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arXiv 2023年

作者： Gupta, Aaryan Bläser, Markus Department of Computer Science & Engineering Indian Institute of Technology Bombay Mumbai India Saarland University Saarland Informatics Campus Saarbrücken Germany

Linear structural causal models (SCMs) are used to express and analyse the relationships between random variables. Direct causal effects are represented as directed edges and confounding factors as bidirected edges. Identifying the causal parameters from correlations between the nodes is an open problem in artificial intelligence. In this paper, we study SCMs whose directed component forms a tree. Van der Zander et al. (AISTATS’22, PLMR 151, pp. 6770–6792, 2022) give a PSPACE-algorithm for the identification problem in this case, which is a significant improvement over the general Gröbner basis approach, which has doubly-exponential time complexity in the number of structural parameters. In this work, we present a randomized polynomial-time algorithm, which solves the identification problem for tree-shaped SCMs. For every structural parameter, our algorithms decides whether it is generically identifiable, generically 2-identifiable, or generically unidentifiable. (No other cases can occur.) In the first two cases, it provides one or two fractional affine square root terms of polynomials (FASTPs) for the corresponding parameter, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Polynomial approximation

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Data analytics and knowledge management approach for COVID-19 prediction and control

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International Journal of Information technology (Singapore) 2023年第2期15卷 937-954页

作者： Hasan, Iqbal Dhawan, Prince Rizvi, S.A.M. Dhir, Sanjay National Informatics Centre Delhi Secretariat IP Estate New Delhi 110003 India Department of Trade and Taxes Government of NCT of Delhi IP Estate New Delhi 110002 India Department of Computer Science Faculty of Natural Science Jamia Millia Islamia Delhi New Delhi 110025 India Department of Management Studies Indian Institute of Technology-Delhi New Delhi 110016 India

The Coronavirus Disease (COVID-19) caused by SARS-CoV-2, continues to be a global threat. The major global concern among scientists and researchers is to develop innovative digital solutions for prediction and control of infection and to discover drugs for its cure. In this paper we developed a strategic technical solution for surveillance and control of COVID-19 in Delhi-National Capital Region (NCR). This work aims to elucidate the Delhi COVID-19 Data Management Framework, the backend mechanism of integrated Command and Control Center (iCCC) with plugged-in modules for various administrative, medical and field operations. Based on the time-series data extracted from iCCC repository, the forecasting of COVID-19 spread has been carried out for Delhi using the Auto-Regressive Integrated Moving Average (ARIMA) model as it can effectively predict the logistics requirements, active cases, positive patients, and death rate. The intelligence generated through this research has paved the way for the Government of National Capital Territory Delhi to strategize COVID-19 related policies formulation and implementation on real time basis. The outcome of this innovative work has led to the drastic reduction in COVID-19 positive cases and deaths in Delhi-NCR. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： ARIMA COVID-19 pandemic Data analytics Forecasting SARS-CoV-2

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Retraction Note: Exploring subcellular location anomalies: a novel quantum bioimaging paradigm

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Kumar, Kailash Abbas, Syed Hauider Gupta, Manish Waris, Saiyed Faiayaz Bordoloi, Dibyahash Kirubakaran, M. K. College of Computing and Informatics Saudi Electronic University Riyadh Kingdom of Saudi Arabia Department of Computer Science and Engineering Integral University Lucknow India Department of Electronics and Communication Engineering GLA University Mathura India Department of Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Vadlamudi Guntur India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Artificial Intelligence and Data Science St. Joseph’s Institute of Technology Chennai India

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Best-Answer Prediction in Q&A Sites Using User Information

arXiv

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arXiv 2022年

作者： Hadfi, Rafik Moustafa, Ahmed Yoshino, Kai Ito, Takayuki Department of Social Informatics Kyoto University Japan Department of Computer Science Nagoya Institute of Technology Japan

Community Question Answering (CQA) sites have spread and multiplied significantly in recent years. Sites like Reddit, Quora, and Stack Exchange are becoming popular amongst people interested in finding answers to diverse questions. One practical way of finding such answers is automatically predicting the best candidate given existing answers and comments. Many studies were conducted on answer prediction in CQA but with limited focus on using the background information of the questionnaires. We address this limitation using a novel method for predicting the best answers using the questioner’s background information and other features, such as the textual content or the relationships with other participants. Our answer classification model was trained using the Stack Exchange dataset and validated using the Area Under the Curve (AUC) metric. The experimental results show that the proposed method complements previous methods by pointing out the importance of the relationships between users, particularly throughout the level of involvement in different communities on Stack Exchange. Furthermore, we point out that there is little overlap between user-relation information and the information represented by the shallow text features and the meta-features, such as time differences. © 2022, CC BY.

