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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Sachs, Christoph Stamer, Fabian Allgeier, Jan Thrimawithana, Duleepa Neuburger, Martin Esslingen University Faculty of Mobility and Technology Germany University of Stuttgart Institute for System Dynamics Germany Robert Bosch GmbH Germany The University of Auckland Dept. of Electrical Computer and Software Engineering New Zealand

ISBN: (纸本)9798350376067

The transition to electric transportation demands efficient and cost-effective powertrains. Optimizing energy use is crucial for extending range and reducing expenses. However, comparing inverter and motor efficiency based on inverter topologies is challenging due to biased methodologies that favor certain designs over others. This document introduces a novel optimization-based approach for enhancing partial load efficiency and minimizing chip area of single and dual traction inverters, indicating potential energy savings and cost reduction. Recent publications of both industry [1], [2] and academia [3] underscore the importance of these design goals achieved by either novel inverter topologies or enhanced control methods. Two promising topologies with the inherent capability of partial load optimization are evaluated regarding chip area and system efficiency to find the most suitable concept for future electric vehicle power trains. © 2024 IEEE.

关键词： Energy utilization

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Optimizing Machine Learning Algorithms to Enhance Blood Donation for Liver Patients: A Case Study 7

Optimizing Machine Learning Algorithms to Enhance Blood Dona...

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7th IET Smart Cities Symposium, SCS 2023

作者： Mijwil, Maad M. El-Kenawy, El-Sayed M. Bala, Indu Aljanabi, Mohammad Abotaleb, Mostafa Ibrahim, Abdelhameed Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood. © The Institution of engineering & Technology 2023.

关键词： Support vector machines

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ML-Driven HR system: Candidate Assessment for Enhanced Recruitment Outcomes 3

ML-Driven HR System: Candidate Assessment for Enhanced Recru...

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3rd International Conference on Automation, Computing and Renewable systems, ICACRS 2024

作者： Dixon, G.C. Srithanujan, S. Ranaweera, H.D.T.N. Ariyarathne, R.M.P.M. Weerasinghe, K.L. Gamage, N. Department of Information Technology SriLanka Institure of Information Technology New Kandy Rd Malabe Sri Lanka Department of Computer System engineering SriLanka Institure of Information Technology New Kandy Rd Malabe Sri Lanka Department of Computer Science and Software Engineering SriLanka Institure of Information Technology New Kandy Rd Malabe Sri Lanka

ISBN: (纸本)9798331532420

This study developed and implemented a machine learning-driven HR system designed to enhance recruitment outcomes, particularly in online interviews. Challenges such as candidate impersonation, interview question memorization, and deepfake manipulations were addressed. The system incorporated several critical processes utilizing advanced algorithms: pre-interview shortlisting employed Support Vector Machines (SVM) achieving 92% accuracy;cognitive skills assessment utilized adaptive testing methods powered by fine-tuned Large Language Models (LLMs) with 88% accuracy;emotional intelligence evaluation was conducted using deep learning-based emotion detection algorithms attaining 85% accuracy;face verification leveraged Convolutional Neural Networks (CNNs) for identity and deepfake detection with 90% accuracy;online video interviews were analyzed through integrated audio-visual processing techniques;language proficiency testing employed Natural Language Processing (NLP) models reaching 87% accuracy;analysis of thinking patterns was performed using sentiment analysis algorithms with 83% accuracy;and candidate grading was facilitated by ensemble learning methods ensuring consistent and reliable evaluations. The results demonstrated the system’s effectiveness in verifying candidate authenticity and assessing competence, providing a robust solution for modern recruitment challenges. © 2024 IEEE.

关键词： Support vector machines

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The One-vs-Rest Method for a Multilabel Patent Classification Machine Learning Approach using a Regression Model

The One-vs-Rest Method for a Multilabel Patent Classificatio...

