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IOT-Enabled Supply Chain Management for Increased Efficiency 2

IOT-Enabled Supply Chain Management for Increased Efficiency

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2nd IEEE international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Nanjundan, Preethi James, Blesson Varghese George, Jossy P. Tiwari, Akhand Department of Data Science India Department of Commerce India

ISBN: (纸本)9798350384277

Deep learning methods have demonstrated potential Supply chain is a set or group of people as well as companies responsible for producing goods and getting it to their consumers. The producers of the raw materials are the first links in the chain, and the vehicle that delivers the finished goods to the client is the last. Lower costs and higher productivity are the benefits of an efficient supply network, which emphasizes the importance of management of supply chain. The internet of things, or IoT, is a network of mechanical and digital technology that can communicate with one another and send data without the need for human contact. Smart items were included into the conventional supply chain system to increase intelligence, automation potential, and intelligent decision-making. The existing supply chain system is offering previously unforeseen chances to increase efficiency and reduce cost. The aim and motive of our research is to analyze the methods of supply chain management where the main elements of IoT in management of supply chain will be highlighted. © 2024 IEEE.

关键词： Supply chain management

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Enhancing Parkinson's Disease Diagnosis through Deep learning-Based Classification of 3D MRI Images 2

Enhancing Parkinson's Disease Diagnosis through Deep Learnin...

引用

2nd international conference on machine learning and data Engineering, ICMLDE 2023

作者： Desai, Shivani Chhinkaniwala, Hitesh Shah, Smit Gajjar, Pranshav Gujarat Technological University Ahmedabad India Department of Computer Science and Engineering Institute of Technology Nirma University India India College of Engineering and Computing George Mason University United States

This paper presents a deep learning-based model developed to accurately classify Parkinson's disease patients using 3D MRI scans and data augmentation techniques. The study aimed to address the research question of whether this approach could effectively detect structural changes indicative of Parkinson's disease and enhance diagnostic accuracy. The model utilized 3D brain MRI scans from the midbrain region and leveraged the domain of data augmentation to expand the dataset and improve generalizability. The testing accuracy of the model was evaluated with and without data augmentation to further understand it's applicability. The paper demonstrated the effectiveness of the proposed model in classifying Parkinson's disease patients from healthy controls, achieving a testing accuracy of up to 90.13% with data augmentation. The incorporation of data augmentation led to a significant 15.53% increase in accuracy compared to the model without augmentation. The utilization of 3D MRI scans allowed for a more comprehensive assessment of structural changes associated with the disease, and it is intuitive to believe that shuch efficient predictions can facilitate earlier treatment and better patient outcomes. © 2024 Elsevier B.V.. All rights reserved.

关键词： Magnetic resonance

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Speech Emotion Recognition Using Deep learning Techniques and Traditional Classifiers

Speech Emotion Recognition Using Deep Learning Techniques an...

引用

2024 international conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Thakur, Sanjeev Bele, Rohit Yarlagadda, Tarun Chaubey, Ved Prakash Sharma, Shamneesh Gochhait, Saikat School of Computer Science and Engineering Lovely Professional University Punjab Jalandhar India Delivery and Student Success upGrad Education Private Limited Karnataka Bangalore India SIDTM Symbiosis International Deemed University Maharashtra Pune India NRI Samara State Medical University Russia

ISBN: (纸本)9798331533137

This paper analyzes the Speech Emotion Recognition (SER) procedure, a tool that helps IT systems increase their intelligence by recognizing persons’ emotions from the signals put into words, by employing deep learning methods. The audio data was preprocessed by extracting the Mel-frequency cepstral coefficients (MFCCs) and later augmented by adding white noise, time shifting, pitch shifting and time stretching. The models used in this paper are Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNNs). CNN architecture has convolutional operation layers, batch normalization layers followed by max-pooling layers, and dropout layers to learn speech features and to avoid overfitting. With these results, we stress the importance of the synergy of CNNs with solid preprocessing and augmentation methods in the context of SER and envision their tremendous potential in HCI, education, and health. It is worth stressing that including models such as SVM and LSTM certifies the novelty and the genetic soundness of the approach. ©2024 IEEE.

关键词： Convolutional neural networks

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Modern Experiment Management Systems Architecture for Scientific Big data 6th

Modern Experiment Management Systems Architecture for Scient...

引用

6th international conference on Software Testing, machine learning and Complex Process Analysis, TMPA 2021

作者： Kaida, Anastasiia Savelev, Aleksei National Research Tomsk Polytechnic University Lenina Ave. 30 Tomsk634050 Russia

ISBN: (纸本)9783031504228

Experiment management systems (EMS) have more than twenty-five years of history. From small desktop prototypes to large-scale distributed systems, they become more and more complicated. The new chapter of EMS was uncovered with the age of Big data surrounded by a special ecosystem to extract, analyze and store data. The big data ecosystem considers new elements that must be taken into account to expand the functionality for EMS to support all data lifecycle stages. One of the challenges is to highlight the key points of a huge variety of EMS evolving through time. Such systems do not usually follow a unified pattern because of special needs for each project. This paper introduces the conceptual high-level architecture as an example of a unified pattern of building EMS for big data ecosystems. The architecture does not consider to be used with the grid computing approach. © 2024, Springer Nature Switzerland AG.

