International Journal of Innovative Computing and Applications (56 papers in press)
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.
A comparison between evolutionary and local search techniques applied to NoC Design Space Exploration
by Jefferson Silva, Silvia Maia, Monica Magalhães, Márcio Kreutz
Abstract: Networks on chip (NoCs) emerged as a communication architecture that overcame the limitations of bus architecture. As more cores were being incorporated in a single die, shared communications architectures reached out limitations in terms of scalability and performance. However, the NoC based communication architecture has many configuration parameters leading to a huge design space to be covered during the design phase. Aiming to find optimized configurations, some methods should be employed to explore the design space and speed up this process. This paper investigates three techniques, Genetic Algorithm (GA), Memetic Algorithm (MME) and Iterated Local Search (ILS). Results have shown the lowest execution time for ILS when compared with the other two. In relation to the quality of the solution, the MME overcame both in almost all scenarios, even considering execution time or number of evaluation as stop criteria.
Keywords: evolutionary computing; networks on chip; genetic algorithm; iterated local search; memetic algorithm; design space exploration.
Human motion tracking under indoor and outdoor surveillance system
by Wafae Mrabti
Abstract: This paper gives an overview of the various potential applications involved in human motion tracking. Also, a review of some relevant algorithms in this area are summarized based on three main components; the human target extraction, the features extraction, and the motion model. In addition, a proposed generative and discriminative human tracking method is presented. This proposed method is based on the hybridization of Kalman Filter (KF) and the Support Vector Machines (SVM). Numerous experiments illustrate the effectiveness of the proposed method against several state of the art trackers. These experiments are applied on several real world image sequences with various challenges that make the tracker vulnerable which are; partial and full occlusions, illumination changes, scale variation, out-of-plane, rotation, motion blur, low resolution, deformation, fast motion and background clutter.
Keywords: Features extraction; Motion model; Kalman filter; Histogram of Oriented Gradient; Support Vector Machines.
Plant propagation algorithm for nurse rostering
by Salim Haddadi
Abstract: This paper investigates the nurse rostering problem (NRP), a challenging combinatorial optimization problem that arises in heath care institutions. We propose to solve it by using the plant propagation algorithm (PPA). As many successful metaheuristics, PPA is inspired by a life process. It emulates the strategy of reproduction and propagation of the strawberry plant. Before applying PPA, a variable-fixing procedure is used for heuristically discarding variables. In practice, it results in removing up to 99% of the variables without sacrificing solution quality. Elite solutions provided by PPA are used to further discard variables, leaving a very sparse NRP that can be solved directly by an IP solver. Computational and comparative results are presented on a widely used set of benchmark instances.
Keywords: Bio-inspired computation; plant-inspired algorithm; plant propagation algorithm; nurse rostering; elite solutions; variable-fixing.
MODIFIED BIO-INSPIRED ALGORITHMS FOR DIAGNOSIS OF BREAST CANCER USING AGGREGATION
by Moolchand Sharma, Shubbham Gupta, SUMAN DESWAL
Abstract: The most widely detectable of all cancers found in women is breast cancer. The mortality rate is also the second-highest among women with a 12% growth rate. It is very pertinent to diagnose breast cancer in the nascent stages so that the survival of the patient is ensured with the help of proper medication. Several algorithms have been proposed in this regard. However, they have failed to achieve the desired level of accuracy. An improved version of the Particle Swarm Optimization & Firefly Algorithm is presented in this paper to overcome the drawbacks of the existing algorithm. The two algorithms are further aggregated to improve the accuracy of the results. The aggregated algorithm is used on the Breast Cancer Wisconsin (Diagnostic) Data Set(real-valued dataset), and results are calculated for different classifiers. An accuracy of 92%-96% is shown by Improved Particle Swarm Optimization and 1%-2% overall hike in the accuracy by Improved Firefly Algorithm, respectively. Finally, the aggregated algorithm shows an accuracy of 93%-97%. Further, Random Forest Classifier has displayed the best accuracy of 97%.
Keywords: Breast Cancer; Bio-inspired Algorithms; Aggregation; Particle Swarm Optimization; Firefly Algorithm; Feature selection; Linear Support Vector Machines; Decision Tree; K-Nearest Neighbor; and Random Forest Classifiers.
Fuzzy Control Mathematical Modeling Method Based On Dynamic Particle Swarm Optimization Training
by Runxia Gao, Houping Jiang
Abstract: Aiming at the false correlation problems under set number limit condition in the fuzzy control mathematic modeling process, combined with dynamic particle swarm optimization training algorithm, the traditional fuzzy control mathematical model based on the Nash equilibrium solution method is difficult to converge to the optimal solution of the state space, leading to bad control performance. This paper proposes the fuzzy control mathematical modeling method based on dynamic particle swarm optimization training, constructs the general structure model of fuzzy control, describes the standard particle algorithm, under the constraint of learning samples of random functional, obtains the global optimal solution of control domain of fuzzy control parameters, and conducts particle swarm optimization training by adopting the position vector fitness updating method. The research results show that the new method can make every step state update get more effective observation information, reduce the error caused by the difficult use of observation data, reduce the computation cost and improve the accuracy of fuzzy control.
Keywords: fuzzy control; observation error; ensemble transform kalman filter (ETKF).
Performance analysis of machine learning techniques for Glaucoma detection based on textural and intensity features
by L.A.W. SINGH, Hitendra Garg, Dr. Pooja, Munish Khanna
Abstract: Glaucoma is one of the significant causes of blindness, so early-stage detection is essential. According to a recent survey, the number of persons suffering from glaucoma is increasing day by day. As it covers about 15% to 20% of the total population. Glaucoma increases the pressure inside the eye due to these reasons optic nerves get permanently damaged,1 which in turn leads to vision loss. This increased pressure also makes the optic cup size more significant as compared to the optic disc. So, it is essential to detect glaucoma in early-stage to prevent blindness. In this paper, we proposed a methodology into two parts: In the first part using the fast fuzzy C-Mean approach calculate CDR and in the second part, we extract textural based features and intensity-based features. Textural Features include Local Binary Pattern, Gray-Level Co-Occurrence Matrix and Harlick Features. Intensity-based features include Color Moment and Skewness. We also extract some other vital features like CDR (cup to disc ratio), Entropy, Horizontal, and Vertical Diameter of Disc and Cup. In the second part after extracting features applying machine learning techniques, Ophthalmologists can correlate the result of both parts and decide to glaucoma or not. We use the dataset of 298 images consisting of both standard and glaucomatous images. This paper proposes an automated glaucoma diagnosis using various Machine Learning algorithms like Support Vector Machine with an accuracy of 95.5%, K-Nearest Neighbour with an accuracy of 93.3%, and Naive Bayes with an accuracy of 93.33% respectively. In the proposed method correlate the result of CDR result and best efficiency of machine learning techniques. So ophthalmologists can make the correct decision for the person eye affected with glaucoma or not.
Keywords: Glaucoma Disease; Retinal Fundus Image; CDR; K-Nearest Neighbour; Naive Bayes; Support Vector Machine.
Cardiac Arrhythmia Classification using Sequential feature selection and Decision tree classifier method
by Durga S, Esther Daniel, Deepa Kanmani S, Jinsa Mary Philip
Abstract: Cardiac arrhythmia is referred to as a condition in which the hearts normal functionality is restricted resulting in cardiovascular diseases. Effective and well-timed monitoring is very much essential to save human life. During the past few years, keeping track of when and how arrhythmias occur has gained a lot of significance as it leads way towards many life-threatening issues like stroke, sudden cardiac arrest and also heart failure. This paper provides an evaluation of various classification algorithms based on feature selection techniques that improve the performance of the cardio monitoring system. The pre-eminent features are sorted out using feature selection methods. The feature selection methods enable to decide the features that can contribute to improve the performance. The paper also gives efficient combinations of distinct classification algorithms along with feature selections which improves the accuracy. Few popular machine learning algorithms namely Na
Keywords: Cardiac arrhythmia; Classification algorithms; Accuracy; Decision tree classifier; Sequential feature selection.
