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9th International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Jenifer, A. Ahmed, Syed Imran Sangeetha, G. Chakkaravarthy, A. Prabhu Kumar, Ashok Bakhare, Ruhi Rmk Engineerinh College Department of Electrical And Electronics Engineering Tamil Nadu Kavaraipettai601206 India Mj College Department of Electronics And Communication Engineering Telangana Hyderabad500053 India R.M.K College of Engineering And Technology Department of Artificial Intelligence And Data Science Tamil Nadu Thiruvallur India Srm Institute of Science And Technology Department of Networking And Communications Tamil Nadu Kattankulathur603203 India Banasthali Vidyapith Department of Computer Science Rajasthan 304022 India Dr. Ambedkar Institute of Management Studies And Research Department of Cse Deekshbhoomi Maharashtra 440010 India

ISBN: (纸本)9798350365092

In an unanticipatedly short period, a patient may seek advice from a computer prior to seeing a doctor, thus establishing the' standard' medical procedure of the future. Thanks to developments in artificial intelligence (AI), the days of misdiagnosing illnesses and treating their symptoms instead of their underlying causes may soon be over. It's possible that something like this will occur soon. To accommodate a complete 3D representation of an organ, imagine how much space would need to be cleared from your laptop or how many years' worth of blood pressure records would need to be eliminated. The image would require both of these things to be made possible. More uses of artificial intelligence and high performance data-driven medicine are made possible by the growing body of data generated in clinics and preserved in electronic medical records as a result of routine testing and medical imaging. These applications have led to the development of methods used by researchers and medical practitioners to address clinical concerns throughout time, and this trend is anticipated to continue. © 2024 IEEE.

关键词： Artificial intelligence

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Automated Evaluation of Student Answers for Geometric Questions Based on the Theorem 'Angles on a Straight Line Add to 180±’

Automated Evaluation of Student Answers for Geometric Questi...

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Artificial Intelligence (SLAAI-ICAI), SLAAI International Conference on

作者： C.P. Edirimanna W. A. C. Weerakoon Department of Statistics & Computer Science University of Kelaniya Kelaniya Sri Lanka

Evaluation of the answers for a geometry-related question is a much more challenging task due to the usage of multiple methods for solving this kind of problem. However, in this research, an automated system to evaluate the students' answers to geometric questions has been introduced. The image of the problem and the image of the student's answer are the inputs of this system. The system can evaluate student's answers by using the identified angles and creating equations in the system. Furthermore, the system allocates the marks for correct steps and provides feedback on whether the answer is correct or incorrect. Here, the set of rules using “sympy” has been used to create the solved answer by the system. Currently, the system evaluates the answers only for the problems related to the theorem” Angles on a Straight Line Add to 180°”. The system has been tested with 30 student answers for 10 questions. 18 were correct out of these 30 answers.

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THE IMPACT OF FEAR AND BEHAVIOUR RESPONSE TO ESTABLISHED AND NOVEL DISEASES

arXiv

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arXiv 2024年

作者： Kaur, Avneet Tyson, Rebecca C. Moyles, Iain R. Department of Computer Science Mathematics Physics and Statistics University of British Columbia KelownaBC Canada Department of Mathematics and Statistics York University TorontoON Canada

We analyze a disease transmission model that allows individuals to acquire fear and change their behaviour to reduce transmission. Fear is acquired through contact with infected individuals and through the influence of fearful individuals. We analyze the model in two limits: First, an Established Disease Limit (EDL), where the spread of the disease is much faster than the spread of fear, and second, a Novel Disease Limit (NDL), where the spread of the disease is comparable to that of fear. For the EDL, we show that the relative rate of fear acquisition to disease transmission controls the size of the fearful population at the end of a disease outbreak, and that the fear-induced contact reduction behaviour has very little impact on disease burden. Conversely, we show that in the NDL, disease burden can be controlled by fear-induced behaviour depending on the rate of fear loss. Specifically, fear-induced behaviour introduces a contact parameter p, which if too large prevents the contact reduction from effectively managing the epidemic. We analytically identify a critical prophylactic behaviour parameter p = pc where this happens leading to a discontinuity in epidemic prevalence. We show that this change in disease burden introduces delayed epidemic waves. © 2024, CC BY.

关键词： Disease control

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STL-DP: Differentially Private Time Series Exploring Decomposition and Compression Methods

STL-DP: Differentially Private Time Series Exploring Decompo...

