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Recent Advances in science and engineering Technology (ICRASET), International Conference on

作者： S Shivananda Lincy Meera Mathews Sanvi Nikkam Prashanth Kambli Rajashree D Ingale Prashant Koparde Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India K S School of Engineering and Management Artificial Intelligence & Data Science Bangalore India K S School of Engineering and Management Computer Science & Business Systems Bangalore India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

In India manually evaluating court records was time-consuming and prone to errors, leading to delays in justice administration. Legal Sarathi, an automated event extraction tool was proposed to address this issue, it utilized Large Language Models coupled with other cutting-edge Natural Language Processing (NLP) techniques. Legal Sarathi analyzes unstructured legal case documents to extract time frames, significant participants and major events to improve speed and accuracy. It featured state-of-the-art Natural Language Processing (NLP) capabilities for prompt and precise case analysis whose aims included speeding up the analysis of court proceedings and real-time information retrieval, while simultaneously providing critical data for informed decision-making. The tools deliverables comprised an automated event extraction system, a highly preprocessed data corpus, a sophisticated NLP model, a user-friendly interface and thorough validation results. Ultimately, this established an improved and equitable legal system in India to ensure that all parties received quick and fair access to the courts and fostered public trust.

关键词： Productivity Visualization Text analysis Law Feature extraction Transformers Solids Natural language processing Libraries User experience

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CMFN: Cross-Modal Fusion Network for Irregular Scene Text Recognition

arXiv

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arXiv 2024年

作者： Zheng, Jinzhi Ji, Ruyi Zhang, Libo Wu, Yanjun Zhao, Chen Intelligent Software Research Center Institute of Software Chinese Academy of Sciences Beijing100190 China The Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China

Scene text recognition, as a cross-modal task involving vision and text, is an important research topic in computer vision. Most existing methods use language models to extract semantic information for optimizing visual recognition. However, the guidance of visual cues is ignored in the process of semantic mining, which limits the performance of the algorithm in recognizing irregular scene text. To tackle this issue, we propose a novel cross-modal fusion network (CMFN) for irregular scene text recognition, which incorporates visual cues into the semantic mining process. Specifically, CMFN consists of a position self-enhanced encoder, a visual recognition branch and an iterative semantic recognition branch. The position self-enhanced encoder provides character sequence position encoding for both the visual recognition branch and the iterative semantic recognition branch. The visual recognition branch carries out visual recognition based on the visual features extracted by CNN and the position encoding information provided by the position self-enhanced encoder. The iterative semantic recognition branch, which consists of a language recognition module and a cross-modal fusion gate, simulates the way that human recognizes scene text and integrates cross-modal visual cues for text recognition. The experiments demonstrate that the proposed CMFN algorithm achieves comparable performance to stateof- the-art algorithms, indicating its effectiveness. © 2024, CC BY.

关键词： Optical character recognition

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A Game-Theoretic Approach for Pricing and Determining Virtual Reality Investment and Cybersecurity Level in a Dual-Channel Supply Chain 19th

A Game-Theoretic Approach for Pricing and Determining Virtua...

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19th International Conference on the Economics of Grids, Clouds, systems, and Services, GECON 2022

作者： Alaei, Amir Mohammad Rasti-Barzoki, Morteza Altmann, Jörn Egger, Bernhard Department of Industrial and Systems Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute of Engineering Research College of Engineering Seoul National University Seoul08826 Korea Republic of Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul08826 Korea Republic of Department of Computer Science and Engineering College of Engineering Seoul National University Seoul08826 Korea Republic of

ISBN: (纸本)9783031293146

Virtual reality has become a new option to inform the customers about product before purchasing. However, providing virtual reality may create new challenges. For instance, consumers may obtain essential information about products by using virtual reality option, but eventually they buy their products from the offline channel. This phenomenon is called webrooming. Another challenge is cyber-attacks. E-tailers and their consumers face risks from cyber-attacks. Thus, e-tailers are investing to improve cybersecurity. We consider a dual-channel supply chain consisting of an offline retailer and an e-tailer who purchase the same product from a manufacturer at the same wholesale price. The e-tailer offers a partial refund policy in order to attract the customers. Also, to reduce consumer valuation uncertainty, the e-tailer faces the decision on whether to introduce virtual reality. We analyze three scenarios. Firstly, the e-tailer does not offer virtual reality. Secondly, the e-tailer offers virtual reality, but he does not invest on cybersecurity. Thirdly, the e-tailer offers virtual reality and invests on cybersecurity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Game theory

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AI-based Bonsai Species Classification and Care Recommendation System using YOLO

AI-based Bonsai Species Classification and Care Recommendati...

