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共 20250 条记录，以下是221-230 订阅

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automated characterization of coal image samples using pretrained deep convolutional neural networks and transfer learning: A comparative Study 3

Automated characterization of coal image samples using pretr...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Samanta, Kalyan Firdaus, Shama Mohapatra, Subrajeet Bit Mesra Dept. of Computer Science & Engg Jharkhand Ranchi India

ISBN: (纸本)9798331528201

Quality of coal are usually determined by manually examining samples under an optical microscope, and then conducting complex chemical tests for further grading. However, such methods are expensive, time-consuming, and dependent on the competency and expertise of the concerned petrologist. Preliminary quality assessment of coal can be performed with the help of image analysis and artificial intelligence-based techniques identifying the presence of coal maceral constituents. The usage of image processing-based methods for intelligent coal gradation is simpler, faster, and less expensive, and it eliminates the subjectivity of petrologists. However, the performance of traditional image processing-based coal gradation methods, on the other hand, is dependent on the accuracy of segmentation and feature extraction methods. Deep learning models, which do not require image segmentation or manual feature engineering, are an alternative solution to the above classification problem. Pretrained deep neural networks eliminates the time-consuming process of determining the best combination of layer architecture, which would be required by standard Convolutional Neural Network models. As a result, the proposed research presents an automated coal gradation system that utilizes pretrained deep convolutional neural networks to classify coal microscopic images. Six different types of pretrained neural networks viz. VGG19, VGG16, GoogLeNet, AlexNet, ResNet50, ResNet18 are simulated over 1700 Australian coal optical microscopic images of three grades. For the considered coal data set, all of the pretrained models achieve classification accuracy of over 80%. © 2024 IEEE.

关键词： Convolutional neural networks

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Space Design Innovation Driven by intelligent Recommendation: The Application and Efficiency Analysis of Hybrid Model 24

Space Design Innovation Driven by Intelligent Recommendation...

引用

3rd international conference on Frontiers of Artificial Intelligence and Machine learning (FAIML)

作者： Zhang, Jiao Wuhan Qingchuan Univ Sch Design Engn Wuhan Hubei Peoples R China

ISBN: (纸本)9798400709777

As technology advances, virtual reality and artificial intelligence play an increasingly important role in interior design. The study proposes an intelligent space design method that optimizes the recommendation effect by combining the recommendation model. This method integrates an improved content recommendation algorithm and collaborative filtering algorithm, and uses a convolutional neural network to extract key features to improve computational efficiency. In the furniture design project, the research solved the problem of data sparseness through image similarity calculation and SlopOne algorithm. Experiments show that when the parameters are set to pcaNum=128 and simNum=4, the recommendation accuracy of the hybrid recommendation model reaches 70% and 88% at TopN=10 and TopN=30 respectively. In addition, when TopN increases to 20, the recommendation accuracy exceeds 80%. The research improves the accuracy of indoor model recommendation, which is of great significance for improving design efficiency and promoting the application of intelligent technology in the field of interior design.

关键词： intelligent recommendation Space design hybrid model Performance analysis Home project

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intelligent Building System based on Multi-Modal Optimization of Energy data Center 3

Intelligent Building System based on Multi-Modal Optimizatio...

引用

3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Du, Danyou School of Architecture Science and Civil Engineering Xiamen Institute of Technology Fujian361000 China

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396157

This study introduces an intelligent building system designed for comprehensive data management, which integrates various subsystems to regulate devices and technologies in buildings. As a fundamental pillar of smart buildings, building mechanization mechanisms improve efficiency, reduce energy consumption, and prioritize human interests. This study specifically focuses on multimodal optimization within energy data centers, employing innovative strategies to improve system efficiency, with special emphasis on energy resource management in data centers. The proposed system integrates ZigBee-based sensor networks, tilt sensors, and temperature sensors for comprehensive architectural security monitoring. A semantic data model based on metadata is applied for improved analysis and representation of multimodal data, contributing to improved clarity and efficiency in data management. The proposed research study also addresses issues related to uninterruptible power supply (UPS) and automatic repair of primary physical connection relationships in building electromechanical systems. The final intelligent building system design includes a hierarchy of integrated platforms with communication interface layers, core control layers, and user interface layers. The effectiveness of the system is demonstrated through a systematic simulation using the METASYS building control system. The experiment shows the successful implementation of Modbus communication for power conversion and distribution systems, highlighting the practical application of the proposed intelligent building system. © 2024 IEEE.

