Essential Feature - Cooperative Gameplay

Although single player and multiplayer is very important in today game, cooperative mode is an essential part of a great game. There are a lot of benefits of playing co-op mode in a game such as education and joy. Communicating, solving problems, handling stress, managing time, making decision, following instructions, acting fast as well as working in a team are skills that students can learn and practice while they are playing cooperative games. These skills are valuable for students to use in education and even in careers

Is Nonfarm Diversification a Way Out of Poverty for Rural Households? Evidence from Vietnam in 1993-2006

school. Using the four high quality household living standards surveys available to date this paper reveals that Vietnam’s rural labour force has been markedly diversifying toward nonfarm activities in the doi moi (renovation) reform period. The employment share of the rural nonfarm sector has increased from 23 percent to 58 percent between the years 1993 and 2006. At the individual level, the results indicate that participation in the rural nonfarm sector is determined by a set of individual-, household-, and community-level characteristics. Gender, ethnicity, and education are reported as main individual-level drivers of nonfarm diversification. Lands as most important physical assets of rural households are found to be negative to nonfarm employment. It is also evident that both physical and institutional infrastructure exert important influences on individual participation in the nonfarm sector. At the household level, a combination of parametric and semi-parametric analysis is adopted to examine whether nonfarm diversification is a poverty exit path for rural households. This paper demonstrates a positive effect of nonfarm diversification on household welfare and this effect is robust to different estimation techniques, measures of nonfarm diversification, and the usage of equivalent scales. However, the poor is reported to benefit less than the non-poor from nonfarm activities. Though promoting a buoyant nonfarm sector is crucial for rural development and poverty reduction, it needs to be associated with enhancing access to nonfarm opportunities for the poor.Rural nonfarm sector, nonfarm diversification, household welfare, Vietnam

An Efficient Data Analytics and Optimized Algorithm for Enhancing the Performance of Image Segmentation Using Deep Learning Model

Image segmentation is the key topic in computer vision and image processing with applications like robotic perception, scene understanding, video surveillance, image compression, medical image analysis, and augmented reality among many others. There are numerous algorithms are developed in the literature for image segmentation. This paper provides a broad spectrum of pioneering works for instance and semantic level segmentation with mask Region based Convolution Neural Network with Monarch butterfly Optimization (RCNN-MBO) architecture. The system is initially constructed in a Python environment with images of people and animals being input. Remove the unnecessary data from the gathered datasets during the pre-processing stage. Next, use a stochastic threshold function to segment the image. Then update the segmented images into a designed model for detecting and classifying a group of images. The main goal of the designed approach is to attain accurate prediction results also improve the performance of the designed model by attaining better results. To enhance the performance, two activation functions were used and MBO fitness is updated in the classification layer. It improves the prediction results and takes less time to detect and classify images. Finally, the experimental outcomes show the reliability of the designed approach by other conventional techniques in terms of accuracy, precision, sensitivity, specificity, F-measure, error rate, and computation time

Remote Health Monitoring IoT Framework using Machine Learning Prediction and Advanced Artificial Intelligence (AI) Model

Real intervention and treatment standards drew attention to remote health monitoring frameworks. Remote monitoring frameworks for disease detection at an early stage are opposed by most conventional works. Even so, it ran into issues like increased operational complexity, higher resource costs, inaccurate predictions, longer data collection times, and a lower convergence rate. A remote health monitoring framework that uses artificial intelligence (AI) to predict heart disease and diabetes from medical datasets is the goal of this project. Patients' health data is collected via smart devices, and the resulting data is then combined using a variety of nodes, including a detection node, a visualisation node, and a prognostic node. People with long-term illnesses (such as the elderly and disabled) are in such greater demand than ever before that a new approach to healthcare delivery is essential. In the evolved paradigm, conventional physical medical services foundations like clinics, nursing homes, and long haul care offices will be old. Due to recent advancements in modern technology, such as artificial intelligence (AI) and machine learning (ML), the smart healthcare system has become increasingly necessary (ML). This paper will discuss wearable and smartphone technologies, AI for medical diagnostics, and assistive structures, including social robots, that have been created for the surrounding upheld living climate. The review presents programming reconciliation structures that are urgent for consolidating information examination and other man-made consciousness instruments to develop brilliant medical care frameworks (AI)

