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arXiv 2023年

作者： Singh, Amritpal Shi, Wenqi Wang, May D. College of Computing Georgia Institute of Technology Georgia United States Electrical and Computer Engineering Georgia Institute of Technology Georgia United States Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Georgia United States

In the context of surgery, robots can provide substantial assistance by performing small, repetitive tasks such as suturing, needle exchange, and tissue retraction, thereby enabling surgeons to concentrate on more complex aspects of the procedure. However, existing surgical task learning mainly pertains to rigid body interactions, whereas the advancement towards more sophisticated surgical robots necessitates the manipulation of soft bodies. Previous work focused on tissue phantoms for soft tissue task learning, which can be expensive and can be an entry barrier to research. Simulation environments present a safe and efficient way to learn surgical tasks before their application to actual tissue. In this study, we create a Robot Operating System (ROS)-compatible physics simulation environment with support for both rigid and soft body interactions within surgical tasks. Furthermore, we investigate the soft tissue interactions facilitated by the patient-side manipulator of the DaVinci surgical robot. Leveraging the pybullet physics engine, we simulate kinematics and establish anchor points to guide the robotic arm when manipulating soft tissue. Using demonstration-guided reinforcement learning (RL) algorithms, we investigate their performance in comparison to traditional reinforcement learning algorithms. Our in silico trials demonstrate a proof-of-concept for autonomous surgical soft tissue retraction. The results corroborate the feasibility of learning soft body manipulation through the application of reinforcement learning agents. This work lays the foundation for future research into the development and refinement of surgical robots capable of managing both rigid and soft tissue interactions. Code is available at https://***/amritpal-001/tissue_retract. © 2023, CC BY.

关键词： Robotic surgery

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Application of Support Vector Machine to Lassa Fever Diagnosis

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2023年 180卷 165-177页

作者： Nwankwo, Wilson Adigwe, Wilfred Umezuruike, Chinecherem Acheme, Ijegwa D. Nwankwo, Chukwuemeka Pascal Ojei, Emmanuel Oghorodi, Duke Department of Computer Science Edo State University Uzairue Nigeria Department of Computer Science Delta State University of Science and Technology Ozoro Nigeria College of Computing and Communication Studies Bowen University Iwo Nigeria Department of Software Engineering Delta State University of Science and Technology Ozoro Nigeria Department of Cyber Security Delta State University of Science and Technology Ozoro Nigeria

Lassa fever is a type of viral hemorrhagic fever with high fatality rate. Precise and prompt diagnosis and successful treatment of this disease is very important in the control and prevention of the spread as unrestricted spread can easily erupt into an epidemic. An insightful decision support system can help make quick and more exact findings on this disease. In this paper, an intelligent model is proposed for predicting Lassa fever is proposed. The Lassa fever dataset was extracted from the clinical records of patients from a known Specialist Teaching Hospital in Southern Nigeria. The relevant feature variables were identified and encoded in a manner appropriate for use in algorithmic analysis. The support vector classifier was used to develop a characterization model for the disease. Assessment and evaluation were based on exactness, accuracy, and affectability. Findings showed that the proposed system could improve and encourage quick decision-making as it utilizes real historical data to effectively characterize Lassa fever spread as well as promote the timely treatment and management of patients. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Support vector machines

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Process Mining Approach to Analyze Engagement in Campaign Activities

Process Mining Approach to Analyze Engagement in Campaign Ac...

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Information Technology International Seminar (ITIS)

作者： Aryo Nugroho Awalludiyah Ambarwati Arsi Raniah Faza Ardi Ilham Falah Oddy Virgantara Putra Ika Widiastuti Department of Information System Universitas Narotama Surabaya Indonesia Department of Accounting UPN Veteran Jawa Timur Surabaya Indonesia Department of Soft Computing AONO Research Center Surabaya Indonesia Department of Informatics Universitas Darussalam Gontor Ponorogo Indonesia Department of Computer Science & Engineering. Ewha Womans University Seoul Republic of Korea

Campaign strategies are evolving because of developments in communication technologies. Campaign activities have become more attractive to the younger generation with the existence of social media. There is much research related to campaigns, but it focuses on data collection and content. This research tries to look at it from the other side, namely from the perspective of the campaign team. How can teams improve performance and ensure that strategies are executed correctly? The analysis was carried out using process mining. The methodology used is the Project Mining Methodology, supported by data, identification, conformity checking, and quality improvement. The data used was taken from a Facebook group that was specifically created and operated during the 100 days of the official campaign. Interactions occurred with 2215 members with 975 active members, of which there were 357 posts, 8755 reactions, 1275 comments, and 280 shares. The results obtained from this process show weekly posting patterns and responses. Furthermore, we can see the involvement of key figures in the activities of the campaign group.

