scholarly journals Review on Image Recoloring Methods for Efficient Naturalness by Coloring Data Modeling Methods for Low Visual Deficiency

2021 ◽  
Vol 3 (3) ◽  
pp. 169-183
Author(s):  
A. Pasumpon Pandian
Keyword(s):  
Performance Measures ◽  
Research Study ◽  
Similarity Index ◽  
Learning Method ◽  
Modeling Methods ◽  
Learning Techniques ◽  
Feature Similarity ◽  
Colour Distribution ◽  
Feature Similarity Index ◽  
New Applications

Recent research has discovered new applications for object tracking and identification by simulating the colour distribution of a homogeneous region. The colour distribution of an object is resilient when it is subjected to partial occlusion, scaling, and distortion. When rotated in depth, it may remain relatively stable in other applications. The challenging task in image recoloring is the identification of the dichromatic color appearance, which is remaining as a significant requirement in many recoloring imaging sectors. This research study provides three different vision descriptions for image recoloring methods, each with its own unique twist. The descriptions of protanopia, deuteranopia, and tritanopia may be incorporated and evaluated using parametric, machine learning, and reinforcement learning techniques, among others. Through the use of different image recoloring techniques, it has been shown that the supervised learning method outperforms other conventional methods based on performance measures such as naturalness index and feature similarity index (FSIM).

An Adaptive Curvelet Based Semi-Fragile Watermarking Scheme for Effective and Intelligent Tampering Classification and Recovery of Digital Images

2018 ◽  
Vol 5 (2) ◽  
pp. 69-94
Author(s):  
K R Chetan ◽  
S Nirmala
Keyword(s):  
Digital Images ◽  
Similarity Index ◽  
Curvelet Transform ◽  
Input Image ◽  
Tamper Detection ◽  
Fragile Watermarking ◽  
Feature Similarity ◽  
Feature Similarity Index ◽  
Index Value ◽  
Watermarking Scheme

A novel adaptive semi-fragile watermarking scheme for tamper detection and recovery of digital images is proposed in this paper. This scheme involves embedding of content and chroma watermarks generated from the first level Discrete Curvelet Transform (DCLT) coarse coefficients. Embedding is performed by quantizing the first level coarse DCLT coefficients of the input image and amount of quantization is intelligently decided based on the energy contribution of the coefficients. During watermark extraction, a tampered matrix is generated by comparing the feature similarity index value between each block of extracted and generated watermarks. The tampered objects are subsequently identified and an intelligent report is formed based on their severity classes. The recovery of the tampered objects is performed using the generated DCLT coefficients from luminance and chrominance components of the watermarked image. Results reveal that the proposed method outperforms existing method in terms of tamper detection and recovery of digital images.

scholarly journals FSITM: A Feature Similarity Index For Tone-Mapped Images

2015 ◽  
Vol 22 (8) ◽  
pp. 1026-1029 ◽  
Author(s):  
Hossein Ziaei Nafchi ◽  
Atena Shahkolaei ◽  
Reza Farrahi Moghaddam ◽  
Mohamed Cheriet
Keyword(s):  
Similarity Index ◽  
Feature Similarity ◽  
Feature Similarity Index

scholarly journals PENINGKATAN KETERAMPILAN SOSIAL SISWA DALAM PEMBELAJARAN IPS MELALUI OUTDOOR ACTIVITY DI SMP NEGERI 1 KALIGONDANG KABUPATEN PURBALINGGA

2015 ◽  
Vol 2 (1) ◽  
pp. 1-11
Author(s):  
Amtorunajah Amtorunajah ◽  
Muhsinatun Siasah Masruri
Keyword(s):  
Social Skills ◽  
Social Studies ◽  
Junior High School ◽  
Social Skill ◽  
Research Study ◽  
Learning Method ◽  
Outdoor Activity ◽  
The Social ◽  
Before And After ◽  
The Mean

