WITHDRAWN: Spearman rank correlation based bagging ensemble bayes optimal classification for genomic predictive pattern analytics

This paper focuses on Dynamic Implementation using Array and Linked list. In the data structure concept, the list plays a major role in the allocation of data where it is a general overview of creating a node in a linked list using an array with the initialization of size. The node contains data and is interconnected to the next node in the sequence via their links which perform the search operation where the space and time complexity are not wasted in these analyses. Linked lists are a great way to store a theoretically infinite amount of data with a small and versatile amount of code. The amount of data stored increases linearly with the number of nodes in the list (space-o(n)) where linked lists are more efficient than arrays. It is observed that considering the time complexity when there is a variation in data set linked array is almost reaches close to array whenever dataset is high also linked array has better time complexity than linked list when the searching element is in middle and last. Considering the space complexity linked array is almost 2 times and 4 times less space is needed than array and linked list respectively.

Prediction is the process of analyzing the current and past events to identify future events. The prediction of the subsequent future conditions is still a revealing stage in many applications to minimize the risk level. Several techniques have been developed for predictive analysis with big data. However, an accurate prediction analysis was not obtained while handling a large volume of data with less complexity. In order to improve prediction accuracy with less complexity, a Gramian symmetric data collection-based random forest bivariate regression and classification (GSDC-RFBRC) technique is developed. Initially, a large volume of data is collected from the dataset. Then, the Gramian symmetric matrix is used for storing the volume of data in rows and columns of a matrix. Then, the classification and regression process is carried out using random decision forests for finding future outcomes. Regression process measures the relationship between a dependent variable (i.e., outcomes) and independent variables (i.e., data) through bivariate correlation. Random decision forest constructs a number of decision trees for classification based on the correlation. Finally, it combines a number of decision trees and applies the voting scheme. The majority vote of classification results is identified for achieving high prediction accuracy. Experimental evaluation is carried out on the factors such as prediction accuracy, prediction time, false-positive rate and space complexity with respect to the number of data (i.e., file). The results confirmed that the proposed GSDC-RFBRC technique improves the performance results of prediction accuracy and minimizes the prediction time, false-positive rate as well as space complexity.

The classic buffer insertion algorithm of van Ginneken has time and space complexity O(n/sup 2/), where n is the number of possible buffer positions. For more than a decade, van Ginneken's algorithm has been the foundation of buffer insertion. In this paper, we present a new algorithm that computes the same optimal buffer insertion, but runs much faster. For 2-pin nets, our time complexity is O(nlogn) and space complexity is O(n). For multipin nets, our time complexity is O(nlog/sup 2/n) and space complexity is O(nlogn). The speedup is achieved by four novel techniques: predictive pruning, candidate tree, fast redundancy check, and fast merging. On industrial test cases, the new algorithms is 2-80 times faster than van Ginneken's algorithm and uses 1/4-1/500 of the memory. Since van Ginneken's algorithm and its variations are used by most existing algorithms on buffer insertion and buffer sizing, our new algorithm significantly improves the performance of all these algorithms. The predictive pruning technique has been applied to buffer cost minimization (Shi et al., 2004), and significantly improved the running time.

We initiate the systematic study of the energy complexity of algorithms (in addition to time and space complexity) based on Landauer's Principle in physics, which gives a lower bound on the amount of energy a system must dissipate if it destroys information. We propose energy-aware variations of three standard models of computation: circuit RAM, word RAM, and transdichotomous RAM. On top of these models, we build familiar high-level primitives such as control logic, memory allocation, and garbage collection with zero energy complexity and only constant-factor overheads in space and time complexity, enabling simple expression of energy-efficient algorithms. We analyze several classic algorithms in our models and develop low-energy variations: comparison sort, insertion sort, counting sort, breadth-first search, Bellman-Ford, Floyd-Warshall, matrix all-pairs shortest paths, AVL trees, binary heaps, and dynamic arrays. We explore the time/space/energy trade-off and develop several general techniques for analyzing algorithms and reducing their energy complexity. These results lay a theoretical foundation for a new field of semi-reversible computing and provide a new framework for the investigation of algorithms.

