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International Conference on Telecommunications (ICT)

作者： Karnika Biswas Hakim Ghazzai Abdullah Khanfor Lokman Sboui CEMSE Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Computer Science Department College of Computer Science & Information Systems Najran University Najran Saudi Arabia Systems Engineering Department École de Technologie Supérieure (ÉTS) University of Québec Montréal Canada

ISBN: (数字)9798350356694

ISBN: (纸本)9798350356700

Urban Air Mobility (UAM) is the envisioned future of inter-city aerial transportation. This paper presents a novel, in-flight connectivity link allocation method for UAM, which dynamically switches between terrestrial cellular and Low Earth Orbit (LEO) satellite networks based on real-time conditions. Our approach prefers cellular networks for cost efficiency, switching to LEO satellites under poor cellular conditions to ensure continuous UAM connectivity. By integrating real-time metrics like signal strength, network congestion, and flight trajectory into the selection process, our algorithm effectively balances cost, minimum data rate requirements, and continuity of communication. Numerical results validate minimization of data-loss while ensuring an optimal selection from the set of available above-threshold data rates at every time sample. Furthermore, insights derived from our study emphasize the importance of hybrid connectivity solutions in ensuring seamless, uninterrupted communication for future urban aerial vehicles.

关键词： Satellites Costs Low earth orbit satellites Transportation Switches Real-time systems Trajectory

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Blockchain driven Evidence Management System

Blockchain driven Evidence Management System

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Shyam Mehta K. Shantha Kumari Paras Jain Harshal Raikwar Shubham Gore Data Science and Business Systems SRM Institute of Science & Technology Chennai India Computer Science and Engineering SRM Institute of Science & Technology Chennai India

When a cognizable violation like murder, abduction, rape, theft, etc. is committed, a victim or someone acting on their behalf must submit an electronic first information report (e-FIR) to the police station. Due to the centralized nature of the e-FIR database, it is possible for the offense’s record to be hacked, and it is also possible for fake e-FIRs to be purposefully registered. Data transparency and integrity are therefore major issues with the e-FIR database. Indian government launched nation-wide Crime and Criminal Tracking Network and systems (CCTNS) during 2009 and it is an efficient e-governance system. This paper provides a blockchain solution to handle the complaints given on both cognizable and non-cognizable complaints. Using real-world examples from previous events, the technology and security underlying the use of blockchain will be discussed. Police will file an e-FIR, which will be validated by the authorities, and on acceptance of the FIR, it will be encrypted and stored as a hash along with the timestamp and hash of the next block. Now, how blockchain makes it secure is that it doesn’t let anybody make changes to the FIR without proof of work and a vote of consensus in which a majority of the blockchain must agree to the change. The hash will be stored in smart contracts using Ethereum. Our findings demonstrate a trade-off between the number of transactions contained in a single block on the blockchain ledger and the security level of various hashing algorithms for the offence data.

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MBConvNet-CNS: A Lightweight Convolutional Neural Network (CNN) Architecture for Plant Disease Detection

MBConvNet-CNS: A Lightweight Convolutional Neural Network (C...

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Intelligent systems and Embedded Design (ISED), International Conference on

作者： Shikhar Burman Nilabh Pandey Chetna Srivastava Manas Ranjan Prusty School of Computer Science and Engineering Vellore Institute of Technology Chennai INDIA Centre for Cyber Physical Systems Vellore Institute of Technology Chennai India

ISBN: (数字)9798331522612

ISBN: (纸本)9798331522629

Plant diseases pose a big threat to agricultural productivity worldwide, so their detection technique should be strong and efficient. Classically, plant disease identifications are time-consuming and usually require expert knowledge; this makes the use of large-scale agriculture difficult. The following paper presents a review of how the use of lightweight CNN models can be used for real-time plant disease detection with models that are computationally efficient enough to be deployed on edge devices such as smartphones and drones. We shall propose a novel model based on state-of-the-art architectures comprising the likes of MobileNet, EfficientNet, Ghost Modules, and Squeeze-and-Excitation blocks that achieve high accuracy for plant disease identification with a low computation footprint. The proposed model was applied to different datasets of images of plant leaves. Experimental results proved it at par to the previous heavyweight and lightweight models based on deep learning for accuracy and time consumed. These findings point out that the correct integration of lightweight CNN architectures and innovative modules shall yield a feasible solution for effective management of plant diseases, making timely interventions possible for reducing crop losses.

