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2023 31st National Conference with International Participation, TELECOM 2023

作者： Sofotasios, Paschalis C. Yoo, Seong Ki Simmons, Nidhi Cotton, Simon L. Valkama, Mikko Khalifa University Center for Cyber Physical Systems Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates Tampere University Department of Electrical Engineering TampereFI-33101 Finland Coventry University Centre for Future Transport and Cities Faculty of Engineering Environment and Computing United Kingdom Queen's University Belfast Institute of Electronics Communications and Information Technology BelfastBT3 9DT United Kingdom

ISBN: (纸本)9798350303292

The μ / inverse gamma and μ / inverse gamma distributions constitute two of the most distinct composite fading models. The present work offers novel results on the achievable ergodic capacity over these composite multipath/shadowing conditions. The derived expressions are corroborated with respective simulation results and are shown to be useful in practical wireless scenarios in the context of device to device communications, such as off-body, and vehicle-to-vehicle communications. © 2023 IEEE.

关键词： Vehicle to vehicle communications

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FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices

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Fire Ecology 2023年第1期19卷 1-19页

作者： Ahmad, Khubab Khan, Muhammad Shahbaz Ahmed, Fawad Driss, Maha Boulila, Wadii Alazeb, Abdulwahab Alsulami, Mohammad Alshehri, Mohammed S. Ghadi, Yazeed Yasin Ahmad, Jawad Faculty of Engineering and Technology Multimedia University Melaka Malaysia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK Department of Cyber Security Pakistan Navy Engineering College NUST Karachi Pakistan Robotics and Internet of Things Laboratory Prince Sultan University Riyadh Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia Department of Computer Science College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Software Engineering Computer Science Al Ain University Abu Dhabi United Arab Emirates

Forests cover nearly one-third of the Earth’s land and are some of our most biodiverse ecosystems. Due to climate change, these essential habitats are endangered by increasing wildfires. Wildfires are not just a risk to the environment, but they also pose public health risks. Given these issues, there is an indispensable need for efficient and early detection methods. Conventional detection approaches fall short due to spatial limitations and manual feature engineering, which calls for the exploration and development of data-driven deep learning solutions. This paper, in this regard, proposes 'FireXnet', a tailored deep learning model designed for improved efficiency and accuracy in wildfire detection. FireXnet is tailored to have a lightweight architecture that exhibits high accuracy with significantly less training and testing time. It contains considerably reduced trainable and non-trainable parameters, which makes it suitable for resource-constrained devices. To make the FireXnet model visually explainable and trustable, a powerful explainable artificial intelligence (AI) tool, SHAP (SHapley Additive exPlanations) has been incorporated. It interprets FireXnet’s decisions by computing the contribution of each feature to the prediction. Furthermore, the performance of FireXnet is compared against five pre-trained models — VGG16, InceptionResNetV2, InceptionV3, DenseNet201, and MobileNetV2 — to benchmark its efficiency. For a fair comparison, transfer learning and fine-tuning have been applied to the aforementioned models to retrain the models on our dataset. The test accuracy of the proposed FireXnet model is 98.42%, which is greater than all other models used for comparison. Furthermore, results of reliability parameters confirm the model’s reliability, i.e., a confidence interval of [0.97, 1.00] validates the certainty of the proposed model’s estimates and a Cohen’s kappa coefficient of 0.98 proves that decisions of FireXnet are in considerable accordance with t

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Blockchain-Based Model and Architecture Public Health System

SSRN

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SSRN 2024年

作者： Fernando, Erick Winanti, Winanti Prabowo, Yulius Denny Johan, Monika Evelin Tjahjana, David Department of Information Systems Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Department of Information Systems Universitas Insan Pembangunan Indonesia Computer Science Department Binus Online Learning Bina Nusantara University Jakarta11480 Indonesia STMIK Insan Pembangunan Indonesia

Blockchain has been identified as one of the technologies that can be used in the healthcare system, especially in the issues of data security, privacy, and interoperability. This article explores the application of blockchain in various medical processes ranging from patient registration, visit registration, initial examination, patient examination, diagnostic input, and drug preparation. This study was conducted at a healthcare facility that utilizes blockchain as a technology to manage patient data, medical records, and drug prescriptions. Through a mixed-method approach, this study evaluates the impact of blockchain on operational efficiency, data security, and system reliability. The results show that blockchain not only improves the security and privacy of patient data but also accelerates operational processes in the healthcare chain. An in-depth discussion of the benefits, challenges, and future potential of blockchain in healthcare is outlined to provide insights for blockchain technology developers and healthcare practitioners. Model validation was conducted through Focus Group Discussion (FGD) with domain experts in the health sector. From the experiments conducted, this study concludes that blockchain can be a transformational solution in addressing major problems in the healthcare sector, although cross-sector collaboration is needed to ensure successful implementation. © 2024, The Authors. All rights reserved.

