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Breast Cancer Classification Using ANN and ML Techniques 7

Breast Cancer Classification Using ANN and ML Techniques

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Bhuta, Nishi Raut, Roshani Pimpri Chinchwad College of Engineering Dept. of Computer Engineering Pune India Pimpri Chinchwad College of Engineering Dept. of Information Technology Pune India

ISBN: (纸本)9798350304268

Millions of individuals throughout the world are impacted by the significant health issue known as breast cancer. To improve patient outcomes, breast cancer must be identified early and correctly diagnosed. In this study, we used TensorFlow to create an artificial neural network (ANN) model to categorise breast cancer. To compare with our ANN model, we also employed seven alternative ML classifiers as basis models, including Line SNM, LR, KNN, NB, DT, RF, and Gradient Booster. For each model, we determined the precision, accuracy, recall, and F1 score. To improve performance, we ensembled all the classifiers. Each classifier's accuracy was analysed and contrasted, and the classification performance was demonstrated via a confusion matrix. Our results show that the ANN have given an accuracy of 97% and the ML model achieved the highest accuracy of 98.8% using LR, followed by SVM with 97.7%, KNN with 96.7%, RF with 95.6%, DT with 95.4%, NB with 95.3% and Gradient Booster with 94.2% the ensemble model gave the overall accuracy to 97.7%. these findings indicate that our proposed ANN model and ensemble approach are effective but LR gave the highest accuracy in classifying breast cancer and can serve as a useful tool for clinicians and researchers in this field. © 2023 IEEE.

关键词： Artificial neural network (ANN) Breast cancer Ensemble Learning ML

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Indoor Autonomous Mobile Bobot Navigation Method Based on Ant Colony Algorithm 7

Indoor Autonomous Mobile Bobot Navigation Method Based on An...

引用

7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Yang, Yan Du, Yanlian Shen, Yijun Liu, Zhiwei Software Engineering Institute of Guangzhou Department of Games Guangzhou510990 China School of Information and Communication Engineering Hainan University Haikou570228 China State Key Laboratory of Marine Resources Utilization in South China Sea Hainan University Haikou570228 China

ISBN: (纸本)9781665478571

the conventional ant colony algorithm exists in such problems as slow convergence velocity, feeble global search capability and tend to fall into local optimality. To cope with this, an indoor mobile robot path planning approach is presented in this study which is based on a modified ant colony algorithm. On the basis of the classical ant colony algorithm, two angle pieces of information are being introduced to enhance the directionality of the algorithm and overcome the local optimum problem. In view of accelerating the capability of global search and convergence speed of the robot, the fluctuation coefficient of the pheromone is adaptively scaled with the iteration number. Results of experiments indicate that our proposed algorithm can not only effectively raise the convergence speed and precision, but also achieve smoother paths. © 2022 IEEE.

关键词： Motion planning

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Comparative Analysis of Deep Learning and Machine Learning Analysis for Kidney Tumor Analysis 7

Comparative Analysis of Deep Learning and Machine Learning A...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Yazhini, A. Kalpana Devi, M. Anitha, M. Praveena, R. Mol, Maheswari K Lawrence, Jinsha Anna University Faculty of Electrical Engineering College of Engineering Chennai600025 India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore10 India KIT - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore641402 India Jai Shriram Engineering College Department of Computer Science and Engineering Tirupur638660 India Veterinary College and Research Institute Thanjavur614625 India Karpagam Academy of Higher Education Department of Computer Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350372816

In order to lower death risks, provide the most effective course of treatment, and improve community healthcare, the majority of recent research has concentrated on examining prevalent illnesses in the population. One of the prevalent ailments that has impacted our society is kidney disease. Around the world, kidney tumors (KT) rank tenth in terms of frequency in both men and women. the organization of cancer is a problem that is attracting increasing attention in the domains of bioinformatics and computational biology. this study presents a comparative analysis of the latest developments in deep learning (DL) and machine learning (ML). While several methods have been proposed to tackle the cancer classification challenge, new research indicates that supervised and deep learning-based methods are the most effective. Healthcare has advanced as a result of the creation of numerous machine learning techniques for awareness evaluation in cancer categorization. It seems that there is a high requirement for the ongoing development of efficient categorization algorithms in order to handle the growth in healthcare applications. the comparative analysis of the proposed classification shows the accuracy as 0.92%. © 2024 IEEE.

关键词： Adversarial machine learning

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Detection of idiopathic pulmonary fibrosis lesion regions based on corner point distribution 7

Detection of idiopathic pulmonary fibrosis lesion regions ba...

