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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Kirubhakaran D. Karthikeswaran M. Arun R. Arun Chakravarthy J. Akshya M. Sundarrajan Mani Deepak Choudhry Department of Computer Science and Engineering Surya Engineering College Erode Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore Department of Electronics and Communication Engineering KGiSL Institute of Technology Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Networking and Communications SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

Dynamic effects in industrial logistics demand more creative solutions towards the pursuit of efficiency and agility in evolving environments. Though static path-planning and taskallocation algorithms have provided the basis for how autonomous vehicles are guided within structured environments, those approaches usually fail to contend with indeterminacy outside of their modelled world, where there is dynamic obstacle appearance and changing task requirements, thus raising inefficiencies and costs of operations. To address these issues, the paper introduces a new multi-agent coordination system that uses a hybrid A* algorithm, integrating path planning with sophisticated detection methods for dynamic obstacles and improving superior task allocation. The system is supposed to provide improvements in real-time coordination in industries of autonomous vehicles to achieve efficient management of tasks and paths in changing environments. We integrate the latest in machine learning techniques for improved and real-time updates within the changes of the vehicle routing process based on both immediate sensory input and environmental interactions. Simulations show that our system performs the task much faster than classic systems: up to 30% improvement in completing tasks compared with classic models, and an average improvement of 25% in allround energy efficiency. It intelligently integrates machine learning techniques with real-time data processing. With the ability to scale up or down, or to adapt itself, this system addresses the chief challenges existing methods face and could revolutionize logistics operations within any industry.

关键词： Industries Adaptation models Heuristic algorithms Real-time systems Path planning Vehicle dynamics Collision avoidance Autonomous vehicles Optimization Logistics

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Prediction of Level of Damage of Brain Due To Tumor Via Integrated CNN Technique

Prediction of Level of Damage of Brain Due To Tumor Via Inte...

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computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Vivek Agrawal Kuldeep Singh Kaswan Sanjay Kumar Department of Information Technology Galgotias College of Engineering and Technology Greater Noida U.P India School of computing science and Engineering Galgotias University Greater Noida U.P India Department of Computer Science Engineering Galgotia College of Engineering and Technology Greater Noida U.P India

Image transform is the individual of ultimate questioning and intelligent field at the period. Tumor is an uneven expansion of container cruel intellect. The lump may be classification as favorable (non-malignant) and diseased (malignant). Earlier stage of swelling is previously in time or order discovered concerning matter through test of figure by doctors and it is a opportunity absorbing task and occasionally gives inaccurate consequence. Today different register forms are secondhand in healing field that supports a fast and correct result. Magnetic Resonance Images (MRI) is ultimate usual image method for resolving within makeup of human bulk. The MRI is secondhand even in disease of most harsh affliction of healing wisdom like mind tumors. The intellect carcinoma discovery process comprise figure prepare methods includes four stages. Image pre-handle, concept separation, feature ancestry, and eventually categorization. There is various existent of methods are handy for intellect carcinoma separation and categorization to discover the intelligence swelling. There are many methods handy presents a study of existent methods for intellect swelling discovery and their benefits and restraints. To overcome these disadvantages, intend a Convolution Neural Network (CNN) located classifier. CNN located classifier used to equate the prepared and test dossier, from this take high-quality result.

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Advancing Urban Traffic Control with IoT and Deep Learning: A YOLOv8 and LSTM-based Adaptive Signal System

Advancing Urban Traffic Control with IoT and Deep Learning: ...

