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5th EAI international conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI international conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770807

The proceedings contain 102 papers. The special focus in this conference is on Cognitive computing and Cyber Physical Systems. The topics include: Comparative Analysis Between Fuzzy Theorem and KNN Methodology for Fault and Anomaly Detection;Production Classification in E-Commerce Based on Product Descriptions with Natural Language Processing (NLP) and Machine learning Models;defense Strategy Security Mechanism for Sensor Networks;an Efficient Approach for Food Demand Forecasting Using an Ensemble Technique and Statistical Analysis;identification of Different Medicinal Plants Using Machine learning and Image Processing;Leveraging the Power of MRMR in Machine learning Models for Multi Class Classification of Rice to Promote Sustainable and Efficient Smart Farming;performance Analysis of Word Recognition System Using Tensor Flow;A Distinct Artificial Feed Forward Neural Network (AF2NN) Model for Predicting Compressive Strength of Geo-Polymer Concrete;Performance Assessment of Deep learning-Models for Kidney Tumor Segmentation using CT Images;investigation of Quantum Machine learning for Smart Eco System Focusing on Energy Optimization;Classification of Skin Cancer Using CNN with Transformer Layer;a Unified Approach to Smart Coconut Farming with IoT and Deep learning for Recommendation of Pesticides and Fertilizers;classifying the Severity of Diabetic Retinopathy Using Deep learning;Exploring Prominent Convolutional Neural Network Frameworks to Identify COVID-19 Deceases by Using Medical Images;non-invasive Technique for Detecting Glycosuria Through Image Processing and Deep learning Approaches;deep learning Strategies for Multiclass Skin Disease Classification;AI-Driven Glaucoma Susceptibility Assessment and Lifestyle Guidance;Deep learning in Cartoon Moderation: Distinguishing Child-Friendly Content with CNN Architectures;THz Wideband Metamaterial Absorber for Different Applications.

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Automated Pneumonia Classification Using DensePneumoNet in Chest CT Scans 5

Automated Pneumonia Classification Using DensePneumoNet in C...

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5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Nithila, Ezhil E. Bibin Christopher, V. Raj, J. Relin Francis Srinivasan, A. Soundiraraj, N. Krishnan, R. Santhana Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Avadi India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai Kattankulathur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Information Technology Madurai India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350379945

'DensePneumoNct' is a specialized deep learning algorithm designed for accurate and efficient detection of pneumonia from CT chest images. Leveraging the DenseNet architecture within TensorFlow, this algorithm demonstrates superior performance across critical metrics including accuracy, recall, precision, and F1 score. The implementation utilizes the ImageNet dataset, preprocessed to ensure standardization and resizing, optimizing input uniformity for robust model training. The DenseNet model architecture features dense blocks and transition layers, fostering strong feature propagation and effective downsampling of feature maps. Hyperparameter tuning, including a learning rate of 0.001, a batch size of 64, and training over 100 epochs, enhances model convergence and performance refinement. Training enhancements such as early stopping and learning rate scheduling further stabilize model training and prevent overfitting. Evaluation on a validation set showcases 'DensePneumoNet''s capability to distinguish COVID-19 pneumonia, viral pneumonia, and healthy lung tissues with unparalleled accuracy. Comparative analysis against ResNet-50, Inception-v3, SqueezeNet, and LeNet confirms DensePneumoNet's superiority in all evaluated parameters, affirming its efficacy for critical medical imaging tasks. 'DensePneumoNet' represents a significant advancement in pneumonia detection, poised to enhance diagnostic accuracy and clinical decision-making in medical settings. © 2024 IEEE.

关键词： Medical imaging

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RecFlex: Enabling Feature Heterogeneity-Aware Optimization for Deep Recommendation Models with Flexible Schedules 24

RecFlex: Enabling Feature Heterogeneity-Aware Optimization f...

