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Data Intelligence and Cognitive informatics (ICDICI), International Conference on

作者： Soundar Ida Mahizha Joseph Annrose Jeyebalaji Mano Christaine Angelo Israel Domilin Shyni Department of Information Technology St. Xavier’s Catholic College of Engineering Nagercoil India Faculty of Dentistry Sri Ramachandra Institute of Higher Education and Research Chennai India Department of Computer Science and Engineering DMI College of Engineering India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Early childhood caries is a common oral health issue in both developed and developing countries. Identifying caries lesions early can help in implementing minimally invasive treatment plans, preserving the remaining tooth structure. Interproximal lesions are typically found between the adjacent surfaces of two teeth. The first step is visual inspection, followed by examination using dental probes to detect the dental caries. Usually, radiographic examination is done for early detection of dental caries. This process is time-consuming. In recent years, AI has been used to detect early caries from X-ray images. In dentistry, proximal caries is difficult to identify from periapical and panoramic radiographs, so Bitewing Radiographs (BWR) are used to detect the interproximal caries. Since the X-ray source is parallel to the sensor, Bitewing Radiographs has least distortion and help in the detection of early proximal caries. Several studies have shown that Bitewing Radiographs have high sensitivity in detecting interproximal caries occurring in existing restorations compared to visual clinical detection. The main objective of this study is to detect dental caries and classify them into various classes enamel, dentin and restorations. In this study, a dataset was collected from Mendeley data and Kaggle. The caries classification utilized a Deep Convolutional Neural Network. A YOLOv8 algorithm was developed based on the severity of the lesions observed in bitewing digital radiographs, enabling convenient and reliable detection of these lesions. The performance of these classifications is evaluated. This study aimed to evaluate the performance of the deep neural network for detecting interproximal caries lesions on Bitewing Radiographs (BWR). Accurate detection and diagnosis of dental caries are crucial for reducing the cost of oral health management and improving the likelihood of preserving natural teeth, leading to better patient outcomes.

关键词： Visualization Sensitivity Teeth Dentistry Image restoration Lesions Convolutional neural networks Diagnostic radiography X-ray imaging Surface treatment

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Deep Attention-Based Alignment Network for Melody Generation from Incomplete Lyrics

Deep Attention-Based Alignment Network for Melody Generation...

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IEEE International Symposium on Multimedia

作者： M Gurunath Reddy Zhe Zhang Yi Yu Florian Harscoet Simon Canales Suhua Tang Digital Content and Media Sciences Research Division National Institute of Informatics SOKENDAI Department of Computer and Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications

ISBN: (纸本)9781665471732

We propose a deep attention-based alignment network, which aims to automatically predict lyrics and melody with given incomplete lyrics as input in a way similar to the music creation of humans. Most importantly, a deep neural lyrics-to-melody net is trained in an encoder-decoder way to predict possible pairs of lyrics-melody when given incomplete lyrics (few keywords). The attention mechanism is exploited to align the predicted lyrics with the melody during the lyrics-to-melody generation. The qualitative and quantitative evaluation metrics reveal that the proposed method is indeed capable of generating proper lyrics and corresponding melody for composing new songs given a piece of incomplete seed lyrics.

关键词： Measurement

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A Unified Approach for Binary-Class and Multi-Class Data Augmented Generation

A Unified Approach for Binary-Class and Multi-Class Data Aug...

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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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Authentication and Authorization in Modern Web Apps for Data Security Using Nodejs and Role of Dark Web 4th

Authentication and Authorization in Modern Web Apps for Data...

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4th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2022

作者： Pant, Piyush Rajawat, Anand Singh Goyal, S.B. Bedi, Pradeep Verma, Chaman Raboaca, Maria Simona Enescu, Florentina Magda School of Computer Sciences and Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya46100 Malaysia Department of Computer Science and Engg Galgotias University Greater Noida India Faculty of Informatics University of Eötvös Loránd Budapest1053 Hungary ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea240050 Romania Department of Electronics Communications and Computers University of Pitesti Pitesti Romania

Authentication and Authorization are the base of security for all the Technologies present in this world today. Starting from your smartphone where a user authenticates himself before he could access the data inside to Entering into the White House, you must authenticate yourself, and based on that you are authorized. In this digital world where every Business, MNC, Government Body, Companies, Users, etc. needs a website to inform the world about their presence on the internet, provide services online and become a "Brand", the risk of leaking user's sensitive information increases. It could be dangerous to the users of the hacked website because their sensitive information like a credit card, bank account details, etc. could be sold in the black market of the "dark web". The role of the dark web is described in the paper and how the data is sold there and what becomes of it. The paper helps to understand how a secure website is developed that promises the user to keep the sensitive information safe, increases the bond of trust between a client and server which results in a long-term relationship. The aim behind developing an authentication system is to keep users' sensitive information safe so that hackers cannot steal and sell the information on the dark web's back market. To perform this, the developer needs to understand how to implement authentication. NodeJS, with the help of its framework expressJS and some other packages, is used to develop the authentication and authorization system of the website by the research. Previous papers on this field covered the authentication topic in general. This paper overcame that by going deeper into the field and being server-side language specific. The common types of authentication methods used in different types of websites are discussed in detail and the best methods are purposed for the developer to be implemented for a more secure website. This research put light on Artificial Intelligence and blockchain as the future

关键词： Websites

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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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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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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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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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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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