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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Gunjan Rabimov Nodir Rakhmatilloevich Arun Khatri Christo Ananth Anooja A Blessy Thankachan Department Of Humanities And Applied Sciences Advanced Institute Of Technology And Management Haryana India Samarkand State University Samarkand City Uzbekistan Mittal School of Business Lovely Professional University Punjab India Faculty of Intelligent Systems and Computer Technologies Samarkand State University Uzbekistan JECRC University Rajasthan India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Quantum teleportation is a new method in quantum communication that allows quantum states to be sent securely using entanglement and classical signals. This research focuses on problems like maintaining strong entanglement, dealing with quantum noise (decoherence) and handling delays in classical communication, which currently limit practical applications. To solve these problems, we built and tested a quantum teleportation circuit using simulation tools and visualized the results using Bloch spheres and statevector analysis. Our results show that quantum states can be teleported accurately while preserving their properties. This proves quantum teleportation is practically achievable and can be used in secure communication, quantum networks, and quantum computers, making it an important step towards building a reliable quantum internet.

关键词： Visualization Quantum entanglement Noise Teleportation Quantum state Reliability theory Vectors Delays System analysis and design Quantum repeaters

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A Comparative Analysis of Artificial Intelligence and Business Intelligence Using Big Data Analytics

A Comparative Analysis of Artificial Intelligence and Busine...

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Advances in computer Science, Electrical, Electronics, and Communication technologies (CE2CT), International Conference on

作者： Bhagath Chandra Chowdari Marella Gopi Chand Vegineni Sireesha Addanki Ehsan Ellahi Arun Kumar K Rishabh Mandal Department of Financial Services Insights & Data Capgemini India Enrollment and Eligibility Team India System soft Technologies llc Information Technology India Department of Information Systems Cleveland State University Ohio United States Department of Computer Science and Engineering Tula's Institute Dehradun UK India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Business intelligence (BI) encompasses the tools and uses to gather, combine, examine, and display data about a specific firm. Using historical and current efficiency comparisons, these structures assist in formulating plans and choices to achieve better outcomes in the future. Additionally, it helps firms determine which business issues and industry trends require attention. It has several benefits, such as increasing responsibility and efficiency, but it carries some disadvantages, such as being expensive, complicated, and time-consuming to adopt. Using big data-driven approaches to optimize talent recruitment is essential for improved decisions in the cutthroat world of BI. The efficacy of many machine learning algorithms to increase recruiting performance and accuracy is compared in this investigation. For optimizing, cross-validating, and hyperparameter modification were employed. The Random Forest (RF) algorithm has the greatest accuracy (92.9%), trailed by the Gradient Boosting Classifying system ( $\mathbf{92.6 \%}$ ) and the neural network (NNs) models ( ${92}.7 \%$ ). These findings imply that sophisticated ML algorithms, in particular RF and NN, can greatly improve BI platforms' hiring procedures. Recruiters can benefit from this research's insightful recommendations for incorporating advanced ML methods into talent recruitment plans.

关键词： Radio frequency Machine learning algorithms Accuracy Artificial neural networks Big Data Market research Classification algorithms Business intelligence Random forests Recruitment

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Leveraging Artificial Intelligence for Advancements in Mental Disorders: A Short Review

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Procedia computer Science 2025年 257卷 676-683页

作者： Selain K. Kasereka Kenny N. Tshibangu Myriam M. Nyembo Lionel K. Tshitenge Marina K. Muzindusi Godwill W.K. Ilunga Soufian Ben Amor Christian D. Bope Tasho Tashev Kyandoghere Kyamakya Mathematics Statistics and Computer Science Department University of Kinshasa Kinshasa DR. Congo Faculty of English Engineering Studies Technical University of Sofia Sofia Bulgaria ABIL Research Center Kinshasa DR. Congo Faculty of Computer Science Université Nouveaux Horizons Lubumbashi DR Congo LI-PaRAD Université de Versailles-Saint-Quentin-en-Yvelines France Institute of Smart Systems Technologies University of Klagenfurt Klagenfurt Austria Polytechnic Faculty University of Kinshasa Kinshasa Democratic Republic of the Congo

