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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Bharanidharan, N. Chakravarthy, S. R. Sannasi Kumar, V. Vinoth Kavya, B. Meghana, G. Rajaguru, Harikumar School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering Dayananda Sagar University Bangalore India Department of Computer Science and Engineering PES University RR Campus Bangalore India

ISBN: (纸本)9783031810794

Gene expression analysis is an efficient modality to detect breast cancer. But analysis of the gene expression data is a complicated task due to the involvement of large number of numerical data for each subject. This research work focuses on the development of computerized diagnosis systems for analyzing the gene expression data and classifying the subjects as either normal or breast adenocarcinoma. Sparse autoencoder is used for dimensionality reduction and Elephant Herding Optimization is used for transforming the features. Four different supervised classifiers namely support vector machine, random forest classifier, decision trees, and naive bayes classifier are examined for classification task. Among them, random forest provides the superior balanced accuracy of 86% while it was further enhanced to 92% with the help of sparse autoencoders and elephant herding optimization algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Transcription

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A Novel RIS-Empowered Base Station: A Practical Implementation and Experimental Validation

A Novel RIS-Empowered Base Station: A Practical Implementati...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Park, Je Hyeon Kim, Suhyun Amri, Muhammad Miftahul Tran, Nguyen Minh In Kim, Dong Choi, Kae Won Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Faculty of Electronics and Telecommunications Vnu - University of Engineering and Technology Hanoi Viet Nam

ISBN: (纸本)9798350368369

This article presents a novel reconfigurable intelligent surface (RIS)-integrated base station (BS) by deploying an RIS very close to the base station antennas (BAs), within its radiative near-field range. We propose a practical algorithm to maximize the performance of uplink communications with the proposed RIS-integrated BS while maintaining reasonable complexity. Furthermore, we have implemented a testbed to experiment with the proposed RIS-integrated BS-based uplink multiple-input-multiple-output (MIMO) system. The experimental data validates the performance of the proposed RIS-integrated BS-based uplink MIMO system. © 2025 IEEE.

关键词： multi-beam design reconfigurable intelligent surface (RIS) RIS-integrated base station

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An Information-Augmented Approach for Graph Similarity Computation 10

An Information-Augmented Approach for Graph Similarity Compu...

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10th IEEE Smart World Congress, SWC 2024

作者： Chen, Junyan Gao, Hui Wu, Xucheng University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China University of Electronic Science and Technology of China Kash Institute of Electronics and Information Industry School of Computer Science and Engineering Chengdu China

ISBN: (纸本)9798331520861

Graph similarity computation is a critical task in various fields, including bioinformatics, social network analysis, and pattern recognition. Traditional methods, such as Graph Edit Distance, face challenges in computational complexity and accuracy. In this paper, we propose IASim, a method for computing graph similarity with information enhancement, leveraging Graph Neural Networks to enhance the accuracy and efficiency of graph similarity computation. IASim incorporates edge information and multi-level node information into the graph embedding process, utilizing a multi-layer neural tensor network architecture and a novel attention mechanism to capture the intricate relationships between nodes and edges. Our experiments on three diverse real datasets demonstrate that IASim outperforms existing methods in both accuracy and computational efficiency, exhibiting outstanding robustness and stability across a range of embedding dimensions. IASim emerges as a promising tool for a variety of applications demanding graph similarity computations. © 2024 IEEE.

关键词： Graph embeddings

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Classification of cervical spine disease using convolutional neural network

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Multimedia Tools and Applications 2024年第41期83卷 88963-88979页

作者： Bezabh, Yohannes Agegnehu Salau, Ayodeji Olalekan Abuhayi, Biniyam Mulugeta Ayalew, Aleka Melese Department of Information Technology University of Gondar Gondar Ethiopia Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Cervical spondylosis myelopathy (CSM) is a degenerative disorder of the cervical spine’s disks and joints, leading to neurological disability. Typically, this condition causes increasing discomfort and neurologic deterioration. Cervical spine pain disease classification using deep learning can assist medical professionals in making early and more precise medical diagnosis of cervical abnormalities. The main focus of the assessment for doctors is the classification of cervical spine diseases from X-rays images, which is valuable for patients to indicate the various symptoms they are faced with. Identifying cervical spine pain injuries (CPI) is a critical aspect of the diseases segmentation and classification. Manually explaining the high-dimensional feature space makes it challenging to identify the exact category and level of severity. This paper introduces a Convolutional Neural Networks (CNN) concatenation feature extraction method to help in the diagnosis of cervical spine pain disease from X-ray images. An innovative CNN-concatenation model-based fully connected classifier and a four-class system are applied to classify X-ray image data in this research. The CNN approach is employed to automatically classify X-ray images into healthy (normal) and with cervical spine disease (abnormal). CSM is currently diagnosed mostly through clinical evaluation using imaging methods such as X-ray and computed tomography (CT). Due to its inexpensive cost and minimal radiation dose, X-ray is commonly used. The proposed approach achieved a training accuracy of 99.98%, a validation accuracy of 98.29%, and a testing accuracy of 97.82% of classification from experiments. These results indicate that the image preprocessing, data augmentation, and CNN approaches provide an efficient classification method for identifying and accurately classifying cervical spine diseases. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Explainable Machine Learning for Drug Classification

