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2023 International Conference on data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Meenakshi, D. Anbarasan, M. Murugesan, S. Selvalakshmi, B. Tagore Engineering College Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India Tagore Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350348910

Deep learning (DL), an evolving technology, allows computers to learn autonomously from historical data. Furthermore, it employs a number of methodologies to generate the precise models and create predictions based on facts or old data. A deep learning system produces the predictions based on mathematical models of prediction which is built from past information once it gets new information. Nowadays, the use of deep learning in medical systems is crucial in various applications such as recognition, segmentation, and classification. Worldwide, lung illness is a prevalent occurrence in human beings which includes Covid-19, pneumonia, emphysema, asthma, lung cancer, etc. The early identification of lung diseases helps to detect the disease more accurately. Several deep learning techniques, including convolutional neural network, vanilla neural networks, visual geometry group-based networks and Caps Net, are used to predict the lung illness. To forecast different lung diseases from chest X-ray and CT scan pictures, typical CNN's performance and learning framework is insufficient. This proposed work aims to build a lung disease detection model using CNN with various hyperparameter tuning to enhance the accuracy of the model. Also, the accuracy is associated with advanced methods to enhance the performance of the proposed work. © 2023 IEEE.

关键词： Convolutional neural networks

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Hybrid Blockchain and IoT Driven Framework for Supply Chain Optimization by Enhancing Transparency, Efficiency and Cost Reduction 3

Hybrid Blockchain and IoT Driven Framework for Supply Chain ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Pradeep Kumar, S. Basha, C.H. Hussaian Prasath, C. Arun Amirthayogam, G. Lathif, Syed Ismail Abdul Leelavathi, R. Rajalakshmi Engineering College Computer Science Design Department Thandalam Chennai602105 India Sr University Department of Electrical and Electronics Engineering Warangal506371 India Mahendra Engineering College Department of Ece Namakkal637503 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600062 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India Velalar College of Engineering and Technology Department of Biomedical Engineering Erode638012 India

ISBN: (纸本)9798331509675

Blockchain technology represents a modern supply chain management revolution which fixes operational challenges while providing enhanced transparency at reduced costs. The research develops HB-SCOF (Hybrid Blockchain-Enabled Supply Chain Optimization Framework) which combines blockchain self-verification databases with automatic smart contracts to maintain continuous supply chain observation and evaluate data credibility. The framework integrates tamper-proof transaction tracking mechanisms with unified supply chain network connectivity for securing data transmission as a trust delivery method through unbreakable shared databases. The retail-industry case study demonstrated a 25% decrease in expenses stemming from agency elimination alongside enhanced flow management and better stock distribution. The system demonstrates its effectiveness through assessments of key performance indicators including transaction speed and data accuracy together with business cost efficiency. This part looks at implementation methods through training stakeholders in addition to transforming organizational policies which produce successful outcomes. Blockchain technology labs verify its capability to enhance supply chain management efficiency while showing realistic deployment methods. © 2025 IEEE.

关键词： Smart contract

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MedX-Net: Hierarchical Transformer with Large Kernel Convolutions for 3D Medical Image Segmentation

