International Journal of Reasoning-based Intelligent Systems (57 papers in press)
Parallel K-Means Algorithm based on Two Stage Clustering of Large Data
by Xia Wendong, Liu Yuanfeng, Chen Deli
Abstract: Aiming at the fact that the algorithm communication time occupation ratio is too high and the practical application value is limited under the Mapreduce mechanism, a Hadoop-based two stage parallel c-Means clustering algorithm is proposed to solve the classification problem of super large data. First, the Hadoop-based two stage parallel fuzzy c-Means clustering algorithm is proposed to process the clustering of large data; and a protocol-based group typical individual reduction strategy is used to improve the time complexity of the MPI communication model of Mapreduce, so as to improve the overall efficiency of the algorithm; secondly, the interference of bad data items can be effectively eliminated by the selective group reduction algorithm, so that the algorithm in this paper has higher operating efficiency and clustering success rate. In terms of parallel rate and speedup ratio, the parallel rate and speedup ratio of the proposed algorithm in this paper on the large data set is better than the performance of the small data set, which means that the algorithm in this paper can adjust itself according to the amount of data in real time. The simulation results show that the performance of PGR-PFCM algorithm is better in the processing of large data.
Keywords: parallel algorithm; fuzzy clustering; K-Means; big data; two stages.
Decision Support for Grape Crop Protection Using Ontology
by Archana Chougule, Vijay Kumar Jha, Debajyoti Mukhopadhyay
Abstract: Weather based decision support for managing pests and diseases of crops requires use of Information Technology. This paper details a system developed using ontology, semantic web rule language and image processing techniques for management of pests and diseases on wines, particularly in hot tropical region of India. It aims at minimizing use of pesticides and fungicides by forecasting pests and diseases occurrence using information about meteorological conditions and its relation with pest and disease occurrence. It is named as PDMGrapes. For system knowledge base, knowledge available in different formats on grape pests and diseases is converted to ontology. Favourable meteorological conditions for pest and disease occurrences are mentioned by SWRL rules. Grapes disease identification is done using image processing techniques. The system helps grape growers to minimize side effects of pesticides on environment. The developed system is validated and verified for accuracy and performance.
Keywords: decision support; ontology building; decision tree; semantic web rule language; grapes; nutrition management.
Double PWM Coordinated Control Based on Model Predictive Algorithm and Power Compensation
by Bo Fan, Ke Wang, Bowen Ding, Ning Guo
Abstract: With analysis on double PWM structure though systems energy flow theory, active power and reactive power of rectifier are controlled by model predictive algorithm. System adopts the method of combine dynamic powers compensation with static powers compensation for system power in order to reduce system power error. A new power compensation algorithm is proposed to designs a new controller to replace PI controller of systems voltage loop, which can restrain fluctuation of DC bus voltage when load power suddenly varies and reduces DC-links capacity of capacitance. As for inverter side, system uses the method of rotor flux linkage oriented to control three phase asynchronous motor. Results of simulation can show that, system can realize the smallest tracking error of active power and reactive power by model predictive control, and restrain fluctuation of DC bus when load power suddenly changes. As the same time, the gird currents waveform is well.
Keywords: Model Prediction; Tracking Error; Power Compensation; Coordination Control.
Psychological Cognition Behavior Model Based on Reinforcement Learning
by Shiyong LIU, Ruosong CHANG, Sang Fu
Abstract: The trust relations in open systems are essentially one of the most complex social relationships, involving a variety of factors, such as hypotheses, expectations, behaviors and environment, etc., which are very difficult to have quantitative expression and forecast accuratly. The goal of the paper is to propose effectively illustrate psychological cognition behaviors using the reinforcement learning. Combined with the trust behavior of human society, an reinforcement learning model based on human trust habits is put forward: (1) Self-adaptive overall knowability decision-making method based on the historical evidence window is constructed, which not only has overcome the subjective judgment method for the determination of weights commonly used in existing models, but also can solve the knowability forecast problem when the direct evidence is insufficient; (2) The concept of reinforcement learning weighted averaging (hereinafter referred to as RLWA for short) operator is introduced, and the direct trust forecast model based on the RLWA operator is established, which can be sued to solve the problem of insufficient dynamic adaptability of the traditional forecast model. The experimental results show that, compared with the existing models, the proposed model has more robust dynamic adaptability and also significant improvement in the forecast accuracy of the model.
Keywords: Distributed System; Information Security; Reinforcement Learning Model; Reinforcement Learning Weighted Averaging Operator.
Cloud Platform Load Balancing Based on Bee Colony Algorithm
by Fan Xue, Zhijian Wu
Abstract: In order to shorten the time needed to execute tasks in cloud system and maximize the utilization of available resources in the system, this article proposes the cloud platform load balancing design under the background of bee colony algorithm (ABC algorithm). First of all, puts forward the designed mathematical model, and then gives the basic algorithm of load balancing based on bee colony algorithm. In addition, in the design of the process, three experiments are respectively carried out. The first set of experiments results show that the result is stochastic and stable and the system overhead will affect the system performance; the second set of experiments results show that there is the presence of outliers, algorithm can guarantee the system to complete the system task implementation within a limited time, and the system consumption continuously rises; the third set of experiments results show that the algorithm has stability and independence, and the algorithm has stable efficiency in the range that the virtual machine can withstand; if it exceeds the range, the results will be unstable. Overall, the ABC algorithm has an effective implementation effect.
Keywords: Bee colony algorithm; Cloud platform; Load balancing.
Visual Automatic Obstacle Avoidance Technology Research in Unmanned Vehicles
by Bo Liu, Liguang Li, Piqiang Tan, Rui Jia, Qing Liu
Abstract: In view of the problems of low obstacle avoidance and low efficiency in traditional unmanned vehicle in automatic obstacle avoidance, multi-feature fusion automatic obstacle avoidance method in unmanned vehicle is proposed. Optimize unmanned vehicle obstacle avoidance objective function, measure target obstacle distance, and calculate the braking distance. Based on this, unmanned vehicle dynamics model is established and the obstacle is located and determined. Multi-feature fusion design of unmanned vehicle visual automatic obstruction steps is made and finally the process of obstacle avoidance is analyzed. Experimental results show that the use of improved visual automatic obstacle avoidance technology has certain advantages in target obstacle positioning and obstacle avoidance accuracy, which are superior to those of traditional obstacle avoidance technology.
Keywords: Vehicle; Unmanned; Visual; Automatic; Obstacle avoidance technology.
Adapting eSpeak to Arabic Language:Converting Arabic Text to Speech Language using eSpeak
by Taha Zerrouki, Mohammed M. Abu Shquier
Abstract: Text to speech (TTS) is a crucial tool needed in many domains, mainly
for visually impaired users. The availability of TTS open sources improves access to
computers and gives more valuable applications. eSpeak provides support for several
languages. It is a tool that provides rules and phoneme files for more than 50 languages, besides, eSpeak is a light, fast, low memory consumption and used in multi-platforms.
In this paper we have explored the possibility to adapt the existing text to speech
converters into Arabic language in eSpeak. we attempt to define new text to speech
conversion rules, adapting existed phonemes and adding missing phonemes for Arabic
under eSpeak. The contributions are quite significant, however, the softwares developers will be able to integrated these enhancements within the new version, so that users who have problems with visual impairments or children with special needs will utilize this development of eSpeak. The availability of such support, open new fields to use arabic in TTS environment, especially for blind persons.
Keywords: TTS; eSpeak; Mbrola; Arabic; open source.
