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12th International Conference and Exposition on Electrical and Power engineering, EPE 2022

作者： Rajawat, Anand Singh Goyal, S.B. Bedi, Pradeep Malik, Shilpa Neagu, Bogdan Constantin Raboaca, Maria Simona Verma, Chaman Sandip University School of Computer Science and Engineering Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Galgotais University Greater Noida201302 India Central Institute of Educational Technology Delhi India 'Gheorghe Asachi' Technical University of Iasi Department of Power Engineering Iasi700050 Romania National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea240050 Romania University of Eötvös Loránd Faculty of Informatics Budapest1053 Hungary

ISBN: (数字)9781665489942

ISBN: (纸本)9781665489942

A recent poll suggests that a lot of people don't vote because they feel like they already have too much to do. This is because of many things, like the fact that everyone has to go to a polling place, wait in a long line, and be mentally and physically tired before they can vote. We have pushed for people to be able to vote online, but there are a growing number of problems and risks that could come with this. When websites are attacked by things like phishing, it might be hard to log in. With the help of Visual Cryptography (VC), We proposed Visual Cryptography and Blockchain for Protecting Against Phishing Attacks on Electronic Voting Systems we made a voting system that lets people discuss and decide on important business ideas without giving away any information. © 2022 IEEE.

关键词： Cryptography

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Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

arXiv

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arXiv 2023年

作者： Zhang, Xuan Wang, Limei Helwig, Jacob Luo, Youzhi Fu, Cong Xie, Yaochen Liu, Meng Lin, Yuchao Xu, Zhao Yan, Keqiang Adams, Keir Weiler, Maurice Li, Xiner Fu, Tianfan Wang, Yucheng Strasser, Alex Yu, Haiyang Xie, YuQing Fu, Xiang Xu, Shenglong Liu, Yi Du, Yuanqi Saxton, Alexandra Ling, Hongyi Lawrence, Hannah Stärk, Hannes Gui, Shurui Edwards, Carl Gao, Nicholas Ladera, Adriana Wu, Tailin Hofgard, Elyssa F. Tehrani, Aria Mansouri Wang, Rui Daigavane, Ameya Bohde, Montgomery Kurtin, Jerry Huang, Qian Phung, Tuong Xu, Minkai Joshi, Chaitanya K. Mathis, Simon V. Azizzadenesheli, Kamyar Fang, Ada Aspuru-Guzik, Alán Bekkers, Erik Bronstein, Michael Zitnik, Marinka Anandkumar, Anima Ermon, Stefano Liò, Pietro Yu, Rose Günnemann, Stephan Leskovec, Jure Ji, Heng Sun, Jimeng Barzilay, Regina Jaakkola, Tommi Coley, Connor W. Qian, Xiaoning Qian, Xiaofeng Smidt, Tess Ji, Shuiwang Department of Computer Science & Engineering Texas A&M University College StationTX United States Department of Chemical Engineering Massachusetts Institute of Technology Cambridge United Kingdom AMLab University of Amsterdam Amsterdam Netherlands Department of Computer Science University of Illinois Urbana-Champaign UrbanaIL United States Department of Electrical & Computer Engineering Texas A&M University College StationTX United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge United Kingdom Department of Materials Science & Engineering Texas A&M University College StationTX United States Department of Physics & Astronomy Texas A&M University College StationTX United States Department of Applied Mathematics & Statistics Stony Brook University Stony BrookNY United States Department of Computer Science Stony Brook University Stony BrookNY United States Department of Computer Science Cornell University IthacaNY United States Department of Computer Science Technical University of Munich München Germany Department of Computer Science Stanford University StanfordCA United States Department of Computer Science & Engineering University of California San Diego La Jolla CA United States Department of Computer Science & Technology University of Cambridge Cambridge United Kingdom Nvidia Santa ClaraCA United States Department of Chemistry and Chemical Biology Harvard University Cambridge United Kingdom Department of Chemistry University of Toronto Toronto Canada Department of Computer Science University of Toronto Toronto Canada Department of Computer Science University of Oxford Oxford United Kingdom Department of Biomedical Informatics Harvard University BostonMA United States Department of Computing & Mathematical Sciences California Institute of Technology PasadenaCA United States Computational Science Initiative Brookhaven National Laboratory UptonNY United States

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science;namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Pilot Study on Using Hybrid–Cascade Filtering on Brain Signals for the Control Purposes

