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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Nithiyanandam Velvizhi, R. Priyadharshini, S.P. Poongavanam, N. SRM Institute of Science and Technology Kattankulathur Campus Department of Computing Technologies Chengalpattu Kattankulathur India Sathyabama Institute of Science and Technology OMR Department of Computer Science and Engineering Chennai India St' Joseph College of Engineering OMR Department of Artificial Intelligence and Machine Learning Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

By linking sensor nodes to fog nodes in critical locations, the Distributing Workloads in Fog Enabled Smart City (DW-FESC) technique efficiently handles the problem of distribution of workloads in smart cities. To get over the processing limits of IoT devices, this setup makes sure that complicated tasks are sent off to neighbouring fog nodes. The plan uses a load balancer and an updated greedy algorithm to evenly distribute work, which speeds up execution and keeps loads from becoming too heavy. Additionally, it has a deadline-based task execution mechanism that makes sure the most important jobs are done on time. The simulation results show that DW-FESC keeps the variance from the mean execution times to a minimum while considerably improving task execution times in comparison to other methods. With the use of fog computing, DW-FESC improves the dependability, efficiency, and responsiveness of smart city applications, leading to greener city life. © 2024 IEEE.

关键词： Fog computing

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Hyper Spectral Spatial Image Segmentation Using machine learning Algorithms 1

Hyper Spectral Spatial Image Segmentation Using Machine Lear...

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1st International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems, iTech SECOM 2023

作者： Saravanan, G. Suresh, A. Jagannathan, J. Thanga Mariappan, L. Majeeth, Najeem Dheen Abdul Kumar, Nithish Veltech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu India Vellore Institute of Technology School of Computer Science and Engineering Tamilnadu Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamilnadu Vellore India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Tamilnadu India

ISBN: (纸本)9798350371185

In recent years, there has been a notable surge in the development of methods dedicated to classifying hyper spectral image samples. However, the inherent high dimensionality of hyper spectral data presents significant challenges for traditional supervised classification algorithms. Prior studies have attempted to address these challenges by incorporating active learning methods for hyper spectral data segmentation. Nevertheless, a crucial drawback of active learning lies in its dependence on labeled data samples, leading to less improved classification results for unlabeled data samples. In response to this limitation, our work introduces Supervised Active learning (SAL) techniques that harness both spectral and spatial information. The key innovation of our research lies in the introduction of a novel hyper spectral image segmentation approach based on the k-dependence Bayesian (KDB) model. We estimate the maximizer of the posterior marginal using Symmetric Iterative Proportional Fitting (SIPF) within this KDB model. By implementing the KDB model for hyper spectral image segmentation, our goal is to enhance the classification accuracy of our SAL classifiers. The SAL framework integrates shape prior probability estimation, class density results from Improved Multi-Level Logistic (IMLL) methods, and the maximization of the posterior marginal estimated through Symmetric Iterative Proportional Fitting (SIPF). We evaluate the accuracy of our proposed MPM-SIPF-SAL system using the real ROSIS Hyper Spectral Dataset, demonstrating that our system outperforms existing methods in terms of classification accuracy. © 2023 IEEE.

关键词： Image segmentation

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Multi-Keywords Based Fully Homomorphic Encryption and Data Classification for Cloud Security and Privacy

Multi-Keywords Based Fully Homomorphic Encryption and Data C...

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Emerging Trends in Engineering and Medical sciences (ICETEMS), International Conference on

作者： Kooragayala Sukeerthi R. Kesavan S.A. Kalaiselvan Department of Computer Science & Engineering Saveetha School of Engineering SIMATS Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

When it comes to processing and storing massive amounts of data efficiently, cloud computing has become the dominant paradigm. Both consumers and suppliers of services in cloud settings are understandably worried about the safety of critical data. When it comes to protecting sensitive information during transmission in the cloud, homomorphic encryption has shown to be an effective method. This study presents a new homomorphic encryption method for efficient and safe computations on the cloud, determined by multistage partial homomorphic encryption. The learning using errors (LWE) issue, primarily an altered form of the BVG method, is the basis of the proposed completely homomorphic encryption technique. It is believed to be difficult to resolve the learning with mistakes issue, which is a challenge in machine learning. We were able to implement the technique by making use of MFHE, an encryption method that can process inputs encrypted under different keys. Our expansion of the technique to search for several keywords was based on this. To make data processing in the cloud safe and privacy protecting, the suggested system combines the strengths of multistage encryption with partial homomorphic encryption.