MedX-Net: Hierarchical Transformer with Large Kernel Convolu...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lu, Lin Zou, Qingzhi Key Laboratory of Computing Power Network and Information Se-curity Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Due to the exceptional performance of Transform-ers in 2D medical image segmentation, recent work has also introduced them into 3D medical segmentation tasks. For instance, Swin UNETR and other hierarchical Transformers have reintroduced prior knowledge from several convolutional networks, further enhancing the volume segmentation capabilities of models. The efficacy of these hybrid methodologies is primarily attributed to the substantial quantity of parameters and the nonlocal self-attention mechanism with a large receptive field. We argue that the behavior of these methods' large receptive fields can be simulated by employing fewer parameters through the utilization of depth-wise convolutions with large kernel. Within this manuscript, we introduce a lightweight volume segmentation model called MedX-Net, which uses convolutional network modules to simulate hierarchical Transformers for robust volume segmentation. Firstly, inspired by the hierarchical Transformer module of Swin UNETR, we investigate large-kernel depth-wise convolutions with different sizes to achieve a reduced model parameter count while maintaining a large global receptive field. Secondly, we replace the multilayer perceptron (MLP) in the hierarchical Transformer module with Inverted Bottleneck with Depthwise Convolution Enhancement(DWCE) to improve model performance with fewer activation and normalization layers, further reducing the parameter count. We validate the effectiveness and efficiency of our model for volume segmentation on three public datasets: Synapse, BTCV and ACDC. On the Synapse dataset, compared to Swin UNETR, our model achieves an improvement from 83.48% to 87.21% in Dice score. Compared to the result of 86.57% achieved by nnFormer, our model achieves superior performance while reducing the model parameter count by 64%. © 2024 IEEE.

关键词： Image segmentation

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Evolutionary Dynamic Optimization-Based Calibration Framework for Agent-Based Financial Market Simulators 13

Evolutionary Dynamic Optimization-Based Calibration Framewor...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Yang, Zhenhua Zhong, Muyao Yang, Peng Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Department of Statistics and Data Science Shenzhen518055 China

ISBN: (纸本)9798350308365

The agent-based financial market simulators serve as an important validation tool for trading strategies. For high-fidelity simulation, it is pivotal to calibrate the parameters of a simulator so that the generated simulation data resembles the observed real market data of interest. In traditional calibration methods, it is typical that the parameters of the simulator are set to be time-invariant. However, the dynamic nature of the real financial market introduces various variability into the behaviors of the market participants over different time intervals, posing in-herent limitations to the traditional methods. A more reasonable approach might involve employing a simulator with time-variant parameters. This suggests that the model parameters can be dynamically adjusted at different stages of the simulation to adapt to the evolving market. Consequently, the calibration problem of the financial market simulators can be treated as a dynamic optimization problem. To dynamically calibrate the simulators, we introduce an Evolutionary Dynamic Optimization (EDO) framework. By monitoring the changes of the best fitness, the whole simulation time interval is adaptively divided into multiple stages. Then the Negatively Correlated Search (NCS) algorithm is employed to effectively adjust the parameters at different simulation stages to better simulate the real financial market. Empirical results on both synthetic and real data verify that our dynamic calibration framework significantly outperforms traditional calibration methods that fixing a parameter for the whole simulation interval. The proposed strategy of detecting dynamic changes is also shown to be more reliable than the naive method of manually segmenting stages. In terms of calibration time, our proposed method significantly improves by nearly 93% compared to the fixed parameter setting, and approximately 61% compared to manual segmentation calibration. © 2024 IEEE.

关键词： Financial markets

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A hierarchical clustering approach for colorectal cancer molecular subtypes identification from gene expression data

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Intelligent Medicine 2024年第1期4卷 43-51页

作者： Shivangi Raghav Aastha Suri Deepika Kumar Aakansha Aakansha Muskan Rathore Sudipta Roy Department of Computer Science&Engineering Bharati Vidyapeeth’s College of EngineeringNew DelhiIndia Artificial Intelligence&Data Science Jio InstituteNavi Mumbai410206India

Background Colorectal cancer(CRC)is the second leading cause of cancer fatalities and the third most common human *** molecular subgroups of CRC and treating patients accordingly could result in better therapeutic success compared with treating all CRC patients *** have highlighted the significance of CRC as a major cause of mortality worldwide and the potential benefits of identifying molecular subtypes to tailor treatment strategies and improve patient *** This study proposed an unsupervised learning approach using hierarchical clustering and feature selection to identify molecular subtypes and compares its performance with that of conventional *** proposed model contained gene expression data from CRC patients obtained from Kaggle and used dimension reduction techniques followed by Z-score-based outlier *** hierarchy clustering was used to identify molecular subtypes,with a P-value-based approach for feature *** performance of the model was evaluated using various classifiers including multilayer perceptron(MLP).Results The proposed methodology outperformed conventional methods,with the MLP classifier achieving the highest accuracy of 89%after feature *** model successfully identified molecular subtypes of CRC and differentiated between different subtypes based on their gene expression *** This method could aid in developing tailored therapeutic strategies for CRC patients,although there is a need for further validation and evaluation of its clinical significance.

