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Comparison of Tumor Segmentation Techniques from Medical Images 7

Comparison of Tumor Segmentation Techniques from Medical Ima...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Gururaj, Bharathi Nair, Prajith Prakash Harish, L. Shanmugasundaram, V. Alaskar, Kamal Maranan, Ramya Ece Acs College of Engineering Karnataka India Cmr Institute of Technology Information Science and Engineering Karnataka India Sona College of Technology Department of Electrical and Electronics Engineering Chennai India Institute of Management Department of Computer Application Maharashtra Kolhapur India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350341485

A Brain Tumor (BT) is one of the leading causes of mortality worldwide. Because of this, early diagnosis is crucial. BT localization and segmentation from magnetic resonance imaging (MRI) is a challenging but crucial task with many medical analytic applications. Many methods have been presented for tumor segmentation. However, they each have their issues, such as reliance on manual intervention from experts and uncertainty over which feature extractor to use. To overcome these obstacles, a novel, hybrid strategy for tumor region segmentation based on a convolutional neural network is suggested. Kaggle's Brain MRI data was used for training and testing the proposed approach. First, a pre-processing strategy is used to operate on a selected portion of the image instead of the entire image to obtain a robust and adaptable BT segmentation system. Reduced processing time and no overfitting issues are the results of this strategy. Second, the Res-SegNet hybrid model was employed, now the model works only with a smaller portion of the MRI Accuracy, loss, Intersection of Union (IoU), and Dice coefficient are some of the metrics that were used to gauge how well the proposed method performed. When applied to tumor segmentation, the model achieved an accuracy of 0.99, an IoU of 0.745, a Dice of 0.831, and a loss of 0.176. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Intelligent System for Early Diagnosis of Skin Cancer using Bilinear CNN and Soft Attention Approach 4

Intelligent System for Early Diagnosis of Skin Cancer using ...

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4th International Conference on electronics and Sustainable Communication Systems, ICESC 2023

作者： Pundir, Aditya Kumar Singh Walid, Md. Abul Ala Adivarekar, Pravin Gopi, Arepalli Malathy, V. Singh, Kamlesh Arya College of Engineering and Information Technology Department of Electronics and Communication Engineering Jaipur India Department of Computer Science and Engineering Khulna9203 Bangladesh A.P. Shah Institute of Technology Maharashtra Thane India Koneru Lakshmaiah Education Foundation Department of CSE Andhra Pradesh Guntur India Sr University School of Engineering Department of Ece Telangana Warangal India Graphic Era Hill University Department of Computer Science and Engineering Dehradun Campus Dehradun India

ISBN: (纸本)9798350300093

Skin cancer is becoming the leading cause of death in the Western world. Although sun exposure is a major risk factor for the development of skin cancer, this malignant neoplasm can appear anywhere in the body's skin. When detected early, most cases of skin cancer can be treated successfully. Skin cancer is fatal if not caught and treated quickly. New tools allow for the first-stage detection of skin cancer. The biopsies are the official method of skin cancer diagnosis. This is accomplished by scraping out a small amount of skin and sending it off to the lab for analysis. It takes a lot of effort and time. The suggested skin cancer detection method makes use of BilinearCNN-SA to identify malignant moles at an early stage. Patients benefit more from it. Preprocessing steps in the diagnostic approach include various elements like noise removal, grayscale conversion, and image enhancement. Once the data has been cleaned up, they apply Otsu segmentation. The ABCD Rule is used for feature extraction. The BilinearCNN-SA Model is then used to make the final classification. When compared to convolutional neural network and support vector machine models, the proposed method fares very well. © 2023 IEEE.

关键词： Convolutional neural networks

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Image Enhancement in Fundus Images using Modified Frangi Vesselness Filter in comparison with Hessian and Gaussian Filter 2

Image Enhancement in Fundus Images using Modified Frangi Ves...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Nanmaran, R. Moorthy, Vijai Meyyappan Srinath, R. Ramasamy, R. Imran Javeed, S. Srimathi, S. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India Vignan Foundation of Science and Research and Technology Department of Advanced Computer Science and Engineering Andhra Pradesh Guntur India Ramco Institute of Technology Department of ECE Tamilnadu Rajapalayam India Saveetha School of Engineering SIMATS Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350365269

