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Leveraging Dual Timestamps to Enhance Process Model Construction Using Time-Infused Inductive Miner

IEEE Access 2025年 13卷 88535-88556页

作者： Effendi, Yutika Amelia Kim, Minsoo Airlangga University Faculty of Advanced Technology and Multidiscipline Department of Robotics and Artificial Intelligence Engineering Surabaya60115 Indonesia Pukyong National University College of Engineering Department of Industrial and Data Engineering Busan48547 Korea Republic of

In business process management, the increasing complexity of modern business processes underscores the need for accurately understanding parallel activities to efficiently allocate and utilize limited corporate resources. However, many existing event log analysis algorithms focus solely on the sequence of executed activities, limiting their ability to model processes involving simultaneous activities effectively. This research seeks to enhance the inductive miner, which is known for its flexibility, formal guarantees, and scalability, by enabling it to better capture and represent the complexities of real-world business processes. The core modification involves leveraging dual timestamps, which utilize both the start and end times of activities. This approach enhances the algorithm's ability to understand parallel structures within process models. Through experimentation, limitations of single timestamp were identified, including reduced accuracy in modeling parallel activities. The proposed approach also demonstrates the capability to effectively minimize dummy activities when discovering process models containing both sequential and parallel activities. Experimental results highlight the improved performance of the proposed algorithm, with metrics showing a fitness score of 0.815, precision of 0.827, generalization of 0.846, and simplicity of 0.834. Moreover, the algorithm achieves the highest conformance results with fewer traces required compared to the alpha miner, fuzzy miner, heuristic miner, and the original inductive miner. © 2013 IEEE.

关键词： Enterprise resource planning

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Multimodality imaging in prostate cancer diagnosis using artificial intelligence: basic concepts and current state-of-the-art

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Multimedia Tools and Applications 2025年 1-30页

作者： Ayyad, Sarah M. Abdel-Hamid, Nahla B. Ali, H. Arafat Labib, Labib M. Computers and Systems Department Faculty of Engineering Mansoura University Mansoura Egypt Faculty of Engineering Mansoura National University Mansoura Egypt Faulty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt

The early diagnosis of prostate cancer (PCa) is highly recommended, as the tumor will not spread to other organs of the body and the bones. Moreover, a late diagnosis of PCa could lower the survival rate. The growing development of artificial intelligence (AI) and Machine Learning (ML) in medical images has led to significant improvement in PCa diagnosis. Multimodality is now commonly applied in medical imaging diagnosis, as it provides comprehensive information about a target (tissue or tumor). It has shown to be useful for advancing the clinical reliability of using medical images and ML for medical diagnostics and analysis. Hence, in this paper, a comprehensive survey is provided to explore the state-of-the-art Computer-Aided Diagnosis Systems (CADs) for PCa detection attributed to multimodality imaging, a background of PCa. different types of medical imaging used in PCa diagnosis, related clinical workflows, future perspectives, and some common limitations of related work. The review exhibits an extensive literature review done on multimodality imaging in PCa, highlights that multimodality imaging has the potential of wide applicability in diagnosis systems. It is expected that this study enhances the understanding necessary for developing CAD systems for PCa diagnosis. Additionally, it is expected to establish a great basis for developing multimodal images, the relevant datasets, some of the challenges, and future topics. © The Author(s) 2025.

关键词： Urology

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Orbit Weighting Scheme in the Context of Vector Space Information Retrieval

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Computers, Materials & Continua 2024年第7期80卷 1347-1379页

作者： Ahmad Ababneh Yousef Sanjalawe Salam Fraihat Salam Al-E’mari Hamzah Alqudah Department of Computer Science Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan Department of Cybersecurity Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346Ajman13306United Arab Emirates Information Security Department Faculty of Information TechnologyUniversity of PetraAmman11196Jordan Department of Data Science and Artificial Intelligence Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan

This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and *** conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size *** proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing *** research focuses on evaluating OWS’s impact on model accuracy using Information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model *** compare OWS-based retrieval models against others using different schemes,including tf-idf variations and *** reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index *** highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.