This study explores the expanding influence of artificial intelligence (AI) on the comprehension and management of mental disorders conditions. By analyzing recent advancements, this article highlights how AI technologies are being applied to diagnose, model, predict, and treat conditions, such as depression, anxiety, and bipolar disorder. It examines innovative approaches, including AI-powered mental wellness apps, that enable users to monitor their symptoms and access personalized support. Additionally, it discusses research on advanced algorithms designed to analyze online social interactions to identify early signs of psychological distress and to facilitate timely interventions. The paper also addresses the challenges involved in integrating AI into mental healthcare and highlights ethical and legal concerns that should be addressed when developing AI mental health models.

关键词： Mental Health Predictive Analytics Mental Health Interventions Machine Learning AI in Healthcare

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Enhancing Building Safety: A Brief Review of Agent-Based Modeling for Fire Evacuation Simulation

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Procedia computer Science 2025年 257卷 668-675页

作者： Selain K. Kasereka Ortega M. Kabwe Maria W.K. Kinyanta Audrey L. Kasongo Godwill W.K. Ilunga Katya Muhambya Tasho Tashev Kyandoghere Kyamakya Mathematics Statistics and Computer Science Department University of Kinshasa Kinshasa DR. Congo Faculty of English Engineering Studies Technical University of Sofia Sofia Bulgaria ABIL Research Center Kinshasa DR. Congo Faculty of Computer Science Université Nouveaux Horizons Lubumbashi DR Congo Ganpat University Faculty of Computer Application Gujarat India Université Libre des Pays des Grands Lacs Faculty of Sciences and Technology Goma DR Congo Institute of Smart Systems Technologies University of Klagenfurt Klagenfurt Austria Polytechnic Faculty University of Kinshasa Kinshasa Democratic Republic of the Congo

The issue of building evacuation in the event of a fire is a significant concern in urban planning and architecture. In the absence of appropriate measures, an emergency situation can potentially result in disastrous outcomes. In this review article, we explore the application of agent-based modeling (ABM) in the simulation of building evacuations in the event of a free breakout. To address this issue, we present a synthesis of findings from several studies, highlighting the advantages of ABM in modeling complex evacuation dynamics such as crowd behavior, consideration of multiple parameters, and decision-making processes. Additionally, we provide insight into the simulation tools and techniques used in studies that demonstrate the practical applications of ABM in enhancing evacuation strategies. Furthermore, we identify current challenges, research gaps, and propose future directions to enhance the accuracy and effectiveness of fire evacuation modeling and simulations. This study aims to contribute to the improvement of proposed solutions and models for the development of successful, effective, and reliable evacuation plans to enhance the safety of occupants in buildings.

关键词： Agent-based Modeling Evacuation Fire Breakout Building Safety Disaster Agent-Based Simulation

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Privacy-Aware Joint DNN Model Deployment and Partition Optimization for Delay-Efficient Collaborative Edge Inference

arXiv

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arXiv 2025年

作者： Cheng, Zhipeng Xia, Xiaoyu Wang, Hong Liwang, Minghui Chen, Ning Fan, Xuwei Wang, Xianbin School of Future Science and Engineering Soochow University Suzhou215006 China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai201804 China Qingdao China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China Department of Electrical and Computer Engineering Western University ON Canada

