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

作者： Gaurav Jindal Vidhika Tiwari Riyaz Mahomad Anita Gehlot Mimoh Jindal Dibyhash Bordoloi Department of Master of Computer Applications G L Bajaj College of Technology and Management Gr. Noida Mechanical Engineering Department DPGITM Gurugram School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Uttaranchal Institute of Technology Uttaranchal University India National Informatics Centre Delhi India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

In this work, we investigate a unique effective framework for projected design inspections in industrial manufacturing combining machine learning methodologies and edges cloud computing technologies. We recommend a thorough approach that involves targeted data gathering and broadcast, forecasting and supplying appropriate, as well as innovation with the current IT plant infrastructure, in contrary to state-of-the-art contributions. To highlight the steps and advantages of the suggested solution, a genuine business use case in the manufacture of SMT is described. The outcomes demonstrate that the proposed strategy can dramatically reduce inspection volumes, leading to economic gains. A key achievement basis for the drawn-out presentation of assembling ventures is the creation of imperfection-free, top-notch items. Indeed, even with the intricacy and assortment of items and the requirement for savvy production, an exhaustive and dependable quality investigation is habitually required. Along these lines, high examination volumes cause producing bottlenecks in the review processes. Thus, the purpose of this paper is to develop a predictive design for quality assessment regarding employing cloud computing and machine learning. Concerning this particular research, a descriptive research design has been used by depending on secondary data due to its reliability and validity. Moreover, it has been found that both cloud computing and machine learning contribute effectively for employing quality assessment because of their innovative features and infrastructure.

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The Malware as a Service ecosystem

arXiv

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arXiv 2024年

作者： Patsakis, Constantinos Arroyo, David Casino, Fran Department of Informatics University of Piraeus 80 Karaoli & Dimitriou str. Piraeus18534 Greece Information Management Systems Institute Athena Research Centre Greece Spain Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Tarragona Spain

The goal of this chapter is to illuminate the operational frameworks, key actors, and significant cybersecurity implications of the Malware as a Service (MaaS) ecosystem. Highlighting the transformation of malware proliferation into a service-oriented model, the chapter discusses how MaaS democratises access to sophisticated cyberattack capabilities, enabling even those with minimal technical knowledge to execute catastrophic cyberattacks. The discussion extends to the roles within the MaaS ecosystem, including malware developers, affiliates, initial access brokers, and the essential infrastructure providers that support these nefarious activities. The study emphasises the profound challenges MaaS poses to traditional cybersecurity defences, rendered ineffective against the constantly evolving and highly adaptable threats generated by MaaS platforms. With the increase in malware sophistication, there is a parallel call for a paradigm shift in defensive strategies, advocating for dynamic analysis, behavioural detection, and the integration of AI and machine learning techniques. By exploring the intricacies of the MaaS ecosystem, including the economic motivations driving its growth and the blurred lines between legitimate service models and cyber crime, the chapter presents a comprehensive overview intended to foster a deeper understanding among researchers and cybersecurity professionals. The ultimate goal is to aid in developing more effective strategies for combating the spread of commoditised malware threats and safeguarding against the increasing accessibility and scalability of cyberattacks facilitated by the MaaS model. © 2024, CC BY-NC-SA.

关键词： Malware

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Modeling Blockage in High Directional Wireless Systems

Modeling Blockage in High Directional Wireless Systems

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Virtual Conference on Communications (VCC), IEEE

作者： Evangelos Koutsonas Alexandros-Apostolos A. Boulogeorgos Stylianos E. Trevlakis Tanweer Ali Theodoros A. Tsiftis Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Research & Development Department InnoCube P.C. Thessaloniki Greece Department of Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Informatics & Telecommunications University of Thessaly Lamia Greece Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China

ISBN: (数字)9798331530099

ISBN: (纸本)9798331530105

While the wireless word moves towards higher frequency bands, new challenges arises, due to the inherent characteristics of the transmission links, such as high path and penetration losses. Penetration losses causes blockages that in turn can significantly reduce the signal strength at the receiver. Most published contributions consider a binary blockage stage, i.e. either fully blocked or blockage-free links. However, in realistic scenarios, a link can be partially blocked. Motivated by this, in this paper, we present two low-complexity models that are based on tight approximations and accommodates the impact of partial blockage in high-frequency links. To demonstrate the applicability of the derived framework, we present closed-form expressions for the outage probability for the case in which the distance between the center of the receiver plane and the blocker's shadow center follow uniform distribution. Numerical results verify the derived framework and reveal how the transmission parameters affect blockage.