关键词： Forecasting

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Construction of Influenza Early Warning Model Based on Combinatorial Judgment Classifier:A Case Study of Seasonal Influenza in Hong Kong

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Current Medical Science 2022年第1期42卷 226-236页

作者： Zi-xiao WANG James NTAMBARA Yan LU Wei DAI Rui-jun MENG Dan-min QIAN Department of Medical Informatics School of MedicineNantong UniversityNantong 226001China Department of Computer Science College of Engineering and Computing SciencesNew York Institute of TechnologyNew York 10023USA Department of Computer Science College of Overseas EducationNanjing University of Posts and TelecommunicationsNanjing 210023China Department of Epidemiology School of Public HealthNantong UniversityNantong 226019China Artificial Intelligence Laboratory Center De Montfort University of LeicesterLeicester LE1 9BHUnited Kingdom

Objective:The annual influenza epidemic is a heavy burden on the health care system,and has increasingly become a major public health problem in some areas,such as Hong Kong(China).Therefore,based on a variety of machine learning methods,and considering the seasonal influenza in Hong Kong,the study aims to establish a Combinatorial Judgment Classifier(CJC)model to classify the epidemic trend and improve the accuracy of influenza epidemic early warning.

关键词： influenza prediction data-driven Support Vector Machine Discriminant Analysis Ensemble Classifier

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Cross-Layer Distributed Attack Detection Model for the IoT

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International Journal of Ambient Computing and Intelligence 2022年第1期13卷

作者： Ahmed, Hassan I. Nasr, Abdurrahman A. Abdel-Mageid, Salah M. Aslan, Heba K. Informatics Department Electronics Research Institute Cairo Egypt Computer Engineering Faculty of Engineering Al-Azhar University Cairo Egypt Computer Engineering Department College of Computer Science and Engineering Taibah University Saudi Arabia Center of Informatics Science Faculty of Information Technology and Computer Science Nile University Egypt

The security of IoT that is based on layered approaches has shortcomings such as the redundancy, inflexibility, and inefficiently of security solutions. There are many harmful attacks in IoT networks such as DoS and DDoS attacks, which can compromise the IoT architecture in all layers. Consequently, cross layer approach is proposed as an effective and practical security defending mechanism. Cross-layer distributed attack detection model (CLDAD) is proposed to enhance security solutions for IoT environments. CLDAD presents a general detection method of DDoS in sensing layer, network layer, and application layer. CLDAD is based on big data analytics techniques, which enable the detection process to be performed in a distributed way, so the model can detect DDoS attacks in any layer on-the-fly and the model support the scalability of the IoT environment. CLDAD is tested based on three datasets, namely artificial jamming attack dataset, BoT-IoT dataset, and BoT-IoT-based HTTP. The results showed that the proposed model is efficient in detecting attacks in the three layers of the IoT. Copyright © 2022, IGI Global.

关键词： Big data

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Artificial Intelligence Enabled IOT System for Football Identification in a Football Match 14th

Artificial Intelligence Enabled IOT System for Football Iden...

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12th International Advanced Computing Conference, IACC 2022

作者： Singh, Mahesh Kumar Singh, Akhilesh Kumar Singh, Pushpendra Kalpana Rishi, Om Prkash Department of Computer Science ABES Engineering College Ghaziabad201009 India Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India Department of Information Technology GL Bajaj Institute of Technology and Management Uttar Pradesh Gr Noida201306 India Department of Information Technology Raj Kumar Goel Institute of Technology Uttar Pradesh Ghaziabad201003 India Department of Computer Science Informatics University of Kota Rajasthan Kota324010 India

ISBN: (纸本)9783031356438

Due to growth of AI enabled IOT devices, their use in sports feature identification can be beneficial in terms of recognition quality. computer Vision is a powerful tool for moving object recognition and tracking. Football identification and recognition problem can be implemented using OpenCV libraries with the help of a programming language like Java, Python etc. OpenCV has special modules that contain specific algorithms and features for moving object recognition and tracking. Internet of things provides a way to connect hardware components through software implementation. In the proposed model the automatic camera module has been implemented through IoT with the help of a servo motor and raspberry pi. The main aim of this paper is to automate the work of a cameraman in a football match by automatically tracking and recognizing the football and broadcasting it with the help of an automatic camera. The initial loss was about 1749, and after 1600+ epochs iterations, the loss reduced to 0.09 in the final model. © 2023, Springer Nature Switzerland AG.

关键词： Cameras

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Machine Learning-Enabled Power Scheduling in IoT-Based Smart Cities

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computers, Materials & Continua 2021年第5期67卷 2449-2462页

作者： Nabeela Awan Salman Khan Mohammad Khalid Imam Rahmani Muhammad Tahir Nur Alam MD Ryan Alturki Ihsan Ullah Department of Computer Science Government Girls Degree College MardanKPK23200Pakistan Department of Electrical Energy System UETPeshawar25000Pakistan Department of Computer Science College of Computing and InformaticsSaudi Electronic UniversityKSA Department of Smart Computing Kyungdong UniversityGlobal CampusGosung24764South Korea Department of Information Sciences College of Computer and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Robotics Engineering Daegu Gyeongbuk Institute of Science and TechnologySouth Korea