关键词： Electronic health record

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Geospatial exploration of geothermal reservoirs in the Souss Massa regions of the anti-atlas and Moroccan Sahara

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Materials Today: Proceedings 2022年 72卷 3911-3926页

作者： El Bouazouli, Abderrahim Baidder, Lahssen Bahi, Hicham Abdehamid, Fadil Rhouzlane, Said Moussaoui, Rabia Oukrich, Nadia Baite, Wissal Hlal, Mohammed Laboratory of Geosciences Faculty of Sciences Aïn Chock University Hassan II of Casablanca Morocco School of Architecture Planning and Design Mohammed VI Polytechnic University Benguerir Morocco Morocco Polytechnic School of Montreal Canada Laboratory of Industrial Equipe Architecture de Systems ENSEM Morocco research laboratory in computer science and education the mohammedia school of engineering Rabat Morocco Laboratory of Industrial Engineering Sciences and Technologies Faculty Sultan Moulay Slimane University Beni Mellal Morocco laboratory Dynamics of arid environments Mohamed Premier University Oujda Morocco

Geothermal reservoirs refer to any heat stored, naturally, in the earth's crust;it is considered as resource coming from a natural complex depending on the geological, geophysical aspect and territories geochemistry. Although this natural resource remains distinguished by his characters to be a renewable, ecological, universal and sustainable mining energy, it stills not, yet, sufficiently exploitable worldwide (El Bouazouli et al., 2020). The same finding was brought up in Moroccan context, despite the presence of some hot resources and capitalizable scientific research to develop this potential. Prospecting for underground geothermal resources is an expensive project that deserves to mobilize scientific and technical methods in order to overcome this risky impasse. Thus, Geomatics has been used, drawing on Remote Sensing for geo-spatial exploration in order to guide prospecting through the development of geothermal deposits potential maps at very low and medium depth on Moroccan territories (El Bouazouli et al., 2019). Furthermore, we explore smart tools to support short and long-term decision-making in terms of creating of clean energy mix and complementary mining energy as well as investment in energy exploration operations. And, as an experiment, we chose for this study, areas distributed mainly in the Moroccan Sahara and the Souss Massa Region, known, probably, by a potentially high geothermal gradient, and therefore constituting geothermal energy reservoirs. As for the exploration methodology adopted, it consists in processing of the imageries: LANDSAT 8, MODIS AND ASTER, in order to extract exploitation indices such as the surface temperature, the layer of lineaments and cracks density, and evidence of hydrothermal alteration that may indicate geothermal activity. The correlation between these three factors, will lead us to conclude that the Moroccan Sahara area is considered as one of the most potential geothermal reservoir areas in Morocco. In short, the a

关键词： Decision making

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Ring Artifacts Elimination Method in Computed Tomography using Sparsity-Induced Norms

Ring Artifacts Elimination Method in Computed Tomography usi...

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2021 IEEE Nuclear science Symposium and Medical Imaging Conference, NSS/MIC 2021

作者： Selim, Mona Rashed, Essam A. Atiea, Mohammed A. Kudo, Hiroyuki University of Tsukuba Division of Information Engineering Faculty of Engineering Information and Systems Tsukuba Japan Nagoya Institute of Technology Department of Electrical and Mechanical Engineering Nagoya466-8555 Japan Suez University Faculty of Computers and Information Department of Computer Science Suez Egypt

ISBN: (纸本)9781665421133

Ring artifacts degrade the visibility of computed tomography images, where they appear as concentric circles in the reconstructed image. They are usually generated due to some limitations/failure associated with imaging hardware. Ring artifacts may destruct effective information in the image, and hence they should be carefully identified and eliminated without damaging other image structures. In this paper, we propose a ring artifacts elimination method that works within the image reconstruction process. With the existence of ring artifacts, the projection data can be represented as a sum of two components. The first contains perfect ideal projection data and the second contains imperfect elements causing the ring artifacts. We propose to minimize sparsity-induced norms such as a combination of 0 and 1-norms of the imperfect elements to remove the ring artifacts efficiently. The proposed algorithm is generated through optimizing the cost function with the alternative direction method of multipliers. The efficiency of the proposed method is tested qualitatively and quantitatively using simulation studies. Results of the reconstructed images show significant elimination of the artifacts. © 2021 IEEE.