The main aim of this work to develop image enhancement algorithm using modified vesselness filter for fundus image. Image enhancement using modified vesselness filter has been designed and compared with hessian and Gaussian filters. Performance of proposed algorithm is measured using high values of Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Normalized Correlation (NC), and Naturalness Image Quality Evaluator (NIQE). Modified frangi vesselness filter provides PSNR = 41.31db, SSIM = 92.9%, NC = 23.25%, NIQE = 30.70%. Hessian filter provides PSNR = 41.37db, SSIM = 10.85%, NC = 18.65%, NIQE = 23.64%. Gaussian filter provides PSNR = 11.33db, SSIM = 95.23%, NC = 99.24%, NIQE = 6.29%. The designed filters were tested against various noises like Gaussian, salt and pepper, speckle noise. All three filters performed good against Gaussian noise over other two types of noises. Based on the simulation results modified vesselness filter performed well when compare other two filters. Keywords: Improved Filter Design, Frangi Vesselness Filter, Hessian Filter, Gaussian Filter, Medical Image Enhancement. © 2024 IEEE.

关键词： Image enhancement

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The Role of 5G Networks in Energy-Efficient Smart Cities Development using IoT 2

The Role of 5G Networks in Energy-Efficient Smart Cities Dev...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Govindasamy, Chinnaraj Dayana, T.R Nisha Vasu, V. Tamilarasi, A. Prakash, R.B.R. Kalaivani, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Vels Institute of Science Technology and Advance Studies Department of Computer Science India St. Claret College Department of Commerce Jalahalli Karnataka Bengaluru560013 India Kongu Engineering College Department of Computer Applications Perundurai India Electrical and Electronics Engineering Koneru Lakshmaiah Education Foundation vaddeswaram522302 India AP/ECE Erode Sengunthar Engineering College India

ISBN: (纸本)9781665456302

In theory, residents in 'smart cities' would have access to a wide range of amenities designed to enhance their daily lives by making better use of public resources and providing a more satisfying experience overall. The efficiency of these services depends on the information sharing across disparate systems serving similar functions. Large amounts of data with a variety of sophisticated, application-specific needs will likely be sent throughout the information exchange process. By better management of public resources and a focus on resident comfort, infrastructure upkeep, and environmental sustainability, 'smart cities,' which rely on ICT, aim to raise the bar on service quality. Fifth-generation (5G) wireless mobile communication paves the way for a new kind of communication network that can connect everyone and everything. Impacting economies and communities, 5G will offer the connectivity infrastructure required by many smart city applications. A glimpse into the future of urban areas may be seen in the rise of 'smart cities' and the IoT. The integration of many systems aimed to monitor varied components of the smart city may be utilized to generate a more sustainable and securer city. This research study presents a construction that mixes the data from diverse systems. In order to lessen the quantity of produced traffic in the 5G network and subsequently the energy consumption, suggest the usage of data aggregation in each antenna. A summary of 5G communication networks and different 5G technologies utilized in smart cities to promote sustainability is provided. The sustainability metrics for 5G networks are then examined across the environmental, social policy, and economic aspects, as well as sub-dimensions such as energy efficiency, energy usage, carbon footprint, contamination, cost, health, safety, and security. The findings indicate that while in an effort to address sustainability in 5G technology and intelligent buildings, the bulk of research publicat

关键词： 5G mobile communication systems

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Edge Intelligence Hybrid Framework for Real-Time IoT Applications using Multi-Stage Adaptive Processing Methods 6

Edge Intelligence Hybrid Framework for Real-Time IoT Applica...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Suneel, Sajja Yadav, Rakesh Kumar Nirmala Devi, K. Teja sree, B. Aarthi, R. Upadhyay, Shrikant Institute of Aeronautical Engineering Dundigal Telangana Hyderabad500043 India Amity School of Engineering and Technology Amity University Bihar Patna India Department of Computer Applications Madanapalle Institute of Technology & Science India Department of IT S.R.K.R Engineering College Bhimavaram India Department of ECE Rajalakshmi Institute of Technology Kuthambakkam Post Tamil Nadu Chembarambakkam600124 India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331522667

The rapid proliferation of IoT devices has created an unprecedented need for real-time data processing and decision-making. This paper proposes an Edge Intelligence Hybrid Framework designed for real-time IoT applications using multi-stage adaptive processing methods. The framework structured into three layers: edge-level processing, fog-level aggregation, and cloud-level computation, enabling efficient distribution of tasks across the hierarchy. At the edge, lightweight models and preprocessing techniques, such as Kalman Filtering and Huffman Coding, ensure low-latency and energy-efficient operation. Intermediate computations and model synchronization occur at the fog level, while high-complexity tasks like deep learning model training performed at the cloud level. Results demonstrate that edge-level processing achieves an accuracy of 85.4%, while fog and cloud levels improve accuracy to 89.4% and 94.9%, respectively. Latency at the edge is as low as 9–13ms, compared to fog (23–29ms) and cloud (117–125ms), ensuring suitability for time-critical IoT tasks. The proposed framework effectively balances latency, accuracy, and resource optimization, making it ideal for applications such as predictive maintenance, healthcare, and smart surveillance. © 2025 IEEE.

