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检索条件"任意字段=4th International Conference on Soft Computing in Data Science, SCDS 2018"

共 1031 条记录，以下是31-40 订阅

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Enhancing Lifetime in Wireless Sensor Networks through Image Processing-Based Clustering with Genetic Algorithm Routing 4

Enhancing Lifetime in Wireless Sensor Networks through Image...

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

作者： Ilampari, M. Amina Begum, M. Mahesh Babu, N. Gopi, S. Mythreyee, M. Deivasigamani, Shanmugavel SIMATS Saveetha School of Law India Oxford College of Engineering Department of ECE Chennai India Anurag Engineering College Suryapet Department of Information Technology Telangana Kodad India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of CSE Chennai India Dr. MGR Educational and Research Institute Department of EEE Chennai India Jeppiaar Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331515720

Wireless Sensor Networks (WSNs) are a very important technology widely used in diverse application domains of environmental monitoring, disaster management, health care, industrial automation etc. these are networks in which spatially distributed sensor nodes monitor physical or environmental conditions and communications data are processed through a base station. Here, we introduced a novel method for enhancing the efficiency and lifetime of WSNs by using the WSN to process images data and genetic programming (GA) to find the best route. A system model was developed to balance a network consisting of homogenous sensor nodes placed randomly in a particular geographical area, representing realistic scenarios. Each sensor node has limited energy resources, hence the operation of the network was partitioned into clustering phase and routing phase to optimal utilization of energy. To improve both the accuracy and efficiency of clustering, the genetic algorithm then identified the optimal path through these clusters using the best combination of pivot nodes for transmission. the proposed method applies image processing techniques to process sensor data, preprocesses sensor data, and then improves the classification process, clustering results, and the genetic algorithm obtains the more energy-efficient paths. Our extensive evaluation results prove substantial optimizations in network performance, energy efficiency and lifetime over known protocols. this method is expected to be effective in such applications as environmental monitoring, disaster management, and other applications that will utilize reliable and sustainable WSNs. © 2024 IEEE.

关键词： Sensor nodes

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A Novel Efficient Approach for Feature Selection for Enhanced Performance in Breast Cancer Prediction 4th

A Novel Efficient Approach for Feature Selection for Enhance...

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4th international conference on Advanced Network Technologies and Intelligent computing-ANTIC-Annual

作者： Bansal, Arin Lohan, Vishal Kumar Khanna, Munish Agnihotri, Shantanu UPES Dehra Dun Uttarakhand India Galgotias Univ Dankaur India

ISBN: (纸本)9783031837920;9783031837937

Modern research relies on computer-aided diagnostic (CAD) tools for efficient utilisation of time and resources. these technologies help medical practitioners make decisions by swiftly identifying anomalies and providing reliable information. the effective selection of features (characteristics) is crucial for these systems to appropriately categorise high-dimensional biological data. these traits may help diagnose linked medical issues. Biomedical data has many unnecessary or redundant information, making pattern identification difficult. therefore, a feature selection (FS) technique should be implemented and used to remove these characteristics from the training data. Eliminating irrelevant or uninformative characteristics is the main objective of FS techniques to enhance classification accuracy along with reduction of training and testing time of machine learning (ML) classifiers. In ML-driven CAD systems, the FS phase is crucial to success. Adding efficient features during training may boost ML model performance. this empirical research proposes an FS-based biological data categorisation approach based on integration of Water Wave Optimization (WWO) soft-computing algorithm with efficient ML classifiers. Using the publicly accessible Wisconsin Diagnostic Breast Cancer (WDBC) standard dataset, this study classifies benign and malignant tumours. the 70:30 train-test split approach is implemented and the suggested strategy's competency is assessed using accuracy, sensitivity (Recall), specificity, precision, F1-Score, Kappa Score, Matthews Correlation Coefficient (MCC), and ROC AUC Score. Best accuracy calculated using the proposed method is 97.96%. the suggested clinical decision support system has a good classification performance, making it a useful secondary opinion tool and lowering the workload of experienced medical physicians.

关键词： Feature Selection soft-computing algorithms Medical data Machine Learning Optimization

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INCS: Design and Development of an Oral Cancer Identification Methodology based on Improved Neural Classification Scheme 4

INCS: Design and Development of an Oral Cancer Identificatio...

