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Road Lane Detection using Region-based Convolutional Neural Network (RCNN) 1

Road Lane Detection using Region-based Convolutional Neural ...

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Thondralnayagi, A. Nandhakumar, T. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

Road lane markings are one of the main factors that cause traffic accidents and jeopardize the safety of both drivers and pedestrians. Both computer vision systems find it challenging to recognize road lanes. The irregular maintenance, insufficient drainage, and traffic patterns. It negatively affects driving comfort, road safety, and vehicle condition, and it could also lead to a lot of accidents. Recently, several machine learning algorithms have been used, although their accuracy and efficiency have not improved significantly. We suggest applying Region-Based Convolutional Neural Networks (RCNN) for path identification to tackle this issue. RCNNs are another kind of neural network that is capable of extracting significant information from picture and time series data. This makes it highly helpful for tasks requiring images, such as shape identification, object classification, and recognition. Image from a dataset with noise removed using a Wiener filter. The next step is to extract features from images using Linear Discriminant Analysis (LDA). Determine which lane line in the RCNN is efficient and provides accuracy, precision, recall, and F1-score as 95.6%, 94.05%, 94.58%, and 94.23%, respectively. © 2025 IEEE.

关键词： Convolutional neural networks

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Energy Efficient and SecurityAware Task off Loading for Multi-Tier Edge Cloud Computing Systems 1

Energy Efficient and SecurityAware Task off Loading for Mult...

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Divya, S. Saranya, S. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

The massive progression of the multi-tier edge cloud computing systems integration has created the possibility of data orchestration and resource control in edge and cloud layers. However, there are many shortcomings, including inappropriate methods of scheduling tasks and security breaches that prevent scalability. To solve these problems, we designed an energy-efficient and security-aware task offloading framework for multi-tier edge cloud computing. Firstly, we estimate the energy ratio of tasks using a Fuzzy Logic Energy Management (FL-EM) algorithm and distribute resources according to different layers. Subsequently, the task-offloading parallel squirrel (TO-PS) method is used to effectively extend the task with better energy consumption and computational efficiency. Finally, it is decided to protect the work data using the Provable Energy-Aware Cryptographic Security (PE-ACS) algorithm. The proposed algorithm achieves a 96.7% success rate in protecting high-level data from cybercrime. Additionally, it enhances security and task performance while reducing energy consumption in multi-tier edge cloud computing systems. © 2025 IEEE.

关键词： Cloud security

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Enhancing Interest Message Forwarding in NDN-based VANETs using Black Hole Optimization 2

Enhancing Interest Message Forwarding in NDN-based VANETs us...

引用

2nd IEEE International Conference on data science and Network Security, ICDSNS 2024

作者： Devi, S. Padma Dhanalakshmi, K. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Psna College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350373110

In Intelligent Transportation Systems (ITS) and smart cities, Vehicular Ad hoc Networks (VANETs) are vital but face challenges due to their dynamic topology, making traditional IP-based content retrieval impractical. Named data Networking (NDN) offers an alternative by using content names for retrieval, though locating content-holding nodes efficiently in NDN-based VANETs remains difficult. This paper introduces an optimized method using Black Hole optimization to enhance interest message forwarding in NDN-based VANETs, aiming to improve packet delivery ratios and reduce latency. Simulation studies comparing proposed method with classic NDN and Particle Swarm Optimization NDN (PSO-NDN) show that proposed approach significantly outperforms these methods, achieving higher packet delivery ratios and lower latency. This research advances efficient communication strategies in NDN-based VANETs, crucial for robust ITS in smart cities, demonstrating the potential of Black Hole optimization to enhance VANET performance and support smart city infrastructure. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Enhancing Dyslexia Detection: A Robust Machine Learning Approach 3

Enhancing Dyslexia Detection: A Robust Machine Learning Appr...

