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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Guan, Jiechao Xiong, Hui AI Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering HKUST China

Hierarchical Bayesian bandit refers to the multi-task bandit problem in which bandit tasks are assumed to be drawn from the same distribution. In this work, we provide improved Bayes regret bounds for hierarchical Bayesian bandit algorithms in the multi-task linear bandit and semi-bandit settings. For the multi-task linear bandit, we first analyze the preexisting hierarchical Thompson sampling (HierTS) algorithm, and improve its gap-independent Bayes regret bound from O(mpn log n log (mn)) to O(m√n log n) in the case of infinite action set, with m being the number of tasks and n the number of iterations per task. In the case of finite action set, we propose a novel hierarchical Bayesian bandit algorithm, named hierarchical BayesUCB (HierBayesUCB), that achieves the logarithmic but gap-dependent regret bound O(m log (mn) log n) under mild assumptions. All of the above regret bounds hold in many variants of hierarchical Bayesian linear bandit problem, including when the tasks are solved sequentially or concurrently. Furthermore, we extend the aforementioned HierTS and HierBayesUCB algorithms to the multi-task combinatorial semi-bandit setting. Concretely, our combinatorial HierTS algorithm attains comparable Bayes regret bound O(m√n log n) with respect to the latest one. Moreover, our combinatorial HierBayesUCB yields a sharper Bayes regret bound O(m log (mn) log n). Experiments are conducted to validate the soundness of our theoretical results for multi-task bandit algorithms. © 2024 Neural information processing systems foundation. All rights reserved.

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IAOMDR: Improved Aquila Optimized Hybrid deep learning Model for Classification of Diabetic Retinopathy 2

IAOMDR: Improved Aquila Optimized Hybrid deep learning Model...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Saju, Binju Barwal, Sahil Asha, V. Kumar, Shubham Tressa, Neethu Nikhil, T. Adi Shankara Institute of Engineering & Technology Department of Computer Science-AI Engineering Kalady India New Horizon College of Engineering Department of Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350328202

One of the scariest illnesses that causes irreversible blindness is Diabetic Retinopathy (DR). As a result, early exposure to Diabetic Retinopathy can help to preserve vision. The study proposes a hybrid model to classify Diabetic Retinopathy images. Initially, pre-processing of the photographs includes resizing them. Grey scale translation and noise removing using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is as opposed to other studies in literature. The proposed model has acquired F1 score of 98.98%, accuracy of 99.2%, sensitivity of 98.9%, specificity of 99.1%, and precision of 99%. © 2024 IEEE.

关键词： Median filters

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Analysis of Magnetic Resonance images for the detection of Alzheimer's 4

Analysis of Magnetic Resonance images for the detection of A...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Patil, Harshal Swati Kamala, L. Chaturvedi, Arpana Veeranjaneyulu, Rayavarapu Vekariya, Vipul Maharashtra Pune India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Gitam School of Technology Department of Computer Science and Engineering Karnataka Bengaluru561203 India New Delhi Institute of Management Department of IT/AI/ML New Delhi110062 India Pace Institute of Technology & Sciences Department of Computer Science and Engineering Andhra Pradesh Ongole523272 India Parul University Parul Institute of Engineering and Technology Department of Computer Science and Engineering Post Limda Gujarat 391760 India

ISBN: (纸本)9798350354218

This study aims to design a CAD tool for Alzheimer's disease (AD) detection using linear SVM classifiers on MRI scans. Existing approaches often face challenges with high-dimensional feature spaces and limited data accessibility from repositories like ADNI. To overcome these, three SVM-based classifier proposals were explored. Results demonstrated promising accuracy, sensitivity (Se), and specificity (Sp), particularly with cross-validation techniques. Notably, classifiers trained with image histograms showed superior performance in differentiating between AD, cognitively normal (CN), and mild cognitive impairment (MCI). Despite successes, initial approaches encountered difficulties, necessitating alternative methods. Challenges in data access and processing highlight the need for improved data management tools. Moving forward, refinements such as feature selection and testing with larger datasets could enhance classifier performance. Exploring deep neural networks as an extension may offer further insights into AD detection methodologies for early diagnosis and intervention. © 2024 IEEE.

关键词： Deep neural networks

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Disease Detection in Finger Millet Plant Using Fine-Tuned Transfer Learning Models

Disease Detection in Finger Millet Plant Using Fine-Tuned Tr...

