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ETL Maturity Model for Data Warehouse Systems:A CMMI Compliant Framework

computers, Materials & Continua 2023年第2期74卷 3849-3863页

作者： Musawwer Khan Islam Ali Shahzada Khurram Salman Naseer Shafiq Ahmad Ahmed T.Soliman Akber Abid Gardezi Muhammad Shafiq Jin-Ghoo Choi Department of Computer Science COMSATS University IslamabadWah Cantt47000Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Information Technology University of the Punjab Gujranwala CampusGujranwala52250Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

The effectiveness of the Business Intelligence(BI)system mainly depends on the quality of knowledge it *** decision-making process is hindered,and the user’s trust is lost,if the knowledge offered is undesired or of poor quality.A Data Warehouse(DW)is a huge collection of data gathered from many sources and an important part of any BI solution to assist management in making better *** Extract,Transform,and Load(ETL)process is the backbone of a DW system,and it is responsible for moving data from source systems into the DW *** more mature the ETL process the more reliable the DW *** this paper,we propose the ETL Maturity Model(EMM)that assists organizations in achieving a high-quality ETL system and thereby enhancing the quality of knowledge *** EMM is made up of five levels of maturity i.e.,Chaotic,Acceptable,Stable,Efficient and *** level of maturity contains Key Process Areas(KPAs)that have been endorsed by industry experts and include all critical features of a good ETL *** Objectives(QOs)are defined procedures that,when implemented,resulted in a high-quality ETL *** KPA has its own set of QOs,the execution of which meets the requirements of that *** brainstorming sessions with relevant industry experts helped to enhance the *** deployed in two key projects utilizing multiple case studies to supplement the validation process and support our *** model can assist organizations in improving their current ETL process and transforming it into a more mature ETL *** model can also provide high-quality information to assist users inmaking better decisions and gaining their trust.

关键词： ETL maturity model CMMI data warehouse maturity model

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Secure Encryption of Satellite Images Using S-Box-Based Two-Fish Algorithm and A Novel Chaotic Map 3rd

Secure Encryption of Satellite Images Using S-Box-Based Two-...

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3rd International Conference on Signal and Data Processing, ICSDP 2023

作者： Jansi, R. Ashwini, K. Aishwarya, N. Muthulakshmi, M. Logeshwari, M. Kanna, Mukesh Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Department of Mathematics Sathyabama Institute of Science and Technology Semmancheri Tamil Nadu Chennai600119 India

ISBN: (纸本)9789819795772

In recent days, encryption of satellite imagery has attracted a wide range of researchers since these images contain confidential information. Thereby, it is necessary to preserve the privacy of this data to avoid unauthorized access. Two-fish algorithm is one of the widely used image encryption schemes. However, the main drawback of this scheme lies with the static nature of the S-box. To avoid this issue and to improve the quality of encryption, in this paper, we propose a novel algorithm based on chaotic map to create a dynamic S-box structure. In the proposed Chaotic S-Box-based Two-Fish (CSTF) algorithm, a novel chaotic map with improved Lyapunov exponent and approximate entropy has been proposed. The sequences produced by the proposed chaotic map are used to randomize the values inside the static S-box structure of Two-fish algorithm. This encryption scheme is suitable for the encryption of satellite images. The proposed algorithm is validated in terms of S-box evaluation metrics like nonlinearity, bit independence criteria-nonlinearity (BIC-nonlinearity), strict Avalanche criterion (SAC) and bit independence criteria-strict Avalanche criterion (BIC-SAC). To validate the proposed algorithm in terms of key sensitivity, tests like Unified Average Changing Intensity (UACI), Hamming Distance (HD) and Number of Pixels Change Rate (NPCR) have been performed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lyapunov functions

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Deep Learning Approaches for Recognizing Daily Human Activities Using Smart Home Sensors 8

Deep Learning Approaches for Recognizing Daily Human Activit...

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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Sanpote, Worrakit Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit Mekruksavanich, Sakorn School of Information and Communication Technology Department of Computer Science University of Phayao Phayao Thailand School of Information and Communication Technology Department of Computer Engineering University of Phayao Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350396546

Nowadays, one of the most important objectives in health-related research is the improvement of the living condition and well-being of people. Smart home systems can provide health protection for residents based on the results of daily activity recognition. Recent advances and developments in sensor technology have increased the need for sensor-compatible goods and services in smart homes. Consequently, the ever-increasing volume of data requires the field of deep learning (DL) for auto-matic human motion recognition. Recent research has modeled spatiotemporal sequences gathered by smart home sensors using long short-term memory networks. In this work, ResNeXt-based models that learn to classify human activities in smart homes were proposed to improve recognition performance. Experiments conducted on Center for Advanced Studies in Adaptive Systems (CASAS) data, a publicly available benchmark dataset, shows that the proposed ResNeXt-based techniques are significantly superior to the existing DL methods and provide better results compared to the existing literature. The ResNeXt model achieved the averaged accuracy over the benchmark method to 84.81%, 93.57%, and 90.38% for the CASAS-Cairo, CASAS-Milan and CASAS-Kyoto3 datasets, respectively. © 2023 IEEE.

