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Intelligent Operational Decision Support System for Sugar Factory Resource Efficiency 5

Intelligent Operational Decision Support System for Sugar Fa...

引用

5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Lutska, Nataliia Zaiets, Nataliia Vlasenko, Lidiia Junge, Stefan National University of Food Technologies Dept. of Automation and Computer Technologies of Control Systems Kyiv Ukraine National University of Life and Environmental Sciences of Ukraine Dept. of Automation and Robotic Systems Kyiv Ukraine State University of Trade and Economics Dept. of Software Engineering and Cyber Security Kyiv Ukraine Berlin University of Applied Sciences Dept. of Packaging Technology Berlin Germany

ISBN: (纸本)9798350359787

With the economic crisis that is looming over the world due to changes in the energy sector and a reduction in the supply of certain types of products, energy and resource efficiency issues are becoming more relevant for the food industry. Also important is the preservation of the environment and the implementation of Cleaner Production trends. Most enterprises follow the concept of Industry 4.0 and increase efficiency through the implementation of intelligent methods, in particular machine learning. The paper proposes a neural network model of sugar production. The model makes it possible to predict the main estimates of resource efficiency used to correct the technological modes of production by the operator-technologist. The structure of the operational decision support system has been developed and is used by personnel to model and predict changes in resource efficiency estimates and KPIs calculated on their basis. Its feature is the integration of methods for achieving resource efficiency, improving environmental performance and ensuring KPIs. To do this, at the initial stage, an analysis of the main flows that affect resource efficiency and an assessment of their importance was carried out. Ensuring Production Performance is confirmed by the simulation and the adequacy of the created neural network accuracy of more than 95%. © 2023 IEEE.

关键词： Decision support systems

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Vertical validation: evaluating implicit generative models for graphs on thin support regions 24

Vertical validation: evaluating implicit generative models f...

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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Mai Elkady Thu Bui Bruno Ribeiro David I. Inouye Computer Science Dept. Purdue University West Lafayette Indiana Electrical and Computer Engineering Dept. Purdue University West Lafayette Indiana

There has been a growing excitement that implicit graph generative models could be used to design or discover new molecules for medicine or material design. Because these molecules have not been discovered, they naturally lie in unexplored or scarcely supported regions of the distribution of known molecules. However, prior evaluation methods for implicit graph generative models have focused on validating statistics computed from the thick support (e.g., mean and variance of a graph property). Therefore, there is a mismatch between the goal of generating novel graphs and the evaluation methods. To address this evaluation gap, we design a novel evaluation method called Vertical Validation (VV) that systematically creates thin support regions during the train-test splitting procedure and then reweights generated samples so that they can be compared to the held-out test data. This procedure can be seen as a generalization of the standard train-test procedure except that the splits are dependent on sample features. We demonstrate that our method can be used to perform model selection if performance on thin support regions is the desired goal. As a side benefit, we also show that our approach can better detect overfitting as exemplified by memorization.

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Digital Twin of Industrial Robots Over the Cloud: Framework, Design and Analysis

Digital Twin of Industrial Robots Over the Cloud: Framework,...

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International Conference on Robotics and Automation sciences (ICRAS)

作者： Hari Ganesan Aaryan M Sandeep Aakash Negi Yuktha M Ashok Kumar Patil Dept. of Computer Science Engineering PES University Bangalore India Dept. of Electronics and Communication Engineering PES University Bangalore India

ISBN: (数字)9798350352504

ISBN: (纸本)9798350352511

This paper presents a novel approach for creating real-time, secure and interactable Digital Twins of industrial robots. This approach diverges from previous approaches that relied on Digital Twin Definition Language (DTDL). The proposed method uses Robotics Operating Systems (ROS) and the Unity Game Engine to build the Digital Twins. The prototype built to showcase this approach comprises 4 sub-systems namely Data Acquisition sub-system, Cloud sub-system, Digital Client sub-system, and Remote-Control sub-system. The methodology focuses on real-time data synchronization using cloud-based communication services like loT servers. There are also multiple security measures employed to secure the data- like passing data through proxy and end-to-end encryption of data. The prototype made using Niryo Ned 2 Robot and a Raspberry Pi 3b+ showcases accurate physical-Digital Twin and effective remote control. Key findings highlight the system's ability to overcome latency, security and data accuracy, which in turn helps improve human-robot collaboration and interactions. This research contributes to the Digital Twin field by providing a modular and scalable framework that can be adapted for various robots and systems. This paper not only provides the general idea for implementation and problems faced during development but also suggests pathways for future work, like - optimization of message rates, integration of machine learning for maintenance and other purposes.

