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共 20256 条记录，以下是651-660 订阅

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Medical application research based on deep learning-cancer cell classification and recognition 3

Medical application research based on deep learning-cancer c...

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3rd international conference on Biomedical and intelligent Systems, IC-BIS 2024

作者： Ruiqi, Che Yi, Wang Shanghai University of Traditional Chinese Medicine Shanghai Institute of Integrated Traditional Chinese and Western Medicine Clinical Medicine Shanghai200082 China Shanghai Integrated Traditional Chinese and Western Medicine Hospital Shanghai200082 China

ISBN: (纸本)9781510681279

This study aims to combine Raman spectroscopy technology with deep learning algorithms to achieve precise classification and identification of cancer cells. Firstly, a large-scale spectral database is established using datasets. Subsequently, deep learning techniques are employed to design and train various deep learning network models for cancer cell classification. The models can extract high-order features from complex spectral data, enabling accurate identification of different types of cancer cells. Through validation and optimization of the models, their accuracy and reliability in cancer diagnosis will be verified, providing clinicians with an effective auxiliary diagnostic tool that is expected to play a significant role in improving the accuracy of cancer diagnosis and treatment efficacy. © 2024 SPIE.

关键词： Cancer cells

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Real-Time Traffic Load Prediction in Cellular Networks using Cutting-Edge Machine learning Approaches 3

Real-Time Traffic Load Prediction in Cellular Networks using...

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

作者： Dhanalakshmi, K.S. Deepak, Kambati Lakshmi Chandu, Chinnam Sai Reddy, Palam Venkata Krishna Reddy, Nagireddy Vishnu Vardhan Manikanta, Kesanasetty Naga Dept. Electronics and Communication Engineering Kalasalingam Academy of Research and Education Srivilliputhur India

ISBN: (纸本)9798331527549

Exponential growth of cellular traffic by the usage of smartphones and streaming services presents challenges to the maintenance of QoS-critical network performance, hence requiring accurate and efficient traffic prediction. This work extends the literature in cellular traffic load prediction with the application of machine learning. This paper discusses the reduction of dimensionality and the efficiency of models. Unlike the existing system that applies LightGBM, SVM, Linear Regression, and DBSCAN for clustering, the proposed system combines Random Forest, XGBoost, and a Stacking Classifier for higher accuracy and improved computational capacity. Collected key features include user throughput, cell throughput, latency, and resource block utilization. Preprocessing is done by filling missing values, normalization, PCA for dimension reduction, and DBSCAN clustering of similar traffic patterns. Mean Squared Error analysis showed that Stacking Classifier results in X% reduction in the prediction error, and Y% reduction in computation time compared with existing models. Random Forest and XGBoost improved feature selection that captured complex traffic patterns, while PCA and clustering reduced the training time without losing accuracy. The proposed framework can scale dynamically to meet the demands of 5G and beyond, providing robust adaptability to changing traffic conditions. In addition, the potential integration with real-time systems and anomaly detection could further improve network reliability. © 2025 IEEE.

关键词： Mean square error

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Advancements in Credit Scoring, Profit Scoring, and Portfolio Optimization for P2P Lending 3

Advancements in Credit Scoring, Profit Scoring, and Portfoli...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Nayaka, Premkumar Hegde, Anusha Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Laboratory Department of Computer Science and Engineering Surathkal India National Institute of Technology Karnataka Brics Laboratory Department of Computer Science and Engineering Surathkal India

ISBN: (纸本)9798331528201

The Peer-to-peer (P2P) lending platform allows borrowers to connect directly with lenders outside traditional banking systems. Therefore, for the sustainability of these platforms, they must accurately assess the credit risk and profitability of the loans. Various credit scoring techniques, including Logistic Regression, neural networks, and ensemble methods, can be used to estimate the likelihood of borrower default. It is imperative to analyze the profit the lenders generated and enhance the credit scoring so that the investors face minimum loss. Once the profit analysis is done, then it is crucial to advise the investors about the portfolio of loans. This paper presents recent credit scoring, profit scoring, and portfolio optimization trends for P2P lending. We highlight the significant issues in incorporating machine learning models into credit scoring systems. The analysis emphasizes the need for a data-driven approach to perfecting lending practices, thus benefiting both borrowers and investors in the rapidly changing P2P landscape. © 2024 IEEE.

关键词： Decentralized finance

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Analysis of unsupervised-learning based approach for the hazard prediction along with the application of the Gaussian method of anomaly detection to reduce the false alarm scenarios in underground coal mines 3

Analysis of unsupervised-learning based approach for the haz...

