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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Koilery, Shriya Sanjay Narikutty Muhammad, Ridha Karalakath, Nabeel Faisal Payyani Rasheed, Azeem A. Revathi, M. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

Electric vehicles (EVs) have emerged as a quick solution and eco-friendly alternative to internal combustion engine vehicles (ICEVs), offering fewer emissions and better energy consumption. Lithium-ion batteries are used to give power to electric vehicles, but their production and disposal add a lot to the upstream Carbon Dioxide (CO2) emissions, which is often otherwise, neglected by the society while considering EV as a 0 to minimal CO2 emission substitute to fossil fuels. This is where a proper understanding of EV's life-cycle emissions is necessary. This paper proposes a novel approach to estimating the CO2 emissions of an EV considering its life-cycle emission, addressing limitations in the already available datasets by creating a new and comprehensive dataset. Four machine learning models, i.e., Linear Regression, Random Forest, Support Vector Regression (SVR) and Bi-LSTM, are evaluated for their accuracy in predicting emissions. These results indicate that Linear Regression and SVR struggled with the dataset's non-linearity, whereas Random Forest performed well but faced issues with unnecessary data. The Bi-LSTM model was the most effective, offering the best accuracy and efficiency in the prediction of CO2 emission by making use of long-term dependencies. The research emphasizes the importance of considering the entire life-cycle emissions of EVs to assess their impact on the environment and highlights Bi-LSTM's efficiency in accurately predicting CO2 emissions. © 2024 IEEE.

关键词： Low emission

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Face Identification of Suspects Using Sequential -Deep Convolutional Neural Network 2

Face Identification of Suspects Using Sequential -Deep Convo...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Munusamy, Vaishnavi Senthilkumar, Sudha School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

In terms of increasing terrorism, criminal behaviour, and anti-social events, there has been a need for safety systems to identify criminals. Face Recognition is one of the vibrant technologies that are very useful for person identification, especially in criminal investigation. Convolutional layers are used in our proposed system for feature extraction, and dense layers are used for classification. ReLU activation functions use four convolutional layers with 3 × 3 kernels and 128/64 filters to add non-linearity. Carefully placing dropout layers prevents overfitting, and Max Pooling layers down sample spatial dimensions. The model produces encouraging outcomes on a customized dataset comprising 4288 photos that capture different angles and concealed/hidden images of four subjects. © 2024 IEEE.

关键词： Face recognition

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Factors affecting forensic electric network frequency matching-A comprehensive study

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Digital Communications and Networks 2024年第4期10卷 1121-1130页

作者： Guang Hua Qingyi Wang Dengpan Ye Haijian Zhang Guoyin Wang Shuyin Xia School of Electronic Information Wuhan UniversityWuhan430072China Institute for Infocomm Research(I2R) Agency for ScienceTechnology and Research(A*STAR)Singapore138632Singapore School of Cyber Science and Engineering Wuhan UniversityWuhan430072China School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China

The power system frequency fluctuations could be captured by digital recordings and extracted to compare with a reference database for forensic timestamp *** is known as the Electric Network Frequency(ENF)criterion,enabled by the properties of random fluctuations and intra-grid *** essence,this is a task of matching a short random sequence within a long reference,whose accuracy is mainly concerned with whether this match could be uniquely *** this paper,we comprehensively analyze the factors affecting the reliability of ENF matching,including the length of test recording,length of reference,temporal resolution,and Signal-to-Noise Ratio(SNR).For synthetic analysis,we incorporate the first-order AutoRegressive(AR)ENF model and propose an efficient Time-Frequency Domain noisy ENF synthesis ***,the reliability analysis schemes for both synthetic and real-world data are respectively *** a comprehensive study,we quantitatively reveal that while the SNR is an important external factor to determine whether timestamp verification is viable,the length of test recording is the most important inherent factor,followed by the length of ***,the temporal resolution has little impact on ***,a practical workflow of the ENF-based audio timestamp verification system is proposed,incorporating the discovered results.

