March 2020 - Journal of Artificial Intelligence and Capsule Networks
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63
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Published By Inventive Research Organization

2582-2012
Updated Monday, 18 October 2021

Author(s):  
C. Vijesh Joe ◽  
Jennifer S. Raj

Cloud applications that work on medical data using blockchain is used by managers and doctors in order to get the image data that is shared between various healthcare institutions. To ensure workability and privacy of the image data, it is important to verify the authenticity of the data, retrieve cypher data and encrypt plain image data. An effective methodology to encrypt the data is the use of a public key authenticated encryption methodology which ensures workability and privacy of the data. But, there are a number of such methodologies available that have been formulated previously. However, the drawback with those methodologies is their inadequacy in protecting the privacy of the data. In order to overcome these disadvantages, we propose a searchable encryption algorithm that can be used for sharing blockchain- based medical image data. This methodology provides traceability, unforgettable and non-tampered image data using blockhain technology, overcoming the drawbacks of blockchain such as computing power and storage. The proposed work will also sustain keyword guessing attacks apart from verification of authenticity and privacy protection of the image data. Taking these factors into consideration, it is determine that there is much work involved in providing stronger security and protecting privacy of data senders. The proposed methodology also meets the requirement of indistinguishability of trapdoor and ciphertext. The highlights of the proposed work are its capability in improving the performance of the system in terms of security and privacy protection.


Author(s):  
A Sathesh ◽  
Edriss Eisa Babikir Adam

Image thinning is the most essential pre-processing technique that plays major role in image processing applications such as image analysis and pattern recognition. It is a process that reduces a thick binary image into thin skeleton. In the present paper we have used hybrid parallel thinning algorithm to obtain the skeleton of the binary image. The result skeleton contains one pixel width which preserves the topological properties and retains the connectivity.


Author(s):  
B. Vivekanandam

As cyber physical systems (CPS) has progressed, there are many applications which use CPS to connect with the physical world. Moreover the use of cloud in CPS revolutionizes the way in which information is stored and computed making it applicable to a wide range of applications. On the other hand, it also has questionable concerns over the energy consumed applications due to their explosive expansion. Hence in order to increase the efficiency of energy utilisation in the cloud environment, applications are hosted by virtual machines while resources are managed using virtualized Technology. However Quality of Service remains a challenge that is yet to be properly addressed. Hence a virtual machine scheduling algorithm which is aware of us is used to save energy in the designed CPS. The first step in a proposed work is to formulate the objective of the work. This is followed by using a genetic sorting algorithm to identify the apt Virtual Machine (VM) VM mitigation solution. MCDM (Multiple Criteria Decision Making) and SAW (Simple Additive Weighting) can also be used to pick the app scheduling strategy. Experimental and simulation results are observed and recorded based on which concrete conclusions are drawn.


Author(s):  
Kottilingam Kottursamy

Recently, the identification and naming of fish species in underwater imagery processing has been in high demand. This is an essential activity for everyone, from biologists to scientists to fisherman. Humans' interests have recently expanded from the earth to the sky and the sea. Robots could be utilized to send mankind to explore the ocean and outer space, as well as for some dangerous professions that human beings are unlikely to perform. Humans have recently shifted their focus from land-based exploration to celestial exploration and the sea. Robots are used for the activities that pose a risk to mankind, like exploration of the seas and outer space. This research article provides a solution to underwater image detection techniques by using an appended transmission map, refinement method and deep learning approach. The features are deeply extracted by multi-scale CNN for attaining higher accuracy in detecting fish features from the input images with the help of segmentation process. Object recognition errors are minimized and it has been compared with other traditional processes. The overall performance metrics graph has been plotted for the proposed algorithm in the results and discussion section.


