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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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Effective Adaptable Framework for Security of IoT Devices 3

Effective Adaptable Framework for Security of IoT Devices

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Sharma, Cheshta Sharma, Tripti Shri Venkateshwara University Department of Computer Science and Engineering Gajraula India Inderprasth Engineering College Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798331528201

The Internet of Things (IoT) becomes the most demanding technology over the past few years. IoT is the basic need of daily life. The security of these devices is very important because they contain large files of sensitive data, the exposure of these devices can be fatal. To ensure the security of different types of IoT devices, we have designed a problem for optimization of weights in this work. The operational goal of this work is to protect different IoT devices alogwith determining a balance between resource and device usage. This framework includes resources and transfers the requirements of heterogeneous IoT devices and modifies different image and implementations using Hungarian algorithm. To attain the objective, five different ways are described to solve the Advanced Encryption Standard (AES). Extensive testing is done to find the best trade with different stocks and weights. The proposed framework is also compared to random greedy methods. The benchmark results show that the proposed system has been modified to secure the system based on IoT while offering better performance and results. Compared to the random method, the proposed method yielded an average of 3% and 13% improvement and in 11% and 17% greedy approach and resource utilization. © 2024 IEEE.

关键词： Encryption algorithms

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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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Generative AI based Secure Wireless Sensing for ISAC Networks

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5195-5210页

作者： Wang, Jiacheng Du, Hongyang Liu, Yinqiu Sun, Geng Niyato, Dusit Mao, Shiwen Kim, Dong In Shen, Xuemin Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Jilin University College of Computer Science and Technology 130012 China Auburn University Department of Electrical and Computer Engineering Auburn United States Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of University of Waterloo Department of Electrical and Computer Engineering Canada

Integrated sensing and communications (ISAC) is one of the crucial technologies for 6G, and channel state information (CSI) based sensing serves as an essential part of ISAC. However, current research on ISAC focuses mainly on improving sensing performance, overlooking security issues, particularly the unauthorized sensing of users. Hence, this paper proposes a diffusion model based secure sensing system (DFSS). Specifically, we first propose a discrete conditional diffusion model to generate graphs with nodes and edges, which guides the ISAC system to appropriately activate wireless links and nodes, ensuring the sensing performance while minimizing the operation cost. Using the activated links and nodes, DFSS then employs the continuous conditional diffusion model to generate safeguarding signals, which are next modulated onto the pilot at the transmitter to mask fluctuations caused by user activities. As such, only authorized ISAC devices with the safeguarding signals can extract the true CSI for sensing, while unauthorized devices are unable to perform the effective sensing. Experiment results demonstrate that DFSS can reduce the activity recognition accuracy of the unauthorized devices by approximately 70%, effectively shield the user from the illegitimate surveillance. © 2005-2012 IEEE.

关键词： Sensors Wireless communication Diffusion models Wireless sensor networks Communication system security Performance evaluation Security Integrated sensing and communication Fluctuations Accuracy

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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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Hybrid technique for lung disease classification based on machine learning and optimization using X-ray images

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Multimedia Tools and Applications 2024年 1-23页

作者： Poloju, Naresh Rajaram, A. Department of Computer Science and Engineering E.G.S Pillay Engineering College Tamilnadu Nagapattinam611002 India Department of Electronics and Communication Engineering E.G.S Pillay Engineering College Tamilnadu Nagapattinam611002 India

The remarkable growth of machine learning has shown that it can perform at an expert level in a number of difficult tasks, such as medical decision-making and image processing. The goal of this research is to take advantage of this potential by creating a new and precise technique for chest X-ray analysis-based lung disease identification. The goal of this work is to research the methods currently used for creating the architecture of machine learning and create our own model, which will then be used for the best possible detection of lung illness using digital medical (X-ray) pictures. Therefore, the focus of this work is on employing various machine learning approaches to diagnose lung illness. The proposed methodology's optimization section consists of 2 primary phases. In the first phase, bagging, voting ensemble learning and boosting techniques are utilized to enhance performance of the support vector machine (SVM), K-nearest neighbours (KNN), and naïve Bayes (NB) algorithms. Emperor Penguin Optimization (EPO) was then used to the system to optimise the parameters and improve the overall system performance. The outcome shows the efficacy of the proposed methodology that compared to other hybrid techniques, the combination of ensemble learning and EPO with SVM produced the highest accuracy of 97.50%. This result suggests that the proposed system has the potential to greatly increase the precision of conventional machine learning methods in the identification of lung illness using chest X-rays. This development may lead to more accurate diagnoses and better patient outcomes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image classification

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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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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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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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