Abstract

Multiple-job-class sequencing problems solve a group of jobs belonging to multiple classes, where to decrease the processing time, jobs in the same class tend to be performed together with the same setup time. In contrast, customer order scheduling problems focus on completing all jobs (belonging to different classes) in the same order at the same time to reduce shipping costs. Because related studies on multiple-job-class sequencing problems with more than one criterion are quite limited in the current research community, this study investigates tri-criteria scheduling problems with multiple job classes and customer orders on a single machine, where the goal is to minimize a linear combination of the makespan, total completion times of all jobs, and sum of the ranges of all orders. Due to the high complexity of the proposed problem, mixed integer programming is developed to formulate the problem, and a branch-and-bound method along with a lower bound and a property is utilized for finding the optimal schedules. Then, two heuristics and a simulated annealing algorithm are proposed to solve the problem approximately. The simulation results of the proposed heuristics and algorithm are evaluated through statistical methods.

Abstract

In today’s society, social networks are vital for communication, allowing individuals to influence each other significantly. Opinion leaders play a crucial role in shaping opinions, attitudes, beliefs, motivations, and behaviors. Recognizing this, companies seek to identify influential users who resonate with their target audience to leverage their impact. Consequently, detecting opinion leaders in social networks has become essential. This paper aims to provide a comprehensive literature review on opinion leader detection. We present a detailed overview of various methods and approaches developed in this field, examining their strengths and weaknesses to identify the most effective strategies for different social networks. Additionally, we highlight key trends, challenges, and future directions in opinion leader detection. Our goal is to equip companies with the necessary knowledge to harness the power of opinion leaders for enhancing marketing and communication strategies. For researchers, this paper serves as a foundational resource, outlining the current state of the art and identifying gaps in the literature for future studies. Ultimately, we strive to advance the understanding of effective opinion leader detection and utilization within the dynamic landscape of social networks.

Abstract

Elderly fatigue, a critical issue affecting the health and well-being of the aging population worldwide, presents as a substantial decline in physical and mental activity levels. This widespread condition reduces the quality of life and introduces significant hazards, such as increased accidents and cognitive deterioration. Therefore, this study proposed a model to detect fatigue in the elderly with satisfactory accuracy. In our contribution, we use video and image processing through a video in order to detect the elderly’s face recognition in each frame. The model identifies facial landmarks on the detected face and calculates the Eye Aspect Ratio (EAR), Eye Fixation, Eye Gaze Direction, Mouth Aspect Ratio (MAR), and 3D head pose. Among the various methods evaluated in our study, the Extra Trees algorithm outperformed all others machine learning methods, achieving the highest results with a sensitivity of 98.24%, specificity of 98.35%, and an accuracy of 98.29%.

Abstract

Large Language Models (LLMs) are being increasingly explored and used in healthcare for their potential applications. These models show the capacity to impact clinical care, research, and medical education significantly. In this research, we shed light on the transformative potential of LLMs in reshaping the healthcare landscape, emphasizing their role in enhancing patient care, improving decision-making processes, and advancing medical research. While the application of LLMs in healthcare presents immense opportunities, this research, also, addresses critical challenges and limitations. Concerns regarding the accuracy, reliability, and ethical implications of LLMs in medical contexts are highlighted, emphasizing the need for continuous monitoring and evaluation to ensure patient safety and data privacy. By exploring the opportunities and challenges associated with LLMs in healthcare, this study contributes to a deeper understanding of the implications and future directions of this technology in the healthcare sector.

Abstract

The Patient Admission Scheduling (PAS) process involves efficiently managing the admission of patients to specific beds within relevant departments while addressing all their medical needs over a defined time horizon. This study delves into PAS within hospital network, emphasizing the collaborative nature of their interactions. Collaborative interactions are a common challenge in hospitals, where they need to collaborate and share resources to allocate patients to a limited number of beds within a specified timeframe, ensuring all necessary medical conditions are met. To address this challenge, a mixed-integer mathematical programming model for the PAS problem within hospital network is proposed with the aim of minimizing a weighted sum of unsatisfied constraints.

