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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Bhuvana, S. Vidhya, K. Sudharsan, S. Suhita, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819788354

Indoor Navigation System (INS) supports seamless movement of objects within confined spaces in smart environments. In this paper, a novel INS that relies on ESP32-based Received Signal Strength Indication (RSSI) measurements for precise estimation of distance using K-Nearest Neighbour (K-NN) algorithm is proposed. The system architecture includes three ESP32 nodes functioning as clients, and ESP8266 combined with ESP32 acting as a server. To overcome the limitations of Global Positioning System (GPS) within an indoor environment, Machine Learning (ML)-based prediction models are employed to estimate distance between nodes and predict user locations along with the distance between source and destination. This approach not only addresses the challenges posed by GPS in indoor settings, but also shows the potential of ESP32 devices and predictive algorithms for enhancing navigation accuracy and reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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Enhanced word vector space with ensemble deep learning model for COVID-19 Hindi text sentiment analysis

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Multimedia Tools and Applications 2025年第9期84卷 5861-5882页

作者： Jain, Vipin Kashyap, Kanchan Lata Department of SCSE VIT Bhopal University Madhya Pradesh Sehore466114 India Department of Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam India

The SARS-CoV-2 virus has spread worldwide since March 2020 and became a global pandemic. Millions of people worldwide have infected with this virus. This research work proposed an enhanced word vector space and deep learning-based hybrid model for COVID-19 Hindi text sentiment analysis. Total 36,863,393 Hindi tweets are collected for analysis. First, various natural language processing techniques are applied for text preprocessing. Next, The word vector space is enhanced by combining various word embedding techniques namely, Word2vec, Fasttext, Glove, POS2Vec, WordPosition2Vec, Lexicon2Vec, and Elmo. Finally, developed a hybrid model for sentiment classification. According to the experimental findings, highest 89.60%, 78.70%, 77.23% and 91.64% precision, recall, F-score, and accuracy, respectively, is given by proposed model. The obtained results indicate that the proposed approach is highly efficient in classifying Hindi sentiments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Vector spaces

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A lossless image encryption technique using chaotic map and DNA encoding

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Multimedia Tools and Applications 2025年 1-23页

作者： Yadav, Anju Jaipuriyar, Ayush Roy, Satyabrata Rawat, Umashankar Department of Information Technology Manipal University Jaipur Rajasthan Jaipur300307 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur300307 India

Image encryption is required to protect digital images from unauthorized access and tampering. With the increasing use of digital images in various fields such as medical imaging, military imaging, and e-commerce, it has become essential to secure the transmission and storage of these images. In this paper, we propose a new image encryption technique that uses a combination of Deoxyribo Nucleic Acid (DNA) encoding and chaotic maps. The proposed technique aims to provide a high level of security for digital images while also maintaining low computational complexity. The encryption process involves encoding the image data using DNA sequences and then performing a chaotic map transformation on the encoded data. The resulting cipher image is highly resistant to attacks such as brute-force and statistical analyses, making it suitable for secure transmission and storage. We evaluate the effectiveness of our proposed technique by comparing it with other popular encryption methods using various performance metrics. The results show that our approach outperforms other methods in terms of security and computational efficiency. For instance, we achieved Number of Pixel Change Rate (NPCR) of 99%, correlation coefficient in order of 10-3 and Unified Average Changing Intensity (UACI) of 33%. Additionally, noise detected was 0%, proving it a lossless scheme. Thus, the proposed image encryption technique has the potential to be used in various applications that require secure image transmission and storage, such as medical imaging, IoT applications etc. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Digital image storage

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Mitigating Risks in Vehicle Fires: An Integrated Approach for Early Detection and Real-Time Alert System 4th

Mitigating Risks in Vehicle Fires: An Integrated Approach fo...

