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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Elshabrawy, Mahmoud Eid, Marwa M. Abdelhamid, Abdelaziz A. El-Kenawy, El-Sayed M. Ibrahim, Abdelhameed Delta Higher Institute of Engineering and Technology Dept. of Communications & Electronics Mansoura Egypt Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Ain Shams University Faculty of Computer and Information Sciences Dep. of Computer Science Cairo Egypt University Mansoura Faculty of Engineering Computer Engineering and Control Systems Dept. Egypt

ISBN: (纸本)9798350323511

This study presents a dipper-throated-based ant colony optimization (DTACO) with the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factor (SARIMAX) model (DTACO+SARIMAX) to forecast monkeypox cases. The work optimizes the SARIMAX model using grid search cross-validation and fine-tunes its hyperparameters using DTACO to improve prediction accuracy. The suggested model's consistency and accuracy are considerable compared to previous studies. Comparisons with state-of-the-art models validate the proposed model's predictions. DTACO+SARIMAX can be used to control disease and monitor monkeypox. Healthcare organizations and governments can better manage and track the pandemic's course by offering accurate predictions, reducing public panic, and enabling effective pandemic planning. The Analysis of Variance (ANOVA) and Wilcoxon signed-rank tests are conducted on the proposed DTACO-SARIMAX model and compared models. © 2023 IEEE.

关键词： Ant colony optimization

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Secure Password Using EEG-based BrainPrint System: Unlock Smartphone Password Using Brain-computer Interface Technology

Secure Password Using EEG-based BrainPrint System: Unlock Sm...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Alkhyeli, Zuwaina Alshehhi, Ayesha Alhemeiri, Mazna Aldhanhani, Salma Albalushi, Khalil Alnuaimi, Fatima Ali Belkacem, Abdelkader Nasreddine UAE University CIT Dep. Computer Science and Software Engineering Al Ain United Arab Emirates UAE University CIT Dep. Information Systems and Security Al Ain United Arab Emirates United Arab Emirates University CIT Dep. Computer & Network Engineering Al Ain United Arab Emirates Autonomous Robotics Research Center TII Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665468190

As security becomes a strong factor in daily activities, finding secure ways to unlock machines and smartphones is a challenge due to hardware limitations and the high risk of hacking. Considering the level of security and privacy in the digital world, attackers tend to be one step ahead. Therefore, this technical paper introduces a brain-computer interface (BCI) for increasing subject-based security using unique biometric features as a solution to build complex passwords. The BCI measures brain changes and extracts relevant bio-features from each subject using non-invasive electroencephalogram (EEG) tests. The proposed system allows users to gain access to their devices using brain waves (bypass) instead of inserting their password manually (normal path), which saves the user time and upgrades the level of privacy as no physical actions are required during this process. This system is also well suited for individuals with mobility impairments. We used the P300-based BCI controlling paradigm which dep.nds on reading the electrical brain activity of the user when observing a particular object. The other feature of the system is that it can extract unique features of each individual brain to produce a network that uniquely identifies them, which is used as a security layer. Users need to enter their unique network to access their device with failed attempts requiring an EEG test to identify the user. The system plays an active role in facilitating user processes for authentication while accessing devices. The system establishes an urgent call whenever the user's brain currents command it to. The project outcomes were assessed by simulating the BCI before real-time implementation to determine errors and resolve the validity of the project scope. © 2022 IEEE.

关键词： Brain computer interface

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Towards Unveiling Vulnerabilities and Securing IoT Devices: An Ontology-Based Approach

Towards Unveiling Vulnerabilities and Securing IoT Devices: ...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Mukund Bhole Wolfgang Kastner Thilo Sauter TU Wien Institute of Computer Engineering Vienna Austria Dep. of Integrated Sensor Systems TU Wien Institute of Computer Technology Danube University Krems Austria

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

In the rapidly expanding landscape of Internet of Things (IoT), sensors have emerged as pivotal components, playing a critical role in sensing and data collection. However, this crucial function also renders them susceptible to potential threats such as unauthorized access and security breaches. This paper investigates the vulnerabilities that lead to these threats and offers a comprehensive set of best practices aimed at fortifying the security of IoT devices. We highlight key measures such as secure device design, robust authentication mechanisms, encrypted communication protocols, regular updates, and emphasize the importance of collaborative efforts among stakeholders using an ontological approach. An Ontology provides a structured framework for organizing knowledge, facilitating clearer communication, efficient data management, and enhanced decision-making. Thus, this paper contributes to the development of a more secure and resilient IoT ecosystem.

