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2nd International Conference for Advancement in technology, ICONAT 2023

作者： Hasan, Muhammad Zulkifl Hussain, Muhammad Zunnurain Mubarak, Zaima Siddiqui, Adeel Ahmad Qureshi, Ali Moiz Ismail, Imran University of Central Punjab Faculty of Computer Science and Information Technology India Bahria University Department of Computer Science Lahore Campus Pakistan National College of Business Administration and Economics Department of Computer Science Lahore Pakistan National College of Business Administration & Economics Department of Computer Science DHA Campus Lahore Pakistan Punjab Board of Technical Education Lahore Pakistan

ISBN: (纸本)9781665475174

Cloud computing security refers to data protection, while data integrity refers to the reliability of the data. Integrity and security of data are not the same things. Therefore, both of them are very important in the field of cloud computing. A primary concern of users related to Cloud Computing is data security and integrity. The purpose of this paper is to discuss the threats and solutions associated with the problems of data security and data integrity that cloud computing users must deal with. Data protection is concerned with data security, whereas data reliability is concerned with data integrity. A data security strategy focuses on reducing the risk of leaks of trade secrets, emails, medical data, and business *** privacy, security, and integrity are essential in information technology. In contrast, data integrity ensures that digital data is not corrupted and that only authorized individuals can access or edit the data (i.e., data consistency, accuracy, and reliability). Several platforms use more accessible language to make their terms more transparent, and users can trust them. Cloud computing is based on distributing computer resources across several users. Several policies are designed to ensure regulatory compliance while protecting the information, applications, and infrastructure associated with Cloud Computing. The paper provides an overview of Cloud Computing, its concepts, its significance in various applications, and solutions to issues and methods for ensuring its integrity and security. This research also discusses multiple challenges associated with cloud computing. To provide high-quality cloud data security, cloud security solutions, comprehensive security policies, and a security-conscious organizational culture are essential. © 2023 IEEE.

关键词： Data privacy

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Bayesian Optimization with Multi-objective Acquisition Function for Bilevel Problems 30th

Bayesian Optimization with Multi-objective Acquisition Func...

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30th Irish Conference on Artificial Intelligence and Cognitive science, AICS 2022

作者： Dogan, Vedat Prestwich, Steven Confirm Centre for Smart Manufacturing School of Computer Science and Information Technology University College Cork Cork Ireland Insight Centre for Data Analytics School of Computer Science and Information Technology University College Cork Cork Ireland

ISBN: (纸本)9783031264375

A bilevel optimization problem consists of an upper-level and a lower-level optimization problem connected to each other hierarchically. Efficient methods exist for special cases, but in general solving these problems is difficult. Bayesian optimization methods are an interesting approach that speed up search using an acquisition function, and this paper proposes a modified Bayesian approach. It treats the upper-level problem as an expensive black-box function, and uses multiple acquisition functions in a multi-objective manner by exploring the Pareto-front. Experiments on popular bilevel benchmark problems show the advantage of the method. © 2023, The Author(s).

关键词： Multiobjective optimization

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A Multi-grained Cascade Structure for Online Sparse Streaming Feature Selection 7

A Multi-grained Cascade Structure for Online Sparse Streamin...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Xu, Ruiyang Wu, Di Chongqing University of Posts and Telecommunications Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences School of Computer Science and Technology Chongqing400065 China Southwest University College of Computer and Information Science Chongqing400715 China

ISBN: (纸本)9798350363173

Online Streaming Feature Selection (OSFS) is an efficient way to handle high-dimensional streaming data. While in real applications, streaming data often have a large amount of missing data due to various reasons, leading to the accuracy loss of OSFS. Latent Factor Analysis (LFA) is one of the most effective methods to pre-estimate the missing data of sparse streaming data. However, the known data density of sparse streaming features is always low, which is not enough for LFA to achieve the satisfactory pre-estimating performance for OSFS. Motivated by this, this paper proposes a sparse OSFS algorithm based on a multi-grained cascaded structure, termed as SO-MCS. Its main idea is two-fold: 1) using LFA to pre-estimate the missing data in sparse flow features in advance, and 2) gradually generating synthetic data to construct a multi-layer LFA architecture, which develops the known data density of the input sparse streaming feature matrix. Extensive experiments are carried out on eight real-world datasets, demonstrating that SO-MCS outperforms five state-of-the-art OSFS algorithms to a significant extent. © 2024 IEEE.

关键词： Feature Selection

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Enhancing Usability: A Grounded Theory Approach-based Scrum Framework for Mobile Application Design 19

Enhancing Usability: A Grounded Theory Approach-based Scrum ...