A Review on Existing Learning Techniques Applied in Solving Optical Character Recognition Problem
by Vijaya Krishna Sonthi, Nagarajan S, Krishna Raj N
Abstract: Optical Character Recognition is treated as the classical problem in Image Processing that can be widely used for application areas related to Computer Vision. This process is highly dependent on the languages, as well as various formats of information like hand written/printed/sculptured documents as input. The chief operations that can be performed while handling this task are training the system and predicting the characters based on the trained knowledge. Conventionally, classification and regression mechanisms are used for this purpose. Due to the recent advents in technological progress, the same mechanism was served with the help of modern Machine Learning and Deep Learning concepts. The main agenda of this paper is to create awareness among the researchers by discussing the basic functionalities of above said techniques.
Keywords: Machine Learning; Deep Learning; Optical Character Recognition; Pattern Recognition; Image Processing;.
A NOVEL APPROACH FOR THE SOLUTION OF GENERALIZED FUZZY ASSIGNMENT PROBLEM
by E. Melita Vinoliah, K. Ganesan
Abstract: In Engineering and other fields, generalized assignment problem plays a vital role. It has widespread applications in routing problems, knapsack problems and other complicated models. In real world problems, the available data may not be known with certainty. Hence to model and solve practical problems, we must deal with uncertainty and vagueness. Fuzzy sets play a vital role to tackle these uncertainty and vagueness. The generalized fuzzy assignment problem became popular and has gained its importance too. In this paper, we consider a generalized fuzzy assignment problem which has restrictions both on tasks and on persons with respect to his/her efficiency / qualification. We propose a unique approach for the solution of generalized fuzzy assignment problem with restrictions without converting the problem to a corresponding crisp form. Costs for handling jth task by the ith person are taken to be trapezoidal fuzzy numbers. The trapezoidal fuzzy numbers are first represented in its parametric form. In view of the decision makers preference, a new ranking method and arithmetic operations are used to bring a desirable solution. A numerical example is given to illustrate the proposed method.
Keywords: Generalized fuzzy; assignment problem; trapezoidal fuzzy numbers; fuzzy arithmetic; fuzzy ranking; extremum difference method; judging matrix; uncertainty and vagueness; generalized assignment problem; trapezoidal fuzzy number; multilevel fuzzy generalized assignment problem,.
Analyzing Smart Power Grid against different Cyber Attacks on SCADA system
by Tafseer Akhtar, Brij Gupta
Abstract: The integration of information and communications technology (ICT) with traditional power grid is transforming the power grid into a more reliable and smarter cyber physical system (CPS). Supervisory Control and Data Acquisition (SCADA) system is generally used to implement the cyber layer for power grid. This system is responsible for real time monitoring and control of power grid and power delivery network. SCADA system with the help of other devices create smart power grid environment, but it also makes power grid vulnerable against different cyber attacks as it needs connection with different kinds of open networks. Smart power grids are extremely critical infrastructure and attack of any kind on it, may affect socio-economical condition of the region. In this paper we present a co-simulation environment for smart power grid using OMNeT++ and openDSS. And different cyber attack scenarios are also taken as a part of the developed framework. Co-simulation involves integration between network simulator and power generation simulator as both works as cyber layer and physical layer respectively. The main contribution of the paper is behavioral analysis of the smart power grid environment while attack performed on cyber layer of the grid and store the response of power grid during these attacks. The findings of the proposed work are vulnerabilities realization of smart power grid, as during cyber attack grid is unstable and power generation is significantly dropped. The Proposed framework is developed by using open source simulators only.
Keywords: Co-simulation; Smart power grid; SCADA system; Cyber attack scenarios.
Research on Intelligent City Parking Guidance Method Based on Ant Colony Algorithm
by Aifeng Chen
Abstract: In order to get the most satisfactory parking space at the fastest speed, an intelligent urban parking guidance method based on ant colony algorithm is proposed. The main factors affecting the selection of parking spaces in parking lots are analyzed, including walking distance, driving distance, walking time, driving time and so on. Each factor is set as multiple attributes of berth, and the optimal berth selection model of smart city is established. Ant colony algorithm is used to solve the model, obtain the optimal parking space, and realize intelligent guidance of intelligent city parking. The simulation results show that the proposed method is feasible and effective.
Keywords: Ant Colony Algorithm; Intelligent City; Parking; Intelligent Guidance.
Business Intelligence & Data Analytics Framework: Case Study of Humanitarian Organizations Refugees Registration System
by Amal Ballout, Mays Al Hasan, Salam Freihat, Ammar Elhassan
Abstract: Business Intelligence and Analytics has gained prominent focus among organizations with information systems that collect and process vast amounts of data. Voluminous, unprocessed data does not lend itself to offering useful insights for businesses, especially with basic statistical methods and traditional reporting techniques. In this work, we design a Business Intelligence and Data Analytics Framework for Refugee Registration System serving over six million refugees to collect, collate and filter demographic data. The proposed reporting mechanism leverages the power of interactive dashboards to offer informative and intuitive reports and visualizations that are accessible and interpretable by stakeholders.
Keywords: Business Intelligence; Analytics; Demographics; Reporting; Dashboards.
Research on Coordinated Control Method of Urban Traffic based on Neural Network
by Lede Niu, Mei Pan
Abstract: Due to the complexity of data in urban traffic coordination and control, there is a lack of relevance between data nodes of existing coordination methods, which leads to urban traffic holdup and congestion.A coordinated urban traffic control method based on neural network is proposed.The traffic flow prediction model is constructed to predict and calculate the urban traffic flow, green signal ratio, phase and period.The coordinated control method based on neural network is used to fine-tune the traffic signal, green signal ratio, phase and cycle, so as to improve the traffic capacity at the intersection during the peak period, and finally realize the coordinated control of urban traffic.The simulation results show that the proposed method can effectively alleviate the traffic capacity at intersections, and the time required for the method to run the whole process is less, which indicates that the proposed method is effective and reliable.
Keywords: Neural network; Urban traffic control; Signal light coordination; simulation.
An Efficient Image Compression Using Pixel Filter for Social media applications
by Ramesh Makala, Ranganath Ponnaboyina, Gowtham Mamidisetti
Abstract: Image data transfer has increased rigorously in present times in social networking sites, mobile apps and live streaming video applications. This phenomenon puts enormous effect on internet bandwidth and speed of image transfer and loading. Image compression deals with this issue by reducing image sizes while maintaining quality aspects. In general filters are applied on a block and deal with quality enhancements. We introduce a new filter which is applied on each pixel and compresses it. Proposed method classifies pixels into different buckets based on filter. Inverse process tries to restore pixel values back from buckets. With experimental results, we show compression and quality aspects variations based on filter selection.
Keywords: Color Map; Image Compression; Filters; Quality Enhancement.
A 600mV +12 dBm IIP3 CMOS LNA with gm smoothening auxiliary path for 2.4 Ghz wireless applications
by Sharath Babu Rao
Abstract: CMOS Low Noise Amplifiers (LNAs) were used in IEEE 802.11 ac/g/n/ad RF Receivers which finds a large scale of applications in gyroscopes, accelerometers, biomedical applications such as neural, nerve, cardiac, biological probes, and other mission sensors. Additionally, LNAs were found in an environment, to amplify weak signals with in-sufficient radiation from WI-FI transmitters operating in the 2.4/5 GHz range, which include cordless telephones, Bluetooth devices, wireless keyboards, and radio equipment. Non-Linearity limitations of the LNA come up with producing altered (or modulated) amplitudes resulting from higher-order frequency component (Especially third-order harmonics) interaction with fundamental frequency (first-order harmonic) components. For radio-frequency (RF) applications where signal frequencies are high, consequences of intermodulation distortion results from the nonlinearities of the MOS Transistors used in the LNA. As it is impracticable to eradicate nonlinearities completely, Second-order nonlinearities were added in out of phase with the third-order nonlinearities in Derivative and Modified Derivative methods found in the literature can able to suppress the third-order nonlinearities up to an Input Intercept Point(IIP3) of 8 dBm only. In this article proposed LNA smoothens the nonlinearity dependence on the transconductance, can able to increase the IIP3 performance up to 12 dBm using the common source differential architecture with PMOS Loads in the auxiliary path used to suppress the nonlinearities in the LNA circuitry. Because of battery operation or limited power supply capacity, these application areas require low power operation. The proposed LNA offers gain (S21) of 12 dB operates at ultra-low voltage supply headroom of 600mV with good stability deemed as a favorable requirement for low power wireless applications.