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2022 International Conference on Information and Knowledge Management Workshops, CIKM-WS 2022

作者： Kim, Kyunghee Kim, Minha Woo, Simon Department of Statistics Sungkyunkwan University Seoul Korea Republic of Department of Artifical Intelligence Sungkyunkwan University Suwon Korea Republic of Department of Applied Data Science Sungkyunkwan University Suwon Korea Republic of

As time series data is collected and used in a variety of fields, the importance of preserving privacy on time series is also on the increase. This paper is a preliminary study of the Differential Privacy (DP) algorithm specially designed to provide privacy to time series data by integrating the time series decomposition technique. In particular, this study extends the Fourier Perturbation Algorithm (FPA) with Seasonal and Trend decomposition using LOESS (STL). In this work, we propose STL-DP, which first performs STL decomposition to the original data. Then we apply the FPA only to the core part of the time series, particularly trend or seasonal components, to provide privacy. In this preliminary study, we show that our approach consistently outperforms other baselines in terms of utility according to the experimental results. Our code is available at https://***/Privacy-DASH/STL-DP. © 2022 Copyright for this paper by its authors.

关键词： Time series

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Predicting Gene Relations with a Graph Transformer Network Integrating DNA, Protein, and Descriptive data

Predicting Gene Relations with a Graph Transformer Network I...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Yibo Xu, Dong Hammer, Richard Popescu, Mihail University of Missouri Columbia Institute for Data Science and Informatics ColumbiaMO United States University of Missouri Columbia Department of Electrical Engineering and Computer Science ColumbiaMO United States University of Missouri Columbia Department of Pathology and Anatomical Sciences ColumbiaMO United States University of Missouri Columbia School of Medicine ColumbiaMO United States

ISBN: (纸本)9798350386226

Gene relation prediction is crucial for understanding cancer pathways and developing targeted treatments. This study proposes a novel Graph Transformer Network to predict gene relations by integrating DNA sequences, amino acids sequences, and gene descriptions. Utilizing the KEGG pathway database, our model aggregates diverse gene information to enhance gene regulatory pathway predictions. We encoded DNA sequences using DNA-BERT2, protein sequences using ESM2, and generated gene descriptions using ChatGPT, subsequently encoded by Bio-BERT. These embeddings were integrated and processed through a graph transformer with multi-head and single-head attention layers. Our results demonstrate that incorporating protein and description embeddings significantly improves predictive performance, while including DNA sequences shows marginal impact. The proposed model outperforms traditional machine learning and graph neural network-based methods, achieving an F-1 score of 0.893 on identifying whether two genes interact. This study highlights the potential of integrating multi-faceted genomic data to advance gene relation prediction, offering valuable insights for precision medicine and bioinformatics. © 2024 IEEE.

关键词： Graph embeddings

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Testing by Betting while Borrowing and Bargaining

arXiv

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arXiv 2024年

作者： Wang, Hongjian Ramdas, Aaditya Department of Statistics and Data Science Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States

Testing by betting has been a cornerstone of the game-theoretic statistics literature. In this framework, a betting score (or more generally an e-process), as opposed to a traditional p-value, is used to quantify the evidence against a null hypothesis: the higher the betting score, the more money one has made betting against the null, and thus the larger the evidence that the null is false. A key ingredient assumed throughout past works is that one cannot bet more money than they currently have. In this paper, we ask what happens if the bettor is allowed to borrow money after going bankrupt, allowing further financial flexibility in this game of hypothesis testing. We propose various definitions of (adjusted) evidence relative to the wealth borrowed, indebted, and accumulated. We also ask what happens if the bettor can "bargain", in order to obtain odds bettor than specified by the null hypothesis. The adjustment of wealth in order to serve as evidence appeals to the characterization of arbitrage, interest rates, and numéraire-adjusted pricing in this setting. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

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Enhanced Water Security and Resilience in Drought-Prone Zones with IoT and SVM classifier for Early Warning Systems

Enhanced Water Security and Resilience in Drought-Prone Zone...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Anandan, P. Saillaja, V. Kovarasan, Rajesh Kambattan Padmaloshani, P. Ishwarya, M.V. Rajmohan, M. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Department Of Electronics And Communication Engineering Tamil Nadu Chennai India Srm Institute Of Science And Technology Faculty Of Management Studies Kattankulathur Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Department Of Computer Science And Engineering Tamil Nadu Chennai India Department Of Electronics And Communication Engineering Tamil Nadu Namakkal India Agni College Of Technology Artificial Intelligence And Data Science Department Tamil Nadu Chennai India Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350371567

