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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sambhare, Praful Devi, S. Rukmani Kumar, J. Ramesh Arul Mary Rexy, V. Priyanka, Nallabothula Jayanthi, L.N. Dr. D. Y. Patil Institute of Technology Department of Artificial Intelligence and Data Science Pune Pimpri India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computer Science Chennai India K.S.R. College of Engineering Department of Ece Tiruchengode India Saveetha College of Liberal Arts and Sciences Simats Department of Commerce Kuthambakkam India Doctor of Pharmacy Krishna Teja Pharmacy College Tirupati India

ISBN: (纸本)9798350371055

Children and newborns are more vulnerable to radiation exposure due to fast cell division, which may interfere with organs and cause skin problems. A system that detects hazardous radiation quickly and efficiently is in great demand. Many new and improved radiation protection and warning technologies are being offered. This research classifies radiation and its effects on newborns to show the development of an IoT-enabled system that utilizes artificial intelligence for radiation monitor and alarms. To help people escape hazardous zones, the suggested system uses audio/visual notifications. Gather the automated sensor system along with a real-time dataset of sensor values and their effects on newborns. Support vector machines, additional trees, bagged classification algorithms, random forests, logistic regression, and boosting adaptive classifiers classify radiation effects based on sensor measurement. This experiment shows the boosting adaptive classifier has the highest precision. © 2024 IEEE.

关键词： additional tree bagged boosting lOT radiation random forest

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Empirical Evaluation of License Plate Recognition Using Tensorflow Framework

Empirical Evaluation of License Plate Recognition Using Tens...

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2024 IEEE International BIT Conference, BITCON 2024

作者： Sowmyayani, S. Harilakshmi, V.S. Kavitha, J. Roselinkiruba, R. Tamilnadu Thoothukudi India Department of Artificial Intelligence and Data Science Tamilnadu Nagercoil India VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331518394

Due to the tremendous increase in vehicles, more attention is required to Intelligent Transport Systems (ITS). License Plate Recognition (LPR) is one such method in ITS. Although there are several researches on LPR, still the issues are partially solved. This is because license plate images suffer from low illumination, side view, etc. This paper developed a method to recognize license plates. It consists of license plate localization, segmentation and recognition. A Convolutional Neural Network (CNN) is designed for recognition. The proposed method is tested on the Chinese City Parking dataset (CCPD), Chinese License Plate dataset (CLPD), Application Oriented License Plate (AOLP) and PKUdata datasets. The performance of the proposed LPR method is evaluated using accuracy. It outperforms other recent methods by achieving an accuracy of 95% © 2024 IEEE.

关键词： Image segmentation

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Precise Asymptotic Analysis of Deep Random Feature Models

arXiv

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arXiv 2023年

作者： Bosch, David Panahi, Ashkan Hassibi, Babak Department of Data Science and AI Computer Science and Engineering Chalmers University of Technology Sweden Department of Electrical Engineering California Institute of Technology United States

We provide exact asymptotic expressions for the performance of regression by an L−layer deep random feature (RF) model, where the input is mapped through multiple random embedding and non-linear activation functions. For this purpose, we establish two key steps: First, we prove a novel universality result for RF models and deterministic data, by which we demonstrate that a deep random feature model is equivalent to a deep linear Gaussian model that matches it in the first and second moments, at each layer. Second, we make use of the convex Gaussian Min-Max theorem multiple times to obtain the exact behavior of deep RF models. We further characterize the variation of the eigendistribution in different layers of the equivalent Gaussian model, demonstrating that depth has a tangible effect on model performance despite the fact that only the last layer of the model is being trained. Copyright © 2023, The Authors. All rights reserved.

