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2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021

作者： Awad, A.H. Saraya, M.S. Elksasy, M.S.M. Ali-Eldin, Amr M. T. Mansoura University Faculty of Engineering Computer Engineering and Control System Dept. Mansoura Egypt

ISBN: (纸本)9781665412438

The Internet of Things refers to the type of network that connects anything to the Internet on the basis of the protocols stipulated by the information sensing equipment for the exchange of information and communication in order to achieve smart recognition, positioning, tracing, monitoring and administration. Movable harbor crane (MHC) is a major part of logistics services. Logistics is the management of the flow of goods between the point of origin and the point of consumption in order to meet the requirements of customers or corporations. MHC is mainly used for the loading and unloading of ships. This paper presents recommendations and practices to be used in the future of the smart industry internet (IoSI) and explores the different applications of smart sensor networks in the movable harbor crane. The main objective of the current paper is to develop a conceptual framework for monitoring and controlling harbor movable crane using a cloud on the IoT-PLC control system. Experimental results show that the proposed system is applicable and effective. © 2021 IEEE.

关键词： Internet of things

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Forecasting Breakdowns of Electric Motors of a Sugar Factory Using Machine Learning Methods 4

Forecasting Breakdowns of Electric Motors of a Sugar Factory...

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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Zaiets, Nataliia Vlasenko, Lidiia Lutska, Nataliia Zhyltsov, Andrii Dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine Dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine Dept. of Electromagnetic Systems The Institute of Electrodynamics The National Academy of Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798350395532

The paper demonstrates the process of developing mathematical models for identifying breakdowns of electric motors using machine learning methods. The authors have developed three mathematical models for identifying breakdowns of electric drives with a power of 55 kW, 1500 rpm on the example of sugar production: model for quadratic discriminant analysis;binary classification decision tree;feedforward neural network model. The best structures and parameters of the models were determined by machine learning methods. The performance of the models on the training and test sets was more than 96% by F-score. It was proposed to use all three models independently for diagnosing breakdowns of electric drives, while the decision maker makes the final decision on the replacement or repair of electrical equipment. This approach is justified and effective because of the diversity of models. The developed models, except for assessing the state of the engine, can also be used for simulation modelling, forecasting, and serve as information support for process operators in organizing equipment maintenance. The availability of real-time information about the state of equipment in production will allow timely repair or replacement of equipment, thereby reducing the risks of stopping production and increasing the resource efficiency of technological processes. © 2023 IEEE.

关键词： Decision trees

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control strategies of model consistency for large-scale parallel and distributed simulation applications 5

Control strategies of model consistency for large-scale para...

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5th International Conference on computer Information Science and Application Technology, CISAT 2022

作者： Linjun, Fan School of Computer Science and Engineering 9 Chengdu610213 China Dept. of Military Management Officers College of PAP Chengdu610213 China

ISBN: (纸本)9781510660076

Model consistency is an important guarantee to ensure good interoperability and correct simulation results of large-scale parallel and distributed simulation applications (DSA). There are many factors affecting the consistency of the model, including internal, external, subjective and objective ones;Model inconsistency will eventually lead to poor interoperability performances and then result in the deviation from reality in DSA;Taking the typical distributed simulation application named Unmanned Aerial Vehicle (UAV) penetration as an example, the paper expounds the problems of model consistency in detail, and puts forward some consistency control strategies of simulation models in DSA from two aspects of technology and management, so as to lay a foundation for improving the accuracy of DSA. © 2022 SPIE.

关键词： Interoperability

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Taxi Origin-Destination Demand Prediction using DNN Algorithm with NYU Database 2

Taxi Origin-Destination Demand Prediction using DNN Algorith...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Shukla, Amogh Reddy, Apoorva Meena, Radhey Shyam Almal, Shubh School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Indian Institute of Technology Dept. of Industrial and Management Engineering Kanpur India

ISBN: (纸本)9781665437899

Because of its colossal possible application in huge scope keen transportation frameworks, taxi request expectation has as of late drawn in expanded research interest. Most past methodologies, then again, just considered taxi request forecast in beginning regions, neglecting the demonstrating of the interesting circumstance of objective travellers. We accept that assigning taxicabs to every area ahead of time founded exclusively on taxi beginning interest is wasteful. In this paper, we present a significant and fascinating errand called taxi beginning objective interest expectation, which means to estimate taxi interest between all-area matches from now on. Its key hardships come from delineating how to proficiently gather a wide scope of relevant information to learn request designs. In this paper, a clever Deep Neural Network (DNN) with Deep learning-based models outflanked conventional AI techniques in an assortment of characterization undertakings, including beginning and objective perspectives. Broad tests and examinations on an expansive dataset plainly show that our DNN beats other looked at techniques for foreseeing taxi beginning objective interest. © 2022 IEEE.

