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2025 AI-Driven Smart Healthcare for Society 5.0, AdSoc5.0 2025

作者： Das, Arpan Kundu, Barnali Sarkar, Taniska Ghosh, Suman Majumder, Banshari Deb Saha, Debasrree Department of Electrical Engineering Guru Nanak Institute of Technology Kolkata India Department of Computer Science Engineering Heritage Institute of Technology Kolkata India Department of Electrical Engineering Narula Institute of Technology Kolkata India

ISBN: (纸本)9798331536336

In recent years, the integration of deep learning techniques into dental diagnostics has shown promising results in enhancing the accuracy and efficiency of disease detection. This paper considers the use of Convolutional Neural Networks for classifying dental conditions based on a dataset that includes 375 images of diverse conditions such as cavities, gingivitis, periodontitis, fluorosis, and healthy teeth. The dataset comprises 300 images for training and 75 for validation. To enhance model generalization and mitigate overfitting, several techniques were employed. Image preprocessing was carried out by resizing all images to a uniform resolution and normalizing pixel values to ensure consistency across the dataset. Using four architectures of CNNs: ResNet-50, VGG16, MobileNet, and ShuffleNet, softmax activation is utilized in determining the classification of the dental conditions. Dropout and L2 weight regularization are utilized for preventing overfitting. It trains models beyond 100 epochs, and to evaluate each of the models one uses accuracy, loss metrics besides drawing out those models which were found out to perform better and generalize the unseen data, and finally to analyze further visualize by taking ROC curves along with AUC scores. The best model, in terms of both classification accuracy and computational efficiency, for the classification of dental conditions will be identified. © 2025 IEEE.

关键词： Convolutional neural networks

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Milestones of Wireless Communication Networks and technology Prospect of Next Generation(6G)

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computers, Materials & Continua 2022年第6期71卷 4803-4818页

作者： Mohammed H.Alsharif Md.Sanwar Hossain Abu Jahid Muhammad Asghar Khan Bong Jun Choi Samih M.Mostafa Department of Electrical Engineering College of Electronics and Information EngineeringSejong University209 Neungdong-roGwangjin-guSeoul05006Korea Department of Electrical and Electronic Engineering Bangladesh University of Business and TechnologyDhaka1216Bangladesh Department of Electrical&Computer Engineering University of OttawaOttawaON K1N 6N5Canada Department of Electrical Engineering Hamdard University KarachiIslamabad CampusIslamabad44000Pakistan School of Computer Science and Engineering&School of Electronic Engineering Soongsil UniversitySeoulKorea Faculty of Computers and Information South Valley UniversityQena83523Egypt Academy of Scientific Research and Technology Cairo11516Egypt

Since around 1980,a new generation of wireless technology has arisen approximately every 10 ***-generation(1G)and secondgeneration(2G)began with voice and eventually introduced more and more data in third-generation(3G)and became highly popular in the fourthgeneration(4G).To increase the data rate along with low latency and mass connectivity the fifth-generation(5G)networks are being installed from ***,the 5G technology will not be able to fulfill the data demand at the end of this ***,it is expected that 6G communication networks will rise,providing better services through the implementation of new enabling technologies and allowing users to connect everywhere.6G technology would not be confined to cellular communications networks,but would also comply with non-terrestrial communication system requirements,such as satellite *** ultimate objectives of this work are to address the major challenges of the evolution of cellular communication networks and to discourse the recent growth of the industry based on the key scopes of application and *** main areas of research topics are summarized into(i)major 6G wireless networkmilestones;(ii)key performance indicators;(iii)future new applications;and(iv)potential fields of research,challenges,and open issues.

关键词： Wireless networks beyond 5G 6G communications terahertz frequency terahertz communications holographic calls

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An Edge Computing System for Fast Image Recognition Based on Convolutional Neural Network and Petri Net Model

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Multimedia Tools and Applications 2024年第5期83卷 12849-12873页

作者： Yang, Cheng-Ying Lin, Yi-Nan Wang, Sheng-Kuan Shen, Victor R.L. Tung, Yi-Chih Lin, Jia-Fu Department of Computer Science University of Taipei 1 Ai-Kao W. Rd. Taipei 100 Taipei Taiwan Department of Electronic Engineering Ming Chi University of Technology 84 Gongzhuan Rd. Taishan District New Taipei City 243 New Taipei Taiwan Department of Electrical Engineering Ming Chi University of Technology 84 Gongzhuan Rd. Taishan District New Taipei City 243 New Taipei Taiwan Department of Computer Science and Information Engineering National Taipei University 151 University Rd. Sanxia District New Taipei City 237 New Taipei Taiwan Department of Information Management Chaoyang University of Technology Taichung Taiwan

