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29th International Conference on Engineering, technology, and Innovation, ICE 2023

作者： Hyrynsalmi, Sami Olsson, Helena Holmström Bosch, Jan Lut University Dept. Software Engineering Lahti Finland Malmö University Dept. Computer Science and Media Technology Malmö Sweden Chalmers University of Technology Dept. Computer Science and Engineering Göteborg Sweden

ISBN: (纸本)9798350315172

Data has been quickly becoming as the fuel, the new oil, of growth and prosperity of companies in the modern age. With useful data and sufficient tools, companies have the ability to enhance their current products, presents new innovations and services as well as generate new revenue streams with a secondary customer base. While there are ongoing efforts to develop machine learning and data science techniques, little attention has been paid to understanding and characterizing data-related business activities in software-intensive *** multiple-case study examines four large international embedded system companies to explore how they are utilizing data and how they have proceeded in their journey in the data business. This study identifies six distinct stages, each with unique challenges, that seems to be common for embedded system companies in their data business. As the result, this study presents an initial data business maturity model for software-intensive embedded system companies. Additionally, this research provides a foundation for future efforts to support software-intensive embedded system companies in establishing data businesses. © 2023 IEEE.

关键词： Embedded systems

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A Lightweight CNN-SVM Explainable AI Approach for Classification and Visualization of Grape Leaf Disease 3

A Lightweight CNN-SVM Explainable AI Approach for Classifica...

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3rd International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Peyal, Hasibul Islam Leion, Zannatul Mawa Abdal, Md. Nazmul Islam, Md. Injamul Miraz, Shafiun Remon, Mahmudur Rashid Kontho, Md. Mahin Rahaman Tasnim, Nishat Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh University of Liberal Arts Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Uttara University Dept. of Computer Science & Engineering Dhaka Bangladesh Rajshahi University of Engineering & Technology Rajshahi Bangladesh North South University Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Sher-e-Bangla Agricultural University Dept. of Agroforestry and Environmental Science Dhaka Bangladesh

ISBN: (纸本)9798350388282

Grape is highly esteemed as a significant agricultural crop in Bangladesh. Plant diseases primarily result from the presence of pathogens and pest insects, leading to a significant decline in productivity if not properly addressed. The fundamental aim of this research is to employ a streamlined CNN-SVM architecture, utilizing deep learning techniques, to accurately categorize grape leaves into three distinct disease classes and one healthy class. The proposed model surpasses the accuracy of the previously trained transfer learning models VGG-16 and VGG-19 while having approximately 257× to 267× times fewer parameters (0.537 M). On average, the proposed model achieves a classification accuracy of 99.18%, which is significantly higher than the 93.42% and 91.94% achieved by the transfer learning models, respectively. With a precision, recall, and F1 score close to 99%, the suggested model provides excellent results. The model's outstanding performance is further validated by its remarkable Area Under Curve (AUC) score of 99.98%. In addition to using less disc space (about 6 MB), the suggested model because of being lightweight shows a significant decrease in parameters. To visually display the disease identified by the proposed model, a transparent AI methodology has been utilized, specifically the Gradient Weighted Class Activation Mapping (Grad-CAM) technique. To better understand which area was responsible for the classification, a heatmap has been created. © 2024 IEEE.

关键词： Deep learning

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Rice Category Identification through Deep Transfer Learning Features and Machine Learning Classifiers: An Intelligent Approach

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第7期51卷 765-784页

作者： Terlapu, Panduranga Vital Prasan, U.D. Kumar, T. Ravi Bendalam, Vijaya Pappu, Sasibhushana Rao Rao, M. Jayanthi Mohitha, Maddula Ratna Jayaram, D. Dept. of Computer Science and Engineering Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam India Dept. of CSE Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Sasibhushana Rao Pappu is an Assistant Professor of Dept. of Department of Computer Science and Engineering GITAM Andhra Pradesh Visakhapatnam India Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Dept. of CSE Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Department of Information Technology Chaitanya Bharathi Institute of Technology Gandipet Telangana Hyderabad500075 India

Rice category identification by image analysis is essential to ensure the quality and safety of rice production. In this study, we propose an intellectual approach to improve rice category identification using deep transfer learning features and machine learning classifiers. Specifically, we extracted features from three pre-trained models (Inception V3, VGG-19 and VGG-16) using transfer learning techniques. These were used as inputs to train MultiLayerPerceptron (MLP) and support vector machine (SVM) classifiers. The results of our experiments show that the proposed strategic results achieve high accuracy in identifying rice categories. The SVM (polynomial kernel) achieves the second-highest accuracy among all models and features, with an accuracy of 0.9948 using the VGG-19 and 0.9912 using Inception V3. The MLP classifier with (30 30) hidden layers achieve the first high accuracy, with an accuracy of 0.9972 (99.72%) using VGG-19 features. The results also show that the choice of deep transfer learning model and machine learning (ML) classifier can significantly affect the accuracy of rice category identification. Among the three pretrained models, VGG-19 features consistently perform the best, followed by Inception V3 and VGG-16. The choice of MLP hidden layer size also affects the accuracy, with 30 HL neurons achieving the best performance. Our proposed approach using deep TL features and ML classifiers shows promising results in improving rice category identification. Our study provides valuable insights into optimizing ML models for agricultural image analysis. © (2024) International Association of Engineers. All rights reserved.

