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2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Kanniga Parameshwari, M. Shobha Rani, P. Chokkalingam, S. Yogesh Kannan, S. Balaji Ganesh, K. Darunesh, R. R.M.K Engineering College Department of Information Technology India R.M.K Engineering College Department of Artificial Intelligence and Data Science India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

In today's fast-paced digital world, connecting distributors and retailers efficiently is crucial for business success in the supply chain market. Traditional methods of networking often involve a lot of travel and can miss potential opportunities due to logistical challenges. That's where 'OneScroll' comes in - a ground breaking mobile app designed to make these connections easier and more effective, similar to how social media works but specifically tailored for business networking. OneScroll stands out from platforms like LinkedIn and Instagram by focusing solely on business connections. It's a space where distributors and retailers can create detailed profiles, find potential business partners, and communicate seamlessly. This paper explores the development of OneScroll, highlighting its unique features and the technology behind it. In doing so, this promises to make business operations smoother and help businesses of various products to reach a wider audience in the evolving world. © 2025 IEEE.

关键词： Sales

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Augmenting traffic flow efficiency using multi-agent systems (MAS)

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International Journal of Information Technology (Singapore) 2025年 1-6页

作者： Hussain, Tarak Urlamma, Dundi Vericharla, Ratnasri Dhatterwal, Jagjit Singh Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India London Metropolitan University London United Kingdom Bapatla Women’s Engineering College Andhra Pradesh Bapatla India School of Computer Science and Artificial Intelligence SR UNIVERSITY Telangana AnanthasagarHasanparthy India

The application of multi-agent systems (MAS) in autonomous vehicle coordination is one such example and has the potential to be an important factor for improving transport systems with increased efficiency, safety, and sustainability. In this paper, a MAS framework is designed for coordinating vehicles, including agent architecture, communication protocols, and decision-making algorithms, as well as coordination strategies. The framework is evaluated in a number of heterogeneous environments (including urban traffic management and highway merging scenarios) using real-time data from case studies and simulations. Traffic flow, network efficiency, and congestion reduction are the performance metrics used to assess the proposed MAS approach. The results show that the proposed framework attains the highest overall network efficiency (92.85%) as compared to existing recent studies, thereby integrating MAS with AVs. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Autonomous vehicles (AVs) Cooperative behavior Decision making Intelligent transport systems Multi-agent systems (MAS)

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Convolutional Neural Network Image Classification Method Integrating Classic Image Features 9

Convolutional Neural Network Image Classification Method Int...

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9th IEEE International Conference on Computational intelligence and Applications, ICCIA 2024

作者： Hao, Qi Huang, Jie Wang, Bin Zhou, Feng Cyber Science and Engineering Southeast University Nanjing China Zhejiang Key Laboratory of Artificial Intelligence of Things Network and Data Security Hangzhou China

ISBN: (纸本)9798350352214

Classic image features were once widely used in image classification but have been almost entirely replaced by neural networks today. While the performance of neural networks, especially convolutional neural networks (CNNs), is indisputable, their lack of interpretability has become a significant limitation in recent years. This paper explores the effective combination of classic image features and convolutional image features, investigating their differences and similarities. Through a series of experiments, a method is proposed to effectively integrate these two types of features by employing a multiplicative attention mechanism. This approach combines standardized MPEG-7 descriptors with convolutional features before feeding them into the fully connected layer for classification. The final model demonstrates an improvement in classification accuracy and indicates the potential of learning traditional image features from the integrated features. © 2024 IEEE.

关键词： Convolutional neural networks

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YouTube Video Summarizer in Regional Language 3

YouTube Video Summarizer in Regional Language

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Jadhav, Ranjana Damre, Prajwal Hire, Atharva Gosavi, Priti Deshmukh, Sanskruti Vishwakarma Institiute of Technology Department of Multidisciplinary Engineering Pune India Vishwakarma Institiute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350396157

The idea and features of the proposed You Tube video summarizer tool created especially for regional languages is presented in this research study. Generally, it is a challenging task for viewers, who prefer regional languages to access and understand videos that are primarily available in a different language in YouTube. The proposed You Tube video summarizer in regional language attempts to overcome this challenge by offering a service to summarize videos in regional languages, making them easier for viewers who might not understand the original language of the video to access and comprehend. The developed program analyses and extracts important information from the video's audio or subtitles using natural language processing (NLP) and machine learning algorithms. This YouTube video summarizer in local languages has a wide range of potential uses. It can improve non-English speakers' access to information, improve video browsing, and promote effective content consumption in localized language groups. © 2024 IEEE.