As a computer system can precisely detect some target objects, many application scenarios will be developed. In this study, the customized object recognition model was trained by using a software tool, You Only Look Once (YOLO), and burned into one hardware component, Maix Bit, for an image edge computing system, so as to fast recognize humans or objects in the images. In the beginning of system development, a Petri net (PN) model was established to detect all possible abnormal processes and to verify the feasibility and completeness of an edge computing system by using Petri net software tool, Workflow Petri net Designer (WoPeD). Compared with many other development boards, Maix Bit is smaller, more flexible, more economical, and more adaptable to those needs of the edge side scenarios. When equipped with an Artificial Intelligence (AI) chip, Kendryte K210, it is suitable for object recognition due to its superior power consumption, film frames, and processors. YOLO, mainly characterized by its fast speed in using the convolutional neural network (CNN) to recognize objects, can be used to predict the positions and types of multiple objects concurrently. Also, CNN can be used to detect and recognize targets from end to end promptly, when configured to train an image recognition model. Finally, the experimental results have shown that the promising mean Average Precision (mAP), 78.6%, was obtained, which outperforms other existing systems. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Object detection

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Gaussian Support Vector Machine Algorithm Based Air Pollution Prediction

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computers, Materials & Continua 2022年第4期71卷 683-695页

作者： K.S.Bhuvaneshwari J.Uma K.Venkatachalam Mehedi Masud Mohamed Abouhawwash T.Logeswaran Department of Computer Sceince and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Electrical and Electronics Engineering M.Kumarasamy College of EngineeringKarur639113India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of Electrical and Electronics Engineering Kongu Engineering CollegeErode638060India

Air pollution is one of the major concerns considering detriments to human *** type of pollution leads to several health problems for humans,such as asthma,heart issues,skin diseases,bronchitis,lung cancer,and throat and eye *** pollution also poses serious issues to the *** from the vehicle industry is the cause of greenhouse effect and CO2 ***,real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary *** monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor *** is the main issue in WSNs;if the sensor node location is unknown,then coverage and power and routing are not *** study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart *** systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants,such as PM2.5 particulate matter,PM10,nitrogen dioxide(NO2),carbon monoxide(CO),ozone(O3),and sulfur dioxide(SO2).The sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding *** dataset is divided into training and testing parts based on 10 *** evaluation on predicting the air pollutant for localization is performed with the training *** error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%.

关键词： Air pollution monitoring air pollutant SVM Gaussian EHO fast correlation WSN localization

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COVID-19 Disease Prediction System Using Genetic Algorithm (Case Study: Nigeria)

COVID-19 Disease Prediction System Using Genetic Algorithm (...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Atanda, Oladayo G. Adebiyi, Marion O. Omolaja, Oladunjoye O. Akindeji, Kayode Adebiyi, Ayodele A. Landmark University Department of Computer Science Omu-Aran Nigeria SDG 3: Good Health and Well-Being Nigeria Durban University of Technology Department of Electrical Power Engineering Durban South Africa Landmark University Department of Computer Sciences Omu-Aran Nigeria

ISBN: (纸本)9798350358155

This research paper presents a COVID-19 disease prediction system designed specifically for Nigeria. The system combines the Genetic Algorithm (GA) approach with Support Vector Regression (SVR) models to improve the accuracy and efficiency of COVID-19 prediction models. The main objective is to enable early detection and effective management of COVID-19 cases. By integrating GA and SVR, the system optimizes feature selection and utilizes advanced regression techniques to forecast the likelihood of COVID-19 infection more accurately. The study utilizes a diverse dataset comprising COVID-19 cases reported in Nigeria. The dataset includes variables such as age, gender, comorbidities, symptoms, and geographic location. The effectiveness of the genetic algorithm-based prediction system is initially evaluated through rigorous assessment and comparison. Then, the SVR model is introduced as an alternative approach, and its performance is evaluated based on accuracy, sensitivity, and specificity. The results show that the genetic algorithm-based system achieves an impressive 100 percent accuracy in predicting the likelihood of COVID-19 infection. Upon integrating the SVR model, the accuracy slightly decreases to 97 percent. However, the SVR model demonstrates strong performance and exhibits promising potential for accurate prediction and early detection of COVID-19 cases. In conclusion, the COVID-19 disease prediction system developed using a combination of genetic algorithms and SVR models is a valuable tool for accurately predicting the likelihood of COVID-19 infection in Nigeria. © 2024 IEEE.

关键词： COVID-19

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Realizing full potential of radiative cooler-integrated solar cells

Realizing full potential of radiative cooler-integrated sola...

