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A location-based fog computing optimization of energy management in smart buildings:DEVS modeling and design of connected objects

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Frontiers of computer science 2023年第2期17卷 179-195页

作者： Abdelfettah MAATOUG Ghalem BELALEM Saïd MAHMOUDI Computer Science Department Faculty of Exact and Applied SciencesUniversity of Oran 1 Ahmed Ben BellaOran 31000Algeria Science and Technology Department Faculty of Science and TechnologyUniversity of TIARETTiaret 14000Algeria Computer Science Department Faculty of EngineeringUniversity of MonsMons 7000Belgium

Nowadays,smart buildings rely on Internet of things(loT)technology derived from the cloud and fog computing paradigms to coordinate and collaborate between connected *** is characterized by low latency with a wider spread and geographically distributed nodes to support mobility,real-time interaction,and location-based *** provide optimum quality of user life in moderm buildings,we rely on a holistic Framework,designed in a way that decreases latency and improves energy saving and services efficiency with different *** EVent system Specification(DEVS)is a formalism used to describe simulation models in a modular *** this work,the sub-models of connected objects in the building are accurately and independently designed,and after installing them together,we easily get an integrated model which is subject to the fog computing *** results show that this new approach significantly,improves energy efficiency of buildings and reduces ***,with DEVS,we can easily add or remove sub-models to or from the overall model,allowing us to continually improve our designs.

关键词： smart building energy consumption IoT fog computing Framework DEVS simulation models

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A Review: Real Estate Price Prediction using Machine Learning with Research and Trends 5

A Review: Real Estate Price Prediction using Machine Learnin...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Sharma, Sumit Kumari, Surbhi Goyal, Suprateek Nirala, Ravikant Department of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350383522

The complex interactions between location, property attributes, prevailing economic conditions, and market trends define the real estate market, which is dynamic and prone to ongoing changes. In order to make educated judgments, buyers, sellers, investors, and legislators are among the stakeholders in the real estate ecosystem that understand how crucial precise price forecasting is. The goal of this project is to create a reliable and accurate real estate price prediction model by utilizing machine learning. The real estate market is known for its dynamism, which is influenced by a wide range of elements including location, property characteristics, prevailing economic conditions, and market trends. For many parties involved in real estate, including buyers, sellers, investors, and legislators, accurate price forecast is essential. The goal of this research is to use machine learning methods to build a solid and accurate real estate price forecast model. In order to conduct this study, we assemble a large dataset of historical real estate transaction records that includes a wide range of property qualities, geographic data, economic indicators, and market trends. To assure the data's quality and relevancy, we preprocess and sanitise it. In order to extract useful information from the dataset, feature engineering approaches are used. Exploratory data analysis (EDA) is then carried out to learn more about the distribution of the data and the relationships between the variables. © 2024 IEEE.

关键词： Algorithm Decision tree Linear Regression modules range SVM

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Predictive Analytics for Parkinson's Disease Detection Leveraging Deep Learning Algorithms

Predictive Analytics for Parkinson's Disease Detection Lever...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart electrical Systems, ICSES 2024

作者： Kalpana, T. Thamilselvan, R. Chitra, K. Priyanka, S. Jahaan, R. Reshmi Thenmalar, T. Kongu Engineering College Department of Computer Application Erode India Kongu Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798331543617

Parkinson disorder is a neurological disease that progresses gradually which is typified by the brain's dopamine-producing neurons becoming exhausted. This neuronal loss results by symptoms including tremors, muscle stiffness, bradykinesia, and balance difficulties. Early detection and accurate prediction of PD progression are especially crucial for improving management and intervention strategies. This study builds a prediction model with the use of MRI datasets. To detect Parkinson's disease from MRI data, this study uses deep learning algorithms, such as Inception, Xception, and VGG19, and evaluates how well they perform in terms of accuracy. © 2024 IEEE.

关键词： Neurons

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Citrus Plant Leaves Disease Detection Using CNN and LVQ Algorithm 1

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Meena, Roop Singh Solanki, Shano Computer Science and Engineering Department NITTTR Chandigarh India

