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Journal articles on the topic 'Time-aware metrics'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

López-Vizcaíno, Manuel, Laura Vigoya, Fidel Cacheda, and Francisco J. Novoa. "Time-Aware Detection Systems." Proceedings 21, no. 1 (2019): 39. http://dx.doi.org/10.3390/proceedings2019021039.

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Communication network data has been growing in the last decades and with the generalisation of the Internet of Things (IoT) its growth has increased. The number of attacks to this kind of infrastructures have also increased due to the relevance they are gaining. As a result, it is vital to guarantee an adequate level of security and to detect threats as soon as possible. Classical methods emphasise in detection but not taking into account the number of records needed to successfully identify an attack. To achieve this, time-aware techniques both for detection and measure may be used. In this w

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Koksal, C. E., and H. Balakrishnan. "Quality-Aware Routing Metrics for Time-Varying Wireless Mesh Networks." IEEE Journal on Selected Areas in Communications 24, no. 11 (2006): 1984–94. http://dx.doi.org/10.1109/jsac.2006.881637.

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MARY, S. A. SAHAAYA ARUL, and R. KRISHNAMOORTHI. "TIME-AWARE AND WEIGHTED FAULT SEVERITY BASED METRICS FOR TEST CASE PRIORITIZATION." International Journal of Software Engineering and Knowledge Engineering 21, no. 01 (2011): 129–42. http://dx.doi.org/10.1142/s0218194011005116.

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Test metrics succeed in analyzing the current level of maturity in testing and give a projection on the way to proceed with testing activities by allowing us to set goals and predict future trends. The objective of test metrics is to capture the planned and actual quantities: the effort, time and resources required to complete all the phases of development of the software project. Test case prioritization is an effective and practical technique in regression testing. It schedules test cases in order of precedence that increases their ability to meet some performance goals, such as code coverag

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Choi, Janghoon. "Target-Aware Feature Bottleneck for Real-Time Visual Tracking." Applied Sciences 13, no. 18 (2023): 10198. http://dx.doi.org/10.3390/app131810198.

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Recent Siamese network-based visual tracking approaches have achieved high performance metrics on numerous recent visual tracking benchmarks, where most of these trackers employ a backbone feature extractor network with a prediction head network for classification and regression tasks. However, there has been a constant trend of employing a larger and complex backbone network and prediction head networks for improved performance, where increased computational load can slow down the overall speed of the tracking algorithm. To address the aforementioned issues, we propose a novel target-aware fe

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López-Vizcaíno, Manuel F., Francisco Novoa, Diego Fernandez, and Fidel Cacheda. "Time Aware F-Score for Cybersecurity Early Detection Evaluation." Applied Sciences 14, no. 2 (2024): 574. https://doi.org/10.3390/app14020574.

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With the increase in the use of Internet interconnected systems, security has become of utmost importance. One key element to guarantee an adequate level of security is being able to detect the threat as soon as possible, decreasing the risk of consequences derived from those actions. In this paper, a new metric for early detection system evaluation that takes into account the delay in detection is defined. Time aware F-score (TaF) takes into account the number of items or individual elements processed to determine if an element is an anomaly or if it is not relevant to be detected. These resu

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Kim, Ga-Yeong, Su-Min Lim, and Ieck-Chae Euom. "A Study on Performance Metrics for Anomaly Detection Based on Industrial Control System Operation Data." Electronics 11, no. 8 (2022): 1213. http://dx.doi.org/10.3390/electronics11081213.

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Recently, OT (operational technology) networks of industrial control systems have been combined with IT networks. Therefore, OT networks have inherited the vulnerabilities and attack paths existing in IT networks. Consequently, attacks on industrial control systems are increasing, and research on technologies combined with artificial intelligence for detecting attacks is active. Current research focuses on detecting attacks and improving the detection accuracy. Few studies exist on metrics that interpret anomaly detection results. Different analysis metrics are required depending on the charac

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Rodríguez-Pupo, Luis, Carlos Granell, and Sven Casteleyn. "An Analytics Platform for Integrating and Computing Spatio-Temporal Metrics." ISPRS International Journal of Geo-Information 8, no. 2 (2019): 54. http://dx.doi.org/10.3390/ijgi8020054.

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In large-scale context-aware applications, a central design concern is capturing, managing and acting upon location and context data. The ability to understand the collected data and define meaningful contextual events, based on one or more incoming (contextual) data streams, both for a single and multiple users, is hereby critical for applications to exhibit location- and context-aware behaviour. In this article, we describe a context-aware, data-intensive metrics platform —focusing primarily on its geospatial support—that allows exactly this: to define and execute metrics, which capture mean

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Kim, Bosung, Gyu-min Lee, and Byeong-hee Roh. "MAC Protocol for Quality-Aware Real-Time Voice Delivery in Cognitive Radio-Enabled WSNs." International Journal of Distributed Sensor Networks 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/952951.

