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Blockchain and Smart Contracts: Evolution, Challenges, and Future Directions

Blockchain and Smart Contracts: Evolution, Challenges, and F...

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2024 International Conference on Knowledge engineering and communication Systems, ICKECS 2024

作者： Singh, Jaibir Rani, Suman Kumar, Parveen Lovely Professional University Computer Science & Engineering Punjab Phagwara India Lovely Professional University Electronics & Communication Engineering Punjab Phagwara India Government College Computer Science Haryana Hisar India

ISBN: (纸本)9798350359688

Blockchain technology and smart contracts are pivotal innovations in the digital era, offering a paradigm shift in how data is stored, shared, and verified across decentralized networks. Initially conceived to underpin cryptocurrencies like Bitcoin, the application of blockchain has since expanded far beyond, promising to revolutionize various industries by ensuring transparency, security, and trust without the need for central authorities. Smart contracts, self-executing contracts with the terms of the agreement directly written into lines of code, further enhance blockchain's utility by automating contract execution, thereby reducing reliance on intermediaries and lowering transaction costs. However, despite their potential, the adoption of blockchain and smart contracts faces significant challenges, including scalability issues, energy consumption, regulatory uncertainties, and security vulnerabilities. This paper provides a comprehensive overview of the evolution of blockchain and smart contracts, identifies the key challenges hindering their widespread adoption, and discusses potential future directions for overcoming these obstacles. Through a detailed literature survey and analysis of current technologies, this study aims to contribute to the ongoing discourse on blockchain and smart contracts, offering insights into their future trajectory and the role they could play in digital transformation. © 2024 IEEE.

关键词： Smart contract

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Robust Method for Accessing IoT Devices and Blockchain for Secure Data Management 5th

Robust Method for Accessing IoT Devices and Blockchain for S...

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5th International Conference on Entrepreneurship, Innovation, and Leadership, ICEIL 2023

作者： Verma, Saweta Chandel, Garima Department of Computer Science Chandigarh University Mohali India Department of Electronics and Communication Engineering Chandigarh University Mohali India

ISBN: (纸本)9789819716814

IoT devices play an important part in the twenty-first century connection environment, allowing for remote access and control of a variety of objects. One of the most notable advantages of these internet-connected devices is their capacity to save time while granting users remote access to resources from anywhere, at any time. However, since its inception, the IoT industry has grappled with several challenges, primarily revolving around data security and privacy. Ensuring confidentiality and integrity is pivotal in establishing a robust security framework. Presently, IoT protocols offer diverse security levels, all intertwined with aspects of authentication and authorization. Despite this, there is a rising need for better security against a variety of threats, such as impounder attacks, Denial of Service (DoS) attacks, replay attacks, password guessing, masquerade attacks, and man-in-the-middle attacks. In this work, a temperature sensor (DHT22) and NodeMCU were used to examine security threats and provide a clever solution. Data was sent and received through IoT protocols at the mobile end. This study examines the secure data transfer from Internet of Things (IoT) devices to mobile devices, revealing the challenges found during prototype development and outlining solutions. In addition, the objective was to create a faultless prototype capable of easily transmitting data to the cloud. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Authentication

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A Proficient Multi-level Data Analytic Suite for Ascertaining Preliminary Gestational Hazards Associated with Its Influences 2nd

A Proficient Multi-level Data Analytic Suite for Ascertainin...

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2nd International Conference on Women Researchers in electronics and Computing, WREC 2023

作者： Bhavani, G. Jeyalakshmi, C. Department of Computer Science and Engineering Thiagarajar College of Engineering Tamil Nadu Madurai India Department of Electronics and Communication Engineering K. Ramakrishnan College of Engineering Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9789819970766

The basis of sustaining positive health outcomes begins from womb in the current era is safeguarding pregnancy from problems including maternal mortality and morbidity. A pregnancy with a hypertensive issue and diabetes is regarded as having a very high risk since it may result in long-term diseases such cardiovascular disease, obesity due to fetal growth restriction, anemia, and thyroid problems. Low birth weight, early delivery, and other serious neonatal disorders can affect babies who go untreated throughout pregnancy. Advances in Intelligence system and health data analytics allow for the early diagnosis of pregnancy concerns, reducing risks. This proposed effort includes the prediction of early gestational problems like diabetes and hypertension as well as the analysis of the risk variables that may be present. In our suggested work, multi-level data analysis is employed to better understand the health of pregnant women throughout the first trimester. In this data analysis suite, summarizing of data with deep insights is done at data preparation using exploratory data analysis (EDA) to look at the core pattern of data with its properties. The KNN algorithm has been used to preprocess data. Pregnancy risks including gestational hypertension and diabetes are identified before 13 weeks of pregnancy machine learning techniques. In order to comprehend analytical data with graphical representations, visual analytical approaches have been utilized. As a consequence, 97.18% accuracy is achieved. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data Analytics