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2023 International Conference on Informatics, Multimedia, Cyber and Information systems, ICIMCIS 2023

作者： Widodo, Slamet Ermatita Setiawan, Deris Pradnyana, I Wayan Widi Universitas Sriwijaya Politeknik Negeri Sriwijaya Faculty of Engineering Department of Computer Engineering Palembang30139 Indonesia University of Sriwijaya Faculty of Engineering Palembang30139 Indonesia University of Pembangunan Nasional Veteran Jakarta Information System Program Jakarta Indonesia

ISBN: (纸本)9798350307306

Redundancy in patent abstracts, that is, the repetition of an International Patent Classification tag or patent class information across documents, poses a challenge in patent classification, which is essential for efficiently managing the growing number of patent documents. This study aims to develop a model to analyze the weights and index values of summary sentences to improve the efficiency and accuracy of patent classification. This model can predict relevant patent data types. Utilizing logistic regression and the one-vs-rest method, this study focuses on the development of models to analyze the weights and index values of summary sentences, with the goal of enhancing the precision and efficiency of patent classification. The main contribution of this research is the creation of predictive models for new patent documents to streamline patent citations based on document type. This study also serves as a reference for evaluating Indonesian patent document classification models, thereby facilitating the patent evaluation process. © 2023 IEEE.

关键词： Efficiency

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Exploring the Relationships between Dynamic Capabilities, Digital Transformation, and IT-Business Alignment 12

Exploring the Relationships between Dynamic Capabilities, Di...

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12th International Conference on Cyber and IT Service Management, CITSM 2024

作者： Kosasi, Sandy Sukmana, Husni Teja Yuliani, I Dewa Ayu Eka Laipaka, Robertus Fitriani, Diana Madhiyono Information System Stmik Pontianak Pontianak West Kalimantan Indonesia Information Engineering Syarif Hidayatullah State Islamic University Jakarta Jakarta Indonesia Computer Science Stmik Pontianak Pontianak West Kalimantan Indonesia

ISBN: (纸本)9798331528799

Business and IT alignment is crucial to business organizations, navigating digital transformation, leveraging dynamic capabilities, facilitating smooth integration of digital technologies, and creating competitive added value in managing digital. Nevertheless, there is no literature discussing the influence of dynamic capabilities in doing business and IT alignment through digital transformation in managing digital business. The objective of the research is to explore and ensure the full impact of the digital transformation construction in bringing business and IT alignment together with every indicator of the effects of dynamic capacity with the availability and readiness to manage a digital business-convergence triangulation research method with explanatory design through follow-up explanation. The research population was taken from all the digital businesses that had run them for at least five years. 198 of the 238 respondents completed the questionnaire online through Google Forms using a random sampling technique with a response rate of 83.19%. The Likert scale is used to process and analyze data using the SEM-PLS method. The results of the research showed it turns out that the assumption of managing digital business is very resistant to the need to harmonize business and IT from the influence of digital transformation is not proven at all, and the existence of the linkage of dynamic capabilities in carrying out the alignment of business and IT. © 2024 IEEE.

关键词： digital businesses digital transformation dynamic capabilities IT-business alignment

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Comprehensive Overview of Multi-agent systems for Controlling Smart Grids

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CSEE Journal of Power and Energy systems 2022年第1期8卷 115-131页

作者： Om Prakash Mahela Mahdi Khosravy Neeraj Gupta Baseem Khan Hassan Haes Alhelou Rajendra Mahla Nilesh Patel Pierluigi Siano Power System Planning Division Rajasthan Rajya Vidyut Prasaran Nigam Ltd.Jaipur-302005India Media Integrated Communication Laboratory Graduate School of EngineeringOsaka UniversityOsakaJapan Computer Science and Engineering Department Oakland UniversityRochesterUSA Department of Electrical and Computer Engineering Hawassa UniversityHawassa 1530Ethiopia Faculty of Mechanical and Electrical Engineering Tishreen UniversityLattakiaSyria Department of Electrical Engineering National Institute of Technology Kurukshetra Department of Management&Innovation Systems University of Salerno84084 SalernoItaly