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Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Prajwal B Subhash Mondal Raghu M Shashank N Computer Science & Engineering (AI & ML) Dayananda Sagar University Bengaluru India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Plant diseases are becoming one of the major threats to global food security, particularly in carbohydrate rich staple crops like finger millet (Eleusine coracana) which requires minimal water and can be grown in diverse climate conditions, highlighting the need for early disease detection to minimize yield losses and promote sustainable agriculture. We employed web scraping for data collection from different open source, data augmentation techniques like flip, rotation, zoom to enhance the model's performance and enhance generalization capabilities. This research focuses on developing an effective disease detection system for finger millet using Convolutional Neural Networks (CNNs) and transfer learning methodologies. We analyzed with pretrained CNN architectures, such as DenseNet121, VGG16, and VGG19, using a dataset of more than thousands of labelled images collected from diverse agricultural settings. Fine tuning these models ensures optimising model's performance and achieving better accuracy. Among the tested models, DenseNet121 achieved the highest accuracy, reaching 96.88%. Additionally, we developed a user-friendly real-time application to disease detection using Streamlit library helping the farmers to detect the disease in mobile for remote access. This study demonstrates the significant use of deep learning techniques like CNN architectures, particularly transfer learning, for developing better crop disease detection systems. By detecting the disease early and prevention at earlier stage will save tonnes of crops. This solves the major issues like food security and also malnutrition.

关键词： Plant diseases Accuracy Transfer learning Fingers Food security Crops computer architecture Convolutional neural networks Sustainable development Tuning

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ABSPYRAMID: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph

ABSPYRAMID: Benchmarking the Abstraction Ability of Language...

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2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Wang, Zhaowei Shi, Haochen Wang, Weiqi Fang, Tianqing Zhang, Hongming Choi, Sehyun Liu, Xin Song, Yangqiu Department of Computer Science and Engineering HKUST Hong Kong Tencent AI Lab Bellevue United States

ISBN: (纸本)9798891761193

Cognitive research indicates that abstraction ability is essential in human intelligence, which remains under-explored in language models. In this paper, we present ABSPYRAMID, a unified entailment graph of 221K textual descriptions of abstraction knowledge. While existing resources only touch nouns or verbs within simplified events or specific domains, ABSPYRAMID collects abstract knowledge for three components of diverse events to comprehensively evaluate the abstraction ability of language models in the open domain. Experimental results demonstrate that current LLMs face challenges comprehending abstraction knowledge in zero-shot and few-shot settings. By training on our rich abstraction knowledge, we find LLMs can acquire basic abstraction abilities and generalize to unseen events. In the meantime, we empirically show that our benchmark is comprehensive to enhance LLMs across two previous abstraction tasks. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Self-moving Window with Long-Short Term Memory based Anomaly Detection in Edge-Assisted IoT Environments

Self-moving Window with Long-Short Term Memory based Anomaly...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Chanti, Yerrolla Adnan, Myasar Mundher Devi, R. Renuga Lakshmanan, M. Madhavi, Arelli Sr University School of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram India Nitte Meenakshi Institute of Technology Department of Eee Bengaluru India Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Bachupally India

ISBN: (纸本)9798350375442

Internet of Things (IoT) systems is widely used in many applications which generate huge amounts of sensory signals that involves to signify the state of systems. However, due to changes in IoT environment impact the detection of anomalies accurately which lead to risk of losing information. To overcome this limitation, a Self-moving Window with Long Short-Term Memory (SW-LSTM) is proposed for accurate detection of anomalies. The self-moving window improved the ability of LSTM to identify subtle anomalies that evolve over time, leading to more accurate detection of unusual patterns or device behavior in IoT environments. Initially, the data about attacks and malicious nodes are acquired from the UNSW-15 dataset and preprocessed by score normalization technique. Then, the optimal set of features are selected by Particle Swarm Optimization (PSO) algorithm. Finally, the proposed SW-LSTM model detects the anomalies in edge assisted IoT network effectively. The experimental results of proposed method achieved accuracy of 93.4% which is greater when compared to existing detection approaches such as Recurrent Neural Network (RNN), Convolutional Neural Network (CNN). © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Heart disease prediction using machine learning, deep Learning and optimization techniques-A semantic review

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Multimedia Tools and Applications 2024年第39期83卷 86895-86922页

作者： Bhavekar, Girish Shrikrushnarao Das Goswami, Agam Vasantrao, Chafle Pratiksha Gaikwad, Amit K. Zade, Amol V. Vyawahare, Harsha Department of Artificial Intelligence & Data Science CSMSS Chh. Shahu College of Engineering Aurangabad431136 India Department of SENSE VIT-AP University Andhra Pradesh 522237 India Department of AI & DS CSMSS Chh. Shahu College of Engineering Maharashtra Aurangabad431136 India Department of Computer Science & Engineering GH Raisoni University Maharashtra Amravati444701 India Department of Computer Science & Engineering Sipna College of Engineering & Technology Maharashtra Amravati444701 India

