European Journal of Molecular & Clinical Medicine

One of the biggest revolutions of modern history is the invention of agriculture for a healthier lifestyle. It significantly changed the human culture and played an important role in the development of the population and biological improvements in food production and domestication. Study into agriculture is then planned by improving the disease diagnostics method with the use of newer information technology to enhance efficiency and quantity for agricultural production and its allied operation. This project focuses on the identification and diagnosis of plant leaf diseases of tomatoes and pomegranate based on visual symptoms, anthracnose, and powdery mildew. Machine learning and image processing using SVM, KNN require many steps to identify and distinguish disease signs

Almost all over the world, the economy mainly depends on the production of food.
Computer vision technology plays a pivotal role in the field of agriculture. The dream of this
research is to produce successful crops in the agricultural sector. Successful farming can
increase crop production in terms of both quality and quantity. The farming performs eight
major phases which begin from crop selection to harvesting. At any of these phases, the
disease and pest may destroy plants. However, the leaves are found to be the most damaged
part in disease identification. A lot of articles are taken out for the survey that endorses the
mechanism of image processing and deep learning for the detection and classification of
diseases from the crop leaves. This survey provides an overview of the pros and cons of all
such articles on various research aspects. The effectiveness of state-of-the-art methods is
explored to identify the techniques that seem to work well across different crops. This paper
indicates that algorithms like Support Vector Machine and Neural Network play an important
role in the crop disease identification and classification.

Feature Extraction assumes a significant function in the region of picture handling. Before getting Features, different picture pre-processing strategies like binarization, thresholding, resizing, standardization and so forth are applied on the inspected picture. From that point onward, Feature extraction methods are applied to get Features that will be helpful in arranging and acknowledgment of pictures. Feature extraction strategies are useful in different picture handling applications for example character acknowledgment. As Features characterize the conduct of a picture, they show its place regarding capacity taken, proficiency in arrangement and clearly in time utilization moreover. Here in this paper, we will talk about different kinds of Features, include extraction procedures and clarifying in what situation, which Features extraction method, will be better. Thus, in this paper, we will mention Features and Feature extraction strategies if there should be an occurrence of character identification application.

Approximate computer arithmetic circuits based on CMOS technology have been extensively studied. Designs of approximate adders, multipliers and dividers for both fixed point and floating-point formats have been proposed. As a new paradigm in the nano scale technologies, approximate computing enables error tolerance in the computational process, it has also emerged as a low power design methodology for arithmetic circuits. Majority logic (ML) is applicable to many emerging technologies and its basic building block (the 3-input majority voter) has been extensively used in digital circuit design. In this project, we propose the design of a one bit approximate full adder based on majority logic.Furthermore, multi-bit approximate full adders are also proposed and studied, the application of these designs to quantum-dot cellular automata (QCA) is also presented as an example. The designs are evaluated using hardware metrics (including delay and area) as well as error metrics. Compared with other circuits found in the technical literature, the optimal designs are found to offer superior performance. Approximate half adder and full adder is designed. These Half adder and Full adder combinations are used to implement the Brent Kung Adder and multiplier. In the present work fast adders like RCA adders using compressor methodology and multiplication operations are performed by utilizing Majority gates. This paper also proposes the Wallace tree multiplier using proposed majority based Full adder .The designed multiplier is effective and efficient in terms of area-delay trade off, delay(speed) and power utilization.Project will be developed using verilog HDL. Xilinx ISE tool is used to perform the Simulation and Synthesis.

The traditional system for detecting the infection has been the manual process of diagnosing the stained slides under a microscope. This manual process might consume more time for producing the results and the availability of medical experts is not always assured. Considering this as the primary concern we proposed a strategy which limits the human error while recognizing the presence of malarial parasite in the blood sample by using Image Processing. Hence by automating the diagnosis process, results can be acquired relatively quicker and more accuracy can be expected. The technologies and techniques to patently extract the required features and efficiently classify the infected samples are surveyed. This paper presents a survey of various approaches to automate the detection and classification of infected and uninfected cells.

Crops suffering from various diseases can be a big turndown for crop yield. This can affect effective crop production, if left unnoticed. Hence, it is extremely important to examine the plant diseases in its initial stages so that felicitous actions can be taken by the farmers at the nick of time, to avoid further losses. It focuses on the method which is based on image processing way for identification of diseases of leaf in a plant .so let’s introduce a system which uses convolutional neural networks that helps farmers to identify any possible plant disease by loading a leaf image in to the system. The system consists of a collection of algorithms which identifies the type of disease with which the leaf is affected by a disease. Input image given by the user goes through many pre-processing steps to identify the disease and results are returned back to the user on a user interface.

