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International conference on Smart systems and Wireless Communication, SSWC 2024

作者： Dharmaraj, N. Suresh Kumar, A. Lakshmikanth, J. Charulatha, R.T. Murugasami, R. Amarnath, M.A. Department of ECE Rajalakshmi Institute of Technology Chennai India Department of ECE M.I.E.T. Engineering College Trichy India Department of CSBS Rajalakshmi Institute of Technology Chennai India Nandha Engineering College Erode India JJ College of Engineering and Technology Trichy India

ISBN: (纸本)9789819613472

Tumors are aberrant tissue growths that can develop in any body organ. Numerous varieties of human tumors have been discovered in recent years, including brain, bone, and lung. image processing is essential in tumor analysis and categorization. Medical image processing is an essential field of study since the results are utilized to improve health issues. When cells within the bone divide uncontrollably, they produce lumps or lumps of aberrant tissue. There are numerous types of bone tumors, each with its own set of characteristics. Bone tumors are classified as noncancerous (benign) or cancerous (malignant). Our project primarily focuses on picture segmentation and classification of skeletal images. Initially, the Fast Non-Native Media (FNLM) filter is used for preprocessing. For the segmentation procedure, a sophisticated algorithm is employed to distinguish malignant nodules from lung pictures. In feature extraction, distinct features are extracted using a gray-level co-occurrence matrix (GLCM). The GoogleNet ranking algorithm is then used to rank the specified features. The suggested classifier is capable of detecting bone cancer with high accuracy. The proposed approach was built in MATLAB, utilizing the dataset from the Bone image database. Various performance measures are assessed and connected with existing classifiers and cutting-edge algorithms. Simulation results show that the devised scheme has a high classification accuracy (99%) compared to previous methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image segmentation

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Role, Significance, and Impact of Method Noise in Multi-Modal Medical image Fusion 5

Role, Significance, and Impact of Method Noise in Multi-Moda...

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5th International conference on Emerging systems and Intelligent Computing, ESIC 2025

作者： Bhosekar, Shailesh Singh, Prabhishek Garg, Deepak Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798331522100

Multi modal medical image fusion in, for instance, combining information from computed tomography (CT) scans, positron emission tomography (PET) and magnetic resonance imaging (MRI) into one single dataset, improves the accuracy of diagnostics due to atomistic and functional details of the images. In this paper, the role, importance, and effect of method noise in multi-modal medical image fusion, is considered to assess and improve fusion algorithms. The effectiveness of an algorithm can be determined in terms of its efficacy at preserving salient image content and removing unwanted artifacts while maintaining the former, which is defined by method noise, the residual noise after image processing. In this paper, I review the theoretical basis of method noise, discuss its function in both fusion and denoising processes, and discuss its practical and conceptual implications, showing its importance for quality assessment and algorithm refinement. We apply method noise to the Non-Subsampled Shearlet Transform (NSST) to demonstrate application in multi-modal medical image fusion. Comparative experiments using NSST based fusion with /without method noise demonstrate significantly different visual clarity and quantitative metrics as measured with different metrics. Results show that method noise both maintains image integrity by preventing detail loss and aids in the integration of complementary detail from different modalities. The need for method noise in advancing image fusion techniques is emphasized in this paper, and a framework for future research and application of fusion in diagnostic imaging is presented. © 2025 IEEE.

关键词： Positron emission tomography

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Artificial Intelligence (AI) Assisted Literature Review 3

Artificial Intelligence (AI) Assisted Literature Review

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3rd International conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Hilda, Jabanjalin Jeevaa, S.V. Bhadhrinath, U. Solanki, Darshini Sr. School of Computer Science Engineering Vellore Institute of Technology Vellore India Vellore Institue of Tehnology Vellore Vellore India

ISBN: (纸本)9798331527549

This research aims to address the inefficiencies and inaccuracies in traditional literature review methodologies by integrating advanced Artificial Intelligence (AI) technologies, specifically transformer-based models. The study focuses on optimizing PDF-to-text conversion through image and character recognition, thereby minimizing resource wastage and errors associated with manual transcription. Additionally, the research employs advanced Natural Language processing (NLP) techniques to automate the identification of critical phrases such as keywords and organizational entities, significantly reducing manual errors and improving accuracy. The methodology also includes the use of sophisticated summarization algorithms and sentiment analysis, streamlining the process of summarizing extensive literature and identifying study limitations. By leveraging state-of-the-art transformer models and industry-standard libraries, this research introduces a novel, efficient framework for literature analysis. The outcome is a comprehensive, AI-powered toolkit that enhances the speed, accuracy, and precision of literature reviews, ultimately providing researchers with a more reliable and resource-efficient approach to navigating scholarly content. © 2025 IEEE.

