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Automatic computer Shutdown with Image Processing via Webcam to Save Energy 2

Automatic Computer Shutdown with Image Processing via Webcam...

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2nd International Conference on Cybernetics and Innovations, ICCI 2023

作者： Chumuang, Narumol Ketcham, Mahasak Ganokratanaa, Thittaporn Pramkeaw, Patiyuth Yimyam, Worawut Chuamoo Dep. of Digital Media Technology Thailand King Mongkut's University of Technology North Bangkok Dep. of Information Technology Management Thailand King Mongkut's University of Technology Thonburi Applied Computer Science Program Bangkok Thailand King Mongkut's University of Technology Thonburi Media Technology Program Bangkok Thailand Phetchaburi Rajabhat University Dep. of Business Information Management Thailand

ISBN: (纸本)9798350336573

The purpose of this research is to help save electricity within organizations and workplaces that use computers. Due to the behavior of general users today, they do not give importance to saving electricity. The researcher has developed an automatic computer shutdown system. with image processing via a webcam By using image processing techniques (Image Processing) applied to a webcam. By using a webcam to capture the user on the computer screen. Then the images are analyzed and examined with the principles of image processing with the Face Detection technique using the algorithm of Haar-like Features to detect human faces and ROI. Define a specific area of interest The system will then check the amount of inactivity on the screen. or does not show the face image within 10 minutes, the system will automatically turn off the computer. with image processing This research can make it easier to turn off computers when not in use. and helps to save electricity for the organization or agencies as well. © 2023 IEEE.

关键词： Face recognition

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A Smart Aquaponic System for Enhancing The Revenue of Farmers in Sri Lanka 9

A Smart Aquaponic System for Enhancing The Revenue of Farmer...

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9th International Conference on ICT for Smart Society, ICISS 2022

作者： Ekanayake, Dhanushka De Alwis, Pubudu Harshana, Pasindu Munasinghe, Dilusha Jayakody, Anuradha Gamage, Ms. Narmada Sri Lanka Institute of Information Technology Dep. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (数字)9781665471350

ISBN: (纸本)9781665471350

Sri Lanka's agricultural sector confronts serious challenges from fertilizer shortages and agriculture-related chemical scarcity. Innovations comparable to aquaponic systems may be offered to Sri Lankan farmers to overcome these difficulties using IoT and ML technology. This research scope is to implement a smart and secure aquaponic environment monitoring system to forecast plant and fish growth factors, provide Sri Lankan farmers with insights into the environment's behaviors, and take measures according to the predictions utilizing control mechanisms. In this research, more exact predictions have been generated by the Random Forest algorithm model rather than the LSTM model, and most of the investigated parameters given good accuracy according to the absolute mean error (Media TDS-1.95, Media pH-0.06, Media Temperature-0.49, Env. Temperature- 0.94, Env. Humidity-2.70) except the environment light intensity (64.11). The ML solution studied in this research paper would increase the quality of traditional agriculture in Sri Lanka for greater productivity and economic benefit. © 2022 IEEE.

关键词： Internet of things

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Clinical Informatics System to Understand Lung Cancer Using Transfer Learning-Based computer-Aided Application 7

Clinical Informatics System to Understand Lung Cancer Using ...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Abugabah, Ahed Mehmood, Atif Shahid, Farah College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates Zhejiang Normal University Dep. of Computer Science and Technology Jinhua321002 China

ISBN: (纸本)9798350342154

Lung cancer is the second leading cause of death worldwide after cardiovascular disease. Due to late diagnosis, lung cancer patients have an extremely low survival rate when compared to other cancer patients. Hence, early lung cancer detection is critical for patients to undergo early therapies, boosting their chances of surviving or potentially becoming cancer-free. Over the past few years, researchers have developed several deep learning-based computer-aided diagnostic tools to detect lung cancer early. As a consequence, deep learning models are readily susceptible to over-fitting for fewer data samples and can be computationally time-consuming to pick hyper-parameters, which usually leads to a reduction in performance. Our proposed technique came up with a solution to this challenge of lung cancer classification tasks that used a layer-wise transfer learning strategy. In this work, three types of data samples such as Lung adenocarcinoma (Lung a), lung squamous cell carcinoma (Lung scc), and lung benign (Lung b) have been taken and implemented models, namely Group-I, II, III with frozen layers and baseline algorithms as VGG-19. To check the validity of the proposed models on lung samples and attain the best classification accuracy on Lung scc vs. Lung b 97.53%, also discriminate between Lung a vs. Lung b testing accuracy 92.59%. In contrast, the remaining Lung scc vs. Lung a attained 89.30%. Finally, a comparison to other studies demonstrating the suggested model superiority over state-of-the-art models in testing accuracy © 2023 IEEE.