Edge inference (EI) is a key solution to address the growing challenges of delayed response times, limited scalability, and privacy concerns in cloud-based Deep Neural Network (DNN) inference. However, deploying DNN models on resource-constrained edge devices faces more severe challenges, such as model storage limitations, dynamic service requests, and privacy risks. This paper proposes a novel framework for privacy-aware joint DNN model deployment and partition optimization to minimize long-term average inference delay under resource and privacy constraints. Specifically, the problem is formulated as a complex optimization problem considering model deployment, user-server association, and model partition strategies. To handle the NP-hardness and future uncertainties, a Lyapunov-based approach is introduced to transform the long-term optimization into a single-time-slot problem, ensuring system performance. Additionally, a coalition formation game model is proposed for edge server association, and a greedy-based algorithm is developed for model deployment within each coalition to efficiently solve the problem. Extensive simulations show that the proposed algorithms effectively reduce inference delay while satisfying privacy constraints, outperforming baseline approaches in various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： R.S. Ramya Keren Lois Daniel S. Rajkumar Sruthi S Madhavan Akshya. J Mani Deepak Choudhry Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Department of Computer Science and Business Systems KGiSL Institute of Technology Coimbatore Department of Information Technology Sona College of Technology Salem Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision.

关键词： Training Analytical models Adaptation models Statistical analysis Atmospheric modeling Computational modeling Decision making Predictive models Autonomous aerial vehicles Safety

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Many-Problem Surrogates for Transfer Evolutionary Multiobjective Optimization With Sparse Transfer Stacking

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IEEE Transactions on Evolutionary Computation 2025年

作者： Li, Hao Wan, Fanggao Gong, Maoguo Qin, A.K. Wu, Yue Xing, Lining Xidian University School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Shaanxi Province Xi'an710071 China Inner Mongolia Normal University School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi'an China and Academy of Artificial Intelligence College of Mathematics Science Hohhot China Swinburne University of Technology Department of Computing Technologies HawthornVIC3122 Australia Xidian University School of Computer Science and Technology Xi'an710071 China

For expensive multiobjective optimization problems, there exists useful knowledge, e.g., the trained surrogate models, can be transferred to assist the optimization of a target optimization problem, which is termed as multi-problem surrogates. Stacking transfer is able to combine the pretrained source surrogate models and the preliminary target model with a meta-regression algorithm to transfer knowledge from source to target. However, when large-scale source models are involved in the many-problem scenarios, the less correlated sources may hurt the target performance, which is known as negative transfer. In this paper, sparse representation of the coefficients of meta-regression is considered to automatically select the most relevant source models for largely avoiding negative transfer. In the proposed many-problem surrogates, the coefficients of the source and target models are assumed to be sparse under the non-negativity and sum-to-one constraints. Then a sparse transfer stacking model is established with l1-norm of the coefficients. Next, the alternating direction method of multipliers is employed to solve the resulting constrained optimization problem by converting it into several much simpler problems. Most of the previous works assume that the costs for evaluation have no much difference and this assumption rarely holds in the real-world applications. In order to further reduce the total costs, an improved surrogate model with a cost-sensitive measure is designed to estimate the cost and select new solutions for real evaluation based on their estimated fitness, uncertainty and cost. Experimental results on synthetic and practical problems have demonstrated the superiority of the proposed many-problem surrogates. © 1997-2012 IEEE.

关键词： Multiobjective optimization sparse representation surrogate-assisted evolutionary algorithm transfer stacking

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Exploiting Operator-Level Concurrency Control to Guide Deployment for Real-Time Tasks in Edge AI Cluster 25

Exploiting Operator-Level Concurrency Control to Guide Deplo...

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Proceedings of the 23rd ACM Conference on Embedded Networked Sensor systems

作者： Changyao Lin Ziyang Zhang Jie Liu School of Computer Science and Technology Harbin Institute of Technology Harbin Heilongjiang China NESLab Politecnico di Milano Milano Italy National Key Laboratory of Smart Farm Technologies and Systems Harbin Institute of Technology Harbin Heilongjiang China International Research Institute for Artificial Intelligence Harbin Institute of Technology Shenzhen Guangdong China

ISBN: (纸本)9798400714795

Existing task deployment frameworks for edge clusters optimize at the model-level, lacking fine-grained resource awareness and concurrency control, where the urgent tasks are frequently blocked and miss their deadlines. Therefore, we propose a multi-level collaborative deployment framework Coconut for real-time deep learning tasks in the typical heterogeneous edge GPU cluster. Coconut collaboratively optimizes model deployment and fine-grained concurrency control. To address the high complexity of multi-level collaborative optimization, we employ an efficient learning-based search algorithm. Based on the operator-level information, we also pre-train an accurate latency predictor for each device, enabling centralized optimization to further accelerate the search. We conduct a preliminary evaluation in an edge cluster and validate the effectiveness of Coconut.

