Abstract

Green manufacturing imposes higher expectations on manufacturing engineering, not only with respect to classic competitive factors such as cost, time and quality, but also with sustainable factors such as resources and energy. In this paper, we investigate green flexible job shop scheduling problem (GFJSP) with variable processing speeds. To solve the GFJSP problem, we propose an adaptive Variable Neighborhood Search to minimize the makespan and the total energy consumption. A number of experiments have been conducted to evaluate the performance of our proposed adaptive VNS algorithm. A comparative study was presented and have verified the out performance of the proposed algorithm against other VNS variants.

Abstract

Fake news has a worldwide impact and the potential to change political scenarios and human behavior, especially in a critical time like the COVID-19 pandemic. This work suggests a Soft Three-Level Voting Model (Soft T-LVM) for automatically classifying COVID-19 fake news. We train different individual machine learning algorithms and different ensemble methods in order to overcome the weakness of individual models. This novel model is based on the soft-voting technique to calculate the class with the majority of votes and to choose the classifiers to merge and apply at every level. We use the Grid search method to tune the hyper-parameters during the process of classification and voting. The experimental evaluation confirms that our proposed model approach has superior performance compared to the other classifiers.

Abstract

Identifying opinion leaders in social networks, particularly in social media, is a crucial marketing strategy. These individuals have a considerable influence on the purchasing decisions of their communities. Companies can benefit from collaborating with relevant opinion leaders in their market as this can increase their visibility, establish their credibility, and gain consumer trust, leading to increased sales, improved brand perception, and an expanded market share. Additionally, by gaining a comprehensive understanding of opinion leaders, companies can better comprehend the trends and preferences of their target audience. This allows them to tailor their marketing and product strategies more effectively. Identifying suitable influencers to endorse their products or services is a significant challenge for companies. The identification of opinion leaders is complicated by their informal and unstructured nature, as well as the varying selection criteria depending on the marketing campaign’s goals. While numerous research studies have focused on detecting opinion leaders in social networks based on content, interactions, or a combination of both, few have explored sentiment analysis of post content, received interactions, and user comments in relation to published posts. The purpose of this paper is to present an hybrid approach to detect opinion leaders in Facebook. This approach involves analyzing the trajectory of post content by examining interactions on the post, as well as mining the text content of the post itself and analyzing the users’comments sentiments.

Abstract

Abstract

Fake news has a worldwide impact and the potential to change political scenarios and human behavior, especially in a critical time like the COVID-19 pandemic. This work suggests a Soft Three-Level Voting Model (Soft T-LVM) for automatically classifying COVID-19 fake news. We train different individual machine learning algorithms and different ensemble methods in order to overcome the weakness of individual models. This novel model is based on the soft-voting technique to calculate the class with the majority of votes and to choose the classifiers to merge and apply at every level. We use the Grid search method to tune the hyper-parameters during the process of classification and voting. The experimental evaluation confirms that our proposed model approach has superior performance compared to the other classifiers.

Abstract

Electrocardiogram (ECG) data are usually used to diagnose cardiovascular disease (CVD) with the help of a revolutionary algorithm. Feature selection is a crucial step in the development of accurate and reliable diagnostic models for CVDs. This research introduces the dynamic threshold genetic algorithm (DTGA) algorithm, a type of genetic algorithm that is used for optimization problems and discusses its use in the context of feature selection. This research reveals the success of DTGA in selecting relevant ECG features that ultimately enhance accuracy and efficiency in the diagnosis of CVD. This work also proves the benefits of employing DTGA in clinical practice, including a reduction in the amount of time spent diagnosing patients and an increase in the precision with which individuals who are at risk of CVD can be identified.

Abstract

Protecting patient data has become a top priority for healthcare providers in the digital age. ECG steganography is a technique for concealing electrocardiogram (ECG) signals during Internet transmission along with other medical data. This strategy aims to recover all embedded patient data while minimizing degradation of the cover signal caused by embedding. Quantization techniques make it possible to include patient information in the ECG signal, and it has been discovered that multiple scaling factors (MSFs) provide a superior trade-off than uniform single scaling factors. In this paper, we present a novel contribution to the field: a discrete wavelet transforms and singular value decomposition-based dynamic Thresholding GA (DTGA)-based ECG steganography scheme. Using the MITIH database, we demonstrate the efficacy of this method, and our findings corroborate that DTGA significantly improves data security.

Abstract

Efficient inventory management (IM) presents an
important key driver for supply chain (SC) sustainability.
This latter becomes a crucial concern for decision-makers and
managers in all domains, particularly in the matter of sensitive
areas that affect human well-being, namely the pharmaceutical
industry. Medicines IM for a sustainable Pharmaceutical Sup-
ply Chain (PSC) brought further particularities compared to
the traditional SCs. Besides the economic preoccupation, social
and environmental issues might be considered. In this work, we
assess the impact of the Lateral Transshipment (LT) strategy on
the sustainability of the IM process. We compare the total costs
of two cases, IM with and without LT strategy. We propose an
IM model that seeks the optimal replenishment order quantity
of multiple types of products and the shipment time in a
two-echelon PSC under a centralized setting. The considered
PSC consists of a pharmaceutical company (PC), a Pharma-
distributor (PD), and multiple hospitals. The mathematical
model takes into account the transportation costs including LT
costs -in the case when LT is included- as well as shortage,
and products with high deterioration rate costs. We attempt
to minimize unused medicines leftover by minimizing the
deterioration rate of products at both distributor and hospital
sites.

Abstract

Since more and more organizations deploy their applications through the cloud, an increasing demand for using inter-cloud solutions is noticed. Such demands could inherently result in overutilization of resources, which leads to resource starvation that is vital for time-intensive and life-critical applications. In this paper, we are interested in the scheduling problem in such environments. On the one hand, a new taxonomy of criteria to classify task scheduling problems and resolution approaches in inter-cloud environments is introduced. On the other hand, a bi-level multi-follower model is proposed to solve the budget-constrained dynamic Bag-of-Tasks (BoT) scheduling problem in heterogeneous multi-cloud environments. In the proposed model, the upper-level decision maker aims to minimize the BoT’ makespan under budget constraints. While each lower-level decision maker minimizes the completion time of tasks it received. Experimental results demonstrated the outperformance of the proposed bi-level algorithm and revealed the advantages of using a bi-level scheme with an improvement rate of 32%, 29%, and 21% in terms of makespan for the small, medium, and big size instances, respectively.

Abstract

Feature construction represents a crucial data preprocessing technique in machine learning applications because it ensures the creation of new informative features from the original ones. This fact leads to the improvement of the classification performance and the reduction of the problem dimensionality. Since many feature construction methods require discrete data, it is important to perform discretization in order to transform the constructed features given in continuous values into their corresponding discrete versions. To deal with this situation, the aim of this paper is to jointly perform feature construction and feature discretization in a synchronous manner in order to benefit from the advantages of each process. Thus, we propose here to model the discretization-based feature construction task as a bi-level optimization problem in which the constructed features are evaluated based on their optimized sequence of cut-points. The resulting algorithm is termed Discretization-Based Feature Construction (Bi-DFC) where the proposed model is solved using an improved version of an existing co-evolutionary algorithm, named I-CEMBA that ensures the variation of concatenation trees. Bi-DFC performs the selection of original attributes at the upper level and ensures the creation and the evaluation of constructed features at the upper level based on their optimal corresponding sequence of cut-points. The obtained experimental results on ten high-dimensional datasets illustrate the ability of Bi-DFC in outperforming relevant state-of-the-art approaches in terms of classification results.