ISBN: (数字)9789819965441

ISBN: (纸本)9789819965434

This study introduces a unique method for disease identification in citrus plants by combining convolutional neural network (CNN) and learning vector quantization (LVQ) techniques. The suggested technology is meant to aid in the early identification and diagnosis of citrus plant diseases, which is important for preserving crop yields and avoiding crop loss. Features are extracted from pictures of citrus plant leaves using a convolutional neural network. Furthermore, the LVQ algorithm is used to identify the retrieved features as either healthy or unhealthy. When tested on a dataset consisting of photographs of citrus plant leaves, the suggested system achieved a high accuracy of 96.33% in disease classification. A total of 3570 pictures were used in this analysis, including both healthy and diseased citrus plants, representing different pathogens (citrus canker, citrus scab, citrus rust, other diseases, and healthy images) classes. There are 500 test photographs from each class and 1070 full-size images throughout test categories. An F1-score of 96.54%, a recall score of 96.54%, and a precision score of 96.69% were all obtained using the proposed strategy. Based on the obtained data, it appears that the proposed method achieves superior accuracy in disease identification compared to the state-of-the-art methods. The citrus industry stands to benefit greatly from this strategy, as it may be used for early disease identification and prevention in citrus plants. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diagnosis

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E-commerce Products Image Classification using EfficientNetB5 with Transfer Learning 7

E-commerce Products Image Classification using EfficientNetB...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Shah, Archi Goel, Parth Gandhi, Vaibhav C. Computer Science & Engineering Department Anand Changa India

ISBN: (纸本)9798350359299

The image classification is a research field where images are classified without human intervention. E-commerce image classification is the application of machine learning where techniques automatically categorize and label product images in E-commerce platforms. This study focuses on the complex task of e-commerce product image classification, which aims to develop a model capable of accurately classifying diverse products. This approach helps to take an acquired image from a designated device, decoding the acquired image, and applying a classification algorithm to categorize the products. The dataset has been organized into four categories, which are T-shirts, TVs, couches, and jeans. The E-commerce product dataset has been utilized with 796 images. The proposed work is based on transfer learning. In the proposed framework, pre-trained EfficientNetB5 model is used for fine-tuning for the e-commerce product classification task. The proposed framework achieved a 98% accuracy, 97.5% precision, 97% recall, and 97.75% F1- Score. © 2024 IEEE.

关键词： Image classification

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A Dual Contrastive Learning Framework for Enhanced Hate Speech Detection in Low-Resource Languages 1

A Dual Contrastive Learning Framework for Enhanced Hate Spee...

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1st Workshop on Challenges in Processing South Asian Languages, CHiPSAL 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Chavinda, Krishan Thayasivam, Uthayasanker Department of Computer Science & Engineering University of Moratuwa Sri Lanka

ISBN: (纸本)9798891762015

Hate speech on social media platforms is a critical issue, especially in low-resource languages such as Sinhala and Tamil, where the lack of annotated datasets and linguistic tools hampers the development of effective detection systems. This research introduces a novel framework for detecting hate speech in low resource languages by leveraging Multilingual Large Language Models (MLLMs) integrated with a Dual Contrastive Learning (DCL) strategy. Our approach enhances detection by capturing the nuances of hate speech in low-resource settings, applying both self-supervised and supervised contrastive learning techniques. We evaluated our framework using datasets from Facebook and Twitter, demonstrating its superior performance compared to traditional deep learning models such as CNN, LSTM, and BiGRU. The results highlight the efficacy of DCL models, particularly when fine-tuned on domain-specific data, with the best performance achieved using the TwHIN-BERT model. This study underscores the potential of advanced machine learning techniques in improving hate speech detection for under-resourced languages, paving the way for further research in this domain. © 2025 International Committee on Computational Linguistics.

关键词： Contrastive Learning

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Deep Learning-Based Diagnosis System for Liver Cancer Through Multimodal Determination for Early Treatment 4

Deep Learning-Based Diagnosis System for Liver Cancer Throug...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Dandotiya, Monika Singh, Anupam Agarwal, Nishi Jaheda, Shaik Gulati, Samridhi Poornima University Department of Computer Science & Engineering Jaipur India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Dev Bhoomi Uttrakhand University Department of Computer Science and Engineering Dehradun India Cvr College of Engineering Department of Information Technology Hyderabad India Raj Kumar Goel Institute of Technology Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350364729

Liver cancer is still one of the top causes of worldwide mortality from cancer, which highlights the demand for earlier diagnostic tools aimed at improved treatment outcomes. Although these traditional diagnostic practices are helpful, they may take time and also be subject to human error. The present study aims to fill the gap in existing diagnostic tools for liver cancer by evaluating prostate deep learning models (VGG 16, VGG 19 and ResNet 50) on CT scan images. This problem statement is related to improving the accuracy and reliability of liver cancer detection using powerful computational techniques. We processed a dataset of 1020 CT scan images by resizing, then normalizing the data, and performing an augmentation and some noise reduction. The performance of each model was evaluated in terms of precision, recall and F1 score using Dice Similarity Coefficient to identify the liver tumours. VGG 19 was the top performer in terms of accuracy and another score, followed by VGG 16 and ResNet 50. The use of such models in clinical settings can significantly improve diagnostic accuracy and, ultimately, diagnosis time, which will lead to better patient outcomes. The study ultimately underscores the promise of deep learning models (specifically VGG 19) as an essential resource in medical imaging by developing new and prospective methods for computerized liver cancer diagnosis that will help improve healthcare. © 2024 IEEE.

