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International Conference on Computing and Networking Technology (ICCNT)

作者： Sheetal Temara Sagar Varma Samanthapudi Piyush Rohella Ketan Gupta T. Kiruthiga Dept of Computer & Information Sciences University of the Cumberlands Williamsburg KY USA Applied Data Science The Polis Center at Indiana University Purdue University Indianapolis Indianapolis Indiana USA Payments &Commerce GoDaddy Edmonds Washington USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Department of ECE Vetri Vinayaha College of Engineering and Technology Trichy Tamil Nadu INDIA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This technical abstract offers an outline of the ability of artificial Intelligence (AI) and device studying (ML) answers for managing leverage threats in banking. Leverage is a key element in banking danger management, as it could expose the organization to disproportionate liabilities if left unchecked. AI and ML may be used to automate the tracking system of leverage hazards, allowing extra correct and welltimed identification and management of exposures. The abstract examines how AI/ML-driven solutions can leverage to be had facts to detect subtle modifications in leverage through the years, combining techniques from supervised and unsupervised ML to generate actionable insights. It then opinions viable implementation fashions and capacity challenges and gives first-rate practices for a hit implementation. The summary concludes that AI/ML-driven hazard management structures offer a promising new approach to leverage threat management inside the banking sector. In addition, research is warranted on this location.

关键词： Analytical models data analysis Banking Organizations Machine learning Hazards data models Pattern recognition Artificial intelligence

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Evaluation of Deep Training and Adapting Algorithms in Medical with De-Noising EEG

Evaluation of Deep Training and Adapting Algorithms in Medic...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Ali Hadi Abdulwahid Girija Rani Karetla KVB. Ganesh A Suresh Kumar K. Radha K. Shivakumar Control and Automation Engineering Department Engineering Technical College Southern Technical University Basra Iraq School of Computer Data and Mathematical Sciences Western Sydney University Australia KL Business School Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of CSE Graphic Era Deemed to be University Dept. of IT St. Martin's Engineering College Secunderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

An electroencephalogram that was captured with electrodes placed can easily get contaminated with a variety of artifacts. Here is a comparative of various electroencephalogram (EEG) de-noising techniques. Three alternative techniques are used to eliminate five different kinds of noise, and also the outcomes are evaluated. These three methods are just the Least Mean Square (LMS) technique, the Connected Neural Network, and also the Recursive Least - square (RLS) adaptable algorithm (FCNN). Period charts of the actual EEG signal, the noise EEG signal, and thus the predicted EEG signal are used to display the results. RRMSE and signal- to-noise ratio were utilized to compare the effectiveness of the 3 de-noising strategies in this study. Investigating the model parameters in this case, we discover the FCNN forecasts a better outcome compared to the other two methods.

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Public Kaggle Competition "IceCube – Neutrinos in Deep Ice" 38

Public Kaggle Competition "IceCube – Neutrinos in Deep Ice"

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38th International Cosmic Ray Conference, ICRC 2023

