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Image Segmentation Techniques for Brain Tumor Detection: A Review 14

Image Segmentation Techniques for Brain Tumor Detection: A R...

引用

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Chhikara, Ankita Fairy Kait, Ramesh Department of Computer Science & Applications Kurukshetra University Kurukshetra India

The detection of brain tumors is the most complex and extended task in medical image planning. Identifying, segmenting, and detecting the region of infection in brain tumor MRI images is a lengthy process. Image processing concepts can be used to think about the various anatomical structures of the human body. It is extremely hard to see irregular human brain frameworks utilizing simple imaging approaches. Here, the paper describes some techniques for segmenting images in order to identify brain tumors. Integrating the neural network with evolutionary algorithms leads to the evolution of the neural network and genetic algorithm. This paper incorporates a flexible approach resolving brain tumor detection using a genetic algorithm. © Grenze Scientific Society, 2023.

关键词： Image segmentation

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Implementation of Efficient Machine Learning Techniques for Prediction of Cardiac Disease using SMOTE 5

Implementation of Efficient Machine Learning Techniques for ...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Narayanan Jayashree Department of Computer Applications College of Science and Humanities SRM Institute of Science and Technology Kattankulathur603203 India

The leading cause of mortality in the world for the past few decades has been cardiovascular disease (CVD) or cardiac arrest, which is caused by conditions that affect the Heart. With Machine Learning (ML), predictive models are refined and adapted to test health industries in a more efficient way. ML algorithms will helpful to make an early prediction for those who may need to make a change to their lifestyle and receive appropriate medical treatment. Our research presents supervised ML classifiers capable of detecting significant features using machine learning techniques to predict Heart disease. An over sampling method is presented here for learning from unbalanced datasets. Several classification techniques are employed with different features in our prediction model. Performance metrics are analyzed to determine their effectiveness when applied to the UCI heart dataset using ML techniques by SMOTE oversampling;A maximum of 96.6 percentage of accuracy, 90 percentage of sensitivity, and 100 percentage of specificity and precision were achieved by the Random Forest method. © 2024 The Authors. Published by Elsevier B.V.

关键词： Diseases

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Healthcare Data Analysis Using Proposed Hybrid Harmony Genetic Diagnostic Model 5th

Healthcare Data Analysis Using Proposed Hybrid Harmony Genet...

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5th International Conference on Recent Innovations in Computing , ICRIC 2022

作者： Sharma, Manju Tyagi, Sanjay Department of Computer Science and Applications Kurukshetra University Kurukshetra India

ISBN: (纸本)9789819906000

Healthcare data analysis is a decision-based process that depends on past historical data. Analyzing such a huge volume of data as well as retaining accuracy in such sensitive data is an essential task. Henceforth, there is a necessity to design an intelligent diagnostic system that can assist humans in a proper diagnosis of disease along with making the precise results. In this paper, a new improved harmony search-based diagnostic model has been proposed for healthcare dataset analysis. Before integrating the harmony search for modeling the system, some enhancements have been done in the harmony improvement phase of the classical harmony algorithm by parameter tuning. Moreover, instead of random selection, the harmony vector is selected by a polygamous mechanism, and the crossover operator is used to increase the exploitation capability of the harmony search algorithm. The performance of proposed hybrid harmony search genetic algorithm (HHSGA)-based disease diagnostic clustering model has been analyzed based on precision, recall, accuracy, and G-measure quality indicators. The quality of the proposed algorithm is compared with other basic metaheuristic models by implementing it on four UCI repository healthcare datasets. Simulation results show that the proposed model is more robust and efficient in terms of achieving highest accuracy and correct classification of data points into clusters. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sensitive data

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Comparison of Optimizers for Lung Cancer Classification using Deep Convolution Neural Network 2

Comparison of Optimizers for Lung Cancer Classification usin...

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2nd International Conference on Artificial Intelligence and Machine Learning applications, AIMLA 2024

作者： Dev, R.B. Shanthi, S. Brindha, V. Arunkarthi, T. Ashwinth, K. Kuppuchamy, R. Kongu Engineering College Department of Computer Science and Engineering Perundurai India PSNA College of Engineering and Technology Department of Computer Applications Dindigul India

