Combined emission economic load dispatch problem using hybrid combination of flower pollination algorithm and moderate random search particle swarm optimization
The total cost of electricity generation is minimized while fulfilling the total load demand and considering all constraints in the Combined Economic Emission Dispatch (CEELD) problem. Electricity generation from fossil fuel negatively impacts the environment. Therefore, various optimization techniques have been deployed for the CEELD problem. In the literature, the Modified Random Search Particle Swarm Optimization (MRSPSO) and Flower pollination algorithm (FPA) are used as a solution for CEELD known as Combined Economic Emission Load dispatch problem. However, MRSPSO is easy to settle into local optima in high-dimensional space and delivers a low convergence rate in the iterative process, whereas in the FPA, the diverse population make it prone to being limited to the local optima. Thus, in order to overcome these limitations, in this paper, we have hybrid the FPA and MRSPSO algorithm that improves the convergence rate to meet the optimal solution. Initially, we have implemented MRSPSO and FPA algorithm; after that, combined it for CEELD. The experimental results were performed in MATLAB. The experimental results show that the hybrid approach gives better results as compared to the MRSPSO and FPA. Thus, the proposed technique is efficient and can be deployed for real-time CEELD problem.
Published by: Jaspreet Singh, Puneet Jain
Author: Jaspreet Singh
Paper ID: V6I4-1286
Paper Status: published
Published: July 29, 2020
Lightweight privacy-preserving scheme for the smart grid data using ANU and Perturbation Algorithm
Smart Grid collects the data of smart meter and communicates the data to electricity generation, pricing, and billing departments. The departments used this information for electricity forecasting, real-time pricing, and generate electricity bills. The smart grid data contains customer personal information as well as electricity consumption details. Thus, sharing all information with the departments violates customer privacy. In addition, if no security mechanism provided for the data makes it prone to the attacks. In this paper, we have proposed a privacy-preserving algorithm for smart grid data security. The algorithm has two-phase. In the first phase, customer personal information and electricity consumption details separated. In the second phase, the customer's personal information is secured using a lightweight algorithm ANU and electricity consumption details are secured using noise addition on the data by applying the perturbation algorithm. The algorithm is coded and simulated in the MATLAB 2013a. The experimental results show that the proposed technique consumes less memory and provides better security as compared to the existing algorithms.
Published by: Ravinderpal Singh, Puneet Jain
Author: Ravinderpal Singh
Paper ID: V6I4-1285
Paper Status: published
Published: July 29, 2020
Eye blink to voice format communication for paralyzed patients
The wide growth of technology in the medication field reduces the difficulties of patients to a large extent. Motor Neuron Disease is one such major classes of physical disabilities resulting in disfunction.MND patients are unable to figure like walk and communicate caused by weakness of muscles. The patient has management solely upon his Eye movement. This technique contains strategies like face detection, eye detection, eye pursuit, conversion of blink to voice, Video-Oculogragphy methodology is employed to create communication between patient and caretaker. The image process module incorporates a digital camera and therefore the eye movement-image is captured with OpenCv to get the coordinate of the eyeball. The system permits the patients to speak with caretaker victimization blink pattern converted into voice format The sensible resolution for image process is obtained be Python programming with Open CV
Published by: Namratha K. S., Jeevitha B. S., Sneha P. H., Veda D.
Author: Namratha K. S.
Paper ID: V6I4-1229
Paper Status: published
Published: July 28, 2020
Computer vision and its role in driving safety
Every year there are more than 1.2 million road accidents happening across the globe, which accounts for more than 2.2% of deaths on a global scale. There has been an alarming increase in road accidents in today’s time and a major reason behind this can be attributed to how the driver is behaving during his driving. Some of them may be unavoidable, yet a major portion of the hazards may be averted if there are means to keep a check on driver state ranging from their physical condition to monitoring their reckless driving patterns. This is where the advent of technology and the role of having a robust monitoring ecosystem come into the picture. Computer Vision more or less is a sought after technology that automotive companies today are chasing, be it telematics-based connected cars or autonomous self-driving vehicles. It can help solve this purpose by monitoring the driver drowsiness through advanced image processing solutions and providing the user with an integrated report showcasing how concentrated their driving was and what needs to be improved. This image processing technique may also be integrated into Telematics products to provide results on what the eco-driving score of the user is and maybe alerted via notifications on smartphones as to what daily trends of their driving are. This solution proves to be an effective approach to counter and restrict the increasing number of road accidents happening across the globe and meet end goal of achieving the maximum safety out of the road-network ecosystem.
