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Analysis of pharmacy drug consumption following extreme weather conditions 25

Analysis of pharmacy drug consumption following extreme weat...

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25th KES international conference on Knowledge-Based and intelligent Information & engineering Systems (KES)

作者： Spichkova, Maria Simic, Milan RMIT Univ Sch Comp Technol Melbourne Vic Australia RMIT Univ Sch Engn Melbourne Vic Australia

This paper presents the results of the project conducted within the Research embedded in teaching initiative in collaboration with Nostradata. The main objective of the research was to analyse if the peak values of such weather conditions, like rainfall and wind speed, can impact the drug purchase transactions within shorts time periods (one, three, and five days) of time, after the peak values of the weather parameters have appeared. The basis for the automated analysis were the data collected over more than six years, restricted to the Melbourne region in Australia. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-nd license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES international.

关键词： Software engineering project-based learning data analytics

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AI-Driven Mobile App for Personalized Health Monitoring

AI-Driven Mobile App for Personalized Health Monitoring

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international Workshop on Research and Education in Mechatronics (REM)

作者： Natheer Almtireen Hashem Altaha Aws Alissa Mutaz Ryalat Hisham Elmoaqet Mechatronics Engineering German Jordanian University Amman Jordan Mechanical Engineering German Jordanian University Amman Jordan

ISBN: (数字)9798331505974

ISBN: (纸本)9798331505981

The Smart Health Monitoring App. (SHMA) revolutionizes personal healthcare through continuous monitoring and intelligent data analysis. This study presents a novel health monitoring system using a mobile application paired with a smartwatch. The smartwatch collects real-time physiological data (heart rate, body temperature, and step count), while the app uses AI for speech analysis to detect stress and facial recognition to identify fatigue. This integrated approach offers comprehensive health insights from combined physiological and behavioral data. A React Native mobile app integrates with the smartwatch, offering users real-time health data and personalized advice. SHMA empowers users to manage their health proactively, promoting overall well-being and setting new standards for smart health systems.

关键词： Temperature measurement Temperature sensors Wearable Health Monitoring Systems Temperature distribution Fatigue Real-time systems Mobile applications Biomedical monitoring Monitoring Stress

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intelligent Assessment of the Visual Ecology of the Urban Environment 2

Intelligent Assessment of the Visual Ecology of the Urban En...

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2nd international conference on Technological Advancements in Computational Sciences, ICTACS 2022

作者： Ather, Danish Rashevskiy, Nikolay Parygin, Danila Gurtyakov, Alexander Katerinina, Svetlana Amity University Department of It and Engineering Tashkent Uzbekistan Architecture and Civil Engineering Volgograd State Technical University Department of Digital Technologies for Urban Studies Volgograd Russia

ISBN: (数字)9781665476577

ISBN: (纸本)9781665476577

The article is devoted to the study of the visual ecology of the urban environment. The main factors of visual pollution are analyzed and a classification of sources of visual pollution is made. A three-stage approach to the intellectual assessment of the state of the visual ecology of the urban environment is proposed. The technology for collecting, preparing and analyzing data for calculating the integral level of area visual ecology using machine learning and geo information methods is described. The developed approach was tested on two cases: survey of visual pollution of vertical surfaces (walls, fences) for the presence of unauthorized graffiti;control over the filling of garbage containers and waste collection sites. Software solutions for monitoring urban infrastructure for violations of the normal state and notification of city services with visualization of information on a city map are described in the paper. © 2022 IEEE.

关键词： Containers

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OFFICERS: Operational Framework for intelligent Crime-and-Emergency Response Scheduling 32

OFFICERS: Operational Framework for Intelligent Crime-and-Em...

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32nd international conference on automated Planning and Scheduling, ICAPS 2022

作者： Chase, Jonathan Goh, Siong Thye Tran, Phong Lau, Hoong Chuin Singapore Management University Singapore

ISBN: (纸本)9781577358749

In the quest to achieve better response times in dense urban environments, law enforcement agencies are seeking AI-driven planning systems to inform their patrol strategies. In this paper, we present a framework, OFFICERS, for deployment planning that learns from historical data to generate deployment schedules on a daily basis. We accurately predict incidents using ST-ResNet, a deep learning technique that captures wide-ranging spatio-temporal dependencies, and solve a large-scale optimization problem to schedule deployment, significantly improving its scalability through a simulated annealing solver. Methodologically, our approach outperforms our previous works where prediction was done using Generative Adversarial Networks, and optimization was performed with the CPLEX solver. Furthermore, we show that our proposed framework is designed to be readily transferable between use cases, handling a wide range of both criminal and non-criminal incidents, with the use of deep learning and a general-purpose efficient solver, reducing dependence on context-specific details. We demonstrate the value of our approach on a police patrol case study, and discuss both the ethical considerations, and operational requirements, for deployment of a lightweight and responsive planning system. © 2022, Association for the Advancement of Artificial Intelligence.

