The recent rise of Artificial Intelligence (AI) has already revolutionized human lives and is improving the quality of human life in many ways. The field of AI, Natural Language Processing (NLP), helps to understand, comprehend and even generate new content. NLP is used for various content analysis tasks such as sentiment analysis, fake news detection, etc. However, human personality traits detection is a new research domain. Analyzing users generated content has a significant role in identifying and understanding users’ views and behaviors. Users’ traits detection can be helpful in analysis of consumers’ personalization, finding top candidates for recruitment, career counselling, etc. In this research study, our aim is to predict personality of extroversion behaviors using machine and deep learning approaches. Extroversion means whether a person is an introvert or extrovert as this trait is relevant to certain jobs like team management, social ties etc. For empirical analysis, we investigate MBTI dataset with various feature engineering techniques including textual features like Term Frequency-Inverse Document Frequency (TF-IDF), Parts of Speech (PoS) tagging, as well as deep word embeddings ok word2vec, GloVe. The state-of-the-art shallow machine learning, ensemble modelling and deep learning models are applied. The main novelty is the exploration of latest sentence embeddings which captures semantic information of content in a better manner. Thus, the comprehensive results analysis reveals sentence embeddings as features to Bi-LSTM achieves highest accuracy of 92.52% and outperforms the existing studies in the relevant literature.

The workflows of the predominant datacenter services are underlaid by various Fork-Join structures. Due to the lack of good understanding of the performance of Fork-Join structures in general, today's datacenters often operate under low resource utilization to meet stringent service level objectives (SLOs), e.g., in terms of tail and/or mean latency, for such services. Hence, to achieve high resource utilization, while meeting stringent SLOs, it is of paramount importance to be able to accurately predict the tail and/or mean latency for a broad range of Fork-Join structures of practical interests. In this paper, we propose a black-box Fork-Join model that covers a wide range of Fork-Join structures for the prediction of tail and mean latency, callled ForkTail and ForkMean, respectively. We derive highly computational effective, empirical expressions for tail and mean latency as functions of means and variances of task response times. Our extensive testing results based on model-based and trace-driven simulations, as well as a real-world case study in a cloud environment demonstrate that the models can consistently predict the tail and mean latency within 20% and 15% prediction errors at 80% and 90% load levels, respectively, for heavy-tailed workloads, and at any load levels for light-tailed workloads. Moreover, our sensitivity analysis demonstrates that such errors can be well compensated for with no more than 7% resource overprovisioning. Consequently, the proposed prediction model can be used as a powerful tool to aid the design of tail-and-mean-latency guaranteed job scheduling and resource provisioning, especially at high load, for datacenter applications.

The COVID-19 pandemic has exerted a widespread influence on a global scale, leading numerous nations to prepare for the endemicity of COVID-19. The polymerase chain reaction (PCR) swab test has emerged as the prevailing technique for identifying viral infections within the current pandemic. Following this, the application of chest X-ray imaging in individuals provides an alternate approach for evaluating the existence of viral infection. However, it is imperative to further boost the quality of collected chest pictures via additional data augmentation. The aim of this paper is to provide a technique for the automated analysis of X-ray pictures using server processing with a deep convolutional generative adversarial network (DCGAN). The proposed methodology aims to improve the overall image quality of X-ray scans. The integration of deep learning with Xtreme Gradient Boosting in the DCGAN technique aims to improve the quality of X-ray pictures processed on the server. The training model employed in this work is based on the Inception V3 learning model, which is combined with XGradient Boost. The results obtained from the training procedure were quite interesting: the training model had an accuracy rate of 98.86%, a sensitivity score of 99.1%, and a recall rate of 98.7%.

The coronavirus disease 2019 (Covid‐19) pandemic has affected most countries of the world. The detection of Covid‐19 positive cases is an important step to fight the pandemic and save human lives. The polymerase chain reaction test is the most used method to detect Covid‐19 positive cases. Various molecular methods and serological methods have also been explored to detect Covid‐19 positive cases. Machine learning algorithms have been applied to various kinds of datasets to predict Covid‐19 positive cases. The machine learning algorithms were applied on a Covid‐19 dataset based on commonly taken laboratory tests to predict Covid‐19 positive cases. These types of datasets are easy to collect. The paper investigates the application of decision tree ensembles which are accurate and robust to the selection of parameters. As there is an imbalance between the number of positive cases and the number of negative cases, decision tree ensembles developed for imbalanced datasets are applied. F‐measure, precision, recall, area under the precision‐recall curve, and area under the receiver operating characteristic curve are used to compare different decision tree ensembles. Different performance measures suggest that decision tree ensembles developed for imbalanced datasets perform better. Results also suggest that including age as a variable can improve the performance of various ensembles of decision trees.

Task offloading in distributed computing involves complex tradeoffs among delay, scalability, cost, and resource utilization. Cloud platforms face long communication delays, while edge nodes have constrained capacity. Static, rule-based schedulers cannot adapt to fluctuating loads or per-instance heterogeneity. Similarly, Reinforcement Learning (RL) schemes typically address only a single layer or assume homogeneous servers, overlooking the hierarchical and multi-instance nature of deployments. To address these challenges, we introduce a server-aware Proximal Policy Optimization (PPO) framework that performs fine-grained offloading across a three-tier (Edge, Regional, Cloud) multi-instance architecture. We formulate offloading as a Markov Decision Process whose state vector includes per-instance delay, CPU/memory utilization, network congestion, cost, and energy metrics. The PPO agent learns to offload tasks to the best server in real time. Our developed RegionalEdgeSimPy simulation shows that PPO agent makes optimal offloading choices for over 90% of tasks, keeping each server near; however, below a 70% utilization. This optimized decision making drives up to 66.9% delay reduction, 78.6% energy savings, and 47.8% cost reductions relative to cloud-only and edge-only baselines.

This research investigates the potential of integrating eye-tracking technology with finite state machines (FSMs) to enhance gameplay control in 2D platform games. The purpose of this study is to develop and evaluate a gaze-based control system for 2D platform games, addressing the need for more inclusive gaming experiences. By utilizing eye gaze as an input method, the proposed system aims to provide a more inclusive gaming experience for individuals with and without disabilities. The methodology involves the development of a 2D platform game that captures eye gestures and translates them into game actions, using FSMs to interpret complex eye movements and map them to specific game commands. Through extensive playtesting with 34 participants, the findings demonstrate the feasibility of the proposed system, indicating that it allows for intuitive and smooth control of game characters. The originality of this work lies in the specific integration of eye-tracking with FSMs for 2D platform control, with a focus on minimizing unintended actions and enabling smooth transitions between gameplay and information gathering. The impact of this research is the potential to expand accessibility in gaming and provide a foundation for future development of gaze-based control systems in various game genres. Future research can explore the potential of applying this approach to a wider range of game genres, further expanding the accessibility and inclusivity of gaming experiences.