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Effectiveness of Recurrent Neural Network with Biomimic Components 3

Effectiveness of Recurrent Neural Network with Biomimic Comp...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Singh, Paras Nath Sakhare, Anupama Sarada, T. Cmr Institute of Technology Dept. of Computer Science and Engineering Bangalore560037 India Rtm Nagpur University Dept. of Electronics and Computer Science Nagpur440033 India

ISBN: (纸本)9798350343274

In order to improve the energy efficiency and computational capacity of our data processing systems, bioinspired, or neuromorphic, computing draws inspiration from how the brain computes. Emulation of natural systems, patterns, and components is known as biomimetics or biomimicry, and it is done to address challenging challenges in society. The term 'spintronics' refers to the application of a fundamental property of particles (the spin of an electron) known as the spin to information processing. Hopfield was the first to establish a direct connection between physical models and neural networks. To learn any of these models, the neural network must be able to adjust the coupling between each pair of spins. To implement multi-layer perceptron (neural network) structures, current technologies, however, have necessitated the extensive usage of complementary metal-oxide-semiconductor (CMOS) devices, which raises the perceptron's complexity of manufacture, space need, and power consumption. In this paper, it is suggested that the first trainable CMOS-free recurrent neural network with biomimetic components can be built using biomimetic neurons made of domain wall-magnetic tunnel junctions. This analysis shows how well this system performs computationally on benchmark tasks and how much more efficiently it performs computationally than alternative recurrent neural network systems. © 2023 IEEE.

关键词： Biomimetics

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An IoT-Based Sustainable Automation System for the Garments Industry in Bangladesh 3

An IoT-Based Sustainable Automation System for the Garments ...

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3rd International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2023

作者： Ahmed, Khandakar Rabbi Salam, Towfika Rabbu, Mohammad Mahmudul Haque, F.M. Hasan, Nazmul Miraz, Shafiun Rana, Md. Masud Mansur, Maliha Tabassum, Humayra Shayer, Khandokar Shadman Miyan Research Institute International University of Business Agriculture and Technology Dhaka Bangladesh Dept. of Mechatronics Engineering Rajshahi6204 Bangladesh International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Dept. of Computer Science & Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Dept. of Electrical & Electronic Engineering Dhaka Bangladesh Varendra University Dept. of Computer Science and Engineering Rajshahi Bangladesh Dept. of Computer Science and Engineering Rajshahi Bangladesh Ahsanullah University of Science & Technology-Bangladesh Dept. of Electrical & Electronic Engineering Dhaka Bangladesh American International University-Bangladesh Dept. of Electrical & Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350307399

The Readymade Garments sector accelerates Bangladesh's development. The 'Made in Bangladesh' brand has also helped to boost Bangladesh's reputation and brand recognition on a global scale. Despite its limited resources, the country has maintained a 6% annual GDP growth rate while attaining extraordinary social and human development. On the other hand, the phrase 'Internet of Things' (IoT) refers to a broad concept that can be summarized as an ecosystem of intelligent gadgets equipped with networks, sensors, and processing tools. IoT is a new technological platform that enables the many objects in our environment to connect via sensors and the Internet to improve our quality of life. Manufacturing and the industrial sector, agriculture, the health sector, smart cities, security, and emergency services, to name a few. IoT implementation in the garment sector would open a new door to growth in production and worker safety. Implementing home automation and safety systems in the garment sector will help in production and ensure the safety of the workers. The suggested IoT -based sustainable automation system for the Bangladeshi garments sector is efficient, eco-friendly, secure, reliable, and user-friendly. The system's user-friendly interface makes it straightforward to use and manage. The Bangladeshi garments industry's IoT -based sustainable automation system is a comprehensive and integrated solution that addresses the industry's primary difficulties and provides a sustainable and viable future. © 2023 IEEE.

关键词： Internet of things

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Decomposition Algorithm for Security-Constrained Unit Commitment with AC Optimal Power Flows

Decomposition Algorithm for Security-Constrained Unit Commit...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Renl, Weihang Li, Mingze Fan, Lei Guan, Yongpei University of Florida Dept. of Industrial and Systems Engineering FL United States University of Houston Dept. of Electrical and Computer Engineering TX United States University of Houston Engineering Technology TX United States

ISBN: (纸本)9798331541125

This paper presents a comprehensive approach to solving the security-constrained unit commitment with alternating current power flows (SCUC-ACPF) problem in contemporary power systems. We introduce algorithms that decompose the problem into subproblems suitable for specialized solvers, so that the large-scale mixed-integer nonlinear program-ming SCUC-ACPF problem can be solved. In case studies, we validate the efficiency and effectiveness of our algorithms on synthetic and industry-scale power system networks. © 2025 IEEE.

关键词： Lagrange multipliers

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Non-Invasive Human Body Temperature Detection for Online Event Data Recorder 3

Non-Invasive Human Body Temperature Detection for Online Eve...