关键词： Asymptotic analysis

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Rethinking Time Encoding via Learnable Transformation Functions

arXiv

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arXiv 2025年

作者： Chen, Xi Tang, Yateng Xu, Jiarong Zhang, Jiawei Zhang, Siwei Peng, Sijia Zheng, Xuehao Xiong, Yun Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Tencent Weixin Group Shenzhen China School of Management Fudan University Shanghai China IFM Lab Department of Computer Science University of California Davis CA United States

Effectively modeling time information and incorporating it into applications or models involving chronologically occurring events is crucial. Real-world scenarios often involve diverse and complex time patterns, which pose significant challenges for time encoding methods. While previous methods focus on capturing time patterns, many rely on specific inductive biases, such as using trigonometric functions to model periodicity. This narrow focus on single-pattern modeling makes them less effective in handling the diversity and complexities of real-world time patterns. In this paper, we investigate to improve the existing commonly used time encoding methods and introduce Learnable Transformation-based Generalized Time Encoding (LeTE). We propose using deep function learning techniques to parameterize nonlinear transformations in time encoding, making them learnable and capable of modeling generalized time patterns, including diverse and complex temporal dynamics. By enabling learnable transformations, LeTE encompasses previous methods as specific cases and allows seamless integration into a wide range of tasks. Through extensive experiments across diverse domains, we demonstrate the versatility and effectiveness of LeTE. © 2025, CC BY-NC-SA.

关键词： Linear transformations

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Impact of E-Waste on Pregnant Lady, Newborn Baby, and Fetus 4

Impact of E-Waste on Pregnant Lady, Newborn Baby, and Fetus

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Tiwari, Ankita Babu, N. Ramesh Rakesh, Nitin Kumar, Mandeep Dhattarwal, Jagjit Singh Bhola, Abhishek College of Engineering Koneru Lakshmaih Education Foundation Department of Engineering Mathematics Guntur India School of Advnced Sciences Vellore Institute of Technology Department of Mathematucs Chennai India Department of Computer Science and Engineering Nagpur Campus Pune India School of Law Sharda University Greater Noida India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence & Data Science Andhra Pradesh Vaddeswaram India Chaudhary Charan Singh Haryana Agricultural University College of Agriculture Bawal India

ISBN: (纸本)9798350387490

Hazardous materials included in electronic trash (e-waste) can affect fetuses, neonates, and expectant mothers. Preterm birth, birth irregularities, and miscarriage are among the risks associated with pregnant women who are exposed to e-waste chemicals. Exposure to e-waste puts newborns at risk for respiratory and neurodevelopmental disorders. Developmental irregularities and growth restrictions are possible in fetuses. To reduce these risks and protect mother and child health, effective methods including appropriate e-waste discarding and public health programmes are important. © 2024 IEEE.

关键词： E-waste Environmental health Fetal development Neonatal health Neuro developmental disorders maternal pregnancy Public health policy recycling technologies Toxic chemicals

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"Two Means to an End Goal": Connecting Explainability and Contestability in the Regulation of Public Sector AI

arXiv

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arXiv 2025年

作者： Schmude, Timothée Yurrita, Mireia Alfrink, Kars Goff, Thomas L.E. Viard, Tiphaine University of Vienna Faculty of Computer Science Research Network Data Science Doctoral School Computer Science Austria Delft University of Technology Department of Sustainable Design Engineering Netherlands i3 Télécom Paris Institut Polytechnique de Paris France

Explainability and its emerging counterpart contestability have become important normative and design principles for the trustworthy use of AI as they enable users and subjects to understand and challenge AI decisions. However, the regulation of AI systems spans technical, legal, and organizational dimensions, producing a multiplicity in meaning that complicates the implementation of explainability and contestability due to the difficulty of defining them. Resolving this conceptual ambiguity requires specifying and comparing the meaning of both principles across regulation dimensions, disciplines, and actors. This process, here defined as translation, is essential to provide guidance on the principles’ realization. To this end, we present the findings of a semi-structured interview study with 14 interdisciplinary AI regulation experts. We report on the experts’ understanding of the intersection between explainability and contestability in public AI regulation, their advice for a decision subject and a public agency in a welfare allocation AI use case, and their perspectives on the connections and gaps within the research landscape. We provide differentiations between descriptive and normative explainability, judicial and non-judicial channels of contestation, and individual and collective contestation action. We further outline three main translation processes pertaining to the alignment of top-down and bottom-up regulation, the assignment of responsibility for interpreting regulations, and the establishment of interdisciplinary collaboration. Our contributions include an empirically grounded conceptualization of the intersection between explainability and contestability and recommendations on implementing these principles in public institutions. We believe our contributions can inform policy-making and regulation of these core principles and enable more effective and equitable design, development, and deployment of trustworthy public AI systems. Copyright © 2025, T