Author(s):  
Shajulin Benedict ◽  
Deepumon Saji ◽  
Rajesh P. Sukumaran ◽  
Bhagyalakshmi M

The biggest realization of the Machine Learning (ML) in societal applications, including air quality prediction, has been the inclusion of novel learning techniques with the focus on solving privacy and scalability issues which capture the inventiveness of tens of thousands of data scientists. Transferring learning models across multi-regions or locations has been a considerable challenge as sufficient technologies were not adopted in the recent past. This paper proposes a Blockchain- enabled Federated Learning Air Quality Prediction (BFL-AQP) framework on Kubernetes cluster which transfers the learning model parameters of ML algorithms across distributed cluster nodes and predicts the air quality parameters of different locations. Experiments were carried out to explore the frame- work and transfer learning models of air quality prediction parameters. Besides, the performance aspects of increasing the Kubernetes cluster nodes of blockchains in the federated learning environment were studied; the time taken to establish seven blockchain organizations on top of the Kubernetes cluster while investigating into the federated learning algorithms namely Federated Random Forests (FRF) and Federated Linear Regression (FLR) for air quality predictions, were revealed in the paper.


Author(s):  
Akey Sungheetha

Due to unfavorable weather circumstances, images captured from multiple sensors have limited the contrast and visibility. Many applications, such as web camera surveillance in public locations are used to identify object categorization and capture a vehicle's licence plate in order to detect reckless driving. The traditional methods can improve the image quality by incorporating luminance, minimizing distortion, and removing unwanted visual effects from the given images. Dehazing is a vital step in the image defogging process of many real-time applications. This research article focuses on the prediction of transmission maps in the process of image defogging through the combination of dark channel prior (DCP), transmission map with refinement, and atmospheric light estimation process. This framework has succeeded in the prior segmentation process for obtaining a better visualization. This prediction of transmission maps can be improved through the statistical process of obtaining higher accuracy for the proposed model. This improvement can be achieved by incorporating the proposed framework with an atmospheric light estimation algorithm. Finally, the experimental results show that the proposed deep learning model is achieving a superior performance when compared to other traditional algorithms.


Author(s):  
A. Pasumpon Pandian

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).


Author(s):  
Milan Tripathi

The government's months-long total lockdown in response to the COVID19 outbreak has resulted in a lack of physical connection with others. This resulted in a massive increase in social media communication. Twitter has become one of the most popular places for people to communicate their thoughts and opinions. As a result, massive amounts of data are created every day. These data can assist businesses in making better judgments. In the case of Nepal, there has been relatively little investigation into the text's analysis. Because few researchers are working in the field, development is slow. In this study, Four language-based models for sentiment analysis of Nepali covid19 tweets are designed and evaluated. Because the number of individuals using social media is expected to skyrocket in the next few days, companies will benefit from an AI-based sentiment analysis system. It will greatly assist firms in adapting to the changing climate.


Author(s):  
Prachu J. Patil ◽  
Ritika V. Zalke ◽  
Kalyani R. Tumasare ◽  
Bhavana A. Shiwankar ◽  
Shivani R. Singh ◽  
...  

One of the many challenges that the world faces is traffic hazard. The major cause of this traffic risk is the presence of a huge number of vehicles on the road. As a result, it generates the most challenging issues, leading to an increase in the death toll due to road accidents that occur throughout the world. As a result, it necessitates the need to provide adequate transportation facilities, which will reduce the number of collisions and save human lives. The GPS, GSM, accelerometer, Arduino UNO technology, and vibration sensor are used to design and develop a vehicle accident detection model. The proposed approach is classified into three stages to prevent and detect the vehicular accidents. At the detection stage, a vibration sensor will be utilized to determine the position of the accident and to alert the user by sending SMS via the GSM module, which will include the user's data stored in Android applications. This data will be taken from the GPS module. The second phase occurs when moderate accidents occur and in such situation, the location will be detected by using a GPS module. After that, the nearby hospital receives a message about the accidents and accordingly they provide services to the accidents. At the same time, after detecting the location, a patient receives a message from the hospital urging them to take precautions. .


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