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Improving Food Safety by IoT-based Climate Monitoring and Control Systems for Food Processing Plants

Remote Sensing in Earth Systems sciences 2025年第2期8卷 387-399页

作者： Kolikipogu, Ramakrishna Shivaputra Muniyandy, Elangovan Maroor, Jnaneshwar Pai Lakshmi, Gundala Venkata Rama Konduri, Bhagavan Naveenkumar, R. Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India Department of Electronics and Communication Engineering Dr. Ambedkar Institute of Technology Bengaluru India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Business Administration Nitte Institute of Professional Education Nitte (Deemed to Be University) Karnataka Mangaluru India Department of Computer Science and Engineering CVR College of Engineering Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Chandigarh Colleges of Engineering Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India

Web different IoT apps offer lot more options for every player in the meat supply chain to monitor, control, and optimize their manufacturing method. Beginning within late 2017, industrial IoF2020 program will concentrate toward massive deployments of IoT via three use cases in the meat supply chain: pig farm management, poultry chain tracking, and for livestock visibility with openness. This initiative will include eleven collaborators spanning 5 nations. Installing and integrating IoT sensors for environmental and livestock management would alleviate producers’ difficulty getting knowledge that data needed regarding continuously tracking how well they are doing. Alert detection is additionally being created, integrating multiple sources of data in order to give farmers helpful input along with communicating news with other partners. By carrying out this, preventative or remedial measures can be done for infections, swine taint, bird mortality, feed waste, the natural world, et cetera. Additionally, EPCIS-based traceability from farm to fork will be allowed to ensure customers may get precise data about the source and caliber of their livestock. Each of these three use scenarios’ existing stage in work along with future plans are expected to be discussed. IoF2020 seeks to support the digital revolution in smart farming by tackling various educational and commercial problems while also promoting its use throughout Europe. It maintains mechanical necessary degree and security against physical along with toxic contaminants during industrial butchering of chicken, a lot measures have been taken. Buyers’ worries about food safety and health are the result of eating chicken increasingly. Our authorities have begun many kinds of initiatives aimed at improving safeguards for food. Our goal is to create an apparatus that alerts the factory farms affecting potential threats for product hygiene of food. With such setup, the detector can be mounted on top of the poultry slaugh

关键词： Food safety Internet of Things Meat processing Monitoring Supply chain

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LEAVEN - Lightweight Surface and Volume Mesh Sampling Application for Particle-based Simulations 29

LEAVEN - Lightweight Surface and Volume Mesh Sampling Applic...

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29th International Conference in Central Europe on computer Graphics, Visualization and computer vision, WSCG 2021

作者： Sommer, Alexander Schwanecke, Ulrich Computer Vision and Mixed Reality Group RheinMain University of Applied Sciences Wiesbaden Rüsselsheim Germany

ISBN: (纸本)9788086943343

We present an easy-to-use and lightweight surface and volume mesh sampling standalone application tailored for the needs of particle-based simulation. We describe the surface and volume sampling algorithms used in LEAVEN in a beginner-friendly fashion. Furthermore, we describe a novel method of generating random volume samples that satisfy blue noise criteria by modifying a surface sampling algorithm. We aim to lower one entry barrier for starting with particle-based simulations while still pose a benefit to advanced users. The goal is to provide a useful tool to the community and lowering the need for heavyweight third-party applications, especially for starters. © 2021 University of West Bohemia. All rights reserved.

关键词： Mesh generation

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MEASURING COMPUTATIONAL THINKING - DEVELOPING A SHORT PERFORMANCE TEST FOR HIGHER EDUCATION 21

MEASURING COMPUTATIONAL THINKING - DEVELOPING A SHORT PERFOR...

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21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024

作者： Guggemos, Josef Rietsche, Roman Aier, Stephan Strecker, Jannis Mayer, Simon Department of Vocational Education and Training University of Education Schwäbisch Gmünd Germany Institute for Digital Technology Management Bern University of Applied Sciences Switzerland School of Computer Science University of St.Gallen Switzerland

ISBN: (纸本)9789898704610

Technological advancements, particularly in artificial intelligence, significantly transform our society and work practices. Computational thinking (CT) has emerged as a crucial 21st-century skill, enabling individuals to solve problems more effectively through an automation-oriented perspective and fundamental concepts of computer science. To ensure the effective integration of CT into educational curricula, it is crucial to develop efficient assessment frameworks that allow teachers to measure and promote student CT proficiency. Therefore, our aim is to develop a short test to measure CT among undergraduate students. To this end, we consider two performance tests: the Computational Thinking test (CTt) and the Algorithmic Thinking Test for Adults (ATTA). We use items from both instruments to compile a short test. Based on a sample of 290 second-year non-computer science undergraduate students, we provide evidence on the quality of our test. Besides classical test theory, we apply item response theory, namely Rasch modeling, and confirmatory factor analysis. Our test shows favorable properties, e.g., Cronbach's alpha > .75, and may be suitable for the efficient assessment of CT across higher education programs. © 2024 Proceedings of the 21st International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2024. All rights reserved.

