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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Saranya, S.S. Kavitha, M.N. Pragatheeswari, E. Dharani, S.A. Selya dharsnee, M. Selya varsnee, M. Kongu Engineering College Department of Computer Science and Engineering Perundurai India Kongu Engineering College Department of Computer Technology-UG Perundurai India

ISBN: (纸本)9798350385793

Providing students with a top-notch education is the main goal of educational establishments. This study intends to use a variety of data mining tools in order to evaluate their effectiveness and determine the impact of different factors on students' academic performance. The res ults of this study establish the groundwork for well-informed decision-making for educators, parents, and legislators by advancing our understanding of the complex interactions between social media and academia. Through identifying plausible positive or negative correlations between social media engagement and academic performance, this research contributes to the development of tactics to optimize the advantages of digital platforms while reducing inherent risks. To examine the dataset, a variety of classification algorithms were used, such as decision trees, random forests, SVM classifiers, and LR classifiers. © 2024 IEEE.

关键词： Support vector machines

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Learning continuous network emerging dynamics from scarce observations via data-adaptive stochastic processes

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science China(Information sciences) 2024年第12期67卷 240-255页

作者： Jiaxu CUI Qipeng WANG Bingyi SUN Jiming LIU Bo YANG College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Public Computer Education and Research Center Jilin University Department of Computer Science Hong Kong Baptist University

Learning network dynamics from the empirical structure and spatio-temporal observation data is crucial to revealing the interaction mechanisms of complex networks in a wide range of domains. However,most existing methods only aim at learning network dynamic behaviors generated by a specific ordinary differential equation instance, resulting in ineffectiveness for new ones, and generally require dense *** observed data, especially from network emerging dynamics, are usually difficult to obtain, which brings trouble to model learning. Therefore, learning accurate network dynamics with sparse, irregularly-sampled,partial, and noisy observations remains a fundamental challenge. We introduce a new concept of the stochastic skeleton and its neural implementation, i.e., neural ODE processes for network dynamics(NDP4ND), a new class of stochastic processes governed by stochastic data-adaptive network dynamics, to overcome the challenge and learn continuous network dynamics from scarce observations. Intensive experiments conducted on various network dynamics in ecological population evolution, phototaxis movement, brain activity, epidemic spreading, and real-world empirical systems, demonstrate that the proposed method has excellent data adaptability and computational efficiency, and can adapt to unseen network emerging dynamics, producing accurate interpolation and extrapolation with reducing the ratio of required observation data to only about 6% and improving the learning speed for new dynamics by three orders of magnitude.

关键词： complex networks network dynamics emerging spatio-temporal dynamics neural processes

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An AI-Based Intrusion Prevention System to Enhance Cloud Security 4

An AI-Based Intrusion Prevention System to Enhance Cloud Sec...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sarumathy, P. Rajasree, S. Chandrasekar, A. St. Joseph's College of Engineering Computer Science and Engineering Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523923

Data breaches have become increasingly prevalent in cloud environments, demanding robust security measures. In an era where data breaches pose significant threats to organizational security and individual privacy, the protection of sensitive data has become paramount. A comprehensive strategy is proposed for analyzing sensitive data and preventing intrusions using a multi-modal framework. The methodology integrates AI techniques, Long Short-Term Memory (LSTM) networks for classification of data as sensitive/non-sensitive and Parameter Efficent Fine-Tuning(PEFT) for categorization of sensitive data. Additionally, organization-specific regular expression (regex) pattern matching algorithms are utilized. Together, these components form an Intrusion Prevention System (IPS) capable of quickly identifying and alerting against unauthorized changes, with a focus on detecting sensitive data. The system also addresses pivot attacks, where attackers exploit vulnerabilities to access data across servers within the same network. By representing system components and their relationships as graphs, our approach enhances the detection and prevention of such attacks. This graphical representation facilitates a better understanding of component relationships and helps identify redundant storage elements, optimizing resource allocation. Our rigorous evaluation demonstrates the effectiveness of our approach in mitigating data breach risks and protecting sensitive information. © 2025 IEEE.

