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Asian Test Symposium (ATS)

作者： Li-Wei Chen Xianyue Zhao Ziang Chen Nan Du Ilia Polian Institute of Computer Architecture and Computer Engineering University of Stuttgart Stuttgart Germany Department of Quantum Detection Institute for Solid State Physics Friedrich Schiller University Jena Leibniz Institute of Photonic Technology (IPHT) Jena Germany

Quick progress in memristive technologies has led to their consideration for several potential applications, many of which are security-critical. New possibilities of memristors, including their unique combination of non-volatile storage and compute capabilities, make them particularly attractive to edge applications, which are physically exposed to their users and therefore to potential attackers. Therefore, practical deployment of memristive circuitry for, e.g., cryptographic (sub-)modules or on-chip neural network inference, is only feasible when their vulnerability to physical attacks is understood and addressed. We evaluate experimentally one relevant class of physical attacks, namely side-channel attacks, under varying external conditions, namely temperature and magnetic fields. Using a small cryptographic construction, we evaluate both white-box and black-box attack varieties, using respective cryptanalytic techniques. Our results show that, while non-nominal conditions can complicate attacks, the information leakage remains and the secrets are extractable with additional knowledge about the memristive devices. This suggests the need to consider possible external disturbances during security evaluation.

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Swarm Intelligence in Internet of Medical Things 10

Swarm Intelligence in Internet of Medical Things

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10th IEEE Jubilee International Conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2022

作者： Roshanzamir, Mohamad Darbandy, Mohammad Tayarani Roshanzamir, Mahdi Alizadehsani, Roohallah Shoeibi, Afshin Ahmadian, Davood Fasa University Faculty of Engineering Department of Computer Engineering Fasa74617-81189 Iran Islamic Azad University Taft School of Architecture Taft*** Iran University of Tabriz Department of Electrical and Computer Engineering Tabriz Iran VIC Australia K. N. Toosi University of Technology FPGA Laboratory Faculty of Electrical Engineering Tehran Iran University of Tabriz Faculty of Mathematics Statistics and Computer Sciences Tabriz Iran

ISBN: (数字)9781665481779

ISBN: (纸本)9781665481779

Advancement in information and communication technology leads to different communication channels. One of them is the Internet of Things (IoT) which plays an important role in the new world. The Internet of Medical Things (IoMT) is the sub-branch of IoT which focuses on the communication between medical devices to share their data and information. It helps the healthcare industry to present better services for physicians and patients. It improves the quality of life by increasing the quality and decreasing the time and cost of services. An important challenge in IoMT is optimization in its different sub-problems and Swarm Intelligence (SI) algorithms are the best solution for them. In this paper, we tried to investigate the status of using SI in IoMT. Its applications and their corresponding performance were analyzed and investigated. The reported results show that using SI can efficiently solve the optimization problems in IoMT. © 2022 IEEE.

关键词： Internet of things

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A New IoT-Based Smart Greenhouse for Cultivation of Water Convolvulus

A New IoT-Based Smart Greenhouse for Cultivation of Water Co...

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International Electrical Engineering Congress (iEECON)

作者： Prayoot Akkaraekthalin Tanee Somwong Nonchanutt Chudpooti Nuttawut Hirun Suwaluck. Meesomklin Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electronic and Telecommunications Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electronics Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper presents a new IOT system for monitoring and controlling plant cultivation in greenhouse system. The system aims to regulate and monitor the environmental conditions suitable for plant cultivation in real-time. The system utilizes sensors to measure environmental conditions, such as soil moisture sensor, light intensity sensor, temperature sensor, and air humidity sensor. The Node MCU ESP32 is used for processing and controlling these operations. The system is designed to send notifications through Line application and allows farmers to monitor the system's performance and plant growth in real-time via a smartphone application. It can utilize the Blynk IoT application on a smartphone for remotely monitoring and controlling the environmental conditions and provides historical data. This experiment demonstrates that the designed system can successfully cultivate plants and promote better growth compared to traditional cultivation methods with a 13.38% improvement. Additionally, it provides convenience and ease of management for plant cultivation.