关键词： Protocols Ecosystems Collaboration Authentication Ontologies Sensors Internet of Things Stakeholders Cryptography Best practices

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From Manual to Semi-Automated Safety and Security Requirements engineering: Ensuring Compliance in Industry 4.0

From Manual to Semi-Automated Safety and Security Requiremen...

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Annual Conference of Industrial Electronics Society

作者： Mukund Bhole Wolfgang Kastner Thilo Sauter Institute of Computer Engineering TU Wien Vienna Austria Dep. of Integrated Sensor Systems Institute of Computer Technology TU Wien Danube University Krems Austria

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

In response to the growing need for compliance with diverse safety and security regulations, crucial for safeguarding both humans and machines in the era of Industry 4.0 (I4.0), it has also become increasingly crucial for industries to ensure adherence to safety and security standards. However, navigating the complexities of regulations and standards can be daunting, often leading to inefficiencies in compliance processes. Our goal is to foster a comprehensive understanding of an effective approach to meeting compliance requirements in these related fields. Therefore, this paper introduces a semi-automated method for ensuring compliance with unified safety and security standards within the context of I4.0. This approach empowers management teams to thoroughly analyze the specific requirements necessary to maintain a protected environment. Compliance can be achieved at both the asset component and system levels, aligning with the relevant standards. Additionally, we present a detailed analysis of a use case and its output, demonstrating the practical application and efficacy of our approach.

关键词： Industries Accuracy Standards organizations Manuals Regulation Safety Fourth Industrial Revolution Complexity theory Security Certification

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IT Security Solutions for IT/OT Integration: Identifying Gaps and Opportunities

IT Security Solutions for IT/OT Integration: Identifying Gap...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

In the contemporary landscape of convergence between Information Technology (IT) and Operational Technology (OT), maintaining asset visibility within the Industrial Control systems (ICS) infrastructure is paramount. Yet, the escalating targeting of legacy systems and air-gapped networks by cyber threats underscores the need for robust security measures and solutions to counter unauthorized access and malicious activities. This paper presents an analysis aimed at identifying gaps and opportunities in integrating IT Security Solutions with OT environments. These solutions offer comprehensive features such as real-time threat detection, asset identification, network monitoring, and incident response capabilities tailored specifically for the unique challenges posed by industrial control networks. Leveraging the Festo MPS 403–1 system as a case study, we delve into the intricacies of IT/ OT integration and highlight areas necessitating improvement to achieve seamless convergence and harness the advantages of unified systems across industries.

关键词： Industries Industrial control Security management Logic gates Software Threat assessment Security Monitoring Industrial Internet of Things Convergence

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Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly Detection in Industrial Wood Planers

Planing It by Ear: Convolutional Neural Networks for Acousti...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Anthony Deschênes Rémi Georges Cem Subakan Bruna Ugulino Antoine Henry Michael Morin Dep. of Wood and Forest Sciences Université Laval Canada Dep. of Computer Science and Software Engineering Université Laval Canada FORAC Research Consortium Université Laval Canada Mila-Quebec AI Institute Canada Concordia University Canada FPInnovations Canada Dep. of Operations and Decision Systems Université Laval Canada

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, the wood product industry has been facing a skilled labor shortage. The result is more frequent sudden failures, resulting in additional costs for these companies already operating in a very competitive market. Moreover, sawmills are challenging environments for machinery and sensors. Given that experienced machine operators may be able to diagnose defects or malfunctions, one possible way of assisting novice operators is through acoustic monitoring. As a step towards the automation of wood-processing equipment and decision support systems for machine operators, in this paper, we explore using a deep convolutional autoencoder for acoustic anomaly detection of wood planers on a new real-life dataset. Specifically, our convolutional autoencoder with skip connections (Skip-CAE) and our Skip-CAE transformer outperform the DCASE autoencoder baseline, one-class SVM, isolation forest and a published convolutional autoencoder architecture, respectively obtaining an area under the ROC curve of 0.846 and 0.875 on a dataset of real-factory planer sounds. Moreover, we show that adding skip connections and attention mechanism under the form of a transformer encoder-decoder helps to further improve the anomaly detection capabilities.