关键词： Kalman filters

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A WSN-Based, Intelligent Medical IoT Systems Integrating computer Vision Platform 3

A WSN-Based, Intelligent Medical IoT Systems Integrating Com...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Singh, Charanjeet Bhavani, G. Lakshmi Maginmani, Umme Habiba Singh, Rajesh Kumar, A Suresh Rebelli, Shashank Deenbandhu Chhotu Ram University of Science and Technology Electronics and Communication Department India KG Reddy College of Engineering and Technology Electronics and Communication Engineering India SECAB Institute of Engineering and Technology Department of Computer Science and Engineering Vijayapur Karnataka India Uttaranchal University India Uttaranchal Institute of Technology India Graphic Era Deemed to Be University Department of CSE India SR University Department of ECE Warangal Telangana State India

ISBN: (纸本)9798350399264

As wireless connectivity technology advanced, wireless sensor technologies also became more frequently used in many societal sectors. This research introduces wireless sensors to the world of health care systems, and we present a sensor network-based medical imaging. Through the communication system, it may send the participant's physiological data as well as pictures to the healthcare monitoring ground station, allowing for continuous pathologic growth tracking and prompt treatment diagnosis. We discussed the use of remote sensing techniques in the remote health care surveillance system and expanded on the ideas for its implementation on the theory of sensor networks, present circumstances, and trends in China's medical care. The design of a network infrastructure, the OS with the interposes communication, as well as the software are the challenges of a wireless sensor network. Our suggested framework is capable of handling the issues in an acceptable manner. To evaluate the overall effectiveness of the model, the suggested approach is contrasted with other recent methods. © 2023 IEEE.

关键词： Internet of things

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The Future of Smart Grids: Revolutionizing Energy Distribution with Advanced IoT, and Renewable Integration 5

The Future of Smart Grids: Revolutionizing Energy Distributi...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Avinash, S. Vinitha, J.C. Dharinya, S. Sree Samuthira Pandi, V. Lakshmi Priya, J. Jaishika, S. St. Joseph's College of Engineering Department of Mba Chennai India Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vit University School of Computer Science Engineering and Information Systems Vellore India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Saveetha Engineering College Department of Ece Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India

ISBN: (纸本)9798350376685

Smart grids, which integrate modern Internet of Things (IoT) technology and renewable energy sources to boost efficiency, sustainability, and dependability, are poised to bring about a dramatic shift in the future of energy distribution. Intelligent grids are expected to bring about this shift. Focusing on the role of the Internet of Things (IoT) in enabling real-time monitoring and control, as well as the incorporation of renewable energy to support a low-carbon future, this research investigates the potential for smart grids to change the energy sector. This study gives insights into how smart grids can be efficiently deployed to satisfy the growing energy needs of society by studying the technological breakthroughs, obstacles, and benefits of smart grids. Specifically, the study's focus is on smart grids. The research highlights the essential role that smart grids play in aiding the shift towards a more sustainable and resilient energy infrastructure by conducting an in-depth analysis of case studies, pilot projects, and industry trends all around the world. Providing recommendations for policymakers, industry stakeholders, and utilities to exploit the full potential of smart grids in determining the future of energy distribution, the findings contribute to the continuing discourse on the strategic development and implementation of smart grid technologies. Both of these topics are currently being discussed. © 2024 IEEE.

