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检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

共 811 条记录，以下是431-440 订阅

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Detection of Tumor-Infiltrating Lymphocytes in the Images of Immunohistochemistry using Deep learning Techniques 7

Detection of Tumor-Infiltrating Lymphocytes in the Images of...

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7th international conference on Image Information Processing, ICIIP 2023

作者： Priya, S.Baghavathi Karuparthi, Manojna Madeswaran, TamilSelvi Computer Science And Engineering Amrita Vishwa Vidyapeetham School of Computing Chennai India University of Technology And Applied Sciences Information Technology Nizwa Oman

ISBN: (纸本)9798350371406

Deep learning is playing a pivotal role in revolutionizing the field of medicine, particularly in its ability to enhance the interpretation of histopathological images and unravel the intricate relationship between the immune system and cancer. this technology has the potential to offer a comprehensive understanding of immune cell behavior within cancer, ultimately leading to more effective diagnostic and therapeutic strategies and, in turn, improving patient outcomes. this survey is dedicated to exploring the intricate dynamics of lymphocytes in balancing pro- and anti-inflammatory activities, underscoring the critical role of tumor-infiltrating lymphocytes (TILs) as essential cancer biomarkers. With the limitations of traditional methods such as immunohistochemistry and manual cell counting in mind, the medical community is increasingly turning to machine learning. the ultimate objective is achieving precise lymphocyte detection and spatial analysis through whole-slide imaging, promising substantial advancements across various medical domains, with a primary focus on enhancing patient quality of life and healthcare precision. Notably, the use of automated methods, particularly deep learning, is being explored to address inter-individual variability in the assessment of crucial prognostic factors like Ki-67 and TILs. the introduction of the LYON19 dataset and PathoNet addresses the demand for publicly available benchmarks, with PathoNet exhibiting superior performance to date, particularly in terms of the harmonic mean measure, thus contributing significantly to medical progress. © 2023 IEEE.

关键词： Tumors

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A Detailed Analysis on Spam Emails and Detection Using machine learning Algorithms 1

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7th international conference on Inventive Systems and Control, ICISC 2023

作者： Sulthana, Razia Verma, Avani Jaithunbi, A.K. Department of Computing and Mathematical Sciences University of Greenwich Old Royal Naval College LondonSE10 9LS United Kingdom Department of Computer Science Birla Institute of Technology & Science Pilani Dubai Campus Dubai345055 United Arab Emirates Department of Computer Science RMD Engineering College TamilNadu Kavaraipettai601206 India

ISBN: (数字)9789819916245

ISBN: (纸本)9789819916238

Spam email is the unwanted junk and solicited email sent in bulk to the receivers, using botnets, spambots, or a network of infected computers. these spam emails can be phishing emails that trick users to get their sensitive information, download malware into the user devices or scam the users stealing confidential data. this paper shows a systematic analysis of spam and its types. It also details the procedure of how the spammers get the email addresses of the receivers. It analyses the problems with spamming. A detailed state of the art on spam filters and the factors that put an email into the spam or ham category is also explained. the paper also discusses spam filtering methods of Gmail, Yahoo and Outlook. Finally, it brings out several solutions to detect spam using principles of machine learning and Data Mining. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electronic mail

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Federated learning for Wearable Sensor-Based Human Activity Recognition 7th

Federated Learning for Wearable Sensor-Based Human Activity...

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7th international conference on Intelligent Technologies, ICIT 2022

作者： Sachin, D.N. Annappa, B. Ambesenge, Sateesh Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India

ISBN: (纸本)9789819919116

computing devices that can identify various human behaviors or motions may be used to aid individuals in multiple contexts, including sports, healthcare, and interactions between humans and robots. Data readily accessible for this purpose may be collected from smartphones and wearable devices used daily. therefore, efforts are made to classify real-time activity data effectively utilizing various machine learning models. However, current methods for human activity recognition do not sufficiently consider user data privacy. To mitigate privacy issues, federated learning can be employed to build generic activity classification model by aggregating a locally trained model at a user-edge device. this paper adopted a deep-learning neural network model called the transformer for motion signal time-series analysis. It uses the attention mechanism to provide context for each point in the time series. It also compares federated learning’s performance to centralized learning. Experimental results show that federated learning outperformed centralized training without com- promising user data privacy, with an accuracy of 96.87%. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data privacy

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machine learning Approach in Melanoma Cancer Stage Detection

Machine Learning Approach in Melanoma Cancer Stage Detection

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Aniruddha Chitte Aniket Pawar Mukund Nale Department Of Electronics and Telecommunication JSPM's RSCOE Pune India

Finding the stage of melanoma cancer is important for cancer research. Because of the lack of awareness of the signs, the number of mortalities has dramatically increased, making early detection of skin cancer essential. Due to its capacity to learn from data and create predictions based on that learning, the machine learning techniques that use deep learning algorithms such as Convolutional Neural Network (CNN) have been widely used in this sector.

