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Masked Face Recognition by Applying SSD and ResNet Model for Attendance System

Masked Face Recognition by Applying SSD and ResNet Model for...

2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2021

作者： Efanntyo Mitra, Aditya R. Universitas Pelita Harapan Electrical Engineering Faculty of Science and Technology Tangerang Indonesia Universitas Pelita Harapan Information Systems Faculty of Computer Science Tangerang Indonesia

ISBN: (纸本)9781665499699

The rapid development of technology in artificial intelligence (AI) includes face recognition. Some of these developments in face recognition systems can be found easily in everyday life, such as entry access, payment, or attendance recording systems. Notably, the demand for image-based face recognition methods to record attendance increases due to its effectiveness and efficiency. Compared to conventional methods like fingerprint or RFID, facial recognition offers better results. On the other hand, with the arising needs in dealing with the novel coronavirus (COVID-19) pandemic, official health protocols require mask-wearing and maintaining a minimum distance of 1 meter between individuals to prevent the spread of the virus. Along with such a situation, a face recognition system to record attendance can reduce the occurrence of direct contact and allow each individual to maintain a safe distance, including from the attendance device. This paper presents the performance of a masked-faced recognition system that implemented SSD (Single Shot Detection) and ResNet feature extraction. The face recognition system application developed using Python and related libraries show a stable level of masked face recognition accuracy. This evaluation was made at predetermined distances between the face and the camera and measured at room lighting of 200 lux with an average accuracy of 67%. The application also has a feature to send notification emails to every employee who is unable to attend work on their scheduled workdays. © 2021 IEEE.

关键词： Face recognition

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A Data Transmission Approach Based on Ant Colony Optimization and Threshold Proxy Re-encryption in WSNs

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Journal of Artificial Intelligence and technology 2022年第1期2卷 23-31页

作者： Jing Liu Zenghui Liu Chenyu Sun Junxi Zhuang College of Computer Science Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of EducationBeijing 100124China College of Automation Engineering Beijing PolytechnicBeijing 100176China

Wireless sensor networks(WSNs)have become increasingly popular due to the rapid growth of the Internet of *** open wireless transmission media are easy to attack,security is one of the primary design concerns for *** solutions consider routing and data encryption as two isolated issues,providing incomplete ***,in this paper,we divide the WSN communication process into a data path selection phase and a data encryption *** propose an improved transmission method based on ant colony optimization(ACO)and threshold proxy re-encryption for WSNs,and we named it as *** method resists internal and external attacks and ensures safe and efficient data *** the data path selection stage,the ACO algorithm is used for network *** improvement of the pheromone concentration is *** order to resist attacks from external attackers,proxy re-encryption is extended to WSN in the data encryption *** threshold secret sharing algorithm is introduced to generate a set of re-encryption key fragments composed of random numbers at the source *** confirm the performance of our model via simulation studies.

关键词： wireless sensors network ant colony optimization pheromone proxy re-encryption threshold

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Investigating Privacy and Security Concerns in a Running eHealth information System

Investigating Privacy and Security Concerns in a Running eHe...

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Communications and Networking (MeditCom), IEEE International Mediterranean Conference on

作者： Viktor Denkovski Irena Stojmenovska Goce Gavrilov Vladimir Radevski Vladimir Trajkovik School of Computer Science and Information Technology University American College Skopje Skopje Macedonia Faculty of Computer Science and Engineering University "Ss. Cyril and Methodus" Skopje Macedonia

The healthcare authorities should be seriously dedicated in maintaining a high level of data security and data privacy, due to the highly sensitive nature of patient data The study investigates the results explored via interview and survey examination on the privacy and security concerns related to medical providers' use of eHealth information systems in Macedonia. The purpose is to determine whether medical professionals are familiar with the security measures taken by healthcare authorities and to evaluate their opinions regarding the demand for system upgrades. About a dozen medical providers underwent in-depth email interviews to discover the privacy and security concerns in using the eHealth systems. This was a cornerstone to conduct an online questionnaire in which 370 medical providers including professionals from primary and secondary healthcare responded. The results underline how crucial it is to implement strong security controls and deal with system usability issues to guarantee the confidentiality and integrity of sensitive patient data. The study's conclusions highlight the importance of thorough privacy and security protocols in the context of eHealth technologies.

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ImPORTance - Machine Learning-Driven Analysis of Global Port Significance and Network Dynamics for Improved Operational Efficiency

arXiv

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arXiv 2024年

作者： Carlini, Emanuele Di Gangi, Domenico Monteiro de Lira, Vinicius Kavalionak, Hanna Spadon, Gabriel Soares, Amilcar National Research Council Institute of Information Science and Technologies Pisa Italy Federal University of Ceará Department of Computer Science CE Fortaleza Brazil Dalhousie University Faculty of Computer Science HalifaxNS Canada Linnaeus University Department of Computer Science & Media Technology Växjö Sweden

Seaports play a crucial role in the global economy, and researchers have sought to understand their significance through various studies. In this paper, we aim to explore the common characteristics shared by important ports by analyzing the network of connections formed by vessel movement among them. To accomplish this task, we adopt a bottom-up network construction approach that combines three years’ worth of AIS (Automatic Identification System) data from around the world, constructing a Ports Network that represents the connections between different ports. Through such representation, we use machine learning to measure the relative significance of different port features. Our model examined such features and revealed that geographical characteristics and the depth of the port are indicators of a port’s significance to the Ports Network. Accordingly, this study employs a data-driven approach and utilizes machine learning to provide a comprehensive understanding of the factors contributing to ports’ importance. The outcomes of our work are aimed to inform decision-making processes related to port development, resource allocation, and infrastructure planning in the industry. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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A Low-power High-gain Inverter Stacking Amplifier with Rail-to-Rail Output 3

A Low-power High-gain Inverter Stacking Amplifier with Rail-...