Agents are intelligent entities that act flexibly and autonomously and make wise decisions based on their intelligence and experience.A multi-agent system(MAS)contains multiple,intelligent,and interconnected collaborating agents for solving a problem beyond the ability of a single agent.A smart grid(SG)combines advanced intelligent systems,control techniques,and sensing methods with an existing utility power *** controlling smart grids,various control systems with different architectures have already been ***-based control of power system operations has been shown to overcome the limitations of time required for analysis,relaying,and protection;transmission switching;communication protocols;and management of plant *** systems provide an alternative for fast and accurate power network *** paper provides a comprehensive overview of MASs used for the control of smart *** paper provides a wide-spectrum view of the status of smart grids,MAS-based control techniques and their implementation for the control of smart *** of MASs in the control of various aspects of smart grids-including the management of energy,marketing energy,pricing,scheduling energy,reliability,network security,fault handling capability,communication between agents,SG-electrical vehicles,SG-building energy systems,and soft grids—have been critically *** than a hundred publications on the topic of MAS-based control of smart grids have been critically examined,classified,and arranged for fast reference.

关键词： Coordinated control multi-agent systems renewable energy sources smart energy infrastructure smart grid

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λGrapher: A Resource-Efficient Serverless system for GNN Serving through Graph Sharing 24

λGrapher: A Resource-Efficient Serverless System for GNN Se...

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33rd ACM Web Conference, WWW 2024

作者： Hu, Haichuan Liu, Fangming Pei, Qiangyu Yuan, Yongjie Xu, Zichen Wang, Lin National Engineering Research Center for Big Data Technology and System The Services Computing Technology and System Lab Cluster and Grid Computing Lab in the School of Computer Science and Technology Huazhong University of Science and Technology 1037 Luoyu Road Wuhan China Peng Cheng Laboratory Huazhong University of Science and Technology China School of Mathematics and Computer Science Nanchang University China Paderborn University Paderborn Germany

ISBN: (纸本)9798400701719

Graph Neural Networks (GNNs) have been increasingly adopted for graph analysis in web applications such as social networks. Yet, efficient GNN serving remains a critical challenge due to high workload fluctuations and intricate GNN operations. Serverless computing, thanks to its flexibility and agility, offers on-demand serving of GNN inference requests. Alas, the request-centric serverless model is still too coarse-grained to avoid resource waste. Observing the significant data locality in computation graphs of requests, we propose λGrapher, a serverless system for GNN serving that achieves resource efficiency through graph sharing and fine-grained resource allocation. "Grapher features the following designs: (1) adaptive timeout for request buffering to balance resource efficiency and inference latency, (2) graph-centric scheduling to minimize computation and memory redundancy, and (3) resource-centric function management with fine-grained resource allocation catered to the resource sensitivities of GNN operations and function orchestration optimized to hide communication latency. We implement a prototype of λGrapher based on the representative open-source serverless platform Knative and evaluate it with real-world traces from various web applications. Our results show that λGrapher can achieve an average savings of 61.5% in memory resource and 47.2% in computing resource compared with the state of the arts while ensuring GNN inference latency. © 2024 ACM.

关键词： Efficiency

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Advanced Data Segmentation and Encryption Approach using Fog Computing

Advanced Data Segmentation and Encryption Approach using Fog...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Singh, Kamred Udham Varshney, Neeraj Kumar, Pushpendra Sharma, Pradeep Kumar Mishra, Umang Deep Singh, Teekam Graphic Era Hill University School of Computing Dehradun India Gla University Department of Computer Engineering & Applications UP Mathura India Scanno System Greater Noida India Hcl Technology UP India Unique Institute of Management & Technology Muradnagar India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350382723

