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13th international conference on Frontier Computing, FC 2023

13th International Conference on Frontier Computing, FC 2023

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13th international conference on Frontier Computing, FC 2023

ISBN: (纸本)9789819995370

the proceedings contain 180 papers. the special focus in this conference is on Frontier Computing. the topics include: Optimization of English machine Translation Model Based on Neural Network;exploration on Text Detection Optimization Algorithm Based on Neural Network Technology;CO2 Emission Prediction of Vehicle Fuel Consumption Based on EMD-LSTM;application of Data Fusion Algorithms in the Data Processing of Intelligent Greenhouses;intelligent Film and Television Communication Optical Technology Based on Network New Media;design of Heterogeneous Database Encryption and Decryption System Based on Data Mining;research and Implementation of Music Recommendation System Based on Particle Swarm Algorithm;design of High-Speed Multi-channel Data Transmission System Based on Single-Chip Microcomputer;application of Distributed Database System in Financial Management;Research on the Development of Japanese MOOC System Based on BP Neural Algorithm;research on Default Risk Prediction of Listed Companies’ Green Credit Based on Deep learning Algorithm;improvement of Cloud Platform Utilization Based on Evaluation and Optimization of Computing Resource Runtime Surplus Capacity;design and Implementation of Operation and Maintenance Monitoring Platform Based on Data Mining Algorithm;civil Aviation Logistics Transportation Route Optimization Algorithm Based on A+ Algorithm;design of Interactive Japanese Translation System Based on Feature Extraction Algorithm;a Preliminary Study on Low-Carbon City Planning Methods Supported by the Cluster Optimization Algorithm;object Detection Algorithms in Embedded Systems and their applications;application of computer VR Technology in Digital Media System Design;discuss the Technical application of Cloud Data Center and Cloud Management Platform;Design of ARM-Based Program Automatic Shelling Data Acquisition System.

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Navigating Agricultural Frontiers: Federated learning CNNs for Banana Leaf Disease Resilience 4

Navigating Agricultural Frontiers: Federated Learning CNNs f...

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4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Sharma, Vikrant Kukreja, Vinay Mehta, Shiva Verma, Aditya Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Chitkara University Institute Of Engineering And Technology Chitkara University Punjab India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350384628

To provide a synopsis of the study that explores banana leaf diseases and the novel use of federated learning using CNNs for six different customers. this research categorizes diseases into four different severity categories to tackle the urgent need for accurate illness categorization. the model works in tandem with six customers via federated learning;these clients provide distinct datasets that reflect different agro-climatic conditions. With an input size of 354x354, the CNN architecture can function thanks to its three convolutional layers, three max-pooling layers, and two fully connected layers. the global model, which is an average of federated localized client models, produces impressive results. the investigation's meat and potatoes, the Result Analysis part, shows off the model's strength. Identifying genuine positives with an accuracy of 84.59% to 94.87% is a testament to its ***, recall scores between 84.87% and 94.84% demonstrate that the model can accurately identify a large number of true positives. the range of F1-score values, which show a balanced connection between accuracy and recall, is 84.66% to 94.86%. the scalability of the model, which can handle different amounts of data, is reflected in the support values. the accuracy rates, which range from 92% to 97%, show that the model is generally quite good at disease classification for banana leaves. the federated average of worldwide client data also introduces complex metrics. the macro-average accuracy, recall, and F1-score, which consistently fall within the range of 84.71% to 94.86%, provide a comprehensive view of the model's performance. Weighted average accuracy accounts for differences in dataset size and ranges from 84.76% to 94.82%. Accumulating data for overall performance, the micro-average accuracy falls between 84.75% and 94.82%. © 2023 IEEE.

关键词： Fruits

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Efficient Method for Tomato Leaf Disease Detection and Classification based on Hybrid Model of CNN and Extreme learning machine 4

Efficient Method for Tomato Leaf Disease Detection and Class...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Chaturvedi, Abhay Yagnasree, Sirivella Chhabra, Gunjan Chidambara Rajan, P. Chauhan, Amit Maranan, Ramya Gla University Department of Electronics and Communication Engineering Uttar Pradesh Mathura India Lovely Professional University Department of Electronics & Communication Engineering Punjab Phagwara India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Dr. N. G. P. Arts and Science College Department of Biotechnology Tamilnadu Coimbatore India Department of Life Sciences Karnataka Bengaluru India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350300093

through India, most people make a living through agriculture or a related industry. Crops and other agricultural output suffer significant quality and quantity losses when plant diseases are present. the solution to preventing losses in the harvest and quantity of agricultural products is the detection of these illnesses. Improving classification accuracy while decreasing computational time is the primary focus of the suggested method for identifying leaf disease in tomato plant. Pests and illnesses wipe off thousands of tons of tomatoes in India's harvest every year. the agricultural industry is in danger from tomato leaf disease, which generates substantial losses for producers. Scientists and engineers can improve their models for detecting tomato leaf diseases if they have a better understanding of how algorithms learn to identify them. this proposed approaches a unique method for detecting diseases on tomato leaves using a five-step procedure that begins with image preprocessing and ends with feature extraction, feature selection, and model classification. Preprocessing is done to improve image quality. that improved K-Means picture segmentation technique proposes segmentation as a key intermediate step. the GLCM feature extraction approach is then used to extract relevant features from the segmented image. Relief feature selection is used to get rid of the categorization results. finally, classification techniques such as CNN and ELM are used to categorize infected leaves. the proposed approach to outperforms other two models such as CNN and ELM. © 2023 IEEE.