关键词： Simulated annealing

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An Efficient learning Model Selection for Dengue Detection 2nd

An Efficient Learning Model Selection for Dengue Detection

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2nd international conference on intelligent and Cloud Computing, ICICC 2021

作者： Katta, Miranji Sandanalakshmi, R. Srilakshmi, Gubbala Adireddi, Ramkumar Sir C R Reddy College of Engineering Andhrapradesh Eluru534007 India Pondicherry Engineering College Puducherry605014 India Vishnu Institute of Technology Andhrapradesh Bhimavaram534202 India

ISBN: (纸本)9789811698729

Significant advances are being made in the development of diagnostic tools in the biomedical sector nowadays. However, finite precision flaws in diagnostic technologies significantly mislead comprehensive medical therapies. In this regard, machine learning is an important programming tool in the diagnosis of early detection of the most life-threatening diseases with existing clinical findings. The machine-learning method offers a framework for creating sophisticated, automated, and objective algorithms with decision-making capability for the processing of multimodal and high-dimensional biomedical data. The prediction performance of several machine-learning algorithms in disease diagnosis using pre-processed datasets based on percentage selection is simulated and compared in this article. The dengue ailment would be used to apply several machine-learning algorithms in order to evaluate performance, and the best of these machine-learning techniques is then incorporated in the Raspberry Pi. The random forest approach outperforms the simulation findings with a 95.60% true positive rate, 94.39% classification accuracy, 93.08% precision, and 0.69 and 0.494% mean absolute and root mean square errors, respectively. In practice, the experimental setting for an analogous machine-learning method yields 78.56% accuracy. During this project, Jupyter, RaspberryPi, and Python idle with OpenCV, Google Collab, and Weka toolbox are used for simulation to evaluate the performance of various methods. The same is implemented in RaspberryPi-V.4 for validation. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diagnosis

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Intrusion Detection System for In-vehicle Networks with Incremental learning Based on Cloud-Edge Collaborative Architecture∗ 19

Intrusion Detection System for In-vehicle Networks with Incr...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and Computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable Computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy Computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Lin, Jiaying Wei, Yehua Jiang, Haoran Long, Jing Hunan Normal University College of Information Science and Engineering Changsha China

ISBN: (纸本)9798350346558

The development of vehicle networking and self-driving technology has increased the integration of communication interfaces into modern vehicles and the interaction between the connected vehicles and roadside unites. This situation also brings security threats. Controller area network (CAN) is the main bus system in modern automobiles. It easily suffers from cyberattacks because it lacks security protection mechanisms. Intrusion detection system is an effective method to defend cyberattacks. However, the detection accuracy may be affected by the change in driving environment and the occurrence of unknown attacks. A detection model based on incremental learning is used to improve detection performance. In addition, a cloud-edge collaborative architecture is designed to implement the detection model for reducing the burden of calculation and storage of on-board system and maintaining the timeliness of detection. Deep Neural Network(DNN) is applied in the offline training stage to obtain a basic classification model using marked actual CAN data. Then, the trained model is stored in the cloud server. After the edge server receives detection request from a vehicle, it will download the model from the cloud server and perform intrusion detection. At the same time, it will update the model with incremental learning technology based on new unlabeled data. Experimental results show the effectiveness of the proposed method. © 2022 IEEE.

关键词： Intrusion detection

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Deep learning for a low-data drug design system 22

Deep learning for a low-data drug design system

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22nd IEEE international conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Qian, Yuchen Xing, Yuan Dong, Liang Baylor University Electrical and Computer Engineering United States University of Wisconsin-Stout Engineering and Technology Department United States

ISBN: (纸本)9781728162676

Molecule design is the process of discovering potential compound candidates for drug discovery. Deep learning technique shows significant advantages in data mining and can be used for molecule design. However, most drug discovery projects are limited to low-data situations, and it is difficult to train deep learning neural networks. This paper proposes a novel drug design system that is based on deep learning. It adopts one-shot learning and reinforcement learning, and it can operate under low-data conditions. Once trained, the system can generate new molecules with the desired properties. © 2021 IEEE.