关键词： Diseases

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Exploring Genetic Programming Models in computer-Aided Diagnosis of Skin Cancer Images 13

Exploring Genetic Programming Models in Computer-Aided Diagn...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Ain, Qurrat Ui Al-Sahaf, Harith Xue, Bing Zhang, Mengjie School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington New Zealand College of Engineering and Info. Technology AlShaab University Dep. of Cybersecurity Engineering Baghdad Iraq

ISBN: (纸本)9798350308365

Extracting important information from complex skin lesion images is vital to effectively distinguish between different types of skin cancer images. In addition to providing high classification performance, such computer-aided diagnostic methods are needed where the models are interpretable and can provide knowledge about the discriminative features in skin lesion images. This underlying information can significantly assist dermatologists in identifying a particular stage or type of cancer. With its flexible representation and global search abilities, Genetic Programming (GP) is an ideal learning al-gorithm to evolve interpretable models and identify important features with significant information to discriminate between skin cancer classes. This paper provides an in-dep.h analysis of a recent GP-based feature learning method where different well-developed feature descriptors are integrated into the learning algorithms to extract high-level features for skin cancer image classification. The study explores the effectiveness of utilizing feature learning for this complex task and designing program structure to suit the problem domain as it has shown promising results compared to commonly used feature descriptors and an existing GP-based feature learning method developed for general image classification. This study analyzes the GP-evolved models to identify the prominent features and most effective feature descriptors important for the classification of these skin cancer images. The evolved models are interpretable, they provide knowledge that can assist dermatologists in making diagnoses in real-time clinical situations by identifying prominent skin cancer characteristics captured by the feature descriptors and learned during the evolutionary process. © 2024 IEEE.

关键词： Genetic programming

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When Do Neuromorphic Sensors Outperform cameras? Learning from Dynamic Features

When Do Neuromorphic Sensors Outperform cameras? Learning fr...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Daniel Deniz Eduardo Ros Cornelia Fermller Francisco Barranco Dep. of Computer Architecture and Technology University of Granada Spain Perception and Robotics Group University of Maryland USA

Visual event sensors only output data when changes in the scene happen at very high frequency. This allows for smartly compressing the scene and thus, enabling real-time operation. Despite these advantages, works in the literature have struggled to show a niche for these event-driven approaches compared to conventional sensors, especially when focusing on accuracy performance. In this work, we show a case that fully exploits event sensor advantages: for manipulation action recognition, learning events achieves superior accuracy and time performance. The recognition of manipulation actions requires extracting and learning features from the hand pose and trajectory and the interaction with the object. As shown in our work, approaches based on event sensors are the best fit for extracting these dynamic features contrarily to conventional approaches based on full frames, which mostly extract spatial features and need to reconstruct the dynamics from sequences of frames. Finally, we show how using a tracker to extract the features to be learned only around the hand, we obtain an approach that is scene- and almost object-agnostic and achieves good time performance with a very limited impact in accuracy.

关键词： Visualization Neuromorphics Neural networks Human-robot interaction Sensor phenomena and characterization Feature extraction Real-time systems

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Preventing Overfitting on Noisy Labels Through Adaptive Checkpointing

Preventing Overfitting on Noisy Labels Through Adaptive Chec...

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Aref Azizian Rouhollah Ahmadian Mehdi Ghatee Mostafa Shamsi Dep. of Math. & Computer Science Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

Overfitting and the presence of noisy labels are significant challenges in the training of machine learning models, particularly in complex datasets. This paper introduces a novel checkpointing method designed to mitigate overfitting while maintaining model calibration in the presence of noise. Our approach leverages the entropy of the two largest predicted probabilities from the model's output to determine when to save the model during training. This entropy-based criterion allows for effective checkpointing without necessitating a separate validation set. We evaluate our method across various datasets characterized by different noise levels and model architectures. The results demonstrate that our entropy-based checkpointing offers a more robust solution for managing model performance during training. This work contributes to the ongoing efforts to enhance model reliability in real-world applications where data imperfections are prevalent.

关键词： Checkpointing Training Adaptation models Prevention and mitigation Noise Machine learning Predictive models Robustness Noise measurement Overfitting

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Lightweight SRAM-PUF Identity Authentication for Edge Devices

Lightweight SRAM-PUF Identity Authentication for Edge Device...

