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Breast Cancer Classification via the Use of ML and DL: A Comprehensive Review 3

Breast Cancer Classification via the Use of ML and DL: A Com...

引用

3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Sarang, Ruvanshi Dabhi, Vipul Shah, Arpit Dept. of Computer Science Engineering Parul University Vadodara India Dept. of Computer Engineering Parul University Vadodara India

ISBN: (纸本)9798331532420

New and improved methods of diagnosis are needed because breast cancer is still the leading cancer-related killer worldwide. Updates to the methods used to categorize breast cancer have emerged because of recent advances in DL and ML. This review paper’s goal in conducting this research is to bring together existing breast cancer diagnostic and classification methods that make use of deep learning and machine learning techniques. We examine the many approaches, datasets, and performance indicators used in the industry and point out the pros and cons of each as part of the review paper. This review aims to thoroughly examine all the methods that are now in use, spot any trends, and propose new areas for research. This review helps researchers and practitioners understand the current state of breast cancer categorization by combining the findings of past research. Discuss recent advances in ML/DL techniques for improving classification, such as ensemble methods, transfer learning, or hybrid models. Additionally, propose the use of fine-tuning of hyperparameters or novel architectures to enhance classification. © 2024 IEEE.

关键词： Adversarial machine learning

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Smart Shopping Cart using CNN 14

Smart Shopping Cart using CNN

引用

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

作者： Vidhya, S.G. Dhruva, M.S. Deekshitha, H.C. Kshama, S. Tejaswini, K.R. Dept. of Information Science and Engineering BGSIT India Dept. of Computer Science and Engineering BGSIT India

According to present scenario, now a days it is often observed during shopping groceries, vegetables, fruits and other needs in malls, markets, bazaars etc. There is a huge rush, billing of the cart products will be delayed due to more number of customers standing in queues at every bill counter especially on holidays and weekends. This leads to loss of valuable time of customers, To overcome these kind of situations, a simple solution is introduced that is "Smart Shopping Cart" using Convolution Neural Network (CNN). This smart cart is connected to cameras that are connected to webpage through hot spot which acts as a sensor, which sense the image of the products and process that image, the model processes the image only if the product is trained, tested and stored in the database. After processing the product image it identifies the product and add the product to the bill. To add the product to the bill we have used a high level pre-trained API known as Tensor flow which acts as a communication link between the sensor and webpage. If the product is removed from the cart then the product will be removed from the bill list. By implementing this model we can save the time of both seller and consumer and avoid misuse of billings. © Grenze Scientific Society, 2023.

关键词： Websites

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Electronic Market for Farmers 14

Electronic Market for Farmers

引用

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

作者： Vidhya, S.G. Dhruva, M.S. Gowda, Nisha J. Shilpa Shree, H. Gowda, Shravana P. Dept. of Information Science and Engineering BGSIT India Dept. of Computer Science and Engineering BGSIT India

The Covid-19 pandemic is a global health crisis that has affected everyone. One negative impact has left local farmers struggling to find other means of income besides marketing their produce to customers. It is difficult for customers to find good quality fresh goods in super markets. The objective of this paper is two-part: first, to evaluate the existing and manual system of how consumers buy their fresh produce during the Covid19 pandemic, to design the and implement the delivery system for local farmers to sell fresh product through mobile app. © Grenze Scientific Society, 2023.

关键词： Sales

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Image based and Point Cloud based Methods for 3D View Reconstruction in Real-time Environment 14

Image based and Point Cloud based Methods for 3D View Recons...

引用

2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Agrawal, Arya Sharma, Teena Verma, Nishchal K. Vasikarla, Shantaram Vellore Institute of Technology School of Computer Science and Engineering Vellore India Indian Institute of Technology Mehta Family School of Data Science and Artificial Intelligence Guwahati India Indian Institute of Technology Dept. of Electrical Engineering Kanpur India California State University Dept. of Computer Science NorthridgeCA United States

ISBN: (纸本)9798350360134

3D view reconstruction in a real-time unknown environment poses several challenges to vision-based applications. Herein, this paper outlines real-time experimental results and analysis of 3D view reconstruction methods. Since real-time experimentation requires inputs from different sensors, this paper outlines the results of methods from the following categories: image based methods and point cloud based methods. This paper explains the detailed steps involved in 3D view reconstruction followed by an explanation of the methods. It provides detailed discussions, advantages, limitations, and empirical results, along with presenting differences between algorithms. For analysis of experimental results, two benchmarked datasets i.e., KITTI 3D benchmark suite and ShapeNet are considered. Furthermore, the paper explores potential future scopes, including the utilization of generative models, unsupervised learning, advanced sensors, and many more. © 2024 IEEE.