关键词： Deep neural networks

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Fall Detection from Indoor Videos using MediaPipe and Handcrafted Feature

Fall Detection from Indoor Videos using MediaPipe and Handcr...

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International Conference on computer and Information Technology (ICCIT)

作者： Fatima Ahmed Parag Biswas Abdur Rashid Md. Khaliluzzaman Dept. of Computer Science and Engineering International Islamic University Chittagong (IIUC) Chittagong Bangladesh Dept. of Master of Science in Engineering and Management Westcliff University California United States

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Falls are a common cause of fatal injuries and hospitalization. However, having fall detection on person, in particular for senior citizens can prove to be critical. Presently, there are handheld, ambient detector and vision-based detection techniques being utilized for fall detection. However, the approaches have issues with accuracy and cost. In this regard, in this research, an approach is proposed to detect falls in indoor environments utilizing the handcrafted features extracted from human body skeleton. The human body skeleton is formed using MediaPipe framework. Results on UR Fall detection show the superiority of our model, capable of detecting falls correctly in a wide number of settings involving people belonging to different ages and genders. This proposed model using MediaPipe for fall classification in daily activities achieves significant accuracy compare to the present existing approaches.

关键词： Ankle Accuracy Computational modeling Lighting Feature extraction Skeleton Indoor environment Fall detection Older adults Videos

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Predicting Mobile Prices with Machine Learning Techniques

Predicting Mobile Prices with Machine Learning Techniques

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Computational Intelligence and Computing Applications (ICCICA), International Conference on

作者： Deepak Singh Rana Shiv Ashish Dhondiyal Sumeshwar Singh Sanjeev Kukreti Ashish Dhyani Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Computer Science and Engineering Jaypee Institute of Information Technology Department of Hospitality Management Graphic Era Deemed to be University Dehradun Uttrakahnd

ISBN: (数字)9798350306446

ISBN: (纸本)9798350306453

Smartphones are increasingly vital to people on a daily basis. Telephones are utilized in all aspects of life, ranging from personal to professional, due to technological advancements. It serves a function beyond making phone calls. It enables internet connectivity and email reading. when not using the computer. The characteristics of a mobile phone are a crucial consideration when buying *** overall objective of this research is to find the best way to apply machine learning to estimate the retail pricing of smartphones based on their individual specs. Individuals who frequently use their phone are more attentive to selecting features. When purchasing a cell phone, a comparison is done based on the price-performance ratio. Phone features are regarded as performance. This research aims to forecast if mobile phones with certain features are considered economical or *** work is capable of being utilized in various marketing and business contexts to assist in making informed purchasing decisions by maximizing features while minimizing costs.

关键词： Support vector machines Machine learning algorithms Costs Machine learning Pricing Predictive models Prediction algorithms

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Web Application-Based Enhanced Esophageal Disease Diagnosis in Low-Resource Settings

Web Application-Based Enhanced Esophageal Disease Diagnosis ...

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IEEE International Conference on Biomedical engineering, computer and Information Technology for Health

作者： Abdullah Al Masum Zishad Hossain Limon Md Ariful Islam Md Shahidur Rahman Mahbub Khan Sheikh Shemanto Afridi Md. Redwan Ahmed Shafiur Rahman Dept. of Information Technology Westcliff University CA USA Dept. of Computer Science Westcliff University CA USA Dept. of Business Administration International American University CA USA Dept. of Engineering management Westcliff University CA USA Dept. of Management Information System Pacific State University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798331534356

ISBN: (纸本)9798331534363

An essential part of the digestive system, the esophagus permits food and liquids to move easily from the throat to the stomach. Traditional diagnostic techniques, such as endoscopy and biopsy, are invasive and resource-intensive, making them less practical in resource-constrained environments. To address these challenges, we proposed utilizing a hybrid Deep Learning (DL) model called GAN-EfficientNet, which combines the power of Generative Adversarial Networks (GAN) and EfficientNet to effectively classify esophageal diseases. Our approach employs advanced image preprocessing techniques and data augmentation methods to enhance the diversity and balance of the dataset. Feature extraction using the Spatial Gray Level Dependence Method (SGLDM) and Principal Component Analysis (PCA) significantly reduces computational complexity without compromising accuracy. We explored both binary and multiclass classification methods, recognizing their crucial roles in various diagnostic scenarios. Binary classification distinguishes between normal and diseased states, essential for initial screening, while multiclass classification identifies specific esophageal diseases, aiding in personalized treatment plans. The GAN-EfficientNet model demonstrated exceptional performance, achieving an impressive F1 score of 99.44% for binary classification and 99.64% for multiclass classification. Additionally, we developed a web application that facilitates quick and precise disease diagnosis, particularly advantageous in areas with limited resources. This tool significantly improves patient outcomes through early detection, leading to reduced healthcare costs and less burden from advanced esophageal diseases.

