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Materials property prediction using feature selection based machine learning technique

Materials Today: Proceedings 2022年 69卷 710-715页

作者： Valli Priyadharshini, K. Vijay, A. Swaminathan, K. Avudaiappan, T. Banupriya, V. Department of Computer Science and Engineering K.Ramakrishnan College of Technology Trichy India Department of Information Technology M.Kumarasamy College of Engineering Karur India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India Department of Computer Science and Business Systems M.Kumarasamy College of Engineering Karur India

An essential role in increasing quality and reducing environmental damage and public safety is played by material performance prediction in materials research. There is still a long way to go before traditional regression analysis models in materials science can accurately capture non-linearities in data, time-consuming predictions, and enormous amounts of data. Because the more features you have, the less accurate your classification will be, hence you need a feature selection method to improve your results. This necessitates the use of modern computer science methods to analyse materials data. Machine learning (ML) has arose in fresh years as a powerful tool for establishing correlations between data and is finding application in the field of materials science due to its generalizability, noise tolerance, and error tolerance. Different materials servicing varied settings can be predicted for their various properties using machine learning techniques based on feature selection. In this research, feature selection-based ML techniques are demonstrated. Comparisons with various computing and input-ranking approaches demonstrate the importance of the suggested model in materials-related challenges. © 2023

关键词： Forecasting

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Advancements in Human Activity Recognition: A comprehensive Review of Smartphone and Wearable Sensor Integration 7

Advancements in Human Activity Recognition: A comprehensive ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Dhohada, Cheruku Sumalatha, Konatham Vellore Institute of Technology Computer Science And Engineering Vellore India Vellore Institute of Technology Department of Database Systems Vellore India

ISBN: (纸本)9798350359299

In recent years, deep learning and machine learning methods have been extensively employed in almost every field due to their capability of data processing and analysis. These are the subdomains of Artificial intelligence that gather information in different formats and from different sources. Human Activity Recognition using smartphones and wearable sensors has evolved into a dynamic field with numerous applications and challenges. Ongoing research efforts continue to refine methodologies, address challenges, and expand the scope of Human activity recognition, making it an exciting area with significant potential. Nowadays everyone is using smartphones, so it is easy to find their applications in health monitoring, fitness tracking, context-aware computing, and security. The ability to automatically detect activities such as walking, running, and sleeping opens avenues for personalized services and interventions that help in people's daily lives. While traditional machine learning and deep learning models offer valuable insights individually, ensemble learning presents an opportunity to further enhance accuracy and robustness. This research work aims to give a concise overview of the various deep learning and machine learning approaches, their convergence, and an analysis of the applications of these algorithms in the field of smartphone sensor-based human activity recognition. © 2024 IEEE.

关键词： Learning systems

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Tomato Leaf Diseases Prediction Using Deep Learning Algorithms

Tomato Leaf Diseases Prediction Using Deep Learning Algorith...

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2024 International Conference on Advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Ananthi, P. Devi, K. Nirmala Gopinath, D. Shanmugapriya, P. Gowtham, S. Kongu Engineering College Department Of Computer Technology Tamilnadu Perundurai India Bangaluru India Kristu Jayanti College Department Of Computer Science Bangaluru India Software Systems Kongu Engineering College Erode India

ISBN: (纸本)9798350364828

In the realm of agriculture, infections on tomato leaves pose a worldwide danger to established tomato production, impacting a large number of farmers worldwide. To ensure healthy tomato plant growth and food security for the world's expanding population, early disease detection, treatment, and solution identification are essential. The use of computer-assisted technologies for disease detection in plant leaf is prevalent in modern agriculture. The goal was to develop an effective deep learning model for the detection of diseases in tomato plant leaf. To address memory and computational resource constraints, the proposed model incorporates a lightweight 'Region Convolutional Neural Network (R-CNN)' head and conducted experiments with various neural network architectures, including Densenet-169, optimized Mobilenet-V2 and Resnet-50, to enhance detection accuracy and computational efficiency. The research extends the capabilities of these neural network architectures by optimizing anchor proportions in the Conventional Neural Network (CNN) and modifying feature extraction topologies. These optimizations resulted in improved detection accuracy and computational efficiency and compared proposed technique against cutting-edge models to evaluate its viability and robustness. The outcomes of the research were promising. Impressive outcomes were obtained by the proposed approach regarding accuracy, F1-score, recall, and precision. © 2024 IEEE.