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We propose a quality-aware media access control (MAC) protocol for real-time voice delivery in cognitive radio- (CR-) enabled wireless sensor networks (WSNs). The temporal structure of the system model is addressed by using periodic timeslots in order to make more efficient use of the spectrum. In our proposed temporal structure, a bandwidth broker in such centralized CR networks (CCRNs) is selected as a central counterpart to synchronize with secondary users (SUs) and assign spectral resources to them. We develop an analytical model for SUs for the Call Admission Control (CAC) of voice traffi

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Hossain, Georges, Rondeau, and Divoux. "Energy, Carbon and Renewable Energy: Candidate Metrics for Green-aware Routing?" Sensors 19, no. 13 (2019): 2901. http://dx.doi.org/10.3390/s19132901.

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There are all sort of indications that Internet usage will go only upwards, resulting in an increase in energy consumption and CO2 emissions. At the same time, a significant amount of this carbon footprint corresponds to the information and communication technologies (ICT) sector, with around one third being due to networking. In this paper we have approached the problem of green networking from the point of view of sustainability. Here, alongside energy-aware routing, we have also introduced pollution-aware routing with environmental metrics like carbon emission factor and non-renewable energ

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Rotar, Raul, Sorin Liviu Jurj, Flavius Opritoiu, and Mircea Vladutiu. "Fault Coverage-Aware Metrics for Evaluating the Reliability Factor of Solar Tracking Systems." Energies 14, no. 4 (2021): 1074. http://dx.doi.org/10.3390/en14041074.

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This paper presents a mathematical approach for determining the reliability of solar tracking systems based on three fault coverage-aware metrics which use system error data from hardware, software as well as in-circuit testing (ICT) techniques, to calculate a solar test factor (STF). Using Euler’s named constant, the solar reliability factor (SRF) is computed to define the robustness and availability of modern, high-performance solar tracking systems. The experimental cases which were run in the Mathcad software suite and the Python programming environment show that the fault coverage-aware m

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Gao, Yong Hong. "Study on QoS Routing Protocol for New Wireless Mesh Networks." Applied Mechanics and Materials 303-306 (February 2013): 1952–55. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1952.

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A QoS aware routing protocol for Wireless Mesh Networks is studied in this paper, the QoS metrics taken into account are bandwidth in our studies, end to end delay and stability of the routes. The routing metric reflects the maximal available bandwidth of a routing path, which is calculated based on the available bandwidth of mesh nodes with the consideration of the interference caused by the hidden terminals in WMNs. With the help of admission control decision, QRBC uses the routing metric in the route discovery process to find a QoS guarantee route for the real-time applications. The simulat

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Hassani, Abdelhadi Eloudrhiri, Aicha Sahel, Abdelmajid Badri, and El Mourabit Ilham. "A hybrid objective function with empirical stability aware to improve RPL for IoT applications." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (2021): 2350. http://dx.doi.org/10.11591/ijece.v11i3.pp2350-2359.

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The diverse applications of the internet of things (IoT) require adaptable routing protocol able to cope with several constraints. Thus, RPL protocol was designed to meet the needs for IoT networks categorized as low power and lossy networks (LLN). RPL uses an objective function based on specific metrics for preferred parents selection through these packets are sent to root. The single routing metric issue generally doesn’t satisfy all routing performance requirements, whereas some are improved others are degraded. In that purpose, we propose a hybrid objective function with empirical stabilit

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Abdelhadi, Eloudrhiri Hassani, Sahel Aicha, Badri Abdelmajid, and Mourabit Ilham El. "A hybrid objective function with empirical stability aware to improve RPL for IoT applications." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (2021): 2350–59. https://doi.org/10.11591/ijece.v11i3.pp2350-2359.

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The diverse applications of the internet of things (IoT) require adaptable routing pro- tocol able to cope with several constraints. Thus, RPL protocol was designed to meet the needs for IoT networks categorized as low power and lossy networks (LLN). RPL uses an objective function based on specific metrics for preferred parents selection through these packets are sent to root. The single routing metric issue generally doesn’t satisfy all routing performance requirements, whereas some are improved others are degraded. In that purpose, we propose a hybrid objective function with empirical

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Siraj, Mohammad, and Zeeshan Abbasi. "Impact Of Load Balancing Interference Routing Metric On Multi Channel Multi Radio Wireless Mesh Network." ITM Web of Conferences 42 (2022): 01002. http://dx.doi.org/10.1051/itmconf/20224201002.