关键词： Network coding

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Some decidability issues concerning Cn real functions 39

Some decidability issues concerning Cn real functions

引用

39th Italian Conference on Computational Logic, CILC 2024

作者： Buriola, Gabriele Cantone, Domenico Cincotti, Gianluca Omodeo, Eugenio G. Spartà, Gaetano T. Dept. of Computer Science University of Verona Italy Dept. of Mathematics and Computer Science University of Catania Italy Dept. of Mathematics Informatics and Geosciences University of Trieste Italy Pontifical Gregorian University Rome Italy

This paper adapts preexisting decision algorithms to a family RDF = {RDFa | n ∈ N} of languages regarding one-argument real functions;each RDFn is a quantifier-free theory about the differentiability class Cn, embodying a fragment of Tarskian elementary algebra. The limits of decidability are also highlighted, by pointing out that certain extensions of RDFn are undecidable. The possibility of extending RDFn into a language RDF∞ regarding the class C∞, without disrupting decidability, is briefly discussed. Two sorts of individual variables, namely real variables and function variables, are available in each RDFn. The former are used to construct terms and formulas that involve basic arithmetic operations and comparison relators between real terms, respectively. In contrast, terms designating functions involve function variables, constructs for addition of functions and scalar multiplication, and-outermost-i-th order differentiation Di[ ] with i ≤ n. An array of predicate symbols designate various relationships between functions, as well as function properties, that may hold over intervals of the real line;those are: function comparisons, strict and non-strict monotonicity / convexity / concavity, comparisons between a function (or one of its derivatives) and a real term. The decidability of RDFn relies, on the one hand, on Tarski’s celebrated decision algorithm for the algebra of real numbers, and, on the other hand, on reduction and interpolation techniques. An interpolation method, specifically designed for the case n = 1, has been previously presented;another method, due to Carla Manni, can be used when n = 2. For larger values of n, further research on interpolation is envisaged. © 2024 Copyright for this paper by its authors.

关键词： Computability and decidability

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Detecting Helmets Of The Bike Riders Using Deep Learning Algorithms 14

Detecting Helmets Of The Bike Riders Using Deep Learning Alg...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Mia, Yousuf Rahman, Md. Solaimanur Basak, Animesh Hossain, Sheikh Mufrad Zulfiker, Md. Sabab Sumaia, Merina Akter Daffodil International University Dept. of Computer Science & Engineering Dhaka Bangladesh Model Institute of Science & Technology Dept. of Computer Science & Engineering Gazipur Bangladesh

ISBN: (纸本)9798350335095

We propose real-time bike helmet detection in our study. A lot of people ride bikes in our nation. Motorbikes are more popular than vehicles because they are cheaper to maintain, take up less parking space, and provide more mobility and adaptability in urban situations. Bike riding is entertaining yet risky. Bicyclist safety is the planned system's main purpose. Many drivers don't wear helmets even though they're mandated by law. In emerging countries, mortality has been growing steadily. A helmet detection technology that identifies drivers without helmets is needed to safeguard the public. We employ a real-time 3202 dataset for this approach. We gather wearing helmet 1911 and no helmet 1291 data and utilize algorithms like VGG16, Resnet50, MobileNet v.02, Inception V3, EfficientNet, and CNN. The EfficientNet achieved 98% accuracy. Each person's comparison statement techniques are in the implementation section. To construct the optimum model for the conditions, this inquiry uses model validation approaches. © 2023 IEEE.

关键词： Deep learning

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BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations

BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neur...

引用

2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Han, Kaiqiao Yang, Yi Huang, Zijie Kan, Xuan Guo, Ying Yang, Yang He, Lifang Zhan, Liang Sun, Yizhou Wang, Wei Yang, Carl University of California La Dept. of Computer Science Los Angeles United States Duke University Dept. of Computer Science Durham United States Emory University Dept. of Computer Science Atlanta United States Emory University Dept. of Biostat. & Bioinf. Atlanta United States Zhejiang University Dept. of Computer Science Hangzhou China Lehigh University Dept. of Computer Sci. & Eng. Bethlehem United States University of Pittsburgh Dept. of Ece and BioE Pittsburgh United States

ISBN: (纸本)9798350351552

Brain network analysis is vital for understanding the neural interactions regarding brain structures and functions, and identifying potential biomarkers for clinical phenotypes. However, widely used brain signals such as Blood Oxygen Level Dependent (BOLD) time series generated from functional Magnetic Resonance Imaging (fMRI) often manifest three challenges: (1) missing values, (2) irregular samples, and (3) sampling misalignment, due to instrumental limitations, impacting down-stream brain network analysis and clinical outcome predictions. In this work, we propose a novel model called BrainODE to achieve continuous modeling of dynamic brain signals using Ordinary Differential Equations (ODE). By learning latent initial values and neural ODE functions from irregular time series, BrainODE effectively reconstructs brain signals at any time point, mitigating the aforementioned three data challenges of brain signals altogether. Comprehensive experimental results on real-world neuroimaging datasets demonstrate the superior performance of BrainODE and its capability to address the three data challenges. © 2024 IEEE.

