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检索条件"任意字段=4th International Conference on Machine Learning and Soft Computing, ICMLSC 2020"

共 564 条记录，以下是11-20 订阅

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Model of Sentiment Analysis in Different Platforms on Social Media 4

Model of Sentiment Analysis in Different Platforms on Social...

引用

4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Vasanthi, P. Madhu Viswanatham, V. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515720

Reviews provide useful information for making decisions. Online stores allow customers to express their opinionson goods and services. Many of these opinions are available tothe public as reviews. Customer reviews are very interesting to manufacturers, retailers, and customers. Such reviews are valuable to consumers since they help them choose which brand or product to purchase. these subjective reviews can help manufacturers identify areas where their product needs improvement. Because there are so many reviews available online foranalysis, manually reading them is not cost effective. An automated system that offers a summary of user sentiments is required to optimize this laborious task. Sentiment analysis is a branch of study that examines how reviews and opinionated literature reflect the sentiments or opinions of readers. In the process of sentiment analysis, machine learning, deep learning, and optimized algorithms are used to assess sentiments and emotions and generate a distilled output for use in decision making. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

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Enhancing Consumer Behavior Insights in Digital Marketing through Artificial Intelligence Integration 4

Enhancing Consumer Behavior Insights in Digital Marketing th...

引用

4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Nivetha, V. Ramu, Murugan Mohan, Anju Chennai77 India

ISBN: (纸本)9798331515720

In today's digital age, understanding consumer behavior is paramount for businesses aiming to optimize their marketing strategies. the vast amount of data generated across various digital platforms, such as social media, e-commerce sites, and web analytics, presents both opportunities and challenges. this study presents a novel approach to enhancing consumer behavior insights in digital marketing through the integration of a Cascaded Long Short-Term Memory (LSTM) network with a Support Vector machine (SVM). Over a two-year period, a comprehensive dataset comprising approximately 100,000 data points was collected from various digital platforms, including social media, e-commerce sites, and web analytics tools. the proposed model leverages the sequential learning capabilities of LSTM and the robust classification power of SVM, achieving an impressive testing accuracy of 98.86%. Extensive experimentation and comparison with other machine learning models, such as Random Forest, XGBoost, and Neural Networks, demonstrated the superior performance of the proposed method. this research not only highlights the effectiveness of AI in consumer behavior analysis but also paves the way for more personalized and efficient marketing strategies. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

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COVID-19 Detection from CT Scan Images using Transfer learning Approach 24

COVID-19 Detection from CT Scan Images using Transfer Learni...

引用

8th international conference on machine learning and soft computing, icmlsc 2024

作者： Bajaj, Navjot Singh Yadav, Pavinder Gupta, Nidhi National Institute of Technology Hamirpur Himachal Pradesh Hamirpur India National Institute of Technology Kurukshetra Haryana Kurukshetra India

ISBN: (纸本)9798400716546

In the past years, since 2020, the outbreak of COVID-19 has alarmed the world with the speed and its spread around the world. this raised the demand of early, accurate and automated detection system for the COVID-19 as there is a scarcity of manpower in medical field. this attracted many researches using deep learning to build COVID-19 detection model. For the diagnosis of COVID-19, computed tomography scanning are being used as more accurate, non-invasive and efficient method in real-time. In this work, we have proposed a model using six different image classification techniques of deep learning on CT scan images and compared the accuracy to find the most suitable and reliable model for transfer learning to achieve best result on ResNet50 as 97.19% training and 98.05% testing accuracy. the model will automate the process of detection of the COVID-19, leading to the advancement in the field of smart health-care. © 2024 ACM.

关键词： COVID-19

来源：评论

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ESIC 2024 - 4th international conference on Emerging Systems and Intelligent computing, Proceedings

ESIC 2024 - 4th International Conference on Emerging Systems...

引用

4th international conference on Emerging Systems and Intelligent computing, ESIC 2024

ISBN: (纸本)9798350349856

the proceedings contain 137 papers. the topics discussed include: recognition of emotional speech using MFCC and machine learning technique;target driven autoencoder: a supervised learning approach for tumor segmentation;early detection of Parkinson’s using machine learning;predicting the stock market index using GRU for the year 2020;detection of bone fracture using deep learning;a pragmatic approach of heart and liver disease;predominant musical instrument detection using ensemble learning;skin cancer detection and classification using deep learning;air pollutant concentration analysis and prediction using ai;a comparative study of deep learning techniques for network intrusion detection;and an efficient approach for optimizing the CA selection search space in a blockchain network.

