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2023 International Conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2023

作者： Azeroual, Otmane Nikiforova, Anastasija Sha, Kewei Berlin Germany University of Tartu Faculty of Science and Technology Institute of Computer Science Chair of Software Engineering Tartu Estonia European Open Science Cloud Task Force 'Fair Metrics and Data Quality' Brussels Belgium University of Houston Clear Lake College of Science and Engineering Houston United States

ISBN: (纸本)9798350339291

Data quality in companies is decisive and critical to the benefits their products and services can provide. However, in heterogeneous IT infrastructures where, e.g., different applications for Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), product management, manufacturing, and marketing are used, duplicates, e.g., multiple entries for the same customer or product in a database or information system, occur. There can be several reasons for this, but the result of non-unique or duplicate records is a degraded data quality. This ultimately leads to poorer, inefficient, and inaccurate data-driven decisions. For this reason, in this paper, we develop a conceptual data governance framework for effective and efficient management of duplicate data, and improvement of data accuracy and consistency in large data ecosystems. We present methods and recommendations for companies to deal with duplicate data in a meaningful way. © 2023 IEEE.

关键词： Ecosystems

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A Comprehensive Experimentation on Security-Conserving Storage Verifications and Data Dynamics Over Cloud Storage Environment 1

A Comprehensive Experimentation on Security-Conserving Stora...

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1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Abhilash, K. Sravan Vasudevan, M. Jayaprakash, M. Anitha, C. Karthikeyan, Kavitha Maisa, Soujanya CMR Engineering College Department of Electronics and Communication Engineering Medchal Kandlakoya India Andhrapradesh Madanapalle India R.M.K. Engineering College Department of Information Technology Tamil Nadu Kavaraipettai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Civil Engineering Tamil Nadu Chennai India Medchal Kandlakoya India

ISBN: (纸本)9798350364590

As an IT firm moves into the future, cloud computing is quickly becoming the standard. The data centre serves as a centralized server from which data may be accessed. Businesses are quickly moving their operations to the cloud in order to take use of the most efficient resources. Both data dynamic operation and public verifiability at a single instance are supported by the current technologies. Instead of involving the client in auditing to detect cloud-stored data, the suggested approach takes into account the duty of a third-party auditor to guarantee the data's integrity. Data operations for inserting, deleting, and modifying data on the block are provided by data dynamics. A novel cloud data enrichment technique dubbed Advanced Cipher based Data Dynamics and Storage Model (ACDDSM) is presented to avoid the flaws in the previous system. To clearly assess the suggested model's performance, it is cross-validated with the traditional Rivest, Shamir, and Adleman (RSA) method. One possible solution to this issue is a cloud server. Managing storage on a grand scale is within its capabilities. But since it is a third-party service, data security is a concern. Strong encryption methods can be used to safeguard data from the cloud server. We must first distinguish between sensitive and non-sensitive data in order to implement appropriate data security measures. Prioritizing sensitive data above potential dangers is essential. Finding valuable information within a reasonable amount of time to compute is the main topic of this research. © 2024 IEEE.

关键词： Data integrity

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Efficient Optimization of Energy Consumption at Home through Machine Learning

Efficient Optimization of Energy Consumption at Home through...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Sharada Prasad N Lakshmi Shree M S Manjunatha S Vipul Vekariya Ganga D Benal Harshal Patil Department of Electrical and Electronics Engineering RNS Institute of Technology Bangalore Karnataka India Department of Computer Science and Engineering Cambridge Institute of Technology Bangalore Karnataka India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Waghodia Gujarat India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India

In recent years, the imperative need for efficient energy utilization in residential buildings has become increasingly evident due to the unwarranted wastage of electrical energy. This has spurred significant interest in optimizing energy consumption while maintaining user comfort. Accurate energy prediction is a crucial component of this optimization. This study focus on minimizing energy consumption by accurately predicting it through advanced Machine Learning (ML) models and optimization techniques. The study begins with the collection of energy data from a reliable source, Kaggle, comprising 29 features. To streamline the dataset, unnecessary features are discarded, and data normalization is performed to ensure consistency and reliability. Subsequently, ML models, specifically Long Short-Term Memory (LSTM), are designed and optimized through the Genetic Algorithm (GA) and Grey Wolf Optimization (GWO) to fine-tune hyperparameters. The prediction results are evaluated using error values to assess the accuracy and reliability of the models. Notably, our findings indicate that the GWO-LSTM model outperforms the others, exhibiting minimal errors and therefore showcasing its superior predictive capabilities. Accurate energy prediction not only serves as a valuable tool in its own right but also plays a pivotal role in enabling proactive energy management. By accurately forecasting future energy requirements, it becomes possible to optimize energy consumption further. Such precision paves the way for intelligent scheduling of home appliances, which, in turn, leads to significant reductions in energy consumption.