关键词： Deep learning Accuracy Computational modeling Generative adversarial networks Feature extraction Data augmentation Data models Medical diagnosis Diseases Principal component analysis

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Smart Agriculture-Based Food Quality Analysis with Healthcare Security System Using Cloud Machine Learning Model

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Remote Sensing in Earth Systems Sciences 2024年第4期7卷 389-398页

作者： Vaithianathan, V. Rebecca, B. Begum, M. Fathima Sundarraj, Antony Allwyn Kumar, M. Prema Kiran, Ajmeera Department of Electronics and Communication Engineering Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Chennai 603110 India Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Hyderabad Telangana Dundigal 500043 India Assistant Professor Department of Computer Science and Engineering (Cyber Security) Madanapalle Institute of Technology and Science Andhra Pradesh-517325 Madanapalle India Department of Food Technology Sri Shakthi Institute of Engineering and Technology Coimbatore Tamilnadu Coimbatore India Dept. of ECE Shri Vishnu Engineering College for Women (A) Andhra Pradesh Bhimavaram 534202 India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

The primary sector of our Indian economy is agriculture. Today’s world’s most important and vital industries are agriculture and irrigation. Information and communication technology must be used in our agriculture sectors in order to support farmers and agriculturalists in improving their practices across the entire crop-growing and post-harvest processes. It is still challenging to define processed food, even in light of mounting research that suggests consuming more ultra-processed food has negative health effects. This research proposes novel technique in smart agriculture model based on cloud machine learning in cloud machine learning and healthcare system. By leveraging IoT and cloud-based data to create data-driven product decisions, cloud computing in agriculture can assist farmers with smart agriculture practices. Here, the input has been collected as food quality based on smart agriculture using a cloud model as well as processed for noise removal and normalisation. Then, based on this data, the healthcare security system has been analysed using reinforcement adversarial stacked encoder with Markov fuzzy algorithm. The experimental analysis has been carried out for various food quality datasets in terms of training accuracy, random precision, normalised correlation ratio, F-1 score, and AUC. The proposed technique achieved training accuracy of 96%, F-1 score of 94%, random precision of 97%, normalised correlation ratio of 93%, and AUC of 92%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Adversarial stacked encoder Cloud machine learning Cloud machine learning Healthcare system Smart agriculture

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Alpha-x Framework for Assessing Network Robustness in Nakagami-m Fading and Co-Channel Interference Environments

Alpha-x Framework for Assessing Network Robustness in Nakaga...

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Delhi Section Conference (DELCON), IEEE

作者： Deepak Upadhyay Karan Siwach Shiv Ashish Dhondiyal Abhay Upadhyay Mridul Gupta Kunj Bihari Sharma Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management Greater Noida India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

This article explores how the Alpha-x framework can improve network robustness under Nakagami-m fading and co-channel interference (CI), The study evaluates network performance using key metrics such as outage probability, spectral efficiency, and capacity degradation. The simulations show a 50% reduction in outage probability, a 20% boost in spectral efficiency, and a 33.3% lower capacity degradation compared to traditional models. These results demonstrate how the Alpha-x framework enhances network performance by adapting to various fading conditions and interference levels in wireless communication systems. The framework not only improves reliability and efficiency but also shows practical relevance in network planning and management. This can potentially lead to better user experiences and increased operational resilience. Future research could focus on optimizing the underlying algorithms and testing their performance on a larger scale in real-world environments. As telecommunications systems evolve, frameworks like Alpha-x could play a crucial role in ensuring reliable and efficient wireless networks.

关键词： Fading channels Degradation Adaptation models Spectral efficiency Probability Robustness Power system reliability Telecommunication network reliability Interchannel interference Capacity planning

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Fuzzy Logic controller for the Chemotherapy of Brain Tumor

Fuzzy Logic Controller for the Chemotherapy of Brain Tumor

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Muhammad Zubair Daniela Iacoviello Iqra Shafeeq Mughal Dept. Computer Control and Management Engineering Sapienza University of Rome Rome Italy Dept. di Ingegneria Electrica Electronica e Informatica DIEEI University of Catania Catania Italy

An advanced Fuzzy Logic controller (FLC) that considers all the states of the brain tumor system is designed for the chemotherapy treatment. A Mamdani-type FLC is proposed for dynamically controlling the chemotherapy drug for the tumor system; the chemotherapy treatment of brain tumors requires advanced strategies which mainly depend upon the severity of the tumor. In this work, the advanced FLC designed aims both at determining the amount of chemotherapy to eliminate tumor cells, and at preserving the minimum amount of healthy and immune cells. The controller's performance is verified using MATLAB software based on different control parameters, showing its effectiveness in reducing the tumor cells. It has shown favorable results in terms of steady-state error, rate of convergence, and amount of drug consumed.

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