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

作者： Manimaran, V. Shanthi, N. Aravindhraj, N. Rakesh, K. Revanth, K. Saran, S. Department of Computer Science and Engineering Kongu Engineering College Erode India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Dyslexia, recognized as a common learning disability, adversely affects reading, spelling, and word decoding capabilities, frequently resulting in considerable academic and social obstacles if not identified at an early stage. Conventional diagnostic approaches, encompassing neuroimaging and standardized assessments, prove to be effective yet are characterized by high costs and limited scalability. In response to this challenge, the present study introduces a hybrid classification model that integrates Random Forest and XGBoost methodologies to improve dyslexia detection through binary classification. The dataset employed comprises both numerical and categorical variables, such as "Gender" and "Native Language," which were processed via one-hot encoding to facilitate the transformation of categorical information. The Synthetic Minority Oversampling Technique (SMOTE) was utilized to mitigate class imbalance, while Principal Component Analysis (PCA) was implemented to diminish dimensionality, thereby enhancing model performance. Preliminary assessments of individual classifiers indicated that Random Forest surpassed both Decision Tree and Neural Network (MLPClassifier), achieving an accuracy of 95.31%, an F1-score of 0.95, and precision metrics of 0.94 for class 1 and 0.97 for class 0. Expanding upon these findings, a hybrid model that merges Random Forest and XGBoost through the application of a VotingClassifier was constructed. This hybrid framework attained an elevated accuracy of 96.23%, accompanied by consistently robust precision, recall, and F1-scores of 0.96. The results underscore the efficacy of the hybrid methodology in offering a scalable and dependable solution for the early detection of dyslexia within both clinical and educational contexts. © 2025 IEEE.

关键词： Adversarial machine learning

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Heart Disease Classification using Adaptive Recurrent Neural Network Method 1

Heart Disease Classification using Adaptive Recurrent Neural...

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Divya, M. Karthikeyan, A. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

Cardiovascular diseases are still among the leading causes of mortality, which require fast and reliable classification methods for diagnosing and preventing the disease. This work thus presents an elaborate framework involving an ARNN for heart disease categorization. First, the technique of Variational Autoencoders (VAE) is used to detect and remove irrelevant or undesirable data records, purging the dataset. Subsequent to this, the Information Gain Feature Selection (IGFS) technique is employed to select the most informative features that lower dimensionality and increase the model's accuracy. Finally, the ARNN is used for classification using the fact that it is a dynamic network that can accommodate temporal characteristics of the data. The experimental results also show that the accuracy, precision, and recall of the approach are higher and higher than those of the conventional method. The proposed framework demonstrates tremendous promise for real-world implementation in medical diagnostics and is a sound and scalable approach to heart disease prediction. Even more striking, these results stress the meaning of applying modern techniques to machine learning in healthcare-sector development. The ARNN achieve 95.6% accuracy for predict the cardiovascular disease. © 2025 IEEE.

关键词： Spatio-temporal data

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A Hybrid Deep Learning Approach for Automatic Melanoma Diagnosis: Integrating Convolutional Neural Networks with Vision Transformers 4

A Hybrid Deep Learning Approach for Automatic Melanoma Diagn...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Priyanka, G. Mahalakshmi, A. Priya, N. Sivaranjani, N. Archana, B. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Hindusthan college of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

The most lethal type of skin lesion is melanoma. The likelihood of survival for melanoma is significantly increased by early detection. Nevertheless, a number of characteristics, such as diminished contrast between the skin and abrasion and visual similarities between non-melanoma and melanoma, make it challenging to identify melanoma with accuracy. The novel melanoma recognition method described in this study consists of three primary phases: preprocessing, segmentation and classification. To maximize image quality and get the data ready for segmentation, the preprocessing step included denoising techniques, grayscale conversion, Gaussian thresholding, and Contrast Limited Adaptive Histogram Equalization (CLAHE). Three widely used edge detection algorithms-Canny, Sobel, and Prewitt-were applied to the segmentation process in order to identify lesion boundaries and extract features unique to melanoma. Performance criteria such as Root Mean Square Error (RMSE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR) were used to assess the accuracy of the segmentation. Based on the comparison analysis, Sobel was found to be the best algorithm for correctly identifying melanoma lesions. The outcomes showed how well the preprocessing methods worked to enhance image quality and how well the segmentation algorithms identified melanoma lesions. This research's subsequent phase will concentrate on classifying images using deep learning models. The segmented images are used to train deep learning model namely Convolutional Neural Networks with Vision Transformer. Superior performance is obtained in terms of accuracy, precision, recall, and F1 score when denoising, segmentation, and classification techniques are combined. This highlights the potential of deep learning and image processing fusion in improving skin disease recognition and performance of classification systems. © 2024 IEEE.

关键词： Dermatology

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Early Stage Pepper Leaf Disease Detection using Deep Learning based Capsule Network with Convolutional Neural Network 1

Early Stage Pepper Leaf Disease Detection using Deep Learnin...

