Empowering Cybersecurity: A Deep Dive into AI-Driven Security Intelligence Modelling
Smart Incubator for Empowering Poultry Farmers by Maximizing Egg Hatching Success
Early Disease Detection of Black Gram Plant Leaf using Cloud Computing Based YOLO V8 Model
Data Retrieval in Cancer Documents using Various Weighting Schemes
Securing Healthcare Data in Smart Cities: A Machine Learning Approach
The new generation of personal authentication technologies based on individual biological characteristics is the core of various applications of the real or the virtual society. The volume of the medical data is increasing due to the presence of vast amount of features, the conventional rule mining technique is not competent to handle the data and to perform precise diagnosis. For instance, this paper intends to implement the improved rule mining technique to overcome limitations. The proposed method has two main contributing stages. The first stage is the robust feature extraction process using the improved Multi-Linear Principal Component Analysis (PCA), whereas the second stage is the classification process using Support Vector Machine. Principal Components Analysis (PCA) is the most commonly-used dimensionality reduction technique employed for the feature extraction of neural spikes.
If the network is able to work, of course with degraded efficiency, in the presence of faults in critical components then the network is called as Fault-Tolerant. A network is single Fault-Tolerant, if it can work with full access in the presence of fault in single SE. If the network is able to provide connections from all sources to all destinations in the presence of k faults in the network, then this network is called as k Fault-Tolerant network. In this paper, routes available in the proposed MIN Modified Alpha Network (MALN) have been evaluated and compared with the existing Alpha (ALN) MIN.
Mining data from databases have seen many upgrades since few decades considering the development of huge data repositories with the advent of internet revolution worldwide. Inherently, the importance of search algorithms to mine data has gained prominence over a period of time. Many programming languages have been used to retrieve relevant information from databases. In this paper, the author presents the E-Literature database created in MySQL with all possible entries, such as ISSN, Publisher name, publication type, etc. Authors from different geographical regions can also be searched from the database. The search algorithm code implemented in the work is used to search the database with varied options, such as 'Abstract', 'keywords', 'affiliation', 'country', 'ISSN', etc. Each search option and the relevant code were written in PHP. Binary search algorithm has been implemented in the work to perform search routine. Apart from general search option, a robust search method which combines various search combinations called 'combination search' can be used to efficiently mine data.
This paper presents the role of neuro-fuzzy logic in real world problems. An Artificial Neural network will process the data set using different algorithms like Perceptron, Adaline, and back propagation algorithms such that the weight should be trained for better performance. To do this, different types of activation functions are required to meet the optimum result of the data set. Due to adaptable characteristic of neural network; it can adjust their weights for new data set predictors through learning phase. With respect from fuzzy logic, which produces uncertain values that can be mapped from zero to one along with all the intermediate values that lies in-between them through fuzzy sets. So, neural network can work on maximum implementation with learning and training while fuzzy logic deals with imprecise information. But Neural Networks when combined with Fuzzy Logic generate intelligent systems that can resolve a number of complex problems in no time with less mean error. We can apply fuzzy rules with neural data set that can integrate to resolve specific problems. The concept of Adaptive Neuro Fuzzy Inference System was introduced under the concept of adaptability of bias and weight values of the neuron with the processing power of fuzzy inference system. Various types of real world problems and new directions might be achieved for neuro-fuzzy with multiple applications. The goal of this paper is to explain the role of neuro-fuzzy systems; and to implement one of the sample instances of weather prediction by using WEKA Tool. This tool clearly represents that multilayer perceptron algorithm that is common in neural networks when related with fuzzy logic would produce better results as prescribed in data set.
For the past few years, there is an increasing demand for reliable data transmission and storage media. During transmission of data, noisy channels often introduce errors in received information bits. Froward error correction, FEC is one of the methods used to enhance reliability of data transmission. The basic idea of FEC is to systematically add redundancy at the end of the messages so as to enable the correct retrieval of messages despite errors in the received sequences. This eliminates the need for retransmission. Reed Solomon codes are a type of algebraic Forward Error Correction code which is found to be an optimal code for maintaining data integrity during wireless transmission. Modulation techniques used in Wireless communication applications must be robust and at the same time should be bandwidth efficient. Thus, there is a need for the analysis of coding schemes to evaluate the optimal code for use in wireless communication applications. From the analysis of linear block codes, Reed Solomon code with n=255 and k=223 is observed to offer optimal performance. Detailed analysis of this code is done to ensure its use in Deep Space and Mobile communication applications.
According to WHO According to World Health Organization (WHO) survey, anemia is one of the most commonly encountered medical deficiencies during pregnancy [5]. BP network has been successfully used for anemia diagnosing in pregnant ladies, however BP network's drawbacks, such as long execution time and its easy fall into local optima have restricted its wider applications. Recently proposed stochastic optimization method Particle Swarm Optimization (PSO) is also been discussed. Also the way BP network's initial weights and bias are optimized; Particle swarm optimization is also carefully discussed. In this paper, firstly BP is used to initially train and test the BP network, then the Particle Swarm Optimization Based Back-Propagation (PSO-BP) networks is used to train and diagnose the anemia in pregnant ladies. While concluding the experimental results, it shows variation in the taken parameters, execution time, and accuracy [10, 11], [16].
The present study aims to determine the attitude of teachers towards internet applications. Survey method was employed for the study. The sample consisted of 50 school teachers. An attitude scale towards internet applications was developed and standardized by the investigator. t-test and ANOVA were used to analyse the data. It was concluded from the study that a majority of the teachers maintained a positive attitude towards internet applications. In addition to this it was concluded that gender, teacher qualification, and teaching experience of teachers have no role on the attitude towards internet application of teachers.
