International Journal on Future Revolution in Computer Science & Communication Engineering

Numerical Modeling and Design of Machine Learning Based Paddy Leaf Disease Detection System for Agricultural Applications

2023-02-02T09:57:41+00:00

In order to satisfy the insatiable need for ever more bountiful harvests on the global market, the majority of countries deploy cutting-edge technologies to increase agricultural output. Only the most cutting-edge technologies can ensure an appropriate pace of food production. Abiotic stress factors that can affect plants at any stage of development include insects, diseases, drought, nutrient deficiencies, and weeds. On the amount and quality of agricultural production, this has a minimal effect. Identification of plant diseases is therefore essential but challenging and complicated. Paddy leaves must thus be closely watched in order to assess their health and look for disease symptoms. The productivity and production of the post-harvest period are significantly impacted by these illnesses. To gauge the severity of plant disease in the past, only visual examination (bare eye observation) methods have been employed. The skill of the analyst doing this analysis is essential to the caliber of the outcomes. Due to the large growing area and need for ongoing human monitoring, visual crop inspection takes a long time. Therefore, a system is required to replace human inspection. In order to identify the kind and severity of plant disease, image processing techniques are used in agriculture. This dissertation goes into great length regarding the many ailments that may be detected in rice fields using image processing. Identification and classification of the four rice plant diseases bacterial blight, sheath rot, blast, and brown spot are important to enhance yield. The other communicable diseases, such as stem rot, leaf scald, red stripe, and false smut, are not discussed in this paper. Despite the increased accuracy they offer, the categorization and optimization strategies utilized in this work lead it to take longer than typical to finish. It was evident that employing SVM techniques enabled superior performance results, but at a cost of substantial effort. K-means clustering is used in this paper segmentation process, which makes figuring out the cluster size, or K-value, more challenging. This clustering method operates best when used with images that are comparable in size and brightness. However, when the images have complicated sizes and intensity values, clustering is not particularly effective.

Analysis and Design of Detection for Liver Cancer using Particle Swarm Optimization and Decision Tree

2023-03-04T10:46:51+00:00

Liver cancer is taken as a major cause of death all over the world. According to WHO (World Health Organization) every year 9.6 million peoples are died due to cancer worldwide. It is one of the eighth most leading causes of death in women and fifth in men as reported by the American Cancer Society. The number of death rate due to cancer is projected to increase by45 percent in between 2008 to 2030. The most common cancers are lung, breast, and liver, colorectal. Approximately 7, 82,000 peoples are died due to liver cancer each year. The most efficient way to decrease the death rate cause of liver cancer is to treat the diseases in the initial stage. Early treatment depends upon the early diagnosis, which depends on reliable diagnosis methods. CT imaging is one of the most common and important technique and it acts as an imaging tool for evaluating the patients with intuition of liver cancer. The diagnosis of liver cancer has historically been made manually by a skilled radiologist, who relied on their expertise and personal judgement to reach a conclusion. The main objective of this paper is to develop the automatic methods based on machine learning approach for accurate detection of liver cancer in order to help radiologists in the clinical practice. The paper primary contribution to the process of liver cancer lesion classification and automatic detection for clinical diagnosis. For the purpose of detecting liver cancer lesions, the best approaches based on PSO and DPSO have been given. With the help of the C4.5 decision tree classifier, wavelet-based statistical and morphological features were retrieved and categorised.

IOT based Security System for Auto Identifying Unlawful Activities using Biometric and Aadhar Card

2023-03-21T06:51:51+00:00

In today’s era, where thefts are consecutively increasing, especially in banks, jewelry shops, stores, ATMs, etc, there is a need to either develop a new system or to improve the existing system, due to which the security in these areas can be enhanced. However, the traditional methods (CCTV cameras, alarm buttons) to handle the security issues in these areas are still available, but they have lots of limitations and drawbacks. So, in order to handle the security issues, this paper describes how the biometric and IoT (Internet of Things) techniques can greatly improve the existing traditional security system. Our proposed system uses biometric authentication using the fingerprint and iris pattern with the strength of IoT sensors, microcontroller and UIDAI aadhar server to enhance the security model and to cut the need of keeping extra employees in monitoring the security system.

The Study and Efficacy of Conventional Machine Learning Strategies for Predicting Cardiovascular Disease

2023-03-23T07:01:39+00:00

Regarding medical science, cardiovascular disease is the main cause of death. Testing patient samples for cardiac disease can save lives and lower mortality rates. During a subsequent visit, the right remedies should be outlined and prescribed. One of the most important factors in preemptive cardiac disease diagnosis is accuracy. Based on this factor, many research approaches were examined and compared. According to the analysis of these approaches, new procedures appear to be more advanced and reliable in detecting cardiac illness. A notation of the methods and their underlying themes and precision levels will be discussed. This paper surveys many models that use these methods and methodologies and evaluates their performance. Models created utilizing supervised learning methods, such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), Decision Trees (DT), Random Forest (RF), and Logistic Regression Units, are highly valued by researchers. For benchmark datasets like the Cleveland or Kaggle, the methodologies are derived from data mining, machine learning, deep learning, and other related techniques and technologies. The accuracy of the provided methods is graphically demonstrated.

Visual Cryptography-Based Secure QR Payment System Design and Implementation

2023-03-23T07:02:14+00:00

It is important to validate the Merchant and the Client to increase confidence in online transactions. At present, only the Client is checked against the merchant server. The research in this paper will show you how to create and launch a QR code-based payment system that is both secure and convenient for users. As a result of their capacity to facilitate instantaneous transactions and offer unparalleled ease of use, QR codes have seen explosive growth in the past few years. QR-based online payment systems are easy to use but susceptible to various assaults. So, for the level of security given by transaction processing to hold, the secrecy and integrity of each payment procedure must be guaranteed. In addition, the online payment system must verify each transaction from both the sender's and the recipient's perspectives. The study's QR-based method is kept safe through visual cryptography. The suggested approach takes advantage of visual cryptography via a web-based application.