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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1856 条记录，以下是61-70 订阅

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Maximizing Development Efficiency: A Comprehensive Guide to Frameworks and Libraries

Maximizing Development Efficiency: A Comprehensive Guide to ...

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Disruptive Technologies (ICDT), international conference on

作者： Bilal Ahmed Rohini Mahajan Amit Jain Vikas Attri Prince Arora Mukesh Kumar School of Computer Applications Lovely Professional University Phagwara Punjab INDIA Department of Computer Application Chandigarh School of Business Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Department of Computer Application Advanced Centre of Research & Innovation (ACRI) Chandigarh School of Business Chandigarh Group of Colleges Jhanjeri Mohali Punjab India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

This article discusses the differences between the baking of coding structures and libraries giving an easy-to-understand perspective for anyone. The frameworks are compared to blueprints; they determine the consistent construction of the software by emphasizing how components should be assembled as well as how they interact with each other. On the contrary, libraries appear as a box of toolkits, each containing everyday programs and functions programmers can utilize to formulate instructions corresponding to different tasks. Frameworks equip developers with standards and uniformity to make their work faster, more efficient, and more cohesive. At the same time, the framework structure may restrict developers from getting too creative or to add features that does not sit well with their projects, such as relying on conventions. In contrast, library encourages flexibility and modularity as developers are given the freedom to decide which functionalities they want and be able to integrate the libraries to provide the desired effects for their application. It enables the developers to craft solutions which cater for the specific requirements of their project and it also allows code reuse and promoting rapid development. In this way, developers express their suppositions which in essence helps to find the most suitable tool for their projects. Such comprehension grants the developers the wisdom to weight structure and freedom and thus ultimately yielding more proficient and scalable software programs.

关键词： Java Video on demand Organizations machine learning Libraries Software Encoding Teamwork Web sites Standards

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Predicting Customer Lifetime Value in E-Commerce: A Data-Driven Approach to Enhance Customer Retention Strategies

Predicting Customer Lifetime Value in E-Commerce: A Data-Dri...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Sonali Dey Radha D. V. S. Kirthika Devi Department of Computer Science Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Department of Electrical and Electronics Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Predicting Customer Lifetime Value (CLV) is one of the most critical tasks that businesses undertake in order to improve customer retention and optimize marketing strategies. The present paper proposes a predictive model for CLV using several machine learning techniques. The features considered include customer demographics, purchasing behavior, and transaction history. All these features were preprocessed using feature engineering and missing value handling. Several regression models, including Linear Regression, Decision Tree, and Random Forest Regressor, were compared in terms of predictive performance. The study includes the decision trees, hash tables as advance data structures such that they can handle large datasets effectively. Of the ones tested, Random Forest Regressor proved to be superior to the others, having the lowest Mean Squared Error (MSE) and highest R 2 score, thereby validating its robustness in identifying the complex patterns within the data. The work highlights the time and space complexities of different models. This study contributes to the application of machine learning in customer analytics and provides a foundation for future research to integrate more diverse data sources and enhance model performance through hyperparameter optimization. Future work will also explore real-time prediction capabilities and the integration of multimodal data to further refine CLV forecasting.

关键词： machine learning algorithms Soft sensors Linear regression Predictive models Hyperparameter optimization Robustness Real-time systems Time complexity Regression tree analysis Random forests

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Email Spam Detection Ensemble Methods

Email Spam Detection Ensemble Methods

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Disruptive Technologies (ICDT), international conference on

作者： Sudhir Prakash Gond Sudeept Singh Yadav Md Arfat Alam Satyajit Sahoo School of Computer Application and Technology (SCAT) Galgotias University Greater Noida Uttar Pradesh India School of Computer Science and Engineering (SCSE) Galgotias University Greater Noida Uttar Pradesh India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Finding on-demand messages has become a key task in today's generation of virtual verbal exchanges as emails and unsolicited messages flood inboxes. Spammers maximize email messaging system provisioning, especially for the scope of unwanted content that can dampen net overall performance, staff. Loan the records can catch up, and degrade the user experience. This paper provides a thorough look at using the device for unwanted mail detection methods to identify models. Several styles were observed including Naive Bayes, Support Vector machine (SVM), Decision Trees, and Random Forest, Voting Classifier, and other ensemble methods. The data set used consisted of 1,00,000 text messages, equally cut into spam and ham (unsolicited mail) between. Our experimental results show that ensemble methods, especially random forest and alternative classification trees, provide the highest accuracy and precision in junk mail detection, achieving an accuracy of 0.97975. The experiment demonstrates the tool's knowledge-enhancing capabilities; it will likely enhance the security of e-mail messages with powerful junk mail detection.

