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2024 International Conference on Information Technology, Data science, and Optimization, I-DO 2024

作者： Zhang, Qiaoyun Yang, Wan-Chi Chang, Hsiang-Chuan Ho, Chia-Ling Chang, Chih-Yung Chuzhou University School of Computer and Information Engineering China National Taipei University of Nursing and Health Sciences General Education Center Taiwan Tamkang University Department of Transportation Management Taiwan Tamkang University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798400709180

In the manufacturing process involving grinding wheels, challenges arise in fine-tuning grinding machines, typically addressed by craftsmen through subjective observations of sparks and sounds. This paper introduces a novel mechanism comprising two pivotal phases aimed at optimizing grinding wheel production line efficiency and accuracy. Firstly, an AutoEncoder is employed for spectrogram denoising, effectively isolating grinding sounds from environmental noise. Convolutional Neural Networks (CNNs) in the Encoder extract features across time and frequency domains, while deconvolution in the Decoder gradually restores features. ReLU activation ensures computational efficiency and effectively handles nonlinear features. Secondly, an AI-based assessment determines parameter adjustments using a combination of 3DCNN and CNN. By integrating classification results from both networks, features from video and audio data are identified, thereby enhancing classification effectiveness. Anomalies during grinding operations are detected through combined outputs, indicating the need for parameter adjustments. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Anomaly detection

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Real-Time Energy Consumption Insights for Hospitals with IoT and Machine Learning 5

Real-Time Energy Consumption Insights for Hospitals with IoT...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Uthayakumar, C. Mohankumar, N. Elakkiya, K. Lakshmi, E. Rama Ganeshbabu, T.R. Srinivasan, C. Vivekanandha College of Engineering For Women Department of Electronics and Communication Engineering Tamil Nadu Tiruchengode India Symbiosis Institute of Technology Nagpur Campus Maharastra Pune India Vel Tech High Tech Dr. Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Computer Science and Design Andhra Pradesh Bhimavaram India Department of Electronics and Communication Tamil Nadu Namakkal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331522100

Reducing operating expenses and minimizing environmental effects requires efficient hospital energy management. This research provides a comprehensive approach for tracking and analyzing energy consumption in real time. With the Internet of Things (IoT) and Machine Learning, especially Support Vector Regression (SVR). Energy consumption, environmental factors, and operating parameters are continually monitored by the proposed system's network of IoT sensors. A centralized cloud platform receives data streams from these sensors in real-time, with high resolution. A powerful machine learning method, SVR, processes and analyzes data. It is well known for its accuracy in predicting continuous variables. SVR models energy consumption trends, spot outliers, and predict future energy use. The predictive features of the system allow hospital managers to spot inefficiencies, forecast peak energy needs, and take proactive actions to optimize energy consumption. Energy data can be easily interpreted, and decisions can be made with the help of the system's visualization and analytics features. This system's combination of IoT and SVR offers a scalable and inexpensive method to improve healthcare facilities' energy efficiency. According to experimental data from pilot installations, there was a notable improvement in overall energy management techniques and a considerable decrease in energy waste. According to this research, modern IoT and machine learning technology may drive sustainable hospital operations and environmental stewardship. © 2025 IEEE.

关键词： Power management (telecommunication)

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GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images

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Mathematical Biosciences and engineering 2024年第8期21卷 6847-6869页

作者： Aggarwal, Sonam Gupta, Isha Kumar, Ashok Kautish, Sandeep Almazyad, Abdulaziz S. Mohamed, Ali Wagdy Werner, Frank Shokouhifar, Mohammad Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Model Institute of Engineering and Technology J&K Jammu India Chandigarh University Punjab Mohali140413 India Department of Computer Engineering College of Computer and Information Sciences King Saud University P.O. Box 51178 Riyadh11543 Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza12613 Egypt Applied Science Research Center Applied Science Private University Amman11931 Jordan Faculty of Mathematics Otto-von-Guericke University Magdeburg39016 Germany Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam

Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpreting endoscopic images, due to their capacity to provide results equivalent to or exceeding those of medical specialists. This capability is particularly crucial in the realm of gastrointestinal disorders, where even experienced gastroenterologists find the automatic diagnosis of such conditions using endoscopic pictures to be a challenging endeavor. Currently, gastrointestinal findings in medical diagnosis are primarily determined by manual inspection by competent gastrointestinal endoscopists. This evaluation procedure is labor-intensive, time-consuming, and frequently results in high variability between laboratories. To address these challenges, we introduced a specialized CNN-based architecture called GastroFuse-Net, designed to recognize human gastrointestinal diseases from endoscopic images. GastroFuse-Net was developed by combining features extracted from two different CNN models with different numbers of layers, integrating shallow and deep representations to capture diverse aspects of the abnormalities. The Kvasir dataset was used to thoroughly test the proposed deep learning model. This dataset contained images that were classified according to structures (cecum, z-line, pylorus), diseases (ulcerative colitis, esophagitis, polyps), or surgical operations (dyed resection margins, dyed lifted polyps). The proposed model was evaluated using various measures, including specificity, recall, precision, F1-score, Mathew’s Correlation Coefficient (MCC), and accuracy. The proposed model GastroFuse-Net exhibited exceptional performance, achieving a precision of 0.985, recall of 0.985, specificity of 0.984, F1-score of 0.997, MCC of 0.982, and an accuracy of 98.5%. ©2024 the Author(s), licensee AIMS Press.

关键词： Endoscopy

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Prompt Based Tri-Channel Graph Convolution Neural Network for Aspect Sentiment Triplet Extraction

Prompt Based Tri-Channel Graph Convolution Neural Network fo...

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2024 SIAM International Conference on Data Mining, SDM 2024

作者： Peng, Kun Jiang, Lei Peng, Hao Liu, Rui Yu, Zhengtao Ren, Jiaqian Hao, Zhifeng Philip, S.Yu. Institute of Information Engineering Chinese Academy of Sciences China School of Cyberspace Security University of Chinese Academy of Sciences China School of Cyber Science and Technology Beihang University China Faculty of Information Engineering and Automation Kunming University of Science and Technology China College of Science University of Shantou China University of Illinois at Chicago Department of Computer Science United States

ISBN: (纸本)9781611978032

Aspect Sentiment Triplet Extraction (ASTE) is an emerging task to extract a given sentence's triplets, which consist of aspects, opinions, and sentiments. Recent studies tend to address this task with a table-filling paradigm, wherein word relations are encoded in a two-dimensional table, and the process involves clarifying all the individual cells to extract triples. However, these studies ignore the deep interaction between neighbor cells, which we find quite helpful for accurate extraction. To this end, we propose a novel model for the ASTE task, called Prompt-based Tri-Channel Graph Convolution Neural Network (PT-GCN), which converts the relation table into a graph to explore more comprehensive relational information. Specifically, we treat the original table cells as nodes and utilize a prompt attention score computation module to determine the edges' weights. This enables us to construct a target-aware grid-like graph to enhance the overall extraction process. After that, a triple-channel convolution module is conducted to extract precise sentiment knowledge. Extensive experiments on the benchmark datasets show that our model achieves state-of-the-art performance. The code is available at https://***/KunPunCN/PT-GCN. Copyright © 2024 by SIAM.

关键词： Extraction

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Customer Prioritization for Medical Supply Chain During COVID-19 Pandemic

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computers, Materials & Continua 2022年第1期70卷 59-72页

作者： Iram Mushtaq Muhammad Umer Muhammad Imran Inzamam Mashood Nasir Ghulam Muhammad Mohammad Shorfuzzaman Department of Management Sciences Sir Syed CASE Institute of Technology45230IslamabadPakistan Department of Operation&Supply Chain Management NUST Business SchoolNational University of Sciences&Technology45200IslamabadPakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

During COVID-19,the escalated demand for various pharmaceutical products with the existing production capacity of pharmaceutical companies has stirred the need to prioritize its customers in order to fulfill their *** study considers a two-echelon pharmaceutical supply chain considering various pharma-distributors as its suppliers and hospitals,pharmacies,and retail stores as its *** studies have generally considered a balanced situation in terms of supply and demand whereas this study considers a special situation of COVID-19 pandemic where demand exceeds supply Various criteria have been identified from the literature that influences the selection of customers.A questionnaire has been developed to collect primary data from pharmaceutical suppliers pertaining to customerselection *** criteria have been prioritized with respect to eigenvalues obtained from Principal Component Analysis and also validated with the experts’domain-related knowledge using Analytical Hierarchy *** potential appeared to be the most important criteria of customer selection followed by trust and service convenience brand loyalty,commitment,brand awareness,brand image,sustainable behavior,and ***,Multi Criteria Decision Analysis has been performed to prioritize the customerselection criteria and customers with respect to selection *** experts with seven and three and ten years of experience have participated in the *** of the study suggest large hospitals,large pharmacies,and small retail stores are the highly preferred ***,findings of prioritization of customer-selection criteria fromboth Principal Component Analysis and Analytical Hierarchy Process are ***,this study considers the experience of three experts to calculate an aggregate score of priorities to reach an effective *** traditional supply chain problems of supplier selection,this study considers a selection