关键词： Machine learning

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Enhanced Precision and Safety in Minimally Invasive Surgery: The Role of AI and Robotics Integration 4

Enhanced Precision and Safety in Minimally Invasive Surgery:...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Kedar, Rani Sunil Puri, Chetan G. Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Meghe Maharashtra Wardha442001 India Maharashtra Wardha442001 India

ISBN: (纸本)9798331529635

A revolutionary development in surgical practice, the incorporation of robotics and artificial intelligence (AI) into minimally invasive surgery (MIS) improves patient outcomes, safety, and precision. This review article examines the development of minimally invasive surgery (MIS) and highlights its notable advantages over open surgery, including shorter recovery periods, less discomfort following surgery, and a decreased rate of complications. The article explores the function of robotic systems, in particular the da Vinci Surgical System, which has transformed surgical operations by giving doctors more dexterity and visualization using robotic arms and high-definition 3D imagery. Additionally, the application of AI technology is explored, demonstrating how it enhances real-time decision-making, intraoperative assistance, and preoperative planning, ultimately improving surgical results. The paper also discusses the ethical and regulatory issues raised by surgical robots' growing autonomy, highlighting the necessity of strong frameworks to guarantee patient safety and fair access to these cutting-edge technologies. This study highlights how artificial intelligence (AI) and robotics have the ability to change the surgical medicine landscape by combining the most recent research and clinical practices. It also calls for continued communication among stakeholders to effectively traverse the complexity of this rapidly developing sector. © 2024 IEEE.

关键词： Clinical research

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Reversible data hiding scheme using prediction neural network and adaptive modulation mapping

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Multimedia Tools and Applications 2025年第9期84卷 6665-6686页

作者： Chen, Si-Sheng Chang, Chin-Chen Horng, Ji-Hwei School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fujian Fuzhou35030 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan Department of Electrical Engineering National Quemoy University Kinmen89250 Taiwan

Prediction error expansion (PEE) is an attractive approach for reversible data hiding (RDH). The key issue for PEE-based RDH is to improve the prediction accuracy and design a better modulation mapping. This paper proposes a novel prediction error modulation (PEM) scheme, which comprises a prediction neural network and a modulation mapping rule generation algorithm. We use the multi-scale feature extraction (MSFE) module and the residual dense networks (RDN) to construct the prediction neural network. In the proposed RDH scheme, the cover image is divided into two parts, the reference pixel set and the cover pixel set, based on the chessboard pattern. The prediction network serves to predict the values of the cover pixel set using the reference pixel set. An optimization model is set up to adaptively determine the optimal modulation mapping for data hiding based on the specific prediction-error histogram and payload constraint. Experimental results show the superiority of the proposed prediction neural network and the optimized modulation mapping compared with related works. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Mapping

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Fine-Tuned Large Language Model for Banking 12th

Fine-Tuned Large Language Model for Banking

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Limkar, Suresh Nale, Soham Darade, Shreyash Chaudhari, Ashwin Taras, Atharva Department of Computer Science and Engineering Central University of Jammu Jammu and Kashmir Jammu India Department of Artificial Intelligence and Data Science AISSMS Institute of Information Technology Pune India

ISBN: (纸本)9789819601387

A new, improved computer program made specifically for banking helps in solving problems like inadequate guidance and makes it convenient for people who cannot visit banks for some reason. This helps people answer questions faster and makes it easier for bank workers. Making the program better involved picking the right model and making a special set of data. After making these improvements, the llama2_sharded model is really good at answering banking questions, doing better than others by 2.67%. This shows how important it is to give programs lots of practice with different information, proven by reaching a low error rate after many tries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Banking

