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Non-Invasive Technique for Fingerprint-Based Blood Group Identification

Non-Invasive Technique for Fingerprint-Based Blood Group Ide...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Devyani Thakre Manswi Panpatte Richa Khandelwal Electronics and Computer Science Department Shri Ramdeobaba College of Engineering And Management Nagpur India Department of Electronics Engineering Ramdeobaba University Nagpur India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

This work delves into the development of a novel, non-invasive method for determining blood groups using finger-print analysis. Traditional blood group testing methods, such as blood sampling, are invasive, time-consuming, and require specialized equipment. In contrast, our approach utilizes image processing techniques to analyze the unique patterns present in fingerprints, aiming to accurately predict an individual's blood type without the need for direct blood contact. The methodology begins with the acquisition of high-resolution fingerprint images, which are then processed through a series of image enhancement and normalization steps to ensure consistent quality. Feature extraction techniques are employed to identify distinctive patterns and characteristics in the fingerprints that correlate with specific blood groups. These extracted features are subsequently fed into advanced classification algorithms, such as Convolutional Neural Networks (CNNs), which are trained to recognize and classify the fingerprint data into one of the primary blood groups: A, B, AB, and O, with both positive and negative Rh factors. Through extensive training and validation, the classification model achieved a training accuracy of 99.47% and a validation accuracy of 80%, demonstrating its robustness and reliability. Additionally, the model achieves high F1-scores across most blood groups, with an average F1-score of 0.83, highlighting its precision and recall balance. Compared to traditional blood testing methods, this approach offers a faster, cost-effective, and painless alternative, achieving comparable accuracy while significantly reducing time and costs. This work leverages state-of-the-art machine learning and image processing methods, optimizing accuracy and reliability by utilizing a large dataset of labeled fingerprint images. By eliminating the need for invasive procedures, this work aims to enhance patient comfort, reduce the risk of infection, and streamline the process of

关键词： Training Accuracy Image matching Fingerprint recognition Feature extraction Classification algorithms data mining Convolutional neural networks Blood Testing

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Enhancing Real-Time Threat Detection with YOLOv8 and LSTM Integration for Intelligent Surveillance Systems

Enhancing Real-Time Threat Detection with YOLOv8 and LSTM In...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Prakash Chandra Behera V. Kanpur Rani Deepak Gupta Noor Kaylan Hamid Vuda Sreenivasa Rao Dhaaraani R Department of Science St. Claret College Autonomous Bangalore India Department of EEE Hindustan Institute of Technology and Science Chennai India Department of Mechanical Engineering Graphic Era Hill University Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Education Al Kitab University Kirkuk Iraq Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram A.P. India Department of AI & DS K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

In the face of increasing security threats, efficient surveillance systems are crucial for timely threat detection and response. This study presents a comprehensive approach that integrates the YOLOv8 object detection framework with Long Short-Term Memory (LSTM) networks to enhance real-time threat detection capabilities. The UCF-Crime dataset, which encompasses a variety of crime-related scenarios, was utilized to evaluate the proposed model's effectiveness. Traditional methods often struggle with accurately detecting objects and understanding complex behavioral patterns in dynamic environments. The existing methods primarily rely on conventional object detection techniques that lack the ability to capture temporal dependencies, leading to limitations in anomaly detection. In contrast, our proposed YOLOv8-LSTM model combines fast and accurate object detection with advanced sequential analysis, enabling the identification of unusual activities such as loitering or aggressive behavior over time. Performance metrics, including precision, recall, and accuracy, were employed to assess both object detection and anomaly recognition capabilities, demonstrating significant improvements in real-time threat detection. The results indicate that the integrated model not only enhances the reliability of surveillance systems but also opens pathways for future enhancements, such as incorporating multimodal data and optimizing the model for edge device deployment. This research highlights the potential of advanced deep learning techniques to improve security measures in public spaces, ultimately contributing to safer communities. The successful integration of YOLOv8 and LSTM marks a significant advancement in the development of intelligent surveillance systems, addressing modern security challenges effectively.

关键词： Sequential analysis Accuracy Surveillance Object detection Threat assessment Real-time systems data models Security Reliability Long short term memory

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Deep Learning-Driven Cloud Intrusion Detection: Internet of Things and Cloud Networks

Deep Learning-Driven Cloud Intrusion Detection: Internet of ...