关键词： Information retrieval orbit weighting scheme semantic text analysis Tf-Idf weighting scheme vector space model

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A Surrogate-assisted Multi-objective Grey Wolf Optimizer for Empty-heavy Train Allocation Considering Coordinated Line Utilization Balance

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Journal of Bionic engineering 2025年第1期22卷 383-397页

作者： Zhigang Du Shaoquan Ni Jeng-Shyang Pan Shuchuan Chu School of Transportation and Logistics Southwest Jiaotong UniversityChengdu610031China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China National and Local Joint Engineering Lab for Integrated Transportation Intelligence Chengdu610031China National Engineering Lab of Comprehensive Transportation Big Data Application Technology Chengdu610031China

This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization *** integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization *** effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective ***,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy *** research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.

关键词： Surrogate-assisted model Grey wolf optimizer Multi-objective optimization Empty-heavy train allocation

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MCFF-Net: Multi-Scale Contextual Feature Fusion Network for Polyp Segmentation

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Journal of Shanghai Jiaotong University (science) 2025年 1-13页

作者： Ma, Lei Yan, Jiangkai Shao, Dangguo Li, Jingtao Wang, Jiawei Yan, Yukun Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Provincial Key Laboratory of Artificial Intelligence Kunming650500 China

In clinical practice, technologies for automatically segmenting polyps might greatly improve the speed and accuracy of medical diagnosis, significantly reducing the chances of patients developing colorectal cancer. Nevertheless, the segmentation of polyps presents a considerable challenge due to their low contrast, presence of occlusion, and intricate background disturbances. In this paper, we propose an innovative approach for segmenting polyps, called multi-scale contextual feature fusion network for polyp segmentation (MCFF-Net). Specifically, MCFF-Net includes three modules: feature enhancement module (FEM), global-local feature fusion module (GLFF), and cross-scale feature fusion module (CFFM). Among them, FEM is used to improve the features at each scale. Building on this, the CFFM enables comprehensive interaction among the information across various scales, enhancing the diversity of the feature scales extracted. Moreover, the GLFF fuses global-local features to generate a rough object localization map. This map further modulates the fusion features from the CFFM, facilitating more precise segmentation. On five publicly available datasets, our approach not only segments various polyp types but also surpasses existing methods in terms of accuracy and generalizability. © Shanghai Jiao Tong University 2024.

关键词： Diseases

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Leveraging machine learning for intelligent agriculture

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. Meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. Mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf

关键词： Smart agriculture

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The Future of Driving: A Review on the Combination of artificial intelligence and Autonomous Vehicles 2

The Future of Driving: A Review on the Combination of Artifi...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Zade, Nikita Gawande, Mayur Verma, Prateek Gundewar, Swapnil Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

The convergence of artificial intelligence (AI) and Autonomous Vehicles (AV) are poised to revolutionize the automotive industry, offering safer, more efficient, and sustainable transportation solutions. This review article explores the current state of AVs, role of AI in AV, challenges and hurdles, Real world applications & development, its critical technological components, and their impact on society. This paper presents a wide array of applications of AI in AV, elaborating on the way complex sensor fusion, computer vision, and Machine Learning (ML) algorithms makes AVs perceive their surroundings, form critical opinions, and carry out accurate motions. AI is making real time interpretations of great quantities of data which are paving the way for safer and more effective navigation under a diversity of traffic circumstances, from sensor fusion to decision-making algorithms. The next section of this paper elaborates on the role of AI in the field of AV in perceiving, planning, and navigating through real world environment, while also talking about issues of integration and ethics. This paper further identifies its social, economic, and environmental consequences, resulting from mass use of AV. This article discusses the latest developments and opportunities in the regulatory environment, gives an overview of how AV define transportation in the near future. This paper also explores the levels of autonomy of AVs as defined by the Society of Automotive Engineers (SAE) and defines the process from driver assistance to full automation. It examines the ethical and technical considerations arising at each instant and emphasizes the key role AI continues to play in providing more autonomy while preserving security and dependability. © 2024 IEEE.

