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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Goswami, Mausumi Bandi, Uday Sai Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

Healthcare organizations need this information to understand and treat the patient's concerns. The motivation for this kind of analysis is how patients provide this information while wrapping it in their thoughts and emotions. It is less practicable to manually study all the free and abundant health-related knowledge accessible online to arrive at decisions that might contribute to an immediate and beneficial decision. Sentiment analysis methods perform this function through automated procedures with minimal human intervention. In this paper, an investigation is conducted to compare the region-wise, language-wise, and sentiment analysis of the tweets collected from Russia and India. The results obtained through research have shown some significant characteristics of the language models used for language detection. The inferenc and analysis obtained from the observations are included in this paper. © 2023 IEEE.

关键词： Health care

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Customized Movie Recommendation for Enhanced Entertainment

Customized Movie Recommendation for Enhanced Entertainment

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Minu, M.S. Talapalli, Srikar Chevuri, Saahithi Medhasri, T.S. SRM Institute of Science and Technology Department of Computer Science Engineering Ramapuram Chennai India SRM Institute of Science and Technology Department of Computer Science Engineering with Specialization in Big Data Analytics Ramapuram Chennai India

ISBN: (纸本)9798331542344

The surging popularity of online movie databases has created a challenge for viewers: choosing a film from a massive library can be overwhelming. In this paper, it proposes to design a new hybrid movie recommendation powered by machine learning techniques as a solution to improve user experience. Among those techniques, two are combined into this paper: content-based filtering, using the real features of a movie such as genre and a director, and collaborative filtering, which uses the rating provided by users. This hybrid approach thus improves the intrinsic weaknesses that reside in both these methods individually. On the other hand, content-based filtering tends to reduce exploration to movies of the exact type one is doing well at, whereas, inversely, collaborative filtering settings often mean that a user will be exposed only to films within his or her already-recognized taste profile. It is expected that the proposed hybrid system will solve both problems by balancing this line between exploring familiar cinematic territory and making new recommendations based on both user preferences and the entire collective intelligence of the user base. The approach would aim to enrich user satisfaction by facilitating the selection of movies and providing a richer and more diversified movie-viewing experience. © 2024 IEEE.

关键词： Collaborative filtering

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Monitoring Clinical Parameters through Machine Learning and Image Processing

Monitoring Clinical Parameters through Machine Learning and ...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Aditya Pai, H. Agarwal, Jyoti Chandrappa, S. Mittal, Tanusha Joshi, Kapil School of Computer Science and Engineering Bengaluru India Computer Science and Engineering Dehradun India Uttaranchal University Uttaranchal Institute of Technology Department of CSE Dehradun India

ISBN: (纸本)9798350394412

The purpose of the paper is to find the clinical parameters of an individual using normal webcam. The paper aims to help the healthcare domain. In recent times there has been emergence of smartphone devices which monitor vital information's. These devices have special sensors like Electrocardiogram (ECG) and Photoplethysmography (PPG) to check heart and respiratory rate. The smartphone camera reads the heart and respiratory data from the user's face. The method used in the paper mentions about the variation in the Hue that reads both heart and respiration rate from the face of a user. The result in the paper explains how the proposed approach gives more accurate results than the other detection algorithms. © 2024 IEEE.

关键词： Photoplethysmography

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Real-Time Caption Generation for the American Sign Language Using YOLO and LSTM

Real-Time Caption Generation for the American Sign Language ...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Jana, Urmi Paul, Subhashish Bhandari, Dinabhandhu Heritage Institute of Technology Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350382693

The lack of captions available for live video events on online platforms makes them inaccessible to the speech and hearing deprived communities. In this study, we examine two models: the pre-trained You Only Look Once (YOLO) framework and a Long Short Term Memory (LSTM) model that uses the Mediapipe framework from Google. The LSTM model is constructed by extracting the landmark features of the subjects face, torso and hands from the video frames and using them to predict the depicted gesture. This sets it apart from existing deep learning methods that use only a Convolutional Neural Network (CNN) framework to extract spatial features. We evaluate the performance and demonstrate the superiority of the proposed model against a LRCN model using our custom dataset. The proposed model offers a valuable approach to making live videos accessible to both viewers and speakers of the disabled community, who are currently depending on human translators. The ability to extract keypoints beforehand further enhances the models capabilities and speed. © 2024 IEEE.

