Forthcoming articles

International Journal of Computational Complexity and Intelligent Algorithms

International Journal of Computational Complexity and Intelligent Algorithms (IJCCIA)

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International Journal of Computational Complexity and Intelligent Algorithms (10 papers in press)

Regular Issues

  • Complexity Verification through Design and Analysis of Computer Experiments   Order a copy of this article
    by Niraj Singh, Soubhik Chakraborty, Dheeresh Mallick 
    Abstract: This research article is a systematic study towards exploring the parameterized behavior of smart sort, a comparison based sorting algorithm. Our observation for quick sort led us to conjecture that for sufficiently large samples of fixed size, the runtime complexity is: yavg(n, td) = Oemp(td). Performance of heap sort is better for discrete inputs with low k values (or equivalently high td values) and the runtime reaches to maximum beyond a threshold k. These two observations are opposite in their behavior. The smart sort, which is a designed of key functions of these two standard algorithms is expected to behave optimally with respect to all input parameters. The robustness of average case Oemp(nlog2n) complexity for smart sort is conjectured as result of study for various regression models and factorial design experiments.
    Keywords: Average case complexity; statistical bound; Empirical-O; quick sort; smart sort; parameterized complexity; factorial design; statistical significance.

  • GIS based Design and Analysis of Preventive Health Management System for Vehicles using ANFIS   Order a copy of this article
    by Sushma Kamlu 
    Abstract: The health of a vehicle gets affected by different parameters having uncertainties such as past running hours, vehicle operating condition, the consumption rate of fuel, etc., which in turn influence the health of a transportation system as a whole. In this work, a geographical information system (GIS) based Adaptive-Network-Based Fuzzy Inference System (ANFIS) has been utilized for the advanced prognostic and health management strategy of the vehicle to assess the condition of the vehicle from a precautionary preservation perspective, so as to enhance the ability of credentials of proactive malfunction circumstances. The case study corroborates the effectiveness of the proposed ANFIS technique. It provides the proposal of safeguarding the operation for pragmatic applications with consideration of all uncertainties, the domino effect on the health of the transportation system.
    Keywords: Vehicle health management system (VHMS); Geographical information system (GIS); Adaptive-Network-Based Fuzzy Inference System (ANFIS).