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3rd IEEE International Conference on Design and Test of Integrated Micro and Nano-Systems, DTS 2021

作者： Shad, Erwin H. T. Moeinfard, Tania Molinas, Marta Ytterdal, Trond Faculty of Information Technology and Electrical Engineering Engineering Cybernetics Trondheim Norway York University Faculty of Electrical Engineering and Computer Science Toronto Canada Faculty of Information Technology and Electrical Engineering Electronic Systems Trondheim Norway

ISBN: (纸本)9780738132631

In this article, a rail-to-rail low-power amplifier is presented based on stacking inverter-based amplifiers. The output voltages of each inverter-based amplifier are converted to a current and then mirrored to the output so that a rail-to-rail output is achieved. Besides, extensive simulations have been carried out to show the effect of drain-source voltage on the intrinsic gain of a transistor. Based on these simulations, a minimum supply voltage is chosen to achieve high open-loop gain and low closed-loop gain error. All the simulations are carried out in a commercially available 0.18 μm CMOS technology. The proposed amplifier achieves 88 dB open-loop gain. It is exploited in a capacitively-coupled amplifier structure. The closed-loop gain is 40 dB in the bandwidth of 0.1 Hz to 10 kHz when the power consumption is 0.54 μW at a 1.2 V supply voltage. The total input-referred noise is 4.7 μVrms in the whole bandwidth. The proposed neural amplifier achieved 0.02 SEF in the bandwidth from 200 Hz to 10 kHz. The proposed amplifier achieved a rail-to-rail output swing while the SEF is among the best reported SEF in the literature. Besides, to show the robustness of the proposed structure in the presence of process and mismatch variation, 500 Monte Carlo simulations are carried out. The PSRR and CMRR mean values are 89 dB and 68 dB, respectively. Finally, the proposed neural amplifier area consumption is 0.03 mm2 without pads. © 2021 IEEE.

关键词： Bandwidth

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Adaptive Fuzzy-Logic Traffic Control Approach Based on Volunteer IoT Agent Mechanism

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SN computer science 2022年第1期3卷 68页

作者： Hewei, Guan Sadiq, Ali Safaa Taheir, Mohammed Adam School of Information Technology Monash University Subang Jaya Malaysia School of Mathematics and Computer Science University of Wolverhampton Wulfruna Street Wolverhampton WV1 1LY United Kingdom Faculty of Technology Sciences Zalingei University Zalingei Sudan

Traffic congestion is an extremely common phenomenal issue;it occurs in many cities around the world, especially in those cities with high car ownership. Traffic congestion not only causes air pollution and fuel wastage, but it also leads to an increased commuting time and reduces the work time availability. Due to these reasons, traffic congestion needs to be controlled and reduced. The traffic light is the most widely adopted method to control traffic;however, most traffic lights in use are designed based on the pre-defined interval, which cannot cope with traffic volume change very well. Therefore, Internet of Things (IoT)-based traffic light or adaptive traffic light systems are developed in the recent years as a complement of the traditional traffic lights. The adaptive traffic light can be built based on monitoring current traffic situation or using vehicle-to-vehicle and vehicle-to-infrastructure communication. This paper proposes a new design of adaptive traffic light, this traffic light system is based on fuzzy logic, and it introduces for the first time the use of volunteer IoT agent mechanism, which introduces more accurate results. To note that the presented work in this paper is the extended version of the work presented in (Hewei et al. in Fuzzy-logic approach for traffic light control based on IoTtechnology, 2021). © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2021.

关键词： Adaptive traffic light Fuzzy logic Internet of Things Traffic control

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IoT, 6G and Digital Twin For Smart Campus

IoT, 6G and Digital Twin For Smart Campus

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Research, Invention, and Innovation Congress (RI2C)

作者： Tanapeak Pexyean Kobkiat Saraubon Prachyanun Nilsook Division of Information and Communication Technology for Education Faculty of Technical Education King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Computer Science and Information Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

Smart Campus This research aims to present the use of the Internet of Things and smart technologies that work with 6G networks to intelligently improve the energy management capabilities of campuses. Whether it's personal. Teachers and students, including those with disabilities, have a better quality of life. Within the university, energy consumption data is collected. Organizations to manage energy management systems Reduce energy consumption Reduce management by using technology to manage and reduce campus costs, transparently monitor energy consumption and use of campus educational equipment, especially cost and expense management, and sustainable energy use. Control usage data, energy consumption results, and estimate budget allocation related to educational institutions. IoT and Cloud Computing are also integrated into 6G networks embedded in state-of-the-art devices for use in environmental management and intelligent energy management, as well as creating new energy models and management that can solve future emergencies with external on-premises virtual energy control anytime, anywhere. Smart Building Management with twin technologies helps to intelligently manage and control indoor energy systems, such as automation for temperature control in buildings. Air conditioner energy consumption control system Electricity consumption control in lighting system This saves energy and increases the usability of buildings within smart campuses.

