Modified Golomb-Rice Algorithm for Color Image Compression

The memory required to store the color image is more. We have reduced the memory requirements using Golomb-rice algorithm. Golomb-rice algorithm consists of the following two steps. In Golomb-Rice algorithm the first step is to compress the image using discrete wavelet transform. By using DWT compression the 8 × 8 image is converted into m × n sub-windows and it is converted into raster file format for producing m × n-1 differential data. Encoding is done by using Golomb-Rice coding.  After encoding, the process length, code word and size are calculated by using GR coding.In the second step decoding is done by GR coding based on the obtained length and code word. After that decoded image is decompressed in order to get the original image by using the inverse discrete wavelet transform.&nbsp

Recognition of Anthracnose Injuries on Apple Surfaces using YOLOV 3-Dense

Plant ailment is one of the essential drivers of harvest yield decrease. With the advancement of PC vision and profound learning innovation, independent discovery of plant surface sore pictures gathered by optical sensors has become a significant research bearing for convenient yield ailment analysis. Right now, anthracnose injury identification strategy dependent on profound learning is proposed.  Right  off the bat, for  the  issue  of  lacking  picture  information brought about by the irregular event of apple illnesses, notwithstanding conventional picture expansion strategies, Cycle-Consistent Adversarial Network (CycleGAN) profound learning model is utilized right now achieve information  increase. These strategies adequately  enhance  the  decent  variety of preparing information and give  a  strong  establishment to  preparing  the  identification  model.  Right now, the premise of picture information increase, thickly associated neural system (DenseNet) is used to streamline highlight layers of the YOLO-V3 model which have lower goals. DenseNet extraordinarily improves the  usage  of  highlights in  the  neural  system  and  upgrades  the identification consequence of the YOLO-V3  model.  It  is  checked in tests that the improved model surpasses Faster  R-CNN with VGG16 NET, the  first  YOLO-V3  model,  and  other  three  cutting  edge  arranges  in  discovery   execution,  and it can understand continuous recognition. The proposed technique can be all around applied to the recognition of anthracnose injuries on apple surfaces in-plantations

Monitoring of Drainage System in Urban Using Device Free Localization Neural Networks and Cloud computing

Wireless Sensor Network is a Wi-Fi community consisting of spatially propagated and self-sufficient devices using sensors to detect physical or environmental conditions. During heavy rainfall, the urban drainage system cannot drain the water. A wireless sensor with many interconnected wireless sensor nodes captures real-time data from the network environment and transmits this data to a base station for analysis and operation. With wireless sensor nodes, it is possible to capture and monitor the amount of water in drainages and the difference in water flow between the two points in the drainage system. Nevertheless, the majority localization techniques aims on device based localization, which can find target with festinated devices. It is not suitable for applications such as terrain, drainage flow and flooding. Here device free wireless localization system using artificial neural networks and a cluster based wireless sensor network system to monitor urban drainage is proposed. There are two stages in the system. During the off-line preparation stage, Acceptable Signal Strength (RSS) differential metrics are calculated between the RSS metrics together while the monitor area is empty and calculated by a specialized in the region. Some RSS dissimilarity values ??are selected in the RSS Difference Matrix. The RSS dissimilarity standards ??and associated matrix indices are taken as the inputs of the ANN representation in addition to the identified position coordinate are in its outputs. The real-time data collected from the wireless sensor network is used to detect overflow and provide alarms before disturbances arise

Efficient Cluster Formation Protocol in WSN

WSN which is called as Wireless Sensor Networks plays vital role in many applications. Most WSNs exploit clustering method for data communication from sensor destination nodes to the sink. So, Clustering should be made as efficient as possible. In most of the existing clustering protocols, residual nodes (non-cluster nodes) may be formed during clustering. Though these nodes can send their data directly to the base station, it needs large amount of energy. In the proposed method, PSO algorithm which is termed as Particle Swarm Optimization is used for cluster configuration which evades the formation of residual nodes. The base station performs cluster formation. Network Simulator-2 (NS-2) tool is used to achieve simulation. Simulation outcomes reveal enhanced operation of the proposed protocol than existing LEACH and OEERP protocols