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2025 International Conference on Intelligent Computing and Control systems, ICICCS 2025

作者： Derry Jeswyn, S. Ramalakshmi, K. Shirly, S. Venkatesan, R. Naveen Sundar, G. Saravana Kumar, C.S. Karunya Institute of Technology and Sciences Division of CSE Coimbatore India Alliance University Department of CSE & IT Bangalore India Karunya University Computer Science and Engineering India Karunya University Division of CSE Coimbatore India Bosch Global Software Technologies Global Tech Park Coimbatore India

ISBN: (纸本)9798331512088

Bonsai, an ancient art form that blends horticultural precision with artistic expression, poses unique challenges to both beginners and seasoned practitioners due to its species-specific care requirements and intricate maintenance processes. This research introduces an innovative AI-based solution combining YOLOv8, an advanced object detection model, and Google Gemini LLM, to simplify bonsai species classification and care recommendations. The system is designed to accurately identify bonsai species and provide personalized, context-aware guidance on essential care aspects such as watering, pruning, and pest management, while dynamically adapting to environmental and seasonal variations. Featuring an intuitive interface with sub-100ms response times, the proposed solution enhances accessibility and engagement for users of all expertise levels. Testing affirms its capability to improve understanding, usability, and appreciation of bonsai cultivation. Beyond its application in bonsai care, this hybrid AI integration demonstrates the potential for broader adoption in fields requiring precise classification and tailored insights, showcasing a seamless blend of tradition and technology. © 2025 IEEE.

关键词： AI for Traditional Practices AI in Horticulture AI-Driven Bonsai Care Bonsai Care Recommendations Bonsai Cultivation Bonsai Species Classification Contextual Care Advice Data Augmentation Deep Learning in Horticulture Fine-Grained Classification Google Gemini LLM Horticultural AI Applications Hybrid AI systems Machine Learning in Agriculture Natural Language Processing (NLP) Object Detection Precision Cultivation Real-Time Object Detection Species Identification YOLOv8

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Adaptive Deep Reinforcement Learning-based Resource Management for Complex Decision Making in Industry Internet of Things Applications

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Procedia computer science 2025年 252卷 231-240页

作者： Niharika Karne Chandra Shekar Ramagundam Ranga Rao Patnala Shashishekhar Ramagundam Sai Nitisha Tadiboina Sr. Principal Systems Engineer in C&RS Platform services DT Amtrak USA University of south alabama Department of electrical engineering Masters in electrical engineering Senior Software Engineer in Cloud platform services City Of Philadelphia Pennsylvania USA Vice president Neuberger Berman US Software Development & Engineering in Content Discovery & AI Comcast USA Troy University Montgomery Campus AL. Department of computer Science Masters In Computer Science Department of Information Technology GEICO - Chevy Chase Chevy Chase MD 20815 United States

An independent industrial system may transform into a connected network through the assistance of Industrial Internet of Things (IIoT). The deployed sensors in the IIoT maintain surveillance of the industrial machinery and equipment. As a result, safety and reliability emerge as the primary concerns in IIoT. This presents a variety of well-known and increasing issues related to the industrial system. The IIoT devices are exposed to a wide range of malware, threats, and assaults. To prevent the IIoT devices from malware effects, effective protection plans must be implemented. But adequate security mechanisms are not be incorporated in IIoT devices with limited resources. It is essential to ensure the accuracy and dependability of information gathered by IIoT devices. Decisions taken with incomplete or inaccurate data might be devastating. To overcome these difficulties deep learning with reinforcement learning for complex decision-making in industry applications is developed in this research work. In this developed model, an Adaptive Deep Reinforcement learning (ADRL)-based resource management is performed to reduce the operation cost associated with IIoT deployments. Energy efficiency is essential in IIoT ecosystem, particularly for the devices that run on batteries. Through dynamic resource allocation based on workload needs and energy limits, ADRL-based resource management optimizes the usage of energy. The reliability of the designed model is enhanced by fine-tuning the parameters from DRL using the Ship Rescue Optimization (SRO) algorithm. Thus, ADRL-based resource management systems make real-time decisions based on current environmental conditions and system requirements. This helps the IIoT systems to react quickly to change demands and optimize resource allocation. Finally, the experimental analysis is performed to find the success rate of the developed resource management system via various metrics. Throughout the validation, the statistical analysis of the

