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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： P Sathish Kumar P. Suthanthiradevi C Arul Stephen B Ebenezer Abishek S. Sivakumar M Mathiyarasu Computer and Communication Engineering Rajalakshmi institute of technology Chennai Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai Electronics and Communication Engineering Vels Institute of science Technology and Advanced studies Chennai Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai Electronics and Communication Engineering BIHER Chennai

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The Fake news becomes a more critical issue nowadays, it may reduce the trust of the public about the particular information. This project focuses on the advancement of fake news detection model using machine learning. The main goal of this project is to develop, analyze a model that can effectively classify the news articles based on their Subject or contents. Natural preprocessing Algorithm and features such as Stemming, Tokenization, Stop word and TF-IDF Vectorization are used to train the machine learning model. Then the Machine learning Algorithms likeLogistic Regression and Random Forest are used to classifying the dataset which gives 98% accuracy and 99% of accuracy score. For this Project we have taken a dataset of 22413 Real news and 23481 Fake News from the online resources. This model has a potential to reduce the impact of fake news by grant the promotion of accurate and reliable information in a society.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Tokenization Reliability Internet of Things

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Blockchain-Based Model for Secure and Fair Data Provision in Crowdsourced Drone Services

IEEE Open Journal of the Communications Society

引用

IEEE Open Journal of the Communications Society 2025年

作者： Akram, Junaid Akram, Awais Sharma, Vandana Anaissi, Ali Jhaveri, Rutvij H. Verma, Priyanka UNSW College Kensington 2033 NSW Australia The University of Sydney School of Computer Science Camperdown 2008 NSW Australia Korea Advanced Institute of Science and Technology School of Computing Daejeon South Korea Christ University Bengaluru India Pandit Deendayal Energy University Department of Computer Science and Engineering School of Technology India University of Galway School of Computer Science Ireland

Current centralized systems for crowdsourced drone services face problems in maintaining data integrity, fairness in data exchanges, and efficient resource allocation. These issues are critical in applications such as bushfire management, where accurate and timely data are essential. In response, we propose a blockchain-based model that creates a decentralized marketplace for secure data provisioning. In this system, drone operators send real-time environmental data to bushfire management authorities, and the data are recorded on a blockchain to ensure traceability. The model includes a time commitment-based mutually verifiable fairness mechanism to prevent dishonest behavior and to ensure that both data providers and consumers follow the agreed terms. Two new consensus mechanisms, Proof-of-Data Integrity (PoDI) and Proof-of-Service (PoSv), are introduced to confirm data authenticity and service quality. Additionally, a dynamic trust model that combines direct and indirect trust metrics is implemented to further support system reliability. Ethereum smart contracts are used to automate secure payment processing and to enforce transaction rules. This approach addresses the shortcomings of current systems and provides a clear framework for secure and fair data management in emergency response scenarios. © 2020 IEEE.

关键词： Access Control Bushfire Management Crowdsourced Drone Services Interoperability Verifiable Credentials

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Remote Patient Monitoring System with Wearable IoT Devices and Biosensors for Vital Signs

Remote Patient Monitoring System with Wearable IoT Devices a...

引用

Device Intelligence, computing and Communication Technologies, (DICCT), International Conference on

作者： S. Naveen T.B. Sivakumar B. R. Supreeth R. Niranjana Vishwa Priya V Saranya R Department of Mechanical Engineering Saveetha School of Engineering_Saveetha Institute of Medical and Technical Sciences_SIMATS Saveetha University Chennai Tamil Nadu India Department of CSE School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science Oxford Degree and PG College Sir. M. Visveshwaraiah Layout Ullal India Department of CSE Velammal College of Engineering and Technology Madurai Tamil Nadu India Department of Computer Science and Information Technology Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India Department of English KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Due to infrequent patient monitoring and the reactive character of the traditional healthcare system, delayed diagnoses and inadequate management of chronic diseases are common outcomes. The study presents a solution to these drawbacks: a Remote Patient Monitoring (RPM) system that combines advanced biosensors with wearable IoT devices to track vital signs continuously. By ensuring proactive healthcare through real-time data collecting and analysis, the entire system overcomes the drawbacks of conventional techniques. Early anomaly detection and timely medical interventions are made possible by the method's utilization of scalable cloud infrastructure and advanced machine learning (ML) methods, such as CNNs (Convolutional Neural Networks) and Long Short-Term Memory (LSTMs). The proposed system achieves a 90% early detection rate, 95% sensitivity, 97% specificity in anomaly detection, and 92% patient satisfaction, which is a substantial improvement over existing techniques, according to the results. It also exhibits better vital sign measurement accuracy than existing systems, indicating its potential to completely transform patient care through better results and economical resource utilization.