引用

7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Yu, Xueqing Han, Yicen Pu, Tian Guo, Lu Peng, Zhenming Peng, Shengkun Pu, Hong Uestc School of Information and Communication Engineering Chengdu China Department of Respiratory and Critical Care Medicine Sichuan Academy of Medical Sciences Sichuan Provincial People's Hospital Chengdu China Sichuan Academy of Medical Sciences Department of Radiology Sichuan Provincial People's Hospital Chengdu China

ISBN: (纸本)9781665478571

Idiopathic pulmonary fibrosis (IPF) is one of the most malignant types of interstitial lung disease, severely affecting the quality of life and survival time of patients. Physicians diagnose lesions by reading CT images, but the diverse imaging manifestations of IPF lead to heavy workload and low efficiency. As far as we know, only one automatic IPF detection method is reported. In this paper, we propose a lesion region detection method for IPF, which is divided into two stages, firstly extracting the lesion candidate regions from CT images, and subsequently doing classification by extracting features from the candidate regions to obtain the lesion regions. the experimental results show, the method in this paper can accurately detect the lesion regions and effectively improve the detection accuracy compared with the k-means clustering-based IPF detection method. © 2022 IEEE.

关键词： Computerized tomography

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Modified Artificial Hummingbird Algorithm with Deep Learning for Pest Detection through Leveraging Pattern Recognition-based Fusion Approach 7

Modified Artificial Hummingbird Algorithm with Deep Learning...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Rubadevi, B. Suresh, Helina Rajini Abdullah, Ibrahim Choudhary, Prashant Kumar Choudhary, Minu Mathurkar, Piyush Arunai Engineering College Department of Computer Science and Engineering Tamilnadu Tiruvannamalai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Al-Turath University Computer Science Department Baghdad Iraq Lovely Professional University Department of Electronics and Communication Engineering Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Vishwakarma Institute of Information Technology Department of Electronics and Telecommunication Engineering Pune India

ISBN: (纸本)9798350372816

the limitations of manual inspection and simple algorithms highlight the need for advanced insect detection systems in agriculture. the study presents the Modified Artificial Hummingbird Algorithm with Deep Learning (MAHADL), a significant breakthrough in pest detection. MAHADL combines the speed and efficacy of the Artificial Hummingbird Algorithm (AHA) with deep learning models such as Recurrent and Convolutional Neural Networks. Combining pattern recognition with the fusion approach improves pest detection accuracy while reducing false positives and negatives. Accurate pest identification is essential for effective agricultural management, which MAHADL provides through data collection, preprocessing, and algorithmic integration. MAHADL outperforms existing systems in terms of accuracy (0.94), recall (0.95), precision (0.92), computational economy (12 hours of training time, 0.5 seconds/image inference time), and decrease in false positives and negatives. the innovative technique provides enhanced agricultural output and sustainability, representing a significant leap in intelligent pest management. © 2024 IEEE.

关键词： Convolutional neural networks

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An Improved Deep Learning Model for Forecasting Red Stripe Disease 7

An Improved Deep Learning Model for Forecasting Red Stripe D...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Tanwar, Vishesh Lamba, Shweta Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

ISBN: (纸本)9798350397253

Sugarcane is the principal crop used to manufacture sugar and ethanol across the world. One concern in the sugar industry is that if sugarcane diseases are not treated and discovered early, they can annihilate growing crops contaminated with the illness, costing poor farmers money. the inquiry was prompted by the ever-expanding categories of ailments and farmers' inadequate capacity for disease detection and identification. there are many cases where the use of advanced technologies can assist researchers in finding solutions to their problems. this research study intends to apply Convolutional Neural Network(CNN) models for disease prediction and classification based on the sugarcane plant which has been reported as a severe critical issue in sugarcane crops. A better understanding of the sugarcane Red Stripe virus' pathogenesis and development can help improve disease management strategies, a Convolutional Neural Network(CNN) model is used to determine if a sugar cane plant is infected with Red Stripe. the data is from secondary sources such as Kaggle and uses this information to predict the illness and classify it. this study shows a 98.5% prediction rate for Red Stripe Sugarcane Disease. © 2023 IEEE.

关键词： Diseases

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Day Ahead Demand Response Using Load Shifting Technique in Presence of Increased Renewable Penetration 7

Day Ahead Demand Response Using Load Shifting Technique in P...