Published by: Probhakar Sarkar, Umair Siddiqui
Author: Probhakar Sarkar
Paper ID: V6I4-1217
Paper Status: published
Published: July 28, 2020
Handwritten character recognition
Character recognition is one of the most important research fields of image processing and pattern recognition. Character recognition is generally known as Handwritten Character Recognition (HCR) or Optical Character Recognition (OCR). HCR is the process of electronic translation of handwritten images or typewritten text into machine editable text. It becomes very difficult if there are lots of paper based information on companies and offices. Because they want to manage huge volume of documents and records. Computers can work much faster and more efficiently than human. It is used to perform many of the tasks required for efficient document and Content management. But computer knows only alphanumeric characters as ASCII code. So computer cannot distinguish character or word from a scanned image. In order to use the computer for document management, it is required to retrieve alphanumeric information from a scanned image. There are so many methods which are currently used for OCR and are based on different languages. The existing method like Artificial Neural Network (ANN based on English Handwritten character recognition needs the features to be extracted and also the performance level is low. So Convolutional Neural Network (CNN) based English handwritten character recognition method is used as a deep machine learning method for which it doesn't want to extract the features and also a fast method for character recognition.
Published by: Addala Tejaswini, Y. Lakshmi Pratyusha, A. Rajashekar Reddy
Author: Addala Tejaswini
Paper ID: V6I4-1268
Paper Status: published
Published: July 28, 2020
A study to assess the effectiveness of structured teaching programme regarding knowledge and practices of mother on prevention of accidents among toddlers
Introduction: Mother is an important care provider and she is strongly responsible to the safety of the children. Children are the most vulnerable group of our population. Childhood accident is a sudden cause of death in children. Accident means “sudden, unexpected harmful event", An accident is often a harmful event that could be avoided by a little careful thought. Child accidents are very common among toddlers. Objectives: This study was done to assess the effectiveness of structure teaching programme regarding knowledge and practice of mothers. to assess the knowledge of mothers regarding prevention of accidents among toddlers, to assess the practice of mothers regarding prevention of accidents among toddlers, to find out the effectiveness of structured teaching program regarding knowledge and practices of mothers on the prevention of accidents among toddlers, to find out the association between the selected demographic variables with knowledge and practice score of mothers regarding prevention of accidents among toddlers. Material and Method: The present study has been carried out in Mashobra village and sample was included 320 mothers of toddlers. A structured questionnaire sheet was developed by the researcher to collect data. A simple random sampling approach was followed in this study and collection of data was performed by interviewing each mother at her home. The main results obtained from the study were as follows: most of the mothers were very poor knowledge of prevention of common accidents among toddlers and nearly all home had at least two or three potential environmental hazards. RESULTS: The present study revealed that the overall pre-test mean knowledge score of mothers regarding prevention of accidents among toddlers was found 31.67% and post test score was 73.33% in area of overall accidents, According to mothers practice in different type of accidents the present study indicated that the majority of mothers had 100% poor practice and post practice score was 62.5% mothers have moderate practice and 37.5% of mothers have good practice score regarding prevention of accidents among toddlers. After administration of STP the knowledge and practice level of mothers increased tremendously. The present study recommended health promotion classes about causes of accidents, first aid, preventive measures and safety for mothers at MCH center, in service educational program toward first aid should be established for community health nurses at rural health units and MCH center, a well planned health education program about causes of accidents, first aid and prevention introduced to the curriculum. Interpretation and Conclusion: The study findings revealed that structured teaching programme was highly effective in improving knowledge and practice of mothers regarding prevention of accidents among toddler. Key Word: Effectiveness, Knowledge, Practice, Structured teaching programme, toddler,
Published by: Prabha Kashyap
Author: Prabha Kashyap
Paper ID: V6I4-1263
Paper Status: published
Published: July 24, 2020