关键词： Cloud computing Data privacy Privacy machine learning Data processing Market research Safety Servers Homomorphic encryption Secure storage

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Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populations 40

Stable Heterogeneous Treatment Effect Estimation across Out-...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Zhang, Yuling Wu, Anpeng Kuang, Kun Du, Liang Sun, Zixun Wang, Zhi Tsinghua University Tsinghua Shenzhen International Graduate School China Zhejiang University College of Computer Science and Technology China Mohamed Bin Zayed University of Artificial Intelligence Machine Learning Department United Arab Emirates Interactive Entertainment Group Tencent Tsinghua University Tsinghua-Berkeley Shenzhen Institute China

ISBN: (纸本)9798350317152

Heterogeneous treatment effect (HTE) estimation is vital for understanding the change of treatment effect across individuals or subgroups. Most existing HTE estimation methods focus on addressing selection bias induced by imbalanced distributions of confounders between treated and control units, but ignore distribution shifts across populations. Thereby, their applicability has been limited to the in-distribution (ID) population, which shares a similar distribution with the training dataset. In real-world applications, where population distributions are subject to continuous changes, there is an urgent need for stable HTE estimation across out-of-distribution (OOD) populations, which, however, remains an open problem. As pioneers in resolving this problem, we propose a novel Stable Balanced Representation learning with Hierarchical-Attention Paradigm (SBRL-HAP) framework, which consists of 1) Balancing Regularizer for eliminating selection bias, 2) Independence Regularizer for addressing the distribution shift issue, 3) Hierarchical-Attention Paradigm for coordination between balance and independence. In this way, SBRL-HAP regresses counterfactual outcomes using ID data, while ensuring the resulting HTE estimation can be successfully generalized to out-of-distribution scenarios, thereby enhancing the model's applicability in real-world settings. Extensive experiments conducted on synthetic and real-world datasets demonstrate the effectiveness of our SBRL-HAP in achieving stable HTE estimation across OOD populations, with an average 10% reduction in the error metric PEHE and 11% decrease in the ATE bias, compared to the SOTA methods. © 2024 IEEE.

关键词： Population distribution

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Assessing Potential of Organizations with Fuzzy Entropy

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Operations Research Forum 2023年第1期4卷 11页

作者： Aggarwal, Manish Krishankumar, R. Ravichandran, K.S. Senapati, T. Yager, R.R. School of Artificial Intelligence and Data Science IIT Jodhpur Jodhpur India Digital Humanities IIT Jodhpur Jodhpur India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Department of Mathematics Amrita School of Physical Sciences Amrita Vishwa Vidyapeetham Coimbatore India School of Mathematics and Statistics Southwest University Chongqing Beibei 400715 China Machine Intelligence Institute Iona College New Rochelle 10801 NY United States

Assessing the performance of organizations in the near future has been a challenging problem because of the factors of subjective assessment of the uncertainty. To cater to such applications, a novel form of fuzzy entropy is proposed to unify the concepts of fuzzy entropy and the conventional Shannon’s entropy. The proposed entropy provides a direct and convenient framework to quantify the uncertainty associated with a firm in the near future. It is also shown that the proposed form is amenable in extending the fuzzy entropy functions to the probabilistic-fuzzy domain. Thus, the proposed form of fuzzy entropy finds a special significance in the human decision-making problems. A case study illustrates the applicability of the proposed functions in assessment of future performance of a firm. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Attitudinal character Entropy Fuzzy logic Probabilistic-fuzzy Uncertainty

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learning Discrete Latent Variable Structures with Tensor Rank Conditions 38

Learning Discrete Latent Variable Structures with Tensor Ran...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Zhengming Cai, Ruichu Xie, Feng Qiao, Jie Wu, Anpeng Li, Zijian Hao, Zhifeng Zhang, Kun School of Computer Science Guangdong University of Technology China Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Department of Applied Statistics Beijing Technology and Business University China Department of Computer Science and Technology Zhejiang University China College of Science Shantou University Guangdong Shantou China Department of Philosophy Carnegie Mellon University United States