Keywords: 802.11 b/g/n; Local Area Network (LAN); Wireless LAN(WLAN); Wireless Sensor Networks (WSN’s); Wireless Personal Area Networks (WPAN’s); Low Noise Amplifier (LNA); Generic Process Design Kit (GPDK); Berkeley Short-Channel IGFET Model version 3 (BSIMv3); Cascode LNA; Folded Cascode LNA. Modified derivative superposition (MDS) technique.
PERFORMANCE ANALYSIS OF RECONSTRUCTED IMAGE USING COLOR DEMOSAICING ALGORITHM FOR DIGITAL IMAGES
by Allwin Devaraj
Abstract: Demosaicing algorithm is emerging as one of the most promising techniques in Digital Image Processing because of providing high-quality image enhancement with low power and high speed. Edge detector, weighting block of anisotropic type, and a compensator with filters are the components of the proposed algorithm. Spatial filters and Laplacian methods are used in the filter-based compensation methodology. These improve edge detection, and the blurring effect is reduced. Hardware sharing reduces the cost of the system. Portable and fast systems are required to produce the test results in a short span. For the above-mentioned purpose, the system is designed in VLSI technology. Matlab and Xilinx tools are used for the system design. A novel strategy to address shading contrasts in multiview video successions is proposed, and it utilizes a thick coordinating based worldwide enhancement structure. The proposed vitality work guarantees to safeguard nearby structures by controlling deviations from the first picture angles. When compared with the existing works 8% and 90.6% of power reduction is observed in the proposed work. It also improves the average color signal-to-noise ratio quality by more than 1.6db.
Keywords: Very Large Scale Integration; Peak Signal-to-Noise Ratio; Field Programmable Gate Array; Green Interpolator.
Modified Adaptive Inertia Weight Particle Swarm Optimization for Data Clustering
by Vikash Yadav, Indresh Kumar Gupta
Abstract: Data clustering is widely applied in many real world domain including marketing, anthropology, medical science, engineering, economics, and others. It concern with partition of unlabelled dataset objects into clusters (groups) based on similarity measure. Partitioning approach of dataset objects must follow that, intra-cluster distances are smaller and inter-cluster distances are larger. In current work particle swarm optimization (PSO) is employ for clustering. Sometime PSO may stuck into local optima, to overcome the PSO algorithms trapping in local optima a modified adaptive inertia weight particle swarm optimization (MAIWPSO) is developed for data clustering based on fitness value of particles. K-means, PSO and MAIWPSO for clustering have been simulated on six standard dataset namely iris, thyroid, heart, breast cancer, crude oil and pima. Simulation results confirm MAIWPSO is better approach for clustering against K-means and PSO.
Keywords: Data clustering; K-means clustering; Particle swarm optimization; fitness function.
Instance-based novelty detection in passive sonar signals
by Victor Hugo Da Silva Muniz, João Baptista De Oliveira e Souza Filho, Eduardo Sperle Honorato
Abstract: In submarines, sonar operators have the main task of identifying potential threats, named as contacts in the military jargon. The principal tool exploited when dealing with such situations is the passive sonar system. Automatic contact classification models may relieve the huge sonar operator workload but require mechanisms capable of identifying any contact not considered during system development. This paper discusses the development of a hierarchical instance-based detector of unknown contact classes for passive sonar signals, focusing on practical strategies for its hyperparameter tuning and performance assessment. Experimental data exploited in system evaluation comprises the acoustic noise irradiated by 28 ships belonging to 8 classes. These ships were submitted to different operational conditions in several runs conducted in an acoustic range. The kNN algorithm has performed best, achieving a novelty detection rate of 78.0%, associated with an average known case identification rate of 95.0%, considering a five unknown class evaluation scenario.
Keywords: passive sonar; novelty detection; automatic classification systems; instance learning; kNN; k-nearest neighbours.
Constrained Neural Classifier Training Method for Flaw Detection in Industrial Pipes Using Particle Swarm Optimization
by Gilvan Silva, Edmar Souza, Eduardo Simas, Paulo Farias, Maria Albuquerque, Ivan Silva, Cláudia Farias
Abstract: A novel method for constrained training of multi-class artificial neural network classifiers is proposed in this work. The traditional training procedure is usually based on mean square error minimization and thus, all classes of interest are considered as having the same relevance for system performance. This is not always the case for real-world applications in which the class relevance may be unbalanced. In this paper, cost functions designed to introduce classification performance constraints for specific classes are presented and particle swarm optimization is used as global optimization method. The proposed method is applied to a non-destructive evaluation decision support problem using pulsed eddy currents signals. Experimental results obtained from thermally insulated industrial pipes indicate the efficiency of the proposed method in comparison to neural networks trained from the traditional back-propagation algorithm.
Keywords: Artificial Neural Networks; Particle Swarm Optimization; Pulsed-Eddy Current Evaluation; Signal Processing.
Convolutional neural networks applied in the detection of pneumonia by x-ray images
by Luan Silva, Leandro Araújo, Victor Souza, Raimundo Neto, Adam Santos
Abstract: According to the World Health Organization (WHO), pneumonia kills about 2 million children under the age of 5 and is constantly estimated as the leading cause of child mortality, killing more children than AIDS, malaria, and measles together. The application of deep learning techniques for medical image classification has grown considerably in recent years. This research presents three implementations of convolutional neural networks (CNNs): ResNet50, VGG-16, and InceptionV3. These CNNs are applied to solve the classification problem of medical radiographs from people with pneumonia, as a manner to assist in the disease diagnosis. The three architectures used in this research obtained satisfactory results. The ResNet50 outperformed InceptionV3 and VGG-16, achieving the highest percentage of training and testing precision, as well as superior recall and f1-score. For the normal class, the f1-score related to ResNet50 was 88.42%, compared to 81.54% for InceptionV3 and 81.42% for VGG-16. For the pneumonia class, this metric was 95.10% against 92.82% for InceptionV3 and 92.54% for VGG-16.
Keywords: Deep learning; Pattern Recognition; Convolutional neural networks; Pneumonia; X-ray.
An EfficientOppositional Crow Search Optimization-based Deep Neural Network Classifier for Chronic Kidney Disease Identification
by Pramila Arulanthu, Eswaran Perumal
Abstract: The Internet of Things (IoT) enables the total gathering of patient data and patient information that can incite logically exact and minute finish of prosperity events. Distributed computing along with the Internet of Things (IoT) thought is another example of gainful regulating and treatment of sensor data. This paper displays a stage-dependent on IoT and Cloud-based capable choice emotionally supportive network for identification of Chronic Kidney Disease (CKD). Additionally, a Deep Neural Network DNN classifier is utilized for the assurance of CKD.An Oppositional Crow Search (OCS) optimization approach is acquainted with selects the necessary features and takes out the undesirable features and also it enhances the process of DNN.The presented structure gathers the patient information utilizing the IoT gadgets joined to the client which will be put away in the cloud alongside the related therapeutic records from the UCI vault.The exhibitions of proposed strategies are gotten by utilizing not many of the assessment measurements for example accuracy, specificity, execution time,and sensitivity. A similar investigation demonstrates that our proposed system achieves better arrangement sensitivity, accuracy, execution time, and specificity estimate when contrasted and different classifiers. The proposed model produces the 97.71% accuracy, 98.88% sensitivity and 93.44% of specificity for the analysis and the exploratory outcomes exhibit that the created OCS-DNN is outflanked.
Keywords: OCS; DNN; Optimization; Cloud; IoT and CKD.