ISBN: (纸本)9798350349221

The process of identifying and categorizing lung cancer in its early stages is difficult, yet doing so will improve patient survival rates. There is a wealth of research that segments and categorizes lung nodules using convolution neural networks (CNN). Numerous factors, including batch size, epochs, learning rate, optimizers, activation function, and weight initialization, affect the model's performance. To categorize CT scan images as benign, malignant, or normal, a deep CNN model is developed. Optimizers are crucial to the training process of the model because they adjust the weight and learning rate of the network in each epoch, which lowers loss and raises classifier accuracy. Using ReLU and selu as activation functions, this work offers a thorough analysis of the performance for a number of optimizers, including Nesterov Adaptive Momentum (Nadam), Adaptive Delta (Adadelta), Adaptive Max Pooling (Adamax), Stochastic Gradient Descent (SGD), Adaptive gradient (Adagrad), Adaptive Momentum (Adam), Adaptive Gradient Descent (Adagrad), and Root Mean Square Propagation (RMSProp)[1][1][1][1][1][1]. The best training accuracy for the ReLu activation function is 92.67%, with a validation accuracy of 97.92, and for the Selu activation function, it is 95.42%, with a validation accuracy of 96.88, according to the Adam optimizer. The study aims to assess various optimization techniques in deep CNNs for lung cancer classification using CT scan images. It analyzes different optimizers to understand their impact on model convergence, accuracy, and generalization. This insight is crucial for selecting the most effective optimization methods in lung cancer diagnosis. © 2024 IEEE.

关键词： Biological organs

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Predicting Sham Claims in Vehicle Insurance using Machine Learning Techniques 2

Predicting Sham Claims in Vehicle Insurance using Machine Le...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Hema Ambiha, A. Subithra, V. Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Karpagam Academy of Higher Education Department of Computer Applications Coimbatore India

ISBN: (纸本)9798350368413

The insurance industry is experiencing rapid growth and as a result, faces the challenge of managing vast amounts of data. One of the foremost concerns within this sector is the issue of fraudulent claims, which refers to deceptive attempts made for personal financial gain. This deceitful practice affects both insurance companies and their honest policyholders due to the prevalence of such claims. Traditionally, domain expertise and conventional methods have been employed to detect fraudulent activities in insurance claims. However, more recently, data mining techniques have emerged as valuable tools in the field of insurance analysis. These techniques enable the calculation of insurance premium amounts and the prediction of both fraudulent claims and accident occurrences. The premium amount is determined based on individual customers' confidential and financial information. Implementing this research significantly reduces human effort and financial losses in the examination of automobile insurance claims. Effective classifiers play a crucial role in determining whether a claim is fraudulent or not. Predicting the occurrence of accidents is a fundamental step in identifying fraudulent claims. Decision tree classifiers, such as Simple CART, SVM, and LMT, available in the WEKA tool, are employed to analyze accidents and claims. The execution of these classifiers is compared in terms of accuracy, with Random Forest demonstrating superior accuracy in predicting accident occurrences and yielding the best results among the classifiers. Furthermore, the SVM algorithm outperforms other classifiers in predicting fraudulent claims. © 2024 IEEE.

关键词： Machine learning

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Cumulative Summary List Driven Lightweight Frequent Closed High Utility Itemset Mining 2

Cumulative Summary List Driven Lightweight Frequent Closed H...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Siva, S. Chaudhari, Shilpa REVA University School of Computer Science and Applications Bangalore India Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350320923

Pattern mining has always been a dominant approach in data mining to identify the object(s) of interest from a humongous large data space to make real-time decisions. In sync with diverse business intelligence or other enterprise demands, pattern mining methods have gained wide-spread attention to serve purposes like high-utility itemset identification, segmentation and top-(itemsets) recommendation. In this paper, a robust cumulative summary list-structure driven lightweight frequent closed high-utility itemsets mining model (CSLFC-HUIM) is proposed that uses an improved cumulative summary list structure (CULS) to ensure optimal set of candidate itemsets in search space. Unlike classical support value driven strict-thresholding, CSLFCHUIM applied lift-score as minimum threshold called cumulative support/utility threshold to prune the search space. The strategic amalgamation of cumulative support value, utility threshold with CULS and cumulative summary list structure (CSLS) concept guarantee optimal set of itemsets in nested-list structure. It reduces unwanted database-scanning, execution-time and memory exhaustion, which makes it a potential solution for different pattern mining and business intelligence tasks. © 2023 IEEE.

