboring nodes and fall of premature death, leading energy gap in the network. Energy gap affects other nodes in the network nodes that share the power load port has a load to the other nodes in the network. As a result, life of a network will end soon. Our research is aimed at maximizing the coverage area, increasing the detection range of remaining sensor nodes when a node fall in premature death, to prevent the formation of holes in the supply network of wireless sensors.

“Mahoshadha” the Sinhala Question Answering Systems aims at retrieving precise information from a large Sinhala tagged corpus. This paper describes a novel architecture for a Question Answering System which summarizes a tagged corpus and uses the summarization to generate the answers for a query. The summarized corpuses are categorized according to a set of topics enabling fast search for information. K-Nearest Neighbor Algorithms is used in order to cluster the summarized corpuses. The query will be tagged, the tagged query will be used to get more accurate results. Through the tagged query the question will be identified clearly with the category of the query. Support Vector Machine is used in order to both automate the summarization and question understanding. This will enable “Mahoshadha” to answer any type of query as well as summarize any type of Sinhala corpus. This enables the Question Answering System to be more useable through many applications.

The Mobile Ad hoc network is the most growing field as it is dynamic in nature and requires no central authority to manage it. In this paper we have worked on routing layer of MANET. We have worked upon AODV (Ad hoc on-demand distance vector) routing protocol. In this paper we have developed a very vulnerable network and a number of attackers and also modified AODV with our trust-based approach. To the best of our knowledge no one has implemented strong attackers and prevented them. In our approach each node monitors its neighbours’ activities and develops a trust table which in turn is very difficult to maintain when there is no cooperation between nodes. Based on the trust table it decides whether to eliminate the attacker. We have also optimized HELLO packet of AODV in such a way that it propagates information about malicious node among other available nodes and isolates the attacker from the entire network. At the end we present the results which clearly show that proposed protocols perform better than AODV in vulnerable situation. To evaluate the performance, we have carried out an extensive simulation study of AODV and modified AODV using NS2.35 with and without attacker showing significant improvement in Packet Delivery Fraction (PDF), Packet Drop Ratio (PDR), Average Throughput (AT), Normalized Routing Load (NRL) and E2E (Average End-2-End delay).

Finite battery power (energy) of the nodes in Mobile Adhoc Networks (MANETs) leads to frequent route failures if not handled properly by a routing protocol. Conditions become worst when a pre assumption is made that all the nodes of the network have equal battery power to communicate. Network scenarios are usually heterogeneous when they are used and realized in real life practical situations. Therefore, considering only homogeneous scenarios restrict the designer to benchmark the performance of a routing protocol to actual practical aspects. Existing protocols and network designs for MANETs not only consider homogeneous scenarios but they are also tested for small size of networks with few numbers of nodes. The throughput achieved by MANETs has been extended its applications to commercial acceptability also. Therefore, to render MANETs services in terms of internet or large number of users, existing protocols should be tested with both large size homogeneous and heterogeneous networks. Thus, scalability becomes another big issue of concern for the better acceptability of a routing protocol for large networks. These limitations make the task of routing very difficult in MANETs. Therefore, routing protocols especially in heterogeneous MANETs (H-MANETs) must be energy efficient and scalable. In this paper, the behavior of some well accepted communication protocols namely AODV, DSR and AOMDV is analyzed for H-MANETs. These protocols have been comprehensively analyzed considering different performance parameters (both traditional and energy related). This paper is aimed to judge the competence of considered protocols in an energy constrained environment with varying network size.

The conceptual ABC framework for requirements prioritization for software products development is analyzed mathematically adapting sets for each layer of the framework. Weights are associated with classes of each set and requirements priorities are determined based on contribution from membership in each set. A unique number sequencing scheme is proposed to visually interpret the priorities associated with requirements.

