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21st International Computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Renxiang, Huang Jia, Chen Jianping, Li School of Artificial Intelligence and Big Data Sichuan University of Arts and Science Chengdu635002 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

This paper reviews the research progress of deep learning-based household waste classification algorithms. It first introduces the importance of household waste classification and the application value of deep learning in this field. Next, it outlines the theoretical foundations of deep learning technology in waste classification, including the principles of convolutional neural networks and their applications in image recognition. The article provides a detailed analysis of the application of different deep learning models in waste classification, such as MobileNetV2, ShuffleNetV1, EfficientNetV1, and YOLOV5, as well as their optimizations and innovations in practical applications. Furthermore, it discusses optimization and innovation methods for deep learning models, including learning rate adjustment, optimizer selection, attention mechanisms, data augmentation, and lightweight network design. Finally, it summarizes the current research shortcomings and challenges, and looks forward to future research directions. This review provides a systematic perspective for a deeper understanding of the application of deep learning in waste classification and offers directions for future research. © 2024 IEEE.

关键词： Convolutional neural networks

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An Enhanced Product Quality Evaluation using Hybrid LSTM-FCNN Model and GWO Algorithm for Social Media Sentiment Analysis 8

An Enhanced Product Quality Evaluation using Hybrid LSTM-FCN...

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

作者： Raj Kumar, V.S. Kumaresan, T. Kanna, P. Rajesh Jagadeesan, S. Showmiya, P. Nithin, P. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India Nandha Engineering College Department of Computer Science and Engineering Erode India Nehru Institute of Technology Department of Computer Science - Cyber Security Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India

ISBN: (纸本)9798350367904

Sentiment analysis is still in its developing era, and sometimes, struggles are faced in evaluating user sentiment due to the use of traditional models to analyze the relationship structures in social media interactions. To tackle the issue, we proposed an innovative approach Hybrid LSTM neural network and a fully connected neural network (FCNN) integrated with the Grey Wolf Optimization (GWO) algorithm. The objective of this proposed method is to improve sentiment analysis by employing the LSTM to capture the sequence of data and improve the ability of FCNNs to improve feature extraction and the analysis process. The proposed system process initiated with the data preprocessing of input Twitter sentiment dataset transformed into a suitable input to the Hybrid LSTM-FCNN neural network. Then, temporal dependency of the data captured by LSTM and FCNN is used to extract the features. The GWO method mimics the grey wolf hunting behaviour, for optimizing the proposed Hybrid LSTM-FCNN model's parameters to enhance the classification accuracy. The system models start with the initialization of the model, parameters, and training the models using the dataset to reduce the cross-entropy loss, estimating the testing dataset from unseen data and fine-tuning using GWO as necessary. Results demonstrate significant improvements in sentiment categorization compared to traditional methods. The Hybrid LSTM-FCNN model, enhanced with GWO, effectively captures nuanced sentiment patterns, leading to more accurate classification. Additionally, GWO's computational efficiency contributes to scalability. In conclusion, our proposed approach offers a promising solution for enhancing product quality evaluation through advanced neural network architectures and optimization algorithms applied to social media sentiment analysis. © 2024 IEEE.

关键词： Tweets

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Customized CNN Model for Alzheimer's Disease Stages Classification and Comparison with Transfer Learning 1

Customized CNN Model for Alzheimer's Disease Stages Classifi...

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1st International Conference on Recent Advances in Information technology for Sustainable Development, ICRAIS 2023

作者： Sindhu, B. Prasad, Rishitha Kodipalli, Ashwini Kumar Swamy, S. Rao, Trupthi Global Academy of Technology Department of Computer Science and Engineering Karnataka Bangalore India Global Academy of Technology Department of Artificial Intelligence and Data Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350306637

Alzheimer's disease, the predominant form of dementia, progressively worsens over time, initially causing mild memory loss and eventually severely hampering daily tasks and communication abilities. This degenerative condition affects regions of the brain integral to cognition, memory, and language. Due to its intricate nature, symptom experiences and progression can vary among individuals. This study proposes a predictive model to discern Alzheimer's stages. Leveraging 'deep learning' techniques through convolutional neural networks (CNNs), the algorithm is trained on an extensive dataset of brain MRI scans. It learns to recognize pivotal features and correlate them with diverse disease phases. The Customized CNN model's accuracy of 94.37% surpasses alternative machine learning methods, including transfer learning. This precision empowers early prediction, significantly benefiting patient care and therapeutic strategies, thereby enhancing Alzheimer's disease management. © 2023 IEEE.