Unobserved discrete data are ubiquitous in many scientific disciplines, and how to learn the causal structure of these latent variables is crucial for uncovering data patterns. Most studies focus on the linear latent variable model or impose strict constraints on latent structures, which fail to address cases in discrete data involving non-linear relationships or complex latent structures. To achieve this, we explore a tensor rank condition on contingency tables for an observed variable set Xp, showing that the rank is determined by the minimum support of a specific conditional set (not necessary in Xp) that d-separates all variables in Xp. By this, one can locate the latent variable through probing the rank on different observed variables set, and further identify the latent causal structure under some structure assumptions. We present the corresponding identification algorithm and conduct simulated experiments to verify the effectiveness of our method. Our results elegantly expand the application scope of causal discovery with latent variables. © 2024 Neural information processing systems foundation. All rights reserved.

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A Quantum Algorithm for Evaluating the Hamming Distance

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computers, Materials & Continua 2022年第4期71卷 1065-1078页

作者： Mohammed Zidan Manal G.Eldin Mahmoud Y.Shams Mohamed Tolan Ayman Abd-Elhamed Mahmoud Abdel-Aty Department of Artificial Intelligence Hurghada Faculty of Computers and Artificial IntelligenceSouth Valley UniversityEgypt Faculty of Engineering King Salman International UniversitySouth SinaiEgypt Department of Mathematics and Computer Science Faculty of ScienceBeni-Suef UniversityBeni-SuefEgypt Department of Machine Learning and Information Retrieval Faculty of Artificial IntelligenceKafrelsheikh UniversityEgypt Mechanical Department Faculty of Technology and EducationSuez UniversitySuezEgypt Faculty of Technological Industries King Salman International UniversitySouth SinaiEgypt Faculty of Engineering at Mataria Helwan UniversityCairoEgypt Department of Mathematics Faculty of SciencesSohag University82524 SohagEgypt

We present a novel quantum algorithm to evaluate the hamming distance between two unknown oracles via measuring the degree of entanglement between two ancillary *** particular,we use the power of the entanglement degree based quantum computing model that preserves at most the locality of interactions within the quantum model *** model uses one of two techniques to retrieve the solution of a quantum computing problem at *** the first technique,the solution of the problem is obtained based on whether there is an entanglement between the two ancillary qubits or *** the second,the solution of the quantum computing problem is obtained as a function in the concurrence value,and the number of states that can be generated from the Boolean *** proposed algorithm receives two oracles,each oracle represents an unknown Boolean function,then it measures the hamming distance between these two *** hamming distance is evaluated based on the second *** is shown that the proposed algorithm provides exponential speedup compared with the classical counterpart for Boolean functions that have large numbers of Boolean *** proposed algorithm is explained via a case ***,employing recently developed experimental techniques,the proposed algorithm has been verified using IBM’s quantum computer simulator.

关键词： Quantum computing quantum algorithm quantum circuit

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Speech Emotion Recognition with Web Speech API 9

Speech Emotion Recognition with Web Speech API

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Anand, M. Vijay Rajasekaran, A. Rajendra Thilahar, C. Bharathiraja, N. Department of Computer Science and Engineering Chennai India Simats Saveetha School of Engineering Department of Artificial Intelligence and Machine Learning Chennai India VelTech MultiTech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798350384208

Speech is one of the natural ways that we as humans communicate with one another. We rely on it so much that we have come to understand its significance in electronic communication, including text messages and emails, where we frequently utilize emojis to convey the emotions we are feeling at the time. Since emotions are a major component of communication, it is critical to identify and analyze them in the digital world of remote communication that we live in today. We use the SER technique because emotion detection is a difficult task because emotions are subjective. The goal of Speech Emotion Recognition (SER) is to identify the affective components of speech, independent of its semantic content. The goal of research on automatic emotion recognition systems is to develop effective, real-time techniques for identifying emotions in a range of application domains, such as caller-agent conversation analysis and interactive voice assistants. In this project, Web-Speech API - pre-existing software for analyzingthe nuances and annotations of speech - is used to analyze and identify the emotional aspects of *** we will be recognizing the words we speak using the scores which are predefined in the AFINN database. The outputwill be displayed in the webpage in the form of emojis as we speak in real-time. The approximate accuracy obtained in this project is around 80%-85%.Thus the sentiment in any content published as a speech or a live recording can be analysed through this project. © 2023 IEEE.

