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5th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Bharathi Mohan, G. Kumar, R. Prasanna Elakkiya, R. Veer, V. Druva Amrita Vishwa Vidyapeetham School of Computing Computer Science and Engineering Chennai India Institute of Technology and Science School of Computing Computer Science and Engineering Birla Dubai345055 United Arab Emirates

ISBN: (纸本)9798350305449

Duplicate question detection is a crucial task in question-and-answer platforms, aiming to improve user experience and minimize redundancy. This project utilizes NLP techniques and machine learning models to predict whether a pair of questions are duplicates accurately. The project involves comprehensive data pre-processing, including tokenization, stopword removal, and lemmatization, to prepare the textual data for analysis. Here we are using various feature engineering techniques, such as bag-of-words, TF-IDF, and word embedding, are applied to extract meaningful representations of the questions. Multiple machine learning models, including Logistic Regression, Random Forest, SVM, XGBoost, and a Combined model, are trained on labeled data. Evaluation metrics focus on precision and recall minimizing misclassification costs. New sample data is used to validate the models. Predictions are made to classify question pairs as duplicates or non-duplicates, showcasing the model's generalization capabilities. The results highlight the potential of NLP techniques and machine learning models in accurately identifying duplicate question pairs, improving knowledge sharing, and reducing redundancy. This project provides valuable insights and methodologies for future research in duplicate question detection, contributing to the field of NLP and enhancing user experiences on question-and-answer platforms. © 2023 IEEE.

关键词： Embeddings

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Deep Convolutional Neural Networks for Early-Stage Detection and Prognostication of Lung and Colon Cancer 2

Deep Convolutional Neural Networks for Early-Stage Detection...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Laxmikant, Kumar Arthi, A. Vinodhini, V. Natarajan, B. Bhuvaneswari, R. Selvam, Prabu Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Karpagam College of Engineering Computer Science and Technology Coimbatore India University College of Engineering Department of Electronics and Communication Engineering Ariyalur India VIT University School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350317558

Lung and colon cancers are significant health issues across the world, prompting the need for inventive methods of diagnosis. This study takes the lead in introducing advanced Deep ConvNets (CNNs) to enhance the accuracy of early detection. The model is trained on extensive datasets, resulting in impressive outcomes. During training, it achieves an accuracy of 92.54% and a loss value of 0.0161. These results are mirrored in testing, where the accuracy reaches 96.59% and the loss value is 0.0441. Beyond just numerical achievements, this research revolutionizes cancer diagnostics by providing a sophisticated tool for personalized and early detection. This study surpasses the mere influence on individuals and extends its reach to bring hope for society and the economy by tackling the pervasive weight of cancer. It distinguishes itself in the realm of advanced medical investigation, affirming the efficacy of the model and inspiring additional inquiry, encompassing the utilization of more extensive sets of data and implementation in diverse medical domains. © 2024 IEEE.

关键词： Colon Cancer Convolutional Neural Network Lung Cancer

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Deep Learning for Wind Speed Forecasting Using Bi-LSTM with Selected Features

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Intelligent Automation & Soft computing 2023年第3期35卷 3829-3844页

作者： Siva Sankari Subbiah Senthil Kumar Paramasivan Karmel Arockiasamy Saminathan Senthivel Muthamilselvan Thangavel Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management StudiesChittoor517127India School of Information Technology&Engineering Vellore Institute of TechnologyVellore632014India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India School of Computing College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India

Wind speed forecasting is important for wind energy *** the modern era,the increase in energy demand can be managed effectively by fore-casting the wind speed *** main objective of this research is to improve the performance of wind speed forecasting by handling uncertainty,the curse of dimensionality,overﬁtting and non-linearity *** curse of dimensionality and overﬁtting issues are handled by using Boruta feature *** uncertainty and the non-linearity issues are addressed by using the deep learning based Bi-directional Long Short Term Memory(Bi-LSTM).In this paper,Bi-LSTM with Boruta feature selection named BFS-Bi-LSTM is proposed to improve the performance of wind speed *** model identiﬁes relevant features for wind speed forecasting from the meteorological features using Boruta wrapper feature selection(BFS).Followed by Bi-LSTM predicts the wind speed by considering the wind speed from the past and future time *** proposed BFS-Bi-LSTM model is compared against Multilayer perceptron(MLP),MLP with Boruta(BFS-MLP),Long Short Term Memory(LSTM),LSTM with Boruta(BFS-LSTM)and Bi-LSTM in terms of Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Square Error(MSE)and *** BFS-Bi-LSTM surpassed other models by producing RMSE of 0.784,MAE of 0.530,MSE of 0.615 and R2 of *** experimental result shows that the BFS-Bi-LSTM produced better forecasting results compared to others.