Pilot Study on Using Hybrid–Cascade Filtering on Brain Sign...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Mariusz Pelc Dariusz Mikołajewski Edward Jacek Gorzelańczyk Anna Wieczorek Henryk Racheniuk Adam Sudoł Kayhan Latifzadeh Luis A. Leiva Aleksandra Kawala-Sterniuk Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland Faculty of Physical Education and Physiotherapy Opole University of Technology Opole Poland Faculty of Natural and Technical Sciences University of Opole Opole Poland Department of Computer Science University of Luxembourg Luxembourg Luxembourg

We present an initial study conducted on fNIRS signals using Hybrid-Cascade filters for the purpose of their quality improvement. Whilst many studies focus on filtering brain signals, so that their frequency domain properties would allow e.g. widely understood diagnostics, here we focus on the study of time-domain signal characteristics, which is relevant for potential control purposes. Taking into account various kinds of artifacts, we propose a novel cascade 1D Kalman filter to handle fNIRS signals.

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Deep Learning and CNN-Based Approach for Efficient Detection of Potato Leaf Diseases

Deep Learning and CNN-Based Approach for Efficient Detection...

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IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing technology (AGERS)

作者： Taher M. Ghazal Jamshaid Iqbal Janjua Walid Abushiba Muhammad Salah-ud-Din Iqbal Waqas Iqbal Munir Ahmad Muhammad Saleem Faculty of Information Science and Technology College of Arts & Science Applied Science University Manama Bahrain & Center for Cyber Security Universiti Kebangsaan Malaysia Selangor Malaysia Al-Khawarizimi Institute of Computer Science (KICS) University of Engineering & Technology (UET) Lahore Pakistan College of Engineering Applied Science University Bahrain School of Computer Science Minhaj University Lahore Pakistan Department of forestry and Range Management University of Agriculture Faisalabad Pakistan College of Informatics Korea University Seoul Republic of Korea

ISBN: (数字)9798331543105

ISBN: (纸本)9798331543112

Potato is a maj or food crop worldwide, providing essential nutrients to millions. However, various diseases can severely affect potato crops, leading to reduced yield and quality. Early disease detection is crucial for effective disease management and control. This study presents a deep learning-based approach using CNN to autonomously diagnose potato leaf diseases. The model leverages the Plant Village dataset, which contains images of potato leaves in both healthy and diseased states. The proposed method demonstrates high accuracy in identifying multiple potato leaf diseases, offering promising results for application in precision agriculture. By utilizing CNN s, the model effectively detects and classifies disease symptoms, providing a reliable tool for early intervention in agricultural practices. The approach's potential impact on improving crop yield and minimizing losses due to diseases is highlighted, making it a significant contribution to smart farming techniques. The study underscores the importance of deep learning in modern agricultural practices and its role in enhancing disease management strategies.

关键词： Deep learning Precision agriculture Smart agriculture Plants (biology) Geoscience Nutrients Reliability Remote sensing Diseases Testing

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Evaluation of the Montenegrin Academic Digital Innovation Hub 27

Evaluation of the Montenegrin Academic Digital Innovation Hu...

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27th International Conference on Information technology, IT 2023

作者： Lakovic, Luka Kara, Peter A. Ognjanovic, Ivana Bokor, Laszlo Sendelj, Ramo Reich, Christoph Roganovic, Milovan Zoulias, Emmanouil Erakovic, Jevto Radusinovic, Tanja Rakocevic, Nada Simon, Aniko University of Donja Gorica Faculty for Information Systems and Technologies Podgorica81000 Montenegro Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary Faculty of Science Engineering and Computing Kingston University Wireless Multimedia and Networking Research Group Department of Computer Science School of Computer Science and Mathematics LondonKT1 2EE United Kingdom University of Donja Gorica Humanistic Studies Podgorica81000 Montenegro Furtwangen University Institute of Data Science Cloud Computing and It Security Furtwangen78120 Germany Clinical Centre of Montenegro Clinic for Neurology Podgorica81000 Montenegro National and Kapodistrian University of Athens Faculty of Nursing School of Health Sciences Goudi 11527 Greece Chamber of Economy of Montenegro Podgorica81000 Montenegro Sigma Technology Budapest1093 Hungary

ISBN: (纸本)9798350397512

In the era of global digitalization, digital innovation hubs have become essential, as they function as transforming forces. In this paper, we provide an analysis of the Montenegrin Academic Digital Innovation Hub by applying multi-criteria qualitative evaluation over several criteria: process flow of fully digitized operations of the Hub (with aspects of the innovation life-cycle, ethics, and security), resource management, strategic goals, and feasibility. The paper also proposes a model of comprehensive resource management via the perspective of smart specialization priorities of Montenegro. © 2023 IEEE.