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International Conference on ent computing and Control Systems (ICICCS)

作者： K. Priya K. Priyadharshini R. Santhana Krishnan J. Relin Francis Raj Imran Javeed Settu A. Srinivasan Department of Computer Science and Engineering Ramco Institute of Technology India Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Urban traffic congestion remains a significant challenge due to increasing vehicle density, inefficient signal control, and unpredictable traffic patterns. Traditional fixed-time signal systems fail to adapt dynamically to varying congestion levels, leading to increased delays, fuel consumption, and environmental pollution. To address these limitations, this research proposes an IoT and deep learning-based adaptive traffic signal system integrating YOLOv8 for real-time vehicle detection and LSTM for congestion prediction. Traffic data is collected using IoT sensors, including cameras, ultrasonic sensors, RFID modules, GPS trackers, and air quality sensors. YOLOv8 processes real-time camera feeds to detect vehicles, classify them based on type, and estimate traffic density. Concurrently, an LSTM-based predictive model analyzes historical traffic patterns and external factors such as peak hours and weather conditions to forecast congestion trends. The system dynamically adjusts traffic signal durations based on congestion probability scores, optimizing road utilization and minimizing vehicle waiting times. Extensive training and evaluation using real-world traffic datasets demonstrate significant improvements in congestion reduction compared to conventional traffic control methods. Performance metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) validate the reliability of the predictive model. Additionally, air quality monitoring highlights the environmental benefits of reduced emissions. This intelligent traffic management framework enhances urban mobility, improves emergency vehicle response times, and contributes to sustainable smart city development.

关键词： Adaptive systems Smart cities Roads Traffic control Predictive models Air quality Real-time systems Fuels Time factors Traffic congestion

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Cloud-Based Road Safety for Real-Time Vehicle Rash Driving Alerts with Random Forest Algorithm

Cloud-Based Road Safety for Real-Time Vehicle Rash Driving A...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Presitha Aarthi M Chilukala Mahender Reddy A. Anbarasi N. Mohankumar Ishwarya M.V S. Murugan Department of Computer Science and Engineering Shree Sathyam College of Engineering and Technology Salem Tamil Nadu India Department of Computer Science and Engineering GITAM University Hyderabad Telangana India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering B.V. Raju Institute of Technology Hyderabad Telangana 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

The cloud-based road safety technology introduced in this research improves real-time vehicle behavior monitoring and warns for rash driving. It uses cloud computing to gather and interpret data from onboard car sensors and other traffic monitoring equipment. Powerful machine learning technology the Random Forest (RF) algorithm analyses this data intelligently. The RF algorithm is trained on a varied array of driving metrics to detect rash driving tendencies. Real-time data streams may be processed efficiently and scalable on the cloud, enabling speedy decision-making for rash driving detection. A network sends alerts to authorities and nearby vehicles. The system is adaptable to different road conditions, resistant to sensor data noise, and able to enhance rash driving detection accuracy via model updates. Cloud architecture makes the system available worldwide. With its focus on real-time notifications utilizing the RF algorithm, this cloud-based road safety system may reduce the hazards of reckless driving and avoid accidents.

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Seeking the Necessary and Sufficient Causal Features in Multimodal Representation Learning

arXiv

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arXiv 2024年

作者： Chen, Boyu Liu, Junjie Li, Zhu Yang, Mengyue Institute of Health Informatics University College London United Kingdom Department of Aeronautics Imperial College London United Kingdom Gatsby Computational Neuroscience Unit University College London United Kingdom Department of Computer Science University College London United Kingdom School of Engineering Mathematics and Technology University of Bristol United Kingdom

Probability of necessity and sufficiency (PNS) measures the likelihood of a feature set being both necessary and sufficient for predicting an outcome. It has proven effective in guiding representation learning for unimodal data, enhancing both predictive performance and model robustness. Despite these benefits, extending PNS to multimodal settings remains unexplored. This extension presents unique challenges, as the conditions for PNS estimation—exogeneity and monotonicity—need to be reconsidered in a multimodal context. We address these challenges by first conceptualizing multimodal representations as comprising modality-invariant and modality-specific components. We then analyze how to compute PNS for each component while ensuring non-trivial PNS estimation. Based on these analyses, we formulate tractable optimization objectives that enable multimodal models to learn high-PNS representations. Experiments demonstrate the effectiveness of our method on both synthetic and real-world data. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Construction of Anti-Terrorism Intelligence Graph Based on DIKW Framework