关键词： Support vector machines Accuracy Filtering Unsolicited e-mail Clustering algorithms Feature extraction Classification algorithms Ensemble learning Random forests Postal services

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Leveraging Social Media Data to Improve Disaster Response and Recovery Efforts Using Artificial Intelligence Techniques: A Comprehensive Review 3rd

Leveraging Social Media Data to Improve Disaster Response an...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Pimpalkar, Shilpa P. Sai Madhavi, D. Rao Bahadur Y. Mahabaleswarappa Engineering College Ballari Visvesvaraya Technological University Karnataka Belagavi590018 India AISSMS Institute of Information Technology Pune India

ISBN: (纸本)9783031862984

The increased use of social media platforms, which provide user-generated data in real-time, has significantly impacted the disaster response and recovery field. The application of artificial intelligence (AI) techniques to parse multimodal datasets from social media has emerged as a practical and feasible strategy to enhance disaster management efficacy in response to this paradigm shift. This research provides a thorough overview of the most recent advancements in AI-driven social media data analysis methods to aid in disaster response and recovery operations. The study explains the wide range of available AI-driven solutions, covering state-of-the-art techniques such as computer vision, deep learning, machine learning, and natural language processing (NLP). This study presents a review based on 44 recent papers, 32% of which are survey papers, 64% on an implementation approach and 4% case study. Survey papers selected for the comprehensive review provide valuable insights covering 1715+ papers. The findings offer valuable input related to the dataset used in recent research, the proposed methodology, and future directions. The research makes a significant contribution to synthesizing the findings for future work in the proposed area. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Performance Enhancement of DBSCAN Density-Based Clustering Algorithm in Data Mining

Performance Enhancement of DBSCAN Density-Based Clustering A...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Neha Tyagi Rashmi Mishra Sitanshu Kumar Patel Somraj Karki Department of Computer Science and Engineering Amity University Noida India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

In order to facilitate better data analysis, clustering is a prominent data mining approach which separates data into separate categories based on their similarities and distinctions. It’s a method of unsupervised learning which may generate a model with a training dataset. Throughout this study, the Euclidian distance and EPS (Epsilon) value has been determined using the DBSCAN technique based on the similarities as well as disparity for the input data. Moreover, Euclidian distance is dynamically determined using the back propagation method; a simulation study is carried out that demonstrates both speed and precision improvements. DBSCAN, or spatial clustering based on density of application with noise, is a common clustering technique for geographic datasets that can locate clusters of any shape and automatically detect noise points. To deal with the problem, an improved DBSCAN technique based on the selection of features (FS-DBSCAN) has been suggested. However, the algorithm proved unstable while handling huge dimensional data. Many simulation via real-world datasets testify the algorithm’s efficiency. Comparison of the proposed algorithm to the DBSCAN algorithm demonstrates its benefits. Structures within the collections may be identified by dividing of collections of products into classes and clusters, with a focus on increasing the resemblances of objects within each cluster and lowering the similarities of things belonging to different clusters.

