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Enhanced Blood Group Prediction with Fingerprint Images using Deep Learning

Enhanced Blood Group Prediction with Fingerprint Images usin...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： C. Sivamurugan B. Perumal Yelchuri Siddarthha Vavilala Krishna Murthi Yaramati Nanda Sankar Yerra Varun Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Blood group prediction is a crucial process in medical diagnostics and emergency care, traditionally carried out through serological tests that require blood samples and specialized laboratory equipment. These conventional methods, while accurate, are invasive, time-consuming, and may be impractical in situations where resources are limited or unavailable in urgent situations. This research explores a novel approach of using fingerprint images for blood group detection, applying deep learning, specifically Convolutional Neural Networks (CNN) with good architectures. By analyzing the unique patterns in fingerprints, these models may offer a non-invasive, faster, and more efficient alternative to traditional methods. This study examines the feasibility of this approach, providing insights into how deep learning could potentially be utilized for blood group prediction.

关键词： Deep learning Renewable energy sources Analytical models Automation Image matching computer architecture Fingerprint recognition Convolutional neural networks Medical diagnosis Blood

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IoT: An Objective Analysis of Architecture, Challenges and Performance Comparison of Various Application Layer Protocols 5

IoT: An Objective Analysis of Architecture, Challenges and P...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Lalit, Mohit Naz, Sardar M N Islam Singh, Surender Rana, Ajay Rana, Arun Kumar Chandigarh University Computer Science and Engineering Department Mohali Punjab Gharaun India Victoria University Distinguished Visiting Artificial Intelligence Australia Chandigarh University Computer Science Mohali Punjab Gharaun India Amity University Computer Science and Engineering Department Uttar Pradesh Greater Noida India Electronics and Communication Engineering Galgotias College of Engineering & Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350398267

IoT has made a significant development in recent years. Few years ago, capitalist's do not take interest to invest in development of this field due to various challenges, however, last decade witnessed increasing demand of IoT devices, investors started taking interest in development of IOT and a major development can be been seen in this domain. With the diversified application areas, IoT covers a large number of devices and every device have their own working limitations. Varying number of devices have their interoperable and configuration issues along with non-consistent architecture and protocols. The aim of this paper is to discuss architecture, overlooked challenges and application layer protocols. Application layer protocol is responsible for conclusive delivery of data to end user through various protocols and the protocol will came in effect, depends upon the device it is working for. Later, a comparative study of various performance variables of protocols has been shown. © 2022 IEEE.

关键词： Network architecture

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A Hybrid Deep Learning and Statistical Framework for Cardiac Arrest Detection in Neonatal Intensive Care Units

A Hybrid Deep Learning and Statistical Framework for Cardiac...

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International Conference on computer and communications Technologies (ICCCT)

作者： Tamezheneal R. B. Sathiyaprasad Vivitha M. G. R. N. Gopika S. Senthur Velan A. Yaswanth Department of Electronics and Communication Engineering Sri Sairam Engineering College Sai Leo Nagar West Tambaram Chennai Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Jeppiaar Nagar Rajiv Gandhi Salai Chennai

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Neonatal cardiac arrest, particularly in preterm infants admitted to Neonatal Intensive Care Units (NICUs), is a serious condition with a need for early and accurate detection. This study proposes a hybrid framework combining deep learning models with statistical techniques that can improve the reliability and accuracy of predictions for the occurrence of cardiac arrest in neonates. Using the Preterm Infant Cardio- Respiratory Signals Database, we extract spatial features from ECG and respiratory signals using Convolutional Neural Networks (CNNs) and capture temporal dependencies using Long Short-Term Memory (LSTM) networks. The framework is complemented by statistical models, including Support Vector Machines (SVM) and Random Forests, to enhance classification robustness and interpretability. The proposed system preprocesses raw physiological signals to remove noise, extracts meaningful features, and generates real-time predictions with high Accuracy of 92. Comparative analysis with existing methods demonstrates significant improvements in performance, highlighting the system's potential for early detection and real-time intervention in NICUs. This hybrid approach not only improves neonatal survival rates but also ensures interpretability, enabling healthcare providers to make confident and informed decisions.

关键词： Support vector machines Deep learning Pediatrics Accuracy Cardiac arrest Medical services Feature extraction Data models Real-time systems Long short term memory

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Identification of Proactive Cyber Threats in Cloud-Managed Internet of Things Systems Utilizing Machine Learning Techniques 2

Identification of Proactive Cyber Threats in Cloud-Managed I...