关键词： LSTM Mediapipe Sign lanuage YOLO

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A contemporary review of machine learning-based heart disease prediction 2

A contemporary review of machine learning-based heart diseas...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Gupta, Sukriti Tripathi, A. Srivastava, Chaynika Buddha Institute of Technology Department of Computer Science & Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Heart disease remains a leading cause of global mortality, necessitating effective predictive tools. As per the World Heart Federation, it was found that over a billion people globally continue to be affected by heart disease and 20.5 million deaths in 2021 occurred and there is no one-size-fits-all approach which can improve cardiovascular health worldwide (***, 2023). Machine learning emerges as a valuable tool for predicting heart diseases, offering improved accuracy compared to conventional approaches. The study focuses on addressing the global challenge of heart disease by leveraging machine learning for predictive analysis. It focused on increasing early detection so that treatment can be made and save the lives of individuals. The investigation unfolds the way machine learning can be applied to the early identification of cardiac conditions in the modern era. While analyzing machine learning, it appeared as a powerful as well as efficient technology that operates on testing as well as training principles. The research aims to employ classification techniques so it would able to build predictive models that will further uncover hidden patterns and increase accuracy in diagnosis. © 2024 IEEE.

关键词： Adversarial machine learning

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Unwinding the Significance of Federated Learning for Smart Agriculture

Unwinding the Significance of Federated Learning for Smart A...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Bhat, Asif Nazir Sahil Kumar, Gautam National Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350379716

Smart agriculture leverages cutting-edge information and communication technologies to optimize agricultural practices, and machine learning plays a significant role in achieving this goal. However, due to the conventional nature of centralized learning, machine learning-oriented solutions in agriculture face significant challenges, primarily the network overburden, time-delayed response and limited computational capabilities of edge nodes. This leads to the requirement of a more efficient and decentralized approach for learning, which can distribute the computational load and enable real-time data processing closer to the data sources. The presented paper un-winds the significance of Federated Learning in addressing these challenges associated with Centralized Learning, particularly in agriculture. The paper explores Federated Learning's ability to leverage the collective intelligence of distributed data sources while maintaining computational efficiency and scalability. It presents it as an ideal solution for the agricultural sector, where data is often fragmented and collected in diverse environments. In this work, a methodology is adopted to compare and analyze the performance of Federated Learning with Centralized Learning using classification-based statistical parameters viz. Accuracy, Precision, Recall and F1-Score and hosting nodes' parameters viz. Memory usage, CPU utilization, Model Converging Time and Node Energy Consumption. The experimental results clearly depict the edge of Federated Learning in analyzing the crop recommendation, as compared to the Centralized Learning. © 2024 IEEE.

关键词： Smart agriculture

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An Efficient Scalp Inspection and Diagnosis System Using Multiple Deep Learning-Based Modules un système efficace d'inspection et de diagnostic du cuir chevelure utilisant plusieurs modules basés sur l'apprentissage profond

IEEE Canadian Journal of Electrical and Computer Engineering

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IEEE Canadian Journal of Electrical and computer engineering 2024年第1期47卷 22-35页

作者： Chen, Liang-Bi Chang, Wan-Jung Chiu, Yi-Chan Huang, Xiang-Rui National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu880011 Taiwan National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung807618 Taiwan Southern Taiwan University of Science and Technology Department of Electronic Engineering Tainan710 Taiwan

The conventional approach to scalp inspection in the hairdressing industry relies on manually interpreting scalp symptom images. Hairdressers provide treatments based on visual assessment, leading to potential inaccuracies and misjudgments. To address these shortcomings, this article proposes a novel multimodal deep learning-based scalp inspection and diagnosis system. The proposed system employs various artificial intelligence (AI) object recognition modules, such as single-shot multibox detector (SSD)-MobileNetV2, SSD-InceptionV2, Faster region-based convolutional neural network (R-CNN)-InceptionV2, and Faster R-CNN-Inception-ResNetV2-Atrous (2). These modules form a diverse scalp symptom recognition module integrated into an AI recognition server. This study included nine scalp symptoms, encompassing four primary conditions (dandruff, hair loss, gray hair, and oily hair), as well as five special conditions (folliculitis, chemical residue, mold, fungi, fungus, and psoriasis). The efficiency of the proposed system is evaluated through experiments, and adjustments are made to the neural network architecture to achieve optimal performance across diverse symptoms. The experimental results showed that Faster-R-CNN-Inception-ResNetV2-Atrous (2) excels in recognizing chemical residue and oily hair symptoms (accuracies of 89.33% and 87.75%, respectively);Faster-R-CNN-Inception-ResNetV2-Atrous (4) outperforms in recognizing dandruff, folliculitis, fungal, and psoriasis symptoms (accuracies ranging from 88.77% to 99.72%);and Faster-R-CNN-Inception-ResNetV2-Atrous (4) is the best-performing method overall. © 2021 IEEE.

