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检索条件"机构=Department of Computer Engineering-AI & Big Data Analytics"

共 303 条记录，以下是31-40 订阅

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Augmenting Cybersecurity in WSN: ai-Based Clone Attacks Recognition Framework 1

Augmenting Cybersecurity in WSN: AI-Based Clone Attacks Reco...

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1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Bhaskar, Seelam Ch Vijaya Kalyani, Gedela Al-Fatlawy, Ramy Alsalamy, Fatima Kurdi, Waleed Hadi Madhloom Al-Aboudy, H.M. Sujatha, G. MVSR Engineering College Department of IT Telangana Hyderabad India Karpaga Vinayaga College of Engineering and Technology Big Data Analytics Tamil Nadu Chengalpattu India The Islamic University College of Technical Engineering Department of Computer Technical Engineering Najaf Iraq Al-Mustaqbal University Medical Laboratories Techniques Department Hillah Iraq Altoosi University College Department of Computer Science Iraq Mazaya University College Department of Computer Techniques Engineering DhiQar Iraq Arunai Engineering College Department of ECE Tamil Nadu Tiruvannamalai India

ISBN: (纸本)9798350367003

Applications such as industrial automation, healthcare, and environmental monitoring need the use of wireless sensor networks (WSNs). However, due to their dispersed organizational makeup, they have become vulnerable to security risks, particularly clone assaults. To protect confidentiality, availability, and confidentiality, several attacks must be recognized and prevented. This project aims to offer an effective method for identifying and averting clone assaults. To identify cloned both nationally and internationally use a low-cost verification process. In this study, we offer a new adaptive sea-horse optimized light gradient boosting machine (ASHO-LGBM) technique for protecting the network against node identity duplicates. The ASHO approach is used in the ASHO-LGBM framework to improve the recognition accuracy of the light gradient boosting machine (LGBM) characteristics. The replications with the nodes intrusion detection (ID) are used to choose a most trustworthy communication mode. The procedure is intended to be implemented and used for gathering data through an internet component. Using a Python tool, the suggested technique is simulated and its delay, packet delivery ratio, packet drop, and energy are evaluated. When compared to other approaches, the study's results show that the ASHO-LGBM strategy's performance analysis achieves the highest accuracy rate. © 2024 IEEE.

关键词： Sensor nodes

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Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Learning Approach 12

Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Le...

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12th International Conference on Smart Grid, icSmartGrid 2024

作者： Oyucu, Saadin Sagiroglu, Seref Aksoz, Ahmet Bicer, Emre Faculty of Engineering Department of Computer Engineering Adiyaman Turkey Gazi University Artificial Intelligence and Big Data Analytics Security R&d Center Ankara Turkey Sivas Cumhuriyet University Mobilers Team Sivas Turkey Sivas University of Science and Technology Faculty of Engineering and Natural Sciences Battery Research Laboratory Sivas Turkey

ISBN: (纸本)9798350361612

Smart grids are faced with a range of challenges, such as the development of communication infrastructure, cybersecurity threats, data privacy, and the protection of user information, due to their complex structure. Another key challenge faced by smart grids is the stability issues arising from variable energy sources and consumption patterns. In these complex grid systems where energy demand and supply need to be balanced instantly, stability predictions play a significant role in foreseeing potential disruptions and optimizing energy flow. Therefore, within the scope of this study, a hybrid structure utilizing Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks is employed for stability classification to predict grid stability. This hybrid model combines the ability of RNN to recognize relationships between consecutive data points with LSTM's capability to preserve long-term dependencies. The results obtained indicate that the model exhibited stable performance with accuracy rates of 98.06% and 98.02% at 50 and 100 epochs, respectively. The findings of this study contribute valuable insights to research on the management and stability of smart grids, enabling energy systems to be operated more reliably and efficiently. © 2024 IEEE.

关键词： Smart power grids

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Improving Power Allocation and Installation in WSN Using Novel Nature-Inspired Optimization for Cyber Security Applications 1

Improving Power Allocation and Installation in WSN Using Nov...