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

作者： Karthikeyan, Bhuvaneshwari Shaik, Reddi Khasim Vijayan, V. Balaji Geo, A V. Allin thiagarajan, R. Krishnamoorthy, R. Symbiosis Medical College for Women Department of Dentistry Pune India Vishnu Institute of Technology Department of Electrical and Electronics Engineering Bhimavaram India HKBK College of Engineering Department of Information Science and Engineering Bengaluru India St. Joseph's Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Prathyusha Engineering College Department of IT Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Chennai India

ISBN: (纸本)9798331515720

the early detection of oral cancer is critical to improving patient outcomes and survival rates. this paper presents the Improved Neural Classification Scheme (INCS) for identifying oral cancer using deep learning techniques. the proposed model integrates MobileNet and EfficientNet, two state-of-the-Art convolutional neural networks (CNN), to create an ensemble capable of extracting both fine-grained and high-level semantic features from oral cancer images. the dataset used consists of 500 oral cancer images and 450 non-cancerous images sourced from Kaggle. A comprehensive preprocessing pipeline, including resizing, normalization, and data augmentation, was implemented to optimize the dataset for model training. the INCS model achieved an accuracy of 98.65%, outperforming other models such as MobileNet, EfficientNet, ResNet, and VGG16. the model also achieved high scores in precision (96.89%), recall (97.45%), and F1score (97.17%), demonstrating its robustness in distinguishing between cancerous and non-cancerous lesions. this high accuracy, combined with its low false positive and false negative rates, highlights the potential of INCS for clinical application in oral cancer diagnosis. Future work will focus on expanding the dataset and exploring real-Time deployment in clinical settings. © 2024 IEEE.

关键词： Lung cancer

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Enhancing the Accuracy of Forecasting of the partern of load usage of the Electrical appliences via soft computing 4

Enhancing the Accuracy of Forecasting of the partern of load...

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4th IEEE international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Nagaraju, Subuddi Bhat, C. Rohith Baskar, M. Rajaram, Gnanajeyaraman Rani, L. Pattathu Bhaskar, K. Vijaya Aditya Institute of Technology and Management Department of Eee Andhra Pradesh Tekkali India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Chennai India Chennai Institute of Technology Department of Mechatronics Chennai India Saveetha Institute of Medical and Technical Sciences Simats Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Simats University Saveetha School of Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350360165

precise estimations of consuming load profiles are crucial for the successful oversight of electrical energy systems in both financial & environmental terms. Based on this foundation, it is feasible to strategize & execute the utilization of manageable power generation and storage systems, along with power acquisition, considering the necessary preparation as well as technological and contract limitations. the documented electrical load profiles will significantly rise throughout the digitization of the energy sector. To enhance prediction accuracy, it is essential to create and analyze models that consider the increasing data volume and aim to enhance predicting accuracy through an increased number of data. Artificial neural networks (ANN) are being more commonly employed to address not linear issues involving large datasets influenced by humans and various other unexpected factors. the artificial neural network (ANN) technique is selected to forecast load patterns. the study aims to simulate and evaluate the efficacy & optimal performance of prediction models using an Artificial Neural Network for power consumption profiles. © 2024 IEEE.

关键词： ANN Electrical Load Prediction Energy generation Energy storage Load Profile ML

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Impact of data Preprocessing on Wind Forecasting Using Machine Learning Techniques 4

Impact of Data Preprocessing on Wind Forecasting Using Machi...

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4th international conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Gaikwad, Nidhi Haribhau Mitra, Arghya Payal, Jangilwar Das, Soumyabrata Keshri, Ritesh kumar Department of Electrical Engineering VNIT Nagpur India

ISBN: (纸本)9798331530402

there is a high demand for renewable energy sources as non renewable energy sources are harmful to the environment due to its effects such as global warming. Among non renewable energy source, the wind energy is unpredictable in nature, making Its integration with the grid a concern. By accurately forecasting wind energy, the stability of the grid can be ensured. the aforementioned can be achieved with the help of Machine Learning (ML) techniques. this paper investigate four ML techniques Multiple Linear Regression, Support Vector Regression, Random Forest Regression, and Decision Trees which are utilized and compared based on the metrics such as root mean square error, mean square error, mean absolute error, and R2 for three different cases. Initially errors are calculated without data preprocessing and later with data preprocessing. It is observed that preprocessing reduces error. this paper also investigates the effect of different seasons on the errors generated by these ML techniques. For this research, data is taken from National Solar Radiance database (NSRD) over two year period from January 2018 to December 2019. © 2025 IEEE.

关键词： Multiple linear regression

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Temporal Analysis and Prediction of Lung Tumor Growth Using LSTM Networks: A Deep Learning Approach 4

Temporal Analysis and Prediction of Lung Tumor Growth Using ...

