Spanning Tree Based Community Detection Using Min-Max Modularity

AbstractCommunity refers to the group of entities which have similar behavior or characteristic among them. Usually community represents basic functional unit of social network. By understanding the behavior of elements in a community, one can predict the overall feature of large scale social network. Social networks are generally represented in the form of graph structure, where the nodes in it represent the social entities and the edges correspond to the relationships between them. Detecting different communities in large scale network is a challenging task due to huge data size associated with such network. Community detection is one of the emerging research area in social network analysis.In this paper, a spanning tree based algorithm has been proposed for community detection which provides better performance with respect to both time and accuracy. Modularity is the well known metric used to measure the quality of community partition in most of the community detection algorithms. In this paper, an extensive version of modularity has been used for quality assessment

An Intelligent Method Based Medical Image Compression

Compression methods are important in many medical applications to ensure fast interactivity through large sets of images (e.g. volumetric data sets, image databases), for searching context dependant images and for quantitative analysis of measured data. Medical data are increasingly represented in digital form. The limitations in transmission bandwidth and storage space on one side and the growing size of image datasets on the other side has necessitated the need for efficient methods and tools for implementation. Many techniques for achieving data compression have been introduced. Wavelet transform techniques currently provide the most promising approach to high-quality image compression, which is essential for Teleradiology. This paper presents an approach of intelligent method to design a vector quantizer for image compression. The image is compressed without any loss of information. It also provides a comparative study in the view of simplicity, storage space, robustness and transfer time of various vector quantization methods. The proposed approach presents an efficient method of vector quantization for image compression and application of SOFM

Torque Quality Improvement in Induction Motor for Electric Vehicle Application Based on Teamwork Optimization

The tailpipe emission caused by the vehi- cles using internal combustion engines is a significant source of air pollution. To reduce the health hazards caused by air pollution, advanced countries are now adopting the use of Electric Vehicles (EVs). Due to the advancement of electric vehicles, research and devel- opment efforts are being made to improve the perfor- mance of EV motors. With a nominal reference sta- tor flux, the classical induction motor drive generates significant flux, torque ripple, and current harmon- ics. In this work, a Teamwork Optimization Algorithm (TOA)-based optimal stator flux strategy is suggested for torque ripple reduction applied in a Classical Direct Torque Controlled Induction Motor (CDTC-IM) drive. The suggested algorithm’s responsiveness is investi- gated under various steady-state and dynamic operat- ing conditions. The proposed Direct Torque Controlled Induction Motor (DTC-IM) drive’s simulation results are compared to those of the CDTC-IM and Fuzzy Di- rect Torque Controlled Induction Motor (FDTC-IM) drives. The proposed system has been evaluated and shown to have reduced torque ripple, flux ripple, cur- rent harmonics, and total energy consumption by the motor. Further, a comparative simulation study of the above methods at different standard drive cycles is presented. Experimental verification of the proposed algorithm using OPAL-RT is presented. The results represent the superiority of the proposed algorithm compared to the CDTC- and FDTC-IM drive. The torque ripple reduction approach described in this study can also be applied to all types of induction motors, not only those for electric vehicles or Hybrid Electric Vehicles (HEVs)

Parallel energy-efficient coverage optimization using WSN with Image Compression

Energy constraint is an important issue in wireless sensor networks. This paper proposes a distributed energy optimization method for target tracking applications. Sensor nodes are clustered by maximum entropy clustering. Then, the sensing field is divided for parallel sensor deployment optimization. For each cluster, the coverage and energy metrices are calculated by grid exclusion algorithm and Dijkstra’s algorithm, respectively. Cluster heads perform parallel particle swarm optimization to maximize the coverage metric and minimize the energy metric. Particle filter is improved by combing the radial basis function network, which constructs the process model. Thus, the target position is predicted by the improved particle filter. Dynamic awakening and optimal sensing scheme are then discussed in dynamic energy management mechanism. A group of sensor nodes which are located in the vicinity of the target will be awakened up and have the opportunity to report their data. The selection of sensor node is optimized considering sensing accuracy and energy consumption. Experimental results verify that energy efficiency of wireless sensor network is enhanced by parallel particle swarm optimization, dynamic awakening approach, and sensor node selection

