Mobile Cloud Computing (MCC) becomes an emerging computing paradigm, where Mobile Devices (MDs) are in the place for offloading task to the nearest resource-rich cloud servers. To promote the system’s performance, the MCC is performed. However, it holds with more overhead complexity in storage and energy, which degrades the network efficiency. Hence the scholar concentrates on decreasing the overhead issue by applying the task offloading process. The major issue in this mechanism is having most cost-effective communication among the devices. This research paper suggests a new optimization strategy for performing the offloading task in MCC. The developed hybrid approach offloads the task to the nearby server to enhance the performance of the MCC by finishing the task within the deadline. A new cost function is derived with the adoption of the average delay of tasks, the energy consumption level, battery lifetime, processing capabilities, storage capacity, response time, communication cost, etc for optimizing the task offloading. Thus, a new task offloading is optimized via a newly recommended hybrid optimizer with the adoption of Probability Condition of Satin Bowerbird Forensic Optimization (PCSBFO), which is developed with the combination of Satin Bowerbird Optimization (SBO) and Forensic- Based Investigation (FBI) to achieve optimal solutions. Additionally, the developed PCSBFO considers the multi-objective constraints such as average delay, energy consumption, and offloading expenditure for ensuring the quality of service, and satisfactory level of the end user in the MCC. This suggested lightweight paradigm addresses the difficulties and minimizes the efforts while developing, deploying, and managing to offload using optimization algorithms to help better available frameworks. Further, the creation of APAs is done to enable the mobile applications to extract maximum utility out of the volumes of available resources. The experiment results show that the suggested hybrid optimization-based task …

The check-in process is a crucial aspect of airport management, requiring effective coordination between the terminal and airlines. Emergencies and the pandemic have exacerbated challenges in managing the check-in process, resulting in long queues and extended waiting times, particularly during peak departure periods. Predicting check-in waiting times accurately can optimize terminal operations and enhance passengers’ departure experience. Therefore, there is an urgent need for airports to possess predictive capabilities to fully leverage their facilities. This paper presents a machine learning-based approach for predicting passenger check-in waiting time. Firstly, this paper collects real data from one of the largest worldwide airports in its major domestic terminal from September 2021 to January 2022. Next, the collected data is analyzed and processed, with continuous features categorized to derive meaningful response variables. Moreover, this paper compares various machine learning classifiers and optimizes the best-performing classifiers, such as Gradient Boosting Machine (GBM) and Random Forest (RF), and discusses the impact of thresholds and features on the accuracy of the models. Based on real-world data analysis, Gradient Boosting Machine exhibits the highest multi-class classification accuracy (0.790; 0.731) and F1-score (0.648; 0.479) compared to other models, achieving an overall AUC of 0.95. The experimental findings suggest practical applications for airport management in both current and future prediction scenarios. This model has been applied in the airport system to facilitate the rational allocation of check-in resources.

A brain tumor is defined by the abnormal growth of brain cells, some of which may become cancerous. Early detection and treatment of the disease are critical for improving the patients’ quality of life and increasing their lifespan. Artificial intelligence and medical imaging technologies have made significant advances in disease analysis and prediction, particularly in the detection of brain tumors. Extracting relevant features from Magnetic Resonance Imaging (MRI) scans is an important step in the diagnostic process, and several methods have been proposed. Traditional approaches frequently result in treatment delays, which can negatively affect patient outcomes. To address these issues, this study aimed to develop a precise brain tumor detection and classification system using advanced deep learning techniques. Initially, a homomorphic wavelet filter is used during the preprocessing stage to enhance MRI images by minimizing noise and improving image clarity. Subsequently, segmentation is performed using the Fuzzy C-Means (FCM) clustering algorithm combined with the Salp Swarm Algorithm (SSA). SSA’s optimization capabilities of SSA refine the clustering process, resulting in a more accurate delineation of tumor regions. For feature extraction, the ResNet-101 model was employed owing to its deep residual learning framework, which captures complex patterns and features from the segmented images. The classification was carried out using an enhanced GoogLeNet model, which leverages its advanced convolutional architecture to improve tumor detection accuracy by effectively managing extracted features and differentiating between tumor types. Comparative analysis demonstrates that the proposed model outperforms other classifiers, such as SqueezeNet, MobileNetv2, VGG-16, and AlexNet, achieving an accuracy of 98.17%, specificity of 91.34%, and sensitivity of 98.79%.

Leveraging algorithms to provide performance feedback to employees has become widespread in organizations. Algorithm-generated feedback is quite different from human’s feedback in feedback form and employees’ perceptions, so it is hard to directly predict the effect of algorithm-generated feedbacks. Despite the widespread use of algorithm-generated feedback in workplace, there is scant empirical evidence revealing its impacts. To address this gap, we empirically examine the effects of the implementation of an algorithm-generated feedback system through a field experiment conducted in the logistics industry. The results indicated that the algorithm-generated feedback significantly reduces customer complaints by about 20%. Additionally, employees with less work experience or lower workloads benefit from algorithm-generated feedback more. This work offers empirical evidence on the business value of algorithm-generated feedback and highlights the importance of employee characteristics in understanding and managing the effects of algorithmic supervision in the workplace.

In social interaction, customers can observe the number of other customers receiving service and select queues with more customers. Simultaneously, customers in the queue anticipate waiting times and worry about utility loss. This study explores the impact of loss aversion psychology on customer queuing strategies, service provider pricing, and revenue in the context of social interaction. Firstly, we consider homogeneous customers and analyze the influence of loss aversion psychology on their queuing decisions and service choices in social interaction. Subsequently, we extend our investigation to heterogeneous customers, considering differences in customers’ sensitivity to social interaction. Social interaction and loss aversion are crucial in customer queuing decisions, affecting their perception of service utility and equilibrium decision-making. Social interaction and loss aversion also influence service providers’ revenue, necessitating tailored service pricing and strategies. This research provides profound insights into customer loss aversion behavior in the context of social interaction and offers practical service strategies for service providers.