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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Nicholas G. Mellor Sylvia A. Cheung Pierrette Michaux Joe Firth E. Scott Graham Bryan W. Day Charles P. Unsworth PhD Student With the Department of Engineering Science The University of Auckland Auckland New Zealand Postdoctoral Research Fellow With The Lowy Cancer Centre University of New South Wales Australia Process Engineers at the Australian National Fabrication Facility and School of Electrical Engineering & Telecommunications University of New South Wales Australia Associate Professor With the Department of Molecular Medicine and Pathology & the Centre for Brain Research The University of Auckland New Zealand Professor With the QIMR Berghofer Medical Research Institute Brisbane Australia Associate Professor (and Main Supervisor of The Work) With the Department of Engineering Science The University of Auckland Auckland New Zealand

Glioblastoma (GBM) is the most aggressive high-grade brain cancer with a median survival time of <15 months. Due to GBMs fast and infiltrative growth patient prognosis is poor with recurrence after treatment common. Investigating GBMs ability to communicate, specifically via Ca 2+ signaling, within its functional tumour networks may unlock new therapeutics to reduce the rapid infiltration and growth which currently makes treatment ineffective. This work aims to produce patterned networks of GBM cells such that the Ca 2+ communication at a network level can be repeatedly and reliably investigated.

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The Convergence of Block chain and Network Security: Opportunities and Challenges

The Convergence of Block chain and Network Security: Opportu...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Kapil Rajput Ganesh Chandrasekaran Manisha Aeri Rishi Prakash Shukla P Jeyanthi Dr Hariom Gurjar Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Computer and Communication Engineering Sri Eshwar College of Engineering Coimbatore India Professor Marketing Jaipuriya Institute of Management Jaipur India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Centre of Excellence AIT Management Chandigarh University

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Block chain generation, acknowledged for its decentralized and immutable nature, has emerged as an ability solution to cozy networks from cyber-attacks. The convergence of block chain and community safety has opened up a new paradigm inside the discipline of cyber security. This generation can decorate network safety with the aid of imparting a decentralized and incredibly cozy network infrastructure. One of the key possibilities of this convergence is the usage of block chain’s distributed ledger machine to create a tamper-proof and obvious repository of community sports and transactions. This can help in figuring out and preventing capability cyber-attacks, mitigating community vulnerabilities, and ensuring a comfy statistics switch. Another gain of the block chain era is its capacity to offer tamper-proof identity management, ensuring secure admission to networks and preventing unauthorized rights of entry. It could additionally be used to cozy community gadgets and structures, developing comfy and trustless surroundings for conversation.

关键词： Scalability Switches Network security Trustless services Regulation Blockchains Safety Cyberattack Convergence Sports

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Predicting Price Direction of Bitcoin based on Hybrid Model of LSTM and Dense Neural Network Approach 4

Predicting Price Direction of Bitcoin based on Hybrid Model ...

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

作者： Karahyla, Jai Kishan Sharma, Neelam Chamoli, Sushant Shirgire, Anil Kant, Ravi Chauhan, Amit Pgcc Hospital Management Professor and Head Respiratory Medicine Mmimsr Mm Deemed University Human Rights HaryanaMullana Ambala India Lovely Professional University Physical Education Punjab Phagwara India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Dr D y Patil Institute of Technology Department of Civil Engineering MaharashtraPimpri Pune India Shoolini University Cse Department Himachal Pradesh Solan India Department of Life Sciences Karnataka Bengaluru India

ISBN: (纸本)9798350300093

Bitcoin is a rapidly growing but extremely risky cryptocurrency. It marks a watershed moment in the history of cash. These days, digital currency is preferred to actual money. Bitcoin has decentralized authority and placed it in the hands of its users. Many people are joining the largest and most well-known Bitcoin mining pools as the risk of working alone is too great. In order to enhance their chances of creating the next block in the Bitcoins blockchain and decrease the mining reward volatility, users can band together to form Bitcoin pools. This tendency toward consolidation may also be seen in the rise of large-scale mining farms equipped with powerful mining resources and speedy processing capability. Because of the risk of a 51% assault, this pattern shows that Bitcoin's pure, decentralized protocol is moving toward greater centralization in its distribution network. Not to be overlooked is the resulting centralization of the bitcoin network as a result of cloud wallets making it simple for new users to join. Because of the easily hackable nature of Bitcoin technologies, this could lead to a wide range of security vulnerabilities. The proposed approach uses normalization and filling missing values in preprocessing, PCA for feature Extraction and finally training the model using LSTM-DNN Models. The proposed approach outperforms other two models such as CNN and DNN. © 2023 IEEE.