关键词： K-means clustering

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Recognizing Indian Classical Dance Forms Using Transfer learning 4th

Recognizing Indian Classical Dance Forms Using Transfer Lear...

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4th international conference on machine Intelligence and Signal Processing, MISP 2022

作者： Reshma, M.R. Kannan, B. Jagathyraj, V.P. Sabu, M.K. Department of Computer Applications CUSAT Kerala Kochi India School of Management Studies CUSAT Kerala Kochi India

ISBN: (纸本)9789819900466

the rise of cultural tourism in India and massive digitization over the last decade has necessitated preserving Indian art forms. Recent advances in artificial intelligence (AI) have provided the tools and techniques to make these efforts more efficient. the potential research areas involved are digital image processing, computer vision, and machine intelligence. Since researchers have not much explored the scope of the digital preservation of Indian classical dance, this paper contributes a method to classify different Indian classical dance forms. computerized dance modeling offers a wide range of uses, including cultural conservation, online learning, entertainment, and even remedial treatment. Because the dataset is small, the paper uses transfer learning models for feature extraction and deep neural networks for classification. the proposed method showed a remarkable accuracy of 97.05 percent. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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2022 4th international conference on Sustainable Technologies for Industry 4.0, STI 2022

2022 4th International Conference on Sustainable Technologie...

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4th international conference on Sustainable Technologies for Industry 4.0, STI 2022

ISBN: (纸本)9781665490450

the proceedings contain 125 papers. the topics discussed include: effects of aging factors on PV modules output power: an experimental investigation;book review sentiment classification in Bangla using deep learning and transformer model;anomaly based network intrusion detection using ensemble classifiers;exploiting deep learning based classification model for detecting fraudulent schemes over Ethereum blockchain;identification of influential genes for colorectal cancer using machine learning approaches;an application programming interface to recognize emotion using speech features;a new approach to analysis of public sentiment on Padma bridge in Bangla text;an energy efficient model of software development life cycle for mobile application;deep learning enabled state of charge estimation for electric vehicle batteries under noise effects;design and implementation of a data-driven fuel management system;identification of influential genes for lung cancer using machine learning approaches;and energy management system for micro grid environment.

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Deep learning Methods for fMRI Classification 4th

Deep Learning Methods for fMRI Classification

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Barillaro, Luca Agapito, Giuseppe Data Analytics Research Center Department of Medical and Surgical Sciences University "Magna Græcia" of Catanzaro Catanzaro88100 Italy Department of Law Economics and Social Sciences University "Magna Græcia" of Catanzaro Catanzaro88100 Italy

ISBN: (纸本)9783031812460

this paper aims to review some deep learning-based methods for the classification of functional Magnetic Resonance Imaging (fMRI) brain images. fMRI is one of the MRI modalities which provides information about the activity of the neurons in the brain. In particular, the fMRI signal is sensitive to blood dynamic changes, which drive the neuron firing. this relationship is known as the Blood Oxygenation Level Dependent (BOLD) effect. Two main applications are possible: a task-specific activity and a resting state. the main difference is that in the first case, a patient is asked to perform a specific task, and the fMRI shows the brain response, while in the resting state, no particular action is required. the study of brain activity and response represents one of the significant research challenges to understand diseases and potential damages better. Using automated techniques based on artificial intelligence, such as machine learning or deep learning, allow the analysis of large fMRI datasets. the classification task is essential because it enables the possibility to support the decision-making process by healthcare experts by identifying particular diseases from images. this paper reviews deep learning methods for fMRI classification, their corresponding accuracies, and the required computational resources for massive fMRI datasets. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurons

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AI Enabled Invoice Management application 8

AI Enabled Invoice Management Application

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8th international conference on Communication and Electronics Systems, ICCES 2023

作者： Kumar, Naveen Katoch, Sahil Tripathi, Shivam Singh, Bhanu Pratap Sajal, Sajal Yadav, Anup Lal Chandigarh Univeristy Department of Cse Mohali India

ISBN: (纸本)9798350396638

Invoices are a critical part of the financial management process for any business. Invoice management involves generating, processing, and tracking invoices, which can be a time-consuming and error-prone task when done manually. With the advent of Artificial Intelligence (AI), businesses can now automate their invoice management processes to improve efficiency, reduce costs, and make better decisions. this paper reviews recent research and industry developments in AI-enabled invoice management applications, including the use of machine learning, natural language processing (NLP), and computer vision. this study begins by discussing about the challenges of traditional invoice management processes and how AI can address these challenges. Next, reviews the current state of AI-enabled invoice management applications and highlights the different types of AI techniques used in these applications. Finally, this study discusses about the future prospects of AI-enabled invoice management applications and the potential impact of it on businesses. © 2023 IEEE.