关键词： Data mining

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A Survey on Breast Cancer Prediction using Various Deep Learning and Machine Learning Techniques 9

A Survey on Breast Cancer Prediction using Various Deep Lear...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Gurupakkiam, G. Ilayaraja, M. Department of Computer Applications India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Anand Nagar Krishnankoil626126 India

ISBN: (纸本)9798350377972

Breast Cancer continues to be a predominant contributor to mortality associated with cancer among women on a global scale. To improve survival rates and treatment consequences, it's necessary to detect issues early and create an accurate diagnosis. In recent years, Machine Learning (ML) and Deep Learning (DL) techniques have shown great promise in medical diagnostics, offering the potential to enhance the accuracy and efficiency of breast cancer detection and prognosis. This research work provides breast cancer prediction using deep learning and machine learning studies on mammograms, ultrasounds, magnetic resonance imaging, and digital pathology images conducted over the past decade. An evaluation of studies from the past ten years highlights how important deep learning and machine learning have been in better patient outcomes and enlarges the accuracy of breast cancer detection. The automation of medical image assessment, machine learning, and deep learning technologies facilitate radiologists and pathologists in rendering more knowledgeable and accurate decisions. This reduction in the probability of human errors culminates in superior diagnostic accuracy and treatment methodologies, thereby ultimately improving the quality of patient care. © 2024 IEEE.

关键词： Lung cancer

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An IoT-Based Approach to Monitor the Health of Water Bodies Nearby Indian Villages 1

An IoT-Based Approach to Monitor the Health of Water Bodies ...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Singh, Shweta Agarwal, Prashant Gupta, Amit Kumar Bhardwaj, Manish Rishika Nigam, Kratika KIET Group of Institutions Department of Computer Applications Ghaziabad India KIET Group of Institutions Department of Computer Science and Information Technology Ghaziabad India

ISBN: (纸本)9798350353648

In 2018, 351 contaminated river segments in India were identified by the Central Pollution Control Board (CPCB). Thirty-one provinces and the union territories (UT) have rivers and streams that failed to meet the criteria for the evaluation of water quality to identify polluted river lengths. to protect everyone's life on the planet. In recent years, a number of writers have put forth architectural-based solutions that emphasise increased efficiency and adaptability. New sensor capabilities are being created by improvements in sensor detection and communication. Water Quality Monitoring (WQM) is an economical and effective method that uses Internet of Things (IOT) technology to monitor the quality of water. These sensors were interfaced with a Microcontroller Unit (MCU), and additional processing was carried out on a Personal computer (PC). An IOT-based ThingSpeak application is used to transmit the acquired data to the cloud in order to track the water quality. The accuracy of the prediction is increased with the proposed system, and the power of drawing conclusions is enhanced with IOT technology. © 2024 IEEE.

关键词： Rivers

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Real Time Car Speed Detection Using CNN Algorithm 3

Real Time Car Speed Detection Using CNN Algorithm

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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Panigrahi, Jangyaseni France, K. Hindustan Institute of Technology and Science Department of Computer Applications India

ISBN: (纸本)9798350399264

In addition to determining car speed, the method can be used to track traffic patterns along a road or motorway. The speed of the car is rarely determined with the security cameras already in use. The majority of the currently used methods require ongoing maintenance and employ expensive sensors. As a result, we suggest a technique for using security cameras to monitor traffic and estimate car speed. To detect a car, pinpoint its location, determine the distance it has travelled over time, and ultimately determine its speed, various image processing techniques are applied. The proposed method's ability to predict speed even in videos with low resolutions or quality will be evaluated. The traffic surveillance system (TSS) is a crucial instrument for gathering important data (speed). Convolution neural networks (CNNs) with a single pipeline have been the focus of a significant amount of research up to this point. Even though this research produced highly accurate results, they needed a sizable dataset and an implementation on specialized hardware. © 2023 IEEE.

关键词： Target tracking

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Sustainable Energy Generation from Speed Breakers for Smart Street Lighting Application 2

Sustainable Energy Generation from Speed Breakers for Smart ...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Vijayakumar, P. Pyingkodi, M. Karuppusamy, S. Prasaath, J.M. Dhyaneshvar Jeysaravanan, S. Manojkumar, V. Kongu Engineering College Department of Computer Applications Perundurai Erode India Nandha Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350368413

This research explores the potential of integrating rack and pinion mechanisms with electromagnetic induction to generate electricity for street light control systems. By converting mechanical energy into electrical energy, this approach offers a sustainable and environmentally friendly solution. The proposed system utilizes Light Dependent Resistors (LDRs) and a Raspberry Pi microcontroller to dynamically control street lights based on ambient light conditions. This intelligent control strategy optimizes energy consumption and reduces operational costs. This research study analyzes the theoretical foundations, design considerations, and implementation methodologies of the proposed system. Experimental validation demonstrates the feasibility and effectiveness of the approach. By promoting the use of renewable energy sources and reducing reliance on conventional power grids, this innovative solution contributes to the development of sustainable and resilient urban infrastructure. © 2024 IEEE.

关键词： Proportional control systems

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