Automatic Three-dimensional Sorting System based on Internet and Database
by Xiao-dan Zhang, Yanming Cheng
Abstract: Automatic three-dimensional sorting system is the core part of supply chain operation for logistics and warehousing department. It has some difficulty in design. The sorting distance and sorting time optimization in traditional sorting operation is not enough. Therefore, automatic three-dimensional sorting system based on Internet and database is designed. The system uses buffer zone and cargo moving area for cargo delivery through shelf area storage of goods, uses cargo sorting area to distinguish different logistics receiving area and different types of goods. This paper introduces goods sorting process in and out of storage using Internet agent to realize sorting control, and designs the control, negotiation and communication principle for Internet agent. At the same time SQL Server 2014 development database is selected to establish the required information entity relation graph and feature table. Experimental results show that the sorting distance and sorting time of the system are short and it can play a great role in sorting operation.
Keywords: Automatic three-dimensional sorting system; Internet; Agent; Database; Entity relation graphrnrn.
Adaptive Multi-crossover Evolutionary Algorithm for Real-world Optimization Problems
by Mohd Khaled Shambour
Abstract: Evolutionary algorithms (EAs) have been extensively used since their invention. EAs are considered as a powerful tool to solve numerous optimization problems in various fields. Their search mechanisms have been actively developed to improve their search efficiency toward global optima solutions. This study aims to investigate the effects of using different types of recombination (crossover) schemes. It introduces an adaptive version of EA called adaptive multi-crossover evolutionary algorithm (AMCEA). The proposed AMCEA offers multiple forms of heuristic crossover operators based on genetic algorithm (GA) and harmony search algorithm (HSA). The proposed technique improves the search attitude by allowing the effective utilization of exploration and exploitation strategies during the evolution process. The quality of the proposed AMCEA is evaluated on six real-world numerical optimization problems (IEEE-CEC2011), and results are compared with those obtained with five variants of GA and HSA. Results demonstrate the superiority of the AMCEA over previously improved algorithms in terms of solution quality; it achieves the lowest mean results and lowest best results in 75% and 66% of the total experiment cases, respectively.
Keywords: Evolutionary algorithms; crossover; optimization problems; genetic algorithm; harmony search algorithm; global optima.
Temperature Aware Power Optimization based 8-bit MAC Architecture for Low Power DSP Applications
by Haripriya D, Govindaraju C, Sumathi M
Abstract: Temperature aware adaptive voltage scaling based low power 8 bit Multiplier-Accumulator (MAC) architecture for Digital Signal Processing (DSP) has been presented in this paper. Temperature increases dynamic power, static power and leakage power dissipation in the electronic circuits, hence it is mandatory to construct a circuit which minimizes the dynamic power, static power and leakage power adaptively according to the current temperature so that the performance of the overall system is not degraded much. The proposed temperature aware adaptive voltage scaling is very effective method to minimize the dynamic power, static power and leakage power consumption without degrading the performance of the system. The dynamic power consumed by the conventional MAC is 3.441mW when the temperature is 150
Keywords: Adaptive dynamic voltage scaling; Digital signal processing; Dynamic power;; Leakage power; Multiply and accumulate; Static power; Temperature aware.
INCORPORATING NOUN COMPOUNDS IN DISTRIBUTIONAL-BASED SEMANTIC REPRESENTATION APPROACHES FOR MEASURING SEMANTIC RELATEDNESS
by Abdulgabbar Saif, Nazlia Omar, Ummi Zakiah Zainodin
Abstract: Identifying noun compounds in natural language documents is very important for handling their various linguistic features, such as semantic, syntactic, and pragmatic features. In this study, we introduce a knowledge-based method for incorporating noun compounds in distributional-based semantic representation approaches. Wikipedia is exploited as a knowledge resource for extracting noun compounds based on its structural features. The categories are then used to classify the extracted noun compounds as linguistic terms and named entities. Next, the look-up list technique is employed to identify the noun compounds when extracting the semantics of the terms using the corpus-based approach for semantic representation. To obtain the semantic representation, we use five well-known distributional-based approaches: latent semantic analysis (LSA), hyperspace analogue to language (HAL), correlated occurrence analogue to lexical semantic (COALS), bound encoding of the aggregate language environment (BEAGLE), and explicit semantic analysis (ESA). The proposed method was evaluated by measuring the semantic relatedness using five benchmark datasets employed in previous studies. The experimental results demonstrate that incorporating noun compounds in the distributional-based semantic representation helps to improve the semantic evidence for the relationships among words.
Keywords: Distributional-based approach; Noun compound; Semantic analysis; Semantic relatedness.
Data Mining and Economic Forecasting in DW-based Economical Decision Support System
by Min Zhang, Rui Qi
Abstract: Decision demand has hierarchies for different users and the decision analysis demand in various area and field have particularity according to different topics. Since traditional MIS is hard to meet the demand of analysis and processing of growing mass data, a novel decision support system(DSS) is urgent to be proposed for decision makers. Based on data warehouse, data mining and OLAP technology, we propose a DSS with modular design, and explain the structure and key technologies of it in this article. Our study establishes multidimensional data-set for OLAP analysis to perform slicing, dicing, drilling and rotation operation. In data mining, for the problems of large data-set such as long learning time and decreasing generalization ability, an SVM accelerating algorithm based on boundary sample selection is put forward. The system test results demonstrate that the data mining has better prediction effects on economical forecasting. Therefore, the research has better practicability and higher accuracy, which shows certain value of popularization and implementation.
Keywords: data mining; data warehouse; DSS; OLAP; SVM; Economic forecasting.
Special Issue on: Artificial Intelligent Techniques Applied to the Study of Engineering Applications
A Hybrid Algorithm for efficient Task Scheduling in Cloud Computing Environment
by Roshni .T, Uma Maheswari, Bijolin Edwin
Abstract: Cloud is a boon to the generation which provides services that can reduce the overhead in maintenance and computational complexities.Scheduling the user’s job in the cloud resources plays an important role for the better performance. Task scheduling is an NP-Hard problem, since it may have more than one solution to fit in. In this paper a hybrid algorithm is proposed by the amalgamation of Artificial Bee Colony Algorithm and Particle Swarm Optimization named as ABPS algorithm. The proposed ABPS algorithm optimizes the task scheduling on the cloud environment by providing minimized makespan, cost, and maximized resource utilization and to balance the load. The experiments were simulated using cloudSim tool and the ABPS algorithm results outperform the original ABC and PSO algorithm. The comparative study proves the performance of the proposed ABPS algorithm over the original ABC and PSO algorithm.
Keywords: Cloud Computing, Task Scheduling, ABC algorithm, PSO algorithm, Makespan, Cost, Load Balancing, Resource Utilization.
A Survey on Contrastive Opinion Summarization
by Lavanya SK
Abstract: Contrastive OpinionSummarization (COS) is jointly generating summaries for two entities in order to highlight their differences based on the features.COS comprises of feature extraction, Sentiment prediction and summarization.Recently, the research focus in COS has been in using semantics associated with words and multi-word expressions to shift from syntactic to semantic level. This survey paper covers different methods used forfeature extraction, various similarity measures and different types of summarization. In addition to these, various datasets and performance measures are also addressed. Finally, future research directions are also suggested.
Keywords: Opinion mining, Feature based opinion summarization, Feature extraction, Sentiment prediction,Contrastive Opinion Summarization (COS).
ANALYSIS OF HEURISTIC BASED MULTILEVEL THRESHOLDING METHODS FOR IMAGE SEGMENTATION USING R PROGRAMMING
by Suresh .K, Sakthi .U
Abstract: The conventional way in analyzing image segmentation algorithms manually is difficult since it requires a lot of human effort in keeping all data for analysis. Various heuristic algorithms are bundled with Otsu’s and Kapur’s objective function in finding optimal fitness and quality segmentation. In this work Otsu’s and Kapur’s objective function are bundled with heuristics such as Harmony Search Optimization (HSO) and Electro Magnetic Optimization (EMO) to compare the solution accuracy of segmented images In order to statistically analyze such algorithms, an automated tool is developed which takes an input image of any image category under consideration and extracts the segmentedfitness values and quality parameters of the image. The extracted values are stored in a central database server constrained with image type, image category, methodology and heuristic used, no of thresholds and quality parameters. The central repository information is fed into data mining and data analytic tools to statistically rank the segmentation algorithms.