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3rd International Conference on Intelligent Cybernetics technology and Applications, ICICyTA 2023

作者： Sani, Muhammad Ikhsan Ibnugraha, Prajna Deshanta Sari, Marlindia Ike Rizal, Moch Fahru Hanifa, Fanni Husnul Salasa, Salsabila Anza Reksa, Anggi Angga Hartono, Aditya Firmansyah Utama, Nur Cahya Telkom University Dept. of Computer Technology Bandung Indonesia Telkom University Dept. of Digital Marketing Bandung Indonesia Telkom University Dept. of Informatics Bandung Indonesia

ISBN: (纸本)9798350394559

The concept of black-box or Event Data Recorder (EDR) implementation on ground vehicle provides an alternative method for preemptive or prescriptive safety procedure. Utilizing all accessible technologies for accident prevention is crucial for ensuring transportation safety and regulation. Much effort has been devoted to developing thermal imaging as an additional sensor for EDR. However, several factors must be considered when using thermal imaging device on Event Data Recorder. Given that fever is a prevalent indication of several contagious illnesses, it is crucial to emphasize the monitoring of human body temperature for the purpose of fever screening. The goal is to reduce the number of individuals with elevated body temperatures from utilizing public transportation. This work aims to introduce a thermal camera application as a supplementary data source for the Event Data Recorder (EDR) in public transportation. The paper also proposed an alternative solution based on the use of a portable Forward-looking Infrared (FLIR) thermal imaging camera. The FLIR thermal camera is incorporated into the online EDR system. The system has been evaluated and can collect data for future analysis, as demonstrated by both indoor and outdoor tests. It may function in a somewhat limited range within small space area up to 3-meter detection range. Further studies will have to clarify on increasing the accuracy with another algorithm or methodology. © 2023 IEEE.

关键词： Automobile bodies

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Investigating Principle Component Analysis Impact on the Performance of Machine Learning Classifiers: A Health informatics Application 25

Investigating Principle Component Analysis Impact on the Per...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Banimustafa, Ahmed Almatarneh, Sattam Bulkrock, Olla Alazaidah, Raed Almahasneh, Hossam Samara, Ghassan Higher Colleges of Technology Computer Information Science Dept. Abu Dhabi United Arab Emirates Zarqa University Data Science & Artificial Intelligence Dept. Zarqa Jordan University of Business and Technology College of Engineering Jeddah Saudi Arabia Princess Sumaya University for Technology Software Engineering Dept. Amman Jordan Zarqa University Computer Science Dept. Zarqa Jordan

ISBN: (纸本)9798331540012

Principal Components Analysis (PCA) is a popular dimensionality reduction and feature selection technique. It works by swiping non-informative features while retaining those that are still potentially useful. However, regardless of its wide applications in statistical analysis, the influence of PCA on the performance of classification algorithms has been rarely studied, particularly in the domain of medical image classification. In this paper, we present a thorough analysis of the influence of applying PCA on the classification of medical images. The dataset used in this study consists of thousands of images that represent four types of blood cells. These images are used to train three machine-learning classifier models after transforming images into numerical vectors using a CNN pre-trained embedding algorithm, where each image is encoded using one thousand numerical vectors. The study involves building three classification models using (1) K-nearest Neighbor (KNN), (2) Logistic Regression (LR), and (3) Random Forests (RF). Each model was built twice, once after the application of PCA to reduce the dimensionality of the embedded features and once with the original embedded features. The performance of each model is evaluated using Classification accuracy, F1, AUC, Recall, precision, and specificity metrics. The recorded performance is then compared to drawing a decisive conclusion. © 2024 IEEE.

关键词： Dimensionality reduction

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Proposal for the Support Method Presentation Model Utilizing Nursing Home Insights 9

Proposal for the Support Method Presentation Model Utilizing...

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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Nakajima, Yoko Yuki, Masamune Ptaszynski, Michal Honma, Hirotoshi Akiba, Tomoyoshi Masui, Fumito Institute of Technology Kushiro Colloege Dept. of Creative Engineering Kushiro Japan School of Regional Innovation and Social Design Engineering of Kitami Institute of Technology Kitami Japan Toyohashi University of Technology Dept. of Computer Science and Technology and Engineering Toyohashi Japan

ISBN: (纸本)9798350383010

One of the significant problems faced by dementia group homes is the high caregiver turnover rate. In particular, there is an urgent need to prevent the turnover of new caregivers and work toward ensuring and improving the quality of care. To address this issue, we are developing a care method support system that utilizes two databases: a personal attributes database and a care case database ([1]-[3]). The personal attributes database extracts personal attributes such as hobbies and previous occupations of residents from care records and care conference minutes. The care case database extracts cases of resident problems, their solutions, and the original text containing them. The care method support system presents candidates for appropriate responses to problems faced by residents based on these databases. In this study, we generated two care suggestion models: Care Suggestion Model: This model applies text similarity to the care case database to suggest candidates for appropriate responses to unregistered problems. Personalized Care Suggestion Model: This model applies clustering to the personal attributes database and generates a care suggestion model that considers personal attributes. We compared the two models and tested the usefulness of the model. © 2024 IEEE.