Explainable Machine Learning for Drug Classification

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International Conference on Electrical and electronics engineering, ICEEE 2023

作者： Mridha, Krishna Bappon, Suborno Deb Sabuj, Shahriar Mahmud Sarker, Tasnim Ghosh, Ankush Computer Engineering Marwadi University Gujarat Rajkot India Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Electrical and Electronics Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh University Center for Research & Development Chandigarh University Punjab Ajitgarh India

ISBN: (纸本)9789819986606

This article provides a machine learning-based drug categorization research effort. The public repository Kaggle is where the dataset for this study was obtained. Age, sex, blood pressure (BP), cholesterol, and the Na-to-potassium ratio are the feature sets with the medication type as the target feature. In this work, five machine learning methods were applied: CatBoost, LightGBM, extreme gradient boosting machine, and extra tree. The findings indicated that, except for extra tree, all four algorithms had 100% accuracy, with CatBoost doing the best. The training and testing performance of the models was displayed using the learning curve. The model performance and key characteristics were understood using explicable approaches like SHAP and feature permutation significance. The findings indicated that the most critical characteristics for medication categorization are age, sex, and blood pressure. This work sheds light on how to classify drugs using machine learning. The findings demonstrate that machine learning may be used to classify drugs with high accuracy. The study's usage of explicable approaches can aid in understanding the model's performance as well as the key elements that can be employed to enhance it. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blood pressure

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Advanced Graduate Admission Prediction 8

Advanced Graduate Admission Prediction

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8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Nalam, Srujan Aleemuddin, Mohd Kadari, Yuva Teja Nanda Kumar, M. Sreenidhi Institute of Science and Technology Dept. of Electronics and Computer Engineering Hyderabad India

ISBN: (纸本)9798350334012

The process of choosing and narrowing down universities for graduate program admission plays a crucial role in the overall application procedure. This project aims to examine the uses of machine learning models to predict the likelihood of a student being accepted into a master's program. Hence this will provide students with an early indication of their admission prospects. In the past, models were constructed using different algorithms such as random forest, multiple linear regression and k-nearest neighbor. Results have shown that logistic regression out performs these algorithms. The admission of students into educational institutions is a critical issue, and this study addresses the application of machine learning algorithms to predict the chance of admission of students into master's programs. These models include SVM, Gaussian Naive Bayes, and Logistic Regression, with experiments demonstrating that the Logistic Regression model surpasses the others. This will give students a better understanding of their admission prospects in advance. © 2023 IEEE.

关键词： Students

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Reservation Systems Cancellation Determination Using Machine Learning 1

Reservation Systems Cancellation Determination Using Machine...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Kumar, Abhishek Kumari, Archana Burman, Ravi Kumar Pandey, Muskan Kumar, Nishant Kamaldeep, Kamaldeep Jharkhand University of Technology Computer Science Engineering Ranchi India Ramgarh Engineering College Computer Science Engineering Jharkhand India Electronics & Communication Engineering BIT Sindri Jharkhand India St. Xavier's College Computer Science Engineering Ranchi India

ISBN: (纸本)9798331518943

The increasing reliance on online reservation systems across various industries, including hospitality, airlines, and e-commerce, has heightened concerns over last-minute cancellations, may lead to monetary losses and ineffective operations. This study examines the application of machine learning techniques, particularly the Random Forest classifier (a bagging ensemble method), to predict reservation cancellations. In order to enable businesses to put proactive measures in place to reduce these risks, the research attempts to identify important variables that affect cancellations, such as customer demographics, booking lead times, and cancellation policies. The model was trained using 80% of the datasets, with the remaining 20% being utilized for testing. The model was assessed using key performance metrics such F1-Score, precision, recall, and accuracy. The model obtained a 0.37 F1-Score, recall of 0.38, precision of 0.37, and accuracy of 42%, indicating reasonable performance. The model's ROC-AUC score of 0.42 indicates that it has to be improved in order to better differentiate between canceled and non-canceled reservations. Overall, the findings show that Random Forest and other machine learning techniques, can be useful for predicting reservation cancellations, though further improvements are needed. Future research could concentrate on improving the model through hyper parameter tuning, feature engineering, and exploring more advanced methods to enhance predictive performance across different industries. © 2024 IEEE.

