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Securing data in the Cloud: The Application of Fuzzy Identity Biometric Encryption for Enhanced Privacy and Authentication 1

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4th international conference on Image processing and Capsule Networks, ICIPCN 2023

作者： Venkatachalam, Chandrasekar Manivannan, K. Venkatachalam, Shanmugavalli Karnataka Bengaluru India Department of Computer Science and Engineering KSR College of Engineering Tamil Nadu Tiruchengode India

ISBN: (数字)9789819970933

ISBN: (纸本)9789819970926

Cloud computing has emerged as a highly innovative technology, offering various benefits to users. To ensure the security of user data, cloud storage schemes have been introduced, aiming to protect sensitive information from unauthorized access. In particular, the sharing of personal health records (PHR) is gaining prominence as a method for securely sharing healthcare data among users on cloud servers. The use of fuzzy techniques plays a crucial role in transforming original data into an encrypted form, known as ciphertext, which is challenging for unauthorized individuals to comprehend. This technique enhances the confidentiality of data, ensuring that only authorized parties can access and understand it. However, while cloud services provide a convenient platform for data sharing, they often lack efficiency in terms of data sharing capabilities. To address these challenges, a novel approach called fuzzy identity biometric encryption (FIBE) is introduced for PHR management. FIBE combines the benefits of fuzzy techniques and biometric authentication to achieve both high-security levels and user convenience simultaneously. This approach enables authorized users to have control over access to PHR data and ensures secure data sharing within a cloud environment. By integrating biometric authentication, FIBE enhances the security of PHR systems, as biometric characteristics are unique to each individual, making it difficult for unauthorized users to gain access. Moreover, the approach improves user convenience by eliminating the need for remembering complex passwords or using traditional authentication methods. In conclusion, the utilization of fuzzy identity biometric encryption (FIBE) in PHR systems offers enhanced security and efficient data sharing in the cloud. This approach combines the advantages of fuzzy techniques and biometric authentication, providing authorized users with control over data access while maintaining a high level of security and user convenienc

关键词： Cloud storage

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Feature Selection Implementation on High-Dimensional data using Firefly Algorithm

Feature Selection Implementation on High-Dimensional Data us...

引用

international conference on Informatics and computing (ICIC)

作者： Tessy Badriyah Iwan Syarif Danang Djehan Lukmanto Prakoso Politeknik Elektronika Negeri Surabaya Indonesia

High-dimensional data often presents a challenge in machine learning tasks due to the curse of dimensionality. Feature selection is a common technique used to overcome this problem by reducing the number of features and selecting the most relevant ones. In this research, we propose the use of the firefly algorithm for feature selection in high-dimensional data. The aim of this study is to evaluate the performance of the firefly algorithm in selecting informative features and compare it to other feature selection algorithms. We used several datasets with different numbers of features and instances to test the performance of the firefly algorithm. The results demonstrated the capability of the firefly algorithm to enhance accuracy after feature selection process: in the Breast Cancer dataset, it improved from 0.959 to 0.969; in the Lymphoma dataset, it improved from 0.479 to 0.575; in the arrhythmia dataset, it improved from 0.622 to 0.714; and in the leukemia dataset, it improved from 0.621 to 0.733. However, for the Central Nervous System (CNS) dataset, the accuracy remained the same before and after at 0.633. We also compared the results of the firefly algorithm with three other well-known feature selection algorithms: the genetic algorithm, particle swarm optimization, and ant-bee colony algorithm. Using performance metrics such as accuracy, precision, and recall, the firefly algorithm proved to be efficient in handling high-dimensional datasets with satisfactory results in several cases. It outperformed the other algorithms, particularly the ant-bee colony algorithm, which also surpassed the others in certain cases.

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Bridge-IF: Learning Inverse Protein Folding with Markov Bridges 38

Bridge-IF: Learning Inverse Protein Folding with Markov Brid...

