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A Novel Method for Clustering High-Dimensional data Using K-Means 1

A Novel Method for Clustering High-Dimensional Data Using K-...

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1st international conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kunchakuri, Naveen Kandari, Chandana Patchigolla, Veera Lakshmayya Clarksburg MD United States Germantown MD United States

ISBN: (纸本)9798331518578

The k-means approach is a fundamental tool in unsupervised machine learning (ML) for grouping high-dimension data. data mining, recognizing patterns, analyzing images, bioinformatics, and ML are just a few of the fields that use clustering, an efficient quantitative data processing method. data can be divided into categories of items with unique properties by using clustering. Numerous activities, management, and service data utilized by managers for system functional assurance are generated by cellular radio systems. Although there are strong clustering techniques like Self Organizing Mapping (SOM), there is almost no research demonstrating how well these techniques function with OAM databases that have an abundance of features (>20) gathered from real-world system adoption. Furthermore, there are still a few unresolved issues regarding the relevance of the clustering techniques. For example, when employing SOM, the beneath data fails to explicitly provide data on clusters following SOM training;therefore, the k-means method for classifying SOM components must be used afterward. In this regard, an approach for clustering radio units depending on a mix of SOM and K-means techniques is described in this study. The technique is used to identify the short-term conduct (hourly observational intervals) and long-term conduct (15 days observational duration) of wireless units by extracting cell shapes. The investigation makes use of OAM statistics gathered from a real 4G/LTE system installed in a significant European town. © 2025 ieee.

关键词： Conformal mapping

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Masking the Bias: From Echo Chambers to Large Scale Aspect-Based Sentiment Analysis 16th

Masking the Bias: From Echo Chambers to Large Scale Aspect-B...

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16th international conference on Social Networks Analysis and Mining

作者： Lee, Yeonjung Cetinkaya, Yusuf Mucahit Kulah, Emre Toroslu, Hakki Davulcu, Hasan Arizona State Univ Tempe AZ 85287 USA Middle East Tech Univ Ankara Turkiye

ISBN: (纸本)9783031785375;9783031785382

Aspect-based sentiment analysis (ABSA) is a natural language processing (NLP) task, ascribing precise sentiment linkages to specific entities and issues in text data. This paper addresses critical shortcomings in current ABSA methods, particularly the issues of limited aspects, training set biases, and lack of comprehensive stance-coded datasets. First, we develop a scalable MaskedABSA approach that masks aspect terms in training sentences to enable unbiased sentiment inference from the context alone. We show that the proposed method surpasses the state-of-the-art solutions in accuracy for the aspect term sentiment classification task, as verified by the SemEval datasets. Furthermore, we tackle the perennial challenges of limited training resources and the prohibitive costs of manual annotation in ABSA dataset creation by introducing an innovative weak supervision technique capitalizing on the inherent community clustering properties found within social media datasets. We utilize community detection algorithms to partition a share network into polarized groups with homogeneous adversarial stances, allowing large-scale aspect-based sentiment analysis dataset curation without labor intensive manual labeling. Our methodology is also validated using a real-world polarized dataset comprising diverse aspects and stances to showcase its efficacy and scalability.

关键词： aspect-based sentiment analysis social networks weakly labeled data social media

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Cross-Channel Unlabeled Sensing over a Union of Signal Subspaces

Cross-Channel Unlabeled Sensing over a Union of Signal Subsp...

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2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Koka, Taulant Tsakiris, Manolis C. Haro, Benjamín Béjar Muma, Michael Robust Data Science Group Technische Universität Darmstadt Darmstadt Germany Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Swiss Data Science Center Paul Scherrer Institute Villigen Switzerland

ISBN: (纸本)9798350368741

Cross-channel unlabeled sensing addresses the problem of recovering a multi-channel signal from measurements that were shuffled across channels. This work expands the cross-channel unlabeled sensing framework to signals that lie in a union of subspaces. The extension allows for handling more complex signal structures and broadens the framework to tasks like compressed sensing. These mismatches between samples and channels often arise in applications such as whole-brain calcium imaging of freely moving organisms or multi-target tracking. We improve over previous models by deriving tighter bounds on the required number of samples for unique reconstruction, while supporting more general signal types. The approach is validated through an application in whole-brain calcium imaging, where organism movements disrupt sample-to-neuron mappings. This demonstrates the utility of our framework in real-world settings with imprecise sample-channel associations, achieving accurate signal reconstruction. © 2025 ieee.

关键词： Cross-Channel Unlabeled Sensing Sampling Sparsity Unlabeled Sensing

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Recommender systems in E-Commerce: A Deep Dive into AI-Driven Optimization 1

Recommender Systems in E-Commerce: A Deep Dive into AI-Drive...

