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Machine learning algorithms for surface plasmon resonance bio-detection applications, A short review 5

Machine learning algorithms for surface plasmon resonance bi...

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5th Photonics Meeting 2022, PM 2022

作者： Zain, H.A. Batumalay, M. Harith, Z. Rahim, H.R.A. Harun, S.W. Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Faculty of IT INTI International University Negeri Sembilan Nilai71800 Malaysia Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka Melaka Malaysia

Surface plasmon resonance (SPR) sensors have many applications in detecting toxic gases, water pollutants, and biomarkers of many diseases. Surface plasmon resonance sensors are a good candidate for future sensing platforms due to their high sensitivity and fine resolution. However, the challenges of high cost, cross-sensitivity, and large amount of generated data need to be addressed to unlock surface plasmon resonance potential. Machine learning (ML) algorithms can address these challenges. In this short review, recent studies integrating the algorithms of Artificial Intelligence (AI) and Machine Learning (ML) with (SPR) sensing mechanisms for bio-detection applications are presented here. this short review shows how the integrated approach can help mitigate some of the challenges faced by traditional SPR sensing. © Published under licence by IOP Publishing Ltd.

关键词： Surface plasmon resonance

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Exploring Dual-Module Approaches for Sentiment Analysis in Call Recordings

Exploring Dual-Module Approaches for Sentiment Analysis in C...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Nikita Dinkar Dhobale Krisha Ramesh Elle Pratiksha Vilas Deshmukh Sakshi Sidram Nirne Prof. Ashwini Jarali Department of Computer Engineering International Institute of Information Technology Pune India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

the system entails the development of a sentiment analysis system consisting of two integral modules aimed at analysing audio recordings of calls. the first module predicts sentiment-positive, negative, or neutral-by converting speech to text using speech-to-text APIs and subsequently applying machine learning algorithms including Random Forest, Naive Bayes, SVM, and Logistic Regression. the second module employs audio processing techniques utilising Librosa for feature extraction and Convolutional Neural Networks (CNNs) for emotion recognition. the system facilitates comparisons between the results obtained from both modules for call recordings. Users, starting with system administrators, can choose between two options: browsing for uploading pre-recorded audio files or live recording. In both scenarios, the system executes sentiment analysis and emotion detection, presenting results derived from speech-to-text and audio processing analyses.

关键词： Support vector machines Sentiment analysis Emotion recognition Logistic regression Machine learning algorithms Communication systems Feature extraction Convolutional neural networks Random forests Speech to text

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A Target Tracking Algorithm Based on Hybrid Pooling and Transformer

A Target Tracking Algorithm Based on Hybrid Pooling and Tran...

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Sensors and Information Technology (ICSI), international conference on

作者： Qiang Fu Zhian Xie Yuanfa Ji Somsack Inthaseone School of Information and Communication Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology International Joint Research Laboratory of Spatio-temporal Information and Intelligent Location Services Guilin University of Electronic Technology Guilin China GUET-Nanning E-Tech Research Institute Co. Ltd. Nanning China Department of Computer Science National University of Laos Vientiane Lao PDR

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

To enhance the robustness and accuracy of target tracking in complex backgrounds and dynamic scenes, this paper presents a target tracking algorithm that integrates hybrid pooling with a Transformer module. the Transformer module captures global context information, effectively mitigating background interference while strengthening the discriminative power of target features. the hybrid pooling technique improves the model's spatial context modeling capability by fusing multi-scale features. Experimental results demonstrate that the proposed method performs exceptionally well on the OTB100 and GOT-10k datasets.

关键词： Target tracking Attention mechanisms Heuristic algorithms Interference Transformers Robustness Sensors Object tracking Information technology Context modeling

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Evaluation of DBSCAN algorithm on different programming languages: An exploratory study 5

Evaluation of DBSCAN algorithm on different programming lang...