关键词： Temperature sensors Temperature measurement Smart agriculture System performance Green products Sensor systems Real-time systems Sensors Internet of Things Monitoring

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Deep Learning based vehicle image detection using Yolo V5 with Region-Based Convolutional Neural Network

Deep Learning based vehicle image detection using Yolo V5 wi...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： S. Swapna Rani Aditya Mudigonda S V Hemanth P.N. Sundararajan G. Vinoda Reddy G. Amirthayogam Department of Electronics and Communication Engineering Maturi Venkata Subba Rao (MVSR) Engineering College Hyderabad India JNIAS School of Planning and Architecture Hyderabad Telangana India Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Hyderabad Telangana Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India Department of Computer Science and Engineering (AI&ML) CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Information Technology Hindustan Institute of Technology and Science Chennai Tamilnadu India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Detecting and classifying vehicles is a modern technology with numerous uses. Administration and regulation of traffic is one of the primary uses. Projects utilizing image processing to prevent traffic accidents heavily rely on vehicle monitoring and detection. Monitoring and recording human movement in surveillance situations require the ability to follow moving objects. Considering its significance, it provides a valuable image processing-based vehicle detection technique—a vehicle tracking and detection system based on images. The recently released high-resolution road vehicle dataset supports deep learning-based vehicle detection and monitoring on the Python platform. It includes over 100 well-defined pictures taken from movies from various locations. This data is produced using a range of image processing methods. The most recent iteration of the YOLO model, the v5 model, was employed for detection. R-CNN is additionally utilized for model detection. The four stages in the Region-Based Convolutional Neural Network (RCNN) process are preprocessing, segmentation, feature selection, and classification. The first step is identifying moving vehicles accurately. Preprocessing the original image is necessary. This enhances the image’s quality, valuable information extracted, or unwanted areas cropped out. Segmentation is the second step. The region’s borders are established, and the entire image is divided into numerous smaller areas using a threshold. The vehicle image’s most pertinent aspects or characteristics (features) that aid in precise detection are found in the third stage, feature selection. A region-based convolutional neural network (RCNN) is employed to determine the categorization. The Yolov5 method can be used to identify and categorize items. The Region-based Convolutional Neural Network (RCNN) approach enhances accuracy and temporal complexity over current segmentation algorithms.

关键词： YOLO Deep learning Image segmentation Accuracy Surveillance Transportation Transforms

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COVID-19 Biomarkers Detection Using ‘KnowSeq’ R Package 1st

COVID-19 Biomarkers Detection Using ‘KnowSeq’ R Package

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1st International Conference on Bioengineering and Biomedical Signal and Image Processing, BIOMESIP 2021

作者： Bajo-Morales, Javier Castillo-Secilla, Daniel Herrera, Luis Javier Rojas, Ignacio Department of Computer Architecture and Technology University of Granada C.I.T.I.C. Periodista Rafael Gomez Montero 2 Granada18014 Spain

ISBN: (纸本)9783030881627

‘KnowSeq’ R Package includes all the essential tools to carry out transcriptomic analysis, providing intuitive functions to build efficient and robust pipelines. In this paper, its capacities are demonstrated in a practical COVID-19 biomarkers detection problem using RNA-Sequencing data. Through Machine Learning techniques such as feature selection and supervised classification models, a clinical decision system for COVID-19 was developed using four genes proposed as COVID-19 signature: OAS3, CXCL9, IFITM1 and IFIT3. These four genes are highly related to different processes that affect the immune system behaviour and its response when facing viruses such as SARS-CoV-2. The final model reaches an accuracy over 97% when predicting over unseen samples. © 2021, Springer Nature Switzerland AG.

