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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Sophia S Getzi Jeba Leelipushpam Paulraj Yuvarajan D Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Unmanned Aerial Vehicle (UAV) networks play an important role in different application areas such as military surveillance, emergency services, and infrastructure. However, these networks face significant challenges such as data leakage, unauthorized access, limited processing capacity, and energy constraints. Available techniques such as Elliptic Curve Cryptography (ECC), hash-based techniques, and dynamic clustering algorithms have been proposed to enhance security and operational performance; however, they often encounter challenges concerning scalability, adaptability to real-time changes within the network, and resource-related constraints. This research presents a streamlined authentication framework with the intent to facilitate secure and effective functionalities in turbulent and fluctuating settings. The proposed protocol uses Dijkstra's algorithm for optimal real-time routing, Adaptive Optimization Clustering (AOC) for dynamic resource distribution, and K-means clustering for noise classification. The proposed methodology achieves substantial improvements in power efficiency, latency, and Packet Delivery Rate (PDR) due to reduced computational overhead as well as risks such as data interception and replay attacks. Simulation results conclude that the system suggested here would be secure, scalable, and energy-efficient to be used for applications in demanding scenarios, such as mission-critical scenarios.

关键词： Protocols Heuristic algorithms Clustering algorithms Autonomous aerial vehicles Routing Elliptic curve cryptography Real-time systems Security Vehicle dynamics Optimization

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Detection of Emergency Vehicles in Traffic and Assign Traffic Free Path Using Deep learning 4

Detection of Emergency Vehicles in Traffic and Assign Traffi...

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4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Vikruthi, Sriharsha Singasani, Tejesh Reddy Ram Pavan Kumar, V.T.M. Nagendrudu, P.V.V.S.D. Raghavendra, Ch Sahith, R. B v Raju Institute of Technology Dept of Cse Telangana Medak Narsapur502313 India University of Cumberlands LouisvilleKY United States Kakaraparti Bhavanarayana College Dept of Computer Science Andhrapradesh Vijayawada India Aditya Univeristy Department of Artificial Intelligence and Machine Learning Suramplaem India Andhra Pradesh Guntur India Cvr College of Engineering Dept of Computer Science and Engineering Telangana Hyderabad501510 India

ISBN: (纸本)9798331523923

Too much of Traffic jams are in a highly populated country. Sometimes emergency vehicles such as fire-fighter and ambulance get stuck in traffic, putting lives at threat in many cases. Priority should be given to special vehicles and their way should be cleared. However, traffic police find it difficult or sometimes impossible to handle with it. Emergency vehicles have visual and audio warning devices help them to overtake traffic by demanding as little space as possible from other vehicles. Many death happen due to delay in doctors, ambulance and fire engine vehicles while reaching their destinations. The traditional systems didn't provide path free route accurately. Therefore, by using detection of emergency vehicles in traffic and assign traffic free path using deep learning system, emergency vehicles can get free path or fast route by observing the traffic cameras. So, those emergency vehicles can reach the location on time by using RNN system. Therefore, the main objective is to improve this system for better results interms of accuracy, detection rate and precision. © 2025 IEEE.

关键词： Recurrent neural networks

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Movie Recommendation System Using Hybrid Based Method 3

Movie Recommendation System Using Hybrid Based Method

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Kamble, Khushi Bhadarge, Pradnyanand Bhakte, Shreejit Kulkarni, Omkaresh Kadam, Rutuja Pinjarkar, Latika Vishwakarma Institute of Information Technology Computer Science and Engineering - Artificial Intelligence and Machine Learning Department Pune India Maharashtra Pune India Nagpur Campus Pune India

ISBN: (纸本)9798331542375

The rise of digital platforms has increased user engagement and satisfaction through personalized recommendation systems. The research paper presents a hybrid approach to movie recommendation, which uses content-based filtering and collaborative filtering techniques to mitigate the cold start problem, data sparsity, and scalability issues. The proposed system will utilize methodologies from machine learning, such as cosine similarity and vectorization, and the K-Nearest Neighbors (KNN) algorithm, for recommending movies based on their precise and personalized values. The dataset TMDB5000 will then be used for two datasets involving movie features, cast, and crew information while preprocessing techniques in cleaning, feature extraction, and merging are undertaken to ensure robust foundation for the model building. The hybrid model uses the bag-of-words technique to vectorize and compute cosine similarity to determine movies' relationships in a multidimensional space. The KNN algorithm then refines the collaborative filtering process by the clustering of user preferences and makes recommendations using nearest-neighbor analysis. Our current approach overcomes these weaknesses by combining both content-based and collaborative methods, resulting in a system which handles diverse user preferences while maintaining high recommendation accuracy. As the evaluation of precision, recall, F1-score, and mean squared errors further validates that this system performance is good and also scalable and efficient for processing large datasets for which it explains some practical aspects related to implementation in real time. The future work includes deep learning models like neural collaborative filtering to further improve semantic understanding and recommendation accuracy. This paper addresses the current system's limitations by proposing a novel, efficient, and user-centric hybrid approach, which can be extended to other domains, such as e-commerce, music, and book recommendati

