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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Samuel-Soma M. Ajibade Angela Lee Siew Hoong Muhammed Basheer Jasser Adedotun O. Adetunla Anthonia Oluwatosin Adediran Department of Data Science and Artificial Intelligence School of Engineering and Technology Sunway University Selangor Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Sunway University Selangor Malaysia Dept. of Mechanical and Mechatronics Engineering Afe Babalola University Ado Ekiti Nigeria Dept. of Mechanical Engineering Science University of Johannesburg Johannesburg South Africa Department of Real Estate Faculty of Built Environment Universiti Malaya Kuala Lumpur

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

The application of machine learning for power-related purposes represents a novel notion in artificial intelligence for predicting power generation. While prediction models are employed to forecast various global situations, their application in predicting energy-related themes will be distinctive and specialized. Scholars and AI engineers, in collaboration with professionals in the energy sector, are conducting investigations to develop AI models that assist in their work. Consequently, due to the distinct nature of the situation and the diversity of datasets, several AI models are chosen and built to aid in their tasks. This study examines the application of AI models for enhancing energy efficiency in power generating in Malaysia. A Linear Regression model and an artificial Neural Network are developed to forecast future power consumption across various industries in Malaysia, compute the growth rate and average growth rate of the projected energy consumption, and identify a viable area for investment. It also formulates a power strategy based on the aggregate anticipated energy usage. Both models are developed, analyzed, and compared to ascertain the most suitable model for this scenario.

关键词： Energy consumption Computational modeling Linear regression Machine learning artificial neural networks Predictive models Energy efficiency data models Forecasting Power generation

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Correction: Divine Religions algorithm: a novel social-inspired metaheuristic algorithm for engineering and continuous optimization problems

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Cluster Computing 2025年第5期28卷 1-2页

作者： Mozhdehi, Ali Toufanzadeh Khodadadi, Nima Aboutalebi, Mohaddeseh El-kenawy, El-Sayed M. Hussien, Abdelazim G. Zhao, Weiguo Nadimi-Shahraki, Mohammad H. Mirjalili, Seyedali Faculty of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran Department of Civil Architectural and Environmental Engineering University of Miami Coral Gables USA Department of Computer Engineering Azad University of Lahijan Lahijan Iran School of ICT Faculty of Engineering Design and Information & Communications Technology (EDICT) Bahrain Polytechnic Isa Town Bahrain Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer and Information Science Linköping University Linköping Sweden Faculty of Science Fayoum University Fayoum Egypt School of Water Conservancy and Hydropower Handan China Faculty of Computer Engineering Najafabad Branch Islamic Azad University Najafabad Iran Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley Australia University Research and Innovation Center (EKIK) Obuda University Budapest Hungary

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AI-DRIVEN DAY-TO-DAY ROUTE CHOICE

arXiv

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arXiv 2024年

作者： Wang, Leizhen Duan, Peibo He, Zhengbing Lyu, Cheng Chen, Xin Zheng, Nan Yao, Li Ma, Zhenliang Department of Data Science and Artificial Intelligence Monash University Melbourne Australia Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge United States Chair of Transportation Systems Engineering Technical University of Munich Munich Germany School of Civil Engineering The University of Queensland Brisbane Australia Department of Civil Engineering Monash University Melbourne Australia School of Computer Science and Engineering Southeast University Nanjing China Department of Civil and Architectural Engineering KTH Royal Institute of Technology Stockholm Sweden

Understanding travelers’ route choices can help policymakers devise optimal operational and planning strategies for both normal and abnormal circumstances. However, existing choice modeling methods often rely on predefined assumptions and struggle to capture the dynamic and adaptive nature of travel behavior. Recently, Large Language Models (LLMs) have emerged as a promising alternative, demonstrating remarkable ability to replicate human-like behaviors across various fields. Despite this potential, their capacity to accurately simulate human route choice behavior in transportation contexts remains doubtful. To satisfy this curiosity, this paper investigates the potential of LLMs for route choice modeling by introducing an LLM-empowered agent, "LLMTraveler." This agent integrates an LLM as its core, equipped with a memory system that learns from past experiences and makes decisions by balancing retrieved data and personality traits. The study systematically evaluates the LLMTraveler’s ability to replicate human-like decision-making through two stages of day-to-day (DTD) congestion games: (1) analyzing its route-switching behavior in single origin-destination (OD) pair scenarios, where it demonstrates patterns that align with laboratory data but cannot be fully explained by traditional models, and (2) testing its capacity to model adaptive learning behaviors in multi-OD scenarios on the Ortuzar and Willumsen (OW) network, producing results comparable to Multinomial Logit (MNL) and Reinforcement Learning (RL) models. Additionally, the study assesses lightweight, open-source LLMs, highlighting their effectiveness in route choice simulation and their potential as cost-effective alternatives to more advanced closed-source models. These experiments demonstrate that the framework can partially replicate human-like decision-making in route choice while providing natural language explanations for its decisions. This capability offers valuable insights for transportation poli

关键词： Reinforcement learning

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A Virtual Assistance for Visually Impaired People to Recognize Fabric Pattern and Color Using Human computer Interaction Logic

A Virtual Assistance for Visually Impaired People to Recogni...