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Fabricating Tasks of a Helpful Customer Association Carrying out Energy-Proficient Yield on a Microgrid Supporting an Extensive Variety of Burden

Fabricating Tasks of a Helpful Customer Association Carrying...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Kukreti, Sanjeev AL-Attabi, Kassem Dixit, Krishna Kant Upadhyay, Shrikant Ramya, R. Nijhawan, Ginni Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical Engineering GLA University Mathura India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India Department of Electronics and Communication Engineering Saveetha school of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Lovely Professional University Phagwara India

ISBN: (纸本)9798350355338

Manufacturers are seeking strategic and operational modifications to improve energy efficiency in manufacturing, considering mounting concerns about global warming and rising fuel prices. However, on-site energy generation's highly unpredictable and intermittent nature makes it difficult for businesses to plan. The use of real-time Demand Side Management (DSM) to reduce carbon emissions is becoming increasingly popular. In DSM, an on-site controller uses a smart algorithm to make instantaneous decisions on sequencing the line's electrical loads. In this research, we present a framework for an intelligent algorithm in a job shop manufacturing facility with the purpose of minimizing overall electricity costs while controlling total order delays. One example of on-site renewable electricity generation is a factory with a windmill. This paper describes an intelligent system that uses a Recurrent Neural Network to predict the output of wind turbines for upcoming time periods. The results of the simulations show that the proposed online algorithm framework for job shop scheduling is an effective DSM technique with superior performance compared to standard methods and results that are on par with those of the benchmark solutions. © 2024 IEEE.

关键词： Windmill

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Structural plasticity-based hydrogel optical Willshaw model for one-shot on-the-fly edge learning

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InfoMat 2023年第4期5卷 48-59页

作者： Dingchen Wang Dingyao Liu Yinan Lin Anran Yuan Woyu Zhang Yaping Zhao Shaocong Wang Xi Chen Hegan Chen Yi Zhang Yang Jiang Shuhui Shi Kam Chi Loong Jia Chen Songrui Wei Qing Wang Hongyu Yu Renjing Xu Dashan Shang Han Zhang Shiming Zhang Zhongrui Wang Department of Electrical and Electronic Engineering The University of Hong KongHong Kongthe People's Republic of China ACCESS-AI Chip Center for Emerging Smart Systems InnoHK CentersHong Kong Science ParkHong Kongthe People's Republic of China School of Computer Science and Engineering Faculty of Innovation EngineeringMacao University of Science and TechnologyMacaothe People's Republic of China Key Laboratory of Microelectronics Devices and Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijingthe People's Republic of China Collaborative Innovation Center for Optoelectronic Science Technology International Collaborative Laboratory of 2D Materials for OptoelectronicsScience and Technology of Ministry of EducationInstitute of Microscale OptoelectronicsShenzhen UniversityShenzhenthe People's Republic of China School of Microelectronics Southern University of Science and TechnologyShenzhenthe People's Republic of China Microelectronics Thrust Function Hub of the Hong Kong University of Science and Technology(Guangzhou)Guagndongthe People's Republic of China

Autonomous one-shot on-the-fly learning copes with the high privacy,small dataset,and in-stream data at the *** such learning on digital hardware suffers from the well-known von-Neumann and scaling *** optical neural networks featuring large parallelism,low latency,and high efficiency offer a promising ***,ex-situ training of conventional optical networks,where optical path configuration and deep learning model optimization are separated,incurs hardware,energy and time overheads,and defeats the advantages in edge ***,we introduced a bio-inspired material-algorithm co-design to construct a hydrogel-based optical Willshaw model(HOWM),manifesting Hebbian-rule-based structural plasticity for simultaneous optical path configuration and deep learning model optimization thanks to the underlying opto-chemical *** first employed the HOWM as an all optical in-sensor AI processor for one-shot pattern classification,association and *** then leveraged HOWM to function as a ternary content addressable memory(TCAM)of an optical memory augmented neural network(MANN)for one-shot learning the Omniglot *** HOWM empowered one-shot on-the-fly edge learning leads to 1000boost of energy efficiency and 10boost of speed,which paves the way for the next-generation autonomous,efficient,and affordable smart edge systems.

