International Journal of Innovative Computing and Applications (22 papers in press)
Evaluating Nondeterministic Signal Machine Relative Complexity: A case study on Dominating Set Problem
by Sahar Ardalan, Sama Goliaei, Ayaz Isazadeh
Abstract: Signal machine is an abstract geometrical model of computation, which can be viewed as a continous space and time generalisation of cellular automata. Almost all studies that have been made are about deterministic signal machines. In spite of few studies that have been made on nondeterministic signal machines, the present paper shows their high efficiency in solving problems using a well-known combinatorial problem. We provide a method to solve the graph dominating set problem using nondeterministic signal machines. First we show how to design a signal machine for each specific instance of the dominating set problem. Then we propose a signal machine which solves the dominating set problem for any instance of the problem, and show how to reduce the space complexity of solution using nondeterminism.
Keywords: Abstract Geometrical Computation; Nondeterministic Signal Machines; Dominating Set Problem; Computational Model.
Sentiment Analysis: An empirical comparison between various training algorithms for Artificial Neural Network
by Ankit Thakkar, Dhara Mungra, Anjali Agrawal
Abstract: The proliferated increase in the commercial benefits of sentiment analysis has accumulated a huge interest in the domain of sentiment classification. Sentiment analysis categorizes a given text into positive or negative class. With the availability of a significant amount of electronic data, machine learning is becoming popular for text classification. In this paper, we present an empirical comparison between different training algorithms Gradient Descent (GD), Gradient Descent with Momentum backpropagation (GDM), Gradient descent adaptive learning rate backpropagation (GDA), Gradient descent with momentum and adaptive learning rate backpropagation (GDX), and Levenberg-Marquardt backpropagation (LM), used for training the neural network for the domain of sentiment classification. The performance of all the methods is compared and evaluated using three balanced binary datasets from various domains with different features using various performance metrics such as accuracy, precision, recall, $f$-score, mean squared error (mse), and training time. The experiments are performed $5$ times with different random seed values using 10-fold cross-validation. The values for the minimum, maximum, mean, median, standard deviation (SD), and the top-three values of $5250$ classification accuracies indicate that GDX and LM outperform other methods in terms of classification accuracy. The paper also outlines the effectiveness of these methods regarding the limitations, advantages, and accuracy for different domains.
Keywords: Sentiment Analysis; Artificial Neural Network; Training Algorithms; Binary class; Different Domains.
Slime Mould Foraging: An inspiration for algorithmic design
by Anthony Brabazon, Sean McGarraghy
Abstract: The metaphor of `foraging as search' provides a rich source of inspiration for the design of optimisation algorithms. An extensive literature has resulted in computer science over the past twenty years based on this, with algorithmic families such as ant colony optimisation and honeybee optimisation amongst others, being successfully applied to a wide range of real-world problems. Of course, all organisms must forage to acquire necessary resources and in recent years, increasing attention has been paid to the mechanisms by which nonneuronal organisms, in other words organisms without a central nervous system, forage. The vast majority of living organisms, encompassing some 99.5% of all biomass on earth, are nonneuronal. In this paper we introduce the plasmodial slime mould Physarum polycephalum. Inspiration has been drawn from some of its foraging behaviours to develop algorithms for graph optimisation and exemplars of these algorithms along with some suggestions for future research are presented in this paper.
Keywords: Slime mould algorithms; Foraging-inspired algorithms; Graph optimisation; Nonneuronal organisms.
Hybrid metaheuristic for generalized assignment
by Salim Haddadi
Abstract: This paper investigates the classical generalized assignment problem (GAP), a challenging combinatorial optimization problem that arises in numerous applications and that has attracted a great deal of research. For solving it we propose a hybrid metaheuristic combining guided search (GS), iterated local search (ILS), and very large-scale neighborhood search (VLSN). The hybrid method is iterative. It starts with a random assignment, and in every iteration it acts in the following way: (i) The best current assignment is perturbed. (ii) An exponential size neighborhood of the perturbed assignment is constructed. It is the feasible solution set of a special GAP where only two fixed machines can execute a job. The neighborhood construction is guided by arntechnique penalizing poor machine/job selections. (iii) The exponential neighborhood is searched for improvement. Exploring the neighborhood amounts to solving a monotone binary program (BP) a monotone BP is one with two non-zero coefficients of opposite sign per column. We prove that the proposed metaheuristic runs in polynomial-time when applied to a variation of GAP. Goodrncomputational results in terms of solution quality, as well as of computation speed, are obtained with two new best values on hard instances.