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

作者： Lakkshmanan, Ajanthaa Reddy Sai Geethesh, D. Saketh Reddy, N. SRM Institute of Science and Technology Department of Computing Technologies 603 203 India

ISBN: (纸本)9798331515720

Lung cancer is one of major causes of cancer related disease globally, and early detection, combined with precise tumor growth prediction, is essential for efficient treatment planning and better patient outcomes. this study offers a deep learning-based technique that employs Long-Short-Term Memory (LSTM) networks to predict the growth rate of lung tumors over time by using sequential imaging data from longitudinal CT scans. the proposed method, which analyses temporal sequences of lung scans, ensures not only detect tumors but also predict their progression, so aiding in the proactive management of lung cancer. this technique includes several steps. Initially, sequential CT scan data from the NSCLC radio genomics dataset is preprocessed to identify the region of interest (ROI) that contains the tumor in each scan. these ROIs are used to extract features such as tumor volume, shape and texture which then fed into the LSTM network. the LSTM method is then trained on these temporal features sequences to identify patterns that represent tumor growth. the model's architecture is intended to capture temporal dependencies in the data, allowing it to generate accurate predictions about the upcoming tumor size and growth rate. the effectiveness of the proposed method is tested using common performance metrics such as Root Mean Squared Error (RMSE), Mean squared error (MSE), R-Squared(R2) and mean absolute error. Experimental results show that the LSTM based method can reliably forecast tumor developed parts offering useful advancements for therapeutic decision such as modifying treatments based on expected tumor growth. this study demonstrated the potential of temporal analysis with LSTM networks to improve lung cancer growth. By predicting tumor growth, this method can help doctors make prompt and personalized treatment decisions, ultimately improving the quality of the care for lung cancer patients. Future work will concentrate on improving the method and investigating it

关键词： Lung cancer

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Comparative Analysis of Apriori, FP-Growth, and ECLAT Algorithms for Effective Grocery Store Management and Product Recommendations 4

Comparative Analysis of Apriori, FP-Growth, and ECLAT Algori...

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

作者： Sivaramakrishnan, S. Anusha, V. Madhumitha, A. Karthika, M. Harshitha, N. Chourasia, Kumari Vaishnavi Dept of Information Science and Engineering New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798331515720

In the dynamic landscape of grocery retail, administrators face the challenge of efficiently managing inventory and making informed decisions to enhance sales and customer satisfaction. Traditional methods of inventory management and product recommendation may not fully leverage the wealth of transactional data available. this study aims to tackle these obstacles through the introduction of a Grocery Recommendation System that utilizes Market Basket Analysis (MBA). the goal of this system is to successfully recommend products and anticipate client preferences by utilizing transactional data taken from the custom e-commerce website. the website is made to record a variety of transactional data when a customer makes a purchase. By using sophisticated algorithms like Apriori, FP-Growth (Frequent Pattern Growth), and ECLAT (Equivalence Class Clustering and Bottom-Up Lattice Traversal), the system detects recurring itemsets and association rules that emphasize the significant connections across products. this study evaluates the performance of all three algorithms, focusing on metrics like time complexity, support, confidence, and lift. FP-Growth demonstrates superior time efficiency, particularly with large datasets, due to its memory-efficient frequent itemset generation. Apriori's straightforward solution, meanwhile, might be appropriate for smaller datasets that need to be interpretable. ECLAT's vertical transaction method offers advantages in dense data scenarios. Selection among Apriori, FP-Growth, or ECLAT should align with specific dataset characteristics and business goals. these algorithms provide actionable insights crucial for inventory management and customer engagement strategies. Administrators are then able to increase inventory turnover, adjust stocking techniques , and eventually increase sales. © 2024 IEEE.

关键词： Apriori Association Rules ECLAT FP-Growth Grocery retail Recommendation System

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Enhancing Predictive Accuracy in Cardiovascular Disease Diagnosis: A Hybrid Approach Using RFAP Feature Selection and Random Forest Modeling 4

Enhancing Predictive Accuracy in Cardiovascular Disease Diag...

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

作者： Pasupuleti, Vikram thuraka, Bharadwaj Kodete, Chandra Shikhi Priyadarshini, V. Kumar Tirumanadham, Koti Mani Shariff, Vahiduddin Eastern Illinois University School of Technology CharlestonIL61920 United States Northwest Missouri State University School of Computer Science and Information Systems MaryvilleMO64468 United States SRKR Engineering College Department of CSE Bhimavaram India Sir C R Reddy College of Engineering Department of CSE Eluru India