关键词： Bi-directional long short term memory boruta feature selection deep learning machine learning wind speed forecasting

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Explaining Parkinson's Disease Detection by Ensemble Classifiers with Local Interpretable Features 8th

Explaining Parkinson's Disease Detection by Ensemble Classif...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Karn, Alok Kumar Dixit, Aniket Pati, Peeta Basa Mahalingam, Anand Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031736162

Parkinson's disease (PD) is a neurodegenerative condition and a progressive nervous system disorder condition, marked by its characteristic symptoms such as tremors, muscle stiffness, and slow, imprecise body movements. Typically affecting the middle-aged and elderly, the most crucial challenge in the treatment of PD is posed in the form of its early diagnosis. While there is no cure for the disease, early intervention is found to have alleviated the symptoms and to have also improved the overall quality of life, potentially slowing down the disease’s progression. With the rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML), almost every industry seems to be benefiting from its incorporation and the field of biomedical sciences is no different. AI-ML techniques have seemed to be specifically beneficial in classification-based biomedical problems. This research is built upon this very same idea. The application of 14 ML algorithms is studied on a dataset that consists of vocal characteristics of several individuals, both the healthy and the PD patients, to classify based on a patient’s speech whether or not, they have Parkinson’s. To improve the overall performance of the algorithms, SMOTE and PCA pre-processing techniques have also been applied to deal with the dataset’s shortcomings. The study observes that Random Forest, Gaussian Process, Extra Trees, and CatBoost are the 4 best performing algorithms for the task, providing an accuracy of 96.61%. The paper concludes with the deployment of an Explainable AI (XAI) LIME module on the best-performing algorithms to provide transparency and interpretability to the decision-making process of these AI systems. © IFIP International Federation for Information Processing 2025.

关键词： Neurodegenerative diseases

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A Comparative Exploration of Time Series Models for Wild Fire Prediction 4

A Comparative Exploration of Time Series Models for Wild Fir...

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4th International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Sri Sowmya, V. Sasikala, D. Theetchenya, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350343670

Wildfires pose a great threat to human safety and property arising from both natural and human causes. According to a technical assessment by the Forest Survey of India more than 95% of fires are of anthropogenic origin. Wildfires erupt due to burning of fossils by local communities for crop rotation, camp fires without proper supervision etc. Climate change further elevates the risk by fostering dry conditions. Traditionally, wildfire prediction relied on statistical models and expert judgment. However, the emergence of Machine Learning (ML) and Deep Learning (DL) techniques has significantly improved the accuracy of forest fire prediction. The objective of this work is to prevent wildfires and save the ecosystem. In this work, LightGBM(Light Gradient Boosting Machine) and LSTM(Long Short-Term Memory) machine learning models are utilized to predict the forest fire. Both models exhibit high F1 scores of 97% and 95% accuracy in forest fire prediction, enabling the development of reliable prediction systems. The results of these ML-based models may aid in identifying highrisk areas, optimizing prevention measures, refining evacuation plans, and guiding firefighting efforts. © 2024 IEEE.

关键词： Deep Learning(DL) LightGBM LSTM Machine learning(ML) Wildfires

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Real-Time Traffic Prediction And Optimization using Machine Learning 3

Real-Time Traffic Prediction And Optimization using Machine ...

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

作者： Vineetha, Kundrapu Tharun Reddy, M. Jaffer Ali, Md. Prathima, Narisetty Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The Real-Time Traffic Prediction and Optimization System may be described as enriched, spacious, algorithm-oriented, and designed to help contribute to the anti-symptomatic deprievement of increasingly congested urban traffic streams. The system with the help of integrating real time data of numerous sources like GPS tracker, traffic sensor as well as weather API, the system using advanced machine learning algorithm in order to predict traffic accurately and optimize routes based on it. This predictive capability then greatly improves the flow of vehicles, the time taken, and the occurrence of choke points. The structure of the system is fully based in the cloud, which makes it easy to scale and very compatible with other systems to amalgamate a vast array of data inputs with high dependability. Fully customizable and intuitive control panel gives live view of traffic, offers actionable stats, and instant control mechanisms to traffic operators to make correct decisions quickly. Congestion in urban areas has an influence on time taken to cover a certain distance, fuel consumption and emission to the atmosphere. On the basis of the above analysis, this work extends a Real-Time Traffic Prediction and Optimization System (RTPOPS) using machine learning approach to predict the traffic density and to further optimize traffic signals and routes. The used system demonstrated successful management and reduction of vehicle density by 30%, and increasing speed limits by 18% and proved that Intelligent Urban Mobility has the potential to be achieved. © 2025 IEEE.