关键词： Energy policy

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An Enhanced Product Quality Evaluation using Hybrid LSTM-FCNN Model and GWO Algorithm for Social Media Sentiment Analysis

An Enhanced Product Quality Evaluation using Hybrid LSTM-FCN...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： V. S. Raj Kumar T. Kumaresan P. Rajesh Kanna S. Jagadeesan P. Showmiya P. Nithin Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Nandha Engineering College Erode India Department of Computer Science – Cyber Security Nehru Institute of Technology Coimbatore India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Sentiment analysis is still in its developing era, and sometimes, struggles are faced in evaluating user sentiment due to the use of traditional models to analyze the relationship structures in social media interactions. To tackle the issue, we proposed an innovative approach Hybrid LSTM neural network and a fully connected neural network (FCNN) integrated with the Grey Wolf Optimization (GWO) algorithm. The objective of this proposed method is to improve sentiment analysis by employing the LSTM to capture the sequence of data and improve the ability of FCNNs to improve feature extraction and the analysis process. The proposed system process initiated with the data preprocessing of input Twitter sentiment dataset transformed into a suitable input to the Hybrid LSTM-FCNN neural network. Then, temporal dependency of the data captured by LSTM and FCNN is used to extract the features. The GWO method mimics the grey wolf hunting behaviour, for optimizing the proposed Hybrid LSTM-FCNN model’s parameters to enhance the classification accuracy. The system models start with the initialization of the model, parameters, and training the models using the dataset to reduce the cross-entropy loss, estimating the testing dataset from unseen data and fine-tuning using GWO as necessary. Results demonstrate significant improvements in sentiment categorization compared to traditional methods. The Hybrid LSTM-FCNN model, enhanced with GWO, effectively captures nuanced sentiment patterns, leading to more accurate classification. Additionally, GWO’s computational efficiency contributes to scalability. In conclusion, our proposed approach offers a promising solution for enhancing product quality evaluation through advanced neural network architectures and optimization algorithms applied to social media sentiment analysis.

关键词： Sentiment analysis Analytical models Accuracy Social networking (online) Neural networks Blogs Feature extraction data models Optimization Long short term memory

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Evolutionary Dynamic Optimization-Based Calibration Framework for Agent-Based Financial Market Simulators

Evolutionary Dynamic Optimization-Based Calibration Framewor...

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Congress on Evolutionary Computation

作者： Zhenhua Yang Muyao Zhong Peng Yang Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Statistics and Data Science Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

The agent-based financial market simulators serve as an important validation tool for trading strategies. For high-fidelity simulation, it is pivotal to calibrate the parameters of a simulator so that the generated simulation data resembles the observed real market data of interest. In traditional calibration methods, it is typical that the parameters of the simulator are set to be time-invariant. However, the dynamic nature of the real financial market introduces various variability into the behaviors of the market participants over different time intervals, posing in-herent limitations to the traditional methods. A more reasonable approach might involve employing a simulator with time-variant parameters. This suggests that the model parameters can be dynamically adjusted at different stages of the simulation to adapt to the evolving market. Consequently, the calibration problem of the financial market simulators can be treated as a dynamic optimization problem. To dynamically calibrate the simulators, we introduce an Evolutionary Dynamic Optimization (EDO) framework. By monitoring the changes of the best fitness, the whole simulation time interval is adaptively divided into multiple stages. Then the Negatively Correlated Search (NCS) algorithm is employed to effectively adjust the parameters at different simulation stages to better simulate the real financial market. Empirical results on both synthetic and real data verify that our dynamic calibration framework significantly outperforms traditional calibration methods that fixing a parameter for the whole simulation interval. The proposed strategy of detecting dynamic changes is also shown to be more reliable than the naive method of manually segmenting stages. In terms of calibration time, our proposed method significantly improves by nearly 93% compared to the fixed parameter setting, and approximately 61% compared to manual segmentation calibration.