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1st international conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Mishra, Manas Kumar Ramavath, Shiva Kumar Gupta, Gopal Kumar Desai, Piyush Bipinkumar Somavarapu, Sushira Katyayan, Shashank Shekhar G.L. Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida201306 India University of North Texas DentonTX United States Nagpur Campus Pune440008 India Tamil Nadu Vellore632014 India Louisiana State University Baton RougeLA70802 United States Eli Broad College of Business Michigan State University East LansingMI United States

ISBN: (纸本)9798331518578

In e-commerce, recommender systems are the most widely used systems that satisfy the user with personalized recommendations, and they have a significant influence on e-business success. In the new era of artificial intelligence (AI) and deep learning techniques, modern recommender systems have evolved from the classical models of content-based filtration systems to highly sophisticated and accurate models. In this post, we take a closer look at specific optimization techniques that are applied in the context of AI-based recommender systems in the e-commerce industry. We start with the data needed to train these systems and the different ways to collect and clean the data. Next, we delve into getting recommendations and the methods behind them - collaborative filtering (CF), content-based filtering (CB), etc. Next, we explain how deep learning approaches such as natural language processing and neural networks improve the accuracy of these systems. We look at what sort of problems you face while implementing these systems (cold-start issue, data sparsity issues et al) and how are you working to address them. We also consider ethical concerns regarding recommender systems: bias and privacy. Finally, we summarize the various use cases of AI-based recommender systems in e-commerce, including product recommendations, personalized promotions, and user profiling. This paper intends to present a general survey of the techniques which are being state-of-the-art in e-commerce AI recommender systems and a brief discussion of how they can massively boost user satisfaction in unison with business goals. © 2025 ieee.

关键词： Marketplaces

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Computer-Aided English Translation system based on Text-to-Text Transfer Transformer model 1

Computer-Aided English Translation system based on Text-to-T...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Cheng, Yue Chengdu College University of Electronic Science and Technology of China School of Arts and Sciences Chengdu China

ISBN: (纸本)9798331529246

In recent years, design and application of a computer-aided English translation system have become essential in the context of globalization for effective crosslinguistic communication in various sectors such as academic, professional, and social interactions. Even though there are many traditional translation methods still they face issues related to failing to preserve context, and linguistic nuances. Therefore, this research aims to provide a robust solution to enhance translation quality and efficiency by incorporating Text-to-Text Transfer Transformer (T5) model for performing translations with unified framework that handles a wide variety of language-based tasks, solving text-to-text transformation. Initially, the data collected from the Workshop on Statistical Machine Translation (WMT) dataset. The collected data is preprocessed by utilizing tokenization for obtaining efficient data. The proposed T5 model generates initial word vectors, capturing contextual meaning for text generation and organized using phrase and rule encoders. Further, encoder divided the data into monolingual and bilingual encoders, that framework enhances translation accuracy while systematically processing input text while maintaining linguistic coherence and contextual integrity. The results of proposed T5 model shows that evaluation of English translation model achieved high performance than existing model with Bilingual Evaluation Understudy (BLEU) as 25.4%. © 2025 ieee.

关键词： Computer aided language translation

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Smart irrigation for soil moisture prediction using hybrid AI and fog computing 14

Smart irrigation for soil moisture prediction using hybrid A...

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14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Bindal, Ruchika Kaur, Mandeep Tandon, Righa Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331531935

Irrigation control is essential for optimizing water usage in smart agricultural systems, especially where there is water scarcity. The fast development of IoT and fog computing brings up new opportunities for developing new and advanced systems that are both efficient and flexible. Further integration of fog computing with machine learning will bring more developments to smart agriculture systems. This paper has framed an irrigation supervision algorithm to predict irrigation needs in the smart agriculture system. The methodology uses a hybrid algorithm that incorporates Gradient Boosting (GB), Logistic Regression (LR) and Random Forest (RF). The system accurately forecasts the irrigation requirement by processing ecological information such as humidity, temperature, and time-based factors. 97.78% of accuracy illustrates the reliability of the resultant model, and initial processing of the data illustrates that information is concise and accurate. Recall, precision and F1- score are computed as metrics for the performance. The system is integrated into fog computing and machine learning to manage real-time data processing to achieve faster and more accurate predictions. © 2025 ieee.

关键词： Random forests

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Generalizable Audio Deepfake Detection via Latent Space Refinement and Augmentation

Generalizable Audio Deepfake Detection via Latent Space Refi...

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2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Wen Gu, Yanmei Wang, Zhiming Zhu, Huijia Qian, Yanmin Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China SJTU Paris Elite Institute of Technology Shanghai China Ant Group Shanghai China

ISBN: (纸本)9798350368741

Advances in speech synthesis technologies, like text-to-speech (TTS) and voice conversion (VC), have made detecting deepfake speech increasingly challenging. Spoofing countermeasures often struggle to generalize effectively, particularly when faced with unseen attacks. To address this, we propose a novel strategy that integrates Latent Space Refinement (LSR) and Latent Space Augmentation (LSA) to improve the generalization of deepfake detection systems. LSR introduces multiple learnable prototypes for the spoof class, refining the latent space to better capture the intricate variations within spoofed data. LSA further diversifies spoofed data representations by applying augmentation techniques directly in the latent space, enabling the model to learn a broader range of spoofing patterns. We evaluated our approach on four representative datasets, i.e. ASVspoof 2019 LA, ASVspoof 2021 LA and DF, and In-The-Wild. The results show that LSR and LSA perform well individually, and their integration achieves competitive results, matching or surpassing current state-of-the-art methods. © 2025 ieee.