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A Scientific Study for Breast Cancer Detection Using Various machine learning Algorithms 7th

A Scientific Study for Breast Cancer Detection Using Various...

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Proceedings of the 7th international conference on Advances in computing and Data Sciences, ICACDS 2023

作者： Soni, Prashant Kumar, Sanjeev Kumar, Dilip Department of Computer Science Engineering United University U.P Prayagraj211012 India Department of Computer Science and Engineering United College of Enginnering and Research U.P Prayagraj211001 India

ISBN: (纸本)9783031379390

A noncommunicable chronic illness, cancer impacts people of all ages, races, nationalities, and socioeconomic statuses indistinctly. Having this diagnosis confirmed illness is usually concerning for the patient. Because it is extremely difficult to predict. A significant spot in the ranking is taken by breast cancer, particularly in women. However, there is a very good possibility of a cure if it is discovered quickly. the cancer of the breast is among the most common type of cancer in women. Mammography also is one of the most successful methods for the early diagnosis and detection of breast cancer lowers the death rate. Mammograms, which are digital images of the breast, are used to identify the beginning signs of breast cancer. these radiographic images improve diagnostic precision while decreasing human error in cyst detection and diagnosis time. this study provides a scholarly analysis of machine learning techniques for cancer in breast detection and classification. In this research, we investigate how different machine-learning approaches affect the automation of breast imaging categorization. this study gathers examples that show how the machine learning approach is used to address various issues that have been identified through various analytical scientific investigations. this work aims to gather and compare the various screening techniques, classifiers, and their efficacy. Along with details about the kind of dataset used, the author has stated a Research Gap analysis. Numerous authors have used various machine learning algorithms on this common dataset, but the outcomes of the algorithms vary. the author has discussed these various algorithms and analyzed the findings in tabulation format. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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USE-Based Deep learning Approach to Detect Spammers on Twitter 7th

USE-Based Deep Learning Approach to Detect Spammers on Twitt...

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7th international conference on Smart Trends in computing and Communications, SmartCom 2023

作者： Dhaka, Deepali Saleem, Saima Mehrotra, Monica Department of Computer Science Jamia Millia Islamia New Delhi110025 India

ISBN: (纸本)9789819907687

Spammers disseminating obscene content on Twitter have been studied and detected using various hybrid features and machine learning approaches in past. To have greater insight into data prevailing in form of text on platforms like Twitter, their correct vector representation is paramount. Our goal is to understand what encoding techniques are more suitable for representing long text documents. We proposed a novel deep learning model consisting of a Universal Sentence Encoder (USE) as a feature extractor and an artificial neural network (ANN) as a classifier. We transform all the sentence vectors representing the tweets of a user into a document vector. these vectors are used as high-quality features to be processed by the artificial neural network for classification. To check the effectiveness of our proposed model, different sentence embedding techniques such as Doc2Vec, Infersent, and Sentence-Bert have been used and compared with the proposed model. Experimental results show that the proposed model outperforms all of them in terms of recall, precision, f1-score, and AUROC. Our results show that a simple ANN combined with USE-based deep learning approach can be a robust solution for the detection of spammers on Twitter. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Embeddings

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Smart-Contract Vulnerability Detection Method Based on Deep learning 7th

Smart-Contract Vulnerability Detection Method Based on Deep ...

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7th international conference on Smart computing and Communication, SmartCom 2022

作者： Hu, Zimu Tsai, Wei-Tek Zhang, Li Digital Society and Blockchain Laboratory Beihang University Beijing China Beijing Tiande Technologies Beijing China

ISBN: (纸本)9783031281235

With the rapid development of blockchain technology, smart contracts (SCs) applied in digital currency transactions have been widely used. However, SCs often have vulnerability in their code that allow criminals to exploit them to steal associated digital assets. Benefiting from the development of machine learning technology and the improvement of hardware performance, one can use deep learning techniques to analyze code and detect vulnerabilities. this paper proposes an innovative combination of opcode sequences and abstract syntax trees for source code parsing. And a method based on the combination of self-attention mechanism and bidirectional long-short term memory neural network is proposed to detect the vulnerability of SCs after word embedding. Experimentation results show that the two parsing methods can complement each other and effectively improve the accuracy of vulnerability detection. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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Identification of Cracks in High-Rise Buildings Using Drones and Deep learning 1