关键词： Resource Management Industrial Internet of Things Applications Complex Decision Making Adaptive Deep Reinforcement Learning Ship Rescue Optimization

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CMANet: A CNN-Mamba aggregation network for face super-resolution

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Pattern Recognition 2025年 168卷

作者： Wu, Ziyi Lu, Tao Zhang, Yanduo Chai, Xiaoyu Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China Computer School Hubei University of Arts and Science Xiangyang China National Engineering Research Center for Multimedia Software Wuhan University Wuhan China

Currently, most face super-resolution (FSR) methods rely on convolutional neural networks (CNNs) or Transformers, both of which have inherent limitations: CNNs suffer from a limited receptive field, while Transformers introduce quadratic computational complexity due to self-attention. In recent years, Mamba has effectively addressed these challenges using the state space model (SSM), excelling in global modeling. However, due to the highly structured nature of face images, relying solely on global modeling is insufficient for high-quality reconstruction. It is crucial to preserve global structural information while enhancing local texture details. While Mamba excels in global modeling, it lacks the ability to effectively extract local details. To eliminate this issue, we propose the CNN-Mamba Aggregation Network (CMANet) for FSR, which consists of two branches: a CNN branch and a Mamba branch. The CNN branch is a traditional end-to-end FSR network, in which the proposed Multi-Scale Residual Block (MSRB) extracts multi-scale local details. The Mamba branch, the core of the network, is built upon a UNet architecture. Its central component is a carefully designed Dual Scan Mamba Block (DSMB), which combines the Dual Scan Mamba Module (DSMM) and Feature Refinement Feed-Forward Network (FR-FFN). The DSMM enhances the global modeling capabilities of the SSM in both plane and channel scanning directions, while the FR-FFN refines the expressiveness and adaptability of the features. Finally, CMANet reconstructs high-fidelity face images by aggregating features from both branches. Extensive experimental results show that our model significantly outperforms many advanced methods in both objective metrics and subjective image quality. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

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Understanding Older Adults' (Dis)Engagement with Design Materials 25

Understanding Older Adults' (Dis)Engagement with Design Mate...

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2025 CHI Conference on Human Factors in Computing systems, CHI 2025

作者： Pradhan, Alisha Jelen, Ben Thangaraj, Ramprabu Siek, Katie A. Jette, Shannon Lazar, Amanda Department of Informatics Ying Wu College of Computing New Jersey Institute of Technology NewarkNJ United States Computer Science and Software Engineering Rose-Hulman Institute of Technology Terre HauteIN United States Ying Wu College of Computing New Jersey Institute of Technology NewarkNJ United States Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States University of Maryland College ParkMD United States College of Information Studies University of Maryland College ParkMD United States

ISBN: (纸本)9798400713941

Design workshops are a popular approach to include older adults in the technology design process. However, formative design sessions with older adults have had unexpected outcomes such as the non-use of traditional design materials like craft-based prototyping supplies or disengagement from design activities. Analyzing the engagement of 32 older adults across two design workshops, this paper sheds insights on some of these outcomes. Contributing to a growing body of HCI research on understanding older adults' participation in design, we provide an understanding of how design materials can shape older adults' engagement in formative design activities. Our discussion furthers research on understanding who older adults design for and why, argues for a different understanding of creative expression, and offers considerations for choosing design materials. © 2025 Copyright held by the owner/author(s).

关键词： Design for manufacturability

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Empowering Leather Quality Assurance: Leveraging Convolutional Neural Networks for Precise Defect Detection and Classification 3

Empowering Leather Quality Assurance: Leveraging Convolution...