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Dynamic Clustering-based Task Orchestrator in Mobile Edge Computing

Dynamic Clustering-based Task Orchestrator in Mobile Edge Co...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Mona Alghamdi Atm Alam Arumugam Nallanathan Asma Cherif School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Information Technology Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King AbdulAziz University Saudi Arabia

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Multi-access Edge Computing (MEC) is an emerging paradigm designed to provide storage, computing and communication capabilities in the proximity of end-user devices. This approach facilitates the deployment of real-time on mobile devices with limited capabilities. To realize the MEC goals, it is essential to effectively manage and offload computing tasks to both edge and cloud-based resources. However, the dynamic nature, uncertainty and mobility within edge computing environments pose significant challenges to resource management. Furthermore, the inherent software and hardware heterogeneity, coupled with the distributed nature of architecture, complicates the development of efficient task offloading strategies that can adeptly manage resources across both edge and cloud platforms. In this paper, we propose a cluster-based task edge orchestrator, where edge servers are grouped based on service demands, resource ability and other factors to improve the overall service. Our proposed method leverages the K-Medoids clustering algorithm to dynamically form clusters of suitable edge servers for offloading computing tasks with minimum response time. To validate our proposed solution, we have orchestrated a comprehensive series of tests using EdgeCloudSim. Results show that our approach outperforms its competitor in terms of average service time by around 8 %.

关键词： Wireless communication Wide area networks Multi-access edge computing Uncertainty Sensitivity Software Servers

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Multi-Criteria Prediction Mechanism for Vehicular Wi-Fi Offloading

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computers, Materials & Continua 2021年第11期69卷 2313-2337页

作者： Mahmoud Alawi Raed Alsaqour Abdi Abdalla Maha Abdelhaq Mueen Uddin Department of Computer Engineering and IT Faculty of EngineeringZanzibar UniversityZanzibarTanzania Department of Information Technology College of Computing and InformaticsSaudi Electronic UniversityRiyadh93499Saudi Arabia Department of Electronics and Telecommunications Engineering College of ICTUniversity of Dar es SalaamTanzania Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Faculty of Science Universiti Brunei DarussalamNegara Brunei Darussalam

The growing demands of vehicular network applications,which have diverse networking and multimedia capabilities that passengers use while traveling,cause an overload of cellular *** scenario affects the quality of service(QoS)of vehicle and non-vehicle ***,wireless fidelity access points Wi-Fi access point(AP)and fourth generation long-term evolution advanced(4G LTE-A)networks are broadly ***-Fi APs can be utilized by vehicle users to stabilize 4G LTE-A ***,utilizing the opportunistic Wi-Fi APs to offload the 4G LTE-A networks in a vehicular ad hoc network environment is a relatively difficult *** condition is due to the short coverage of Wi-Fi APs and weak deployment strategies of *** studies have proposed that offloading mechanisms depend on the historical Wi-Fi connection patterns observed by an interest vehicle in making an offloading ***,depending solely on the historical connection patterns affects the prediction accuracy and offloading ratio of most existing mechanisms even when AP location information is *** present study proposed a multi-criteria wireless availability prediction(MWAP)mechanism,which utilizes historical connection patterns,historical data rate information,and vehicular trajectory computation to predict the next available AP and its expected data capacity in making offloading *** proposed mechanism is decentralized,where each vehicle makes the prediction by *** characteristic helps the vehicle users make a proactive offloading decision that maintains the QoS for different applications.A simulation utilizing MATLAB was conducted to evaluate the performance of the proposed mechanism and benchmark it with related state-of-the-art mechanisms.A comparison was made based on the prediction error and offloading ratio of the proposed mechanism in several *** MWAP mechanism exhibited a lower prediction error(i.e.,below 20%)and higher offloading rat

关键词： Vehicular network markov predictor 4G LTE-A Wi-Fi offloading prediction model heterogeneous network

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification 19

Deep Learning in Smart Healthcare: A GAN-based Approach for ...

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19th International Conference on Future Networks and Communications, FNC 2024 / 21st International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th International Conference on Sustainable Energy information technology, SEIT 2024

作者： Tufail, Hina Ahad, Abdul Puspitasari, Ira Shayea, Ibraheem Coelho, Paulo Jorge Pires, Ivan Miguel Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga Indonesia School of Software Northwestern Polytechnical University Shaanxi Xian China Turkey Polytechnic University of Leiria Leiria Portugal Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which affects patients' lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD. © 2024 The Authors. Published by Elsevier B.V.

关键词： Generative adversarial networks

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