关键词： Accuracy Service robots Prototypes Industrial robots Real-time systems Digital twins Security

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Towards Robust mmWave-based Human Activity Recognition using Large Simulated Dataset for Model Pretraining

Towards Robust mmWave-based Human Activity Recognition using...

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IEEE International Conference on Big Data

作者： Vinay Joshi Shengkai Xu Qiming Cao Yi Zhu Pu Wang Hongfei Xue Dept. of Computer Science UNC Charlotte Charlotte USA Electrical and Computer Engineering Purdue University West Lafayette USA Dept. of Computer Science Wayne State University Detroit USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Human activity recognition (HAR) is crucial for real-world applications such as healthcare, surveillance, and smart homes. Among sensing technologies, millimeter wave (mmWave) sensors stand out due to their contactless nature, high sensitivity, and ability to operate in low-light environments while preserving privacy. However, the scarcity of mmWave sensing data limits the generalizability of mmWave-based HAR systems. To address this, we propose mmAP, a data augmentation and pretraining framework that synthesizes a large mmWave dataset using human mesh data, followed by pretraining a robust and general mmWave heatmap encoder using a multi-modal masked autoencoder framework using the synthesized data. We enhance the model’s robustness with heatmap-specific data perturbations and perform task-specific fine-tuning on a small real-world dataset. The experiment results over the baseline demonstrate the effectiveness of the proposed mmAP framework.

关键词： Heating systems Perturbation methods Surveillance Autoencoders Smart homes Data augmentation Data models Robustness Human activity recognition Millimeter wave communication

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AIM-HKR: AI-Driven Molecular Retrosynthesis Using Heterogeneous Knowledge Representations

AIM-HKR: AI-Driven Molecular Retrosynthesis Using Heterogene...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Bomgni, Alain Bertrand Ceskoutsé, Ribot Fleury Téné Njingang, Kevin Jordan Njike Bouetou, Thomas Bouetou Gadhamshetty, Venkataramana Etienne Gnimpieba, Z. Oglala Lakota College KyleSD United States University of Yaounde 1 Ecole Nationale Supérieure Polytechnique Yaounde Cameroon University of Dschang Dept. of Computer Science Dschang Cameroon South Dakota School of Mines and Technology Dept. of Civil and Environmental Engineering Rapid CitySD United States University of South Dakota Dept. of Biomedical Engineering VermillionSD United States

ISBN: (纸本)9798350386226

The synthesis of small molecules is a crucial task across multiple scientific domains, including drug discovery, materials science, and sustainable chemistry. As AI and Machine Learning (ML) continue to advance, these technologies offer transformative potential in molecular synthesis, enhancing efficiency and expanding synthetic possibilities. In particular, small molecule synthesis has profound implications for creating novel therapeutic agents, high-performance materials, and greener chemical processes, but a major challenge remains in designing efficient synthetic routes for target molecules. Retrosynthesis, the process of mapping out synthetic pathways by working backwards from a target molecule, represents a vital step;however, traditional retrosynthesis methods often struggle to predict complex or novel transformations. To address this limitation, we introduce AIM-HKR: AI-Driven Molecular Retrosynthesis Using Heterogeneous Knowledge Representations, a model that leverages graph neural network techniques to enhance retrosynthetic predictions. AIM-HKR integrates information from heterogeneous knowledge graphs, capturing the intricate relationships and analogical reasoning required for retrosynthesis. This model generates type-specific embeddings that reflect both network topology and semantic connections across different entity types, such as molecules, reactions, and functional groups. AIM-HKR's unique capacity to leverage heterogeneous graphs allows it to propose synthetic pathways that extend beyond established precedents, enabling predictions of chemically feasible but previously uncharted transformations. We believe AIM-HKR has the potential to significantly advance molecular synthesis through AI-driven retrosynthesis, establishing a new paradigm in AI-assisted chemistry. © 2024 IEEE.