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4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Suryawanshi, Ranjeetsingh Bhagat, Harshita Karande, Aryan More, Parth More, Vaishali Department of Computer Engineering Vishwakarma Institute of Technology Pune India Department of Information Technology Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9789819751990

Every year, there are about 170,000–220,000 car accidents each year that involve fire. The cause of the fire ranges from collision, engine malfunction, electric short circuits, and many more. Irrespective of the cause, the damage incurred is close to $2 billion in direct property damage and in severe accidents, even the passengers’ lives. The rapid detection and timely notification of fire incidents are crucial for minimizing property damage and ensuring public safety. The current vehicle fire safety system relies on manual notification, leading to delayed responses and potential risks, especially in low-density areas and nighttime scenarios. Additionally, the lack of proactive fire detection technology hinders early identification, increasing the likelihood of extensive damage and harm to passengers. This paper proposes an integrated alert system automatically notifies fire stations, hospitals, and police upon fire detection, streamlining coordination and reducing response times. Rigorous evaluations are undertaken to assess the system's accuracy, sensitivity, and reliability under various conditions, solidifying its efficacy in addressing vehicle fire incidents. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fires

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AR-Light: Enabling Fast and Lightweight Multi-user Augmented Reality via Semantic Segmentation and Collaborative View Synchronization

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IEEE Transactions on computers 2025年第6期74卷 2073-2086页

作者： Wen, Yu Shah, Aamir Bader Cao, Ruizhi Zhang, Chen Chen, Jiefu Wu, Xuqing Xie, Chenhao Fu, Xin University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Beihang University School of Computer Science and Engineering Beijing100191 China University of Houston Department of Information Science Technology TX77204 United States

Multi-user Augmented Reality (MuAR) allows multiple users to interact with shared virtual objects, facilitated by exchanging environment information. Current MuAR systems rely on 3D point clouds for real-world analysis, view synchronization, object rendering, and movement tracking. However, the complexity of 3D point clouds leads to significant processing delays, with approximately 80% of overhead in commercial frameworks. This hampers usability and degrades user experience. Our analysis reveals that maintaining the facing side of the real-world scene in a stable environment provides sufficient information for virtual object placement and rendering. To address this, we introduce a lightweight quadtree structure, representing 2D scenes through semantic segmentation and geometry, as an alternative to 3D point clouds. Additionally, we propose a novel correction method to handle potential shifts in virtual object placement during view synchronization among users. Combining all designs, we implement a fast and lightweight MuAR framework named AR-Light and test our framework on commercial AR devices. The evaluation results on real-world applications demonstrate that AR-Light can achieve high performance in various real-world scenes while maintaining a comparable virtual object placement accuracy. © 1968-2012 IEEE.

关键词： Semantic Segmentation

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Review of Steganography and Cryptography: Tools for Covert Communications

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International Journal of Network Security 2025年第2期27卷 431-438页

作者： Fatima, Kanza Wu, Nan-I Chan, Chi-Shiang Hwang, Min-Shiang Department of Computer Science and Information Engineering Asia University Taichung41354 Taiwan Department of Information Management Lunghwa University of Science and Technology Taiwan Department of Artificial intelligence Asia University Taiwan

In today’s rapidly evolving digital media landscape, safeguarding content privacy and preventing unauthorized access to copyrighted material are major challenges. Cryptography plays a crucial role in modern digital media security, providing the essential tools to protect the confidentiality, reliability, and genuineness of digital media information. This paper examines cryptographic and steganographic techniques employed for securing digital media content. It provides an overview of key cryptographic principles, including symmetric and asymmetric encryption. It also provides different branches of steganography. This paper also discusses different evaluation matrices for cover object quality. Overall, this research offers a comprehensive exploration of steganography, highlighting its importance as a flexible communication method in the digital age and its future scope. © (2025), (International Journal of Network Security). All rights reserved.

关键词： information hiding

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An Enhanced Aspect-Based Sentiment Analysis Model Based on RoBERTa For Text Sentiment Analysis

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第14期49卷 193-202页

作者： Chauhan, Amit Sharma, Aman Mohana, Rajni Jaypee University of Information Technology HP Solan173234 India Department of Computer Science Amity School of Engineering and Technology Amity University Punjab Punjab Mohali140306 India

Using an aspect-based sentiment analysis task, sentiment polarity towards specific aspect phrases within the same sentence or document is to be identified. The process of mechanically determining the underlying attitude or opinion indicated in the text is known as sentiment analysis. One of the most important aspects of natural language processing is sentiment analysis. The RoBERTa transformer model was pretrained in a self-supervised manner using a substantial corpus of English data. This means it was pretrained solely with raw texts and an algorithmic process to generate inputs and labels from those texts. No human labelling was involved, allowing it to utilise a vast amount of publicly available data. The authors of this work provide a thorough investigation of aspect-based sentiment analysis with RoBERTa. The RoBERTa model and its salient characteristics are outlined in this work, followed by an analysis of the model's optimisation by the authors for aspect-based sentiment analysis. The authors compare the RoBERTa model with other state-of-the-art models and evaluate its performance on multiple benchmark datasets. Our experimental results show that the RoBERTa model is effective for this important natural language processing task, outperforming competing models on sentiment analysis tasks. Based on the SemEval-2014 variant benchmarking datasets, the restaurant and laptop domains have the highest accuracy, scoring 92.35 % and 82.33 %, respectively. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Natural language processing systems