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7th international conference on Smart Trends in computing and Communications, SmartCom 2023

作者： Tyagi, Ria Mehta, Vishad Sankaranarayanan, Suresh Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science College of Computer Science and Information Technology King Faisal University Al Hofuf Saudi Arabia

ISBN: (数字)9789819907694

ISBN: (纸本)9789819907687

the conventional way of identifying structural defects and cracks in high-rise buildings is very labor-intensive, time-consuming, and requires a lot of equipment too. there has been work done in the past where machine learning and deep learning models were used for identification of cracks using image classification. this paper proposes a better way of identifying these cracks using drones and deep learning models. A custom-made shallow convolutional neural network was implemented and compared against other pretrained models like VGG16, RESNET50, Xception, Inception, and DenseNet on three different datasets based on several performance metrics. After analyzing which model performs the best in terms of higher accuracy, the CNN and VGG16 models were integrated with a drone to identify cracks. this would be beneficial in informing the inspection team on the development of cracks in the building walls and hence necessary actions can be taken. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Drones

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machine learning-Based Social Distance Detection: An Approach Using OpenCV and YOLO Framework 7th

Machine Learning-Based Social Distance Detection: An Approac...

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7th international conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2022

作者： Shetty, Deepthi Sarojadevi, H. Kakamari, Onkar Bharatesh Shetty, Savitha Shetty, Saritha Shenoy, Radhika V. Rashmi, B.N. Sneha Dechamma, M.S. Tanmaya, G. Department of CS and E NMIT Bengaluru India Department of CSE NMAM Institute of Technology Karkala India Department of MCA NMAM Institute of Technology Karkala India

ISBN: (纸本)9789811954818

In the fight against coronavirus, social distance has proven to be a very effective tool. To minimize the risk of the virus spreading through physical contact or proximity, the public is being advised to limit their contact with one another. It has previously been demonstrated that deep learning can solve a variety of issues. In our proposed system, we utilize Python, image analysis, and other learning techniques to monitor social distance in public areas and offices to corroborate the social distancing protocol. By analysing live video feeds from cameras, this tool will track if people remain within a safe distance from each other. With this tool, it is possible to predict people at malls, company offices, and stores to see if they are at an appropriate distance from one another. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Carbon-Energy Composite Flow for Transferred Multi-searcher Q-learning Algorithm with Reactive Power Optimization 7th

Carbon-Energy Composite Flow for Transferred Multi-searcher ...

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7th international conference on soft computing in Data Science (SCDS)

作者： Zhou, Jincheng Xue, Hongyu Qiannan Normal Univ Nationalities Sch Comp & Informat Duyun 558000 Guizhou Peoples R China Baoji Univ Arts & Sci Sch Comp Sci Baoji 721007 Peoples R China Key Lab Complex Syst & Intelligent Optimizat Guiz Duyun 558000 Guizhou Peoples R China

ISBN: (纸本)9789819904044;9789819904051

In the conventional carbon emission computation paradigm, the primary obligation falls on the electricity-generating side. However, according to the theory of carbon emission flow, the grid side and the user side are the primary producers of carbon emissions and must carry the majority of the obligation. To minimize carbon dioxide emissions, it is required to apply the carbon emission flow analysis approach to move the carbon footprint from the power generation side to the grid side and the user side in order to create more efficient energy saving and emission reduction plans. In order to accomplish the low-carbon, energy-saving, and cost-effective operation of the power system, the carbon-energy composite flow is included in the objective function of reactive power optimization in this study. In order to solve the reactive power optimization model of carbon-energy composite flow and to demonstrate the superiority of the Q-learning algorithm of migration multi-searcher, this paper designs a carbon-energy composite flow optimization model on the IEEE 118 node system and adds six algorithms, such as the genetic algorithm, in order to solve the reactive power optimization model of carbon-energy composite flow. Come in for a simulation exercise, including comparison. the simulation and verification outcomes of an example demonstrate that the suggested model and algorithm may achieve economical, low-carbon, and secure functioning of the power system.

关键词： Carbon emission Carbon-energy composite flow Transfer multi-searcher Q-learning algorithm Reactive power optimization

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