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检索条件"机构=Artificial Intelligence and Data Science Engineering"

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Learning Heuristics via Genetic Programming for Multi-Mode Resource-Constrained Project Scheduling 13

Learning Heuristics via Genetic Programming for Multi-Mode R...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Tian, Yuan Mei, Yi Zhang, Mengjie Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington6140 New Zealand

ISBN: (纸本)9798350308365

The multi-mode resource-constrained project scheduling problem (MRCPSP) is a challenging problem for researchers and practitioners in operations research and project management. MRCPSP involves both selecting the execution mode for each activity and sequencing the activities in the schedule. Thus, activity prioritisation and mode selection are the two main decisions in building a project schedule. A rule-based heuristic approach is commonly used for solving this problem in practical complex scenarios. However, designing effective project scheduling rules highly relies on the expertise of professionals and domain knowledge. To address the above issue, this paper proposes a genetic programming-based hyper-heuristic (GPHH) to design heuristic rules automatically. Various decision strategies based on decision orders are proposed and their impact on the capacity of GPHH to learn effective scheduling rules is investigated. The experiment results demonstrate the evolved rules generated by GPHH outperform the existing manual heuristic rules and making two decisions simultaneously is identified as the most effective strategy. © 2024 IEEE.

关键词： Genetic programming

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A Multiclass Semi-Supervised Deep Convolutional Generative Adversarial Network for Music Genre Classification Using Mel-Frequency Cepstral Coefficients 2

A Multiclass Semi-Supervised Deep Convolutional Generative A...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Satapathy, Ashutosh Duda, Neelima MacHcha, Jaswanthi Thottempudi, Kokila Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science and Engineering Vijayawada India Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798350306415

The growing consumer base and expanding market for various music styles highlight the necessity of classifying music genres to cater to people's preferences. Manual music ranking is a labor-intensive process for listeners, prompting the need for a more efficient approach. This involves the extraction of Mel-frequency cepstral coefficient feature maps from the log Mel-spectrograms of audio clips. The extracted feature maps are supplied to a multiclass semi-supervised deep convolutional generative adversarial network where the discriminator behaves as a classifier. The training of models involves utilizing the GTZAN standardized dataset, a publicly accessible collection of thousands of audio files spanning ten different genres, from which 80% and 20% of the data are used for training and testing, respectively. Finally, the paper discusses the performance of the semi-supervised deep convolutional generative adversarial network through the RMSprop and Adam optimizers on the original and augmented labeled and unlabeled MFCC feature maps. Without any data augmentation, the discriminator achieves a training accuracy of 97.9% and a test accuracy of about 45.67%. In contrast, the discriminator's training accuracy is about 98.3%, and the test accuracy is 84.75% with data augmentation. © 2024 IEEE.

关键词： data Augmentation GTZAN Indian Music Genre Mel-frequency Cepstral Coefficients Multiclass Classification Semi-Supervised Deep Convolutional Generative Adversarial Network

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AI and Blockchain Based Platform for Empowering Content Creators: Enabling NFT - Driven Content Sharing, Inspiration Generation, and Collaborative Interaction 4

AI and Blockchain Based Platform for Empowering Content Crea...

引用

4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Adru, Sri Likhita Johnson, Sandra Hemalatha, M. R.M.K. Engineering College Department of Artificial Intelligence and Data Science Kavaraipettai India

ISBN: (纸本)9798350300888

The digital era has revolutionized the way content is created, consumed, and monetized. Content creators face new opportunities and challenges in reaching their audiences and establishing sustainable revenue streams. This research paper explores the potential of an innovative platform that leverages the power of artificial intelligence (AI) and blockchain technology to empower content creators in sharing and selling their work through Non-Fungible Tokens (NFTs). This platform integrates unique features, including text-to-image generation for creative inspiration, a recommendation engine for content discovery, and a collaborative chat room fostering direct interaction among creators and their audiences. The objective of this paper is to provide a thorough examination of the platform's architecture, functionalities, and the potential impact it can have on the content creation ecosystem, thereby offering a comprehensive understanding of its structure and design. © 2023 IEEE.

关键词： Blockchain

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Early Detection of Coconut Palm Leaflet Diseases using Convolutional Neural Networks based Deep Learning Approach 4

Early Detection of Coconut Palm Leaflet Diseases using Convo...

