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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Kanataria, Nikunj Patel, Kunj Pareshbhai Patel, Hetul Niteshbhai Goel, Parth Patel, Krishna Garg, Dweepna Devang Patel Institute of Advance Technology and Research Computer Science & Engineering Department Changa Anand India Computer Science & Design Department Vallabh vidyanagar Anand India

ISBN: (纸本)9798350377972

The explosion of online information necessitates efficient and accurate methods for retrieving and analyzing relevant data. This research proposes a novel framework that leverages Retrieval-Augmented Generation (RAG) techniques to address this challenge. The framework utilizes web scraping to extract information from various sources and employs advanced language models like Llama 3, Mistral, and Gemini to generate concise and informative summaries. Furthermore, the framework incorporates embedding models such as nomic-embed-text and Gemini embedding model text embedding 001 to create semantic representations of the retrieved data and utilize FAISS for efficient indexing and retrieval. The RAGAS framework was used to evaluate the performance of different LLMs, with Llama 3.1 demonstrating the highest accuracy at 86.67%. This framework has significant implications for various applications, including personalized education, where it can be used to provide students with tailored learning materials and personalized tutoring experiences. © 2024 IEEE.

关键词： Semantics

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A Review on Leveraging Language Agnostic Automated Text Data Augmentation with Explainable Semantic Integrity 5

A Review on Leveraging Language Agnostic Automated Text Data...

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5th International Conference on Advanced research in computing, ICARC 2025

作者： Wijesundera, Sarith Peiris, Suresh Michael University of Westminster Department of Computer Science and Engineering London United Kingdom Informatics Institute of Technology School of Computing Colombo Sri Lanka

ISBN: (纸本)9798331530983

Data Augmentation (DA) is an effective strategy to increase model generalisation. In Natural Language Processing (NLP), DA remains in its early stages, primarily due to the inherent sensitivity of textual data, which complicates the augmentation process. These complexities are further increased in multilingual contexts, as DA techniques effective in one language may not yield comparable results in others. As a result, manually identifying optimal DA techniques that preserve semantic integrity and are language agnostic remains a significant challenge. It emphasises the necessity for automating the text DA process with Explainable Artificial Intelligence (XAI), which offers a promising approach by calculating word importance scores to effectively guide the augmentation. In this paper, a comprehensive review of existing approaches in auto-text DA is presented, beginning with an exploration of the concept of auto-text DA, followed by a discussion on its cross-lingual applications, and concluding with the integration of XAI for semantic preservation. The paper highlights the critical need for further research to enhance the effectiveness and applicability of XAI in auto-text DA, enabling its use across diverse languages. © 2025 IEEE.

关键词： Transfer learning

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A Novel Method to Predict Chronic Kidney Disease using Optimized Deep Learning Algorithm 21

A Novel Method to Predict Chronic Kidney Disease using Optim...

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21st International Learning and Technology Conference, L and T 2024

作者： Preethi, I. Dharmarajan, K. Sharma, Bhisham Chowdhury, Subrata Dhaou, Imed Ben Vistas School of Computing Sciences Chennai India Vistas Department of Information Technology Chennai India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Andhra Pradesh Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing Design Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Chronic Kidney Disease (CKD), a significant global health issue and has a major impact on a vast number of people. In the early stages, it is impossible to identify the disease's symptoms. Very few people are aware of this illness and are able to foresee the symptoms earlier. The disorder is considered as potential risk factor. As a result, a potent deep learning framework is used in this paper to reduce the risk of acquiring these diseases as a way to prevent them. The machine learning algorithms used in early disease prediction, has been found to be computationally expensive, frequently overfit, and underperform in terms of accuracy since they must examine the large amount of clinical data until the model converges. Therefore, in this paper three novel work has been proposed an efficient novel hybrid feature selection strategy RFITLO algorithm is used to find the optimal features that gives the major contribution is classifying the CKD disease. Then two proposed classification algorithms namely Enhanced Multi-Layer Perceptron (HW-MLP) and Optimized Multi-Layer Perceptron (PKD-OMLP) are used in prediction model to capture the complex patterns and optimize the learning algorithm to predict the CKD at prior stage from the data gathered in Kaggle, Real Time and UCI Machine Learning Repository Dataset. In order to measure the classifications of disease, performance measures including accuracy, precision and recall are analyzed. The experimental findings show that the PKD-OMLP strategy produces better results than the proposed HW-MLP and other conventional approaches like Support Vector Machine (SVM), Linear Regression (LR) and Multi-Layer Perceptron (MLP). Among the preceding four models PKD-OMLP renders the best outcome as per its performance level producing a high accuracy of 94.89% on testing Real Time (RT) CKD dataset comparatively with other datasets such as Kaggle and UCI repository. Therefore, these proposed algorithms can support clinicians to enable secured an