关键词： Graph neural networks

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Workload Prediction and its Impact on QoS of Microservices Architecture Based Applications

Workload Prediction and its Impact on QoS of Microservices A...

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Progressive Innovations in Intelligent Systems and Data science (ICPIDS), International Conference on

作者： Neha Kaushik Harish Kumar Vinay Raj Dept. of Computer Engineering JC Bose University of Science & Technology Faridabad India Dept. of Computer Applications NIT Tiruchirappalli Tiruchirappalli India

ISBN: (数字)9798331534691

ISBN: (纸本)9798331534707

Microservices is an architectural style used for developing modular and scalable applications. The distributed and de centralised architectural property of microservices applications makes it difficult to manage unpredictable workload variations and sometimes lead to degraded QoS of the applications. Accurate workload prediction is important for efficient resource allocation and maintaining high QoS of the MSA based applications. This paper proposes an LSTM-RNN based model to predict the workload of each microservice instance and after that an analysis is done to identify the effects of proactive workload management on the QoS metrics such as response time, throughput, and CPU utilisation. The results show that the workload prediction accuracy significantly improves the latency, resource utilisation and performance of microservices applications.

关键词： Technological innovation Accuracy System performance Microservice architectures Quality of service Predictive models Throughput Time measurement Resource management Time factors

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Visual Exploratory Data Analysis of the Covid-19 Pandemic in Nigeria: Two Years after the Outbreak 5

Visual Exploratory Data Analysis of the Covid-19 Pandemic in...

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5th International Conference on Information Technology for Education and Development, ITED 2022

作者： Orji, Ugochukwu. E. Ukwandu, Elochukwu Obianuju, Ezugwu. A. Ezema, Modesta. E. Ugwuishiwu, Chikaodili. H. Egbugha, Malachi. C. University of Nigeria Dept. of Computer Science Nsukka Enugu Nigeria Cardiff School of Technologies Cardiff Metropolitan University Dept. of Applied Computing and Engineering United Kingdom Ahmadu Bello University Dept of Computer Engineering Zaria Nigeria

ISBN: (纸本)9781509064229

The outbreak of the coronavirus disease in Nigeria and all over the world in 2019/2020 caused havoc on the world's economy and put a strain on global healthcare facilities and personnel. It also threw up many opportunities to improve processes using artificial intelligence techniques like big data analytics and business intelligence. The need to speedily make decisions that could have far-reaching effects is prompting the boom in data analytics which is achieved via exploratory data analysis (EDA) to see trends, patterns, and relationships in the data. Today, big data analytics is revolutionizing processes and helping improve productivity and decision-making capabilities in all aspects of life. The large amount of heterogeneous and, in most cases, opaque data now available has made it possible for researchers and businesses of all sizes to effectively deploy data analytics to gain action-oriented insights into various problems in real time. In this paper, we deployed Microsoft Excel and Python to perform EDA of the covid-19 pandemic data in Nigeria and presented our results via visualizations and a dashboard using Tableau. The dataset is from the Nigeria Centre for Disease Control (NCDC) recorded between February 28th, 2020, and July 19th, 2022. This paper aims to follow the data and visually show the trends over the past 2 years and also show the powerful capabilities of these data analytics tools and techniques. Furthermore, our findings contribute to the current literature on Covid-19 research by showcasing how the virus has progressed in Nigeria over time and the insights thus far. © 2022 IEEE.

关键词： Data Analytics

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Predicting the Likelihood of a Recurrent Febrile Seizure Using Thermographic Images and Deep Learning with Rules Generation

Predicting the Likelihood of a Recurrent Febrile Seizure Usi...

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AIML-Applications for engineering & Technology (ICAET), International Conference on