关键词： Information management

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Deep learning-based Text News Classification using Bi-directional LSTM Model 3

Deep Learning-based Text News Classification using Bi-direct...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Anand, Shreyas Prakasam, P. Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Electronics Engineering Vellore India

ISBN: (纸本)9798350372120

The advancement of technology has resulted in increased news consumption through various online platforms and social media channels. Hence, there is a requirement for intelligent systems that may categorize the news that exists on the social media/online platform. Hence, this paper explores creating a news classifier utilizing a Bidirectional Long ShortTerm Memory (Bi-LSTM) deep learning model, which is a type of recurrent neural network (RNN) specifically designed to overcome the challenge of vanishing gradient often faced in deep learning. The machine learning was performed on a Kaggle dataset with over 400,000 records of news stories from various publishers. Natural language processing techniques such as stemming, one-hot encoding, vectorization, and embedding are used to prepare the data for machine learning. The multiclass classification is achieved and the performance of the model is analyzed. © 2024 IEEE.

关键词： Classification (of information)

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Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM 3

Hybrid Approach for Electricity Price Forecasting using Alex...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sambana, Bosubabu Devi, Kotamsetty Geethika Reddy, Bandi Rajeswara Hussain, Galeti Mohammad Siddartha, Gownivalla School of Computing Mohan Babu University Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331527549

The recent development of advanced machine learning methods for hybrid models has greatly addressed the need for the correct prediction of electrical prices. This method combines AlexNet and LSTM algorithms, which are used to introduce a new model with higher accuracy in price forecasting. Despite RNN and ANN being effective, they often fail to deal with forex time sequence data. The traditional methods do not accurately forecast the prices. These traditional methods only focus on demand and price which leads to insufficient analysis of data. To address this issue, using the hybrid approach, which focuses on external variables that also effect the predicted prices. Nevertheless, due to AlexNet's excellent feature extraction and LSTM's learning sequential patterns, the prediction accuracy is vastly increased. The model is built on the past data, which has been supplied with the most significant elements like demand, temperature, sunlight, and rain. For example, the model applies methods, such as minimum-maximum scaling and a time window, to predict the electricity prices of the future. The results show that this hybrid model is good than the standalone ones in terms of accuracy. Although we got our accuracy rating of 97.08, it shows higher accompaniments than remaining models RNN and ANN with accuracies of 96.64 and 96.63 respectively. © 2025 IEEE.

关键词： Prediction models

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A Deep learning Approach to Stock Trend Forecasting using CNNs with Technical Indicators 3

A Deep Learning Approach to Stock Trend Forecasting using CN...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Bharathiraja, M. Krishna Kumar, K. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University TamilNadu Chennai India Department of Mechatronics Engineering Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

The approach encompassed under this model introduces stock trend forecasting using a new approach that involves Convolutional Neural Networks (CNNs) supplemented with technical indicators like the Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), and Relative Strength Index (RSI). This study reacts to the limitations of traditional methods like ARIMA and GARCH due to their shortcomings with non-linear dependence on stock data. The robust solution for trend prediction closely follows the hierarchical learning of CNN. This model attains a remarkable accuracy of 99% in stock trend prediction using Convolutional Neural Networks, fine-tuning crucial but elaborate patterns for stock price movements. The forecast results categorize stock trends as Neutral, Bullish, and Bearish, with validation shown from high precision, recall, and F1-scores computed from complete classification reports and confusion matrices. The capability of forecasting provides tremendous assistance in making informed investment decisions. These results prove that CNNs more efficiently predict stocks if combined with technical analysis and give portions of insight to traders. This study shows the capacity to apply various deep learning techniques in finance to enhance stock trend predictions and shape on-the-ground investment strategies. The study fills an existing gap in research with the introduction of combining CNN with technical indicators in the prediction models for improvement with predictive power and accuracy. © 2025 IEEE.

