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arXiv 2024年

作者： Cao, Yuchen Dai, Jianglai Wang, Zhongyan Zhang, Yeyubei Shen, Xiaorui Liu, Yunchong Tian, Yexin Khoury College of Computer Science Northeastern University United States Department of EECS University of California Berkeley United States Center for Data Science New York University United States School of Engineering and Applied Science University of Pennsylvania United States Georgia Institute of Technology College of Computing United States

The global increase in mental illness requires innovative detection methods for early intervention. Social media provides a valuable platform to identify mental illness through user-generated content. This systematic review examines machine learning (ML) models for detecting mental illness, with a particular focus on depression, using social media data. It highlights biases and methodological challenges encountered throughout the ML lifecycle. A search of PubMed, IEEE Xplore, and Google Scholar identified 47 relevant studies published after 2010. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was utilized to assess methodological quality and risk of bias. The review reveals significant biases affecting model reliability and generalizability. A predominant reliance on Twitter (63.8%) and English-language content (over 90%) limits diversity, with most studies focused on users from the United States and Europe. Non-probability sampling methods (approximately 80%) limit representativeness. Only 23% of studies explicitly addressed linguistic nuances like negations, crucial for accurate sentiment analysis. Inconsistent hyperparameter tuning was observed, with only 27.7% properly tuning models. About 17% did not adequately partition data into training, validation, and test sets, risking overfitting. While 74.5% used appropriate evaluation metrics for imbalanced data, others relied on accuracy without addressing class imbalance, potentially skewing results. Reporting transparency varied, often lacking critical methodological details. These findings highlight the need to diversify data sources, standardize preprocessing protocols, ensure consistent model development practices, address class imbalance, and enhance reporting transparency. By overcoming these challenges, future research can develop more robust and generalizable ML models for depression detection on social media, contributing to improved mental health outcomes globally. © 2024, CC BY.

关键词： Adversarial machine learning

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Real-Time Pill Identification for the Visually Impaired Using Deep Learning

arXiv

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arXiv 2024年

作者： Dang, Bo Zhao, Wenchao Li, Yufeng Ma, Danqing Yu, Qixuan Zhu, Elly Yijun Computer Science San Francisco Bay University FremontCA United States School of Software Engineering University of Science and Technology of China Anhui Hefei China School of Electronics and Computer Science University of Southampton Southampton United Kingdom Computer Science Stevens Institute of Technology HobokenNJ United States College of Computing Georgia Institute of Technology AtlantaGA United States

The prevalence of mobile technology offers unique opportunities for addressing healthcare challenges, especially for individuals with visual impairments. This paper explores the development and implementation of a deep learning-based mobile application designed to assist blind and visually impaired individuals in real-time pill identification. Utilizing the YOLO framework, the application aims to accurately recognize and differentiate between various pill types through real-time image processing on mobile devices. The system incorporates Text-to-Speech (TTS) to provide immediate auditory feedback, enhancing usability and independence for visually impaired users. Our study evaluates the application's effectiveness in terms of detection accuracy and user experience, highlighting its potential to improve medication management and safety among the visually impaired community. Copyright © 2024, The Authors. All rights reserved.

关键词： Mobile computing

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A Systematic Review of Machine Learning Approaches for Detecting Deceptive Activities on Social Media: Methods, Challenges, and Biases

arXiv

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arXiv 2024年

作者： Liu, Yunchong Shen, Xiaorui Zhang, Yeyubei Wang, Zhongyan Tian, Yexin Dai, Jianglai Cao, Yuchen School of Engineering and Applied Science University of Pennsylvania United States Khoury college of computer science Northeastern University United States Center for Data Science New York University United States Georgia Institute of Technology College of Computing United States Department of EECS University of California Berkeley United States

Social media platforms like Twitter, Facebook, and Instagram have facilitated the spread of misinformation, necessitating automated detection systems. This systematic review evaluates 36 studies that apply machine learning (ML) and deep learning (DL) models to detect fake news, spam, and fake accounts on social media. Using the Prediction model Risk Of Bias ASsessment Tool (PROBAST), the review identified key biases across the ML lifecycle: selection bias due to non-representative sampling, inadequate handling of class imbalance, insufficient linguistic preprocessing (e.g., negations), and inconsistent hyperparameter tuning. Although models such as Support Vector Machines (SVM), Random Forests, and Long Short-Term Memory (LSTM) networks showed strong potential, over-reliance on accuracy as an evaluation metric in imbalanced data settings was a common flaw. The review highlights the need for improved data preprocessing (e.g., resampling techniques), consistent hyperparameter tuning, and the use of appropriate metrics like precision, recall, F1 score, and AUROC. Addressing these limitations can lead to more reliable and generalizable ML/DL models for detecting deceptive content, ultimately contributing to the reduction of misinformation on social media. © 2024, CC BY.

关键词： Adversarial machine learning

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Cyber (Re-)Insurance Policy Writing Is Np-Hard in Iot Societies

Cyber (Re-)Insurance Policy Writing Is Np-Hard in Iot Societ...

