Xbee-Based WSN Architecture for Monitoring of Banana Ripening Process Using Knowledge-Level Artificial Intelligent Technique

Real-time monitoring of fruit ripeness in storage and during logistics allows traders to minimize the chances of financial losses and maximize the quality of the fruit during storage through accurate prediction of the present condition of fruits. In Pakistan, banana production faces different difficulties from production, post-harvest management, and trade marketing due to atmosphere and mismanagement in storage containers. In recent research development, Wireless Sensor Networks (WSNs) are progressively under investigation in the field of fruit ripening due to their remote monitoring capability. Focused on fruit ripening monitoring, this paper demonstrates an Xbee-based wireless sensor nodes network. The role of the network architecture of the Xbee sensor node and sink end-node is discussed in detail regarding their ability to monitor the condition of all the required diagnosis parameters and stages of banana ripening. Furthermore, different features are extracted using the gas sensor, which is based on diverse values. These features are utilized for training in the Artificial Neural Network (ANN) through the Back Propagation (BP) algorithm for further data validation. The experimental results demonstrate that the projected WSN architecture can identify the banana condition in the storage area. The proposed Neural Network (NN) architectural design works well with selecting the feature data sets. It seems that the experimental and simulation outcomes and accuracy in banana ripening condition monitoring in the given feature vectors is attained and acceptable, through the classification performance, to make a better decision for effective monitoring of current fruit condition

A Robust Neutrosophic Modeling and Optimization Approach for Integrated Energy-Food-Water Security Nexus Management under Uncertainty

Natural resources are a boon for human beings, and their conservation for future uses is indispensable. Most importantly, energy-food-water security (EFWS) nexus management is the utmost need of our time. An effective managerial policy for the current distribution and conservation to meet future demand is necessary and challenging. Thus, this paper investigates an interconnected and dynamic EFWS nexus optimization model by considering the socio-economic and environmental objectives with the optimal energy supply, electricity conversion, food production, water resources allocation, and CO2 emissions control in the multi-period time horizons. Due to real-life complexity, various parameters are taken as intuitionistic fuzzy numbers. A novel method called interactive neutrosophic programming approach (INPA) is suggested to solve the EFWS nexus model. To verify and validate the proposed EFWS model, a synthetic computational study is performed. The obtained solution results are compared with other optimization approaches, and the outcomes are also evaluated with significant practical implications. The study reveals that the food production processes require more water resources than electricity production, although recycled water has not been used for food production purposes. The use of a coal-fired plant is not a prominent electricity conversion source. However, natural gas power plants’ service is also optimally executed with a marginal rate of production. Finally, conclusions and future research are addressed. This current study emphasizes how the proposed EFWS nexus model would be reliable and beneficial in real-world applications and help policy-makers identify, modify, and implement the optimal EFWS nexus policy and strategies for the future conservation of these resources

Generalized OWA operators for uncertain queuing modeling with application in healthcare

The weighted averaging operators are one of the popular methods for aggregating information. In recent years, ordered weighted averaging operators (OWA) have attained a great attention by researchers. These OWA operators due to their versatility are very useful to model many real world situations. Several extensions of OWA operators are presented in the literature which can handle a situation with uncertainty. Although many queuing models have been proposed in numerous healthcare studies, the inclusion of OWA operators is still rare. In this research study, we propose a novel method using the uncertain generalized ordered weighted average and illustrate its application to the uncertain queue modeling in a hospital emergency room; where incoming flux of patients and the required level of service for each patient is unknown and uncertain. The model with multilateral decision making process has been described which will provide several alternatives to decision makers to select the best alternative for their challenging situations. The proposed method has resulted an improved performance of the queuing system, increased customer satisfaction as well as a significant reduction in the operational cost. This study will enable decision makers to operate a flexible and cost-effective system in the event of uncertainty, uncontrollable and unpredicted situations

Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network

The voltage supply of induction motors of various sizes is typically provided by a shared power bus in an industrial production powerline network. A single motor’s dynamic behavior produces a signal that travels along the powerline. Powerline networks are efficient at transmitting and receiving signals. This could be an indication that there is a problem with the motor down immediately from its location. It is possible for the consolidated network signal to become confusing. A mathematical model is used to measure and determine the possible known routing of various signals in an electricity network based on attenuation and estimate the relationship between sensor signals and known fault patterns. A laboratory WSN based induction motors testbed setup was developed using Xbee devices and microcontroller along with the variety of different-sized motors to verify the progression of faulty signals and identify the type of fault. These motors were connected in parallel to the main powerline through this architecture, which provided an excellent concept for an industrial multi-motor network modeling lab setup. A method for the extraction of Xbee node-level features has been developed, and it can be applied to a variety of datasets. The accuracy of the real-time data capture is demonstrated to be very close data analyses between simulation and testbed measurements. Experimental results show a comparison between manual data gathering and capturing Xbee sensor nodes to validate the methodology’s applicability and accuracy in locating the faulty motor within the power network

Hand Gesture Recognition with Symmetric Pattern under Diverse Illuminated Conditions Using Artificial Neural Network

