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Artificial Intelligence (CAI), IEEE Conference on

作者： Frederic Rizk Rodrigue Rizk Dominick Rizk Patrick Rizk Chee-Hung Henry Chu The Center for Advanced Computer Studies University of Louisiana at Lafayette Lafayette LA U.S.A. Department of Computer Science University of South Dakota Vermillion SD U.S.A. Department of Electrical Engineering and Computer Science The Catholic University of America Washington DC U.S.A. Department of Mechanical Engineering University of Quebec at Trois-Rivières Drummondville QC Canada The Center for Advanced Computer Studies Informatics Research Institute University of Louisiana at Lafayette Lafayette LA U.S.A.

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Deep neural networks excel in a wide range of tasks but require diverse datasets to prevent overfitting. Overfitting occurs when a network fits training data too precisely, leading to poor generalization. Data Augmentation is often used to mitigate overfitting aiming at enlarging and improving the quality of training datasets, facilitating the construction of superior deep learning models. MAGAN algorithm emerges as an innovative approach that functions as a Meta-Analysis of Generative Adversarial Networks (GANs). MAGAN harnesses the latent space capabilities of GANs to confront the challenges presented by binary-class, multi-class, grayscale, and RGB images, effectively covering a wide spectrum of scenarios. In this paper, we propose the use of MAGAN algorithm for binary-class and multi-class data augmented generation. We also undertake an in-depth experimental analysis, evaluating the performance of the proposed MAGAN-based approach in comparison to two alternative baseline scenarios: one without any augmentation and another utilizing a conventional augmentation method. To gauge the effectiveness of the proposed technique, we employed diverse classification metrics, including accuracy, loss, precision, recall, F1-score, and the confusion matrix. Our results demonstrate that the proposed approach surpasses the other two scenarios achieving improvements in terms of accuracy by a factor of x1.15 and x1.03, respectively. This underscores the significant advantages of harnessing MAGAN, a meta-analysis of GANs, for data augmentation across a range of image types and classification tasks.

关键词： Deep learning Measurement Accuracy Training data Artificial neural networks Gray-scale Data augmentation

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Clinical resistance predictors to first-line VEGFR-TKI monotherapy for metastatic renal cell carcinoma: a retrospective multicenter real-life case series

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Journal of Cancer Metastasis and Treatment 2023年第1期9卷 445-458页

作者： Gaetano Pezzicoli Silvana Quaglini Valentina Tibollo Melissa Bersanelli Camillo Porta Mimma Rizzo Department of Interdisciplinary Medicine University of Bari"Aldo Moro"Bari 70124Italy Department of Electrical Computer and Biomedical EngineeringUniversity of PaviaPavia 27100Italy Laboratory of Informatics and Systems Engineering for Clinical Research Scientific Clinical Institute Maugeri(ICS Maugeri)Pavia 27100Italy Medical Oncology Unit University Hospital of ParmaParma 43121Italy Division of Translational Oncology Scientific Clinical Institute Maugeri(ICS Maugeri)Pavia 27100Italy Medical Oncology Unit University Hospital-Polyclinic of BariBari 70124Italy

Aim:For many years,systemic treatment of metastatic Renal Cell Carcinoma(mRCC)was based on sequential targeted agent *** this real-life case series,we evaluated easily accessible clinical factors useful for disease course ***:We exploited patients'clinical pathological characteristics and systemic treatment outcomes in a real-world population of 365 mRCC patients who received sequential monotherapies in the targeted therapy era,and we identified an early progressors subpopulation,resistant to first-line VEGFR-TKI monotherapy in less than 6 ***:Early progressors(n=124)show a far worse OS compared with patients progressing beyond the sixth month of therapy(13.5 vs.44.8 months,P-value<0.0001,HR=0.41,95%CI:0.29-0.53).However,these patients did not show far worse performance in second and third-line settings compared to first-line *** the univariate analysis,IMDC risk class,sarcomatoid features,and Systemic Inflammation Index(SII)were correlated with first-line therapy Progression-Free Survival(PFS1).In multivariate analysis,variables correlated with first-line outcome were IMDC risk class,histotype,and number of metastatic sites at the ***:Real-world data can contribute to developing easy-to-use prognostic factors associated with refractory disease that could support clinicians in identifying the most appropriate treatment strategy for each patient.