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3rd International Conference on Intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Logeswaran, K. Suresh, P. Savitha, S. Sah, Swati Kr, Prasanna Kumar Rajadevi, R. Vasugi, M. Sujit, B. Akilesh, T. Kongu Engineering College Center of Excellence in Real-Time Analytic Department of Artificial Intelligence Tamilnaud Erode India Vellore Institute of Technology Department of Database Systems Tamil Nadu Vellore India K.S.R College of Engineering Department of Computer Science and Engineering Tamil Nadu Tiruchengode India Sharda University School of Engineering and Technology Uttar Pradesh Greater Noida India Kongu Engineering College Department of Information Technology Tamilnaud Erode India Kongu Engineering College Department of Artificial Intelligence Tamilnaud Erode India

ISBN: (纸本)9798331523893

Ensuring the development of high-quality leather products in the industrial sector requires a strong focus on leather quality assurance. In this study, a unique method for enhancing leather quality assurance using Convolutional Neural Networks (CNNs) is presented. Our suggested solution is able to detect and classify leather defects with amazing accuracy and efficiency by utilizing deep learning. CNN integration for leather fault categorization and detection offers a more reliable and affordable solution, ensuring better product quality and satisfied customers. Existing methods for inspecting leather products often suffer from inconsistent quality control, subjective manual inspection processes, and the complexity of accurately identifying and classifying various types of leather defects. By addressing these challenges, the proposed system aims to empower leather manufacturers with a reliable and efficient solution for ensuring consistent product quality and reducing production costs. Convolutional Neural Networks (CNNs) are integrated to provide a highly automated and effective solution that overcomes these scaling challenges. The proposed CNN-based method uses deep learning algorithms to decode images, which allows the system to swiftly and precisely analyse enormous volumes of visual data. To make sure the CNN can differentiate between various defect classes and keep a low rate of false positives and false negatives, its accuracy, precision, recall, and F1 score should be carefully assessed. © 2025 IEEE.

关键词： Fault tolerance

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Detection of DDoS Attacks with Gaussian Mixture Model 9

Detection of DDoS Attacks with Gaussian Mixture Model

引用

9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Cecchetto, Alessandro Conte, Giuseppa Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

A Distributed Denial of Service (DDoS) is an attack which aim is to stop or tamper with an online service incapacitating a server with a flood of packages or requests, using internet or intranet. The main aim of the DDoS attack is to collapse the network or server with abnormal traffic to make the service unavailable for the legitimate users. This problem is particularly profound, due to the development of emerging technologies, such as cloud computing, the Internet of Things or artificial intelligence techniques, from which attackers can take advantage by launching a huge volume of DDoS attacks at a lower cost, and it is much harder to detect and prevent DDoS attacks, because DDoS traffic is similar to normal traffic. In this paper we implement a novel technique implementing an unsupervised Gaussian Mixture Model (GMM) based algorithm. Using a real traffic dataset, the CIC-DDoS2019, for benchmark, the proposed GMM can achieve recall, precision, and accuracy up to 99%. Experiments reveal that this can be a promising solution for the detection of DDoS attacks. © 2023 Copyright for this paper by its authors.

关键词： Denial-of-service attack

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Energy-Efficient Flying LoRa Gateways: A Multi-Agent Reinforcement Learning Approach

arXiv

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arXiv 2025年

作者： Ahmed, Abdullahi Isa Bentahar, Jamal Amhoud, El Mehdi Benguerir Morocco Department of Computer Science Khalifa University Abu Dhabi United Arab Emirates Concordia Institute for Information Systems Engineering Concordia University Montreal Canada

As next-generation Internet of Things (NG-IoT) networks continue to grow, the number of connected devices is rapidly increasing, along with their energy demands. This creates challenges for resource management and sustainability. Energy-efficient communication, particularly for power-limited IoT devices, is therefore a key research focus. In this paper, we deployed flying LoRa gateways mounted on unmanned aerial vehicles (UAVs) to collect data from LoRa end devices and transmit it to a central server. Our primary objective is to maximize the global system energy efficiency of wireless LoRa networks by joint optimization of transmission power, spreading factor, bandwidth, and user association. To solve this challenging problem, we model the problem as a partially observable Markov decision process (POMDP), where each flying LoRa GW acts as a learning agent using a cooperative multi-agent reinforcement learning (MARL). Simulation results demonstrate that our proposed method, based on the multi-agent proximal policy optimization algorithm, significantly improves the global system energy efficiency and surpasses the popular MARL and other conventional schemes. © 2025, CC BY.

关键词： Energy efficiency

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