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International Conference on Communication and Electronics Systems (ICCES)

作者： M.S Bravishma Panicker R. Mohandas Shanmugasundaram Hariharan K. Nirmala Devi M.S.K. Chaitanya Rashmi Mishra Saveetha School Of Law Saveetha Institute of Medical and Technical Science (SIMATS) Chennai-77 Tamil Nadu India Department of ECE Chennai Institute of Technology Chennai Department of Artificial Intelligence and Data Science Vardhaman College of Engineering India Department of Computer Applications Madanapalle Institute of Technology & Science Department of CIVIL S.R.K.R Engineering College chinamiram Bhimavaram Andra pradesh Department of Applied Science & Humanities G L Bajaj Institute of Technology & Management Greater Noida

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Modern advancements in technology have paved the way for innovative solutions to assist visually impaired individuals in their daily lives. This study presents a virtual assistance system designed to recognize fabric patterns and colors using a combination of deep learning and human-computer interaction techniques. The system employs high-quality image acquisition methods, bilateral filtering, and histogram equalization to preprocess images, enhancing their quality for subsequent analysis. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are integrated to form a Hybrid Fabric Pattern Recognition Network (HFPRN) that accurately identifies fabric patterns. Additionally, the system utilizes RGB color space conversion for precise color detection. Voice command capabilities via Amazon Alexa and tactile feedback mechanisms further enhance user interaction. Experimental results demonstrate the proposed system's high accuracy, with the HFPRN achieving 97% accuracy in fabric pattern recognition and 95% accuracy in color detection. This comprehensive solution significantly improves the autonomy and quality of life for visually impaired individuals by providing real-time, accurate feedback on fabric patterns and colors.

关键词： Human computer interaction Accuracy Recurrent neural networks Image color analysis Virtual assistants Tactile sensors Fabrics Real-time systems Pattern recognition Logic

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Revisiting Few-Shot Learning from a Causal Perspective

arXiv

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arXiv 2022年

作者： Lin, Guoliang Xu, Yongheng Lai, Hanjiang Yin, Jian School of Computer Science and Engineering Sun Yat-Sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Artificial Intelligence Sun Yat-Sen University China

Few-shot learning with N-way K-shot scheme is an open challenge in machine learning. Many metric-based approaches have been proposed to tackle this problem, e.g., the Matching Networks and CLIP-Adapter. Despite that these approaches have shown significant progress, the mechanism of why these methods succeed has not been well explored. In this paper, we try to interpret these metric-based few-shot learning methods via causal mechanism. We show that the existing approaches can be viewed as specific forms of front-door adjustment, which can alleviate the effect of spurious correlations and thus learn the causality. This causal interpretation could provide us a new perspective to better understand these existing metric-based methods. Further, based on this causal interpretation, we simply introduce two causal methods for metric-based few-shot learning, which considers not only the relationship between examples but also the diversity of representations. Experimental results demonstrate the superiority of our proposed methods in few-shot classification on various benchmark datasets. Code is available in https://***/lingl1024/causalFewShot. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning

arXiv

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arXiv 2025年

作者： He, Yuting Wang, Boyu Ge, Rongjun Chen, Yang Yang, Guanyu Li, Shuo Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Nanjing China School of Instrument Science and Engineering Southeast University Nanjing China Department of Computer Science Western University LondonONN6A 3K7 Canada Department of Biomedical Engineering Department of Computer and Data Science Case Western Reserve University ClevelandOH44106 United States

Dense contrastive representation learning (DCRL) has greatly improved the learning efficiency for image dense prediction tasks, showing its great potential to reduce the large costs of medical image collection and dense annotation. However, the properties of medical images make unreliable correspondence discovery, bringing an open problem of large-scale false positive and negative (FP&N) pairs in DCRL. In this paper, we propose GEoMetric vIsual deNse sImilarity (GEMINI) learning which embeds the homeomorphism prior to DCRL and enables a reliable correspondence discovery for effective dense contrast. We proposes a deformable homeomorphism learning (DHL) which models the homeomorphism of medical images and learns to estimate a deformable mapping to predict the pixels’ correspondence under the condition of topological preservation. It effectively reduces the searching space of pairing and drives an implicit and soft learning of negative pairs via gradient. We also proposes a geometric semantic similarity (GSS) which extracts semantic information in features to measure the alignment degree for the correspondence learning. It will promote the learning efficiency and performance of deformation, constructing positive pairs reliably. We implement two practical variants on two typical representation learning tasks in our experiments. Our promising results on seven datasets which outperform the existing methods show our great superiority. We will release our code at a companion website. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Hyperbolic Geometric Latent Diffusion Model for Graph Generation

arXiv

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arXiv 2024年

作者： Fu, Xingcheng Gao, Yisen Wei, Yuecen Sun, Qingyun Peng, Hao Li, Jianxin Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Topology

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Novel Deep Learning Approaches to Environmental Management with Sustainability

Novel Deep Learning Approaches to Environmental Management w...