关键词： Bitcoin

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Machine Learning in K–12 Educational Settings

Machine Learning in K–12 Educational Settings

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Ramy Riad Al-Fatlawy Dadamuxamedov Alimjon R. Udayakumar Ashish Sharma Jaspreet Kaur S. Padmanaban Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Senior Lecturer at the Department of "Modern Information and Communication Technologies" of International Islamic Academy of Uzbekistan Tashkent Uzbekistan Dean CS & IT Kalinga University India Department of Computer Engineering and Applications GLA University Mathura India Assistant Professor Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Department of Science and Humanities Karpagam Institute of Technology Coimbatore India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The many real-world applications of machine learning-based approaches over the past several decades have shown the potential of data-driven methodology in a variety of computing fields. Higher education computer curricula are beginning to include machine learning, and more and more institutions are incorporating it into K–12 computer instruction. Given the growing prevalence of computational learning in K–12 computer teaching, it is imperative to investigate how agency and intuition develop in these kinds of settings. However, considering the challenges educators and schools already have in integrating conventional learning, understanding the challenges connected with teaching algorithms for learning across grades K–12 provides an even more challenging barrier for computer education research. the curriculum's incorporation of artificial intelligence and *** article outlines the potential for data mining education in grades K–12. These advancements include modifications to technology, philosophy, and practice. The study puts the present findings in the larger framework of computing education and discusses some differences that K–12 computer educators should keep in mind while tackling this issue. The study focuses on key components of the fundamental shift that is required in order to successfully integrate machine learning into more thorough K–12 computing curricula. A critical first step is to abandon the notion that next-generation computational thinking requires rule-based, "traditional" programming.

关键词： Training Technological innovation Philosophical considerations Education Machine learning Learning (artificial intelligence) Market research Telecommunication computing Programming profession Next generation networking

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Early Localized Lyme Disease prediction using Image Processing and Machine Learning Techniques

Early Localized Lyme Disease prediction using Image Processi...

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Integration of Emerging Technologies for the Digital World (ICIETDW), International Conference on

作者： Saravanan M.S G. Sreehitha Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences and Assistant Professor Sri Padmavati Mahila Visvavidyalayam Tirupathi Andhra Pradesh India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

The purpose of this study is to predict the early Lyme disease with the set of images and applying few machine learning algorithms. For this dataset taken from the public medical image repositories such as DermNet NZ, HAPiD, etc., using this two Convolutional Neural Network (CNN) machine learning algorithms such as VGGNet and GoogLeNet. The dataset size predicted using gpower tool with eighty percent of sample setting. With the sample size prediction the CNN type of VGGNet and GoogLeNet both are iterated and the results are recorded using training and testing data. The results were such as accuracy, precision, recall, F1-score, and ROC-AUC are determined to predict early localized Lyme disease. It was found that the novel CNN VGGNet model achieved an accuracy of 96.84% and other performance metrics compared to the CNN GoogLeNet algorithm’s 91.34% for the Lyme disease dataset. Hence this research study can help the healthcare practitioners or experts to identify the early growth of Lyme also this study result accepted the statistical significant of p value of 0.001, which is less than 0.05 in the independent t-test on both the machine learning algorithms. Therefore, the results indicate that the CNN VGGNet machine learning algorithm proves useful for early Lyme disease prediction and is effective at recognizing skin lesions.

关键词： Measurement Training Machine learning algorithms Accuracy Image processing Machine learning Skin Convolutional neural networks Lesions Diseases

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Utilizing Artificial Neural Networks and Multivariate Patient Data for Anemia Detection using WEKA based Approach for Diagnosis

Utilizing Artificial Neural Networks and Multivariate Patien...