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machine learning for Real-Time Language Translation: A Server-Side Approach to Overcome Cross-Cultural Communication Challenges 4

Machine Learning for Real-Time Language Translation: A Serve...

引用

4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Elango, S. Mohanaprakash, T.A. Siva, D. Gowtham, A. Sasikumar, A.N. Sunitha, T. Department of Computer Science and Engineering SOET CMR University Bengaluru India Department of Computer Science and Applications SRM Institute of Science and Technology Chennai India Madanapalle Institute of Technology & Science AP India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Artificial Intelligence & Data Science Saveetha Engineering College Chennai India

ISBN: (纸本)9798331515720

the evolution of human civilization has been intrinsically linked to advancements in technology, leading to the development of multiple languages as mediums of communication. However, this linguistic diversity poses significant challenges in cross-cultural communication. Despite numerous efforts and achieving near state-of-the-Art results in language translation, a universal solution remains elusive. this research study proposes an advanced approach to machine translation by utilizing server-side machine learning to enable seamless communication across various languages. By sending GET or POST requests through JQuery, users can efficiently translate text. the process is structured into three key steps: speech recognition (conversion of spoken words to text), translation using machine learning algorithms, and speech synthesis (conversion of translated text to audio). this approach aims to enhance real-Time translation capabilities, offering a more effective and user-friendly solution for overcoming language barriers. © 2024 IEEE.

关键词： Adversarial machine learning

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Bi-objective lion swarm optimization based on teaching and learning algorithm 2022

Bi-objective lion swarm optimization based on teaching and l...

引用

6th international conference on machine learning and soft computing (icmlsc)

作者： Zhang, Qi Jiang, Mingyan Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Peoples R China

ISBN: (纸本)9781450387477

To address the problem that it is difficult to obtain a good quality and uniformly distributed Pareto optimal solution set for a complex biobjective system model, this paper proposes a Teaching-learning-based Bi-objective Lion Swarm Optimization algorithm (TLBLSO) for solving the bi-objective system uniform Pareto solution set problem. the idea and mechanism of teaching and learning optimization algorithm are introduced into the Lion Swarm algorithm. that is, the knowledge level of the whole group is improved by the way of individual teachers imparting knowledge and students exchanging knowledge, which effectively improves the spatial searching ability of the lion swarm. By comparing with other optimization algorithms, the experimental results show that the proposed TLBLSO can obtain well-distributed optimization solutions, which verifies the superiority of the proposed algorithm in this paper.

关键词： teaching and learning bi-objective lion swarm optimization algorithm uniform distribution

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Improved Alzheimer's Disease Early Prediction using machine learning Method : A Comprehensive Diagnostic Approach with Random Forest Classifier 4

Improved Alzheimer's Disease Early Prediction using Machine ...

引用

4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Munawar, Madiha Monika Singh, T. Kishor Kumar Reddy, C. Hanafiah, Marlia Mohd Stanley College of Engineering and Technology for Women Department of Computer Science and Engineering Hyderabad India Universiti Kebangsaan Malaysia Faculty of Science and Technology Malaysia

ISBN: (纸本)9798331515720

Alzheimer's Disease (AD) often affects the elder persons and is the prevalent kind of Dementia. AD has a huge expense, especially when it comes to the treatment. AD is a main reason the older generations die whereas when Alzheimer Disease is detected early there will be minimal deaths and accurate diagnosing likely occurs in most cases. Using anonymized Oasis longitudinal MRI data, the study trains several machine learning algorithms to identify AD patients. Analyzing the test result includes utilizing different learning strategies or algorithms e.g., Ensemble Random Forest (RF), Hierarchical Decision Tree (DT), Logistic Regression (LR), Convolutional Neural Network (CNN), Deep Neural Network binary classifier (DNN), soft voting classifier (SVC), transfer learning approaches (TLA), Support Vector machine (SVM) and boosting AdaBoost in reference of their Accuracy, Error Rate, AUC (area under the curve), Time Taken and Recall for each method. the obtained results concluded that the proposed Random Forest classifier achieves higher accuracy (92.84%), Error Rate (7.16%), AUC (95%) (Area Under Curve) and Recall (89%). the Random Forest classifier also achieves higher Time Taken that is 483.654 sec. © 2024 IEEE.