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Iranian Conference on Electrical Engineering (ICEE)

作者： Alireza Shafiei Mehrnaz Monajati Dep. of Electrical and Computer Engineering Graduate University of Advanced Technology Kerman Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

The expansive and intricate nature of the Internet of Things (IoT) network necessitates intelligent and real-time computation. Edge computing, as an innovative concept and trend within the IoT landscape, addresses this demand by leveraging advanced technologies to overcome challenges such as timely responsiveness, enhanced security, and reduced power consumption. Among the technologies available for securing edge devices, Physical Unclonable Functions (PUFs) stand out. This paper introduces an SRAM-PUF that explores the utilization of adiabatic logic with carbon nanotube field-effect transistors (CNTFETs) to design lightweight edge devices capable of addressing the aforementioned challenges. The proposed computing platform holds promise for enhancing security and energy efficiency in IoT applications.

关键词： Electrical engineering Power demand Physical unclonable function Market research Real-time systems Energy efficiency CNTFETs

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Predictions of COVID-19 Spread by Using Supervised Data Mining Techniques

Predictions of COVID-19 Spread by Using Supervised Data Mini...

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2020 Ibn Al-Haitham International Conference for Pure and Applied Sciences, IHICPS 2020

作者： Awadh, Wid Akeel Alasady, Ali Salah Mustafa, Hadeel Ismail Dep. of Computer Information System College of Computer Science and Information Technology University of Basrah Iraq Dep. of Computer Science College of Computer Science and Information Technology University of Basrah Iraq

In the wake of the outbreak of the new coronavirus, the countries in the world have fought to combat the spread of infection and imposed preventive measures to compel the population to social distancing, which led to a global crisis. Important strategies must be studied and identified to prevent and control the spread of coronavirus COVID-19 disease 2019. In this paper, the effect of preventive strategies on COVID-19 spread was studied, a model based on supervised data mining algorithms was presented and the best algorithm was suggested on the basis of accuracy. In this model, three classifiers (Naive Bayes, Multilayer Perceptron and J48) dep.nded on the questionnaires filled out by Basra City respondents. The questionnaires consisted of 25 questions that covered fields most related to and that affect the prevention of COVID-19 spread, including demographic, psychological, health management, cognitive, awareness and preventive factors. A total of 1017 respondents were collected. This model was developed using Weka 3.8 tool. Results showed that quarantine played an important role in controlling the spread of the disease. By comparing the accuracy of the algorithms used, the best algorithm was found to be J48. © Published under licence by IOP Publishing Ltd.

关键词： COVID-19

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Machine Learning Application to Transmission Quality Assessment in Optical Networks 30

Machine Learning Application to Transmission Quality Assessm...

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30th International Conference on Software, Telecommunications and computer Networks, SoftCOM 2022

作者： Kozdrowski, Stanislaw Cichosz, Pawel Sujecki, Slawomir Warsaw University of Technology Institute of Computer Science Warsaw Poland University of Science and Technology Telecommunications and Teleinformatics Dep. Wroclaw Poland

ISBN: (数字)9789532901177

ISBN: (纸本)9789532901177

This paper presents the application of 2 selected machine learning methods for transmission quality assessment in optical networks. The proposed solution uses real data to train classification models that predict whether a given optical channel meets transmission quality requirements. The training data is extracted directly from the running network through the control plane. Several attribute selection strategies have been analysed. The available attributes are divided into several subsets. The performance of the selected machine learning algorithms for each subset is compared using ROC and PR curves. The results are used to evaluate the predictive performance of the selected machine learning algorithms when applied to the optical channel classification task and to determine how this performance is related to the set of attributes used. © 2022 University of Split, FESB.

关键词： Machine learning

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The Role of the Internet of Things in Monitoring Air Pollution-A Survey 11

The Role of the Internet of Things in Monitoring Air Polluti...

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11th International Conference on Information technology, ICIT 2023

作者： Abusukhon, Ahmad Alghannam, Bareeq Al-Zaytoonah University of Jordan Faculty of Science and Information Technology Computer Science Dep. Amman Jordan College of Business Studies the Public Authority for Applied Education and Training Department of Computer Science and Information System Kuwait City Kuwait

ISBN: (纸本)9798350320060

Air pollution is one of the most common problems that the world is facing today. In fact, there are numerous causes of air pollution, including the large number of industries and automobiles that emit carbon dioxide (CO2) and carbon monoxide. Pollution is increasing on a daily basis, making air quality monitoring and management a major challenge for governments in both developed and developing countries. Air pollution affects human health as well as the atmosphere, causing climate change. One of the techniques used for monitoring air pollution is the Internet of Things (IoT). In this paper, we demonstrate various techniques for monitoring air quality using IoT and make a comparison based on various factors elicited from the previous work. © 2023 IEEE.

关键词： Internet of things

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