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3rd IEEE international conference on Technology, engineering, Management for Societal Impact using Marketing, Entrepreneurship and Talent, TEMSMET 2023

作者： Sharma, Mayank Maity, Tanmoy Banerjee, Soumyadip Dhanbad Deparment of Electrical Engineering Jharkhand India

ISBN: (纸本)9798350397819

Safety is a prime concern in the underground mining industry. The wireless sensor networks of hybrid architectures are proposed for monitoring underground mines. Very little work is observed in the field to make an intelligent monitoring system;many are supervised learning-based approaches. Unsupervised and reinforcement learning approaches are significantly fewer in number, and these areas are still unexplored for the underground mine scenarios. This paper proposes the analysis of the K-means clustering algorithm for an intelligent gas hazard monitoring system based on an unsupervised learning method. However, before hazard detection, an anomaly detection algorithm is applied to remove anomalies so there will be fewer chances of false alarms. The F 1 score metric is used to verify the anomaly detection algorithm. Principal component analysis (PCA) is used to visualize the multidimensional gas data. After training the model in various scenarios, it is observed that the model is capable of detecting a hazardous situation with a significant accuracy level. © 2023 IEEE.

关键词： Unsupervised learning

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Personalized Confederation Training for Smart Iot Systems: A Cloud - Delivered Structure 3

Personalized Confederation Training for Smart Iot Systems: A...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Katariya, Jitendra Kumar Nivesh Vivekananda Global University Department of Computer Science Engineering India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

The Internet of Things (IoT) has significantly impacted many facets of contemporary life, and several sophisticated IoT applications and services are already being developed. Recently, the concept of federated learning has been proposed in order to train a globally shared model on IoT devices utilising a large number of user-generated data samples while stopping data leak. Due to the widespread heterogeneities in device, statistics, and model in complex IoT contexts, traditional federated learning is not suitable for instant implementation. In this paper, we present a cloud-edge architecture for intelligent IoT applications that includes a customised federated learning framework. To address the heterogeneity worries in IoT conditions, we examine imaginative altered united learning calculations that could diminish the unfortunate results brought about by heterogeneities in various aspects. The advantages of edge registering empower smart IoT applications to meet the models for high handling pace and low inertness. At long last, we give a contextual investigation of human movement recognition in light of Web of Things to show the advantages of customisable combined learning for intelligent IoT applications. © 2023 IEEE.

关键词： Internet of things

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3rd international conference on Smart Manufacturing, Industrial and Logistics engineering, SMILE 2023 and the 7th Asian conference of Management Science and Applications, ACMSA 2023

3rd International Conference on Smart Manufacturing, Industr...

引用

3rd international conference on Smart Manufacturing, Industrial and Logistics engineering, SMILE 2023 and the 7th Asian conference of Management Science and Applications, ACMSA 2023

ISBN: (纸本)9789819701933

The proceedings contain 79 papers. The special focus in this conference is on Smart Manufacturing, Industrial and Logistics engineering and Management Science and Applications. The topics include: An Optimal Period-Based Switching Model for Assembly Lines Considering Ergonomic Evaluation;a Mathematical Analysis on Optimal Assignment in Limited-Cycle Multiple Periods Considering Quality and Due Time ~the Case of Two Worker Levels~;Average TSP Tour Length Approximations for Territory Design;motion Capture Analysis of learning Effect for Assembly Tasks;scheduling Optimization of Underground Material Transport Vehicle in X Coal Mine Based on Whale Algorithm;an Optimal Allocation Model for Smart Production System in Limited-Cycle Problem with Multiple Periods Considering Quality and Due Time ~the Case of Different Worker Levels~;integrated Assembly Job Shop Scheduling Considering Fuzzy Operation Time;a Hybrid Multi-objective Genetic Algorithm Combined with Dispatching Rule for Wafer Test Scheduling;deep Reinforcement learning for Dynamic Flexible Job-Shop Scheduling with automated Guided Vehicles;an Integrated Shop Floor Dispatching Rule by Considering Urgent State of Jobs Based on Workload Control: An Assessment by Simulation;intelligent Cooperation by Solving a Two-Stage Production Assembly Scheduling Problem with a Heuristic Algorithm in Canned Food Plant;solving an intelligent Scheduling Problem in an Automobile Factory;research on Mixed-Model Assembly Line Rebalancing Considering Skill Differences and Series–Parallel Design;distributed Dual-Resource Flexible Job Shop Scheduling Optimization Based on Multi-objective Gray Wolf Algorithm;scheduling of Design Engineers in an Engineer-To-Order Production System;Development of an Integrated GA and PSO Scheduling Method Considering the Skill Level of the Workers.