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Visual Analytics and Exploration of Calcium Transient Imaging Data using Event-Based Clustering

Visual Analytics and Exploration of Calcium Transient Imagin...

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IEEE International Conference on Big Data

作者： Shiaofen Fang Michal Lange Haoying Fu Yingying Chen Yaoying Ma Luddy School of Informatics Computing and Engineering Indiana University Indianapolis IN USA Department of Computer & Information Science Purdue University Indianapolis Department of Pharmacology and Toxicology School of Medicine Indiana University Indianapolis IN USA Department of Pharmacology and Toxicology Stark Neuroscience Institute School of Medicine Indiana University Indianapolis IN USA

Advances in imaging technology and fluorescent calcium indicators have increased the popularity of the application of miniature microscopes (miniscope) in freely-moving rodents when they are performing sophisticated behaviors. The recently established analysis tools, such as Minian, allow for the efficient extraction of neuronal calcium transients from raw videos. However, there is still a gap in front of neuroscientists to draw any conclusions based on the neuronal calcium signals detected during behavioral tests. The poor signal-to-noise ratio and unreliable neuron registration in miniscope images bring significant challenges to the data analysis tasks. This paper describes a visual analytics approach to the exploration of potential neuronal clustering based on the calcium signals and the behavioral tasks. This new interactive solution can be applied for the analysis and exploration of the correlations between calcium imaging data and addiction behavioral data using a combination of visualization, user interaction, visual clustering, and event-related feature selection techniques. The associated interactive software tool is designed to help neuroscientists develop neurobiological hypotheses underlying both physiological and pathological behaviors, which could be a milestone in the field of neuroscience to understanding behavioral coding in the brain at the neuronal level.

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Human Age Estimation Based on Dental Images

Human Age Estimation Based on Dental Images

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Nur Ain Raidzan Itaza Afiani Mohtar Wan Azani Mustafa Muntather Almusawi Abbas Hameed Abdul Hussein Teknologi MARA Perak Branch Computer Science Studies College of Computing Informatics and Mathematics Universiti Perak Malaysia Faculty of Electrical Engineering & Technology Campus Pauh Putra University Malaysia Perlis Advanced Computing (AdvComp) Centre of Excellence (CoE) Universiti Malaysia Perlis (UniMAP) Campus Pauh Putra Arau Perlis Malaysia Faculty of Engineering The Islamic University Najaf Iraq College of MLT Ahl Al Bayt University Karbala Iraq

Predicting age is one of the most important characteristics in the forensic context for human identification because biological profiles can only be effectively rebuilt by estimating the most precise age. Humans usually count their age using calendars that are pre-programmed to keep track of days. It is not difficult for any living human being to measure their age, but it becomes a challenge when forensics needs to determine the exact age of a dead person involved in a criminal case. By comparing the stage of tooth development in X-rays to established dental growth norms, forensic scientists may estimate age. To deal with such problems, an age estimate system that predicts a person's real age group is required. In this paper, Convolutional Neural Network (CNN) was used to build Human Age Estimation System that can predict a person's actual age group. The system employs Inception-v3 architecture to create the model. The dataset consists of 1404 dental X-ray images of pre-teen in Malaysia. The model was trained using 60% of the dataset, and the remaining 40% was used for testing. Accuracy, precision, recall, and f-measure were used to assess the model. The model achieved 82%, 78%, 76%, and 77%, respectively. As a conclusion, a large amount of data is required for the training and testing process to acquaint the model with the pattern of tooth growth by age group. It is also crucial to rule out any illnesses that impact tooth growth, as well as radiographic images that are not of good quality.

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Dual role of sprouty2 as an inhibitor of RAS/ERK-driven proliferation and a promoter of cancer invasion in KRAS wild-type colorectal cancer

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Molecular Carcinogenesis 2023年第7期62卷 951-962页

作者： Lee, Chung-Ta Chu, Chien-An Wang, Yu-Ming Wang, Yi-Wen Chen, Yi-Lin Ho, Chung-Liang Yeh, Yu-Min Lin, Peng-Chan Lin, Bo-Wen Chen, Po-Chuan Chen, Shang-Hung Chan, Ren-Hao Chang, Chen Chow, Nan-Haw Department of Pathology College of Medicine National Cheng Kung University Hospital National Cheng Kung University Tainan Taiwan Department of Pathology National Cheng Kung University Hospital Dou-Liou Branch Yunlin County Douliu City Taiwan College of Medicine Institute of Molecular Medicine National Cheng Kung University Tainan Taiwan Department of Oncology College of Medicine National Cheng Kung University Hospital National Cheng Kung University Tainan Taiwan Department of Genomic Medicine College of Medicine National Cheng Kung University Hospital National Cheng Kung University Tainan Taiwan College of Electrical Engineering and Computer Science Institute of Medical Informatics National Cheng Kung University Tainan Taiwan Department of Surgery College of Medicine National Cheng Kung University Hospital National Cheng Kung University Tainan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan College of Medicine Institute of Basic Medical Sciences National Cheng Kung University Tainan Taiwan