Enhanced Ai-Based Machine Learning Model for an Accurate Segmentation and Classification Methods

Phone Laser Scanner becomes the versatile sensor module that is premised on Lamp Identification and Spanning methodology and is used in a spectrum of uses. There are several prior editorials in the literary works that concentrate on the implementations or attributes of these processes; even so, evaluations of all those inventive computational techniques reported in the literature have not even been performed in the required thickness. At ToAT that finish, we examine and summarize the latest advances in Artificial Intelligence based machine learning data processing approaches such as extracting features, fragmentation, machine vision, and categorization. In this survey, we have reviewed total 48 papers based on an enhanced AI based machine learning model for accurate classification and segmentation methods. Here, we have reviewed the sections on segmentation and classification of images based on machine learning models

Study of Factors Affecting the Leadership Capacity of CEO in Industrial SMEs in Vietnam

The leadership capabilities of management team greatly influence the performance of management activities in an enterprise. Capturing and evaluating the impact of elements influencing leadership ability might help enterprises to formulate policies to optimize the capacity of the management team. The scope of the study in this paper is the survey of SMEs in Vietnam. The research team conducted 141 surveys by executives and managers working as heads of departments within current industrial SMEs in Vietnam. Research has indicated the three influential factors including self-efficacy, environment and personal characteristics, with personal traits making the least impact. Keywords: Leadership capabilities, CEO, small and medium industry enterprises

Damage Detection in Structural Health Monitoring using Hybrid Convolution Neural Network and Recurrent Neural Network

The process of damage identification in Structural Health Monitoring (SHM) gives us a lot of practical information about the current status of the inspected structure. The target of the process is to detect damage status by processing data collected from sensors, followed by identifying the difference between the damaged and the undamaged states. Different machine learning techniques have been applied to attempt to extract features or knowledge from vibration data, however, they need to learn prior knowledge about the factors affecting the structure. In this paper, a novel method of structural damage detection is proposed using convolution neural network and recurrent neural network. A convolution neural network is used to extract deep features while recurrent neural network is trained to learn the long-term historical dependency in time series data. This method with combining two types of features increases discrimination ability when compares with it to deep features only. Finally, the neural network is applied to categorize the time series into two states - undamaged and damaged. The accuracy of the proposed method was tested on a benchmark dataset of Z24-bridge (Switzerland). The result shows that the hybrid method provides a high level of accuracy in damage identification of the tested structure

Effects of plant essential oils and their constituents on Helicobacter pylori : A Review

Essential oils (EOs) obtained from different medicinal and aromatic plant families by steam distillation have been used in the pharmaceutical, food, and fragrance industries. The plant EOs and their broad diversity of chemical components have attracted researchers worldwide due to their human health benefits and antibacterial properties, especially their treatment of Helicobacter pylori infection. Since H. pylori has been known to be responsible for various gastric and duodenal diseases such as atrophic gastritis, peptic ulcer, gastric adenocarcinoma, and mucosa-associated lymphoid tissue lymphoma, several combination antibiotic therapies have been increasingly used to enhance the eradication rate of the bacterial infection. However, in the last decades, the efficacy of the therapies has decreased significantly due to widespread emergence of multidrug resistant strains of H. pylori. In addition, side-effects from commonly used antibiotics and recurrence of the bacterial infection have drawn public health concern globally.Therefore, this review focuses on in vitro effects of plant EOs and their bioactive constituents on the growth, cell morphology and integrity, biofilm formation, motility, adhesion, and urease activity of H. pylori. Their inhibitory effects on expression of genes necessary for growth and virulence factor productions of the bacterial pathogen are also discussed. Further in vivo and clinical evaluations are required so that plant EOs and their bioactive constituents can be possibly applicable in pharmacy or as adjuvants to the current therapies of H. pylori infection