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MANETs Connectivity Management for Age of Information Decreasing in Real-Time Applications 5

MANETs Connectivity Management for Age of Information Decrea...

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5th International Science and Technology Conference Modern Network Technologies, MoNeTec 2024

作者： Mikov, Alexander I. Mikov, Alexander A. Kuban State University Information Technologies Dept. Krasnodar Russia Kuban State University Computer Technologies & Applied Mathematics Dept. Krasnodar Russia

ISBN: (纸本)9798350369007

Mobile networks that implement real-time distributed tasks of searching for certain regions on the surface are considered. It is shown that the time for solving such problems significantly depends on the age of the information available in network nodes, and the age, in turn, depends on the duration of disconnectedness periods of network. Algorithms for controlling the connectivity of mobile sensor search networks have been proposed, which significantly increase the speed of finding areas containing objects of a given type. The effectiveness of the algorithms has been assessed. Based on the simulation results, the possibility of solving optimization problems for determining the composition of network node equipment is shown. © 2024 IEEE.

关键词： Graph theory

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Using IOT and WSNs Technology for Monitoring an Aquaponic Greenhouse System 4

Using IOT and WSNs Technology for Monitoring an Aquaponic Gr...

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4th International Conference on Embedded and Distributed Systems, EDiS 2024

作者： Touhami, Achouak Bourouis, Amina Benahmed, Tariq Touhami, Sana Touhami, Nawal Benahmed, Khelifa Ali Kafi university center Dept. Mathematics and Computer Science Tindouf Algeria Tahri Mohammed university Dept. Mathematics and Computer Science Bechar Algeria Ahmed Draria university Dept. Electrical Engineering Adrar Algeria

ISBN: (纸本)9798331519872

The increasing population, climate fluctuations, soil erosion, limited water resources, and concerns about food security highlight the need for effective and sustainable approaches to food production. Aquaponics represents a sustainable method that conserves land and water while minimizing its environmental impact. This technique operates as a closed-loop, soil-less farming system, requiring meticulous monitoring, control, and management. Monitoring an aquaponic greenhouse system involves tracking various parameters to maintain the health and productivity of both plants and fish. Our research introduces a design and algorithms for monitoring an aquaponic greenhouse, utilizing wireless sensor networks (WSN), aquaponic systems, and Internet of Things (IoT) technology. © 2024 IEEE.

关键词： Aquaponic greenhouse Closed-loop Fish Internet of things Plant Wireless sensor networks

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Measuring Motor Planning Using an Affordable Sound-Based Virtual Reality Setup for Accessible Perception-Action Studies

Measuring Motor Planning Using an Affordable Sound-Based Vir...

引用

2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Barumerli, Roberto Privitera, Alessandro Giuseppe Carollo, Alessandro Fontana, Giovanni Patarini, Eros Zaninelli, Alberto Cesari, Paola Geronazzo, Michele Dept. of Neuroscience Biomedicine and Movement Italy Dept. Mathematics Computer Science and Physics Italy Dept. of Engineering and Management Italy

ISBN: (纸本)9798331514846

The recent availability of consumer head-mounted displays (HMDs) for virtual reality (VR) provides an immersive and affordable platform to study sound perception and action outside laboratory settings. While traditional perception-action studies rely on complex systems like motion capture, HMDs integrate dynamic sound delivery and real-time movement tracking. In this study, we adapted the experimental protocol from Geronazzo et al. (2023) to test the feasibility of measuring user behaviour with a consumer-grade HMD. Instead of muscular activity measured from surface electromyography, we analysed reaction times using kinematic data from the HMD's onboard sensors and controllers. Data from ten participants showed that spatial properties of looming sounds influence auditory perception-action loops, aligning with findings from the original study. Our results demonstrate that reaction time is a scalable and meaningful measure for studying space perception through sound, providing a possible alternative to muscular activity measurements and showcasing the potential of consumer-grade VR for accessible and robust experimental research despite current limitations. © 2025 IEEE.