Keywords: Otsu &Kapur Objective function, Electro-magnetic Optimization, Harmony Search Optimization, Rank test.
Aggregated Clustering for Grouping of Users based on Web Page Navigation Behavior Aggregated Clustering for User Grouping
by GeethaRamani .R, Revathy .P, Lakshmi B
Abstract: In this epoch, a significant amount of patterns are retrieved using data mining techniques. Application of data mining techniques to the World Wide Web is referred as Web Mining. Clustering is one of the data mining technique that plays an vital role in the field of Web mining. This paper works on the server logs from the MSNBC dataset for the month of September 1999. Users with the average access length of 6 are used for analysis. This research aims to cluster the user based on their navigation behavior. An iterative aggregated clustering is proposed, in which various clustering algorithms such as EM clustering, Farthest First, K-Means Clustering, Density based cluster, Filtered cluster are applied on the dataset. The resultant clusters from various algorithms are aggregated correspondingly and the frequency of instances in each cluster is determined. If frequency of a instance in a cluster is greater than or equal to two-third majority, then the instance is grouped in that cluster. The work revealed that the system guaranteed to cluster 91% of users in the first iteration under 17 clusters for each page category and 99% of users are clustered in the subsequent iterations in another 17 clusters and rest of the users are grouped as one cluster, resulting in 35 hard clusters. The proposed framework is believed to serve in clustering user groups there by enabling suitable customized web environment.
Keywords: Clustering algorithm, Data mining, MSNBC, Web usage mining, Hard Clusters
Exploiting Ontology to map requirements derived from informal descriptions
by Murugesh Sundaram, Jayal A
Abstract: Requirements are narration of the services which a software system should make available along with the constraints that should be satisfied when the system operates. Software requirements have to be arrived from descriptions that are often incomplete, inconsistent, informal and ambiguous . Such informal descriptions have to be preprocessed and information constructs have to be extracted. This article deals with use of an ontology specific to Automatic Teller Machine(ATM) operations domain that contains the concepts, the relationships that exists among the concepts and the focus is to decide on the feasibility of the requirement by mapping the extracted requirement with the requirement defined in the background ontology. The developed ontology is queried using Simple Protocol and RDF Query Language (SPARQL), if the derived requirement is present in the ontology it is said to be feasible; else decision may be taken to eliminate the requirements that are invalid and infeasible. Ontology is a formal specification of concepts with their attributes and relationship in a particular domain. As standard description formalism, the Web Ontology Language (OWL) that is based on Resource Description Framework (RDF) is to be used.
Keywords: Ontology; Requirements elicitation; Simple Protocol And RDF Query Language (SPARQL); Web Ontology Language (OWL); Resource Description Framework (RDF); Unstructured documents; Natural Language Processing (NLP).
Application of mutation inspired Constrained Factor PSO considering voltage stability and losses by locating and rating TCSC during N-1 Contingency
by Jayachitra S, Baskar G, Feridinand T
Abstract: This paper describes, a strategy for optimal placement and setting of series FACTS controller- Thyristor Controlled Series Capacitor (TCSC) under single line contingency (N -1) using Particle Swarm Optimization(PSO), Constriction Factor PSO(CFPSO), Cauchy mutation- CFPSO(CM-CFPSO) & Gaussian Mutation CFPSO(GM-CFPSO) algorithm in order to reduce over loading and power loss in transmission lines and to improve voltage stability of a power system. In this proposed CM-CFPSO & GM-CFPSO methods, a new-fangled position equation is framed and the features of the Constriction Factor Approach (CF) is incorporated with the proposed approach. To detect the most severe transmission line, Composite Severity Index (COSI) is calculated under N-1 contingency and top three severe lines are taken for this research work. To validate the consequence of proposed approach, simulation studies are carried out on a standard IEEE 30-bus network. Appraisals are made in provisions of eminence solution, execution time and stable convergence behaviour
Keywords: Particle Swarm Optimization; Composite Severity Index; Mutation; Contingency; Optimal placement; Constraint Factor; Thyristor Controlled Series Capacitor;.
Survey on Data Analytics Techniques in Healthcare Using IOT Platform
by GOKULNATH CHANDRA BABU, SHANTHARAJAH S P
Abstract: The large amount of data generated by IOT has high impact values, the mining algorithms with IOT used to get the meaningful information that has been hidden in the data. In this paper designed a system that reviews the data in the knowledge view, technique view and application view, including clustering, classification, time series, association and outlier analysis and the application used in mining algorithm are surveyed. As the many devices connected with the IOT it produces large volumes of data and it also analyzed. So the algorithm should be modified so it can be used for mining algorithms. At last architecture has been suggested for big data mining system.
Keywords: Internet of Things (IOT); RFID; FMMEA; Near Field Communication (NFC); Prognostics and Health Management (PHM); CBM; Time to Failure (TTF); SVM; Ultra-Wide Bandwidth (UWB); DFT.
Octagonal Picture Languages
by Ramya Govindaraj, Anand M
Abstract: A picture grammar is the generation of pictures through description of words. The picture is represented in matrix form of finite alphabet using various grammars .Picture grammar can be achieved through context free grammar or regular expressions. Here we extend hexagonal picture language to octagonal picture language thereby introducing Octagonal Wang System (OWS) and octagonal picture language. Previously hexagonal wang system coincides with hexagonal tile system. Here we determine the same using octagon that is to check the coincidence of octagonal wang system and octagonal tile system. We use octagons since, they form several interior angles that leads to the base for drawings and architectural planning.
Keywords: Formal languages;picture languages;Hexagonal picture language;octagonal picture language;octagonal tiling system;Octagonal wang system.
FlowForensic: FlowRule Enforcement for Control Plane Attacks in Software Defined Networking
by NITHYA SAMPATH, Jayakumar Chinnappan
Abstract: Due to the lack of security in the traditional network, a new reprogrammable network called Software Defined Networking has been introduced. It is a layered abstraction network with easy programmable, flexible, and extensible by managing the networks by segregating the control plane from the data plane. This separation provides a way for developing more complex and advanced applications efficiently. OpenFlow is an interface between switches and controllers. It simplifies network management and programming of the network devices. The landscape of digital threats and cyber-attacks is evolving tremendously. The impact of various network attacks in software defined network environment is studied and implemented.The throughput results are compared and analyzed between normal packet and spoofed packet. In accordance with the analysis of spoofed packet, rules are enforced for protection.
Keywords: Software Defined Networking; Attacks; POX controller; rule; Mininet; throughput.
Trusted computing in Social Cloud
by Priya Govindaraj, Jaisankar Natarajan
Abstract: Social Cloud is a new paradigm which allows a user to share the system resources for computing purposes when it is not otherwise being used. This paper focuses on the computing model Social Cloud where the computing nodes are managed by the social bonds determined from a trust retaining social graph. It can be considered as a situation of a computing method in which cloud users gather a group of resources to do computational tasks for the sake of a social friend. So whenever a task is given by a user, the burden of computing is passed to the friend, i.e. to the nodes which are directly connected to them in the social network. We have used two different scheduling algorithms namely FCFS and Round Robin to check the performance statistics of the Social Cloud and state which algorithm is best suitable for the implementation. Apart from this we have proposed a new index called Trust Index for calculating the trust value of a user and also to find out whether a user is trustworthy or not. The proposed Trust Index mainly fills the gap of human interaction in evaluating a users trustworthiness, since here a user decides whether or not to trust another user. To show the efficiency of this trust index we have created a social network with a feature of verifying a friend, by using which we can verify our friends and make them trustworthy.
Keywords: Trust; Trust index; Scheduling; Social cloud; Social graph.