关键词： Wireless communication Closed-form solutions Receivers Probability Propagation losses Power system reliability Numerical models

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CCGRID 2023: A Holistic Approach to Inclusion and Belonging

CCGRID 2023: A Holistic Approach to Inclusion and Belonging

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IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Beth Plale Preeti Malakar Meenakshi D'Souza Hemangee K. Kapoor Yogesh Simmhan Ilkay Altintas Manohar Swaminathan School of Informatics Computing and Engineering Indiana University Bloomington IN USA Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kalyanpur Kanpur India International Institute of Information Technology Bangalore Bengaluru Karnataka India Department of Computer Science and Engineering Indian Institute of Technology Guwahati Guwahati Assam India Department of Computational and Data Sciences Indian Institute of Science Bangalore India San Diego Supercomputer Center University of California San Diego MC USA Microsoft Research Lab - India Bengaluru Karnataka India

“CCGRID will act with responsibility as its primary consideration; with equity, diversity, and inclusion as its central goals.” from the CCGRID 2023 web site [1]

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PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutralization Prediction

PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutra...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhang Wan Zhuoyi Lin Shamima Rashid Shaun Yue-Hao Ng Rui Yin J. Senthilnath Chee-Keong Kwoh School of Computer Science and Engineering Nanyang Technological University Singapore Institute for Infocomm Research Agency for Science Technology and Research (A*STAR) Singapore School of Chemical and Biomolecular Engineering Nanyang Technological University Singapore Department of Health Outcome and Biomedical Informatics University of Florida Gainesville FL USA

Prediction of neutralization antibodies is important for the development of effective vaccines and antibody-based therapeutics. Traditional methods rely on features based on first principles derived from the binding interface. However, they are burdened by arduous data preprocessing from a limited quantity of protein structures. In comparison, deep learning allows automatic substructure characterization and representation without hand-crafted feature engineering. In particular, large language models (LLMs) based method predicts neutralization using Fv sequences of antibody and antigen. Despite LLM’s success, incorporating full-length Fv sequences suffers from: 1) inaccurate sequence-level labels in existing datasets, 2) inefficient modeling due to noisy non-contributing motifs, and 3) ignorance of non-bonded interactions that play a key role in facilitating epitope-paratope pairing. In this paper, we propose a novel approach that incorporates only the paratope and epitope for antibody-antigen neutralization prediction while adopting a novel set modeling that regards the paratope and epitope as bags of residues. Specifically, we hand-crafted a dataset containing neutralizing paratope-epitope pairs where epitopes are potentially generalizable to future unseen variants of SARS-CoV-2. Training on such a dataset enables deep learning models to predict neutralizing antibodies for prospective mutated variants of SARS-CoV-2, meanwhile addressing the problem of inaccurate sequence-level labels. A higher modeling efficiency is also achieved by disregarding non-contributing motifs. Furthermore, we also propose paratope-epitope set interaction (PESI), a set modeling model inspired by first principles that learns intra-inter non-covalent interactions through a global attention mechanism. To validate PESI, we perform a 10-fold cross-validation on our dataset. Experimental results show that PESI achieves a more balanced overall performance and a significant improvement on MCC as c

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Prediction of water quality parameters by IoT and machine learning

Prediction of water quality parameters by IoT and machine le...

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International Conference on computer Communication and informatics (ICCCI)

作者： Srinivas Kolli M. Ranjani P. Kavitha D. Arul Pon Daniel Awadhesh Chandramauli Department Of Information Technology VNR Vignana Jyothi Institute of Engineering & Technology Hyderabad India Computer Applications Jawaharlal Nehru college for women Ulundurpet India Muthayammal center for Advanced Research Muthayammal College of Arts & Science Department of Computer Science and Applications Loyola College of Arts & Science Department of Civil Engineering Uttaranchal Institute of Technology Uttaranchal University Dehrradun India

Increases in domestical applications and other applications drive the need for better clean Water Management in India. What makes up the “chemical, physical, and biological” quality of freshwater There are three stages in the conventional method of monitoring water quality: sample, analysis, and inquiry. The scientists do these by hand. This method needs to be foolproof and predict a water supply’s quality. They’re Using the development of wireless sensor technologies, has also been some investigation into the possibility of monitoring water quality with wireless sensors put in water and communicating with farmers through brief messages regarding water. There has also been a study into the possibility of employing machine learning algorithms to analyze water quality. In light of recent advances in Machine Communication, which allow for devices to communicate with one another and smart ly analyze the data they collect. We have developed an “smart water quality monitoring system by using wireless sensonrs” for residential areas that rely on water storage tanks. A pH sensor and a total dissolved solids meter are used in this system to provide accurate readings of the water’s pH and dissolved solids levels. K-Means clustering, a machine learning technique, has been used to forecast water quality using a training data set consisting of various water samples. This system is now in its prototype stage, used with low-cost embedded devices like the Arduino Uno and Raspberry Pi3.