ISBN: (纸本)9798331515720

Cardiovascular disease remains one of the leading causes of death all over the world and is characterized by ailments affecting cardiovascular structures, such as the heart failure, arrhythmia, and coronary artery diseases. It has multiple roots based on genetic influences on the disease, meals, drinks, physical inactivity, or presence of other diseases including hypertension, and diabetes. Heart diseases are still rampant in the world despite the development in the research on diseases and in healthcare. this is mainly because of the increasing average life span of individuals as well as increased cases of urbanization and adoption of poor-quality diets and leading inactive or sedentary lives. Such problems further escalate due to shortcomings in healthcare provision and preventive options by socioeconomic differences, especially affecting the needy populace. Hence, there is a rising focus on using complex technologies such as artifices and machine intelligence, along with big data analytics and predictive models, in handling these issues. these methodologies utilize computational methods and large-scale data to enhance risk assessment, optimize treatment pathways, and alleviate the overall burden of cardiac disease on healthcare systems and individuals. the concentration of this research is on the systematic application of rigorous data preprocessing, class imbalance correction using techniques such as SMOTE, and outlier management using methods such as IQR, in order to improve the effectiveness of predictive modelling. this study intends to improve predictive accuracy and model generalizability, thereby contributing to improvements in healthcare decision-making and patient care outcomes, by utilizing the RFAP hybrid technique for feature selection and Random Forest for modelling. © 2024 IEEE.

关键词： Risk assessment

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Lunar Crater Detection Using Customized YOLOv8 Model 4th

Lunar Crater Detection Using Customized YOLOv8 Model

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4th international conference on Advanced Network Technologies and Intelligent computing-ANTIC-Annual

作者： Chaini, Chinmayee Jha, Vijay Kumar Birla Inst Technol Dept Comp Sci & Engn Ranchi 835215 Bihar India

ISBN: (纸本)9783031837920;9783031837937

Object detection is crucial in analyzing imagery from satellite sensing, with developments in geospatial monitoring technology and deep convolutional networks driving significant progress. Nevertheless, difficulties remain, especially in recognizing objects across different sizes and precisely identifying minor features. this paper presents a novel network, Customized YOLOv8, to tackle these issues and enhance object detection capabilities. Many researchers use digital elevation models (DEMs) for crater classification, but their low resolution often limits the detection of smaller craters. In response, this paper leverages lunar CCD images from NASA's 100-m global mosaic, captured by the Lunar Reconnaissance Orbiter Camera (LROC) WAC. the Customized Yolov8 model is built with DenseNet121 as the backbone for efficient feature reuse, improving gradient flow and minimizing vanishing gradients. this paper used Bi-FPN as the neck for fusing multi-scale features, which is essential for recognizing objects of various dimensions. Additionally, soft-NMS enhances accuracy by reducing missed detections in closely overlapping objects. this architecture achieves 76.0% precision, 68.6% recall, and a minimum average precision of 78.7% on test data without any prior preprocessing applied to the raw image.

关键词： YOLOv8 Dense-Net121 Bi-FPN CCD image soft-NMS

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Temporal Fusion Transformer -LSTM-based Improved State of Charge Estimation of Li-ion battery for Real Drive Cycle 4

Temporal Fusion Transformer -LSTM-based Improved State of Ch...

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4th international conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Pandian, Anbumalar Shruthi, S. Dhanush, V. Sundarajan, Sriranganayaki Abishek, P. Subbian, Sutha Natarajan, Pappa Dept. of Instrumentation Engineering Anna University MIT Campus Chennai India Dept. of Artificial Intelligence and Data Science Meenakshi Sundararajan Engineering College Chennai India

ISBN: (纸本)9798331530402

Precise measurement of State of Charge (SoC) in Li-ion battery is crucial for effective utilization of energy storage system. However, it is challenging with physics-based model due to computation complexity while data-driven model often lacks generalization for unseen data. To improve the robustness of data-driven model, hybrid algorithms are employed. this paper focuses on combining Temporal Fusion Transformer (TFT)Long Short-Term Memory (LSTM) to improve the generalization of state-of charge estimation of unseen drive cycles. By leveraging the attention mechanism feature, temporal feature extraction of TFT along with memory capability of LSTM TFT-LSTM model is developed with optimal hyperparameters using Optuna framework. this model is used to estimate the SoC during the discharging cycle of a Turnigy Graphene Li-Ion Battery (5000mAh, 65C) for the LA92 drive cycle at an ambient temperature of 40°***, uncertainty analysis is performed against US06 drive cycle. Finally, the performance of TFT-LSTM is compared with LSTM model. the results demonstrate that TFT-LSTM achieves better performance compared to LSTM with Root Mean Square Error (RMSE) of 1.1962 for unseen US06 drive cycle. these findings demonstrate the robustness of hybrid TFT-LSTM against unseen drive cycle and highlight its potential under realistic conditions with variability. © 2025 IEEE.

关键词： Mean square error

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