关键词： Convolutional neural networks

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An intelligent approach for Speed Bump Detection 3

An intelligent approach for Speed Bump Detection

引用

3rd international conference on Computing and Machine Intelligence (ICMI)

作者： Hussein, Abrar Ahmed Omar, Mariam Mohamed Altamimi, Jawaher Suliman Ead, Waleed M. Beni Suef Univ Comp & Artificial Intelligence Bani Suwayf Egypt Najran Univ Appl Coll Dept Comp Najran Saudi Arabia

ISBN: (纸本)9798350372977;9798350372984

In this paper, we propose an intelligent approach that uses YOLO algorithms to detect marked and unmarked bumps. The proposed approach for speed bumps based on real data collected from the Upper Egypt region in Egypt. Several data augmentations were performed on it. The proposed approach reached a mAP of 81% in the model while also calculating the distance from the car to the bump. We have used the Jetson Nano and the Xbox Kinect camera hardware to achieve this objective. Based on road preview using a camera, this work can be used to inform and alert the driver via a mobile application to make the ride more safe, prevent accidents, and reduce vehicle damage.

关键词： Speed bumps intelligence systems safe roads detection Deep learning Kinect camera

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ISME-automated Facial Recognition Mobile APP 3

ISME-Automated Facial Recognition Mobile APP

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Raja Sekar, R. Vivek, Anigalla Pavan Guptha, Chekka Sai Asish Sai, Adini Parimal Sesha Reddy, Bondala Charan Kumar Department of Computer Science & Engineering Kalasalingam Academy of research and education Tamilnadu Krishnan koil India

ISBN: (纸本)9798331527549

In real-time, attendance tracking is a labour-intensive process in schools and universities, prone to proxy participation when done manually. The ISME (Identification, Security, Modern, and Education) application is a mobile solution designed to modernize attendance tracking using facial recognition technology. Leveraging the phone’s camera and the Haar Cascade algorithm for real-time face detection and identification, the app ensures accurate and efficient attendance management. Firebase integration supports secure and scalable data management, enabling role-based access for faculty and students. Teachers can capture live images or videos during lessons, and the app compares detected faces to a preregistered dataset of students’ facial images collected during ID card registration. Recognized faces automatically update attendance records in Firebase, reducing manual errors. The app’s user-friendly interface allows instructors to manage and view attendance trends, while students can access their attendance history. By employing real-time database synchronization and Firebase authentication, the app enhances the educational experience by ensuring secure, accurate, and seamless attendance tracking. © 2025 IEEE.

关键词： Students

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Elevating Ocular Diagnosis: Harnessing the Power of EfficientNet for Eye Disease Classification 3

Elevating Ocular Diagnosis: Harnessing the Power of Efficien...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mohith, Vankadari Raja, Kalpana Oviya, I.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Yale University Section for Biomedical Informatics and Data Science School of Medicine New Haven United States

ISBN: (纸本)9798350372120

In response to the evolving landscape of healthcare technology this study addresses the need for automated diagnostic tools in the field of ophthalmology. By utilizing deep learning methods specifically leveraging the EfficientNetV2S framework we achieve an overall accuracy rate of 94.6% in classifying images depicting common eye conditions like cataract, diabetic retinopathy, glaucoma, and normal cases. Our model exhibits capabilities across a diverse spectrum of medical conditions due to meticulous preprocessing and fine-tuning procedures. The results highlight the potential for automated technology integration in healthcare settings, which might improve patient care and treatment plans. This paper highlights the use of advanced neural network designs in the diagnosis of ocular illnesses, offering insights at the nexus of learning and medical image processing. © 2024 IEEE.

关键词： Ophthalmology

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Knee Osteoarthritis detection and classification using a customized CenterNet with DenseNet201 3

Knee Osteoarthritis detection and classification using a cus...

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3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Shirisha, N. Shilpa, K. Meghana, C.H. Rahman, Mohammed Abdul MLR Institute of Technology Dept. of Computer Science and Engineering Hyderabad India MLR Institute of Technology Dept. of Computer Science and Engineering Nalgonda India

ISBN: (纸本)9798331523893

Knee osteoarthritis (KOA) is a prevalent joint disorder diagnosed using imaging modalities like MRI, CT scans, and X-rays, with X-rays being the most cost-effective. Early detection is crucial for effective management. This study presents an automated deep learning approach to detect and classify KOA severity based on the Kellgren-Lawrence (KL) grading system using single posteroanterior standing knee X-ray images. Utilizing the Osteoarthritis Initiative dataset, we employed transfer learning to fine-tune DenseNet-201, enhancing model performance. Additionally, knowledge distillation was applied to reduce computational complexity while maintaining accuracy. Our model achieved over 95% accuracy on both testing and cross-validation datasets, outperforming existing methods. This approach offers a reliable tool for early KOA diagnosis and grading, potentially aiding clinical decision-making © 2025 IEEE.

关键词： Diagnostic radiography

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