关键词： Technological innovation Sentiment analysis Prevention and mitigation Cloud computing security Data breach IP networks Servers Resource management Pattern matching Long short term memory

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An Automated Bird Species Classification Using Deep Learning 2

An Automated Bird Species Classification Using Deep Learning

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Saritha, N. Shruthi Department of Computer Science and Engineering Adichuchanagiri Institute of Technology Chikkamagalur India Department of Computer Science and Engineering Navkis College of Engineering Hassan India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

Birds are nice-looking from their aesthetic looks and life styles, around 50 billion of birds are living on the earth where some birds are in threat of extinction. For people, distinguishing and classification of bird species appears to be troublesome with exposed eye. In this document, we aim to construct a deep learning-driven approach. an automated bird species identification system using convolution neural network (ResNet50V2 architecture) and with assorted dataset. This system will able to identify and classify the bird species with an accuracy of 99. ©2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

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computer Modeling in engineering & sciences 2024年第8期140卷 1339-1370页

作者： Ali S.Alghamdi Mohamed A.Zohdy Saad Aldoihi Department of Electrical Engineering College of EngineeringMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical and Computer Engineering Department Oakland UniversityRochesterMI48309USA Institute of Earth and Space Science King Abdulaziz City for Science and Technology(KACST)Riyadh11442Saudi Arabia Computer Science and Systems Engineering Institute Polytechnique de ParisPalaiseau CedexFrance

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network *** study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic *** primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss ***,a carbon tax is included in the objective function to reduce carbon *** scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal *** results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution ***,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)*** research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local *** emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

关键词： Renewable energy integration optimal power flow stochastic renewable energy sources gaussian-bare-bones levy cheetah optimizer electrical network optimization carbon tax optimization

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Prediction of Pesticide Goodness in Potato Crop Across Various Agriculture Lands Using Machine Learning Algorithm 2

Prediction of Pesticide Goodness in Potato Crop Across Vario...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Pragatheeswari, E. Saranya, S.S. Kavitha, M.N. Deepika, S. Gopihaa, S. Keerthana, M. Kongu Engineering College Department of Computer Science and Engineering Perundurai India Kongu Engineering College Department of Computer Technology - UG Perundurai India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

Agriculture as the cornerstone of food production, involves key phases like crop cultivation and animal breeding. Beyond sustenance, agriculture significantly contributes to pharmaceutical industries by producing medicinal plants and plant extracts, serving as fundamental resources for drug development. Additionally, agriculture plays a pivotal role in providing raw materials for fabric production, such as cotton, wool and leather. India is a prominent agricultural producer, focusing on food cultivation and security. Farmers use pesticides to enhance crop protection and yield. Pesticides are the composition of materials such as plant hormones that regulate the plant growth, defoliants that prevent the plants from dying due to certain factors and also other substances that improve the yield of food products. However, predicting the impact of pesticides on crop productivity, especially in diverse agricultural lands like potato cultivation, poses challenges. This study aims to predict pesticide effectiveness in potatoes using various machine learning models, including K-Nearest Neighbors, Random Forest, Support Vector Regressor, and Linear regression. By analyzing the 'Indian Crop Production' dataset, which includes crop-specific data and pesticide usage information, the research aims to provide valuable insights for informed decision-making in agriculture. © 2024 IEEE.

关键词： Crops

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Deformable Dual Graph Aggregation Transformer Convolutional Networks with Spider Wasp Optimizer for Ovarian Tumor Classification Using Magnetic Resonance Imaging

Biomedical Materials and Devices

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Biomedical Materials and Devices 2025年 1-19页

作者： Shanmugaveni, V. Jotheeswari, M. Abarnaswara, R. VijayaKumar, M. Manojanani, A. Department of Computer Science and Engineering (Cyber Security) Dr. Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Computer Science and Engineering PSNA College of Engineering and Technology Tamil Nadu Dindigul India Department of Artificial Intelligence & Data Science Dr. Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Computer Science and Engineering RVS Technical Campus Tamil Nadu Coimbatore India

The diagnosis of Ovarian Tumor (OT) remains a significant challenge as there is presently no practical non-invasive technique to determine true benign or malignant lesions before treatment. This study proposes a unique Deformable Dual Graph Aggregation Transformer Convolutional Network (DeDGATCNet) with Spider Wasp Optimizer (SWO) for the classification of ovarian tumors acquired from magnetic resonance imaging (MRI). In contrast to traditional deep learning models, DeDGATCNet + SWO directly integrates bending graph-based forming transformer convolutional learning and improves representation for better classification. The SWO is combined to optimize the parameters of the network, thereby improving its performance. The procedures include pre-processing the MRI data from the study of ovarian tumor TCGA-OV datasets with an Adaptive Bilateral Texture Filter (ABTF) for removing artifacts and normalization of intensity, accompanied by a segment using a Masked-Attention Mask Transformer (MAMT). Feature extraction is achieved through a One-Dimensional Quaternion Discrete Fourier Transform (1D-QDFT), which captures features from both the spatial and frequency domains. DeDGATCNet + SWO performance is way ahead and sets a new accuracy record of 99.9% for state-of-the-art models. This research introduces a new type of hybridization between deformable graph transformers, quaternion-based spectral analysis, and nature-inspired optimization techniques, setting non-invasive OT at a new height for classification and improving early diagnosis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Adaptive bilateral texture filter Deformable dual graph aggregation transformer convolutional networks Masked-attention mask transformer One-dimensional quaternion discrete fourier transform