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advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： Iffath Tanjim Moon Md. Nazrul Islam Mondal Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

In this paper, we have utilized deep learning approaches to detect cloud intrusion for the Internet of Things (IoT). The emerging growth of IoT and cloud environments has revolutionized many industries by allowing real-time data sharing and storage. Cloud platforms offer the essential infrastructure for managing the massive volumes of data produced by IoT devices. However, due to several flaws, including insufficient encryption, insecure APIs, and weak authentication, intruders can exploit the vulnerabilities to interrupt services and obtain unauthorized access. Therefore, deep learning can be an effective solution to detect cloud intrusion in IoT. In this research, we have developed individual convolutional neural network (CNN) and long short-term memory (LSTM) models, along with a hybrid CNN-LSTM model for multiclass classification and binary classification to detect cloud intrusion. In addition, we have examined which models perform the best for cloud intrusion detection for IoT among the individual and hybrid models. The proposed models are effective in detecting cloud intrusion, as CNN can extract spatial features, and LSTM can capture temporal dependencies from network traffic data. Furthermore, we have utilized one of the most recent and comprehensive datasets for IoT, the ToN-IoT dataset, containing real-world data. In multiclass classification, the hybrid CNN-LSTM has given the best performance, achieving an accuracy of 97.65%, precision of 97.72%, recall of 97.65%, f1 score of 97.67%, and AUC of 99.93%. In binary classification, all CNN, LSTM, and hybrid CNN-LSTM have demonstrated high performance, where LSTM has achieved the highest accuracy of 99.97%. Moreover, we have evaluated the performance of multiclass classification for detecting nine distinct kinds of attacks. In this instance, CNN has shown better performance in detecting backdoor attacks. Thus, this research can make the IoT platform more secure by efficiently detecting cloud intrusion.

关键词： Deep learning Cloud computing Accuracy Intrusion detection Telecommunication traffic Feature extraction Real-time systems Internet of Things Convolutional neural networks Long short term memory

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A Real-Time Object Detection System with Audio Feedback for Visually Impaired Persons

A Real-Time Object Detection System with Audio Feedback for ...

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Intelligent Control, computing and Communications (IC3), international conference on

作者： Neha Yadav Saloni Kumawat Sushil Kumar Mishra Mahek Jaseja Shanu Khare Department of Computer Science & Engineering Chandigarh University Chandigarh India Department of Computer Science & Engineering Chandigarh University Unnao India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Object detection technologies offer attractive pos-sibilities to visually impaired people, enabling them to avoid full-on collision with obstacles and recognize objects. This paper represents a modern real-time object detection system with integrated audio feedback is presented in this study in an effort to improve accessibility and situational awareness. The YOLO (You Only Look Once) algorithm is used by the system to efficiently recognize objects in images, videos, and real-time object detection (WebCam), and the IDE we used is Jupyter Notebook. It is implemented with OpenCV. Our method combines visual and audio output using text-to-speech technology to announce the presence of detected items, while also showing bounding boxes and labels on them. The method exhibits great precision in distinguishing numerous objects in intricate settings, maintaining a 0.5 confidence threshold to guarantee dependable identifications. Important features include threaded audio announcements to eliminate interface lag, dynamic label positioning for clear visual feedback, and nonmaximum suppression to reduce overlapping detections. This multimodal method creates opportunities for applications in autonomous systems, augmented reality, and assistive technologies, in addition to improving the interpretability of detection data. Our study advances computer vision by providing an integrated system that combines real- time auditory feedback with visual detection. This technology could help visually impaired users and improve human-computer interaction in a range of applications.

关键词： YOLO Visualization Computer vision Webcams Blindness Real-time systems Text to speech Object recognition Wearable devices Videos

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Head and Neck Tumor Segmentation Using Pre-RT MRI Scans and Cascaded DualUNet 1st

Head and Neck Tumor Segmentation Using Pre-RT MRI Scans and ...

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1st Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, HNTS-MRG 2024, Held in Conjunction with 27th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Saukkoriipi, Mikko Sahlsten, Jaakko Jaskari, Joel Al-Tahmeesschi, Ahmed Ruotsalainen, Laura Kaski, Kimmo Department of Computer Science Aalto University Espoo Finland Department of Computer Science University of Helsinki Helsinki Finland Department of Electronic Engineering University of York York United Kingdom The Alan Turing Institute London United Kingdom

ISBN: (纸本)9783031832734

Accurate segmentation of the primary gross tumor volumes and metastatic lymph nodes in head and neck cancer is crucial for radiotherapy but remains challenging due to high interobserver variabil- ity, highlighting a need for an effective auto-segmentation tool. Tumor delineation is used throughout radiotherapy for treatment planning, ini- tially for pre-radiotherapy (pre-RT) MRI scans followed-up by mid- radiotherapy (mid-RT) during the treatment. For the pre-RT task, we propose a dual-stage 3D UNet approach using cascaded neural networks for progressive accuracy refinement. The first-stage models produce an initial binary segmentation, which is then refined with an ensemble of second-stage models for a multiclass segmentation. In Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Task 1, we utilize a dataset consisting of pre-RT and mid-RT T2-weighted MRI scans. The method is trained using 5-fold cross-validation and eval- uated as an ensemble of five coarse models and ten refinement models. Our approach (team FinoxyAI) achieves a mean aggregated Dice simi- larity coefficient of 0.737 on the test set. Moreover, with this metric, our dual-stage approach highlights consistent improvement in segmentation performance across all folds compared to a single-stage segmentation method. © The Author(s) 2025.

