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How does artificial intelligence and accountability optimises energy management and cost reduction in Nigeria's manufacturing firms?

How does artificial intelligence and accountability optimise...

引用

2024 international conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Akinadewo, Israel S. Dagunduro, Muyiwa E. Akinadewo, Jeremiah O. Oluwagbade, Isaiah O. Akpan, James U. Afe Babalola University Ado-Ekiti Department of Accounting Ekiti State Nigeria

ISBN: (纸本)9798350358155

Manufacturing firms face challenges in aligning technological interventions with effective energy management. The study addresses gaps in understanding the nuanced relationships between AI components and energy outcomes and the sustainable energy practices thereof. The study, therefore, examined the impact of Data Analytics (DA), automation (AUT), and the Internet of Things (IoT), with a focus on the moderating influence of Accountability (ACC) in selected Nigerian manufacturing firms on the optimization of energy management and cost reduction. Utilizing a Likert scale questionnaire, data from 84 Accountants from selected manufacturing firms were gathered. Statistical analyses and correlations were applied to interpret the relationships among variables. Findings reveal a strong positive correlation (r = 0.940) between Data Analytics and Energy Management and Cost Reduction (EMCR). Accountability (ACC) significantly moderates the relationship between Data Analytics and EMCR. There is also a positive impact of automation (AUT) on EMCR (0.235, p © 2024 ieee.

关键词： accountability artificial intelligence automation data analytics energy management and cost reduction

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A Survey of Screen Shooting Resilient Image Watermarking Algorithms

A Survey of Screen Shooting Resilient Image Watermarking Alg...

引用

2023 international conference on Culture-Oriented science and Technology, CoST 2023

作者： Guan, Hu Huang, Ying Zhang, Shuwu Liu, Jie Institute of Automation Chinese Academy of Sciences Key Laboratory of Digital Rights Services Beijing China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Institute of Automation Chinese Academy of Sciences Beijing Digital Content Engineering Research Center Beijing China

ISBN: (纸本)9798350357998

Image watermarking is an important copyright protection scheme which has been developed for over 30 years. Screen Shooting Resilient Image Watermarking (SSRIW) is a technique that after the watermarked image is displayed on a computer or projection screen, and be captured from this screen by a camera, from which the watermark can still be accurately extracted. That has very broad and practical application value. Several researchers have conducted some academic research and made a series of progress in this field, but as far as we know, there is no systematic analysis of SSRIW in literatures. This paper focuses on a comprehensive review and analysis of SSRIW, including algorithms based on traditional methods and deep learning, points out the main difficulties in SSRIW research, and puts forward our suggestions for the future development and the research focus which can be continued of this technique. © 2023 ieee.

关键词： Image watermarking

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Efficient In-Training Adaptive Compound Loss Function Contribution Control for Medical Image Segmentation 46

Efficient In-Training Adaptive Compound Loss Function Contri...

引用

46th Annual international conference of the ieee engineering in Medicine and Biology Society, EMBC 2024

作者： Al-Battal, Abdullah F. Duong, Soan T. M. Nguyen, Chanh D. Tr. Truong, Steven Q. H. Phan, Chien Nguyen, Truong Q. An, Cheolhong Uc San Diego Electrical and Computer Engineering Department La Jolla CA United States King Fahd University of Petroleum and Minerals Electrical Engineering Department Saudi Arabia Vinbrain Jsc Hanoi Viet Nam Le Quy Don Technical University Department of Computer Science Hanoi Viet Nam VinUniversity College of Engineering and Computer Science Hanoi Viet Nam University Medical Center Diagnostic Imaging Department Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350371499

Image segmentation plays a crucial role in many clinical applications, including disease diagnosis and monitoring. Current state-of-the-art segmentation approaches use deep neural networks that are trained on their target tasks by minimizing a loss function. Class imbalance is one of the major challenges that these networks face, where the target object is significantly underrepresented. Compound loss functions that incorporate the binary cross-entropy (BCE) and Dice loss are among the most prominent approaches to address this issue. However, determining the contribution of each individual loss to the overall compound loss function is a tedious process. It requires hyperparameter fine-tuning and multiple iterations of training, which is highly inefficient in terms of time and energy consumption. To address this issue, we propose an approach that adaptively controls the contribution of each of these individual loss functions during training. This eliminates the need for multiple fine-tuning iterations to achieve the desired precision and recall for segmentation models. © 2024 ieee.

关键词： Image segmentation

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Digit Recognition and Separation of Overlapped Digits using Deep CapsNet 2

Digit Recognition and Separation of Overlapped Digits using ...