引用

38th conference on Neural Information processing Systems, NeurIPS 2024

作者： Zhu, Yiheng Wu, Jialu Li, Qiuyi Yan, Jiahuan Yin, Mingze Wu, Wei Li, Mingyang Ye, Jieping Wang, Zheng Wu, Jian College of Computer Science & Technology Liangzhu Laboratory Zhejiang University China College of Pharmaceutical Sciences Zhejiang University China Alibaba Cloud Computing China School of Public Health Zhejiang University China School of Artificial Intelligence and Data Science University of Science and Technology of China China The Second Affiliated Hospital Zhejiang University School of Medicine China

Inverse protein folding is a fundamental task in computational protein design, which aims to design protein sequences that fold into the desired backbone structures. While the development of machine learning algorithms for this task has seen significant success, the prevailing approaches, which predominantly employ a discriminative formulation, frequently encounter the error accumulation issue and often fail to capture the extensive variety of plausible sequences. To fill these gaps, we propose Bridge-IF, a generative diffusion bridge model for inverse folding, which is designed to learn the probabilistic dependency between the distributions of backbone structures and protein sequences. Specifically, we harness an expressive structure encoder to propose a discrete, informative prior derived from structures, and establish a Markov bridge to connect this prior with native sequences. During the inference stage, Bridge-IF progressively refines the prior sequence, culminating in a more plausible design. Moreover, we introduce a reparameterization perspective on Markov bridge models, from which we derive a simplified loss function that facilitates more effective training. We also modulate protein language models (PLMs) with structural conditions to precisely approximate the Markov bridge process, thereby significantly enhancing generation performance while maintaining parameter-efficient training. Extensive experiments on well-established benchmarks demonstrate that Bridge-IF predominantly surpasses existing baselines in sequence recovery and excels in the design of plausible proteins with high foldability. The code is available at https://***/violet-sto/Bridge-IF. © 2024 Neural information processing systems foundation. All rights reserved.

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A data encryption scheme combined symmetric encryption algorithm with asymmetric encryption algorithm

A data encryption scheme combined symmetric encryption algor...

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5th international conference on Artificial Intelligence and Advanced Manufacturing (AIAM 2023)

作者： Y. Wang School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan People's Republic of China

In order to explore the research of computer network data security encryption technology. The author proposes a data encryption scheme that combines symmetric encryption algorithms with asymmetric encryption algorithms, by combining AES algorithm with RSA algorithm and ECC algorithm, improved hybrid encryption algorithms AES-RSA and AES-ECC are obtained. In order to verify the correctness of the scheme, a Hadoop experimental platform was built and tested using Hadoop Eclipse, the time required for different algorithms to encrypt the same size of data was measured. Through the analysis of the obtained experimental data and the safety of the experimental plan, the experimental results indicate that, the AES-RSA algorithm takes slightly longer to encrypt the same data than the AES-ECC algorithm, this is because in these two hybrid encryption algorithms, the RSA algorithm defaults to using a 1024 bit key, while the ECC algorithm selects a key length of 160 bits. A 160 bit ECC key can provide security equivalent to a 1024 bit RSA key, so based on the advantage of small key bits, under the same security strength, the AES-ECC algorithm has higher encryption efficiency than the AES-RSA algorithm. It has been proven that the improved encryption algorithm has the advantages of efficiency and high security, and can achieve secure storage of data in cloud computing environments.

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Utilizing Machine Learning to Develop an Improved Real-Time data Analysis System

Utilizing Machine Learning to Develop an Improved Real-Time ...

