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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Alain Bertrand Bomgni Ribot Fleury Téné Ceskoutsé Kevin Jordan Njike Njingang Thomas Bouetou Bouetou Venkataramana Gadhamshetty Etienne Gnimpieba Z. Math Science and Technology Oglala Lakota College Kyle SD USA Ecole Nationale Supérieure Polytechnique University of Yaounde 1 Yaounde Cameroon Dept. of Computer Science University of Dschang Dschang Cameroon Dept. of Civil and Environmental Engineering South Dakota School of Mines and Technology Rapid City SD USA Dept. of Biomedical Engineering University of South Dakota Vermillion SD USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The synthesis of small molecules is a crucial task across multiple scientific domains, including drug discovery, materials science, and sustainable chemistry. As AI and Machine Learning (ML) continue to advance, these technologies offer transformative potential in molecular synthesis, enhancing efficiency and expanding synthetic possibilities. In particular, small molecule synthesis has profound implications for creating novel therapeutic agents, high-performance materials, and greener chemical processes, but a major challenge remains in designing efficient synthetic routes for target molecules. Retrosynthesis, the process of mapping out synthetic pathways by working backwards from a target molecule, represents a vital step; however, traditional retrosynthesis methods often struggle to predict complex or novel transformations. To address this limitation, we introduce AIM-HKR: AI-Driven Molecular Retrosynthesis Using Heterogeneous Knowledge Representations, a model that leverages graph neural network techniques to enhance retrosynthetic predictions. AIM-HKR integrates information from heterogeneous knowledge graphs, capturing the intricate relationships and analogical reasoning required for retrosynthesis. This model generates type-specific embeddings that reflect both network topology and semantic connections across different entity types, such as molecules, reactions, and functional groups. AIM-HKR’s unique capacity to leverage heterogeneous graphs allows it to propose synthetic pathways that extend beyond established precedents, enabling predictions of chemically feasible but previously uncharted transformations. We believe AIM-HKR has the potential to significantly advance molecular synthesis through AI-driven retrosynthesis, establishing a new paradigm in AI-assisted chemistry.

关键词： Technological innovation Chemistry Accuracy Scalability Semantics Machine learning Predictive models Reliability engineering Drug discovery Chemicals

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Hyper-IIoT: A Smart Contract-inspired Access Control Scheme for Resource-constrained Industrial Internet of Things

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2025年

作者： Li, Dun Li, Hongzhi Crespi, Noel Minerva, Roberto Li, Ming Liang, Wei Li, Kuan-Ching Tsinghua University Department of Industrial Engineering China Institut Polytechnique de Paris Samovar Telecom SudParis Palaiseau91120 France Chizhou University College of Big Data and Artificial Intelligence Chizhou247100 China The Hong Kong Polytechnic University Research Institute for Advanced Manufacturing Hung Hom Hong Kong Hunan University of Science and Technology School of Computer Science and Engineering Hunan University of Science and Technology Sanya Research Institute Hunan Key Laboratory for Service Computing and Novel Software Technology China Providence University Dept. of Computer Science and Information Engineering Taiwan

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data in Industrial Internet of Things (IIoT) devices have highlighted the need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. Besides, we defined a storage model and data format for private data by designing and deploying smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in real-world simulations, Hyper-IIoT demonstrates excellent contract execution time control, stable system throughput, and rapid consensus processes that are capable of handling high throughput and efficient consensus in distributed systems, even in large-scale request scenarios. © 2016 IEEE.

关键词： Smart contract

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Improving the SNBWAN Efficiency based on the Improvement of Signal-Code Constructions 13

Improving the SNBWAN Efficiency based on the Improvement of ...

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13th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2021

作者： Bobrovsky, Vadim Sartakov, Anatoly Kirichek, Ruslan Pham, Van Dai Semakov, Leonid Zhuravel, Evgeny University of Telecommunications Bonch-Bruevich Saint Petersburg State Dept. of Software Engineering and Computer Science Saint-Petersburg Russia KB 'Mars' Novosibirsk Russia

ISBN: (纸本)9781665402194

The paper discusses the issues of increasing the efficiency of an IoT network - SNBWAN based on improving signal-code constructions. Two ways to improve efficiency are considered: based on the use of complementary code keying and advanced signal construction (SC) with quadrature amplitude modulation (QAM). The results of analytical calculations and computer simulation are presented. and copy;2021 IEEE. © 2021 IEEE computer Society. All rights reserved.

