Performance Of Approach Research Articles

A prescriptive framework to support express delivery supply chain expansions in highly urbanized environments

PurposeWith the proliferation of e-commerce companies, express delivery companies must increasingly maintain the efficient expansion of their networks in accordance with growing demands and lower margins in a highly uncertain environment. This paper provides a framework for leveraging demand data to determine sustainable network expansion to fulfill the increasing needs of startups in the express delivery industry.Design/methodology/approachWhile the literature points out several hub assignment methods, the authors propose an alternative spherical-clustering algorithm for densely urbanized population environments to strengthen the accuracy and robustness of current models. The authors complement this approach with straightforward mathematical optimization and simulation models to generate and test designs that effectively align environmentally sustainable solutions.FindingsTo examine the effects of different degrees of demand variability, the authors analyzed this approach's performance by solving a real-world case study from an express delivery company's primary market. The authors structured a four-stage implementation framework to facilitate practitioners applying the proposed model.Originality/valuePrevious investigations explored driving distances on a spherical surface for facility location. The work considers densely urbanized population and traffic data to simultaneously capture demand patterns and other road dynamics. The inclusion of different population densities and sustainability data in current models is lacking; this paper bridges this gap by posing a novel framework that increases the accuracy of spherical-clustering methods.

Extending multivariate Student's- semiparametric mixed models for longitudinal data with censored responses and heavy tails.

This article extends the semiparametric mixed model for longitudinal censored data with Gaussian errors by considering the Student's -distribution. This model allows us to consider a flexible, functional dependence of an outcome variable over the covariates using nonparametric regression. Moreover, the proposed model takes into account the correlation between observations by using random effects. Penalized likelihood equations are applied to derive the maximum likelihood estimates that appear to be robust against outlying observations with respect to the Mahalanobis distance. We estimate nonparametric functions using smoothing splines under an EM-type algorithm framework. Finally, the proposed approach's performance is evaluated through extensive simulation studies and an application to two datasets from acquired immunodeficiency syndrome clinical trials.

High school students’ achievement goal adoption: Evidence from neglected rural high schools in Iran

The study aimed to examine rural high schoolers’ achievement goal preferences and the potential gender variations in a neglected rural context in Iran. To this end, a sample of 252 high schoolers answered Elliott and Murayama’s (2008) Achievement Goal Questionnaire-Revised (AGQ-R). Quantitative results indicated the prevalence of mastery approach (MAp) and performance approach (PAp) goals, followed by performance avoidance (PAv) and mastery avoidance (MAv) goals. Further, an examination of gender differences indicated no significant differences between male and female high schoolers in their adoption of MAp, PAp and MAv goals. However, a significant difference was observed between male and female students in their resort to PAv goal. Results may have implications for different individuals in educational settings such as educational authorities, teachers, and counselors as well as researchers.

Sensing matrix optimization for multi-target localization using compressed sensing in wireless sensor network

In the multi-target localization based on Compressed Sensing (CS), the sensing matrix's characteristic is significant to the localization accuracy. To improve the CS-based localization approach's performance, we propose a sensing matrix optimization method in this paper, which considers the optimization under the guidance of the t%-averaged mutual coherence. First, we study sensing matrix optimization and model it as a constrained combinatorial optimization problem. Second, the t%-averaged mutual coherence is adopted as the optimality index to evaluate the quality of different sensing matrixes, where the threshold t is derived through the K-means clustering. With the settled optimality index, a hybrid metaheuristic algorithm named Genetic Algorithm-Tabu Local Search (GA-TLS) is proposed to address the combinatorial optimization problem to obtain the final optimized sensing matrix. Extensive simulation results reveal that the CS localization approaches using different recovery algorithms benefit from the proposed sensing matrix optimization method, with much less localization error compared to the traditional sensing matrix optimization methods.

A Novel Effective Lightweight Homomorphic Cryptographic Algorithm for data security in cloud computing

Cloud computing is a technology in which the resources are delivered as services. Users can access them anywhere, anytime via the Internet without any need to know the infrastructure knowledge, experience, or even authority that provides such services. It has become an important medium for enterprises to develop their networks because of these resources. With the growing need for cloud computing, security is becoming important for both individuals and business needs. Many researchers have studied the security of cloud computing. Still, security gaps or threats are increasing as the demand for cloud computing connectivity increases. Because a third party mostly provides cloud platforms to the cloud client, data protection in the cloud is the primary issue. This paper presents a novel, effective lightweight homomorphic cryptographic algorithm which contains two layers of encryption. The first layer uses the new effective, light-weight cryptographic algorithm and the second layer multiplicative homomorphic schemes considered for improving security data in cloud computing. This approach offers both symmetric and asymmetric cryptography features. The proposed approach's performance is evaluated using a variety of metrics, including memory, computational time and (key sensitivity), statistical analysis, image histogram, and entropy change analysis. The proposed algorithm's experimental findings showed a high level of security and an apparent improvement in encryption execution time, memory usage, and throughput. When compared to the cryptographic systems widely used in cloud computing.