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Smart Generation computing, Communication and Networking (SMART GENCON), international conference on

作者： Harsimrat Khandari Girija Shankar Sahoo Raja Praveen K N Ritu Shree Umamaheswari B Yashwant V Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India

With the constantly growing volume of facts being produced, conventional data evaluation structures are regularly insufficient in terms of presenting an correct overview of the most modern-day data. The utilization of system mastering provides the potential to create an improved real-time records evaluation device this is more able to making experience of and knowledge the records. The development of an progressed actual-time statistics evaluation device utilizing system studying can hugely enhance statistics analysis techniques by means of integrating relevant data and structured analyses of the identical. this will include strategies inclusive of natural language processing and clustering algorithms to perceive trends, sentiment evaluation to understand customer feedback, and predictive analytics to better perceive areas for improvement. Moreover, the realtime statistics analysis system can be further enhanced by means of incorporating AI to make extra correct predictions, as well as reinforcement studying to evolve present models as new statistics is integrated. this would allow the system to learn more efficaciously and modify its algorithms to continually enhance its overall performance. Universal, by using machine studying to develop an advanced actual-time statistics evaluation system, extra correct and comprehensive insights can be generated from information, and groups can use this facts to better tell their choice making. moreover, temporal records evaluation can be advanced with faster response times as a result of the device being able to process records

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Comparative Analysis of Decision Trees, Random Forest, and k-Nearest Neighbors in Predictive Analytics for Orange Telecom's Customer Complaint data

Comparative Analysis of Decision Trees, Random Forest, and k...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), international Engineering conference on

作者： Rayan Dayyeh Waed AlSawareah Bassam Kasasbeh Raneem Qaddoura Sinan Kamal School of Computing and Informatics Al Hussein Technical University Amman Jordan Data & Artificial Intelligence Department Orange Jordan Amman Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

The study examines the application of machine learning algorithms, namely Decision Trees, Random Forest, and k-Nearest Neighbors, for improving predictive analytics in customer complaint management at Orange Telecom. An analysis is conducted to evaluate the accuracy, efficiency, and scalability of these algorithms in this specific context. Emphasis is placed on thorough data preparation and algorithm assessment, using established performance metrics. Findings provide valuable insights into the handling of complex customer data by these algorithms, underscoring the importance of detailed data preparation for predictive analysis in the telecommunications sector.

关键词： Measurement Machine learning algorithms Scalability Nearest neighbor methods Prediction algorithms Telecommunications Classification algorithms

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The Smart Application of data Mining in the Detection of Fraudulent Transactions

The Smart Application of Data Mining in the Detection of Fra...

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Smart Generation computing, Communication and Networking (SMART GENCON), international conference on

作者： Aniruddha Bodhankar Upendra Singh Aswal D. Arivazhagn Sachin S. Pund Nandini Kulkarni Sudhir Kumar Chaturvedi Department of Data Science & Information Systems Dr. Ambedkar Institute of Management Studies and Research- Nagpur Nagpur India Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Symbiosis School of Planning Architecture and Design Nagpur Symbiosis International University Nagpur India Department of AMET Business school Academy of Maritime Education and Training Deemed to be university Chennai Department of School of Engineering UPES Dehradun India

Records mining is a powerful analytical device used to find out styles, correlations, and traits in large facts sets. Smart application of data mining methods within the detection of fraudulent transactions has finished an extensive quantity of success. It normally entails statistical algorithms, facts evaluation, and machine getting to know techniques to stumble on suspicious sports associated with fraud. Using statistics mining techniques, it's far possible to music the behaviour of clients, discover anomalous transaction styles and developments in huge datasets, come across frauds and generate signals on suspicious activities. It also allows to identify which forms of merchandise are frequently centered, which price methods are used, which places are problem to danger, and which traders have capacity credit card fraud. The utility of facts mining within the detection of fraudulent sports entails principal techniques. The first method makes use of conventional techniques used by banks, credit groups and other retailers, which includes account reconciliation, annualized billing and complex question processing. Those methods are used to generate reviews that are used by investigators to perceive probably suspicious activities. The second technique entails the usage of superior analytical techniques such as neural networks, decision timber, and clustering algorithms. These strategies have the functionality to stumble on facts irregularities in several conditions that were formerly unseen or unknown.

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Detection of Misinformation Related to Pandemic Diseases Using Machine Learning 7th

Detection of Misinformation Related to Pandemic Diseases Usi...

