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DERNNet:Dual Encoding Recurrent Neural Network Based Secure Optimal Routing in WSN

computer Systems science & engineering 2023年第5期45卷 1375-1392页

作者： A.Venkatesh S.Asha School of Computer Science and Engineering Vellore Institute of TechnologyChennaiIndia Centre for Cyber Physical Systems&School of Computer Science and Engineering Vellore Institute of TechnologyChennaiIndia

A Wireless Sensor Network(WSN)is constructed with numerous sensors over geographical *** basic challenge experienced while designing WSN is in increasing the network lifetime and use of low *** sensor nodes are resource constrained in nature,novel techniques are essential to improve lifetime of nodes in *** energy is considered as an important resource for sensor node which are battery powered *** WSN,energy is consumed mainly while data is being transferred among nodes in the *** research works are carried out focusing on preserving energy of nodes in the network and made network to live ***,this network is threatened by attacks like vampire attack where the network is loaded by fake ***,Dual Encoding Recurrent Neural network(DERNNet)is proposed for classifying the vampire nodes s node in the ***,the Grey Wolf Optimization(GWO)algorithm helps for transferring the data by determining best solutions to optimally select the aggregation points;thereby maximizing battery/lifetime of the network *** proposed method is evaluated with three standard approaches namely Knowledge and Intrusion Detection based Secure Atom Search Routing(KIDSASR),Risk-aware Reputation-based Trust(RaRTrust)model and Activation Function-based Trusted Neighbor Selection(AF-TNS)in terms of various *** existing methods may lead to wastage of energy due to vampire attack,which further reduce the lifetime and increase average energy consumed in the ***,the proposed DERNNet method achieves 31.4%of routing overhead,23%of end-to-end delay,78.6%of energy efficiency,94.8%of throughput,28.2%of average latency,92.4%of packet delivery ratio,85.2%of network lifetime,and 94.3%of classification accuracy.

关键词： Wireless sensor network vampire nodes lifetime optimal routing energy encryption decryption trust value optimization

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Efficient Road Segmentation Techniques with Attention-Enhanced Conditional GANs

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SN computer science 2024年第1期5卷 176页

作者： George, Glenn Varghese Hussain, Mohammad Shahil Hussain, Rizwan Jenicka, S. School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology (VIT) Vellore India

Road segmentation from aerial images is a challenging yet crucial task, underpinning significant applications in urban planning, navigation, and transportation systems. In this study, we employ a conditional Generative Adversarial Network (GAN) architecture that synergistically integrates the strengths of Attention U-Net and PatchGAN to address this task. The Attention U-Net, serving as the generator, is trained on the publicly available Massachusetts Roads dataset, with an emphasis on critical regions while concurrently disregarding the irrelevant ones, thereby enhancing the accuracy of road segmentation. Simultaneously, the PatchGAN discriminator ensures the generation of sharp, high-quality segmentations. Through this cooperative approach, we have achieved an overall accuracy of 98.2%, a recall of 82.30%, a precision of 78.66%, an Intersection over Union (IoU) of 67.19%, and an F1 score of 80.44% on our dataset. While these results are promising, they also highlight areas for improvement, particularly in reducing false positives and enhancing the identification of all road pixels, underscoring the potential for future refinements in this research domain. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Aerial image analysis Attention U-Net Conditional generative adversarial network (CGAN) Deep learning-artificial neural network Road segmentation

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Automatic Query Generation Based on Adaptive Naked Mole-Rate Algorithm

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Multimedia Tools and Applications 2024年第16期84卷 1-32页

作者： Kinikar, Mayura Saleena, B. School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Pune India

In the growing information retrieval (IR) world, selecting suitable keywords and generating queries is important for effective retrieval. Modern database applications need a sophisticated interface for automatically updating the connections between users and databases. Most database applications are intelligent;however, some may be complex to understand when generating queries for effective retrieval. Therefore, this paper develops a new Adaptive Naked Mole-Rate algorithm (ANMR) for an automatic query generation (AQG) based IR system. The query generation approach primarily generates the query with expanded keywords to enhance IR. Modified sealion optimization (MSO) is applied to select several features. The selected features are combined in the feature fusion process using the Fuzzy based Search and Rescue approach (FSR). Similarity matching is performed using the hybrid cosine and Jaccard similarity measures. At last, the ranking process is performed using the Dynamic Global Local Attention Network based on Capsules (DGLANC). The developed AQG-ANMR system improves the performance of the entire information retrieval system. Then, to analyze the performance, the proposed approach is implemented in the python platform and evaluated in terms of accuracy, recall, precision, and F1-Score by employing TREC-3 and CISI datasets. Besides, the performance of the proposed approach is compared with that of state-of-the-art approaches. Finally, the simulated results clearly showed that the proposed approach outperformed the state-of-the-art approaches better. The maximum accuracy attained by the proposed approach is 97.2% with AQG-ANMR and 94.69% without AQG-ANMR for the TREC 3 dataset. In the same way, using the CISI dataset, the proposed approach reached a maximum accuracy of 98.6% with AQG-ANMR and 95.8% without AQG-ANMR, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Information retrieval