关键词： Life cycle

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AI in Industry: Real-World Applications and Case Studies

TechRxiv

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TechRxiv 2023年

作者： Rahmaniar, Wahyu Maarif, Alfian ul Haq, Qazi Mazhar Iskandar, Muchammad Edo Institute of Innovative Research Tokyo Institute of Technology Kanagawa226-8503 Japan Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55166 Indonesia International Bachelor Program in Informatics and Computer Science Yuan Ze University Taoyuan320315 Taiwan Ezy Industries Jakarta Indonesia

Artificial intelligence (AI) has advanced rapidly and is becoming a cornerstone technology that drives innovation and efficiency in various industries. This paper examines the real-world application of AI in multiple sectors, including healthcare, finance, agriculture, retail, energy, and automotive. Several case studies are described to understand better the practical applications, results, and challenges of implementing AI. While many industries have reaped enormous benefits from AI, inherent challenges include data privacy, the potential for bias, and the continuing demand for skilled labor. This comprehensive review aims to provide AI application insights to professionals and researchers. Thus, as AI grows, there may be challenges and avenues for future research. © 2023, CC BY.

关键词： Agriculture

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Experimental Analysis for Position Estimation using Trilateration and RSSI in Industry 4.0 11

Experimental Analysis for Position Estimation using Trilater...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Chouhan, Robin Singh Rajawat, Anand Singh Goyal, S.B. Bedi, Pradeep Constantin, Neagu Bogdan Raboaca, Maria Simona Verma, Chaman Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India School of Computer Science and Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Galgotais University Greater Noida India Gheorghe Asachi Technical University of Iasi Department of Power Engineering Iasi Romania National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea Romania University of Eötvös Loránd Faculty of Informatics Budapest1053 Hungary

ISBN: (纸本)9781665487320

With the introduction of industry 4.0, the role of robots especially autonomous bots has increased exponentially. The next thing in order to improve the navigation for these bots is the need for a proper indoor navigation system. The paper proposes experimental analysis for estimating the position using trilateration. The experiment includes three wifi nodes operating at 2.4GHz working as wifi beacons. The paper provides an efficient and cost-effective solution to estimate the approximate position of the node in terms of coordinates. The paper also includes a working mobile application to showcase the node's position in real-time in the form of a 2d map. © 2022 IEEE.

关键词： Indoor positioning systems

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Assistive Technical Communication of Montenegrin eServices: a Case Study 27

Assistive Technical Communication of Montenegrin eServices: ...

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27th International Conference on Information technology, IT 2023

作者： Simon, Aniko Lakovic, Luka Kovacevic, Pavle Kara, Peter A. Ognjanovic, Ivana Sendelj, Ramo Reich, Christoph Roganovic, Milovan Mantas, John Bokor, Laszlo Sigma Technology Budapest1093 Hungary University of Donja Gorica Faculty for Information Systems and Technologies Podgorica81000 Montenegro Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary Kingston University Wireless Multimedia and Networking Research Group Department of Computer Science School of Computer Science and Mathematics Faculty of Science Engineering and Computing LondonKT1 2EE United Kingdom University of Donja Gorica Humanistic Studies Podgorica81000 Montenegro Furtwangen University Institute of Data Science Cloud Computing and It Security Furtwangen78120 Germany Clinical Centre of Montenegro Clinic for Neurology Podgorica81000 Montenegro National and Kapodistrian University of Athens Faculty of Nursing School of Health Sciences Goudi 11527 Greece

ISBN: (纸本)9798350397512

With the advancement of information technology in the recent decades, digital and online services have become essential components of everyday life in Europe. Montenegro is no different in this regard. However, as digital literacy in the country is still in a developing phase in comparison with most countries of the EU, a higher portion of its citizens may rely on assistive technical communication. In this paper, we provide a case study on the assistive technical communication of Montenegrin eServices. The work addresses the sectors of online banking, healthcare services, higher education, national administrative services, and utilities. Our findings highlight the diversity in terms of implemented assistive technical communication, and distinguish usage contexts on the level of adequacy. Yet as the functionalities of Montenegrin eServices are expected to gradually expand in the upcoming years, assistive technical communication must aim to handle the higher extents of service complexity. © 2023 IEEE.