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A Comprehensive Digital Solution for Identifying and Addressing Academic Risk in Middle Education 25th

A Comprehensive Digital Solution for Identifying and Address...

引用

25th international conference on intelligent data engineering and automated learning

作者： Magalhaes, Renata Duraes, Dalila Costa, Antonio Machado, Jose Novais, Paulo Univ Minho Algoritmi Ctr LASI Braga Portugal

ISBN: (纸本)9783031777370;9783031777387

Smart schooling seeks to enhance the educational experience through technology. In this effort, a digital educational platform has been developed and empirically tested to identify students at risk of academic failure and dropout, while also promoting effective study and learning habits. Machine learning algorithms are employed to assess academic failure risk based on students' responses to a questionnaire created by educational psychologists. The platform predicts students' academic performance by analyzing factors related to their school and home environments, as well as their motivation to learn. Additionally, a decision support system is integrated to alert the class director and recommend preventive actions when risks or unusual behaviors are identified. A decision support system is incorporated to alert the class director and suggest preventive measures after risk or unusual behaviour is detected.

关键词： academic failure conversational agent smart education machine learning decision support system

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Triplet CNN-based word spotting of Gujarati Handwritten Document 3

Triplet CNN-based word spotting of Gujarati Handwritten Docu...

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3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Bodana, Yuvrajsinh Hingrajiya, Heli Thakkar, Amit Suthar, Sanket Department of Computer Science and Engineering India

ISBN: (纸本)9798331528201

Word spotting of Gujarati handwritten documents is a highly challenging task due to the complexity of the handwritten text in the Gujarati language. This paper presents a novel approach to word spotting, which includes a transfer learning-based triplet CNN model for generating word embeddings and calculating triplet loss. During training, the CNN processes a set of triplet samples consisting of anchor, positive, and negative images. Triplet loss is then calculated to create word embeddings by minimizing intra-class distances and maximizing inter-class distances, which are further evaluated using Euclidean distance for comparison. The proposed approach is tested on handwritten image documents, and the experimental results demonstrate its effectiveness. © 2024 IEEE.

关键词： Embeddings

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data Selection Driven by Item Difficulty: On Investigating data Efficient Practice for Hyperparameter Search 24

Data Selection Driven by Item Difficulty: On Investigating D...

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IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI (CAIN)

作者： dos Reis, Gustavo Rodrigues Mos, Adrian Cornax, Mario Cortes Labbe, Cyril NAVER LABS Europe Meylan France Univ Grenoble Alpes CNRS INRIA Grenoble INPLIG Grenoble France

ISBN: (纸本)9798400705915

Foundation Models shift the interest to adapting models instead of creating proprietary models from scratch. Despite this change, performing hyperparameter optimization (HPO) is still needed. Users adapting systems powered by those models on proprietary data should not considerably increase the overall resource footprint with extensive hyperparameter search. Given that this footprint is also proportional to the data used in HPO, we aim to investigate how a user can effectively reduce the amount of data used, leveraging the deep learning model's empirical facility to output the expected correct result for an item in the dataset. In this work, we describe a methodology for accomplishing this data reduction through estimating a measure of an item's difficulty. This method allows keeping only a portion of data that conserves the overall proportions of item difficulty throughout the dataset while helping order them meaningfully. The rationale is derived from results from curriculum learning research as we try to answer if the adapted models could help organize and select subsets of data representative of the whole. Preliminary results of evaluating the method are provided for image recognition and scientific name entity recognition (NER). We observe that the amount of data for HPO can be reduced as far as 60% and still point to the same choice of hyperparameters compared to using the whole training set.

关键词： Image recognition

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Shape Statistical Model-Based Regression for Predicting Anatomical Landmarks 3

Shape Statistical Model-Based Regression for Predicting Anat...

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3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Ting, Tong China University of Geosciences Wuhan430074 China

ISBN: (纸本)9798350380989

Determining appropriate shapes and establishing corresponding relationships based on 3D models remains a significant challenge. This research employs shape statistical models and direct correspondence between two morphologies via support vector regression. The displacement of sampled points to the standard model, calculated through non-rigid registration, represents the shape state. Constructing shape statistical models based on the deformation fields of sampled points from different models and subsequently mapping high-dimensional cranial features using support vector regression allows for establishing relationships between different models. The study used 77 mandibles and maxillas for the validation. Predicting the mandible using the maxilla resulted in a 2.43mm average error, while predicting the maxilla using the mandible yielded a 1.95mm average error. It offers potential avenues for further research and applications in craniofacial reconstruction. © 2024 IEEE.

关键词： craniofacial reconstruction Machine learning mandibles maxillas SVR

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