Keywords: Generalized assignment problem; hybrid metaheuristic; very large scale neighborhood; iterated local search; guided search; variable-fixing.
Implementation of Fuzzy Logic Controller based Quadratic Buck Converter for LED Lamp Driver Applications.
by Ravindranath Tagore Yadlapalli, Anuradha Kotapati
Abstract: This paper focuses mainly on design of quadratic buck converter (QBC) for LED lamp driver applications. The LED current regulation is the critical issue in the family of LED lamp drivers. The continuous mode based QBC is well designed for 60V/20mA at 100 kHz. The QBC performance is analysed with Digital average current mode control (ACMC) based QBC and fuzzy logic control-ACMC based QBC. The simulation is fulfilled using MATLAB/Simulink software.
Keywords: Pulse Width Modulation; Amplitude modulation (FLC); Organic LEDs.
Hybrid Algorithm for Materialized View Selection
by Mayata Raouf, Boukra Abdelmadjid
Abstract: Data warehouses store current and historical data, which are used for creating reports, for the purpose of supporting decision-making. A data warehouse uses materialized views in order to reduce the query processing time. Since materializing all view is not possible, due to space and maintenance constraints, materialized view selection became one of the crucial decisions in designing a data warehouse for optimal efficiency. In this paper the authors present a new hybrid algorithm named (QCBO) based on both quantum evolutionary algorithm (QEA) and colliding bodies optimization (CBO) to resolve the materialized view selection (MVS) problem. Also, some aspects of the well-known greedy algorithm (HRU) are included. The experimental results show that QCBO provides a fair balance between exploitation and exploration. Comparative study reveals the efficiency of the proposed algorithm in term of solution quality compared to well-known algorithms.
Keywords: Data warehouse; materialized view selection; metaheuristic; quantum evolutionary; colliding bodies optimization.
Special Issue on: Cognitive Computing for Emerging Internet of Things
HOG Features and Connected Region Analysis-Based Workpiece Object Detection Algorithm
by Yu Ting, Tian Maoyi
Abstract: In order to solve the problem of bearing workpiece object, namely, the insuffi-cient detection ability of the algorithm caused by the complex edge features and inconspicuousness of the surface as well as the uncertainty and interference of the background, a HOG features and connected region analysis-based workpiece object detection algorithm is proposed in this paper starting from the calculation of HOG features, the image gradient direction, the connected region analysis and object detection. The image is processed in accordance with the color chroma-tography of foreign matters to separate the foreign matter from the background. Firstly, the target images of standard workpiece in the training set are meshed to calculate the pixel gradient in the grid, count the gradient histogram and com-plete the extraction and training of HOG features. Then interval division of the single peak threshold is refined, and a two-threshold segmentation mechanism is proposed to convert the two-valued image into a label image by combining the connected region analysis, and the evaluation of pixel attribute and the filtering of interference is conducted to achieve the purpose of accurately detecting the workpiece object. The experimental results show that the bearing workpiece ob-ject detection algorithm in this paper has higher accuracy and stability.
Keywords: Workpiece object detection; Image gradient; Chromatography; Edge feature; Connected region; Meshing; Histogram.
SLFNs Interpolation Fingerprint Particle Filter-Based Shared Bicycle Tracking Algorithm
by Cao Honghua, Yan Xiaoyan, Li Yan
Abstract: In order to improve the performance of traditional fingerprint detection method in the process of tracking the shared bicycle, the inertial sensor is used for data measurement. The particle filter (PF) method is a widely used sensor fusion al-gorithm, but the initialization and weighting processes are problematic in shared bicycle positioning systems. In this paper, a new PF scheme is proposed, and it can produces smooth and stable localized knowledge. However, the feed-forward network that uses the single hidden layer is used to simulate the estimation and improvement of the performance of multiple probability to achieve the distinc-tion of similar fingerprints. At the same time, the random sample consensus al-gorithm (RANSAC) is used to initialize the algorithm so as to reduce the conver-gence time. Experiments show that the tracking error of this scheme is less than 1.2m, which is superior to the selected comparison method.
Keywords: Feed-forward network; Particle filter; Shared bicycle; Tracking algorithm; Strength indicator of signal.
Markov Model-Based Low delay Data Aggregation tree Algorithm
by Huang Luyu
Abstract: The data aggregation technology can save resources of wireless sensor networks, but it can also add extra delays. To this end, specific to the special scenario where data transmission must be completed under specified delay constraints, the Markov model-based low delay data aggregation tree (MLDGT) algorithm is proposed. Firstly, the formal expression of the problem of constructing data ag-gregation tree under delay constraints is given. This problem has been confirmed as a NP problem. Then, the Markov approximate model is used to find a subop-timal solution, and further obtain the low delay data aggregation tree. Finally, the effectiveness of the MLDGT algorithm is analyzed by simulation and compari-son. The experimental results show that the MLDGT algorithm can reduce the data aggregation delay.
