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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kamkar, Spandana Anilkumar Sanjay, Srushti Vaishnavi, P.S. Karthik, S. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331515911

Oral health is vital to overall well-being but is often overlooked due to inefficient monitoring tools and delayed diagnosis. This project presents a smart handheld device featuring a miniaturized camera for detecting oral issues like plaque, cavities, teeth discoloration and gum diseases in real-time. Using image processing and deep learning algorithms, the device provides precise feedback and actionable insights. Paired with a mobile application offering detailed analysis and recommendations, it empowers users to adopt proactive oral hygiene practices to prevent teeth disorder. © 2025 IEEE.

关键词： Electronic health record

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Smart ANPR: A Checkpoint-Based Toll and Parking Management System Using Hybrid OCR and Blockchain 3

Smart ANPR: A Checkpoint-Based Toll and Parking Management S...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Samarth, M.N. Gopisetti, Sampriti Chaitra, M. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331515911

Automated Number Plate Recognition is one of the most important applications of computer vision, employed in the enforcement of traffic law, vehicular management, security, and even at toll collection. This paper proposed an advanced system for ANPR that can move away from the traditional booth and establish a checkpoint. The strategically located cameras with checkpointing will automatically capture the plates of passing vehicles, permitting road tax and parking fees to be deducted without making contact. A hybrid approach that combines the capabilities of OpenCV, Tesseract OCR, and deep-learningbased OCR models helps tackle such challenges as stable images from live video feeds and highly accurate text recognition. To this end, since the system integrates high-resolution frame stabilization using OpenCV to ensure crystal clear captures, it further employs the use of YOLOv8 to boost the precision of real-time detection. The blockchain technology is further implemented to ensure that transactions are tamper proof, therefore ensuring secure and transparent billing systems. This makes the ANPR solution deployable on edge devices, which opens real-time processing capabilities for high-speed and high-volume environments, thereby offering a scalable and efficient approach for intelligent transportation and smart city applications. © 2025 IEEE.

关键词： Blockchain

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Deep Learning-Driven Cloud Intrusion Detection: Internet of Things and Cloud Networks 1

Deep Learning-Driven Cloud Intrusion Detection: Internet of ...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Moon, Iffath Tanjim Islam Mondal, Md. Nazrul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331518578

In this paper, we have utilized deep learning approaches to detect cloud intrusion for the Internet of Things (IoT). The emerging growth of IoT and cloud environments has revolutionized many industries by allowing real-time data sharing and storage. Cloud platforms offer the essential infrastructure for managing the massive volumes of data produced by IoT devices. However, due to several flaws, including insufficient encryption, insecure APIs, and weak authentication, intruders can exploit the vulnerabilities to interrupt services and obtain unauthorized access. Therefore, deep learning can be an effective solution to detect cloud intrusion in IoT. In this research, we have developed individual convolutional neural network (CNN) and long short-term memory (LSTM) models, along with a hybrid CNN-LSTM model for multiclass classification and binary classification to detect cloud intrusion. In addition, we have examined which models perform the best for cloud intrusion detection for IoT among the individual and hybrid models. The proposed models are effective in detecting cloud intrusion, as CNN can extract spatial features, and LSTM can capture temporal dependencies from network traffic data. Furthermore, we have utilized one of the most recent and comprehensive datasets for IoT, the ToN-IoT dataset, containing real-world data. In multiclass classification, the hybrid CNN-LSTM has given the best performance, achieving an accuracy of 97.65%, precision of 97.72%, recall of 97.65%, f1 score of 97.67%, and AUC of 99.93%. In binary classification, all CNN, LSTM, and hybrid CNN-LSTM have demonstrated high performance, where LSTM has achieved the highest accuracy of 99.97%. Moreover, we have evaluated the performance of multiclass classification for detecting nine distinct kinds of attacks. In this instance, CNN has shown better performance in detecting backdoor attacks. Thus, this research can make the IoT platform more secure by efficiently detecting cloud intrusion. © 2025

关键词： Network intrusion

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Lightweight Attention Based Deep CNN Framework for Human Facial Emotion Detection from Video Sequences

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SN computer Science 2025年第1期6卷 1-12页

作者： Kant, Krishna Shah, Dipti B. Computer Science Dept. P.G Department of Computer Science and Technology Sardar Patel University V.V. Nagar Gujrat Anand 388120 India

Emotions with their intensities are associated with the action of humans which decides the behaviour of an *** recent research has gained enormous attention in the domain of emotion detection due to automatic facial emotion detection. The prime goal of facial emotion recognition (FER) is intended to map different facial expressions from video sequences to specific emotional states. Human facial emotion recognition (HFER) has captivated the attention of researchers for its excellent real time application which serves the society. This research work has proposed a novel lightweight attention based deep convolution neural framework for human facial emotion detection from video sequences on CK + dataset. A dense convolutional network (DenseNet-201) has been applied to strengthen feature propagation to find a solution to the vanishing-gradient problem, and significantly lower the parameter *** provide the model for better visual perceptibility, an attention block is applied between the convolution and dense layer. The proposed model achieves good accuracy of 98.8% while using less memory and processing in comparison to state-of-the-art approaches to reach good performance on CK + data set. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Attention network Deep convolution neural network DensNet-201 Emotion detection Human face detection Intensity Video surveillance