关键词： Deep learning

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Efficient Chromosome Abnormal Detection using Yolo-V8 model

Efficient Chromosome Abnormal Detection using Yolo-V8 model

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International Symposium on Advanced Computing and Communication (ISACC)

作者： V. UmaRani Augustian Isaac K. Premnath Kannan G Sanjay A Hari Haran.L. K Dept of Computer Science and Engineering Saveetha Engineering College Chennai TamilNadu India Dept of Artificial Intelligence & Machine Learning Saveetha Engineering College Chennai TamilNadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Human chromosomes exhibit complex and intricate patterns, making their analysis and data interpretation challenging. Studying chromosomes is crucial for identifying genetic disorders, such as Down syndrome, and various types of cancer. Traditional karyotyping methods depend heavily on manual method, which causes human error and high cost. To address these challenges, a deep learning-based approach is proposed to automate the karyotyping process, improving both accuracy and efficiency while minimizing human involvement and errors. This research leverages advanced deep learning techniques, specifically the YOLOv8 object detection framework, to enhance chromosomal analysis. From experiments, the proposed YOLO V8 model achieves a 95.2% accuracy, surpassing other models such as DCNN, CNN, and VGG-19. This work represents a significant step toward developing a more reliable and efficient diagnostic tool for detecting genetic disorders.

关键词： Deep learning YOLO Training Analytical models Accuracy Manuals Genetics Reliability Intelligent systems Biological cells

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Privacy-Preserving Data Mining Methods for Sensitive Information 3

Privacy-Preserving Data Mining Methods for Sensitive Informa...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Singh, Sukhvinder Jasim, L.H. Dari Dhote, Vijay Suseela, D. Venkatasubramanian, R. Pune India College of Technical Engineering The Islamic University Najaf Iraq Ies College of Technology Department of Computer Science & Engineering Bhopal India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350342338

This study explores innovative Privacy-Preserving Data Mining (PPDM) methods for handling sensitive information, addressing the critical balance between data utility and privacy. We identify prevalent issues arising from traditional data mining techniques and evaluate advanced PPDM approaches like differential privacy, homomorphic encryption, and federated learning using real-world datasets. Through a comprehensive analysis, our results indicate that hybrid models, which combine multiple PPDM techniques, are particularly effective in enhancing privacy without compromising data utility. The findings underscore the need for adaptable and robust PPDM strategies in the era of Big Data, contributing valuable insights to both the academic and practical domains. Our research paves the path for future studies and developments in creating a harmonious ecosystem where data mining efficiency and privacy coexist, promoting an insightful yet private data-centric world. © 2023 IEEE.

关键词： Analytically Valuable Patterns Data Privacy Data Repositories Homomorphic Encryption and Utility Privacy-Preserving Data Mining Sensitive Information

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Cross-Temporal Snapshot Alignment for Dynamic Multi-Relational Networks 2

Cross-Temporal Snapshot Alignment for Dynamic Multi-Relation...

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2022 2nd International Conference on Electrical, Electronics and Computing Technology, EECT 2022

作者： Chen, Lvjia Liang, Shangsong School Of Computer Science And Engineering Sun Yat-sen University Guangzhou China Department Of Machine Learning Mohamed Bin Zayed University Of Artificial Intelligence Abu Dhabi United Arab Emirates

A dynamic network is often represented as a sequence of snapshots evolving over time. In certain real-world scenarios, the identities of nodes in snapshots of a dynamic network are unknown and need to be figured out. To deal with such a challenge, recently, the task of cross-temporal snapshot alignment for dynamic networks is proposed, which aims to match equivalent nodes across temporal snapshots of a dynamic network given a small set of identified nodes. However, in many dynamic multi-relational networks like temporal knowledge graphs, the relation type information of edges, which can be useful for the alignment task, is neglected by existing methods. In this paper, we focus on a similar task but pay special attention to dynamic multi-relational networks. We propose a Relation-Aware Cross-Temporal Snapshot Alignment model (RCTSA) that incorporates time-dependent topological structure information into temporal node embeddings and temporal relation embeddings. To disentangle time-dependent information and time-independent information of the dynamic multi-relational network, RCTSA maintains a time embedding for each snapshot to preserve the temporal information which is incorporated into entity embeddings and relation embeddings to get the temporal embeddings. After training, the learned entity embeddings of unidentified nodes together with time embeddings can be used for the alignment task. Experimental results on real-world dynamic multi-relational networks demonstrate the effectiveness of our model. © Published under licence by IOP Publishing Ltd.