关键词： Computational efficiency

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Distance Matrix and Markov Chain Based Sensor Localization in WSN

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computers, Materials & Continua 2022年第5期71卷 4051-4068页

作者： Omaima Bamasaq Daniyal Alghazzawi Surbhi Bhatia Pankaj Dadheech Farrukh Arslan Sudhakar Sengan Syed Hamid Hassan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversitySaudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipurRajasthanIndia University of Engineering and Technology LahorePakistan Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveliTamilnaduIndia

Applications based on Wireless Sensor Networks(WSN)have shown to be quite useful in monitoring a particular geographic area of *** geometries of the surrounding environment are essential to establish a successful WSN *** it is literally hard because constructing a localization algorithm that tracks the exact location of Sensor Nodes(SN)in a WSN is always a challenging *** this research paper,Distance Matrix and Markov Chain(DM-MC)model is presented as node localization technique in which Distance Matrix and Estimation Matrix are used to identify the position of the *** method further employs a Markov Chain Model(MCM)for energy optimization and interference *** are performed against two well-known models,and the results demonstrate that the proposed algorithm improves performance by using less network resources when compared to the existing *** probability is used in the Markova chain to sustain higher energy ***,the proposed Distance Matrix and Markov Chain model decrease energy use by 31%and 25%,respectively,compared to the existing DV-Hop and CSA *** experimental results were performed against two proven models,Distance VectorHop Algorithm(DV-HopA)and Crow Search Algorithm(CSA),showing that the proposed DM-MC model outperforms both the existing models regarding localization accuracy and Energy Consumption(EC).These results add to the credibility of the proposed DC-MC model as a better model for employing node localization while establishing a WSN framework.

关键词： Wireless sensor network resource optimization routing distance matrix Markov chain

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Enhancing Credit Card Fraud Detection through LSTM-Based Sequential Analysis with Early Stopping 2

Enhancing Credit Card Fraud Detection through LSTM-Based Seq...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Jhansi Ida, S. Balasubadra, K. Skandarsini, R.R. Lakshmi Narayanaa, T. RMK Engineering College Dept. of Computer Science and Business Systems Thiruvallur India RMD Engineering College Department of Information Technology Thiruvallur India

ISBN: (纸本)9798350365269

There is still a serious problem with credit card fraud in the world of financial transactions, requiring innovative solutions to effectively detect and halt illicit behavior. This research presents a unique method of detecting credit card fraud by strategically implementing early pausing in a Long Short-Term Memory (LSTM) neural network. Leveraging the sequential dependencies inherent in transaction data, the LSTM model demonstrates remarkable efficacy in discerning fraudulent from legitimate transactions. The early stopping mechanism ensures that the model's training halts at the optimal epoch, preventing over fitting and enhancing generalization. Our thorough analysis highlights the model's versatility by demonstrating a dynamic compromise between recall and accuracy throughout training. Practical implications include enhanced fraud detection accuracy, reduced false alarms, and real-time decision support for financial institutions. The model's adaptability and generalization make it a valuable asset in the ongoing battle against credit card fraud, contributing to a more secure financial environment. Future research directions may explore additional features and alternative architectures to further advance fraud detection capabilities. © 2024 IEEE.

关键词： Long short-term memory

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EnsembleCAM: Unified Visualization for Explainable Cervical Cancer Identification

EnsembleCAM: Unified Visualization for Explainable Cervical ...