Cardiovascular disease holds the position of being the foremost cause of death worldwide. Heart Disease Prediction (HDP) is a difficult task as it needs advanced knowledge with better experience. Moreover, it encounters numerous significant challenges in clinical data analysis. While many researchers have focused on predicting heart disease, the performance metric, namely prediction accuracy, remains suboptimal. The accurate HDP can help the person to prevent himself from life threats and at the same time, inaccurate prediction can prove to be fatal. To solve these issues, in this review work several Deep Learning (DL), Machine Learning (ml) and optimization based HDP techniques are discussed. In recent times, many researchers have been utilizing different DL and ml algorithms to help the professionals and health care industry for the prediction of heart disease. Further, it discussed about various optimization-based algorithms and its performance analysis. Therefore, this review paper suggests that the optimization-based HDP algorithm could assist doctors in predicting the occurrence of heart disease in advance and offering suitable treatment. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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Towards intelligent and trustworthy task assignments for 5G-enabled industrial communication systems

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Digital Communications and Networks 2025年第1期11卷 246-255页

作者： Mingfeng Huang Anfeng Liu Neal N.Xiong Athanasios V.Vasilakos School of Computer and Communication Engineering Changsha University of Science&TechnologyChangsha410114China School of Electronic Information Central South UniversityChangsha410083China Department of Computer Science and Mathematics Sul Ross State UniversityAlpineTX 79830USA College of Mathematics and Computer Science Fuzhou UniversityFuzhou350116China Center for AI Research University of AgderGrimstad 999026Norway

With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient *** this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few ***,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each ***,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate *** when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start *** importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker ***,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.

关键词： Industrial Internet of Things Insufficient workers Trust evaluation Social relation Task assignment

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A Novel Approach to Optimizing Spark: Detecting Misconfigurations for Enhanced Efficiency

A Novel Approach to Optimizing Spark: Detecting Misconfigura...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Sanjana S V Rachana Uday Umesh Suraj Rao R. Jayashree Department of Computer Science and Engineering PES University Bengaluru India Department of Computer Science and Engineering (AI & ML) PES University Bengaluru India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Configuration errors constitute 60% of contemporary system failures in distributed computing platforms. These errors lead to data breaches, system crashes, or silent failures requiring extensive technical support and knowledge of the application architecture. This research work introduces a dataset that can be used to detect misconfigurations in a Spark cluster. The dataset encompasses detailed system performance metrics such as CPU usage and memory allocation that were recorded by running CPU intensive and memory intensive Spark jobs under various configuration settings to capture performance variations. Analysis of this dataset provides insights into optimizing the values of Spark cluster configurations and mitigating the impact of configuration errors. By using machine learning techniques on this dataset, it is possible to construct automated methods for proactively detecting and mitigating misconfigurations, which improves the stability and efficiency of distributed computing environments.

关键词： Measurement System performance Machine learning Data breach Stability analysis computer crashes Sparks Resource management Distributed computing Intelligent systems

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Enhancing Real-Time Stream Processing with Apache Kafka by integrating Scaled-Hidden Markov Models

Enhancing Real-Time Stream Processing with Apache Kafka by i...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： V Rachana Sanjana S R Jayashree Department of Computer Science and Engineering PES University Bangalore India Department of Computer Science and Engineering(AI & ML) PES University Bangalore India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

This research study explores the integration of Kafka, a distributed streaming platform, in the context of enhancing speech recognition systems utilizing Hidden Markov Models (HMMs). Speech recognition is a vital aspect of ai and Machine Learning which plays an important role in the interaction between humans and computers. Hidden Markov models (HMMs) are a popular choice for speech recognition systems due to sequential data modeling. In this paper, we conduct a comprehensive study integrating Big Data technologies like Kafka into Hidden Markov Model (HMM) based speech recognition, aiming to recognize spoken words from audio wave files and classify them into predefined word classes. The integration involves a multifaceted approach, employing Kafka for efficient data processing and communication between distinct system modules. To prevent underflow during HMM training, a Scaled HMM Module is employed. We address challenges posed by unseen data, by incorporating smoothing techniques such as add-delta smoothing which mitigates zero probabilities and improves the generalization of the model. We also experiment the use of pruning techniques which disregards the low-probability paths during the Viterbi algorithm, which results in improvement in the accuracy. By incorporating the integration of Big Data technologies like Kafka, along with advanced techniques such as pruning, smoothing, and scaling of Hidden Markov Models (HMMs), we delve into a comprehensive strategy for advancing speech recognition capabilities.

关键词： Adaptation models Analytical models Smoothing methods Accuracy Viterbi algorithm Hidden Markov models Speech recognition Big Data Data models Robustness

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