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IEEE Recent Advances in Intelligent Computational Systems (RAICS)

作者： Senthil Kumar Seeni P. Maheswari Jothibabu Konidhala M. Raja Suguna M.V. Ishwarya S. Srinivasan Department of Mobile Application Development Cognizant Technology Solutions Buffalo Grove Illinois USA Faculty of Management Studies SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering University of Technology and Applied Sciences- Al Musannah Musanna South Al Batina Oman Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

The research uses Internet of Things technology for predictive maintenance in the construction sector. The system addresses incorporating Internet of Things sensors into construction machinery to enable real-time monitoring of operating parameters. The study suggests a proactive maintenance strategy that may anticipate equipment breakdowns by examining the data obtained and using sophisticated prediction models. With this strategy, construction teams can arrange maintenance activities systematically, which cuts down on downtime and helps avoid expensive interruptions. The abstract emphasizes the advantages of this practice, which include reducing unexpected downtime as much as possible, extending the life of equipment, and making the most of available resources. The study proves the usefulness of the IoT-based predictive maintenance approach via empirical investigations and the analysis of case studies. This validates the strategy's efficacy, contributing to enhanced equipment performance and increased work efficiency in the construction industry.

关键词： Electric breakdown Predictive models Real-time systems Sensor systems Maintenance Sensors Trajectory Internet of Things Machinery Predictive maintenance

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Towards Symbolic XAI – Explanation Through Human Understandable Logical Relationships Between Features

arXiv

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arXiv 2024年

作者： Schnake, Thomas Jafari, Farnoush Rezaei Lederer, Jonas Xiong, Ping Nakajima, Shinichi Gugler, Stefan Montavon, Grégoire Müller, Klaus-Robert Berlin Institute for the Foundations of Learning and Data – BIFOLD Berlin10623 Germany Machine Learning Group Technical University of Berlin Berlin10623 Germany RIKEN AIP Tokyo103-0027 Japan Department of Artificial Intelligence Korea University Seoul136-713 Korea Republic of Max Planck Institut für Informatik Saarbrücken66123 Germany Department of Mathematics and Computer Science Free University of Berlin Berlin14195 Germany

Explainable Artificial Intelligence (XAI) plays a crucial role in fostering transparency and trust in AI systems, where traditional XAI approaches typically offer one level of abstraction for explanations, often in the form of heatmaps highlighting single or multiple input features. However, we ask whether abstract reasoning or problem-solving strategies of a model may also be relevant, as these align more closely with how humans approach solutions to problems. We propose a framework, called Symbolic XAI, that attributes relevance to symbolic queries expressing logical relationships between input features, thereby capturing the abstract reasoning behind a model’s predictions. The methodology is built upon a simple yet general multi-order decomposition of model predictions. This decomposition can be specified using higher-order propagation-based relevance methods, such as GNN-LRP, or perturbation-based explanation methods commonly used in XAI. The effectiveness of our framework is demonstrated in the domains of natural language processing (NLP), vision, and quantum chemistry (QC), where abstract symbolic domain knowledge is abundant and of significant interest to users. The Symbolic XAI framework provides an understanding of the model’s decision-making process that is both flexible for customization by the user and human-readable through logical formulas. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

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Spreading dynamics of information on online social networks

arXiv

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arXiv 2025年

作者： Meng, Fanhui Xie, Jiarong Sun, Jiachen Xu, Cong Zeng, Yutian Wang, Xiangrong Jia, Tao Huang, Shuhong Deng, Youjin Hu, Yanqing School of Systems Science and Engineering Sun Yat-sen University Guangzhou510275 China Center for Computational Communication Research Beijing Normal University Zhuhai519087 China School of Journalism and Communication Beijing Normal University Beijing100875 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Department of Statistics and Data Science College of Science Southern University of Science and Technology Shenzhen518055 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China College of Computer and Information Science Chongqing Normal University Chongqing401331 China College of Computer and Information Science Southwest University Chongqing400715 China Institute of Neuroscience Technical University of Munich Munich80802 Germany Department of Modern Physics University of Science and Technology of China Hefei230026 China MinJiang Collaborative Center for Theoretical Physics College of Physics and Electronic Information Engineering Minjiang University Fuzhou350108 China Center for Complex Flows and Soft Matter Research Southern University of Science and Technology Shenzhen518055 China