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2024 International Research Conference on Smart Computing and systems engineering, SCSE 2024

作者： Sritharan, Niruthikka Gnanavel, Nishaanthini Inparaj, Prathushan Meedeniya, Dulani Yogarajah, Pratheepan University of Moratuwa Department of Computer Science and Engineering Moratuwa Sri Lanka Ulster University School of Computing Engineering and Intelligent Systems Londonderry United Kingdom

ISBN: (纸本)9798350375688

Cervical cancer, which is ranked fourth among cancers affecting women, is highly treatable when detected early through the pap smear test. Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), analyze pap smear images, yet their Black-Box nature raises transparency concerns in medical diagnostics. This paper introduces a solution named EnsembleCAM to enhance interpretability by unifying visual explanations through the combination of diverse Class Activation Maps (CAMs). Using the Herlev Dataset, we employ data pre-processing, data augmentation techniques, develop an XceptionNet based binary classification model with an accuracy of 89% and apply GradCAM, GradCAM++, Score-CAM, Eigen-CAM and LayerCAM on this classifier. Then, the novel EnsembleCAM is constructed taking the median of activation maps from the five individual CAM methods. The analysis of activation maps of each CAM method and EnsembleCAM confirmed that in activation maps of EnsembleCAM, higher activation values were more concentrated around the nucleus which is the most important region in indicating cervical malignancy. The evaluation using pixel flipping performance metric also proved that the EnsembleCAM effectively recognises regions vital to the model's decision-making through its steepest drop in the mean prediction score when the pixels in the region contributing most to the model's decision were flipped. © 2024 IEEE.

关键词： Visualization

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Enhancing Skin Cancer Detection Through Automated Classification Using Convolutional Neural Networks 1

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7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Hossain, Md Jakir Bhuiyan, M. D. Wahidul Islam Raisa, Sadia Afrin Tomal, Minhazul Amin Jennifer, Sanjeda Sara Reza, Ahmed Wasif Siddique, Nazmul Department of Computer Science and Engineering East West University Dhaka Bangladesh School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

Skin cancer presents a significant public health concern, necessitating early detection and intervention to mitigate life-threatening consequences. Despite its prevalence, skin cancer is not always given the attention it deserves. In this paper, we propose an automated procedure for distinguishing skin cancers using several neural network algorithms. We used the MobileNet architecture whose accuracy is not very high and this is why we have also used other algorithms such as Sequential CNN (Convolution Neural Network), VGG16, and Xception to improve the accuracy of our model. By comparing these algorithms, we found that the Sequential CNN performed best with an accuracy of 94%. The proposed model utilizes the Sequential CNN algorithm to achieve a high level of accuracy, which is of utmost importance for the early detection of skin cancer. The high accuracy of the proposed model makes it a promising tool for detecting skin cancer early. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Lung cancer

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Improved Adaptive Fairness-Indexed Technique for Congestion Control in Underwater Networks 3

Improved Adaptive Fairness-Indexed Technique for Congestion ...

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3rd International Conference on Communication, Control, and Intelligent systems, CCIS 2024

作者： Balaji Vijayan, V. Manojee, K.S. Mohammed Ibrahim, M. Nivethitha, R. Thamaraimanalan, T. Ranjithkumar, M. Hkbk College of Engineering Department of Information Science and Engineering Karnataka Bengaluru India Kongunadu College of Engineering and Technology Department of Information Technology Tamilnadu Trichy India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India K.S.R. College of Engineering Department of Computer Science and Engineering Tamilnadu Thiruchengode India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamilnadu Coimbatore India Knowledge Institute of Technology Department of Computer Science and Business Systems Tamilnadu Salem India