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Breast Cancer Diagnosis Using Elephant Herding Optimization and Sparse Autoencoder Through Gene Expression Analysis 15th

Breast Cancer Diagnosis Using Elephant Herding Optimization ...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Bharanidharan, N. Chakravarthy, S. R. Sannasi Kumar, V. Vinoth Kavya, B. Meghana, G. Rajaguru, Harikumar School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering Dayananda Sagar University Bangalore India Department of Computer Science and Engineering PES University RR Campus Bangalore India

ISBN: (纸本)9783031810794

Gene expression analysis is an efficient modality to detect breast cancer. But analysis of the gene expression data is a complicated task due to the involvement of large number of numerical data for each subject. This research work focuses on the development of computerized diagnosis systems for analyzing the gene expression data and classifying the subjects as either normal or breast adenocarcinoma. Sparse autoencoder is used for dimensionality reduction and Elephant Herding Optimization is used for transforming the features. Four different supervised classifiers namely support vector machine, random forest classifier, decision trees, and naive bayes classifier are examined for classification task. Among them, random forest provides the superior balanced accuracy of 86% while it was further enhanced to 92% with the help of sparse autoencoders and elephant herding optimization algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Transcription

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Manhole Detection and System Monitoring Controlled by IoT 8

Manhole Detection and System Monitoring Controlled by IoT

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8th International Conference on electronics, communication and Aerospace Technology, ICECA 2024

作者： Niranjane, Vaishali. B Niranjane, Pornima Punwatkar, Krushil.M. Yeshwantrao Chavan College of Engineering Electronics and Telecommunication Maharashtra Nagpur India Babasaheb Naik College of Engineering Computer Science and Engineering Maharashtra Pusad India Babasaheb Naik College of Engineering Electronics and Communication Maharashtra Pusad India

ISBN: (纸本)9798350367904

The IoT-based Manhole Monitoring System is a solution that aims to improve city drainage system management by effectively addressing the challenges that today's drainage networks face. Using a network of sensors and GSM modules, this system not only detects and notifies authorities of potential hazards such as gas leaks and sewage overflows, but also provides them with real-time data to help them make informed decisions. Its strategic placement of sensors, which includes water flow rate sensors, temperature sensors, and tilt sensors, ensures precise monitoring of critical parameters, allowing for proactive measures to prevent blockages and system malfunctions. Furthermore, the seamless integration with Thing Speak enables secure data storage, thorough analysis, and insightful visualization, allowing the government to gain comprehensive insights into system performance and emerging trends over time The system's ability to send SMS notifications ensures smooth communication and timely responses in emergency situations, encouraging a collaborative approach to urban infrastructure management. In the face of global urbanization and environmental challenges, the IoT-based Manhole Monitoring System emerges as a new sewage system that prioritizes resilience, efficiency, and sustainability in urban development. Its implementation represents a significant advancement in safeguarding. © 2024 IEEE.

关键词： Gas hazards

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Utilizing the Power of Residual and Attention Properties with Binary Focal Loss Optimization for Underwater Image Segmentation Using UNet Architecture 7th

Utilizing the Power of Residual and Attention Properties wit...

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7th International Conference on communication, Device and Networking, ICCDN 2024

作者： George, Geomol Anusuya, S. Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India

ISBN: (纸本)9789819764648

Recent developments in deep-sea exploration, environmental surveillance, and ocean research, accurate segmentation of underwater images is essential. This study pursues this goal by exploring underwater image segmentation from a deep learning perspective. It specifically looks at how well the Attention Residual UNet architecture, a more sophisticated version of the U-Net, works with the focal loss technique to achieve accuracy in this crucial task. Using attention mechanisms and residual connections, the Attention Residual UNet architecture, based on the U-Net framework, can capture fine details while maintaining contextual coherence. Model loss and accuracy measures are considered as this study carefully assesses the architecture’s performance. Notably, the model exhibits outstanding accuracy, obtaining 97.23% and 92.76% accuracy on the training and validation sets, respectively. The Jaccard coefficient further demonstrates the model’s efficiency, which measures the intersection between predicted and actual segments and has coefficients for the model and validation sets of 74.31% and 66.51%, respectively. The Mean Intersection over Union (MIoU) statistic, which boasts a value 91.24%, validates the model’s superiority. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Underwater imaging

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Expression Analysis of Tweets using NLP in Machine Learning

Expression Analysis of Tweets using NLP in Machine Learning

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Sabeenian, R.S. Abishiek, A. Harivignesh, S. Dinesh, P.M. Lakshmi, G. Prasanna Alagarsamy, Manjunathan Sona College of Technology Electronics and Communication Engineering Salem India Sandip University School of Computer Science and Engineering Maharashtra Nashik India K.Ramakrishnan College of Technology Department of Electronics and Communication Engineering Tamil Nadu Trichy621112 India