引用

2nd international conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Moudgil, Aditi Sareen, Bhavna Ghantasala, G S Pradeep Rajesh Sharma, R. Sungheetha, Akey Soubache, I.D. Vidyullatha, Pellakuri Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Alliance University Department of Computer Science and Engineering Bengaluru India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Pondicherry University Department of Eee Pondicherry India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798350364699

Separation of overlapping digits is observed as a very difficult task when done with human eye, but subsequently it becomes easy when computer vision is used for the same task. The authors have constructed a capsule network which recognizes and separates the overlapped digits. Small sized convolutional kernels were used for the first step of feature extraction which provides grained features this reducing the training parameters. The same process could also possibly be applied for overlapping alphabets and characters also. The process was further followed by insertion of capsule layer into the model which resulted in 93.08% accuracy. Finally the overlapped digits were separated and the image of each digit was reconstructed. © 2024 ieee.

关键词： Deep neural networks

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Comparative Analysis of ML Algorithms for Forecasting Time Series Stock Returns 4

Comparative Analysis of ML Algorithms for Forecasting Time S...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Bondili, Bala Karthikeya Singh Bolagani, Sai Pavan Thatikonda, Rakshith Vnr Vignana Jyothi Institute of Engineering and Technology Computer Science and Engineering Telangana India Koneru Lakshmaiah Education Foundation Computer Science and Engineering Andra Pradesh India Cmr Technical Campus Electronics and Communication Engineering Telangana India

ISBN: (纸本)9798331523923

Forecasting stock returns is a challenging task due to high data variability, noise, and the inherent unpredictability of financial markets. Traditional methods like ARIMA and regression lack scalability and fail to capture complex data patterns. Recent advancements in machine learning, including ensemble methods such as Random Forest and Boosting, have shown promise in improving predictive accuracy. However, these methods face challenges such as overfitting, computational complexity, and data dependency. This paper evaluates the performance of various machine learning algorithms and their ability to address these challenges to improve forecast reliability. By using techniques like dimensionality reduction and hyperparameter tuning, the study identifies strategies to enhance prediction accuracy while reducing computational complexity and costs. © 2025 ieee.

关键词： Dimensionality reduction

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Review of Heave Compensation Systems: Design and Control Strategies 11

Review of Heave Compensation Systems: Design and Control Str...

引用

11th ieee international conference on computer science and Network Technology, ICCSNT 2023

作者： Xu, Jianan Yi, Bailin Zhan, Yong College of Mechanical & Electrical Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9798350311594

Heave compensator has significant application value in the field of offshore engineering, involving offshore lifting operation, floating wind power installation, etc. In this paper, the basic characteristics of heave compensation, including passive heave compensation (PHC), active heave compensation (AHC), and active/passive heave compensation (APHC), are reviewed, with an emphasis on recent years. The performance differences of the various systems are compared and analyzed, and in the final section, potential future development opportunities are proposed. © 2023 ieee.

关键词： Offshore oil well production

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Deep Learning for Fraud Detection in Bitcoin Transactions: An AI Approach

Deep Learning for Fraud Detection in Bitcoin Transactions: A...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Shravan, M. Ajitha, E. Arnone, Gioia Renu, Nekkanti Tiwari, Mohit Chouhan, Kuldeep Sr University School of Business Telangana Warangal India St. Joseph's Institute of Technology Computer Science and Engineering India Vrije Universiteit Brussel Department of Private and Economic Law Bruxelles Belgium Department of Computer Science and Engineering Duvvada India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi Delhi India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida201310 India

ISBN: (纸本)9798331508685

This research looks into how deep learning methods can be used to find fake Bitcoin transfers. It's mostly about the unique problems and chances that come with trading in cryptocurrencies and blockchain. Deep learning models are used to look at transaction trends and find oddities that could be signs of fraud. This is because Bitcoin's open structure makes it harder to use traditional methods of fraud detection. We offer a structure that connects CNNs (convolutional neural networks) and RNNs (recurrent neural networks) so that strange patterns can be found in big sets of blockchain transactions. An AI-driven method aims to improve detection accuracy while lowering false positives by taking on scammers' complicated and always-changing strategies. Experienced researchers have shown that the model is good at finding strange things, which means it can be used to safely stop theft in Bitcoin networks in real time. It looks like deep learning could make digital money safer and give us data that can be used in bigger blockchain-based money systems. © 2025 ieee.