关键词： Renal cell carcinoma targeted therapy tyrosine kinase inhibitor drug resistance early progressor rapidly progressive prognostic factor real-world data

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Games for Scrum Team Collaboration in the Global Software Development Environment: A Literature Review

Games for Scrum Team Collaboration in the Global Software De...

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International Conference on informatics and Computing (ICIC)

作者： Anita Hidayati Iklima Ermis Ismail Ade Rahma Yuly Henry Edison Department of Computer and Informatics Engineering Politeknik Negeri Jakarta Depok Indonesia Faculty of Computer Science Universitas Dian Nuswantoro Semarang Indonesia Department of Software Engineering Blekinge Institute of Technology Sweden Swedia

ISBN: (纸本)9798350345728

Software development companies commonly use Global Software Development (GSD) in their industry. A competent Scrum team supports the success of the GSD project. This research aims to identify the game components in the form of types, elements, and technologies that contribute to building experience for the Scrum team in a GSD environment. We used a Systematic Literature Review (SLR) with the Kitchenham and snowball method. From the 12 papers, we concluded that none of the games specifically focused on the competence of the Scrum team at GSD. The SLR results also identify ten game elements and technologies as a reference for game development to meet the challenges in GSD. The conclusion is that there are still great opportunities to develop a game that can improve the competence of the GSD Scrum team. Game development should refer to elements and identified technologies.

关键词： Industries Systematics Bibliographies Games Software Teamwork Object recognition

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An Innovative Way to Increase the Speed and Efficiency of Reverse Mapping of Elliptic Curve Points

An Innovative Way to Increase the Speed and Efficiency of Re...

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Emerging Smart Computing and informatics (ESCI), Conference on

作者： Ryan Saptarshi Ray Debendranath Das Aritro Sengupta Pankaj Kumar Arun Kumar Sahani Information Technology Govt. College Of Engineering & Ceramic Technology Kolkata India Cryptology and Security Research Unit Indian Statistical Institute Kolkata India Department of Computer Science & Engineering Lingaya's Vidyapeeth Faridabad Haryana India

Elliptic curve cryptography is a widely recognized method of using a public key cryptosystem for encrypting and decrypting data. The message must be transformed to points on an Elliptic Curve using a mapping algorithm in order to send the message. This process of mapping the message to the point on the elliptic curve is done by applying a well-known mapping algorithm. Elliptic curve cryptography is widely acknowledged to have simpler mathematical operations compared to other cryptographic methods. However, researchers in the field of cryptography have faced challenges in mapping plain messages onto points on an elliptic curve and reversing the process at the receiver's end for decryption. Recent advancements have led to the development of mapping techniques that are resistant to frequency analysis. In this paper, we have discussed a well-known mapping scheme that has been used to map plain text messages to points on elliptic curves. However, the time taken to map a message and encrypt the points takes a substantial amount of time. This latency can be reduced if we parallelize the mapping process. In this paper, we have parallelized the reverse mapping phase of the scheme at the receiver's end to reduce the total communication time. Performance results after parallelization have been recorded and provided in the later section of the paper in tabular and graphical form.

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Konstantina-Helen Tsarapatsani Vasilios D. Tsakanikas Antonis Sakellarios Hans J. Trampisch Efterpi Karapintzou Henrik Rudolf George K. Matsopoulos Dimitrios I. Fotiadis Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Department of Medical Informatics Biometry and Epidemiology Ruhr University Bochum Bochum Germany Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greeece Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina and Biomedical Research Institute-FORTH Ioannina Greece

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction.

关键词： Accuracy Sensitivity Explainable AI Biological system modeling Peptides Receivers Predictive models Indexes Cardiovascular diseases Random forests

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Time-Domain Channel Estimation for Extremely Large MIMO THz Communication Systems Under Dual-Wideband Fading Conditions

arXiv

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arXiv 2023年

作者： Vlachos, Evangelos Kaushik, Aryan Eldar, Yonina C. Alexandropoulos, George C. Industrial Systems Institute ATHENA Research and Innovation Centre Rio-Patras26504 Greece School of Engineering and Informatics University of Sussex United Kingdom Faculty of Mathematics and Computer Science Weizmann institute of Science Israel Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens15784 Greece Department of Electrical and Computer Engineering Department University of Illinois Chicago ChicagoIL60601 United States