关键词： Wireless communication Meters Wireless sensor networks Water storage Training data Prototypes Water quality

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Power of Sequential Protocols in Hidden Quantum Channel Discrimination

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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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Enhanced algorithmic modelling and architecture in deep reinforcement learning based on wireless communication Fintech technology

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Optik 2023年 272卷

作者： Upreti, Kamal Syed, Mohammad Haider Khan, Mohiuddin Ali Fatima, Huda Alam, Mohammad Shabbir Sharma, A.K. Department of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Technology & Management Delhi India College of Computing and Informatics Saudi Electronic University Saudi Arabia Department of Computer & Network Engineering Jazan University Jazan Saudi Arabia Department of Information Technology & Security Jazan University Jazan Saudi Arabia Department of Computer science College of Computer Science and Information Technology Jazan University Jizan Saudi Arabia Department of Creative Technologies & Product Design National Taipei University of Business Taipei City Taiwan

Financial technology" or "FinTech" refers to use of information technologies to derive financial solutions. FinTech is now widely regarded as a hotly debated blend of financial services and information technology. A combination of AI and IoT approaches will significantly increase the extraction of valuable financial data as well as provide better services to customers. This research proposed a novel protocol in data transmission using fuzzy rule-based secure transmission with data optimization technique using deep reinforcement neural network by wireless communication. This technique for data transmission will reduce the credit risk and enhances the data optimization by fuzzy rule-based protocol with DRNN. The logic utilized is fuzzy logic, which is a multi-valued logic with truth values for variables ranging from 0 to 1. When truth values range from entirely true to completely false, it is used. The end user's searching experience is improved by fuzzy logic-based semantic search, which finds and retrieves exact matching files for relevant search files provided by user. When exact matches aren't possible, method leverages semantic similarities to determine most relevant matches. A grading method is utilized to minimize number of false positives. For this, proposed technique was used, which may decide on a collection of suitable storage servers on which the data must be saved and resulting in a reduction in execution time while maintaining a higher level of security. The experimental results show the execution time of 54 %, network performance of 96 %, the overall complexity of 71 %, data optimization of 95 % and end-end delay of 67 %. © 2022

关键词： Semantics

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Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking

arXiv

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arXiv 2025年

作者： Liu, Zhang Niyato, Dusit Wang, Jiacheng Sun, Geng Huang, Lianfen Gao, Zhibin Wang, Xianbin Department of Informatics and Communication Engineering Xiamen University Fujian361102 China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Jilin University Changchun130012 China Navigation Institute Jimei University Fujian Xiamen361021 China Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada

Lyapunov optimization theory has recently emerged as a powerful mathematical framework for solving complex stochastic optimization problems by transforming long-term objectives into a sequence of real-time short-term decisions while ensuring system stability. This theory is particularly valuable in unmanned aerial vehicle (UAV)-based low-altitude economy (LAE) networking scenarios, where it could effectively address inherent challenges of dynamic network conditions, multiple optimization objectives, and stability requirements. Recently, generative artificial intelligence (GenAI) has garnered significant attention for its unprecedented capability to generate diverse digital content. Extending beyond content generation, in this paper, we propose a framework integrating generative diffusion models with reinforcement learning to address Lyapunov optimization problems in UAV-based LAE networking. We begin by introducing the fundamentals of Lyapunov optimization theory and analyzing the limitations of both conventional methods and traditional AI-enabled approaches. We then examine various GenAI models and comprehensively analyze their potential contributions to Lyapunov optimization. Subsequently, we develop a Lyapunov-guided generative diffusion model-based reinforcement learning framework and validate its effectiveness through a UAV-based LAE networking case study. Finally, we outline several directions for future research. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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AIM-AHEAD Connect: Online Collaboration, Mentoring, and Data Science Training Platform to Increase researcher Diversity and Advance Health Equity 11

AIM-AHEAD Connect: Online Collaboration, Mentoring, and Data...

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11th IEEE International Conference on Healthcare informatics, ICHI 2023

作者： Ahmed, Toufeeq Hoyal, Aidan Stinson, Katie Johnson, Jay Latif, Zainab Burge, Legand L. Libin, Alexander Gordon Gao, Guodong Shara, Nawar Vishwanatha, Jamboor K. Vanderbilt University Medical Center Department of Biomedical Informatics Nashville United States Howard University Department of Electrical Engineering and Computer Science WashingtonDC United States Georgetown University MedStar Health Research Institute WashingtonDC United States Johns Hopkins University Carey Business School Baltimore United States University of North Texas Health Science Center at Fort Worth Institute for Health Disparities Fort Worth United States

ISBN: (纸本)9798350302639

AIM-AHEAD is an NIH-funded consortium whose goal is to advance health equity and researcher diversity via artificial intelligence and machine learning. In our first year of operations, we launched AIM-AHEAD Connect, a centralized platform for the consortium stakeholders containing mentoring, networking, and professional development resources. This paper describes the resources available in AIM-AHEAD Connect as well as collaborative efforts to provide tools and training for the entire consortium. © 2023 IEEE.

关键词： Artificial intelligence

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