Due to the advancement of computer technology image-processing techniques have become increasingly important in a wide variety of applications. Image segmentation plays a important role in image processing. Image segmentation refers to partition of an image into different regions that are similar and different in some characteristics like color, intensity or texture. Different algorithms and techniques have been developed for image segmentation. This paper investigates and compiles some of the technologies used for image segmentation. The various segmentation techniques like Edge Detection, Threshold, Region based, Feature Based Clustering and Neural Network Image Segmentation were discussed in this paper

With increase in population the need for food is on rise, in such circumstances, plant diseases prove to be a major threat to agricultural produce and result in disastrous consequences for farmers. Early detection of plant disease can help in ensuring food security and controlling financial losses. The images of diseased plants can be used to identify the diseases. Classification abilities of Convolutional Neural Networks are used to obtain reliable output. Google’s pretrained model ‘Inception v3’ is used. The Inception v3 model is trained over a dataset of diseased plants obtained from ‘Plant Village Dataset’. The developed detection approach is evaluated on measures of F1 score, precision and recall.

Breast Cancer is one of the life threatening disease among females all over the world. This killer disease however when it can be detected in its early stages can be a life saver for many. Radiologists uses the mammography images to detect the presence and absence of Breast Cancer. The field of Bio-informatics leverages the Machine learning techniques for diagnosis of Breast cancer in particular. This research work experiments with the two most popularly used Supervised Machine Learning Algorithms, K-Nearest Neighbour and Naive Bayes. This work predicts Breast Cancer on the The Breast Cancer Data Set (BCD) taken from the UCI Machine Learning Repository. A comparative analysis between the two approaches are made in terms of its performance metrics using CV techniques. The proposed work has achieved a best accuracy of 97.15% by employing the KNN algorithm and a lowest error rate of 96.19% using NB classifier.

In the paper, a system is proposed which is used to identify the old and soiled Indian paper currency notes. When the new currency notes are introduced and put into circulation, they get passed from person to person. As the time passes by, these notes gets soiled, dirtier, and also get wrinkled. Identification and recognition of such notes inside the automated teller machines (ATMs) becomes difficult. Thus in the paper, a system is developed to handle such soiled, old and dirty Indian paper currency notes. The system works on three denominations of Indian paper currency which are 50, 200 and 500 Indian paper currency.

Produces from agriculture which feeds the entire population is dependent on proper farming practices. The growth of technology must pay a way for increasing the produce per acre and also help in reducing the onset of frequently affecting plant disease. Timely help in detecting the diseases coupled with solution helps in productivity and quality of the produce. This paper aims to detect the plant leaf disease based on image detection and using machine learning to identify the disease with accuracy and suggest the solution. The product must cater to the needs of urban and rural farmer and also the person with only lay man knowledge of taking photo. This project mainly focuses on leaf disease like Anthracnose, Bacterial Blight, Cercospora, Alternaria Altermata diseases in the Pomegranate, Indian Beech, Tobacco, and Bitter Gourd leaves. This project aims to identify the disease even with lesser region of Interest and predict the leaf diseases using Convolutional Neural Network Algorithm

Object detection plays an important role in real time applications. It is used in many applications such as surveillance monitoring, human machine interaction, army base etc. The main aim of this paper is to detect the object and to detect the colour of the object using Image processing technique. Pi camera. Raspberry pi 11 kit interfaced with pi camera is used for detection of object. Raspbian os with python coding is used for object detection and colour recognition.

Documents of the ancient time gift several opposition for standalone gesture recognition systems, among them, the division and classification steps. Fastidiously gloss wordings square measure is required to coach a system. In some eventualities, written document square measure solely offered at the subdivision level. During this activity, we have a tendency to demonstrate the way to train the system with few tagged information. We have a tendency to additionally propose a model-based social control theme that considers the variability within the writing scale at the popularity section. We have a tendency to apply this approach to the publically offered browse dataset. Our system achieved the competitor result. Humans have distinctive handwriting designs that prove to be an obstacle for handwriting recognition algorithms. To date, multiple researches are done to acknowledge these totally different handwriting designs, most notable mistreatment the synthetic neural network (ANN) with back propagation algorithms that has additionally been verified to relinquish adequately high accuracies. By mistreatment real time method image capturing, this technique and algorithmic rule will be enforced to use multiple written entry information for faculties and universities, wherever the written information of a regular score sheet from totally different people will be transferred to a computer program.