关键词： Natural language processing systems

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An application of smart visual air conditioning controller to enhance control efficiency in office environments

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RESULTS IN ENGINEERING 2025年 25卷

作者： Tian, Yu Zhao, Zilong Loo, Junn Yong Univ Malaya Fac Engn Dept Mech Engn Kuala Lumpur 50603 Malaysia Monash Univ Malaysia Sch Informat Technol Jalan Lagoon Selatan 47500 Bandar Sunway Selangor Darul Malaysia

Innovative solutions for sustainability and energy efficiency are crucial in green building management. This study presents a novel approach to optimizing air conditioning (AC) system operations in commercial buildings, with a focus on real-time control aimed at reducing energy consumption. We propose the Smart Visual Air Conditioning Controller (SVACC), which utilizes computer vision and deep learning-based human detection to intelligently manage AC operation, minimizing unnecessary runtime. By detecting human presence in meeting rooms, the system dynamically adjusts AC activation based on occupancy, thereby significantly reducing energy waste. A statistical analysis conducted over five months across ten conference rooms demonstrated that the SVACC reduced AC usage time by 33.60 %. We validate and optimize the SVACC across various building types, including commercial office spaces, industrial warehouses and laboratories, and residential apartments. The system achieved an optimal balance with 96.55 % precision and 93.33 % recall, resulting in an F1 score of 0.9492, demonstrating high performance across various environments. Our results underscore the effectiveness of the SVACC, which highlights the potential of integrating advanced deep learning models with HVAC systems to optimize energy consumption. This approach offers a promising solution for improving HVAC design and energy management across diverse building environments. Future work will focus on refining sensor technology and control algorithms to further optimize energy efficiency.

关键词： HVAC Building management Deep learning image processing Computer vision

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Invisible Backdoor Attack with image Contours Triggers 24th

Invisible Backdoor Attack with Image Contours Triggers

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24th International conference on algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Hou, Yangzhi Yang, Ying Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9789819615445

In recent years, backdoor attack techniques on neural networks have been widely studied and researched. In this attack mode, the model implanted with a backdoor behaves normally when processing normal inputs, but once it encounters a special trigger designed by the attacker, its prediction results will be maliciously manipulated. Different backdoor attack strategies have been proposed to improve the stealth and robustness of the triggers. However, most of the existing backdoor attack strategies use fixed triggering patterns or input-independent triggering patterns, and thus are easily detected and blocked by existing defense systems. To address these limitations, we propose a backdoor attack method based on image contours. Specifically, we first use image contours as the target region for backdoor injection and fill them with specific color information to generate trigger patterns. Then, using our designed Generative Network (GN) and the Auxiliary Extraction Network (AE), the trigger patterns are covertly embedded into the images to be contaminated to generate a poisoned dataset, which contaminates the training model for the purpose of malicious attacks. Compared with traditional methods, our trigger patterns are more natural and harder to detect. Experimental results show that our proposed attack method achieves excellent performance in terms of both stealth and robustness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Medical image Noise Reduction Methods Based on Cutting-Edge Hybrid Filtering Designs

Medical Image Noise Reduction Methods Based on Cutting-Edge ...

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International conference on Smart systems and Wireless Communication, SSWC 2024

作者： Saha, Tanusree Vishal, Kumar Magadh University Bodh Gaya India Department of Information Technology JIS College of Engineering Kalyani India Department of Mathematics Magadh University Bodh Gaya India

ISBN: (纸本)9789819613472

A high-resolution noise free medical image is an essential requirement for accurate disease detection. Medical image processing and analysis techniques have become utmost important in recent decades as a supplementary competence for medical professionals in the fields of diagnosis and prevention. Obtaining high-quality images without any interference is a crucial requirement for effectively managing therapeutic images. Medical images may contain many types of noises for involvement of simultaneous operation of multiple equipment involved in data capturing and transmission. Various filters have been suggested for eliminating different types of noises, each having its own merits and drawbacks depending on its capabilities and projected benefits. Our research depicts a technique for removing noises from medical images using a hybrid filtering approach based on ensemble adaptive mean filtering and Block-matching-3D filtering. The proposed methodologies specifically aim to reduce the speckle noise in brain MRI images obtained from ultrasonic therapy by employing hybrid filtering techniques. This proposed hybrid filtering algorithms integrate the most effective aspects of high-performance filters to achieve a de-noised image that retains fine details, edges, and textures. The statistical performance measurement metrics like Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR) are employed to evaluate the improved visual quality of the images. The results indicate that our proposed hybrid filtering technique functioning efficiently and provide better quality de-noised image compare with other existing de-noising techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image enhancement

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AI-Based Bone Cancer Detection Using image processing and CNN 5th

AI-Based Bone Cancer Detection Using Image Processing and CN...