作者： Pratima Patil Deepa S Deshpande Dept. of Computer Science and Engineering Jawaharlal Nehru Engineering College MGM University Chhatrapati Sambhajinagar India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Recurrent febrile seizures are a common concern in children, often triggered by rapid changes in body temperature during fever episodes. For rapid action and care, early and accurate seizure recurrence prediction is crucial. Limited techniques were developed for predicting the likelihood of a Recurrent Febrile Seizure using a Thermographic image. Several drawbacks were considered for recurrent febrile seizure prediction such as lower performance, high time consumption, and error rate. Hence, the proposed technique is introduced to provide an efficient Deep Learning (DL)-based technique. Initially, the data is gathered from datasets, and the Intra-Attentional Convolutional ResNet (InAtt-CRNet) with the Yolov5-Face model detects the face and reduces the processing time by tacking the face with a bounding box. The patient's forehead or nose temperature is monitored using the Region of Interest (ROI) finding approach following face detection and tracking in each frame. Then, the temperature is predicted using the Enhanced Convolutional Capsule Network (EC2Net). The body temperature, age, duration of fever, and family history are used to construct the clinical decision rule. The Twin-directional Gated Long Short-term memory (TdG-LSTM) is used to predict the likelihood of a subsequent febrile seizure. The proposed technique achieves 98.16% accuracy, 98.45% precision, 0.12 RMSE, and 29 seconds as processing time.

关键词： Deep learning YOLO Temperature sensors Accuracy Forehead Nose Face detection History Long short term memory Faces

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Assessment of IT Governance Maturity Level for Yemeni Telecom Sector Using COBIT 2019 Framework 1

Assessment of IT Governance Maturity Level for Yemeni Teleco...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Ghallab, Abdullatif Alswari, Tahani Alhazbi, Saleh Al-Fuhaidi, Belal Hadwan, Mohammed Al-Fahaidy, Farouk Al-Dowail, Mohammed Maqtary, Naseebah Department of Computer Science University of Science and Technology Sana’a Yemen Department of IT Open University Malaysia Sana’a Yemen Dept. of Computer Science & Eng. Qatar University Doha Qatar Department of IT Qassim University Buraydah Saudi Arabia Department of Electrical Engineering Ibb University Ibb Yemen Department of Information Systems University of Science and Technology Sana’a Yemen

ISBN: (纸本)9798331533557

Information technology (IT) plays a crucial role in business operations most organizations rely on IT resources for gaining competitive advantages and creating innovative and continuous values. This increased the importance of enterprise IT governance (EGIT) and developed the recent version of the control objectives for information and related technology (COBIT), COBIT 2019 framework. It identifies the seven components of an effective EGIT system: processes, organizational structures, information flows and items, people, skills and competencies, policies and procedures, culture, ethics and behavior, services, infrastructure, and applications. The EGIT maturity level (ITGML) assessment helps to align the EGIT with strategic objectives. This study aims to assess the ITGML of the Yemeni telecom sector using COBIT 2019 framework. A quantitative method was applied, with a 73.6% response rate (148 out of 201 questionnaires collected). The study revealed that the current ITGML is 3.48, reflecting a defined maturity level. The findings suggested that IT governance practices based on COBIT 2019 are well documented and standardized within the Yemeni telecom sector. © 2024 IEEE.

关键词： Telecommunication industry

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Advanced Image Ciphering: 1-D Group Cellular Automata and S-Box Strategies

Advanced Image Ciphering: 1-D Group Cellular Automata and S-...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Biswarup Yogi Satyabrata Roy Umashankar Rawat Shweta Redkar Dept. of Computational Sciences Brainware University Kolkata India Dept. of Computer Science and Engineering Manipal University Jaipur Rajasthan India Dept. of Data Science and Engineering Manipal University Jaipur Rajasthan India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

This work explores advanced methodologies for encrypting images by utilizing one-dimensional Group Cellular Automata (1-D GCA) and S-Box techniques, with a focus on their pivotal role in ensuring the security of digital images. The proposed approach amalgamates Cellular Automata and S-Box mechanisms within a framework of symmetric-key cryptography to augment the strength of encryption. Through the generation of pseudorandom sequences using 1-D GCA and the utilization of S-Box for substitution, the method accomplishes efficient pixel diffusion and confusion. The assessment criteria demonstrate noteworthy enhancements: Number of Pixels Change Rate (NPCR) at 99.9864%, Unified Average Changing Intensity (UACI) at 33.6130%, Peak Signal-to-Noise Ratio (PSNR) at 9.2336 dB, and Mean Squared Error (MSE) at 0.1193. These findings underscore the robustness of the algorithm against statistical and differential attacks. The study justifies the promise of integrating GCA and S-Box strategies to reinforce cryptographic systems, proposing future avenues for enhancing computational efficiency and broadening applicability across diverse digital contexts.

关键词： PSNR Digital images Instruments Automata Information processing Robustness Encryption S Box Resilience Random sequences

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