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Enhanced prediction of agricultural CO2 emission using ensemble machine learning-based imputation approach

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Neural Computing and Applications 2024年第36期36卷 22867-22887页

作者： Thendral, M. Sathya Abinaya, S. Devi, M. K. Kavitha Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai625015 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India

The agricultural sector contributes significantly to greenhouse gas emissions, which cause global warming and climate change. Numerous mathematical models have been developed to predict the greenhouse gas emissions from agriculture. However, the database utilized for prediction has thousands of missing values and imbalanced data records due to various factors, such as environmental disasters, sensor failure, maintenance issues, and budgetary constraints. Many researchers have either completely discarded records with missing values or have only partially addressed the issue, leading to less precise predictions. This study proposes a machine learning-based ensemble approach that uses multiple imputation by chained equations (MICE), K-nearest neighbors (KNN), and MissForest techniques to impute the missing values by reducing uncertainties and improves robustness in predictions. Furthermore, Synthetic Minority Oversampling Technique (SMOTE R) addresses data imbalance in CO2 emissions prediction by generating synthetic data to balance the target variable's distribution in the regression problem. Thereby the proposed SMOTE R and ensemble imputation approach aims to improve prediction accuracy and reliability by tackling data completeness and distribution issues simultaneously. The experiments are conducted on the GRACEnet database shows that the proposed approach outperforms the existing work in measure of R2, MAE, and RMSE metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Greenhouse gas emissions

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Novel Darknet traffic data synthesis using Generative Adversarial Networks enhanced with oscillatory Growing Cosine Unit activated convolution layers

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 58-65页

作者： Pradeep C, Antony Amali D, Geraldine Bessie Noel, Mathew Mithra Ghalib, Muhammad Rukunuddin Subramaniam, Prabhakar Rontala Venugopal, Chitra School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Electrical Engineering Vellore Institute of Technology Vellore India School of Computing De Montfort University Dubai United Arab Emirates Information Systems and Technology Westville South Africa Electrical Engineering & Renewable Energy Oregon Institute of Technology United States

The Darknet is an anonymous, encrypted collection of websites, with a passive listening nature - accepting incoming packets, while not supporting outgoing packets. Thus, it can potentially host criminal or malicious activity and software, becoming a cyber security threat. Network Detection Systems are effective in identifying dark net traffic and mitigating its ill effects. However, capturing and extracting data from raw network traffic for training these systems can be time-intensive and costly. Using the CIC-Darknet 2020 dataset, this paper proposes using a Novel Generative Adversarial Networks (GAN) Architecture generating the required training and testing data for these systems. This uses a combination of Growing Cosine Unit (GCU) activated convolution layers and Dense layers for the Generator. Feature selection with statistical correlation methods is used to select the most relevant features. An independently trained Evaluator network is used to evaluate the generated data. The proposed system is compared to other established Tabular data GANs like Conditional Tabular GAN (CTGAN) and Copula GAN with similar parameters and on the same data. The Proposed GAN architecture outperforms CTGAN and CopulaGAN by 20 % and 10 % in ters of accuracy while also taking 90 % and 30 % less time to train respectively. Results from measuring similarity of data using the Inverted Kolmogorov - Smirnov D statistic also show significantly better results for the Proposed GAN Architecture. This shows significant promise in using Generative models to reduce the time and effort costs associated with collecting and formatting data to use in research and for training detection systems. © 2024

关键词： Generative adversarial networks

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Music Emotion Classification using Harris Hawk Optimization based LightGBM Classifier 10

Music Emotion Classification using Harris Hawk Optimization ...

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10th International Conference on Biosignals, Images and Instrumentation, ICBSII 2024

作者： Patil, Hemprasad Yashwant Bhagwan Patil, Priyanka Balasubramaniam, Anishkaa Shanmugasundaram, Lackesh Vellore Institute of Technology School of Electronics Engineering Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350350951

This research investigates the relationship between music and human emotions, focusing on emotion classification in Turkish music and the role of acoustics in music emotion recognition (MER). Leveraging a unique dataset stored in the UCI-MLR, the study employs machine learning techniques, including Linear Discriminant Analysis and Gradient Boost Classifier, to build a robust model. The accuracy enhancement is primarily driven by the feature selection process, which is powered by the Harris Hawks optimizer. This optimizer, when used with the LightGBM model, enables a more precise selection of features, thereby significantly improving the model's accuracy. The research achieves notable accuracies - 81.25% for Linear Discriminant Analysis and 83.75% for the Gradient Boosting Classifier - surpassing previous records. The paper discusses findings in the context of affective computing, emphasizing potential applications in music recommendation systems and therapy, concludes with a comprehensive analysis, and outlines future possibilities. This research contributes to advancing MER, laying the groundwork for innovations in diverse domains, including the potential integration with IoT-based intelligent systems for real-time emotion analysis and personalized music experiences based on camera frames. © 2024 IEEE.