关键词： Students

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The Integration of 6G and Blockchain into an Efficient AIoT-Based Smart Education Model 6

The Integration of 6G and Blockchain into an Efficient AIoT-...

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6th World Symposium on Communication Engineering, WSCE 2023

作者： Lioupa, Anna Memos, Vasileios A. Stergiou, Christos L. Ishibashi, Yutaka Psannis, Konstantinos E. School of Information Sciences University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Graduate School of Engineering Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan

ISBN: (纸本)9798350339505

The technological advances impact the future classrooms and the whole education system. The evolution of 6G promises faster speeds based on terahertz, wireless communication, the leverage of artificial intelligence (AL), virtually reality (VR) and generally a fully connected world. The use of applications of IoT and AIoT improves the education environment, but the intelligent network services undoubtedly gather, store and analyze big data which are vulnerable to attackers. As a solution we may consider the technology of blockchain. The trust and security of next generation intelligent networks are essential part of their design. In this article we suggest a data security system based on blockchain for AL applications in 6G networks. First, we present two Al-enabled applications, indoor positioning and mobile payment within the framework of 6G in the university area. We present the efficiency of blockchain in data security for Al-enabled systems using two case studies of indoor positioning system and mobile payment. The 6G system which is fully supported by Al and the integration of this in technology of blockchain, is ensured the security in future wireless communication. © 2023 IEEE.

关键词： Blockchain

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Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning 22

Selectively Sharing Experiences Improves Multi-Agent Reinfor...

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22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023

作者： Gerstgrasser, Matthias Danino, Tom Keren, Sarah John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States The Taub Faculty of Computer Science Technion - Israel Institute of Technology Haifa Israel

We present a novel multi-agent RL approach, Selective Multi-Agent Prioritized Experience Relay, in which agents share with other agents a limited number of transitions they observe during training. The intuition behind this is that even a small number of relevant experiences from other agents could help each agent learn. Unlike many other multi-agent RL algorithms, this approach allows for largely decentralized training, requiring only a limited communication channel between agents. We show that our approach outperforms baseline no-sharing decentralized training and state-of-the art multi-agent RL algorithms. Further, sharing only a small number of highly relevant experiences outperforms sharing all experiences between agents, and the performance uplift from selective experience sharing is robust across a range of hyperparameters and DQN variants. A reference implementation is available under https://***/mgerstgrasser/super. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Reinforcement learning

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Revolutionizing Tuberculosis Treatment: Smart Technology Innovations for Mycobacterium tuberculosis Management

Revolutionizing Tuberculosis Treatment: Smart Technology Inn...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Pramanik, Atreyi Singh, Ajay Rani, Gouri Dubey, Shival Aluvala, Srinivas Uttaranchal University School of Applied and Life Sciences Division of Research and Innovation Dehradun India Uttaranchal University School of Applied and Life Sciences Dehradun India Uttaranchal University India University of Leeds Institute of Design Optimization and Robotics School of Mechanical Engineering United Kingdom SR University School of Computer Science & Artificial Intelligence India

ISBN: (纸本)9798350394412

Tuberculosis remains a significant global problem. The modification in smart technology (ST) invention generates novel chances to reform tuberculosis (TB) controlling. This work explores the potential of ST for filtering MTB diagnostics and therapy. ST includes telemedicine, mobile health uses (mHealth), wearable devices (WD) and data analytical that deliver many options of improving TB control. Telemedicine licenses distant consulting thus empowering medics to access patients in remote areas, offer quick detects as well as recommend treatment. mHealth apps certify that individuals have right of entry to education materials, can remind them when it's time for medication and offers self-monitoring tools which enhance adherence to the therapy by keeping the patient involved during the course of treatment. WD support real-time (RT) monitoring of heart rates, blood pressure level, physical activity and other vital signs that are important in coming up with modified remedy plans while also detecting response to cure earlier enough. Data analytics combined with AI can examine large amounts of patient data to identify patterns, forecast drug effects and optimize treatment strategies resulting in more efficient individualized care. These novelties through intellectual technologies may be used as solutions for tasks experienced during TB management, leading to improved patient outcomes and a reduction in the TB burden. © 2024 IEEE.