Detection of Atrial Fibrillation from Cardiac Signal using Convolutional Neural Network
by Saumendra Kumar Mohapatra
Abstract: Multi-disciplinary research including engineering and medicine paves the way of modern research. In this work, authors have taken attempt to classify long duration ECG signal. The data is of 30-60 seconds and is collected from form '2017 Physionet/Computing in Cardiology Challenge database'. Use of this database for analysis of long duration signal in terms of data mining is the novelity. This analysis is performed for ambulatory ECG diagnosis and monitoring. As the pre-processing of the signals Savitzky-Golay (SG) filter is used. The filtered signals are classified with the designed deep neural network model. Convolutional neural network (CNN) is used for data classification. The classification task carried for four classes: normal, atrial fibrillation, alternative rhythm, noisy. A ten layer CNN model is designed to classify these four classes of signals. Sensitivity, specificity, and accuracy, these measuring parameters are used for the performance evaluation. Result found from the proposed method is promising one as compared to earlier methods and exhibited in result section. The accuracy with this data is 95.89%. For the comparison purpose RBFN, SVM, MLP, and PNN methods have been verified and presented.
Keywords: ECG; Neural Network; Deep learning; Convolutional Neural Network.
Fuzzy Logic Control based High Step Up Converter for Electric Vehicle Applications
by Ravindranath Tagore Yadlapalli, Anuradha Kotapati
Abstract: At present, the Electric Vehicles (EVs) have a dominant role for visualizing the pollution less environment. Morover, it is important to design efficient power conditioning units for fulfilling the desired tasks. With advancements in the technology, the Fuel Cells (FCs) can provide a better solution for powering the EVs. However, their downside is the production of very low output voltage. Therefore, this necessitates a high step ratio DC-DC converter for providing the desired dc link voltage for DC-AC converter. This paper focuses on fuzzy logic control (FLC) based high gain DC-DC converter for EV applications. The fuzzy controllers are superior in dealing the nonlinearities and plant parameter variations without the need of strict mathematical modeling of the converter system. The converter is simulated with simple voltage mode control as well as FLC based voltage mode control. The MATLAB simulation results are compared for the above two control strategies with an emphasis on the steady state and dynamic regulations.
Keywords: Fuel cells; electric vehicles; power converters; control strategies.
Special Issue on: Security, Privacy and Trust in Cognitive-inspired Computing and Applications
An Improved Sensorless Strategy on the basis of Improved PI regulator
by Haigang Zhang, Piao Liu, Xiaoqi Sun, Xiangsheng Kong, Xuan Chen, Bulai Wang, Heng Wan
Abstract: The sensorless control strategy was implemented by replacing the conventional PI regulator with a improved fuzzy PI regulator, meanwhile a adjusted sliding mode observer was established for replacing the traditional rigid position sensor based on the principle of non-position sensor. The signals of rotor volocity and location were extracted from the counter electromotive force for reducing the error and complexity of the calculation of the angular and rotational speed. Compared with conventional PI observer, the regulate system of the PMSM has a preferable influence on the steady-state and dynamic response. A Matlab platform has been built to validate this method, and the experimental results showed that the fuzzy PI sliding mode observer had strong robustness and restrained the chattering to a certain extent. On the other hand it also could improve the stability, rapidity and dynamic performance of the SMO system.
Keywords: Fuzzy Control?Non-position Sensorless?PMSM?Sliding Mode Observer.
Cryptologic Characteristics of Circulunt Matrices
by Haiqing HAN, Qin Li
Abstract: A 4
Keywords: Circulant Matrix; Branch Number; Orthormorphic Matrix; Cryptologic Characteristic; Symmetrical Permutation.
Application of EPC Internet of Things and Radio Frequency Identification Technology in Logistics
by Xiaobei Wang, Yaya Wang
Abstract: E-commerce is growing stronger, more and more flexible and diverse application to various industries of society, Initiating innovation in shopping and trading methods. In particular, major changes have taken place in the circulation of goods. Then, How to make electronic money safely and reliably purchase real-world items in a virtual network? Logistics plays a key role in this. Realizing informationization, automation and intelligence has become a new trend in the development of modern logistics. The continuous development of network development provides a broad development prospect and technical support for logistics. Modern logistics and the Internet complement each other, and support the commercial application of modern networks. How to apply existing network technology to effectively improve the logistics level is an important topic of our research. This paper focuses on the practical application of EPC and RFID technology in the field of logistics. The Internet is the foundation of the Internet of Things, IOT using wireless communications, RFID technology, computer network technology etc, construct a huge system that covers the world. In order to meet the identification and efficient identification of individual products, the Automatic Identification Laboratory of the Massachusetts Institute of Technology proposed the concept of EPC (Electronic Product Code), each item is assigned a unique identification code. The EPC code is usually stored in an electronic tag of a silicon chip material, and the tag is attached to the identified article. When an EPC tag is embedded in an item, the item and the product electronic code in the EPC tag are in one-to-one correspondence. It is identified, transmitted, and queried by high-level information processing software?On the basis of the Internet, a logistics model that provides various information services for the supply chain is formed . As a wireless version of the barcode, RFID technology has its own advantages and features non-contact automatic identification. It mainly identifies the object objects in the logistics by issuing specific RF signals and obtains related item information. The perfect integration of electronic tags, EPC codes and Internet technologies has created the Internet of Things known as the next generation Internet.
Keywords: EPC; RFID; Computer network technology; Internet of Things.
Terrain Frames Classification Based on HMC for Quadruped Robot (February 2017)
by Zhe LI, Yibin LI, Xuewen RONG, Hui ZHANG
Abstract: As a multi-body nonlinear rigid-flex system, the quadruped robot must maintain the correct perception and classification capabilities for the external environment. This ability is necessary to help quadruped robots make path planning, gait adjustment and attitude control while maintaining complex interactions with the external environment. This paper proposes a terrain classification algorithm based on HMC (HMRF-MAP-CNN) as the basis for robot motion control strategy selection. Different from the classification method based on image features, the terrain-based classification method has higher accuracy and better computational efficiency. The raster map terrain frames classification method proposed in this paper includes a rugged terrain recognition algorithm based on HMRF and a rugged formation algorithm based on convolutional neural network. In the process of solving the actual terrain classification problem, the algorithm firstly uses HMRF to classify the obtained terrain frames into two categories, flat and rugged, and then use CNN to filter, according to the causes of rugged terrain frames. Through the simulation experiment and comparative analysis, the superiority of HMC terrain frame classification algorithm is confirmed.
Keywords: quadruped robot; terrain classification; HMRF; MAP; CNN.
Implementation of Cloud Service Platform for Monitoring Charging Facility Status of Electric Vehicle based on MQTT
by Lei Li, Weidong Liu, Xiaohui Li, Guang Yang, Dan Li
Abstract: Aiming at the heterogeneous charging facilities of many manufacturers, the difficulty of communication and information interaction among these devices, and the lack of real-time information sharing, this paper makes full use of Message Queuing Telemetry Transport(MQTT) which is a lightweight message publishing and subscribing protocol, designs a cloud service platform for monitoring the status of charging facilities of electric vehicles, and collects monitoring data of charging facilities of electric vehicles to store in cloud server. Firstly, the network topology structure of cloud service platform is given. Then, the logical architecture and functions of the platform are designed. The information collection, pushing process and security design of the cloud service platform are described in detail. Finally, the implementation and performance tests of the presented system are carried out. The MQTT-based Internet of Things Middleware in the cloud platform can provide remote parameter configuration and information acquisition functions for charging facilities in smart grid environment, shield the underlying hardware devices, realize data interaction between the sensor layer and the upper application, and realize the integration of multi-source information of heterogeneous devices. The research results have important application value for improving the automation level of electric vehicle charging facilities monitoring and the real-time information interaction among heterogeneous devices of electric vehicles.
Keywords: Electric Vehicle; Charging Facility Monitoring; Cloud Computing; MQTT; Middleware.
Safety Evaluation on Central Separation Opening of Reconstructed Freeway Based on Surrogate Safety Assessment Model
by Wei Hou, Zijun Du, Zhaoxin Liu, Xu Wang
Abstract: In order to reduce the risk of driving at the opening of the median strip of the old road of the freeway, the driving safety was quantified by taking the opening length of the central section of the old road and the main line flow as independent variables. Based on the VISSIM micro-simulation software, a typical simulation scenario is established. The vehicle speed, the position of the vehicle's change point, the number of conflicts, the severity of the conflict, and the conflict points are analyzed by the Surrogate Safety Assessment Model (SSAM). The influence of the opening length of different median strips and the influence of flow on safety is quantitatively evaluated, so as to obtain a reasonable opening length of the median strip. The research results show that the flow has a greater impact on the number of collisions, and the length of the open section has a significant impact on the severity of the conflict. The conflict points are found to be concentrated at the front of the 400m open section. The results of the study provide a theoretical basis for the opening length of the median strip and the setting of traffic signs.