ISBN: (纸本)9798350390049

Nowadays the social media has become one of the most essential platforms for reviewing and commenting their own opinions and perspectives. Sentiment analysis is the study of analyzing reviews of the people from texts, products and also analyzing the customer opinions. Expression or Sentiment analysis uses machine learning concepts particularly Natural Language Processing and various ML algorithms. Mining of the data helps to draw conclusions from various fields. We have proposed a paper where we analyze the sentiments of twitter data, identify and segregate positive tweets and negative tweets using machine learning and displaying the number of positive and negative tweets, plotting them using histogram and displaying the classification report. We have done this project since it is one of the most important tasks of classifying the positive and negative texts and analyze the sentiments which is very crucial for any organization's growth © 2024 IEEE.

关键词： Tweets

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Principles and Potentials of F5G: The Fifth Generation Fixed Network 1

Principles and Potentials of F5G: The Fifth Generation Fixed...

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1st International Conference on Circuits, Power, and Intelligent Systems, CCPIS 2023

作者： Routray, Sudhir K. Javali, Abhishek Sharmila, K.P. Pappa, M. Jha, Mahesh K. Sarangi, Susanta K. Cmr University Department of Computer Science and Engineering Bangalore India Cmr Institute of Technology Department of Electronics and Communication Engineering Bangalore India University Department of Electronics and Communication Engineering Bhubaneswar India

ISBN: (纸本)9798350315769

In general, 5G is understood to be the fifth generation of mobile communications. However, there is also a fifth generation of wired communications which is led by the optical fiber technologies. This technology too has several attractive features like the wireless mobile 5G. In this article, we present the basic concepts of Fifth Generation Fixed Network (F5G). We show its characteristics and explain how it is going to change the communication landscape. We present the key benefits of F5G and show the main motivations behind its development. There are several applications of F5G and many emerging applications are also being introduced every year. Therefore, this newly developed technology has a lot of commercial importance in the modern world. In addition to the above, F5G is going to be one of the main rivals of 5G in the fixed environment. It is expected that F5G will outperform all its contemporary access area technologies in the fixed domain in the next few years. In this article, we briefly present its applications and future economic prospects. © 2023 IEEE.

关键词： Optical fibers

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Weapon Detection System For Security And Surveillance Using YOLOv8 2

Weapon Detection System For Security And Surveillance Using ...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Arivalagan, Divya Rubankumar, D. Thushiyanth, K. Suresh Kumar, S. Priyadharsini, V. Vignesh, O. Madras Institute of Technology Anna University Department of Electronics Engineering Chennai India QIS College of Engineering and Technology Department of Electronics and Communication Engineering Ongole India Akshaya College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350371314

The study focuses on developing and refining an advanced object detection model for integration into security systems. It begins with an exploration of challenges during early training epochs, aiming to enhance precision, recall, and overall performance. The training process involves meticulous data preprocessing, augmentation, and iterative optimization of hyperparameters to foster model convergence. Significantly, this project holds implications for security applications, offering an innovative approach to threat detection through advanced computer vision technologies. Notably, the study incorporates the usage of YOLOv3 and YOLOv8 models. The YOLOv8 model showed a mAP50 of 0.936 compared to YOLOv3, which had an mAP50 of 0.719, demonstrating superior performance with YOLOv8. Considerations for real-time processing, integration of temporal information, and collaboration with security professionals are vital for further advancements. © 2024 IEEE.

关键词： Object detection

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Prospects of Deep Learning with Blockchain for Securing the Digital Radiography Data in Smart Healthcare 2

Prospects of Deep Learning with Blockchain for Securing the ...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Sudhakar, K. Mahaveerakannan, R. Saveetha School of Engineering Simats Saveetha University Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Simats Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350382952

Blockchain knowledge is gaining traction in today's information and digital age, and it's expected to become extremely popular among the next generation for reasons that go well beyond bitcoin and other cryptocurrencies. Blockchain technology's potential use in healthcare may be observed through data-sharing, which enables users to pick certain data and limit data access based on user type, and is therefore crucial for the upkeep of EHRs. Despite the broad deployment of Internet of Things (IoT) integrated cloud for remote patient accuracy of sickness prediction remain important problems in today's health care system. Multiple machine learning techniques were used to successfully forecast illnesses at an early stage. Although it offers the possibility of precise categorizations, machine learning is time-consuming and inefficient. This research suggests combining Attribute based Searchable Honey Encryption with SqueezeNet to better analyse sickness and secure IoT-cloud healthcare data. To reduce variability and provide consistency across IoT data, means-mode normalisation is an essential first step. The final output was saved in view the prediction results and relevant patient evidence. We discovered that the projected model has higher accuracy (94%), sensitivity (94%), and specificity (94%), compared to the state-of-the-art models (also 94%). © 2024 IEEE.

关键词： Diseases

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