关键词： Predictive analytics

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Sensor Enabled Vehicle Parking System using Blynk IoT Application 5

Sensor Enabled Vehicle Parking System using Blynk IoT Applic...

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5th International Conference on Smart electronics and Communication, ICOSEC 2024

作者： Sekar, G. Benson Mansingh, P.M. Prasad, J. Britto Manoj, A. Vsb College of Engineering Department of Electronics and Communication Engineering Technical Campus Coimbatore India Vignan's Foundation for Science Technology and Research Department of Advanced Computer science and Engineering Guntur India Kpr Institute of Engineering and technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331504403

In the current scenario, finding the parking in full slot area is very difficult and also there is no online platform to check those parking slot availability details. The proposed system provides a parking slot availability information over phone and reducing a waiting time to check the availability. Using the 'Smart parking system' the parking slot availability can be accessed easily over the internet. This system can completely automate the entry and exit of car and also payment process. The Node MCU ESP8266 is connected with IR sensors. The IR sensors will show the availability of the parking slot. The entry and exit gates are connected with servo motors. RFID scanner used to show the information of the user while entering. Online service software was used (Blynk app) it shows the slot through your mobile world. © 2024 IEEE.

关键词： Sensor nodes

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Prediction of Electric Vehicle Price in India using Simple Linear Regression

Prediction of Electric Vehicle Price in India using Simple L...

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2023 International Conference on Trends in Automotive Parts Systems and Applications, TAPSA 2023

作者： Raj, Joshua Daniel Immanuel, J. Samson Karthik, P. Jayanthi, M. New Horizon College of Engineering Computer Science &E India Karunya Institute of Science and Technology Electronics India K S School of Engineering and Management Electronics and Co India New Horizon College of Engineering Electronics and Communic India

The demand for electric vehicles (EVs) has been steadily increasing in recent years, led by the factors like environmental concerns, government incentives, and improvements in EV technology. The EV's growth is expected to increase in the coming years as EVs become more affordable and more models become available on the market. Predicting the price of electric vehicles provides valuable insights on the EV market and inform a range of business, consumer, financial, and policy decisions. Predicting the price of electric vehicles using simple linear regression involves building a linear regression model with a single independent variable usually the vehicle's characteristics or features to predict the dependent variable the *** work has predicted the price of Electric Vehicle using a data set prepared for the Indian context. It has been predicted that there is significant correlation between battery capacity in Ah and the vehicle price. The measured RMSE value is 26274.942642891292. The measured value indicates that the model is better at predicting the price of an electrical vehicle. © 2024 SAE International. All rights reserved.

关键词： Electric vehicles

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A Survey on Digital Twins: Enabling Technologies, Use Cases, Application, Open Issues, and More

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 84-107页

作者： Hassija, Vikas Chamola, Vinay De, Rajdipta Das, Soham Chakrabarti, Arjab Sangwan, Kuldip Singh Pandey, Amit Deemed to be University School of Computer Engineering Bhubaneswar751024 India BITS-Pilani Department of Electrical and Electronics Engineering Pilani Campus Pilani333031 India BITS Pilani Department of Mechanical Engineering Pilani Campus Pilani333031 India Bennett University Computer Science Engineering and Technology Department Greater Noida201310 India

Digital Twins, sophisticated digital replicas of physical entities, have been gaining significant attention, especially after NASA's endorsement, and are poised to revolutionize numerous fields, such as medicine and construction. These advanced models offer dynamic, real-time simulations, leveraging enabling technologies, such as artificial intelligence, machine learning, IoT, cloud computing, and Big Data analytics to enhance their functionality and applicability. In the medical field, Digital Twins facilitate personalized treatment plans and predictive maintenance of medical equipment by simulating human organs with precision. In construction, they enable efficient building design and urban planning, optimizing resource use, and reducing costs through predictive maintenance. Startups are innovatively employing Digital Twins in various sectors, from smart cities - where they optimize traffic flow, energy consumption, and waste management - to industrial machinery, ensuring predictive maintenance and minimizing downtime. This survey delves into the diverse use cases, market potential, and challenges of Digital Twins, such as data security and interoperability, while emphasizing their transformative impact on industries. The future prospects are promising, with continuous advancements in AI, ML, IoT, and cloud computing driving further expansion and application of Digital Twin technologies. © 2024 IEEE.

关键词： Predictive maintenance

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