关键词： Classification (of information)

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Privacy Leakage in Federated Learning for Smart Grid Short-Term Load Forecasting 50

Privacy Leakage in Federated Learning for Smart Grid Short-T...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Aly, Hussein Al-Ali, Abdulaziz Al-Ali, Abdulla Malluhi, Qutaibah College of Engineering Department of Computer Science and Engineering Qatar University Doha Qatar KINDI Computing Research Center Qatar University Doha Qatar

ISBN: (纸本)9781665464543

Federated Learning (FL) for household-level Short-Term Load Forecasting (STLF) has emerged as a solution to privacy concerns in smart grids, enabling clients to collaboratively train models without sharing their consumption data with a central server. However, sharing model updates can still introduce privacy risks. A common solution to mitigate this risk is the use of differential privacy during the federation process. However, there is a lack of empirical privacy analysis of these techniques in smart grid scenarios. This paper proposes a property inference attack utilizing a single update per client to evaluate privacy risks in federated learning within smart grid contexts. We assess the privacy risk associated with the standard FedAvg algorithm and a differentially private noise-before-aggregation (NBA) FL scheme. Furthermore, we investigate the trade-off between privacy and utility in the NBA-FL scheme. Our empirical findings reveal significant information leakage with standard FedAvg scheme. An adversary with access to a single model update can identify global data properties of the FedAvg client local dataset with an Area Under the Curve (AUC) of 72%. This privacy leakage can be reduced using NBA-FL, which reduces the AUC to 60%. However, the addition of noise to the model updates results in a utility loss of up to 70% in the model’s predictive power. This significant degradation in model performance outweighs the advantages of the scheme. © 2024 IEEE.

关键词： Federated learning

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Water Level Controller with Weather Forecasting Using 8051 Microcontroller 7

Water Level Controller with Weather Forecasting Using 8051 M...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Kaniskaa, M.S. Madanagopal, C. Deepa, M. Hariprasath, K. PSG Institute of Technology and Applied Research Department of Electronics and Communications India Vellore Institute of Technology and Applied Research Department of Computer Science India

ISBN: (纸本)9798350340600

Water management is a salient role of engineers due to its importance in everyday activities. The proposed work aims to eliminate over- and under-irrigation, as well as water waste, by monitoring soil moisture and enabling automatic switching of the pumping system to adjust the moisture level. Along with the soil moisture levels, the weather forecast is also considered to regulate the moisture level. The proposed design implements a water level controller by tracking the soil moisture levels using an 8051 microcontroller and interfacing it with sensors. A threshold value is set, for both lower and higher values of soil moisture and the live value is constantly compared. When the value is below or above the respective lower and higher threshold values, the motor pumping system is automatically turned on or off respectively. The output values are observed using a web application via an Arduino board. The design also uses Node MCU which is integrated with ThingSpeak to aggregate and store data. The web application has an additional feature of weather forecasts, which is live data obtained using an Application Programming Interface (API) key with Weatherstack API. This weather forecast is used to control the motor and automatically change its state based on the soil's present state of moisture level and the weather that day. This feature makes the design more reliable and practical for integrating it with mainstream agriculture and gardening activities. © 2023 IEEE.

