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IEEE Conference on Global Communications (GLOBECOM)

作者： Chun-An Yang Shih-Chieh Chen Yi-Hsuan Peng Yu-Wen Chen Jian-Jhih Kuo Ming-Jer Tsai Dept. of Computer Science National Tsing Hua University Hsinchu Taiwan Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan Computer Systems Technology New York City College of Technology Brooklyn New York USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Telecom carriers have announced new content services by making a partnership with content providers and offered popular video streaming to customers. Via the integration with edge networks, telecom carriers can procure various contents from content providers and place them on edge servers proximate to end users to serve real-time requests with high bandwidth and ultra-low latency. Nevertheless, it is challenging to consider the content procurement, placement, and services jointly due to the user preference, user distribution, storage capacity of edge server, economic costs, etc. Telecom carriers would like to balance the procuring, placing, and transfer costs. To address this problem, the paper formulates an optimization problem and then proposes an approximation algorithm. Finally, the simulation results manifest that our algorithm outperforms other baselines.

关键词： Procurement Costs Simulation Streaming media Approximation algorithms Real-time systems Telecommunications Servers User preference Optimization

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Mitigating Crop Losses: AI-enabled Disease Detection in Tomato Plants 20

Mitigating Crop Losses: AI-enabled Disease Detection in Toma...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Ashfaq, Hassan Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Ali Khan, Talha Ijaz, Ali Zeeshan Ali, Nisar Siddique, Abu Bakar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

Fostering crop health is vital for global food security, underscoring the need for effective disease detection. This research introduces an innovative artificial intelligence (AI) model designed to enhance the detection and diagnosis of diseases in tomato plants, particularly focusing on Early Blight and Late Blight. Significantly, our model leverages cutting-edge image processing techniques to improve disease detection efficiency, outperforming traditional methods in terms of speed and accuracy. Our results demonstrate an impressive model accuracy of 92.58% on training data and 86.83% on validation data, showing the effectiveness of AI in diagnosing plant diseases. These high accuracy rates underline the potential of our model for timely disease classification, allowing for immediate and appropriate interventions. However, our research also identified a potential overfitting problem in the model's performance. To address this, we propose using regularization and data augmentation techniques to enhance the model's generalizability on unseen data. Additionally, we delve into inherent challenges that plague AI-based plant disease detection, such as the scarcity of diverse datasets and the difficulty of achieving broad generalizability across different plant species. In identifying potential solutions for these issues, our research lays the groundwork for the wider and more practical implementation of AI technologies in agriculture. © 2023 IEEE.

关键词： Crops

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Assessing Respiratory Changes Induced by STN-DBS in a Rat Model 19

Assessing Respiratory Changes Induced by STN-DBS in a Rat Mo...

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19th International Conference on Emerging Technologies, ICET 2024

作者： Inam, Shehla Teixeira, Livia De Mesquita Waqar, Muhammad Ishaq, Muhammad Iqbal, Shahzaib Ullah, Fasih Muthuswamy, Jitendran School of Biological & Health Systems Engineering Arizona State University Tempe United States Balochistan University of Engineering & Technology Dept. of Biomedical Engineering Khuzdar Pakistan National University of Computer & Emerging Sciences Dept. of Electrical Engineering Islamabad Pakistan Balochistan University of Engineering & Technology Dept. of Mechanical Engineering Khuzdar Pakistan Cecos University of It & Emerging Sciences Dept. of Electrical Engineering Peshawar Pakistan

ISBN: (纸本)9798350379587

Deep brain stimulation of the subthalamic nucleus (STN-DBS) is a technique used to treat motor symptoms of movement disorders including Parkinson's Disease (PD), a neurogenerative disorder caused by the progressive loss of striatal dopamine in the brain that leads to tremors, rigidity, and postural instability. Despite its therapeutic effects, DBS is still known to cause some non-motor symptoms including breathlessness due to unknown reasons in PD patients. In this paper, the changes in breathing rates were evaluated from peak detection for 20 trials in the time domain for the preliminary data obtained from a rat model after the application of DBS on the STN for frequencies 10Hz and 130Hz with stimulation intensities of 150 μA and 450 μA. Results revealed a significant change in breathing rates for all intensities and corresponding frequencies, confirming that the degree of disruption in breathing rate may depend on the stimulation frequency. The study may further suggest potential neuronal networks that mediate these changes. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Survey on LED lamps for residential applications: Characterization of performance 23

Survey on LED lamps for residential applications: Characteri...