引用

5th international conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2021

作者： Amiruzzaman, Md Rahman, Rashik Islam, Md. Rajibul Nor, Rizal Mohd West Chester University West ChesterPA United States University of Asia Pacific Dhaka1205 Bangladesh International Islamic University Kuala Lumpur53000 Malaysia

ISBN: (纸本)9781665495226

DBSCAN is a well-known clustering algorithm that is often used to find associations and structures in large spatial data. Due to its popularity, built-in functions for DBSCAN have been implemented on top of many different programming languages. Researchers and practitioners (i.e., data scientists) have been using these built-in functions to cluster and analyze a prolific area of research in data science. Due to the many implementations of DBSCAN and its utilization in many different languages, the output of each built-in DBSCAN function is assumed to be identical. In this paper, we present a systematic approach to evaluate the built-in functions of DBSCAN algorithms and to identify discrepancies in their output. the evidence from the study shows that there are some discrepancies and recommends caution in dealing with built-in functions. © 2021 IEEE.

关键词： Clustering algorithms

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Certain Investigations on Classification of Amyotrophic Lateral Sclerosis

Certain Investigations on Classification of Amyotrophic Late...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Shamik Tiwari Saikat Gochhait Ritam Chatterjee School of Computer Sceince & Engineering IILM University Gurugram India Symbiosis Institute of Digital and Telecom Management Constituent of Symbiosis International Deemed University Pune India

ISBN: (数字)9798350368710

ISBN: (纸本)9798350368727

Amyotrophic Lateral Sclerosis (ALS) is a persistently progressing neurological disease with restricted treatment choices. the advent of extensive global datasets and advanced machine learning models offers new opportunities to evaluate potential prognostic, inspection, and diagnostic indicators. Additionally, emerging categorization and staging systems aim to accurately stratify patients into distinct prognostic categories. this study evaluates several prominent machine learning classifiers for ALS classification. these experiments have exposed that the CatBoost classification algorithm attained the maximum performance, with an accuracy of 0.85 and an AUC of 0.97. Other significant models include XGB, Random Forest, and Extra-Trees classifiers, each showing an accuracy of around 0.75 but with varying AUC values.

关键词： Neurological diseases Industries Accuracy Data analysis Inspection Boosting Classification algorithms Random forests Business

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Neural Network-Based Hybrid Recommendation System

Neural Network-Based Hybrid Recommendation System

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Emerging Technology (INCET), international conference for

作者： Bhuvan Singh Mangat Sahil K. Shah Vidya Kumbhar T.P. Singh Symbiosis Institute of Geoinformatics Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

the main goal of this study is to improve the user experience by providing personalized product recommendations based on user preferences and behaviors. the study examines the various methods and frameworks commonly used in e-commerce recommendation processes, including collaborative filtering, content filtering, and using these methods in neural networks system. Additionally, the proposed algorithm solves the “cold start problem” by newly visited users. Results obtained by proposed approach that use popularity-based algorithm to create the initial set of recommendations highlights how effective the system is at providing accurate and relevant recommendations to users, ultimately increasing customer satisfaction and sales conversion rates of supply. these findings and insights contribute to the development of e-commerce recommendation systems, and present possible avenues for future research and development.

关键词： Filtering Collaborative filtering Neural networks Filtering algorithms User interfaces User experience Electronic commerce

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ICCDA 2021 - Proceedings of the 2021 5th international conference on Compute and Data Analysis

ICCDA 2021 - Proceedings of the 2021 5th International Confe...

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5th international conference on Compute and Data Analysis, ICCDA 2021

ISBN: (纸本)9781450389112

the proceedings contain 30 papers. the topics discussed include: a data-driven productivity assessment framework for collaborative research teams;finding clusters and patterns in big data applications: state-of-the-art methods in clustering environments;discovering risk factors of high intraocular pressure infection using data mining algorithms;prediction of chronic kidney disease risk using multimodal data;the applications of zero-failure data’ dependability analyzing to estimating mine sweeping;an extended bond pricing model and its algorithm based on the big data simulation;incorporating knowledge bases and databases to an effective repair of data errors;algorithmic fairness in applied machine learning contexts;and clustering of functionally related genes using machine learning techniques.