Abstract

Internet users are overwhelmed by the vast number of services and products to choose from. This data deluge has led to the need for recommender systems. Simultaneously, the explosion of interactions on social networks is constantly increasing. These interactions produce a large amount of content that incites organizations and individuals to exploit it as a support for decision making. In our research, we propose, Twit-CoFiD, a hybrid recommender system based on tweet sentiment analysis which performs a demographic filtering to use its outputs in an enhanced collaborative filtering enriched with a sentiment analysis component. The demographic filtering, based on a Deep Neural Network (DNN), allows to overcome the cold start problem. The sentiment analysis of Twitter data combined with the enhanced collaborative filtering makes recommendations more relevant and personalized. Experiments were conducted on 1M and 100K Movielens datasets. Our system was compared to other existing methods in terms of predictive accuracy, assessed using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. It yielded improved results, achieving lower RMSE and MAE rates of 0.4474 and 0.3186 on 100K Movielens dataset and of 0.3609 and 0.3315 on 1M Movielens dataset.

Abstract

The escalating challenges of urban traffic congestion pose a critical issue that calls for efficient traffic management system solutions. Traffic forecasting stands out as a paramount area of exploration in the field of Intelligent Transportation Systems. Various traditional machine learning techniques have been employed for predicting traffic congestion, often requiring a significant amount of data to train the model. For that reason, historical data are usually used. In this paper, our first concern is to use real-time traffic data. We adopted Stochastic Gradient Descent, an online learning method characterized by its ability to continually adapt to incoming data, facilitating real-time updates and rapid predictions. We studied a network of streets in the city
of Muscat, Oman. Our model showed its accuracy through comparisons with actual traffic data.

Abstract

Business Process as a Service (BPaaS) has recently emerged from the synergy between business process management and cloud computing, allowing companies to outsource and migrate their businesses to the cloud. BPaaS management refers to the set of operations (decomposition, customization, placement, etc.) that maintain a high-quality of the deployed cloud-based businesses. Like its ancestor SaaS, BPaaS placement consists on the dispersion of its composing fragments over multiple cloud availability zones (CAZ). These latter are characterized by their huge, diverse and dynamic data, which are exploited to select the high-performance servers holding BPaaS fragments, while preserving their constraints. These fragments’ relations and their placement schemes constitute a dynamic BPaaS information network. However, the few existing BPaaS solutions adopt a static placement strategy, while it is important to take the CAZ dynamic and uncertain nature into account. Also, current solutions do not properly model the BPaaS environment. To offer an efficient BPaaS placement scheme, we combine prediction and learning capabilities, which will help identify the migrating fragments and their new hosting servers. We first model the BPaaS context as a heterogeneous information network. Then, we apply an incremental representation learning approach to facilitate its processing. Using the principles of proximity-aware representation learning, we infer useful knowledge regarding BPaaS fragments and the available servers at different CAZ. Finally based on the degree of closeness between the BPaaS environment’s entities (e.g., fragments, servers), we select the optimum cloud availability zone on which the resource-consuming BPaaS fragments are migrated based on a proposed placement scheme. Obtained results were very promising compared to traditional BPaaS placement solutions.

Abstract

Abstract

In a rapidly evolving digital landscape, recommender systems have become essential tools for helping users navigate overwhelming amounts of information in various domains. In e-learning contexts, these systems aim to support learners by identifying educational resources or relevant academic content that are significant for their learning experience. However, research incorporating domain knowledge, such as course-related concepts, to improve recommendation quality remains limited. This paper presents a new personalized recommender system in e-learning context, which is called KA-ERN: Knowledge-based Attention Ensemble Recurrent Network. Specifically, KA-ERN leverages a Knowledge Graph to capture the dependencies and semantic relationships between users, courses, and their related concepts and then, performs ensemble learning by combining the Bidirectional Long Short-Term Memory network (Bi-LSTM) with the Artificial Neural Network (ANN). An attention mechanism is added to enhance recommendation quality. Our approach is based on a two-stage architecture. First, entities and relations embeddings are generated and then concatenated as sequences allowing the model to capture complex relationships and contextual dependencies of user preferences. Secondly, these embeddings are provided as inputs to the KA-ERN model. The proposed combination of Bi-LSTM network, ANN, and attention mechanisms shows the advantage of using Knowledge graph embeddings over bipartite graph embeddings capturing only user-item interactions and experimental results achieving the best recommendation metrics, with RMSE of 0.045 and MAE of 0.034, outperforming baseline methods.