Formulating layered adjustable autonomy for unmanned aerial vehicles

Purpose - In this paper, we propose a Layered Adjustable Autonomy (LAA) as a dynamically adjustable autonomy model for a multi-agent system. It is mainly used to efficiently manage humans and agents share control of autonomous systems and maintain humans’ global control over the agents. Design/Methodology/Approach - We apply the LAA model in an agent-based autonomous Unmanned Arial Vehicle (UAV) system. The UAV system implementation consists of two parts, software, and hardware. The software part represents the controller and the cognitive and the hardware represents the computing machinery and the actuator of the UAV system. The UAV system performs three experimental scenarios of dance, surveillance and search missions. The selected scenarios demonstrate different behaviors in order to create a suitable test plan and ensure significant results. Findings - The results of the UAV system tests prove that segregating the autonomy of a system as multidimensional and adjustable layers enables humans and/or agents to perform actions in a convenient autonomy levels. Hence, reducing the adjustable autonomy drawbacks of constraining the autonomy of the agents, increasing humans’ workload and exposing the system to disturbances. Originality/value - The application of the LAA model in a UAV manifests the significance of implementing dynamic adjustable autonomy. Assessing the autonomy within three phases of agents run cycle (task-selection, actions-selection, actions-execution) is an original idea that aims to direct agents’ autonomy towards performance competency. The agents’ abilities are well exploited when an incompetent agent switches with a more competent on

The role of chatterbots in enhancing tourism: a case study of Penang tourism spots

Chatterbots have been widely used as a tool for conversational booking assistance mainly for hotels such as the Expedia. This paper extends the use of chatterbot beyond booking by presenting the proof of concept of a chatterbot expert system called the VIZARD. The proposed VIZARD is developed using an expert system shell called verbot. The core of Vertbot 5 is the natural language processing (NLP) engine based on pattern matching. The core Verbot 5 engine is responsible for finding matches to a given user input string and firing the appropriate rule. The findings from the user acceptance test concluded that majority of the respondents agreed that the VIZARD expert system stands at an unbiased state while being more aligned on supporting the usefulness of the system

Review of different strategies for coordinative planning of multi-agent systems

Agent-based systems have been widely examined in the literature for various type of tasks. Within this examination, various strategies and modeling have been employed. Several surveys and reviews have been depicted in the literature regarding agent-based systems. However, minimal efforts have been made in the context of feature extraction and feature selection. This paper aims to review the strategies used for feature extraction and selection agentbased systems. In terms of the nature of agent communications, this paper tackles two types, centralized and decentralized. In terms of the workflow, this paper tackles three types, including coordinative, collaborative and emergent-based systems. Finally, a discussion is presented comparing the strategies and the frequent use of the strategies in the literature. Based on this review, most of feature extraction agent-based systems rely on either coordinating or emergent-based strategies, while feature selection agent-based systems rely on collaborative strategies. However, there are several aspects that we can consider to be classify agent-based strategies. This review develops a classification scheme for systems used for specific tasks, including feature extraction and feature selection

Review of local binary pattern operators in image feature extraction

With the substantial expansion of image information, image processing and computer vision have significant roles in several applications, including image classification, image segmentation, pattern recognition, and image retrieval. An important feature that has been applied in many image applications is texture. Texture is the characteristic of a set of pixels that form an image. Therefore, analyzing texture has a significant impact on segmenting an image or detecting important portions of an image. This paper provides a review on LBP and its modifications. The aim of this review is to show the current trends for using, modifying and adapting LBP in the domain of image processing

A study of annexin-V labeled-lymphocytes apoptosis in pediatric-onset systemic lupus erythematosus in comparison to juvenile rheumatoid arthritis

Background: In systemic lupus erythematosus (SLE), which is the prototype of autoimmune diseases, the autoimmune process seems to be antigen driven. Apoptosis is responsible for eliminating cells from the immune system that are autoreactive, and defects in apoptosis may contribute to autoimmune diseases such as SLE and juvenile rheumatoid arthritis (JRA). Objective: This work is aimed to study the apoptotic peripheral blood lymphocytes in patients with pediatric- onset SLE, to trace its correlations, if any, with the disease activity and clinical presentation, and to compare the apoptotic process to that in JRA, as an example of another rheumatologic disorder. Methods: The study was conducted on 32 patients with pediatric- onset SLE; their ages ranged between 5 and 25 years (mean + SD = 15.5 + 4.4). In addition to various laboratory investigations needed for diagnosis, assessment of different system involvement as well as disease activity, the percentage of early circulating apoptotic lymphocytes was measured by flowcytometry using Annexin –V. The results were compared to that of 20 age and sex matched clinically healthy children and adolescents as well as 10 JRA patients. Results: The percentage of circulating early apoptotic lymphocytes was significantly higher in SLE patients (mean ± SD = 7.02 ± 7.29 %) and JRA patients (mean ± SD=5.91± 6.00 %) as compared to healthy controls (mean ± SD = 1.89 ± 2.21 %; p=0.0003 and 0.023, respectively). The levels of apoptotic lymphocytes seemed higher in SLE patients than in JRA patients but the difference was statistically insignificant (p=0.58). There was no correlation between the percentage of circulating apoptotic lymphocytes and the disease activity markers (SLEDAI and ESR), different system involvement and the dose or duration of corticosteroids therapy. Conclusion: The general increase of circulating apoptotic lymphocytes seen in SLE patients may not be specific to SLE and could be seen with other autoimmune diseases. It seems that disturbance in the apoptotic process contributes more to the phenomenon of autoantigenicity rather than the prediction of the disease clinical activity or specific organ involvement.Keywords: SLE, apoptosis, annexin V, autoimmune diseases, JRA, PediatricEgypt J Pediatr Allergy Immunol 2003; 1(2): 118-2

