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5th IEEE International Conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2023

作者： Shanmugavalli, R. Krishnaveni, M. Dhivyaprabha, T.T. Subashini, P. Center for Machine Learning Intelligence Avinashilingam Institute for Home Science and Higher Education for Women Department of Computer Science Coimbatore India

ISBN: (纸本)9798350338287

Wireless Sensor Network (WSN) is a self-configured and infrastructure-less network that is used to monitor the environmental conditions and transfer sensor data to the desired destination in a particular region. Energy consumption is the most important concern in WSN, which is considered as an active research area. Routing selection is one method that is used to optimize energy in WSN. There are many protocols for discovering a route between two nodes. However, the performance of the Ad hoc On-Demand Distance Vector (AODV) routing protocol is a more suitable one. It is a generic reactive protocol for routing mostly used in MANET (Mobile Ad Hoc Networks) and WSNs (Wireless Sensor Networks). This protocol supports unicasting and multicasting and will also identify the shortest path. In this paper, energy-aware routing is implemented in the AODV protocol which is derived from nodes remaining energy. The remaining energy of node is computed by Max-Min energy algorithm in order to extend the network's life span and facilitates to keep the network lively. The performance of AODV is compared with Modified AODV protocols. The comparison is done by various performance metrics such as PDR (Packet Delivery Ratio), throughput, delay time, loss rate, and energy consumption. Analysis on the experimental results showed that MAODV protocol gives better results than traditional AODV protocol and it is also inferred that MAODV avoids too much energy consumption of nodes in the network. © 2023 IEEE.

关键词： Energy utilization

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Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty? 39

Is the Volume of a Credal Set a Good Measure for Epistemic U...

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39th Conference on Uncertainty in Artificial Intelligence, UAI 2023

作者： Caprio, Michele Hüllermeier, Eyke Sale, Yusuf Germany PRECISE Center Department of Computer and Information Science University of Pennsylvania United States Munich Center for Machine Learning Germany

Adequate uncertainty representation and quantification have become imperative in various scientific disciplines, especially in machine learning and artificial intelligence. As an alternative to representing uncertainty via one single probability measure, we consider credal sets (convex sets of probability measures). The geometric representation of credal sets as d-dimensional polytopes implies a geometric intuition about (epistemic) uncertainty. In this paper, we show that the volume of the geometric representation of a credal set is a meaningful measure of epistemic uncertainty in the case of binary classification, but less so for multi-class classification. Our theoretical findings highlight the crucial role of specifying and employing uncertainty measures in machine learning in an appropriate way, and for being aware of possible pitfalls. © UAI 2023. All rights reserved.

关键词： machine learning

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Computationally Efficient machine learning Methodology for Indian Nobel Laureate Classification 6

Computationally Efficient Machine Learning Methodology for I...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Banerjee, Swastik Joy, Helen. K. Artificial Intelligence & Machine Learning Christ Deemed to be University Department of Computer Science Bengaluru India Christ Deemed to be University Department of Computer Science Bengaluru India

ISBN: (纸本)9798350381689

A computationally efficient methodology for Indian Nobel Laureate classification is proposed in this study, emphasizing the optimization of image categorization through supervised learning techniques. Leveraging advancements in Convolutional Neural Networks (CNNs), the research aims to enhance the efficiency and precision of image classification tasks. The study utilizes Logistic Regression for dataset analysis, initially employing browser extensions for mass downloading categorized image data. Haar cascade classifiers are then used for data wrangling, focusing on facial, nose, and mouth recognition. Following this, feature engineering through wavelet transformation reduces image dimensionality, preparing the dataset for the chosen ML model, Logistic Regression. The primary focus is to simplify technology for improved image categorization. Support Vector machines (SVM), Random Forest, and Logistic Regression are examined, with Logistic Regression emerging as the most effective model, achieving an accuracy rate of 87.5%. A thorough evaluation using Confusion Matrices reveals Logistic Regression's superior performance in classifying images of Indian Nobel laureates. A strategic up-sampling approach is implemented to address dataset inconsistencies, ensuring balanced representation across classes. The Haar wavelet transform is then applied for feature extraction, optimizing the dataset for ML models. The dataset is split into training and testing sets (80-20), and the three models are trained and evaluated for accuracy. Logistic Regression proves to be the best performer, offering insights into prominent leaders' identification. The research offers a detailed pipeline for data preprocessing, feature engineering, and model assessment, culminating in a robust image categorization system. Logistic Regression emerges as a reliable method for biographical picture identification, demonstrating superior accuracy over SVM and Random Forest. This research underscores the import

关键词： Convolutional neural networks

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Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics 38

