Search

Affect detection from text has captured the attention of researchers recently. This is due to the rapid use of social media sites (e.g. Twitter, Facebook), which allows users to express their feelings, emotions, and thoughts in textual format. Analyzing emotion-rich textual data of social networks has many real-life applications. The context of an emotional text can be measured by analyzing certain features of this rich source of emotional information. Classifying text into emotional labels/intensities is considered a difficult problem. This paper resolves one of the state-of-the-art NLP research emotion and intensity detection tasks using Deep Learning and ensemble implementations. In this paper, we developed several innovative approaches; (a) bidirectional GRU_CNN (BiGRU_CNN), (b) conventional neural networks (CNN), and (c) XGBoost regressor (XGB). The ensemble of BiGRU_CNN, CNN, and XGB is used to solve an emotion intensity (EI-reg) task of the SemEval-2018 Task1 (Affect in Tweets). Our proposed ensemble approach was evaluated using a reference dataset of the SemEval-2018 Task1. Results show that our approach is well above the baseline for this task. It also achieved a Pearson of (69.2%), with an enhancement of 0.7% in comparison with previous best performing models.

Affective gaming has been an active research field recently. This is due to the importance of the player’s emotions while playing computer games. Emotions can be detected from various modalities such as facial, voice, and physiological signals. In this study, we evaluate an XGBoost ensemble method and deep neural network for detecting naturalistic expressions of emotions of video game players using physiological signals. Physiological data was collected from twelve participants while playing PUBG mobile game. Both Discrete and dimensional emotion models were evaluated. We evaluated the performance of classification models using individual physiological channels and a fusion of these channels. A comparison between user-dependent, and user-independent is also provided. Our results indicated that the use of the dimensional valence and arousal model can provide more accurate accuracy than the discrete emotion model. The results also showed that ECG features and a fusion of features from all physiological channels provide the highest affect detection accuracy. Our deep neural network model based on user-dependent model achieved the highest accuracy with 77.92% and 78.58% of detecting valence, and arousal respectively using a fusion of features. The user-independent models were not feasible, presumably due to strong individual differences of physiological responses.

The impact of the COVID-19 pandemic has led to the use of M-Health apps for tracking COVID-19 infected people.So, the aim of this study was to develop a model that explains the adoption of M-Health apps for tracking COVID-19 infected people in developing countries. We tested the model based on data collected using an online survey with 283 participants. For data analysis , the partial least squares technique was used. The results revealed that the intention to use the Aman application was positively influenced by performance expectancy, social influence, Aman application quality, perceived self-efficacy, facilitating conditions, and perceived credibility. Moreover, it was negatively influenced by the perceived financial cost and the effort expectancy. M-Health apps are found as an approach to fighting COVID-19. Therefore, it is imperative to address the reasons that prevent the use of these apps. The implications of this are discussed.

Image analysis is an important technique that can help specialists localize, detect, and segment objects in different types of medical images such as MRI, CTs, and ultrasounds (US). In this research, we use US images to identify and segment the enlarged veins in the pampiniform venous plexus, which is called varicocele. The proposed method aims to determine whether a potential patient is affected or not. This method was evaluated using 90 US images that were taken of the left testicles of 90 patients in the Supine position. This system analyzes US images in three stages which are; preprocessing, processing, and edge detection. The Region Of Interest (ROI) of the pampiniform plexus area was extracted using Otsu segmentation with different parameters (0.1, 0.2, 0.17) and different color modes (Grayscale, YCbCr, RGB). In the processing stage, different denoising filters were used. Eventually, in the edge detection stage, four edge detectors were applied which are Canny, Soble, Prewitt, and Roberts. Results showed that the best accuracy in detecting varicocele was 78% when the YCbCr color mode yellow (y) channel was used with 0.1 Otsu segmentation and the Canny edge detector. The system also showed a Sensitivity of 91%, as the test was able to detect 91% of the people with Varicocele, and the Specificity value was 39%.

Entrepreneurial leadership with a pure intellectual starting point has received the attention of high-quality international business organizations because of its need for leadership that possesses strategic thinking which can change with the changing circumstances surrounding it, hence the interest in the topic of entrepreneurial leadership with strategic thinking due to the acceleration of the complex environmental conditions surrounding these organizations, represented by the emergence of the phenomenon of globalization and the cognitive economy and technological progress, which may be represented here as the techniques of artificial intelligence, which prompted these leaders and their organizations to search for mechanisms for their continuity and stability in a competitive world. Therefore, it is expected that entrepreneur leaders possess the qualities of intellectual stimulation, which is an important matter in entrepreneurial leadership, and that they are also ready to change the existing status of their organizations by creating major changes, through taking advanced technological technologies. This is expected to help them achieve the visions of their organizations to reach local and global entrepreneurship. Therefore, the current study worked on knowing the impact of artificial intelligence on the entrepreneurship of the leadership of international business organizations to reach the added value of its strategic operations at the local and global levels.

Fog computing is a promising technology that is used by many organizations and end-users. It has characteristics and advantages that offer services such as computing, storage, communication, and application services. It facilitates these services to end-users and allows to increase the number of devices that can connect to the network. In this paper, we provide a survey of Fog computing technology in terms of its architecture, features, advantages and disadvantages. We provide a comparison of this model with Cloud Computing, Mobile-Edge Computing, and Cloudlet Computing. We also present challenges and issues that face Fog Computing such as privacy and security, control and management, fog networking and task scheduling. Finally, we discuss aspects of Fog computing security and the benefits of integration between Fog computing and other techniques like Internet of Things and Cloud Computing.