ISBN: (纸本)9798331528201

In underwater acoustic sensor networks (UASNs), effective congestion control is essential for ensuring reliable data transmission and resource efficiency, especially in the face of challenging environmental factors such as water velocity, pressure, temperature, and salinity. This paper introduces a novel Adaptive Fairness-Indexed Congestion Control (AFICC) mechanism that employs Q-learning to dynamically adjust transmission rates among nodes, ensuring equitable resource distribution and minimizing congestion. Our simulations, conducted with 100 nodes at depths of up to 1000 meters, reveal that AFICC significantly outperforms two existing algorithms: Rate-Based Congestion Control (RBCC) and Energy-Aware Congestion Control (EACC). Specifically, AFICC achieved a Packet Delivery Ratio (PDR) of 93%, compared to 85% for RBCC and 80% for EACC. Additionally, the End-to-End Delay was reduced to an average of 150 milliseconds with AFICC, while RBCC and EACC recorded delays of 220 milliseconds and 210 milliseconds, respectively. AFICC also demonstrated a 25% improvement in Energy Efficiency over EACC and maintained a Jain's Fairness Index of 0.92, surpassing the 0.82 and 0.78 indices achieved by RBCC and EACC, respectively. These results confirm that AFICC offers a robust framework for congestion control in UASNs, effectively adapting to fluctuating underwater conditions while ensuring reliable communication and equitable resource allocation. © 2024 IEEE.

关键词： Energy efficiency

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Psychological structure of human trust toward autonomous vehicles, using structural equation model

Advances in Transportation Studies

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Advances in Transportation Studies 2024年 64卷 73-90页

作者： Park, H. Lee, K. Kim, S.-H. Department of Industrial and Manufacturing Systems Engineering College of Engineering and Computer Science University of Michigan-Dearborn DearbornMI48128 United States Department of Marketing Global Business and Economics College of Business and Public Management Kean University UnionNJ07083 United States

This study aimed to validate the influence of latent variables on human trust in autonomous vehicles and elucidate the underlying psychological framework. A survey with 114 queries, drawn from previous studies on trust factors, was conducted online, garnering 195 valid responses. Employing correlation and factor analysis, 51 queries were identified as significantly impacting trust levels. A structural equation model incorporating these 51 variables was constructed, revealing five higher-level psychological constructs: "Interpersonal," "System Feature," "Risk Perception," "Behavioral Intention," and "Trust," each comprising sub-factors derived from associated queries. Notably, the "Interpersonal" construct exhibited the greatest influence on "Behavioral Intention" and "Trust," while "Risk Perception" showed a negative correlation with trust, indicating higher perceived risk diminishes trust. The findings highlight the importance of understanding user interpersonal characteristics in enhancing trust in autonomous vehicles. Unlike previous studies focusing on individual variables, this research unveiled a comprehensive psychological structure by categorizing variables, factors, and constructs hierarchically. The developed model facilitates comprehension, prediction, and enhancement of user trust, guiding the development of tailored trust-fostering strategies and interventions for specific user groups and contexts. © 2024, Aracne Editrice. All rights reserved.

关键词： Risk perception

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Multimodal Emotion Recognition through Deep Fusion of Audio-Visual Data 26

Multimodal Emotion Recognition through Deep Fusion of Audio-...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Sultana, Tamanna Jahan, Meskat Uddin, Md. Kamal Kobayashi, Yoshinori Hasan, Mahmudul Comilla University Department of Computer Science and Engineering Cumilla Bangladesh NSTU Department of Computer Science & Telecommunication Engineering Noakhali Bangladesh Saitama University Interactive Systems Lab. Saitama Japan

ISBN: (纸本)9798350359015

The field of emotion recognition in artificial intelligence focuses on enabling machines to comprehend and react to the range of emotions experienced by humans. This paper presents a novel approach that integrates the Convolution Neural Network (CNN) with audio and visual modalities. The study employs the RAVDESS database as a resource to train two distinct models for the analysis of both video and audio data. When it comes to audio pre-processing, advanced signal-processing techniques are applied to extract relevant elements and capture basic acoustic characteristics. A one-dimensional Convolutional Neural Network (CNN) architecture receives the audio data as input, enabling the model to learn complicated patterns and representations from the audio domain. In the context of video pre-processing, sophisticated algorithms are employed to extract essential facial characteristics. In order to capture the changing periods of facial expressions, the video frames are analyzed using a three-dimensional CNN framework following that they have been compressed and converted to grayscale. The fusion technique involves concatenating and extending the outputs of the audio and visual models. The fused features are subsequently sent into a softmax layer, which facilitates the development of a resilient emotion identification system. © 2023 IEEE.

关键词： Audio-visual recognition Convolution Neural Networks Emotion recognition Multi-modal fusion

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