关键词： Speech recognition

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Advancing Vascular Dementia Diagnosis Through Salp Swarm Optimized Convolutional Neural Network 8

Advancing Vascular Dementia Diagnosis Through Salp Swarm Opt...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Mathkunti, Nivedita Manohara Ananthanagu, U. Abdulpur, Vidya N. Mangaiyarkarasi, D. Anitha, S. Alliance School of Advanced Computing Alliance Univerisity Department of Computer Science & Engineering Karnataka Bengaluru India Dr.D.Y Patil Pratishthan's College of Engineering Department of Electrical Engineering Maharastra Kolhapur India Institute of Science and Technology Avadi India Department of Artificial Intelligence & Machine Learning Karnataka Bengaluru562106 India

ISBN: (纸本)9798331505462

Vascular Dementia (VD) is a form of cognitive impairment seen in individuals, restricting from problems with blood vessels supplying the brain. These issues lead to strokes or reduced blood flow and gradually lead to the development of vascular dementia. Since it tends to progress slowly, diagnosis often comes after significant brain damage making prevention is crucial. Early identification of risk factors are vital to mitigate the risk of irreversible cognitive decline associated with vascular dementia. Accordingly, this research proposes an optimized neural network model for predicting vascular dementia at its earlier to improve the quality of life. The initial stage in this research involves preprocessing, where the input data is cleaned and normalized. These steps ensure that the vascular dementia dataset is cleaned of inconsistencies and prepared with normalized features for accurate analysis and modeling. Following preprocessing, relevant data features are selected using the chi-square approach, making the classification task easier. Finally, classification is performed utilizing a Convolutional Neural Network (CNN) and for fine-tuning of CNN parameters Salp Swarm Optimization (SSO) Algorithm is implemented. The execution of proposed work is processed using Python software and the outcome reveals superior performance in terms of accuracy and precision. Thereby the proposed method provides better performance with an accuracy of 94.5%, contrasted to existing methods in predicting VD. © 2024 IEEE.

关键词： Convolutional neural networks

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GPT Assisted Annotation of Rhetorical and Linguistic Features for Interpretable Propaganda Technique Detection in News Text

arXiv

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arXiv 2024年

作者： Hamilton, Kyle Longo, Luca Božić, Bojan School of Computer Science and SFI Centre for Research Training in Machine Learning Technological University Dublin Dublin Ireland Artificial Intelligence and Cognitive Load Research Lab School of Computer Science and SFI Centre for Research Training in Machine Learning Technological University Dublin Dublin Ireland

While the use of machine learning for the detection of propaganda techniques in text has garnered considerable attention, most approaches focus on "black-box" solutions with opaque inner workings. Interpretable approaches provide a solution, however, they depend on careful feature engineering and costly expert annotated data. Additionally, language features specific to propagandistic text are generally the focus of rhetoricians or linguists, and there is no data set labeled with such features suitable for machine learning. This study codifies 22 rhetorical and linguistic features identified in literature related to the language of persuasion for the purpose of annotating an existing data set labeled with propaganda techniques. To help human experts annotate natural language sentences with these features, RhetAnn, a web application, was specifically designed to minimize an otherwise considerable mental effort. Finally, a small set of annotated data was used to fine-tune GPT-3.5, a generative large language model (LLM), to annotate the remaining data while optimizing for financial cost and classification accuracy. This study demonstrates how combining a small number of human annotated examples with GPT can be an effective strategy for scaling the annotation process at a fraction of the cost of traditional annotation relying solely on human experts. The results are on par with the best performing model at the time of writing, namely GPT-4, at 10x less the cost. Our contribution is a set of features, their properties, definitions, and examples in a machine-readable format, along with the code for RhetAnn and the GPT prompts and fine-tuning procedures for advancing state-of-the-art interpretable propaganda technique detection. © 2024, CC BY.

关键词： Cost engineering

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