引用

4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Santhiya, S. Krishnasamy, Lalitha Sharmila, C. Jayadharshini, P. Dharshan, P.P. Haripreetha, S. Kongu Engineering College Department of Artificial Intelligence Erode India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode638052 India Srm Institute of Science and Technology Department of Cse Tiruchirappalli India Kongu Engineering College Erode India

ISBN: (纸本)9798331518981

In tropical regions, coconut palms are an essential part of the economy and daily life, providing food, oil, and many other products. However, their health and productivity can be affected by various diseases. Early and precise disease identification is essential for the management and prevention of many diseases. Leaflet diseases in coconut plants are those that impact individual leaflets, or leaf segments, on the fronds of coconut palms. Drying leaflets in coconut tree leaves can result from a variety of factors, including biotic (living organisms) and abiotic (environmental or non-living) impacts. In coconut trees, chlorosis, or the yellowing of the leaves, is a prevalent issue that can be caused by some factors. The CNN model is trained on a sizable set of high-resolution images of both healthy and diseased coconut leaves. Each disease's unique characteristics and patterns are automatically acquired with the aid of the deep learning architecture, enabling accurate categorization. Using an additional set of annotated photos of coconut tree leaves, the CNN model is assessed with remarkable accuracy in identifying illnesses. Comparing the system to traditional manual diagnosis procedures demonstrates the technology's efficacy and potential for early disease identification. Using this strategy raises the algorithm's accuracy to 95% © 2024 IEEE.

关键词： Plant diseases

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Enhanced Diabetes Detection with Deep Learning: An Iris Image Analysis Approach 5

Enhanced Diabetes Detection with Deep Learning: An Iris Imag...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Rajarajeswari, T.S. Snehanjali, Beeram Rizwana, Rapthadu Kasi Vardhan, Bethi Phanindra Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science and Engineering Vijayawada India Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798331530013

Diabetes is a chronic and progressive condition that, if not diagnosed early, can lead to serious health complications, often we will see that patients learn they have diabetes only years after its emergence this points out the significance of precise and early identification. Thus, diabetes is a global health issue that requires timely detection. Traditional methods for diagnosing diabetes often involve expensive procedures, which can lead to delays in diagnosis. Using iris images for diabetes detection through deep learning algorithms offers a more accurate and cost-effective alternative. In general, a person with diabetes may experience changes in the iris, such as alterations in pattern, texture, or pigmentation. To identify these altered features, high-resolution observation is required. For this purpose, we use advanced imaging techniques and deep learning algorithms to analyze and interpret the changes in iris characteristics. CNN's, known for their powerful feature extraction capabilities, effectively captured and analyzed complex details in iris patterns. This approach utilizes several types of algorithms available in deep learning like Convolutional Neural Networks (CNNs), MobileNetV2, InceptionV3, ResNet50 and CNNs along with transformers. All these algorithms are widely known for their efficiency in image analyzing applications. Thus, we have chosen to implement the same procedural approach but for different algorithms. Finally, we had found an efficient algorithm in such a way that it gives the highest accuracy for the same dataset as compared with the other algorithms. It proved that CNNs are effective in identifying features and patterns in images and it gave the accuracy of about 93.91 % showing its effectiveness. Thus, ultimate aim of this approach is to provide an accurate, non-invasive and cost-effective way of diabetes disease diagnosis. © 2024 IEEE.

关键词： Convolutional Neural Networks Disease diagnosis InceptionV3 Iris MobileNetV2 ResNet50 Transformer Networks

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Comparative analysis of Large Image Model and Pre-trained CNN for the Classification of White Blood Cells 2

Comparative analysis of Large Image Model and Pre-trained CN...

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2nd International Conference on Self Sustainable artificial intelligence Systems, ICSSAS 2024

作者： Dahanukar, Soham Ajit Nagarhalli, Tatwadarshi P. Pawar, Shruti Sunil Vartak, Rujuta Vidyavardhini's College of Engineering and Technology Dept of Artificial Intelligence and Data Science Mumbai India Vidyavardhini's College of Engineering and Technology Mumbai India

ISBN: (纸本)9798350368413

White blood cells (WBCs) must be accurately classified in order to be used in medicine for both therapeutic and diagnostic purposes. The performance of two domains that are very efficient in classifying blood cells are examined in this work, with particular emphasis on the models' robustness and accuracy of classification. The development of Convolutional Neural Networks (CNNs) and, more recently, Large Image Models (LIMs) has drastically changed the landscape of image recognition. This study examines how well these two architectures perform in comparison when used for image-based tasks with uncommon datasets. Although CNNs have proven to be exceptionally powerful in certain fields, LIMs hold the potential to achieve more broad picture understanding. To do this, this research study compares a pre-trained CNN and a cutting-edge LIM. These results clarify the classification of each strategy, showing that CNNs can potentially beat LIMs at jobs requiring specialized expertise. This work adds to the current discussion on model selection in image analysis by highlighting the significance of dataset properties in choosing the best architecture. This research study intends to direct the creation of more efficient image recognition systems for a variety of applications by comprehending the trade-offs between generalization and specialization. © 2024 IEEE.

关键词： Convolutional neural networks

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond 3

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Dharshan, A. Kumar, Purushottam Ravimaran, S. Srinivasulu Reddy, U. Saranathan College of Engineering Department of Artificial Intelligence and Data Science Trichy India National Institute of Technology Artificial Intelligence Machine Learning & Data Analytics Lab Trichy India National Institute of Technology Department of Computer Applications Machine Learning & Data Analytics Lab Trichy India

ISBN: (纸本)9798350381931

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems. © 2024 IEEE.