引用

1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Balamurugan, K.S. Kalyani, Gedela Al-Fatlawy, Ramy Kurdi, Waleed Hadi Madhloom Alsalamy, Fatima Al-Aboudy, H.M. Manikandan, K. Karpaga Vinayaga College of Engineering and Technology Department of ECE Tamil Nadu Chengalpattu India Karpaga Vinayaga College of Engineering and Technology Big Data Analytics Tamil Nadu Chengalpattu India The Islamic University College of Technical Engineering Department of Computer Technical Engineering Najaf Iraq Altoosi University College Department of Computer Science Najaf Iraq Al-Mustaqbal University Medical Laboratories Techniques Department Hillah Iraq Mazaya University College DhiQar Department of Computer Techniques Engineering Iraq Sona College of Technology Department of Biomedical Engineering Tamil Nadu Salem India

ISBN: (纸本)9798350367003

Wireless sensor network (WSN) installation and power allocation optimization for cybersecurity purposes are difficult tasks which include a well-thought-out method that strikes a balance between goals such as the energy efficiency, connectivity and cyber threat resistance while improving overall system performance and cybersecurity. A novel multi-task walrus optimization (MTWO) technique is offered in this study. The installation and power allocation problem (IPAP) with several jobs is established in this research. The suggested MTWO is used to split the IPAP into numerous scalar components, which are consequently grouped and treated by their desired goals. We evaluate the proposed MTWO approach using simulations and show that it works well in practice. The findings demonstrate that power distribution and installation for cybersecurity activities on WSNs can be improved by the MTWO. The superiority of the problem specific MTWO over the MOGA has been demonstrated by simulation outcome in several network cases, offering a wide range of excellent network designs to aid in the decision-maker selection. © 2024 IEEE.

关键词： Energy efficiency

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Automated Sentiment Analysis for Web-Based Stock and Cryptocurrency News Summarization with Transformer-Based Models

Automated Sentiment Analysis for Web-Based Stock and Cryptoc...

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2023 IEEE Asia-Pacific Conference on computer Science and data engineering, CSDE 2023

作者： Hasan, Mehedi Rahman, Md. Tahmid Alavee, Kazi Ahnaf Zillanee, Abu Hasnayen Uddin, Jia Alam, Md. Golam Rabiul Brac University Department of Computer Science and Engineering Dhaka1212 Bangladesh Brac Business School Brac University 66 Mohakhali Dhaka1212 Bangladesh Endicott College Woosong University Ai and Big Data Department Daejeon Korea Republic of

ISBN: (纸本)9798350341072

In the fast-paced realm of global financial markets, characterized by rapid trading of both stocks and cryptocurren-cies, it has become essential to grasp the influence of sentiment on market dynamics. With more than 630,000 publicly traded companies worldwide and major stock exchanges like the NYSE handling a substantial portion of global equity transactions, the inherent volatility of the stock market is well-established. Over the past decade, various factors have contributed to the consistent fluctuations in stock prices. One key factor is the influence of investor reviews sourced from diverse news outlets and social media platforms such as Twitter. Understanding how these reviews can be collected and effectively summarized is crucial. This paper centers on the intricate field of market sentiment analysis and its profound impact on user sentiment, subsequently affecting price fluctuations in both stocks and cryptocurrencies. In this study, we present a comprehensive exploration of the development and evaluation of an automated sentiment analysis system tailored for summarizing web-based news related to stocks and *** have implemented BERT (Bidirectional Encoder Representations from Transformers) in combination with NLTK for text summarization, a highly accurate model with a performance level of 95.84%, as part of our proposed approach. © 2023 IEEE.

关键词： Sentiment analysis

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How Graph Neural Networks Learn: Lessons from Training Dynamics 41

How Graph Neural Networks Learn: Lessons from Training Dynam...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Yang, Chenxiao Wu, Qitian Wipf, David Sun, Ruoyu Yan, Junchi School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University China Amazon Web Services United States School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data China

A long-standing goal in deep learning has been to characterize the learning behavior of black-box models in a more interpretable manner. For graph neural networks (GNNs), considerable advances have been made in formalizing what functions they can represent, but whether GNNs will learn desired functions during the optimization process remains less clear. To fill this gap, we study their training dynamics in function space. In particular, we find that the gradient descent optimization of GNNs implicitly leverages the graph structure to update the learned function, as can be quantified by a phenomenon which we call kernel-graph alignment. We provide theoretical explanations for the emergence of this phenomenon in the overparameterized regime and empirically validate it on real-world GNNs. This finding offers new interpretable insights into when and why the learned GNN functions generalize, highlighting their limitations in heterophilic graphs. Practically, we propose a parameter-free algorithm that directly uses a sparse matrix (i.e. graph adjacency) to update the learned function. We demonstrate that this embarrassingly simple approach can be as effective as GNNs while being orders-of-magnitude faster. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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A Comparative Study of Diverse Intrusion Detection Methods using Machine Learning Techniques 12

A Comparative Study of Diverse Intrusion Detection Methods u...