Keywords: safety evaluation; conflict; trajectory; median strip; freeway; single side widening.
The Effects of Green Product Trust and Perception on Green Purchase Intention in China
by Pinghao Ye, Liqiong Liu, Linxia Gao, Quanjun Mei
Abstract: According to the theory of planned behavior (TPB), we created a model of factors affecting customer green purchase intention, with such factors including green product experience, green purchase behavior, subject social norms, perceived environmental protection value, green product trust, and green product perception. Then, a questionnaire was developed based on the model. 307 respondents were involved and the feedback was analyzed, using a structural equation model, to verify the data reliability and validity. Results show that green product trust and green product perception exerted significant positive effects on green purchase intention; green product experience had significant positive effects on green product trust; and subjective norms and perceived environmental protection value positively affected green product perception. These findings provide valuable information for studying factors affecting the green purchase intention of customers.
Keywords: Green product trust; Green product perception; Green purchase intention; Perceived environmental protection value.
Special Issue on: Artificial Intelligence for Sustainable Future Computing
Hybrid approach for semantic similarity calculation between Tamil words
by Deepa Karuppaiah, Durai Raj Vincent P M
Abstract: Semantic similarity, sometimes referred as semantic relatedness, is one of the important concepts that help in various applications that involve Natural Language Processing. In literature, there are plenty of similarity measures to compute the relationship among words in monolingual and cross-lingual documents. They help us in understanding text, finding plagiarism, information retrieval etc. They can be categorized based on the resources used into corpus based and knowledge based measures. These measures are plenty for English language. For Tamil language, hardly there are any works in calculating the similarity between words. In this paper, we proposed a similarity finding technique that exploits the knowledge from the resources like Tamil Indo Wordnet, Tamil Wikitionary and Oxford Tamil Dictionary. We have used the definitions and example sentences of each word that are available through each of these resources for similarity calculation. The proposed approach is evaluated using human evaluated Miller Charles and Rubenstein Goodenough datasets.
Keywords: Semantic similarity; Tamil words similarity; Indo Wordnet; Knowledge based similarity.
Semantics-based Key Concepts Identification for Documents Indexing and Retrieval on the Web
by Mohammed Maree
Abstract: Bridging the semantic gap on the web remains one of the crucial challenges for current horizontal as well as domain-specific information retrieval systems. This challenge becomes even more pronounced when users express their information needs using short queries that are formulated using a few number of keywords. Therefore, relying on keywords for indexing web documents results in degrading the quality of the returned results. To tackle this issue, new approaches across multiple disciplines propose to incorporate semantic resources to overcome the query-document mismatch problem. Although these approaches have proved to assist users in finding relevant documents, many results are still irrelevant; failing to adequately meet the desired information needs. This is because of the imprecise expansion of query terms and the limited depth and breadth of the employed resources. In this article, we discuss these limitations and introduce an approach that employs knowledge captured by large-scale knowledge resources to identify key query terms for retrieving semantically-relevant documents. Unlike conventional approaches that treat query terms independently, key terms are mapped to their semantic correspondences and variable term weights are assigned based on the semantic and taxonomic relations for each term. To demonstrate the effectiveness of the proposed approach, we have conducted experimental evaluation using Glasgows NPL test collections. Findings indicate that precision results have improved by employing the proposed method against four conventional similarity metrics that are based on the bag of words similarity model.
Keywords: Key concepts; Large-scale ontologies; Semantic matching; Information indexing; Data analysis; Precision measures.
Development of Scheduling Algorithm for Reconfigurable Architecture using FPGA
by Pushpa Bangare, M.B. Mali
Abstract: The present title discloses a scheduling algorithm for scheduling multiple tasks to allow multiple tasks to reconfigure in reconfigurable architecture. In the proposed architecture, multiple tasks are considered. With the provision of operating multiple tasks concurrently or on priority-based, routine tasks are executed on the ideal state of the higher priority tasks. A concurrent round-robin scheduling architecture is proposed to execute the task which needs to be executed through reconfigurable architecture. The proposed architecture is described using Very High-Speed Integrated Circuit Hardware Description Language. For systematic verification, timing simulation is done through the Modelsim simulator. Synthesis is targeted using the Xilinx ISE platform for Xilinx FPGA devices.
Keywords: Task scheduling; field programmable gate array; round robin scheduling; general purpose processor.
Face Pose and Blur Normalization for Unconstraint Face Recognition from Video/Still Images
by T. Shreekumar, K. Karunakara
Abstract: Face recognition has reached maturity level using Deep learning which works on a very large data set of Face images. In a considerable lot of circumstances, it is exceptionally hard to get the huge number of Face Images for the confirmation purpose, particularly from Village individuals. The main bottle-neck in face recognition are Pose variation, illumination variation, Occlusion, and Noise. To solve these problems we are presenting a system that will recognize the countenances from an extremely small dataset. This method initially removes the blur from the image and, then normalizes the Pose by generating virtual frontal Face using the Local Linear Regression (LLR) method. Then the combined score of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are obtained from the image. This score is used to identify the Face using Support Vector Machine (SVM) which gives more accurate results compared to simple Euclidean measures. The experiment results show a maximum accuracy of 93.5% .
Keywords: Face Recognition; Support Vector Machine; Motion Blur; Principal Component Analysis; Local Linear Regression. Pose Variation.
Modified Iterative Learning Controller (MILC) for efficient power management of Hybrid AC/DC Micro grid
by Angalaeswari Sendraya Perumal, K. Jamuna
Abstract: In this paper, modified ILC is proposed for maintaining stable voltage and frequency and performing efficient power management in a hybrid micro grid. The hybrid micro grid (HMG) is modeled with solar, battery, DC loads at DC bus and wind turbine, utility grid, AC loads at AC bus. An interlinking converter (IC) is connected between the AC and DC bus to facilitate bidirectional power flow. Due to the intermittent nature of the distributed sources and the variable loads, the voltage and frequency deviation is occurred. The voltage control at dc and ac bus has to be done and the power balance has to meet in all modes of operation. The proposed objective has been obtained in the modeled hybrid micro grid with Set Point Weighting Iterative Learning Controller (SPW-ILC) by controlling the Interlinking Converter both in autonomous and grid connected mode of operation. To minimize the error signal to the controller, the classical optimization method of Sequential Quadratic Programming (SQP) has been employed to improve the performance of the controller. The simulation results show that the proposed controller have better performance than other controllers under variable source and load conditions.
Keywords: Iterative Learning Controller; Hybrid Micro grid; Power Management; Sequential Quadratic Programming; Voltage stability.
A hybrid cum enhanced minutiae feature extraction approach for security oriented Content based fingerprint image retrieval for patient history identification and authentication
by Raghavan R, John Singh K
Abstract: The role of security in fingerprint is having lot of available features like ledger maintenance in a distributed fashion, security through finger print authentication and validation, data access. There are many forms of security technologies which are already in existence in the domain of company view point as well as institute viewpoint. Both the companies and institutes have started to discover applications towards healthcare. Some of the applications include smart contracts, fraud detection and identity verification. Identity verification in healthcare industry sometimes facing challenge in correctly identifying the patients in terms of medication errors, errors while undertaking tests, some form of error related to transfusion, and more particularly discharge of infants to the wrong families. In this paper we proposed an improved content based fingerprint image retrieval system for patient history identification application. A measure is proposed as a pre-step to avoid handling the less clarity input with a noisy image using median filter to initiate the improvisation of retrieval process. Binarization is done using rough sets and thinning processes are further carried out after the initial steps. Then using a novel proposed approach using rough sets the features are extracted from the noise free finger print image based on the minutiae details. After initiating the proposed matching algorithm using rough sets the correct patient history details of the authorized person is fetched and delivered only to the authorized individual. The testing are performed using Multimodal dataset and IRMA dataset to evaluate the proposed retrieval technique. The accuracy obtained in fetching and validating the fingerprint in this hybrid approach is found to be more accurate and hence more secure towards avoiding misuse of patient medical data.