Social media is profoundly changing our society with its unprecedented spreading power. Due to the complexity of human behaviors and the diversity of massive messages, the information spreading dynamics are complicated, and the reported mechanisms are different and even controversial. Based on data from mainstream social media platforms, including WeChat, Weibo, and Twitter, cumulatively encompassing a total of 7.45 billion users, we uncover a ubiquitous mechanism that the information spreading dynamics are basically driven by the interplay of social reinforcement and social weakening effects. Accordingly, we propose a concise equation, which, surprisingly, can well describe all the empirical large-scale spreading trajectories. Our theory resolves a number of controversial claims and satisfactorily explains many phenomena previously observed. It also reveals that the highly clustered nature of social networks can lead to rapid and high-frequency information bursts with relatively small coverage per burst. This vital feature enables social media to have a high capacity and diversity for information dissemination, beneficial for its ecological development. © 2025, CC BY.

关键词： Tweets

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Dynamic Lane Management with IoT for Real-time Lane Configuration and Traffic Flow

Dynamic Lane Management with IoT for Real-time Lane Configur...

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computer, Communication and Control (IC4), International Conference on

作者： Mohammed Rehaman Pasha S. Latha R. Usha V. Bini Marin M.V Ishwarya M. Muthulekshmi Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India Department of Department of Biotechnology Vinayaka Mission’s Kirupananda Variyar Engineering College Vinayaka Mission’s Research Foundation(DU) Salem Tamil Nadu India Department of Mathematics S. A. Engineering College Chennai Tamil Nadu India Faculty of Management Studies SRM Institute of Science and Technology Ramapuram Chennai Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Thazhambur Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Traffic may be reduced and transportation efficiency improved by properly controlling traffic flow in urban areas. This research provides a new method for managing lanes in real-time by using IoT connectivity. To optimize traffic flow in response to shifting demand patterns, the suggested system prioritizes making lane design changes in real time depending on the current traffic conditions. Using information collected from Internet of Things (IoT) sensors and linked automobiles, the system may dynamically allot lanes in response to variations in traffic volumes and congestion. It describes developing and deploying a dynamic lane control system based on the Internet of Things, stressing the technology’s potential to improve traffic flow in congested metropolitan areas. The simulation results show its efficiency in decreasing traffic and increasing throughput. It demonstrates the potential of intelligent transportation systems for creating smarter and more efficient cities by combining IoT, real-time data processing, and traffic control.

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Mobile Application for Water Management and Monitoring System in Residential Building

Mobile Application for Water Management and Monitoring Syste...

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International Conference on Futuristic Technologies (INCOFT)

作者： Nalla Akhila Aswadhati Sirisha P. Dass P M Dinesh T Sujatha Amit Kamalakar Gaikwad Computer Science and Engineering Aditya Engineering College Surampalem Jawaharlal Nehru technological university Kakinada Kakinada East Godavari district Information Technology (IT) Department Vignans institute of information technology (A) Duvvada India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Department of ECE Sona College of Technology Salem Department of Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore Department of Computer Science G.H.Raisoni University Amravati Maharashtra

Water scarcity is one of the major threats to human society. Though there are many reasons for water scarcity like water pollution, drought and global warming, overuse of domestic water is one of the major causes of water scarcity. More than one billion people are deprived of clean drinking water and water for other purposes. One of the main solutions that were found to this problem is rainwater harvesting. Many countries encouraged the citizens to collect the rainwater for further uses. Though the rainwater is preserved, another issue named water over usage comes into existence. Most of the time, the water supplied for domestic reasons may undergo several preprocessing like filtration, osmosis, etc. to ensure the cleanliness of the water. When this water is wasted for petty reasons like washing dishes and watering plants, it may lead to the shortage of freshwater for necessary needs like cattle feeding, food production, farming, etc. Many researchers and scientists have found a lot of techniques and processes to minimize the usage of fresh water in households. This project aims the development of a mobile application that constantly monitors the water usage by a house in a community. There will be a prefixed value for the amount of water each house can use. If the water usage exceeds the prefixed value, the inmates of the house are asked to pay a certain fine amount which is calculated by the number of extra liters used and the prefixed fine amount for each liter. If the fine amount was not paid, the water supply to that particular house will be blocked. All of the above-mentioned processes are monitored by this application. Hardware components like multiple sensors, adaptors, motors are used for this purpose. The whole working of the hardware is controlled by a microcontroller namely Arduino Uno. This application helps in the control of the over usage of water in residential buildings. This can be equipped in apartments, community houses, quarters, etc. to constan