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Crossover Operators between Multiple Scheduling Heuristics with Genetic Programming for Dynamic Flexible Job Shop Scheduling 13

Crossover Operators between Multiple Scheduling Heuristics w...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Zhu, Luyao Zhang, Fangfang Feng, Mengyuan Chen, Ke Zhu, Xiaodong Zhang, Mengjie School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9798350308365

Dynamic flexible job shop scheduling (DFJSS) is an important combinatorial optimisation problem that aims to optimise machine resources to improve production efficiency. Multi-tree genetic programming (MTGP) has been widely used to learn the routing rule and the sequencing rule for DFJSS simultaneously. Unlike traditional genetic programming that only operates crossover on a single tree, MTGP has various cases to conduct crossover since a genetic programming individual consists of more than one tree. Different crossover operators may affect the performance of MTGP for DFJSS. However, the investigation into different crossover operators in MTGP for DFJSS is rare. Specifically, it is not clear what influence will have on MTGP if involving both the routing rule and the sequencing rule for crossover. To this end, this paper provides a comprehensive investigation of four possible crossover cases between multiple scheduling heuristics with MTGP for DFJSS. The four operators are designed according to the number of trees/rules that crossover operator works on, and whether swapping full trees between parents. The results show that although the compared algorithms have comparable results in most scenarios, MTGP with both rules for crossover and the swapping strategy is ranked as the best one. Further analyses show that the sizes of learned rules are highly related to the crossover operators, and crossover involving more rules can increase the rule sizes, and vice versa. In addition, the population diversity and the number of unique features in the learned rules of MTGP with both rules for crossover are increased to learn effective rules. © 2024 IEEE.

关键词： Genetic programming

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Classification of Sarcoma Based on Genomic data Using Machine Learning Models 4

Classification of Sarcoma Based on Genomic Data Using Machin...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Gala, Pratham Pandloskar, Yash Godbole, Shubham Hakim, Fayed Kanani, Pratik Kurup, Lakshmi Dwarkadas J. Sanghvi College of Engineering Mumbai India Dept. of Artificial Intelligence and Data Science Dwarkadas J. Sanghvi College of Engineering Mumbai India

The proposed work provides a new machine-learnt classification approach for the various types of soft tissue sarcoma based on genomics data which addresses a considerable gap in sarcoma diagnostics. The previous studies have investigated various aspects of sarcoma but this study is unique in that it targets the predicting sarcoma variant types using genetic information, which has not been done before. Random Forest was used as the meta-estimator and a stacking ensemble model comprising of Random Forest, Extreme Gradient Boosting and LightGBM were used for this study. The model which was trained and validated on a complete dataset of 206 adult soft tissue sarcoma samples containing genomic alterations, transcriptomic, epigenomic and proteomic data achieved an accuracy of 89.44% at a precision level as high as 91%. Stratified k-fold cross validation is employed to ensure that class imbalance is not a hindrance to performance. This innovative approach outmatches single classifiers and traditional single model methods at great length hence making it possible and effective to use machine learning on genomic data for predicting sarcoma variants. Thus, the findings from this research could change cancer diagnosis forever;they promise more accurate classification as well as personalized treatment modalities while also providing a framework for analogous applications in other rare complex cancers. © 2025 The Authors.

关键词： Adversarial machine learning

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From 5G to 6G: The Role of AI, Machine Learning, and Deep Learning in Wireless Systems 4

From 5G to 6G: The Role of AI, Machine Learning, and Deep Le...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Gote, Pradnyawant M. Kumar, Praveen Verma, Prateek Yesankar, Prajyot Pawar, Adesh Saratkar, Saniya Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

The swift progression of wireless communication technologies-specifically from 5G to 6G is an approach that could be the most significant revolutionary leap towards changing connectivity and data transmission forever. Additionally, algorithms of machine learning (ML) and deep learning (DL) in 5G and 6G can offer promising advancements toward predictive maintenance, dynamic spectrum management, and enhanced security. The review of the potentially transformative nature of these technologies underlines the need for future directions of research efforts called for in circumventing challenges towards making full capability exploitation of 6G possible. © 2025 IEEE.

关键词： Adversarial machine learning

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