关键词： COVID-19

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Machine Learning based Cybersecurity Technique for Detection of Upcoming Cyber Attacks

Machine Learning based Cybersecurity Technique for Detection...

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International Conference on Communication and Electronics Systems (ICCES)

作者： Kanchi Lohitha Lakshmi S.Shobana Bathula Prasanna Kumar K.B.Glory Bonda Kiran Kumar Sandya Rani Department of Computer Science and Engineering Vasireddy Venkatadri Institute Of Technology Guntur Department of IT R.M.K Engineering College Department of Computer Science and Engineering KKR & KSR Institute of Technology and Sciences Guntur AP Department of Engineering English Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh State India Department of Architecture KL School of Architecture KL Deemed to be University Vaddeswaram Dept. of IT St. Martin's Engineering College Secunderabad Telangana India

Cyberattacks are prevalent in the age of the Internet. Each year, both the quantity and severity of cybercrimes increase. Protection against cyber-attacks has become a primary responsibility, Significant in the internet society of today. However, providing cyber security is a highly difficult task that requires experience in the field of attacks and the ability to evaluate the possibility of threats. The continual evolution of cyberattacks is the biggest challenge in this industry. This article describes the significance of cyber security and enumerates the hazards that exist in the present digital environment. The statistics and evaluation of cyberattacks demonstrate the seriousness of these occurrences. Several sorts of cyber security threats are outlined, along with the machine learning approaches that may be used to detect these attacks.

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3D visualization of hurricane storm surge impact on urban infrastructure

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Urban Informatics 2024年第1期3卷 1-14页

作者： Ye, Xinyue Li, Shoujia Gao, Ge Retchless, David Cai, Zhenhang Newman, Galen Du, Jiaxin Duffield, Nick Department of Landscape Architecture & Urban Planning Texas A&M University College Station USA Department of Computer Science & Engineering Texas A&M University College Station USA Center for Geospatial Sciences Applications and Technology Texas A&M University College Station USA Department of Marine and Coastal Environmental Science Texas A&M University Galveston USA Department of Electrical and Computer Engineering Texas A&M University College Station USA

As climate change intensifies, resulting in more severe rainfall events, coastal cities globally are witnessing significant life and property losses. A growingly crucial component for flood prevention and relief are urban storm flood simulations, which aid in informed decision-making for emergency management. The vastness of data and the intricacies of 3D computations can make visualizing the urban flood effects on infrastructure daunting. This study offers a 3D visualization of the repercussions of hurricane storm surge flooding on Galveston, TX residences, illustrating the impact on each structure and road across varied storm conditions. We employ target detection to pinpoint house door locations, using door inundation as a metric to gauge potential flood damage. Within a GIS-based framework, we model the damage scope for residences exposed to varying storm intensities. Our research achieves three core goals: 1) Estimating the storm inundation levels on homes across different storm conditions; 2) Assessing first-floor elevations to categorize housing damages into three distinct groups; and 3) Through visualization, showcasing the efficacy of a proposed dike designed to shield Galveston Island from future storm surge and flood events.

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Identifying Challenges and Opportunities for Intelligent Data-Driven Health Interfaces to Support Ongoing Care

Identifying Challenges and Opportunities for Intelligent Dat...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Knoche, Hendrik Abdul-Rahman, Alfie Clark, Leigh Curcin, Vasa Huo, Zhiqiang Iwaya, Leonardo Horn Lemon, Oliver Mikulík, Robert Neate, Timothy Roper, Abi Skovfoged, Milo Marsfeldt Verdezoto, Nervo Wilson, Stephanie Ziadeh, Hamzah Department of Architecture Design and Media Technology Aalborg University Aalborg Denmark Department of Informatics King's College London London United Kingdom Bold Insight London United Kingdom School of Population Health and Environmental Sciences King's College London London United Kingdom Department of Population Health Science King's College London London United Kingdom Research Group Department of Mathematics and Computer Science Karlstad University Karlstad Sweden Department of Computer Science Heriot-Watt University Edinburgh United Kingdom Neurology Department The St. Anne's University Hospital Brno Brno Czech Republic School of Health and Psychology City University of London London United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science City University London Londond United Kingdom