Recent advancements in hardware and communication technologies have enabled worldwide interconnection using the internet of things(IoT).The IoT is the backbone of smart city applications such as smart grids and green energy *** smart cities,the IoT devices are used for linking power,price,energy,and demand information for smart homes and home energy management(HEM)in the smart *** complex smart gridconnected systems,power scheduling and secure dispatch of information are the main research *** challenges can be resolved through various machine learning techniques and data *** this paper,we have proposed a particle swarm optimization based machine learning algorithm known as a collaborative execute-before-after dependency-based requirement,for the smart *** proposed collaborative execute-before-after dependencybased requirement algorithm works in two phases,analysis and assessment of the requirements of end-users and power distribution *** the rst phases,a xed load is adjusted over a period of 24 h,and in the second phase,a randomly produced population load for 90 days is evaluated using particle swarm *** simulation results demonstrate that the proposed algorithm performed better in terms of percentage cost reduction,peak to average ratio,and power variance mean ratio than particle swarm optimization and inclined block rate.

关键词： PSO IBR machine learning IoT smart cities CDBR

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Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models

arXiv

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arXiv 2025年

作者： Lai, Yuxiang Zhong, Jike Li, Ming Zhao, Shitian Yang, Xiaofeng Li, Yuheng Psounis, Konstantinos Department of Computer Science and Informatics Emory University AtlantaGA30322 United States Department of Computer Science Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Department of Computer Science University of Tokyo Tokyo113-8654 Japan Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA30332 United States Department of Radiation Oncology Winship Cancer Institute Emory University AtlantaGA30322 United States

Vision-language models (VLMs) have achieved impressive progress in natural image reasoning, yet their potential in medical imaging remains underexplored. Medical vision-language tasks demand precise understanding and clinically coherent answers, which are difficult to achieve due to the complexity of medical data and the scarcity of high-quality expert annotations. These challenges limit the effectiveness of conventional supervised fine-tuning (SFT) and Chain-of-Thought (CoT) strategies that work well in general domains. To address these challenges, we propose Med-R1, a reinforcement learning (RL)-enhanced vision-language model designed to improve generalization and reliability in medical reasoning. Built on the DeepSeek strategy, Med-R1 adopts Group Relative Policy Optimization (GRPO) to encourage reward-guided learning beyond static annotations. We comprehensively evaluate Med-R1 across eight distinct medical imaging modalities. Med-R1 achieves a 29.94% improvement in average accuracy over its base model Qwen2-VL-2B, and even outperforms Qwen2-VL-72B—a model with 36× more parameters. To assess cross-task generalization, we further evaluate Med-R1 on five question types. Med-R1 outperforms Qwen2-VL-2B by 32.06% in question-type generalization, also surpassing Qwen2-VL-72B. We further explore the thinking process in Med-R1, a crucial component for the success of Deepseek-R1. Our results show that omitting intermediate rationales (No-Thinking-Med-R1) not only improves in-domain and cross-domain generalization with less training, but also challenges the assumption that more reasoning always helps. These findings suggest that in medical VQA, it is not reasoning itself, but its quality and domain alignment, that determine effectiveness. Together, these results highlight that RL improves medical reasoning and generalization, enabling efficient and reliable VLMs for real-world deployment. © 2025, CC BY.

关键词： Reinforcement learning

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Enhancing Security and Scalability of Metaverse with Blockchain-based Consensus Mechanisms 15

Enhancing Security and Scalability of Metaverse with Blockch...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Rajawat, Anand Singh Goyal, S.B. Goyal, Aarti Rajawat, Kavita Raboaca, Maria Simona Verma, Chaman Mihaltan, Traian Candin School of Computer Science & Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Mind Intellect Technology Indore India National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea Romania Eötvös Loránd University Faculty of Informatics Department of Media and Educational Informatics Budapest Hungary Technical University of Cluj-Napoca Faculty of Building Services Cluj-Napoca Romania

ISBN: (纸本)9798350321388

This study examines how blockchain technology and consensus mechanisms can safeguard and grow the metaverse ecosystem. Blockchain is transparent and decentralized. It is unchangeable, cryptographically verified, and runs without a central authority. Blockchain technology can secure the metaverse and users' data. This study compares blockchain-based consensus algorithms such as POW(PoW), PoS, and PBFT to ensure metaverse safety. These consensus mechanisms provide safe, decentralized transaction verification, waste reduction, and network consensus. Studying blockchain technology's metaverse scaling restrictions and scaling solutions, including sharding, sidechains, and off-chain protocols. These methods increase throughput, parallel computing, and transaction speed. They improve the metaverse's eco-system. Quantitative analysis, simulations, and experiments determine the security and scalability of blockchain-based metaverse consensus processes. Changes will be quantified by measuring transaction speed, network throughput, and resource consumption. This study enables safe, scalable metaverse environments. Blockchain-based consensus mechanisms allow the metaverse to retain user data safely, halt undesirable conduct, and allow users to join without trusting each other. Scalability solutions declutter networks, enabling the metaverse to handle more individuals and transactions. Blockchain-based consensus mechanisms could make the metaverse more secure, private, and scalable. Users will trust each other, create new apps, and be more open to this groundbreaking digital world. © 2023 IEEE.

关键词： Scalability

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