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检索条件"任意字段=3rd International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2021"

共 2040 条记录，以下是31-40 订阅

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Bubble Prediction in Stock Markets: Leveraging SVM and Technical Indicators for High Accuracy 3

Bubble Prediction in Stock Markets: Leveraging SVM and Techn...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Manogar, E. Doss, P.Lourdu Mahimai Gunasekaran, M. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering TamilNadu Chennai India B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Chennai India

ISBN: (纸本)9798331509675

Predicting stock market bubbles is indispensable for risk control and refinement of trading strategies. Therefore, this study presents a machine-learning Framework to detect price bubble formations based on Support Vector machine (SVM) and through important technical indicators. Among these are momentum indicators, such as the Relative Strength Index (RSI), and the Moving Average Convergence Divergence (MACD) histogram, which are complemented with volatility measures from Bollinger Bands and with normalized volume indicators, all to improve predictive accuracy. Recent historical stock data were fed to the model during training and testing, yielding exceptional accuracy: 98.54% on the test set and 98.95% on the training set. The performance evaluation was carried out based on Classification Reports, Confusion Matrices, Receiver Operating Characteristic (ROC) curve, and Precision-Recall metrics, thus confirming the performance efficiency of the developed framework. With this approach, bubbles' early detection allows traders and analysts a powerful tool to make informed decisions in risk mitigation or strategic enhancement. © 2025 IEEE.

关键词： Support vector machines

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Comprehensive Analysis on Optimal Routing in Wireless Mesh Network Using Optimization and machine/Deep learning Algorithms 3

Comprehensive Analysis on Optimal Routing in Wireless Mesh N...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Kumar, Kaata Ranjith Bala, K. Srinivas, Kalyanapu Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Vaagdevi Engineering College Department of Computer Science and Engineering Warangal India

ISBN: (纸本)9798331508456

Wireless Mesh Networks (WMNs) is an important component of wireless communications by enhancing the network coverage. The mesh network topology is interconnecting all nodes in the cluster. However, the existing approaches utilized for optimal routing in WMN face challenges such as increased delays, packet loss, network congestion that lead to inefficient data transmission. WMN has an ability to deliver efficient performance even with a large number of devices. Additionally, WMNs support a wide range of applications, by using its effectiveness in various scenarios. The fundamental architecture of a mesh network integrates multiple wireless nodes interconnected through base stations for efficient routing communication. Mesh networks able to adopt different client-based structures, including infrastructure and hybrid configurations. Optimization, learning techniques and Software defined network methods are utilized for efficient optimal routing to enhance the efficiency of data transmission, while reducing delay. In this study, routing-based optimization and machine (ML), Deep learning (DL) algorithms are reviewed along with its advantages and limitations. The performance ML or DL model is utilized for optimal routing in WMN is evaluated based on performance metrics such as Delay, Throughput, Packet Delivery Ratio (PDR) and Connected Client Ration (CCR). © 2025 IEEE.

关键词： Wireless mesh networks (WMN)

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A Concise Survey on Meta-learning and Its applications 3rd

A Concise Survey on Meta-Learning and Its Applications

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3rd international conference on machine Vision and Augmented Intelligence, MAI 2023

作者： Ramkumar, D. Maithili, Chigurupati Palaskar, S.P. Toley, Minal Jadhav, Ajita Pande, Sagar Dhanraj School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Sipna College of Engineering and Technology Maharashtra Amravati India Ajeenkya D Y Patil School of Engineering Charholi Via Lohegaon Maharashtra Pune India Dr. Vishwanath Karad MIT WORLD PEACE UNIVERSITY Maharashtra Pune India School of Engineering and Technology Pimpri Chinchwad University Maharashtra Pune India