Fog computing is one of the most important technologies and promising computing model used by many end-users, organizations, and companies. This model has many qualities, characteristics, advantages and offers many services to end-users like compute, and application services. So, it facilitates the services that provided it to the end-users and allows us to increase the number of devices that connect the network. In this research paper, we survey the Fog technology in terms of the architecture and the primary features (i.e. advantages, and disadvantages). Then, we compare this model with Cloud computing, Mobile-Edge Computing, and Cloudlet Computing. Also, this paper is constructed by illustrating the importance of several challenges and issues that facing Fog computing like "privacy and security: issues and solutions", control and management issues, fog networking and task scheduling. Finally, we mention the six aspects of fog computing security and the benefits of merging between Fog computing and other techniques like “Internet of Things and Cloud”. This paper will contribute to providing the researchers who want to study the Fog computing in terms of aspects that previously mentioned.

This study aimed to explore the impact of human resources management practices (training, participating in decision-making, motivation, and controlling), trying to build theoretical and practical frame for the effect of human resource practices in fulfilling innovations. And trying to clarify the impact of these practices on crisis management. In addition to introduce suggestions and recommendations to the firms, and to future research in similar topics. The population consisted of the estimated 11 commercial bank employees, operating in Irbid province, out of a total 34 branches, and a randomized judgmental sample were taken from (80) employees working in human resources departments. (71) resolutions deemed fit for analysis recovered. The most important research findings that there is positive statistically significant influence for human resources management practices (training, participating in decision-making, motivation, and controlling)) in crisis management. The study recommends that outsourcing like consultants is another option, consultant that can lead the process.

Blockchain technology (BC) has recently emerged as a potential solution of security and privacy issues in cloud computing systems. Particularly, the decentralization concept is driven by Blockchain technology. This paper aims to define the cloud computing and Blockchain technologies as recent technological trends. It also surveys the latest trends of Blockchain inclusion within cloud computing. A systematic review method is conducted and evaluated in a dozen studies in time period between 2016 and 2019. The methodologies and results of these studies were explored, and it was found that these studies focused on two main aspects. First, does the data provenance, integrity, and privacy tackle in real data sets such as medical sets and business datasets? Second aspect is the data security in which various studies suggested and proposed for handling this issue. It has been noticed that there is a great variety of designs that are not contradictory, as well as variation of the interests of studies and adopted data sets. In this paper, the authors will analyze the Blockchain technology within cloud computing environments to demonstrate advantages and disadvantages in distributed data and protection.

Mobile-to-mobile (M2M) is considered one of the new vital technologies that has a great attraction for mobile communication systems research. In this paper, the modelling and simulation of multiple-input multiple-output (MIMO) M2M channels is studied for both isotropic- and non-isotropic scattering cases, so in this context, three parameterization methods for MIMO simulation channel models are studied: Extended Method of Exact Doppler Spread (EMEDS), Modified Method of Equal Areas (MMEA) and Method Lp-Norm (LPNM). Three new methods are also suggested to design the simulation model of MIMO channel: Modified Extended Method of Exact Doppler Spread (MEMEDS), New Modified Method of Equal Areas (NMMEA) and Modified Lp-Norm method (MLPNM). A performance comparison between each proposed method and the original method is carried out.

Affect detection form physiological signals has been an active research field recently. It has many applications in education, computer games, car driver monitoring, and health. Electrocardiography, electromyography, respiration, and skin conductivity have all been considered as potential physiological channels for recognizing affective states. There is a large amount of literature on physiology-based affect detection that reports on high detection rates of affective states (either discrete emotions, or dimensional valence/arousal) under laboratory conditions. However, one challenge is that physiological measures exhibit considerable variations over time, non-stationary, making classification of future data difficult. In this study, we aim to investigate the changing physiological features over multiple sessions. In our experiments, we use a dataset collected from four participants who viewed a set of images from the International Affective Picture System (IAPS) over multiple days. This study also provides a comparison between self-reported and IAPS ratings for both valence and arousal dimensional affective model. In addition, the study provides an analysis of the performance of individual physiological channels for affect detection. Results indicate that a Deep Neural Network achieved accuracy with 61.12%, 67.14% of detecting self-report compared with IAPS rating for valence and arousal. Results also show that EMG zygomatic features and a fusion of features from all physiological channels have the highest affect detection accuracy for both valence and arousal.

IoT is a network of connected objects in which these objects can collect data and exchange them using embedded sensors. It permits communication between objects and things, and it binds together units in our universe under a regular infrastructure. It allows not just controlling things around us, but also keeping us educated on the state things. Internet of Things (IoT) devices are used in daily life in many domains such as health, agriculture and many others. In the IoT world, the embedded sensors of connected devices play a major role. These sensors have limited energy capabilities, and any network of such devices is called Low power and Lossy network (LLNs). Many implementations and techniques have been trying to address this issue of energy consumption. This paper focuses on Application Layer techniques and specifically on one of the most important protocols, the Constrained Application Protocol (CoAP). We discuss ten of the best energy-efficient techniques to implement the CoAP, and conduct a comparison among these techniques on different levels.