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Remote Fish Nourishing and Water Quality Tracking Using a Mobile Application 5th

Remote Fish Nourishing and Water Quality Tracking Using a Mo...

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5th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Rajesh, Singamaneni Bezawada, Deepika Jaya Sri Kasireddy, Venkata Mamatha Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada India

ISBN: (纸本)9789819788606

Generally, in many households, offices, and private spaces it is common to have an aquarium. However, a major problem occurs when dealing with monitoring and feeding of fish in the absence of an aquarium caretaker. there are many automatic fish feeder devices that can be used by setting up it with specific instructions. these devices are not operated remotely. there is no specific mobile application for both water quality checking and feature of fish feeding. the research presents an innovative approach to the development of an automatic fish feeder system integrated with a mobile application. Taking data from temperature and turbidity sensors, the system uses advanced machine learning techniques, specifically Long Short-Term Memory (LSTM) networks, to analyze environmental conditions and adapt feeding schedules accordingly. this system allows users to schedule feeding sessions, adjust portion sizes, and ensure the well-being of their aquatic pets. thus, this project brings up a modernistic approach by connecting fish feeders with IoT devices and mobile applications. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： LSTM Remote Feeder Schedule feeding sessions Temperature sensor Turbidity sensor Water quality monitoring

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Emerging 5G, IoT and WSN: Opportunities, Challenges and Remedies

Emerging 5G, IoT and WSN: Opportunities, Challenges and Reme...

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international conference on Web Research (ICWR)

作者： Mohammad Reza Saadatmand Mohsen Ghotbi Technical & Network Operation Department Information Technology Business Intelligence MCI Tehran Iran Research & Development Department MCI Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

the Convergence of 5th generation (5G) technology with the Internet of things (IoT) and Wireless sensor networks (WSNs) will be investigated in this paper. this convergence of these concepts will bring many benefits to the quality of life in communities by increasing the number of connected devices more safely and resulting in higher data rates. However, like any other uprising, it will have challenges, concerns, and bottlenecks. We offer resolutions and remedies to make applying the above three elements more feasible, accessible, and tangible. Future research directions for industries, academia, and policymakers will also be suggested.

关键词： Wireless communication Industries Wireless sensor networks Quantum computing 5G mobile communication Merging Ultra reliable low latency communication Internet of things Sustainable development Convergence

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An Acoustic Method for Inspection of CNC Cutting Operations 5th

An Acoustic Method for Inspection of CNC Cutting Operations

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5th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Sondkar, Shilpa Kerkar, Kimaya Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9789819788606

CNC machining enables fine motion control of tools and workpieces simultaneously along multiple axes by a set of commands, simplifying manufacture of complex 3D components. Since the movements are not manually controlled, the operator does not have the tactile feedback based on which the cutting parameters can be fine-tuned to maintain and enhance the quality of cutting. One such aspect that is lost is the registration of the actual instant that the cutting tool makes contact with the workpiece. this can happen earlier or later than programmed due to variations in workpiece size and tool wear. this project uses commercially available sensors to analyse the change in sound and vibration from non-cutting movement to cutting operations, and applies MIR (Musical Information Retrieval) algorithms to determine the onset of cutting. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： CNC MIR Onset detection RNN sensor Spectral flux

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Leveraging AI in Prevention and Protection of Women Against Cybercrime in India: A Paradigm Shift of Criminal Law in the Making 5th

Leveraging AI in Prevention and Protection of Women Against ...

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5th international Ethical Hacking conference, eHaCON 2024

作者： Dey, Alice Dey, Sadhan Kumar IEM’s International Institute of Juridical Sciences University of Engineering and Management Kolkata Kolkata700160 India Department of ESM RCC Institute of Information Technology Kolkata700015 India