关键词： Backpropagation Training machine learning algorithms Shape Noise Clustering algorithms Spatial databases Data mining Object recognition Unsupervised learning

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CNN-KNN Model for Assessing Hair Health in Telogen Effluvium

CNN-KNN Model for Assessing Hair Health in Telogen Effluvium

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Gotte Ranjith Kumar Shubham Deepak Banerjee School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The abstract provides a concise overview of a study that focuses on predicting hair breakage levels using a CNN-KNN hybrid model. The study evaluates the model's performance across five Breakage Degrees (BDs) by utilizing precision, recall, and F1-score metrics. Among these degrees, BD2 stands out with precision, recall, and F1-score values of 94.20%, indicating the model's ability to accurately classify hair damage levels between 21% and 40%. The support values, which indicate the number and proportion of instances in each BD, demonstrate a well-balanced distribution across all categories, ensuring the model's robust training. Researchers used a dataset of 10,050 images to reach an impressive 94.02% accuracy in predicting hair breakage levels caused by Telogen Effluvium in the research. Moreover, the model achieves an impressive overall accuracy of 98%, showcasing its effectiveness in distinguishing between various degrees of hair breakage severity. This high accuracy level highlights the model's potential application in clinical settings for dermatological diagnostics and personalized treatment planning in hair care management. The abstract underscores the CNN-KNN hybrid approach as a promising methodology for automated hair health assessment, providing healthcare professionals with a dependable tool to improve diagnostic accuracy and treatment efficacy. By incorporating machine learning techniques, specifically convolutional and k-nearest neighbor algorithms, the study contributes to the advancement of computational methods in dermatology and cosmetology. The results suggest practical implications for enhancing patient care through early detection and intervention of hair-related conditions, ultimately supporting better health outcomes and quality of life for individuals affected by hair breakage concerns.

关键词： Hair Training Measurement Accuracy Dermatology Medical services machine learning Predictive models Planning Reliability

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An Artificial Bee Colony Improved Deep Neural Network Prototypical for Controlling Unprovoked Stroke Data in Iot Environment 1

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3rd international conference on machine Intelligence and Smart Systems, MISS 2023

作者： Fernandez, Shilpa Jackson Chacko, Prabha Biju Khambra, Geetanjli Department of Computer Science and Application The Bhopal School of Social Sciences M.P. Bhopal India

ISBN: (数字)9783031317231

ISBN: (纸本)9783031317224

There is a wealth of information available about diseases, environments, organized and unstructured data, and people’s awareness of their own health status, making healthcare a promising new topic of study. Smart watches, fitness bands, sensors, and healthcare apps are all examples of how technological progress is improving the healthcare system. The development of these tools marked a significant advancement in medical care. That has resulted in widespread anxiety about the current health situation and its potential implications. Therefore, precise health data analysis and healthcare services constitute a vital component in the healthcare area. Meanwhile, machine learning is a well-known method that has been widely embraced for healthcare data analysis and prediction. The purpose of ML is to make correct diagnoses and quick decisions. The primary focus of this study is on the need of prompt and correct disease diagnosis. Data imputation, accuracy, outliers, and environmental difficulties are chosen as potential problems, and the best possible solution for each of these is sought for here. Optimal feature selection is a key factor in boosting DNN’s performance. Therefore, an artificial bee colony based method is used to extract the relevant features for the stroke dataset. The effectiveness of the DNN approach is measured using a variety of criteria, including accuracy, precision, and recall. All of the features from the stroke dataset, together with the feature weighting technique, are used in the simulations (ABC-FS). It can be seen that the prediction rate of the DNN technique is greatly enhanced by the feature weighting technique. It is also said that the prediction rate of the DNN model is improved by up to 7.5% when relevant features are included. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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Methodologies for Ensuring Robust Cybersecurity in Health Care Agencies for the Purpose of Securing Medical Internet of Things Devices

Methodologies for Ensuring Robust Cybersecurity in Health Ca...

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Disruptive Technologies (ICDT), international conference on