Construction of Anti-Terrorism Intelligence Graph Based on D...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： GAO Peng YUAN Haoran LIU Yanfei MAO Bowen DUAN Yucong WANG Zhenhua Gansu Police Vocational College Gansu China Artificial Intelligence and Big Data Academy Chongqing City Science and Technology College Chongqing China Department of Intelligence and Computing Department of Infor Security Tianjin University Chongqing Police College Chongqing China Department of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Technology Hainan University Haikou China Department of Tactics Chongqing Police College Chongqing China

“Construction of anti-Terrorism Intelligence Graph Based on the DIKW Framework” simplifies the process of handling decentralized and diverse sources of intelligence in counter-terrorism. It uses the DIKW model to consolidate and structure data for better efficiency. It creates a data graph to illustrate relationships, making data more accessible. This data is then used to form an information graph, connecting data with relevant knowledge. From this, intelligent models are extracted, aiding in counter-terrorism prevention and prediction. This approach emphasizes transforming data into intelligence and knowledge hierarchically to improve counter-terrorism efforts.

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Hiding Scrambling Text Message in Speech Signal Based on Lightwight Hyperchaotic Map and Conditional Lsb Mechanisms

SSRN

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SSRN 2023年

作者： Al Sibahee, Mustafa A. Abduljaleel, Iman Qays Luo, Chengwen Zhang, Jin Huang, Yijing Abduljabbar, Zaid Ameen Ma, Junchao National Engineering Laboratory for Big Data System Computing Technology Shenzhen Technology University 518060 China Department of Computer Science College of Computer Science and Information Technology University of Basrah 61004 Iraq College of Computer Science and Software Engineering Shenzhen University 518060 China Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Technical Computer Engineering Department Al-kunooze University College Basrah61001 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518118 China College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China

This study proposed a lightweight and secure audio steganography system for hiding text messages during transmission over the Internet to address the computational cost exaggeration, and Insufficient levels of security in earlier studies. The paper proposes a two-phase functioning mechanism. Text characters are transformed into ASCII code and then stored in a vector, which is then divided into three sub-vectors. The sub-vectors are scrambled using a two low complexity operation, namely a forwardbackward reading technique and an odd-even index. Two scrambling circuits can be performed: the first on tiny sub-vectors and another on the vector as a whole. In the hiding phase, the speech signal samples were divided into 256 blocks using only 200 values per block, and low complexity Quadratic and the Hénon maps to hide the speech signal in a random manner. This method utilizes the conditional LSB as low complexity algorithm to identify hidden bits and a special hyperchaotic map algorithm for randomly choosing locations. The proposed approach provides good security for a scrambled text message with high SNR and PSNR, small MSE error and PESQ, SSIM near one, BER close to zero, and MOS near five as well as the lowest computational hiding cost. © 2023, The Authors. All rights reserved.

关键词： Steganography

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Fuzziness Based Semi-supervised Deep Learning for Multimodal Image Classification 1st

Fuzziness Based Semi-supervised Deep Learning for Multimoda...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Asma, Abeda Mostafa, Dilshad Noor Akter, Koli Mahmud, Mufti Patwary, Muhammed J. A. Department of CSE International Islamic University Chittagong Chittagong Bangladesh Department of Computer Science Nottingham Trent University Clifton Lane NottinghamNG118NS United Kingdom Medical Technologies Innovation Facility Nottingham Trent University Clifton Lane NottinghamNG118NS United Kingdom Computing and Informatics Research Center Nottingham Trent University Clifton Lane NottinghamNG118NS United Kingdom

ISBN: (纸本)9783031346217

Predicting a class or label of text-aided image has practical application in a range of domains including social media, machine learning and medical domain. Usually, supervised learning model is used to make such predictions where labeled data is mandatory, which is time consuming and required manual help. Classification of images are accomplished on visual features only by utilizing deep learning. Employing semi-supervised learning is a viable answer to these issues that needs a few label sample to classify huge unlabeled samples. The paper suggests a novel semi-supervised deep learning method based on fuzziness, called (FSSDL-MIC) for multimodal image classification to tackle the challenge of web image classification. For the first time in this scenario, we integrate Multilayer perceptron for textual features and MobileNetV2 for visual features to create a multimodal paradigm. Using data from PASCAL VOC’07, experiments have revealed that the proposed framework achieves significant improvement and outperforms modern techniques for multimodal image categorization. We also see a positive impact of low fuzzy sample when final model trained with visual features only. © 2023, ICST Institute for computer sciences, Social informatics and Telecommunications Engineering.