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2024 international conference for High Performance computing, Networking, Storage and Analysis

作者： Pan, Zaifeng Zheng, Zhen Zhang, Feng Xie, Bing Wu, Ruofan Smith, Shaden Liu, Chuanjie Ruwase, Olatunji Du, Xiaoyong Ding, Yufei Renmin Univ China Beijing Peoples R China Microsoft Redmond WA 98052 USA Univ Calif San Diego San Diego CA 92103 USA

ISBN: (数字)9798350352917

ISBN: (纸本)9798350352924;9798350352917

Industrial recommendation models typically involve numerous feature fields. The embedding computation workloads are heterogeneous across these fields, thus requiring varied optimal code schedules. While existing solutions apply basic fusion optimization for embedding operations, they inefficiently treat all feature fields with identical schedules, leading to suboptimal performance. In this paper, we introduce RecFlex, which generates fused kernels with distinct schedules for different feature fields. RecFlex employs the interference-aware schedule tuner to tune schedules and the heterogeneous schedule fusion compiler to generate fused kernels, addressing two major challenges. To determine optimal schedules of different feature fields within the fused kernel, RecFlex proposes a two-stage interference-simulated tuning strategy. To handle dynamic workloads that challenge tuning and fusion, RecFlex combines compile-time schedule tuning with runtime kernel thread mapping. RecFlex surpasses state-of-the-art libraries and compilers, achieving average speedups of 2.64x, 20.77x, and 11.31x over TorchRec, HugeCTR, and RECom, respectively. RecFlex is publicly available at https://***/PanZaifeng/RecFlex.

关键词： recommender system machine learning compiler

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5th EAI international conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI international conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770777

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An Optimized Deep learning for Efficient Defect Inspection in Smart Industry with Fog computing 2

An Optimized Deep Learning for Efficient Defect Inspection i...

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2nd international conference on Data Science and Information System, ICDSIS 2024

作者： Narayana, Potu Keerthi, K. Adnan, Myasar Mundher Madhura, G.K. Ghamya, K. Stanley College Of Engineering And Technology For Women Department Of Cse Telangana Hyderabad India Methodist College Of Engineering And Technology Department Of Cse Telangana Hyderabad India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Artificial Intelligence And Machine Learning Bengaluru India Tirupati India

ISBN: (纸本)9798350381665

With the development of the constantly expanding Internet of Things (IoT) devices and communications infrastructure, the majority of sensors have been used, resulting in a vast amount of data. One of the most frequent instances is the manufacturer's inspection to precisely detect product flaws;which allows for adaptive optimization of the model's parameters. This research proposes a novel Q-learning Reinforcement learning (RL)-based Optimization Algorithm (ROA) for optimizing the Fog computing based Convolutional Neural Network (FC-CNN) that can discover probable faulty items precisely, to develop an effective inspection system. Given the possibility of several assembly lines operating in a single plant, one of the primary issues is the real-time processing of this data. Subsequently, adjusting the CNN model's hyperparameters significantly enhances its faults detection capabilities. The suggested FC-CNN-ROA transfers the burden from the central servers to the fog nodes, allowing it to handle very huge volumes of data. When compared with existing techniques such as You Only Look Once version4 combined with DL (YOLO v4-DL), MobileNet-V2, the proposed FC-CNNROA obtains a higher accuracy of 99.97%, 99.89%, 99.75%, 99.71%, and 99.16% for different sets of datasets such as 10,20, 30,40,50 respectively. © 2024 IEEE.

关键词： Internet of things

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Automated Symptom Tracking And Prediction Of Angioedema Disease Using Machine learning

Automated Symptom Tracking And Prediction Of Angioedema Dise...

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2024 international conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Arunkumar, M. Palanisamy, M. Viknesh, K. Mugilarasan, M. Abinesh, B. Ifet College Of Engineering Dept. Of Information Technology Tamilnadu Villupuram India Dhanalakshmi Srinivasan Engineering College Dept. Of Information Technology Tamilnadu Perambalur India Ifet College Of Engineering Dept. Of Information Technology Tamilnadu Villupuram India