作者： Eller, Philipp Abbasi, R. Ackermann, M. Adams, J. Agarwalla, S.K. Aguilar, J.A. Ahlers, M. Alameddine, J.M. Amin, N.M. Andeen, K. Anton, G. Argüelles, C. Ashida, Y. Athanasiadou, S. Axani, S.N. Bai, X. Balagopal, A.V. Baricevic, M. Barwick, S.W. Basu, V. Bay, R. Beatty, J.J. Becker Tjus, J. Beise, J. Bellenghi, C. Benning, C. BenZvi, S. Berley, D. Bernardini, E. Besson, D.Z. Blaufuss, E. Blot, S. Bontempo, F. Book, J.Y. Boscolo Meneguolo, C. Böser, S. Botner, O. Böttcher, J. Bourbeau, E. Braun, J. Brinson, B. Brostean-Kaiser, J. Burley, R.T. Busse, R.S. Butterfield, D. Campana, M.A. Carloni, K. Carnie-Bronca, E.G. Chattopadhyay, S. Chau, N. Chen, C. Chen, Z. Chirkin, D. Choi, S. Clark, B.A. Classen, L. Coleman, A. Collin, G.H. Connolly, A. Conrad, J.M. Coppin, P. Correa, P. Cowen, D.F. Dave, P. De Clercq, C. DeLaunay, J.J. Delgado, D. Deng, S. Deoskar, K. Desai, A. Desiati, P. de Vries, K.D. de Wasseige, G. DeYoung, T. Diaz, A. Díaz-Vélez, J.C. Dittmer, M. Domi, A. Dujmovic, H. DuVernois, M.A. Ehrhardt, T. Eller, P. Ellinger, E. El Mentawi, S. Elsässer, D. Engel, R. Erpenbeck, H. Evans, J. Evenson, P.A. Fan, K.L. Fang, K. Farrag, K. Fazely, A.R. Fedynitch, A. Feigl, N. Fiedlschuster, S. Finley, C. Fischer, L. Fox, D. Franckowiak, A. Fritz, A. Fürst, P. Gallagher, J. Ganster, E. Garcia, A. Gerhardt, L. Ghadimi, A. Glaser, C. Glauch, T. Glüsenkamp, T. Goehlke, N. Gonzalez, J.G. Goswami, S. Grant, D. Gray, S.J. Gries, O. Griffin, S. Griswold, S. Groth, K.M. Günther, C. Gutjahr, P. Haack, C. Hallgren, A. Halliday, R. Halve, L. Halzen, F. Hamdaoui, H. Ha Minh, M. Hanson, K. Hardin, J. Harnisch, A.A. Hatch, P. Haungs, A. Helbing, K. Hellrung, J. Henningsen, F. Heuermann, L. Heyer, N. Hickford, S. Hidvegi, A. Hill, C. Hill, G.C. Hoffman, K.D. Hori, S. Hoshina, K. Hou, W. Huber, T. Hultqvist, K. Hünnefeld, M. Hussain, R. Hymon, K. In, S. Ishihara, A. Jacquart, M. Janik, O. Jansson, M. Japaridze, G.S. Technical University of Munich Munich Data Science Institute Walther-von-Dyck-Strasse 10 Garching85748 Germany III. Physikalisches Institut RWTH Aachen University AachenD-52056 Germany Department of Physics University of Adelaide Adelaide5005 Australia Dept. of Physics and Astronomy University of Alaska Anchorage 3211 Providence Dr. AnchorageAK99508 United States Dept. of Physics University of Texas at Arlington 502 Yates St. Science Hall Rm 108 Box 19059 ArlingtonTX76019 United States CTSPS Clark-Atlanta University AtlantaGA30314 United States School of Physics Center for Relativistic Astrophysics Georgia Institute of Technology AtlantaGA30332 United States Dept. of Physics Southern University Baton RougeLA70813 United States Dept. of Physics University of California BerkeleyCA94720 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Institut für Physik Humboldt-Universität zu Berlin BerlinD-12489 Germany Fakultät für Physik & Astronomie Ruhr-Universität Bochum BochumD-44780 Germany Université Libre de Bruxelles Science Faculty CP230 BrusselsB-1050 Belgium Dienst ELEM BrusselsB-1050 Belgium Department of Physics Laboratory for Particle Physics and Cosmology Harvard University CambridgeMA02138 United States Dept. of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Dept. of Physics The International Center for Hadron Astrophysics Chiba University Chiba263-8522 Japan Department of Physics Loyola University Chicago ChicagoIL60660 United States Dept. of Physics and Astronomy University of Canterbury Private Bag 4800 Christchurch New Zealand Dept. of Physics University of Maryland College ParkMD20742 United States Dept. of Astronomy Ohio State University ColumbusOH43210 United States Dept. of Physics Center for Cosmology and Astro-Particle Physics Ohio State University ColumbusOH43210 United States Niels Bohr Institute University of Copenhagen Copenh

The reconstruction of neutrino events in the IceCube experiment is crucial for many scientific analyses, including searches for cosmic neutrino sources. The Kaggle competition "IceCube – Neutrinos in Deep ice" was a public machine learning challenge designed to encourage the development of innovative solutions to improve the accuracy and efficiency of neutrino event reconstruction. Participants worked with a dataset of simulated neutrino events and were tasked with creating a suitable model to predict the direction vector of incoming neutrinos. From January to April 2023, hundreds of teams competed for a total of $50k prize money, which was awarded to the best performing few out of the many thousand submissions. In this contribution I will present some insights into the organization of this large outreach project, and summarize some of the main findings, results and takeaways. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Neutrons

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Enhancing engineering Pedagogy through Deep Learning: Applications of Explainable AI and Neural Networks

Enhancing Engineering Pedagogy through Deep Learning: Applic...