关键词： Alzheimer's Disease Decision Trees Deep learning Dementia learning Approaches machine learning Random Forest

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Severity estimation of Coffee leaf disease using U-Net and pixel counting mechanism 24

Severity estimation of Coffee leaf disease using U-Net and p...

引用

8th international conference on machine learning and soft computing, icmlsc 2024

作者： Deb, Sagar Deep Kashyap, Rachit Abhishek, Arjun Jha, Rajib Kumar Department of Electrical Engineering Indian Institute of Technology Patna India

ISBN: (纸本)9798400716546

Coffee leaf disease (CLD) is a major threat to coffee production worldwide, causing significant economic losses for farmers. Accurate and timely estimation of the severity of CLD is crucial for implementing effective control measures. In this paper, we propose a novel approach for severity estimation of CLD using a combination of the U-Net deep learning architecture and a pixel counting mechanism. the U-Net architecture is used to generate the segmented masks of the coffee leaf images. Finally to estimate the severity of CLD, we introduce a pixel counting mechanism that quantifies the proportion of affected pixels in the segmented regions. By summing the total number of diseased pixels and dividing it by the total number of pixels in the region, we obtain a severity score that reflects the extent of CLD infections. the algorithm proposed by us achieves a five class accuracy of 89.10%. © 2024 ACM.

关键词： Deep learning

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Integrating Deep learning, machine learning, AI, IoT and Data Science for Future Innovations 4

Integrating Deep Learning, Machine Learning, AI, IoT and Dat...

引用

4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Gupta, Shipra Kumar, Vijay Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Hill University Department of Physics Uttarakhand Dehradun India

ISBN: (纸本)9798331515720

machine learning (ML), Deep learning (DL), Internet of things (IoT), Artificial Intelligence (AI) and Data Science are integrated to create innovative solutions that are powering industries today like never before. this paper reviews these technologies and how they can work together to transform industries like healthcare, finance, and manufacturing, as well as the immensely important space of smart cities. With ML and DL-driven analytics for predictions, IoT-based real-Time data capture mechanisms, AI making informed decisions along data science generating insights from the data makes organizations efficient in every aspect as well as reliable and innovative. Some example applications are predictive maintenance in manufacturing, personalized medicine in healthcare, algorithmic trading in finance and the development of smart/sustainable urban environments. While these potboilers offer a hopeful advantage, they also come with some hurdles, data privacy/security, integration complexity and the need for high-end technical skills being the topmost. In this manuscript, some case studies of success are highlighted in the belonging field and discuss the challenges for incorporation today along with future directions for seamless integration aimed at promoting even greater innovation. © 2024 IEEE.

关键词： Smart manufacturing

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A Systematic Review of Data Augmentation Techniques in Natural Language Processing for Sentiment Analysis using machine learning Techniques 4

A Systematic Review of Data Augmentation Techniques in Natur...

引用

4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Patil, Uday Dhanaji Sudhakaran, Pradeep SRM Institute of Science and Technology Department of Computer Science & Engineering Tamil Nadu Kattankulathur603203 India Kolhapur India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798331515720

Due to the enormous growth of mobile technology, Social media has created a crucial platform to express the feelings and opinions of people. Based on these opinion considerations, it is very beneficial for making strategic decisions in organizations, businesses, trading, political entities, and so on. To achieve effective improvement, sentiment analysis is taken into account, and the public opinion polarity for further reference. In this perspective, the main contribution of the study is to explore the benefits and limitations of various learning techniques under the evaluation of sentiment analysis. Generally, Sentiment analysis is evaluated by Natural language processing tasks with various learning techniques, to achieve effective generalization ability and efficiency, data augmentation techniques are integrated, which reduces the overfitting problem effectively. To explore the limitations of the traditional methods, 25 literary papers were taken into account to analyze various approaches, and methods under the utilization of distinct datasets. Together with this, the achieved evaluation metrics along with the achievements are also discussed briefly in the study. Additionally, the study provides the shortcomings and future scope of the conventional methods with a detailed description. the main objective of the survey includes knowledgeable categorization, which aims to explore a new model with the illustration of recent trends and achieve effective data augmentation in future analysis. © 2024 IEEE.

关键词： Adversarial machine learning

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