Programmed recognition of miniaturized scale aneurysms (MA) in shading retinal pictures is proposed in this paper. At present days acknowledgment of MA is a pivotal stride in conclusion and evaluating of diabetic retinopathy. Customarily directional cross segment profile acknowledges MA location on nearby most extreme pixels of pre-prepared retinal picture. Top acknowledgment is connected on every pixel, and an arrangement of traits like measurement, size(length), tallness and state of every pixel computed precisely and accordingly. However, cross-segment profile examination is not material for location of MA as for dimensional in retinal pictures. Customarily propose to create CPHC (Classification by Pattern-Based Hierarchical Clustering) a semi managed order calculation that uses example based group chain of importance as the immediate which means, of agreement. In any case, a few insufficiencies like the effort of emphasize removal and complicated are available in SIFT (Scale Invariant Function Transformation)-based ID. To take care of these problems, a novel preprocessing technique with CPHC in perspective of the Enhanced Round Gabor Convert suggested. After planning by the iterated spatial an isotropic sleek technique, the quality of the uninformative SIFT key concentrates is reduced considerably. Tried on the VARIA and eight duplicated retina data source collaborate rotate and climbing, designed technique provides appealing outcomes and reveals heartiness to radical changes and range changes.

Soft set is a new mathematical approach to solve the uncertainty problems. It is a tool which has the prospects of parameterization. Maji et al. defined intuitionistic fuzzy soft set (IFSS). However, using the approach of provided by Tripathy et al. (2015) we re-define IFSS and use it in deriving group decision making (GDM). An application is used for illustration of the process.

Recommendation system is a special type of information filtering system that attempts to present information/objects that are likely to the interest of user. Any organization, provides correct recommendation is necessary for maintain the trust of their customers. Collaborative filtering based algorithms are most widely used algorithms for recommendation system. However, recommender systems supported collaborative filtering are known to be extremely prone to attacks. Attackers will insert biased profile information or fake profile to have a big impact on the recommendations made. This paper provide survey on effect of shilling attack in recommendation systems, types of attack, knowledge required and existing shilling attack detection methods.

The study of digraphs provides a proving ground where mathematicians’ ability to bind together multiple disciplines of mathematics becomes evident. The new class of graph called Arithmetic graph was introduced on the basis of Number theory, particularly the Theory of Congruence. Graham and Spencer brought forth the idea of using quadratic residues to construct a tournament with p vertices where p ≡ 3 (mod 4) is a prime. These tournaments were appropriately named Paley digraphs in honor of the late Raymond Paley, who used quadratic residues 38 years earlier to construct Hadamard matrices. In this paper we prove that quadratic residue digraphs over a finite field called Paley tournament allows Edge product cordial labeling, K edge graceful labeling and H_n cordial labeling.

With the increase in the data storage and data acquisition technologies there is an increase in huge image database. Therefore we need to develop proper and accurate systems to manage this database. Here in this paper we focus on the transformation technique to search, browse and retrieve images from large database. Here we have discussed briefly about the CBIR technique for image retrieval using Discrete Cosine Transform for generating feature vector. We have researched on the different retrieval algorithms. The proposed work is experimented over 9000 images from MIRFLIKR Database (Huskies ACM International Conference on Multimedia Information Retrieval (MIR’08), 2008 [1]). We have focused on showing the difference between the precision and recall and also the time of different methods and its performance by querying an image from the database and a non-database image.

The present research work has been done to assess the groundwater quality and its drinking suitability by developing a Water Quality Index (WQI) for Nanded Tehsil, Maharashtra. The representative groundwater samples (50) were collected from Dug/Bore wells during pre monsoon 2012. By clutching Hydrochemical analysis and GIS based IDW technique were used to represent spatial variation of WQI in study area. The physicochemical parameters viz. pH, EC, TDS, TH, Ca2+, Mg2+, Na+, K+, CO₃−, HCO₃−, Cl−, NO₃−, SO₄−, PO₄− were determined to assess the groundwater quality and compared with BIS standards 2003. Water quality index used to classify water into four categories. Water quality index shows that 14 % samples are excellent, 84 % samples are good and 2 % Poor for drinking purpose. The GIS tools and statistical techniques used for spatial distribution and representation of water quality.

