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A Novel Approach for Enhancing Customer Retention Using machine learning Techniques in Email Marketing Application 2

A Novel Approach for Enhancing Customer Retention Using Mach...

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2nd international conference on Paradigm Shifts in Communications Embedded Systems, machine learning and signal processing, PCEMS 2023

作者： Yadav, Dharmveer Singh, Jagriti Verma, Priti Rajpoot, Vikram Chhabra, Gunjan St. Xavier's College Department of Computer Science Jaipur India Manipal University Department of Management Jaipur India Sharda University Sharda School of Business Studies Greater Noida India Madhav Institute of Technology & Science Department of Information Technology Gwalior India Graphic Era Hill University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9798350310719

Customers are becoming more concerned about the QoS (Quality of Service) that businesses can give them in today's modern world. Because the services offered by many vendors are not very distinguishable, businesses have increased rivalry to maintain and improve their quality of service. Customer Relationship Management (CRM) systems are used to provide businesses with the capacity to boost their profitability by acquiring new consumers, establishing a continuous relationship with existing customers, as well as keeping more of their existing customers. machine learning (ML) algorithms are used in CRM (Customer Relationship Management) systems to evaluate personal & behavioral data from clients. This gives a company a competitive edge by improving the percentage of consumers they keep as clients. This research attempts to evaluate and assess the performance of several machine learning (ML) approaches to solve the subscriber prediction issue in email marketing. Different analytical machine learning methods that belong to diverse types of learning are selected for this work, especially classification and regressor techniques. Models were used on the dataset of emails that comprises twenty-three features. The experimental outcome demonstrates that RF (Random Forest) & Adaboost outperform all other machine learning methods with an almost similar accuracy of 95%. KNN and ensemble approach achieved the highest 89.8% and 91% R2 scores. The comparison found that the ensemble approach outperforms state-of-arts machine learning methods regarding accuracy, error value, and R2 score. © 2023 IEEE.

关键词： Quality of service

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machine learning Approaches for Automated Detection and Classification of Dysarthria Severity 2

Machine Learning Approaches for Automated Detection and Clas...

引用

2nd international conference on Electronics, Energy and Measurement, IC2EM 2023

作者： Hamza, Amina Addou, Djamel Kheddar, Hamza University of Sciences and Technology Houari Boumediene Speech and Signal Processing Lab Algiers Algeria University of Medea Lsea Lab. Faculty of Technology Dept. Electrical Engineering Medea Algeria

ISBN: (纸本)9798350314243

Dysarthria, a speech disorder caused by neuro-motor problems resulting in impaired articulation, requires an assessment of its severity for diagnostic and monitoring purposes. Additionally, accurate severity classification facilitates the development of automated dysarthric speech detection and classification systems. This paper presents a comprehensive investigation into detecting dysarthric voices within a collection of normal voice samples, followed by the dysarthria severity classification utilizing neural network frameworks, specifically long short-term memory network (LSTM) and recurrent neural network (RNN). The study employs various features including Mel frequency cepstral coefficients (MFCC), formants, prosodic parameters, and voice quality. The performance of these models is evaluated against a baseline support vector machine (SVM) classifier using the Nemours corpus database. Remarkably, the highest classification accuracy achieved for this corpus is 99.69%. Detailed analysis demonstrates that selecting an appropriate neural network architecture yields superior performance compared to the conventional SVM classifier. © 2023 IEEE.

关键词： Support vector machines

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Classification of Plant Leaf Diseases Identification Using Deep learning 2

Classification of Plant Leaf Diseases Identification Using D...