关键词： Telemedicine

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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN computer Science 2024年第7期5卷 911页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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Prediction of Condensation Heat Transfer of Passive Cooling Systems in Nuclear Power Plants through Machine Learning

Prediction of Condensation Heat Transfer of Passive Cooling ...

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International Conference on Energy Conservation and Efficiency (ICECE)

作者： Rehan Zubair Khalid Tauseeq Hussain Atta Ullah Asifullah Khan Department of Chemical Engineering Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad Pakistan Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad Pakistan

The accurate prediction of steam-air mixture condensation plays crucial role in managing severe accidents such as main steam line break (MSLB) and loss of coolant accident (LOCA) using passive cooling systems within nuclear power plant containment. However, this prediction task remains challenging due to the intricate characteristics of two-phase flow phenomena and the absence of comprehensive underlying physics. While numerous empirical correlations have been proposed to estimate steam condensation heat transfer coefficients (HTC), there is still a need for more precise and reliable models because of limited range of applicability. This study addresses this challenge through a multidisciplinary approach that leverages machine learning techniques on a large dataset collected from existing literature. A comprehensive dataset consisting of 2658 experiments collected from published sources is employed to develop the model. Five different machine learning models (ML), namely artificial neural networks (ANN), k-nearest neighbors (KNN), support vector regression (SVR), random forest (RF) and Adaboost are utilized to predict steam condensation HTC. The findings of this investigation illustrate the superiority of ML models, particularly the ANN, in accurately predicting steam condensation HTC with wide range of applicability. These ML models outperform traditional empirical correlation commonly used for such predictions. By leveraging the power of ML, this study provides a more accurate and reliable approach to estimating steam condensation HTC under air-inclusive conditions.

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Detection of Internet Layer DoS Attack on Cloud Using Deep Learning

Detection of Internet Layer DoS Attack on Cloud Using Deep L...

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International Bhurban Conference on applied sciences & Technology, IBCAST

作者： Mansoor-ul-haque Hassan Asghart Muhammad Hanif Durad Anila Usmanl Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences Islamabad Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

Attacks on cloud infrastructure and services have become a significant concern due to the increasing reliance on cloud computing. Various types of attacks pose threats to the cloud ecosystem. Denial-of-Service (DoS) attacks aim to disrupt services by overwhelming the cloud infrastructure with excessive traffic, rendering them inaccessible. Distributed Denial-of-Service (DDoS) attacks take this a step further by utilizing a network of compromised devices to launch coordinated attacks, causing widespread disruptions. Traditional rule-based detection methods may struggle to keep pace with the sophistication and diversity of modern DoS attacks. Machine learning algorithms, on the other hand, can learn from historical attack data, network behavior, and anomalies to detect and classify malicious traffic accurately. In this paper, we propose a classifier based on deep learning algorithms that can detect modern sophisticated DoS attacks more accurately, enabling early detection and timely response. The proposed classifier demonstrated high detection rates, often exceeding 99%, in effectively identifying DoS attacks on cloud infrastructure.

关键词： Deep learning Cloud computing Machine learning algorithms Telecommunication traffic Denial-of-service attack Feature extraction Rendering (computer graphics) Real-time systems Classification algorithms computer crime

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Digital Twin in Industrie 4.0 for Embedded Systems

Digital Twin in Industrie 4.0 for Embedded Systems

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IEEE International Workshop on Factory Communication Systems (WFCS)

作者： Nico Braunisch Santiago Soler Perez Olaya Uwe Schmidt Marko Ristin Marcin Sadurski Hans Wernher van de Venn Martin Wollschlaeger Institute of Applied Computer Science TU Dresden Dresden Germany Institute of Mechatronic Systems Zurich University of Applied Sciences (ZHAW) Zurich Switzerland

ISBN: (数字)9798350319347

ISBN: (纸本)9798350319354

The realization of Industrie 4.0 potential depends on the utilization of digital twins, commonly implemented as Asset Administration Shells. AAS provides a strong framework for describing assets and their functionalities, and is expected to become a key component of digital twin for embedded systems. In the fields of Industrial Cyber Physical Systems and Industrial Internet Of Things, AAS serves as a bridge between microcontroller and high-performance *** Software Development Kits for AAS focus rather on cloud and PC applications. Building on top of transpilation-based approaches, we explore novel ways how to adapt the SDKs for AAS in order to bring them to industrial embedded systems. Our method is designed to streamline the development process in particular for micro-controller and Industrial Control Systems contexts.

关键词： Industries Cloud computing Embedded systems Microcontrollers Communication systems Process control Software

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