Lung cancer classification using data mining and supervised learning algorithms on multi-dimensional data set

These With recent developments in machine learning, data mining and computer vision, there is great potential for improvements in early detection of lung cancer using scans and data available. This paper details the methods and techniques used in our project, where the objective is to develop algorithms to determine whether a patient has or is likely to develop lung cancer using dataset images using data mining and machine learning for the classification and examination. We explore approaches to address the problem. Cancer is the most important cause of death globally. The disease diagnosis is a major process to treat the patients who are affected by cancer disease. The diagnosis process is more difficult comparatively known about the cancer disease detection. Developing a proposed data mining model is useful to diagnose the cancer disease once the cancer detection is accomplished using data mining for the examination and classification of machine learning supervised algorithms

Clustering algorithms subjected to K-mean and gaussian mixture model on multidimensional data set

This paper explored the method of clustering. Two main categories of algorithms will be used, namely k-means and Gaussian Mixture Model clustering. We will look at algorithms within thesis categories and what types of problems they solve, as well as what methods could be used to determine the number of clusters. Finally, we will test the algorithms out using sparse multidimensional data acquired from the usage of a video games sales all around the world, we categories the sales in three main standards of high sales, medium sales and low sales, showing that a simple implementation can achieve nontrivial results. The result will be presented in the form of an evaluation of there is potential for online clustering of video games sales. We will also discuss some task specific improvements and which approach is most suitable

Speaker Identification Model Based on Deep Neural Networks

This study aims is to establish a small system of text-independent recognition of speakers for a relatively small group of speakers at a sound stage. The fascinating justification for the International Space Station (ISS) to detect if the astronauts are speaking at a specific time has influenced the difficulty. In this work, we employed Machine Learning Applications. Accordingly, we used the Direct Deep Neural Network (DNN)-based approach, in which the posterior opportunities of the output layer are utilized to determine the speaker’s presence. In line with the small footprint design objective, a simple DNN model with only sufficient hidden units or sufficient hidden units per layer was designed, thereby reducing the cost of parameters through intentional preparation to avoid the normal overfitting problem and optimize the algorithmic aspects, such as context-based training, activation functions, validation, and learning rate. Two commercially available databases, namely, TIMIT clean speech and HTIMIT multi-handset communication database and TIMIT noise-added data framework, were tested for this reference model that we developed using four sound categories at three distinct signal-to-noise ratios. Briefly, we used a dynamic pruning method in which the conditions of all layers are simultaneously pruned, and the pruning mechanism is reassigned. The usefulness of this approach was evaluated on all the above contact databases

Enhancing Multilevel Inverter Performance: A Novel Dung Beetle Optimizer-based Selective Harmonic Elimination Approach

This paper introduces a novel approach for enhancing the performance of multilevel inverters by applying a dung beetle optimizer (DBO)-based Selective Harmonic Elimination (SHE) technique. Focusing on a 3-phase multilevel inverter (MLI) with a non-H-bridge structure, the proposed method offers advantages such as cost-effective hardware implementation and eliminating the traditional H-bridge inverter requirement. To assess its efficacy, we compare the presented DBO-based SHE technique (DBOSHE) with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), evaluating their ability to determine optimal switching angles for achieving low-distorted load voltage. Unlike methods reliant on time-consuming calculations or fixed solutions, DBO provides a flexible approach, considering multiple possibilities to yield accurate switching angles. Using Simulink, harmonic component values and Total Harmonic Distortion (THD) are obtained for each optimization technique, specifically emphasizing on 9-level and 11-level MLI topologies. Our study aims to identify the most effective optimization technique for achieving lower THD and THDe values while eliminating odd-order harmonics from the 3-phase load voltage. Finally, we demonstrate the effectiveness of employing DBO for THD and THDe optimization within the SHE technique

Unlocking Solar Potential: Advancements in Automated Solar Tracking Systems for Enhanced Energy Utilization

The use of solar tracking systems has become vital and has established itself as a vital element in the generation of solar energy by enhancing the collection efficiency. This paper seeks to understand the necessity of shifting from conventional energy sources and why issues like scarcity of fossil fuel, and pollution are some of the hurdles toward achieving sustainable energy. Solar power, in particular, is one of the lights at the end of this tunnel since it pioneers a shift towards the usage of clean energy in the world. The subject of interests of the study is on how tracking systems help in maximizing energy collection from solar systems by interchanging it with the movement of sun’s path. It discusses the method that was followed, which involves selecting component, designing circuit and developing software together with presenting empirical data that was obtained from a three-day, Twenty-four-hour experiment. Outcomes show that there is an improvement on voltage stability, the level of solar irradiation and temperature regulation when the system is applied as compared to static system and its applicability for the enhancement of the renewable energy harnessing methods by using the solar tracking technology. Finally, it outlines the future research directions to continue exploring the proposed methods and its wider impact on renewable energy generation