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5th EAI International conference on Cognitive Computing and Cyber Physical systems, IC4S 2024

作者： Srividya, K. Reddy, Gangannagari Varunteja Bakki, Vishwaja Adilakshmi, T. Department of Computer Science and Engineering Vasavi College of Engineering Hyderabad India

ISBN: (纸本)9783031770777

This study presents a new Technology-driven approach for early detection of bone cancer using preliminary image processing technologies and neural networks (CNN) used for diagnosing cancer from pathological images. The main aim is to provide a powerful and reliable tool for cancer diagnosis done in clinics. Survival rates of cancer decrease with an increase in age, urging the need for early diagnosis and precaution. The project aims to use machine learning and image preprocessing to provide solutions for the detection of cancer, necessary for timely intervention. Using carefully selected datasets and advanced image preprocessing techniques, the project aims to provide doctors with reliable tools to accurately diagnose bone cancer. Analysis of existing literature demonstrates the effectiveness of CNN and deep learning algorithms in the analysis of medical images as the basis of the proposed method. The proposed methodology includes image enhancement, data acquisition, CNN development, training, validation, transformation, and model evaluation. The CNN model is evaluated on accuracy, precision, recall, and f1-scores of bone cancer diagnosis images. It provides insight into model development by addressing issues such as classification error, class inequality, and biases. While comparisons with existing literature demonstrate the model's uses, practical considerations are also discussed. The findings suggest a significant potential to improve bone cancer diagnosis and stimulate further research on improving CNN model architecture and developing real-world deployment strategies. The training accuracy of the CNN model came out to be 94.64%. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Diseases

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image processing in Retail Marketing: Innovations for Customer Engagement, Sales, and Operational Efficiency 12th

Image Processing in Retail Marketing: Innovations for Cust...

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12th International conference on Recent Trends in Computing, ICRTC 2024

作者： Rastogi, Isha Sinha, Utkrishta Gaur, Anubha Department of Information Technology KIET Group of Institutions Ghaziabad India

ISBN: (纸本)9789819788354

The retail industry, marked by fierce competition and evolving consumer preferences, demands innovative approaches to boost customer satisfaction, drive sales, and streamline operations. This study presents a comprehensive strategy leveraging cutting-edge image processing technologies such as facial recognition, object detection, and emotional analysis. The methodologies proposed encompass emotional profiling through facial recognition, personalized offline store experiences enabled by facial recognition technology, and automated inventory management via object detection algorithms. Real-world applications and case studies validate the efficacy of these techniques, showcasing their transformative potential in redefining the retail landscape. By integrating advanced image processing solutions, retailers can not only meet the dynamic demands of modern consumers but also gain insights to optimize business processes and deliver unparalleled shopping experiences. This research underscores the pivotal role of technology in shaping the future of retail, offering practical insights for industry stakeholders to navigate and thrive in an increasingly competitive market landscape. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Inventory control

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Robust Non-convex Model-Based Approach for Deep Learning-Based image processing 4th

Robust Non-convex Model-Based Approach for Deep Learning-Ba...

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4th International conference on Numerical Computations: Theory and algorithms, NUMTA 2023

作者： Morotti, Elena Evangelista, Davide Loli Piccolomini, Elena Department of Political and Social Sciences University of Bologna Bologna Italy Department of Mathematics University of Bologna Bologna Italy Department of Computer Science and Engineering University of Bologna Bologna Italy

ISBN: (纸本)9783031812408

Total p-norm Variation (TpV) is a well-established technique in image processing, used to denoise and preserve edges. However, the related non-convex minimization is still a challenging task in optimization, both for the computational cost and for convergence guarantees toward a good local minimum. We propose a framework, called (TpV)2, embedding a convolutional neural network to speed up the iterative reconstruction while preserving converging features. The resulting hybrid method is robust and accurate. Verifications and comparisons illustrate that the proposed method is effective and promising. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Inverse Problems Neural Network Non-convex optimization

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Advancements in image Deblurring and Performance Metrics Using Deep Learning Technique 12th

Advancements in Image Deblurring and Performance Metrics Usi...

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12th International conference on Recent Trends in Computing, ICRTC 2024

作者： Dhavalagimath, Shankramma S. Rajesh, T.M. Singh, Rakesh Kumar Department of Mathematics Dayananda Sagar University Bangalore562112 India Department of Computer Science and Engineering Dayananda Sagar University Bangalore562112 India

ISBN: (纸本)9789819788354

In computer vision and image processing, image deblurring is a crucial phase that attempts to restore the sharpness of the image and clarity of images that have been damaged due to motion blur, defocus, or other factors. The multiple image deblurring methods are thoroughly investigated in this work, along with the performance evaluation metrics that were employed to evaluate each method's efficiency. We started by examining the most recent deblurring techniques, which include both deep learning-based and traditional algorithms. Every technique's advantages, disadvantages, and suitability are examined in different scenarios. Furthermore, we investigate the finer details of performance metrics that are frequently used to rate the quality of deblurred images. These metrics include subjective assessments from user research &objective measurements like PSNR and SSIM. This study aims to elucidate the benefits and drawbacks of current deblurring methods in diverse contexts through extensive experimentation. Moreover, we examine the applicability and consistency of performance evaluation metrics for evaluating the accuracy and perceived quality of deblurred images. By supporting researchers and professionals in identifying suitable techniques and assessment standards for their particular applications, the knowledge gained from this study improves the field of image deblurring. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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