Data acquisition using PCL-207 card

The field of data acquisition and control encompasses a very wide range of activities. At its simplest level, it involves reading electrical signals into a computer from some form of sensor. These signals may represent the state of a physical process, such as the position and orientation of machine tools, the temperature of a furnace or the size and shape of a manufactured component. The acquired data may have to be stored, printed or displayed. Often the data have to be analyzed or processed in some way in order to generate further signals for controlling external equipment or for interfacing to other computers. This may involve manipulating only static readings, but it is also frequently necessary to deal with time-varying signals as well. In less than a decade, the PC has become the most widely used platform for data acquisition and control. The main reasons for the popularity of PC-based technology are low costs, flexibility and ease of use, and, last but not the least, performance. Data acquisition with a PC enables one to display, log and control a wide variety of real world signals such as pressure, flow, and temperature. This ability coupled with that of easy interface with various stand-alone instruments makes the systems ever more desirable. Until the advent of the PC, data acquisition and process monitoring were carried out by using dedicated data loggers, programmable logic controllers and or expensive proprietary computers. In this project we have used the thermocouple(J-type) to acquire the temperature of water being heated by a heater , which we have got in mili volts range. This has been further converted approximately into the range of 5 volt by using an amplifier of suitable gain (1000). We have used the data acquisition card PCL-207 to interface the amplified output to PC

Application of plastic funnel in blast hole to improve blasting efficiency of opencast coal mine at West Bokaro

Blasting being one of the key activities of mining, its efficiency in terms of lower explosives consumption, improved rock fragmentation, decreased fly-rock, reduced noise and vibration level is very much desired for an effective mining operation which can be achieved by maximizing the utilization of explosive energy in the blast hole. Use of ‘reverse plastic funnel’ into the blast hole is one of the techniques for more utilization of explosives energy to improve blasting efficiency. The reverse plastic funnel is placed between explosive and stemming column in the blast hole which eliminates the contamination of explosive from drill cuttings (used for stemming), thus increases the Velocity of Detonation (VoD) of the explosive. Also, the conic shape of funnel creates a ‘Wedge effect’ guiding more of the explosive energy into the rock rather than upward out of the blast hole which helps in utilizing more explosive energy for rock breakage and reducing fly rock generation. In order to establish the benefit, trials were carried out in OB (overburden) benches of opencast coal mine at West Bokaro. In-hole VoD is measured by using Micro Trap VoD Recorder. It was found that the in-hole Velocity of Detonation (VoD) of the explosive is more in blast hole having funnel which means more strength of explosive. It was also observed that the fly rocks generation is negligible from blast holes in which funnels are placed

A PROSPECTIVE STUDY ON UNSUPERVISED REHABILITATION PROTOCOL AFTER ROTATOR CUFF REPAIR.

Abstract Background The postoperative rehabilitation following rotator cuff repair yields favourable outcomes; however, it necessitates a significant investment of both time and financial resources. The primary aim of this study was to evaluate the efficacy of a home-based rehabilitation protocol in the management of patients who underwent rotator cuff repair. Methods The present study utilized a prospective design to investigate a group comprising 84 individuals who underwent surgical intervention for rotator cuff repair. The participants were provided with detailed instructions regarding the exercise protocol, which they diligently executed within the confines of their individual residences. Results The average duration of follow-up was 14 months. The Visual Analogue Score (VAS) for pain demonstrated a significant improvement, with the preoperative mean of 7.3 decreasing to a follow-up mean of 1.2. Additionally, the Disability of the Arm, Hand, and Shoulder (DASH) score, assessed in a sample of 52 patients, exhibited notable enhancement, as the preoperative mean of 33.1 decreased to a follow-up mean of 4.4. Furthermore, an improvement in the range of motion was observed in a group of 78 patients. A total of 74 patients achieved complete reintegration into the workforce within a three-month timeframe. Conclusion Given that the patients are engaging in postoperative rehabilitation within the confines of their own residences, this approach serves to diminish financial burdens, mitigate time constraints associated with travel, and minimize susceptibility to external influences. Recommendation Limit home-based rehabilitation to well-evaluated cases without substantial tears, emphasizing optimal repair procedures for complex cases. Implement remote monitoring tools to track patient progress, ensuring adherence and early identification of potential issues. Conduct a thorough cost-benefit analysis to assess the economic advantages of home-based rehabilitation, considering reduced financial burdens and time constraints