关键词： Helmet mounted displays

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Enhancing the Coexistence of 802.11 WLAN and 5G Networks using a Multi-Task Learning with Convolutional Neural Network Model 2

Enhancing the Coexistence of 802.11 WLAN and 5G Networks usi...

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2nd International Symposium on Information Technology and Digital Innovation, ISITDI 2024

作者： Putra, Dody Ichwana Ekariani, Shelvi Universitas Andalas Dept. of Computer Engineering Padang Indonesia Universitas Andalas Dept. of Mathematics and Data Science Padang Indonesia

ISBN: (纸本)9798350389241

The increasing need for high-speed internet for wireless devices has driven the development of 5G New Radio (NR) technology. The 5G NR operates in various spectrums such as sub-1 GHz, sub-6 GHz, and millimeter wave (mmWave) in the 26-28 GHz range. Conversely, the new standard Wi-Fi 6E operates on the unlicensed band and currently utilizes 2.4 GHz, 5 GHz, and 6 GHz. The coexistence of 5G NR sub-6 GHz and Wi-Fi 6E networks can result in interference and reduced performance for both technologies. To address this challenge, we introduce a Multi-Task Learning (MTL) approach using Convolutional Neural Networks (CNN) to improve the coexistence of 5G and Wi-Fi 6E networks. This proposes identifying and classifying packets transmitted and its modulation over both types of networks. The MTL-CNN model is trained to classify packets based on their type (e.g., 5G or Wi-Fi) and to identify the specific protocol used within each packet. We confirm the accuracy of this method with Wi-Fi and 5G signals produced by the MATLAB waveform simulator and subsequently deploy it on a hardware testbed using SDR. Our experimental results show that this method improves the accuracy of packet identification and classification, thus enhancing the coexistence between 5G and Wi-Fi 6E networks. MTL-CNN potentially leads to more efficient use of the wireless spectrum and improves the overall performance of wireless communication systems. © 2024 IEEE.

关键词： Wi-Fi

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Classification of Depression, Anxiety and Stress from Handwriting and Drawing with Stacking Models 8

Classification of Depression, Anxiety and Stress from Handwr...

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8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Bayrak, Semra Golgiyaz, Sedat Aykut, Murat Karadeniz Technical University Graduate School of Natural and Applied Science Dept. of Computer Engineering Trabzon61080 Turkey Bingöl University Dept. of Computer Engineering Bingöl Turkey Karadeniz Technical University Dept. of Computer Engineering Trabzon Turkey

ISBN: (纸本)9798331540104

Recently, emotion analysis from online handwriting and drawings has become an important research topic. This study aims to determine the optimal model combination using dynamic and statistical features obtained from handwriting and drawings for detecting emotional states. The feature vectors are derived from physical (kinematic), statistical and signal processing (spectral, cepstral and frequency domain) analyses. To address the class imbalance problem, ADASYN (Adaptive Synthetic Sampling) and Tomek Links methods were applied together. In this way, the number of samples of the minority class has been increased, while over-sampled classes have been reduced. Dimensionality reduction was realized using PCA, and Gradient Boosting Classifier (GBC) feature selection. After determining the fewest and most effective features, the samples were classified using the two-level stacking ensemble method. eXtreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), GBC, Random Forest (RF) methods are selected for base and meta models. Model and hyperparameter optimization was carried out by Optuna frameork. The performance of the models is evaluated through experiments in publicly available EMOTion recognition from HAndWriting and draWing (EMOTHAW) database. The aim was to determine the emotional states of depression, anxiety and stress with the data obtained from 7 different drawing/writing tasks of 129 people. The results obtained from accuracy, f1-score, precision and recall metrics show that the model provides remarkable performance. © 2024 IEEE.