关键词： Deep learning

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Energy Consumption for Cognitive Radio Network Enabled Multi-Access Edge Computing 3

Energy Consumption for Cognitive Radio Network Enabled Multi...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Elnaim, Areeg Ali Babeker, Amin Barakat, Mohammed Gaid, Abdulguddoos S.A. Saeed, Mamoon M. Ahmed, Zainab. E. Saeed, Rashid A. College of Engineering and Communication Alneelain University Khartoum Sudan School of Electronics Engineering Sudan University of Science and Technology Khartoum Sudan Taiz University Dept. of Communication & Computer Engineering Taiz Yemen Faculty of Engineering Department of Communications and Electronics Engineering Yemen Gezira University Department of Electrical and Computer Engineering Khartoum Sudan College of Computers and Information Technology Taif University Department of Computer Engineering Taif21944 Saudi Arabia

ISBN: (纸本)9798350305333

This article discusses optimizing energy Consumption in CRN-enabled Multi-access Edge Computing (MEC) systems, presenting a novel approach to optimize the energy Consumption of a cognitive radio network-enabled MEC system using iterative optimization techniques. The proposed model aims to minimize the system's energy consumption while maintaining its performance and quality of service requirements. The iterative optimization algorithm is used to find the optimal allocation of resources, such as computing, communication, and power, among different nodes in the MEC system. As a result, the cognitive radio network enables efficient spectrum utilization by dynamically allocating available spectrum to different users based on their requirements. The performance evaluation approach is evaluated through simulations, and the results show that it can significantly improve the energy consumption of the MEC system while maintaining its performance and QoS requirements. This article contributes to developing sustainable and energy-efficient communication systems for future wireless networks. © 2023 IEEE.

关键词： Cognitive radio

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Flexible Localization Method with Motion Estimation for Underwater Wireless Sensor Networks 26

Flexible Localization Method with Motion Estimation for Unde...

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26th International Conference on Advanced communications Technology, ICACT 2024

作者： Ismail, A.S. Hawbani, Ammar Wang, Xingfu Aziz, Samah Abdel Zhao, Liang Saeed, Nasir School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230027 China Zagazig University Faculty of Science Zagazig44519 Egypt School of Computer Science Shenyang Aerospace University Shenyang110136 China United Arab Emirates University Department of Electrical and Communication Engineering Al Ain15551 United Arab Emirates

ISBN: (纸本)9791188428120

Due to the challenging conditions of underwater environments, such as node mobility and large-scale networks, achieving localization in large-scale mobile underwater sensor networks (UWSN) is a difficult task. This paper introduces a scheme known as the Flexible Localization Method with Mobility Estimation (FLMME) for UWSNs by utilizing the expected mobility patterns of underwater objects. FLMME performs localization hierarchically by splitting the process into anchor and ordinary node localization. Each node estimates its next mobility pattern based on previous location information, enabling estimates about its next location. Anchor nodes, holding known locations, manage the localization process to balance accuracy and error trade-offs. Extensive simulations demonstrate that FLMME reduces localization errors and hence increases localization accuracy. © 2024 Global IT Research Institute - GIRI.

关键词： Location

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Application of Evolutionary Computing for Optimal Design of Base Station Antenna 7

Application of Evolutionary Computing for Optimal Design of ...

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7th IEEE International Conference on Electronic Information and communication Technology, ICEICT 2024

作者： Khan, Naveed Hamid Nawaz Feng, Botao Ding, Xiao Deng, Li Gu, Wenzhe Chung, Kwok L. Shenzhen University College of Physics and Optoelectronic Engineering Shenzhen518060 China Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Macau University of Science and Technology Faculty of Humanities and Arts Macau999078 China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China Huizhou University School of Computer Science and Engineering Huizhou China Huizhou University School of Computer Science and Engineering Huizhou516007 China

ISBN: (纸本)9798350384437

This study outlines the development and refinement of an innovative base station antenna for contemporary wireless communication systems, which operates within the frequency range of 1.9 to 4.9 GHz. The antenna is made of copper and has a radiating patch thickness of 0.2 mm. The ground patch measures 38×38 mm and the total height of the antenna is 16 mm. The performance of the system relies on four reflecting walls that have been carefully carved to reduce out-of-band radiations and minimize interference. The antenna attains a peak gain of 6 dBi throughout the operational range, while keeping the |S11| parameters below -10 dBi, ensuring effective signal transmission. The process of optimization involves the utilization of evolutionary computers, more specifically a Genetic Algorithm, to meticulously adjust the design parameters. This approach guarantees that the antenna fulfills the demanding criteria of high gain and wide bandwidth, offering a strong and reliable solution for modern communication requirements. © 2024 IEEE.

关键词： Antenna grounds

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Transfer Learning for Efficient Node Placement in Dynamic Wireless Sensor Networks 16

Transfer Learning for Efficient Node Placement in Dynamic Wi...

