The relationship between blood type and personality has long been one of the more challenging issues of scientific studies. Several large-scale surveys were conducted to address the issue, and some of them had shown statistically significant associations. Presently, more than half of Japanese people feel that the relationship is legitimate. This pilot study analyzed data from two large-scale surveys (Survey 1: N = 1,859, Survey 2: N = 3,750) to examine the relationship between blood type and personality. AI predicted blood types of participants more than by chance. The ANOVA results of large-scale surveys showed that respondents displayed the personality traits corresponding to their own blood type more strongly than respondents who had different blood types did. This finding was consistent across all traits, and all differences were statistically significant. The same differences in scores were found in the groups who reported no blood type personality knowledge, although the values were smaller. We observed a clear and significant relationship between blood type and personality in large-scale surveys.
Knowing the value of PID parameters is important to tune the PID controller. There are different kinds of process to know the value of PID parameters. Genetic Algorithm is applied to find out the best value of PID parameters. Simulation process has been done by using code in MATLAB to initiate PID controller. In this work it has been shown that how to get the suitable value of PID parameters to tune the PID controller. Researching on different kinds of paper to get information about the process, at which we get the best value of PID parameters. The operation of PID controller depends on the value of PID Parameters. A block diagram shown the methodology part at which explain the whole process of this work. Performance Analysis of the procedure that applied to find the value of PID parameters. The value, which getting by using Genetic Algorithm is applied in (1st to 10th) order to calculate the fitness function. Different kinds of calculation, figure, graph and table shown in this work. After calculating the fitness value, it is obtained that which value is suitable for tuning the PID controller. These value is the resulting value to tune the PID controller.
Hand gesture recognition has gotten so many areas of application such as in human-computer interaction, hearing impaired communication and systems control. Recognizing gestures in videos however is a challenging task. Many techniques and features have been adopted in the literature but some of the methods still need to be improved upon. There are basically two types of gestures: the static and the dynamic gestures. In this work, a computer vision-based system for recognition of static hand gesture is proposed using a fusion of the histogram of oriented gradient and the Hu invariant moments as features. The proposed system consists of three phases: preprocessing, feature extraction and classification. The images are first pre-processed using segmentation and morphological operations. The histogram of oriented gradient and the Hu invariant moments are then extracted as features. The extracted features are concatenated and passed as input into a multilayer neural network model to recognize the static hand gesture. The proposed system is implemented and tested on the hand gesture database collected online. The model was trained using 500 features which consist of 20 gestures each from 25 gesture types. The model is then tested with another 500 gestures which also consist of 20 gestures each from the 25 gesture types. The experimental results show that the proposed system is able to recognize the static gestures with accuracy of 96.4%.
The aim of this paper is to design a feed forward artificial neural network (Ann) to estimate two-dimensional Henon dynamical map by selecting an appropriate network, transfer function and node weights. The proposed network side by side with using Fast Fourier Transform (FFT) as transfer function is used. For different cases of the system, chaotic and noisy, the experimental results of proposed algorithm will compared empirically, by means of the mean square error (MSE) with the results of the same network but with traditional transfer functions, Logsig and Tagsig. The performance of proposed algorithm is best from others in all cases from both sides, speed and accuracy.
RPA, or Robotic Process Automation, is a software that mimics the steps a human takes to complete rules-based, repetitive tasks. The robot carries out work with speed and precision, utilizing the same applications your employees use every day. In traditional automation, all the actions are primarily based on the programming/scripting, APIs or other ways of integration methods to the backend systems or internal applications. In distinction, RPA automates software that can migrate the work from the human to the computer which can stop paying humans to do work ripe for automation, faster front and back office transaction processing, near "Instant On" integration at the lowest cost, optimization of User Interface to drive long call/transaction times down, accelerate digital transformation objectives, eliminate errors thereby improving productivity by making workers smarter.
This paper considers the problem of observer-based hierarchical sliding mode control approach for uncertain underactuated systems with time delay and input saturation in strict-feedback form. Based on sliding mode control technique and the concept of hierarchical design, an adaptive fuzzy hierarchical sliding mode controller is designed for the uncertain nonlinear system by using fuzzy logic systems to approximate the uncertain functions. Additionally, input saturation which is one of the most important input constraints usually appear in many industrial control systems. By choosing an appropriate Lyapunov–Krasovskii function, the proposed controller is designed to demonstrate that all the signals in the closed-loop system can not only guarantee uniformly ultimately bounded, but also achieve good tracking performance. Finally, some computer simulation results of a practical example are illustrated to verify the effectiveness of the proposed approach.
