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A Neural Computing-Based Access Control Protocol for AI-Driven Intelligent Flying Vehicles in Industry 5.0-Assisted Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3573-3581页

作者： Mahmood, Khalid Tariq, Tayyaba Sangaiah, Arun Kumar Ghaffar, Zahid Saleem, Muhammad Asad Shamshad, Salman National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology International Graduate School of AI Douliu64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102 2801 Lebanon University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China The University of Lahore Department of Software Engineering Lahore54590 Pakistan

The continuous revolution in Artificial Intelligence (AI) has played a significant role in the development of key consumer applications, including Industry 5.0, autonomous decision-making, fault diagnosis, etc. In practice, autonomous and intelligent robots and flying vehicles (i.e., drones) are also becoming the integral part of Industry 5.0. These vehicles are attaining significant importance nowadays because of their applications in consumer electronics. However, there may be numerous privacy issues in such an infrastructure. Nevertheless, neural computing-driven security solutions have played a significant role in overcoming the security and privacy issues of AI-driven intelligent flying vehicles. Motivated by these facts, this article presents a neural computing-based access control protocol for AI-driven flying vehicles to prevent all possible security threats. A detailed security evaluation of the devised protocol is done formally using the extensively recognized Real-Or-Random (ROR) oracle model. Similarly, the informal security analysis ensures the resistance of our scheme against various potential attacks under the assumptions of CK and DY adversarial models. Moreover, detailed performance scrutiny of the devised protocol with various existing schemes shows the robustness and supremacy of our scheme in terms of computation, communication overhead, and security attributes. © 1975-2011 IEEE.

关键词： Authentication

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Domain Reductions After Preprocessing: Effects on Dynamic Variable Ordering Heuristics in Constraint Satisfaction Search 23rd

Domain Reductions After Preprocessing: Effects on Dynamic V...

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23rd EPIA Conference on Artificial Intelligence, EPIA 2024

作者： Wallace, Richard J. Insight Centre for Data Analytics and Department of Computer Science University College Cork Cork Ireland

ISBN: (纸本)9783031734991

We still do not have an adequate understanding of heuristic methods used for solving constraint satisfaction problems (CSPs). An example involves the effects of preprocessing, an essential means of improving CSP search. The traditional explanation for its beneficial effect is that the resulting "problem reduction" leaves fewer possibilities to explore. Recently, however, it was shown that when dynamic variable ordering heuristics are used, other factors related to domain size reduction are much more important. This paper extends this analysis and explores some implications of this new perspective. The key idea is that pattern of domain reductions produced by preprocessing transmits information that guides heuristic decisions. Treating domain reduction as a code and the set of reductions as a message of length n transmitted to the search algorithm, we distinguish two factors, message discriminability and code quality, and show that greater problem reduction enhances both. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Constraint satisfaction problems

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Bayesian Regression for Predicting Subscription to Bank Term Deposits in Direct Marketing Campaigns

Bayesian Regression for Predicting Subscription to Bank Term...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Tanvir, Muhammad Farhan Hossain, Md Maruf Asifuzzaman Jishan, Md Universitat Potsdam Department of Computer Science Potsdam Germany Technische Universitat Dortmund Department of Statistics Dortmund Germany

ISBN: (纸本)9798350369106

In the highly competitive environment of the banking industry, it is essential to precisely forecast the behavior of customers in order to maximize the effectiveness of marketing initiatives and improve financial consequences. The purpose of this research is to examine the efficacy of logit and probit models in predicting term deposit subscriptions using a Portuguese bank's direct marketing data. There are several demographic, economic, and behavioral characteristics in the dataset that affect the probability of subscribing. To increase model performance and provide an unbiased evaluation, the target variable was balanced, considering the inherent imbalance in the dataset. The two model's prediction abilities were evaluated using Bayesian techniques and Leave-One-Out Cross-Validation (LOO-CV). The logit model performed better than the probit model in handling this classification problem. The results highlight the relevance of model selection when dealing with complicated decision-making processes in the financial services industry and imbalanced datasets. Findings from this study shed light on how banks can optimize their decision-making processes, improve their client segmentation, and boost their marketing campaigns by utilizing machine learning models. © 2024 IEEE.

关键词： Logistic regression

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Integrating Multimodal AI Models for Enhanced Oral Cancer Detection and Diagnosis 2

Integrating Multimodal AI Models for Enhanced Oral Cancer De...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Shantha Kumari, K. Udhya shree, S. Calarany, C. Department of Data Science and Business Systems SRM Institue of Science and Technology Kattankulathur India Department of Computer Science and Engineering Rajiv Gandhi College of Engineering and Technology Pondicherry India Department of Computer Science and Engineering Tagore Government Arts and Science College Pondicherry India