关键词： Image classification

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Shield-U: Safeguarding Traffic Sign Recognition Against Perturbation Attacks

Shield-U: Safeguarding Traffic Sign Recognition Against Pert...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Shengmin Xu Jianfei Sun Hangcheng Cao Yulan Gao Ziyang He Cong Wu College of Computer and Cyber Security Fujian Normal University China School of Computing and Information Systems Singapore Management University Singapore Department of Computer Science City University of Hong Kong China Division of Information Science and Engineering KTH Royal Institute of Technology Sweden School of Cyber Science and Engineering Zhengzhou University China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Traffic sign recognition systems are crucial for the navigation and situation awareness of autonomous vehicles. They leverage deep learning technologies to swiftly and accurately identify traffic signs, even in the most challenging traffic environments. However, security researchers have uncovered a critical vulnerability in these systems: learning-based TSRs are particularly susceptible to physical-world perturbation attacks. Through subtle modifications (i.e., attaching well-designed patches on traffic signs), attackers can deceive the recognition system into making erroneous judgments, which can further lead to serious traffic accidents. Although several defense mechanisms have been proposed to enhance the security of sign recognition systems, these solutions generally target only specific types of malicious perturbations and thus lack robustness. To address this issue, we present a robust defense mechanism named Shield-U, which restores traffic sign images contaminated by physical patch perturbations, providing credible data for the recognition model. In the process of implementing Shield-U, we first design a feature difference-aware perturbation generator that outputs potential sign contamination patterns. Incorporating generated perturbations during the training phase enables our restoration model to gain sufficient understanding of diverse perturbation types, thus enhancing its ability to repair various perturbed signs. Following this, we build an attention-driven restoration network to repair sign images. Finally, we evaluate the effectiveness of Shield-U using widely used sign recognition models and public datasets. The results demonstrate that our defense mechanism excels in resisting potential perturbations, increasing the average sign recognition accuracy by 50.4%.

关键词： Training Image recognition Target recognition Perturbation methods Maintenance engineering Robustness Image restoration Safety Security Public transportation

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An Adaptive Acm Watermarking Technique Based on Combined Feature Extraction and Non-Linear Equation

SSRN

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SSRN 2025年

作者： Bounceur, Ahcene Mostefa, Kara Ferik, Brahim Laouid, Abdelkader College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Information & Computer Science Department King Fahd University of Petroleum & Minerals Street Academic Belt Road Dhahran31261 Saudi Arabia University of Tebessa Tebessa12002 Algeria LIAP Laboratory University of El Oued PO Box 789 El Oued39000 Algeria

Digital watermarking is crucial for protecting data integrity and intellectual property, especially in medical imaging, where authenticity and privacy are critical. Embedding hidden information within images aids authentication, copyright protection, and integrity verification. This work proposes a robust palmprint-based watermarking approach based on adaptive ACM watermarking with a non-linear equation technique to enhance patient security. The proposed method integrates local binary patterns and histograms of oriented gradients for robust feature extraction, discrete wavelet transforms for multi-level image decomposition, Arnold Cat Map for chaotic mapping, and singular value decomposition for stable watermark embedding. The contribution goals are improving security, imperceptibility, and resilience against image processing attacks. Experimental results demonstrate the method’s effectiveness, with a peak signal-to-noise ratio of 63.52 and a structural similarity index 1.00, indicating high image quality retention. An equal error rate of 0.035 confirms the method's reliability in watermark detection. These findings underscore the proposed method’s suitability for secure medical image watermarking applications, enhancing data integrity and patient privacy. © 2025, The Authors. All rights reserved.

关键词： Wavelet decomposition

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