引用

2nd IEEE international conference on Networking and Communications, ICNWC 2024

作者： Revathy, Nehru Meenakshi, A. SRM Institute of Science and Technology Dept of Computer Science and Applications Tamil Nadu Chennai600026 India

ISBN: (纸本)9798350365269

This research addresses the imperative task of plant disease identification, specifically focusing on leaves. The approach involves a meticulous methodology encompassing image acquisition, pre-processing, and Convolutional Neural Networks (CNNs), emphasizing the crucial need for early disease detection in effective agricultural management. Recognizing the limitations of existing models, often confined to specific diseases or plants, the study advocates for a universal approach to enhance the adaptability of CNNs across diverse plant species, promoting accurate disease identification. Augmentation techniques are integrated to enhance dataset variability, ensuring the training of robust models capable of accurately identifying a broad spectrum of plant diseases. Implemented in MATLAB 2019b with its deep learning toolbox, the methodology achieves remarkable training, testing, and validation accuracies of 96.7%, 98.9%, and 98.7%, respectively, showcasing superior model performance. This research not only contributes to technological advancements in agriculture but also underscores the significance of accurate and timely disease detection for sustainable agricultural practices. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on fault prediction optimization of CNC machine tools based on ensemble learning 5

Research on fault prediction optimization of CNC machine too...

引用

5th international conference on signal processing and Computer Science, SPCS 2024

作者： Yao, Li Wan, Yuwen Damjanovic, Srdjan Xin, Zhengfei Shanghai Polytechnic University 2360 Jinhai Road Pudong Shanghai China University of East Sarajevo Faculty of Business Economics Bijeljina Sarajevo Bosnia and Herzegovina

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

As the manufacturing industry develops towards high precision and intelligence, CNC machine tools play an important role in production. The occurrence of failures not only reduces production efficiency but also increases costs. In order to improve the accuracy and efficiency of fault prediction, this paper establishes an integrated learning model for CNC machine tool fault prediction by stacking ensemble learning algorithms and combining decision trees, support vector machine (SVM), random forests and other algorithms. The fault-related features are optimized through data preprocessing and feature engineering, and the results are finally obtained. The experimental results show that the ensemble learning model is superior to the single model in result indicators, especially recall rate and F1 score, reaching 0.6393 and 0.7027. This verifies that the ensemble learning model proposed in this paper has better performance in improving the performance of CNC machine tool fault prediction and can better solve the problems related to prediction in CNC machine tool faults. © 2025 SPIE.

关键词： Prediction models

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'Empowering Education: The Role of Artificial Intelligence in Supporting Students with Disabilities' 2

'Empowering Education: The Role of Artificial Intelligence i...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Tripathi, Veena Palak Bali, Antra Sharma, Plaksha Chadha, Sukriti Sharma, Bhoomi Model Institute of Engineering & Technology J&K Jammu India

ISBN: (纸本)9798350366570

The integration of artificial intelligence (AI) in education has revolutionized adaptive learning technologies and greatly supports the learners with disabilities. This study explores how AI solutions help make education more inclusive by addressing challenges related to mental, visual, and hearing impairments. By reviewing existing research and survey data, we highlight tools like speech-to-text applications, intelligent tutoring systems, and personalized platforms that improve learning experiences for students with disabilities. Key AI tools - such as speech-to-text applications, language translation services, intelligent tutoring systems, and personalized learning platforms - are crucial in meeting the diverse needs of students. Moreover, the study addresses the ethical, technical, and adoption challenges that are associated with the integration of AI in educational settings. These include the issues of privacy, algorithmic bias, and the need for adequate training for educators. The paper offers recommendations for policymakers, educators, and researchers encouraging the development and responsible adoption of these technologies to ensure disabled students have the same academic opportunities as their non-disabled peers. Additionally, the findings highlight the necessity for increased awareness and education on current AI trends for disabled children to maximize the benefits of these technologies. This paper contributes to the ongoing discourse on AI in education and disability studies, advocating for a future where AI-powered adaptive learning technologies empower all students to reach their full potential. © 2024 IEEE.

关键词： Contrastive learning

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Multiband Radar signal Fusion and Extrapolation Method Based on Transformer Model 2

Multiband Radar Signal Fusion and Extrapolation Method Based...