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Wireless Mesh Network (WMNs) is a promising technology that has been accepted favorably by the Internet Service Providers (ISPs). ISPs use it to provide last mile broadband Internet connectivity to the end users. This popularity arises from the fact, that it inter operates well with diverse wireless systems and provides robust fault tolerance with a high degree of redundancy and reliability. Routing metrics plays a crucial part in the performance of WMN. When routing protocols are implemented, the routing metric is assigned to different paths. It calculates the optimum path to predict the best

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Stuart, David. "Metrics for an increasingly complicated information ecosystem." Online Information Review 39, no. 6 (2015): 848–54. http://dx.doi.org/10.1108/oir-06-2015-0174.

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Purpose – The purpose of this paper is to encourage recognition of the potential impact of an increasingly complicated information ecosystem on scientometric indicators. Design/methodology/approach – The paper considers how new web technologies have impacted the role of time in scientometric indicators. Findings – The paper suggests that it is important to be aware of the limitations of scientometrics indicators in an increasingly complicated information environment, although without a more developed semantic web there is little that can be done. Practical implications – Users of scientometric

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Shi, Yan, Long Hao Zou, and Shan Zhi Chen. "A Mobility Pattern Aware Clustering Mechanism for Mobile Vehicular Networks." Applied Mechanics and Materials 130-134 (October 2011): 317–20. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.317.

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Clustering is one of the promising technologies for group mobility in mobile vehicular networks. MPAC (Mobility Pattern Aware Clustering) mechanism is proposed in this paper. MPAC aims at improving the stability of the cluster depending on GR (Group Relation), which is derived from some basic mobility metrics such as speed, direction, acceleration and vehicle location. The cluster formation and cluster maintenance operation based on the above mobility metrics are defined. Simulation results show that MPAC has obvious performance advantages in cluster stability, which is illustrated by the mean

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Cheng, Shulin, and Yuejun Liu. "Time-Aware and Grey Incidence Theory Based User Interest Modeling for Document Recommendation." Cybernetics and Information Technologies 15, no. 2 (2015): 36–52. http://dx.doi.org/10.1515/cait-2015-0027.

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Abstract Document recommendation involves the recommendation of documents similar to those that a user has preferred in the past. The Vector Space Model (VSM) is commonly adopted to denote the document objects and user interests. The user interests are extracted from the documents that a user has browsed. The interest degree of the user is calculated using the TF-IDF method, but the time factor is not considered. The recent documents that a user has browsed embody much more his/her interests. This study proposes a time-aware and grey incidence theory based user interest model to improve docume

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Khan, Samiullah, Khalid Saeed, Muhammad Faran Majeed, et al. "Range aware message drop policy for delay tolerant networks." PeerJ Computer Science 10 (July 12, 2024): e2099. http://dx.doi.org/10.7717/peerj-cs.2099.

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In delay tolerant networks (DTNs) the messages are often not delivered to the destination due to a lack of end-to-end connectivity. In such cases, the messages are stored in the buffer for a long time and are transmitted when the nodes come into the range of each other. The buffer size of each node has a limited capacity, and it cannot accommodate the new incoming message when the buffer memory is full, and as a result network congestion occurs. This leads to a low delivery probability and thus increases the overhead ratio. In this research work, a new buffer management scheme called Range Awa

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Yan, An, and Bill Howe. "Fairness-Aware Demand Prediction for New Mobility." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 1079–87. http://dx.doi.org/10.1609/aaai.v34i01.5458.

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Emerging transportation modes, including car-sharing, bike-sharing, and ride-hailing, are transforming urban mobility yet have been shown to reinforce socioeconomic inequity. These services rely on accurate demand prediction, but the demand data on which these models are trained reflect biases around demographics, socioeconomic conditions, and entrenched geographic patterns. To address these biases and improve fairness, we present FairST, a fairness-aware demand prediction model for spatiotemporal urban applications, with emphasis on new mobility. We use 1D (time-varying, space-constant), 2D (

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Amer, Ali A., Muna Al-Razgan, Hassan I. Abdalla, Mahfoudh Al-Asaly, Taha Alfakih, and Muneer Al-Hammadi. "Neighboring-Aware Hierarchical Clustering." International Journal on Semantic Web and Information Systems 20, no. 1 (2024): 1–24. http://dx.doi.org/10.4018/ijswis.346377.

