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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Saxena, Akshit Singh, Jaspreet Chandigarh University Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350335095

Early diagnosis and treatment planning are greatly aided by the early identification of brain tumors. Because of its high resolution, low radiation, and low risk of patient discomfort, magnetic resonance imaging (MRI) is frequently used to diagnose brain tumors. Brain tumor identification is only one area where recent developments in convolutional neural networks (CNNs) have shown exceptional effectiveness. This article summarizes recent progress in detecting brain tumors by utilizing MRI images and bespoke CNN layers with transfer learning. The review kicks off with a discussion of the difficulties of detecting brain cancers, such as the tumors' complexity and heterogeneity and the scarcity of available annotated data. The article proceeds to go into the foundations of CNNs and their applicability to MRI image processing. To improve detection accuracy, we incorporate custom CNN layers that are tailored to capture salient tumor-specific information. The concept of transfer learning, in which CNN models trained on large-scale datasets are repurposed for brain tumor detection, is also discussed at length in the review. Using transfer learning, we can take advantage of what we've learned about general image identification to better train models to spot brain tumors. Fine-tuning, feature extraction, and other transfer learning methods are addressed at length. Recent research using custom CNN layers and transfer learning approaches to detect brain cancers in MRI images is thoroughly analyzed in this study. Among the benefits and drawbacks discussed are the methods' adaptability to small datasets, enhanced detection accuracy, and decreased training time. Also, the significance of using metrics for measuring performance and benchmark datasets for comparing methods fairly is discussed. The analysis concludes with suggestions for future study, such as the combination of functional and diffusion tensor imaging with conventional MRI scans to better detect brain tumors. Further

关键词： Magnetic resonance imaging

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IoT Enabled Water Flow Management and Monitoring System for Hydroelectric Power Generation

IoT Enabled Water Flow Management and Monitoring System for ...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Shaikat, Abu Salman Tasnim, Rumana Hasan, Jahid Zonayed, Md Hussein, Molla Rashied Shithil, Shaekh Mohammad Al Amin, Abdullah Laurentian University of Sudbury Dept. of Bharti School of Engineering and Computer Science Ontario Canada World University of Bangladesh Dept. of Mechatronics Engineering Dhaka Bangladesh Dhaka University of Engineering & Technology Dept. of Mechanical Engineering Dhaka Bangladesh University of Asia Pacific Dept. of Computer Science & Engineering Dhaka Bangladesh University Technology Malaysia Dept. of Mechanical Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350369106

Water energy sources play a crucial role, particularly in providing electrical energy through hydroelectric power. Both the water flow rate and the water head determine the potential energy available from water. The hydroelectric system's energy generation directly depends on the water flow rate. A lower flow rate means less water is available to generate energy, even if the head is high. It is essential to increase the potential of the water flow so that it can generate enough electricity. Traditionally, the flow rate is restricted due to the limited capacity of single penstock. This work addresses this issue by introducing multiple penstock-based water flow management systems. Also, conventional water flow monitoring systems often face limitations, like relying on manual measurement, as it only provides periodic data, making it difficult to respond quickly to changes in flow conditions. IoT-based flow sensors leverage IoT technology to monitor water flow and transmit flow data in real-time, enabling operators to detect fluctuating flow conditions and implement timely adjustments to processes, therefore enhancing the overall efficiency. This work aims to design and develop an IoT-enabled prototype hydroelectric power generation system using multiple water flows for enhancing power generation efficiency. The prototype indicates the use of multiple pipes as penstocks in a dam. Additionally, if generating a significant amount of electricity is not required, the water flow can be discontinued, thereby automatically closing each pipeline. This work uses flow sensors, Kitsware software, Arduino IoT Cloud, and an LCD display to generate flow measurement data every ten seconds from multiple pipelines. Moreover, the system automatically generates Excel data and graphically represents multiple water flows, enabling the user to compare the water flow data. The completed work resulted in a hydropower system demonstrating enhanced output and effective compatibility with IoT for

关键词： Penstocks

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Exploratory Testing of Software Product Linesusing Distance Metrics

Exploratory Testing of Software Product Linesusing Distance ...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Saini, Ashish Kumar, Satendra Kumar, Raj Kumar, Mukesh Kumari, Amrita Quantum University Dept. of Computer Science and Engineering Roorkee India Moradabad Institute of Technology Dept. of Computer Science and Engineering Moradabad India Gurukula Kangri Deemed to Be University Dept. of Computer Science Haridwar India Quantum University Dept. of Mechanical Engineering Roorkee India

ISBN: (纸本)9798350323917

Quality is very important in any software product, so that the customer trusts the efficiency of the product. The quality or validity of a product is measured by its testing. The test measures the quality of a product as well as shows the validity of the product. For example, testing and measuring the validity of a product in a single software system is not easy. Similarly, the software product line, which produces many different products of the same family at once, makes it even more difficult to check their validity and quality. Several techniques and models have been proposed to test the validity of the software line products and to ascertain the quality. In the same direction, we also propose a method for testing the products of software product line. The proposed method isbased on the distance between the features of the product (Desired and Original). We have used the proposed technique on product line with less features and found that the method presented gives fast and better results than traditional methods. © 2023 IEEE.