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2021 Winter Simulation Conference, WSC 2021

作者： Pal, Ranjan Lu, Taoan Liu, Peihan Yin, Xinlong Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States College of Computing Georgia Institute of Technology AtlantaGA30332 United States

ISBN: (纸本)9781665433112

The last decade has witnessed steadily growing markets for cyber (re-)insurance products to mitigate residual cyber-risk. In this introductory effort, we prove that underwriting simple cyber re-insurance policies can be worst case computationally hard, i.e., NP-Hard, especially for upcoming IoT societies. More specifically, let alone human underwriters, even a computer cannot compute an optimal cyber re-insurance policy in a reasonable amount of time in worst case scenarios. Here, optimality of a contract is judged based on the extent of information asymmetry induced negative externalities it mitigates between a re-insurance seller and a buyer. Our result does not challenge the existence of cyber re-insurance markets that we feel will be a necessity in the IoT age, but only rationalizes why their growth might be slow, and would subsequently need regulatory intervention. As a direct applicability of our methodology, we argue that optimal traditional cyber-insurance underwriting in IoT societies is also NP-Hard. © 2021 IEEE.

关键词： Insurance

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MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge

arXiv

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arXiv 2023年

作者： Ni, Bo Buehler, Markus J. Massachusetts Institute of Technology 77 Massachusetts Ave. CambridgeMA02139 United States Center for Computational Science and Engineering Schwarzman College of Computing Massachusetts Institute of Technology 77 Massachusetts Ave. CambridgeMA02139 United States

Solving mechanics problems using numerical methods requires comprehensive intelligent capability of retrieving relevant knowledge and theory, constructing and executing codes, analyzing the results, a task that has thus far mainly been reserved for humans. While emerging AI methods can provide effective approaches to solve end-to-end problems, for instance via the use of deep surrogate models or various data analytics strategies, they often lack physical intuition since knowledge is baked into the parametric complement through training, offering less flexibility when it comes to incorporating mathematical or physical insights. By leveraging diverse capabilities of multiple dynamically interacting large language models (LLMs), we can overcome the limitations of conventional approaches and develop a new class of physics-inspired generative machine learning platform, here referred to as MechAgents. A set of AI agents can solve mechanics tasks, here demonstrated for elasticity problems, via autonomous collaborations. A two-agent team can effectively write, execute and self-correct code, in order to apply finite element methods to solve classical elasticity problems in various flavors (different boundary conditions, domain geometries, meshes, small/finite deformation and linear/hyper-elastic constitutive laws, and others). For more complex tasks, we construct a larger group of agents with enhanced division of labor among planning, formulating, coding, executing and criticizing the process and results. The agents mutually correct each other to improve the overall team-work performance in understanding, formulating and validating the solution. Our framework shows the potential of synergizing the intelligence of language models, the reliability of physics-based modeling, and the dynamic collaborations among diverse agents, opening novel avenues for automation of solving engineering problems. © 2023, CC BY-NC-SA.

关键词： Elasticity

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Forte: A Suite of Advanced Multireference Quantum Chemistry Methods

arXiv

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arXiv 2024年

作者： Evangelista, Francesco A. Li, Chenyang Verma, Prakash Hannon, Kevin P. Schriber, Jeffrey B. Zhang, Tianyuan Cai, Chenxi Wang, Shuhe He, Nan Stair, Nicholas H. Huang, Meng Huang, Renke Misiewicz, Jonathon P. Li, Shuhang Marin, Kevin Zhao, Zijun Burns, Lori A. Department of Chemistry Cherry Emerson Center for Scientific Computation Emory University AtlantaGA30322 United States Key Laboratory of Theoretical and Computational Photochemistry Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Center for Computational Molecular Science and Technology School of Chemistry and Biochemistry School of Computational Science and Engineering Georgia Institute of Technology AtlantaGA30332-0400 United States

Forte is an open-source library specialized in multireference electronic structure theories for molecular systems and the rapid prototyping of new methods. This paper gives an overview of the capabilities of Forte, its software architecture, and examples of applications enabled by the methods it implements. Copyright © 2024, The Authors. All rights reserved.

关键词： Electronic structure

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Classification of Dermoscopy Textures with an Ensemble Feedback of Multilayer Perceptron 1st

Classification of Dermoscopy Textures with an Ensemble Feedb...

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1st International Conference on Deep sciences for computing and Communications, IconDeepCom 2022

作者： Prabhu Chakkaravarthy, A. Saravanan, T.R. Udayakumar, Sridhar Subasini, C.A. Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Information Technology Mettu University Mettu Ethiopia St. Joseph’s Institute of Technology Old Mamallapuram Road Chennai India