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

作者： Prof Neeraj Bhargava Pramod Singh Rathore Parul Vaishnav Manish Rai Professor School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India M.Tech Research Scholar MDS University Ajmer Department of Computer Science and Engineering (AI& ML) Manipal University Jaipur India

This research study describes a method that uses neural networks to figure out if a woman has anemia. An artificial neural network is a type of computer network that is based on the real neural networks that make up the structure of the human brain. In this paper, the WEKA tool is used for the classification process carried out using an ANN structure, which is referred to as one of the data mining approaches and is based on multiple perceptions. The network has seven inputs (RBC, HCT, MCV, MCH, MCHC, RDW, and WBC) and an output (decision). Simulations on 567 patients demonstrate that the Artificial Neural Network (ANN) accurately diagnose the disease. This study investigates the utilization of Artificial Neural Network (ANN)-supervised machine learning techniques for predicting anemia by using the data from Kaggle.

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Enhancing strength of cement using aluminium sulfate accelerator with aluminium formate

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Magazine of Concrete Research 2024年第14期76卷 816-826页

作者： Wan Zhang Zhihong Wu Wang Huang Jiawei Wang Yuexin Pan Zhishun Li Shufang Ren Hongjin Xu Master's student College of Materials Science and Engineering Xi'an University of Architecture and Technology Xi'an China Associate Professor College of Materials Science and Engineering Xi'an University of Architecture and Technology Xi'an China (corresponding author: zhihong@*** ) Technical Supervisor Department of Technical Service Shanxi Kangrui Building Materials New Technology Co. Ltd Yuncheng China Technical Supervisor Department of Technology Research and Development Shanxi ZTS Construction Co. LTD Engineering Branch Yuncheng China

An alkali-free liquid accelerator was prepared with aluminium sulfate and aluminium formate as the main components. The effects of the accelerator on the setting of paste and the mechanical strength of mortar were studied, and the impact of the accelerator on the early hydration behaviour and hydration products of the paste were investigated by isothermal calorimeter analysis, X-ray diffraction analysis, scanning electron microscopy and thermogravimetric analysis. The results show that the accelerator is excellent for the rapid setting and hardening of the cement. When the content of the accelerator was 8 wt%, the initial setting time and final setting time of the paste were 85 s and 180 s, respectively, and the strengths of the mortar at 1 day and 28 days were 9.52 MPa and 44.42 MPa, respectively. The proposed accelerator can accelerate the dissolution and hydration of tricalcium aluminate and tricalcium silicate and promote the formation of ettringite, formate and portlandite.

关键词： admixtures cement cementitious materials

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Cloud-Based Smart Sensing System for Real-Time Health Management of Dementia Patients

Cloud-Based Smart Sensing System for Real-Time Health Manage...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： R. Latha R. Ramya U. Arul Nilamadhab Mishra S. Dhanalakshmi Professor Department of Computer Science and Engineering K. Ramakrishnan College of Engineering Trichy Tamil Nadu India Department of Mathematics S.A Engineering College Chennai Tamil Nadu India Professor Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Chennai Tamil Nadu School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Assistant Professor Department of Electronics and Communication Engineering Sri Muthukumaran Institute of Technology Chennai Tamil Nadu India

The paper describes an innovative technique of monitoring updates on the health of people with dementia by integrating smart sensing technologies with a cloud-based platform. Because dementia is a degenerative neuropsychiatric disease that develops with time, constant patient monitoring is essential for ensuring the health and safety of those living with the condition. The proposed system uses smart sensing devices, including wearable and ambient sensors, to monitor dementia patients' health in real-time. Data like heart rates, movements used, and ambient conditions are collected and sent to a server in the cloud for analysis. The camera can monitor and analyze visual data, capture crucial information, and help evaluate the patient's well-being. Multiple patients can be monitored effectively in real time because of the cloud-based system's scalability, accessibility, and storage capacities. For healthcare providers to recognize irregular movements, identify possible risks to health, and implement preventative measures, doctors utilize the moment data analytics devices to gather valuable insights from the acquired data. In addition, the cloud-based solution improves collaborative care and enhances patient support by allowing for continuous communication and information sent among caregivers, doctors, nurses, and family members. Predefined thresholds or anomalous occurrences may trigger alerts and messages, allowing for quick action in reaction to emergencies. Modern technology and data analytics increase the health and happiness of people with dementia.