关键词： Machine learning Colorectal cancer Feature selection Classification Clustering

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Empowering NGOs with Remote Sensing and CNN-LSTM Models for Social and Environmental Transformation

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 554-568页

作者： Ramachandran, Jayakarthik Gupta, Ashwani Kumar Syamala, Maganti Banerjee, Sudipta Baburao, D. Ranadive, Amol Deivasigamani, Shanmugavel Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Chennai India School of Business and Commerce Chandigarh University Unnao India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur India Department of Computer Science and Engineering Symbiosis Institute of Technology Pune India Campus Symbiosis International (Deemed University) (SIU) Maharashtra Pune India Department of Computer Science and Engineering Vidya Jyothi Institute of Technology (VJIT) Telangana Hyderabad India BIMHRD Sri Balaji University Pune India Department of Artificial Intelligence and Data Science Jeppiaar Institute of Technology Anna University Tamilnadu Chennai India

Non-governmental organizations (NGOs) play a pivotal role in addressing some of the most pressing environmental and social challenges across the globe. With the rise of advanced technologies, NGOs have an unprecedented opportunity to enhance their impact through the adoption of innovative tools. Among these, remote sensing technology has emerged as a cornerstone in environmental monitoring, offering real-time data on land, water, and atmospheric conditions. The integration of remote sensing with CNN-LSTM models offers a transformative approach for NGOs to tackle environmental and social challenges. This study explores the efficacy of these technologies in monitoring deforestation and habitat loss. By leveraging high-resolution satellite imagery and ground truth data, our proposed CNN-LSTM model successfully identifies and tracks changes in land cover. Throughout various tests, the model demonstrated a high accuracy of 0.97, showcasing its potential in providing reliable, real-time data for environmental conservation efforts. The findings suggest that combining CNNs and LSTMs enhances the ability to interpret complex spatiotemporal data, thus supporting NGOs in their decision-making processes. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： CNN-LSTM models Deforestation Environmental monitoring Habitat loss Non-governmental organizations (NGOs) Remote sensing Satellite imagery Spatial pattern recognition Temporal dynamics

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Using Blue Whale Technology: An ML Edge Self-Adaptable Vehicle Slowdown Earliest Warning Information System 8th

Using Blue Whale Technology: An ML Edge Self-Adaptable Vehic...

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Murali, N. David, D. Beulah Research Scholar Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of Data Analytics Institute of Information Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9789819784219

Human life is challenged by this main work’s ultimate goal of reducing accidents and ensuring life safety due to the enormous growth of vehicles and those based on safety. Here, cases of suspected drunk driving, reckless driving, etc., are present. In this scenario, vehicles are traveling at full speed and irresponsibly using technology on all roads. An early warning system is used to stop auto accidents. In order to accommodate the city's steadily increasing automobile population, intelligent traffic systems must be designed in a resourceful and sustainable manner using all available technology in both light and dark. The advanced ML's work on unexpected traffic flow and all road conditions resulted in significant real-time changes in the flow of traffic for every significant issue that arose. Here, the Blue Whale technology makes use of clever intelligence to quickly identify, detect, and modify warning systems. The primary objective of the study was to train CNN/BWT on the collected data sets in order to operationalize the unsupervised approach for the prediction of critical situations. Additionally, it makes an effort to clarify the situation so that guests can feel more at ease and help them decide on transfers in time-sensitive situations. IOT is a practical way to locate the nearest object using machine learning methods that are accurate and apply received data sets, resulting in significant findings that support object recognition. The paperwork fully focuses on improving driving knowledge and offering new approaches for quick object recognition. The Fast Warning System Initiative, which aims to increase daytime safety and security, is providing a prior warning system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Backward wave tubes