关键词： Patient monitoring Accuracy Medical services Real-time systems Biosensors Internet of Things Biomedical monitoring Medical diagnostic imaging Anomaly detection Diseases

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Machine Learning for Drug Discovery: Predicting Drug-Protein Binding Affinities using Graph Convolutional Networks

Machine Learning for Drug Discovery: Predicting Drug-Protein...

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Recent Trends in computer science and Technology (ICRTCST), International Conference on

作者： S. RadhaMahendran Akriti Dogra Dinesh Mendhe S. B G Tilak Babu Shriniket Dixit Surya Pratap Singh Department of Bioinformatics School of Life Sciences Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India Department of Oral & Maxillofacial Surgery Manav Rachna Dental College Faridabad Haryana Computer and Information Research Scientist Office of Research Computing Rutgers State University of New Jersey New Brunswick New Jersey Department of ECE Aditya Engineering College Surampalem School of Computer Science Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Multi-Disciplinary Research Unit UPUMS Etawah UP

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

Determining the potential interactions between ligands and proteins is a crucial first step in developing new drugs. Graph BAR, our novel prediction model, makes accurate guesses about the strength of ligand-protein bindings by means of a graph convolutional neural network. When compared to graph convolutional neural networks, standard convolutional neural network models are more resource and time intensive to manage. The interaction between proteins and ligands is shown via a network consisting of many adjacency meshes. The atomic distances dictate the alterations to these matrices. Additionally, the atomic properties can be shown in a feature matrix. Using the PDBbind datasets, we verified the functionality of graph convolution and evaluated Graph BAR's ability to forecast the binding strengths of proteins and ligands. In order to maximize The model's performance was improved by utilizing ways for adding additional input, which took advantage of the processing capability of graph convolutional neural networks. Graph BAR's predictions appear to have potential for use in drug creation due to its great accuracy and quickness. The results of our research suggest that Graph Convolutional Networks have the potential to expedite the discovery of new medications by properly predicting the interactions between pharmaceuticals and proteins.

关键词： Proteins Drugs computer science Accuracy Convolution Machine learning Predictive models

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Optimal Routing Protocols for Reconfigurable Atom Arrays

arXiv

引用

arXiv 2024年

作者： Constantinides, Nathan Fahimniya, Ali Devulapalli, Dhruv Bluvstein, Dolev Gullans, Michael J. Porto, J.V. Childs, Andrew M. Gorshkov, Alexey V. Joint Quantum Institute NIST University of Maryland College ParkMD20742 United States Joint Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States Department of Physics Harvard University CambridgeMA02138 United States Department of Computer Science Institute for Advanced Computer Studies University of Maryland College ParkMD20742 United States

Neutral atom arrays have emerged as a promising platform for both analog and digital quantum processing. Recently, devices capable of reconfiguring arrays during quantum processes have enabled new applications for these systems. Atom reconfiguration, or routing, is the core mechanism for programming circuits;optimizing this routing can increase processing speeds, reduce decoherence, and enable efficient implementations of highly non-local connections. In this work, we investigate routing models applicable to state-of-the-art neutral atom systems. With routing steps that can operate on multiple atoms in parallel, we prove that current designs require Ω(√N log N) steps to perform certain permutations on 2D arrays with N atoms and provide a protocol that achieves routing in O(√N log N) steps for any permutation. We also propose a simple experimental upgrade and show that it would reduce the routing cost to Θ(log N) steps. Copyright © 2024, The Authors. All rights reserved.

关键词： Routing protocols

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Support Vector Machine for Satellite Images Classification Using Radial Basis Function Kernel Method 9th

Support Vector Machine for Satellite Images Classification U...