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In this work, a simple yet robust neighboring-aware hierarchical-based clustering approach (NHC) is developed. NHC employs its dynamic technique to take into account the surroundings of each point when clustering, making it extremely competitive. NHC offers a straightforward design and reliable clustering. It comprises two key techniques, namely, neighboring- aware and filtering and merging. While the proposed neighboring-aware technique helps find the most coherent clusters, filtering and merging help reach the desired number of clusters during the clustering process. The NHC's performance, w

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Arney, David, Yi Zhang, Lauren R. Kennedy-Metz, Roger D. Dias, Julian M. Goldman, and Marco A. Zenati. "An Open-Source, Interoperable Architecture for Generating Real-Time Surgical Team Cognitive Alerts from Heart-Rate Variability Monitoring." Sensors 23, no. 8 (2023): 3890. http://dx.doi.org/10.3390/s23083890.

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Clinical alarm and decision support systems that lack clinical context may create non-actionable nuisance alarms that are not clinically relevant and can cause distractions during the most difficult moments of a surgery. We present a novel, interoperable, real-time system for adding contextual awareness to clinical systems by monitoring the heart-rate variability (HRV) of clinical team members. We designed an architecture for real-time capture, analysis, and presentation of HRV data from multiple clinicians and implemented this architecture as an application and device interfaces on the open-s

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Lohith, Lakkakula. "Speech Enhancement via Metric GAN and Kolmogorov-Arnold Networks: A Deep Learning Approach in Python." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49208.

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Abstract - Speech enhancement in noisy environments remains a critical challenge for robust voice communication systems. Traditional signal processing techniques and supervised deep learning models often struggle to generalize to diverse noise conditions and fail to optimize for human perceptual quality. This paper proposes a novel Metric GAN+KAN architecture, which integrates a Generative Adversarial Network (GAN) with Kolmogorov-Arnold Networks (KAN) to enhance speech signals by focusing both on perceptual fidelity and structural consistency. The GAN-based generator learns to map noisy speec

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R, Samrrutha, Stephi Jacob, Akalya A, Karthika L, and Anisha C.D. "Investigating Process Scheduling Techniques for Optimal Performance and Energy Efficiency in Operating Systems." Journal of Information Technology and Digital World 6, no. 4 (2024): 388–400. https://doi.org/10.36548/jitdw.2024.4.006.

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Process scheduling is a critical component of operating systems, determining the order in which processes are allocated CPU time. Traditionally, scheduling algorithms have aimed to optimize performance metrics such as throughput, latency, and CPU utilization. However, with increasing emphasis on energy efficiency in modern computing, particularly in mobile devices and data centers, energy consumption has become a key factor in evaluating scheduling strategies. This survey explores various process scheduling algorithms, focusing on their impact on energy efficiency. A comparative analysis is pr

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Navaz, Alramzana Nujum, Hadeel T. El Kassabi, Mohamed Adel Serhani, and Ezedin S. Barka. "Resource-Aware Federated Hybrid Profiling for Edge Node Selection in Federated Patient Similarity Network." Applied Sciences 13, no. 24 (2023): 13114. http://dx.doi.org/10.3390/app132413114.

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The widespread adoption of edge computing for resource-constrained devices presents challenges in computational straggler issues, primarily due to the heterogeneity of edge node resources. This research addresses these issues by introducing a novel resource-aware federated hybrid profiling approach. This approach involves classifying edge node resources with relevant performance metrics and leveraging their capabilities to optimize performance and improve Quality of Service (QoS), particularly in real-time eHealth applications. Such paradigms include Federated Patient Similarity Network (FPSN)

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Alasmary, Hisham. "ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring." Sensors 24, no. 4 (2024): 1346. http://dx.doi.org/10.3390/s24041346.

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Addressing the increasing demand for remote patient monitoring, especially among the elderly and mobility-impaired, this study proposes the “ScalableDigitalHealth” (SDH) framework. The framework integrates smart digital health solutions with latency-aware edge computing autoscaling, providing a novel approach to remote patient monitoring. By leveraging IoT technology and application autoscaling, the “SDH” enables the real-time tracking of critical health parameters, such as ECG, body temperature, blood pressure, and oxygen saturation. These vital metrics are efficiently transmitted in real tim

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Krishna Kanth Vangaru. "Adaptive Heterogeneity-Aware CPU Scheduling using Deep Reinforcement Learning for Energy-Efficient Real-Time VR/AR on Mobile Platforms." Journal of Computer Science and Technology Studies 7, no. 7 (2025): 908–20. https://doi.org/10.32996/jcsts.2025.7.7.100.