关键词： Dimensionality reduction

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Unleashing Machine Learning for Hepatitis C Prediction: A Holistic Exploration of Clinical Insights

Unleashing Machine Learning for Hepatitis C Prediction: A Ho...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Al-Amain Janin, Fatema Tuj Ahmed Robin, Faizul Ahmed, Shifat Mohi Uddin, Khandaker Mohammad Southeast University Dept. of Computer Science and Engineering Dhaka Bangladesh Hamburg University of Applied Sciences Dept. of Information and Electrical Engineering Hamburg Germany

ISBN: (纸本)9798331529765

Chronic diseases, characterized by long-term effects and persistent symptoms, pose significant challenges to human health and well-being. The hepatitis C virus (HCV) is the cause of chronic hepatitis C, which affects 58 million people globally. It also causes 290,000 deaths annually due to liver cancer and cirrhosis, two conditions connected to HCV. Taking advantage of machine learning (ML) algorithms' ability to analyze large datasets and identify patterns and relationships that aid in diagnosis, this effort focuses on employing algorithms for machine learning to identify and forecast hepatitis C precisely. Various techniques were utilized, including Random Forest, Cat Boost, SVM, and others to create predictive models using blood values from 615 patients, including both hepatitis patients and healthy blood donors. The dataset underwent comprehensive preprocessing, including handling missing values, scaling, and dimensionality reduction via Principal Component Analysis (PCA). Various ML algorithms were evaluated, and the Cat Boost classifier achieved the highest accuracy of 99.17%, recall of 92.00%, precision of 100.00% and F1-score is 96.00%. This study demonstrates the potential of ML in enhancing HCV diagnosis, offering less invasive and cost-effective alternatives to traditional methods, and providing predictive insights for personalized treatment plans. The findings underscore the importance of advanced data preprocessing and feature selection in improving model efficiency. This study adds to the ongoing efforts to lessen the prevalence of hepatitis C worldwide, aligning with the World Health Organization's goal to significantly decrease new HCV cases and related deaths by 2030. © 2024 IEEE.

关键词： Adversarial machine learning

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Real-Time Stress Detection Using Facial Images and Convolutional Neural Networks

Real-Time Stress Detection Using Facial Images and Convoluti...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Sindu, M. Ashwin, T. Shyam Sunder, V. Shivasankaran, S. Poongothai, P. Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu India Chennai Institute of Technology Dept. of Computer Mathematics Tamil Nadu India

ISBN: (纸本)9798331535476

Stress is one of the prevailing issues in today's world, deeply impacting the actual well-being of individuals. In this paper, we introduce an innovative method to detect stress that deviates from the mainstream approaches, which rely solely on physiologic signals and self-reporting. Our non-invasive, efficient, and scalable real-time system, built on the facial image data processed by CNNs, makes real-time human stress detection possible. The system captures facial expressions through video or static images, which it then processes within a CNN model to identify stress-related features. CNNs are particularly innovative in that they are adept.at automatically extracting and learning hierarchical features from extremely complex, unstructured data like facial images. Facial expression datasets were used to train and test the proposed model in different stress conditions. The model achieved a very good accuracy level in differentiating stressed from non-stressed states, thus putting it on par with professionals. Real-time testing of the system's performance was done on edge devices such as smartphones to prove its potential for use in real-life applications related to mobile health. As facial image data is utilised by the system, thereby killing the need for wearable sensors or invasive physiological monitoring, the approach would be more accessible for detecting stress levels. This research showed that CNN models developed based on facial images can transform stress monitoring. Its implications are wide, applicable in health care, workplace settings, and even self-manageable personal stress. In future work, the model should be generalised for diverse populations, the system's real-time capabilities enhanced, and multimodal data further integrated to increase detection accuracy and reliability. © 2024 IEEE.

关键词： Convolutional neural networks

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Advances and Adaptation in Artificial General Intelligence Based on Novel Q∗ Framework 27

Advances and Adaptation in Artificial General Intelligence B...