Sprouty2 (SPRY2) is known to inhibit the RAS/MAPK/ERK pathway, and is a potential study target for cancer. The effect of SPRY2 in colorectal cancer (CRC) and whether it is influenced by KRAS mutation are not known. We manipulated SPRY2 gene expression and used an activating KRAS-mutant plasmid to determine its effect on CRC cell function in vitro and/or in vivo. We performed SPRY2 immunohistochemical staining in 143 CRC specimens and analyzed the staining results with various clinicopathological characteristics in relation to KRAS mutation status. SPRY2 knockdown in Caco-2 cells carrying the wild-type (WT) KRAS gene upregulated phosphorylated ERK (p-ERK) levels and increased cell proliferation in vitro, but inhibited cell invasion. However, SPRY2 knockdown in SW480 cells (activating KRAS mutant) or Caco-2 cells transfected with KRAS-mutant plasmid did not significantly alter p-ERK levels, cell proliferation, or invasion. The xenografts of SPRY2-knockdown Caco-2 cells were larger with less deep muscle invasion than those of control cells. The clinical cohort study revealed a positive association of SPRY2 protein expression with pT status, lymphovascular invasion, and perineural invasion in KRAS-WT CRCs. However, the associations were not observed in KRAS-mutant CRCs. Interestingly, high SPRY2 expression was related to shorter cancer-specific survival in both KRAS-WT and KRAS-mutant CRC patients. Our study demonstrated the dual role of SPRY2 as an inhibitor of RAS/ERK-driven proliferation and as a promoter of cancer invasion in KRAS-WT CRC. SPRY2 may promote the invasion and progression of KRAS-WT CRC, and might also enhance KRAS-mutant CRC progression through pathways other than invasion. © 2023 Wiley Periodicals LLC.

关键词： cetuximab colorectal cancer invasion KRAS sprouty2

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Applications of Knowledge Distillation in Remote Sensing: A Survey

arXiv

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arXiv 2024年

作者： Himeur, Yassine Aburaed, Nour Elharrouss, Omar Varlamis, Iraklis Atalla, Shadi Mansoor, Wathiq Al Ahmad, Hussain College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates MBRSC Lab University of Dubai Dubai2713 United Arab Emirates Department of Computer Science and Software Engineering United Arab Emirates University United Arab Emirates Department of Informatics and Telematics Harokopio University of Athens AthensGR-17778 Greece

With the ever-growing complexity of models in the field of remote sensing (RS), there is an increasing demand for solutions that balance model accuracy with computational efficiency. Knowledge distillation (KD) has emerged as a powerful tool to meet this need, enabling the transfer of knowledge from large, complex models to smaller, more efficient ones without significant loss in performance. This review article provides an extensive examination of KD and its innovative applications in RS. KD, a technique developed to transfer knowledge from a complex, often cumbersome model (teacher) to a more compact and efficient model (student), has seen significant evolution and application across various domains. Initially, we introduce the fundamental concepts and historical progression of KD methods. The advantages of employing KD are highlighted, particularly in terms of model compression, enhanced computational efficiency, and improved performance, which are pivotal for practical deployments in RS scenarios. The article provides a comprehensive taxonomy of KD techniques, where each category is critically analyzed to demonstrate the breadth and depth of the alternative options, and illustrates specific case studies that showcase the practical implementation of KD methods in RS tasks, such as instance segmentation and object detection. Further, the review discusses the challenges and limitations of KD in RS, including practical constraints and prospective future directions, providing a comprehensive overview for researchers and practitioners in the field of RS. Through this organization, the paper not only elucidates the current state of research in KD but also sets the stage for future research opportunities, thereby contributing significantly to both academic research and real-world applications. © 2024, CC BY.

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Smart AmbuBag: A Cloud-Based Automated Respiratory Ventilator 13

Smart AmbuBag: A Cloud-Based Automated Respiratory Ventilato...