Penelitian ini bertujuan untuk meningkatkan keterampilan sosial siswa dalam pembelajaran IPS melalui outdoor activity sebagai metode pembelajaran. Penelitian ini merupakan penelitian tindakan kelas dengan subjek penelitian siswa kelas VIIA SMP Negeri 1 Kaligondang Kabupaten Purbalingga. Penelitian ini terfokus pada peningkatan keterampilan sosial siswa. Data peningkatan keterampilan sosial diperoleh melalui pengamatan, wawancara, dokumentasi, yang selanjutnya dianalisis untuk dibandingkan: (1) hasil pengamatan sebelum tindakan dan sesudah tindakan, dan (2) hasil pengamatan keterampilan sosial tiap siklus yang didukung oleh tanggapan berbagai pihak. Sebelum dilakukan tindakan, peneliti melakukan pengamatan keterampilan sosial dengan hasil rerata 2,79. Pada penerapan outdoor activitiy siklus I diperoleh skor rerata sebesar 3,12 (kategori cukup). Pada penerapan outdoor activity siklus II diperoleh skor rerata sebesar 3,69 (kategori baik). Pada penerapan outdoor activity siklus III mengalami peningkatan dengan skor rerata 4,16 (kategori baik). Berdasarkan hasil pengamatan keterampilan sosial siswa pada prasiklus, siklus I, siklus II, dan siklus III, dapat disimpulkan bahwa penerapan metode outdoor activity dalam pembelajaran IPS dapat meningkatkan keterampilan sosial pada siswa. Dengan demikian metode pembelajaran dengan outdoor activity layak diterapkan dalam pembelajaran IPS sebagai salah satu kegiatan pembelajaran, khususnya untuk meningkatkan keterampilan sosial siswa. Kata kunci: keterampilan sosial, outdoor activity, pembelajaran IPS SMP______________________________________________________________ IMPROVING STUDENTS’ SOCIAL SKILLS IN SOCIAL STUDIES LEARNING THROUGH OUTDOOR ACTIVITY IN SMP NEGERI 1 KALIGONDANG PURBLINGGA REGENCY Abstract This study aims to improve the social skills of students in social studies learning through outdoor activity as a learning method. This was a classrooms action research study involving grade VIIA students of SMP Negeri 1 Kaligondang Purbalingga regency. This study focused on improving the social skill. The data of improvement of social skills were collected through observation, interviews, documentation, which is then analyzed for comparison: (1) the result of observations before and after the actions, and (2) the result of observation of social skills of each cycle supported by responses from different  parties. Before the actions, the researchers conducted observations of social skills and the mean score was 2.79 (in the moderate category). Through the application of the outdoor activitiy in cycle I obtained mean score of 3.12 (in the moderate category). Through the application of the outdoor activity in cycle II obtained mean score of 3.69 (in the good category). Through the application of outdoor activity in cycle III, the student’ social skill improved, with a mean score of 4.16 (in the good category). Based on the result of the observations of students' social skills on precycle, cycle I, cycle II, and  cycle III, it can be concluded that the application of the learning method of outdoor activity in Social Studies can improve social skills in students. Thus the method of learning with outdoor activity feasible in learning social studies as one of the learning activities, in particular to improve the students’ social skills. Keywords: social skills, outdoor activity, social studies learning at junior high school.

Intelligent ubiquitous computing model for energy optimization of cloud IOTs in sensor networks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Deepa S.N.
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Energy Level ◽  
Computational Model ◽  
Ubiquitous Computing ◽  
Sensor Network ◽  
Research Study ◽  
Content Type ◽  
Computing Model ◽  
Learning Techniques

Purpose Limitations encountered with the models developed in the previous studies had occurrences of global minima; due to which this study developed a new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization. Ubiquitous machine learning computational model process performs training in a better way than regular supervised learning or unsupervised learning computational models with deep learning techniques, resulting in better learning and optimization for the considered problem domain of cloud-based internet-of-things (IOTs). This study aims to improve the network quality and improve the data accuracy rate during the network transmission process using the developed ubiquitous deep learning computational model. Design/methodology/approach In this research study, a novel intelligent ubiquitous machine learning computational model is designed and modelled to maintain the optimal energy level of cloud IOTs in sensor network domains. A new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization is developed. A new unified deterministic sine-cosine algorithm has been developed in this study for parameter optimization of weight factors in the ubiquitous machine learning model. Findings The newly developed ubiquitous model is used for finding network energy and performing its optimization in the considered sensor network model. At the time of progressive simulation, residual energy, network overhead, end-to-end delay, network lifetime and a number of live nodes are evaluated. It is elucidated from the results attained, that the ubiquitous deep learning model resulted in better metrics based on its appropriate cluster selection and minimized route selection mechanism. Research limitations/implications In this research study, a novel ubiquitous computing model derived from a new optimization algorithm called a unified deterministic sine-cosine algorithm and deep learning technique was derived and applied for maintaining the optimal energy level of cloud IOTs in sensor networks. The deterministic levy flight concept is applied for developing the new optimization technique and this tends to determine the parametric weight values for the deep learning model. The ubiquitous deep learning model is designed with auto-encoders and decoders and their corresponding layers weights are determined for optimal values with the optimization algorithm. The modelled ubiquitous deep learning approach was applied in this study to determine the network energy consumption rate and thereby optimize the energy level by increasing the lifetime of the sensor network model considered. For all the considered network metrics, the ubiquitous computing model has proved to be effective and versatile than previous approaches from early research studies. Practical implications The developed ubiquitous computing model with deep learning techniques can be applied for any type of cloud-assisted IOTs in respect of wireless sensor networks, ad hoc networks, radio access technology networks, heterogeneous networks, etc. Practically, the developed model facilitates computing the optimal energy level of the cloud IOTs for any considered network models and this helps in maintaining a better network lifetime and reducing the end-to-end delay of the networks. Social implications The social implication of the proposed research study is that it helps in reducing energy consumption and increases the network lifetime of the cloud IOT based sensor network models. This approach helps the people in large to have a better transmission rate with minimized energy consumption and also reduces the delay in transmission. Originality/value In this research study, the network optimization of cloud-assisted IOTs of sensor network models is modelled and analysed using machine learning models as a kind of ubiquitous computing system. Ubiquitous computing models with machine learning techniques develop intelligent systems and enhances the users to make better and faster decisions. In the communication domain, the use of predictive and optimization models created with machine learning accelerates new ways to determine solutions to problems. Considering the importance of learning techniques, the ubiquitous computing model is designed based on a deep learning strategy and the learning mechanism adapts itself to attain a better network optimization model.

Sign in / Sign up

Export Citation Format

Share Document