  • Algorithm Design, Software Simulation and Mathematical Modeling of Subthreshold Leakage Current in CMOS Circuits   Order a copy of this article
    by Debasis Mukherjee, B. V. R. Reddy 
    Abstract: In this paper, concepts of mathematics and computer science were applied to electronics engineering field, specifically very large scale integration (VLSI) design and semiconductor industry. Presently one of the major challenges faced by the semiconductor industry is continuously growing leakage current with technology scaling. Transistor is the smallest structural unit of any chip or semiconductor device. Subthreshold leakage current is known as one of the most dominant leakage current components of transistor. In this paper, mathematical relationship between transistor structure and subthreshold leakage current was found. An algorithm was designed for automatic tracking of the transistor structure. Simulation setup was formed by applying some mathematical formula on the outputs of the algorithm. Results of TCAD software simulation were found to be very close to a well known mathematical formula. As complementary metal oxide semiconductor (CMOS) is the most popular technology for semiconductor device fabrication in present days, the same was used for simulation purpose.
    Keywords: 20 nm; bulk; CMOS; device level; leakage current; MOSFET; NMOS; subthreshold; TCAD; VLSI.
    DOI: 10.1504/IJCCIA.2018.10016451
  • A Novel Method Based on Pole Clustering Technique and Differential Evolution for Model Order Reduction   Order a copy of this article
    by Shilpi Lavania, Deepak Nagaria 
    Abstract: This paper strives to present a model order reduction (MOR) method for complex high order linear time-invariant (LTI) single input-single output (SISO) systems. The recommended method utilises the benefits of pole clustering method and differential evolution algorithm. In this suggested method, approximated denominator polynomial is obtained by pole clustering method whereas; approximated numerator is obtained using differential evolution algorithm. To indicate the effectiveness of the suggested method over existing MOR techniques, a comparison on the basis of a performance index known as integral square error (ISE) is depicted in this paper by using simulation graphs and in tabular form. Further, the suggested method is extended for MIMO systems also. Numerical examples are solved to give better understanding of the propound technique for SISO and MIMO systems. The recommended method derives and guarantees a stable approximated ROM if the original higher order system (HOS) is stable.
    Keywords: Model Order Reduction (MOR); Single Input Single Output (SISO) systems; Integral Square Error (ISE); Pole clustering; Differential evolution; Performance Index (PI).
    DOI: 10.1504/IJCCIA.2018.10019032
  • Classifiers for Arabic NLP: Survey   Order a copy of this article
    by Moustafa Al-Hajj, Marwan Al Omari 
    Abstract: In this paper, we reviewed most common-used models and classifiers that used for the Arabic language to classify texts into categories, classes, or topics in tasks of opinion mining, sentence categorisation, part of speech tagging, language identification, name entity recognition, authorship attribution, word sense disambiguation, and text classification. Comparisons between classification tasks conducted in terms of models' performances and accuracies. Classification approaches are three types: lexicon-based, machine and deep learning, or hybrid ones. Research sample is 34 articles in the classification domain. Challenges facing the Arabic language discussed with further solutions: 1) solid research training on both approaches: lexicon-based and corpus-based (machine and deep learning); 2) research contribution mainly corpus, approach technique, and free accessibility; 3) fund increase to the research development in the Arab world.
    Keywords: Lexicon-based Approach; Corpus-based Approach; Machine Learning; Deep Learning; Classification; Big Data;.
    DOI: 10.1504/IJCCIA.2018.10019805
  • Big Data Analytics Framework to Analyze Student’s Performance   Order a copy of this article
    by Syedibrahim.sp Sp 
    Abstract: Performance of the students is the most important element for any educational institution. Knowing the areas where student lags proficiency helps the student as well as the teacher to improve and make the education system to be in par with this competitive world. Aim of this paper is to provide a framework that can analyse the performance of the students. This framework was experimented with a first semester course which has 5,962 students registered. Students were given a problem, taught programming concepts relevant to solving it and they are made to experiment them immediately on a machine. Three categories of exercises practice problems; assessment problems and challenging task were given to the students through an online portal. Rubrics were designed to evaluate the performance of students. A scalable MapReduce-based analytics model using Spark framework was developed and results were visualised. Then K-means clustering algorithm was also applied to group the students based on their performance.
    Keywords: Big Data Analytics; reflective learning and teaching.
    DOI: 10.1504/IJCCIA.2018.10021266
  • Improved low power implicit pulse triggered flip-flop with reduced power dissipation   Order a copy of this article
    by Ravi G, Senthilkumar J. P, Vinothini Jane S 
    Abstract: In this paper, an improved implicit pulse triggered flip-flop is proposed based on conditional pulse enhancement scheme. The pass transistor logic AND gate creates a faster discharge path. Then a conditional pulse enhancement scheme is used in order to conditionally enhance the pulse only when required. This avoids unnecessary switching action in the flip-flop. Then in order to reduce the power dissipation further, the pseudo NMOS logic is replaced with a NAND gate structure. This reduces the static power consumption and as well as the switching power consumption. Thus results in an overall power saving. The results are obtained in 180 nm CMOS technology using mentor graphics. The results are compared with four conventional flip-flops and its power saving is improved.
    Keywords: Flip-Flop; CMOS; Implicit pulse triggered; low power.
    DOI: 10.1504/IJCCIA.2018.10021267
  • Energy Routing Algorithm Based on OSPF Protocol and Virtual Circuit Switching Mode in Energy Local Area Network   Order a copy of this article
    by Babar Ali, Ghulam Murtaza, Tariq Mahmood, Hafiz Muhammad Bilal, Rizwan Zahoor, Saleemullah Memon 
    Abstract: The advance management of the power supply to load such as in homes, offices etc. and power from source to central network in smart grid system with lowest possible loss is considered here in this paper using the concept of energy internet. It also includes the complete details of power flow and control. In this paper, the efficiency of transmission is increased by using energy routers based on open shortest path first (OSPF) protocol, a new concept in the field of energy internet. By using energy local area network (e -LAN) concept, all the energy routers are connected by using OSPF protocols and virtual circuit concept theory. An algorithm is proposed for efficient power transmission and effective selection of sources to minimize the overall cost in the network.
    Keywords: Smart Grid; e-LAN; OSPF; Energy Routers.
    DOI: 10.1504/IJCCIA.2019.10024515
  • Layout Detection Using Computer Vision   Order a copy of this article
    by Nayan Shende, Syedibrahim.sp Sp 
    Abstract: Remote sensing technology has opened the possibility of performing large object detection from satellite imagery. computer vision which concentrates on the theory and technology for building AI systems that extract features from images. Layout designing is as interesting yet complicated part of an architectural design which is particularly focused on artistic and usability quality of a layout, which is hard to define formally. Layout detection is also a similar concept where objects such as buildings, trees, and roads are extracted from the satellite images so as to collect the coordinates from the extracted objects and replicate the same on another parcel here we are using two different approaches to extract the objects such as for roads canny edge algorithm and model-based object detection similarly for buildings and trees pixel-based object detection and using tensor flow CNN model By using all this algorithm, we will be replicating layout in parcel.
    Keywords: Computer Vision; Layout Detection; Canny Edge Detection; Road Detection; Building Detection; Tensor Flow CNN model; image Trimming or Boundary Detection and Object Detection; OpenCV.
    DOI: 10.1504/IJCCIA.2018.10025093
  • Scheming a new algorithm for Dynamic Price Prediction of vegetable commodities using Statistical Price Prediction (SPP) Technique   Order a copy of this article
    by R. Deepalakshmi, S. Padma Devi, J. Shanthalakshmi Revathy, T. Grace Shalini 
    Abstract: Agriculture is the back bone of our nation and plays major role in our nation. Data mining and big data plays a vital role in making a decision related to the agriculture field. The major drawback of using these algorithms for price prediction methodology is that only small sample of data set is taken for the processing and predicting the price of vegetable in one particular area. This paper proposes a new statistical price prediction (SPP) methodology to overcome this. The present study was undertaken with the objectives to build appropriate forecasting model and to forecast the groundnut price of Madurai market of Tamil Nadu. SPP is used along with the statistical measurement and calculation for the unknown sample of data and a time series analysis is made for the appropriate price prediction of sample data collected. This technique provides a view for estimating the price of vegetable in future and provides a possible way to analyses the price
    Keywords: agriculture; data mining; machine learning algorithm; time series analysis; price prediction algorithm.
    DOI: 10.1504/IJCCIA.2019.10025191