In this paper, we study the problem of extremely large (XL) multiple-input multiple-output (MIMO) channel estimation in the terahertz (THz) frequency band, considering the presence of propagation delays across the entire array apertures at both communication ends, which naturally leads to frequency selectivity. This problem is known as beam squint and may be pronounced when communications are subject to multipath fading conditions. Multi-carrier (MC) transmission schemes, which are usually deployed in THz communication systems to address these issues, suffer from high peak-to-average power ratio, which is specifically dominant in this frequency band where low transmit power is mostly feasible. Furthermore, the frequency selectivity caused by severe molecular absorption in the THz band necessitates delicate consideration in MC system design. Motivated by the benefits of single-carrier (SC) waveforms for practical THz communication systems, diverging from the current dominant research trend on MC systems, we devise a novel channel estimation problem formulation in the time domain for SC XL MIMO systems subject to multipath signal propagation, spatial wideband effects, and molecular absorption. An efficient alternating minimization approach is presented to solve the proposed mixed-integer sparse problem formulation. The conducted extensive performance evaluation results validate that the proposed XL MIMO estimation scheme exhibits superior performance than conventional SC- and MC-based techniques, approaching the idealized lower bound. © 2023, CC BY.

关键词： Channel estimation

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A quantum-linguistic multi-attribute group consensus method based on trust networks

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Advanced engineering informatics 2025年 66卷

作者： Xu, Yizhao Yan, Shuli Cabrerizo, Francisco Javier School of Management Science and Engineering Research Institute for Risk Governance and Emergency Decision-Making Nanjing University of Information Science & Technology Nanjing210044 China Department of Computer Science and Artificial Intelligence University of Granada Granada18071 Spain

In the context of uncertain social networks, belief biases and behavioral interactions among decision-makers often lead to interference effects during the decision-making process. To address this issue, this paper proposes a multi-attribute group consensus decision-making model based on quantum decision theory. Firstly, linguistic Z-numbers are extended into quantum states to represent uncertain information. The interference effects among trust paths are comprehensively considered, and trust relationships are aggregated based on quantum probability theory to complete the trust network. On this basis, the comprehensive relative out-degree centrality index is used to determine the weights of decision-makers. Secondly, a minimum adjustment group consensus optimization model based on trust relationships is constructed to analyze the impact of decision-makers’ trust levels on group opinion adjustments. Furthermore, considering the risk preference psychology of decision-makers in the decision-making process, attributes’ weights are determined using different probability distributions, and the score values of alternatives are calculated to obtain the ranking results. Finally, the proposed model is validated through a case study of production line configuration in an electronics manufacturing company, and sensitivity analysis and comparative analysis are conducted to demonstrate the rationality and robustness of the proposed method. The results show that the proposed model can effectively characterize interference effects and promote the achievement of group consensus. © 2025

关键词： Distribution functions

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Human Expansion-Induced Biodiversity Crisis over Asia from 2000 to 2020

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research 2024年第2期2023卷 647-658页

作者： Chao Yang Qingquan Li Xuqing Wang Aihong Cui Junyi Chen Huizeng Liu Wei Ma Xuanyan Dong Tiezhu Shi Fanyi Meng Xiaohu Yan Kai Ding Guofeng Wu MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area&Guangdong Key Laboratory of Urban Informatics&Shenzhen Key Laboratory of Spatial Smart Sensing and Services Shenzhen UniversityShenzhen 518060China School of Architecture and Urban Planning Shenzhen UniversityShenzhen 518060China College of Civil and Transportation Engineering Shenzhen UniversityShenzhen 518060China Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Center for Hydrogeology and Environmental Geology China Geological SurveyBaoding 071051China Department of Geography Hong Kong Baptist UniversityHong Kong Special Administrative Region 999077China Faculty of Land Resource Engineering Kunming University of Science and TechnologyKunming 650093China institute for Advanced Study and Tiandu-Shenzhen University Deep Space Joint Laboratory Shenzhen UniversityShenzhen 518060China School of Civil Engineering Chongqing Jjiaotong UniversityChongging 400074China Department of Cil and Environmental Engineering Tohoku UniversitySendai 980-8579Japan School of Artificial Intelligence Shenzhen PolytechnicShenzhen 518055China School of Computer Science and Technology Dongguan University of TechnologyDongguan 523419China