关键词： Adaptation models Fluctuations Heuristic algorithms Computational modeling Scalability Manuals data models

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Classification of Single Tuition Fee with Imbalanced data Using XGBoost and SMOTE

Classification of Single Tuition Fee with Imbalanced Data Us...

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Information Technology and Digital Innovation (ISITDI), International Symposium on

作者： Muhammad Isa Dadi Hasibuan Maya Silvi Lydia Amalia Romi Fadillah Rahmat Department of Data Science and Artificial Intelligence Universitas Sumatera Utara Medan Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Department of Information Technology Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798350389241

ISBN: (纸本)9798350389258

Tuition fees at the nation's public universities in Indonesia are determined using a Single Tuition Fee (STF) system, which aligns educational costs with students' family economic capabilities. However, the current STF group determination process, involving manual verification of various data points (parental income, number of siblings, electricity bills, etc.) for each student, is not only time-consuming and inefficient but also prone to human error due to the sheer volume of data to be processed. This research proposes the integration of technology, specifically Machine Learning, to enhance the efficiency, accuracy, and reliability of this process. We utilize the Extreme Gradient Boosting (XGBoost) algorithm to process and analyze data related to students' economic capabilities and other relevant variables, combined with the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance issues in the data. Our approach significantly improves the F-score from an initial 0.7506 to 0.9214 and achieves an F-score of 0.8563 when tested on a different dataset, demonstrating the model's robustness and generalizability. This approach not only streamlines the STF determination process but also reduces the risk of errors, contributing to a more equitable and accessible higher education system in Indonesia.

关键词： Technological innovation Accuracy Sensitivity Machine learning algorithms Electricity Education Manuals Robustness data models Information technology

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Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks

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IEEE Internet of Things Journal 2025年

作者： Yu, Hanzhi Liu, Yuchen Yang, Zhaohui Sun, Haijian Chen, Mingzhe University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral GablesFL33146 United States North Carolina State University Department of Computer Science RaleighNC27695 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China University of Georgia School of Electrical and Computer Engineering AthensGA30602 United States

In this paper, we investigate an accurate synchronization between a physical network and its digital network twin (DNT), which serves as a virtual representation of the physical network. The considered network includes a set of base stations (BSs) that must allocate its limited spectrum resources to serve a set of users while also transmitting its partially observed physical network information to a cloud server to generate the DNT. Since the DNT can predict the physical network status based on its historical status, the BSs may not need to send their physical network information at each time slot, allowing them to conserve spectrum resources to serve the users. However, if the DNT does not receive the physical network information of the BSs over a large time period, the DNT's accuracy in representing the physical network may degrade. To this end, each BS must decide when to send the physical network information to the cloud server to update the DNT, while also determining the spectrum resource allocation policy for both DNT synchronization and serving the users. We formulate this resource allocation task as an optimization problem, aiming to maximize the total data rate of all users while minimizing the asynchronization between the physical network and the DNT. The formulated problem is challenging to solve by traditional optimization methods, as each BS can only observe a partial physical network, making it difficult to find an optimal spectrum allocation strategy for the entire network. To address this problem, we propose a method based on the gated recurrent units (GRUs) and the value decomposition network (VDN). The GRU component allows the DNT to predict future status using the historical data, effectively updating itself when the BSs do not transmit the physical network information. The VDN algorithm enables each BS to learn the relationship between its local observation and the team reward of all BSs, allowing it to collaborate with others in determining whe

关键词： Resource allocation

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