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Progressive Index Modulation Based CH Selection in ICIC Scheme for Multi-Hop WSN 11

Progressive Index Modulation Based CH Selection in ICIC Sche...

引用

11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Monika, P. Vijayalakshmi, S. Sri Ramakrishna College of Arts and Science for Women Department of Computer Science Coimbatore India Sri Ramakrishna College of Arts and Science for Women Department of Data Analytics Coimbatore India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350357

Wireless Sensor network is the communication network in which the sensor nodes are located in various parts of the network and the base station in same network. The sensor nodes gather the data and communicate gathered information to the base station. The major issue in the transmission process of WSN is energy consumption. Some techniques and algorithms are suggested in many researches for reducing energy consumption to make the node to sustain in network for longer duration. This paper proposes the progressive index modulation ICIC strategy for cluster head selection and used indexing transmitting data. In intra cluster mode, cluster head is chosen based on probability factor and minimized energy utilized route is identified and indexing is initialized for that route in intra clustering scheme. The simulation is performed using different metrics like packet delivery rate, number of dead nodes and energy utilization. All the above metrics are evaluated in two different strategies like number of rounds and number of nodes. Proposed evaluation results provides better results when compared with ICIC strategy and it helps to utilize less energy and better data transmission. © 2024 IEEE.

关键词： Energy utilization

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Enhance Efficient of Support Vector Machine with CNN and PSO for solving Handwritten Digits Recognition 28

Enhance Efficient of Support Vector Machine with CNN and PSO...

引用

28th International computer science and Engineering Conference, ICSEC 2024

作者： Janprasit, Siwach Punkong, Narong Ratanavilisagul, Chiabwoot Kosolsombat, Somkiat Faculty of Applied Science Department of Computer and Information Science Bangkok Thailand Digital Technology for Business Faculty of Management Science Kanchanaburi Thailand Faculty of Applied Science Department of Computer and Information Sciences Bangkok Thailand Thammasat University Data Science and Innovation College of Interdisciplinary Studies Thailand

ISBN: (纸本)9798350366860

handwritten digit recognition is a crucial task in various fields such as postal mail sorting, bank check processing, and digitizing handwritten documents. This research aims to compare the effectiveness of using Convolutional Neural Network (CNN) for feature extraction combined with Support Vector Machine (SVM) for classification, against the traditional method of using Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) for feature extraction. Additionally, Particle Swarm Optimization (PSO) is applied to optimize SVM parameters to further enhance model performance. The experiments are conducted on two datasets: The MNIST dataset and an individual handwriting dataset from 50 participants. The results indicate that the proposed method of using CNN for feature extraction and PSO-optimized SVM achieves superior accuracy compared to traditional DWT and DCT methods. Specifically, the proposed method achieved an accuracy of 99.05% on the MNIST dataset and 67.79% on the individual handwriting dataset, demonstrating its robustness and effectiveness in handwritten digit recognition. © 2024 IEEE.

关键词： Convolutional neural networks

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Novel Spectral Algorithms for the Partial Credit Model 41

Novel Spectral Algorithms for the Partial Credit Model

引用

41st International Conference on Machine Learning, ICML 2024

作者： Nguyen, Duc Zhang, Anderson Ye Department of Computer & Information Science University of Pennsylvania United States Department of Statistics & Data Science Wharton School University of Pennsylvania United States

The Partial Credit Model (PCM) of Andrich (1978) and Masters (1982) is a fundamental model within the psychometric literature with wide-ranging modern applications. It models the integer-valued response that a subject gives to an item where there is a natural notion of monotonic progress between consecutive response values, such as partial scores on a test and customer ratings of a product. In this paper, we introduce a novel, time-efficient and accurate statistical spectral algorithm for inference under the PCM model. We complement our algorithmic contribution with in-depth non-asymptotic statistical analysis, the first of its kind in the literature. We show that the spectral algorithm enjoys the optimal error guarantee under three different metrics, all under reasonable sampling assumptions. We leverage the efficiency of the spectral algorithm to propose a novel EM-based algorithm for learning mixtures of PCMs. We perform comprehensive experiments on synthetic and real-life datasets covering education testing, recommendation systems, and financial investment applications. We show that the proposed spectral algorithm is competitive with previously introduced algorithms in terms of accuracy while being orders of magnitude faster. Copyright 2024 by the author(s)

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Transparency in Translation: A Deep Dive into Explainable AI Techniques for Bias Mitigation

Transparency in Translation: A Deep Dive into Explainable AI...