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This article presents an innovative approach to CPU scheduling for mobile Virtual Reality (VR) and Augmented Reality (AR) applications utilizing Heterogeneous Multi-Processors (HMPs). The proposed system leverages Machine Learning (ML) and Reinforcement Learning (RL) techniques to develop an adaptive, heterogeneity-aware scheduler that dynamically optimizes task placement and frequency scaling. Traditional schedulers face significant challenges when managing the highly variable workloads characteristic of immersive applications, particularly in balancing real-time performance requirements with

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Zahmat Doost, Elmira, and Jamie C. Gorman. "Enhancing Human-Autonomous System Interaction and Team Dynamics in Automated Driving Systems (ADS)." Proceedings of the AAAI Symposium Series 5, no. 1 (2025): 127–30. https://doi.org/10.1609/aaaiss.v5i1.35577.

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As Automated Driving Systems (ADS) advance toward higher levels of automation (SAE Levels 3-5), the role of human drivers is shifting from active control to supervision and intervention. However, traditional human-automation interaction frameworks do not fully account for the dynamic team-based coordination required for effective ADS integration. Poor adaptability and coordination between drivers and ADS can result in critical safety failures, as seen in real-world incidents. This research focuses on human-autonomy teaming (HATs) in ADS, emphasizing collaborative adaptation, shared decision-ma

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Kong, Ruoyan, Ruixuan Sun, Charles Chuankai Zhang, Ye Yuan, and Joseph A. Konstan. "Supporting Organizations in Improving Employee Bulk E-mail --- A Tool Design and Evaluation Study." Proceedings of the ACM on Human-Computer Interaction 8, CSCW2 (2024): 1–28. http://dx.doi.org/10.1145/3686911.

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Organizations often send bulk emails to employees to make them aware of policy changes, organization plans, and events. Many of these emails, however, are long digests with many separate messages that waste employees' time and reduce their awareness. This study introduces CommTool--a prototype tool to help organizational communicators better understand their emails' performance and cost. We first interviewed 5 communicators and identified the need to measure the performance of each message within bulk email. Then we iteratively designed and deployed an organizational bulk email evaluation plat

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Sarita, S. Gaikwad. "SECURE POWER AWARE ROUTING TO SUPPORT REAL TIME TRAFFIC IN MOBILE ADHOC NETWORKS." International Journal on Cloud Computing: Services and Architecture (IJCCSA) 2, February (2018): 01–13. https://doi.org/10.5281/zenodo.1444092.

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A mobile Ad hoc network (MANET) is a collection of wireless nodes that forms a network without central administration. The nodes in such kind of network serve as routers as well as hosts. The nodes can forward packets on behalf of other nodes and run user applications. These devices are operated on battery which provides limited working capacity to the mobile nodes. Power failure and the energy consumption of the nodes is a critical factor in the operation of a mobile ad hoc network. The performance of the node can be hampered by power failure, which affects the ability of node to forward the

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Keat, Low Choon, Chong Li Chiao, Ng Yen Phing, and Tew Yiqi. "Enhancement of QoE-Aware Application Allocation and Energy-Aware Module Allocation with Offloading Applications in Fog Computing." International Journal of Research and Innovation in Social Science IX, no. I (2025): 4290–396. https://doi.org/10.47772/ijriss.2025.9010333.

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The Internet of Things (IoT) and other rapidly evolving technologies have profoundly affected daily life and created an exponential rise in the amount of data generated and processed. By extending cloud capabilities to the network edge, fog computing lowers latency and boosts the effectiveness of data processing. But it also brings with it new difficulties, especially regarding resource management and energy usage. This study starts with a thorough analysis of the current state of fog computing systems, pointing out weaknesses and areas for improvement. We suggest enhancing the current QoE-Awa

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Czarnul, Pawel, Jerzy Proficz, and Adam Krzywaniak. "Energy-Aware High-Performance Computing: Survey of State-of-the-Art Tools, Techniques, and Environments." Scientific Programming 2019 (April 24, 2019): 1–19. http://dx.doi.org/10.1155/2019/8348791.

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The paper presents state of the art of energy-aware high-performance computing (HPC), in particular identification and classification of approaches by system and device types, optimization metrics, and energy/power control methods. System types include single device, clusters, grids, and clouds while considered device types include CPUs, GPUs, multiprocessor, and hybrid systems. Optimization goals include various combinations of metrics such as execution time, energy consumption, and temperature with consideration of imposed power limits. Control methods include scheduling, DVFS/DFS/DCT, power

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Wang, Yuxiang, Zhangyang Peng, Xiangyu Ke, Xiaoliang Xu, Tianxing Wu, and Yuan Gao. "Cohesiveness-aware Hierarchical Compressed Index for Community Search on Attributed Graphs." Proceedings of the ACM on Management of Data 3, no. 1 (2025): 1–27. https://doi.org/10.1145/3709672.