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A Hybrid Approach Using Cryptography and K-Least Significant Bit Steganography Algorithm in Cloud Computing 1

A Hybrid Approach Using Cryptography and K-Least Significant...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Goyal, Anshika Gupta, Bhumika VMSB Uttarakhand Technical University Dept. of Computer Science & Engineering Dehradun India G.B. Pant Institute of Engineering & Technology Dept. of Computer Science & Engineering Pauri India

ISBN: (纸本)9798331518578

The exponential growth of communication technologies has broadened the cloud computing ecosystem horizon to meet main communication needs. However, in-parallel upsurge in online attacks has alarmed industries to ensure optimal data security. The combination of steganography and cryptosystems has been recognized as an enhanced security model for data transfer among the main security systems now in use. In this paper, a hybrid approach using cryptography and K- Least Significant Bit Steganography algorithm in Cloud Computing (HYBC-KLSBS-CC) are proposed. Initially, input text data is retrieved from cloud storage and compressed using Huffman Compression Coding (HCC) to reduce data size. The compressed data is then encrypted with a Hybrid Cryptography Algorithm (Hyb-MRFC-ALC), combining Modified Random Fibonacci Cryptography and Asymmetric Lightweight Cryptography to ensure robust encryption. Finally, encrypted data is embedded within an image using the K-Least Significant Bit (KLSB) steganography technique, providing additional layer of security. The proposed method enhances data confidentiality, integrity, and storage efficiency in cloud environments. The proposed HYBC-KLSBS-CC approach attains 26.89%, 29.49% and 18.64% lower PSNR and 26.89%, 29.49% and 18.64% lower MSE when analyzed through existing techniques like data security dependent on cryptography steganography related with OTP procedure and Huffman coding in the cloud environment (CS-OTP-CE), a novel hybrid cryptographic framework for secure data storage in cloud computing: Integrating AES-OTP and RSA with adaptive key management and Time-Limited access control (AES-OTP-RSA-CC) and Crypto-Stegno dependent model for securing medical information on IOMT platform (CS-SMI-IOMT). © 2025 IEEE.

关键词： Cloud platforms

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Flood Susceptibility Assessment in the Teesta River Basin, India: An Advanced Geospatial Artificial Intelligence Approach Leveraging Spatial Data Augmentation

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SN computer Science 2025年第4期6卷 1-22页

作者： Sen, Deepanjan Das, Swarup Dept. of Computer Applications Dr. B. C. Roy Academy of Professional Courses West Bengal Durgapur India Dept. of Computer Science and Technology University of North Bengal West Bengal Siliguri India

Floods are a critical natural catastrophe that has a significant impact on the Teesta River Basin in India, resulting in substantial losses to agriculture, property, and life. This investigation introduces a sophisticated geospatial artificial intelligence framework for the evaluation of flood susceptibility, which employs spatial data augmentation for enhancing models’ reliability. The study uses six machine learning algorithms, involving K-Nearest Neighbours (KNN), Na¨ıve Bayes (NB), Decision Tree (DT), Conditional Inference Tree (CIT), BlackBoost (BBoost), and Model Averaged Neural Network (MANN), to integrate multisource geospatial data, including 30 × 30 m Cartosat-1 satellite derived Digital Elevation Model (DEM). A thorough flood inventory found 317 inundated areas, with original data sources systematically enhanced at a 1:3 ratio to expand the dataset, and the Boruta feature selection technique identified 12 essential flood conditioning elements. The models were assessed using metrics such as accuracy, Kappa, precision, recall, F1 score, and Area Under the Curve (AUC). The KNN model has the maximum accuracy of 91.62% (Kappa = 0.8325), precision of 90.41%, recall of 93.12%, and AUC of 97.80%, classifying around 35% of the basin as high-susceptibility zones. This study underscores the efficacy of machine learning and geospatial analysis in enhancing flood risk management tactics, offering critical insights for disaster management policymakers. Future efforts will concentrate on improving model robustness via comprehensive evaluation across many geographical settings, incorporating real-time meteorological data for dynamic flood forecasting, and creating user-friendly decision support tools for stakehol-ders in disaster management. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Boruta analysis Digital elevation model Flood susceptibility Geospatial artificial intelligence Spatial data augmentation

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Improved Bounds For Online Convex Optimization

Improved Bounds For Online Convex Optimization

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Nayak, Tanvi S. Bharath, B.N. Dept. of Computer Science and Engineering Indian Institute of Technology Dharwad Dharwad India Dept. of EECE Indian Institute of Technology Dharwad Dharwad India

ISBN: (纸本)9798350368741

In this paper, we consider the problem of online learning with convex objectives. Most of the existing work shows that static and dynamic regrets scale logarithmically or sub-linearly with the time horizon T. On the contrary, for the strongly convex setting, it is shown that the static and dynamic regrets scale with the variation term that measures the rate at which the function changes over time. In the convex setting, we show that a simple Online Gradient Descent (OGD) algorithm with single round at each time achieves a regret that is comparable with the strongly convex setting with an additional assumption of Quadratic Growth (QG) condition. More specifically, we show that the static and dynamic regrets scale as O(Var3(T)) and O(Var1(T)) + O(Var2(T)), respectively. Here, Vari, i = 1, 2, 3 represent variation terms that capture different aspects of changes in the optimal solution over time. We use experiments to corroborate some of our theoretical findings. © 2025 IEEE.