Energy Efficient Data Compression and Aggregation Technique for Wireless sensor Networks[TELSOB MOTES]
by Karthikeyan B, Kumar R, Srinivasa Rao Inabathini
Abstract: This paper present and analyze an energy efficient data compression and data aggregation algorithm which results in the whole network lifetime prolonged by about 24% . In this paper, a new idea is proposed for sensor values compression based on a technique that involves feedback mechanism. In this technique, the base node in the sensor network generates Huffman code for the sensor data that needs to be compressed and broadcast the Huffman code in to the sensor network. All nodes in the sensor network receives Huffman code, compress the sensor data and transmit to base node. For data aggregation, secure data aggregation algorithm is used which does not necessitate additional phase for data integrity verification and also it eludes extra transmissions and computational overhead on the sensor nodes to reduce the amount of energy used up by the network. The whole idea was tested on TelosB sensor network platform, programmed in nesC language and also analyses the performance of the algorithm in the Contiki OS- simulator Cooja. A comparison is also done with existing compression algorithms in terms of lifetime of the sensor network.
Keywords: Sensor Network; Huffman code; Secure Hierarchical aggregation; Cooja; Telosb.
An Intelligent Neuro-Genetic Framework for Effective Intrusion Detection
by Rama Prabha Krishnamoorthy Pakkirisamy, Jeyanthi N
Abstract: Intrusion detection systems are useful for improving the network performance by safeguarding the networks from attacks including flooding attacks. Intrusion detection systems can be developed by identifying the important features from the network data to be analysed and by classifying the network traffic using the most contributing and important features. In this paper, a new intelligent neuro-genetic framework is proposed for detecting the intruders in networks by analysing their behaviour. For this purpose, a new Genetic Algorithm based Feature Selection Algorithm (GAFSA) and a Neuro-Genetic Fuzzy Classification Algorithm (NGFCA) have been proposed in this paper which are used to identify the malicious users through classification of user behaviours. The main advantage of this proposed framework is that it reduces the attacks by identifying the intruders with high accuracy and reduced false positive rate. This work has been tested through simulations and also using bench mark dataset for analysing the performance of the proposed algorithms. From the experiments conducted in this work using full features and selected features by applying the existing classification algorithms as well as the proposed classification algorithm, it is proved that the proposed framework detects the intruders more accurately and reduces the attacks leading to increase in packet delivery ratio and reduction in delay.
Keywords: Intrusion Detection System; Feature Selection; Classification; GAFSA; NGFCA; false positive rate; neuro-genetic framework.
Cluster Based EA-PATM Protocol for Energy Consumption in Hierarchical WSNs
by Meenatchi SS, Prabu Sevagan
Abstract: Consumption of energy by the sensor node in wireless sensor node is the main criteria affecting the wireless sensor network. The message transmission of wireless sensor network requires high power consumption and quality of service, which affects the energy consumption in WSN. To overcome the criteria the energy consumption of node is reduced by the proposed EA-PATM protocol. The proposed protocol consists of pillar k-mean clustering method to cluster the network in to set of nodes. For the selection of cluster heads, the ant lion optimization algorithm check the clustered node for effective QoS parameters. Ant lion is a nature inspired optimization algorithm proposed in this paper to generate cluster head for the evaluation of energy consumption in the WSN. The TDMA based MAC protocol is proposed in the paper to evaluate the energy consumed in transmission of information from one cluster node to other during routing. The proposed formulation offer a stable definition for estimating the quality of service performance of network and hence by consuming less energy in wireless sensor network. The proposed technique is carried out in network simulator and the results are plotted in terms of processing energy, nodes remaining energy and QoS parameters such as packet delivery ratio, packet loss ratio, delay, latency, throughput and overheads. From the concluded results, it clearly mentioned that the proposed EA-PATM protocol is an efficient method for consumption of sensor node processing energy and quality of service in wireless sensor networks.
Keywords: Wireless Sensor Networks (WSN); Ant Lion Optimization Algorithm; Pillar K- means Clustering; TDMA based MAC; Network Simulator; Quality of Service (QoS).
Real Time implementation of Multivariable Centralized FOPID controller for TITO process
by Lakshmanaprabu Sk
Abstract: The development and real time implementation of multivariable centralized FOPID (MC-FOPID) controllers for two interacting frustum conical tank level process (TICFTLP) is presented. The Modeling and control of TICFTLP is difficult due to its dynamic coupling between inputs and outputs. The black box model is developed from the open loop experimental data using process reaction curve method (PRC). The multivariable centralized FOPID (MC-FOPID) controller with five tuning parameters is designed based on the steady state gain matrix of the process and then the controller parameters are tuned using bat optimization algorithm. The comparison of proposed MC-FOPID controller with multiloop PID controller is demonstrated in the simulation study. The simulation results of the controllers are compared in terms of settling time and integral error criteria. It is found that the MC-FOPID controller has better servo and regulatory response than multiloop PID control. The real time implementation of MC-FOPID is done in MATLAB/SIMULINK using USB based DAQ module.
Keywords: Multiloop PID control; Centralized Control; Fractional order control; FOPID; BAT optimization algorithm; Two input Two Output Process; Two Interacting frustum conical tank process.
A novel feature extraction approach for tumor Detection and Classification of data based on Hybrid SP Classifier
by Nandha Gopal, Roheet Bhatnagar
Abstract: This paper deals with how to identify the cancer affected region of the brain. There have been many tools and techniques such as SOM (Self Organizing Map), PSVM (Proximal Support Vector Machine) classifiers, discovered to find out the cancer affected region in the brain. There is a rapid growth in the brain tumour cases in the recent past. Technology failed to find out the root cause behind it. Recent reports reveal that different types of brain tumours can be treated either through surgery or in rare cases, with radiation. The role of image segmentation in identifying and the treatment of brain tumours are enormous, because image segmentation will help to find out the volume and the growth of the tumours using the techniques like human edge correction, outer edge coloring and inter active thresholds holdings. In order to reduce the human error and to get the accurate results in MRI images there is an urgent need to find out an automatic or semi automatic method for the classification of brain tumor images .Finally a system called Hybrid SP classifier has been developed for the detection and classification of brain cancer, this type of mechanisms uses a system dependent actions to find the block and different types of brain tumours. It also makes the use of some of the mechanisms like Image enhancement, segmentation and Equalization of histogram.
Keywords: Image processing; Segmentation; Medical Image;.
DESIGN AND IMPLEMENTATION OF ENERGY EFFICIENT RECONFIGURABLE NETWORKS (WORN-DEAR) FOR BAN IN IOT ENVIRONMENT (BIOT)
by Kumaresan P, Prabukumar M
Abstract: Embedded Systems are pervasive with the advent of Internet of Things. This has led smart devices to be omnipotent. In future, this will convert any object (living, non-living, smart devices) into smarter devices which finds applications in an unimaginable way. Even though the technology becomes omnipotent, several research problems arise in design and implementation. Problems such as energy consumption, security, quality of information, performance and intelligence are needed to be addressed when it is applied in the health care system, wireless communication, defence, agriculture and so on. Here, we have concentrated on the Low Power Health Care System based on Body Area Networks (BAN) in which the technology embeds the Body Area Networks with Internet of Things (IOT) which can be jointly coined as BIOT (BAN Internet of Things). BIOT finds application as wearable devices for monitoring and care giving systems for patients. Due to BIOTs nature of omnipotent application in the health care, maintaining its life time remains in the darker side of the research. To overcome this problem, a new algorithm for the BIOT called WORN (Wake - On Reconfigurable Networks) has been proposed. The proposed algorithm works on the DEAR (Distance Energy Adaptive Rule) rule sets. This algorithm calculates distance based on RSSI and selects frequency using DEAR rule sets for minimum energy. It has been tested with different transceivers on different architectures. The results obtained from different testbeds have shown a 20-30%of increase in lifetime of the BIOT network. By increasing the life time of the devices, BIOT with WORN-DEAR power model will be the bridge between the human and the machine Interface.
Keywords: BAN; IOT; BIOT; WORN; DEAR; RECONFIGURABLE NETWORKS; NETWORK LIFETIME.