引用

2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Deokar, Ruchira Nanjundan, Preethi Mohanty, Sachi Nandan Christ University Department of Data Science Pune Lavasa India School of Computer Science & Engineering VIT -AP University Andhra Pradesh India

ISBN: (纸本)9798350361537

In an era dominated by artificial intelligence (AI), concerns about bias and discrimination loom large. The quest for fairness and equity in AI-driven decision-making has led to the exploration of Explainable AI (XAI) as a viable solution. This paper undertakes a thorough examination of the bias ingrained within AI systems and posits XAI as a potent antidote. Beginning with an exploration of the origins and aftermath of bias in AI, the analysis traverses the evolution of XAI techniques, including SHAP, LIME, and counterfactual explanations, clearly stating their advantages and drawbacks. With each XAI method thoroughly inspected, the study unravels their applicability across diverse AI models and domains. Furthermore, a compelling case study is presented, showcasing XAI's practical application in a language translation app, where it guarantees transparency and equity in the translation process. This tangible example serves as a testament to XAI's efficacy in mitigating bias within real-world applications. As the analysis concludes, it underscores the pivotal role XAI plays in fostering accountability and trustworthiness in AI systems. By shedding light on how XAI mitigates bias and offering concrete examples of its utility, the paper advocates for its widespread adoption as an imperative step towards the development of ethically robust AI systems. In a landscape filled with concerns about bias, XAI emerges as a beacon of hope, promising a future where AI decisions are transparent, fair, and equitable for all. © 2024 IEEE.

关键词： Sensitivity analysis

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Optimizing Healthcare: Enhancing Disease Management with Recommendation Systems 5th

Optimizing Healthcare: Enhancing Disease Management with Rec...

引用

5th International Symposium on Signal and Image Processing, ISSIP 2024

作者： Gupta, Ritwika Das Chatterjee, Soham Ramadani, Lauresha Department of Statistics and Data Science CHRIST Deemed to be University Bangalore India Department of Computer Science AAB College Pristina Kosovo

ISBN: (纸本)9789819795147

This paper explores a data-driven disease recommendation system for medical professionals based on symptoms. The technology examines symptom patterns to recommend diseases from large datasets by utilizing collaborative filtering and data analytics. To provide individualized disease recommendations based on symptom severity, it goes through data preprocessing and uses techniques like collaborative filtering and cosine similarity. Even if the technology is promising, disease predictions might be strengthened. It seeks to support early disease prediction and offer patients and healthcare professionals individualized guidance. This system demonstrates the potential of technology in healthcare decision-making using a basic Tkinter application. More improvements are anticipated as a result of data-driven approach advancements, which will improve patient care and optimize healthcare procedures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Personalized medicine

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ALRS: An Attention Loss Function based on Row-Sparsity for Incremental Learning 8

ALRS: An Attention Loss Function based on Row-Sparsity for I...

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8th International Conference on computer and Communication Systems, ICCCS 2023

作者： Xia, Yingying Lu, Bo Ji, Jianmin School of Data Science University of Science and Technology of China Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9781665456128

Incremental learning has received significant attention, but the problem of catastrophic forgetting remains a major challenge for existing approaches. This issue hinders models from accumulating knowledge over long stretches. To address this problem, we propose a new approach called Attention Loss function based on Row-Sparsity (ALRS) that mines significant patches by simultaneously learning patch weights and logits (class vectors) using the same parameters. We integrate the attention mechanism with the novel loss function to avoid catastrophic forgetting. This innovative approach enables the model to conflate the newly introduced classes with the existing ones, without the need to store any data or models from the previous steps' base classes. To assess its efficacy, we incorporate ALRS into the distillation loss for validation and conduct a thorough evaluation of the approach's performance on three datasets: CIFAR-100, Caltech-101, and CUBS-200-2011. Compared to LWM, which also does not store data, our method achieves an average improvement of more than 8 percentage points with an absolute advantage on CIFAR-100. Additionally, on Caltech-101 and CUBS-200-2011, our new approach provides comparable accuracy to baseline. © 2023 IEEE.

关键词： Distillation

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A Transformer-Based Approach with OpenNMT for Indonesian to Gayo Language Translation 2

A Transformer-Based Approach with OpenNMT for Indonesian to ...

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2nd International Symposium on Information Technology and Digital Innovation, ISITDI 2024

作者： Bengi, Mahara Amalia, Amalia Budiman, Mohammad Andri Universitas Sumatra Utara Department of Data Science and Artificial Intelligence Medan Indonesia Universitas Sumatra Utara Department of Computer Science Medan Indonesia

ISBN: (纸本)9798350389241

The Gayo language has been used by the Gayo community for approximately 7,500 years, but its usage has been continuously declining, and its digital documentation is minimal. To address this challenge, various efforts are necessary to document, preserve, and develop the Gayo language to keep it relevant for future generations. Machine translation is an automated process that converts text or speech from one language to another using computer algorithms without involving human translators. Machine translation can be utilized to revitalize regional languages through its documentation function. This research conducts an experiment by training a Neural Machine Translation (NMT) model using the OpenNMT framework and transformer architecture to translate Indonesian into Gayo. A parallel corpus was collected from the Indonesian-Gayo II Dictionary and through preprocessing steps, including case folding and punctuation removal. The dataset was divided into three parts: training data, validation data, and test data. data augmentation was performed to increase the variation and size of the training dataset. The experimental results showed an accuracy rate of 90% for the augmented training data, with a BLEU score of 79.90. This research is an initial step in developing an NMT model for the Gayo language. The model's prediction results indicate high accuracy when the input sentences match the corpus, but are less accurate if the input sentences contain words not present in the corpus. © 2024 IEEE.