ART is a fixed parameter in AODV routing, which tells that how long route state information should be kept in the routing table. However, as per this simulation study, an optimal value of ART is the function of node’s mobility. Therefore, the presented research work attempts to analyse the impact of variation in Active Route Timeout (ART) value on the Quality of Service (QoS) metrics for different values of node’s mobility in AODV routing of the ad hoc network. This research work concludes that the lower values of ART perform better, especially at higher node’s mobility. Moreover, it is also concluded that the network performs better in terms of throughput at ART = 1 s and in terms of delay as well as jitter at ART = 0.5 s, which is less than its default QualNet ART value (i.e. 3 s).

Mobile Phone used not to be matter of luxury only, it has become a significant need for rapidly evolving fast track world. This paper proposes a spatial context recognition system in which certain types of human physical activities using accelerometer and gyroscope data generated by a mobile device focuses on reducing processing time. The benchmark Human Activity Recognition dataset is considered for this work is acquired from UCI Machine Learning Repository, which is available in public domain. Our experiment shows that Principal Component Analysis used for dimensionality reduction brings 70 principal components from 561 features of raw data while maintaining the most discriminative information. Multi Layer Perceptron Classifier was tested on principal components. We found that the Multi Layer Perceptron reaches an overall accuracy of 96.17 % with 70 principal components compared to 98.11 % with 561 features reducing time taken to build a model from 658.53 s to 128.00 s.

The technological advancements in the medical field generate a massive number of medical images and are saved in the database in order to make easy in the future. This paper presents a new approach for retrieving the similar medical images from a huge database for supporting computer aided diagnosing system to improve the quality of treatment. This approach is a two step process based on the concept of multi-scale orientation structure, firstly the region of the object is detected with the facilitation of segmentation method and secondly the texture patterns are extracted with the help of higher order steerable texture description. The related medical images are retrieved by computing similarities matching among the given queries of medical image feature vector and the consequent database image feature vector by means of Euclidian distance. The efficiency of the projected scheme is tested and exhibited with a variety of medical images. With the investigational outcome, it is understandable that the higher order oriented steerable features yields better results than the classical retrieval system.

Automatic text summarization provides a solution to the problem of information overload. Summarization technique preserves important information while reducing the original document. This paper focuses on enhancing Fuzzy text summarization technique by Genetic approach. The Genetic approach is used for optimizing the feature set given to fuzzy system. The optimization is achieved on feature set by natural evolution. The optimized input features are entrusted to fuzzy system which concentrates on the membership function and fuzzy rules. The analysis is performed on documents related to Earth, Nature, Forest and Metadata. The comparative study shows that using genetic approach would improve recall and F-measure.

The era of modern developments enhanced the technological advancements in all domains like medical, transportation, manufacturing, space and aviation, accounting, agriculture etc. The rule based and expert systems have proven their capabilities in all the well established and emerging domains. Manufacturing is one of the emerging fields, having major impact on global market. Rules and expert systems are mainly governed by knowledge, facts, empirical theorems etc. These systems are very useful to user for better prediction/approximation of the outputs. It also servers certain advantages like easy to operate, reduced human error, low and semi skilled person can handle etc. Here, a review has been done to converge and highlight the major applications of such systems for different manufacturing processes.

Reinforcement learning (RL) comes from the self-learning theory. RL can autonomously get optional results with the knowledge obtained from various conditions by interacting with dynamic environment. It allows machines and software agents to automatically determine the ideal behavior within a specific context, in order to maximize its performance. Neural network reinforcement learning is most popular algorithm. Advantage of using neural network is that it regulates RL more efficient in real life applications. In this paper, we firstly survey reinforcement learning theory and model. Then we present various main RL algorithms. Then we discuss different neural network RL algorithms. Finally we introduce some application of RL and outline some future research of RL with NN.

Metadata Harvestings is one of the prime research fields in information retrieval. Metadata is used to references information resources. Metadata play an significant role in describing and searching document. In early stages of metadata harvesting was manually. Later on automatic metadata harvesting techniques were invented; still they are human intensive since they require expert decision to identify relevant metadata also this is time consuming. Also automatic metadata harvesting techniques are developed but mostly works with structured format. We proposed a new approach to harvesting metadata from document using NLP. As NLP stands for Natural Language Processing work on natural language that human used in day today life.