Keywords: Content based image retrieval; noisy image; rough sets; median filter; binarization; thinning; fingerprint; minutiae; matching algorithm; rough sets.
ANALYSIS OF PERFORMANCE OF STUDENTS USING STATISTICAL APPROACH - ANOVA TEST
by Hemamalini B H, Suma V, Suresh L, Shankar M M
Abstract: Educational Data Mining (EDM) throws light on the various techniques and strategies that affects the performance of students. The present work has considered engineering students from a reputed institution in Karnataka for study. The present paper focuses on applying one of the statistical techniques Anova Test on the student dataset. The dataset comprises of the details from the institutional repository of a prestigious institution in Karnataka. The pre-processed data consists of 1186 records. The Anova test is applied to know if the averages of two or more collections are considerably different from each other. Descriptive F value and T test is applied on the given dataset. The probability distribution is also calculated. It is claimed that the fathers occupation has an impact on the performance of student. If father is in defense, the student has performed excellent. Also, if the student is a day scholar, his performance is better than the hostel student. If the student is from ICSE or CBSE background, the student performance is better than the student from State board or other boards. By predicting the parameters that affect the performance of students, we can produce better results, thus contributing to the society. However, mothers occupation does not have any significant association with outcome variables.
Keywords: Anova; Descriptive Statistics; Multiple comparison; f-value; variance; degree of freedom; p-value; significant; T test; sumsquare; meansquare.
A comparative study of techniques for polygonal approximation of digital image boundary
by Kiruba Thangam Raja, Bimal Kumar Ray
Abstract: Polygonal approximation (PA) technique have been widely applied in the field of pattern recognition and classification, shape analysis and identification, 3D reconstruction and medical imaging, digital cartography and geographical information system (GIS). In this study, we focus on some of the key techniques used in implementing the PA algorithms. The PA can be broadly divided into three main category, dominant point detection, threshold error method with minimum number of break points and break points approximation by error minimization. Of the above three methods, there has been always a tradeoff between the three classes and optimality, specifically the optimal algorithm works in a computation intensive way with a complexity ranges from O (N2) to O (N3), on the other hand heuristic methods approximate the curve in a speedy way, however they lack in the optimality but have linear time complexity. Here a comparative study on major PA methods for digital planar curve approximation is presented.
Keywords: : contour; break point; split and merge; dominant point; polygonal approximation; digital planar curve; computation intensive; geographical information system; digital image boundary; 2D or 3D images; redundant breakpoints; Freeman chain code; Heuristic algorithms; optimal approximation; dominant points.
Sematic based Road Traffic Prediction using Moving
Weighted Average model
by Prathilothamai Manikandan, Viswanathan V
Abstract: In this emerging world, peoples are running behind the time and wasted their time in travelling. Drastic increase in population results in rapid increase of number of vehicles. A semantic based road traffic model is proposed to predict the traffic and to inform the public about the current traffic condition to all persons who belongs to the same lane. Real time data is acquired from Ultrasonic, PIR sensor and camera. Proposed system uses the vehicle count, distance between the vehicles and speed of the vehicle from both sensors and camera and it applies semantic interpretation of those data uses moving weighted average model to predict the traffic condition. In order to have time efficient prediction, the work is experimented in Apache Spark which will reduce disk latency when compared to hadoop. Prediction result is sent it as alert message to the public as a location based messages. Therefore, the traffic prediction system results are more helpful in goods transportation and accident prediction system etc.
Keywords: Big Data; Hadoop; Spark; Real-Time Applications; Road Traffic Prediction.
Person Identification using Fusion of Deep Net Facial Features
by Haider Mehraj, Ajaz Hussain Mir
Abstract: Face base identification is the method of recognizing individuals through face
images having application domains such as smart cards, mobile phones, information
security, law enforcement and surveillance system. Deep networks have proved to be
successful for facial identification and involve some pre-processing steps like sampling to be done before the images are applied. The complete images are passed as input to Deep Net, and the network does feature extraction as well as classification. However, such a process requires millions of images to work with and implementing the same sometimes becomes complex and time-consuming. This Paper utilizes Deep Networks Alex net and VGG-16 as feature extractors in which contribution to more significant level layers are utilized as feature vectors. Alex net and VGG-16 are pre-trained Deep Nets, and such the number of input images can be significantly lower in comparison to training a network from scratch. The Feature vectors are then diminished using a combination of PCA and LDA. After the reduction in the dimensionality of highlight vectors, they are intertwined and characterized using Support Vector Machines. The proposed framework is assessed utilizing freely accessible database VIDTIMIT, highlighting the performance as far as exactness or precision, accuracy, and review or recall.
Keywords: Biometrics; DNN; CNN; Alex net; VGG-16; Feature vector; SVM,
Recognition; Multi-Algorithm Biometric System; Neural Network; Deep Networks.
Mathematical modeling and Kinematic analysis of 3-RRR Parallel Planar Manipulator
by Shaik Himam Saheb, G.Satish Babu
Abstract: Parallel mechanisms are found as positioning platforms in several applications in robotics and production engineering. Today there are various types of these mechanisms based on the structure, type of joints and degree of freedom. An important and basic planar mechanism providing three degree of freedom at the end-effector (movable platform) is a 3-RRR linkage. The forward kinematics in parallel mechanisms is a multi-solution problem and involves cumbersome calculations compared to inverse kinematics. With inverse kinematics, the input kinematic parameters for a known table center coordinate are determined. In present work, the workspace and Jacobian matrices are computed at corresponding solution and dexterous workspace analysis is discussed. Main objective is to fabricate a model of this planar manipulation mechanism with calculated dimensions and observe the practical workspace and dexterous workspace available at end effector. This final output data is useful for manipulator designers to design manipulators for different applications in addition to this the stress analysis is performed with the help of Ansys software to estimate the failure zone of moving platform.
Keywords: 3RRR Parallel Planar Manipulator; Work space analysis; Regular Dexterous Workspace; fabrication of 3RRR PPM Model; Performance analysis; Stress analysis; load carrying capacity; precise; accurate position; Kinematic analysis; Ansys software; Degree of freedom; mechatronic system; Dexterous workspace; Isometric view of prototype,.
Thyroid Disease Classification with Hybrid C5.0 and Cultural Algorithm
by M. Deepika, K. Kalaiselvi
Abstract: Data mining plays a prominent role in disease classification and diagnosis. The data mining technique helps physician in making reliable and accurate disease diagnosis and prognosis. In this work the thyroid disorder is classified as hyperthyroidism and hypothyroidism based on C5.0 and Cultural Algorithm. The C5.0 algorithm reduces over fitting of data in dataset. The best cost function minimizes the load of knowledge discovery from the missing data in dataset with the help of cultural algorithm. The Cultural algorithm is derived from social evolution which includes a population space, communication protocol and a belief space. The experimental results show C5.0 paired with cultural algorithm provides better thyroid classification with minimal cost function.
Keywords: Classification; Cultural Algorithm; C5.0; Decision Trees; Thyroid analysis; Prediction.
A novel approach towards content based image retrieval for querying framework application using enhanced first type of pessimistic covering based lower approximation multi-granular rough sets
by Raghavan R, John Singh K
Abstract: Content based image retrieval is also referred as query by image content in which image is taken as a query and the method retrieves and return the result in the form of set of images related to the query. Most of the obtained results in prior approaches are with no assurance that there are still some set of images found in the result which are partially related to the query. For this reason, the performance of such retrieval systems is found to be with less clarity in obtaining the required result. In this paper, we propose to improvise the accuracy through investigating the precision performance by considering series of images as input from a dataset and also getting the input preference from the user for the query system in the form of preferred color and preferred object that needs to be retrieved from the dataset. The process of color feature extraction from the given image is performed using color averaging approach. The concept of covers from the covering based rough sets are introduced and used to store the concern image of the identified color. The second set of input received from the user is the user preferred object to search. According to the input preference the shape of the object is identified using moment invariants method by comparing the pixels of the query image with the image in the data set. The image of the concern color is moved to the concern cover, the image of the identified shape is moving to the second cover which is to store the collection of such object. The extracted features are readily available after underwent the training towards color matching and shape matching. The features are now in two different covers and ready to identify and detect the common images which belongs to both the covers using first type pessimistic covering based lower approximation multi-granulation rough sets by accumulating the knowledge received from the prior steps over iterations. The experiments are performed using Corel dataset which consists of 10,908 images from 10 different classes. Experimental result shows that the proposed approach performs better in terms of precision and recall as compared to the existing systems.