ISBN: (纸本)9789819784561

Innovative strategies are required for the efficient prevention and defense of legal rights considering the serious problems that the growth of cybercrime has presented to established legal systems in recent years. the paper additionally explores how India's criminal law is evolving due to technological developments in artificial intelligence (AI), with an emphasis on preventing cybercrime and strengthening the laws that protect women's rights. India is among the digital economies that growing at the fastest pace, that necessitates to fortify its legal defenses against cyberattacks promptly. the expansive presence of technology inclusive of its uses and misuses poses a set of different challenges both for international and National legal frameworks to combat them. In this respect, technologies powered by AI provide promising substitutes by giving legal domain the state-of-the-art tools for threat detection, evidence processing, and pro-active crime prevention. With the use of AI-enabled technologies, enormous volumes of data from various sources—such as social media, online forums, and digital communication channels—would be analyzed to look for trends that can point to cybercrimes directed at women according to NCRB Report. thus, to tackle cyber dangers and defend the rights of women in India, this abstract idea calls for the creation of progressive legislation that integrates AI technologies, data privacy measures, and gender-sensitive policies. In conclusion, a paradigm shifts in the battle against cybercrime and the defense of women's legal rights in India is represented by the incorporation of AI into the legal sphere. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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A Unified Approach to Smart Coconut Farming with IoT and Deep Learning for Recommendation of Pesticides and Fertilizers 5th

A Unified Approach to Smart Coconut Farming with IoT and Dee...

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Sai Baba, B. Ch. S. N. L. S. Hasavilasini, Siddhani Teja, Siddani Sarojini, Presingu Lakshmi Deepak, Reddi Vishnu Institute of Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9783031770746

Coconut production has a significant impact on the livelihoods of many farmers worldwide, serving as a primary source of income. However, recent observations have revealed a concerning decline in tree health and leaf condition, attributed to prevalent diseases and soil nutrition deficiencies. this deterioration poses a direct threat to productivity, gradually weakening coconut trees’ strength, nutrient absorption capacity, and yield production. Recognizing the urgency of this issue, this paper proposes an innovative approach leveraging Deep Learning, specifically Convolutional Neural networks (CNNs), and the interconnected network of physical devices commonly referred to as the Internet of things (IoT). the primary objective of this project is to develop a comprehensive web application for early coconut disease detection using CNN. Advanced technologies, including classification and image processing are integrated to assess tree health, identify disease symptoms, and detect pest infestations. Beyond disease detection, the project incorporates a recommendation system for pesticides to effectively cure identified diseases. Additionally, Internet of things (IoT) technology is employed to analyze soil conditions, providing insights that inform personalized fertilizer recommendations. this proactive system not only mitigates current challenges faced by coconut farmers but also establishes a sustainable framework for optimizing productivity. the overall performance of this architecture is validated with an impressive accuracy measure of 97.2%. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Convolutional neural networks

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HEMIT: H&E to Multiplex-Immunohistochemistry Image Translation with Dual-Branch Pix2pix Generator

HEMIT: H&E to Multiplex-Immunohistochemistry Image Translat...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Bian, Chang Phillips, Beth Cootes, Tim Fergie, Martin Division of Informatics Imaging and Data Sciences School of Health Sciences University of Manchester Manchester United Kingdom Division of Cancer Sciences University of Manchester Manchester United Kingdom

ISBN: (纸本)9783031845246

Computational analysis of multiplexed immunofluorescence histology data is emerging as an important method for understanding the tumour micro-environment in cancer. this work presents HEMIT, a dataset designed for translating Hematoxylin and Eosin (H&E) sections to multiplex-immunohistochemistry (mIHC) images, simultaneously featuring DAPI, CD3, and panCK markers. Distinctively, HEMIT’s mIHC images are multi-component and cellular-level aligned with H&E, enriching supervised stain translation tasks. To our knowledge, HEMIT is the first publicly available cellular-level aligned dataset that enables H&E to multi-target mIHC image translation. this dataset provides the computer vision community with a valuable resource to develop novel computational methods which have the potential to gain new insights from H&E slide archives. We also propose a new dual-branch generator architecture combining residual Convolutional Neural networks (CNNs) and Swin Transformers, with a feature map fusion module to integrate information from both branches. this architecture achieves superior translation outcomes compared to other popular algorithms. Evaluations on the HEMIT dataset show it outperforms pix2pixHD, pix2pix, U-Net, and ResNet, with the highest scores in Structural Similarity Index Measure (SSIM), Pearson correlation (R), and Peak Signal-to-Noise Ratio (PSNR). Additionally, we discuss the limitations of commonly used metrics in certain stain translation scenarios and provide recommendations for future use. Additionally, we also designed a downstream analysis to further validate the quality and utility of the generated mIHC images from a clinical-focused perspective. these results set a new benchmark in the field of stain translation tasks. the proposed dataset can be accessed at: https://***/BianChang/HEMIT-DATASET. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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sensor Placement Based on Cellular Communication Systems