引用

27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Upreti, Meenakshi Bhatnagar, Abhinav Birla Institute of Applied Sciences Dept. of Master of Computer Applications Bhimtal India Birla Institute of Applied Sciences Dept. of Electronics and Communication Engineering Bhimtal India

ISBN: (纸本)9798350392319

A priority in the researches on artificial intelligence is the creation of Artificial General Intelligence. Even though the regular artificial intelligence was making some progress in certain areas, there were challenges and untried domains. This study introduces a novel Q∗ framework which is intended for improving AGI. Understanding, obtaining and using machine-generated knowledge across multiple domains are some of the aspects of AGI. It integrates concepts form cognitive psychology and reinforcement learning to produces a cohesive approach that includes learning as well as introspection and flexibility. By leveraging the advantages of Q-learning and refining existing methods, achieving AGI through Q∗ is feasible. Q* prioritizes metalearning and hierarchical representations over traditional AI systems that perform well within narrow contexts yet fail when asked to generalize more broadly. This makes it possible to be adaptable and learn well in unfamiliar settings. The paper gives a detailed explanation of the theoretical underpinnings and possible applications of AGI, emphasizing Q∗′ 's commitmentto advancing its pursuit. We discuss the conceptual framework proposed by Q∗ and speculate on its implications for future AI research. © 2024 IEEE.

关键词： Adversarial machine learning

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Evaluation of the Effectiveness of Vulnerable Road User Clustering in C-V2X Systems

Evaluation of the Effectiveness of Vulnerable Road User Clus...

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2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Rupp, Matthias Wischhof, Lars Munich University of Applied Sciences HM Dept. of Computer Science and Mathematics Munich Germany

ISBN: (纸本)9798350346473

Vulnerable Road Users (VRUs) are at special risk in road traffic. The dissemination of information about VRUs in Intelligent Transportation Systems (ITS) can help to protect VRUs from various risks. The Vulnerable Road User Awareness Message (VAM) format has been specified by ETSI as a way of distributing VRU-related information. The standard also describes a clustering mechanism that can help to reduce the number of generated messages and therefore prevent computing overload and network congestion in certain situations. This paper presents initial results on the effectiveness of the clustering approach proposed in the ETSI standard as well as its impact on position accuracy. Therefore, the clustering schemes are implemented in coupled network-and mobility simulations, modeling traffic situations with pedestrians and vehicles. In addition, the impact of one of the most relevant parameters for forming clusters, the maxClusterVelocityDifference, on clustering is assessed. The results indicate that VRU clustering can signifi-cantly reduce the number of VAM messages but also leads to a larger position error between the actual-and received position of individual VRUs. © 2023 IEEE.

关键词： Roads and streets

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Exploiting Impact of Eavesdropping Attacks on Secrecy Performance in WPT-based Secure Multi-hop Transmission 14

Exploiting Impact of Eavesdropping Attacks on Secrecy Perfor...

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14th International Conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Shim, Kyusung An, Beongku Hankyong National University School of Computer Engineering & Applied Mathematics Korea Republic of Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (纸本)9798350335385

In this paper, we study the impact of various eaves-dropping attacks on the secrecy performance in wireless power transfer (WPT)-based secure multi-hop transmission. Since each node has a limited power supply, each node harvests energy from the power beacon before data transmission. After then, each node fully consumes the harvested energy for data transmission. Meanwhile, according to the nature of the wireless medium, eavesdroppers can overhear the confidential message at each hop. In order to exploit the relationship between network parameters and secrecy performance, we derive the closed-form expression for secrecy outage probability (SOP) with different eavesdropping attacks, respectively. From the numerical results, we show that the Monte-Carlo simulation and analysis results are tightly matched. Additionally, we evaluate and discuss the impact of the power beacon's transmit power, the number of eavesdroppers, and time switching ratio on the secrecy performance. © 2023 IEEE.

关键词： Monte Carlo methods

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