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9th International Conference on computing and Informatics, ICOCI 2023

作者： Mansor, Nur Suhaili Awang, Hapini Malami, Sarkin Tudu Shehu Zolkafli, Amirulikhsan Taiye, Mohammed Ahmed Maulana, Hanhan Institute for Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Kedah Sintok06010 Malaysia Department of Computer Science and Information Technology Faculty of Science Sokoto State University Sokoto Sokoto852101 Nigeria Department of Cultural Sciences Faculty of Arts and Humanities Linnaeus University Vaxjo351 95 Sweden Department of Informatics Engineering and Computer Science Universitas Komputer Indonesia Bandung40134 Indonesia

ISBN: (纸本)9789819995882

Machine learning, particularly Support Vector Machines (SVM), has gained popularity in geospatial data processing and image classification. Geospatial data from various sources may contain errors, impacting image classification accuracy. Traditional pixel-based and object-based methods struggle to classify complex land cover classes accurately. Previous studies explored machine learning algorithms like Random Forests, K-Nearest Neighbors, and Neural Networks. Still, they faced challenges capturing intricate relationships within images and required substantial labeled training data, leading to computational expenses. SVM with polynomial kernels was attempted in some studies, but it suffered from potential overfitting and inefficiency for large datasets. To overcome these issues, this study employed SVM with RBF and Linear kernels to classify multispectral satellite images from the SPOT-6 Satellite Imagery dataset in Sungai Kelang, Malaysia. Previous research evaluated each kernel’s performance accuracy compared using a test dataset, utilizing open-source tools like Jupyter Notebooks and Python libraries to explore SVM’s potential as a high-performance satellite image classification technique. The findings revealed that SVM with RBF kernel outperformed SVM with polynomial or linear kernels in classifying satellite images. The RBF kernel’s robustness allowed SVM to model intricate decision boundaries and capture complex patterns in the image data, making it suitable for tasks with non-linearly separable data. The study introduces a new methodology and theoretical contribution to image classification-related literature, shedding light on the efficacy of SVM-RBF for geospatial data processing. It provides an alternative to traditional approaches for complex image classification tasks. Moreover, the research assists in selecting the optimal algorithm for remote sensing and satellite imagery applications. © 2024, The Author(s), under exclusive license to Springer Nature Si

关键词： Image classification

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NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points

arXiv

引用

arXiv 2025年

作者： Zhang, Qianyi Luo, Wentao Liu, Boyi Zhang, Ziyang Wang, Yaoyuan Liu, Jingtai Institute of Robotics and Automatic Information System Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin China Advanced Computing and Storage Lab Huawei 2012 Lab China Department of Electronic and Computer Engineering The Kong Kong University of Science and Technology Hong Kong

Motion planning in navigation systems is highly susceptible to upstream perceptual errors, particularly in human detection and tracking. To mitigate this issue, the concept of guidance points—a novel directional cue within a reinforcement learning-based framework—is introduced. A structured method for identifying guidance points is developed, consisting of obstacle boundary extraction, potential guidance point detection, and redundancy elimination. To integrate guidance points into the navigation pipeline, a perception-to-planning mapping strategy is proposed, unifying guidance points with other perceptual inputs and enabling the RL agent to effectively leverage the complementary relationships among raw laser data, human detection and tracking, and guidance points. Qualitative and quantitative simulations demonstrate that the proposed approach achieves the highest success rate and near-optimal travel times, greatly improving both safety and efficiency. Furthermore, real-world experiments in dynamic corridors and lobbies validate the robot’s ability to confidently navigate around obstacles and robustly avoid pedestrians. © 2025, CC BY.

关键词： Travel time

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Details Enhancement in Unsigned Distance Field Learning for High-fidelity 3D Surface Reconstruction

arXiv

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arXiv 2024年

作者： Xu, Cheng Hou, Fei Wang, Wencheng Qin, Hong Zhang, Zhebin He, Ying State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China School of Advanced Interdisciplinary Sciences University of Chinese Academy of Sciences China Department of Computer Science Stony Brook University United States InnoPeak Technology United States College of Computing and Data Science Nanyang Technological University Singapore