关键词： Microcontrollers Buildings Production Water pollution Hardware Osmosis Sensors

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Unleashing The Power Of 6g Assisted Wireless Communication Network To Define A New Routing Strategy To Preserve Communication Energy

Unleashing The Power Of 6g Assisted Wireless Communication N...

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International Conference on Electrical Energy Systems (ICEES)

作者： S. Avinash S. Sujatha D. Poornima Vedhashree S.K V. Saravanan P. Malathi Department of MBA St. Josephs College of Engineering Chennai Tamil Nadu India Department of EEE Sri Sairam College of Engineering Anekal Bangalore Karnataka Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Department of Electronics and Communication Engineering M.Kumarasamy College of Engineering Chennai Tamil Nadu India Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353785

The incorporation of cutting-edge technology into intelligent power grid management becomes a top priority as the globe keeps searching for effective and environmentally friendly energy solutions. The arrival of 6G wireless networks is expected to completely transform the methods used to track, manage, and enhance energy grids. An effort to provide high data speeds has already been made with the deployment of commercial fifth-generation (5G) wireless communications networks. The advent of IoT applications and the proliferation of smart devices will put a heavy strain on 5G wireless networks since they need extremely dependable and lowlatency connection. So, the massive continuous data traffic boom is probably going to be too much for 5G networks to handle. Research into enhancing current wireless networks in preparation for the 6G cellular systems, the next generation of mobile communication, has been spurred by this. This paper introduces the Neural 6G Wireless Routing Protocol (N6GWRP), a new routing protocol that supports intelligent communication. To evaluate the effectiveness of the suggested approach, it is cross-validated with the Multiple-Access Routing Protocol (MARP), a traditional routing model. Users should not experience any disruptions when mobile power and data transmission systems interact. Another major obstacle is connecting the vast array of devices and people with varying degrees of processing power in a way that is both sustainable and environmentally conscious. This study delves into the smart solution to the aforesaid difficulties and examines how 6G wireless networks might improve smart energy grid resilience and dependability through seamless interaction with other sophisticated technologies like blockchain. A more environmentally friendly and smart vision regarding electrical grid administration is possible thanks to the extensive study that strives to illuminate the revolutionary significance of 6G wireless technologies.

关键词： 6G mobile communication 5G mobile communication Wireless networks Transforms Routing Routing protocols Smart grids Sustainable development Smart devices Strain

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

arXiv

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arXiv 2024年

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. © 2024, CC BY-NC-ND.

关键词： Large datasets

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Wearable Technology and Cloud Computing Integration for Adaptive Cognitive Load Management

Wearable Technology and Cloud Computing Integration for Adap...

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Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： S P Vimal Palaniraj Rajidurai Parvathy John Benito Jesudasan Peter Brinda P S. Srinivasan R Sankar Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Mphasis Corporation Chandler Arizona USA Cyber │ Data Science & Engineering Capital One Services LLC Richmond Virginia USA Department of Computer Science and Engineering Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Master of Computer Applications S.A. Engineering College (Autonomous) Chennai Tamil Nadu India

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

Nowadays, people must deal with more complex cognitive tasks; hence they need ways to regulate their cognitive load effectively. One potential solution to the problem of cognitive load is wearable technology, which can track physiological and behavioral data in real-time. To create adaptive cognitive load management systems, this article investigates the possibility of integrating wearable devices with cloud computing infrastructure. Wearable devices may gather data, process it, and analyze it to fine-tune cognitive load treatments in real-time by tapping into the storage and processing capacity of the cloud. Individuals' cognitive capacities and the needs of their specific situation may be considered via the proposed integration, which allows for individualized feedback and treatments. Cloud analytics also make it easy to aggregate data from several users, which is great for building adaptive algorithms and getting a better comprehensive understanding of how cognitive load patterns play out on a larger scale. Many fields might benefit greatly from this integration, such as healthcare, education, and workplace performance enhancement. To fully use cloud computing and wearable technologies for cognitive load management, however, issues including data protection, interoperability, and algorithm improvement must be resolved. Human performance and well-being may be improved, which can be achieved through multidisciplinary cooperation and technological improvements.