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12th International Conference on computer Communication and Informatics, ICCCI 2022

作者： Kathiresan, V. Karthik, S. Divya, P. Rajan, D. Palanivel Marwadi University Computer Engineering- Ai and Big Data Analytics Gujarat Rajkot India S. N. S College of Technology Department of CSE Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India Cmr Engineering College Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9781665480352

Establishing robust and efficient intrusion detection systems (IDS) and Intrusion prevention systems (IPS) are inevitable in today's security world. The major role of IDS is detecting the anomaly in network traffic using effective methods. Machine learning techniques take a vital role in intrusion detection based on anomaly detection. This paper deals with the survey on machine learning techniques used in intrusion detection. The classification algorithms of machine learning like logistic regression, naïve Bayes, KNN, Decision Tree, Random Forest, and SVM are suitable for intrusion detection. This paper analyses the behaviour and properties of the machine learning-based classification techniques which is used for intrusion detection application. © 2022 IEEE.

关键词： Intrusion detection

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Enhancing the Long-Term Sustainability of Healthcare data Optimization Through Deep Federated Learning 3

Enhancing the Long-Term Sustainability of Healthcare Data Op...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Kumar, Balraj Evin, Munni Byeon, Haewon Sivaprakash, M. Sheeba, P. Maria Sunil, J. School of Computer Application Lovely Professional University Punjab Phagwara India School of Engineering and Computing American International University Kuwait College of Ai Convergence Inje University Department of AI-Big Data Gimhae50834 Korea Republic of Stella Mary's College of Engineering Department of Mechanical Engineering Tamil Nadu 629202 India St. Mother Theresa Engineering College Department of Electronics and Communication Engineering Thoothukudi India Annai Vailankanni College of Engineering Tamil Nadu Kanyakumari India

ISBN: (纸本)9798350361186

The medical industry regards the prevention and detection of skin diseases as a top priority. The number of people suffering from skin diseases is increasing faster than ever before due to increased levels of pollution and poor diet. Although it's not the major priority, having bad skin makes us less certain. Regularly inspecting your skin is an important step in identifying any degenerative or early changes in your skin that could lead to skin disease. Machine learning algorithms can be utilized to create robust models that can categorize a wide range of skin conditions. First, the skin and non-skin must be distinguished in order to identify skin conditions. In order to determine the unambiguous a group of skin conditions A selection of five distinct machine learning algorithms has been employed to compile a dataset of skin infections. These algorithms include Kernel Self- pattern, Random Forest, Naive Bayes, Logistic Regression, and CNN, among others. A comparable investigation based on training accuracy and skepticism matrix criterion has been carried out and illustrated with graphs. It has been determined that, of all the applicants, CNN offers the highest level of training accuracy for the appropriate assumption of skin disorders. © 2024 IEEE.

关键词： Deep learning

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Real-Time Energy Efficient Communication for Large-Scale Industrial Internet of Things 2nd

Real-Time Energy Efficient Communication for Large-Scale Ind...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Khan, Musfira Khan, Muazzam A. Farman, Haleem Malik, Asad W. Ramzan, Muhammad Sher Islamabad Pakistan Department of Computer Science Quaid-I-Azam University Islamabad Pakistan ICESCO Chair for Big Data Analytics and Edge Computing Quaid-I-Azam University Islamabad Pakistan Smart Systems Engineering Lab College of Engineering Prince Sultan University Riyadh Saudi Arabia King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9789819783441