关键词： Deep neural networks

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Optimizing Soccer Match Prediction with Deep Convolutional Neural Networks and Grey Wolf Optimization

Optimizing Soccer Match Prediction with Deep Convolutional N...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Tushar Dhar Shukla V.Kanpur Rani Indrajeet Kumar Omar Mohsen Hussein Shaik Balkhis Banu Bhavani P University Institute of Physical Education & Sports Chandigarh University Mohali India Department of EEE Hindustan Institute of Technology and Science Chennai India Department of CSE Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Biomedical Engineering Al-Farahidi University Baghdad Iraq Department of Physiotherapy Fatima College of Health Sciences Al Ain UAE Dept of AI & DS K. Ramakrishnan college of Engineering Trichy India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Predicting soccer match outcomes has become an essential area of interest in sports analytics, yet many existing studies rely on traditional machine learning methods that may not adequately capture the complex dynamics inherent in the sport. This study aims to address these limitations by proposing a novel approach utilizing a Deep Convolutional Neural Network (DCNN) optimized through Grey Wolf Optimization (GWO) to significantly enhance prediction accuracy. The methodology leverages a comprehensive dataset from the European Soccer database, gathered from Kaggle, which includes detailed match statistics, player attributes, and betting odds, allowing for a richer analysis. The results of the study indicate that the proposed DCNN model outperforms several baseline models, achieving notable improvements in accuracy, precision, and F1-scores, thereby showcasing its ability to learn complex patterns and relationships within the data effectively. Despite these promising findings, the study acknowledges certain limitations, such as the potential for further enhancement through the integration of additional features like spatiotemporal tracking data and more detailed player performance metrics. The outcomes suggest that the combination of advanced deep learning techniques with effective optimization strategies can yield substantial improvements in predictive analytics in sports. In conclusion, this research contributes significantly to the field by demonstrating the potential of deep learning in the accurate prediction of soccer match outcomes. It encourages future exploration of diverse datasets and methodologies to further refine predictive capabilities, ultimately supporting more informed decision-making processes for teams and coaches within the competitive landscape of soccer. Overall, the study underscores the transformative potential of machine learning in sports analytics.

关键词： Deep learning Accuracy Europe Predictive models data models Spatiotemporal phenomena Convolutional neural networks Predictive analytics Optimization Sports

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AI-Driven Green computing for 6G Network: Improve User Experience and Security

AI-Driven Green Computing for 6G Network: Improve User Exper...

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advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： Rakesh Kumar Yadav Adarsha Harinaiha Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar District Karnataka India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

A dynamic Computational Thinking resolution can be likely in a 6G environment by way of Green-IoT and Artificial Intelligence (AI), important parts of computational thinking. This order requires lowering difficult problems to tinier, more controllable parts and recognizing patterns and friendships at intervals in miscellaneous parts. Machine intelligence approaches concentrate on algorithms capable of learning from data, making judgments and predictions, and executing complex tasks beyond explicit programming. To appreciate the far-reaching quantities of data presented by 6G-connected schemes, computational inference and machine intelligence can be used for the Green-IoT. This has the potential to reinforce the computerization of many processes and bring about a smarter and more efficient use of possessions. Computational thinking that combines Green-IoT, AI, and 6G sciences can transform many areas, including education, and reinforce society's lives through the production of brisker, more adept, and more tailor-made uses and services. The research has been started by outlining key limits utilizing Green-IoT-authorized sensor electronics in the framework of 5G and 6G surroundings, as well as Artificial Intelligence, in this place study's review on computational thinking. In the subsequent section, delineate the fundamental structural requirements for a digital thinking paradigm centered on artificial intelligence and the Net of Things, significant aspects of prospective beneficial applications, and evaluate the advantages and disadvantages of each within the context of shared resources. Lastly, several security risks to an AI-based architecture that might compromise the system's ability to provide the computational thinking services that social media platforms need

关键词： 6G mobile communication Social networking (online) Green products Transforms User experience Security Internet of Things Artificial intelligence Programming profession Machine intelligence

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IoT Security in the Quantum Era: State of the Art and Open Challenges

IoT Security in the Quantum Era: State of the Art and Open C...