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Community search on attributed graphs (CSAG) is a fundamental topic in graph data mining. Given an attributed graph G and a query node q , CSAG seeks a structural- and attribute-cohesive subgraph from G that contains q . Exact methods based on graph traversal are time-consuming, especially for large graphs. Approximate methods improve efficiency by pruning the search space with heuristics but still take hundreds of milliseconds to tens of seconds to respond, hindering their use in time-sensitive applications. Moreover, pruning strategies are typically tailored to specific algorithms and their

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Dheerendra, Yaganti. "Sustainable Microservice Deployment in Azure Using KEDA and Carbon Intensity Metrics for .NET Workloads." European Journal of Advances in Engineering and Technology 12, no. 1 (2025): 24–28. https://doi.org/10.5281/zenodo.15241009.

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The rising demand for cloud-native applications has intensified the energy consumption of modern software systems, calling for more sustainable deployment strategies. This thesis proposes a carbon-aware scheduling framework for .NET microservices deployed in Azure, leveraging Kubernetes Event-Driven Autoscaling (KEDA) and real-time carbon intensity metrics. The approach dynamically scales workloads based on both application demand and the environmental impact of power usage, enabling green DevOps practices. By integrating carbon intensity APIs with Azure Container Apps and KEDA, deployment dec

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Mehfuz, Shabana, and M. N. Doja. "Swarm Intelligent Power-Aware Detection of Unauthorized and Compromised Nodes in MANETs." Journal of Artificial Evolution and Applications 2008 (February 7, 2008): 1–16. http://dx.doi.org/10.1155/2008/236803.

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Characteristics of mobile ad hoc networks (MANETs) such as lack of central coordination, mobility of hosts, and limited availability of resources make quality of service (QoS) provisioning very challenging. Limited resource availability such as battery power and insecure medium is one of the major QoS issues to be dealt with. In this paper, we have proposed a new secure power-aware ant routing algorithm (SPA-ARA) for mobile ad hoc networks that is inspired from ant colony optimization (ACO) algorithms which are a swarm intelligent technique. In this algorithm, we have introduced a new metric,

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Choudhury, Nazim. "Community-Aware Evolution Similarity for Link Prediction in Dynamic Social Networks." Mathematics 12, no. 2 (2024): 285. http://dx.doi.org/10.3390/math12020285.

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The link prediction problem is a time-evolving model in network science that has simultaneously abetted myriad applications and experienced extensive methodological improvement. Inferring the possibility of emerging links in dynamic social networks, also known as the dynamic link prediction task, is complex and challenging. In contrast to the link prediction in cross-sectional networks, dynamic link prediction methods need to cater to the actor-level temporal changes and associated evolutionary information regarding their micro- (i.e., link formation/deletion) and mesoscale (i.e., community fo

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Furnadzhiev, Radoslav, Mitko Shopov, and Nikolay Kakanakov. "Efficient Orchestration of Distributed Workloads in Multi-Region Kubernetes Cluster." Computers 14, no. 4 (2025): 114. https://doi.org/10.3390/computers14040114.

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Distributed Kubernetes clusters provide robust solutions for geo-redundancy and fault tolerance in modern cloud architectures. However, default scheduling mechanisms primarily optimize for resource availability, often neglecting network topology, inter-node latency, and global resource efficiency, leading to suboptimal task placement in multi-region deployments. This paper proposes network-aware scheduling plugins that integrate heuristic, metaheuristic, and linear programming methods to optimize resource utilization and inter-zone communication latency for containerized workloads, particularl

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Anan, Muhammad, Khalid Kanaan, Driss Benhaddou, et al. "Occupant-Aware Energy Consumption Prediction in Smart Buildings Using a LSTM Model and Time Series Data." Energies 17, no. 24 (2024): 6451. https://doi.org/10.3390/en17246451.

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Accurate energy consumption prediction in commercial buildings is a challenging research task. Energy prediction plays a crucial role in energy efficiency, management, planning, sustainability, risk management, diagnosis, and demand response. Although many studies have been conducted on building energy predictions, the impact of occupancy on energy prediction models for office-type commercial buildings remains insufficiently explored, despite its potential to improve energy efficiency by 20%. This study investigates energy prediction using a Long Short-Term Memory (LSTM) model that incorporate

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Tam, Prohim, Sa Math, and Seokhoon Kim. "Priority-Aware Resource Management for Adaptive Service Function Chaining in Real-Time Intelligent IoT Services." Electronics 11, no. 19 (2022): 2976. http://dx.doi.org/10.3390/electronics11192976.