关键词： Inspection

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Design and Optimization of Adaptive Network Coding Algorithms for Wireless Networks 15

Design and Optimization of Adaptive Network Coding Algorithm...

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

作者： Nadeem, Syed Mohammed Rao, Deepak Dasaratha Arora, Arpit Dongre, Yashwant V. Giri, Rakesh Kumar Jaison, B. G Pulla Reddy Engineering College Department of Computer Science and Engineering India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India College Sunrise University Department Computer Science & Engineering Alwar301028 India R.M.K.Engineering College Department Computer Science and Engineering Kavarapettai601206 India

ISBN: (纸本)9798350370249

This paper specializes in the layout and optimization of adaptive community coding algorithms for WiFi networks. The goal of those algorithms is to maximize the performance of network transmissions at the same time as heading off collisions and losses. We analyze the performance of numerous current network coding algorithms, examine their strengths and weaknesses and advocate techniques to optimize their performance. We also increase and compare new algorithms based on the adaptive network coding method. Those algorithms are designed if you want to mechanically adapt to the environment and take full benefit of the available assets to maximize the overall network's performance. The results of our simulations endorse that the proposed adaptive network coding algorithms outperform conventional network coding algorithms in phrases of both throughput and latency. We further propose an optimized model of our adaptive network coding algorithms and evaluate its performance to that of the unique algorithms. Our results demonstrate significant performance profits in terms of both throughput and latency. © 2024 IEEE.

关键词： Algorithms Latency Optimization Throughput Transmissions

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Development of a Naive Bayes-based Framework for Optimizing Crop Recommendation System and Enhancing Agricultural Yield Prediction 4

Development of a Naive Bayes-based Framework for Optimizing ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swetha, P. Senthilkumar, J. Kalaignar Karunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

This research study proposes a comprehensive Crop Recommendation System that uses advanced machine learning to boost agricultural productivity. By analyzing key environmental and soil factors, including soil composition, pH, temperature, humidity, and rainfall, the system offers customized, data-driven crop suggestions to optimize yield and promote sustainable farming. The system evaluates several algorithms, including Naive Bayes, Decision Tree, Logistic Regression, and Random Forest, and identifies Naive Bayes as the top performer for accurately predicting crop suitability. This multi-algorithm approach represents a significant advancement in precision agriculture, empowering farmers and policymakers with actionable insights to drive improvements in the agricultural sector. Leveraging historical data, the Crop Recommendation System supports farmers, consultants, and policymakers in making more informed and strategic crop selection decisions. The system's precision and adaptability make it a valuable resource for stakeholders focused on improving resource efficiency and fostering sustainable agriculture, ultimately enhancing food security and minimizing environmental impact. © 2025 IEEE.

关键词： Agriculture Crop Recommendation System Decision Tree Logistic Regression Machine Learning Naive Bayes Random Forest

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Maximizing Utility and Quality in Smart Healthcare with Incentive Driven Hierarchical Federated Learning 17

Maximizing Utility and Quality in Smart Healthcare with Ince...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Singh, Himanshu Patidar, Sourabh Pratap, Ajay Yasumoto, Keiichi Department of Computer Science and Engineering Varanasi India Nara Institute of Science and Technology Nara Japan

ISBN: (纸本)9798331531195

Recent development of federated learning based smart healthcare techniques facilitates distributed processing of patient data while protecting privacy. However, two significant challenges can notably degrade model learning performance. Firstly, patients often show reluctance to participate in model training due to a lack of incentives. Secondly, the quality of updated models can change significantly due to different aspects such as the size and training data quality. The incorporation of low-quality updates can subsequently degrade global model quality in FL. Considering the above scenarios, this paper formulates a utility maximization problem keeping model quality function and budget into deliberation. To solve the formulated problem, this paper proposes association algorithm utilizing Utility Maximization and Payment (UMaxP) algorithm based on matching approach to select Wireless Body Area Networks (WBANs) and hospitals in a way that increases the utility and improves model quality using Hierarchical FL (HFL). In UMaxP, the allocation of patients to hospitals is optimized to maximize both the sum of local model qualities and the overall utility of the patients. The effectiveness of the proposed algorithmic model is demonstrated through theoretical analysis, experimental validation, and comparative evaluation with various methods using real-world data. © 2025 IEEE.

关键词： Federated learning

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