A HIGH PERFORMANCE COGNITIVE FRAMEWORK (SIVA SELF INTELLIGENT VERSATILE AND ADAPTIVE) FOR HETEROGENOUS ARCHITECTURE IN IOT ENVIRONMENT
by Yokesh Babu Sundaresan, Saleem Durai M A
Abstract: The advent of the Internet of Things in todays technology brings automation to the footsteps of every human. But still the technology is in darker side when it is required to implement machine learning on Internet of Things for the intelligent detection. Several Machine learning algorithms like Artificial Neural Networks, Support Vector Machines, Deep learning Algorithms are applied for bringing the Cognitive aspects in the Internet of Things. But these machine learning algorithms finds their application in face recognition, emotion recognitions etc., on the hardware. Still there is a need for developing low power, high accurate, more intelligent machine learning framework for embedded architectures when they are used for dynamic inputs in health care solutions. Hence we propose a framework named SIVA (Self Intelligent Versatile and Adaptive) for dynamic inputs in IOT based health care solutions. This framework is based on Neural Network and Cognitive rule sets for self learning and adaptability. The proposed learning algorithm works on self adaptive principles which make the framework suitable for the biomedical wearable devices for dynamic inputs. This framework has been evaluated for different biomedical sensors and embedded heterogeneous architectures. Various performance parameters viz. recognition rate, accuracy, execution time and energy are measured and analysed. The results indicate that the framework not only have superiority on complexity, but also have low power consumption over existing neural network and svm algorithms.
Keywords: SIVA; iot; svm; cognitive rule sets; deep learning; self-adaptive.
Encrypted Image based Data Hiding Technique Using Elliptic Curve ElGamal Cryptography
by JAYANTHI RAMASAMY, John Singh K
Abstract: Most of the data hiding techniques used RSA based encryption algorithms for encrypting the images and the messages. However, the security provided by elliptic key cryptography is higher with a lower size key than the RSA algorithm. Therefore, a new image encryption scheme which can be reversed during decryption is proposed in this paper which uses an elliptic curve key based ElGamal encryption scheme for effective data hiding in images. Moreover, it uses the difference scheme available in the existing work for data hiding of images. Form the experiments conducted in this work, it is proved that the proposed scheme is more efficient with respect to security and it reduces the computation complexity when it is compared with other related schemes.
Keywords: Elliptic key cryptography; ElGamal encryption; Data hiding; Difference expansion; Encrypted image; Public key cryptography.
Design of CMOS Full Subtractor using 10T for Object Detection Application
by M. Mahaboob Basha, K. Venkata Ramanaiah, P. Ramana Reddy
Abstract: This paper presents the design of full subtractor (FS), which is able to operate at low voltage and low power. In this method, 2 XOR gates with 1 MUX circuit are used to design the 10T full subtractor in 45nm CMOS technology. In this paper, Low Cost Thresholded Full Subtractor (LCTFS) method is presented to utilize the subtractor circuit with minimum number of transistors, which is mostly used in digital circuits and high-speed applications. Multi threshold CMOS (MTCMOS) circuit is introduced in FS to avoid the thresholding problem. From this subtractor, Restoring Array Divider (RAD) is designed for object detection application. Simulation results have shown that with the help of LCTFS circuit, area, power, delay and power delay product have minimized in LCTFS, RAD, and object detection application with compared to the conventional methods.
Keywords: Full subtractor; Multi threshold CMOS; Integer restoring divider; Area; Power; Delay;.
An Efficient Raindrop Parameter Estimation using Image Processing
by Pandharinath Appasaheb Ghonge, Kushal R. Tuckley
Abstract: Nowadays, image processing algorithms play a key role in the rain drop distribution estimation. This paper deals with Number of drops and Drop size distribution and its volume in particular time. We are using the raindrop image to calculate the amount of rainfall in a particular time. The proposed Image Processing based Rain Drop Parameter Estimation (IPRDPE) by using Double-Density Dual-Tree DWT (DDDT DWT) and thresholding based segmentation. By using effective image fusion technique, rain drop images from different angles are fused and using segmentation and morphological operations raindrop parameters estimated. To get better fused output max-based effective image fusion rules are used. The system using advanced image fusion technique and estimation for rain drop parameter, produce more accuracy and error free system compared to the existing techniques and also achieved better accuracy with respect to the real-time measurement.
Keywords: Raindrop image; Double-Density Dual-Tree Discrete wavelet transform; segmentation; image processing based rain drop parameter estimation;.
IDENTIFICATION OF PERSON OR DATA USING MODIFIED SQUARE BLOCKWISE APPROACH
by Denslin Braja R, Dharun V. S.
Abstract: Nowadays authentication is important to identify the originality of a person or document. Visual Secret Sharing scheme is one of the best methods to provide authentication without any complex computations. In this paper, we proposed novel method called modified square blockwise approach to generate shares. Here the personage photo or any biometric data can be used as an authenticated image. This approach will generate two shares, one share is printed on identity card and the other one is stored on database. To verify the originality of a document or a person, first receive the identity card and scan or take a photo of the share. Now this share is compared with the stored one, if it is reveal the authenticated image then it is accepted, otherwise simply rejected. This approach is used to authenticate any confidential data such as medical document, bank details and administration details also. Using this approach, we can restrict the use of confidential documents without knowing the originality of the document.
Keywords: Authentication; Modified square blockwise approach; Visual Secret Sharing.
Grouping of Users based on User Navigation Behavior using Supervised Association Rule Tree Mining
User Clusters based on User Navigation Behavior using Supervised Association Rule Tree Mining
by GeethaRamani .R, Revathy .P, Lakshmi .B
Abstract: In this internet world, an increased interest of users in the search of World Wide Web results in wide relevance of Web Mining, which is an application of Data Mining. According to the type of data to be mined, web mining is broadly classified into three categories namely web content mining, web usage mining and web structure mining. Clustering has been widely used for web usage mining. One of the major challenges in the study of clustering is finding the initial cluster center and the number of clusters to be generated. This is overcome in this work by grouping of users based on the target class value. The benchmark dataset MSNBC is collected for msnbc.com and news-related portions of msn.com for the entire day of September, 28, 1999. Supervised Association Rule Tree mining is used to find the frequent itemset for the targeted class value and thus generating "if then rules". Users are automatically clustered based on the rules satisfying the ground truth, resulting in 36 clusters in two iterations. The results revealed that the renowned clustering algorithms such as K-Means takes 22 iterations for forming 36 clusters, wherein the proposed work generates 36 clusters in two iterations efficiently with less computation.
Keywords: Clustering algorithm; Data Mining; MSNBC; Web usage mining; Supervised Association Rule Tree Mining.
Integrated cloud based risk assessment model for Continuous Integration
by VIJAYAKUMAR .K, Arun .C
Abstract: During the development stages of a software application or product there is a natural tendency to focus on the functional aspects rather than the architectural, security aspects. In the current age of changing dynamics in Internet and mobility environments the risks in the areas of application being deployed and faces a critical security threat is very high. Its always better to prevent than react. With the advent of the technology and practices like continuous integration and dev ops are gaining importance it would be very meaningful for implementation of such framework as it would help in continuous risk assessment during the application development stages itself. This conference paper provides a novel framework through which can implement a continuous risk assessment approach on the software development projects for cloud based scenarios.
Keywords: Cloud Security; Risk Assessment; Cloud Migration; Continuous Risk Assessment.