Keywords: Wireless sensor network; Aggregation tree; Data aggregation; Formal expression; Markov model.
MeTis Meshing-Based Bayes 3D Ship Model Geometry Reconstruction
by Yue Jingya
Abstract: In order to improve the compression efficiency of 3D model geometry reconstruction process, a MeTiS meshing-based Bayes 3D ship model geometry reconstruction algorithm is proposed. The original 3D mesh is subnetted by the MeTiS method at the coding end, and the geometrical shape of the subnet is coded by a random linear matrix, and the neighbor node of the boundary node is considered to construct the data sequence by the pseudo random number generator; then the Bayes algorithm is used to design the geometric model reconstruction algorithm, and the learning rules for the mean, variance matrix and model parameter are theoretically given, realizing the geometric reconstruction of 3D model; finally, on the 3D model standard test library and 3D ship model, the simulation comparison with the GFT, LSM and CSGFT and other algorithms show that the proposed method has a relatively high bit rate compression index and a low reconstruction error, leading to significantly improved computational efficiency.
Keywords: 3D vessel model; Geometric reconstruction; MeTiS meshing; Bayes; Neighbor node.
Dynamic Node Adaptive Incremental Interaction Optimization in Micro-blogging Community
by Fei Shang, Xiaobo Nie
Abstract: Most community discovery methods are based on network topology and edge density for best community determination, but these methods have very high computational complexity and are very sensitive to the form and type of network. In order to solve these problems, this paper proposes a micro-blogging community interaction optimization algorithm based on dynamic node adaptive increment model, which is based on optimizing the interaction of members in each community, and uses greedy algorithm to search the best candidate for the optimal community effectively without traversing all nodes. The model can quickly and accurately measure the interaction difference between the community and the community. Finally, the simulation tests on the datum test network and the Sohu micro-blogging platform show that the proposed algorithm is better than the selected contrast algorithm in the index of recall, accuracy, algorithm calculation time and network coverage.
Keywords: Complex network; Edge density; Community discovery; Self- adaptive; Interaction optimization; Incremental model.
Quantitative Structure-Activity Analysis of Predicted Drug Targets Based on Adaboost-SVM
by Fujun Gao
Abstract: This paper first constructs two sets of data sets to demonstrate the effectiveness of the proposed method, one data set consists of all human protein data, and the other is composed of human G protein-coupled receptor data, which accounts for a high proportion of drug targets. It extracts the corresponding primary structure, polypeptide characteristics and basic physicochemical properties of each protein in the data set, feature selection is used to reduce the learning burden of classifier as the feature space of training classifier. Then the data are preprocessed and the optimal classifier is constructed by adjusting the parameters of the model. Data sets are classified by SVM classifier and Adaboost-SVM classifier respectively in the experimental construction and analysis part, analyzed and compared the experimental results of two classifiers applied to two sets of data sets before and after data preprocessing, the classification results of the two groups were verified each other to increase the reliability of the classification results. The experimental results verify the effectiveness of the proposed method. At the same time, it shows that the method proposed in this paper can effectively predict drug targets, and provide a preliminary reference for drug research and development workers.
Keywords: Direct push type; Support vector machine; Predictive drugs; Target quantification; Structure-activity analysis.
Energy Sensing Streaming Media Data Transmission Protocol Based on Implicit Markov Algorithm in WSNs
by Guozhong Li
Abstract: Streaming media transmission protocols can be divided into traditional streaming media push protocol and streaming media pull protocol. Traditional streaming media push protocols such as RTP, whose server determines the channel state according to the RTP feedback from the client, and then decides to send data packets suitable for the current channel state. The pull protocol of streaming media sends data packets according to the content of the client when the transmission rate meets the requirement on the contrary. Streaming media pull protocol greatly reduces the complexity of servers in streaming media transmission technology, it can also support the application of different adaptive algorithms in the transmission process compared with the traditional universal server transmission algorithm mechanism. Therefore, this kind of pull protocol cannot only improve the quality of service of streaming media transmission, but also meet the requirements of different channel states and different clients.
Keywords: WSNs; Data aging; Mesh area; Energy aware; Data transmission protocol.