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AI Framework for Keys Instruction and Music Notation

AI Framework for Keys Instruction and Music Notation

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Krishnaraj Rao N S Smitha GL P. S. Ramesh Nandhini T J P. Nagaveni A Balaji Assistant Professor Department of Information Science and Engineering Nitte (Deemed to be University) NMAM Institute of Technology (NMAMIT) Nitte Karnataka India Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Science (SIMATS) Chennai India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Saveetha School of Enginnering Saveetha Institute of Medical and Technical Science – SIMATS Saveetha University Chennai India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Department of IT Panimalar Engineering College

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The study of artificial intelligence focuses on the principles that guide all human intellectual endeavors. The integration of several academic disciplines, including as computer science, philosophy, politics, linguistics, systems engineering, neurophysiology, and behavioral science, has allowed it to grow. This article will describe how to build an intelligent machine, consisting of both hardware and software, that can replicate and exceed the abilities of the human brain in order to handle complicated issues that need specialized knowledge. It will specifically examine how to more effectively use artificial intelligence support systems into piano instruction. An Introduction, an oral language and music-assisted education system based on the ARM and SA algorithm, is proposed by this study in order to ascertain the implementation principle guiding the functioning of the piano automation recording system. It also delves deeply into the theory and operation of automated music recording systems, compiling and evaluating data that these systems generate via internal databases. is an effort to improve music education via the use of artificial intelligence support systems. The results of the experiment show that the training system for the automated notation algorithms for playing the piano and songs has a 56.81 and fast growing machine learning auxiliary technology utilization rate. Thus, we may infer that using an AI support system is necessary while teaching performance piano and automated notation.

关键词： Training Machine learning algorithms Education Music Machine learning Software Recording Multiple signal classification Telecommunication computing Artificial intelligence

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Optimizing Battery Management Systems For Electric Vehicles: Current Trends And Future Directions

Optimizing Battery Management Systems For Electric Vehicles:...

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Global Conference for Advancement in Technology (GCAT)

作者： V.S.Prakash Annamalai Muthu B. Anandan V. Velmurugan T. Alex Stanley Raja Purushothaman V Department of Computer Science Kristu Jayanti College Bengaluru Electrical Section Engineering Department University of Technology and Applied Science Nizwa Oman Department of Mechanical Engineering Siddharth Institute of Engineering & Technology Puttur Andhrapradesh Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Assistant Professor Level III Bannari Amman Institute of Technology Sathyamangalam Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The rapid global shift towards electric vehicles (EVs) marks a transformative era in transportation, driven by the imperative to reduce carbon emissions and reliance on fossil fuels. Central to the performance and longevity of EVs are Battery Management Systems (BMS), which optimize battery usage, enhance safety, and extend operational life. This comprehensive review explores current trends and future directions in BMS optimization, focusing on key technological advancements and their implications for electric mobility. Battery Management Systems (BMS) serve as critical components in electric vehicles, orchestrating the efficient operation of battery packs through real-time monitoring, state-of-charge (SoC) estimation, thermal management, and fault diagnosis. These systems not only ensure optimal performance but also safeguard against potential hazards, thereby enhancing vehicle reliability and user confidence. Current advancements in BMS technology emphasize enhanced accuracy in SoC estimation, leveraging sophisticated algorithms and sensor integration to provide precise information on battery charge levels. Coupled with innovative thermal management strategies, which include active cooling and heating systems, BMS can mitigate thermal stress and optimize battery performance across diverse environmental conditions. By synthesizing technological advancements, emerging trends, and potential challenges, it offers valuable insights for researchers, engineers, and policymakers aiming to advance the efficiency, reliability, and sustainability of electric mobility solutions worldwide.

关键词： Technological innovation Electric potential Battery management systems Estimation Electric vehicles Thermal management Market research Sustainable development Thermal stresses Optimization

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