关键词： Automotive industry

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Efficient segmentation of exudates in color fundus images using wavelets and generative adversarial network

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Multimedia Tools and Applications 2025年 1-31页

作者： Kar, Mithun Kumar Nath, Malaya Kumar Neog, Debanga Raj Amrita School of Artificial Intelligence Coimbatore Amrita Vishwa Vidyapeetham Coimbatore641112 India Department of Electronics and Communication Engineering National Institute of Technology Puducherry Karaikal609609 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Guwahati Guwahati781039 India

Due to the progression of diabetic retinopathy (DR), retinal features change and lead to the appearance of microaneurysms, hemorrhages, and exudates on its surface. The presence of exudates appears over the retina, giving a clear early sign of diabetic retinopathy, which is an irreversible eye disease. Automatic segmentation and detection of exudates enhance the computer-aided diagnosis of the disease. Various deep learning techniques are employed for the automatic detection and segmentation of exudates, but their performance is unsatisfactory due to the small size of exudates. Here, a novel method is proposed, using generative adversarial network on wavelet subbands. Wavelet decomposition un-correlates exudates from other retinal features in subbands due to its frequency-selective nature. Generative adversarial network (GAN) is employed with modified loss function with multi-scale residual convolutional neural network (MSR-Net) to accurately segment exudates. The proposed method is experimented on using IDRiD and e-optha databases, and different quantitative and qualitative performance measures are computed and compared with state-of-the-art methods. The proposed method achieves an accuracy, sensitivity, and F1Score of 0.9886, 0.8306, and 0.8246, respectively, for the IDRiD database. For the e-ophtha database, the above-mentioned performance measures are reported to be 0.9861, 0.8512, and 0.8444, respectively. This method performed well in presence of other features. For generalization purposes, the proposed method is applied for skin cancer cell segmentation using University of Waterloo’s skin cancer database. It achieved highest accuracy of 0.9943 and sensitivity of 0.9885 for segmenting the skin cancer cells. It has the potential for analyzing the diagnostic information about various ophthalmic diseases such as: glaucoma and diabetic retinopathy (DR). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Wavelet decomposition

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Graph-based Named Entity Information Retrieval from News Articles using Neo4j 18

Graph-based Named Entity Information Retrieval from News Art...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Chaudhary, Shikha Vyas, Hirenkumar Arora, Naveen D'Mello, Sejal Vidyavardhini's College of Engineering and Technology Dept. of Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9789380544519

The internet, social media, and other cutting-edge technologies are all contributing to the exponential growth of digital information. This makes interpreting and applying this enormous amount of data both possible and challenging. Our research proposes a smart system that can convert unprocessed text into a knowledge graph in order to address this. This system makes use of the graph database Neo4j. Although there are currently available tools for linking entities and models to extract relationships, such as Spacy, NLTK, and Flair, their combined use is inefficient. Our suggested method seeks to efficiently blend entity linkage and relation extraction, allowing us to convert raw data into a knowledge graph. There are many real-world uses for this strategy, particularly in data analysis and decision-making. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Cypher Queries Graph database Information Retrieval Named Entity Recognition Neo4j News Articles Relation extraction

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Disaster Management Ecosystem Using Crowdsourced data, Deep Learning and NLP 5

Disaster Management Ecosystem Using Crowdsourced Data, Deep ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Savale, Vaishali Patil, Siddhi Raut, Atharva Phadke, Mrunmayee Sundkar, Rahul Dongre, Rajkumar Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Engineering Maharashtra Pune411037 India

ISBN: (纸本)9798331530013

In recent years, the frequency and intensity of natural disasters have increased, necessitating more efficient and responsive disaster management systems. This paper presents a novel disaster management solution leveraging crowdsourced data to enhance disaster response and coordination. The system bridges the gap between citizens and emergency management officials by composing a mobile application for citizens to send real-time disaster alerts and a web application for administrators to monitor and manage these alerts. A customized DBSCAN algorithm, adapted to use haversine distance, is deployed to detect clusters of disaster alerts in real time, indicating potential disaster events. Upon cluster detection, administrators can delineate affected areas on a map, triggering notifications to users within these regions and enabling them to send help requests with images and messages. These help requests are processed using the BLIP model for image captioning and the ChatGPT API for categorization, assigning relevant tags to each request. The system provides administrators with real-time data analysis features, including map filtering and citizen help request category visualization, enabling more informed decision-making. Utilizing a cloud-based architecture ensures scalability, reliability, and efficient processing of large volumes of data. This research demonstrates the system's capability to provide timely alerts and comprehensive situational awareness, ultimately contributing to more efficient disaster management. © 2024 IEEE.

关键词： Information management

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