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

作者： Karthiga, R. Jaishika, S. Pandi, V. Samuthira Shobana, D. Lakshmi Priya, J. Ramesh, T. St Joseph's Institute of Technology Department of Cse OMR Chennai India Vinayaka Mission's Research Foundation Aarupadai Veedu Institute of Technology Department of Electronics and Communication Engineering Salem India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Mechatronics Engineering Chennai India Saveetha Engineering College Department of Ece Chennai India R.M.K Engineering College Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388916

IoT gadgets and cloud computing have revolutionized how we utilize technology. Increasing connectivity and dependence on cloud-managed IoT technology has raised cyber threat attack surfaces. Reactive security methods often fail against increasingly complex cyber threats. Thus, proactive threat identification systems must detect cyber dangers before they occur. ML is used to provide a new paradigm for proactive cyber threat assessment in cloud-managed IoT systems. Data from telemetry devices, user behaviors, and network traffic is analyzed using ML algorithms to identify cyberattack tendencies. The approach proactively identifies IoT security issues to reduce data breaches and service outages. First, the paper examines IoT cyber threats and insufficient security protocols. It then examines how ML methods including supervised and unsupervised learning, anomaly detection, predictive modeling, and threat identification are used. The study evaluates decision trees, support vector machines, neural networks, and ensemble techniques for proactive cyber threat detection. The research uses real-world and simulated IoT data to test the methods. The results show that ML can detect malware infections, unlawful access attempts, and DDoS attacks. The framework's performance is measured by detection accuracy, false positive rate, and reaction time to balance security and usability. In addition, the research covers using cloud-managed IoT security protocols with ML-based threat identification. It addresses data privacy, generalizing models, and ML algorithm scalability in big IoT rollouts. ML techniques can transform cloud-based IoT security, the study concludes. It emphasizes ML models that learn and adapt to stay up with new cyber threats. Further research should investigate advanced ML methods like deep learning and reinforcement learning and create AI models that are easy to comprehend and utilize for open threat identification. ML-based proactive threat identification is propos

关键词： Cyber attacks

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Advancements in Flood Mapping Methodologies: A Comprehensive Review

Advancements in Flood Mapping Methodologies: A Comprehensive...

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IEEE Recent Advances in Intelligent Computational Systems (RAICS)

作者： Divya V Chandran J Anitha Amrutha Vijayakumar Anjana Raveendran Dept. of Electronics and Communication Engineering Adi Shankara Institute of Engineering and Technology Kalady India Dept.of Computer Science Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

This review aims to comprehensively assess the recent advancements in flood mapping methodologies and technologies. By analyzing diverse techniques and approaches employed in flood mapping, this review seeks to present an in-depth overview of the current landscape of this field. The primary focus is on evaluating the strengths and limitations inherent in various flood mapping methods. Special attention will be given to exploring the integration of remote sensing technologies and geographic information systems (GIS) in flood mapping. Additionally, the review will delve into the transformative impact of machine learning and artificial intelligence on enhancing the precision and efficiency of flood mapping processes. Recent developments in flood mapping have significantly elevated the accuracy and reliability of the techniques employed. The integration of AI technologies with remote sensing methods has notably contributed to this advancement. Conventional approaches to flood mapping have traditionally relied on multiple methodologies to comprehend potential flood impacts. However, these methods often prove laborious, resource-intensive, and may lack access to real-time data, posing limitations for precise flood mapping and prediction. These identified limitations underscore the critical need for the development of more robust flood mapping techniques. Addressing these challenges is essential for enhancing flood risk assessment, improving management strategies, and ensuring better preparedness for future flood events.

关键词： Accuracy Reviews Heuristic algorithms Real-time systems Satellite images Floods Reliability

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CNN based encoder and decoder model for sign language communication

CNN based encoder and decoder model for sign language commun...

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International Conference on Computing and Networking Technology (ICCNT)

作者： S Preethi T Meeradevi V Sathya Narayanan S Dharshika V.M Ashvathan K Sreedharshan Department of Electronics and Communication Engineering Kongu Engineering College Perundurai Tamil Nadu India Department of Computer Science Engineering Kalasalingam Academy of Research and Eduation Virudhunagar Tamil Nadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

World Health Organization’s report says that there are more than 466 million individuals worldwide who have hearing impairments, with 72 million of them experiencing deafness. In this paper, a model is proposed for two-way sign language communicationto assist these *** proposed system consists of two components, one that converts image to voice and the other to convert voice back to an image. A dataset comprising 55,500 samples and 37 classes were used to train the proposed Convolutional Neural Networkmodel(CNN) to recognize 37 classes *** model contains three convolutional layers and 2 hidden layers and output layer. The model was trained with a batch size of 32, to achieve a maximum accuracy of 98.84% in training and $\mathbf{9 6. 5 8 \%}$ in validation. For the voice to image conversion a pretrained open source model was used.

关键词： Training Performance evaluation Sign language Deafness Accuracy Auditory system Service-oriented architecture Decoding Convolutional neural networks Testing

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Link Correlation Procedure for Improving Energy Efficiency in Homogeneous WSN 3

Link Correlation Procedure for Improving Energy Efficiency i...

