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Improving the accuracy of box office revenue forecasts is conducive to stimulating the creation, market investment, infrastructure construction, and rational allocation of public resources in the film market, as well as promoting social welfare and cultural prosperity. Since the existing box office revenue prediction algorithm does not consider film industry structure, the prediction accuracy is not satisfying. This paper firstly builds a two-stage human-machine collaborative feature processing framework. In the first stage, based on the box office data, the regression decision tree algorithm is used to process all the box office features preliminarily and delete the unimportant features automatically. In the second stage, feature processing is coupled with the built Artificial Neural Network (ANN). In this stage, the features processed by the machine are manually classified, and multiple, incompatible feature sets are divided. After designing the incompatible set network pruning algorithm, the neural network is pruned. We construct the data set with a total of 7098 movies crawled online on four platforms. Numerical experimental results show that the Mean Absolute Error (MAE) of the two-stage algorithm is significantly better than the baseline model, which can effectively reduce the noise caused by encoding between incompatible features directly, improve the prediction accuracy of ANN, accelerate the forward inference speed of ANN and reduce the consumption of computing resources. [ABSTRACT FROM AUTHOR]

The cycles in an interconnection network are one of the communication types that are considered as a factor to measure the efficiency and reliability of the networks’ topology. The network is said to be panconnected if there are cycles of length l between two nodes u and v, for all l = d(u, v), d(u, v) + 1, d(u, v) + 2, …, n - 1 where d(u, v) is the shortest distance between u and v in a given network, and n is the total number of nodes in the network. In this paper, we propose an algorithm that proves the existence of panconnectivity of Eisenstein-Jacobi networks by constructing all cycles between any two nodes in the network of length l such that 3 ≤ l < n. The correctness of the proposed algorithm is given with the time complexity O(n4 ). The proposed algorithm adopts and modifies the idea of Dynamic Source Routing (DSR) to find all possible shortest paths. The results of some test cases using the proposed algorithm are provided. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of sequential content load algorithm on regional transfer in OpenSimulator framework. It, then, presents an abstract parallel content load algorithm, which is implemented using the basic parallel for and chunk partitioning constructs to exploit the parallelism of multi-core architectures. This work used a set of five example virtual worlds content to investigate the effectiveness of the proposed work and its impact on the reallocation process of regions in OpenSimulator. Simulation results revealed that the implementation using parallel for reduced the timings by 47% to 57% while the implementation using chunk partitioning minimized it by 63% to 72%. Time impact on regional transfer was reduced by 39% to 51% using parallel for while 52% to 66% using chunk partitioning. [ABSTRACT FROM AUTHOR]

For online education platforms, a personalized recommendation system is crucial, and the collaborative filtering algorithm is the primary recommendation algorithm used. This study took the recommendation of crowdfunding platforms as a sample, and enhanced the collaborative filtering algorithm based on the user score and project attribute features of the crowdfunding platform, intending to resolve the cold start issue brought on by the platform's reliance on a single data source. The study concludes with experimental proof of the paper's suggested better method. This approach can alleviate the cold start issue to some degree. The prediction accuracy has been much enhanced in comparison with the conventionally advised method. The method can also adapt to user tastes over time, learning what they like and what they don't. It also has an excellent real-time suggestion impact. The performance verification of the algorithm in this research is also conducted using data from a live crowdfunding site, lending credence to the study's claim of greater practicality. [ABSTRACT FROM AUTHOR]

The trend of inter-disciplinary conceptual design synthesis requires designers to involve more and more distributed multi-disciplinary design resources. Therefore, this paper proposes a graph-based computerized optimal conceptual design synthesis to help designers explore novel design schemes within the distributed multi-disciplinary resource environment. The design resources tightly related to the design goal can be extracted from the huge resource environment by a proposed searching engine. The optimal design scheme can be generated from these related design resources by a proposed graph-based algorithm. A set of computer applications called Automatic Conceptual Design System (ACDS) is established to verify the feasibility of this proposed conceptual design synthesis, and a garbage power system’s conceptual design is completed by this software prototype. [ABSTRACT FROM AUTHOR]

The Lower Goru Formation (LGF) has produced from E-sand in Kadanwari gas field, composed of sand and shale. The estimation of reservoir properties, including porosity, mineralogy, and thickness, which could help to identify new possible drilling sites, is hindered by the heterogeneous nature of LGF, and intermix shale, and sand. Integration of elastic and petrophysical properties worked as an effective tool for evaluating reservoir properties, especially for handling the heterogeneities of sand reservoirs associated with pores and their fluids. The probabilistic neural networking (PNN) technique has been proven the most effective and is a widely applied algorithm for predicting petrophysical parameters away from control points. It can create nonlinear correlations between input and estimated properties by efficiently handling the intermix shale. For this study, PNN prediction demonstrated a reliable statistical approximation. The trends of the actual and predicted properties such as effective porosities (PHIE = ∅e) and water saturation (Sw) for E-sand level exhibited an excellent correlation of 0.98 and 0.99 with an error rate of 0.006 and 0.03 respectively, which is also evident through the regression lines. The promising zones of the reservoir are further assessed by the multivariate seismic attributes, i.e., amplitudes, sweetness, and RGBA blending of appropriate frequency bands, which illuminated the potential channelized sands. The methodology applied in this study eventually minimized the uncertainty associated with petrophysical properties such as ∅e and Sw of the producing sands in LGF. The findings suggest that when dealing with heterogeneous reservoirs, a similar approach could be employed to locate feasible prospects. [ABSTRACT FROM AUTHOR]

Automated recitation plays an important role in improving self-learning. It is based on Speech/Text recognition. The research in Arabic speech recognition is very limited. The few existing applications are only based on the Holy Qur’an. This article proposed a new system (Samee’a - سميع) to facilitate memorizing any kind of text such that poems, speeches and the Holy Qur’an. Samee’a system is based on Google Cloud Speech Recognition API to convert the Arabic speech to text and Jaro Winkler Distance algorithm to determine the similarity between the original and converted texts. The system has been tested using 70 collected files ranging between 12 to 400 words and some chapters from the Holy Qur’an. The average similarity achieved 83.33% for the 70 files and 69% for the selected chapters of the Holy Qur’an. These results were enhanced to 91.33 % and 95.66% after applying preprocessing operations on the text files and the Holly Qur’an respectively. To validate the obtained results, two comparison studies were performed. The Jaro Winker distance was successfully compared to the cosine and the Euclidean distance. In addition, the proposed system outperformed the related work with an improvement of the similarity reaching 5% when using section 30 of the Holy Qur’an. Finally, the user experience testing was carried out by 10 users of different ages (between 5 and 50-year-old) using small texts and some small chapters of the Holy Qur’an. The proposed system proved its efficiency. [ABSTRACT FROM AUTHOR]

Genetic algorithm (GA) and differential evolution (DE) are metaheuristic algorithms that have shown a favorable performance in the optimization of complex problems. In recent years, only GA has been widely used for single-objective optimal design of reinforced concrete (RC) structures; however, it has been applied for multiobjective optimization of steel structures. In this article, the total structural cost and the roof displacement are considered as objective functions for the optimal design of the RC frames. Using the weighted sum method (WSM) approach, the two-objective optimization problem is converted to a single-objective optimization problem. The size of the beams and columns are considered as design variables, and the design requirements of the ACI-318 are employed as constraints. Five numerical models are studied to test the efficiency of the GA and DE algorithms. Pareto front curves are obtained for the building models using both algorithms. The detailed results show the accuracy and convergence speed of the algorithms. [ABSTRACT FROM AUTHOR]