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A regularized CNN approach for detecting cervical cancer

AIP Conference Proceedings 2024年第1期3220卷

作者： Jesse Jeremiah Tanimu Mohamed Hamada Mohammed Hassan Tsentob Joy Samson Jane Simon Parah Department of computer Science Bayero Unoiversity Kano Kano Nigeria Division of information systems University of Aizu Aizu Japan Department of Software Engineering Bayero University Kano Kano Nigeria Department of computer Science Air Force Institute of Technology Kaduna Kaduna Nigeria Department of Mathematical and Physical Science Glorious Vision University Ogwa Benin Nigeria

This study delved into leveraging deep learning techniques to classify different types of cervical cancer images, recognizing the disease’s prevalence and criticality. We employed a dataset comprising cervical images and investigated the utilization of the VGG16 model as a foundational architecture. To enhance performance, we incorporated various improvements, such as L2 regularization and data augmentation. The outcomes underscored the efficacy of these enhancements, with the refined model achieving a remarkable accuracy of 94%, surpassing the VGG16 model’s baseline accuracy of 89%. These findings demonstrate the potential of deep learning to improve the classification of cervical cancer.

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INNOVATIONS IN computer-AIDED-DESIGN OF MARINE TURBINES USING INTERACTIVE GRAPHICS

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NAVAL ENGINEERS JOURNAL 1980年第2期92卷 207-217页

作者： GINGRICH, JK WINTER, RL Mr. John K. Gingrich joined the General Electric Company after his graduation from Lafayette College in 1948. His early assignments were with the General Engineering Laboratory and the Knolls Atomic Power Laboratory in Schenectady. N. Y. He joined the Marine Turbine and Gear Department in 1968 as Manager Engineering Administration. and assumed his present assignment as Manager Engineering Resource Planning & Administration in 1976. Mr. Gingrich was instrumental in procuring the Interactive Graphics System for the Medium Steam Department. having headed up the Study Team and is now responsible for the Interactive Graphics Systems management including new procurements. He is a member of the American Institute of Design Drafting and is currently the Chairman of the Computer-Aided Drafting Committee in that organization. Mr. Reinhold L. Winter is a graduate of the General Electric Apprentice Program and Salem State College from which he received his B.S. degree in Business Administration. He has nineteen years experience with the General Electric Company including eight years in Business Systems and Programming two years in Computer Operations Management and six years in his present position as Manager Engineering Business Systems. He was a member of the original Study Team that investigated and selected the present Applicon Interactive Graphics (IAG) equipment and currently is responsible for the management of software and hardware support for the ZAG Facility.

This paper discusses the Interactive Graphics System used by the General Electric Company, Medium Steam Turbine department (engineering & Manufacturing) for designing, drafting, and manufacturing applications. A brief overview of the hardware malting up the system is described, followed by a more detailed description of the actual applications. Two-dimensional applications described include a Heat Balance Analysis, Flow Diagrams, and Electrical Schematics. A more fruitful area for increased productivity gains is described in the three-dimensional or mechanical applications including turbine design & layout and bucket design. coordination of the design with manufacturing for numerical control tape generation is described through CAM and Plate Frame Cutting applications. Finally, a short review of the engineering design work using Interactive Graphics is discussed. Productivity gains of 2.6 to 1 are being realized, and the overall savings to the Medium Steam department are outlined.

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CHANCES AND CHALLENGES OF CHATGPT AND SIMILAR MODELS FOR EDUCATION IN M&S

TechRxiv

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TechRxiv 2023年

作者： Scherer, William T. Tolk, Andreas Loper, Margaret L. Barry, Philip Rabadi, Ghaith Yilmaz, Levent The MITRE Corporation Modeling and Analysis Innovation Center 1001 Research Park Blvd #200 CharlottesvilleVA22911 United States Georgia Tech Research Institute Georgia Institute of Technology AtlantaGA30308 United States University of Virgina Department of Systems and Information Engineering CharlottesvilleVA22904 United States L3Harris Corporation Mission Analysis Group 2235 Monroe Street HerndonVA20171 United States University of Central Florida School of Modeling Simulation and Training 3100 Technology Pkwy OrlandoFL32826 United States Auburn University Samuel Ginn College of Engineering Computer Science and Software Engineering AuburnAL36849 United States

The position paper summarizes the inputs of a set of experts from academia and industry presenting their view on chances and challenges of using ChatGPT within the Modelling and Simulation education. They also address the need to evaluate continuous education as well as education of faculty members to address scholastic challenges and opportunities while meeting the expectation of industry. © 2023, CC BY.