关键词： associative memory hydrogel one-shot learning optical neural network structural plasticity Willshaw model

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Photovoltaic Panel Quality Evaluation Based on Power Generated Involving Copper Indium Gallium Selenide Solar Cells

Photovoltaic Panel Quality Evaluation Based on Power Generat...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Srivastava, Yashi Almusawi, Muntather Badhoutiya, Arti Dhasmana, Gulshan Jain, Alok Aishwarya, Bk Boob, Nandini Shirish Srivastava, Arun Pratap Lloyd Institute of Engineering & Technology Greater Noida India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical Engineering GLA University Mathura India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India

ISBN: (纸本)9798350355338

When it comes to energy-efficient manufacturing, this report tackles some of the most important concerns raised by companies and academic institutions. Both production management and the integration of information and communication technology systems with respect to energy efficiency performance criteria are encompassed in the research question (RQ). The requirements of the corporate sector were considered alongside solutions that had previously been detailed in the literature. Findings show that methods exist for keeping tabs on, managing, and improving production processes. Organizational, facility, and procedure-level energy management in manufacturing is an area where these methods are seldom effective. A gap persists in industrial organizations between possible solutions and their actual execution. To close the gap between theory and practise, research into effective and efficient energy management in manufacturing is necessary. Managers in the manufacturing sector may use this data to make more informed, faster business decisions based on real energy usage. Key performance indicators (KPIs) for energy efficiency should be defined and specified in a way that allows for quantitative measurement at the plant level. Seeing how energy efficiency stacks up against other metrics is essential for decision-makers looking at production performance areas from multiple angles. © 2024 IEEE.

关键词： Quality control

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An Efficient Distributed Framework for Secured Multimedia Content using Cloud Storage 2

An Efficient Distributed Framework for Secured Multimedia Co...

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2nd International Conference on computer Technologies, ICCTech 2023

作者： Faniband, Yunus Parvej Ishak, Iskandar Sidi, Fatimah Jabar, Marzanah A. Yap, Deloria Shaniska Razin C.H. Center for Communication & IT Research Research Institute King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia UPM Serdang Selangor 43400 Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Putra Malaysia UPM Serdang Selangor 43400 Malaysia

ISBN: (纸本)9781665455824

Applications that support multimedia data such as medical pictures and videos, video broadcasting, and secure video conferencing consume vast quantities of information that require large numbers of servers to provide services for the many client users who access them. Cloud-based sensitive data becomes increasingly important for the respective users to be kept confidential without infringing confidentiality, integrity, and availability. We propose a framework based on the OpenStack cloud-computing platform for multimedia data security. A prototype framework with a helper tool EncSwift is implemented, which allows stringent data access control and applying the data-at-rest Over-Encryption approach. The implementation is evaluated for computation of cryptography operations, cost of communications, and update policy consequences for the framework. With OpenStack Swift’s open source and modular design, our multimedia data management and management framework is well suited to protecting and managing clouds’ multimedia with low computational costs as shown in the experimental results. © 2023 IEEE.

关键词： Cryptography

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AI-Driven Strategic Foresight: Anticipating Future Trends and Modelling Business Strategies

AI-Driven Strategic Foresight: Anticipating Future Trends an...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Qader Ismail Alnajem, Ahmed Abdel Ibrahim, Amer Abdulrahman, Mayasa M. Abdulrazak Al-Kubaisi, Abdulrazak A. Tawfeq, Jamal Fadhil Khishe, Mohammad Saeed, Ali Baghdad College of Economic Sciences Business Administration Department Iraq College of Information Technology Department of Computer Science and Software Engineering United Arab Emirates College of Engineering Computer Engineering Department University of Baghdad Baghdad Iraq Medical Technical College Department of Medical Instrumentation Technical Engineering Al-Farahidi University Baghdad00965 Iraq Yuan Ze University Innovation Center for Artificial Intelligence Applications Taiwan Jadara University Jadara Research Center Jordan Northern Technical University Computer Center Ninevah Iraq