Navigating the Maze of Explainable AI: A Systematic Approach...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Klein, Lukas Lüth, Carsten Schlegel, Udo Bungert, Till El-Assady, Mennatallah Jäger, Paul Interactive Machine Learning Group Germany ETH Zürich Department of Computer Science Switzerland Germany Heidelberg University Department of Computer Science Germany University of Konstanz Department of Computer Science Germany

Explainable AI (XAI) is a rapidly growing domain with a myriad of proposed methods as well as metrics aiming to evaluate their efficacy. However, current studies are often of limited scope, examining only a handful of XAI methods and ignoring underlying design parameters for performance, such as the model architecture or the nature of input data. Moreover, they often rely on one or a few metrics and neglect thorough validation, increasing the risk of selection bias and ignoring discrepancies among metrics. These shortcomings leave practitioners confused about which method to choose for their problem. In response, we introduce LATEC, a large-scale benchmark that critically evaluates 17 prominent XAI methods using 20 distinct metrics. We systematically incorporate vital design parameters like varied architectures and diverse input modalities, resulting in 7,560 examined combinations. Through LATEC, we showcase the high risk of conflicting metrics leading to unreliable rankings and consequently propose a more robust evaluation scheme. Further, we comprehensively evaluate various XAI methods to assist practitioners in selecting appropriate methods aligning with their needs. Curiously, the emerging top-performing method, Expected Gradients, is not examined in any relevant related study. LATEC reinforces its role in future XAI research by publicly releasing all 326k saliency maps and 378k metric scores as a (meta-)evaluation dataset. The benchmark is hosted at: https://***/IML-DKFZ/latec. © 2024 Neural information processing systems foundation. All rights reserved.

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Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality 41

Transformers are SSMs: Generalized Models and Efficient Algo...

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41st International Conference on machine learning, ICML 2024

作者： Dao, Tri Gu, Albert Department of Computer Science Princeton University United States Machine Learning Department Carnegie Mellon University United States

While Transformers have been the main architecture behind deep learning's success in language modeling, state-space models (SSMs) such as Mamba have recently been shown to match or outperform Transformers at small to medium scale. We show that these families of models are actually quite closely related, and develop a rich framework of theoretical connections between SSMs and variants of attention, connected through various decompositions of a well-studied class of structured semiseparable matrices. Our state space duality (SSD) framework allows us to design a new architecture (Mamba-2) whose core layer is an a refinement of Mamba's selective SSM that is 2-8× faster, while continuing to be competitive with Transformers on language modeling. Copyright 2024 by the author(s)

关键词： Distribution transformers

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THE HUMAN-AI SUBSTITUTION GAME: ACTIVE learning FROM A STRATEGIC LABELER 12

THE HUMAN-AI SUBSTITUTION GAME: ACTIVE LEARNING FROM A STRAT...

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12th International Conference on learning Representations, ICLR 2024

作者： Yan, Tom Zhang, Chicheng Machine Learning Department Carnegie Mellon University United States Department of Computer Science University of Arizona United States

The standard active learning setting assumes a willing labeler, who provides labels on informative examples to speed up learning. However, if the labeler wishes to be compensated for as many labels as possible before learning finishes, the labeler may benefit from actually slowing down learning. This incentive arises for instance if the labeler is to be replaced by the ML model once it is trained. In this paper, we initiate the study of learning from a strategic labeler, who may abstain from labeling to slow down learning. We first prove that strategic abstention can prolong learning, and propose a novel complexity measure and representation to analyze the query complexity of the learning game. Next, we develop a near-optimal deterministic algorithm, prove its robustness to strategic labeling, and contrast it with other active learning algorithms. We also analyze extensions that encompass more general learning goals and labeler assumptions. Finally, we characterize the query cost of multi-task active learning, with and without abstention. Our first exploration of strategic labeling aims to consolidate our theoretical understanding of the imitative nature of ML in human-AI interaction. © 2024 12th International Conference on learning Representations, ICLR 2024. All rights reserved.

关键词： learning algorithms

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UNCCER: Unified Network for Cancer Classification and Efficient Representation using Microarray Data 18

UNCCER: Unified Network for Cancer Classification and Effici...

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Younis, Haseeb Brahmi, Horiya Imane Byrne, Jonathan Minghim, Rosane University College Cork Department of Computer Science Ireland Intel Ireland Limited Machine Learning Group Ireland

ISBN: (纸本)9798331508692

In recent years, there has been a significant advance in the use of machine learning (ML) techniques to extract gene expression data from microarray databases, particularly in cancer-related research. There no unified method for classifying cancer microarray data, even after ML adoption. Due to the high dimensionality of microarray data, it is difficult to extract the relevant features and provide insights that can be helpful in identifying cancer types and stages. In this paper, we propose a Unified Network for Cancer Classification and Efficient Representation (UNCCER) using Deep learning (DL) on cancer microarray data. To implement this methodology, we employed a microarray database (CuMiDa) that has 78 carefully curated datasets for different types of cancers. Our single model has the capability to learn the patterns, cluster instances into their corresponding classes, and classify the cancer. We also used the Uniform Manifold Approximation and Projection (UMAP) to visualise, in low dimension, the instance separation both on original data and transformed data by our methodology. Using the proposed methodology, we achieved average 94% average accuracy, precision, recall, F1 Score, and 91% G-Mean. © 2024 IEEE.