This paper investigated the effects of variant lighting conditions on the recognition process. A framework is proposed to improve the performance of gesture recognition under variant illumination using the luminosity method. To prove the concept, a workable testbed has been developed in the laboratory by using a Microsoft Kinect sensor to capture the depth images for the purpose of acquiring diverse resolution data. For this, a case study was formulated to achieve an improved accuracy rate in gesture recognition under diverse illuminated conditions. For data preparation, American Sign Language (ASL) was used to create a dataset of all twenty-six signs, evaluated in real-time under diverse lighting conditions. The proposed method uses a set of symmetric patterns as a feature set in order to identify human hands and recognize gestures extracted through hand perimeter feature-extraction methods. A Scale-Invariant Feature Transform (SIFT) is used in the identification of significant key points of ASL-based images with their relevant features. Finally, an Artificial Neural Network (ANN) trained on symmetric patterns under different lighting environments was used to classify hand gestures utilizing selected features for validation. The experimental results showed that the proposed system performed well in diverse lighting effects with multiple pixel sizes. A total aggregate 97.3% recognition accuracy rate is achieved across 26 alphabet datasets with only a 2.7% error rate, which shows the overall efficiency of the ANN architecture in terms of processing time

Healthcare Human Resources: Trends and Demand in Saudi Arabia

This paper estimates the impact of policies on the current status of Healthcare Human Resources (HHR) in Saudi Arabia and explores the initiatives that will be adopted to achieve Saudi Vision 2030. Retrospective time-series data from the Ministry of Health (MOH) and statistical yearbooks between 2003 and 2015 are analyzed to identify the impact of these policies on the health sector and the number of Saudi and non-Saudi physicians, nurses and allied health specialists employed by MOH, Other Government Hospitals (OGH) and Private Sector Hospitals (PSH). Moreover, multiple regressions are performed with respect to project data until 2030 and meaningful inferences are drawn. As a local supply of professional medical falls short of demand, either policy to foster an increase in supply are adopted or the Saudization policies must be relaxed. The discrepancies are identified in terms of a high rate of non-compliance of Saudization in the private sector and this is being countered with alternative measures which are discussed in this paper. The study also analyzed the drivers of HHR demand, supply and discussed the research implications on policy and society. The findings suggest that the 2011 national Saudization policy yielded the desired results mostly regarding allied health specialists and nurses. This study will enable decision-makers in the healthcare sector to measure the effectiveness of the new policies and, hence, whether to continue in implementing them or to revise them

Radio-Frequency-Identification-Based 3D Human Pose Estimation Using Knowledge-Level Technique

Human pose recognition is a new field of study that promises to have widespread practical applications. While there have been efforts to improve human position estimation with radio frequency identification (RFID), no major research has addressed the problem of predicting full-body poses. Therefore, a system that can determine the human pose by analyzing the entire human body, from the head to the toes, is required. This paper presents a 3D human pose recognition framework based on ANN for learning error estimation. A workable laboratory-based multisensory testbed has been developed to verify the concept and validation of results. A case study was discussed to determine the conditions under which an acceptable estimation rate can be achieved in pose analysis. Using the Butterworth filtering technique, environmental factors are de-noised to reduce the system’s computational cost. The acquired signal is then segmented using an adaptive moving average technique to determine the beginning and ending points of an activity, and significant features are extracted to estimate the activity of each human pose. Experiments demonstrate that RFID transceiver-based solutions can be used effectively to estimate a person’s pose in real time using the proposed method

Dynamic Key Extraction Technique Using Pulse Signal and Lightweight Cryptographic Authentication Scheme for WBAN

As a key component of ubiquitous computing, the wireless body area network (WBAN) can be used in a variety of disciplines, including health monitoring. Our everyday routines have been transformed by wearable technology, which has changed the medical industry and made our lives more convenient. However, the openness of the wireless network has raised concerns about the privacy and security of patient’s data because of the latent threat imposed by attackers. Patients’ sensitive data are safeguarded with authentication schemes against a variety of cyberattacks. Using pulse signals and a lightweight cryptographic approach, we propose a hybrid, anonymous, authentication scheme by extracting the binarized stream (bio-key) from pulse signal. We acquired 20 different sample signals to verify the unpredictability and randomness of keys, which were further utilized in an authentication algorithm. Formal proof of mutual authentication and key agreement was provided by the widely known BAN logic, and informal verification was provided by the Automated Validation of Internet Security Protocol and Applications (AVISPA) tool. The performance results depicted that storage cost on the sensor side was only 640 b, whereas communication cost was 512 b. Similarly, the computation time and energy consumption requirements were 0.005 ms and 0.55 µJ, respectively. Hence, it could be asserted that the proposed authentication scheme provided sustainable communication cost along with efficient computation, energy, and storage overheads as compared to peer work

A Voltage Multiplier Circuit Based Quadratic Boost Converter for Energy Storage Application

In this paper, a new transformerless high voltage gain dc-dc converter is proposed for low and medium power application. The proposed converter has high quadratic gain and utilizes only two inductors to achieve this gain. It has two switches that are operated simultaneously, making control of the converter easy. The proposed converter’s output voltage gain is higher than the conventional quadratic boost converter and other recently proposed high gain quadratic converters. A voltage multiplier circuit (VMC) is integrated with the proposed converter, which significantly increases the converter’s output voltage. Apart from a high output voltage, the proposed converter has low voltage stress across switches and capacitors, which is a major advantage of the proposed topology. A hardware prototype of 200 W of the proposed converter is developed in the laboratory to validate the converter’s performance. The efficiency of the converter is obtained through PLECS software by incorporating the switching and conduction losses