Sensor Placement Based on Cellular Communication Systems

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Shaobo Yuan Jiaxu Chen Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

this paper presents a novel approach for optimizing sensor placement within a designated area, leveraging a combination of cellular communication systems and feed-forward neural networks to streamline the selection process for operators. Initially, a modified hill-climbing algorithm, a form of local search, is implemented to generate preliminary data for sensor positioning. However, to prevent the algorithm from converging on local optima, a simulated annealing algorithm is subsequently employed to validate and refine the results. During the construction phase of the cellular communication system, flaws were detected in the evaluation metrics when using Modal Assurance Criterion (MAC) and singular value ratios, which led to the adoption of a feed-forward neural network. this neural network is utilized for both further optimization and a more accurate assessment of the sensor distribution. the study highlights the seamless integration of cellular communication systems with local search methods and underscores the significant improvements in optimization achieved through the use of neural networks. Ultimately, the research successfully develops a method for determining the optimal sensor positions, providing a robust solution for the effective deployment of cellular sensors in complex environments.

关键词： Measurement sensor placement Search methods Clustering algorithms Simulated annealing Computer applications Mobile communication Power electronics Feedforward neural networks Optimization

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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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Non-invasive Technique for Detecting Glycosuria through Image processing and Deep Learning Approaches 5th

Non-invasive Technique for Detecting Glycosuria Through Imag...

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Teja, Chitturi Siva Nitheesha, Gokaraju Kumar, Boyidi Ravi Krishna, Bandaru Radha Naik, Desavath Madhu Pareek, Prakash Singh, Lokendra Department of ECE Vishnu Institute of Technology Bhimavaram534202 India Dehradun248002 India

ISBN: (纸本)9783031770746

Glycosuria level monitoring is a fundamental aspect of diabetes management, enabling proactive and personalized care to prevent complications, improve overall health, and enhance the quality of life for individuals with diabetes. Conventional glycosuria monitoring methods often involve invasive procedures, potentially risking the immune system, especially with repeated injections. the research focuses on a non-invasive method utilizing urine samples for glycosuria testing to address these concerns. the proposed method integrates image processing techniques, such as Gaussian filtering and image resizing, to optimize input data with machine learning approaches. Deep learning, especially convolutional neural networks is employed for their ability in feature extraction and pattern recognition. the system aims to achieve high accuracy in categorizing glycosuria levels into groups such as diabetes, prediabetes, and normal, providing a reliable and non-invasive alternative to traditional glycosuria monitoring techniques. this approach shows promise in enhancing patient compliance and overall health monitoring for individuals requiring regular glucose assessment. the accuracy of the proposed method based on CNN achieves 98.54% where the existing method based on support vector machine (SVM) achieves only 88.46%. it is evident from simulation results, the proposed method attained 11.39% improved accuracy when compared to existing SVM. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Accuracy Classification Convolutional Neural Network Deep learning gaussian filter Glycosuria

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Enhanced Security-Aware Beamforming and Trajectory Design for UAV-Assisted Integrated Sensing and Communication

Enhanced Security-Aware Beamforming and Trajectory Design fo...

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sensors and Information Technology (ICSI), international conference on

作者： Zeyu Yang School of Information Engineering Guangdong University of Technology Guangzhou China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

Integrated Sensing and Communication (ISAC) en-ables simultaneous sensing and communication functions and is regarded as a cutting-edge technology for future mobile networks. In this paper, we propose a UAV-enabled joint sensing and communication (ISAC) system, where the UAV serves as a base station. In the presence of eavesdroppers, the system aims to ensure that multiple sensing targets are effectively illuminated while maximizing the sum of achievable secrecy rates for multiple communication users under secure communication constraints. To achieve this, we develop a solution based on alternating optimization (AO) and successive convex approximation (SCA). Simulation results validate the effectiveness of the proposed method, demonstrating its ability to balance communication and sensing requirements while significantly enhancing communication security.

关键词： Array signal processing Simulation Radar Physical layer security Integrated sensing and communication Autonomous aerial vehicles Vectors Security Trajectory optimization sensor arrays

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