关键词： Lung cancer

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Design of Weed Detection Technique Using Yolo Models

Design of Weed Detection Technique Using Yolo Models

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2024 IEEE Students Conference on Engineering and Systems, SCES 2024

作者： Sreehari, M. Jain, Ankur Vellore Institiute of Technology Artificial intelligence and Machine Learning Department of computer science engineering specialization Bhopal India

ISBN: (纸本)9798350374711

The use of the ADAM (Adaptive Moment Estimation) and SGD (Stochastic Gradient Descent) algorithms to optimize the YOLOv7(You Only Look Once), YOLOv8, and YOLO-NAS models for weed detection in agricultural landscapes is investigated in this study. Offering a thorough comparative study based on mean Average Precision (mAP), recall, and precision measures is the goal. When compared to YOLOv7, YOLOv8 regularly performs better, exhibiting balanced recall and higher precision. With remarkable mAP scores at IOU (Intersection Over Union) 0.50, YOLOv8 ADAM in particular sticks out, demonstrating strong weed identification abilities. The YOLOv7 ADAM exhibits a precision-recall trade-off, with precision being slightly sacrificed for recall. Model superiority is not solely determined by the optimization algorithm used (ADAM vs. SGD);YOLOv8 variations perform admirably. For real-world implementation, practical factors like processing efficiency and deployment viability are even more important than quantitative criteria. This work establishes the foundation for comprehending the complex trade-offs and advantages that are present in every model and algorithm, in addition to helping with the educated selection of the best YOLO models for weed detection. Future research directions in object detection in agriculture, including ensemble techniques and hyperparameter tuning, promise more advancements in this subject as technology develops. © 2024 IEEE.

关键词： Stochastic systems

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Secure Healthcare Monitoring: Algorithms and Techniques for Heart Disease Detection and Data Privacy 1

Secure Healthcare Monitoring: Algorithms and Techniques for ...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Poonam Mallapur, Sujata V. Sharnbasva University Department of Computer Science and Engineering Kalaburagi India Sharnbasva University Department of Artificial Intelligence & Machine Learning Kalaburagi India

ISBN: (纸本)9798331529246

This survey offers the review of Healthcare Monitoring Systems (HMS) and Privacy Preservation (PP) approaches. The main objective is based on the detection of heart disease and maintain the security for patient data. The machine learning models such as SVM, decision trees, and neural networks for heart disease detection and encryption approaches such as AES, ECC, and homomorphic encryption for securing the data are the major topics that reviewed in this survey. Moreover, the survey highlights the performance evaluation for heart disease prediction and the datasets used for heart disease. It also ensures the privacy to tackle the challenges in real time monitoring. A higher accuracy of 99.8 was reached by FHE-based encryption method. Moreover, it discusses the research gaps like scalability, efficiency, and regulatory compliance which motivates the need of novel privacy-preserving techniques for healthcare systems, which is highlighted in the future scope. © 2025 IEEE.

关键词： Differential privacy

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Improved fuzzy C means with deep learning-based MANET routing model for multimedia communication

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International Journal of Networking and Virtual Organisations 2024年第4期31卷 306-338页

作者： Biradar, Manjula A. Mallapur, Sujata Department of Computer Science and Engineering Sharnbasava University Sharan Nagar Karnataka Kalaburagi585105 India Department of Artificial Intelligence and Machine Learning Sharnbasava University Sharan Nagar Karnataka Kalaburagi585105 India

The rapid growth of wireless communication amazed the researchers to done multimedia communication via wireless networks. The significant rise of mobile ad-hoc networks (MANET) in wireless communication is widely researched by researchers for an efficient transmission of multimedia data. MANET works on non-infrastructure topology with high quality of service (QoS) for better user experience. Though, it suffers from limitations such as routing, limited bandwidth, dynamic mobile node nature, etc. That’s why, this research paper is dedicated to introduce a MANET routing model for multimedia communication by providing reliable retransmission for better communication. An optimal cluster-based routing via improved fuzzy C-means (FCM) clustering takes place before it gets started nodal energy prediction is done by modified deep CNN model. And optimal cluster head selection is done via self-improved coati optimisation (SICO) under certain constraints. Finally, it is finished off with reliable retransmission to enhance the reliability of communication. Further, the efficiency of the proposed model is proved by various analysis. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Mobile ad hoc networks

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