关键词： Efficiency

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Comparative Study of Deep learning Techniques for Automated Classification of Lung Diseases 4

Comparative Study of Deep Learning Techniques for Automated ...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Rekha, H. Kumaravel, T. Natesan, P. Brinda, B.M. Sangeetha, S. Dharanesh, S. Shridevi Institute of Engineering and Technology Department of Information Science and Engineering Karnataka Tumkur India Kongu Engineering College Department of Computer Science and Engineering Tamil Nadu Erode India Paavai College of Engineering Department of Computer Science and Engineering Tamil Nadu Pachal India Kongunadu College of Engineering and Technology Department of Information Technology Tamilnadu Trichy India

ISBN: (纸本)9798350300888

Accurate classification of lung requires overcoming limited availability of labeled data, particularly for rare conditions like COVID-19, and the risk of overfitting due to the high dimensionality of medical images. the use of transfer learning with VGG16, VGG19, and DenseNet201 architectures enables leveraging pre-trained models' knowledge of general image features, allowing effective learning on a smaller medical image dataset and reducing the risk of overfitting. this project proposes three deep learning methods based on the VGG16, VGG19, and DenseNet201 architectures. these approaches aim to automatically classify these three lung diseases using chest X-ray images. To enhance the data, preprocessing techniques and image augmentation methods were employed. the VGG16, VGG19, and DenseNet201 models were trained using transfer learning on this dataset, and performance was evaluated on a separate test set. the achieved accuracies for VGG16, VGG19, and DenseNet201 models were 95%, 96%, and 97%, respectively. these impressive accuracies demonstrate the capacity of the approaches for precise and early detection of lung diseases. Among the three architectures, DenseNet201 consistently exhibited the highest performance for all three disease classifications. In summary, the proposed deep learning techniques utilizing VGG16, VGG19, and DenseNet201 architectures offer promising tools for the automated classification of lung diseases based on chest X-ray images. their implementation can greatly assist medical professionals in promptly diagnosing and treating these diseases. DenseNet201, in particular, showcased superior performance among the three architectures. © 2023 IEEE.

关键词： Deep learning

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application of machine learning algorithm for prediction of heart disease 4

Application of machine learning algorithm for prediction of ...

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Upadhyay, Ankit Kumar Saleem, Sharzeel Anubhav, Adarsh Sagar, Shrddha Galgotias University Department of Computer Science and Engineering Greater Noida India Department of Electrical Electronics and Communications Galgotias University Greater Noida India

ISBN: (纸本)9781665474368

Healthcare is the one the domain in which every organization is working on it globally. It need to make it better and better via different researches and the innovations in this sector. machine learning is the most used algorithm in every sector and organization. It not merely transforming the field of healthcare but also able to emulate the various human intelligence from the surrounding in order to solve various problems. machine learning in health care sector has shown its various application in it. Disease prediction is one of the applications of it. this study helps to predict heart diseases with the help of its algorithm to determine the risk and treat the patient in the healthcare system. In this paper we have taken out the data from the kaggle and implemented it using the Jupiter notebook tool. the three algorithm we have implemented (naïve bayes, decision tree and random forest) out of which random forest is giving. Hence, with the help of the following algorithms it is possible for us to predict the heart disease with more accuracy. this research will able to predict various disease over its various symptoms more easily and accurately. © 2022 IEEE.

关键词： Forecasting

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Channel Estimation for Intelligent Reflecting Surface-Assisted Wireless Communication Systems Based on Deep learning 4

Channel Estimation for Intelligent Reflecting Surface-Assist...

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4th international conference on Signal Processing and computer Science, SPCS 2023

作者： Zhang, Xuhui Xu, Yan Zhang, Xiaoqi School of Electronics and Information Engineering Lanzhou Jiaotong University Lanzhou730070 China

ISBN: (纸本)9781510672505

Intelligent Reflecting Surface (IRS) is capable of performing phase and amplitude transformations on incoming signals, thereby achieving precise signal transmission and improving coverage and transmission efficiency. However, these advantages can only be achieved when the precise Channel State Information (CSI) is known. Due to the passive nature of IRS elements, obtaining accurate CSI is challenging. therefore, a combination of Compressive Sensing (CS) algorithm and Deep learning (DL) is proposed to address this issue. Firstly, joint-link structure is used to optimize the traditional CS algorithm, enabling better Normalized Mean Square Error (NMSE) performance at lower pilot overhead and Signal-to-Noise Ratio (SNR). Secondly, the channel estimation problem is regarded as a denoising problem, where the output of the optimized CS algorithm is treated as noisy CSI. A multi-layered deep denoising block network is utilized to further denoise the CSI, resulting in more accurate estimation. Finally, experimental results demonstrate that the proposed algorithm outperforms the comparison algorithms under the same SNR, yielding better accuracy. © 2023 SPIE.

关键词： Channel state information

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