Binary embedding refers to methods for embedding points in $\mathbb {R}^{d}$ into vertices in the Hamming cube of dimension ${\mathcal{O}}(d)$ , such that the normalized Hamming distance between the codes preserves a prespecified distance between vectors in the original space. A common approach to binary embedding is to use random projection , followed by one-bit quantization to produce binary codes. Of particular interest, in this letter, is sparse circulant binary embedding (SCBE), where a sparse random circulant matrix with random sampling at a rate of $1-s$ for $s\in (0, 1)$ is used for random projection. The SCBE has the space complexity ${\mathcal{O}}((1-s)d)$ , while unstructured random projection has the space complexity ${\mathcal{O}}(d^2)$ . We present an asymptotic analysis of SCBE, when $d$ approaches $\infty$ , showing that the performance of SCBE is comparable to that of binary embedding with unstructured random projection whilethe former has the space complexity ${\mathcal{O}}((1-s)d)$ and the time complexity ${\mathcal{O}}(d\log d)$ but the latter has both space and time complexities ${\mathcal{O}}(d^2)$ .

A faster FPTAS for knapsack problem with cardinality constraint

This paper introduces a new heuristic function with high efficiency for an optimum solving of the 8 - puzzle, this one being the double of the Chebyshev distance. The comparative study is realized among this new heuristic (Chebyshev heuristic), the Hamming h euristic and the Manhattan heuristic using A* algorithm implemented in Java. The Chebyshev heuristic function is more informed than Hamming and Manhattan heuristics. This paper also presents the necessary stages in object oriented development of an interactive software dedicated to simulate the A* search algorithm for 8 -puzzle. The modeling of the software is achieved through specific UML diagrams representing the phases of analysis, design and implementation, the system thus being described in a clear and practical manner. In order to confirm the obtained theoretical results which show that Chebyshev heuristic is more efficient, two performance criteria were used: space complexity and time complexity. The space complexity was measured by the number of generated nodes from the search tree, by the number of the expanded nodes and by the effective branching factor. The time complexity was measured by the running time. From the experimental results obtained by using the Chebyshev heuristic, improvements were obs erved regarding space and time complexity of A* algorithm versus Hamming and Manhattan heuristics.

This paper proposes a novel learning algorithm- SVM based MLP neural network algorithm (SVMMLP), which based on the Maximal Margin (MM) principle and take into account the idea of support vectors. SVMMLP has time and space complexities O(N) while usual SVM training methods have time complexity O(N3) and space complexity O(N2), where N is the training-dataset size. Intrusion detection benchmark datasets – NSL-KDD used in experiments that enable a comparison with other state-of-the-art classifiers. The results provide evidence of the effectiveness of our methods regarding accuracy, AUC, and Balanced Error Rate (BER).KeywordsMLP neural networkconvergenceSVMMLPintrusion detection

Big Data - Towards a New Techno-Determinism?

A number of algorithms are available for computing the simulation relation on Kripke structures and on labelled transition systems representing concurrentsystems. Among them, the algorithm by Ranzato and Tapparo~[2007] has the best time complexity, while the algorithm by Gentilini et al.~[2003]~--~successivelycorrected by van Glabbeek and Ploeger~[2008]~--~has thebest space complexity. Both space and time complexities are critical issues in a simulation algorithm, in particular memory requirements are crucial in the context of model checking when dealing with large state spaces.We propose here a new simulation algorithm thatis obtained as a space saving modification of the time efficient algorithm by Ranzato and Tapparo: a symbolic representation of sets is embedded in thisalgorithm so that any set of states manipulated by the algorithm can be efficiently stored as a set of blocks of a suitable state partition. It turns out that this new simulation algorithm retains a space complexity comparable with Gentilini et al.'s algorithm while improving on Gentilini et al.'s time bound.