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CryptoASCII-ASCII based cryptosystem for cloud data storage

CryptoASCII-ASCII based cryptosystem for cloud data storage

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Pavithra, M. Prakash, M. Vennila, V. Tamil Nadu Komarapalayam India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computer Science and Engineering K. S. R. College of Engineering Tamil Nadu Tiruchengode India

ISBN: (纸本)9781032669663

Cloud computing plays a huge role in IT industry, Education sector, government sector, medical field, defense management, Natural disaster control, weather reporting, road traffic maintenances, e-money transaction etc. The cloud provides wide amount of services can include delivery of data, software, hardware, infrastructure, platform, networking and so on. The merits of cloud computing are numerous to the user for data storing, accessing, maintenance in the cloud from anywhere at any time at low cost. On the other hands, the data in the cloud were not maintained by the direct owner. Despite, the data security has always been big challenge in cloud storage. data integrity, confidentiality and privacy protection are the prominent factors of the user’s side from the cloud environment. In this proposed paper, we introducing a new method CryptoASCII to provide data security in the cloud storage. By CryptoASCII, we confirmed that the user’s data are maintained with integrity and confidentiality. CryptoASCII can provide encryption and decryption algorithms to ensure data privacy as well as it provide redundancy checking proof for data integrity. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Redundancy

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Prognostic Phenotype Clustering of Uterine Sarcoma Patients

Prognostic Phenotype Clustering of Uterine Sarcoma Patients

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Zhang, Tuao Yuan, Suzhen Zhou, Xue Wang, Wenwen Zhu, Xin Graduate School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan Department of Obstetrics and Gynecology Tongji Hospital Huazhong University of Science and Technology Wuhan China Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Tokyo Japan

ISBN: (纸本)9798350350548

Uterine sarcoma is a rare but highly aggressive malignancy with poor prognosis. In this study, prognostic phenotypes corresponding to the end point of mortality were explored by unsupervised machine learning (ML) on training (n = 1985) and validation (n = 498) datasets. The data were extracted from patients with uterine sarcoma in the Surveillance, Epidemiology, and End Results (SEER) database. A decision-making process of phenotype identification was performed and the findings were compared with existing research. Three phenotypes associated with stratified levels of mortality risks (high, intermediate and low) were identified. Kaplan-Meier survival analysis revealed significant differences among these phenotypes (pairwise comparison p © 2024 IEEE.

关键词： Contrastive Learning

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Dimensionality Reduction for Classification Using Divide-and-Conquer Based Genetic Programming 13

Dimensionality Reduction for Classification Using Divide-and...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wang, Peng Xue, Bing Liang, Jing Zhang, Mengjie School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand School of Electrical Engineering and Automation Henan Institute of Technology Xinxiang China

ISBN: (纸本)9798350308365

Dimensionality reduction (DR) is to obtain meaningful low-dimensional representation concealed within high-dimensional data. Genetic programming (GP) has been used to achieve DR for classification because of the flexible representations and promising performance. However, one drawback of many current GP-based DR methods is the high computational demands, potentially limiting their utility in high-dimensional scenarios. To tackle this issue, this work develops a new GP algorithm to achieve DR for classification. A divide-and-conquer strategy based on feature clustering is proposed to split the original feature set into multiple small feature groups. Each tree in the multi-tree GP is used to learn one high-level feature from each feature group. The performance of the proposed method is examined on 10 classification datasets with varying difficulty. The results indicate that the new GP method achieves significantly better classification performance than commonly used feature construction and feature selection methods. More importantly, the proposed GP method uses fewer nodes but achieves better or comparable classification accuracy than the baseline multi-tree GP method. © 2024 IEEE.

关键词： Dimensionality reduction

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