关键词： Support vector machines

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HPC OpenStack Cloud with Optimized Software-Defined Storage 2

HPC OpenStack Cloud with Optimized Software-Defined Storage

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Prabha, V.A. Varalakshmi, P. Madras Institute of Technology Anna University Dept. of Computer Technology Tamilnadu Chennai India

ISBN: (纸本)9798350315905

High Performance Computing (HPC) clouds offer researchers access to powerful computing resources with the ability to handle a wide range of workloads without compromising the performance. As these workloads are diverse in nature, the traditional Lustre parallel file system does not suit mostly for a scalable storage solution. Ceph, the software defined storage is a distributed storage system that is designed to provide scalable, highly available storage for a variety of use cases, including HPC. One of the challenges with using Ceph in HPC environments is that HPC workloads often have very high performance and bandwidth requirements, and Ceph's performance can depend heavily on the underlying hardware and network infrastructure. We propose an optimization method for HPC Cloud ecosystem with OpenStack and optimized storage deployment using Ceph software-defined storage as the best option for their needs and to offer an efficient persistent storage for both VMs and container workloads. © 2023 IEEE.

关键词： Platform as a Service (PaaS)

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WorkoutNet: A Deep Learning Model for the Recognition of Workout Actions from Still Images 3

WorkoutNet: A Deep Learning Model for the Recognition of Wor...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Dey, Arnab Dutta, Arpita Biswas, Samit Indian Institute of Engineering Science and Technology Dept. of Computer Science and Technology Howrah India

ISBN: (纸本)9798350338607

Workouts or Exercises are some repetitive physical activities essential for maintaining good health. Workouts make us relax and happy, give us more energy, lower the risk of chronic disease, and rejuvenate our bodies and minds. Proper technique and posture are essential to get the most out of any workout;the same workout may lead to injury if performed incorrectly. This paper presents a method to identify a person's workout action from realistic still images. In contrast to video-based action recognition, the challenge is more difficult for still photographs because the Spatio-temporal information is not present in images. This study proposes a deep learning-based model named WorkoutNet to recognize workout actions. The method achieved remarkable results on the Workout Action Image dataset, consisting of 2796 images prepared by us and on the publicly available Exercise image dataset. The suggested approach predicts different workout actions and classifies them into ten categories with high accuracy. The proposed WorkoutNet model attains 92.75% validation accuracy in classifying the workout action images, succeeded by the XceptionNet, VGG-19, DenseNet121, InceptionV3, MobileNet and VGG-16. Further, the classification performance has been evaluated using the F1-Measure and Confusion Matrix. The suggested model can be utilized in real-life to identify workout action images and assist humans doing workouts without a trainer or guide in understanding the right way. © 2023 IEEE.

关键词： Deep learning

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An Effective Prediction of Rainfall Using Machine Learning Technique 5

An Effective Prediction of Rainfall Using Machine Learning T...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Rana, Nisha Guru Prasad, M. Sharma, Ashutosh Pratap, M.S. Anand Kumar, M. Aditya Pai, H. Dept. of Computer Science and Engineering Rishikesh India Graphic Era Deemed University Dept. Computer Science and Engineering Dehradun India Rishikesh India School of Computing and Information Technology Reva University Bengaluru India Dept. of Computer Application Dehradun India Dept. of Computer Science and Engg. Dehradun India

ISBN: (纸本)9781665493604

Climatic information such as temperature, humidity, and precipitation are useful for agriculturists, businesses, researchers, and the government. Estimating precipitation is a major factor that affects the environment and is one of the most important aspects of meteorological science. In this study, factual approaches and machine learning methods are used to estimate and forecast meteorological parameters for the government. Estimating precipitation is a major factor that affects the environment and is one of the most important aspects of meteorological science. Approaches are used to forecast and estimate precipitation. Daily observations were used as part of the experiment. Validation of results using real-world data is used to check the accuracy of forecasting model experimentation. The experimental results show that distribution, correlation, line-plots, and neural networks perform well for forecasting meteorological characteristics and have the best classification accuracy when compared to other types of machine learning methods for precipitation forecasting. © 2023 IEEE.