Brain-derived neurotrophic factor in asthmatic children

Background: Brain-derived neurotrophic factor (BDNF) regulates the cross-talk between the immune and nervous systems which may play an important role in asthma pathophysiology. Objective: This study was aimed to investigate the relation between BDNF and asthma exacerbation and severity, and to study its possible correlation to eosinophilic counts in blood and sputum. Methods: Twenty-seven asthmatic children were studied during both exacerbation and remission. According to acute exacerbation severity as assessed clinically and by peak expiratory flow rate (PEFR), they were equally subdivided into 3 groups (mild, moderate and severe). Serum and sputum BDNF levels as well as blood and sputum eosinophilic counts were estimated in all patients in comparison to 30 healthy children with no personal or family history of atopy. Results: BDNF levels (in serum and sputum) and eosinophilic counts (in blood and sputum) were significantly elevated in asthmatic patients, whether studied as one group or subgrouped into mild, moderate and severe as compared to controls. Patients with mild, moderate and severe acute asthma exacerbation had significantly higher values of BDNF (in serum and sputum) and eosinophilic count (in blood and sputum) than the corresponding values measured during remission. The latter values were still higher than those of the control group. BDNF in serum and sputum indirectly correlated with asthma severity as evidenced by their negative correlation with PEFR. However, sputum BDNF correlated better with the severity of asthma exacerbation as evidenced directly by its significant increase with clinical severity. Both serum and sputum BDNF levels revealed significant positive correlations with eosinophilic count in blood and sputum among all studied groups. Conclusion: BDNF probably plays a role in the evolution of asthma exacerbation and it reflects the degree of asthma severity during exacerbation. It might also represent an objective indicator of remission and treatment efficacy. Studies with specific BDNF receptor antagonists or synthesis inhibitors are required as BDNF may prove to be a reasonable target for a new therapy in future.Keywords: BDNF, neurotrophins, bronchial asthma, asthma severity, neurogenic inflammationEgypt J Pediatr Allergy Immunol 2003; 1(2): 102-

A Hybrid Algorithm for Improving the Quality of Service in MANET

A mobile ad-hoc network (MANET) exhibits a dynamic topology with flexible infrastructure. The MANET nodes may serve as both host and router functionalities. The routing feature of the MANET is a stand-alone multi-hop mobile network that can be utilized in many real-time applications. Therefore, identifying paths that ensure high Quality of Service (QoS), such as their topology and applications is a vital issue in MANET. A QoS-aware protocol in MANETs aims to find more efficient paths between the source and destination nodes of the network and, hence, the requirements of the QoS. This paper proposes a different hybrid algorithm that combines Cellular Automata (CA) with the African Buffalo Optimization (ABO), CAABO, to improve the QoS of MANETs. The CAABO optimizes the path selection in the ad-hoc on-demand distance vector (AODV) routing protocol. The test results show that with the aid of the CAABO, the AODV manifests energy and delay-aware routing protocol

Prediction of player position for talent identification in association netball: a regression-based approach

Among the challenges in industrial revolutions, 4.0 is managing organizations’ talents, especially to ensure the right person for the position can be selected. This study is set to introduce a predictive approach for talent identification in the sport of netball using individual player qualities in terms of physical fitness, mental capacity, and technical skills. A data mining approach is proposed using three data mining algorithms, which are Decision Tree (DT), Neural Network (NN), and Linear Regressions (LR). All the models are then compared based on the Relative Absolute Error (RAE), Mean Absolute Error (MAE), Relative Square Error (RSE), Root Mean Square Error (RMSE), Coefficient of Determination (R2), and Relative Square Error (RSE). The findings are presented and discussed in light of early talent spotting and selection. Generally, LR has the best performance in terms of MAE and RMSE as it has the lowest values among the three models

Deep Learning Approach for Predicting Prostate Cancer from MRI Images

According to medical data, prostate cancer has been one of the most lethal malignancies in recent years. Early detection of prostate cancer significantly influences the tumor's treatability. Image analysis software that operates using a machine learning or deep learning algorithm is one of the techniques utilized to aid in the early and rapid identification of prostate cancer. This paper evaluates the performance of three deep learning Convolutional neural network (CNN) algorithms in detecting prostate cancer. Using Python, three deep learning models, ResNet50, InceptionV3, and VGG16, are subsequently created on the Kaggle platform. These three models have been applied to various medical image diagnostic problems and have won several contests. This study used 620 image samples from the Cancer Imaging Archive (TCIA) data source. Accuracy, f1 score, recall, and precision are used to evaluate the performance of the three models. The extracted test results indicate that the VGG16 achieves the highest level of accuracy at 95.56 percent, followed by the ResNet50 at 86.67 percent and the InceptionV3 at 85.56 percent

A Model for Predicting Entrepreneurship Intentions based on Social Cognitive Theory and Entrepreneurship Characteristics

This research studies whether entrepreneurial characteristics comprising passion, creativity, and self-efficacy are significantly associated with entrepreneurial intentions. Therefore, the final year students from various disciplines from a public university who were potential entrepreneurs and have attended the advance level courses on entrepreneurship were chosen for the data collection. Most of these students have participated in the on-campus business related activities and received some experience through these activities. It was found that there are strong positive correlation between entrepreneurial characteristic factors with entrepreneurial intention. Overall, the R2 of the entrepreneurship’s characteristics comprising entrepreneurial passion, self-efficacy, and creativity to predict entrepreneurship intention to become an entrepreneur shows very strong explanation power. It implies that this studys proposed model can serve as an assessment tool for selecting students with the higher entrepreneurial intention to engage them in entrepreneurship and activities at the university