关键词： Pandemic customer prioritization pharmaceutical supply chain principal component analysis multi-criteria decision-making

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A Smell Sensor based Improved Waste Management System for Green and Smart Cities 6

A Smell Sensor based Improved Waste Management System for Gr...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Saud, Hadiba Naaz, Sameena Saiyeda, Anam School of Engineering Sciences and Technology Jamia Hamdard Department of Computer Science and Engineering New Delhi India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

IoT is crucial for smart city applications to improve. The typical waste management method starts with garbage generated by municipal residents and deposited in garbage cans at the source. Outdoor containers are not kept up with by municipalities or trash management businesses, production it incredible to know when they need to be cleaned or when they are jam-packed. The author presents a smart city based IoT-allowed solid waste management system in this paper. Its goal is to break beyond the limits of existing waste management techniques. PBLMU and HBLMU end sensor nodes are used to pathway trash bins in residential zones and public area, respectively, in the proposed design. Using an intuitive Graphical User Interface, the garbage collection authority can view and also analyse the unfilled status of every waste bin (GUI). Multiple dustbins can be found throughout the city, university, or home in this paper. These garbage cans are equipped with a sensor that allows them to track their level and weight. Every rubbish bin in the city will be equipped with PBLMU and HBLMU, making it easy to determine which bin is full. The gadget will broadcast the reading along with the Lora WAN Gateway when the bin's level and weight approach the threshold limit. Harmless chemicals are put to the smell sensor, which is used to detect the rotting smell, in order to avoid the decaying scent around the bin. © 2022 IEEE.

关键词： Smart city

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Anomaly detection using deep learning approach for IoT smart city applications

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Multimedia Tools and Applications 2024年 1-21页

作者： Shibu, S. Kirubakaran, S. Remamany, Krishna Priya Ahamed, Suhail Chitra, L. Kshirsagar, Pravin R. Tirth, Vineet Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering CMR College of Engineering and Technology Telangana Hyderabad501401 India Engineering Department College of Engineereing and Technology University of Technology an Applied Sciences PC Musandam811 Oman Machine Learning Software Engineer Paragon Semvox GmbH Saarland Kirkel Germany Department of Electrical and Electronics Engineering Aarupadai Veedu Institute of Technology Vinayaka Missions Research Foundation Tamil Nadu Chennai603104 India Department of Electronics & amp Telecommunication Engineering J D College of Engineering & ampManagement Maharashtra Nagpur India Department of Mechanical Engineering College of Engineering King Khalid University Asir Abha61421 Saudi Arabia

With the advancements of IoT devices, many smart applications start to rule this era. In particular, smart cities has been adapted and realized by many countries around the world. In smart cities, vas amount of data is generated at every second. This vast data need a transmission medium which could be wireless standard. However, security is the main concern in such applications since the smart transmission always binds with anomalies. The existing anomaly detection systems need improvement in accuracy due to inefficient feature extraction and selection procedure. This paper proposes an accurate anomaly detection technique that built upon deep learning approach. We proposed a Combined Deep Q-Learning (CDQL) algorithm for anomaly detection. Priory, optimal features are selected by using Spider Monkey Optimizer (SMO). With the optimal features, CDQL detects anomalies accurately. In addition, the CDQL algorithm learns the environment in order to monitor the network data continuously. This continuous monitoring and optimum features helps in accuracy improvement up to 98%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Reinforcement learning

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Design of IoT Based Agricultural Farm Protector Drone Using Raspberry Pi 6