关键词： Emotion Recognition

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Epilepsy Seizure Detection based on EEG Signals using Machine Learning 3

Epilepsy Seizure Detection based on EEG Signals using Machin...

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

作者： Goel, Aadya School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India

ISBN: (纸本)9798331515911

Seizure detection is a critical aspect of epilepsy management, and accurate identification can significantly improve patient outcomes. This study explores the application of machine learning techniques, specifically the Random Forest classifier, for the detection of seizures using electroencephalogram (EEG) data. By employing a systematic approach that includes feature extraction and channel reduction, we enhanced the model's performance. Relevant features were extracted from EEG signals, including time-domain statistics and frequency-domain characteristics, which are vital for effective classification. The model was evaluated using standard classification metrics such as precision, recall, F1-score, and accuracy, yielding high performance, particularly in identifying non-seizure events. These findings highlight the potential of ensemble learning methods for real-time seizure monitoring and underscore the importance of this research in developing reliable diagnostic tools. Overall, our results demonstrate that machine learning approaches can significantly contribute to improved patient outcomes in epilepsy management, paving the way for future advancements in clinical applicability. © 2025 IEEE.

关键词： Electroencephalography

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Deep Learning-based Text News Classification using Bi-directional LSTM Model 3

Deep Learning-based Text News Classification using Bi-direct...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Anand, Shreyas Prakasam, P. Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Electronics Engineering Vellore India

ISBN: (纸本)9798350372120

The advancement of technology has resulted in increased news consumption through various online platforms and social media channels. Hence, there is a requirement for intelligent systems that may categorize the news that exists on the social media/online platform. Hence, this paper explores creating a news classifier utilizing a Bidirectional Long ShortTerm Memory (Bi-LSTM) deep learning model, which is a type of recurrent neural network (RNN) specifically designed to overcome the challenge of vanishing gradient often faced in deep learning. The machine learning was performed on a Kaggle dataset with over 400,000 records of news stories from various publishers. Natural language processing techniques such as stemming, one-hot encoding, vectorization, and embedding are used to prepare the data for machine learning. The multiclass classification is achieved and the performance of the model is analyzed. © 2024 IEEE.

关键词： Classification (of information)

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Collision Avoidance System using YOLO-based Object Detection and Distance Estimation 3

Collision Avoidance System using YOLO-based Object Detection...

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

作者： Raj, Shiv Akash Dhinakar Singh, Ashita Shekhawat, Tarakshraj Singh Manav Muthanna, M. Sivasankaran, K. School of Electronics Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331527549

Road accidents are a significant global issue, with human error causing a substantial number of collisions. This emphasizes the need for advanced collision avoidance systems to improve road safety. In response, this study integrates object detection and distance estimation techniques to enhance vehicle safety. Utilizing recent iterations of the YOLO architecture—YOLOv6n, YOLOv8n, YOLOv9t, and YOLOv10n—the models were trained and evaluated on the KITTI dataset, a benchmark suite for driving applications. This research study provides a comparative analysis of these models, focusing on performance metrics such as precision, mean average precision, recall, and F1 score. A novel single-camera distance estimation method was also implemented to assess object proximity accurately. Among the evaluated models, YOLOv8n achieved the best results, with a F1 score of 0.74 and a mAP@0.5 of 0.782, demonstrating superior detection accuracy and reliability. Its swift convergence and low training losses make it well-suited for real-world use cases. While YOLOv9t and YOLOv10n also performed well, they were slightly less effective. The system enhances its capabilities by accurately estimating distances, which improves collision avoidance and road safety applications. Overall, YOLOv8n is the most efficient and reliable option for developing advanced safety features in vehicle systems. © 2025 IEEE.

关键词： Vehicle safety

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Leveraging Artificial Neural Networks for Breast Cancer Detection and Prognosis

Leveraging Artificial Neural Networks for Breast Cancer Dete...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Goel, Aadya Mishra, Pallavi Bhatia, Rachna School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Advanced Sciences Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331512965

Breast cancer remains one of the leading causes of mortality among women worldwide, making early detection and accurate classification critical for improving patient out-comes. This study explores the application of Artificial Neural Networks (ANN) for breast cancer multiclass classification and prediction using the CBIS-DDSM: Breast Cancer Image Dataset. The model was trained to classify mammographic images into three categories: benign, malignant, and benign without callback, achieving an impressive accuracy of 99.6%. The choice of ANN was driven by its proven capability to model complex, nonlinear relationships within data, offering a significant improvement over traditional machine learning technique. Despite the challenges posed by the limited size and heterogeneity of the dataset, the ANN demonstrated robust performance, highlighting its potential for clinical applications. The high accuracy of the model underscores its value as a reliable tool for aiding radiologists in the early detection and prognosis of breast cancer, which could lead to more effective treatment planning and better patient outcomes. This study lays the foundation for integrating advanced AI technologies into clinical settings, offering promise for more precise, timely breast cancer diagnoses. © 2025 IEEE.

关键词： Lung cancer

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