关键词： Internet of things

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"MealShare: Using Blockchain Technology and a Reward-Based System to Combat Global Food Waste"

TechRxiv

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TechRxiv 2023年

作者： Kumara, H.A.P. manjula Jayakodi, J.K.R.P. Gamage, I.D. Wickramasinghe, R.G.M.N. De Zoysa, R.R.P. De Sliva, D.I. Dept. of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology New Kandy RD Malabe Sri Lanka

"MealShare" is an upcoming internet application that aims to reduce food waste and promote sustainable dietary habits while supporting a more sustainable global food system. It uses blockchain technology and a rewards-based system to incentivize companies to donate their excess food. This innovative approach ensures transparency, traceability, security, and decentralization throughout the entire food donation process. The use of blockchain technology enables the monitoring and recording of each contribution of surplus food, providing an immutable record of the entire supply chain, increasing accountability, and reducing the possibility of food waste due to negligence or mismanagement. Furthermore, "MealShare" uses blockchain smart contracts to automate verification and identification, speeding up the processing of food donations while saving money. The automatic and open incentive structure offered by smart contracts encourages companies to take part in the food donation program and make frequent contributions, compensating them based on specified criteria such as the quantity or quality of leftover food supplied. This unique approach makes use of blockchain technology and a rewards-based system to promote sustainable food practices, reduce food waste, and motivate companies to donate extra food. It is a potential tool in the worldwide effort to reduce food waste due to its openness, traceability, security, and automated incentive structure. © 2023, CC BY.

关键词： Blockchain

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Distress Study Through Machine-Learning Techniques

Distress Study Through Machine-Learning Techniques

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International Conference on Biomedical Innovations and Applications (BIA)

作者： Ginka Marinova Neli Kalcheva Dept. of Computer Science and Engineering Technical University of Varna Varna Bulgaria Dept. of Software and Internet Technologies Technical University of Varna Varna Bulgaria

ISBN: (纸本)9781665445825

Machine learning as a contemporary approach in research provides an opportunity to cover and process a large amount of data. This is a condition for the synthesis of reliable characteristic descriptors of distress. The process of model selection is a factor in achieving the necessary adequacy and improving the quality of results. Age is a risky reason to take into account the development of cancer. This paper analyzed classification models for predicting distress in patients with the oncological disease, segmented into three age groups. Experimental results of a study conducted on the age of the patient and assessment of distress, using three methods of machine-learning are presented.

关键词： Support vector machines Technological innovation Analytical models Biological system modeling Machine learning Predictive models Reliability

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Methodology for the Study of Defects in Technical Structures at Construction Sites

Methodology for the Study of Defects in Technical Structures...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Liliya R. Snitsar Kazizat T. Iskakov Makpal G. Zhartybaeva Bakhytzhan K. Suleimenov Samat A. Boranbaev dept. of Computer and Software Engineering L.N. Gumilyov Eurasian National University Nur-Sultan Kazakhstan

ISBN: (数字)9781665498043

ISBN: (纸本)9781665498050

The article deals with the problem of analyzing existing approaches to the survey of construction sites. It is given an explanation of the relevance of the study of objects by non-destructive diagnostic methods. In practice, the use of one of the methods of non-destructive testing of construction objects - the ultrasonic method is shown. The mathematical approach to solving the problems is also described in the article, which presents the basic formulas used in solving the problem. Authors also characterized the available non-destructive testing devices and their technical characteristics. The results and algorithms of various methods for solving the problem are presented. An experimental study is described using one of the considered devices. The process of preparation for measurements and installation of sensors is described in detail. There were given the results of experimental studies in laboratory conditions and on the construction site. According to the results of the experiments, conclusions were made for each of them.

关键词： Space technology Propagation Sensor phenomena and characterization Acoustics Electron devices Testing

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Aerial Vehicle Detection and Classification Through Fusion of Multi-Domain Features of Acoustic Signals

Aerial Vehicle Detection and Classification Through Fusion o...

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engineering & Computing Technologies (ICECT), International Conference on

作者： Malaika Sumble Sumair Aziz Muhammad Umar Khan Khushbakht Iqtidar Raul Fernandez-Rojas Department of Electronics Engineering University of Engg. & Tech. Taxila Taxila Pakistan Faculty of Science and Technology University of Canberra Canberra ACT Australia Dept. of Computer & Software Engineering National University of Sciences & Tech. Islamabad Pakistan

ISBN: (数字)9798350349719

ISBN: (纸本)9798350349726

Detection of aerial vehicles is a challenging task. Over time, the significance of developing such vehicles has increased rapidly. However, an utmost concern arises from the potential misuse of these small-sized vehicles in illicit activities such as unauthorized surveillance, smuggling contraband, or disrupting critical infrastructure significant security threats. Recognizing the gravity of this issue, we have opted to introduce an innovative approach to automate the detection processes for aerial vehicles. This initiative aims to deploy effective drone detection systems to mitigate risks and safeguard against illegal activities. We put use to two datasets from GitHub depot. Cepstral, spectral, and time domain features were extracted from the data, followed by classification. We conducted two experiments, with the first (Drone, No-Drone) yielding 98.6% accuracy, using the Ensemble (Bagged Trees) classifier. The second set of experimentation addressed five classes: Background Noises, Bebop Drone, Drone, Helicopter, and Mambo Drone. Ensemble (Bagged trees) again outperformed all other classifiers and achieved 98.3% accuracy. The results highlight that our proposed framework gives effective results based on audio signals of different aerial vehicles.