关键词： Image reconstruction

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Architecture development of middleware and API for IOT-based Red Onion farming

Architecture development of middleware and API for IOT-based...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Alvin Steven Suharnawi Farrikh Alzami Filmada Ocky Saputra Rama Aria Megantara Pulung Nurtantio Andono Dept. of Information Systems Universitas Dian Nuswantoro Indonesia Dept. of Information Engineering Universitas Dian Nuswantoro Indonesia Faculty of Computer Science Universitas Dian Nuswantoro Indonesia

IoT plays a crucial role in transforming the agricultural industry by offering diverse use cases starting from crop monitoring and precision farming to yield optimization. The fundamental reason why the agricultural community benefits immensely from IoT is due to its capacity to gather real-time data straight from their fields. Farming requires considerable attention, and wireless sensor networks enable farmers to deploy IoT devices throughout various parts of their farm so they can obtain information on soil moisture levels, temperature, humidity, among other essential parameters that are integral for effective agriculture management. This gathered data empowers farmers with accurate weather forecasts through machine learning techniques which assist them in making informed decisions related to planting or harvesting of crops - vital components critical for successful outcomes towards better yields. Moreover, middleware architectures/APIs developed specifically for IOT-based red onion farming utilize cloud computing services wherein these enlightening patterns generated get stored safely using analytics tools &automated systems. These intensify insights derived as it simplifies accessing important metrics aiding ease while analyzing historical trends advantageously helping make prudent future course corrective measures concerning planting/harvesting schedules based entirely on solid empirical evidence combined with artificial intelligence ensuring unprecedented efficiency gains and higher returns than previously achievable without technology guidance.

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DTIAM: A unified framework for predicting drug-target interactions, binding affinities and activation/inhibition mechanisms

arXiv

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arXiv 2023年

作者： Lu, Zhangli Lei, Chuqi Wang, Kaili Qin, Libo Tang, Jing Li, Min School of Computer Science and Engineering Central South University Changsha410083 China Research Program in Systems Oncology Faculty of Medicine University of Helsinki HelsinkiFI00014 Finland

Accurate and robust prediction of drug-target interactions (DTIs) plays a vital role in drug discovery. Despite extensive efforts have been invested in predicting novel DTIs, existing approaches still suffer from insufficient labeled data and cold start problems. More importantly, there is currently a lack of studies focusing on elucidating the mechanism of action (MoA) between drugs and targets. Distinguishing the activation and inhibition mechanisms is critical and challenging in drug development. Here, we introduce a unified framework called DTIAM, which aims to predict interactions, binding affinities, and activation/inhibition mechanisms between drugs and targets. DTIAM learns drug and target representations from large amounts of label-free data through self-supervised pre-training, which accurately extracts the substructure and contextual information of drugs and targets, and thus benefits the downstream prediction based on these representations. DTIAM achieves substantial performance improvement over other state-of-the-art methods in all tasks, particularly in the cold start scenario. Moreover, independent validation demonstrates the strong generalization ability of DTIAM. All these results suggested that DTIAM can provide a practically useful tool for predicting novel DTIs and further distinguishing the MoA of candidate drugs. DTIAM, for the first time, provides a unified framework for accurate and robust prediction of drug-target interactions, binding affinities, and activation/inhibition mechanisms. Copyright © 2023, The Authors. All rights reserved.

关键词： Chemical activation

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Novel Lightweight Method for Detecting Backdoor Attacks in Edge-Deployed Neural Networks

Novel Lightweight Method for Detecting Backdoor Attacks in E...