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The growth of the Internet of Things (IoT) in various mission-critical applications generates service heterogeneity with different priority labels. A set of virtual network function (VNF) orders represents service function chaining (SFC) for a particular service to robustly execute in a network function virtualization (NFV)-enabled environment. In IoT networks, the configuration of adaptive SFC has emerged to ensure optimality and elasticity of resource expenditure. In this paper, priority-aware resource management for adaptive SFC is provided by modeling the configuration of real-time IoT ser

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MA, LIANG, and MIESO K. DENKO. "ENHANCED ROUTING METRIC FOR LOAD-BALANCING IN WIRELESS MESH NETWORKS." Journal of Interconnection Networks 08, no. 04 (2007): 407–26. http://dx.doi.org/10.1142/s0219265907002107.

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Wireless mesh networks (WMNs) have been drawing significant attention in recent years due to their flexibility in providing extensive wireless backbone. WMNs typically consist of mesh routers and mesh clients with each node operating not only as a host but also as a router. Due to the traffic patterns in WMNs, load-balancing becomes an important issue and may degrade the performance of the entire network. This paper proposes a routing metric known as Weighted Cumulative Expected Transmission Time with Load-Balancing (WCETT-LB) for wireless mesh networks. WCETTT-LB enhances the basic Weighted C

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Yingchia Liu, Hao Tan, Guanghe Cao, and Yang Xu. "Enhancing user engagement through adaptive UI/UX Design: A study on personalized mobile app interfaces." Computer Science & IT Research Journal 5, no. 8 (2024): 1942–62. http://dx.doi.org/10.51594/csitrj.v5i8.1457.

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This paper presents a comprehensive study on developing and evaluating an adaptive UI/UX framework to enhance user engagement in mobile applications through personalized interfaces. The research investigates key factors influencing user engagement, including demographics, cognitive abilities, and contextual variables. A context-aware adaptation engine was designed to adjust interface elements based on real-time user data dynamically. The proposed framework was implemented in a mobile learning application and subjected to rigorous usability testing and user engagement analysis. Results demonstr

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Xia, Yanting, Lin Zhang, Zhou Yang, Chichen Zhang, and Ting Guo. "Temporal-Aware Neural Networks for Balancing Dynamic Preferences and Long-Term Interests in Recommendation Systems." Journal of Computing and Information Technology 33, no. 2 (2025): 123–38. https://doi.org/10.20532/cit.2025.1006009.

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Recommendation systems face the challenge of balancing dynamic short-term preferences with stable long-term interests to deliver personalized and timely recommendations. Traditional methods often treat these aspects separately, leading to suboptimal integration and limited adaptability to evolving user behavior. This paper introduces Temporal-Aware Neural Networks (TANR), a novel framework that leverages a time-aware Transformer architecture to dynamically balance short-term and long-term user preferences. The proposed model incorporates a time decay mechanism within the attention layer to adj

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Zimbelman, Eloise G., and Robert F. Keefe. "Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety." PLOS ONE 17, no. 12 (2022): e0278645. http://dx.doi.org/10.1371/journal.pone.0278645.

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Real-time data- and location-sharing using mesh networking radios paired with smartphones may improve situational awareness and safety in remote environments lacking communications infrastructure. Despite being increasingly used for wildland fire and public safety applications, there has been little formal evaluation of the network connectivity of these devices. The objectives of this study were to 1) characterize the connectivity of mesh networks in variable forest and topographic conditions; 2) evaluate the abilities of lidar and satellite remote sensing data to predict connectivity; and 3)

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N.V, Babu, Abhinav Kumar, and J. Joshua Thomas. "To design and implement the QoS -Aware Energy Efficient Routing Mechanism for the BAN-IoT networks in Smart Health care Applications." Journal of Smart Internet of Things 2024, no. 2 (2024): 55–78. https://doi.org/10.2478/jsiot-2024-0012.

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Abstract The integration of Internet of Things (IoT) and miniature implantable sensors has significantly advanced Body Area Networks (BAN) for real-time monitoring of patients' physiological vital signs, including Electrocardiogram (ECG), Electromyogram (EMG), and Electroencephalogram (EEG). However, the limited bandwidth of wearable nodes and frequent body movements result in recurrent topological changes, causing unreliable and delayed transmissions. To address these challenges, a reliable and low-latency routing mechanism is required to ensure lossless data transfer and support timely clini

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Alam, Muhammad Morshed, and Sangman Moh. "Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks." Electronics 11, no. 7 (2022): 1099. http://dx.doi.org/10.3390/electronics11071099.