The worldwide risk of droughts to water security and agricultural sustainability is growing. Using Internet of Things (IoT) technology and a Support Vector Machine (SVM) classifier to create drought-prone early warning systems is an innovative solution to these difficulties. Better water resource management and resilience in water-scarce locations are the study goals. The IoT infrastructure collects real-time data from sensors strategically positioned over the research area to monitor soil moisture, weather, and water levels. SVM classifiers, which are effective in classification, get this large dataset. The SVM model uses data to predict and classify drought events, alerting decision-makers and stakeholders. The system shows that this integrated strategy works. The IoT-SVM early warning system reliably predicts droughts and alerts authorities in advance, enabling proactive mitigation and intervention. The system's drought prediction accuracy helps drought-prone areas achieve water security and resilience by allocating resources, managing water sustainably, and reducing effects on agriculture and people. It uses IoT and machine learning to improve drought preparation and response, improving long-term water sustainability and resilience in water-scarce areas. © 2024 IEEE.

关键词： Forecasting

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Effective Short Packet Communication for Resource Allocation in Wireless Powered Iot Networks 9

Effective Short Packet Communication for Resource Allocation...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Selvi, K. Kanchana, D. Sankar, R. Princy, B Anni Saravanan, S. Balamurugan, A. R M K Engineering College Department of Information Techology Tamil Nadu India SRM Institute of Science and Technology Department of Computer Applications Tamil Nadu India Chennai Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu India Panimalar Engineering College Department of Computer and Communication Engineering Tamil Nadu India Jeppiaar Institute of Technology Department of Aritificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798350397376

The Internet-of-things (IoT) is a technology that might be used to link huge amounts of equipment and gadgets in the future. This research uses a hybrid access point (HAP) to explore short packet communication (SPC), in which the IoT devices broadcast their short data packets to the HAP using the energy they gather. As compared to regular wireless networks, SPC has a lower transmission rate and a higher packet error rate. With unlimited block length codes, the allocation of resources in the literature is no longer the best it could possibly be. Based on a user's unique effective amount of information r, their transmission time and packet error rate are tuned to maximize or decrease total effective throughput or total transmission time. Nonconvex issues need the development of efficient ways for unacceptable solutions. These algorithms outperform current benchmarks and are on par with an exhaustive search for the optimum solution in terms of performance. © 2023 IEEE.

关键词： Resource allocation

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MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization

arXiv

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arXiv 2023年

作者： Awad, Noor Sharma, Ayushi Muller, Philipp Thomas, Janek Hutter, Frank Department of Computer Science University of Freiburg Germany Department of Statistics University of Munich Germany

Hyperparameter optimization (HPO) is a powerful technique for automating the tuning of machine learning (ML) models. However, in many real-world applications, accuracy is only one of multiple performance criteria that must be considered. Optimizing these objectives simultaneously on a complex and diverse search space remains a challenging task. In this paper, we propose MO-DEHB, an effective and flexible multi-objective (MO) optimizer that extends the recent evolutionary Hyperband method DEHB. We validate the performance of MO-DEHB using a comprehensive suite of 15 benchmarks consisting of diverse and challenging MO problems, including HPO, neural architecture search (NAS), and joint NAS & HPO, with objectives including accuracy, latency and algorithmic fairness. A comparative study against state-of-the-art MO optimizers demonstrates that MO-DEHB clearly achieves the best performance across our 15 benchmarks. © 2023, CC BY.

关键词： Multiobjective optimization

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Machine Learning Models for Speech-Based Depression Screening

Machine Learning Models for Speech-Based Depression Screenin...

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2025 IEEE International Conference on Cybernetics and Innovations, ICCI 2025

作者： Kongchatree, Kunanon Suriyo, Ongon Pichedpan, Natvara Kongkachandra, Rachada Songmuang, Pokpong Data Science and Innovation College of Interdisciplinary Studies Thammasat University Bangkok Thailand Thammasat University Department of Computer Science Bangkok Thailand

ISBN: (纸本)9798331533267

Mental health issues significantly affect society, requiring long-term treatment that is both costly and impacts personal relationships. This research paper proposes a speech-based method to screen for depression risk as an accessible alternative. The research included five machine learning models-Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Naive Bayes, and Linear Discriminant Analysis (LDA)-were used to analyze speech data from 25 participants with varying mental health conditions. Three data preparation techniques-Leave-One-Out Cross-Validation, SMOTE, and raw data-were applied to optimize the models. Results showed that SVM with SMOTE achieved the highest accuracy (98 %), emphasizing the importance of effective data preparation. This research demonstrates the potential of using speech analysis as a cost-effective tool for early mental health screening and provides a foundation for practical applications in healthcare and beyond. © 2025 IEEE.

关键词： mHealth

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