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Sampedro, Gabriel Avelino Cobar, Jose Mari Abaja, Keith Daniel Baluis, Ronnie Huyo-A, Shekinah Lor Abisado, Mideth University of the Philippines - Open University Faculty of Information and Communication Studies Los Baños Philippines Research and Development Center Philippine Coding Camp Manila Philippines Technological Institute of the Philippines Computer Engineering Department Quezon City Philippines College of Computing and Information Technologies National University Manila Philippines

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

With the Bag-Valve-Mask (commonly known as an AmbuBag or BVM) being widely used to provide emergency ventilation to patients, it is one of the most critical instruments used in the medical industry. As was emphasized in some studies, there is an urgency to implement a resolution to the human error-induced risks posed by the manual BVM. Hence, this study explores how an IoT-Based Automated Valve Mask can aid in resolving accidents born from the use of the manual counterpart. This device aims to propose a solution to the potential root causes of such adverse events in the field. It uses an GSM-based technology to gather the patient's vital signs and the quality of the air. After being tested on both Child and Adult groups, it was remarked that the accuracy rate of the device surpassed 95%. © 2022 IEEE.

关键词： Internet of things

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Investigating Retrieval-Augmented Generation in Quranic Studies: A Study of 13 Open-Source Large Language Models

arXiv

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arXiv 2025年

作者： Khalila, Zahra Nasution, Arbi Haza Monika, Winda Onan, Aytug Murakami, Yohei Radi, Yasir Bin Ismail Osmani, Noor Mohammad Department of Informatics Engineering Universitas Islam Riau Pekanbaru28284 Indonesia Department of Library Information Universitas Lancang Kuning Riau28266 Indonesia Department of Computer Engineering College of Engineering and Architecture Izmir Katip Celebi University Izmir35620 Turkey Faculty of Information Science and Engineering Ritsumeikan University Shiga Kusatsu525-8577 Japan Perlis Kuala Perlis02000 Malaysia Department Of Qur’an And Sunnah Studies Ahas Kirkhs International Islamic University Malaysia Malaysia

Accurate and contextually faithful responses are critical when applying large language models (LLMs) to sensitive and domain-specific tasks, such as answering queries related to quranic studies. General-purpose LLMs often struggle with hallucinations, where generated responses deviate from authoritative sources, raising concerns about their reliability in religious contexts. This challenge highlights the need for systems that can integrate domain-specific knowledge while maintaining response accuracy, relevance, and faithfulness. In this study, we investigate 13 open-source LLMs categorized into large (e.g., Llama3:70b, Gemma2:27b, QwQ:32b), medium (e.g., Gemma2:9b, Llama3:8b), and small (e.g., Llama3.2:3b, Phi3:3.8b). A Retrieval-Augmented Generation (RAG) is used to make up for the problems that come with using separate models. This research utilizes a descriptive dataset of Quranic surahs including the meanings, historical context, and qualities of the 114 surahs, allowing the model to gather relevant knowledge before responding. The models are evaluated using three key metrics set by human evaluators: context relevance, answer faithfulness, and answer relevance. The findings reveal that large models consistently outperform smaller models in capturing query semantics and producing accurate, contextually grounded responses. The Llama3.2:3b model, even though it is considered small, does very well on faithfulness (4.619) and relevance (4.857), showing the promise of smaller architectures that have been well optimized. This article examines the trade-offs between model size, computational efficiency, and response quality while using LLMs in domain-specific applications. © 2025, CC BY.

关键词： Semantics

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Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia

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computers, Materials & Continua 2021年第3期66卷 2787-2796页

作者： Nawaf N.Hamadneh Waqar A.Khan Waqar Ashraf Samer H.Atawneh Ilyas Khan Bandar N.Hamadneh Department of Basic Sciences College of Science and Theoretical StudiesSaudi Electronic UniversityRiyadh11673Saudi Arabia Department of Mechanical Engineering College of EngineeringPrince Mohammad Bin Fahd UniversityAl Khobar31952Saudi Arabia Department of Mathematics and Natural Sciences College of Sciences&Human StudiesPrince Mohammad Bin Fahd UniversityAl Khobar31952Saudi Arabia College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia Faculty of Mathematics and Statistics Ton Duc Thang UniversityHo Chi Minh City72915Vietnam King Abdullah University Hospital University of Science and TechnologyIrbid22110Jordan

In this study,we have proposed an artificial neural network(ANN)model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17,*** proposed model is based on the existing data(training data)published in the Saudi Arabia Coronavirus disease(COVID-19)situation—*** Prey-Predator algorithm is employed for the *** perceptron neural network(MLPNN)is used in this *** improve the performance of MLPNN,we determined the parameters of MLPNN using the prey-predator algorithm(PPA).The proposed model is called the MLPNN–*** performance of the proposed model has been analyzed by the root mean squared error(RMSE)function,and correlation coefficient(R).Furthermore,we tested the proposed model using other existing data recorded in Saudi Arabia(testing data).It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi *** results reveal that the number of infected persons will increase in the coming days and become a minimum of *** number of recoveries will be 2000 to 4000 per day.

关键词： COVID-19 ANN modeling multilayer perceptron neural network prey-predator algorithm

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