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International Conference on Advanced Communication Technologies and Networking (CommNet)

作者： Salmane Douch M. Riduan Abid Khalid Zine-Dine Driss Bouzidi Driss Benhaddou National School of Computer Science and Systems Analysis (ENSIAS) Mohammed V University in Rabat Rabat Morocco TSYS School of Computer Science Columbus State University Columbus GA USA Faculty of Sciences (FSR) Mohammed V University in Rabat Rabat Morocco College of Engineering Alfaisal University Riyadh Saudi Arabia

ISBN: (数字)9798350367027

ISBN: (纸本)9798350367034

The increasing utilization and demand for intelligent tasks at the end-user level, such as autonomous vehicles, virtual assistants, telesurgery, etc., has elevated the risk of various attacks targeting intelligent models used in these applications. One of the most prominent types of attacks is the backdoor attack. In backdoor attacks, adversaries clandestinely inject poisoned data using a trigger formula, blending it with clean data during the training phase. Subsequently, they exploit the same trigger formula to deceive the model during production. With the emergence of collaborative training (such as Federated Learning) at the edge, adversaries can now easily share their poisoned data during the training, causing critical misclassifications on data infused with poison and potentially leading to serious consequences such as autonomous vehicle crashes or incorrect authentication, etc. To mitigate these threats and enhance the safe deployment of deep learning models at the edge, we introduce a lightweight technique for detecting backdoor attacks. Our method begins by integrating an early exit layer into the model, as a model with multiple outputs can learn different information from poisoned data. We then compare the Earth Mover’s Distance between the early and final exits to effectively detect backdoor images. During our experiments with LeNet-5 and ResNet-20 models on the MNIST and GTSRB datasets, we successfully detected between 95% and 100% of backdoored samples, achieving an error rate of less than 5% on clean data. Furthermore, our method effectively detected a wide range of state-of-the-art backdoor attacks, demonstrating superior performance and innovation in this critical area.

关键词： Training Technological innovation Toxicology Federated learning Image edge detection Virtual assistants Neural networks Production Data models Autonomous vehicles

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Trajectory Planning and Tracking Control for the Pendubot

Trajectory Planning and Tracking Control for the Pendubot

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Chinese Automation Congress (CAC)

作者： Bin Xu Xin Xin Yuhang Yan Yannian Liu School of Automation Southeast University Nanjing China Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama Japan

In this paper, we study the trajectory planning and tracking control for the Pendubot, which is a two-link robot has a single actuator at the base joint of the first link. We realize a fast swing-down control under the sinusoidal-derivative (SD) controller which introduces a damper and spring, and obtain a desired trajectory of the state and the control input by using the swing-down controller and the time-reversal method. To track such a trajectory, we derive the linearized time-varying model of the Pendubot around the trajectory, and design a linear state feedback control along with a feedforward term determined by the trajectory of the control input. We study several obtained trajectories and focus on two issues: 1) the linear controllability of the corresponding linearized time-varying model for each trajectory; 2) the sufficient condition in the literature for checking the stability of the tracking control of the linearized time-varying model. Through our numerical investigation, we find that we have to choose appropriate control gains of the SD controller to guarantee the linear controllability, and that the sufficient condition is difficult to be satisfied.

关键词： Sufficient conditions State feedback Trajectory planning Trajectory tracking Controllability Stability analysis Trajectory

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Hierarchical Continual Learning for Domain-Knowledge Retention in Healthcare Federated Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Choudhry, Imran Arshad Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia University of Central Punjab Faculty of Information Technology and Computer Science Department of Software Engineering Lahore Pakistan

Internet of Medical Things (IoMT) applications encounter issues with data protection, continual adaptation, and domain-specific knowledge retention, especially in consumer-centric IoMT scenarios. We overcome these obstacles and facilitate effective knowledge retention and task adaptation in IoMT applications. This study attempts to create a unique privacy-preserving federated learning framework that combines a hierarchical learning structure with Continual Learning (CL). Despite the advancements in Federated Learning (FL), current models have trouble integrating changing datasets in real-time while protecting privacy, as well as catastrophic forgetting, which occurs when previously learned knowledge is lost when adjusting to new tasks. We present a hierarchical learning framework that makes use of three levels of models -Junior Model (JM), Consultant Model (CM), and Senior Consultant Model (SCM) -to overcome these drawbacks. Each level of the model aids in archived retention and domain-knowledge adaptation. To guarantee that the model maintains valuable information over time and adapts to new tasks with ease, our method blends domain adaptation strategies with ongoing learning approaches like knowledge distillation and elastic weight consolidation (EWC). We compare the suggested methodology with current state-of-the-art (SOTA) models on healthcare datasets for tasks like illness diagnosis and medical image categorization. According to our findings, the hierarchical continual learning model performs better than SOTA techniques in terms of accuracy, task adaptability, and privacy protection. In the healthcare industry, our study sets a new standard for privacy-preserving, continuously adaptable federated learning systems, allowing for real-time, scalable IoMT applications that can adapt dynamically to a variety of changing datasets. © 1975-2011 IEEE.

关键词： Patient treatment

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