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Jeevitha, S. Venkadesh, R. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

Agriculture plays a vital part in the economy of some countries, providing benefits such as food, national income, and employment. This results in substantial growth in agricultural produce, financial losses, and a decline in quality and quantity. Farmers have traditionally detected diseases through eye tests, but this method has drawbacks. However, previous research suggests that binary classification needs improvement because some types are more challenging to determine. To address these issues, various pepper diseases are classified based on DL techniques to address these issues. First, image pre-processing uses a Median Filter (MF) to reduce noise and increase brightness. Furthermore, the Active Contour Segmentation (ACS) method detects affected and unaffected areas in the image. Moreover, the Texture Color Intensity Feature Selection (TCIFS) method is employed to select the optimal features of pepper disease. Finally, the proposed Capsule Network with Convolutional Neural Network (CapsNet-CNN) technique detects pepper leaf disease. The proposed method attains an accuracy of 98.32%, recall of 97.43%, and precision of 98.04% compared to other methods. © 2025 IEEE.

关键词： Median filters

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Improving Emotion Recognition in Audio Signals: Leveraging Novel Features and Deep Learning for Improved Classification 3rd

Improving Emotion Recognition in Audio Signals: Leveraging N...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Chaudhary, Poonam Choudhary, Neeraj Department of Computer Science and Engineering The NorthCap University Gurugram India Department of Computer Science and Engineering University College of Engineering and Technology Bikaner India

ISBN: (纸本)9789819726707

Recently, there has been interest in classifying emotions using audio inputs and machine learning methods. Because a single statement might be delivered in a variety of emotional circumstances, textual data alone is insufficient for identifying the emotions, necessitating the adoption of novel feature extraction approaches. This paper aims to provide an improved framework which uses a detailed study on audio sound features like mel-frequency cepstral coefficients (MFCCs), zero crossing rate (ZCR), spectral bandwidth and many other features for audio analysis and uses the same for predicting emotions. The paper also emphasises on various audio augmentation techniques to improve the generalising power of the model. With the above-mentioned improvements in the feature vector and a carefully selected deep neural network, state-of-the-art classification accuracy is achieved on the two state-of-the-art datasets Toronto emotional speech set (TESS) and the Ryerson Audio-Visual database of Emotional Speech and Song (RAVDESS). The paper is concluded with the improved approach for emotion recognition using audio signals and leads to future scope of efficient speech recognition applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Emotion Recognition

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An Interactive Augmented Reality Application for Agriculture using Unity 17

An Interactive Augmented Reality Application for Agriculture...

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

作者： Maasthi, Manasvi J. Vidyavardhaka College of Engineering Department of Computer Science and Engineering Mysuru India

ISBN: (纸本)9798331531195

Augmented reality is defined as a 3D computer generated imagery that is embedded into the real world. There are numerous areas in which augmented reality can be applied, including: education, medical care, entertainment, & so on. Unlike VR, AR does not require the use of a HMD (Head Mounted Device);a projection can be performed on a mobile phone making it affordable. Most farmers are facing problems today, for instance, loss of crops. In this Paper an application has been created aimed at enabling farmers to know important things like when to grow certain crops and what to use to assist in soil fertility, through the development of augmented reality applications using Unity and Vuforia. That way, the farmers will have an idea of what other crops they should be able to grow during the other season. So that they will not be undergoing any kind of loss. © 2025 IEEE.

关键词： Virtual environments

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Food Hazines and Tracking System for Farmers using Blockchain Technologies 1

Food Hazines and Tracking System for Farmers using Blockchai...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Jananipriya, R. Ramya, P. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

It is quite challenging to monitor goods quality or security issues due to the intricacy of a tracking system, particularly for the fundamental farming food supply chains that contribute to making up everyday feeds of individuals. There are a number of significant issues with the current agricultural food tracking systems, including a large number of subjects, challenging interactions brought on by lengthy logistics phases, and user and centrally managed distrust of data. The traceability issue in agricultural food tracking systems is successfully resolved by the development of blockchain technology. In order to reduce the requirement for central organizations and departments and enhance the dependability, safety, and authenticity of records of transactions, this work suggests a structure based on smart contracts and consortiums to monitor and document the process of agricultural food tracking system, implement transparency and accessibility, and dismantle data centers between businesses. Farmers simultaneously store file hashes in smart contracts and record environmental and agricultural development information in the InterPlanetary File System (IPFS), which improves the safety of information and mitigates the issue of blockchain storage overflow. The proposed system demonstrated superior performance with reduced delay in response (0.035) and increased transmission speed (265), reception speed (305) and processing power (17.65) compared to Ethereum and Hyperledger Fabric. © 2025 IEEE.

关键词： Blockchain

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