ISBN: (纸本)9798331504960

Oral cancer is a major global health issue marked by high morbidity and mortality rates, primarily due to late-stage diagnosis. This work proposes the Advanced AI models that use multimodal data for early detection and diagnosis of oral cancer. Currently, traditional machine learning algorithms coupled with deep learning algorithms such as Convolutional neural networks (CNNs), focus on image-based analysis for detecting cancer. These works are delimited with minimal robustness and input data efficacy. Through the proposed multimodal AI model, histopathological images and clinical notes of the oral cancer patients are combinedly analyzed by which diagnostic accuracy is enhanced. This framework integrates CNNs for image analysis and Transformer-based models for text analysis, with a sophisticated fusion layer for data integration. Enhanced preprocessing techniques and performance optimization through Vision Transformers (ViTs) and BERT models significantly improve sensitivity, specificity, and F1-score. The multimodal AI model achieved a sensitivity of 0.90, specificity of 0.85, and an F1-score of 0.89, outperforming previous models like SVMs, RF, and CNNs. This indicates a high accuracy in identifying both positive and negative cases, ensuring reliable diagnoses. The results demonstrate a substantial improvement over traditional methods, that lays foundations for early diagnosis. © 2024 IEEE.

关键词： Lung cancer

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Cross-Paradigm Modelling: A Study of Puzznic 36

Cross-Paradigm Modelling: A Study of Puzznic

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Espasa, Joan Gent, Ian P. Miguel, Ian Nightingale, Peter Salamon, Andras Z. Villaret, Mateu School of Computer Science University of St Andrews United Kingdom University of York Department of Computer Science United Kingdom University of Girona Department of CS Mathematics and Statistics Spain

ISBN: (纸本)9798331527235

Puzznic is a tile-matching video game published by Taito in 1989 and ported to many platforms. The player manipulates blocks in a given grid until they match when two or more blocks of the same pattern are adjacent and are removed from play. The goal is to match all patterned blocks in the grid. Puzznic is rich in structure: levels have internal platforms and the blocks are affected by gravity, leading to complex state changes and the possibility of a cascaded series of matches following each move by the player. The puzzle is therefore a significant challenge to model, motivating our study. We study Puzznic from both constraint modelling and AI Planning perspectives, identifying their complementary strengths and weaknesses for this problem. We further exploit our constraint model to produce an automated tool for instance generation, parameterised on the grid, the combination of patterned blocks, and the steps required. © 2024 IEEE.

关键词： Constraint theory

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An Efficient Doubly-Robust Test for the Kernel Treatment Effect 37

An Efficient Doubly-Robust Test for the Kernel Treatment Eff...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Martinez-Taboada, Diego Ramdas, Aaditya Kennedy, Edward H. Department of Statistics and Data Science Carnegie Mellon University PittsburghPA15213 United States Department of Statistics and Data Science Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9781713899921

The average treatment effect, which is the difference in expectation of the counterfactuals, is probably the most popular target effect in causal inference with binary treatments. However, treatments may have effects beyond the mean, for instance decreasing or increasing the variance. We propose a new kernel-based test for distributional effects of the treatment. It is, to the best of our knowledge, the first kernel-based, doubly-robust test with provably valid type-I error. Furthermore, our proposed algorithm is computationally efficient, avoiding the use of permutations. © 2023 Neural information processing systems foundation. All rights reserved.

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Product Quality Enhancement in Social Media using Emotion-Enhanced Bert (EE-BERT) Model 2

Product Quality Enhancement in Social Media using Emotion-En...

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

作者： Raj Kumar, V.S. Kumaresan, T. Mythily, V. Raghavendran, N. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data science Sathyamangalam India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Nandha Engineering College Department of Computer Science and Engineering Erode India Rmk college of Engineering and Technology Department of Artificial Intelligence and Data science Chennai India

ISBN: (纸本)9798350368413

Twitter plays an important role in understanding the consumer sentiment about the products. The advanced analytics and Natural Language Processing (NLP) are used to extract actionable insights from this data and useful for a business to improve features and address issues on time for better customer satisfaction and brand loyalty. Most of the current models suffer from a lack of effective ways to handle subtle sentiment analysis because of a lack of contextual understanding and proper feature extraction. Most traditional models cannot combine and optimize features that lead to obtaining sentiments. This leads to overfitting and problems caused by local minima. The proposed Emotion-Enhanced BERT (EE-BERT) model is an extension of the standard BERT to learn contextual embeddings in user sentiment. In this work, data preprocessing includes text cleaning, tokenization, dealing with emojis and emoticons, and lemmatization with the best quality data. The proposed work is basically a dual-stream architecture extended with butterfly optimizer. First, the advanced tokenization and semantic understanding techniques is used for extracting textual features. Second, the emotion-enhanced features extracted through emotion-hash vectorization to enhance the model's contextual awareness in social media data. This provides a unified framework that combines the feature extraction methods to enhance the accuracy in sentiment analysis. It demonstrated with an impressive accuracy of 98.2% on sentiment in posts and comments with 0.15 losses. The proposed work is free from overfitting and local minima in product quality enhancement. © 2024 IEEE.