作者： S.B. Prakalya Rajesh David Swapna Neerumalla J. Jegan S Sajithra B. Nancharaiah Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Application Architect KUKA U.S. Holdings Company LLC Austin TX USA Department of Computer Science and Engineering Vignana Bharathi Institute of Technology Hyderabad India Department of Computer Science and Engineering School of Technology The Apollo University Chittoor Andhra Pradesh India Department of Computer Science and Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Usha Rama College of Engineering and Technology Telaprolu Andra Pradesh India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Improvements in patient tracking, diagnosis, and treatment have resulted from the widespread use of MIoT devices in healthcare organizations. Patient safety and sensitive health data are at danger due to the increased number of linked devices, which has brought about major cybersecurity issues such as illegal access, data breaches, and service disruptions. This study delves into different approaches to guarantee strong cybersecurity in healthcare organizations, with a particular emphasis on safeguarding MIoT devices. Anomaly detection systems, secure communication protocols, device authentication, and end-to-end encryption are some of the key tactics that are investigated. In addition, the research looks into how machine learning and AI may be used for predictive analytics and real-time threat detection in MIoT networks, and how blockchain technology can guarantee data integrity and safe communication in these networks. Following legal standards like HIP AA and creating multi-layered security measures to reduce risks are also emphasized in the research. The goal of this study is to present a thorough framework for protecting MIoT devices in healthcare settings, guaranteeing patient safety and data confidentiality by integrating established cybersecurity practices with new technology. Additionally, issues like managing a diverse ecosystem of MIoT devices and device resource limits are discussed, along with suggestions for adaptable security solutions that are lightweight. The results provide healthcare organizations with practical advice on how to strengthen the security of their systems and prevent cyberattacks on their MIoT infrastructure.

关键词： Protocols Standards organizations Medical services Organizations Threat assessment Real-time systems Safety Encryption Internet of Things computer security

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A Survey on Artificial Intelligence Models for Endometrial Tumor Detection, Classification and Diagnosis 3rd

A Survey on Artificial Intelligence Models for Endometrial T...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Karthick, N. Nithya, P. Department of Computer Applications Sri Krishna Arts and Science Tamil Nadu Coimbatore641041 India Department of Networking and Mobile Application PSG College of Arts and Science Tamil Nadu Coimbatore641041 India

ISBN: (纸本)9783031862922

An Endometrial Tumor (ET) is a common uterine disease that continues to play a significant role in the mortality linked to a tumor. Advanced ET diagnosis exhibits worse diagnostic accuracy. The medically used ET screening methods require a significant amount of time and money, and not all patients can easily access them. So, oncologists have focused huge attention on molecular modeling as it has grown quickly, which has sped up the invention of computer-aided tumor detection systems. Artificial Intelligence (AI) models offer possibilities for molecular diagnostics, early tumor classification, efficient diagnosis, and diagnosis modality selection. It may be especially pertinent to use AI models in ET detection, classification, and diagnosis. From this perspective, researchers have been actively developing and implementing AI models to create effective ET diagnostic systems. This article studies the background of ET characteristics and earlier diagnosis techniques to stimulate further research in this field. The review is planned to investigate AI models, such as machine learning (ML) and Deep learning (DL) algorithms, for ET detection, classification, and diagnosis. After that, the positives and negatives of each classification model, such as Logistic Regression (LR), Support Vector machine (SVM), Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) networks, are compared. Amongst them, a multi-modal CNN-LSTM model achieves 93% accuracy on the TCGA-UCEC dataset, compared to the other models. At last, promising prospects are emphasized to achieve greater efficiency in classifying and diagnosing ET. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Transformative Influence: AI's application in Improving Risk Prevention and Management in Banking Institutions 3rd

Transformative Influence: AI's Application in Improving Risk...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Kashni, Tarun Shukla, Anurag Dadwal, Neeraj Dadhich, Manish Alliance School of Business Alliance University Bengaluru India Departmet of Management and Commerce Arni University Kangra India School of Management Sir Padampat Singhania University Udaipur India

ISBN: (纸本)9783031862922

This paper examines deeper and profound implications of artificial intelligence on risk management practices for the banking and financial services industry. As AI technology continues to penetrate nearly every facet of finance, more banks embrace AI algorithms, including decision trees, random forests, and neural networks, with some related machine learning techniques aimed at improving their risk assessment processes and decision-making processes and mitigation strategies. In this context, the research study orients itself towards the application of AI in risk management and proceeds further to evaluate the efficacy and accuracy of AI models against conventional methods, using statistical tests and confidence intervals in order to justify such differences. Ethical considerations, such as algorithmic bias and data privacy, will be also discussed in depth, including strategies for mitigating such risks. The study of the present article outlines the transformative capacity of AI in banking risk management and its implications for the industry through an inclusive review of current literature and case studies, particularly in cybersecurity and risk assessment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Risk management

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