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Innovative Research in Applied science, Engineering and Technology (IRASET),conference on

作者： Moussa Diop Papis Ndiaye Doudou Dione Idy Diop Departements of Computer Sciences Polytechnic Institute (ESP) Cheikh Anta Diop University (UCAD) Dakar Senegal Departements of Computer Sciences Assane Seck University of Ziguinchor (UASZ) Ziguinchor Senegal

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

The Internet of Things (IoT) has grown rapidly, transforming various aspects of daily life into intelligent systems capable of collecting and exchanging data in real time. At the same time, advances in quantum computing have given rise to powerful quantum computers capable of processing complex tasks at unprecedented speed. This disparity between the limited resources of IoT devices and the capabilities of quantum computers poses significant security challenges. Quantum computers introduce new vulnerabilities that threaten IoT systems, particularly in critical applications such as connected healthcare, industrial networks, smart infrastructure, and secure embedded systems. This study reviews the existing literature on post-quantum cryptography (PQC) applied to IoT devices. It analyzes the characteristics and security requirements of IoT environments, current security technologies, and unresolved issues. It highlights the growing threat of post-quantum attacks and the need to adapt security strategies. Finally, the study explores post-quantum code-based cryptosystems as solutions to enhance the security of resource-constrained IoT applications and ensure their resilience against future challenges.

关键词： Computers Quantum computing Reviews Medical services Real-time systems Error correction codes Internet of Things Cryptography Intelligent systems Resilience

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Machine and Deep Learning Classifications for IoT-Enabled Healthcare Devices

Machine and Deep Learning Classifications for IoT-Enabled He...

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Pervasive Computational technologies (ICPCT), international conference on

作者： Lalit Kumar Tyagi Anish Gupta Vibhash Singh Sisodia Department of Computer Science & Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali India Sri Bhawani Niketan Institute of Technology and Management Jaipur India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The Internet of Things (IoT) is transforming academic research and will impact various industries, including healthcare. IoT has transformed healthcare from blast-shaped outdated centralized methods to decentralized smart devices. This led to tailored health systems. Recent advances in wearables, sensor networks, and computing cloud have made large-scale health care IoT usage easier. Despite its serious limitations, IoT can change medicine. Maintenance of various appliances, pricing increases, and the need for greater storage space all issues. IoT is a diverse and durable technology, thus this study will analyze its health sector use. How machine learning and deep learning interact is being studied. The Internet of Things (IoT) health system architecture is also covered in this article, along with its benefits and use cases. This helps highlight IoHT difficulties and encourages academics to create innovative solutions. This paper examines the current work of numerous researchers in machine learning and deep learning, which are heavily linked with IoT-based healthcare approaches.

关键词： Deep learning Ethics Accuracy data security Medical services Transforms Internet of Things Predictive analytics Wearable devices Biomedical imaging

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Enhanced Breast Cancer Detection in Ultrasound Images Using LoRA-Fine-Tuned Florence-2 Model

Enhanced Breast Cancer Detection in Ultrasound Images Using ...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Aarushi Agrawal Harleen Bagga Richa Khandelwal Electronics And Computer Science Department Shri Ramdeobaba College of Engineering And Management Nagpur India Department of Electronics Engineering Ramdeobaba University Nagpur India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Breast cancer remains a significant cause of mortality among women globally, where early and precise diagnosis is crucial for improving patient outcomes and lowering mortality rates. Although traditional diagnostic techniques like mammography and biopsy are effective, they have limitations regarding invasiveness, accessibility, and cost. Ultrasound imaging has emerged as a valuable non-invasive method for breast cancer detection, providing greater accessibility and minimizing risks. However, accurate interpretation of ultrasound images is often challenging due to issues like low contrast and noise. In this study, we introduce an innovative deep learning-based approach for breast cancer detection using ultrasound imaging by fine-tuning the Florence-2 base model with Low-Rank Adaptation (LoRA). This method leverages transfer learning, enabling the model to learn efficiently from pre-trained features while minimizing the need for additional computational resources during fine-tuning. LoRA enhances the fine-tuning process by introducing low-rank parameter updates, which improves the model's generalization capabilities without leading to overfitting. Our approach classifies breast ultrasound images into three categories: normal, benign, and malignant, thereby streamlining the diagnostic workflow. The model achieved a mean Average Precision (mAP) of 0.53 at an Intersection over Union (IoU) threshold of 0.5, demonstrating its robustness in differentiating various breast tissue types. This level of accuracy indicates the potential of the model to be utilized in clinical settings, offering a cost-effective and scalable solution for early breast cancer diagnosis. Future work will focus on further optimizing the model and validating it on larger, more diverse datasets to enhance its clinical applicability.

关键词： Adaptation models Ultrasonic imaging Accuracy Computational modeling Transfer learning LoRa Mortality Breast cancer Robustness Synthetic data

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