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16th IEEE International Conference on Computational Intelligence and communication Networks, CICN 2024

作者： Priyadarshi, Rahul Bagri, Shiva Singh Ranjan, Rakesh Sundaram Faculty of Engineering and Technology Bhubaneswar India National Institute of Technology Patna Department of Mathematics Bihar 800005 India School of Computer Science Upes Uttarakhand Dehradun248007 India Government Engineering College Sheikhpura Department of Electronics and Communication Engineering Bihar 811105 India

ISBN: (纸本)9798331505264

Transfer learning is employed in this paper to introduce a novel method for node allocation in dynamic Wireless Sensor Networks (WSNs). In dynamic situations, traditional node placement techniques typically face challenges in terms of flexibility and efficiency. We propose a transfer learning approach that utilizes information from past network deployments to improve the design of node placement techniques in novel situations. In comparison to traditional approaches, the suggested method exhibits substantial enhancements in coverage ratio, energy efficiency, and execution time. The findings of our study suggest that transfer learning has the potential to significantly enhance network performance, thereby presenting a viable approach for dynamic WSNs. Improved network management and flexibility in different settings are among the ramifications of this study. © 2024 IEEE.

关键词： Network Management Network Performance Node Allocation Transfer learning Wireless Sensor Networks

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Advanced Joint Monitoring System Utilizing Real-time Data and KNN Algorithm for Prediction of Joint Health 4

Advanced Joint Monitoring System Utilizing Real-time Data an...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Antony Joseph Rajan, D. Raman, Ramakrishnan Mookherjee, Ujal Kumar Vekariya, Daxa Ganeshbabu, T.R. Rajmohan, M. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Maharashtra Pune India Maharashtra Pune India Parul University Faculty of Engineering and Technology Parul Institute of Engineering and Technology Gujarat Vadodara India Department of Electronics and Communication Tamil Nadu Namakkal India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350307757

This research proposes a novel strategy for the field of tele-orthopedics by incorporating a K-Nearest Neighbors (KNN) algorithm into a Raspberry Pi-based remote joint monitoring system. The KNN model can provide accurate predictions about the present condition of the joint health of orthopedic patients by using real-time data from sensors that capture joint movement measures. The technology makes it easier for medical experts to intervene at the appropriate moment, which in turn makes it possible to design treatment programs that are more individualized and data-driven. The solution enables patients and healthcare practitioners to communicate without interruptions by using a protected platform hosted in the cloud. The malleability and interpretability of the KNN algorithm contribute to a sophisticated framework for tele-orthopedics, which offers precise insights into the development of orthopedic therapies. This study lays a framework for future developments in remote patient monitoring for orthopedic treatment. It proves that it is possible to use machine learning in telemedicine, which is a significant contribution to the field. © 2024 IEEE.

关键词： Orthopedics

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Utilization of ECC in Smart Grids: A Review

Utilization of ECC in Smart Grids: A Review

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Gopala Krishna, K. Shekhawat, Jyoti Agarwal, Trapty Faculty of Engineering and Technology Department of Electronics and Communication Engineering Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Maharishi University of Information Technology Engineering Department Uttar Pradesh India

ISBN: (纸本)9798350357769

Within the realm of smart grids, a power service provider plays a crucial function in coordinating predicted power production with actual power demand in order to support power system stability. Insufficient protection means that, even with these advancements, data sent inside smart grids is still vulnerable to a variety of security risks and assaults. To enhance the security of electronic grid networks, a robust and effective authentication procedure must be put in place. The trouble of ensuring the security of communications among substations and electronic machines stays, even in shrewd lattice settings where SCADA (supervisory controls and data acquisition) frameworks assume a part in shielding associations between the administration place and substations. In this present circumstance, predominant security strategies frequently neglect to accomplish an unpretentious harmony among security and execution. With the expansion of an safe part on the smart machine side, this work incorporates security insurance into a moderation verification framework in view of elliptic bend cryptography. This clever convention handles the requirement for a finely tuned balance between the security and execution parts of shrewd lattices. Key arrangement, common verification, and character security are significant parts of the proposed convention. The convention's viability is upheld by a complete assessment utilizing the Gong-Needham-Yahalom rationale. This procedure is a critical improvement in reinforcing the security position of smart grids, particularly taking into account the continually changing climate of cyber threats and weaknesses. © 2023 IEEE.

关键词： Authentication Elliptic Curve Cryptography Privacy Protection SCADA Systems Security Smart Grids

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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism

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Mathematical Biosciences and engineering 2025年第1期22卷 73-105页

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis require enhancement in the creation of dependable detection approaches. Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). The CNN extracts spatial features, the BiGRU captures long-term temporal dependencies, and the CBAM emphasizes critical spatial and temporal regions, creating a hybrid architecture optimized for EEG pattern recognition. Evaluation of a public EEG dataset revealed superior performance compared to existing methods. The model achieved 99.00% accuracy in binary classification, 96.20% in three-class tasks, 92.00% in four-class scenarios, and 89.00% in five-class classification. High sensitivity (89.00–99.00%) and specificity (89.63–99.00%) across all tasks highlighted the model’s robust ability to identify diverse EEG patterns. This approach supports healthcare professionals in diagnosing epileptic seizures accurately and promptly, improving patient outcomes and quality of life. © 2025 American Institute of Mathematical sciences. All rights reserved.

关键词： Electroencephalography

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