关键词： convex optimization Online learning QG condition regret bounds sparse signal processing

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Artificial Intelligence to Enhance the Security of Internet of Things: A Comprehensive Review 3

Artificial Intelligence to Enhance the Security of Internet ...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dheekshaa, V.M. Harsha, V. Sindhu, M. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

The Internet of Things is the term we call when devices are connected to the network and work together to provide a better experience for users, facilitate better decision-making, and improve operations. As with any invention, this one also has its disadvantages. IoT devices have complex structures and low processing capacities. There are serious security risks in designing IoT like poor authentication, data leakage, and DDoS attacks. In this review paper, how AI can secure IoT is discussed. A few AI-driven strategies that help mask all these weaknesses are also discussed regarding intrusion detection systems (IDS), behavioural analytics, anomaly detection, and real-time threat monitoring. It presents a four-layered IoT architecture consisting of perception, network, support, and application layers. The use of the IoT systems will become more flexible and efficient in data security and collection, secure and encrypted communication, and predictive security analytics. AI will play the same role in improving the threat perception, real-time identification, and response, as this will make it indispensable to ensure the IoT environment security. This work emphasizes the urgent need for solid AI-powered, multilayered adaptive security frameworks to match the evolving demands of cybersecurity. © 2025 IEEE.

关键词： Network intrusion

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Adaptive Weather Routing for Advanced Ship Navigation: A Comprehensive Review 3

Adaptive Weather Routing for Advanced Ship Navigation: A Com...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Venkatesan, Iyyappan Dharanish, M.B. Muthumari Lakshmi, S. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

Maritime navigation offers a comprehensive approach to optimising ship navigation, in such a way that guarantees the safety and effectiveness of the journey through multiple techniques, like the adaptive weather routing technique, which uses the predictive model and the current weather conditions for efficient navigation. The review mainly emphasizes the basic principles and computational simulation for sustainable navigation in advancing the route defining algorithm. Discusses about the conventional route defining algorithms and dynamic route defining algorithm by adjusting for wind, waves, and currents. A comparison analysis shows how well different algorithms work in various weather conditions, and case studies show how they are used in the actual world, while new trends are being discussed, major problems also concern the need for accurate climate predictions, high-speed data processing, and an efficient response plan. The Advancements in artificial intelligence and composite path forecasting also address issues related to major needs, such as accurate weather forecasts, high-speed data processing, and effective reaction tactics. Through this insights of this review provides on future directions for adaptive weather routing research and development that the sustainable growth of intelligent naval navigation is supported. © 2025 IEEE.

关键词： Weather forecasting

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ViTYoga: Vision Transformer for Real-Time Yoga Pose Estimation and Analysis

ViTYoga: Vision Transformer for Real-Time Yoga Pose Estimati...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Wadawadagi, Ramesh Ajay, V.G. Desai, Vinod Varon, E. Patil, Shantakumar B. Sai Vidya Institute of Technology Dept. of Computer Science and Engg. Bangalore560064 India

ISBN: (纸本)9798331538538

Recognizing and analyzing yoga poses is a critical aspect of yoga practices and is vital when it is supported by technology. The importance of yoga arises from its ability to promote mental and physical well-being, stress reduction, im-proved focus, emotional stability, resilience, spiritual growth, and a life-centered perspective. As the popularity of yoga continues to grow, technological advancements are becoming increasingly vital to assist yoga instructors and practitioners in monitoring and enhancing their practice. This work elucidates the significance of automated yoga pose detection and its potential benefits for enhancing self-correction, providing real-time feedback, and maximizing yoga benefits. The proposed work utilizes Vision Transformers (ViT) for real-time detection and monitoring of yoga poses, referred to as ViTYoga. The proposed ViTYoga employs ViT model, which is pre-trained on the COCO-pose dataset and further fine-tuned on Yoga Pose Image datasets that comprise specific yoga postures. The performance assessment of proposed ViTYoga is compared and contrasted with other baseline techniques. The experimental results reveal that ViTYoga outperforms several SOTA techniques. Moreover, the proposed design offers a lightweight model that can be deployed as a mobile application and used by yoga practitioners to perform yoga at their convenience. © 2025 IEEE.

关键词： COCO-Pose dataset Deep Learning Deep Vision Vision Transformers Yoga pose estimation

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