Ontology-Enabled Emotional Sentiment Analysis on COVID-19 Pandemic-Related Twitter Streams.

The exponential growth of social media users has changed the dynamics of retrieving the potential information from user-generated content and transformed the paradigm of information-retrieval mechanism with the novel developments on the concept of “web of data”. In this regard, our proposed Ontology-Based Sentiment Analysis provides two novel approaches: First, the emotion extraction on tweets related to COVID-19 is carried out by a well-formed taxonomy that comprises possible emotional concepts with fine-grained properties and polarized values. Second, the potential entities present in the tweet can be analyzed for semantic associativity. The extraction of emotions can be performed in two cases: (i) words directly associated with the emotional concepts present in the taxonomy and (ii) words indirectly present in the emotional concepts. Though the latter case is very challenging in processing the tweets to find the hidden patterns and extract the meaningful facts associated with it, our proposed work is able to extract and detect almost 81% of true positives and considerably able to detect the false negatives. Finally, the proposed approach's superior performance is witnessed from its comparison with other peer-level approaches.

Improved GA based power and cost management system in a grid-associated PV-wind system

Renewable hybrids play an essential part in assisting India with quickening the decarbonisation of power production and lowering power production expense in the medium term. PV and wind energy are complementary to each other, making the system to generate electricity almost throughout the year. In this paper, a grid-associated PV-wind energy system tied with a battery is analysed. PV, wind, grid and battery are the sources to be effectively scheduled for uninterrupted power and cost minimisation. Energy management controllers use optimisation strategies for effective utilisation of sources and cost minimisation. The methodologies are detailed as optimisation problems. Limiting the household energy cost is considered as objective, and the delivery ratio of power offered to the grid and utilised locally is treated as the optimisation variable. In this paper, an improved genetic algorithm is proposed to solve the formulated nonlinear optimisation problems. The time-of-use tariff is becoming popular in India; therefore, this article analyses the improved genetic algorithm based intelligent power and cost management system under time-of-use tariff. Using MATLAB, the proposed approach's performance is presented with the comparative analysis of conventional self-made for self-consumed and rest for sale mode and genetic algorithm-based energy management controller.

Learning to Generate Missing Pulse Sequence in MRI using Deep Convolution Neural Network Trained with Visual Turing Test.

Magnetic resonance imaging (MRI) is widely used in clinical applications due to its ability to acquire a wide variety of soft tissues using multiple pulse sequences. Each sequence provides information that generally complements the other. However, factors like an increase in scan time or contrast allergies impede imaging with numerous sequences. Synthesizing images of such non acquired sequences is a challenging proposition that can suffice for corrupted acquisition, fast reconstruction prior, super-resolution, etc. This manuscript employed a deep convolution neural network (CNN) to synthesize multiple missing pulse sequences of brain MRI with tumors. The CNN is an encoder-decoder-like network trained to minimize reconstruction mean square error (MSE) loss while maximizing the adversarial attack. It inflicts on a relativistic Visual Turing Test discriminator (rVTT). The approach is evaluated through experiments performed with the Brats2018 dataset, quantitative metrics viz. MSE, Structural Similarity Measure (SSIM), and Peak Signal to Noise Ratio (PSNR). The Radiologist and MR physicist performed the Turing test with 76% accuracy, demonstrating our approach's performance superiority over the prior art. We can synthesize MR images of missing pulse sequences at an inference cost of 350.71 GFlops/voxel through this approach.

A novel hybrid framework for wind speed forecasting using autoencoder‐based convolutional long short‐term memory network

A precise forecast of wind speed is a fundamental requirement of wind power integration. The nonlinear and intermittent nature of the wind makes wind speed forecasting (WSF) complicated for linear approaches. Addressing the complications faced by the linear approaches, this paper proposed a novel and robust approach using long short-term memory (LSTM) autoencoder, convolutional neural network (CNN), and LSTM model for enhanced WSF. The proposed hybrid approach is divided into two main components: feature encoding, dimensionality reduction using LSTM autoencoder and forecasting using convolutional LSTM. In the first stage, the LSTM autoencoder eliminates the uncertainties present in raw wind speed data and also reduces the computational load on the forecasting convolutional LSTM approach. Then, in the second stage, CNN is used to extract the optimum features, and the LSTM network is used to forecast the wind speed. Five different benchmark forecasting models are used to evaluate and study the proposed hybrid approach's performance. The experiment is performed with real-time wind speed data from the Garden city wind farm, USA. The proposed hybrid approach performance is verified using various performance metrics. The experimental results demonstrate that the proposed approach improved by 40% over the second best benchmark forecasting approach.