关键词： Big data

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Enhancing Cardiovascular Risk Prediction with Vision Transformer and Retinal Image Analysis 3

Enhancing Cardiovascular Risk Prediction with Vision Transfo...

引用

3rd international conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Nikhil, S. Anjali, T. Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532420

Cardiovascular diseases (CVD) are among the lead-ing causes of deaths. Early prediction of cardiovascular risk can play an important role in the development of improved preventive medicine. This study investigates the feasibility of using deep learning methods with retinal images for cardiovascular risk prediction. To that end, we compare the RNN and ViT models in the analysis of retinal images. Preprocessing techniques and data augmentation were also used to improve model training. The RNN and ViT models are trained and tested on various metrics, such as accuracy and ROC curves. From the results of our experiments, it can be demonstrated that the ViT model performs better compared with the RNN model in predicting the cardiovascular risk by obtaining higher accuracy and a better performance in the ROC curve. This study demonstrates the potential utility of retinal imaging in cardiovascular risk assessment, where the ViT model exhibits capabilities superior to classical RNN models. This research places a special emphasis on higher-order deep learning models, which should be used in the early stages of diagnosis and prevention in healthcare settings. © 2024 IEEE.

关键词： Ophthalmology

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Oral Cancer Detection using Deep learning 15

Oral Cancer Detection using Deep Learning

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Keerthi, Reddy Sree Komati, Navindra Jagadeesh, S.V.V.D. Nalluru, Venkata Hemanth Reddy Lakireddy Bali Reddy College of Engineering Mylavaram India

ISBN: (纸本)9798331300579

The rising frequency of oral cancer and its devastating impact on public health necessitate the development of more accurate methods for early detection and diagnosis. This study presents a groundbreaking approach that utilizes convolutional neural networks (CNN) to improve the precision of medical image-based oral cancer diagnosis. Our meticulously crafted model, fine-tuned and optimized for oral cancer characteristics through transfer learning with pre-trained models, achieves an impressive accuracy rate of 81%. This study underscores the potential of deep learning methods, particularly CNNs, as highly effective tools for achieving precise and early detection of oral cancer. The results unequivocally demonstrate the efficacy of our model in accurately distinguishing between different oral cancer conditions, thereby paving the way for improved diagnostic accuracy, and contributing to better patient outcomes. Overall, our research significantly advances the evolving landscape of medical image analysis and reinforces the indispensable role of advanced technologies in addressing critical healthcare challenges. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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Adapting Deep learning Transformer Model for Depression-Specific Named Entity Recognition: A Transfer learning Approach 3

Adapting Deep Learning Transformer Model for Depression-Spec...

引用

3rd international conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Pawar, Dipti Phansalkar, Shraddha Department of Computer Science Engineering MIT School of Computing MIT Art DesignTechnology University Pune India

ISBN: (纸本)9798331532420

Named Entity Recognition (NER) is essential in the biomedical domain, particularly in mental health studies focused on disorders like depression. It helps extract structured information from unstructured text, enabling accurate recognition of key entities such as symptoms, disorder, antidepressants, brands, and therapies. This step is crucial for recommending diagnoses, personalizing treatments, and advancing research. Traditional methods, like rule-based or dictionary-driven approaches, often fall short in flexibility and scalability, as they rely heavily on predefined lexicons and corpora. The proposed work presents an advanced deep learning model aimed at improving NER performance on a customized dataset. Our model focuses on identifying critical entities disorders, brands, antidepressants, and therapies by addressing the limitations of conventional techniques. It uses a hybrid approach that combines a BERT-based model, fine-tuned through transfer learning, with a knowledge-based dictionary (MedDRA). This integration allows for enhanced semantic enrichment, improving the identification and categorization of biomedical entities. By leveraging BERT’s contextual capabilities alongside the structured knowledge from MedDRA, the model delivers better accuracy and classification in mental health data. Preliminary results show high confidence levels, with substantial improvements in key metrics like precision, recall, and F1 score, further advancing NER's role in mental health research. © 2024 IEEE.

关键词： Semantics

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A Predictive Framework for Failure Detection and Reallocation in Crowdsourcing Platforms 2024

A Predictive Framework for Failure Detection and Reallocatio...