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Cost Effective Decentralized Key Management Framework for IoT

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computer Systems science & engineering 2022年第6期41卷 1057-1070页

作者： Raja Lavanya K.Sundarakantham S.Mercy Shalinie Department of Computer Science and Engineering Thiagarajar College of EngineeringTamil NaduIndia

Security is a primary concern in communication for reliable transfer ofinformation between the authenticated members, which becomes more complexin a network of Internet of Things (IoT). To provide security for group communication a key management scheme incorporating Bilinear pairing technique withMulticast and Unicast key management protocol (BMU-IOT) for decentralizednetworks has been proposed. The first part of the proposed work is to dividethe network into clusters where sensors are connected to and is administered bycluster head. Each sensor securely shares its secret keys with the cluster headusing unicast. Based on these decryption keys, the cluster head generates a common encryption key using bilinear pairing. Any sensor in the subgroup candecrypt the message, which is encrypted by the common encryption key. Theremaining part focuses to reduce communication, computation and storage costsof the proposed framework and the resilience against various attacks. The implementation is carried out and results are compared with the existing schemes thathave given considerably better results. Thus, the lightweight devices of IoT canprovide efficiency and security by reducing their overhead in terms of complexity.

关键词： Bilinear pairing common encryption key key distribution

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Sustainable Energy Generation from Speed Breakers for Smart Street Lighting Application 2

Sustainable Energy Generation from Speed Breakers for Smart ...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Vijayakumar, P. Pyingkodi, M. Karuppusamy, S. Prasaath, J.M. Dhyaneshvar Jeysaravanan, S. Manojkumar, V. Kongu Engineering College Department of Computer Applications Perundurai Erode India Nandha Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350368413

This research explores the potential of integrating rack and pinion mechanisms with electromagnetic induction to generate electricity for street light control systems. By converting mechanical energy into electrical energy, this approach offers a sustainable and environmentally friendly solution. The proposed system utilizes Light Dependent Resistors (LDRs) and a Raspberry Pi microcontroller to dynamically control street lights based on ambient light conditions. This intelligent control strategy optimizes energy consumption and reduces operational costs. This research study analyzes the theoretical foundations, design considerations, and implementation methodologies of the proposed system. Experimental validation demonstrates the feasibility and effectiveness of the approach. By promoting the use of renewable energy sources and reducing reliance on conventional power grids, this innovative solution contributes to the development of sustainable and resilient urban infrastructure. © 2024 IEEE.

关键词： Proportional control systems

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Road Extraction From Remote Sensing Images Using U-Net

Road Extraction From Remote Sensing Images Using U-Net

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Sai Kumar, D. Lokesh Jyothi, V. Esther Vardhani, Chennu Meghana, Bayarla Priya, Bobbili Hema Sharika, Alladi Computer Science And Engineering Pvp Siddhartha Engineering College Vijayawada India Vr Siddhartha Engineering College Department Of Computer Applications Vijayawada India

ISBN: (纸本)9798350371567

The enhancement of the transportation database through updated road networks is a critical aspect with a wide range of applications. Traditional methods rely on onsite measurements or human interpretation of remote sensing images for mapping purposes. However, these methods have significant drawbacks, including high expenses, lengthy processing times, and a substantial workforce requirement. To overcome this challenge, we propose a method leveraging the U-Net architecture for road extraction from multispectral satellite images. The satellite imagery used is sampled from the DigitalGlobe Images dataset, covering images captured over Thailand, Indonesia, and India, with a ground resolution of 50 cm/pixel. The U-Net model is trained on both original and augmented data, employing convolutional layers, max-pooling, and up-sampling to extract and reconstruct road masks. Performance evaluation is conducted using custom validation sets, measuring metrics such as IOU and loss. Results demonstrate the effectiveness of the proposed system, achieving high accuracy and validation scores. The extracted road networks contribute to enhancing transportation databases, offering valuable insights for urban planning and management. © 2024 IEEE.

关键词： Roads and streets

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