关键词： Support vector machines Convolution Autoencoders Forestry Transformers Acoustics Sensors Convolutional neural networks Speech processing Anomaly detection

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Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly Detection in Industrial Wood Planers

arXiv

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arXiv 2025年

作者： Deschênes, Anthony Georges, Rémi Subakan, Cem Ugulino, Bruna Henry, Antoine Morin, Michael Dep. of Wood and Forest Sciences QC Canada Dep. of Computer Science and Software Engineering Dep. of Operations and Decision Systems QC Canada FORAC Research Consortium Université Laval QC Canada Mila-Quebec AI Institute QC Canada FPInnovations QC Canada Concordia University QC Canada

关键词： Competition

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Automated Crop Harvesting, Growth Monitoring and Disease Detection System for Vertical Farming Greenhouse 3

Automated Crop Harvesting, Growth Monitoring and Disease Det...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Jayasekara, Charith Banneka, Sajani Pasindu, Gihan Udawaththa, Yukthi Wellalage, Sasini Abeygunawardhane, Pradeep K.W. Sri Lanka Institute of Information Technology Dep. of Computer Systems Engineering Malabe10115 Sri Lanka

ISBN: (纸本)9781665408622

Greenhouses are a type of cultivation method used to optimize the production of crops by using controlled climatic conditions and other external factors. They are widely used in agriculture both globally and locally. Vertical farming is the modern practice of growing crops in vertically stacked layers in warehouses that aims to optimize and develop plant growth by using controlled environment agriculture. This type of agriculture concept is practiced in Sri Lanka at present. This research paper proposes a robot which consists of a specific navigation system and harvesting mechanism that can be used inside greenhouses. The proposed system can be implemented to harvest lettuce in the vertical farming greenhouses where lettuce needs to be harvested with care to ensure the supermarket quality. The above-mentioned system helps to detect the diseases of plants also where a lot of time can be saved, and less effort is made with the use of this type of implementation. © 2021 IEEE.

关键词： Greenhouses

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Assessment and recognition in technical massive open on-line courses with and without on-line laboratories 14

Assessment and recognition in technical massive open on-line...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Martin, Sergio Castro, Manuel Sancristobal, Elio Diaz, Gabriel Garcia-Loro, Felix Perez, Clara Quintana, Blanca Gil, Rosario Plaza, Pedro Lopez, Africa Carro, German Robles, Antonio Tobarra, Llanos Rodriguez-Artacho, Miguel Ruiperez-Valiente, Jose A. Universidad Nacional de Educación A Distancia Electrical and Computer Engineering Department Madrid Spain Universidad Nacional de Educación A Distancia Dep. Sistemas de Comunicación y Control Madrid Spain Universidad Nacional de Educación A Distancia Dep. Lenguajes y Sistemas Informáticos Madrid Spain Masschusetts Institute of Technology Teaching Systems Lab United States

ISBN: (纸本)9798350399431

This paper describes the organization and results of several MOOCs delivered about technical topics (i.e., digital electronics, VHDL design on FPGAs, open education and OERs repositories and the use of STEAM technologies to encourage diversity and inclusion), where online laboratories have been used in some of them. An analysis of the enrollment, students participating on tasks or quizzes, drop-out rate and certifications requests have been done. © 2023 IEEE.

关键词： Drops

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Logistic Regression Classification for Assessing the Risk of Kidney Tumor

Logistic Regression Classification for Assessing the Risk of...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Dalia Alzu'bi Rabia Emhamed Al Mamlook Ahmad Nasayreh Mohammad Aljaidi Rami AlAzab Hasan Gharaibeh Qais Al-Na'amneh Dep. Information and Systems Engineering Concordia University Montreal Canada Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University Zawia Tripoli Libia Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. Surgery and Urology Jordan University of Science and Technology Irbid Jordan Dep. of Cyber Security Applied Science Private University Amman Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

The kidneys have the important function of filtering waste products and toxins from the blood in the human body. Any problems affecting the kidneys can potentially impact their efficiency and overall function. Kidney Tumor (KT) is a disease that specifically affects kidney cells and causes the abnormal growth of tissue in one or both kidneys. Assessing the risk of developing a kidney tumor and identifying the key factors that contribute to this risk are crucial for ensuring patient well-being. Additionally, this information assists doctors and specialists in making faster and more accurate diagnoses, determining appropriate treatment methods, and potentially influencing the course of the disease to minimize its severity and impact. Machine learning algorithms have recently been introduced to evaluate disease risks, and in this study, we specifically focus on examining the risk of kidney tumor development and investigating the influencing factors. We employed a logistic regression model to predict if a patient is at risk of developing a kidney tumor or not, and further categorized the samples into high-risk and low-risk groups. Our model was trained and tested using a unique dataset obtained from the (KAUH) hospital in Jordan, consisting of well-balanced metadata from 120 patients with kidney issues. Our work demonstrated accuracy results ranging from 90% to 98%. Ultimately, specialists can utilize our model as an additional tool to enhance the speed and accuracy of patient diagnoses

关键词： Logistic regression Analytical models Accuracy Predictive models Genetics History Reliability

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