Design of IoT Based Agricultural Farm Protector Drone Using ...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Kanimozhi, P. Kannan, Muthukumaran Ananth, Christo Kumar, T. Ananth Parkavi, S. IFET College of Engineering Computer Science and Engineering Villupuram India Jaya Engineering College Department of EIE Tamilnadu Chennai India Samarkand State University Faculty of International Education Programs Uzbekistan

ISBN: (纸本)9798350304480

In many cases, the safety of persons and commodities is now assured using surveillance drones, which are crucial technical systems. The drone is critical operational asset during surveillance operations because of its rapid deployment and accurate observation. It is crucial to safeguard farmland and agricultural areas from animals, birds, and other species. Numerous measures are attempted to deter human intrusion but they fall short in defending against the farmer's principal foes-animals that trample their crops, both domestic and wild animals must be kept out of the area. A farm protection drone has been employed to prevent such disasters. With the help of a drone technology with raspberry pi, used as animal protection to monitor the farms. To do this, this project makes use of (GSM) Global System for Mobile Communication and (GPS) Global Positioning System Module. A 'SMS'the area that animals observe and detected by image processing will be sent to the farmer and forest officers. © 2023 IEEE.

关键词： Global Positioning System Global system for mobile communication Internet of Things Open CV Raspberry Pi

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Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images

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computer Systems science & engineering 2023年第10期47卷 375-391页

作者： Sultan Alahmari Saud Yonbawi Suneetha Racharla ELaxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa King Abdulaziz City for Science and Technology P.O Box 6086Riyadh 11442Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of AIML Aditya Engineering CollegeSurempallemAndhra PradeshIndia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Computer Science University of Central AsiaNaryn722600Kyrgyzstan Faculty of Computers and Information South Valley UniversityQena83523Egypt

Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed *** spatial information and spectral signatures of hyperspectral images(HSIs)present greater potential for detecting and classifying fine *** accurate classification of crop kinds utilizing hyperspectral remote sensing imaging(RSI)has become an indispensable application in the agricultural *** is significant for the prediction and growth monitoring of crop *** the deep learning(DL)techniques,Convolution Neural Network(CNN)was the best method for classifying HSI for their incredible local contextual modeling ability,enabling spectral and spatial feature *** article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification(HMAODL-CTC)algorithm *** proposed HMAODL-CTC model mainly intends to categorize different types of crops on *** accomplish this,the presented HMAODL-CTC model initially carries out image preprocessing to improve image *** addition,the presented HMAODL-CTC model develops dilated convolutional neural network(CNN)for feature *** hyperparameter tuning of the dilated CNN model,the HMAO algorithm is ***,the presented HMAODL-CTC model uses an extreme learning machine(ELM)model for crop type classification.A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC *** comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.

关键词： Crop type classification hyperspectral images agricultural monitoring deep learning metaheuristics

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Impact of Approximate QRS Identification on RR Interval-Based Arrhythmia Detection and Classification

Impact of Approximate QRS Identification on RR Interval-Base...

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International Congress on Image and Signal Processing, BioMedical engineering and Informatics (CISP-BMEI)

作者： Taiki Nagatomo Toshinori Sato Department of Electronics Engineering and Computer Science Graduate School of Engineering Fukuoka University Japan Department of Electronics Engineering and Computer Science Faculty of Engineering Fukuoka University Japan

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Wearable devices for arrhythmia diagnosis are battery-powered and require improved power efficiency. In our previous study, we applied approximate computing, which is effective for power reduction, to QRS identification algorithm and achieved significant power reduction. However, the impact of this approximation on arrhythmia detection and classification was not fully investigated. In this study, we investigate how the approximation --- specifically, the use of two approximate multipliers and eight approximate squarers applied to the Pan-Tompkins algorithm --- affects RR interval-based arrhythmia detection and classification. We use 48 samples from the MIT-BIH Arrhythmia Database to evaluate the accuracy of arrhythmia detection and classification. It was found that the approximate multiplier mul16s_GQU is the best choice, achieving nearly 100 % accuracy and less than 1 % false negatives in abnormal/normal classification. Additionally, the most power-efficient approximate squarer, E10C1, achieves relatively high accuracy with less than 5 % false negatives.

关键词： Accuracy Databases Arrhythmia Energy conservation Signal processing algorithms Signal processing Approximation algorithms Classification algorithms Wearable devices Informatics

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