Fast development of industrial robots and its utilization by the manufacturing industries for many different applications is a critical task for the selection of robots. As a consequence, the selection process of the robot becomes very much complicated for the potential users because they have an extensive set of parameters of the available robots. In this paper, gradient descent momentum optimization algorithm is used with backpropagation neural network prediction technique for the selection of industrial robots. Through this proposed technique maximum, ten parameters are directly considered as an input for the selection process of robot where as up to seven robot parameter data be used in the existing methods. The rank of the preferred industrial robot evaluates from the perfectly the best probable robot that specifies the most genuine benchmark of robot selection for the particular application using the proposed algorithm. Moreover, the performance of the algorithms for the robot selection is analyzed using Mean Square Error (MSE), R-squared error (RSE), and Root Mean Square Error (RMSE).

In today’s technology world one may have been attacked or witnessed cyber-attacks on their applications. Currently there are many systems that help you detect as well as prevent various kinds of attacks that your application may be vulnerable to. There is dire necessity to protect your Projects from these kinds of attacks. Using NSA tool, security can be implemented, one can detect and analyze if there is any attack taking place or there has been an attack. NSA helps in detecting all sorts of attacks ranging from databases to network. Cross-Site Scripting, SQL injection, URL rewriting, Buffer Overflow and Cross-Site Request Forgery are amongst the few that are found by NSA. Studies have also showed rapid rise in these attacks, it has become necessary to provide solution to protect the web applications against them. Use of firewalls along with NSA is one of the solutions to mitigate these attacks along with others.

Glaucoma is one of the second most important cause of blindness after cataracts. Detection of glaucoma is essential to prevent visual damage. In India, Glaucoma is the third leading cause of blindness. This paper discusses an algorithm developed for detection and diagnosis of glaucoma. The algorithm calculates the Cup to Disc Ratio (CDR) which is obtained through image processing of the retinal images obtained from stratus OCT from Sudhalkar Eye Hospital, Vadodara. Image processing has been carried out on 120 retinal images obtained from Stratus OCT out which 79 are glaucomatous eyes and 41 are normal eyes. OTSU histogram and watershed algorithm have been used for image segmentation. Image analysis has been carried out through image segmentation, which is the process of dividing an image into regions or object. Morphological operations have been performed for the enhancement of the images to extract optic cup and optic disc region from the eye. The accuracy of the algorithm is 94 %. MATLAB software has been used for image processing on the retinal images.

The paper presents various controllers for damping low frequency oscillations in a single-generator infinite-bus (SMIB) electrical power system. The intent of the Fuzzy Logic based UPFC controller systems are to dampout low frequency oscillations. UPFC controller based ahead amplitude modulation index of exciter m_E has been intended. System response with Damped-UPFC controller and PI-fuzzy logic based UPFC controllers are compared at various loading environment. Relevant models have been designed and simulated in Matlab/Simulink version R2013a. The hybrid PI-Fuzzy Logic based UPFC controller is developed by selecting suitable controller parameters based on the experience of the power system performance. This paper also presents the TLBO (Teacher Learner based Optimization) based PSS design. The simulation results of these models show that the TLBO based PSS modeled has an excellent capability in damping low frequency oscillations on power systems.

Cloud computing delivers digital services over the internet by using various applications which were carried out at distributed datacenters by computer systems. It provides protocol based high performance computing which permits shared storage and computation over long distances. This proposed work bridges the efficient computation and secure storage in cloud environment. Secure cloud storing includes receiving the data by cloud server for storage after applying security steps like authentication, encryption of data and allocation of storage space. In cloud computing, a secured computing infrastructure is provided to cloud user through computing request and commitment generation. Dynamic server stipulation by cuckoo algorithm is utilized after the completion of successful user access. The uncheatable computation, secure access and storage of proposed work achieve confidentiality. It improves the efficiency and manages concurrent users’ requests.