The Spatial neural network model with disruptive technology for property appraisal in real estate industry

Property valuation is a complex issue that has always been the focal point for the real estate industry. The traditional valuation models used for appraisals cannot meet real-world demand anymore due to the improper processing of correlated information of nearby facilities. In this study, we propose a Spatial Neural Network (SNN) model, called Property Appraisal 4.0, that uses disruptive technology to forecast property values and discover hidden neighbourhood features of real estate information in the satellite embedding vectors. The latest deep learning technologies are also employed, such as knowledge distillation, incremental learning, and Deep-Automated Optical Inspection. Class Activation Mapping is also adapted to reinforce the proposed spatial neural network in the model. Experimental results show that our approach's performance is better than that of previous mainstream models, such as the Hedonic Pricing Model and Support Vector Machines.

Detecting home countries of social media users with machine-learned ranking approach: A case study in Hong Kong

Inferring individual's home country from geotagged footprints is widely applied in human mobility research. Previous studies mainly used simple empirical methods that are based on intuitive hypothetical assumptions. Because the exact relationships between users' home countries and geotagged footprints haven't be quantitatively revealed, empirical methods based on human intuitions and past experiences are used for rough approximation. In this study, we propose a machine-learning approach for the task of home country detection, by formulating the task as a query-ranking problem and using a machine-learned ranking model for problem solving. The used model is a Multiple Additive Regression Trees framework that aims to rank regions in specific orders and the region ranked first is designated as the home country. Our approach is data-driven and can adaptively learn the unknown function from input (geotagged footprints) to output (user's home country), thus alleviating the bias introduced by previous empirical methods. We conduct experiments with real-world datasets, and results demonstrate that our approach achieves better performance than previous empirical methods. The model's parameter sensitivity is also investigated, and results show that user's origin may be a factor affecting the approach's performance and that our approach achieves robust good performance with various parameter settings.

An Adaptive Approach for Dynamic Load Modeling in Microgrids

Electric microgrids require accurate dynamic models for operation, control, stability, and protection studies, then adequate load modeling plays an important role. This paper presents a two-stage adaptive approach to improve the generalization capability of load models obtained with the measurement-based modeling. The load model and their respective parameters are obtained through machine learning tools like decision trees (DTs) and optimization algorithms as ant colony (ACO). In the off-line stage of the proposed approach, several parameterized load models are optimally obtained using a database of microgrid disturbances. Then, the best model to represent each disturbance is defined using a similarity criterion. This model and the disturbance characteristics are integrated into a DT (classifier), while the characteristics and the model parameters are related in a second DT (predictor). These DTs are used in an on-line stage to swiftly determine the adequate parameterized load model in the case of a new disturbance in the microgrid. The approach's performance is compared with the conventional measurement-based load modeling in a modified CIGRE benchmark low voltage microgrid. The results evidence the advantages of the proposed adaptive approach for dynamic load modeling.

A simulated annealing based approach for open pit mine production scheduling with stockpiling option

Production scheduling plays a pivotal role in successfully executing any open-pit mining operation. It defines the most profitable extraction sequence of mineralized material from the ground subject to various physical and operational constraints. Different mathematical formulations have been proposed to achieve this goal; however, solving these models for real-sized deposits with multiple constraints is a challenging and computationally expensive task. Moreover, the inclusion of stockpiling option further complicates this task. The stockpile option adds flexibility to the operation by allowing excess low-grade ore storage for processing at a later stage when processing capacity is available. Accurate integration of stockpiling option in the production scheduling process through mathematical approaches leads to nonlinear constraints. This could further complicate the already challenging task since linear approximation of these nonlinear constraints could lead to sub-optimal results. Metaheuristic techniques could play an essential role in handling such situations. Though several attempts have been made to solve this problem through these techniques, little effort has been made to incorporate stockpiling option in the optimization process. This article presents a Simulated Annealing based approach for production scheduling of open-pit mines with stockpiling option. The proposed approach uses a stockpile and a greedy heuristic with a Simulated Annealing algorithm to achieve this goal. The greedy heuristic improves the Simulated Annealing algorithm's computational efficiency by managing its randomness. The proposed approach's performance and efficiency are demonstrated through three case studies (A, B, and C) under different algorithmic settings. Results of these case studies reveals that compared with the CPLEX solver, the proposed approach produced near optimal solution, within reasonable amount of time, proving the applicability of the proposed approach.