关键词： Neural machine translation

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LATTE: Layer Algorithm-aware Training Time Estimation for Heterogeneous Federated Learning 24

LATTE: Layer Algorithm-aware Training Time Estimation for He...

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30th International Conference on Mobile Computing and Networking, ACM MobiCom 2024

作者： Wang, Kun Zhou, Zimu Li, Zhenjiang Department of Computer Science City University of Hong Kong Hong Kong School of Data Science City University of Hong Kong Hong Kong

ISBN: (纸本)9798400704895

Accurate estimation of on-device model training time is increasingly required for emerging learning paradigms on mobile edge devices, such as heterogeneous federated learning (HFL). HFL usually customizes the model architecture according to the different capabilities of mobile edge devices to ensure efficient use of local data from all devices for training. However, due to oversimplification of latency modeling, existing methods rely on a single coefficient to represent computational heterogeneity, resulting in sub-optimal HFL efficiency. We find that existing methods ignore the important impact of runtime optimization of deep learning frameworks, which we call development-chain diversity. Specifically, layers of a model may have different algorithm implementations, and deep learning frameworks often have different strategies for selecting the algorithm they believe is the best based on a range of runtime factors, resulting in different training latencies and invalid predictions from existing methods. In this paper, in addition to considering this diversity to ensure synchronized completion time of model training, we also study how to select the best algorithm each time to reduce the latency of the per-round training, thereby further improving the overall efficiency of federated training. To this end, we propose LATTE, which consists of a novel selector that identifies the best algorithm at runtime based on relative runtime factors. By further integrating it into our training latency model, LATTE provides accurate training time estimation. We develop LATTE as middleware, compatible with different deep learning frameworks. Extensive results show significantly improved training convergence speed and model accuracy compared to state-of-the-art methods. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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LULC Analysis of Green Cover Loss in Bangalore 15

LULC Analysis of Green Cover Loss in Bangalore

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mathew, Joice Ann Eapen, Naived George George, Jossy P. Christ University Department of Data Science Karnataka Bangalore560029 India Christ University Department of Computer Science Karnataka Bangalore560029 India

ISBN: (纸本)9798350370249

Urbanization of the cities especially the Indian City of Bangalore has led to the creation of an important discourse concerning development and conservation. The study carries out a detailed LULC study with special reference to Green Cover Loss in city of Bangalore. Using satellite images from 2014 to 2023 period and machine learning tools, the study establishes declines in green spaces with economic, environmental and health consequences of the city's uncontrolled expansion. The innovations afforded to the study regard methodologically on the use of ResNet50 for accurate LULC classification with an accuracy of 92% Hence the study reveals the interaction between urbanization and conservation, the efficiency of which requires policy adjustments that depend on existing knowledge. The study not only accustomizes the progression in the geography of Bangalore but it also shapes the technology and methodology for the further geospatial research in the areas under rapidly urbanizing in the future. © 2024 IEEE.

关键词： Green development

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Advancements in Skin Cancer Detection: A Comprehensive Review of Convolutional Neural Network Approaches 14

Advancements in Skin Cancer Detection: A Comprehensive Revie...

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14th International Conference on Software Technology and Engineering, ICSTE 2024

作者： Thinakaran, Rajermani Somasekar, J. Neerugatti, Vikram Ganga, Krishna INTI International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia Department of Computer Science and Engineering Karnataka Banglore India

ISBN: (纸本)9798350378955

Skin cancer is one of the most prevalent types of cancer globally, with its incidence steadily rising over the past decades. Early and accurate detection of skin cancer plays a pivotal role in improving patient outcomes and reducing mortality rates. In recent years, Convolutional Neural Networks (CNNs) have emerged as powerful tools for automated image analysis and classification, offering promising results in various medical imaging tasks, including skin cancer detection. This paper provides a comprehensive review and analysis of CNN-based approaches for skin cancer classification. We discuss the challenges associated with traditional methods of skin cancer diagnosis and highlight the potential of CNNs in addressing these challenges. Furthermore, we survey state-of-the-art CNN architectures, dataset collections, preprocessing techniques, and evaluation metrics commonly used in skin cancer classification tasks. We also delve into the transfer learning and data augmentation strategies employed to improve model generalization and robustness. Additionally, we review recent advancements in interpretability techniques for CNNs, which enhance the transparency and trustworthiness of automated skin cancer diagnosis systems. Finally, we discuss the limitations and future directions of CNN-based approaches in skin cancer classification, emphasizing the need for large-scale, diverse datasets, standardized evaluation protocols, and interdisciplinary collaborations to facilitate the clinical translation and deployment of CNN-based diagnostic tool. © 2024 IEEE.

关键词： Lung cancer

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