Asia stands out as a priority for urgent biodiversity conservation due to its large protected areas(PAs)and threatened *** the 21st century,both the highlands and lowlands of Asia have been experiencing the dramatic human ***,the threat degree of human expansion to biodiversity is poorly ***,the threat degree of human expansion to biodiversity over 2000 to 2020 in Asia at the continental(Asia),national(48 Asian countries),and hotspot(6,502 Asian terrestrial PAs established before 2000)scales is investigated by integrating multiple large-scale *** results show that human expansion poses widespread threat to biodiversity in Asia,especially in Southeast Asia,with Malaysia,Cambodia,and Vietnam having the largest threat degrees(~1.5 to 1.7 times of the Asian average level).Human expansion in highlands induces higher threats to biodiversity than that in lowlands in one-third Asian countries(most Southeast Asian countries).The regions with threats to biodiversity are present in~75%terrestrial PAs(including 4,866 PAs in 26 countries),and human expansion in PAs triggers higher threat degrees to biodiversity than that in *** findings provide novel insight for the Sustainable Development Goal 15(SDG-15 Life on Land)and suggest that human expansion in Southeast Asian countries and PAs might hinder the realization of *** reduce the threat degree,Asian developing countries should accelerate economic transformation,and the developed countries in the world should reduce the demands for commodity trade in Southeast Asian countries(i.e.,trade leading to the loss of wildlife habitats)to alleviate human expansion,especially in PAs and highlands.

关键词： Human terrestrial integrating

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Investigating the Use of Artificial Intelligence for Secure and Transparent Record Keeping in Education

Investigating the Use of Artificial Intelligence for Secure ...

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informatics (ICI), International Conference on

作者： B. Ravindar Darshana A. Naik Neetu Jain Melanie Lourens Mohit Tiwari Pankaj Kunekar School of Sciences SR University Warangal Telangana Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore Bharati Vidyapeeth (Deemed to Be University) Institute of Management and Research New Delhi India Faculty of Management Sciences Durban University of Technology South Africa Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi Department of Information Technology Vishwakarma Institute of Technology Pune Maharashtra

Numerous businesses, particularly education, are being rapidly transformed by artificial intelligence (AI). AI is being utilised in school management to enhance student results, the learning experience, and administrative chores. This study intends to investigate the use of AI in educational management, comprising advantages and disadvantages. The study effort examines the literature on AI in school management using a systematic review method. According to the research, AI has several benefits, such as increased student engagement, personalised instruction, and cost effectiveness. But AI also presents several difficulties, including ethical issues, possible biases, and the necessity to reskill the workforce. The study finds that AI may significantly enhance educational management, but its application must be done so carefully and cautiously.

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Brain Tumor Classification and Segmentation Using Dual-Outputs for U-Net Architecture: O2U-Net

Brain Tumor Classification and Segmentation Using Dual-Outpu...

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21st International Conference on informatics, Management, and technology in Healthcare, ICIMTH 2023

作者： Zargari, Seyed Aman Kia, Zahra Sadat Nickfarjam, Ali Mohammad Hieber, Daniel Holl, Felix Electrical Engineering Department Faculty of Electrical and Computer Engineering Semnan University Semnan Iran Department of Health Information Management and Technology School of Allied Medical Sciences Kashan University of Medical Science Kashan Iran Health Information Management Research Center Kashan University of Medical Science Kashan Iran Institute DigiHealth Neu-Ulm University of Applied Sciences Neu-Ulm Germany

ISBN: (纸本)9781643684000

We propose a modified version of the U-Net architecture for segmenting and classifying brain tumors, introducing another output between down- and up-sampling. Our proposed architecture utilizes two outputs, adding a classification output beside the segmentation output. The central idea is to use fully connected layers to classify each image before applying U-Net's up-sampling operations. This is achieved by utilizing the features extracted during the down-sampling procedure and combining them with fully connected layers for classification. Afterward, the segmented image is generated by U-Net's up-sampling process. Initial tests show competitive results against comparable models with 80.83%, 99.34%, and 77.39% for the dice coefficient, accuracy, and sensitivity, respectively. The tests were conducted on the well-established dataset from Nanfang Hospital, Guangzhou, China, and General Hospital, Tianjin Medical University, China, from 2005 to 2010 containing MRI images of 3064 brain tumors. © 2023 The authors and IOS Press.

关键词： Tumors

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