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Power of Sequential Protocols in Hidden Quantum Channel Discrimination

引用

Physical Review Letters 2024年第24期132卷 240805-240805页

作者： Sho Sugiura Arkopal Dutt William J. Munro Sina Zeytinoğlu Isaac L. Chuang Physics and Informatics Laboratory NTT Research Inc. 940 Stewart Drive Sunnyvale California 94085 USA Laboratory for Nuclear Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics Co-Design Center for Quantum Advantage Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA NTT Basic Research Laboratories and Research Center for Theoretical Quantum Physics 3-1 Morinosato-Wakamiya Atsugi Kanagawa 243-0198 Japan Okinawa Institute of Science and Technology Graduate University Onna-son Okinawa 904-0495 Japan Department of Physics Harvard University Cambridge Massachusetts 02138 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

In many natural and engineered systems, unknown quantum channels act on a subsystem that cannot be directly controlled and measured, but is instead learned through a controllable subsystem that weakly interacts with it. We study quantum channel discrimination (QCD) under these restrictions, which we call hidden system QCD. We find sequential protocols achieve perfect discrimination and saturate the Heisenberg limit. In contrast, depth-1 parallel and multishot protocols cannot solve hidden system QCD. This suggests sequential protocols are superior in experimentally realistic situations.

关键词： Quantum measurements Quantum metrology Quantum sensing

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Exploring DeepDream and XAI Representations for Classifying Histological Images

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SN computer Science 2024年第4期5卷 362页

作者： Martinez, João Manoel Cardoso Neves, Leandro Alves Longo, Leonardo Henrique da Costa Rozendo, Guilherme Botazzo Roberto, Guilherme Freire Tosta, Thaína Aparecida Azevedo de Faria, Paulo Rogério Loyola, Adriano Mota Cardoso, Sérgio Vitorino Silva, Adriano Barbosa do Nascimento, Marcelo Zanchetta Department of Computer Science and Statistics (DCCE) São Paulo State University (UNESP) Rua Cristóvão Colombo 2265 SP São José do Rio Preto 15054-000 Brazil Department of Informatics Engineering - Faculty of Engineering University of Porto Dr. Roberto Frias sn Porto 4200-465 Portugal Science and Technology Institute (ICT) Federal University of São Paulo (UNIFESP) Av. Cesare Mansueto Giulio Lattes 1201 SP São José dos Campos 12247-014 Brazil Department of Histology and Morphology Institute of Biomedical Science Federal University of Uberlândia (UFU) Av. Amazonas S/N MG Uberlândia 38405-320 Brazil Area of Oral Pathology School of Dentistry Federal University of Uberlândia (UFU) R. Ceará - Umuarama MG Uberlândia 38402-018 Brazil Faculty of Computer Science (FACOM) Federal University of Uberlândia (UFU) Av. João Naves de Ávila 2121 MG Uberlândia 38400-902 Brazil

The application of machine learning methods to analyze histological images has led to significant advances in computer-aided diagnosis. In this work, a study was carried out to classify patterns present in histological samples stained with hematoxylin-eosin, representative of breast cancer, colorectal cancer, liver tissue, and oral dysplasia. The proposed method explored convolutional neural networks (VGG19, ResNet50, InceptionV3, and EfficientNet B2), gradient-weighted class activation mapping, local interpretable model-agnostic explanations and representations based on the inceptionism effect (DeepDream). The deep features were analyzed via multiple classifiers (rotation forest, multilayer perceptron, logistic, random forest, decorate, k-nearest neighbor, KStar, and support vector machine) to indicate the main associations. The best solution was obtained through the ResNet50 model with the representations from the VGG19 architecture, exploring feature vectors with 40 and 100 attributes. The results were AUC rates ranging from 0.994 (breast cancer dataset) to 1.0 (colorectal dataset). The most explored deep features were obtained via original images (highest occurrence), followed by local interpretable model-agnostic explanations, DeepDream, and gradient-weighted class activation mapping representations. These results can support the development of techniques addressed to pattern recognition of H &E images with more robust computer-aided diagnoses. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Deep features DeepDream representations Histological images XAI

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