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23rd International Scientific Conference on Electric Power engineering, EPE 2023

作者： Hudač, Tomáš Drapela, Jiri Kukačka, Leoš Meyer, Jan Stiegler, Robert Brno University of Technology Faculty of Elect. Eng. and Communication Dept. of Electrical Power Engineering Brno Czech Republic Institute of Mechatronics and Computer Engineering Technical University of Liberec Liberec Czech Republic Institute of Electrical Power Systems and High Voltage Engineering Technische Universität Dresden Dresden Germany

ISBN: (纸本)9798350335934

Aim of the paper is to conduct a survey of the present market, focusing on the best-selling retrofit LED lamps and to analyze experimentally their fundamental EMC performance for subsequent classification of the LED drivers. Two stage experimental characterization testing approach is adopted. First, input current and output luminous flux in steady-state under nominal supply conditions is acquired. Next, transfer of input supply voltage interharmonics to output visible flicker components in terms of gain factor curves is obtained. Based on the achieved and discussed testing results, classification of the LED lamps is developed. © 2023 IEEE.

关键词： Light emitting diodes

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Leveraging AI and NLP in Chatbot Development: An Experimental Study 20

Leveraging AI and NLP in Chatbot Development: An Experimenta...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Paracha, Abdul Wahab Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Ali Khan, Talha Ijaz, Ali Zeeshan Ali, Nisar Siddique, Abu Bakar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

In the current era of chatbots, this research delves into the advancements in AI chatbots, drawing on artificial intelligence (AI) and natural language processing (NLP) techniques to mimic human-like conversations. A particular focus is given to the potential of chatbots in facilitating multitasking dialogues, offering emotional support, and addressing complex subject matter, all the while respecting user privacy and trust. The implemented chatbot model is trained on a neural network, using Keras and TensorFlow libraries. This model's performance indicates a considerable dependence on the dataset's size, with larger datasets leading to better outcomes by providing more extensive language usage and context examples. Additionally, we also analyze the effect of varying architectures and hyperparameters on chatbot performance. The significance of localizing chatbots to adapt to different languages and cultures is also highlighted. While promising, the study identifies areas of improvement, suggesting future research directions in enhancing language capture techniques, expanding training datasets, and integrating emotional intelligence within chatbot systems. © 2023 IEEE.

关键词： Natural language processing systems

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Performance Evaluation of Popular Deep Neural Networks for Neural Machine Translation 20

Performance Evaluation of Popular Deep Neural Networks for N...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Naeem, Muhammad Siddique, Abu Bakar Ali, Raja Hashim Arshad, Usama Abideen, Zain Ul Ali Khan, Talha Shah, Muhammad Huzaifa Zeeshan Ijaz, Ali Ali, Nisar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

The field of Neural Machine Translation (NMT) has shown impressive performance for quick and easy communication in various languages spoken all over the world. NMT helps us by improving communication between different languages. For this purpose, different sequential models are used such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Units (GRU). Analysis among these different models are important for making language translation better and choose the best model for the right job. This research investigates the performance of these models on two distinct language datasets, English-to-German and English-to-Urdu. Based on accuracy metrics, the findings reveal that GRU having test accuracy (88.22% ) outperforms RNN (87.21% ), and LSTM (85.70% )demonstrating the highest translation accuracy, followed by RNN and LSTM exhibiting comparatively lower accuracy levels. © 2023 IEEE.

关键词： Long short-term memory

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A Centrality Measure for Quantifying Spread on Weighted, Directed Networks

SSRN

引用

SSRN 2023年

作者： Fink, Christian G. Fullin, Kelly Gutierrez, Guillermo Omodt, Nathan Zinnecker, Sydney Sprint, Gina McCulloch, Sean Dept. of Physics Gonzaga University United States Dept. of Computer Science Ashland University United States Dept. of Industrial and Systems Engineering Georgia Institute of Technology United States Dept. of Mechanical Engineering Gonzaga University United States Dept. of Computer Science Gonzaga University United States Dept. of Computer Science Ohio Wesleyan University United States

While many centrality measures for complex networks have been proposed, relatively few have been developed specifically for weighted, directed (WD) networks. Here we propose a centrality measure for spread (of information, pathogens, etc.) through WD networks based on the independentcascade model (ICM). While deriving exact results for the ICM requires Monte Carlo simulations, we show that our centrality measure (Viral Centrality) provides excellent approximation to ICM results for networks in which the weighted strength of cycles is not too large. We show this can be quantified with the leading eigenvalue of the weighted adjacency matrix, and we show that Viral Centrality outperforms other common centrality measures in both simulated and empirical WD networks. © 2023, The Authors. All rights reserved.