ISBN: (纸本)9783031276217

Aim: To build a perfection classification of skin cancer dataset in the form of textured parameters. The input images are taken from ISIC 2019 dataset. The images are converted into textures like contrast, homogeneity, energy and correlation using GLCM feature extraction. Materials and method: Multilayer perceptron’s, or MLPs, can approximate any continuous function with a single hidden layer. They can be used in supervised learning to model the correlation of inputs and outputs between pairs of inputs and outputs. To optimize performance, models are trained to minimize error by adjusting their parameters, or weights and biases. Result: The input classification elements like contrast, correlation, energy and homogeneity are taken for classification, the initial with normal Multi-layer perceptron classification generates the sample input of 65.5% but the object is carried over ensemble classification again with Multi-layer perceptron generates 93.5% classification accuracy. Conclusion: On comparing the outcomes from the normal classification and the ensemble classification with Multi-Layer Perceptron, the ensemble model generates the maximum classification accuracy without breaking the sample of classification dataset. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Efficient Global Algorithms for Transmit Beamforming Design in ISAC Systems

arXiv

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arXiv 2024年

作者： Wu, Jiageng Wang, Zhiguo Liu, Ya-Feng Liu, Fan The School of Mathematics Jilin University Changchun130012 China The College of Mathematics Sichuan University Sichuan Chengdu610064 China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China

In this paper, we propose a multi-input multi-output transmit beamforming optimization model for joint radar sensing and multi-user communications, where the design of the beamformers is formulated as an optimization problem whose objective is a weighted combination of the sum rate and the Cramér-Rao bound, subject to the transmit power budget. Obtaining the global solution for the formulated nonconvex problem is a challenging task, since the sum-rate maximization problem itself (even without considering the sensing metric) is known to be NP-hard. The main contributions of this paper are threefold. Firstly, we derive an optimal closed-form solution to the formulated problem in the single-user case and the multi-user case where the channel vectors of different users are orthogonal. Secondly, for the general multi-user case, we propose a novel branch and bound (B&B) algorithm based on the McCormick envelope relaxation. The proposed algorithm is guaranteed to find the globally optimal solution to the formulated problem. Thirdly, we design a graph neural network (GNN) based pruning policy to determine irrelevant nodes that can be directly pruned in the proposed B&B algorithm, thereby significantly reducing the number of unnecessary enumerations therein and improving its computational efficiency. Simulation results show the efficiency of the proposed vanilla and GNN-based accelerated B&B algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： computational efficiency

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Oral Cancer Detection and Classification Using Deep Learning with DenseNet121-CatBoost Classifier

Oral Cancer Detection and Classification Using Deep Learning...

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Network, Multimedia and Information technology (NMITCON) International Conference on

作者： D Anitha Thota Soujanya Subhra Chakraborty Ahmad Alkhayyat R Revathi Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur India School of Agricultural Sciences SR University Warangal India Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Oral cancer is a growing universal health challenge, which is rising in the current situation. Early detection is vital for effective treatment and better patient results. However, current diagnostic methods, notably manual histopathological analysis, suffer from subjectivity and time limitations due to classifying the images according to their type. The dependence on manual analysis can lead to delays in accurately diagnosing oral cancer. The proposed method DenseNet121-CatBoost classifier addresses this challenge by using the Histopathological Images dataset. The DenseNet121 for the extracting process and the CatBoost classifier for classifying the images according to their classes. DenseNet121 is utilized to extract features and patterns from images and the CatBoost classifier in the classification process helps to classify the dataset according to its classes using the extracted information. The performance of the DenseNet121-CatBoost method is analyzed using accuracy, specificity, sensitivity, f1score, precision, and Area Under Curve (AUC). This paper shows that the proposed DenseNet121-CatBoost outperforms the existing methods such as ResNet50+CatBoost, DenseNet201+SVM (Support Vector Machine), and GoogLeNet+XGBoost in terms of accuracy of 97.62%, specificity of 98.96%, sensitivity of 94.55%, f1-score of 93.87%, precision of 98.65%, and AUC of 95.26%.

关键词： Support vector machines Accuracy Sensitivity Image color analysis Manuals Feature extraction Cancer detection Gabor filters Data mining Cancer

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An Efficient Novel Approach for Iris Recognition and Segmentation Based on the Utilization of Deep Learning

An Efficient Novel Approach for Iris Recognition and Segment...

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Evolutionary Algorithms and Soft computing Techniques (EASCT), International Conference on

作者： Syed Nawaz Pasha Anitha D R Anbunathan Gobinath C Madhura G K School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Mathematics SA Engineering College Chennai India School of Computing and Information Technology Reva University Bangalore India Department of AI and ML Nitte Meenakshi Institute of Technology Bengaluru India

Iris recognition stands out as one of the most stable and accurate biometric identification systems such as facial, voice, fingerprint, handwriting, and iris recognition itself. Deep Learning (DL) plays a pivotal role in enhancing security by leveraging Convolutional Neural Networks (CNNs) for iris recognition. The acquisition of iris images and real-time processing have become focal points demanding heightened attention in the realm of DL. Iris recognition is prized for its high level of protection, making it challenging to reconstruct iris data easily from the database. The CASIA-Iris Dataset serves as a benchmark to verify the uniqueness of iris patterns and evaluate the performance of iris recognition. However, due to limitations in iris image access, the application of CNNs in the segmentation approach remains critical for iris localization and pupil boundary identification, ensuring both efficiency and specificity in iris images. The results obtained from this study reveal that the proposed CNN, utilizing DL models, achieves an impressive accuracy of 99.89% on the CASIA-Iris dataset, better performance of other existing models, such as U-Net and VGG-Net, and ensuring accurate classification.

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