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7th EAI international conference on Robotics and Networks, ROSENET 2023

作者： Naeem, Javaria Gül, Ömer Melih Parlak, Ismail Burak Karpouzis, Kostas Kadry, Seifedine Nimer Salman, Yücel Batu Department of Computer Engineering Bahçeşehir University İstanbul Turkey Department of Computer Engineering Galatasaray University İstanbul Turkey Panteion Uninversity of Social and Political Sciences Kallithea Greece Department of Applied Data Science Noroff College Kristiansand Norway Ajman University Ajman United Arab Emirates Department of Software Engineering Bahcesehir University İstanbul Turkey

ISBN: (纸本)9783031644948

The advent of the COVID-19 pandemic has brought with it not only a global health crisis but also an infodemic characterized by the rampant spread of misinformation on social media platforms. In response to the urgent need for effective misinformation detection, this study presents a comprehensive approach harnessing machine learning and deep learning techniques, culminating in ensemble methods, to combat the proliferation of COVID-19 misinformation on Facebook, Twitter, Instagram, and YouTube. Drawing from a rich dataset comprising user comments on these platforms, encompassing diverse COVID-19-related discussions, our research applies SVM, decision tree, logistic regression, and neural networks to perform in-depth analysis and classification of comments into two categories: positive and negative information. The innovation of our approach lies in the final phase, where we employ ensemble methods to consolidate the strengths of various machine learning and deep learning algorithms. After applying ensemble learning, accuracy reached 91% for Facebook content, 79% for Instagram, 80% for Twitter, and 95% for YouTube. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： COVID-19

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Distributed log collection for business processes based on ELK architecture

Distributed log collection for business processes based on E...

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Electrical Engineering, Big data and algorithms (EEBDA), IEEE international conference on

作者： Yize Zong Beijing Key Laboratory on Integration and Analysis of Large-cale Stream Data North China University of Technology Beijing China

When it comes to executing service solutions in a distributed environment, the service solution is considered as a workflow split into many sub-processes that are scheduled to run independently and autonomously on each processing engine. Each organization’s machines have its own set of limitations. They can't write too much code or rebuild the system, so they'll have to rely on a lightweight, low-code strategy to expand the service solution’s process activities to collect valuable logs. As a result, we created a log collection system for the case, as mentioned earlier. The listener class is used to insert some functional code that sends the logs to the messaging middleware. Then, the service call records, or log data, are transmitted to the central administration platform. The ELK (Elasticsearch, Logstash, Kibana) framework, commonly used in log analysis nowadays, is then used to perform real-time data processing. Finally, the logs are visually displayed to provide intuitive feedback on log information and to make system maintenance and adjustment easier.

关键词： Electrical engineering Codes Microservice architectures Writing Maintenance engineering Load management Real-time systems

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Improved K-Nearest Neighbor Missing data Classification Based on Interval Value Imputation

Improved K-Nearest Neighbor Missing Data Classification Base...

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Electrical Engineering, Big data and algorithms (EEBDA), IEEE international conference on

作者： Ziyang Zhang Can Tang School of Mathematics and Statistics Shandong University of Finance and Economics Jinan China International School Beijing University of Posts and Telecommunications Beijing China

In reality, when processing data sets for classification, there are often missing data sets, which brings inconvenience to the classification work. To this end, this paper proposes a method to impute incomplete data based on the interval value of K nearest neighbors. The method uses the Euclidean distance between the incomplete data and the complete data to find the K closest complete data to the incomplete data, so that the nearest neighbor can be constructed according to the corresponding attribute value of the complete data to the missing attribute value of the incomplete data. interval. Next, the dataset is constructed into an interval-valued dataset. Based on the interval-valued distance algorithm, the incomplete data can be classified by the K-nearest neighbor algorithm. The experimental results show that the improved K-nearest neighbor algorithm based on interval value imputation is more efficient than the traditional 0-value imputation, median imputation and mean imputation K-nearest neighbor algorithm under certain circumstances.

关键词： Training Electrical engineering Euclidean distance Big data Classification algorithms

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