关键词： Nitrates

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Design and Evaluation of a Medical Chatbot Built on BERT Language Model for Remote Health Assistance 3

Design and Evaluation of a Medical Chatbot Built on BERT Lan...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Rao, D. Rajeswara Thottempudi, Kokila Surla, Bharath Kumar Satapathy, Ashutosh Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science and Engineering Vijayawada India Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798350381931

Telemedicine refers to delivering healthcare services, including diagnosis, treatment, and consultations, remotely through digital communication tools such as video calls, phone calls, and online messaging. Access to quality healthcare remains a significant challenge in underserved rural areas worldwide. Due to transportation issues and financial support in remote areas, people can only visit hospitals close to their location. Current telemedicine applications provide video conversations with doctors but need more seamless services due to the huge population increase in cities and the unavailability of internet services for people in rural areas. This article presents a medical chatbot application trained on a customized health assistant corpus to generate fast responses in text format to queries related to common health issues. The solution is built on the BERT model to train the chatbot, which includes MLM and NSP, which is one of the best ways to obtain accurate results. The BERT model trained on customized health assessment corpus consists of multiple health assessment corpora and health assistant queries/responses collected from different sources and achieved 96.5% test accuracy. Since the model is pre-trained, it will generate the response as fast as possible. In addition to that, the application provides 24/7 availability, user-friendly interfaces, remote monitoring, and robust data security. © 2024 IEEE.

关键词： Rural areas

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From Pixels to Payouts -A Satellite Based Solution

From Pixels to Payouts -A Satellite Based Solution

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Deepthi, R.S.S.V. Priya, A Leela Revanth, D. Ajay, K. Madhavi, M. Velagapudi Ramakrishna Siddhartha Engineering College Depratment of Artificial Intelligence and Data Science Vijayawada India Velagapudi Ramakrishna Siddhartha Engineering College Depratment of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350359688

The Pradhan Mantri Fasal Bima Yojana (PMFBY) stands as a crucial initiative for farmers' protection, yet grapples with challenges like delayed claims, inaccurate loss assessments, and uneven aid distribution [1]. This project proposes an innovative solution that addresses these issues by introducing a precision agriculture and risk management framework. The current crop insurance policies rely on an area-based approach and limited weather indices, resulting in an inadequate assessment of individual crop health and losses [2]. This shortfall leads to unjust aid distribution, leaving deserving farmers underserved [3]. The proposed solution advocates for a paradigm shift, incorporating a personalized and technology-driven approach. By integrating remote sensing technology, machine learning algorithms, and comprehensive data analysis, the system optimizes insurance resource allocation, assessing crop and regional risk profiles with precision. Furthermore, the project implements advanced image processing, segmentation, and machine learning models, such as SVM and CNN, for crop detection and yield estimation. The Google Maps API enhances decision support through geo-mapping, providing a transparent and data-driven basis for aid distribution. This innovative approach fosters equity, transparency, and active farmer participation, overcoming the limitations of existing methodologies. With a focus on precision, transparency, and equity, the proposed solution aims to revolutionize the effectiveness of PMFBY, ensuring accurate assessment and targeted aid distribution for farmers facing crop stress and losses. © 2024 IEEE.

关键词： Risk management

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Transfer Learning for Iterative Learning Control Using Gaussian Processes 10

Transfer Learning for Iterative Learning Control Using Gauss...

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10th Indian Control Conference, ICC 2024

作者： Muthiah, Letchumanan Tangirala, Arun K. The Department of Chemical Engineering Department of Data Science and Artificial Intelligence Indian Institute of Technology Madras Tamil Nadu Chennai600036 India

ISBN: (纸本)9798331517212

Iterative Learning Control (ILC) is a technique used to improve the transient tracking performance of a repetitively operated system, such as a batch process. However, the assumption of iteration invariance of the process conditions limits the extension of ILC to similar processes and often demands process modelling and controller design from scratch. data-driven modelling strategies are highly effective but they suffer from large model development cost during training. This is undesirable since operating conditions of industrial batch processes can switch rapidly to meet the requirements of the market leaving little time and resources for model development. In this paper, we present a novel method of transferring optimally tuned ILC across processes using the modern ideas of transfer learning (TL). Specifically, we develop a Gaussian process model using ideas of forward adaptive transfer learning (FAT) to facilitate knowledge transfer between processes. A major advantage of the proposed method is that optimal ILC designed for current process can be transferred to a new process using only limited training data. The proposed method is demonstrated on the Cott-Batch Reactor, showing substantial promise for the proposed TL-based FAT-GP. © 2024 IEEE.

关键词： Federated learning

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