Optimizing ANFIS using simulated annealing algorithm for classification of microarray gene expression cancer data.

In the medical field, successful classification of microarray gene expression data is of major importance for cancer diagnosis. However, due to the profusion of genes number, the performance of classifying DNA microarray gene expression data using statistical algorithms is often limited. Recently, there has been an important increase in the studies on the utilization of artificial intelligence methods, for the purpose of classifying large-scale data. In this context, a hybrid approach based on the adaptive neuro-fuzzy inference system (ANFIS), the fuzzy c-means clustering (FCM), and the simulated annealing (SA) algorithm is proposed in this study. The proposed method is applied to classify five different cancer datasets (i.e., lung cancer, central nervous system cancer, brain cancer, endometrial cancer, and prostate cancer). The backpropagation algorithm, hybrid algorithm, genetic algorithm, and the other statistical methods such as Bayesian network, support vector machine, and J48 decision tree are used to compare the proposed approach's performance to other algorithms. The results show that the performance of training FCM-based ANFIS using SA algorithm for classifying all the cancer datasets becomes more successful with the average accuracy rate of 96.28% and the results of the other methods are also satisfactory. The proposed method gives more effective results than the others for classifying DNA microarray cancer gene expression data. Basic structure of proposed method.

Arabic Semantic Similarity Approach for Farmers’ Complaints

Semantic similarity is applied for many areas in Natural Language Processing, such as information retrieval, text classification, plagiarism detection, and others. Many researchers used semantic similarity for English texts, but few used for Arabic due to the ambiguity of Arabic concepts in both sense and morphology. Therefore, the first contribution in this paper is developing a semantic similarity approach between Arabic sentences. Nowadays, the world faces a global problem of coronavirus disease. In light of these circumstances and distancing's imposition, it is difficult for farmers to physically communicate with agricultural experts to provide advice and find suitable solutions for their agricultural complaints. In addition, traditional practices still are used by most farmers. Thus, our second contribution is helping the farmers solve their Arabic agricultural complaints using our proposed approach. The Latent Semantic Analysis approach is applied to retrieve the most problem-related semantic to a farmer's complaint and find the related solution for the farmer. Two methods are used in this approach as a weighting schema for data representation are Term Frequency and Term Frequency-Inverse Document Frequency. The proposed model has also classified the big agricultural dataset and the submitted farmer complaint according to the crop type using MapReduce Support Vector Machine to improve the performance of semantic similarity results. The proposed approach's performance with Term Frequency-Inverse Document Frequency-based Latent Semantic Analysis achieved better than its counterparts with an F-measure of 86.7%.

Synergy-Based Neural Interface for Human Gait Tracking With Deep Learning

Neural information decomposed from electromyography (EMG) signals provides a new path of EMG-based human-machine interface. Instead of the motor unit decomposition-based method, this work presents a novel neural interface for human gait tracking based on muscle synergy, the high-level neural control information to collaborate muscle groups for performing movements. Three classical synergy extraction approaches include Principle Component Analysis (PCA), Factor Analysis (FA), and Nonnegative Matrix Factorization (NMF), are employed for muscle synergy extraction. A deep regression neural network based on the bidirectional gated recurrent unit (BGRU) is used to extract temporal information from the synergy matrix to estimate joint angles of the lower limb. Eight subjects participated in the experiment while walking at four types of speed: 0.5km/h, 1.0km/h, 2.0km/h, and 3.0km/h. Two machine learning methods based on linear regression (LR) and multilayer perceptron (MLP) are set as the contrast group. The result shows that the synergy-based approach's performance outperforms two contrast methods with R2var. scores of 0.83~0.88. PCA reaches the highest performance of 0.871±0.029, corresponding to RMSE of 3.836°, 6.278°, 2.197° for hip, knee, and ankle, respectively. The effect of walking speed, synergy number, and joint location will be analyzed. The performance shows that muscle synergy has a good correlation will joint angles which can be unearthed by deep learning. The proposed method explores a new way for gait analysis and contributes to building a novel neural interface with muscle synergy and deep learning.