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Green Industrial Electronics and Sustainable Technologies (GIEST), IEEE International Conference on

作者： Umamaheswari. U Pushpa Latha Mamidi Divya Nimma D. Venkateswarlu Mohammed Saleh Al Ansari Ponni Valavan M Saveetha School of law Saveetha Institute of Medical and Technical Sciences SIMATS Chennai India Department of Engineering Mathematics & Humanities S.R.K.R Engineering College Bhimavaram Andhra Pradesh India Computational Science University of Southern Mississippi Data Analyst in UMMC USA Dept. of MBA Lakireddy Balireddy College of Engineering (Autonomous) Permanently Affiliated to JNTUK Mylavaram AP India Department of Chemical Engineering College of Engineering University of Bahrain Bahrain Department of AI & DS K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798331540142

ISBN: (纸本)9798331540159

Conventional engineering pedagogy frequently fails to deliver individualized learning experiences and places a strong emphasis on one-size-fits-all methods of instruction. Meeting the requirements of every individual student and improving learning outcomes become more challenging as a result. Modern instructional tools, however state-of-the-art, typically cannot offer personalized, data-driven insights. Since many systems are clear, it might be challenging for educators and learners to comprehend the recommendations made. The concept-based transformer network presented in this work is intended to improve the personalized learning experience in engineering education. Using focus, this new neural system focuses on the most relevant elements of a student’s education, providing personalized recommendations and assessments. Transparency is achieved through the inclusion of semantic AI, so that teachers and students can understand and validate the recommendations made by the system The proposed approach begins with the collection of large amounts of data, which including demographic information, contact records, and measures of student performance. Pre-processing assures data accuracy and quality. Personalized learning strategies and recommendations are generated through feedforward neural networks and integrated into the instructional platform. Performance measurement and user feedback for incremental change and continuous monitoring. Key metrics such as student engagement, outcomes for learning, and satisfaction levels are used to assess the effectiveness of the model. A comparison of the proposed model with current methods demonstrates its ability to provide scalable, transparent and customizable learning solutions.

关键词： Deep learning Performance evaluation Industrial electronics Accuracy Explainable AI Semantics Transformers Feedforward neural networks engineering education Monitoring

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Advancements in Multiple Sclerosis Disease Classification Through Machine Learning

Advancements in Multiple Sclerosis Disease Classification Th...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Darshanaben D. Pandya Sheshang Degadwala Dhairya Vyas Vishal Narendrakumar Solanki Sharma Vishalkumar Sureshbhai Hiteshkumar Gunvantbhai Patel Dept. of Comp. Sci. SCJCCS (BCA) Sankalchand Patel University Visnagar Gujarat India Dept. of Comm. Engg. Sigma University Gujarat India The Maharaja Sayajirao University of Baroda Vadodara Gujarat India D L Patel Institute of Mgt. & Technology (MCA College) Himatnagar Gujarat India Department of Computer Science (MCA) Sankalchand Patel University Visnagar Gujrat Quality Engineering - Data Tiger Analytics Chennai Tamil Nadu

In recent years, the field of medical diagnosis has witnessed significant advancements due to the application of machine learning algorithms, especially in complex neurological diseases like Multiple Sclerosis (MS). This study delves into the progress made in MS disease classification using various machine learning techniques, including SVM, KNN, DT, RF, NB, and Extra Trees. Through a diverse dataset of clinical and neuroimaging data, this study has systematically compared the performance of these algorithms in identifying different MS subtypes. SVM and Random Forest exhibited the highest accuracy, while KNN and Decision Trees showed competitive performance. Naive Bayes and Extra Trees also demonstrated promising results in specific scenarios. The study discusses the strengths and weaknesses of each approach and explores the interpretability of the models to understand the key features influencing the classification process. These findings contribute to the growing knowledge base of machine learning in MS disease classification and open avenues for more efficient and accurate diagnostic tools, ultimately leading to personalized patient care and improved treatment planning.