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A flying ad hoc network (FANETs), also known as a swarm of unmanned aerial vehicles (UAVs), can be deployed in a wide range of applications including surveillance, monitoring, and emergency communications. UAVs must perform real-time communication among themselves and the base station via an efficient routing protocol. However, designing an efficient multihop routing protocol for FANETs is challenging due to high mobility, dynamic topology, limited energy, and short transmission range. Recently, owing to the advantages of multi-objective optimization, Q-learning (QL)-based position-aware routi

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Wu, Qi, Mingyan Han, Ting Jiang, et al. "Realistic Noise Synthesis with Diffusion Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 8 (2025): 8432–40. https://doi.org/10.1609/aaai.v39i8.32910.

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Deep denoising models require extensive real-world training data, which is challenging to acquire. Current noise synthesis techniques struggle to accurately model complex noise distributions. We propose a novel Realistic Noise Synthesis Diffusor (RNSD) method using diffusion models to address these challenges. By encoding camera settings into a time-aware camera-conditioned affine modulation (TCCAM), RNSD generates more realistic noise distributions under various camera conditions. Additionally, RNSD integrates a multi-scale content-aware module (MCAM), enabling the generation of structured no

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Gao, Ruoyuan. "Toward a fairer information retrieval system." ACM SIGIR Forum 55, no. 1 (2021): 1–2. http://dx.doi.org/10.1145/3476415.3476429.

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With the increasing popularity and social influence of information retrieval (IR) systems, various studies have raised concerns on the presence of bias in IR and the social responsibilities of IR systems. Techniques for addressing these issues can be classified into pre-processing , in-processing and post-processing. Pre-processing reduces bias in the data that is fed into machine learning models. In-processing encodes fairness constraints as a part of the objective function or learning process. Post-processing operates as a top layer over the trained model to reduce the presentation bias expo

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Bouchekara, Houssem R. E. H., Abdulazeez F. Salami, Yusuf A. Sha’aban, Mouaaz Nahas, Mohammad S. Shahriar, and Mohammed A. Alanezi. "TUBER: Time-aware UAV-based energy-efficient reconfigurable routing scheme for smart wireless livestock sensor network." PLOS ONE 19, no. 1 (2024): e0292301. http://dx.doi.org/10.1371/journal.pone.0292301.

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This paper is a follow-up to a recent work by the authors on recoverable UAV-based energy-efficient reconfigurable routing (RUBER) scheme for addressing sensor node and route failure issues in smart wireless livestock sensor networks. Time complexity and processing cost issues connected to the RUBER scheme are consequently treated in this article by proffering a time-aware UAV-based energy-efficient reconfigurable routing (TUBER) scheme. TUBER scheme employs a synchronized clustering-with-backup strategy, a minimum-hop neighborhood recovery mechanism, and a redundancy minimization technique. C

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Venkataraman, Gangadharan. "Operational Excellence in Real-Time AI Systems: Observability, Experimentation, and Scalability." European Journal of Computer Science and Information Technology 13, no. 47 (2025): 61–74. https://doi.org/10.37745/ejcsit.2013/vol13n476174.

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Operational excellence in real-time AI systems requires sophisticated practices beyond model performance metrics. As organizations integrate AI deeper into critical business functions, the need for robust operational frameworks becomes paramount. This article presents key strategies for achieving production-grade reliability in AI systems through three essential pillars: observability, experimentation, and scalability. The observability section details techniques for monitoring both system health and model performance, including drift detection and integration with business metrics. The experi

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Surendra Lakkaraju. "AI-Powered Dynamic Risk Scoring for E-commerce Transactions." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 3515–26. https://doi.org/10.32628/cseit251112363.

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This article presents a comprehensive framework for dynamic risk assessment in e-commerce fraud detection using advanced machine learning techniques. The article introduces an innovative approach combining reinforcement learning, Bayesian networks, and real-time processing architectures to address the challenges of modern fraud detection. The implementation demonstrates significant improvements in fraud detection accuracy, reduction in false positives, and enhanced customer experience through adaptive risk scoring mechanisms. The system incorporates sophisticated feature engineering, network-a

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Khaleghnasab, Roghaye, Karamolah Bagherifard, Hamid Parvin, Samad Nejatian, and Bahman Ravaei. "An Energy and Load Aware Multipath Routing Protocol in Internet of Things." Management Strategies and Engineering Sciences 7, no. 3 (2025): 1–15. https://doi.org/10.61838/msesj.7.3.1.

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IoT is a network of smart things. This indicates the ability of these physical things to transfer information with other physical things. The characteristics of these networks, such as topology dynamicity and energy constraint, challenges the routing problem in these networks. Previous routing methods could not achieve the required performance in this type of network. One of the routing methods is utilization of multipath protocols which send data to its destination using routes with separate links. One of such protocols is RPL routing protocol. In this paper, this method is improved using com

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