Big Data Analysis and Computing Visions
Volume:3 Issue: 3, Sep 2023

In today's fiercely competitive industrial landscape, companies are under mounting pressure to improve process efficiencies, adhere to stringent environmental regulations, and meet their financial objectives. Given the aging infrastructure of many industrial systems and the ever-changing dynamics of the manufacturing market, there is an urgent requirement for intelligent and cost-effective industrial automation systems that can enhance productivity and efficiency. In this regard, Wireless Sensor Networks (WSNs) present a compelling alternative to traditional wired monitoring and control systems, offering numerous advantages.

The current landscape of Cloud Computing predominantly relies on closed data centers, housing a multitude of dedicated servers that cater to cloud services. However, an immense number of underutilized Personal Computers (PCs) are owned by individuals and organizations globally. These dormant resources can be harnessed to form an alternative cloud infrastructure, offering a wide array of cloud services, particularly focusing on infrastructure as a service. This innovative strategy, the "no data center" approach, complements the conventional data center-centric cloud provisioning model. In a research paper, the authors introduce their opportunistic Cloud Computing framework called cuCloud, which effectively utilizes the idle resources of underutilized PCs within a given organization or community. The success of their system serves as tangible evidence that the "no data center" concept is indeed feasible. Beyond conceptualization and philosophy, the authors' experimental findings strongly validate their approach.

Video Quality Assessment (VQA) is a critical component of various technologies, including automated video broadcasting through displaying technologies. Moreover, determining visual quality necessitates a balanced examination of visual features and functionality. Previous research has also shown that features derived from pre-trained models of Convolutional Neural Networks (CNNs) are extremely useful in various image analysis and computer vision activities. Based on characteristics collected from pre-trained models of deep neural networks, transfer learning, periodic pooling, and regression, we created a unique architecture for No Reference Video Quality Assessment (NR-VQA) in this research. We were able to get results by solely employing dynamically pooled deep features and avoiding the use of manually produced features. This study describes a novel, deep learning-based strategy for NR-VQA that uses several pre-trained deep neural networks to characterize probable image and video distortions across parallel. A set of pre-trained CNNs extract spatially pooling and intensity-adjusted video-level feature representations, which are then individually mapped onto subjective peer assessments. Ultimately, the perceived quality of a video series is calculated by combining the quality standards from the various regressors. Numerous researches demonstrate that the suggested approach on two large baseline video quality analysis datasets with realistic aberrations sets a new state-of-the-art. Furthermore, the findings show that combining the decisions of different deep networks can greatly improve NR-VQA.

The study aimed to examine difficulties encountered by higher education agencies through action research and to integrate "Online Activity" into an after-school english program at a university to optimize their teaching strategy. The study was conducted between October and December 2022. "Online Activity" was chosen as a possible optimization strategy because it assisted teachers in applying online activities to current course materials, improving students' learning motivations and learning outcomes. The results showed that online activity was a practical strategy for improving foreign language learning. Meanwhile, collaboration, discussions, and reflections among the action research team assisted professional development in teaching. The researchers recommend that educators choose appropriate action strategies to adapt to various learning situations, which may create opportunities for innovation in the current rigid education and learning environment.

Measuring the performance of a production system has been an important task in management for control, planning, etc. The Balanced Scorecard (BSC) allows us to do just that. BSC is widely used in government and industry because of the clear representation of the relationship and logic between the Key Performance Indicators (KPIs) of 4 perspectives-financial, customer, internal process, and learning and growth. Conversely, traditional studies in Data Envelopment Analysis (DEA) view systems as a whole when measuring efficiency, ignoring the operation of individual processes within a system. We present and demonstrate a multi-criteria approach for evaluating every project in different stages. Our approach integrates the BSC and DEA and develops an extended DEA model. The input and output measures for the integrated DEA-BSC model are grouped in “cards,” which are associated with "BSC". With efficiency decomposition, the process that causes the inefficient operation of the system can be identified for future improvement. Finally, we illustrate the proposed approach with a case study involving six banking branches.

The escalating annual insurance costs nationwide have sparked a growing interest among insurance industry managers and policymakers in analyzing insurance data to forecast future costs. Accurately predicting the number of claims and implementing appropriate policies can help mitigate potential losses for insurance companies and customers. This study focuses on predicting the amount of customer claims and utilizes data from 128 individuals insured by Iran insurance company. The dataset includes various attributes such as the age of the vehicle owner, type of car, age of the car itself, number of claims, and the corresponding claim amounts (measured in 10,000 Tomans) recorded in the year 1400. All features, except the claim amount (the target variable), were discretized into ordinal variables to ensure accurate analysis and address any outliers or data inconsistencies. Multiple linear regression was employed to predict the target variable, enabling an investigation into the influence of each feature on estimating the claim amount. The data analysis was conducted using IBM SPSS MODELER software, allowing for a comprehensive examination of the assumptions associated with the regression model. By leveraging this approach, insurance industry stakeholders can gain valuable insights into predicting claim amounts and make informed decisions to optimize their operations and minimize potential financial risks.