Industrial Internet of Things (IIoTs) comprises large-scale sensors deployed to monitor and control the system. Deploying IoT devices presents challenges due to frequent disconnections and sudden power drainage in intermediate devices responsible for routing data. Consequently, the deployed network becomes sporadic, hindering connectivity with the industrial controller system, especially critical during emergencies. Furthermore, IoT node failures lead to a short network lifetime, which is intolerable in real-time industrial applications. This paper introduces a multi-hop clustering framework designed for reliable communication in industrial environments. The framework dynamically forms clusters, employing cluster heads and backup nodes to stabilize communication in the absence of a cluster head. Each cluster selects a backup node during the initial cluster head selection and later through data piggybacking. The proposed framework provides efficient communication across the IIoT network with the help of a backup and multi-hop clustering mechanism. The proposed framework is compared with two existing protocols i.e. cluster-based coordination routing (CRC) and energy-efficient quality of service routing (EEQSRP) protocols. The results show a considerable decrease in sensor energy consumption and packet delay. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Energy utilization

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An Intelligent Digital System For Foot-Weight Distribution Analysis

An Intelligent Digital System For Foot-Weight Distribution A...

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International Bhurban Conference on Applied Sciences & Technology, IBCAST

作者： Tassadaq Hussain Amna Haider Uman Ahmad Muhammad Haris Muhammad Zeerak Awan Eduard Ayguade Department of Electrical Engineering Centre for AI and Big Data Mianwali Pakistan UCERD Pakistan Supercomputing Foot Analytic Islamabad Pakistan Centre for AI and Big Data Namal University Mianwali Pakistan Computer Sciences Department Barcelona Supercomputing Centre Spain

ISBN: (数字)9798331516680

ISBN: (纸本)9798331516697

Maintaining proper weight distribution on the feet during daily activities can reduce postural and ambulatory issues. Therefore, accurate foot weight distribution analysis is crucial for medical science, occupational rehabilitation, physical therapy, sports, military training, and daily life postural training, influencing diagnostic and preventive healthcare. Despite the growing significance of body weight analysis, existing research in this domain is limited. The complexity of this issue arises from inherent variations in factors such as weight, height, Body Mass Index (BMI), physiological parameters, and accidentally conceived abnormalities. This paper introduces a novel approach to address this challenge by developing a smart digital system designed to analyze human body weight distribution across different parts of the foot. The system uses a data acquisition module connected with Force Sensing Resistor (FSR)-based matrix sensors placed under the plantar surface of the foot. The collected data is processed by an embedded system that uses analytic techniques to identify foot patterns and diagnose defects in the body. The proposed foot weight distribution system can be a helpful diagnostic tool for analyzing foot pressure distribution to identify and suggest potential medical problems (such as osteoporosis and prostheses). To validate the effectiveness of the system, a study involving 200 participants (comprising 100 males and 100 females) was conducted. The results show a significant level of accuracy in identifying abnormalities in osteoporosis and detecting prosthetic misalignment.

关键词： Training Osteoporosis Accuracy Digital systems data acquisition Sensor systems Medical diagnostic imaging Intelligent sensors Surface treatment Foot

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Parameter-Efficient Image Classification with Convolutional Spiking Neural Network via Fast Sigmoid Surrogate Gradient Descent

Parameter-Efficient Image Classification with Convolutional ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Joy Datta Fabliha Afaf Sarwar Rawhatur Rabbi Puja Saha Md. Golam Rabiul Alam Jia Uddin Aniqua Nusrat Zereen Department of Computer Science & Engineering School of Data & Sciences Brac University Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh AI and Big Data Department Endicott College Woosong University Daejeon South Korea

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Spiking Neural Networks (SNNs) offer several advantages over traditional Artificial Neural Networks (ANNs), especially in terms of biologically inspired event-driven mechanisms and energy efficiency. This paper aims to demonstrate parameter efficiency of Convolutional Spiking Neural Networks (CSNNs) by training extremely small CSNNs and comparing those with conventional Convolutional Neural Networks (CNNs) of same parameter count and depth, as well as against state-of-the-art SNN implementations. Through experiments conducted on multiple small-scale CSNN models, we observed that CSNNs are able to achieve exceptional parameter efficiency and performance with the help of a fast sigmoid surrogate gradient descent, proving an astonishing balance between accuracy and compactness. The findings fulfill the promise that CSNNs hold as a feasible solution for resource-constrained and low-power applications at the same time, as it demonstrates the biological plausibility of learning in such complex machine learning tasks.

关键词： Training Image recognition Biological system modeling Neurons Spiking neural networks Machine learning computer architecture Energy efficiency Convolutional neural networks Image classification

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