ISBN: (纸本)9798350352689

The timely and precise identification of skin diseases is essential to optimizing therapy effectiveness and attaining favorable patient outcomes. However depending solely on visual examination and conventional diagnostic techniques adds subjectivity and might take a long time, which increases the risk of misdiagnosis or postponed treatment. This research aims to develop a machine-learning model to detect angioedema disease, a potentially fatal illness marked by sudden swelling of the skin and mucous membranes. Using several feature extraction techniques, the proposed model extracts texture, color, and intensity information from skin photographs. These extracted attributes are then integrated into a transfer learning framework to enhance overall performance, utilizing data from previously trained models. The shortcomings of traditional machine learning methods, such the need for huge datasets or protracted training cycles, are addressed by this innovative methodology The created approach has the potential to help medical practitioners make well-informed diagnoses, especially in areas with limited resources. The methodology has the potential to greatly improve patient outcomes and save healthcare costs by enabling early diagnosis and permitting proactive treatment actions. This development is especially helpful for increasing access to precise diagnoses in a variety of healthcare environments, guaranteeing that patients receive prompt and effective therapy. © 2024 IEEE.

关键词： Local binary pattern

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PRODUCTIVE FAILURE PEDAGOGY IN engineering MECHANICS 25

PRODUCTIVE FAILURE PEDAGOGY IN ENGINEERING MECHANICS

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25TH international conference ON engineering AND PRODUCT DESIGN EDUCATION (E&PDE)

作者： Persaud, Stefan Flipsen, Bas Delft Univ Technol Ind Design Engn Delft Netherlands

ISBN: (纸本)9781912254194

In September 2021, the faculty of Industrial Design engineering (IDE) introduced a revamped bachelor's programme that emphasizes design for higher complexity, teacher as a coach, and autonomous learning. The programme includes Understanding Product engineering (UPE), which teaches first-year design students about product embodiment, manufacturing, and mechanics of materials. However, the traditional approach of teaching engineering using direct instructions and problem-based learning was ineffective, as students failed to apply the engineering knowledge in their capstone design projects. To address this issue and promote autonomous learning, the Productive Failure ( PF) pedagogical framework was introduced as the main pedagogical framework in UPE. However, the general approach of the PF pedagogy as described by Kapur, lacked a translation into an effective design of the workshops. To address this, this paper proposes a hands-on model based on constructive alignment, where learning objectives, activities, and assessment are designed side-by-side. This paper presents our didactical model, which was developed in an agile way during the second run of UPE. The hands-on model proposed aids in applying the PF pedagogy in engineering courses and consists of a method to develop workshop assignments and a didactical approach to guide and coach students through the workshop process.

关键词： Autonomous learning productive failure direct instruction product engineering engineering mechanics

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Efficient Data Sampling and Reduction Methods in Large-Scale Forensic Analysis

Efficient Data Sampling and Reduction Methods in Large-Scale...

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2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Harika, Ala Sharma, Praveen Aravinda, K. Nagpal, Amandeep Praveen Albawi, Ali Institute of Aeronautical Engineering Hyderabad Dundigal India Ies Insititute of Technology and Management Ies University Department of Computer Science & Engineering M. P Bhopal India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India The Islamic University College of Medical Technology Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350373783

In this era of enormous, continuously changing datasets, forensic analysis needs innovative approaches that can adapt to changing data characteristics while maintaining data security. To satisfy this demand, our recommended system includes all algorithms for large forensic investigations. The adaptive stratified sampling algorithm sets the scene. It adjusts strata and sample quantities based on data attributes. The Feature Weighting with Machine learning Algorithm improves selection by weighting features using machine learning. Based on them, the dynamic cluster-based sampling algorithm organizes data points dynamically. By determining how much information each data point has, the entropy-guided data reduction algorithm improves the framework. The Temporal Correlation Analysis Algorithm finally allows you to view changing data. Tables3 and Tables4 indicate that our approach outperforms others in several key areas. The recommended technique outperforms others in accuracy, memory, F1 score, usability, stability, legal acceptability, ease of application, computing complexity, and freedom. The findings in Figures.6-10 indicate that the proposed framework can balance accuracy and memory, decrease data, and be highly usable, stable, and versatile. Finally, our technology represents a novel technique to conduct large-scale research. It is fast, adaptable, and data-safe. Machine learning, dynamic changes, and temporal variables make our platform robust and comprehensive for current forensic investigations. © 2024 IEEE.