关键词： Demand Response Management Distributed Generation Energy Efficiency Energy Storage Systems Grid Resilience Low-Carbon Future Real-Time Monitoring and Control

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Advancements in Artificial Intelligence for Better Image Analysis, Recognition, and Interpretation in the Field of Image Processing 2

Advancements in Artificial Intelligence for Better Image Ana...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Saravanan, V. Adaikkammai, A. Sasikala, G. Iyswariya, A. Babisha, A. Latha, M. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Nano Electronics Materials and Sensors Tamilnadu Chennai India R.M.D. Engineering College Department of Computer Science and Business Systems India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Avadi Chennai India R.M.K. Engineering College Department of Ece Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science India Srm Institute of Science and Technology Department of It India

ISBN: (纸本)9798350388916

The rapid advancement of artificial intelligence (AI) has brought about a significant revolution in image processing. This revolutionary technology has enabled the analysis, recognition, and interpretation of images in ways that were previously inconceivable. An investigation into the most recent developments in artificial intelligence techniques that have improved the efficiency and precision of image processing tasks is presented in this study. We survey the emergence of transformer architectures and the integration of deep learning models, namely Convolutional Neural Networks (CNNs), to explore how these technologies have transformed the understanding and application of images in several industries. An overview of existing image processing methods and the limits of those approaches is presented at the beginning of our research. This highlights the necessity of more advanced AI-driven solutions. After that, we examine the advancements that have been made in artificial intelligence, such as the incorporation of attention processes, the development of more effective CNN architectures, and the utilisation of generative adversarial networks (GANs) for the purpose of picture synthesis and augmentation. The purpose of this research is to investigate the impact that artificial intelligence has on particular applications of image processing, such as autonomous car navigation systems, facial recognition, medical imaging, and satellite images analysis. Specifically, we focus on the enhancements in diagnostic accuracy, environmental monitoring, security, and safety that arose as a result of the adoption of artificial intelligence in these sectors through the use of case studies. Furthermore, we discuss the issues that are associated with artificial intelligence in image processing. These challenges include the necessity for explainable AI models, the requirement for computational resources, and the protection of data privacy. A number of potential solutions to these problems

关键词： Convolutional neural networks

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Towards Wearable Wireless Power Harvesting using Clothing-Integrated Beamforming Structures

Towards Wearable Wireless Power Harvesting using Clothing-In...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： Wagih, Mahmoud Khan, Rayhan Bhardwaj, Shubhendu Vital, Dieff University of Southampton School of Electronics and Computer Science United Kingdom Florida International University College of Engineering and Computing ECE Department MiamiFL United States The University of Illinois-Chicago College of Engineering and Computing ECE Department ChicagoIL United States

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

High-gain wide-beam off-body wearable antennas are essential for efficient wearable RF energy harvesting (RFEH). Here, we report a 5.8-GHz reflector-backed Yagi-inspired antenna with over 2 dB gain and efficiency improvement over a conventional Yagi. Using the reflector, the end-fire patterns are re-directed in the broadside off-body direction while maintaining a simulated 76% efficiency. To demonstrate the feasibility of high-gain RFEH, a beam-forming Rotman lens is designed with a relatively low insertion loss based on conventional fabrics with conductive surfaces designed to be implemented via the automated embroidery of conductive threads. This work is a stepping stone to clothing-integrated low-profile beamforming for sub-6 GHz 5G/6G applications. © 2022 IEEE.

关键词： Textiles

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Automated Digitally Modulated Signal Recognition and Classification using Machine Learning with Multimodal Information 2

Automated Digitally Modulated Signal Recognition and Classif...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Kalaiarasi, N. Walid, Md. Abul Ala Das, Shyamali Nanmaran, R. Srimathi, S. Beschi, I.S. Rmk College of Engineering and Technology Department of Ece India Department of Computer Science and Engineering India Cmr Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India Saveetha School of Engineering Simats Department of Bio Technology Tamilnadu Chennai India Department of Computer Applications Chennai India

ISBN: (纸本)9798350325799

Recently, the pattern detection of communication modulation signals takes obtained important attention in many application regions like civilian field, military, and so on. It develops vital to proposal a safe and robust feature extraction (FE) system to effectively detect the many signal modulation varieties in a difficult platform. Many works have developed novel approaches for extracting the feature parameters like instant features, fractal features, etc. Besides, machine learning (ML) algorithms are generally utilized for modulation signal classification. This study develops an Automated Digitally Modulated Signal Recognition and Classification using Machine Learning (ADMSR-ML) technique. The aim of the ADMSR-ML approach lies in the proficient recognition and categorization of various kinds of digitally modulated signals. At the initial stage, Sevcik Fractal Dimension (SFD) model is employed to produce a set of feature vectors. For modulation detection and classification, deep extreme learning machine (DELM) model is applied. Eventually, Artificial Bee Colony (ABC) optimization algorithm was used for the optimal parameter selection of the DELM model. To examine the classification results of the ADMSR-ML approach, series of experiment are made. The extensive outcomes indicate the superior performance of the ADMSR-ML technique over other models. © 2023 IEEE.

关键词： Fractal dimension

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