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A science cloud for data intensive sciences

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data science Journal 2013年 12卷 WDS139-WDS146页

作者： Murata, Ken T. Watari, Shinichi Nagatsuma, Tsutomu Kunitake, Manabu Watanabe, Hidenobu Yamamoto, Kazunori Kubota, Yasufumi Kato, Hisao Tsugawa, Takuya Ukawa, Kentaro Muranaga, Kazuya Kimura, Eizen Tatebe, Osamu Fukazawa, Keiichiro Murayama, Yasuhiro 4-2-1 Nukui-kita Koganei Tokyo 184-8795 Japan Systems Engineering Consultants Co. Ltd. 4-10-1 Yoga Setagaya Tokyo 158-0097 Japan Dept. Medical Informatics of Medical School of Ehime Univ. Situkawa Toon City Ehime 791-0295 Japan Dept. Computer Science Univ. of Tsukuba Tenoudai 1-1-1 Tsukuba Science City Ibaraki 305-8573 Japan Research Institute for Information Technology Kyushu University 6-10-1 Hakozaki Higashi-Ku Fukuoka 812-8581 Japan Integrated Science Data System Research Lab NICT 4-2-1 Nukui-kita Koganei Tokyo 184-8795 Japan

It is often discussed that the fourth methodology for science research is "informatics". The first methodology is a theoretic approach, the second one is observation and/or experiment, and the third one is computer simulation. Informatics is a new methodology for data intensive science, which is a new concept based on the fact that most scientific data are digitalized and the amount of data is huge. The facilities to support informatics are cloud systems. Herein we propose a cloud system especially designed for science. The basic concepts, design, resources, implementation, and applications of the NICT science cloud are discussed.

关键词： Digital storage

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Uncovering the Benefits of Machine Learning for Automating Financial Regulatory Tasks

Uncovering the Benefits of Machine Learning for Automating F...

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E-mobility, Power Control and Smart Systems (ICEMPS), International Conference on

作者： Sagar Varma Samanthapudi Piyush Rohella Sheetal Temara Ketan Gupta S. Dhanasekaran Applied Data Science The Polis Center at Indiana University Purdue University Indianapolis Indianapolis Indiana USA Principal Product Manager Payments & Commerce GoDaddy Edmonds Washington USA Dept of Computer & Information Sciences University of the Cumberlands Williamsburg KY USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Department of ECE Sri Eshwar College of Engineering Coimbatore Tamil Nadu INDIA

ISBN: (数字)9798350394399

ISBN: (纸本)9798350394405

Machine Learning (ML) gives the ability to automate economic regulatory responsibilities in terms of price savings and more green process control. Current advances in ML have enabled the improvement of models that may interpret complicated sets of facts and automate the technique of extracting the preferred facts from these records. The development of such models gives a price-effective and efficient opportunity to greater luxurious and time-consuming guide labor. Moreover, these fashions are capable of perceiving and correctly categorizing data to ensure information accuracy and compliance with regulatory policies. It allows the company to adhere to policies without human oversight, lowering each value and time constraints. It will be further prolonged to other factors of the enterprise by making use of unsupervised fashions and fashions advanced on switch learning, together with deep learning. Those models allow the automation of responsibilities inclusive of assessing the threat associated with investments or transactions, facilitating the development of quicker and more knowledgeable techniques. Ordinary, the usage of ML fashions for financial law automatization processes is precious for decreasing price and time constraints and making sure of compliance. Through leveraging the ability of ML, financial institutions can take advantage of extra internal manner efficiency and statistics accuracy with fewer risks.

关键词： Accuracy Regulators Biological system modeling Machine learning Switches Time factors Stakeholders

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Investigating the Role of AI in Personalized Consumer Banking Experiences

Investigating the Role of AI in Personalized Consumer Bankin...

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E-mobility, Power Control and Smart Systems (ICEMPS), International Conference on

作者： Piyush Rohella Sheetal Temara Sagar Varma Samanthapudi Ketan Gupta K. Suganyadevi Principal Product Manager Payments & Commerce GoDaddy Edmonds Washington USA Dept of Computer & Information Sciences University of the Cumberlands Williamsburg KY USA Applied Data Science The Polis Center at Indiana University Purdue University Indianapolis Indianapolis Indiana USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Department of ECE Sri Eshwar College of Engineering Coimbatore Tamil Nadu INDIA