A Secured Cloud Storage Auditing with Empirical Outsourcing of Key Updates
by Vijayakumar K, Suchitra S, Swathi Shri P
Abstract: Cloud Computing is emerging and considered next generation architecture for computing. Cloud storage auditing is regarded as a prominent feature to validate the integrity of the data in public cloud. The key exposure resistance is a vital issue in various security applications. The current cloud storage auditing models require the client to update his secret keys in each time period which bring in local burdens to the client. The aim of the project is to make the key updates transpicuous to the client and present a new model for auditing cloud storage with verifiable outsourcing of key updates. In this model, the key updates are outsourced to a third party auditor (TPA) who reduces the local burden on the client. The third party auditor (TPA) is accountable for both cloud storage auditing and safe key updates. The TPA holds an encrypted version of the clients secret key. The client decrypts the secret key, generates authenticators for the file and uploads these files along with authenticators to the cloud. In our design, we employ the Multi Key Encryption Algorithm to achieve faster key updates, short key size and to proficiently encrypt the secret keys. In addition, the TPA will audit whether the files in cloud are stored correctly by a challenge-response protocol between it and the cloud at regular time. The proposed paradigm permits the client to authenticate the validity of the encrypted secret key produced by the third party auditor for uploading data to the cloud. These prominent features are considered to make the entire cloud storage auditing technique as transpicuous as possible for the client.
Keywords: Cloud Computing; Encryption; Decryption; Multi Key Encryption Algorithm.
FUZZY LOGICS ASSOCIATED WITH NEURAL NETWORKS IN INTELLIGENT CONTROL FOR BETTER WORLD
by Prabhakar .G, D. Arul Dalton, Kamakshi .P, K. Sai Prasad
Abstract: Now a days technology is growing wide spread with innovate taught. Day by day changes are made to technology. Fast growing area in computer sciences is Neural Networks. Neural Network is a computer system modeled on the human brain and human nervous system. Neural Networks sometimes referred to as artificial human brain. Human Brain is transformed to plastic brain by neural networks. Neural Networks are formed by interconnecting the number of information cells to process information. There is no guarantee that the system can work for all type of tasks. It may sometimes fail for certain tasks. To perform actions as humans, it is referred to fuzzy-logics. This topic teaches how computers can work as humans. This paper deals with fuzzy logic applications in the real world. Neural networks, computational intelligence techniques, intelligent control, instrument and robotics include fuzzy logics to perform the action in easier way.
Keywords: fuzzy-logics; Neural Networks; Computational intelligence; intelligent control.
A Survey on Resource Allocation Strategies in Cloud
by Chenni Kumaran J, Aramudhan .M
Abstract: Cloud computing has turned into age innovation that has tremendous possibilities in endeavors and markets. Cloud can make it conceivable to get to applications and related information from anyplace. Organizations can lease assets from cloud for capacity and other computational purposes so that their framework cost can be lessened fundamentally. Facilitate they can make utilization of vast access to applications, in view of pay-as-you-go display. Henceforth there is no requirement for getting licenses for individual items. Be that as it may one of the real pitfalls in distributed computing is identified with enhancing the assets being designated. On account of the uniqueness of the model, asset distribution is performed with the target of limiting the expenses related with it. Alternate difficulties of asset portion are taking care of client requests and application prerequisites. In this paper, different asset portion methodologies and their difficulties are talked about in detail. It is trusted that this paper would profit both cloud clients and analysts in defeating the difficulties confronted.
Keywords: cloud computing; cloud services; Resource allocation.
A File Sharing System in Peer-to-Peer Network by a Nearness-Sensible Method
by Vimal .S, Srivatsa S.K
Abstract: For a comprehensive Peer-to-Peer file sharing system dynamic file query is substantial where its performance can be enhanced by clustering of peer that can also considerably improve the efficiency. Depending upon physical nearness and peer interest peers are clustered in current work. File replication algorithm has been employed that creates replicas for the requested file to enhance the efficiency. Compared to unstructured P2P the efficiency is high for structured P2P which is difficult to analyze because of their rigid topology. We have developed Nearness and Interested Cluster (NIC) super peer network to improve the efficiency of file location in current years for P2P system but few works rely on peer interest and physical nearness. Various methods have been used to improve intra-sub-cluster searching. Here the interest is categorized into sub-interest then they are linked according to common-interest. File searching delay is minimized where an overlay is built to link. Flower filter is employed to enhance the efficiency and reduce the overhead. On comparing Nearness Sensible I-clustered System with other system the efficiency has been traced. The effect of enhancing the efficiency using intra-sub-cluster searching is observed in experimental results.
Keywords: File Replication; Peer to Peer Networks; Flower Filter mechanism.
Stimulated RR MAC Protocol for Power Efficient Wireless Sensor Networks
by Kirubakaran M.K., Sankarram N.
Abstract: Wireless Sensor Networks is a collection of sensor nodes scattered across a habitat of interest to collect crucial information from the habitat. The application of these networks is enormous, ranging from video surveillance, medical device monitoring, air traffic control, robot control, target monitoring, border protection, disaster assessments etc. Since the nodes in wireless sensor networks remain in the habitat for long duration, it is necessary they utilize the battery mounted onboard very efficiently. Research has grown along this area where in many protocols are being proposed in order to enable the nodes use the energy efficiently. This paper proposes one such protocol Stimulated RR MAC protocol and discusses its benefits and performance over other existing protocols.
Keywords: Channel allocation; Channel capacity Multipath channels; Multicast protocols; Wireless communication.
MITIGATION OF DDOS THREAT TO SERVICE ATTAINABILITY IN CLOUD PREMISES
by Shiny Duela, Uma Maheswari P
Abstract: Cloud environment provides users with amicable services that help them to attain their organizational and personal goals. The cloud users are provided with immense resources as they require by exploiting the scalability and openness feature of the distributed environment. The heterogeneity masking is an added advantage that facilitates the customers with dissimilar resources to communicate with each other by having common agreed standards. The service models rendered by the cloud on pay as you go basis assist the customers to have large-scale business deals so that the investment to carry out such deals diminishes. To enable full-time availability of such resources, the cloud users should have access to the necessary applications and data when and wherever needed with internet connectivity. The scenario of availability of service differs from the goal of Cloud Service Provider whenever the end user suffers from unavailability and delay of promised services. This is due to Distributed Denial of Service (DDOS) attack where the illegitimate users on cloud who always generate different kinds of bots and zombies to disrupt or breakdown service availability by means of flooding the network and consumption of large network assets. To overcome such scenario, a DDOS Detection Algorithm (DDA) is proposed that trace out the existing attack by employing Action-based Monitoring (AM) approach on the incoming traffic, resource consumption and header information parameters after which the Human Discovery Algorithm (HDA) is implemented by performing a Turing test targeting on human so that bots could be segregated from the normal user. The Supervisor Self Learning Algorithm (SSLA) is another mitigation test that allows future bots to be isolated by referring to the previous attack patterns of bots. This model has shown improvement in terms of client success ratio, load balancing, and detection, prevention and mitigation of DDOS attack with respect to time.
Keywords: Bots; Denial of service; openness; scalability; zombies; availability.
Students Performance Analysis System Using Cumulative Predictor Algorithm
by DafniRose J, VIJAYAKUMAR K, Sakthivel Srinivasan
Abstract: The recent trends in the IT industry indicates that it is moving towards automation to do mundane tasks and the expectations for students already equipped with good programming skills is on the rise. In parallel, there arehas been a rising number of students who find it difficult to attain the skills necessary in order to get the dream IT job they desire. The aim of this project is to bridge the gap between the employer and the future employee of the company by the use of SPAS at college level.Student Performance Analysis System (SPAS) is an online web application system which enables students to know prior hand if their level of skills for the placement is enough to get placed or not, given the necessary inputs.SPAS has an intelligent learning algorithm which utilizes a rich database, analyses the records of previous students traits and develops a model for further prediction.The performance evaluation of students by SPAS is by the cumulative predictor algorithm involving generation of several random forest trees on the available data. SPAS learns and creates its model reaching higher accuracy with increasing data availability.
Keywords: Educational Data mining; decision tree (J48); Naïve Bayes’ classifier; JRip algorithm; bagging method; standard deviation; infogain; entropy gain.