关键词： Complex networks

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Joint Optimization of Throughput and Energy Consumption in Microservices-Based UAV Networks

Joint Optimization of Throughput and Energy Consumption in M...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： José Gómez-DelaHiz Aymen Fakhreddine Juan M. Murillo Jaime Galán-Jiménez Dept. of Computer Systems and Telematics Engineering University of Extremadura Spain Institute of Networked and Embedded Systems University of Klagenfurt Klagenfurt Austria Technology Innovation Institute Abu Dhabi UAE

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

The application of Unmanned Aerial Vehicle (UAV) networks to the coverage problem in rural and low-income areas is actively studied nowadays by the research community. A UAV-based network infrastructure is different from traditional cellular networks relying on Base Stations. By mounting small cells on top of UAVs, coverage can be enhanced in regions where network operators avoid to invest due to the low Return on Investment. In case there is a requirement for an enhanced throughput in a rural area (e.g., users requesting IoT applications with strict QoS requriements), a potential solution is to place several UAV s over the same area, thus maximizing the offered throughput. However, this situation would lead to an increase in the energy consumption. To tackle this problem, this paper proposes an optimal solution to the problem of jointly maximizing the offered throughput in rural scenarios where users request microservice-based IoT applications, while minimizing the energy consumption of the swarm of UAV s. A Mixed Integer Linear programming (MILP)-based formulation is defined and evaluated over realistic scenarios. Experimental results demonstrate how the solution is able to perform UAV placement in a way to maximize the offered throughput in the highest number of areas, while minimizing the total number of deployed UAVs.

关键词： Energy consumption Costs Microservice architectures Quality of service Throughput Autonomous aerial vehicles Mixed integer linear programming

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Throughput-Energy Efficiency Trade-off in Microservices-Based UAV Networks

Throughput-Energy Efficiency Trade-off in Microservices-Base...

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IEEE Symposium on computers and Communications (ISCC)

作者： José Gómez-delaHiz Andrés García-López Santiago García-Gil Diego Ramos-Ramos Aymen Fakhreddine Juan Manuel Murillo Jaime Galán-Jiménez Dept. of Computer Systems and Telematics Engineering University of Extremadura Spain Institute of Networked and Embedded Systems University of Klagenfurt Klagenfurt Austria Technology Innovation Institute Abu Dhabi UAE

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

Rural areas broadband access suffers from the limited investment of network operators, due to the forecasted return on investment. As a consequence, digital services such as eHealth, remote education, or smart agriculture cannot be offered to the rural population. In this context, research on Unmanned Aerial Vehicles (UAV) networks has emerged, which aims to solve the coverage problem by relying on small cells mounted on UAVs to provide coverage. From the network Quality of Service (QoS) point of view, i.e., the performance offered to users according to certain parameters such as delay, reliability, and throughput provisioning can be identified as one of the weak points. For the problem of maximizing throughput, the main solution is to group several UAVs in the same area. However, as the offered throughput increases, the power consumption will also increase. In this context, this paper proposes a genetic algorithm to solve the problem of jointly maximizing the offered throughput in rural scenarios where users request microservice-based IoT applications while minimizing the energy consumption of the swarm of UAVs. The algorithm is defined and evaluated in realistic scenarios, demonstrating its effectiveness on increasing the throughput while decreasing the number of UAVs that are required.

关键词： Smart agriculture Energy consumption Microservice architectures Quality of service Throughput Autonomous aerial vehicles Telecommunications Telecommunication network reliability Investment Genetic algorithms

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A centrality measure for quantifying spread on weighted, directed networks

arXiv

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arXiv 2023年

While many centrality measures for complex networks have been proposed, relatively few have been developed specifically for weighted, directed (WD) networks. Here we propose a centrality measure for spread (of information, pathogens, etc.) through WD networks based on the independent cascade model (ICM). While deriving exact results for the ICM requires Monte Carlo simulations, we show that our centrality measure (Viral Centrality) provides excellent approximation to ICM results for networks in which the weighted strength of cycles is not too large. We show this can be quantified with the leading eigenvalue of the weighted adjacency matrix, and we show that Viral Centrality outperforms other common centrality measures in both simulated and empirical WD networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Complex networks

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