关键词： Convolutional neural networks

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Genetic Algorithm for Optimizing Blade Shape of Horizontally-Oriented Wind Turbine at Ras-Munif, Jordan

Genetic Algorithm for Optimizing Blade Shape of Horizontally...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Al-Omari, Zakaria Khlaifat, Nour Banimustafa, Ahmed Isra University Faculty of Engineering Department of Renewable Energy Amman Jordan Isra University Department of Data Science and Artificial Intelligence Jordan

ISBN: (纸本)9798350303780

The shape and orientation of the blade of wind turbines (WTs) play a significant role in energy yield from wind turbines. This work proposes optimizing the aerodynamic design of the blade shape (B_Shape) of the horizontally orientated wind turbines (HOWT) to enhance its annual energy production taking into consideration the available wind resources at Ras Munif, Jordan. The optimized design considers the B_Shape parameters, which include the chord length and twist angle (CL & TA), maximum allowable annual energy production (AEP), and rotor speed. The Weibull probability distribution function (WPDF), and 'scale and shape parameters of 2.218 and 6.278 m/s, correspondingly' were utilized as input for the optimized design in addition to the statistical wind conditions. 'NREL-20 kW HWT' was used in the experimental study to verify and validate the design's reliability and effectiveness. The 'horizontally oriented rotor performance optimization (HORP_Opt)', and 'National Renewable Energy Laboratory (NREL), Golden, CO, USA' were improved with construction indicators relating to 'nominal revolving velocity', 'B_Shape, and 'pitch angle'. The pitch angle stayed unchanged at 0° whilst increasing wind velocity increases rotational speed to the level of 104.74 rpm at the base speed. According to the optimized WT, the annual energy production achieved was 36011 kWh/yr. © 2023 IEEE.

关键词： Wind turbines

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TMRE:Novel Algorithm for Computing Daily Reference Evapotranspiration Using Transformer-Based Models

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Computers, Materials & Continua 2025年第5期83卷 2851-2864页

作者： Bushra Tayyaba Muhammad Usman Ghani Khan Talha Waheed Shaha Al-Otaibi Tanzila Saba National Center of Artificial Intelligence(NCAI) Al Khwarizmi Institute of Computer Sciences(KICS)Lahore54890Pakistan Department of Computer Science University of Engineering and Technology LahoreLahore54890Pakistan Artificial Intelligence&Data Analytics Lab Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** estimation of ETo is challenging due to its dependency onmultiple climatic variables,including temperature,humidity,and solar radiation,making it a complexmultivariate time-series *** machine learning and deep learning models have been applied to forecast ETo,achieving moderate ***,the introduction of transformer-based architectures in time-series forecasting has opened new possibilities formore precise ETo *** this study,a novel algorithm for ETo forecasting is proposed,focusing on four transformer-based models:Vanilla Transformer,Informer,Autoformer,and FEDformer(Frequency Enhanced Decomposed Transformer),applied to an ETo dataset from the Andalusian *** novelty of the proposed algorithm lies in determining optimized window sizes based on seasonal trends and variations,which were then used with each model to enhance prediction *** custom window-sizing method allows the models to capture ETo’s unique seasonal patterns more ***,results demonstrate that the Informer model outperformed other transformer-based models,achievingmean square error(MSE)values of 0.1404 and 0.1445 for forecast windows(15,7)and(30,15),*** Vanilla Transformer also showed strong performance,closely following the *** findings suggest that the proposed optimized window-sizing approach,combined with transformer-based architectures,is highly effective for ETo *** novel strategy has the potential to be adapted in othermultivariate time-series forecasting tasks that require seasonality-sensitive approaches.

关键词： Reference evapotranspiration ETo transformer informer autoformer FEDformer timeseries forecasting self-attention

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Innovative Video Classification Method based on Deep Learning Approach

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Recent Patents on engineering 2025年第2期19卷 95-107页

作者： Hemamalini, V. Jayasutha, D. Vinothini, V.R. Devi, R. Manjula Kumar, A. Anitha, E. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Arjun College of Technology Coimbatore Thamaraikulam India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode638401 India Department of Computer Science and Design Kongu Engineering College Perundurai Erode638060 India Department of Civil Engineering University Institute of Engineering & Technology Maharshi Dayanand University Rohtak124001 India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore641202 India

Background: The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham science Publishers.

关键词： Convolutional neural networks

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A Python Based Machine Learning Framework for Eye Blink Detection with BlueEye technology 5

A Python Based Machine Learning Framework for Eye Blink Dete...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Musthafa, A. Syed Sujatha, R. Jessie, P. Mithuna, R. Mekala, R. Vijay, A. M.Kumarasamy College of Engineering Department of Information Technology Tamil Nadu Karur639113 India Dr.Mahalingam College of Engineering and Technology AIML Department Pollachi642 003 India Akshaya College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India St.Josephs College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Chennai600119 India

ISBN: (纸本)9798331518097

This research study discusses the results of an Eye Blink technology, Eye Blink technology enables interaction with digital interfaces through eye movement tracking, offering a hands-free method for controlling devices. This technology utilizes computer vision techniques, such as those provided by OpenCV and MediaPipe, to detect and track facial landmarks. It captures real-time video from a webcam and processes each frame to detect the positions of eyes and movements thereof. Mapping eye landmarks to screen coordinates is then used by the system to convert movement of eyes into movement of the cursor, while it lets people interact with the system without touching the monitor- eye blinks translate to clicks. This can be basically credited to the immense potential for access tools, which make access to computers much more natural and effective for limited mobility users. © 2024 IEEE.