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3rd International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Babu, V Suresh Kumar, Pardeep Balasubadra, K. Dineshkumar, T. Hindusthan Institute of Technology Department of Electronics and Communication Engineering Valley Campus Pollachi Highway Tamil Nadu Coimbatore India Anurag University Department of Artificial Intelligence Telangana Hyderabad India R.M.D Engineering College Department of Computer and Science Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Tamil Nadu Thottiam India

ISBN: (纸本)9781665462754

The wireless sensor network (WSN) is a multi-hop wireless network which include s numerous sensor nodes prearranged in a self-organized mode. In this approach all sensor nodes have the same energy, same transmission range. Many clustering approaches have been introduced to reduce the energy utilization during data transmission in the WSN. Nowadays clustering algorithms established on data correlation have been extensively examined. A correlation operation is a centralized greedy clustering. But, in a centralized model, additional energy is necessary to receive a global information, and increase delay. To solve these issues, Link Correlation procedure for improving energy efficiency (LCIE) in homogeneous WSN is introduced. This paper explained clustering approach for a homogeneous WSN that establishes a single-hop communication within cluster using link correlation. The multi-hop communication among Cluster Head (CH) is controlled by network coding. We perform simulations in the Network simulator to validate the performance of the LCIE approach. Simulation results demonstrate that the proposed approach has minimized the transmission energy consumption and improved the network throughput in the WSN. © 2022 IEEE.

关键词： Energy efficiency

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IoT based Smart Shoes for Blind people 3

IoT based Smart Shoes for Blind people

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3rd International Conference on electronics and Sustainable communication Systems, ICESC 2022

作者： Darney, P. Ebby A.E., Muthu K., Jeyakumar R.S., Krishnan K.L., Narayanan Y.H., Robinson Rajarajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India Scad College of Engineering and Technology Department of Computer Science and Engineering Tirunelveli India Scad College of Engineering and Technology Department of Mechanical Engineering Tirunelveli India Scad College of Engineering and Technology Department of Electronics & Communication Engineering Tirunelveli India Vellore Institute of Technology School of Information Technology and Engineering Tamilnadu Vellore India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

India stands third among the world's blind people population. They cannot see what is happening around them. They are facing numerous problems while moving from one place to another. Due to loss of eyesight, they hurt themselves while travelling to many places. Sometimes they even find it difficult to trace the route back to their home. Hence a novel system is required to facilitate them while travelling and to help them to find their way to home when they lost their route to home. To deal with this, the proposed study has designed a novel system called IoT based Smart Shoes for blind people. This can solve aforementioned problems and facilitate blind people to a huge extent. © 2022 IEEE.

关键词： communication systems Sociology Blindness Footwear Statistics

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Transfer Learning with XGBoost for Predictive Modeling in Electronic Health Records 1

Transfer Learning with XGBoost for Predictive Modeling in El...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Badhoutiya, Arti Singh, Durgeshwar Pratap Srivastava, Arun Pratap Raj, J. Relin Francis Lakshmana Chari, S. Khan, Akhilesh Kumar Gla University Department of Electrical Engineering Mathura India Graphic Era Deemed to Be University Department of Mechanical Engineering Uttarakhand Dehradun India Lloyd Institute of Engineering and Technology Greater Noida India Institute of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

The integration of XGBoost and transfer learning for modeling predictions in Electronic Health Records (EHR) is investigated in this study. The study uses a deductive methodology, an interpretive philosophy, a descriptive research design, and secondary data collection. The accuracy of prediction, accessibility, and generalization of the XGBoost models enhanced by transfer learning are assessed in a variety of patient populations. The outcomes demonstrate noteworthy enhancements in precision and flexibility, confirming the effectiveness of the suggested methodology. Metrics of interpretability like SHAP values provide information about the level of openness of a model. The models' equilibrium over time is revealed by temporal analysis. The trade-offs between interpretability and accuracy are brought to light by critical analysis, which inspires suggestions for additional research. © 2023 IEEE.

关键词： Electronic Health Records healthcare predictive modeling interpretability transfer learning XGBoost

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Utilizing Extreme Gradient Boosting (XGBoost) for Apple Scab Disease Detection 3

Utilizing Extreme Gradient Boosting (XGBoost) for Apple Scab...

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3rd International Conference on Advancement in Computation and computer Technologies, InCACCT 2025

作者： Kumari, Sowjanya Y. Jayakrishna, K. Dhanasekar, J. Miniappan, P.K. Balasundaram, N. Vijayakumari, G. Department of Computer Science Engineering QIS College of Engineering and Technology Ongole India Department of Master of Computer Application QIS College of Engineering and Technology Ongole India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India Department of Mechanical Engineering Karpagam Academy of Higher Education Coimbatore India Department of Civil Engineering Karpagam Academy of Higher Education Coimbatore India Department of Electronics and Communication Engineering Builders Engineering College Kangeyam India

ISBN: (纸本)9798331543891

The research in this paper aims at evaluating a technique known as Apple Scab Disease detection using XGBoost (ASD-XG) Extreme Gradient Boosting (XGBoost) in the identification of apple scab on the apples crop which is a detrimental disease globally. The work feeds on a collection of 100 high resolution images of apple leaves on which image processing methodology is used to derive features pertinent to scab condition including color, texture and shape. The above-mentioned features of the dataset have been used to train the XGBoost model on the dataset, which showed a test accuracy of 95% as against the models like SVM, RF, CNN etc. Consequently, the experimental evaluations prove XGBoost’s higher accuracy and readability for early discovery of diseases in agriculturally important crops. The work done in this study indicates that XGBoost could underline the improvement of sustainable farming. © 2025 IEEE.

关键词： Photointerpretation

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