关键词： Digital forensics Audio forensics Data authentication Timestamp verification Electric network frequency criterion

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SQL Injection Testing on Website using Sqlmap 2

SQL Injection Testing on Website using Sqlmap

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Subbulakshmi, T. Josiga, S. Brindha, S. Jishnu, S. Kumar, Adityan Sunil Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

Websites handle the data of the webpages by storing them into a database and reading from it whenever needed. The data may include the content of the webpages or the users who use the website. Security of this data is one of the biggest concerns of websites. The websites need to make sure that the data does not fall into wrong hands. It needs to safeguard the private information of the users and prevent any type of leakage of information due to unauthorized access. In this article, SQL injection testing of a website is done using SQLMAP tool. © 2024 IEEE.

关键词： Information leakage

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Digital Essay Assessment using Machine Learning Algorithms 9

Digital Essay Assessment using Machine Learning Algorithms

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9th International Conference on science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Johnsi, R. Kumar, G. Bharadwaja School of Computer Science And Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350365092

Essay are considered as the most prominent way in assessing the student performance. Manual grading of the essay are considered to be a hectic tasks for both students and instructors due to various reasons such as time consuming, prone to subjectivity, inconsistencies and lack of reliability may be influenced by biases, such as prior knowledge of the student or their back ground. The solution to all these problems has been rectified by using Automated Essay Scoring (AES) system. This technical paper proposes a novel AES system that utilizes various Machine Learning (ML) algorithms such as Linear Regression, Lasso Regression, Ridge Regression, Gradient Boosting Regression, Bayesian Regression, Elastic Net Regression, Random Forest Regression, Decision Tree Regression, Gaussian Process Regression, Support Vector Regression, Polynomial Regression, XG Boost regression and XGBoost with Polynomial Features and discovered that XGBoost with Polynomial features achieved the best results with an average Quadratic Weighted Kappa (QWK) value of 0.68. © 2024 IEEE.

关键词： Text processing

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An Enhanced Feature Fusion Network Model for Cervical Cancer Prediction

An Enhanced Feature Fusion Network Model for Cervical Cancer...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Pragatheeswari, B. Mary, Jameela Priya, S. Roshni Dhivyaa, C.R. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331540821

Healthcare providers and researchers that work with patients who have cervical cancer face a significant challenge because it is one of the world's most common causes of death. Worldwide, cervical cancer is one of the major causes of death among women according to the World Health Organization (WHO). Pap smear, also called the Pap Test, is used to examine the cells present in the cervix for abnormal cells. However, manual examination of these pap smears is prone to human error. This work aims to explore the use of deep learning models to detect cancer from pap smear images and develop an ensemble model to improve the accuracy. Our proposed model uses pretrained CNN models through transfer learning to extract features, perform feature concatenation followed by dimensionality reduction and hyperparameter tuning, which are then fed into a neural network for multiclass classification. Hyperparameters are fine-tuned with the help of Bayesian optimization to improve the model's performance. Our proposed model has achieved an accuracy of 96.4%, sensitivity of 96.41% and specificity of 96.66%. © 2024 IEEE.

关键词： Dimensionality reduction

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Dual-Model Approach for Diabetic Retinopathy and Macular Edema Detection

Dual-Model Approach for Diabetic Retinopathy and Macular Ede...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Micheal, A. Ancy Sai, Ladi Jeevan Vellore Institute of Technology Chennai School of Computer Science and Engineering India

ISBN: (纸本)9798350378092

Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) are major causes of vision loss in diabetic patients. Early detection is crucial for preventing permanent vision impairment. This research paper proposes a novel dual-model architecture for detecting Diabetic Retinopathy and Diabetic Macular Edema. The proposed framework integrates preprocessing techniques such as circular cropping and Ben's preprocessing, followed by data augmentation. The Diabetic Retinopathy model is trained on the APTOS2019 dataset, while the Diabetic Macular Edema model is trained using the IDRid dataset. InceptionV3 outperforms other models with an accuracy of 88% for Diabetic Retinopathy classification, while MobileNetV2 outperforms other models with an accuracy of 83% for DME classification. The dual-model architecture enables simultaneous evaluation of retinal images for DR and DME diseases. The Experimental results demonstrate promising outcomes, suggesting the potential effectiveness of the proposed dual-model architecture in diagnosing diabetic eye diseases. © 2024 IEEE.

关键词： Ophthalmology

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A Novel RoPE-Enhanced Informer Approach for Accurate Remaining Useful Life Prediction of Lithium-Ion Batteries

A Novel RoPE-Enhanced Informer Approach for Accurate Remaini...