关键词： Adaptive Stratified Sampling Algorithm Algorithm Optimization Data Reduction Data Sampling Dynamic Cluster-based Sampling Entropy-guided Data Reduction Feature Weighting Forensic Analysis Machine learning Temporal Correlation Analysis

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DigiTeachVR: Digitally-Enhanced teaching Platform for Improving Data Science Skills and Virtual Reality Competences in Cross-Disciplinary engineering Education 14th

DigiTeachVR: Digitally-Enhanced Teaching Platform for Improv...

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14th international conference on learning Factories (CLF)

作者： Koh, Linus Stricker, Philipp Reisinger, Julia Ansari, Fazel TU Wien Res Grp Prod & Maintenance Management Theresianumgasse 27 A-1040 Vienna Austria TU Wien Dept Integrated Planning & Ind Bldg Karlspl 13 A-1040 Vienna Austria

ISBN: (纸本)9783031653995;9783031654008

Integrated factory design (IFD) requires cross-disciplinary competences obtained inter alia from areas of industrial, mechanical, civil engineering and architecture as well as operations, assets and facility management. However, traditional teaching practices in higher education could neither effectively provide solutions to cross-disciplinary competencies for IFD nor supply evolving job market demands (aka skill gaps). Information and Communication Technologies (ICT) are pivotal in this transformation, shaping smart learning environments that integrate collaborative activities and harness freely available online tools and cloud services. However, challenges persist in cross-disciplinary fields like IFD, where data science and visualization technologies like Virtual Reality (VR) are increasingly applied but often not fully integrated into traditional curricula. To address this educational gap, this paper introduces the digiTeachVR platform, which incorporates VR and data science into a collaborative teaching platform designed for IFD. Undergraduate and graduate students from diverse disciplines participate in a parametric design course that leverages industrial data science and VR within an interactive, self-paced, collaborative environment. The digiTeachVR's approach involves self-taught programming lessons followed by collaborative programming on Python. This is to develop parametric designs, subsequently transformed into VR models using Rhino 3D. A pilot study of digiTeachVR at the TU Wien indicates a significant (79%) increase in student enthusiasm, suggesting improved content delivery and enhanced learning outcomes (63%). The paper presents the approach, summarizes the key achievements and identifies the future research pathways for enhancing quality of cross-disciplinary engineering education.

关键词： Technology-enhanced learning Blended learning engineering Education Industrial Data Science Virtual Reality Integrated Factory Design

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A seven layer DNN approach for social-media multilevel image-based text classification 2nd

A seven layer DNN approach for social-media multilevel image...

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2nd international conference on Next-Generation computing and Information Systems, ICNGCIS 2023

作者： Tripathi, Veena Upadhyay, Navin Mani Sharma, Saurabh Model Institute of Engineering and Technology J&K Jammu India

ISBN: (纸本)9781032738659

In recent years, social media platforms have played a significant role in disaster management. To extract valuable insights and understand the meaning behind social media text content, using conventional machine learning methods, text mining solutions have been created. Through these techniques, messages are intended to be categorized into several themes, such counsel and caution. Nevertheless, these methods frequently focus on a single event and have trouble generalizing to cross events classifications. The effectiveness of traditional models trained on historical data for classifying social media messages from future events remains unclear. Our research focuses on the efficacy of a CNN model in classifying Twitter topics across diverse events. Three geotagged datasets from twitter gathered during the COVID-19 pandemic’s management are the subject of this study. Two conventional machine learning techniques (LR and SVM) are contrasted with the CNN model’s performance. Our experiments conclusively show that the CNN model significantly surpasses the performance of both SVM and LR models in terms of accuracy, regardless of whether the evaluation is based on individual events or across different events. This implies that the CNN model may efficiently categorize messages for impending events and pre-train on Twitter data from past occurrences, improving situational awareness. In conclusion, this study demonstrates the CNN model’s capability compared to traditional machine learning techniques for disaster management. © 2025 The Author(s).

关键词： Disasters

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