International Journal of Computational Complexity and Intelligent Algorithms (5 papers in press)
Complexity Verification through Design and Analysis of Computer Experiments
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
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
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.
A Novel Method Based on Pole Clustering Technique and Differential Evolution for Model Order Reduction
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).
Classifiers for Arabic NLP: Survey
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;.