关键词： Soil moisture

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Improving Energy Theft Detection through Time Series Segmentation and Ensemble Learning 50

Improving Energy Theft Detection through Time Series Segment...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Altamimi, Emran Al-Ali, Abdulaziz Al-Ali, Abdulla Malluhi, Qutaibah College of Engineering Department of Computer Science and Engineering Qatar University Doha Qatar Kindi Center for Computing Research Qatar University Doha Qatar

ISBN: (纸本)9781665464543

Non-technical loss and energy theft detection are crucial for improving the stability and reducing financial losses in smart grid and power grid utilities. Recently, the availability of massive datasets has improved detection capabilities using sophisticated techniques like deep neural networks. However, training models on extensive feature sets, such as multi-year data, can lead to confusion due to varied behavioral changes in electricity consumption. To address this, we propose a reformulation of the energy theft detection problem by segmenting the time series data and training individual models on each segment. These models’ anomaly scores are then aggregated to produce a final classification. Our framework has shown significant improvement, elevating the F1 score from 0.6 to 0.74, outperforming recent state-of-the-art techniques on the SGCC dataset, the only publicly available dataset labeled for energy theft. © 2024 IEEE.

关键词： Adversarial machine learning

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Explainable AI for Healthcare: An Approach Towards Interpretable Healthcare Models 24

Explainable AI for Healthcare: An Approach Towards Interpret...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Zahoor, Kanwal Bawany, Narmeen Zakaria Ghani, Usman Jinnah University for Women Institute of Business Administration Center for Computing Research Department of Computer Science and Software Engineering Karachi Pakistan

ISBN: (纸本)9798350384307

Artificial intelligence (AI) along with deep learning techniques has become an integral part of almost all aspects of life. One of the domains significantly impacted by this technological revolution is healthcare. Deep learning-based AI systems assist clinicians and medical professionals in disease diagnosis, personalized treatment, and monitoring through wearables, among other applications. Despite its expedient integration in healthcare, the trustworthiness of deep learning models remains a concern, primarily due to a lack of understanding of their underlying processes. However, Explainable AI (XAI) offers explanations through various methods, including Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive explanation (SHAP), and GRAD-CAM. XAI is utilized to enhance transparency, allowing users to understand and trust AI decisions. In this study, we present deep learning models for the classification of pneumonia disease in Chest X-ray Images followed by their explanations. Convolutional Neural Networks (CNNs) and other pre-trained models, including VGG16, MobileNetV3, and ResNet50, were used for classification of images as 'normal' or 'pneumonia'. The VGG16 model, known for its exceptional image understanding capabilities, achieved the highest accuracy, with an impressive 93% score. Further, we used XAI techniques including SHAP, LIME, and Grad-CAM for explanation of models. LIME and Grad-CAM provided more accurate results than SHAP in our experiments. This approach was taken to evaluate the fairness and transparency of the model. The insights gained from XAI can be used to refine and improve machine learning models by identifying areas of weakness or misinterpretation which increases overall model robustness. © 2023 IEEE.

关键词： Deep learning Explainable AI Grad-CAM Healthcare LIME SHAP Transfer learning

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Siamese Multiple Attention Temporal Convolution Networks for Human Mobility Signature Identification 27

Siamese Multiple Attention Temporal Convolution Networks for...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Zheng, Zhipeng Jiang, Yuchen Zhang, Shiyao Wei, Xuetao Research Institute for Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computer Science and Engineering China