关键词： Plant diseases Adaptation models Accuracy Computational modeling Crops computer architecture Computational efficiency Complexity theory Convolutional neural networks Smart phones

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Implementation of Malaria Detection Using MobileNetV2 Pretrained Model for Improved Deep Learning Based Diagnostics

Implementation of Malaria Detection Using MobileNetV2 Pretra...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Rahmat Hidayat Achmad Solichin Adhitia Erfina Sudin Saepudin Muhamad Muslih Mohd Zainuri Saringat Master of Computer Science Budi Luhur University Jakarta Indonesia Information Systems Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Tun Hussein Onn Malaysia University

ISBN: (数字)9798331529376

ISBN: (纸本)9798331529383

The Plasmodium parasite, which causes malaria, is an acute fever illness that infects people when a female Anopheles mosquito bites them. It is predicted that malaria would claim 619,000 lives in 2021, with 96% of those deaths occurring in the African continent. We can achieve this by using a microscope to examine thick and thin blood smears. The proficiency of a microscope examiner is crucial for doing microscopic examinations. Consider how time-consuming, ineffective, and costly it would be to examine thousands of malaria cases. Consequently, Creating an automated method for detecting malaria parasites is the aim of this study. We employ a MobileNetV2 pretrained model with CNN technology. Because it has been trained on dozens or even millions of data points, this pretrained model is incredibly light but dependable. There are two main benefits of automatic malaria parasite detection: firstly, it can offer a more accurate diagnosis, particularly in locations with limited resources; secondly, it lowers diagnostic expenses. The optimizer utilizes Adam Weight, the criteria uses NLLLoss, and the model is trained using 32 for batch_size. In the fourteenth epoch, we obtained the maximum accuracy score of 96.26% based on the training data. The outcomes of the predictions demonstrate how excellent this score is. EfficienceNet, DenseNet, AlexNet, and other pretrained models are among the alternatives that scientists are advised to try training with.

关键词： Training Adaptation models Accuracy Costs Malaria Microscopy Data visualization Training data Cost function Data models

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Computation Offloading in NTN-empowered MEC using Multi-Agent Distributed Deep Reinforcement Learning

Computation Offloading in NTN-empowered MEC using Multi-Agen...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Nida Fatima Paresh Saxena Giovanni Giambene Department of Computer Science and Information Systems BITS Pilani Hyderabad India Department of Information Engineering and Mathematical Sciences University of Siena Siena Italy

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, we investigate Non-Terrestrial Network (NTN)-empowered Multi-access Edge Computing (MEC) systems for emerging applications like telemedicine, industrial automation, and augmented reality, which demand significant computational resources from User Equipments (UEs). To address these issues, we consider that UEs can either process tasks locally or offload them to edge servers deployed on Unmanned Aerial Vehicles (UAVs) and Low Earth Orbit (LEO) satellites. The efficacy of such systems crucially depends on formulating an efficient computation offloading policy. We propose a multi-agent distributed deep reinforcement learning (MADDRL) approach for computation offloading in NTN-empowered MEC systems. Our approach utilizes two distributed DRL frameworks: Centralized Training and Decentralized Execution (CTDE), where training occurs on a central controller, and Independent Learners (IL), where training is performed locally on the UEs. In both frameworks, policies are executed by UEs in a decentralized manner. Simulation results show that the CTDE framework reduces the average cost, defined as the weighted sum of delay and energy consumption experienced by the UEs, by 11%, 42%, and 66% compared to IL, local, and random policies, respectively.

关键词： Training Costs Satellites Multi-access edge computing Telemedicine Simulation Low earth orbit satellites Deep reinforcement learning Servers Global communication

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Portable Automatic Sensing System for Sustainable Precision Farm

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2022年 20卷 222-227页

作者： Galvão, J. Fernandes, J. Pereira, D. Galvão, M. Neves, F. Department of Electrical Engineering Polytechnic of Leiria Leiria2411-901 Portugal R&D Unit Institute for Systems Engineering and Computers INESC Coimbra Portugal ISTécnico/Lisbon Technical University Lisboa1049-001 Portugal Department of Computer Science Polytechnic of Leiria Leiria2411-901 Portugal

Taking into account the world´s growing population and climate change, there is a need to decarbonize production processes and, in particular, intensive cultures carried out in greenhouses. There is a need to boost the energy and water efficiency of these cyber-physical systems, taking into account human consumption or floriculture, as well as European Community (EEC) and United Nations agreements and pledges, such as the European Green Deal (EGD) or the REPowerEU Plan [1,2] t is critical to develop systems that use integrated models to control these sorts of intense production, collect physical variable factors more efficiently, and achieve improved and zero greenhouse gas emissions (GHG). Finally, it is crucial for a digitalization in these intensive crops, in precision farm or greenhouses. The main objective of this work focuses on the conception of a portable automatic sensing system (PASS) for pH, electroconductivity and water flow, to control irrigation water quality in precision farm/greenhouses. The data collected by these sensors are made available in a mobile app for consultation by the operator. Another objective will be the local production of electrical and thermal energy from renewable sources (solar and biomass), hybrid systems and the use of cogeneration to supply energy to the greenhouse. A sustainable product policy also has the potential to reduce waste significantly. © 2022, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Sustainable development

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Criteria-based Unsupervised Feature Selection Techniques: A Comparative Study Over Chest CT Scan 25