关键词： Eye movements

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Intelligent Security Monitoring: Machine Learning-based Intrusion Detection

Intelligent Security Monitoring: Machine Learning-based Intr...

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2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Rekha, M. Rani, P.Shobha Aashida, S. Elakkiya, S. Ezhil Arasi, S. Amirtha, A. R.M.K. Engineering College Department of Information Technology Chennai India R.M.K. Engineering College Department of Artificial Intelligence And Data Science Chennai India

ISBN: (纸本)9798331509828

The rapid growth in the field of Information technology has definitely caused a steep rise in the rate and sophistication of cyber-attacks;therefore, cybersecurity is an issue of major concern. Recently, Machine Learning and Deep Learning methodologies have turned out to be really potent tools for network intrusion detection. This paper presents a detailed review of various ML and DL models used in Intrusion Detection Systems (IDS). The models like Naïve Bayes, artificial Neural Networks, Support Vector Machines, Decision Tree, K-Nearest Neighbors, and methods based on Reinforcement Learning are covered. In this paper, the MRF model and examine the model with other ML and DL models are focused. In this analysis, the authors use the KDD dataset, a very popular standard in intrusion detection research. This dataset has different attributes divided into four main classes. Such as Content, Host, Basic, and Traffic. All these are vital classes in deciding the false alarm rate and detection rate of IDS. The empirical evidence that the MRF model outperforms traditional models in both measures of accuracy and reliability are provided. How contribution analysis for each class of attributes towards DR and FAR can be utilized in optimizing the MRF model for better performance of intrusion detection systems are shown. The results obtained in this research work have been pointing toward the importance of choosing suitable data attributes and models so that maximum DR can be achieved with a minimum FAR, which would increase the effectiveness of IDS. The proposed Modified Random Forest-based Intrusion Detection System uses advanced feature selection and preprocessing to improve detection accuracy and reduce false alarms. The real-time adaptability is done by dynamic selection of parameters in order to prevent evolving cyber threats. © 2025 IEEE.

关键词： Network intrusion

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Medical Material Allocation using Multi-Queue Scheduling during Pandemics

Medical Material Allocation using Multi-Queue Scheduling dur...

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2024 International Conference on science, technology, engineering and Management, ICSTEM 2024

作者： Divya Priya, D. Subramaniam, Suthashini Kumar, P. Vinoth Theam, Lim Jit Ayyasamy, Ramesh Kumar Krishnan, Kesavan Department of Computer Science Engineering MLR Institue of Technology Hyderabad India Perak Kampar Malaysia Department of Artificial Intelligence and Data Science St Joseph's Institute of Technology OMR CHENNAI India Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798350376913

In this study, we implemented a multi-level queue scheduling algorithm for a hospital with three wards: General, Pandemic, and Arogya Sree. The Pandemic ward uses priority scheduling, while the others use FCFS. It also calculates distances to nearby airports based on geolocation to identify oxygen airlift points. This work allows us to eliminate human interference in resource allocation by automating the process based on priority derived from the doctor’s diagnosis. This ensures that maximum efficiency is reached. The common people faced many problems during the pandemic, mainly related to costs incurred and resource availability. We want to solve this by removing the human factor from the equation. © 2024 IEEE.

关键词： Scheduling algorithms

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A Real-Time Video Surveillance-Based Framework for Early Plant Disease Detection Using Jetson TX1 and Novel LeafNet-104 Algorithm 4th

A Real-Time Video Surveillance-Based Framework for Early Pla...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Murthy, Jamuna S. Dhanashekar, K. Siddesh, G.M. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru India The Ohio State University Columbus United States

ISBN: (纸本)9789819726134

The precise and timely identification of plant diseases plays a pivotal role in safeguarding crop health and optimizing agricultural yields. This paper introduces an innovative framework designed for plant disease detection using video surveillance-based cameras strategically deployed on the Nvidia Jetson TX1 hardware platform within agricultural farms. Leveraging the capabilities of deep learning and the novel neural network architecture known as LeafNet-104, this framework empowers real-time disease detection on plants. Through the deployment of video surveillance cameras, continuous monitoring and early disease detection become achievable, enabling farmers to take prompt corrective measures and mitigate further crop damage. The Nvidia Jetson TX1, with its robust high-performance computing capabilities, enables on-device processing, rendering it ideal for real-time applications. To assess the efficacy of the proposed framework, comprehensive experiments were conducted on an actual agricultural farm. The dataset encompassed various plant species and disease types. Evaluation metrics such as accuracy, precision, recall, and the F1-score were employed to gauge the performance of the LeafNet-104 algorithm. The experimental findings underscored the framework's capacity to accurately and swiftly detect and classify plant diseases in real time, attaining an impressive accuracy rate of 97.5%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crops

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