引用

2nd IEEE international conference on signal, Information and Data processing, ICSIDP 2024

作者： Gong, Chunxu Li, Weidong Lu, Ruiming Wang, Rui Beijing Institute of Technology Radar Technology Research Institute Beijng China

ISBN: (纸本)9798331515669

The improvement of range resolution is crucial for enhancing the capability of imaging radars to detect small targets, such as drones. However, due to technical and cost limitations, current radars cannot indefinitely increase hardware bandwidth to enhance range resolution. To address this challenge, the multiband fusion and extrapolation technique has emerged. This method uses multiple discrete sub-band radar signals and fuses them to extrapolate the missing frequency bands, effectively achieving a wideband radar signal. Traditional approaches primarily rely on constructing signal models and estimating model parameters to accomplish fusion and extrapolation. This paper is the first to apply machine learning techniques to the field of multiband fusion and extrapolation, proposing a radar multiband signal fusion and extrapolation method based on a Transformer model. By training the Transformer network, the method enables accurate estimation of unknown frequency band signals. The effectiveness of the approach is validated through electromagnetic simulation data of drone echoes, demonstrating significantly improved estimation accuracy compared to traditional signal fusion and extrapolation methods. © 2024 IEEE.

关键词： Drones

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An Analytical framework for Classification of ElectroEncephaloGram (EEG) Motor Imagery tasks using Deep learning Techniques 2

An Analytical framework for Classification of ElectroEncepha...

引用

2nd IEEE international conference on Emerging Research in Computational Science, ICERCS 2024

作者： Sathish, M. Geetha Devasena, M.S. Saravanan, B. Sudharsan Kumar, S. Kumaraguru College Of Technology Computer Science and Engineering Tamil Nadu Coimbatore India Sri Ramakrishna Engineering College Computer Science and Engineering Tamil Nadu Coimbatore India Kumaraguru College Of Technology Information Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331534950

Motor Imagery (MI) on Electroencephalogram (EEG) signals is a common approach within Brain-machine Interface (BMI) models, extensively applied in medical rehabilitation and artificial machinery. BMI serves as a direct link between the brain's electrical activity and external devices, such as computers or artificial limbs. BMIs are regularly expressed at researching, directing, helping, extending, augmenting, and correcting the people cognitive or motor sensory devices. The accomplishment of BMI's ranges from non-invasive to invasive device based on the tight electrodes gets into brain tissue. Now a days, Deep learning (DL) is playing a crucial role in MI EEG based BMI. The framework focuses on how Deep learning (DL) is enhancing MI-based BMIs by improving signal processing and classification. Key aspects covered include preprocessing, feature extraction, classification techniques, and performance evaluation. The analysis highlights the strengths and challenges of various DL approaches, providing insights to advance the development of reliable and efficient BMI systems. The models were analyzed with respect to the domains of undirected and discriminated analysis using belief networks. © 2024 IEEE.

关键词： Deep learning

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Supervised machine learning approaches for medical data classification 2

Supervised machine learning approaches for medical data clas...

引用

2nd international conference on Artificial Intelligence and signal processing, AISP 2022

作者： Dalai, A.K. Jena, A.K. Ramana, B.V. Maneesha, B. Panda, Nibedan School of Computer Science and Engineering VITAP University India Department of Information Technology Aditya Institute of Technology and Management AP Tekkali532201 India Department of CSE Presidency University Yelahanka Bengaluru560064 India

ISBN: (纸本)9781665442909

Recently there is an emergent curiosity among researchers to apply machine learning algorithms over diversified real world complications to get simpler *** notion behind this briefing is to represent the basic machine learning algorithms and its applicability in current research. Broadly machine learning algorithms falls to the category of either supervised or unsupervised learning technique. In this paper we have discussed supervised machine learning techniques with its simplicity to apply over various problem areas and simultaneously the challenges for such algorithms. Furthermore SVM and Random Forest (RF) are utilised learn, categorise, and compare cancer, liver, diabetes, iris, and heart data in this study. For all considered data sets, the results of SVM and RF are compared. The results are properly analysed in order to develop better prediction learning techniques. © 2022 IEEE.