ISBN: (纸本)9781450394222

This workshop will explore future work in the area of intelligent, conversational, data-driven health interfaces both from patients' and health care professionals' perspectives. We aim to bring together a diverse set of experts and stakeholders to jointly discuss the opportunities and challenges at the intersection of public health care provisioning, patient and caretaker empowerment, monitoring provisioning of health care and its quality. This will require AI-supported, conversational decision-making interfaces that adhere to ethical and privacy standards and address issues around agency, control, engagement, motivation, and accessibility. The goal of the workshop is to create a community around intelligent data-driven interfaces and create a road map for their future research. © 2023 Owner/Author.

关键词： User interfaces

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Comparative Assessment Between Statistical and Soft Computing Methods for Accident Severity Classification

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Journal of The Institution of Engineers (India): Series A 2020年第1期101卷 27-40页

作者： Sarkar, Amrita Sarkar, Satyaki Department of Computer Science and Engineering Sarala Birla University Ranchi835103 India Department of Architecture Birla Institute of Technology Mesra Ranchi835215 India

Globally traffic scenario has experienced a vast change over the years as volumes of traffic on urban roads have increased by leaps and bounds. This increase has also marked an increase in the number of road accidents, both fatal and non-fatal. Accident prediction has a significant role in the improvement in traffic safety, and urban traffic management. Different statistical methods and various soft computing techniques are being used for developing accident prediction models. In this study, ensemble ranking method, comprising three filter-based ranking methods such as Information Gain, Gain Ratio, and Symmetric Uncertainty, has been employed to select significant features for prediction of accident severity. Through analysis, eight significant features have been selected for accident severity prediction. These are Annual Average Daily Traffic, Road Width, Percentage of Left-Turn Vehicles, Driver’s Age, Percentage of Right-Turn Vehicles, Types of Vehicles Involved, Spot Speed, and Cause of Accidents. Multiple logistic regression (MLGR) and artificial neural network (ANN) have been applied for accident severity prediction in urban roads. This analysis concludes that ANN model gives more accuracy and minimum errors compared with the MLGR method at the time of severity classification of a vehicular accident. © 2019, The Institution of Engineers (India).

关键词： Forecasting

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Performance Evaluation of Metaheuristic Optimization Techniques in Insulation Problem 8

Performance Evaluation of Metaheuristic Optimization Techniq...

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8th International Conference on Information technology Trends, ITT 2022

作者： Gunal, Ozen Akpinar, Mustafa Akpinar, Kevser Ovaz Turgutlu Vocational School Manisa Celal Bayar University Department of Computer Programming Manisa Turkey Sakarya University Department of Software Engineering Sakarya Turkey School of Engineering Architecture and Interior Design Amity University Dubai Dubai United Arab Emirates Rochester Institute of Technology Department of Computing Security Dubai United Arab Emirates

ISBN: (数字)9781665487429

ISBN: (纸本)9781665487412

Selection of insulation material and determination of its thickness are the two most important parameters that prevent heat loss. Too much thickness complicates the price and use. The low coefficient of thermal conductivity of the insulation material reduces heat loss and increases the cost. One of the most important factors when determining the material thickness and conductivity coefficient is the price. In this study, two cases were examined under certain conditions, independent of the price. In the first case, the insulation material is determined, whereas the material thickness is determined in the second case. Six different current metaheuristic algorithms were used to calculate the two cases. It has been observed that harris hawks optimization (HHO), salp swarm optimization (SSO), and whale optimization (WO) algorithms determine the global optimum point without any error in determining the targets of the problem while the processing times were short. Overall, it has been seen that all six algorithms can create solutions to heat transfer problems with very low errors. © 2022 IEEE.

关键词： Optimization

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