Reservoir engineering is a critical discipline in the oil and gas industry, aimed at optimizing the extraction and management of subsurface resources. Traditionally, reservoir models have relied on static data and conventional simulation methods, which often fall short in capturing the dynamic complexities of reservoirs. With the advent of big data and predictive analytics, there is a significant opportunity to revolutionize reservoir modeling by integrating real-time data for continuous updates and more accurate predictions. This review introduces a theoretical exploration of how big data and predictive analytics can transform dynamic reservoir models, highlighting their potential to improve reservoir management and hydrocarbon recovery. Big data technologies enable the collection, storage, and processing of vast amounts of structured and unstructured data from multiple sources, including well logs, seismic surveys, drilling operations, and production data. Predictive analytics, through machine learning and statistical techniques, can extract actionable insights from these datasets to predict reservoir behavior, enhance decision-making, and optimize production strategies. This integration facilitates real-time monitoring and adaptive reservoir management, allowing engineers to respond to changing reservoir conditions and uncertainties more effectively. The review further explores the implications of big data-driven predictive models for enhanced hydrocarbon recovery techniques, such as water flooding and gas injection. By automating data processing and integrating real-time field data, predictive analytics can improve the accuracy of reservoir simulations, reduce downtime, and increase operational efficiency. Ultimately, the future of dynamic reservoir modeling lies in the seamless integration of big data and predictive analytics, providing a pathway to smarter, more sustainable resource management and maximized recovery factors in increasingly complex reservoir environments. Keywords: Big Data, Predictive Analytics, Reservoir Engineering, Dynamic Reservoir, Models.

Kernel ridge regression (KRR) is a powerful method for nonparametric regression. The time and space complexity of computing the KRR estimate directly are O(n3) and O(n2) , respectively, which are prohibitive for large-scale data sets, where n is the number of data. In this article, we propose a novel random sketch technique based on the circulant matrix that achieves savings in storage space and accelerates the solution of the KRR approximation. The circulant matrix has the following advantages: It can save time complexity by using the fast Fourier transform (FFT) to compute the product of matrix and vector, its space complexity is linear, and the circulant matrix, whose entries in the first column are independent of each other and obey the Gaussian distribution, is almost as effective as the i.i.d. Gaussian random matrix for approximating KRR. Combining the characteristics of the circulant matrix and our careful design, theoretical analysis and experimental results demonstrate that our proposed sketch method, making the estimate kernel methods scalable and practical for large-scale data problems, outperforms the state-of-the-art KRR estimates in time complexity while retaining similar accuracies. Meanwhile, our sketch method provides the theoretical bound that keeps the optimal convergence rate for approximating KRR.

Routing with multiple quality-of-services constraints: An approximation perspective

The precision and efficiency of the similarity computing of documents is the foundation and key of other documents processing. In this paper, the DF and TF-IDF algorithms are improved. First, DF's time complexity is linear which suits mass documents processing, but it has the fault that exceptional useful features may be deleted, so we make up that by adding the count of the words at the important places. Second, we rectify the weight of feature by the result of feature selection phase. In this way, we improve the precision of documents similarity without adding much time and space complexity.

In this paper a fast and space efficient method for simulating a d′-dimensional cellular automaton (CA) on a d-dimensional CA (d<d′) is introduced. For d′=2 and d=1 this method is optimal (under certain assumptions) with respect to time as well as space complexity. Let in this case t(n) be the time complexity and r(n) the side length of the smallest square enclosing all used cells. Then the simulation does not need more than O(r 2) cells and has a running time of d′ = d+1 a version with the time and space complexity of O(t 2) will be presented. Finally it will be shown, how it is possible to simulate a 2d-dimensional CA on a d-dimensional CA in a similarly efficient way.KeywordsTime ComplexityCellular AutomatonSpace ComplexityQuiescent StateComputation StepThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.