ISBN: (纸本)9789819743582

In this paper, a basic idea of Meta-learning and its applications are given. Meta-learning is a derivative of machine learning. It is commonly referred to as "learning to learn". Simply this meta-learning means the learning model that gets knowledge through another learning model or algorithm. The area of meta-learning is growing rapidly year by year because of its ability to get precise results for a given input data in machine learning. There are several machine learning algorithms at present, the role of meta-learning is to understand which algorithm is most suitable for a specific dataset. Converse to the traditional way approach to artificial intelligence where tasks are resolved from the start with the help of established learning models, the main objective of meta-learning is to enhance the model automatically by giving instructions from the knowledge which is gained by observing various other learning algorithms. The meta-learning algorithm alters the existing algorithms for a particular problem and tries to get the best result out of it, this algorithm takes the outcome of the other algorithms as input. It is basically used to refine and upgrade the performance of the learning algorithms by altering some features of the algorithm. This algorithm gives accurate results as it takes the input data from the out data of the other algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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A Flexible Hybrid Resource Allocation Model for Enhanced Efficiency using AI in Wireless Sensor Networks 3

A Flexible Hybrid Resource Allocation Model for Enhanced Eff...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Endla, Purushotham Mohandas, R. Saikia, Achinta Lakshmi, C. Karthikeyan, G. Moharekar, Tejashree Tejpal Sr University School of Sciences and Humanities Department of Physics Telangana Warangal506371 India Chennai Institute of Technology Department of Ece Chennai India Mariani College Department of Statistics Mariani Assam Jorhat785634 India Saveetha Engineering College Chennai India Karpagam College of Engineering Department of Artificial Intelligence & Data Science Coimbatore India Shivaji University Yashwantrao Chavan School of Rural Development Maharashtra Kolhapur India

ISBN: (纸本)9798331509675

Wireless Sensor Networks (WSNs) have become a cornerstone technology in various fields, including environmental monitoring, industrial automation, and healthcare. This paper presents a novel hybrid resource allocation model designed to enhance efficiency in Wireless Sensor Networks (WSNs) using advanced Artificial Intelligence (AI) techniques. The proposed model integrates static and dynamic resource allocation strategies with machine learning and reinforcement learning to optimize key network resources such as energy, bandwidth, and processing power. Comprehensive evaluations demonstrate that the proposed model outperforms nine existing models across multiple metrics, including energy efficiency, network lifetime, throughput, delay, latency, and accuracy of resource allocation. The proposed model achieved an energy efficiency of 0.98 J/bit, extended network lifetime to 620 days, and maintained a high throughput of 620 Kbps. Notably, the model demonstrated the lowest delay (90 ms) and latency (180 ms) while achieving the highest resource allocation accuracy of 97.8%. These results highlight the model's capability to dynamically adjust resource allocation in response to real-time network conditions, ensuring optimal performance and resource utilization. The proposed model's success in efficiently managing WSN resources marks a significant advancement in the field, offering a robust solution for various WSN applications requiring high efficiency and reliability. © 2025 IEEE.

关键词： Artificial Intelligence Energy Efficiency machine learning Network Lifetime Reinforcement learning Resource Allocation Wireless Sensor Networks

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Enhancing Air Quality Index Forecasting through machine learning Models 3

Enhancing Air Quality Index Forecasting through Machine Lear...

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Reddy, Kamalapuram Vigneswara Begum, Chimakurthy Mounika Neeraja, Adhi Jyotsna, C. Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering India

ISBN: (纸本)9798331523893

Air pollution poses serious health risks, including respiratory issues and hospitalization. This study focuses on predicting the Air Quality Index (AQI) in Delhi, leveraging data from monitoring stations and machine learning models like linear regression, support vector regression, and tree-based methods such as decision trees and random forests. Results show that the Decision Tree Regressor outperformed other models, achieving the lowest Root Mean Squared Error (RMSE) and highest R-squared (R²) values. Incorporating and advanced preprocessing enhanced accuracy, addressing seasonal variations in air quality data. The findings emphasize the value of machine learning in accurate AQI forecasting, supporting policy decisions, and improving urban air quality management. This research highlights the critical role of predictive models in mitigating air pollution's impact and protecting public health while suggesting pathways for future advancements in real-world applications. © 2025 IEEE.