ISBN: (纸本)9798331505929

The Human Mobility Signature Identification (HuMID) problem stands as a fundamental task within the realm of driving style representation, dedicated to discerning latent driving behaviors and preferences from diverse driver trajectories for driver identification. Its solutions hold significant implications across various domains (e.g., ride-hailing, insurance), wherein their application serves to safeguard users and mitigate potential fraudulent activities. Present HuMID solutions often exhibit limitations in adaptability when confronted with lengthy trajectories, consequently incurring substantial computational overhead. Furthermore, their inability to effectively extract crucial local information further impedes their performance. To address this problem, we propose a Siamese Multiple Attention Temporal Convolutional Network (Siamese MA-TCN) to capitalize on the strengths of both TCN architecture and multi-head self-attention, enabling the proficient extraction of both local and long-term dependencies. Additionally, we devise a novel attention mechanism tailored for the efficient aggregation of multi-scale representations derived from our model. Experimental evaluations conducted on two real-world taxi trajectory datasets reveal that our proposed model effectively extracts both local key information and long-term dependencies. These findings highlight the model's outstanding generalization capabilities, demonstrating its robustness and adaptability across datasets of varying sizes. © 2024 IEEE.

关键词： Convolution

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A Detailed Structured Monitoting System for ICU Patient Mortality rate by using AI Algorithm 5

A Detailed Structured Monitoting System for ICU Patient Mort...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Joshi, Shivani Kumar, Rajiv Rai, Vipin Dwivedi, Avinash Tripathi, Vikash Chandra G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Nvidia Ai and Learning & Research Center Department of Applied Computational Science and Engineering Greater Noida India Galgotias University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350383522

The complexity and seriousness of diseases in ICU patients, though, make it difficult for medical practitioners to identify high-risk patients who may face irreversible harm. With ANN (Artificial Neural Network), this study will provide an ICU admission prediction model to help identify high-risk patients at an earlier stage. After collecting and analyzing extensive data from various perspectives including demographics, medical history, vital signs, test results and treatment modalities, exploratory data analysis has been conducted. These are used as markers on how statistical techniques and machine learning algorithms can be used to predict mortality among critically ill patients. Consequently, the parameters are designed within the ANN model to create a predictive model that will be able to effectively classify those at higher risk. Advanced machine learning algorithms together with modern tools such as deep learning techniques will aid in the prompt analysis of complex information for accurate forecasts by this predictive model. The ANN model's training set will include a large dataset of ICU patients so that it can become reliable and valid. This makes the predictive model developed capable of accurately identifying high risk cases expeditiously © 2024 IEEE.

关键词： accuracy Artificial Neural Networks (ANN) exploratory data analysis high-risk patients ICU patient admission medical technology mortality outcomes periodic updates prediction model

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Parallel Software-Based Self-Testing with Bounded Model Checking for Kilo-Core Networks-on-Chip

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Journal of computer science & Technology 2023年第2期38卷 405-421页

作者：张颖季鹏飞朱潘玮 Zebo Peng 李华伟江建慧 School of Software Engineering Tongji UniversityShanghai 200092China Department of Computer and Information Science Linkoping UniversityLinkoping 58183Sweden Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Online testing is critical to ensuring reliable operations of the next generation of supercomputers based on a kilo-core network-on-chip(NoC)interconnection *** present a parallel software-based self-testing(SBST)solution that makes use of the bounded model checking(BMC)technique to generate test sequences and parallel *** this method,the parallel SBST with BMC derives the leading sequence for each router’s internal function and detects all functionally-testable faults related to the function.A Monte-Carlo simulation algorithm is then used to search for the approximately optimum configuration of the parallel packets,which guarantees the test quality and minimizes the test ***,a multi-threading technology is used to ensure that the Monte-Carlo simulation can reach the approximately optimum configuration in a large random space and reduce the generating time of the parallel *** results show that the proposed method achieves a high fault coverage with a reduced test ***,by performing online testing in the functional mode with SBST,it effectively avoids the over-testing problem caused by functionally untestable turns in kilo-core NoCs.

关键词： software-based self-testing(SBST) parallel test kilo-core networks-on-chip(NoCs) online testing

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