Keywords: Content based image retrieval; rough sets; color averaging; moment invariant; pessimistic covering based multi-granular rough sets.
Special Issue on: Smart Computational Intelligence and Optimisation Method Applications to Electrical Power Engineering
XGBoost Regression Model Based Electricity Tariff Plan Recommendation in Smart Grid Environment
by Dayal Kumar Behera, Madhabananda Das, Subhra Swetanisha, Janmenjoy Nayak
Abstract: Electrical Power System is an interconnected network of power generation, transmission and distribution of electricity from a power plant to the consumer. Smart Grid is an advancement over the power grid that facilitates an efficient and reliable two-way delivery system. It acts as a backbone infrastructure to enable various cutting-edge models like smart city, efficient smart metering and tariff retailing. Power System deregulation enables the power industry to provide residential customers to choose retailing electricity plan. This allows competition among retailers or traders and also minimizes the energy expenditure with quality of services. Few researches have been proposed for electricity tariff plan recommendation using Collaborative filtering approach. We have proposed an XGBoost regression model for Electricity Tariff Plan Recommendation. Firstly, proposed regression model with basic statistical features is compared with Support Vector Regression (SVR), Decision Tree (DT), Bayesian Ridge and KNN regression model. Secondly, performance of the proposed model is extensively studied by combining the features from other user-based, item-based and matrix factorization based techniques. In this research, dataset shared in the project Smart Grid Smart City (SGSC), Australia is used for conducting experimental analysis. A rating inference approach is designed to infer the choice of electricity consumer for a specific retailing plan. The proposed model achieves better performance as compared to other baseline methods.
Keywords: XGBoost Regression Model; Recommender System; Smart Grid; Power System; Electricity Tariff Plan Recommendation.
Frequency regulation of hybrid power system using firefly algorithm
by Tulasichandra Sekhar Gorripotu, Pilla Ramana
Abstract: In the present work, Firefly Algorithm (FA) based cascaded PD-PID with filter controller (PD-PIDF) is implemented for the hybrid power system to keep the frequency within the limits. At first, a two-area hybrid power system is proposed, considering thermal and distributed power units in area-1, and hydrothermal units in area-2. The cascaded PD-PIDF controller parameter values are optimized using the integral time multiplied absolute error (ITAE) criterion. The proposed system is analyzed under three cases by applying step load perturbations (SLPs) and noise signals at possible areas. The superiority of cascade PD-PIDF controller is shown by comparing with recently published results. Finally, sensitivity analysis is performed to demonstrate the cascaded PD-PIDF controller capability while varying system parameters and conditions.
Keywords: Cascaded PD-PIDF controller; distributed power system; firefly algorithm (FA); frequency regulation; hybrid power system.
A novel modified random walk grey wolf optimization approach for non-smooth and non-convex economic load dispatch
by ARUN SAHOO, Tapas Kumar Panigrahi, Gopal Krishna Nayak
Abstract: In practice, economic load dispatch (ELD) problems have non-smooth and non-convex cost functions which are subjected to numerous non-linear equality and inequality constraints involving multiple decision variables. It makes the problem computationally labyrinthine to solve via any analytic method. Therefore, this study proposes a coming of age improved version of conventional grey wolf optimization (GWO) technique to solve the ELD problem. In the improved GWO, the leadership hierarchy of the grey wolf is ameliorated by taking the random walking behaviour of the grey wolfs into consideration. The algorithm aims to modify the existing leaders with the best leaders in order to overcome the drawbacks of the conventional GWO. The improved GWO guarantees better exploration and exploitation of the search space. The non-linear constraints of generating units like ramp rate constraints, influence of valve-point loading and Prohibited Operating Zones (POZs) are taken into account for both lines with and without losses. The obtained results are compared to that of other contemporary algorithms for demonstrating the superiority of the suggested one. This technique provides the optimum dispatch with a faster convergence rate in comparison to the conventional GWO and some other existing methods.
Keywords: Economic Load Dispatch (ELD); Constraints; Grey Wolf Optimization (GWO); Modified Random Walk Grey Wolf Optimization (MRWGWO); Valve Point Loading.
Special Issue on: Leveraging Opportunistic Networks Using Smart Wireless Digital Devices and IoT for Intelligent Communication Systems Challenges and Emerging Trends
A Novel Genetic Algorithm with CDF5/3 Filter Based Lifting Scheme for Optimal Sensor Placement
by Ganesan T., Soumya Ranjan Nayak, Amandeep Singh Bhatia
Abstract: The generic algorithm has been receiving significant attention due to node placement problem in the field of sensor application in terms of machine learning. Sensor deployment is able to provide maximum coverage and maximum connectivity with less energy consumption to sustain the network lifetime. The maximum quality coverage problem has been solved successfully by evolutionary algorithm while placing node in optimal position. In evolutionary algorithm, genetic algorithm (GA) plays an important technique for deploying the sensor in the form of population matrix. However, the existing techniques unable to place sensor position perfectly. In this paper, a novel genetic algorithm with second generation wavelet transform (SGWT) is proposed for identifying optimal potential position for node placement. In order to improve the quality of population matrix, bi-orthogonal CohenDaubechiesFeauveau wavelet (CDF 5/3) has been employed. The proposed method is performed primarily to generate sensor position with different population. Subsequently, it can extend to CDF5/3 filter based lifting scheme to adjust the sensor position. The proposed method has been compared with random deployment, genetic algorithm and GA with CDF5/3 wavelets in terms of target to cover by the sensor. The result of the proposed method affirms better optimization as compared to state-of-art techniques.
Keywords: Wireless senor network; Sensor deployment; Genetic algorithm; Lifting scheme; Target coverage.
Semantics Interoperability in the Internet of Things- State of the Art and Prospects
by Jameel Ahamed, Mohammad Ahsan Chishti
Abstract: Internet of Things is generally referred to as an internet world of little things with limited storage and processing ability, but facing communication, security, privacy, and interoperability issues. Further, Device, Network, Platform, Syntactic, and Semantic are the major interoperability issues in the Internet of Things. Smart objects, termed as interconnected devices, generate a huge chunk of heterogeneous data and its processing as well as usage becomes the challenge in IoT. Therefore, this heterogeneous Data is semantically annotated using linked vocabularies to make it interoperable information. The main aim of this article is to have a deep understanding of semantic interoperability that will help in sharing heterogeneous data through the development of knowledge-based systems or common ontologies.
Keywords: Internet of Things; Semantic Interoperability; Ontology; Heterogeneous Data.
Impact of Contacts for Message Copies, Mobile Nodes and Buffer Size in Delay-Tolerant Networks
by Md. Sharif Hossen, Khaleel Ahmad, Khairol Amali Bin Ahmad
Abstract: Delay tolerant network (DTN) is a kind of mobile ad hoc networks where there are no predefined routes from one node to other. This network is used often in some areas of applications such as disaster management, military communications, emergency communications, and wild life tracking services where the fixed infrastructures are not found. Under this network, the analysis of node contact is essential, without which, message copies will not be possible to deliver successfully from the source to the destination. In this research study, we take our concentration on single and multi-copy contact duration aware routing techniques and address the issues of the variation of mobile nodes, message copies and nodes buffer size. We have used a java unified opportunistic network environment (ONE) simulator. We observe that contact increases per hour with the increase of mobile nodes, while goes up and down for varying message copies and remains approximately the same for changing buffer size, i.e., buffer size does not affect the achievement of contacts. On the other hand, inter-contact time (ICT) is high at the beginning of the communication for three cases, i.e. for varying message copies, mobile nodes and buffer size. Higher contact time will result in higher ICT, and higher number of contacts in case of varying mobile nodes, while it will be slightly changed for varying message generation rate and fixed for varying buffer size.
Keywords: Ad hoc network; contact; delivery; latency; message copies; routing; simulation.