引用

7th IEEE international conference for Convergence in technology, I2CT 2022

作者： Arun, C. Aswinraj, R. Bijoy, M.T. Nidheesh, M. Rohikaa Micky, R. College of Engineering Department of Electrical and Electronics Engineering Kerala Calicut Vadakara India

ISBN: (纸本)9781665421683

Demand side management strategies help to reduce the peak demand, thereby reducing the need to operate at high cost and with high emission generating units. In this work, a priority-based load shifting demand response (PBLSDR) is proposed to reduce the gap between demand and supply in presence of increased renewables. the case studies are expected to provide an insight into load shifting and curtailments applicable for residential customers in presence of renewable energy sources, which can be interpreted as demand response potentials in residential markets with automated demand response. © 2022 IEEE.

关键词： Renewable energy resources

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Analysis of various Smart Healthcare Technologies 7

Analysis of various Smart Healthcare Technologies

引用

7th international conference on Parallel, Distributed and Grid computing, PDGC 2022

作者： Rastogi, Rashi Bansal, Mamta Deemed University Shobhit Institute of Engineering and Technology Dept. of Cse Meerut India Ch. Charan Singh University Meerut. Sir Chottu Ram Institute of Engineering and Technology Meerut India

ISBN: (纸本)9781665454018

Healthcare technology is the practical application and synthesis of various knowledge for enhancing, preserving, and supporting human health. the improvement of engineering applications to healthcare technologies, which has significant economic, scientific, and societal implications, determines the quality of people's lives. the creation of an efficient and cost-effective system that is completely adequate for the financial, clinical, and ancillary needs in healthcare is a challenge that all health economies face globally. Due to this significant difficulty, it is occasionally insufficient and unsustainable and requires further attention. Nowadays smart healthcare system is widely used in everywhere. the system provides fast health tracking, early prediction of disease etc. It allows patients to exchange their medical records directly with an expert for remote diagnosis, without human participation. In addition, the remote monitoring of patients through the use of wearable sensors, Internet of things (IoT) technology, and artificial intelligence (AI) has made the treatment accessible and affordable. In this paper various IoT based technologies used in smart healthcare system has been presented. Furthermore, the comparative analysis of various healthcare technologies in perspective of wearable devices, BAN and digital healthcare system has been presented. © 2022 IEEE.

关键词： Internet of things

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Fish Species Classifier for Allergic People using CNN Algorithm 7

Fish Species Classifier for Allergic People using CNN Algori...

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7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Reddy, Puchalapalli Surya Vradhan Krishna, Mannepalli Vamsi Aishwarya, R. Yogitha, R. Kumar, K. Ashok Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9781665464086

Some people may be allergic to a variety of crustaceans, including prawns, crab, and lobster, or they may be sensitive to some types of fish. Cross-reactivity is the term for this kind of condition. this approach is useful because it is challenging to predict which fish will cause an allergic reaction in you. It is challenging to determine which fish may cause an allergic reaction. Deep learning has been a huge help in recent years in solving numerous issues. Real-time datasets are not implemented in the current systems, and they additionally the current systems are not very accurate. the proposed system utilizes CNN algorithm for detecting and classifying fish that cause allergies. the implemented system aims to help people with seafood allergies, clinical immunology, or allergies. specialist in image analysis of caught fish, detection, and species classification of fish from images. © 2023 IEEE.

关键词： Deep learning

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Brain Tumor Segmentation and Evaluation Empowered with Deep Learning 7

Brain Tumor Segmentation and Evaluation Empowered with Deep ...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Sadique, Sona Nishanthi, X. Swaathy, V.N. Mabisha, Selva thanka, Roshni Edwin, Bijolin Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397253

A critical stage in the diagnosis and planning of brain tumor treatment is the segmentation of the tumor. Deep learning approaches have lately demonstrated considerable potential for properly segmenting brain cancers from MRI and CT data. In this research study, a deep learning-based comparative strategy is used for segmenting and evaluating brain tumors using U-Net, Residual Network, and Multi ResNet architectures are compared. the deep neural networking model used in the proposed method is trained using a sizable dataset of MRI brain image scans, enabling it to distinguish between tumor-affected regions and normal, healthy brain tissue. the multi-ResNet architecture is utilised to accurately separate the tumor regions and capture the high-level features of the brain images. Proposed experimental comparison demonstrates that the multi residual network achieves high accuracy and outperforms traditional methods on benchmark datasets. Among the three algorithms performed in this study, Multi ResNet marked dice coefficient of 0.89, Precision of 0.91 and Recall of 0.87. © 2023 IEEE.

关键词： Tumors

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