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Communication, computer sciences and engineering (IC3SE), International Conference on

作者： Chunchu Suchith Kumar Chinnem Rama Mohan B Santhosh Kumar Navdeep Singh Pemmadi Lavanya Dyuti Banerjee School of Agriculture SR University Warangal Telangana India Department of Computer Science and Engineering Narayana Engineering College Nellore Andhra Pradesh India Institute of Aeronautical Engineering Dundigal Hyderabad Lovely Professional University Phagwara MBA Vignan's Institute Of Information Technology Visakhapatnam Artificial intelligence & Data Science (AI&DS) Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Guntur District Andhra Pradesh

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

The application of Earth observation data to Deep Learning (DL) has increased dramatically over the last ten years, with ground-breaking initiatives made possible by the creation of foundational models. Environmental concerns have been addressed with the use of these technologies, which have been used to detect air quality, enhance precision agriculture, and provide alerts about deforestation in near real time. The objective of this research is to present cutting-edge studies and uses of these technologies in areas with limited data. The objective is to tackle the problems associated with environmental development while advancing cloud computing, data democracy, open data access, and capacity-building initiatives. This research concludes by analysing the most current breakthroughs and uses of DL and AI in order to encourage sustainability in a range of domains, such as, renewable energy, smart building energy management, environmental health and accomplishing the sustainable developments Goals(SDGs).

关键词： Industries Deep learning Renewable energy sources Ethics Wireless networks Predictive models data models

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Progression Cognition Reinforcement Learning with Prioritized Experience for Multi-Vehicle Pursuit

arXiv

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arXiv 2023年

作者： Li, Xinhang Yang, Yiying Yuan, Zheng Wang, Zhe Wang, Qinwen Xu, Chen Li, Lei He, Jianhua Zhang, Lin The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Centre for Telecommunications Research King's College London LondonWC2R 2LS United Kingdom School of Computer Science and Electronics Engineering University of Essex Colchester United Kingdom The Beijing Big Data Center Beijing China

Multi-vehicle pursuit (MVP) such as autonomous police vehicles pursuing suspects is important but very challenging due to its mission and safety critical nature. While multi-agent reinforcement learning (MARL) algorithms have been proposed for MVP problem in structured grid-pattern roads, the existing algorithms use randomly training samples in centralized learning, which leads to homogeneous agents showing low collaboration performance. For the more challenging problem of pursuing multiple evading vehicles, these algorithms typically select a fixed target evading vehicle for pursuing vehicles without considering dynamic traffic situation, which significantly reduces pursuing success rate. To address the above problems, this paper proposes a Progression Cognition Reinforcement Learning with Prioritized Experience for MVP (PEPCRL-MVP) in urban multi-intersection dynamic traffic scenes. PEPCRL-MVP uses a prioritization network to assess the transitions in the global experience replay buffer according to the parameters of each MARL agent. With the personalized and prioritized experience set selected via the prioritization network, diversity is introduced to the learning process of MARL, which can improve collaboration and task related performance. Furthermore, PEPCRL-MVP employs an attention module to extract critical features from complex urban traffic environments. These features are used to develop progression cognition method to adaptively group pursuing vehicles. Each group efficiently target one evading vehicle in dynamic driving environments. Extensive experiments conducted with a simulator over unstructured roads of an urban area show that PEPCRL-MVP is superior to other state-of-the-art methods. Specifically, PEPCRL-MVP improves pursuing efficiency by 3.95% over TD3-DMAP and its success rate is 34.78% higher than that of MADDPG. Codes are open sourced. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Expression of Concern for: Discovering Innovative Applications for Real-Time data Analysis with Machine Learning

Expression of Concern for: Discovering Innovative Applicatio...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Prranjali Jadhav Raja Praveen K N Prateek Aggarwal Sandeep Gautam Preeti Naval D. Nirmala Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

This paper investigates the capability for discovering modern programs for actual-time information analysis the use of system learning. We will take a look at the technology and strategies used in system getting to know and talk their adaptive potential to interpret and analyze dynamically changing facts inputs. Especially, we can examine existing technological methods to understand the capacity of AI to discover creative solutions to complicated challenges. We will also explore capability techniques to contain gadget gaining knowledge of into present real-time records analysis platforms and architectures. Finally, we are able to speak the programs of machine studying in real-time records evaluation and its potential to allow fast and correct decision making. This paper gives a theoretical framework to apprehend and harness the power of device gaining knowledge of to investigate and interpret real-time facts in modern approaches.

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