关键词： Tweets

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Bipartite Time Series Network for data Imputation 27

Bipartite Time Series Network for Data Imputation

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Amerise, Ilaria Lucrezia Fionda, Valeria Pirrò, Giuseppe Department of Economics Statistics and Finance University of Calabria Italy Department of Mathematics and Computer Science University of Calabria Italy

ISBN: (纸本)9781643685489

The pervasive issue of missing data in statistical analysis and data science significantly impacts the integrity and accuracy of Time-Series Cross-Sectional (TSCS) datasets, extensively used in domains like health sciences and sensor applications. Traditional imputation methods are often inadequate for these datasets as they fail to address the complexities arising from cross-sectional and temporal data gaps. This paper introduces BiTSNet (Bipartite Time Series Network for data Imputation), a novel approach designed to tackle the unique challenges of TSCS datasets by leveraging a dual representation of data. BiTSNet models cross-sectional time series data as sequences of bipartite graphs, where each graph represents a specific time step, and feature values are depicted as edge weights. Thus, missing values are interpreted as missing edge feature values, allowing BiTSNet to comprehensively capture spatial relationships within each time step and temporal relationships across steps. The framework uses Graph Neural Networks (GNNs) to process spatial dependencies within these bipartite graph representations and employs Recurrent Neural Networks (RNNs) to handle temporal dependencies. This integration enables BiTSNet to learn the intricate intra-time step relationships and the inter-time step dynamics effectively. Our approach not only preserves the longitudinal and cross-sectional integrity of the data but also ensures the production of valid and insightful conclusions from the enriched dataset. We evaluated BiTSNet on several datasets and compared it against the state-of-the-art approaches with encouraging results. © 2024 The Authors.

关键词： Spatio-temporal data

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A comparative analysis for deep-learning-based approaches for image forgery detection

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第1期8卷 1-10页

作者： Ch, Ravikumar Radha, Marepalli Mahendar, Maragoni Manasa, Pinnapureddy Department of Artificial Intelligence &Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India Department of Computer Science and Engineering CVR College of Engineering Hyderabad 501510 India Department of Computer Science and Engineering Neil Gogte Institute of Technology Hyderabad 500039 India

The detection of counterfeit photographs is critical in the digital age because of the widespread development of digital media and its significant impact on social networks. The legitimacy of digital content is being threatened by the growing sophistication of picture counterfeiting. With the help of pre-trained VGG-16 models and deep learning techniques that integrate Error Level Analysis (ELA) and Convolutional Neural Networks (CNNs), this study presents a fresh solution to this problem. The study thoroughly assesses and contrasts these models with a dataset that has been carefully chosen to bring the presented findings into perspective. To ensure a reliable evaluation of each model's performance 5000 experiments were carried out in total. With an accuracy rate of 99.87% and an accurate identification rate of 99% of hidden forgeries, the results demonstrate the exceptional effectiveness of the ELA-CNN model. However, despite its robustness, the VGG-16 model only achieves a significantly lower accuracy rate of 97.93% and a validation rate of 75.87%. This study clarifies the relevance of deep learning in the identification of image forgeries and highlights the practical ramifications of various models. Moreover, the research recognizes its constraints, especially for highly advanced counterfeits, and proposes possible paths for enhancing the accuracy and scope of detection algorithms. In the ever-changing world of digital media, the thorough comparative analysis provided in this study offers insightful information that can direct the creation of accurate forgery detection tools, protecting digital content integrity and reducing the effects of image manipulation. © (2023), (Society of Sytematic Innovation). All Rights Reserved.

关键词： Counterfeit images deep learning ELA-CNN Image forgery detection VGG-16 model

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Improved Sparse Coded Features for Automatic Identification and Discrimination of Exudates and Drusen in Retinal Fundus Images

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SN computer science 2025年第1期6卷 1-12页

作者： Kumar, Mukesh Rani, Kumi Department of Mathematics and Statistics University of Houston-Downtown Houston 77002 TX United States Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Ghaziabad India

Diabetic Retinopathy is a medical condition in people having diabetes in which damage occurs to the retina and can cause partial or complete vision loss. It is the most common reason for vision loss in the world. Diabetic Retinopathy is characterized by the appearance of retinal lesions such as microaneurysms, exudates, cotton wool spots, and haemorrhages. Age-Related Macular Degeneration (ARMD) is another eye disease which causes gradual loss of vision with age and is characterized by the appearance of drusen. Exudates and Drusen are known as bright lesions as they appear as yellow deposits in the retina. In this paper, we propose an automated method for the classification of bright lesions in retinal fundus images based on improved sparse coded features and Support Vector Machine (SVM). We employ Scale-Invariant Feature Transform (SIFT) to extract interesting regions in the image in the form of key points and extract local low-level features in the neighbourhoods of key-points. After that, we use the sparse coding technique to get high-level sparse coded features that are used for classification using SVM. Experimental results reveal that the proposed approach gives an accuracy, sensitivity, a specificity of 92.90%, 89.60%, 95.40% for exudate classification and 93.80%, 89.60%, 95.80% for drusen classification, with a dictionary of 300 atoms on a dataset of 1158 images. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diabetic retinopathy Drusen Exudates Region of interest Retinal fundus images Sparse coding

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