关键词： Support vector machines

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Design and Development of an Automated Kiosk for COVID-19 detection and mitigation using machine learning Algorithms 2

Design and Development of an Automated Kiosk for COVID-19 de...

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2nd IEEE international conference on Advances in Information Technology, ICAIT 2024

作者： Kiran Kumar, B.M. Subbarayappa, Shreyanka Dept. of EEE M S Ramaiah University of Applied Sciences Bangalore India Dept. of ECE M S Ramaiah University of Applied Sciences Bangalore India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

'The Corona Virus Disease (COVID-19) is a contagious illness that has spread globally since December 2019. Coughing, sneezing, and even encountering objects used by an infected individual, is the possibility of Corona virus spreading. It is the utmost priority in today's time to control and mitigate this virus's spread. This is achieved by detecting some of the symptoms using non-clinical methods (as clinical methods are time-consuming), which detect the possibility of the presence of the virus in the human body. A solar-powered kiosk with a face-mask detector, a contactless temperature sensor, an oxygen saturation sensor, an automatic hand sanitizer dispenser, and an entry flip gate is designed and built to monitor and manage the spread of covid-19 illness. The kiosk system with a camera placed detects the facial mask of the person taking an entry using machine learning Algorithms (MLA). The system denies access to those without the mask and warns those whose masks are below the nose and denies entry through the Kiosk. Also, a contactless temperature sensor and SpO2 sensor in the Kiosk detect the threshold temperature and oxygen saturation level of the human for access entry. The automated contactless sanitizer is dispensed once the predefined parameters are met. When these tasks are completed, a flip gate will automatically open, allowing people to exit the Kiosk. The Kiosk is driven by a 10-watt solar PV (Photovoltaic) and from an electric outlet. The Raspberry Pi is the system's central processing unit, regulating several actuators. The Kiosk is portable and can be deployed in various locations, including malls, offices, and educational institutions. © 2024 IEEE.

关键词： Temperature sensors

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Classifying Parkinson's Disease Using Speech Features 2

Classifying Parkinson's Disease Using Speech Features

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2nd international conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Soliman, Hala A. Elshourbagi, Mohammed S. Dahy, Malak A. Osman, Mena M. Kamal, Bassant A. Elaziz, Mohamed Abd El-Rashidy, Nora Galala University Faculty of Computer Science and Engineering Suez435611 Egypt Intelligence-Kferelshikh University-kaferelshikh Machine learning and information retrieval-faculty of artificial Egypt

ISBN: (纸本)9798350362633

This scientific paper focuses on the detection of Parkinson's Disease in individuals using vocal/speech features. Specifically, the study records an individual's pronunciation of the short 'a' sound (\\ \\\ \\\ae) and applies various signal processing Algorithms to process the recorded data. To address the data's class imbalance, both oversampling and undersampling techniques are implemented, and their results are compared. Additionally, a feature selection algorithm is employed to eliminate noise from the dataset while retaining the relevant features necessary for the model. Logistic regression, Support Vector machines (SVM), decision trees, and Ensemble learning methods are utilized to determine the likelihood of an individual having Parkinson's Disease. Furthermore, SHAP (SHapley Additive exPlanations) analysis is conducted to gain a deeper understanding of the impact of features on class determination for each instance. The Random Forest model achieves an accuracy of approximately 95% when trained on the oversampled dataset, surpassing the accuracy obtained from the undersampled dataset. Additionally, a graphical user interface (GUI) is successfully developed to predict the diagnosis of Parkinson's disease. © 2024 IEEE.

关键词： Neurodegenerative diseases

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