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Exploratory Data Analysis for Malaysian Cases of Covid-19

Exploratory Data Analysis for Malaysian Cases of Covid-19

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international conference on Smart Sensors and Application (ICSSA)

作者： Sahnius Usman Norulhusna Ahmad Muhd Faiq Nurhakim Nor Iskandar Siti Zura A. Jalil Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia Faculty of Artificial Intelligence Universiti Teknologi Malaysia Kuala Lumpur Malaysia Faculty of Artificial Intelligence Universiti Teknologi Malaysia (UTM) Kuala Lumpur Malaysia

ISBN: (数字)9798350354294

ISBN: (纸本)9798350354300

Risk factor analysis is essential in medical research and patient care, offering insights into the determinants of mortality and severe outcomes. It aids healthcare providers in improving patient management, tailoring interventions, and optimizing resource allocation. the COVID-19 pandemic underscored the importance of rapid and collaborative risk factor analysis. Machine learning (ML) algorithms have emerged as powerful tools for predicting risk factors, but they require thorough data cleaning and preparation. this paper focuses on the exploratory data analysis (EDA) of Malaysian COVID-19 cases to identify feasible variables for subsequent risk factor analysis, enhancing the precision and effectiveness of ML models in healthcare. In this study, data from 128 records from Hospital Sg Buloh were analyzed, focusing on 34 variables. the process involved data cleaning and various analytical techniques, including univariate, bivariate, and multivariate analyses. this exploratory data analysis (EDA) is crucial as it facilitates the identification and extraction of significant features for machine learning algorithms. EDA helps in understanding data patterns, relationships, and structures, ensuring that the resulting ML models are precise and effective. By thoroughly analyzing the data, EDA lays the foundation for robust risk factor analysis, ultimately enhancing decision-making and patient care in healthcare settings.

关键词： COVID-19 Data analysis Machine learning algorithms Pandemics Machine learning Feature extraction Prediction algorithms Cleaning Data models Resource management

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A Haar-Cascading and Deep Learning Driven Enhanced Mechanism on Cloud Platform for Real-Time Facial Emotion Detection

A Haar-Cascading and Deep Learning Driven Enhanced Mechanism...

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international conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Anil Kumar Navdeep Adarsh Kumar Smruti Rekha Swain Ishu Gupta Ashutosh Kumar Singh Department of Computer Applications National Institute of Technology Kurukshetra India Department of Computer Science International Institute of Information Technology Bangalore India Indian Institute of Information Technology Bhopal Madhya Pradesh India

Real-time emotion detection utilizes computer vision that aims to recognize and categorize emotions. However, it contains challenges, including the intricate nature of emotions and the variations in their expression across individuals. the accuracy of the system can also be affected by environmental factors such as background noise, lighting, and other people. Having access to a diverse and comprehensive dataset is essential for training algorithms that can accurately detect emotions in real time. In order to tackle these challenges, we utilized CNN and seven emotions to identify their corresponding facial expressions. By implementing appropriate activation functions like ELU, ReLU, and Leaky ReLU, and training with the dataset Facial Expression Recognition 2013 (FER-2013) that contains over 30,000+ images of facial expressions and emojis, as we increase the epoch the accuracy of this model increases, we achieved an impressive accuracy of 94.74% by using ReLU on 100 enochs.

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Dynamic Identification and Optimization of Key Opinion Leaders in Social Networks Using Advanced GCN Models

Dynamic Identification and Optimization of Key Opinion Leade...

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Machine Learning and Computer Application (ICMLCA), international conference on

作者： Yinuo Chen Shenzhen International Exchange Academy Shenzhen China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

the rapid evolution of social media platforms has significantly increased the speed and reach of information dissemination, making the identification of Key Opinion Leaders (KOLs) crucial for shaping public opinion and maximizing influence. this paper presents a novel approach to identifying KOLs through the construction of dynamic social networks, employing advanced Graph Convolutional Networks (GCNs) and a pseudo-labeling mechanism. Pseudo-labeling is an innovative technique that automatically generates labels for nodes in a social network based on their characteristics, such as connectivity and influence level. By categorizing nodes into leader and edge nodes, we enhance the training data available for the dynamic GCN model, enabling it to better capture the underlying relationships within the network. By transforming user interaction data into a weighted social network and applying a community detection algorithm, we effectively generate a pool of candidate opinion leaders. the integration of dynamic GCN models, optimized through a Mahalanobis distance-based similarity measure, further refines the selection process, ensuring that the most influential leaders are identified. Experimental results on real-world datasets demonstrate that our method, outperforms existing algorithms in influence spread and leader identification accuracy, achieving a 12.35% improvement in KOL identification and enhancing precision in influence propagation and community detection, especially with fewer KOLs or clusters.

关键词： Accuracy Public relations Social networking (online) Graph convolutional networks Heuristic algorithms Image edge detection Clustering algorithms Training data Machine learning Optimization

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