关键词： Nursing

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Exploratory Data Analysis of Titanic Survival Prediction using Machine Learning Techniques 2

Exploratory Data Analysis of Titanic Survival Prediction usi...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Gupta, Anshika Arora, Deepak Tiwari, Shivam Aset Lucknow Amity University Dept. of Computer Science & Engineering Uttar Pradesh India Gl Bajaj Institute Technology & Management Dept. of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9781665456302

It is very important to find out the root causes of human tragedies in the past so that future crises can be avoided. The incident of 15 April 1912 is an example of a human tragedy in which around 1500 passengers and working staff lost their lives. Continuous research in today's time shows that if the proper statistical assessment was done then it would probably be possible that human devastation could be reduced. In today's era, many new and powerful technologies are available, with the help of which accurate statistical calculations can be done. In this research study, Titanic survivals have been studied based on machine learning techniques. In the study, out of the total entities, 891 entities have been used for training and 418 entities have been used for the test set and the comparative study of different machine learning algorithms gives importance to this research study. © 2023 IEEE.

关键词： Machine learning

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Image Processing based Skin Cancer Recognition using Machine Learning 3

Image Processing based Skin Cancer Recognition using Machine...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Singh, Sanjay Pilania, Urmila Kumar, Manoj Awasthi, Satya Prakash Manav Rachna University Dept. of Computer Science & Technology Faridabad India Poornima University Faculty of Computer Engineering Jaipur India

ISBN: (纸本)9798350338607

The skin is the most important part of the human body because it protects bones and muscles., hence, it is the body's largest organ. Numerous people are suffering from skin cancer these days. It has now become the most common type of cancer. Melanoma is known to be a serious and mortal disease. If detected in initial stages, it can be easily treated. Non-melanoma skin cancers have also become common. Rrecognizing skin cancer automatically from the images can help in faster diagnosis and enhanced accuracy saving valuable time. In this paper, 4 types of CNN were used named VGG16, InceptionV3, ResNet152V2 and a special CNN with 12 layers. © 2023 IEEE.

关键词： Machine learning

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Precision Agriculture: Crop Image Segmentation and Loss Evaluation through Drone Surveillance 3

Precision Agriculture: Crop Image Segmentation and Loss Eval...

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3rd International Conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Patidar, Pawan Kumar Tomar, Deepak Singh Pateriya, R.K. Sharma, Yogesh Kumar Maulana Azad National Institute of Technology Dept. of Computer Science Engineering Bhopal India

ISBN: (纸本)9798350300710

Agriculture is the most important source of livelihood. Crop segmentation has become an important role in precision agriculture which helps farmers to make decisions about crop damage and its production. However, it's a challenging task to achieve precision in the agriculture field. Drone Surveillance helps to achieve that crop yield assessment, crop damage, crop health, and other parameters. This paper focuses on image segmentation of crops, classified into categories like sparse and dense crops with the multitemporal data image taken by Drone. This model proposed and studied shows the loss percentage in crop identification by image segmentation process, it helps farmers to get good compensation for crops to survey through Drone (UAV) techniques. A detailed analysis with outcome of thisis explained further. © 2023 IEEE.

关键词： Drones

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Enhancing Telecommunications Customer Retention: A Deep Learning Approach Using LSTM for Predictive Churn Analysis 2

Enhancing Telecommunications Customer Retention: A Deep Lear...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Kumar, M. Ranjith Priyanga, S. Anusha, Janani Srinivasan Chatiyode, Veda Santiago, Jerome Chaudhary, Dharminder School of Computer Science and Engineering Vellore Institute of Technology Chennai India Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science & Engg Chennai India

ISBN: (纸本)9798350373110

The telecommunications industry has a significant challenge in customer churn, especially for companies aiming for sustainable growth in a competitive market. To overcome this issue, we suggested a method utilizing Long Short-Term Memory (LSTM) networks, a deep learning strategy that has revolutionized predictive modeling. We used 4250 customer profiles, each containing 19 features including consumption data and plan options, to train and test the LSTM model. Data preprocessing included encoding categorical variables, normalizing numerical features, and eliminating irrelevant variables to maintain data integrity and authenticity. The LSTM model was fine-tuned to recognize significant patterns rather than simply memorizing data. We also incorporated an additional layer to assess diverse information types, which improved the model's ability to process sequential data effectively. The proposed model achieved a prediction accuracy of 95.3% in identifying potential customer churn, demonstrating its effectiveness in predicting when customers might leave their telecommunications provider. These results demonstrate how LSTM networks can reduce customer attrition in the telecom industry, making them a valuable tool for companies aiming to retain customers and maintain growth in a competitive market. The study's findings pave the way for innovative applications of deep learning techniques in customer retention strategies across diverse industries. This breakthrough has the potential to transform business approaches to customer relationship management and loyalty programs. © 2024 IEEE.

关键词： Commerce

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