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

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

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

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

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

Visual Cryptography-Based Secure QR Payment System Design and Implementation

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

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

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

Analysis of Machine Learning Models for Heart Disease Prediction using Different Algorithms: A Review

Now a days the heart diseases are growing very rapidly making it an important and apprehensive task of prediction of these kinds of diseases in advance. The diagnosis is also a tough chore because it has to be performed in a precise and efficient manner. The emerging technology in modern life style integrated with internet of thing which having sensors and huge amount of data is sent to various clouds for further investigation using different algorithms to fetch out precise information for various domains. Across the world approximately 3 quintillion bytes/day information generated and this data stored for further examination. As data is in huge quantity therefore, appropriate methods applied to examine the perfect analysis so that prediction can be carried out optimally. Clinical decision making is dominant to all patient care happenings which includes choosing a deed, between replacements. These days emerging field like Machine Learning play prime role in healthcare to analyze and predict the diseases. After investigating numerous research article on Machine Learning, it was found that for same data set accuracy was different for various algorithms. In our research work different machine learning techniques will be implemented and will be tested for various parameters like accuracy, precision, recall on validated dataset. ML and Neural Networks are more capable in supporting deciding and predicting from the enormous data formed by health care systems

Smart Grid based Wireless Communication in 5G Network for Monitoring and Control Systems in Renewable Energy Management

Wireless networks are becoming ubiquitous and as the cost of equipment decreases and performance increases, it becomes both economically and technologically feasible to deploy wireless networks in power systems and industrial environments for a wide range of applications. They have advantage of providing diverse controlling features through a unified communication platform. Application of such networks in the smart grid/industrial environments is under active research and expected to become an integral part of the power system. This research propose novel technique smart grid communication in wireless 5G networks for monitoring and controlling management. Here the smart grid designing has been done based on wireless communication networks. The smart grid network for renewable energy has been controlled using Stackelberg equilibrium based SCADA (supervisory control and data acquisition) method. The control method based collected data has been monitored for detection of malicious activities in the network using supervised radial basis fuzzy systems. The experimental analysis has been carried out based on control system and network malicious activities. Here the control system based parameters analysed are Scalability of 65%, QoS of 71%, Power consumption of 41%, Network Efficiency of 92%. Then machine learning based malicious activities detection in terms of accuarcy of 96%, network security of 88%, throughput of 94%, Network delay of 41%. Proposed method supports interoperability of multiple types of inverters, is scalable and flexible, and transmits data over a secure communication channel

Design Analysis and Implementation of Stock Market Forecasting System using Improved Soft Computing Technique

In this paper, a stock market prediction model was created utilizing artificial neural networks. Many people nowadays are attempting to predict future trends in bonds, currencies, equities, and stock markets. It is quite challenging for a capitalist and an industry to forecast changes in stock market prices. Due to the numerous economic, political, and psychological aspects at play, forecasting future value changes on the stock markets is quite challenging. In addition, stock market forecasting is a difficult endeavor because it relies on a wide range of known and unknown variables. Many approaches, including technical analysis, fundamental analysis, time series analysis, and statistical analysis are used to attempt to predict the share price; however, none of these methods has been demonstrated to be a consistently effective prediction tool. Artificial neural networks (ANNs), a subfield of artificial intelligence, are one of the most modern and promising methods for resolving financial issues, such as categorizing corporate bonds and anticipating stock market indexes and bankruptcy (AI). Artificial neural networks (ANN) are a prominent technology used to forecast the future of the stock market. In order to understand financial time series, it is often essential to extract relevant information from enormous data sets using artificial neural networks. An outcome prediction neural network with three layers is trained using the back propagation method. Analysis shows that ANN outperforms every other prediction technique now available to academics in terms of stock market price predictions. It is concluded that ANN is a useful technique for predicting stock market movements globally

Research-Based on Telecommunication in Mobile Service Provider's Performance using Enhanced Naive Bayes Classifier

In recent years, mobile service providers have rapidly expanded across all countries. Considering unpredictable development trends, mobile service providers are essential to knowledge-based service businesses. Performance may be improved by creating and disseminating new information through innovation activities based on the usage of business intelligence. This research examined the performance of mobile service providers across all countries utilizing an enhanced Naive Bayes classifier based on telecommunication. In comparison to quantitative variables, the naive Bayes performs quite well. In the beginning, data is collected and the normalization technique is used for data preprocessing. Feature extraction is carried out using “Term Frequency and Inverse Document Frequency (TF-IDF)”. “Decision Tree algorithm” is used for data analysis. Then the feature is selected using a two-stage Markov blanket algorithm. Enhanced Naïve Bayes Classifier is the proposed algorithm for telecommunication analysis and at last, the performance of the system is analyzed. This proposed algorithm is used to compare the mobile service provider's performances with existing algorithms. The proposed method measures the following metrics as Throughput, Packet loss, Packet duplication, and User quality of experience. The proposed algorithm is more effective and produces better results.&nbsp

Design Simulation and Analysis of Deep Convolutional Neural Network Based Complex Image Classification System

There are 350 families and over 250,000 known varieties of flowering plants. Furthermore, effective flower classification, including content-based image recovery, is essential for the order, plant inspections of buildings, the gardening sector, live plantations, and scientific flower classification guidelines. The representation of flowers has a broad variety of uses. However, manual categorization is time-consuming and exhausting, particularly when the image basis is confusing, has a large number of images, and is perhaps erroneous for several flower groupings. Therefore, effective flower division, discovery, and categorization processes are of great significance. To ensure robust, trustworthy, and ongoing characterization during the preparation stage, new approaches are proposed in this work. On three datasets of flowers that are undeniably known, our technique is tested. Results that are better than the best in this aim for all data sets with accuracy over 98 percent. The categorization of flowers from a wide variety of animal groups is attempted in this research using a unique two-way deep learning method. In order for the foundation box to be placed around the floral area, it was first separated into sections. In a system that uses just convolutional networks, the suggested method for floral distribution is shown to be a parallel classifier. Make a powerful classification using convolutional neural networks in order to recognize the various flower types