The paper deals with the problem of adaptive fuzzy output-feedback sliding mode control for switched uncertain nonlinear systems with input saturation. Based on sliding mode control technique, an adaptive fuzzy sliding mode controller is designed for switched uncertain nonlinear systems by using fuzzy logic systems to approximate the uncertain functions. By means of state variable filters, a state observer is constructed to solve the problem of unmeasured states of the system. Moreover, input saturation which is one of the most important input constraints usually occurs in many industrial control systems. The controller is designed by choosing a suitable Lyapunov function. Actually, it is verified that all the signals in the closed-loop system can not only guarantee uniformly ultimately bounded, but also achieve well performance. Finally, some computer simulation results of a practical example are illustrated to show the performance of the proposed approach.
A traffic light control module base on PSO algorithm has been given to determining the optimal set of adjacent streets that are the candidate to choose the green period time providing the best vehicle flow. In our previous work [19] a visual traffic light monitoring module has been introduced. This module able to define the traffic conditions (crowded, normal and empty). The proposed control module should be able to integrate with the previous monitoring module to develop a new complete intelligent traffic light scheme. The controller module shows its ability to select a set of streets. The green period time will be devoted to these selected streets to reach the optimal vehicle flow through the traffic light’s intersections. The results show that the proposed control module improving the flow ratio about 72% to 96% with a different number of traffic lights.
In this article we study function interpolation problem from interpolating polynomials and artificial neural networks point of view. Function interpolation plays a very important role in many areas of experimental and theoretical sciences. Usual means of function interpolation are interpolation polynomials (Lagrange, Newton, splines, Bezier, etc.). Here we show that a specific strategy of function interpolation realized by means of artificial neural networks is much efficient than, e.g., Lagrange interpolation polynomial.
Development of information and communication technologies, mobile technologies, the creation of laptops, netbooks and pocket personal computers, as well as smartphones, new horizons opened up in improving virtual contact between highly qualified doctors of the central hospitals and medical staff of primary health facilities in the outskirts, and remote settlements. Analyzing the problems and barriers in improving tuberculosis (TB) detection indicators, and the further deterioration of the situation with multi-drug resistant tuberculosis (MDR) worldwide, TB scientists concluded that the cornerstone is the belated identification of the focus of the infection and the impossibility of monitoring the performance of the outpatient treatment regimen. Insufficient financing and staffing, especially in rural areas, exacerbates the situation. In this review article we tried to analyze the effectiveness of using various mobile applications in monitoring tuberculosis therapy conducted in rural areas. To do this, you can use applications such as WhatsApp, SMS. While the patient is taking tuberculosis medications, a medical officer removes the video on the smartphone and sends it to the central hospital. These video materials are archived. Also an employee can send SMS to the central hospital. The use of this technique allows us to monitor the medical process, actually being at great distances. This method is also effective for patient adherence to treatment.
The dynamic modeling of an electromechanical motor system (EMS) for different input voltages based on different weights and the corresponding output revolutions per minute using neural networks (NN) is presented in this paper with a view to quantify the effects of voltages based on different weights on the system output. The input–output data i.e. the electrical input voltage and the revolution per minute (rpm) of a PORCH PPWPM432 permanent magnet direct current (PMDC) motor as the output which is obtained from the EMS have been used for the development of a dynamic model of the EMS. This paper presents the formulation and application of an online modified Levenberg-Marquardt algorithm (MLMA) for the nonlinear model identification of the EMS. The performance of the proposed MLMA algorithm is compared with the so-called error back-propagation with momentum (EBPM) algorithm which is the modified version of the standard back-propagation algorithm for training NNs. The MLMA and the EBPM algorithms are validated by one-step and five-step ahead prediction methods. The performances of the two algorithms are assessed by using the Akaike’s method to estimate the final prediction error (AFPE) of the regularized criterion. The validation results show the superior performance of the proposed MLMA algorithm in terms of much smaller prediction errors when compared to the EBPM algorithm. Furthermore, the simulation results shows that the proposed techniques and algorithms can be adapted and deployed for modeling the dynamics of the EMS and the prediction of future behaviour of the EMS in real life scenarios. In addition, the dynamic modeling of the EMS in closed-loop with a discrete-time fixed parameter proportional-integral-derivative (PID) controller has been conducted using both networks trained with EBPM and the MLMA algorithms. The simulation results demonstrate the efficiency and reliability of the proposed dynamic modeling using MLMA and closed-loop PID control scheme. However, despite the little poor performance of the PID controller, the accuracy of the NN model trained with the MLMA when used in a dynamic operating environment has been confirmed.