Centralized Cloud Service Providers in Improving Resource Allocation and Data Integrity by 4G IoT Paradigm

Due to the expansion of Internet of Things (IoT), the extensive wireless, and 4G networks, the rising demands for computing calls and data communication for the emergent EC (EC) model. By stirring the functions and services positioned in the cloud to the user proximity, EC could offer robust transmission, networking, storage, and transmission capability. The resource scheduling in EC, which is crucial to the accomplishment of EC system, has gained considerable attention. This manuscript introduces a new lighting attachment algorithm based resource scheduling scheme and data integrity (LAARSS-DI) for 4G IoT environment. In this work, we introduce the LAARSS-DI technique to proficiently handle and allot resources in the 4G IoT environment. In addition, the LAARSS-DI technique mainly relies on the standard LAA where the lightning can be caused using the overall amount of charges saved in the cloud that leads to a rise in electrical intensity. Followed by, the LAARSS-DI technique designs an objective function for the reduction of cost involved in the scheduling process, particularly for 4G IoT environment. A series of experimentation analyses is made and the outcomes are inspected under several aspects. The comparison study shown the improved performance of the LAARSS-DI technology to existing approaches

EMPLOYEE DATA MINING BASED ON TEXT AND IMAGE PROCESSING.

Employees of any company need to know whether their employees are happy or sad or they have any problem in their working environment. There should be some mechanism to handle this information about the employee. Employee chatting messages could be analyzed using sentiment analysis and employee mood detection is retrieved based on text analysis. Also, Employee facial expressions can be detected using Image Processing on employee images taken through Web Camera while an employee is chatting with colleagues. Using Image Processing, Emotion of employee such as Happy, Angry, Sad or Normal is detected. Employee analysis report is shown to company management to find whether the employee is satisfied with company or employee is facing some problem in the working environment

EMPLOYEE DATA MINING BASED ON TEXT AND IMAGE PROCESSING

Employees of any company need to know whether their employees are happy or sad or they have any problem in their working environment. There should be some mechanism to handle this information about the employee. Employee chatting messages could be analyzed using sentiment analysis and employee mood detection is retrieved based on text analysis. Also, Employee facial expressions can be detected using Image Processing on employee images taken through Web Camera while an employee is chatting with colleagues. Using Image Processing, Emotion of employee such as Happy, Angry, Sad or Normal is detected. Employee analysis report is shown to company management to find whether the employee is satisfied with company or employee is facing some problem in the working environment

Under Reporting Practices of Adverse Drug Reaction: An Observational Study Corresponding Author

ABSTRACT Objective: The present study was to estimate extend or percentage of Adverse Drug Reaction (ADR) reporting in Bhopal region at pilot level. Our main objective is not only to find out the reporting status but is to find out the possible causable reason behind the under-reporting of the suspected ADRs by the private practioners. Methods: A questionnaire based short intensive survey was conducted on the private practioners of the Bhopal region. The questionnaire consists of ten questions from which most of it was totally based and design so that we can get the much close and exact reason for the under-reporting practices. The survey was conducted by a random sample of approximate 150 private practioners of the Bhopal region. Results: The overall reporting percentage was only approximately 7% or the under-reporting percentage was approximately 93% that clearly indicates somewhat a considerable obstacle for the roadmap forecast by the CDSCO in collaboration with IPC. Conclusion: The under-reporting percentage was quite considerable (93%) so as to look after the issue to resolve or for improvement. As majority of population have their reliability and first exposure of treatment via private practices. So ADRs at this level if reported earlier in the phase of drug exposure could be better controlled as per quality concern and also its global exposure may be prevented