关键词： Functional neuroimaging

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Active Element Arrangement and Prediction Model for Channel Estimation in IRS-Assisted Systems

Active Element Arrangement and Prediction Model for Channel ...

引用

2024 IEEE Region 10 Conference, TENCON 2024

作者： Tsuchiya, Yoshihiko Suga, Norisato Uruma, Kazunori Fujisawa, Masaya Tokyo University of Science Dept. of Information and Computer Technology Tokyo Japan Shibaura Institute of Technology Dept. of Information and Communication Engineering Tokyo Japan Kogakuin University Dept. of Computer Science Tokyo Japan

ISBN: (纸本)9798350350821

Intelligent reflecting surfaces (IRS) that can dynamically control the phase of radio waves and reflect them are attracting attention to realize non line-of-sight communication in the high-frequency band. Channel state information (CSI) is estimated to properly control the phase, however the overhead of the estimation reduces communication efficiency. To reduce the overhead, IRS systems with active elements that perform channel estimation are considered. One approach treats the partial CSI obtained from the active elements as an image and utilizes a super-resolution technique to predict the entire CSI. However, because the active elements are arranged in grid, prediction is difficult when the active elements spacing and the CSI fluctuation period match. In this study, we propose a new arrangement that gathers the active elements in the center of the IRS. Furthermore, we regard prediction as an image-inpainting problem that recovers missing pixels from known pixel values, and we propose applying the U-Net to solve this problem. Numerical experiments show that the proposed method outperforms conventional methods in prediction accuracy and spectral efficiency. © 2024 IEEE.

关键词： Channel estimation

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A Novel Hybrid Deep Neural Network for Early Detection and Classification of Chicken Diseases 5

A Novel Hybrid Deep Neural Network for Early Detection and C...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Uddin, Md Shihab Islam, Md Fakrul Rahman, Sharukh Foysal, Nasir Uddin Islam, Muhammad Nazrul Razzaque, Md. Abdur Al Faisal, Faiz Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka1216 Bangladesh University of Dhaka Dept. of Computer Science and Engineering Dhaka1000 Bangladesh

ISBN: (纸本)9798350394290

Detection and classification of chicken diseases has been a longstanding concern for farmers. Relying solely on expert opinions could be a costly option. Moreover, there are certain chicken diseases that spread quickly, and necessary procedures are immediately needed to carryon. On the other hand, the lack of research activity or low availability of data on this topic leads to many misunderstandings and challenges so as to correctly detect a certain type of chicken disease. The existing techniques show low accuracy on disease classification, which leads to a complex engineering problem and critical analysis is required for sustainable development. In this paper, we develop a novel hybrid deep neural network, namely ChickenNet21, which combines a Convolutional Neural Network (CNN) and a fine-tuned Visual Geometry Group (VGG) model to address this challenge. Using a large dataset of 6,812 farm-labeled fecal images, we assess the effectiveness of ChickenNet21 and it achieves 98.83% accuracy in identifying chicken diseases. © 2023 IEEE.

关键词： Convolutional neural networks

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Investigation of VGG 16 Transfer Learning and CNN Integrated Brain Tumour Detection

Investigation of VGG 16 Transfer Learning and CNN Integrated...

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2024 International Conference on computer, Electronics, Electrical Engineering and their Applications, IC2E3 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Dept. of Electronics and Engineering Dehradun India

ISBN: (纸本)9798350388534

This study had conducted an intensive exploration of the possibilities of VGG-16 and VGG-19 Convolutional Neural Network models for picture categorisation. Using a thorough method comprising substantial training and severe testing, we may observe both steady loss decrease and significant model accuracy improvements. It is assumed that the models may appropriately keep track of subtle variations in data to which they are adversarial while identifying regular patterns they may generalize. The VGG models' architecture is quite sophisticated, but as seen above, they give strong classification accuracy, with performance in nearly ideal 99 percent in many trials. This integration proves that extremely advanced deep learning instruments are competent to handle and complete even very sophisticated picture recognition assignments. Additionally, our experiments identify a few essential aspects of model adjustment and tuning identifying important techniques considerably enhancing CNN outcomes in application scenarios. This study is pioneering for the automatic picture identification discipline since it provided a strong understanding of the generality and model quicker than many real-world models. Additionally, it exhibits that the CNN structures enhance artificial intelligence's power to recognize visual input and could improve many sectors like the surveillance and health systems. The findings defined here may now open the door to more progress in deep learning and picture process analyses while reiterated the importance of CNN in modern technology. © 2024 IEEE.

关键词： Convolutional neural networks

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