关键词： Adversarial machine learning

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Predictive Maintenance of Power Grid Infrastructure using Long Short-Term Memory Networks

Predictive Maintenance of Power Grid Infrastructure using Lo...

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2024 international conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Mittal, Mudit Alsalami, Zaid Kumar, Rakesh Boob, Nandini Shirish Verma, Vikas Sangeeta, K. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering & Applications Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Tamilnadu Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This study examines the application of Long Short-Term Memory (LSTM) networks for the prescient upkeep of a control lattice framework. Leveraging verifiable information comprising sensor readings and support records, the LSTM demonstrate precisely expects potential disappointments or debasement within the control network, empowering proactive support techniques. Through broad experimentation, the LSTM demonstrates accomplishing momentous execution with an exactness of 90%, precision of 88%, review of 92%, F1-score of 90%, and an area beneath the ROC curve (AUC) of 0.95. Comparative examination against standard methods and related works within the writing illustrates the prevalence of the LSTM-based approach in prescient support of control network infrastructure. Optimization of hyperparameters and interpretability examination advance upgrades the model's execution and encourages decision-making in control network administration. This study sheds light on the potential of machine learning strategies to revolutionize control framework upkeep, guaranteeing a more solid, strong, and economic vitality supply. © 2024 ieee.

关键词： Long short-term memory

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Design and Optimization of Sensing-Communication Integration System in Industrial automation Based on FDA and PA 25

Design and Optimization of Sensing-Communication Integration...

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25th ieee international conference on Industrial Technology, ICIT 2024

作者： Feng, Freddy Y.P. Mi, De Guan, Mingxiang Sun, Lichao Tang, Hui Liu, Chang Xiao, Pei Lu, Chen Shenzhen Institute Of Information Technology Shenzhen China College Of Computing Birmingham City University Birmingham United Kingdom School Of Engineering Lancaster University Lancaster United Kingdom Extend Robotics Limited Reading United Kingdom Institute For Communication Systems University Of Surrey Guildford United Kingdom

ISBN: (纸本)9798350340266

Industrial automation is a rapidly evolving field that aims to improve efficiency, productivity, and safety in various verticals. The integration of sensing and communication technologies has become crucial in this automation process. Re-cent advancements in Frequency Diverse Array (FDA) antennas have shown promising performance advantages over traditional phased array antennas in certain aspects, e.g., the FDA-based radar can focus its beam in both angle and range domains simultaneously. In this paper, we propose a novel architecture for sensing-communication integration in the context of industrial automation, where FDA antennas are employed as the radar sensing array, while phased array antennas are utilized for communication purposes. The integration of FDA antennas brings benefits such as more accurate object detection, improved spatial resolution, and efficient data transmission. We present a comprehensive analysis of the proposed architecture, including design considerations, system modeling, and performance evalu-ation. The results demonstrate the potential of FDA antennas in enhancing the sensing-communication integration in industrial automation, leading to improved efficiency, productivity, and safety in industrial environments. © 2024 ieee.

关键词： Integration

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The Seizure Detection Based on a Novel Electroencephalogram Segmentation 46

The Seizure Detection Based on a Novel Electroencephalogram ...

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46th Annual international conference of the ieee engineering in Medicine and Biology Society, EMBC 2024

作者： Shi, Mingxia Yang, Guan Wang, Wen Hu, Yueyang Wan, Lin Li, Qingya Tao, Xiaoyan Chinese Academy of Sciences Laboratory of Brain Atlas and Brain-Inspired Intelligence Institute of Automation Beijing China Seventh Medical Center Chinese Pla General Hospital Senior Department of Pediatrics Beijing China Beijing Forestry University College of Engineering Beijing China

ISBN: (纸本)9798350371499

Due to the non-stationary nature of electroencephalogram (EEG) signals, it is imperative to partition the EEG into quasi-stationary intervals with similar statistical characteristics. This study introduces an innovative method leveraging the relative stability of synchronous brain electrical activity for the automatic segmentation of EEG signals, eliminating the need for human intervention. The experiments illustrate that EEG signals segmented using this method demonstrate a certain level of stability across different frequency ranges and frequency power distribution. This consistency ensures the EEG signals remain quasi-stationary to a significant degree. For validation, this method was applied to seizure detection. The evaluation, utilizing EEG data from the department of pediatrics of Chinese PLA General Hospital, reveals that our method outperforms existing techniques. Notably, the approach achieved a seizure detection accuracy of 92.26%, specificity of 91.43%, and sensitivity of 92.93%. © 2024 ieee.

关键词： Electroencephalography

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