关键词： Molecules

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FOUR-WAY CLASSIFICATION OF TABLA STROKES WITH MODELS ADAPTED FROM AUTOMATIC DRUM TRANSCRIPTION 22

FOUR-WAY CLASSIFICATION OF TABLA STROKES WITH MODELS ADAPTED...

引用

22nd international conference on Music Information Retrieval, ISMIR 2021

作者： Rohit, M.A. Bhattacharjee, Amitrajit Rao, Preeti Department of Electrical Engineering Indian Institute of Technology Bombay India

ISBN: (纸本)9781732729902

Motivated by musicological applications of the four-way categorization of tabla strokes, we consider automatic classification methods that are potentially robust to instrument differences. We present a new, diverse tabla dataset suitably annotated for the task. The acoustic correspondence between the tabla stroke categories and the common popular Western drum types motivates us to adapt models and methods from automatic drum transcription. We start by exploring the use of transfer learning on a state-of-the-art pre-trained multiclass CNN drums model. This is compared with 1-way models trained separately for each tabla stroke class. We find that the 1-way models provide the best mean f-score while the drums pre-trained and tabla-adapted 3-way models generalize better for the most scarce target class. To improve model robustness further, we investigate both drums and tabla-specific data augmentation strategies. © 2021 Proceedings of the 22nd international conference on Music Information Retrieval, ISMIR 2021. All Rights Reserved.

关键词： Information retrieval

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Inferring Questions from Programming Screenshots

Inferring Questions from Programming Screenshots

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international Mining Software Repositories, MSR

作者： Faiz Ahmed Xuchen Tan Folajinmi Adewole Suprakash Datta Maleknaz Nayebi York University Toronto ON Canada

ISBN: (数字)9798331501839

ISBN: (纸本)9798331501846

The integration of generative AI into developer forums like Stack Overflow presents an opportunity to enhance problem-solving by allowing users to post screenshots of code or Integrated Development Environments (IDEs) instead of traditional text-based queries. This study evaluates the effectiveness of various large language models (LLMs)—specifically LLAMA, GEMINI, and GPT-4o in interpreting such visual inputs. We employ prompt engineering techniques, including in-context learning, chain-of-thought prompting, and few-shot learning, to assess each model’s responsiveness and accuracy. Our findings show that while GPT-4o shows promising capabilities, achieving over $60 \%$ similarity to baseline questions for $51.75 \%$ of the tested images, challenges remain in obtaining consistent and accurate interpretations for more complex images. This research advances our understanding of the feasibility of using generative AI for image-centric problem-solving in developer communities, highlighting both the potential benefits and current limitations of this approach while envisioning a future where visual-based debugging copilot tools become a reality.

关键词： Visualization Accuracy Large language models Debugging Programming Software Problem-solving Prompt engineering data mining Few shot learning

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FIG-LP: Feature-Inverse-Graph based Link Prediction in Graph Stream 19

FIG-LP: Feature-Inverse-Graph based Link Prediction in Graph...

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作者： Zhang, Xu Xiao, XiaoQiang Li, GuoWei Ning, WeiXun Song, JianTong National University of Defense Technology School of Computer ChangSha China State Key Laboratory of High Performance Computing ChangSha China

ISBN: (纸本)9798350346558

Link prediction is one of the essential issues in network science, which aims to find the unknown link or estimate the future link in networks. Existing methods are mainly based on the assumption that the network data is completely available and has stable distribution before analysis. In practice, however, complex networks evolve lifelong with massive data. The data in those networks is associated with previous ones, and the distribution will be non-stationary. Compared with conventional link prediction methods, online link prediction in the largescale dynamic network has three main challenges: i) How to analyze massive data with acceptable expenditure;ii) How to predict future links with less topological information;iii) How to make link prediction stably in a dynamic evolutionary network. In this paper, we propose a streaming link prediction model based on lifelong learning and graph neural networks (GNNs), which converts the link prediction problems to graph classification. Our main idea is to design a new topology, the Feature-Inverse-Graph, which turns node pairs into independent graphs and takes the features of the node pair as new vertices. Additionally, we apply a two-phase sampling sketch to deal with the massive data so that the complexity of the model within lifelong evolving networks could be acceptable. Then, the link prediction tasks in the regular graph is converted into a series of individually Feature-Inverse-Graph classifications. In this case, the computational cost of our model will not increase dramatically with the increase of network data, which is further verified by analyzing the computational complexity. The experimental results demonstrate the efficiency and effectiveness of our model by continuously predicting future links of classical datasets. In our experiments, several topological link prediction measures are chosen as features of node pair. For future work, the FIG-LP model can be used as the basic research of intelligent systems su

关键词： intelligent systems

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