关键词： Air Pollution Air Quality Index Delhi machine learning Prediction

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Smart Intelligence-Driven Deep learning Framework for Reliable Detection and Accurate Classification of Skin Cancer Diseases 3

Smart Intelligence-Driven Deep Learning Framework for Reliab...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Rathish Babu, T.K.S. Chandramohan, S. Be, Hajira Niroshini Infantia, H. Srm Institute of Science and Technology Department of Computer Science Engineering Ramapuram Chennai89 India Sri Chandrasekarendra Saraswathi Viswa Maha Vidyalaya Department of Computer Science Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Computer Applications Maduranthakam Taluk India St. Joseph's Institute of Technology Department of Information Technology OMR Chennai600119 India

ISBN: (纸本)9798331509675

The interest in skin cancer detection is growing remarkably because of the prevalence of this health condition worldwide. It improves the chances of survival and enhances general treatment outcomes when diagnosis has been done early. Most the traditional methods for skin lesion detection involve visual inspection performed by dermatologists;hence, it is time-consuming and, to some extent, subjective. Therefore, with the rise of deep learning-Artificial Intelligence, auto-detection of skin cancer has gained a foothold in improving diagnostic accuracy, hence treatment outcomes. The presented paper proposes a GAN-based approach for skin cancer detection by deep learning analysis of dermoscopic images, classifying them as malignant and benign. The proposed approach uses the generator and discriminator parts of GAN to obtain an effective discrimination between cancerous and non-cancerous lesions with high precision. The proposed method incorporates the native abilities of GANs for boosting diagnostic precision. GANs consist of two major components: a generator and a discriminator. These two adversaries collaborate with each other in a way to encourage the classification performance of the framework. The generator synthesizes the realistic data representations by augmenting the dataset with various variations of dermoscopic images. This helps not only to compensate for the insufficiency of annotated data but also to make the model generalize more robustly to diverse skin lesion patterns. At the same time, the discriminator learns to distinguish malignant from benign lesions with high accuracy by using the adversarial nature in GAN training. These results clearly indicate the effectiveness of the proposed GAN-based approach over state-of-the-art traditional deep learning models. GAN makes not only the detection system more robust but also reduces its dependency on large annotated datasets. © 2025 IEEE.

关键词： Adversarial machine learning

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Leveraging Neural Networks with BiLSTM and CNN Models for Enhanced HR Management Practices 3

Leveraging Neural Networks with BiLSTM and CNN Models for En...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Kalva, Yamini Parasar, Anuradha Pujar, Rajlaxmi Pramod Balasubramanian, K. Pasha, Zameer Ramya, S. Sri Ramachandra Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India School of Liberal Education Galgotias University Dankaur India Indira School of Business Studies Pgdm Department of Marketing Pune India Kalasalingam Academy of Research and Education-Deemed to be University Department of Computer Applications Krishnankoil India Department of Management Bengaluru India M.Kumarasamy College of Engineering Department of Artificial Intelligence and Machine Learning Karur India

ISBN: (纸本)9798331508456

The most valuable asset of any business is its employees. Even with technological advances, organizations rely heavily on human resources for system administration. An increasingly competitive sector makes managing and retaining talented workers difficult. An organization's ability to develop, survive, and compete depends on its employees' skills. These challenges can be overcome using preprocessing, feature extraction, and model training. data preparation for machine learning is necessary yet time-consuming. Features selection is critical since it enhances accuracy and reduces overfitting. CNN-BiLSTM trained the model. The CNN- BiLSTM model outperformed BiLSTM and CNN. The model's 90.62 percent accuracy showed it could handle HR tasks. For effective human resource management, this study recommends sophisticated machine learning models. Today's businesses need reliable tools for managing and keeping people, and the CNN-BiLSTM model helps. © 2025 IEEE.