关键词： Alignment

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Biomaterials and Their Applications

Biomedical Materials and Devices

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Biomedical Materials and Devices 2025年 1-16页

作者： Menagadevi, M. Nirmala, M. Thiyagarajan, D. Somasundram, D. Department of Computer Science and Engineering School of Engineering Malla Reddy University Hyderabad India Department of Biomedical Engineering Dr. N G P Institute of Technology Coimbatore India Department of Artificial Intelligence and Machine Learning School of Engineering Malla Reddy University Hyderabad India Department of Micro & Nanoelectronics School of Electronics Engineering Vellore Institute of Technology Vellore India

A biomaterial is a biocompatible substance engineered to interact with biological systems for therapeutic and diagnostic, such as pacemakers, dental implants, vascular stents, artificial joints, drug delivery systems, tissue engineering, biosensors, image contrast agents in computer Tomography, Magnetic Resonance Imaging, optical imaging, and ultrasound. Biomaterials are also used as assays in point-of-care testing devices. Based on the organs’ biological properties, the biomaterials are mainly classified into metals, polymers, ceramics, and composite biomaterials. The success rate of the implant depends on the physical, chemical, and mechanical properties of the selected material. Each biomaterials have unique properties, so it is used in both soft and hard tissue replacements. In this review, various properties and applications of biomaterials are discussed. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Applications Biocompatibility Biomaterials Properties Types

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A Framework for Rainfall Forecasting based Crop Analysis using Deep learning 9

A Framework for Rainfall Forecasting based Crop Analysis usi...

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

作者： Gokuldev, S. Selvapandian, D. Satheshkumar, K. Deepa, T. INurture Education Solutions Zonal Head South - IT Academics Bengaluru India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Learning Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Erode Sathyamangalam India PSNA College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350397376

More than half of India's arable land is susceptible to drought, making the country's agriculture perpetually insecure. Crop productivity and agricultural investment are both negatively impacted by the monsoon's poor performance. Literature states that the existing statistical and numerical procedures often fail to meet the present requirements and strategies for recognizing the important input data for designing efficient rainfall and crop yield forecasting models. In this research, Adaptive Moment Optimization (AMO) based Long Short-term Memory Neural Networks (LSTM-NN) are developed for accurate seasonal rainfall forecasting. The AMO is used to find the relevant features for rainfall prediction and LSTM is used to predict the rainfall. The AMO has a higher learning rate and selects the relevant features that help to progress the performance of the model. The investigational outcomes demonstrated that the proposed strategy accomplished nearly 95% of accuracy in all cases. © 2023 IEEE.

关键词： Rain

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Automating the Selection of Proxy Variables of Unmeasured Confounders 41

Automating the Selection of Proxy Variables of Unmeasured Co...

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41st International Conference on machine learning, ICML 2024

作者： Xie, Feng Chen, Zhengming Luo, Shanshan Miao, Wang Cai, Ruichu Geng, Zhi Department of Applied Statistics Beijing Technology and Business University Beijing China School of Computer Science Guangdong University of Technology Guangzhou510006 China Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Probability and Statistics Peking University Beijing China

Recently, interest has grown in the use of proxy variables of unobserved confounding for inferring the causal effect in the presence of unmeasured confounders from observational data. One difficulty inhibiting the practical use is finding valid proxy variables of unobserved confounding to a target causal effect of interest. These proxy variables are typically justified by background knowledge. In this paper, we investigate the estimation of causal effects among multiple treatments and a single outcome, all of which are affected by unmeasured confounders, within a linear causal model, without prior knowledge of the validity of proxy variables. To be more specific, we first extend the existing proxy variable estimator, originally addressing a single unmeasured confounder, to accommodate scenarios where multiple unmeasured confounders exist between the treatments and the outcome. Subsequently, we present two different sets of precise identifiability conditions for selecting valid proxy variables of unmeasured confounders, based on the second-order statistics and higher-order statistics of the data, respectively. Moreover, we propose two data-driven methods for the selection of proxy variables and for the unbiased estimation of causal effects. Theoretical analysis demonstrates the correctness of our proposed algorithms. Experimental results on both synthetic and real-world data show the effectiveness of the proposed approach. Copyright 2024 by the author(s)

关键词： Higher order statistics

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Towards Conscious Service Robots

arXiv

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arXiv 2025年

作者： Behnke, Sven Autonomous Intelligent Systems Computer Science Institute VI – Intelligent Systems and Robotics Center for Robotics the Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

Deep learning’s success in perception, natural language processing, etc. inspires hopes for advancements in autonomous robotics. However, real-world robotics face challenges like variability, high-dimensional state spaces, non-linear dependencies, and partial observability. A key issue is non-stationarity of robots, environments, and tasks, leading to performance drops with out-of-distribution data. Unlike current machine learning models, humans adapt quickly to changes and new tasks due to a cognitive architecture that enables systematic generalization and meta-cognition. Human brain’s System 1 handles routine tasks unconsciously, while System 2 manages complex tasks consciously, facilitating flexible problem-solving and self-monitoring. For robots to achieve human-like learning and reasoning, they need to integrate causal models, working memory, planning, and metacognitive processing. By incorporating human cognition insights, the next generation of service robots will handle novel situations and monitor themselves to avoid risks and mitigate errors. Copyright © 2025, The Authors. All rights reserved.

关键词： Problem solving

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