关键词： edge computing

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Bidirectional Stacking Ensemble Curriculum Learning for Hyperspectral Image Imbalanced Classification With Noisy Labels

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Wang, Yixin Li, Hao Gong, Maoguo Wu, Yue Gong, Peiran Qin, A.K. Xing, Lining Zhang, Mingyang Xidian University School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Shaanxi Xi’an710071 China Inner Mongolia Normal University Academy of Artificial Intelligence College of Mathematics Science Hohhot010028 China Xidian University School of Computer Science and Technology Xi’an710071 China Swinburne University of Technology Department of Computing Technologies HawthornVIC3122 Australia

Hyperspectral imaging has demonstrated substantial advantages in enhancing classification performance in remote sensing applications due to its abundant spectral information. To address the challenges of label noise and class imbalance in the hyperspectral image (HSI) classification, we propose an end-to-end feature-guided network (FGN) for HSIs. Instead of merely combining spatial and channel attention, the FGN leverages feature-level attention interactions to enhance contextual understanding, leading to better feature extraction, especially for underrepresented classes. Furthermore, a bidirectional loss for curriculum learning (CL) is proposed to rank the HSI training data in descending or ascending order. The top and bottom loss regularizers are designed to make the proposed model suitable for noisy and imbalanced HSI data distributions. In the phase of selecting the pace parameter, a stacking ensemble CL (SECL) model is established to avoid the outliers and noisy HSI data involved in the CL training process. A novel instruction matrix based on sample weights is designed for base classifiers. The outputs of the base models, combined with the expected labels, form the input-output pairs for training the second-level classifier. Experiments conducted on multiple hyperspectral imbalanced datasets with noisy labels demonstrate the superior performance of our method. © 1980-2012 IEEE.

关键词： Image classification

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MediVault DApp: A Electronic Health Record Application Using Blockchain Technology

MediVault DApp: A Electronic Health Record Application Using...

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Machine Learning and Autonomous systems (ICMLAS), International Conference on

作者： Lingeswara Reddy Peddu Aditya Mishra Amirineni Rama L Padmaja Rekha G Palanikumar S R. K. Pongiannan Dept. of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of ECE Geethanjali College of Engg. And Tech. Hyderabad India Dept. of Computer science and business systems Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engg. College Avadi Chennai Tamilnadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The healthcare industry stands to benefit greatly from blockchain technology due to its robust security, privacy protection, confidentiality, and decentralized nature. This study outlines how blockchain can revolutionize current medical data systems and proposes a framework for its implementation. The primary objective of this framework is to ensure secure storage of electronic records by establishing comprehensive access protocols for users. When patients seek medical care, hospitals generate and store patient records in a decentralized application (DApp), which is a blockchain- based platform for maintaining electronic medical records. Medical professionals can modify patient records, while patients can retrieve their medical information from the DApp using public keys. Electronic Health Records (EHRs) allow healthcare providers to monitor information necessary for compliance with insurance companies and government regulations. EHRs serve as a centralized data repository for storing doctors' orders related to laboratory tests, x-rays, and other diagnostic procedures. The proposed system aims to optimize individual medical information management, minimize redundant testing, reduce treatment delays, and equip patients with adequate information to make informed decisions.

关键词： Privacy Scalability Medical services Decentralized applications Regulation Blockchains InterPlanetary File System Electronic medical records Usability Medical diagnostic imaging

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