关键词： Personnel selection

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Scalable Subspace Clustering via Purity Kernel Tensor learning 3

Scalable Subspace Clustering via Purity Kernel Tensor Learni...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Adhvaryu, Rachit Kannan, S. Rajes Sanjay, C.P. Dange, Pravin Parul University Department of Computer Science and Engineering Faculty of Engineering and Technology Gujarat India Chennai Institute of Technology Department of Computer Science and Engineering Kundrathur India Sri Balaji University Pune India Pune India

ISBN: (纸本)9798331508456

Subspace clustering is a fundamental task in machine learning, particularly for high-dimensional datasets where the goal is to group data points that lie in distinct subspaces. Traditional methods, such as Sparse Subspace Clustering (SSC) and Low-Rank Representation (LRR), have demonstrated good performance but often suffer from high computational costs, particularly when applied to large-scale data. To address these limitations, this paper introduces a novel technique called Purity Kernel Tensor learning (PKTL). PKTL combines the power of tensor decomposition with a purity kernel function to improve both the efficiency and effectiveness of subspace clustering. By utilizing tensor-based representations, the method captures higher-order relationships in the data, allowing for a more robust clustering process. The purity kernel function measures the coherence within the clusters, enhancing the identification of subspaces in complex datasets. The proposed approach not only reduces computational complexity but also improves the clustering accuracy compared to existing methods. Experiments on synthetic and real-world datasets, including ORL, Yale, and MNIST, show that PKTL outperforms conventional subspace clustering algorithms, both in terms of clustering accuracy and scalability. The results demonstrate the potential of PKTL in handling large datasets while maintaining high performance, making it a promising approach for scalable subspace clustering in real-world applications. © 2025 IEEE.

关键词： Large datasets

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Object Detection in Images using machine learning 3

Object Detection in Images using Machine Learning

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Nagarajan, G. Piratla, Abhiram Golla, Karthikeya Department of CSE Sathyabama institute of science and technology Chennai India

ISBN: (纸本)9798331527549

This study develops a scalable, effective, and user-friendly solution to tackle the problem of real-time object detection in photos. The suggested approach makes use of YOLOv5 (You Only Look Once version 5) for deep learning-based object detection and Flask for managing REST API queries. Users can submit base64-encoded photos to the system, which will analyze them using the YOLOv5 model to identify and categorize objects, label them with bounding boxes, and then return the base64-formatted images. High-speed performance is ensured by using PyTorch for model inference, while Non-Maximum Suppression (NMS) increases object localization accuracy by removing redundant detections. Because of its lightweight architecture and ease of integration with online applications, this system can be used for a variety of purposes, including surveillance and e-commerce. The study's output is a reliable, approachable object detection API that can effectively manage real-time image processing and deliver precise item recognition, showcasing the potential for implementing deep learning models in intuitive and scalable web-based settings. © 2025 IEEE.

关键词： Image annotation

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Deep learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition 3rd

Deep Learning for Cognitive Task and Seizure Classification ...

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3rd international conference on machine Vision and Augmented Intelligence, MAI 2023

作者： Jain, Shraddha Srivastava, Rajeev Department of Computer Science and Engineering Indian Institute of Technology BHU Varanasi India

ISBN: (纸本)9789819743582

Electroencephalography (EEG) signals are often utilized to study cognitive processes and brain diseases. The non-stationary and non-linear nature of EEG signals makes their analysis difficult. A deep learning framework that suggested classifying seizures based on scalp EEG signals and automating cognitive tasks. We use a pre-processing module based on the Hilbert–Huang Transform (HHT) and Variational Mode Decomposition (VMD) to extract features from raw EEG data. We present an approach that combines deep learning with HHT and VMD for rapid and precise seizure detection. Our method detects seizures with an astounding 98.40% accuracy. Our suggested techniques have great potential for quantifying brain wave patterns and advancing neuroscience research, even outside of classification applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Variational mode decomposition

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