Uncertainty Inspired RGB-D Saliency Detection.

We propose the first stochastic framework to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Existing RGB-D saliency detection models treat this task as a point estimation problem by predicting a single saliency map following a deterministic learning pipeline. We argue that, however, the deterministic solution is relatively ill-posed. Inspired by the saliency data labeling process, we propose a generative architecture to achieve probabilistic RGB-D saliency detection which utilizes a latent variable to model the labeling variations. Our framework includes two main models: 1) a generator model, which maps the input image and latent variable to stochastic saliency prediction, and 2) an inference model, which gradually updates the latent variable by sampling it from the true or approximate posterior distribution. The generator model is an encoder-decoder saliency network. To infer the latent variable, we introduce two different solutions: i) a Conditional Variational Auto-encoder with an extra encoder to approximate the posterior distribution of the latent variable; and ii) an Alternating Back-Propagation technique, which directly samples the latent variable from the true posterior distribution. Qualitative and quantitative results on six challenging RGB-D benchmark datasets show our approach's superior performance in learning the distribution of saliency maps. The source code is publicly available via our project page: https://github.com/JingZhang617/UCNet.

A Study of the Development of a Fuzzy Inference System Using Image Watermarking

The primary purpose of this research is to develop a non-blind image watermarking system that is both robust and unnoticeable using wavelet transforms and fuzzy inference. The emphasis is on developing discrete wavelet transform, human visual system, and fuzzy inference system-based picture watermarking that makes optimal use of human vision constraints. The purpose of this research is to develop a novel technique for embedding perfect measure watermark data into the host image without creating perceptual damage. The methodology is to combine discrete wavelet transforms, the human visual system, and fuzzy inference to enable the utilisation of human vision limits in a viable manner. Additionally, the idea is to insert a significant logo as a watermark rather than the more traditional pseudorandom paired grouping for robust image watermarking. The purpose of this project is to develop image watermarking using the discrete wavelet transform. The objective is to investigate wavelet basis functions that are ideal for picture watermarking and to empirically estimate the strength of the watermark in order to inject the maximum amount of watermark information into the host image without degrading its perceptual quality. To model and execute various assaults on watermarked photos in order to degrade their quality in order to assess performance for resilience. Additionally, performance is evaluated in the presence of the maximum potential degradation of the watermarked image in order to define effective robustness requirements. To evaluate the performance of suggested approaches based on DWT, DWT-HVS, and DWT-HVS-FIS image watermarking, as well as to compare the proposed approach's performance to that of existing approaches utilising performance parameters.

The influence of competition and performance goals on decoding complex emotions

The present research examines the processes through which competitive contexts influence performance in an understudied domain: social perception. In two experiments (one preregistered), we test how competition (relative to control) influences performance on a measure of emotion decoding: The Reading-the-mind-in-the-Eyes (RME) task. Specifically, we examine whether performance-approach (PAP) and performance-avoidance (PAV) goals (as well as mastery-approach [MAP] and mastery-avoidance [MAV] goals) affect the competition-performance relation in this domain. The results indicate no direct effect of competition on RME performance, but that competition promotes the pursuit of both PAP and PAV goals. Moreover, we find that both PAP and PAV goals are positively related to RME performance in a competitive, but not control, context. Mediational analyses indicate that PAP goals, in particular, are the process through which competition indirectly influences RME performance. These findings integrate several different heretofore separate literatures, and lay the foundation for exciting new work in this overlooked area.

Cooperative Tile-Based 360° Panoramic Streaming in Heterogeneous Networks Using Scalable Video Coding

The use of high-quality 360° panoramic video is booming in the video industry. However, existing schemes for smartphones suffer from significant bandwidth consumption as they transmit the entire panoramic views in very high resolutions. This demand for bandwidth becomes even more problematic when multiple adjacent smartphones compete to access the same content, which further challenges a wireless network's capacity, and when the available bandwidth fluctuates much more than wired networks. In this paper, we propose a cooperative streaming scheme for tile-based 360° video using scalable video coding (SVC) to maximize a group of users' quality of experience. We formulate an optimization problem to choose optimal downloading and sharing subsets from a set of all requested SVC layers of tiles to maximize the effective quality of the users' viewport while meeting the feasibility of the bandwidth of heterogeneous networks. We then show that the problem is NP-hard and compose a heuristic approach. In the approach, we rank the SVC layers based on the aggregated group-level preference to guide the devices' downloading and sharing activities. A prototype on the Android platform is developed to test the approach's performance, and the real-world results show that our proposed scheme outperforms baseline alternatives.