关键词： Machine learning

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DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clustering and Deep Learning for Scalable Indoor Localization Using Wi-Fi RSSI 13

DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clusterin...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Mahali, Muhammad Izzuddin Leu, Jenq-Shiou Achmad Sulistyo Putro, Nur Widyawan Prakosa, Setya Avian, Cries National Taiwan University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan Universitas Negeri Yogyakarta Dept. of Electronics and Informatics Engineering Education Yogyakarta Indonesia Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350373332

This research introduces a novel method for indoor positioning by integrating Fuzzy C-Means clustering with deep learning autoencoders, leveraging Wi-Fi RSSI fingerprinting for precise location prediction and building and floor level classification within a unified framework. The approach combines the comprehensibility of Fuzzy C-Means (FCM) with the robust feature extraction capabilities of deep autoencoders through a dual-phase strategy: extensive offline training utilizing RSSI fingerprint data followed by real-time online position prediction. The model's performance was rigorously tested using 5-Fold cross-validation, examining its effectiveness both with and without the integration of FCM on various cluster numbers. Evaluation metrics, including Euclidean distance error for regression and accuracy, F1-score, precision, and specificity for classification effectiveness, were used for comparative analysis. The results were visualized through regression plots, confusion matrices, and learning curves, supplemented by t-SNE visualizations that provide profound insights into the feature space. These detailed analyses highlight the considerable promise of this integrated approach in enhancing the accuracy and reliability of indoor positioning systems. © 2024 IEEE.

关键词： Fuzzy clustering

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A Novel Hybrid Deep Neural Network for Early Detection and Classification of Chicken Diseases 5

A Novel Hybrid Deep Neural Network for Early Detection and C...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Uddin, Md Shihab Islam, Md Fakrul Rahman, Sharukh Foysal, Nasir Uddin Islam, Muhammad Nazrul Razzaque, Md. Abdur Al Faisal, Faiz Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka1216 Bangladesh University of Dhaka Dept. of Computer Science and Engineering Dhaka1000 Bangladesh

ISBN: (纸本)9798350394290

Detection and classification of chicken diseases has been a longstanding concern for farmers. Relying solely on expert opinions could be a costly option. Moreover, there are certain chicken diseases that spread quickly, and necessary procedures are immediately needed to carryon. On the other hand, the lack of research activity or low availability of data on this topic leads to many misunderstandings and challenges so as to correctly detect a certain type of chicken disease. The existing techniques show low accuracy on disease classification, which leads to a complex engineering problem and critical analysis is required for sustainable development. In this paper, we develop a novel hybrid deep neural network, namely ChickenNet21, which combines a Convolutional Neural Network (CNN) and a fine-tuned Visual Geometry Group (VGG) model to address this challenge. Using a large dataset of 6,812 farm-labeled fecal images, we assess the effectiveness of ChickenNet21 and it achieves 98.83% accuracy in identifying chicken diseases. © 2023 IEEE.

关键词： Convolutional neural networks

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A Deep Learning Approach to Analyze the Relationship Between Gender, Height, Weight, and Basal Metabolic Rate from Face Images 2

A Deep Learning Approach to Analyze the Relationship Between...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Siam, Shykul Islam Chowdhury, S.A.H. Ahmed, Nafi Biswas, Swapneel Rajshahi University of Engineering & Technology Dept. of Electronics & Telecommunication Engineering Rajshahi Bangladesh Ahsanullah University of Science & Technology Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798331517984

In response to existing limitations, a novel convolutional neural network (CNN) model tailored for facial attribute estimation is introduced in this study. The methodology encompasses meticulous data preprocessing techniques, including dataset splitting, resizing, cleaning, augmentation, and cropping, to ensure the integrity of the dataset. Through rigorous real-world testing, remarkable accuracy in predicting gender, height, weight, and basal metabolic rate (BMR) from facial photos is demonstrated by our CNN architecture. Notably, unlike conventional approaches where BMR is directly estimated from facial images, three output neurons are employed by our model to simultaneously predict gender, height, and weight, which are sub-sequently utilized for BMR calculation. A primary contribution is made through the development of a comprehensive methodology that advances the reliability of facial attribute estimation by achieving an outstanding accuracy of 98.50% on our dataset named Face-ete and a commendable accuracy of 88.29% on another dataset. Our model outperforms the second-best model by percentage relative improvement factors of 2.19 and 4.28 in terms of BMR prediction accuracy for the 'Faceere' and the other dataset, respectively. This work represents a significant step forward in the field of facial attribute estimation. © 2024 IEEE.

关键词： Convolutional neural networks

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Samarjini - Revolutionizing Household Cleaning with an Affordable IoT-Based Automatic Mopper 3

Samarjini - Revolutionizing Household Cleaning with an Affor...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Joshi, Siddharth Kate, Vandana Bhalerao, Shilpa Tripathi, Prasoon Sharma, Sudhanshu Acropolis Institute of Technology and Research Dept. of Computer Science and Information Technology Indore India

ISBN: (纸本)9798350343274

The title 'Samarjini' draws its inspiration from the Sanskrit word for a cleaning tool, and this research paper introduces the development of an automatic mopper machine designed to streamline household cleaning tasks while maintaining affordability. In a market filled with high-cost alternatives, this IoT-based device stands out by offering autonomous navigation, obstacle detection and avoidance, and effective floor cleaning at a lower price. The paper explores the machine's design, hardware and software components, evaluates its performance in various scenarios, and underscores its relevance in today's fast-paced lifestyles. Extensive experimentation was conducted to assess the machine's performance in diverse environments and scenarios. The outcomes affirm that 'Samarjini' excels in floor cleaning, adept.y avoiding obstacles and autonomously navigating through spaces. © 2023 IEEE.

关键词： Floors

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