Special Issue on: Challenges in Smart Reasoning Intelligent Systems
Clustering Algorithm for Wireless Sensor Network to Improve the Efficiency of Acnode
by Youwei Shao
Abstract: In the vehicular ad hoc networks VANETs, the topological structure with high dynamic and frequent cracked link challenge the vehicle to vehicle communication. By taking VANETs city scene as the background, the Thesis proposes routing VAC-BNR (Vector-angle-cluster and bridge nodes-based routing) protocol based o clustering of direction vector angle and bridge nodes. The VAC - BNR protocol at first divides a road into the intersection area and straight line sections between or among intersections. In the straight line sections, according to the moving direction of vehicles, the vehicles are divided into different clusters, later on, utility value of the nodes in each cluster are calculated, and then priority of node forwarding data packages is arranged according to utility values of nodes; In the intersection area, the stability factor of the vehicles is calculated, which includes relative velocity and distance between the vehicle from surrounding vehicles, and the most stable vehicle in the intersection area is chosen as the forwarding node. The experimental data shows that the proposed VAC - BNR protocol can effectively improve data transmission in the urban scene environment.
Keywords: Topological structure; Clustering routing; Vector angle; Sensor network; Routing protocol.
Fuzzy Neural Network Learning based on Hierarchical Agglomerative T-S Fuzzy Inference
by Tao Duan, Ang Wang
Abstract: It is well-known that the accuracy of classification prediction is relatively high, but the prediction result is obscure in concept since result is given in two-value form (0 or 1) which says that red tide exists or does not exist. On the other hand, the accuracy of numerical prediction is relatively low, but it can offer density value of plankton which influences red tide. In order to combine characteristics of the above mentioned two methods, a prediction method for red tide which is mixed with integration model of hierarchical agglomerative T-S fuzzy inference is proposed. In the Thesis, through using the proposed prediction method mixed with integration model of hierarchical agglomerative T-S fuzzy inference, taking respective advantages of classification prediction and numerical prediction in prediction process for reference, and through experiment and comparison, it is proved that this algorithm is better than LMBP algorithm in prediction accuracy which shows the validity of the proposed algorithm. In the next step, it is mainly to further study the practical application of the algorithm, and to apply this prediction model to red tide warning system, and also to conduct experimental verification for a certain period by using actual marine environment.
Keywords: Fuzzy inference; Neural network; Hierarchical agglomerative; Prediction.
Key Data for Cloud Computing based on Ensemble Clustering Approximate Analysis
by Zou Yu, Qin Zhong Ping
Abstract: To realize multi-label classification of text and meanwhile reduce calculation complexity and keep classification precision, dimensionality-reduction clustering method for fuzzy association of text multi-label based on cluster classification has been proposed. In text classification, it usually involves enormous feature numbers, which may cause curse of dimensionality. In addition, classification region can not always keep convex characteristics. It can be non-convex region composed of several overlapping or intersecting sub-regions. Above mentioned automatic classification system may require enormous memory requirement or has poor classification performance. Hence, new multi-label text classification method is proposed to overcome these problems in combination with fuzzy association technology. Fuzzy association evaluation is adopted to transform high-dimension text to low-dimension fuzzy association vector, thus avoiding curse of dimensionality. Experiment results show that the proposed method can more effectively classify text multi-label problem.
Keywords: Fuzzy transformation; Key data; Integration clustering; Cloud data; Data analysis.
Design of Unsupervised Facial Expression Animation Based on Geometric Grid Measurement
by Niu Chunzhou, Zhu Yukai
Abstract: Many actual application images in the real world are formed by high dimensional data in most cases, while the manifold learning algorithm can explore the nonlinear information hidden in these high dimensional data. As most of manifold learning algorithms can only be defined in training cluster, it is impossible to project the sample on the lower dimensional space. In the Thesis, we introduce a kind of double manifold algorithm based on LLE and Isomap. Different from the traditional LLE algorithm, our algorithm learns two kinds of manifold information in which one group of data relates to many types and it compares two kinds of single LLE algorithm and Isomap algorithm through the setting of the appropriate nearest neighbor number K. No matter for the recognition rate or running time, it is obviously superior to the other two kinds of algorithms and it can effectively achieve the estimation of facial expression and significantly reduce the computation complexity.
Keywords: Facial expression; Geometric grid; Unsupervised; Manifold learning algorithm; K-nearest neighbor.
Factor analysis model of the result of hospitalized patients with neurosis
by Sun Shanhui, Li Hong, Li Zhuangzhuang, Zhang Bingqiu
Abstract: To study the diagnosis of hospitalized patients with neurosis and its influencing factors, this article, on the basis of the data of treating hospitalized patients with neurosis hospitalization, empirically analyzes the relationship between the treating effect and personal basic situation, personal social relations, personal original condition, and makes the corresponding regression analysis and factor analysis. The results show that the patient's social relationship and personal character are obviously related to the diagnosis of neurosis. There are obvious correlations between the original condition in the early diagnosis and patients with neurosis, so it is important to strengthen the understanding and analysis of the original condition. We should strengthen publicity and education of mental health knowledge, encourage people from all walks of life to actively participate in it and improve their awareness of neurosis, and thus to effectively reduce the bias in patients with neurosis. Untimely separation and unharmonious relation with parents are one of socio-psychological factors which cause adults suffer neurological disorders.
Keywords: Neurosis; Diagnosis; Factor Analysis Model;.
Multi Criteria Decision Making Method based on Analytic Hierarchy Process with Intuitionistic Fuzzy Preference Information
by Cai Liang
Abstract: An extension method of multi criteria decision making (MCDM) is proposed in the Thesis for selection and assessment of parting line in mold design. First of all, linguistic variable is used to express grade of alternative parting line scheme and weight of criterion significance; and then, the membership function of final fuzzy assessed value is determined based on these linguistic values; in the end, a new ordering method of maximum and minimum sets will be adopted for normalization of weight grading and defuzzification to clear value and for strength and weakness ordering of alternative scheme. The verification and contrast experiment show effectiveness of the method in the Thesis and the comparison with traditional fuzzy multi criteria decision making method shows the method in the Thesis is more applicable.
Keywords: Fuzzy number; Multi criteria decision making; Selection of parting line; Mold design; Ordering of maximum and minimum sets.
MapReduce Optimization Information Query Method for File Management System
by XuGuang Zhu, Yuzhi Shen
Abstract: MJQO problem is very complicated, query speed influences execution efficiency of database application software. To solve deficiencies such as low rate of convergence, etc of PSO algorithm and improve optimization efficiency of database multi-connection query, this Thesis proposes a MJQO method adapting to escape momentum particle swarm optimization aiming at deficiencies of particle swarm optimization such as early-maturing, partial optimization, etc, and it verifies effectiveness of SAEV-MPSO via emulation contrasted test, and this algorithm can obtain optimal query scheme of MJQO in relatively short time. Crossover mechanism is first introduced by this algorithm of genetic algorithm to particle swarm algorithm to maintain diversity of it and prevent early-maturing phenomenon, and then this Thesis introduces search track of momentum algorithm smoothness particle to accelerate convergence rate of particle swarm; finally this Thesis applies this algorithm to database multi-connection query optimization solution to achieve optimal database query scheme. Emulation result indicates this algorithm improves database query efficiency and shortens query response time.
Keywords: Database query; Archive information management; Genetic algorithm; Mapreduce; Particle swarm.
Dynamic Path Planning of Mobile Robot based on Ant Colony Algorithm
by Long Zhuo-Qun
Abstract: The Thesis makes vehicle in cross-country environment as research object, and uses improved ant colony algorithm to research and analyze the cross-county path planning. First, improved ant colony algorithm is used to research cross-county path planning of vehicle, then Slope Table and Roughness Table are introduced to analyze topographic Slope and land surface propertys affect on path planning, and path optimization algorithm is designed considering restriction of slop and Roughness. Simulation result shows that this algorithm can realize cross-county path planning with speediness and efficiency. Experimental result demonstrates that the improved ant colony algorithm has stronger feasibility and better searching capability.