ISBN: (数字)9798350394399

ISBN: (纸本)9798350394405

This research examines the function of synthetic Intelligence in developing customized purchaser banking reviews. Artificial Intelligent -based total gear is increasingly being followed to offer consumers customized banking reports. Studies into the use of AI in this context will tell the layout of future banking products and services. The primary intention of this research is to understand the modern technological and personal adoption developments related to Artificial Intelligent and customized banking reviews. Particularly, this study will identify the modern use cases of Artificial Intelligent in personalized banking, look into clients' attitudes and motivations towards Artificial Intelligent -enabled banking experiences, and discover the ability possibilities and challenges related to utilizing Artificial Intelligent in growing individualized banking studies for purchasers. Additionally, the role of ethical issues in implementing and the use of Artificial Intelligent in personalized consumer banking experiences may be evaluated. This study is crucial for presenting a better understanding of the function of Artificial Intelligent in developing personalized and moral consumer banking experiences.

关键词： Ethics Reviews Shape Power control Layout Banking Fraud

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Crystal structure prediction by joint equivariant diffusion 23

Crystal structure prediction by joint equivariant diffusion

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Rui Jiao Wenbing Huang Peijia Lin Jiaqi Han Pin Chen Yutong Lu Yang Liu Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University and Institute for AIR Tsinghua University Gaoling School of Artificial Intelligence Renmin University of China and Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China National Supercomputer Center in Guangzhou School of Computer Science and Engineering Sun Yat-sen University Stanford University

Crystal Structure Prediction (CSP) is crucial in various scientific disciplines. While CSP can be addressed by employing currently-prevailing generative models (e.g. diffusion models), this task encounters unique challenges owing to the symmetric geometry of crystal structures—the invariance of translation, rotation, and periodicity. To incorporate the above symmetries, this paper proposes DiffCSP, a novel diffusion model to learn the structure distribution from stable crystals. To be specific, DiffCSP jointly generates the lattice and atom coordinates for each crystal by employing a periodic-E(3)-equivariant denoising model, to better model the crystal geometry. Notably, different from related equivariant generative approaches, DiffCSP leverages fractional coordinates other than Cartesian coordinates to represent crystals, remarkably promoting the diffusion and the generation process of atom positions. Extensive experiments verify that our DiffCSP significantly outperforms existing CSP methods, with a much lower computation cost in contrast to DFT-based methods. Moreover, the superiority of DiffCSP is also observed when it is extended for ab initio crystal generation. Code is available at https://***/jiaor17/DiffCSP.

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Usage of Computer Aided Methods for Detection and Evaluation of Breast Cancer in Mammograms

Usage of Computer Aided Methods for Detection and Evaluation...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： M. Nagabhushana Rao L. Venkateswara Reddy D. Ganesh Lakkireddy. Udanth Reddy R Madana Mohana CSE Marri Laxman reddy institute of Technology & Management Hyderabad Department of CSE Joginapally B. R. Engineering College Yenkepally RR Dist Hyderabad Telangana India CSE Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College(Autonomous) Tirupati Andhra Pradesh India Dept. of ECE VIT-AP University Amaravath Artificial Intelligence & Data Science Chaitanya Bharathi Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Breast cancer accounts for an extremely high annual death rate. Many tools have been created to facilitate earlier diagnosis of breast cancer, which is the most common cancer among females. According to the research, cancer of the breast is also regarded as the second most deadly form of the disease. It is the most prominent kind of the disease and the top reason for death among females worldwide. Any progress made in the early detection and identification of cancer is crucial to maintaining good health. Therefore, it is essential to improve both the therapeutic aspect and the survival standard of patients by utilizing high precision in cancer prognosis. A reliable, effective, and speedy reaction from medical professionals is provided by an automatic disease identification system, which also reduces the danger of death. Recently, breast cancer screening methods that utilise deep learning have shown promising results, allowing for earlier identification and thereby enhancing patients’ chances of survival thanks to the introduction of artificial intelligence (AI). Deep learning reduces the need for manual intervention while extracting features, in comparison to traditional machine learning methods. We provide a brief overview of deep learning techniques, data availability, and the many breast cancer screening options, such as mammography, thermography, ultrasound, and MRI. Using demographic, laboratory, & mammographic data, this investigation sought to forecast breast cancer using various Deep learning algorithms. Ultimately, we apply artificial intelligence to breast cancer clinical trials and compare the proposed approach to current algorithms.

关键词： Deep learning Ultrasonic imaging Manuals Feature extraction Breast cancer Mammography Reliability Prognostics and health management Standards Medical diagnostic imaging

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