Multi-Feature Fusion Energy-Saving Routing in Internet of Things Based on Hybrid Ant Colony Algorithm
by Ren Xiao-Li, Yang Jian-Wei, Li Nai-Qian
Abstract: This paper analyzes the research status of sensor networks and several improved LEACH protocols. It is known that there are some shortcomings in current low-energy clustering protocols:The problem of uneven network cluster and unequal energy consumption of each node in the cluster group leads to excessive energy consumption of some nodes, the whole network life cycle is also greatly shortened. This paper proposes a multi-feature fusion energy-saving routing algo-rithm based on hybrid ant colony algorithm to optimize and upgrade LEACH energy-saving routing model for the Internet of Things on the basis of LEACH.(NPCHS-Leach)to improve the problems of short lifetime and low energy utilization caused by existing clustering routing protocols,it improves and prolongs the network life cycle. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments.
Keywords: Ant colony algorithm; Multi-feature fusion; Internet of things; Energy efficient routing.
Energy-Saving Algorithm for Data Center Network Based on Genetic Algorithm
by Shu Yang, Hua Yang, Hua Yang, Wen Chai, Wen Chai, Zehui Liu, Zehui Liu
Abstract: This thesis focuses on network equipments in the data center which hasrncaused rapidly growth of energy consumption recent years. The switches account for the largest proportion of energy consumption of network equipments, so turning off unneeded switches can reduce energy consumption effectively. Based on this point, we develop an high-efficient routing algorithm based on genetic algorithm(GA) in order to improve energy consumption of network equipments. Genetic algorithm is a kind of a heuristic algorithm which solves the optimization problem rapidly by imitating the way of the natural selection, but to a certain degree, it reduces accuracy. Its a complicated problem to decide routing path in arnshort period, so we choose genetic algorithm to achieve our goals. In ourrnsimulation, we make some improvements of GA in order to fit our problem andrnraise the accuracy of its solution.
Keywords: Data center network; Energy efficient routing; Genetic algorithm.
Damage Prevention Analysis of Heavy-Duty Gear Body Based on Finite Element Neural Network
by Pei Weichi, Dong Jianwei, Long Haiyang, Ji Hongchao, Zhang Wenming, Li Yaogang
Abstract: The method of damage prevention analysis of heavy-duty gear body based on finite element neural network is proposed to improve the effectiveness of damage prevention analysis of heavy-duty gear body. Firstly, a design platform for gearbox gears of caterpillar tractors is developed based on finite element theory, the three-dimensional model of the gear is designed on this platform, and the bending and contact finite element analysis of the gear teeth is carried out, the bending stress and contact stress of the gears are obtained, which provides a basis for the parameter design and reliability of the gears. Secondly, a neural network algorithm is introduced to predict and analyze the impact of damage data of heavy-duty gear body. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments.
Keywords: Finite element; Neural network; Heavy-duty gear body; Destruction prevention.
On-Demand Distance Vector Refinement-Based Car Networking Stable Routing
by Shutao Zhou, Chengxing Li, Hui Yu
Abstract: Due to the high-speed movement of vehicles and obstacles in urban scenes, the communication paths between vehicles become extremely fragile. Specific to the routing problem of VANETs, a path criterion-based on-demand distance vector PA-AODV routing algorithm is proposed. The PA-AODV routing algorithm makes full use of the characteristics of AODV routing, and improves its routing decision. By calculating the path criterion weight and preferentially selecting the path with small weight for data transmission, the path stability is thus improved. The experimental data show that the proposed PA-AODV routing reduces the data packets loss rate and also shortens the end-to-end transmission delay.
Keywords: Car networking; Distance vector refinement; On-demand distance vector; Communication path; Link quality.
Cognitive and Artificial Intelligence System for Logistics Industry
by Jing Zhao, Fengjie XIE
Abstract: With the continuous development of cognitive science, the impact on society is becoming more and more significant.Artificial intelligence is an important branch of cognitive science.Artificial intelligence has been applied to medical, education, security, logistics and other industries, which has broad prospects for development. Logistics industry uses artificial intelligence technology to complete intelligent search, face recognition, combined with large data calculation and planning reasonable path in warehousing, which plays an important role in the process of storage, transportation and distribution.Taking China's logistics industry as the research object, this paper analyzes the application of artificial intelligence technology in the logistics industry. In the warehousing process, artificial intelligence technologies including compile storage code, automatic picking with Automated Guided Vehicle, warehouse robot to improve work efficiency.Intelligent unmanned aerial vehicle (UAV) transport and intelligent sorting technology are implemented by artificial intelligence technology in the transportation link.Logistics distribution links use artificial intelligence technology to plan the best path, improve the recognition rate of express waybill that save a lot of labor.Artificial intelligence technology allocates logistics resources, optimizes logistics links, and improves logistics efficiency and other measures to promote the development of logistics informatization and automation.rn
Keywords: Cognitive Technology;Artificial Intelligence (AI); Logistics Industry.