An application of Poisson hidden Markov model to forecast endometrial cancer cases in Odisha, India

Background: Time series model often used for forecasting, but inadequate when the data series is unbounded and over dispersed in nature. Moreover, if the observations are serially dependent, then Markov dependent mixture model i.e., Poisson Hidden Markov model can be used. The objective of this study was to apply Poisson Hidden Markov model to forecast month wise new endometrial cancer cases. Materials and Methods: Month-wise total number of registered endometrial cancer cases has been collected from a local cancer hospital in Odisha from January, 2017 to December, 2021. In this paper we have applied the Poisson hidden Markov model to forecast, the number of endometrial cancer cases for next 12 months. Results: Three state Poisson hidden Markov model was found as best fitted model and used to forecast the endometrial cancer cases for the next 12 months. The results showed that the number of endometrial cancer cases most likely to lie in state 2 in January and in state 3 in rest of the months in 2022. The monthly forecasted mean of endometrial cancer cases varies between 34 to 38 for the year 2022. Conclusion: This study reveals that the average endometrial cancer cases will increase in future months. It is also suggested that, the three-state hidden Markov model can be used to fit and forecast the distribution of the number of endometrial cancer cases

Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification

As a huge number of satellites revolve around the earth, a great probability exists to observe and determine the change phenomena on the earth through the analysis of satellite images on a real-time basis. Therefore, classifying satellite images plays strong assistance in remote sensing communities for predicting tropical cyclones. In this article, a classification approach is proposed using Deep Convolutional Neural Network (DCNN), comprising numerous layers, which extract the features through a downsampling process for classifying satellite cloud images. DCNN is trained marvelously on cloud images with an impressive amount of prediction accuracy. Delivery time decreases for testing images, whereas prediction accuracy increases using an appropriate deep convolutional network with a huge number of training dataset instances. The satellite images are taken from the Meteorological & Oceanographic Satellite Data Archival Centre, the organization is responsible for availing satellite cloud images of India and its subcontinent. The proposed cloud image classification shows 94% prediction accuracy with the DCNN framework

Pre-hospital care: Data profile from traumatic brain injury registry

Introduction: There are multiple factors from injury spot till patient reach trauma unit, which affect their outcome. The literature of same from developing country is mere. The present study investigates primary care, mode of transportation and emergency management among TBI patients visiting a tertiary institute.Methods: The data of 337 patients was selected from a trauma registry. The data of TBI patients visiting emergency were entered in standard computer interface after obtaining their consent. The standard proforma was developed by FileMaker Pro Advanced 13 (Copyright © 1994-2015, FileMaker, Inc) and web data entry interface Drupal CMS. Data was analyzed using Stats Direct version 3.0.150.Results: Seventy five percent of patients were from rural setup. About 67% of patients visiting emergency had undergone first aid from both rural and urban setup. Forty percent of patients came directly, only about 5% were referred from other hospitals. Majority of patients were accompanied by relatives (87%) followed by spouse (8.6%). Non ambulance mode (31%) was more than ground ambulance (25%) to reach emergency setup. Emergency management of airway, breathing and circulation was significant with outcome at discharge (p<0.001).Conclusion: The study reports that majority of patients had undergone first aid before reaching trauma unit. Non ambulance mode of transportation is more. The study emphasis for detail study on pre hospital care variables with larger sample size