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19th International Conference on Future Networks and Communications, FNC 2024 / 21st International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th International Conference on Sustainable Energy information technology, SEIT 2024

作者： Alnanih, Reem Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

As technology becomes increasingly integrated into our daily lives, the role of software engineering in promoting healthy lifestyles has become increasingly important. Software applications have permeated our routines and well-being. Scrum, unlike traditional methodologies, offers an agile, iterative, and client-centric approach to software engineering. It emphasizes frequent client involvement, adaptability to changing requirements, and early risk mitigation, fostering a dynamic and responsive development environment. This approach is particularly crucial in the realm of mobile application design. Meanwhile, Grounded Theory can enhance the understanding of the alignment between developers and users' needs. This paper presents a comprehensive exploration of the development journey of a mobile application designed for promoting health, utilizing the Scrum framework and Grounded Theory. The iterative process, based on qualitative research, encompasses requirements gathering, database design, interface development, and usability testing. The author introduces concepts and styles to define and simplify sprints for improved clarity and comprehension. The usability testing for the proposed application-based Scrum framework was evaluated by two groups: an expert group familiar with the application's functionality and a participant group with no prior experience. The chi-square analysis revealed no significant differences between the two groups in their performance of the set of tasks, suggesting the application was evaluated similarly. Furthermore, the results of the post-test questionnaire, specifically the System Usability Scale (SUS) measure, indicate that over 83% of the participants scored the application above 82, placing it in the "A"grade range and reflecting an excellent level of user satisfaction. The analysis of the gathered data yields valuable insights with practical implications for organizations aiming to optimize the effectiveness of their Scrum teams. The

关键词： Application programs

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Machine Learning and Data Mining for Predictive Modeling of Stroke Risk and Post-Stroke Care Management 3

Machine Learning and Data Mining for Predictive Modeling of ...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Gobi, N. Singh, B.P. Yadav, Surendra Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350343274

The research investigates the efficacy of a comprehensive machine learning and data mining approach in predictive modeling of stroke risk and post-stroke care management. A holistic methodology is proposed, integrating diverse datasets and advanced algorithms, including Logistic Regression, Random Forest, and Long Short-Term Memory (LSTM) networks. Data preprocessing, feature extraction, and personalized care plan generation are key components. Performance evaluation using accuracy, sensitivity, specificity, precision, F1 Score, AUC, and MAE demonstrates the superior predictive capabilities and efficiency of the proposed method compared to traditional approaches. The study highlights the potential for revolutionizing stroke care, emphasizing personalized risk assessment and post-stroke management for improved patient outcomes and healthcare delivery. © 2023 IEEE.

关键词： Data mining

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Enhancing Preschool Literacy: A Machine Learning Approach to Sinhala Alphabet Recognition

Enhancing Preschool Literacy: A Machine Learning Approach to...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： De Silva, D.I. Athukorala, K.S.N. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka UvaWellassa University Department of Computer Science and Informatics Badulla Sri Lanka

ISBN: (纸本)9798331543617

This paper proposes a new tool to help preschool children learn the Sinhala alphabet, as it employs the K-Nearest Neighbors algorithm for handwritten Sinhala character recognition and prediction. The interactive platform also addresses the challenges that arise from conventional classroom learning systems, such as the lack of quick feedback, one-off learning, and inflexibility for individual learners. A crucial aspect of the Sinhala script is that it contains fifty-six characters, making character recognition particularly tedious for preschool children. The K-Nearest Neighbors algorithm appears to fill that gap impressively, as it can determine the handwriting of individuals and convert it into usable pieces for students. Usability testing indicated that preschoolers achieved a defined accuracy threshold of 90%, while the model's accuracy was 87%. These indicators provide internal evidence of the tool's actual utility in classroom interactions. The present study highlights the cultural relevance of HCI applications for children in early years education and advances the fields of machine learning systems and the preservation of the Sinhala language. The outcomes of this research demonstrate the technical correctness of the solution and its potential usefulness in applying this method to other languages and educational systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Multimodal Data Fusion and Machine Learning for Comprehensive Management of Parkinson's Disease in Healthcare 3

Multimodal Data Fusion and Machine Learning for Comprehensiv...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Ganesh, D. Gautam, Anupam Kumar Bhambu, Pawan Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350343274

Parkinson's disease is a neurological ailment that affects millions of individuals throughout the world. Successful treatment will greatly enhance the quality of life for those who have it. To effectively treat Parkinson's disease, this study investigates how multimodal data and machine learning technology might be combined. To improve illness diagnosis, symptom severity prediction, and medication efficacy evaluation, the suggested technique draws from a broad variety of data sources such as clinical records, medical imaging, and sensor data. In this study, novel data fusion and analytic techniques are used to generate a comprehensive picture of the patient's health. Classification and prediction can be accomplished using either the Random Forest, Convolutional Neural Network, or Long Short-Term Memory Network machine learning techniques. Data fusion and feature extraction, the first steps in building reliable models, are graphically shown by a set of mathematical equations. Data presented in the tables of comparison show how much of an improvement the proposed remedy represents over the status quo. This strategy has the potential to significantly alter the way doctors treat Parkinson's disease, leading to better outcomes for patients and setting a new standard for healthcare in general. © 2023 IEEE.