Keywords: Path planning of robot; Land surface property; Optimal method; Ant colony.
Construction of Evaluation System of Sports Talent Training Scheme based on Data Mining
by Gong Xun, Lin Suxia
Abstract: In order to improve the effectiveness of the evaluation system construction for sports talent training program, this paper puts forward a kind of evaluation system construction method for sports talent training program based on data mining. It provides the description target of sports talent training program, the problem that the upper bound of itemsets only used by the traditional mining algorithm has unsatisfactory effect on the high-expectation weight and downward closure property of mining algorithm in design hierarchy, and presents the proof process, and this property can effectively reduce the processing capacity of candidate set in the premise of ensuring the accuracy, to construct the two-stage data mining process. By comparing with the training program of 2007 version, the proposed method can eliminate the setting compulsory course equivalent to individual professional elective course, to promote the diverse development of students.rnrn
Keywords: Two-stage; Apriori algorithm; Sports talent; Training program.
Design of College Students' Physique Monitoring and Service Platform based on Computer and Network
by Haiyan Wang
Abstract: The problem of college students physique has become a serious problem which restricts the cultivation of high-competent talents in China. In view of the existing deficiencies in the monitoring platform of students physique, we fully utilize the advanced technology of computer and Internet development and follow the basic principles of economical practicality, scalability, user-friendliness and real-time information exchange to establish monitoring and service platform for college students physique. It will have far-reaching practical significance to fulfill the expected goals in different levels such as teaching management, campus sports, and students' individual physical intervention in all universities through the basic and extended functions of platform such as basic assessment, testing organization, data management, information release, fitness guidance, and printout. The main design idea of the platform, the function realization, and the design expansion and operation method of each module will provide reference for universities to establish their own physique monitoring and service platform in line with their own requirements.
Keywords: Computer; College students; Physique; Monitoring; Service.
Decision Making Model of energy Consumption based on Multi Uncertain Factors
by Cai Liang
Abstract: An Energy Consumption Decision Model Method based on multi-factor Agent Fuzzy Game Compromise is proposed in this Article, to promote availability of energy consumption decisions. First, give the Fuzzy Decision Model for Energy System based on multi-agent methods, in which fuzzy decision Agent is the core of whole alliance, able to get the energy demands information of users from user layer Agent, and the energy supply conditions and energy supply availability inside the alliance from energy consumption layer Agent; secondly, give a fuzzy game compromise weight decision method to build a relation matrix based on fuzzy evaluation, and propose an assessment methods for grey Euclid model; and at last, by simulation experiment, verify the availability of energy consumption decision model based on the proposed multi-factor Agent Fuzzy Game Compromise.
Keywords: Multi-factor; Agent model; Fuzzy compromise; Energy consumption; Decision model.
WSNs Heterogeneous Cluster Routing based on Distributed Fuzzy Logic Inference
by Tang JunYong, Chen Xiang
Abstract: Targeted at the wireless sensor network in heterogeneous distribution composed of solar energy supply nodes and zero energy supply nodes, the cluster routing algorithm based on node density and energy size is proposed. In this Thesis, the wireless sensor network of heterogeneous non-uniform distribution is studied, and sun energy harvesting model ISHE based on intensity of illumination is proposed. In IISHE model, at first, the light intensity sensor is used to read solar illuminance under the current environment, and then, energy size obtained by solar energy supply node at specified period can be estimated based on corresponding relation between illuminance and irradiance. The model is characteristic of strong timelines and high calculation accuracy. The second innovation point is to propose cluster routing algorithm of the wireless sensor network of heterogeneous non-uniform distribution based on node intensity and energy size in the Thesis. The experiment results show that invalidation round rate of the first node LEACH in the algorithm proposed in the Thesis is 86.2% higher than the average value and invalidation round rate of the last node is 65.9% higher than the average value; network coverage rate of the algorithm in 86% of lifetime is 38% higher than LEACH; network throughput of the algorithm proposed in the Thesis at round 2000 is 5 times as that of LEACH.
Keywords: Fuzzy inference; Distributed; Wireless sensor network; Cluster routing; Non-uniform distribution.
Facial Feature Extraction based on Principal Component Analysis and Class Independent Kernel Sparse Representation
by Xin Xiong, Li Kefeng
Abstract: Robust Principal Component Analysis (RPCA) and kernel sparse representation technology which have been proposed in recent years provide a new idea for solving problems of the above three aspects. In this Thesis, classification algorithm of kernel sparse representation has been proposed based on robust principal component analysis by using RPCA technology to generate redundant dictionary and kernel sparse representation to structure classifier, and has been used for face recognition. Basic idea of this algorithm is to generate base dictionary and error dictionary by using RPCA technology, and to realize face recognition through classifier structured by kernel sparse representation. Firstly, each training sample matrix has been decomposed into a low rank matrix and a sparse error matrix by using RPCA technology, so as to structure base dictionary and error dictionary by using the low rank matrix and error matrix respectively, and generate redundancy dictionary of sparse representation of test samples. Then, Kernel regularized Orthogonal Matching Pursuit (KROMP) algorithm has been proposed to get sparse representation coefficient which has been used to complete classification and recognition of test samples. Compared with similar algorithms, algorithm in the Thesis is of a high recognition rate for face recognition, and has a strong ability to adapt to noise and error interference.rnrn
Keywords: Principal component analysis (PCA); Image recognition; Sparse representation; Face recognition; Facial feature; Feature extraction.
An Arabic Natural Language Interface for Querying Relational Databases Based on Natural Language Processing and Graph Theory Methods
by Bais Hanane, Mustapha Machkour, Lahcen Koutti
Abstract: Nowadays, databases represent a great source of information. To extract information from these databases, the user needs to write queries using database query languages, such as Structured Query Language(SQL). Generally, for using this type of language, this user must know the structure of the database. However, this task can be difficult for non-expert users. In that, the using of natural language to extract data from the database can be a very important and efficient method. The problems in using natural language query are that it doesn't give any specification about the path access correspond to the required data. For that, many previous works are deal with the problems of freeing users from knowing the detailed structure of the database. But, Almost of this works are designed to the English language. Whereas, for the Arabic language which is the subject of this paper, there is not any proposed system. For that, we present in this paper a model of a natural language interface for databases. This interface allows the user to access data stored in the database by using Arabic language and it obviates the need for users to know the internal structure of the underlying databases. Also, it can function independently of database domain, and it can to improve its knowledge base through experience.
Keywords: Arabic language processing; DataBase; Graph theory; Dijkstra Algorithm; Extended Context Free Grammar.
Glossary Applications Model for Financial Terms with Boyer-Moore-Horspool Method Based on Mobile Application
by Nazori Agani, Arfian Maulidan
Abstract: String Search or the string matching is one aspect that is very important in terms of data processing, in addition the problem string matching is also one of the problems that are well known in the world of informatics. Some examples of the implementation of the string matching problem is in matching a string in a text editor application such as Microsoft Word, or in the case of bigger, ie matching website by entering key words as it has been implemented on search engines such as Google Inc. The general process in the searching a string is looking for a string that consists of some of the characters (called pattern) in a large amount of text. Search string is also used to look for patterns of bits in a large number of binary files. Problems begin to arise if the search process occurs in a lot of data and complex, of course, this would be very time-consuming and resources owned, so the search technique effectively and efficiently will be needed. The purpose of this study is to utilize a String Matching algorithm that is by using the Boyer-Moore-Horspool in the search data is a list of financial terms. Boyer-Moore algorithm-Horspool used to search for any string precision (word or phrase) entered with a high degree of accuracy of search, because it uses pattern matching from right to left. In addition to the HTML5-based application model, application model matching string can later be run multiplatform not only on mobile devices, but also be able to run on a desktop browser.
Keywords: String Matching; Search Engine; Boyer-Moore-Horspool; Mobile; Browser.