Cloud is an ironic solution of the ultimate globalization which is a bunch of dedicated servers, networking elements such as software and hardware networked with the internet. Cloud computing enables the user to utilize the applications, storage solutions, and resources, likewise it also authorizes the data access, data management, and connectivity. All the activities of the cloud infrastructure seem to be transparent to ensure it without any notion of the locale to its user. The rising technology aids to deliver on-demand web access speedily to the computing resources. Cloud user can access and release the computing resources with minimum management effort and least interaction with cloud service provider. A consolidated cloud service i.e., hybrid cloud renders a federation, bridge, secure encrypted connection, information placement decision between the public and private cloud and utilizes the services of the two clouds those are security, availability, and cost effectiveness. Though cloud computing services have bags of inherent benefits, there are likelihood risks in privacy and security heeding’s that should be thought over before collecting, processing, sharing or storing enterprise or individual’s data in the cloud. This survey paper explores the contemporary techniques and methods to furnish a trustworthy and foolproof cloud computing environment.

Abnormal Cardiac beat identification is a key process in the detection of heart ailments. This work proposes a technique for the detection of Bundle Branch Block (BBB) using Bat Algorithm (BA) technique in combination with Levenberg Marquardt Neural Network (LMNN) classifier. BBB is developed when there is a block along the electrical impulses travel to make heart to beat. The Bat algorithm can be effectively used to find changes in the ECG by identifying best features (optimized features). For the detection of normal and Bundle block beats, these Bat feature values are given as the input for the LMNN classifier.

Here in this paper we have proposed a framework for Bot detection and Botnet tracking. The proposed system uses a distributed network of Honeynets for capturing malware samples. The captured samples are processed by Machine learned model for their classification as bots or not-bots. We have used the Native API call sequences generated during the malware execution as feature set for the machine learned model. The samples identified as Bot are clustered based upon their network and system level features, each such cluster thus obtained represents a Botnet family. The Bot samples belonging to such clusters are executed regularly in the sandbox environment for the tracking of botnets.

In the present work, a module is developed to approximate the forces during deep drawing operation. It also predicts the tooling configurations i.e. blank size, punch and die diameters, profiles, number of draws etc. during each stage of operation. Deep drawing is metal forming process, normally used to produce several components like automotive body panel, household utensils etc. Generally, during the deep drawing process, it is required to measure forces for tooling design. Traditionally it is done using hit and miss method. It consumes time and increase indirect costs. Hence in the present study, a module has been made using rules and knowledge based system (if then rules) to estimate forces during deep drawing operation. The codes are famed using VB and interfaced with the AUTOCAD to generate the 2D drawing of the tooling for production. It will help small and medium scale industries to predict the forces before actual operation. Thus it is easier for them to build tools (punch, die, blank holder etc.) for different geometrical and material conditions.

Recent works in web services have employed finite state machines for solving different problems, like matchmaking of web services, modelling of web service composition and verification of web service composition. Annotated Deterministic Finite State Automata (ADFSA) is used for matchmaking of web services. ADFSA is the combination of deterministic finite state automata (DFA) with logical annotation of transitions in state. BPEL4WS is a high level programming language to express the execution behavior of web services but this language is Turing-complete. For matchmaking of web services, only a fragment of BPEL suffice which is equivalent to regular language. A complex web service is obtained from simpler web services where each simpler web service is modeled as Communicating Automata (CA). A CA is a Non-deterministic Finite State Automata (NFA). In this paper, we show how CA can easily be used for matchmaking of services. For this purpose, we give translations of CA to ADFSA. Thus matchmaking of services can be carried out even when the services are modeled using CA.

Context-aware web service composition is a challenging research topic. In context aware application, a context changes over a period of time. In order to satisfy a user request, a set of services will be updated according to the context. Thus, services may be added and/or removed from the set of existing services. A context-aware application may not satisfy a user request because of unavailability of services. In this paper, we address these type of scenarios and suggest a method for web services composition.