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Optical Interference Coatings, OIC 2022

作者： Heo, Se-Yeon Lee, Gil Ju Kim, Do Hyeon Song, Young Min School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology 61005 Korea Republic of Department of Electronics Engineering Pusan National University 46241 Korea Republic of

In this study, we investigated the dependency of the radiative cooling power for various types of solar cell(SC)s. As a result, high efficient multi-junction solar cell(MJSC) is presented by introducing bi-functional ... 详细信息

ISBN: (纸本)9781957171043

关键词： Cooling systems

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Development of Framework to Find Lung Canker Using GAN Technique 5

Development of Framework to Find Lung Canker Using GAN Techn...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Ramakrishnan, Rakesh Muniandi, Balakumar Yennapusa, Haritha Jiwani, Nasmin Kiruthiga, T. University of the Cumberlands Department of Information Technology Union CityCA United States Lawrence Technological University Department of Electrical and Computer Engineering SouthfieldMI United States University of Central Missouri Department of Computer Science AustinTX United States University of the Cumberlands Dept. of Information Technology WilliamsburgKY United States Vetri Vinayaha College of Engineering and Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798350383522

Lung cancer is one of the main reasons of lack of existence globally, accounting for approximately 1. Eight million deaths in past one year. Early prognosis of lung most cancers is crucial for a hit remedy, however it stays a task due to its subtle early signs and symptoms and the limitations of present-day diagnostic techniques. In recent years, the improvement of deep information of era has proven promising consequences in medical photograph analysis and ailment evaluation. On this context, the improved Generative adverse network (GAN) framework has emerged as a powerful device for improving the accuracy and overall performance of lung cancer evaluation. The stepped forward GAN framework is an extension of the conventional GAN, which incorporates a generator and a discriminator working collectively in a competitive gaining knowledge of the approach. The generator generates pics which might be then evaluated with the aid of the discriminator, which identifies if they may be actual or fake. This technique is repeated till the generator efficiently generates sensible photos, fooling the discriminator. Within the context of lung most cancers prognosis, the progressed GAN framework makes use of radiological snap shots, along with CT scans, to generate practical snap shots of lung nodules. Those generated photos are used to enhance the training dataset, developing the range and complexity of the facts, main to a miles higher deep getting-to-know version. This lets in higher differentiation between cancerous and non-cancerous nodules. © 2024 IEEE.

关键词： Better Differentiation Discriminator Extension Prognosis

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Efficient Deep CNN Model for COVID-19 Classification

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computers, Materials & Continua 2022年第3期70卷 4373-4391页

作者： Walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in *** Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death *** X-Ray(CXR)and computerized Tomography(CT)screening of infected persons are essential in diagnosis *** are numerous ways to identify positive COVID-19 *** of the fundamental ways is radiology imaging through CXR,or CT *** comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high ***,automated classification techniques are required to facilitate the diagnosis *** Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical *** deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image *** this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is *** activation functions as well as three optimizers are tested and compared for this *** proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train *** performance is tested and investigated on the CT and CXR *** activation functions:Tanh,Sigmoid,and ReLU are compared using a constant learning rate and different batch *** optimizers are studied with different batch sizes and a constant learning ***,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is ***,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early *** proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR an

关键词： COVID-19 image classification CNN DL activation functions optimizers

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Adaptive Movie Recommendations: A Deep Learning Framework with Mood and Sentiment Integration 5

Adaptive Movie Recommendations: A Deep Learning Framework wi...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Karthikeyan, M. Saravana Shankar, S. Vijay Krishnan, R. Santhana Sornavalli, M. Raj, J. Relin Francis Sundaravadivel, P. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Mathematics Madurai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331530013

This research study proposes an advanced movie recommendation system that combines deep learning models with mood detection and sentiment analysis for highly personalized suggestions. The system collects data from diverse sources, including user watch history from OTT platforms along with real-time social media sentiment from social media platforms. Additionally, wearable devices track heart rate to infer the user's emotional state during media interaction. The recommendation engine uses a multi-layered approach. Neural Collaborative Filtering (NCF) analyzes watch patterns, while BERT-based embeddings capture key movie metadata such as genres and actors for content-based filtering. The system also employs an LSTM model for mood-based recommendations, integrating heart rate data and social media sentiment to align suggestions with the user's current mood. Key advantages include a hybrid recommendation approach that combines collaborative filtering, content-based suggestions, and real-time mood analysis. The system adapts to both long-term preferences and short-term emotional states, delivering more relevant and personalized results. Deployed on scalable cloud infrastructure (AWS EC2), with continuous learning from user feedback, the system ensures dynamic and evolving movie recommendations that enhance user satisfaction. © 2024 IEEE.

关键词： Collaborative filtering

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Improving Recommendation Systems with a Combination of Similarity Measures on the Example of Radio Service Data 12th

Improving Recommendation Systems with a Combination of Simil...

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12th International Workshop of Advanced Manufacturing and Automation, IWAMA 2022

作者： Morozov, Jurij Saradgishvili, Sergei Tumanyan, Polina Voinov, Nikita Drobintsev, Pavel Wu, Jian Institute of Computer Science and Technology Peter the Great St. Petersburg Polytechnic University St. Petersburg195251 Russia College of Mechanical and Electrical Engineering Changshu Institute of Technology Changshu215500 China

ISBN: (纸本)9789811993374

In this paper, we will experiment with a music content recommendation system using a collaborative filtering approach. The experiment will be carried out using a combination of formulas that allow you to calculate similarities when developing collaborative filtering. The Otai Coefficient and Euclidean distance were chosen as metrics, as a result of their combination, a recommendation system is obtained that gives a positive result. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recommender systems

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