关键词： Machine learning algorithms Accuracy Cepstral analysis Surveillance Signal processing algorithms Feature extraction Time-domain analysis

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VR REAL RUN: An immersive Oculus Quest 2-Based Virtual Reality Exergaming 4

VR REAL RUN: An immersive Oculus Quest 2-Based Virtual Reali...

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Mambu, Joe Y. Rismayani Sihotang, Jay Idoan Rantung, Vivi Peggy Computer Science Faculty Universitas Klabat Airmadidi Indonesia Dipa Makassar University Dept. of Software Engineering Makassar Indonesia Faculty of Information Technology Universitas Advent Indonesia Bandung Indonesia Faculty of Information System Universitas Negeri Manado Tondano Indonesia

ISBN: (纸本)9781665453950

There are many different types of exergaming, but one of the most engaging one is the one with virtual reality. This type of gaming allows you to immerse yourself in a completely different world, and it can be a lot of fun. Due to Covid-19 pandemic many people resort to their usual exercise activities and opt to do it at home yet only to find a monotonous and boring fitness routine. VR Real Run is aimed to give a solution on how to have fun and to get fit at the same time. By using the latest and front runner in the VR headsets products, the Oculus Quest 2, we developed an exergame that require user to run in place and jump to play. It also features high score to motivate users in perform better in the next session. A black box test has been well performed and a usability test was came out with a 79 SUS score which translate the application as 'good' therefore its usability is acceptable. © 2022 IEEE.

关键词： Virtual reality

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Optimization of Processing Power by Improving the Performance of Application Service

Optimization of Processing Power by Improving the Performanc...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Nurken Tileubergenov Svetlana N. Romanchikova Ainur K. Zhumadillayeva dept. of &#x201C Computer and Software Engineering&#x201D L.N. Gumilyov Eurasian National University Nur-Sultan Kazakhstan

ISBN: (数字)9781665498043

ISBN: (纸本)9781665498050

This article discusses the problems of optimizing computing power by improving the performance of web applications. The shared responsibility model for the Azure cloud is considered and the area of responsibility of the user and the provider of cloud services for such models as IaaS, PaaS and SaaS is shown. The article discusses methods for optimizing loops and provides the results of performance tests in Microsoft Visual Studio using Performance Profiler and C# language. Methods for optimizing such constructs as algebraic expressions are described and ways to increase performance from caching are considered. Optimization methods are described when using the String type. The article also looks at how boxing/unboxing objects of reference and value types improve application performance. The practical steps given in the article are aimed at writing safe and efficient code. Please, note that the tests and optimization designs carried out and described in the article will help to write code that will not cause poor application performance in the future. These steps will help to avoid buying software to detect optimization problems and, accordingly, significantly reduce material costs for the entire project as a whole.

关键词： Performance evaluation Visualization Codes Costs Computational modeling software as a service Random access memory

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A Review on Data Balancing Techniques and Machine Learning Methods

A Review on Data Balancing Techniques and Machine Learning M...

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International Conference on Smart Systems and Inventive Technology (ICSSIT)

作者： Gaurav Parmar Rimi Gupta Tejas Bhatt G.J. Sahani Brijeshkumar Y. Panchal Hiren Patel Dept. of Computer Engineering S.V.I.T Vasad Gujarat Software Developer TÜV SÜD South Asia Pvt. Ltd

Researchers in the discipline of Data Mining (DM) occasionally disregard the need of ensuring a dataset is evenly distributed. As such, it may have a major impact on how things are ultimately sorted. Most classifiers function on the concept that the data is roughly normally distributed. Therefore, the categorization approach is no longer as successful as it once was and fixing this is essential. This research aims to construct an evaluation of several methods for producing labels for minority classes to prevent the model from developing bias toward protected attributes. In this study, different researcher’s work learned to determine the most effective method for disseminating the data. There will be a discussion of ways to enhance categorization abilities in the conclusion. The future of data-balancing research is determined by weighing the relative merits of the several possible methodological amalgamations.

关键词： Distributed databases Machine learning Data mining

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