关键词： Productivity Cloud computing data analysis data protection Cognitive load Real-time systems Biomedical monitoring Wearable devices Sustainable development Interoperability

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Robust AI-Synthesized Image Detection via Multi-feature Frequency-aware Learning

arXiv

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arXiv 2025年

作者： Cai, Hongfei Liu, Chi Shen, Sheng Qu, Youyang Gui, Peng Faculty of Data Science City University of Macau China Design and Creative Technology Vertical Torrens University Australia NSW Australia Shandong Provincial Key Laboratory of Computer Networks Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

The rapid progression of generative AI (GenAI) technologies has heightened concerns regarding the misuse of AI-generated imagery. To address this issue, robust detection methods have emerged as particularly compelling, especially in challenging conditions where the targeted GenAI models are out-of-distribution or the generated images have been subjected to perturbations during transmission. This paper introduces a multi-feature fusion framework designed to enhance spatial forensic feature representations with incorporating three complementary components, namely noise correlation analysis, image gradient information, and pretrained vision encoder knowledge, using a cross-source attention mechanism. Furthermore, to identify spectral abnormality in synthetic images, we propose a frequency-aware architecture that employs the Frequency-Adaptive Dilated Convolution, enabling the joint modeling of spatial and spectral features while maintaining low computational complexity. Our framework exhibits exceptional generalization performance across fourteen diverse GenAI systems, including text-to-image diffusion models, autoregressive approaches, and post-processed deepfake methods. Notably, it achieves significantly higher mean accuracy in cross-model detection tasks when compared to existing state-of-the-art techniques. Additionally, the proposed method demonstrates resilience against various types of real-world image noise perturbations such as compression and blurring. Extensive ablation studies further corroborate the synergistic benefits of fusing multi-model forensic features with frequency-aware learning, underscoring the efficacy of our approach. © 2025, CC BY-NC-SA.

关键词： Image coding

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Smart Warehouse Solutions for Efficient Onion Buffer Stock Management System

Smart Warehouse Solutions for Efficient Onion Buffer Stock M...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Ramakrishnan Raman L. Ramalingam Kamal Kishorbhai Sutaria Manika Kamthan S. Sangeetha S. Murugan Symbiosis International (Deemed University) Symbiosis Institute of Business Management Pune & Pune Maharashtra India Department of Artificial Intelligence and Data Science Chennai Institute of Technology (Autonomous) Chennai Tamil Nadu India Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Vadodara Gujarat India Symbiosis Law School Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Karpagam College of Engineering Coimbatore Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

This research study presents an innovative smart warehouse system for onion buffer stock management. The system includes an RFID reader, a central controlling unit, a displaying unit, a temperature sensor, a moisture sensor, a relay, a cooling fan, storage nodes, and a cloud server. The RFID reader automatically tracks onion crates or pallets in the warehouse, enabling real-time visibility and traceability. The central controlling unit coordinates activities and collects data from sensors. Onions are stored at appropriate temperatures and moisture levels via sensors. Warehouse workers can monitor this data in real-time on the displaying unit once the central controlling unit processes it. Maintaining temperature and humidity need the relay and cooling fan. Based on sensor data, the main controlling unit activates the cooling fan to avoid onion spoiling. Storage nodes in the warehouse store onion bags and pallets. These nodes have RFID tags or sensors for convenient tracking of stock. data analysis, storage, and integration depend on the cloud server. It allows advanced data analytics, forecasting, and decision-making. It also helps suppliers, distributors, and retailers communicate and distribute products efficiently. Real-time visibility, smart monitoring, and proactive management of storage conditions advance efficient operations, prevent spoiling, and improve onion supply chain efficiency.

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