关键词： Diseases

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A deep convolutional neural network for COVID-19 detection using chest X-rays

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Research on Biomedical engineering 2022年第1期38卷 139-148页

作者： Bassi, Pedro R. A. S. Attux, Romis Department of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Campinas Brazil

Purpose: We present image classifiers based on Dense Convolutional Networks and transfer learning to classify chest X-ray images according to three labels: COVID-19, pneumonia, and normal. Methods: We fine-tuned neural networks pretrained on ImageNet and applied a twice transfer learning approach, using NIH ChestX-ray14 dataset as an intermediate step. We also suggested a novelty called output neuron keeping, which changes the twice transfer learning technique. In order to clarify the modus operandi of the models, we used Layer-wise Relevance Propagation (LRP) to generate heatmaps. Results: We were able to reach test accuracy of 100% on our test dataset. Twice transfer learning and output neuron keeping showed promising results improving performances, mainly in the beginning of the training process. Although LRP revealed that words on the X-rays can influence the networks’ predictions, we discovered this had only a very small effect on accuracy. Conclusion: Although clinical studies and larger datasets are still needed to further ensure good generalization, the state-of-the-art performances we achieved show that, with the help of artificial intelligence, chest X-rays can become a cheap and accurate auxiliary method for COVID-19 diagnosis. Heatmaps generated by LRP improve the interpretability of the deep neural networks and indicate an analytical path for future research on diagnosis. Twice transfer learning with output neuron keeping improved DNN performance. © 2021, Sociedade Brasileira de Engenharia Biomedica.

关键词： COVID-19

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Deep Learning based Approach for Cassava Leaf Disease Detection 8

Deep Learning based Approach for Cassava Leaf Disease Detect...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Kalpana, T. Thamilselvan, R. Chitra, K. Kaviya, S. Kiruthiga, M. Mahesh, C. Kongu Engineering College Department of Computer Applications Erode India Kongu Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350367904

Cassava is a vital staple crop in numerous tropical countries, to enhance plant disease detection using deep learning architecture including ResNet, EfficientNet, Incetion V3 and simple CNN. By levering these advance model significant improve accuracy compared to previous methods. This approach addresses the pressing issue of food insecurity by efficiently identifying diseases in crops. Types of Cassava disease are Cassava Mosaic Disease (CMD), Cassava Green Mottle (CGM), Cassava Brown Streak Disease (CBSD), and Cassava Bacterial Blight (CBB), along with the presence of Whitefly. The proposed work uses Convolution Neural Network (CNN) to identify the leaf diseases to quickly identify the specific disease that is affecting the crop. CNN can automatically detect important features without any human supervision. A vast collection of labelled cassava leaf photos comprising multiple disease classes is utilized for training the CNN model. To advance deep learning architectures, our research integrates techniques for data augmentation and transfer learning to further enchance model performance. © 2024 IEEE.

关键词： Plant diseases

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Hybridization of K-Means and Chaotic K-Best Gravitational Search Algorithm to Solve Clustering Problems 12th

Hybridization of K-Means and Chaotic K-Best Gravitational ...

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12th International Conference on Big Data Analytics, BDA 2024

作者： Das, Sujit Das, Anwesha Department of Computer Science and Engineering NIT Warangal Hanamkonda India

ISBN: (纸本)9783031861925

Clustering is an essential task in data analysis with applications in various fields. Although the widely used K-means algorithm has limitations, several heuristic approaches have been proposed to improve its performance. One such algorithm is GSA-KM, which combines the gravitational search algorithm (GSA) with K-means (KM) clustering. However, GSA itself has certain drawbacks that can be addressed through parameter tuning and the integration of chaotic optimization techniques. The Chaotic k-best GSA (CKGSA) is a hybrid algorithm that utilizes chaos theory to enhance the precision, convergence rate, and global search capability of GSA. This paper proposes a new clustering approach that combines CKGSA and K-Means algorithms. Integrating CKGSA improves the search strategy, while K-Means contributes to effective clustering results. Our proposed algorithm shows promise for enhancing clustering performance in terms of both efficiency and quality. This paper provides a comprehensive overview of the methodology, detailed experimental results, and a comprehensive discussion of the findings. We have used three benchmark datasets (Iris, Wine, and Glass) for the comparative analysis purpose, where the proposed approach shows better performance in terms of intra-cluster distance, standard deviation and average execution times. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： K-means clustering

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