Cloud Computing is known for providing services to variety of users by with the aid of very large scalable and virtualized resources over the internet. Due to the recent innovative trends in this field, a number of scheduling algorithms have been developed in cloud computing which intend to decrease the cost of the services provided by the service provider in cloud computing environment. Most of the modern day researchers, attempt to construct job scheduling algorithms to increase the availability and performance of cloud services as the users have to pay for the available resources/services based on time. Considering all the above factors, scheduling plays a crucial role to maximize the utilization of resources in cloud computing environment. Through this paper, we are doing a comparative study of various scheduling algorithms and the related issues in cloud computing.
Wireless Sensor Networks, in the last few decades, have witnessed significant amount of improvement in research across various areas like Routing, Security, Localization, Deployment and above all Energy Efficiency. Nowadays, the main central point of attraction is the concept of Swarm Intelligence based techniques integration in WSN. Swarm Intelligence based Computational Swarm Intelligence Techniques have improvised WSN in terms of efficiency, performance, robustness and scalability. The main objective of this research paper is to simulate and compare the performance of various existing routing protocols like AODV, DSDV and DSR routing protocols with ACO Based Routing Protocol in terms of End to End Delay, Packet Delivery Rate, Routing Overhead, Throughput and Energy Efficiency. Simulation based results and data analysis shows that overall ACO is 150% more efficient in terms of overall performance as compared to other existing routing protocols for Wireless Sensor Networks.
Cloud deals with maximizing resource usage in an efficient way. Resource Management and efficient use of that is a biggest challenge. For simulation purpose CloudSim 3.0.3 framework and to display the output in eclipse Luna IDE along with JAVA programming language this research used. Fuzzy c-means is a clustering technique for allocation of dynamic virtual machine in Cloud. Comparison in the CloudSim between the proposed system Fuzzy c-means with the existing system K-means which is one of the clustering technique for allocation of dynamic virtual machine in cloud. The two algorithms had two datacenters, two clusters. Keeping same number of virtual machines and cloudlets in both clustering techniques taking different scenarios, result obtained is that proposed system Fuzzy c-means algorithm took less total execution time in CloudSim than existing system K-means, this means that Fuzzy c-means increases performance.
The RFID card is used to create secure access to the patient's personal data and medical records.Thehealth monitoring using RFID project mainly aims at building a better means of storing and retrieving data. This project uses the hardware kit to get the patient id. The hardware kit will send the patient id to the serial port of the system. The patient ID can be accessed by the respective doctor by scanning the RFID card, after logging into doctor's account. The doctor can view and update patient's medical records and prescriptions. The patient can login into his account and he can perform functionalities that is view his previous medical reports and prescriptions. The admin registers doctors and patients and assigns unique doctor id and patient id along with password to the respective doctors and patients. Certain records, like medical records requires high privacy. Using this technology, all medical information is stored and retrieved online at any given point of time. It is easy to update, adapt and grow. Trying to identify an unconscious patient or patient who is unable to communicate can lead to delays in treatment. With this system emergency departments improve efficiency while enhancing the level of patient care.
Preserving the privacy while publishing the medical dataset is one of the techniques that can be implemented to preserve the privacy on the collected large scale of medical dataset. Medical data set contains the information that will include the personal identity of an individual therefore reproducing the same data to third party may gain privacy threats, which will include the personal detail of an individual. This paper proposes a data hiding technique called overlapping slicing for the better privacy preservation of the medical dataset that gets published.
Computers and technology now permeate nearly every aspect of our lives, but few if any lay people can understand how they work at all. While this does not actually matter to the users, other programmers and software specialists would want to see how a program works to try and increase their own knowledge. For experts, reading the documentation and then the programs is a simple matter that gives near total clarity on how a system works. But, when a fledgling, naive programming student attempts to do the same, they are unlikely to understand most of the program designed by an expert. Even mid-level and simplistic programs are often beyond the scope of beginners in programming to understand. Within the sphere of existing technologies, the naive programmer will need to find an expert to explain the program to them, as the current teaching resources use only a standard set of programs that cover the concepts, but fail to extend their scope beyond that. Decipher C is created to take a program written in C from a user and then break down the program to explain each instruction to the user. By doing this, Decipher C will allow the user to input any complex program and have it explained at the required level, ensuring maximum understanding of every concept involved. By also considering the ability to identify any areas of difficulty that the user is facing and provide an appropriate learning tool for naive programmers to use. The target is to create a robust system that can explain any C program regardless of the features it employs.
The fast pace of urbanization has led to increase in number of on-road vehicles. While most business and educational establishments have a parking facility, it requires an operator to be present at all times to verify the users accessing the parking lot. This paper discusses the implementation of a system which can be installed in parking lots to automate the process of verification of its users. The system uses image processing and character recognition to read license plates of users of the system and grant or deny access appropriately.