关键词： Modeling languages

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Accelerating Real-Time Imaging for Radiotherapy: Leveraging Multi-GPU Training with PyTorch

Accelerating Real-Time Imaging for Radiotherapy: Leveraging ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Ruth Obe Brandt Kaufmann Kaelen Baird Sam Kadel Yasmin Soltani Mostafa Cham Matthias K. Gobbert Carlos A. Barajas Zhuoran Jiang Vijay R. Sharma Lei Ren Stephen W. Peterson Jerimy C. PoIf Dept. of Computer Science and of Software Engineering U. of Houston-Clear Lake USA Dept. of Mathematics and Statistics U. of San Francisco USA Dept. of Computer Science and of Mathematics Skidmore College USA Dept. of Computer Science and of Psychology Mount Holyoke College USA Dept. of Biomedical Engineering U. of Houston USA Dept. of Information Systems U. of Maryland Baltimore County Dept. of Mathematics and Statistics U. of Maryland Baltimore County Medical Physics Graduate Program Duke University USA Dept. of Radiation Oncology U. of Maryland School of Medicine USA Department of Radiation Oncology U. of Maryland School of Medicine USA Dept. of Physics U. of Cape Town South Africa H3D Inc. USA

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy.

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Behavioral-based real-time cheating detection in academic exams using deep learning techniques

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AIP Conference Proceedings 2023年第1期2909卷

作者： Zouheir Trabelsi Medha Mohan Ambali Parambil Fady Alnajjar Luqman Ali Department of Information Systems and Security College of Information Technology United Arab Emirates University Al Ain United Arab Emirates Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain United Arab Emirates

Recently, with the development of Artificial Intelligence (AI), the use of automated evaluation in education has increased. Maintaining academic integrity is one of the most challenging aspects of higher education. Cheating is rampant in academic examinations and other forms of educational assessment. The vast majority of students believe that it is unethical to tolerate cheating; therefore, it is vital to devote a significant amount of effort to identifying and avoiding instances of cheating. Examining the student’s behavior is one way to determine whether they are engaged in cheating or not. This paper proposes a deep learning-based cheating detection system that can identify instances of students engaging in dishonest behavior. A YOLOv7 model is trained on a custom dataset collected from various resources. The dataset comprises two classes, i.e., cheating and not cheating, and 2565 images. Evaluation criteria like precision, F1 score, recall, and mAP (mean average precision) are used to validate the performance of the proposed model. The proposed model shows promising performance in categorizing the student’s visible actions into cheating or not cheating and achieved an overall mAP@0.5 of 0.719. Overall, the proposed method can be utilized to reduce the error rate associated with human monitoring by alerting the proper authorities whenever unusual behavior is observed during academic tests.

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Demographical Based Sentiment Analysis for Detection of Hate Speech Tweets for Low Resource Language

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ACM Transactions on Asian and Low-Resource Language Information Processing 1000年

作者： Kamal Safdar Shibli Nisar Waseem Iqbal Awais Ahmad Yawar Abbas Bangash Department of Computer Software Engineering National University of Sciences and Technology (NUST) Islamabad-44000 Pakistan Department of Electrical Engineering National University of Sciences and Technology (NUST) Islamabad-44000 Pakistan Department of Information Security National University of Sciences and Technology (NUST) Islamabad-44000 Pakistan Information Systems Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Saudi Arabia