Data security in conventional applications was only available at the application layer. Within the scope of the intended research, the safety of the merchandise is ensured. The deficiencies that are present in the security systems that are already in place have led to the realisation that a new security system is required. Considering that authentication is not present, the likelihood of decryption should be reduced. Establishing a decentralised security system is essential in order to protect against threats coming from a variety of digital networks. The new encryption standard is said to improve multilayer security, according to the claims. The material is split into two halves using this method, which also provides a reliable transmission mechanism. Because of the technology, hackers are unable to create vulnerabilities in the security system. There was a need for security in both the cloud and the edge environment in order to prevent intruders. Increasing the reliability and security of data transmission over a variety of networks was the motivation for the development of the two fish approach. Because of its security mechanism, it has been able to survive assaults from crackers and hackers. The paper that is being presented advises doing research on the safety of edge transmissions that are based on slicing. In order to prevent attackers from launching attacks from several networks, it is necessary to establish decentralised security. Layered security will help increase encryption standards, which is a significant benefit. Over the course of various investigations, computer security edge generators have been the focus of attention. Quite a few of them have already been extensively discussed. For the purpose of developing a reliable method of transmission, the proposed research splits the data into two parts, each of which is further encrypted at a different level. As a result of the development of multilayer encryption, the reliability and safety of data transm

关键词： Fish

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Fine-Tuning IndoBERT Model for Big Five Personality Prediction from Indonesian Social Media 24

Fine-Tuning IndoBERT Model for Big Five Personality Predicti...

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24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

作者： Pradnyana, Gede Aditra Anggraeni, Wiwik Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Information System Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Department of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350313956

The increasing amount of data generated from social media brings opportunities to produce various helpful knowledge and information, one of which is predicting a person's personality. The advent of attention-based classification techniques and transformers brings promising results on multiple tasks in Natural Language Processing (NLP). In this study, we predicted the Big Five personalities from Indonesian-language social media by fine-tuning the IndoBERT model. The IndoBERT model is a Bidirectional Encoder Representation from Transformers (BERT)-based mono-language model trained in the Indonesian corpus. Based on the experimental results, the prediction model we proposed obtained an accuracy value of 72% and an F1-score of 71% using IndoBERT-base. Meanwhile, using IndoBERT-large can increase the accuracy value by 78% and the F1-score by 74%. The proposed model also outperforms previous models in predicting the Big Five personality. © 2023 IEEE.

关键词： Forecasting

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An Integrated Approach to Robotic Object Grasping and Manipulation

arXiv

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arXiv 2024年

作者： Ahmed, Owais Huzaifa, M. Areeb, M. Khan, Hamza Ali Students B.E Computer & Information System Engineering NED University OF Engineering and Technology Karachi Pakistan

In response to the growing challenges of manual labor and efficiency in warehouse operations, Amazon has embarked on a significant transformation by incorporating robotics to assist with various tasks. While a substantial number of robots have been successfully deployed for tasks such as item transportation within warehouses, the complex process of object picking from shelves remains a significant challenge. This project addresses the issue by developing an innovative robotic system capable of autonomously fulfilling a simulated order by efficiently selecting specific items from shelves. A distinguishing feature of the proposed robotic system is its capacity to navigate the challenge of uncertain object positions within each bin of the shelf. The system is engineered to autonomously adapt its approach, employing strategies that enable it to efficiently locate and retrieve the desired items, even in the absence of pre-established knowledge about their placements. Crucially, the robotic system operates with a paramount emphasis on autonomy. The intricate interplay of algorithms, control mechanisms, and sensor fusion empowers the robot to execute the entire object picking task without human intervention. This unfaltering commitment to autonomy is a pivotal step towards revolutionizing warehouse operations, potentially paving the way for unprecedented levels of efficiency and productivity. This project serves as a testament to the intersection of robotics, computer vision, and artificial intelligence in tackling a complex challenge within the realm of modern logistics. The envisioned robotic system represents a significant advancement in autonomous object picking technology, holding the promise of transforming conventional warehousing practices. As the fusion of cutting-edge technology and logistical innovation unfolds, the outcomes of this endeavor have the potential to redefine the future of warehouse operations and automation within the industry. © 2024, CC BY.

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