关键词： Neurodegenerative diseases

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Ensuring Data Integrity: A Strategic Approach to Reconciliation in Data Migration Projects 15

Ensuring Data Integrity: A Strategic Approach to Reconciliat...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Raju, Mudunuru Krishnamurty Rajesh, Remala Kumar, Thummapudi Jithendra Guravaiah, Koppala Independent Researcher United States Indian Institute of Information Technology Kottayam Department of Computer Science & Engineering India

ISBN: (纸本)9798350370249

In today's corporate landscape, mergers are increasingly prevalent, driven by various factors such as strategic expansion, cost efficiency, and technological innovation. However, post-merger, the migration of data from disparate systems becomes imperative to consolidate information and establish an efficient operational model. Despite the prominence of data migration projects, reconciliation often takes a backseat to ingestion and loading processes. This article emphasizes the critical importance of upfront consideration for reconciliation strategies, drawing from practical experience and research. Neglecting this phase can lead to chaotic testing processes and unnecessary iterations, hindering the migration process. Through this article, we present a comprehensive strategy for designing and implementing reconciliation processes, from proactive planning to meticulous execution. By prioritizing reconciliation upfront, organizations can mitigate risks and streamline their data migration endeavors for enhanced success. Our approach includes unmasking encrypted Personally Identifiable information (PII), reconciling it with source data, and implementing a metadata-driven audit framework enriched with extensive log information. This ensures thorough oversight and accountability, safeguarding data integrity throughout the migration journey. © 2024 IEEE.

关键词： Data integrity

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IndoCL: Benchmarking Indonesian Language Development Assessment

IndoCL: Benchmarking Indonesian Language Development Assessm...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Lin, Nankai Wu, Hongyan Zheng, Weixiong Liao, Xingming Jiang, Shengyi Yang, Aimin Xiao, Lixian School of Computer Science and Technology Guangdong University of Technology China College of Computer National University of Defense Technology China School of Information Technology and Engineering Guangzhou College of Commerce China School of Computer Science and Intelligence Education Lingnan Normal University China Faculty of Asian Languages and Cultures Guangdong University of Foreign Studies China

ISBN: (纸本)9798891761681

Recently, the field of language acquisition (LA) has significantly benefited from natural language processing technologies. A crucial task in LA involves tracking the evolution of language learners' competence, namely language development assessment (LDA). However, the majority of LDA research focuses on high-resource languages, with limited attention directed toward low-resource languages. Moreover, existing methodologies primarily depend on linguistic rules and language characteristics, with a limited exploration of exploiting pre-trained language models (PLMs) for LDA. In this paper, we construct the IndoCL corpus (Indonesian Corpus of L2 Learners), which comprises compositions written by undergraduate students majoring in Indonesian language. Moreover, we propose a model for LDA tasks, which automatically extracts language-independent features, relieving laborious computation and reliance on specific language. The proposed model uses sequential information attention and similarity representation learning to capture the differences and common information from the first-written and second-written essays, respectively. It has demonstrated remarkable performance on both our self-constructed corpus and publicly available corpora. Our work could serve as a novel benchmark for Indonesian LDA tasks. We also explore the feasibility of using existing large-scale language models (LLMs) for LDA tasks. The results show significant potential for improving LLM performance in LDA tasks. © 2024 Association for Computational Linguistics.

关键词： Students

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Anomaly Detection and Classification in Streaming PMU Data in Smart Grids

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computer Systems science & Engineering 2023年第9期46卷 3387-3401页

作者： A.L.Amutha R.Annie Uthra J.Preetha Roselyn R.Golda Brunet Department of Computational Intelligence SRM Institute of Science and TechnologyChennai603203India Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India Department of Computer Science and Engineering Government College of EngineeringSalem636011India

The invention of Phasor Measurement Units(PMUs)produce synchronized phasor measurements with high resolution real time monitoring and control of power system in smart grids that make *** are used in transmitting data to Phasor Data Concentrators(PDC)placed in control centers for monitoring purpose.A primary concern of system operators in control centers is maintaining safe and efficient operation of the power *** can be achieved by continuous monitoring of the PMU data that contains both normal and abnormal *** normal data indicates the normal behavior of the grid whereas the abnormal data indicates fault or abnormal conditions in power *** a result,detecting anomalies/abnormal conditions in the fast flowing PMU data that reflects the status of the power system is critical.A novel methodology for detecting and categorizing abnormalities in streaming PMU data is presented in this *** proposed method consists of three modules namely,offline Gaussian Mixture Model(GMM),online GMM for identifying anomalies and clustering ensemble model for classifying the *** significant features of the proposed method are detecting anomalies while taking into account of multivariate nature of the PMU dataset,adapting to concept drift in the flowing PMU data without retraining the existing model unnecessarily and classifying the *** proposed model is implemented in Python and the testing results prove that the proposed model is well suited for detection and classification of anomalies on the fly.

关键词： Smart Grid PMU data incremental learning classifying anomalies artificial intelligence

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