While Signed Distance Fields (SDF) are well-established for modeling watertight surfaces, Unsigned Distance Fields (UDF) broaden the scope to include open surfaces and models with complex inner structures. Despite their flexibility, UDFs encounter significant challenges in high-fidelity 3D reconstruction, such as non-differentiability at the zero level set, difficulty in achieving the exact zero value, numerous local minima, vanishing gradients, and oscillating gradient directions near the zero level set. To address these challenges, we propose Details Enhanced UDF (DEUDF) learning that integrates normal alignment and the SIREN network for capturing fine geometric details, adaptively weighted Eikonal constraints to address vanishing gradients near the target surface, unconditioned MLP-based UDF representation to relax non-negativity constraints, and DCUDF for extracting the local minimal average distance surface. These strategies collectively stabilize the learning process from unoriented point clouds and enhance the accuracy of UDFs. Our computational results demonstrate that DEUDF outperforms existing UDF learning methods in both accuracy and the quality of reconstructed surfaces. Our source code is at https://***/GiliAI/DEUDF. Copyright © 2024, The Authors. All rights reserved.

关键词： Surface reconstruction

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Big Data Graph Node Importance Using Page Rank

Big Data Graph Node Importance Using Page Rank

引用

Innovations in Communications, Electrical and computer Engineering (ICICEC), International Conference on

作者： R. Roseline B. Vasudevan A. Clementking S. Rani T Sivakumar C. Srinivasan PG Department of Computer Applications St. Joseph’s College of Arts and Science (Autonomous) Cuddalore India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur India Mount Carmel College (Autonomous) Bengaluru Karnataka India Department of BCA/IT Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

Improving the efficiency and accuracy of finding important nodes in large networks is the goal of enhancing Big Data graph node significance analysis using PageRank. To deal with the massive and complicated data sets seen in big data settings, it is necessary to modify and enhance the PageRank algorithm. The objective is to provide a more efficient framework that can speed up computation and improve the accuracy of significance ranking in large-scale networks. To control the algorithm's scalability, it is necessary to use state-of-the-art computational methods, such as parallel processing and effective data structures. The expected result is a scalable and reliable system that can quickly and correctly analyze large networks, yielding useful insights for applications like recommendation systems, online search, and social network analysis. The focus is on making PageRank more feasible for use in large data settings while also increasing its computing efficiency and scalability. The Adjacency Matrix of the Graph in a sample of 5 nodes has a value ranging from 15 to 55 in numbers, according to results from synthetic graph data. In another instance of the same dataset, the Initial PageRank Scores range from 0.2-0.15 in numbers, and in yet another instance, the final PageRank Scores for nodes range from 0.177 to 0.242 in numbers.

关键词： Technological innovation Accuracy Social networking (online) Scalability Big Data Parallel processing Search engines Distance measurement Recommender systems Optimization

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Fault Detection using Opposition-based Learning Orca Predator Algorithm with Random Forest

Fault Detection using Opposition-based Learning Orca Predato...

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Distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： C. Umarani Mohammed I. Habelalmateen Sujata N Patil Rekha Phadke Srinath Chandramohan Department of Computer Science and Applications Christ Academy Institute for Advanced Studies Bangalore India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Radiation Oncology Department (Pathology Lab) Thomas Jefferson University Philadelphia USA Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Machinery equipment stability and safety operations heavily depend on the implementation of intelligent fault detection systems. The system enhances production efficiency while decreasing maintenance costs together with its most significant benefit of preventing safety accidents. Intelligent compound fault detection methods require additional research regarding their interpretability features. The detection results prove difficult to understand which impedes their general acceptance for industrial use in the real world. Therefore, this research proposes an Opposition-based Learning Orca Predator Algorithm (OLOPA) with Random Forest (RF) for fault detection. The OLOPA exploration-exploitation control improves worldwide search performance while reducing convergence time thus enhancing both accuracy and stability in feature extraction. High-dimensional data management and strong generalization abilities characterize the RF classifier during its performance. The combined hybrid method provides dependable and interpretable fault detection capabilities which enables its use in complex industrial environments. Furthermore, this research extracts meaningful features from signals through time-domain and frequency domain to enhance the feature detection and signal quality maintenance. The OLOPA-RF achieves 99.91% accuracy for CWRU dataset which is better than state-of-the-art methods.

关键词： Vibrations Accuracy High dimensional data Fault detection Feature extraction Safety Maintenance Random forests Classification tree analysis Convergence

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