https://i-scholar.in/index.php/CiiTDIP/issue/feedDigital Image Processing2022-10-31T04:55:53+00:00Dr. V. Sivarajeditor@ciitresearch.orgOpen Journal Systems<p> The Aim of Journal on Digital Image Processing is a forum for presenting new advances and research results in the fields of Digital Image Processing. The scope of the journal covers all theoretical and practical aspects of the Digital Image Processing, from basic research to development of application.</p><p> The scope of the journal includes developing technologies in Image generation and display, Enhancement and restoration, Segmentation, Face Recognition & Super-resolution imaging Color and texture analysis Object Detection, Recognition and Categorization Medical Image Analysis ,Motion and Tracking ,Stereo and Structure from Motion Image restoration, feature extraction Bioluminescence, Biomedical optics and cancer detection, Biometric imaging, Blurred and noisy image processing Detectors and Image Formation Digital transforms Document image understanding Edge detection, image analysis and classification Emerging Detection and Imaging Technologies Noise estimation and filtering.</p>https://i-scholar.in/index.php/CiiTDIP/article/view/215972Identification of Medicinal Plants using Visual Characteristics of Leaves and Flowers2022-10-31T04:55:53+00:00N. AkashV. KaushikY. B. PrajwalR. PranavV. C. Rudramurthyrudramurthy@gat.ac.inThe proposed framework helps in ID of plant sickness and gives cures that can be utilized as a safeguard component against the illness. The information base got from the Internet is appropriately isolated and the distinctive plant species are recognized and are renamed to frame a legitimate data set then, at that point get test-data set which comprises of different plant infections that are utilized for checking the exactness and certainty level of the undertaking. Then, at that point utilizing preparing information, the classifier is prepared and afterward yield will be anticipated with ideal exactness. The proposed system comes under Machine learning domain. Machine Learning centers around the improvement of programs that can get to information and use it to find out on their own. Machine Learning has various applications and has been used to tackle real world problems in an efficient manner. It has applications in medicine, communication, entertainment, military and so on. Convolutional Neural Network (CNN) which comprises of different has been used for classification and prediction. The problem with existing systems is that they are limited to a few numbers of plant species or due to use of inefficient algorithms have not been able to achieve the desired levels of accuracy. With the proposed system and training model an accuracy level of 78% was achieved. The proposed framework gives the name of the plant species with its certainty level and the cure that can be taken as fix.2021-05-01T00:00:00+00:00https://i-scholar.in/index.php/CiiTDIP/article/view/215975Knaster–Tarski Fixed Point Theorem in Generalized Two Mappings on Metric Spaces2022-10-31T04:55:53+00:00A. Shaas AhmedThe theorem has applications in abstract interpretation, a form of static program analysis. A common theme in lambda calculus is to find fixed points of given lambda expressions. Every lambda expression has a fixed point, and a fixed-point combinator is a "function" which takes as input a lambda expression and produces as output a fixed point of that expression. An important fixed-point combinator is the Y combinator used to give recursive definitions. In denotational semantics of programming languages, a special case of the Knaster–Tarski theorem is used to establish the semantics of recursive definitions. While the fixed-point theorem is applied to the "same" function (from a logical point of view), the development of the theory is quite different. The same definition of recursive function can be given, in computability theory, by applying Kleene's recursion theorem. These results are not equivalent theorems; the Knaster–Tarski theorem is a much stronger result than what is used in denotational semantics. However, in light of the Church–Turing thesis their intuitive meaning is the same: a recursive function can be described as the least fixed point of a certain functional, mapping functions to functions. The above technique of iterating a function to find a fixed point can also be used in set theory; the fixed-point lemma for normal functions states that any continuous strictly increasing function from ordinals to ordinals has one (and indeed many) fixed points.2021-05-01T00:00:00+00:00https://i-scholar.in/index.php/CiiTDIP/article/view/215977A Guide to New Generalized p - k Mittag-Leffler Function in Fractional Calculus2022-10-31T04:55:53+00:00K. Elakiya ShreeN. AbinayaOne of the applications of the Mittag-Leffler function is in modeling fractional order viscoelastic materials. Experimental investigations into the time-dependent relaxation behavior of viscoelastic materials are characterized by a very fast decrease of the stress at the beginning of the relaxation process and an extremely slow decay for large times. It can even take a long time before a constant asymptotic value is reached. Therefore, a lot of Maxwell elements are required to describe relaxation behavior with sufficient accuracy. This ends in a difficult optimization problem in order to identify a large number of material parameters. On the other hand, over the years, the concept of fractional derivatives has been introduced to the theory of viscoelasticity. Among these models, the fractional Zener model was found to be very effective to predict the dynamic nature of rubber-like materials with only a small number of material parameters. The solution of the corresponding constitutive equation leads to a relaxation function of the Mittag-Leffler type. It is defined by the power series with negative arguments. This function represents all essential properties of the relaxation process under the influence of an arbitrary and continuous signal with a jump at the origin.2021-05-01T00:00:00+00:00https://i-scholar.in/index.php/CiiTDIP/article/view/215978Prediction of Malignant Cell using the Machine Learning Algorithms2022-10-31T04:55:53+00:00N. AkilaB. ManoharanMachine Learning (ML) is one of the core branches of Artificial Intelligence. It’s a system which takes in data, finds patterns, trains itself using the data and outputs an outcome. So what makes a machine better than a trained professional? ML has key advantages over Pathologists. Firstly, machines can work much faster than humans. A biopsy usually takes a Pathologist 10 days. A computer can do thousands of biopsies in a matter of seconds. Machines can do something which humans aren’t that good at. They can repeat themselves thousands of times without getting exhausted. After every iteration, the machine repeats the process to do it better. Humans do it too, we call it practice. While practice may make perfect, no amount of practice can put a human even close to the computational speed of a computer. Another advantage is the great accuracy of machines. With the advent of the Internet of Things technology, there is so much data out in the world that humans can’t possibly go through it all. That’s where machines help us. They can do work faster than us and make accurate computations and find patterns in data. That’s why they’re called computers.2021-05-01T00:00:00+00:00