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2024 IEEE Conference on engineering Informatics, ICEI 2024

作者： Nair, Aayush Bhatnagar, Shashwat Srinivasan, Kathiravan Manoov, R. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798331505776

Lithium-ion batteries are integral to various systems, including electric vehicles and warehouses, where estimating their operational lifespan is critical. This lifespan, known as Remaining Useful Life (RUL), helps in optimizing performance and maintenance. Accurate RUL prediction helps avoid unexpected breakdowns and lowers maintenance costs, making these systems more reliable. This paper introduces a novel approach using a Rotary Positional Embedding (RoPE) Enhanced Informer model to address challenges in long-term time series forecasting for RUL prediction. By integrating RoPE into the Informer, the model efficiently preserves positional information and leverages sparse attention mechanisms to reduce computational complexity, enabling it to handle longer sequences with minimal memory usage. The proposed model was tested on benchmark datasets, where it outperformed peer level approaches in terms of Mean Absolute Error and Root Mean Squared Error. The results show a significant reduction in validation errors, indicating the model's superior generalization ability and its robustness against overfitting. This approach offers a scalable solution suitable for real-time predictive maintenance, particularly in resource-constrained environments. The combination of advanced positional encoding with the Informer architecture demonstrates the potential for this method to enhance battery management and improve the accuracy of RUL prediction across various industrial applications. © 2024 IEEE.

关键词： Lithium-ion batteries

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AoI-Aware Optimization in RIS-Assisted mmWave Network 10

AoI-Aware Optimization in RIS-Assisted mmWave Network

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10th International Conference on Web Research, ICWR 2024

作者： Asadi, Somayeh Sheikhi, Marzieh Hakami, Vesal Iran University of Science and Technology School of Computer Engineering Tehran Iran

ISBN: (纸本)9798350394986

In the era of next-generation wireless networks, real-time applications demand freshness in information delivery, notably in IoT, cyber-physical systems, and industrial IoT. We target the optimization of average Age of Information (AoI) in a multi-user RIS-assisted mmWave network, where the sensitivity of mmWave bands to propagation challenges is mitigated through reconfigurable intelligent surfaces (RIS). Notably, prior studies have overlooked AoI optimization in RIS-enhanced mmWave networks. Our contribution lies in devising a novel Markov Decision Process (MDP) framework and a model-free Deep Reinforcement Learning (DRL) algorithm tailored to high-dimensional action spaces. Our approach orchestrates user transmission timing, power allocation, base station beamforming, and RIS reflective coefficient adjustments. Through extensive simulations, we demonstrate the superiority of our method over existing schemes, showcasing enhanced network performance. This work represents an advancement in the optimization of AoI within the context of RIS-assisted mmWave communications, shedding light on efficient control policy learning for improved network operation. © 2024 IEEE.

关键词： Reinforcement learning

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Pose Prediction for Six-axis Industrial Robots Based on Spatio - Temporal Dense Graph Convolutional Networks 5

Pose Prediction for Six-axis Industrial Robots Based on Spat...

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5th International Symposium on computer engineering and Intelligent Communications, ISCEIC 2024

作者： Fu, Honggang Chen, Deng School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331518677

Pose prediction can timely determine whether the posture of industrial robots is abnormal, thereby reducing the harm caused by abnormal actions of industrial robots. The pose prediction task requires modeling the complex dynamics of industrial robots, so it is necessary to effectively understand the complex spatio-temporal joint dynamics of industrial robot postures. This process requires considering the nonlinear relationships and coupling effects between joints, making this task very challenging. This paper proposes a pose prediction method suitable for six-axis industrial robots, achieving the interaction between space and time. Finally, in the decoding layer, we use a temporal convolution (TCN) to decode the spatio-temporal features of historical frame poses to predict future frame poses. Through long-term prediction MPJPE (mean per joint position error) experiments and long-term pose prediction visualization experiments on a self-made six-axis industrial robot dataset, the average MPJPE for short-term predictions is 7.1 and the average MPJPE for more difficult long-term pose predictions is 11.4. In the long-term prediction visualization experiments, we can accurately predict the long-term future poses. © 2024 IEEE.

关键词： Industrial robots

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