Parameter Aware Utility Proportional Fairness Scheduling Technique in Communication Network
by Neeraj Kumar, Anwar Ahmad, Shashank Awasthi, Kavita Sharma
Abstract: The current paper suggests a parameter aware utility proportional fairness scheduling technique, in short PAPUF, for mixed traffic users. Both elastic traffic type (non-real time) users or inelastic traffic type (real-time) users are scheduled as per their corresponding performance metrics. Conventional techniques separately consider real-time users and non-real time users in resource scheduling. In different scenarios, such as high network traffic, less network coverage, and the case of radio resource constraint, the joint consideration of both elastic and inelastic traffic type users is needed for resource allocations. Further, some of the users can be left without getting radio resources in conventional rate allocating techniques. In the proposed technique, a utility function is defined for each elastic traffic and inelastic traffic type users. Then, it maximizes the total utilities by network utility maximization approach with considering scheduling parameters such as queue length, head of the line delay and experienced channel condition corresponding to each elastic and inelastic traffic user. Rate allocation based on queue awareness improves fairness among elastic traffic performing users, delay awareness improves QoS performance of inelastic traffic performing users and channel condition awareness improves the throughput of all users. Further, the network utility maximization approach provides minimum throughput to all users i.e. no users will be left without rate allocation. Simulation results of the proposed scheduling technique are compared with existing techniques, which show improved communication performance for users.
Keywords: Utility Function; Proportional Fairness; Rate Allocation; Throughput; Queue; Delay; Logarithmic Function; Sigmoidal Function;.
TASRP: A Trust Aware Secure Routing Protocol for Wireless sensor networks
by Tayyab Khan, Karan Singh
Abstract: Over the last decade, considerable secure routing protocols with static sink have been developed for wireless sensor networks (WSNs) to achieve reliable routing paths as well as energy efficiency during information gathering and transmission. Security is vital for sensor networks since sensor devices communicate essential and private data. However, cryptographic security solutions have not proved suitable for wireless sensor networks since they impose high overhead as well as do not deal with severe internal attacks.
Trust-based security models are efficient and reliable over traditional cryptographic schemes (e.g., encryption, key management schemes) to detect and alleviate various internal threats by estimating the trust scores of sensors nodes in a quantitative way. Trust models analyze the trustworthiness of each sensor node to improve reliability, quality of data, prevent damage, and adversely affect. This paper presents a realistic trust-based reliable routing (communication) strategy to counter selfish nodes based on the hybrid trust model. The proposed scheme (TASRP) is a multifactor routing approach that employs trust scores of nodes, residual energy, and path length to provide reliable routing paths among trusted nodes with reduced energy consumption.
This multi-factor strategy helps in selecting trusted nodes to forward data and minimize energy consumption due to shorter routing paths. Simulation results show better performance in terms of robust trust values, throughput, packet delivery rate, and energy consumption of nodes.
Keywords: WSN Routing Protocols; Trust; Security; Internal Threats.
Probabilistic Routing Protocol with Firefly Particle Swarm Optimisation for Delay Tolerant Networks Enhanced with Chaos Theory
by Siddhant Banyal, Kartik Krishna Bharadwaj, Deepak Kumar Sharma
Abstract: In this paper, we propose a proactive routing paradigm for Delay Tolerant Networks (DTNs) using probabilistic routing implemented via Firefly Particle Swarm Optimisation(PSO) and optimised by chaos maps. Delay Tolerant Networks is a specific class of networks where there is absence of an end to end connectivity among nodes and are characterised by long or variable delays, asymmetric data rates and high error rates. The routing scheme considers the constrained environment of DTN nodes and implements a meta-heuristic paradigm to facilitate data transfer. The chaos maps further enhance the probabilistic routing parameters controlling brightness and ageing by enabling randomising them. The protocol is implemented on Opportunistic Network Environment simulator and its performance is compared with traditional DTN routing schemes across Delivery Probability, Average Latency, Overhead Ratio and Buffer Utilisation. The simulation results show that our proposed protocol, Firefly PRoPHET, performs better than its contemporary nature-inspired routing schemes such as Ant PRoPHET across the performance metrics and is suitable for implementation in DTN environment.
Keywords: Chaotic Mapping; DTN; Firefly Algorithm; Nature Inspired Algorithm; Networks; Opportunistic Networks; Probabilistic Routing Using History of Encounters; Routing; Store and Forward.
Hierarchical Search Based Routing Protocol for Infrastructure Based Opportunistic Networks
by Deepak Kumar Sharma, Sanjay Kumar Dhurandher, Shiv Kumar
Abstract: Opportunistic networks (OppNets) comprise of intermittently connected devices where there is no guarantee, that a predefined path from source to destination for message transmission exists. In such a sparsely connected network where nodes relay messages in an opportunistic manner, designing a routing protocol is a challenging task. Moreover, these routing challenges differ in many aspects for infrastructure-dependent OppNets and fully mobile infrastructure-independent OppNets.rnrnIn this paper, a novel routing protocol for infrastructure based OppNets called the Hierarchical Search Based Routing Protocol (HSBRP), has been proposed. This protocol combines the benefits of both fixed and mobile routing techniques, combating the disadvantages posed by each, to give better performance on a multitude of performance metrics like, no single point of failure, fault detection and communication capacity, etc. In the proposed protocol, nodes are arranged in a hierarchical fashion and interactions between different levels of nodes are only allowed through well-defined rules that boost message delivery probability and reduce the overhead of the network. The proposed protocol has been compared to the Global Ferrying Scheme (GFS) routing protocol which it outperforms on multiple performance criteria such as total number of messages delivered and overhead ratio, while varying the speed of nodes, the number of nodes and the speed of ferries.
Keywords: Opportunistic Networks (OppNets); Wireless charging; Selfish nodes; Incentive scheme; The Opportunistic Network Environment (ONE) Simulator.
Managing Levels of CO2 In A Tunnel Using Oppnet Virtual Machine
by Shuroug Almayouf, Atheer Altamimi, Fatemah Kharrat, Hessah Alsaidan, Nabaa Alqahtani, Sarah Bin Nafisah, Mai Alduailij
Abstract: This work aims at managing levels of CO2 inside King Abdulaziz Road Tunnel in Makkah, Saudi Arabia, by using heterogeneous systems and devices that can interact with each other autonomously through various communication media. This can be achieved by providing a tunnel with sensors that can detect the increased levels of CO2 and then search for available devices that can help in achieving the goal which is providing a tunnel with good ventilation system. The sensors should be able to interact with fans to refine the air inside the tunnel and a warning massage is sent to the billboard inside the tunnel to be displayed for drivers to take precautions. This can be done by using Oppnet Virtual Machine (OVM). OVM can be downloaded to any device making it oppnet-enabled. Our goal of managing levels of CO2 inside the tunnel can be achieved by making the sensors, fans and the billboard oppnet-enabled devices to interact with each other and share resources autonomously. The results show that the success rate is 93%, and the time required for our oppnet to succeed is within 7 seconds.
Keywords: ad hoc networks; CO2 level; heterogeneous systems; opportunistic networks; resource sharing; resource utilization networks; tunnels.
Special Issue on: Recent Advances in Intelligent Systems
Agent-based modeling approach for Internet rumor propagation and its empirical study
by Tinggui Chen, Bailu Jing, Jingtao Rong, Zepeng Wang
Abstract: This paper analyzes the current research status of Internet rumor propagation at home and abroad, proposes an expanded SPNR rumor propagation model based on SIR infectious disease model, carries out simulation analysis with agent method, and uses two methods of data set verification and case verification to verify the SPNR model.Firstly, the infection status of rumor spreaders is divided into positive and negative, and the positive and negative infection rates are dynamically set according to the proportion of surrounding infected persons. At the same time, the forgetting mechanism is also increased, and a more applicable SPNR rumor spreading model is proposed. Secondly, the simulation of rumor spreading process is realized based on agent method, and the influence analysis of parameters affecting rumor spreading is carried out by numerical simulation method, which provides the basis for the formulation of rumor control strategy. Finally, from the perspective of data set validation and case validation, the simulation results of SinaWeibo and SPNR model are compared to verify the applicability of SPNR model in the process of rumor propagation.
Keywords: Network rumors; epidemic models; complex networks; information dissemination.