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Cat Swarm Algorithm Generated Based on Genetic Programming Framework Applied in Digital Watermarking

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computers, Materials & Continua 2025年第5期83卷 3135-3163页

作者： Shu-Chuan Chu Libin Fu Jeng-Shyang Pan Xingsi Xue Min Liu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210000China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266000China Department of Information Management Chaoyang University of TechnologyTaichung40601TaiwanChina School of Information Science and Engineering Fujian University of TechnologyFuzhou350000China Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resource Nanjing University of Information Science and TechnologyNanjing210000China

Evolutionary algorithms have been extensively utilized in practical ***,manually designed population updating formulas are inherently prone to the subjective influence of the *** programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world *** paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human *** modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the *** designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update *** Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the *** validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark ***,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking *** experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency.

关键词： Cat swarm algorithm genetic programming digital watermarking update mode mode generation framework

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Efficient Collision-Free Motion Planning in Distributed Multi-Robot Systems 17

Efficient Collision-Free Motion Planning in Distributed Mult...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Sharma, Om Chakraborty, Nilotpal India Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India

ISBN: (纸本)9798331531195

In distributed multi-robot systems, ensuring collision-free motion planning is a complex challenge, especially in dynamic environments where multiple robots are operating simultaneously. Traditional path-planning algorithms are effective for individual robot navigation but struggle when dealing with the unpredictable movements of other robots in the system. To overcome these limitations, this research focuses on advanced motion planning techniques that ensure safe and efficient robot coordination. We model the overall motion planning problem into a two-dimensional grid and our objective is to minimize both the time and the energy taken by the robots to reach their destinations from their respective sources. We enhance standard path-finding algorithms such as A∗ search, along with Deep Reinforcement Learning (DRL) with suitable modifications to adapt to real-time changes. DRL allows robots to learn from their environment and make optimal decisions, improving their ability to avoid collisions and navigate efficiently in uncertain conditions. By integrating these algorithms and conducting a comparative analysis, we propose a framework that enables robots to dynamically adjust their paths, ensuring collision-free movement and optimal coordination. © 2025 IEEE.

关键词： Robot programming

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Predicting Student Behavior Using a Neutrosophic Deep Learning Model

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Neutrosophic Sets and Systems 2025年 76卷 288-310页

作者： Shitaya, Ahmed Mohamed Wahed, Mohamed El Syed Abd El Khalek, Saied Helemy Ismail, Amr Shams, Mahmoud Y. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Computer Science Faculty of computer and information sciences Suez Canal University Egypt Department. of Communication and Computer Engineering Air Defense University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt

We developed an information system using an object-oriented programming language and a distributed database (DDB) consisting of multiple interconnected databases across a computer network, managed by a distributed database management system (DDBMS) for easy access. An intelligent system was designed to assess the difficulty level of preliminary exams and select top-performing advanced students using a Neutrosophic Deep Learning Model. The dataset was randomly split into training (80%) and testing (20%) sets, and the model, trained with the Adam optimizer at a 0.001 learning rate over 50 epochs, incorporated early stopping based on validation loss. This system, implemented at a traditional Egyptian university, achieved a 95% accuracy in predicting student dropout. Student behavior, influenced by personal, environmental, and academic factors, is often evaluated subjectively, leading to inconsistent results. Traditional machine learning approaches struggle with the inherent uncertainty in behavioral data. To address this, we combined neutrosophic theory—a mathematical framework that accounts for truth, falsity, and indeterminacy—with deep learning, which excels at learning complex data relationships, to predict student outcomes such as dropout rates. Evaluating the model on student data, including attendance and grades, showed superior accuracy, achieving a determination coefficient of 0.95, demonstrating the approach's potential for identifying at-risk students and enabling targeted interventions. © 2025, University of New Mexico. All rights reserved.

关键词： Contrastive Learning

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