Criteria-based Unsupervised Feature Selection Techniques: A ...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Al-Daja, Shrouq Alyabrodi, Ala' Moubayed, Abdallah Injadat, MohammadNoor Elrashidi, Ali Zarqa University Data Science & Ai Department Zarqa Jordan Zarqa University Cyber Security Department Zarqa Jordan King Fahd University of Petroleum & Minerals Interdisciplinary Research Center for Intelligent Secure Systems Computer Engineering Department Dhahran Saudi Arabia University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Research on classifying chest CT scans as normal or abnormal using machine learning and deep learning has garnered significant attention. To address this, various feature selection (FS) methods are employed to reduce dimensionality and identify key features. This paper evaluates criteria tailored to unsupervised binary datasets, such as feature subsets, redundancy, and diversity, by comparing Mutual Information-Based Methods (MI) with Clustering-Based Feature Selection (Agglomerative Clustering) ones. Additionally, it examines Graph Structure, Spectral Properties, and Quality criteria by comparing Graph-Based Methods with Spectral Feature Selection-be (SPEC). For feature reduction, Agglomerative Clustering may offer a slight advantage due to better handling of diversity and redundancy, though the difference is minimal. If these aspects are crucial, Agglomerative Clustering might be preferred. Conversely, for Graph-Based vs. SPEC, SPEC Method is more suitable for feature reduction due to its coherent feature selection and focus on capturing significant patterns related to medical interventions. © 2024 IEEE.

关键词： Contrastive Learning

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Lung Cancer Detection using CNN

Lung Cancer Detection using CNN

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Sur Singh Rawat Sarthak Nigam Yash Tiwari Yash Vishwakarma Gyanendra Kumar Mahesh Kumar Singh Lal Pratap Verma Computer Science and Engineering JSS Academy of Technical Education Noida India School of Computing and Intelligent Systems Manipal University Jaipur Jaipur India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

Early detection is crucial in the fight against the widespread impact of lung cancer on global health. This comprehensive review examines how Convolutional Neural Networks (CNNs) have transformed lung cancer detection through advanced image analysis. The review surveys seminal research and recent advancements, explaining different CNN architectures and their performance across datasets. It also discusses preprocessing methods and evolving strategies for characterizing lung nodules. This review emphasizes the potential of CNNs to increase the accuracy of early detection, highlighting their crucial role in lung cancer diagnosis. It also provides pathways for refining precision and ensuring responsible integration of CNNs into the medical practice for lung cancer diagnosis.

关键词： Surveys Accuracy Reviews Transfer learning Refining Lung cancer Data models Convolutional neural networks Artificial intelligence Overfitting

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A Reliable Edge Server Deployment Algorithm Based on Spectral Clustering and a Deep Q-network Strategy using Multi-objective Optimization

A Reliable Edge Server Deployment Algorithm Based on Spectra...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zhou Zhou Taotao Yu Mohammad Shojafar Xia Ou Hongbing Cheng School of Computer Science and Engineering Changsha University Changsha China College of Computer Science Zhejiang University of Technology Hangzhou China 5G/6GIC Institute for Communication Systems (ICS) University of Surrey Guildford UK

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Mobile edge computing (MEC) enables real-time processing and reduces core network congestion by bringing computing resources closer to data sources. However, optimizing edge server deployments to minimize latency, energy consumption, and load imbalance remains challenging. We propose the SC-DQN strategy, combining spectral clustering and deep Q-network (DQN) techniques. Spectral clustering analyzes the spatial distribution of base stations, grouping them and identifying cluster centers as initial deployment sites. A deep reinforcement learning environment then enables each edge server (agent) to iteratively optimize deployment, minimizing latency, energy consumption, and load imbalance. Experiments with Shanghai Telecom data demonstrate that SC-DQN improves load balancing by 23.05%, reduces latency by 45.32%, and lowers energy consumption by 9.64%, outperforming traditional methods.

关键词： Energy consumption Base stations Heuristic algorithms Soft sensors Clustering algorithms Load management Telecommunications Servers Telecommunication network reliability Security

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Peak Period Demand Forecasting with Proxy Data: GNN-Enhanced Meta-Learning

Peak Period Demand Forecasting with Proxy Data: GNN-Enhanced...

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IEEE Winter Applications and computer Vision Workshops (WACVW)

作者： Zexing Xu Linjun Zhang Sitan Yang Nan Jiang Department of Industrial and Enterprise Systems Engineering University of Illinois Urbana-Champaign Department of Statistics Rutgers University Forecasting Science Amazon Department of Computer Science Purdue University

Demand prediction is a crucial task for e-commerce and physical retail businesses, especially during high-stake sales events. However, the limited availability of historical data from these peak periods poses a significant challenge for traditional forecasting methods. In this paper, we propose a novel approach that leverages proxy data from non-peak periods, enriched by features learned from a graph neural networks (GNNs) based forecasting model, to predict demand during peak events. We formulate the demand prediction as a meta-learning problem and introduce the Feature-based First-Order Model-Agnostic Meta-Learning (F-FOMAML) algorithm, which adapts to new tasks by conditioning on the GNN-generated relational metadata. Empirical evaluations on large-scale industrial datasets demonstrate the superiority of our approach, with our model consistently outperforming state-of-the-art baselines in the demand prediction task, by 26.24% on the internal vending machine dataset and 8.7% on the public *** dataset over the Mean Absolute Error.

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