ISBN: (纸本)9798350369106

In a generation characterized with the aid of speedy technological advancements, socio-monetary shifts, and environmental challenges, groups need to undertake proactive strategies to stay aggressive and resilient. Strategic foresight, which entails expecting destiny tendencies and modeling enterprise techniques, accordingly, emerges as a vital device in navigating this uncertainty. The trouble this have a look at addresses is the growing trouble groups face in predicting and making ready for destiny disruptions the use of conventional forecasting techniques. The number one goals of this have a look at are to discover the important thing methodologies and gear of strategic foresight, display how those techniques may be implemented to assume destiny tendencies, and offer insights on integrating foresight into enterprise approach formulation. This has a look at contributes to the present frame of information with the aid of using providing a complete evaluation of strategic foresight techniques, which includes environmental scanning, situation planning, the Delphi method, fashion evaluation, SWOT and PESTLE evaluation, and era street mapping. Research findings show that forward-looking strategies provide an organization with the ability to identify potential risks and opportunities, develop adaptive strategies, and achieve long-term growth Specifically, strategic organizations using anticipation reports a 30% increase in the ability to anticipate market fluctuations, a 25% improvement in strategic haste and a 20% reduction in unexpected disruptions, of consumer goods etc. Case studies of various industries demonstrate the practical application and usefulness of forecasting in real-world situations The study concludes that incorporating forward-looking strategies into business planning does not that it not only prepares organizations for future challenges but empowers them to shape their future and ensure sustainable competitive advantage. © 2024 IEEE.

关键词： Competition

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SkinMarkNet: an automated approach for prediction of monkeyPox using image data augmentation with deep ensemble learning models

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Multimedia Tools and Applications 2025年第18期84卷 20177-20193页

作者： Akram, Aqsa Jamjoom, Arwa A. Innab, Nisreen Almujally, Nouf Abdullah Umer, Muhammad Alsubai, Shtwai Fimiani, Gianluca Bahawalpur Pakistan Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Riyadh Diriyah13713 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy

Monkeypox, a rare but potentially fatal viral disease, poses a significant public health challenge due to its potential for outbreaks and complications. Detecting monkeypox lesions early and accurately is vital to effectively manage and control the disease. This study introduces a novel method for classifying monkeypox lesions, employing data augmentation methods and a framework based on an ensemble of three transfer learning models called "SkinMarkNet". The dataset used in this research consists of skin lesion images collected from the Kaggle data repository, encompassing diverse demographics and lesion characteristics. This research uses image data augmentation techniques to tackle the scarcity of annotated data. This augmentation enriches the training dataset, thereby improving the model’s ability to perform effectively. Moreover, the novelty of this research work lies in the usage of three popular transfer learning models(Inception, Xception, and ResNet) for feature extraction and ensemble learning. The SkinMarkNet achieves promising results showing an accuracy of 90.615% for monkeypox lesion classification, outperforming traditional machine learning and deep learning methods utilized in recent research works. In addition, thorough comparative analysis is done with machine learning models and contemporary approaches to validate the efficacy of the proposed method. Overall, the findings underscore the potential of leveraging advanced deep learning architectures and data augmentation strategies for improving monkeypox lesion classification, thereby facilitating early diagnosis and intervention in clinical settings and public health surveillance efforts. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Architectural analysis of a prototype of smart class

Architectural analysis of a prototype of smart class

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Srivastava, Yashi Adnan, Myasar Mundher Badhoutiya, Arti Nainwal, Ankita Singh, Navdeep Aswini, Kilaru Shrivastava, Anurag Lloyd Institute of Engineering & Technology Greater Noida India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical Engineering GLA University Mathura India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad Dundigal India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India

ISBN: (纸本)9798350355338

Recent developments in wireless technology have accelerated the emergence of intelligent systems in daily life. One difficulty faced by teachers is integrating curriculum material in pertinent contexts. Context-oriented environments, such as smart homes and smart cities, are made possible by the concept of networking embedded computers to carry out daily chores. Automation of the electrical appliances in a room is totally feasible owing to recent technical advancements in areas like IoT (Internet of Things). The potential of IoT is enormous and unbounded in countless fields. The system described in this paper would automate classrooms by handling and regulating electrical equipment including projectors, fans and lights. The major goal of this paper is to create a smart classroom system that automates the electrical appliances while putting an emphasis on energy efficiency. © 2024 IEEE.

关键词： Smart homes

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