关键词： Traffic congestion

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Media Power Measuring via Emotional Contagion

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Journal of Social computing 2024年第1期5卷 15-35页

作者： Xue Lin Hong Huang Zongya Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China School of Journalism Huazhong University of Science and TechnologyWuhan 430074China

Media power,the impact that media have on public opinion and perspectives,plays a significant role in maintaining internal stability,exerting external influence,and shaping international dynamics for nations/***,prior research has primarily concentrated on news content and reporting time,resulting in limitations in evaluating media *** more accurately assess media power,we use news content,news reporting time,and news emotion simultaneously to explore the emotional contagion between *** use emotional contagion to measure the mutual influence between media and regard the media with greater impact as having stronger media *** propose a framework called Measuring Media Power via Emotional Contagion(MMPEC)to capture emotional contagion among media,enabling a more accurate assessment of media power at the media and national/regional *** also interprets experimental results through correlation and causality analyses,ensuring *** analyses confirm the higher accuracy of MMPEC compared to other baseline models,as demonstrated in the context of COVID-19-related news,yielding compelling and interesting insights.

关键词： explainability media power emotional contagion

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Towards Blockchain-Based Secure BGP Routing,Challenges and Future Research Directions

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computers, Materials & Continua 2024年第5期79卷 2035-2062页

作者： Qiong Yang Li Ma Shanshan Tu Sami Ullah MuhammadWaqas Hisham Alasmary Faculty of Information Technology Beijing University of TechnologyBeijing100124China School of Information Science and Technology North China University of TechnologyBeijing100144China Department of Computer Science Shaheed Benazir Bhutto UniversitySheringal18050Pakistan School of Computing and Mathematical Science Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerth6027Australia Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Border Gateway Protocol(BGP)is a standard inter-domain routing protocol for the Internet that conveys network layer reachability information and establishes routes to different *** BGP protocol exhibits security design defects,such as an unconditional trust mechanism and the default acceptance of BGP route announcements from peers by BGP neighboring nodes,easily triggering prefix hijacking,path forgery,route leakage,and other BGP security ***,the traditional BGP security mechanism,relying on a public key infrastructure,faces issues like a single point of failure and a single point of *** decentralization,anti-tampering,and traceability advantages of blockchain offer new solution ideas for constructing secure and trusted inter-domain routing *** this paper,we summarize the characteristics of BGP protocol in detail,sort out the BGP security threats and their ***,we analyze the shortcomings of the traditional BGP security mechanism and comprehensively evaluate existing blockchain-based solutions to address the above problems and validate the reliability and effectiveness of blockchain-based BGP security methods in mitigating BGP security ***,we discuss the challenges posed by BGP security problems and outline prospects for future research.

关键词： BGP security blockchain prefix hijacking trust model secure routing

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Exploring the Cut-Off Between Energy Balance and Delay in In-Network Data Aggregation (I-NDA) for Wireless Sensor Networks 2

Exploring the Cut-Off Between Energy Balance and Delay in In...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kumar, Anuj Kant Agrawal, Krishna School of Computing Science & Engineering Galgotias University Greater Noida India Ajay Kumar Garg Engineering College Department of Computer Science Engineering Ghaziabad India School of Computing Science & Engineering Galgotias University Yamuna Expressway Gautam buddh Nagar Greater Noida India

ISBN: (纸本)9798350371055

In WSN, data aggregation is an important part of the data collection process. It is used to reduce network traffic and reduce energy consumption. However, this process comes with a cut-off between energy balance and delay. This cut-off is due to the competing interests of the nodes in the network: the sensors require energy to transmit data, while the sinks require low delays in data collection. In order to maintain energy balance and reduce delay, the system must select suitable methods of data aggregation that are both energy-efficient and low-delay. To reduce energy consumption, the system can use energy-conserving data aggregation protocols, such as clustering and tree-structured routing. To reduce delay, they system can use low-delay data aggregation techniques, such as time synchronization and distributed aggregation. By understanding the cut-off between energy balance and delay in I-NDA, it is possible to make informed decisions on the selection of suitable protocols and techniques to ensure energy-efficiency and low-delay data collection. It is important to select the right set of protocols and techniques to balance the efficiency and delay parameters. © 2024 IEEE.

关键词： clustering cut-off data networks sensor traffic wireless

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Machine Learning for Malware Detection: A Comprehensive Review of Visual, Textual and Network Traffic Features

Machine Learning for Malware Detection: A Comprehensive Revi...

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2024 International Conference on computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Jolly, Jasmine Ananthanarayanan, V. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Campus Coimbatore641112 India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore641112 India

ISBN: (纸本)9798331510299

IoT devices, constrained by limited resources and weak security measures, are highly vulnerable to malware at- tacks. This review examines malware detection methods using textual, visual, and network traffic features, with experiments fo- cused on network traffic data. Feature extraction techniques such as correlation analysis, eXtreme Gradient Boosting(XGBoost), and the Firefly algorithm were applied to machine learning models, including logistic regression, random forest, Adaptive Boosting(AdaBoost), and perceptron. Results highlight the poten- tial of these approaches for resource-constrained environments. © 2024 IEEE.

关键词： Adaptive boosting

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