Study on oceanic big data clustering based on incremental K-means algorithm
by Yongyi Li, Zhongqiang Yang, Kaixu Han
Abstract: With the increase of marine industry in the Beibu Gulf, data clustering has become an important task of intelligent ocean. Partition clustering methods are suitable for marine data. However, traditional K-means algorithm is not suitable for large scale data. Focusing on the characteristics of oceanic big data, we propose a clustering method based on incremental K-means (IKM) algorithm. First, a vector model is adopted to represent data sets, and the calculation model for mean values and centers is used to initialize arbitrary numbers of data points. Second, the input data vectors are iteratively calculated in an incremental vector form. Finally, by applying incremental vector and distance model, the large-scale data are clustered according to convergence condition. Experiments show that the algorithm can increase the clustering efficiency, reduce time and space complexity, and lower the missing data rate.
Keywords: cluster; K-means; incremental; oceanic big.
A fuzzy comprehensive evaluation model for Smart City Application
by Huaihui Liu, Zhiqing Zhang, Zhijie Sun
Abstract: As one of the basic social relationships in current world, the relationship between the police and citizens directly reflects the relationship between the authority and the public, which play an important role in the social stability. It has an essential significance to properly get to know, to deal with and to evaluate the police-citizen relationship. Firstly, we design a hierarchy evaluation index system model about the harmony degree between the police and the citizens, with the help of questionnaires, based on the principle of designing an evaluation index system and the five major factors that impact the harmony relationship between the police and the citizens. Secondly, we set up a fuzzy comprehensive evaluation model based on an improved analysis hierarchy process (AHP). And then we make an empirical research on the harmony degree between the police and the citizens with the help of the model we set up. Finally, based on the conclusion of the empirical research, we make a propose to the government and the security department about how to promote the construction of the harmony police-citizen relationship. The research enriches the methods and means of evaluation the harmony degree of the police and the citizens and exemplifies the empirical research.
Keywords: the harmony degree of the police and citizens; evaluation index system; the improved AHP; the fuzzy comprehensive evaluation mathematical model.
A Chameleon Hash Authentication Tree Optimization Audit for Data Storage Security in Cloud Calculation
by Yang Bo
Abstract: In order to improve the security of data storage in cloud calculation , a chameleon Hash authentication tree optimization audit method for data storage security in cloud calculation is proposed. First, an optimized public audit agreement is proposed. By storing homomorphic linear validator for user data on TPA sites, the response size of cloud storage server (CSS) is optimized. At the same time, the quasi-random function is used to optimize the query request to CSS; secondly, the chameleon hash and an improved chameleon authentication tree are used to perform efficient dynamic data updating on client data (cloud calculation ) to support block-level updating and fine-grained updating; finally, through thorough security and performance analysis, it is clearly verified that the proposed method is safe and efficient.
Keywords: Cloud calculation; Data storage; Chameleon authentication tree; Third part audit; Quasi random function.
Optimization of CoMP based Cellular Network design and its Radio network parameters for Next Generation HetNet using Taguchi
by Sarosh Dastoor, Upena Dalal, Jignesh Sarvaiya
Abstract: A heterogeneous network (HetNet) is a complex network made of variable cellular dimensions with different network topology. An erratic network design is valueless, unproductive and expensive. Research paper describes coordination of multipoint transmission, in which a collection of transmitting Base Stations (BS) dynamically harmonizes their transmission among themselves, enhancing the coverage to the edge users. The proposed cellular planning strategy uses variable radii cells forming a cluster in a given region to be dimensioned. For a given cluster, minimum distance (dmin) between two cells has been calculated and using proposed (1/3 d_min ) dimensioning technique, the coverage radius of cells in a cluster is made, forming a HetNet. By optimizing the network; coverage, cost and energy requirements could be minimized and optimization of network performance parameters like transmission power, tilt and azimuth angle of antenna with the radius of cell provides cost-efficient deployment of a network. The research paper proposes the mathematical dimensioning model for the design of a HetNet as well as its performance parameters using Taguchi
Keywords: heterogeneous network; optimization; orthogonal array; coordination; Multipoint transmission; Taguchiâ€™s Method; azimuth; tilt; energy conservation; throughput.