关键词： anti-spoofing audio deepfake detection generalization

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A Multi-Modal Smart Switching Based Image Transmission Using Semantic Communication 17

A Multi-Modal Smart Switching Based Image Transmission Using...

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17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Tiwari, Sanjana Tripathi, Sharda Joshi, Sandeep Bhatt, Priyanshu Natarajan, Balasubramaniam Birla Institute of Technology & Science Pilani Department of Electrical & Electronics Engineering 333031 India Kansas State University Department of Electrical & Computer Engineering 66506 United States

ISBN: (纸本)9798331531195

The conventional paradigm of communication primarily concentrates on the transmission of raw data, often disregarding its contextual meaning. However, to tackle the exponential growth in data demands along with the limited availability of transmission bandwidth, there is an increasing need to transition from Shannon's classical information-theoretic communication to a more advanced framework centered on semantics. This work presents a multi-modal semantic-based communication method for the transmission of high-definition images aimed at optimizing the transmitted data volume while maintaining a high throughput and mean intersection over union score. To this end, two architectural models are explored: a denser ResNet-based and a lightweight U-Net-based. Depending on the required QoS and resource availability, the raw image is either semantically segmented to obtain a fine-grained, pixel-level classification of the image or represented as label semantics, which provides only a higher-level, object-based, or region-based classification prior to its transmission. The experimental results show that such an adaptive semantic image processing approach leads to around 63% reduction in the transmitted data volume without compromising on the quality of image reconstruction. © 2025 ieee.

关键词： Semantic Segmentation

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Bayesian-Informed Hyperdimensional Learning for Intelligent and Efficient data Processing 24

Bayesian-Informed Hyperdimensional Learning for Intelligent ...

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43rd international conference on Computer-Aided Design, ICCAD 2024

作者： Barkam, Hamza Errahmouni Das, Tamoghno Poduval, Prathyush Jeong, SungHeon Yeung, Calvin Solitan, Mostafa Imani, Mohsen University of California Irvine IrvineCA United States

ISBN: (纸本)9798400710773

In machine learning (ML), near-sensor AI is transforming edge computing by reducing response times and data transmission, ultimately saving energy and bandwidth. Despite challenges like limited computational resources and the need for transparent decision-making, this approach aims to enhance the intelligence and autonomy of edge devices. Our research presents a novel framework that adds a layer of abstract intelligence to sensors, boosting system efficiency and accuracy through transparent, interpretable sub-symbolic AI. We combine Bayesian algorithms with hyperdimensional computing (HDC), inspired by the human brain’s operational efficiency, to deliver an energy-efficient solution matching the accuracy of traditional cloud systems without constant server dependence. This framework uses a binary classifier with Bayesian insights to choose the best data processing location—locally or in the cloud—adapting to data environments. Our method ensures cloud-level performance while significantly reducing energy consumption, improving the sustainability of sensor-based systems. It also enables continual adaptation and learning directly at the sensor level, enriching cloud models with fresh edge insights. Our results have shown to bridge the gap from around 38% quality loss between the standalone near-sensor HDC model and the SOTA cloud-based model to improve the quality loss to only 9% while simultaneously saving 45.34% of energy by not using the cloud. This framework paves the way for more sustainable, efficient, and accurate edge computing in the ML landscape by bridging the gap between simple near-sensor models and their advanced cloud-based counterparts. © 2024 Copyright is held by the owner/author(s).

关键词： data reduction

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An Ensemble Machine Learning Model for Osteoporosis Risk Prediction from Medical data 2

An Ensemble Machine Learning Model for Osteoporosis Risk Pre...

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Thulasi, M. Thailambal, G. Vels Institute of Science Technology & Advanced Studies Department of Advanced Computing and Analytics Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

The metabolic condition osteoporosis afflicted a large number of people worldwide. Osteoporosis causes major health and its financial implications worldwide. However, the advancement of osteoporosis-related methods for forecasting is inadequate. Machine Learning (ML), a subfield of Artificial Intelligence (AI)I allows computers to 'learn' using programs. In comparison to classic statistical methods, ML places a greater focus on prediction accuracy and can find patterns in multidimensional data sets. This study propose an ensemble model employing Random Forest(RF), XGBoost(XGB) and Light Gradient Boosting(LGBM) techniques for the prediction of osteoporosis from the medical data of an individual. The accuracy, precision, recall, and f1-score were computed to compare the model with four alternative machine learning models: Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). The findings show that the ensemble model outperforms with an accuracy, precision, recall and F1-score of 93.5%, 92.8%, 94.2% and 93.5% respectively. © 2025 ieee.

关键词： Support vector machines

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