引用

16th international conference on Contemporary computing, IC3 2024

作者： Voleti, Sai Anushree Tathipamula, Harini Sai Poolla, Lakshmi Saranya Nair, Lekshmi S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798400709722

Crowdsourcing platforms have become essential tools for managing various tasks, relying on distributed labour practices to enhance productivity and goal achievement. However, they often encounter challenges such as unemployment and low-quality task submissions, impeding project effectiveness. Timely resolution of these issues is crucial for project momentum. To address these challenges, we propose a predictive model based on machine learning techniques. Our framework aims to anticipate mistakes and optimize workload reallocation within community spaces. Leveraging state-of-the-art algorithms, our system analyzes employee behaviours, task features, and platform dynamics to detect problematic situations early. Moreover, we introduce a dynamic reallocation technique to expedite project duration while minimizing delays. This method automatically reassigns tasks to alternative tasks. The experimentation results exhibit that our framework has shown that it is better than other methods at accurately detecting failures and reassigning tasks. Furthermore, our framework's adaptability enables its application across various crowd-sourcing fields, including data annotation and content moderation. Described as a proactive response to failures within crowd-sourcing platforms, our predictive framework offers a promising solution to enhance project efficiency. © 2024 Copyright held by the owner/author(s).

关键词： Crowdsourcing

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3DA-NTC: 3D Channel Attention Aided Neural Tensor Completion for Crowdsensing data Inference

3DA-NTC: 3D Channel Attention Aided Neural Tensor Completion...

引用

international Joint conference on Neural Networks (IJCNN)

作者： Kang, Xu Jia, Jia Jia, Zhiyang Ren, Jiadong China Univ Petr Beijing Karamay Karamay Peoples R China Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing Peoples R China Yanshan Univ Sch Informat Sci & Engn Qinhuangdao Hebei Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Mobile crowdsensing is a promising scheme for performing large-scale urban monitoring, but it always faces the issue of unstable spatiotemporal coverage, which results in the incompletion of data collection. The common solutions for tackling this issue, are to use the existing subset of measurements for inferring the remaining unsensed data by leveraging the latent data correlations. However, existing data inference techniques, both for matrix/tensor factorization based methods and deep learning based methods, cannot well capture the high-order and dynamic data correlations simultaneously under the mobile crowdsensing scheme. In this paper, we propose a novel 3Dimensional (3D) channel attention aided neural tensor completion method, called "3DA-NTC", for more accurate crowdsensing data inference, through leveraging both the multi-dimensional data structure mining ability of tensor factorization as well as the high-order, dynamic correlation learning ability of deep neural network. Specifically, to capture the spatiotemporal and multitype data correlations, we first use a 3D tensor to model the 3Order interaction among crowdsensing data. Then, we combine the traditional inner product based tensor factorization with outer product computing to enhance the modeling of nonlinear data correlations and form an interaction tensor, based on which, we apply a 3D channel attention aided convolutional neural network to further extract the features of high-order and dynamic data interactions for missing value inference. Extensive experiments on two real-world urban sensing datasets, including U-Air and SensorScope, are conducted to evaluate the performance of 3DANTC, and the results demonstrate the superiority of our method compared with the state-of-the-art (SOTA) baselines in missing data recovery.

关键词： data inference attention mechanism neural network tensor completion mobile crowdsensing

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Early Prediction of Rice Leaf Disease Using Deep Neural Network Models 14

Early Prediction of Rice Leaf Disease Using Deep Neural Netw...

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14th international conference on Cloud computing, data science and Engineering, Confluence 2024

作者： Nandita Jaiswal, Arunima Sachdeva, Nitin Indira Gandhi Delhi Technical University for Women Department of Computer Science and Engineering New Delhi India Uttar Pradesh Noida India

ISBN: (纸本)9798350344837

The global significance of rice as a staple food for over half of the world's population underscores the need for robust measures to safeguard its production. Rice crops face threats from various diseases, necessitating swift and precise detection methods. Leveraging advancements in deep learning and computer vision, this study delves into the application of convolutional neural networks (CNNs) for automated rice leaf disease detection. The research emphasizes the urgency of timely identification to ensure consistent rice production, which is crucial for meeting escalating global food demands. By employing sophisticated models, particularly CNN s, this approach proves highly effective in accurately identifying disease-induced anomalies in digital representations of rice leaves. The study covers key aspects such as the training process, data preparation, model architecture, and evaluation metrics essential for crafting efficient disease detection models. Highlighting real-world implications, the paper underscores the transformative potential of deep learning in rice leaf disease detection, citing benefits like accuracy, early detection, automation, scalability, and data-driven insights. Ultimately, this integration of deep learning techniques presents a promising avenue to fortify global food security through early, precise, and scalable automated detection systems. This study explores disease detection in rice leaves by employing self-constructed and pre-trained DL models on two distinct verified datasets. The research compares the performance of five models, revealing that CNN-SVM achieves the highest accuracy in this experiment followed by ResNet50, VGG19, VGG16, and AlexNet. © 2024 IEEE.

关键词： Convolutional neural networks

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