Advancement in IT and communication technology provides the opportunity for social media users to communicate their ideas and thoughts across the globe within no time as well big data promulgated in a result of the communication process itself has immense challenges. Recently, the provision of freedom of speech has witnessed immense promulgation of offensive and hate speech content on the internet aimed the basic human rights violation. The detection of abusive content on social media for rich resource language has become a hot area for researchers in the recent past. However, low-resource languages are underprivileged due to the non-availability of large corpus and its complexity to understand. The proposed methodology mainly has two parts. One is to detect abusive content and the other is to have a demographical analysis of the Indigenously developed dataset. The process starts with the development of a unique unlabeled Urdu dataset of 0.2 M from Twitter through a web scrapper tool named snscraper. The dataset is collected against the 36 districts of Punjab from Pakistan and from the duration 2018- Apr 2022. The dataset is labeled into three target classes Neutral, Offensive, and Hate Speech. After data cleaning, the feature extraction process is achieved with the help of traditional techniques such as Bow and tf-idf with the combination of word and char n-gram and word embedding word2Vec. The dataset is trained on both machine learning algorithms SVM and Logistic regression and deep learning techniques Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). The best F score achieved through LSTM on this dataset is 64 and accuracy is 93 through CNN algorithms. A Choropleth map is used for visualization of the dataset distributed among 36 districts of Punjab and a time series plot for time analysis covers five years duration from 2018-Apr to 22.

关键词： snscrapper CNN Word Embedding LSTM Choropleth map

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A Text-Inception-Based Natural Language Processing Model for Sentiment Analysis of Drug Experiences

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ACM Transactions on Asian and Low-Resource Language Information Processing 1000年

作者： Muhammad Swaileh A. Alzaidi Alya Alshammari Manar Almanea Haneen A. Al-khawaja Hanan Al Sultan Shoayee Alotaibi Wafa Almukadi Department of English Language College of Language Sciences King Saud University Riyadh Saudi Arabia Department of Applied Linguistics College of Languages Princess Nora bint Abdul Rahman University Riyadh Saudi Arabia Department of English College of Languages and Translation Imam Muhammad Ibn Saud Islamic University Riyadh Saudi Arabia Applied Science Private University Amman Jordan Department of English College of Arts King Faisal University Al-Ahsa Saudi Arabia Department of Artificial Intelligence and Data Science? College of Computer Science and Engineering University of Hail Hail Saudi Arabia Department of Software Engineering College of Engineering and computer Science Jeddah University Jeddah Saudi Arabia

The study of sentiment in Natural Language Processing (NLP) is among the most successful research areas because of the availability of millions of user opinions online since the turn of the century. The economic, political, and medical fields are just some of the many that have benefited from studies of sentiment research. While numerous studies have examined more mainstream topics like consumer electronics, movies, and restaurants, relatively few have examined health and medical concerns. Considerable insight into where to direct efforts to improve public health might be gained by a study of how people feel about healthcare as a whole and of individual drug experiences in particular. When it comes to medicine, automatic analysis of online user evaluations paves the way for sifting through massive amounts of user feedback to find information regarding medications' efficacy and side effects that might be used to enhance pharmacovigilance programs. Simple rules-based methods have given way to more complex machine learning approaches like deep learning, which is developing as a technology for many natural language processing jobs. The opensource datasets have been analyzed with models that use word embeddings and term frequency-inverse document frequency (TF-IDF). A feature-enhanced text-inception model for sentiment classification was presented to work in tandem with this approach. The model first employed a cutting-edge text-inception module to glean useful shallow features from the text. K-MaxPooling was subsequently employed to reduce the dimensionality of its shallow and deep includes as well as enhance the generalization of characteristics, and a deep feature extraction module was formed using the bidirectional gated recurrent unit (Bi-GRU) and the capsule neural network to comprehend the text's semantic data. By combining traditional methods with cutting-edge artificial intelligence techniques, this hybrid approach can revolutionize public health initiatives, de

关键词： Natural Language Processing (NLP) User Opinions Healthcare Medical Sentiment Public Health Deep learning

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Efficient Training Approaches for Performance Anomaly Detection Models in Edge Computing Environments

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ACM Transactions on Autonomous and Adaptive systems 1000年

作者： Duneesha Fernando Maria Rodriguez Patricia Arroba Leila Ismail Rajkumar Buyya Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Australia CCS–Center for Computational Simulation Universidad Politécnica de Madrid Spain Intelligent Distributed Computing and Systems Research (INDUCE) Lab Department of Computer Science and Software Engineering United Arab Emirates University United Arab Emirates

Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to performance anomalies caused by resource hogging (e.g., CPU or memory), resource contention, etc., which can negatively impact their Quality of Service and violate their Service Level Agreements. Existing research on performance anomaly detection for edge computing environments focuses on model training approaches that either achieve high accuracy at the expense of a time-consuming and resource-intensive training process or prioritize training efficiency at the cost of lower accuracy. To address this gap, while considering the resource constraints and the large number of devices in modern edge platforms, we propose two clustering-based model training approaches: (1) intra-cluster parameter transfer learning-based model training (ICPTL) and (2) cluster-level model training (CM). These approaches aim to find a trade-off between the training efficiency of anomaly detection models and their accuracy. We compared the models trained under ICPTL and CM to models trained for specific devices (most accurate, least efficient) and a single general model trained for all devices (least accurate, most efficient). Our findings show that ICPTL’s model accuracy is comparable to that of the model per device approach while requiring only 40% of the training time. In addition, CM further improves training efficiency by requiring 23% less training time and reducing the number of trained models by approximately 66% compared to ICPTL, yet achieving a higher accuracy than a single general model.

关键词： Edge computing Microservices IoT Performance anomaly detection Machine learning Deep learning

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Author Correction: Perceived match between own and observed models' bodies: influence of face, viewpoints, and body size

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Scientific reports 2020年第1期10卷 17120页

作者： Lize De Coster Pablo Sánchez-Herrero Carlos Aliaga Miguel A Otaduy Jorge López-Moreno Ana Tajadura-Jiménez DEI Interactive Systems Group Department of Computer Science and Engineering Universidad Carlos III de Madrid Avenida de la Universidad 30 28911 Leganés Madrid Spain. lcoster@inf.uc3m.es. Seddi Labs Madrid Spain. Modeling and Virtual Reality Group Department of Computer Science Universidad Rey Juan Carlos Madrid Spain. Multimodal Simulation Lab Department of Computer Science and Architecture Computer Systems and Languages Statistics and Operative Investigation Universidad Rey Juan Carlos Madrid Spain. DEI Interactive Systems Group Department of Computer Science and Engineering Universidad Carlos III de Madrid Avenida de la Universidad 30 28911 Leganés Madrid Spain.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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CheXMed: A Multimodal Algorithm to Improve Pneumonia Detection in the Elderly

SSRN

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SSRN 2023年

作者： Hao, Ren Jing, Fengshi Chen, Zhurong Liu, Le Zhou, Jiandong Jing, Ran Lian, Wanmin Tian, Junzhang Xu, Zhongzhi Zhang, Qingpeng Cheng, Weibin Institute for Healthcare Artificial Intelligence Guangdong Second Provincial General Hospital Guangzhou510317 China UNC Project-China UNC Global School of Medicine University of North Carolina Chapel HillNC27599 United States College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China College of Foreign Languages Jishou University Zhangjiajie427000 China Nuffield Department of Medicine University of Oxford OxfordOX3 9DU United Kingdom Information Department Guangdong Second Provincial General Hospital Guangzhou510317 China School of Public Health Sun Yat-sen University Guangzhou510080 China School of Data Science City University of Hong Kong Hong Kong

Objectives: Pneumonia can be a deadly illness for particular populations, one of which is older adults. While studies have successfully trained artificial intelligent assisted diagnostic tools to detect pneumonia using chest X-ray images, they were targeted to the general population without stratification on age ***: This study investigated the performance disparities between geriatric and younger patients when using chest X-ray images to detect pneumonia, and developed and tested a multimodal algorithm that incorporates clinical notes together with image data to improve pneumonia detection performance for older *** and Methods: Three most used pneumonia diagnostic models, CheXNet, DenseNet121, and ResNet18, were evaluated to test their performance in older persons. Electronic medical records and frontal-view chest X-ray images of 2,572 inpatients were collected in a local hospital data to train and test a multi-input deep neural network framework named CheXMed, for pneumonia detection among older patients. Accuracy, precision, recall, F1-score, and AUC were used for model performance ***: CheXNet, DenseNet121, and ResNet18 achieved 9.5%, 6.0%, and 6.9% of higher AUC in the younger age group than in the elderly group, respectively. The proposed novel model, CheXMed, outperforms CheXNet by 15.3% in accuracy, 9.0% in precision, 38.3% in recall, and 24.0% in ***: Diagnostic models achieving good performance for young adults or the general population do not guarantee that they are useful for the elderly. Chest X-ray images combined with clinical information significantly improve pneumonia detection performance among elderly patients who are more vulnerable while neglected in general models. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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