关键词： Software testing

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Active Element Arrangement and Prediction Model for Channel Estimation in IRS-Assisted Systems

Active Element Arrangement and Prediction Model for Channel ...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Tsuchiya, Yoshihiko Suga, Norisato Uruma, Kazunori Fujisawa, Masaya Tokyo University of Science Dept. of Information and Computer Technology Tokyo Japan Shibaura Institute of Technology Dept. of Information and Communication Engineering Tokyo Japan Kogakuin University Dept. of Computer Science Tokyo Japan

ISBN: (纸本)9798350350821

Intelligent reflecting surfaces (IRS) that can dynamically control the phase of radio waves and reflect them are attracting attention to realize non line-of-sight communication in the high-frequency band. Channel state information (CSI) is estimated to properly control the phase, however the overhead of the estimation reduces communication efficiency. To reduce the overhead, IRS systems with active elements that perform channel estimation are considered. One approach treats the partial CSI obtained from the active elements as an image and utilizes a super-resolution technique to predict the entire CSI. However, because the active elements are arranged in grid, prediction is difficult when the active elements spacing and the CSI fluctuation period match. In this study, we propose a new arrangement that gathers the active elements in the center of the IRS. Furthermore, we regard prediction as an image-inpainting problem that recovers missing pixels from known pixel values, and we propose applying the U-Net to solve this problem. Numerical experiments show that the proposed method outperforms conventional methods in prediction accuracy and spectral efficiency. © 2024 IEEE.

关键词： Channel estimation

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Deep Learning Model for Emotion Prediction from Speech, Facial Expression and Videos 5

Deep Learning Model for Emotion Prediction from Speech, Faci...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Rajyalakshmi, Chepuri Lakshminadh, K. Reddy, M. Sathyam Dept. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665474672

The rapid development of computer vision and machine learning in recent years has led to fruitful accomplishments in a variety of tasks, including the classification of objects, the identification of actions, and the recognition of faces, among other things. Nevertheless, identifying human emotions remains one of the most difficult tasks to do. To find a solution to this issue, a significant amount of work has been put in. In order to achieve higher accuracy in this reactivity towards a variety of speeches and vocal -based methods, computer intelligence, natural language modelling systems, and other similar technologies have been used. The examination of the emotions has the potential to be useful in a number of different settings. Cooperation with human computers is one example of such a field. computers can help customers recognize emotions, make wiser decisions, and create more lifelike human-robot interactions. In recent times, there has been a lot of focus placed on the ability to forecast dynamic facial emotion expressions in videos. Therefore, this work proposes a deep convolutional neural networks (CNNs) model for emotion prediction from speech samples, facial expression images, and videos with enhanced prediction accuracy and reduced loss. In addition, the speech CNN model also utilizes mel-frequency Cepstrum coefficients (MFCC) as feature extraction from given speech samples. The proposed MFCC-CNN model resulted in superior performance than traditional models. © 2023 IEEE.

关键词： Neural network models

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qCrop: An IoT Based Framework to Enhance Crop Productivity in Smart Agriculture 32

qCrop: An IoT Based Framework to Enhance Crop Productivity i...

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32nd IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2024

作者： Shamsa, Mahdi Rachakonda, Laavanya Mohanty, Saraju P. Kougianos, Elias University of North Texas Dept. of Computer Science and Engineering Denton United States University of North Carolina Wilmington Dept. of Computer Science Wilmington United States University of North Texas Dept. of Electrical Engineering Denton United States

ISBN: (纸本)9798331539672

Food is crucial in our life. Despite food manufacturers' efforts to meet consumers' needs with manufactured food, it cannot attain the identical level of quality and flavor as natural food. Some fruits and vegetables are classified as food that cannot be manufactured. Also, they play an essential role worldwide. This paper focuses on how farmers can protect their farms from an object that might threaten the crops and cause damage to them. It proposes a framework that farmers may utilize to safeguard the crops against any species of birds. Considering the adverse impact of bird attacks on crop production, this system effectively addresses this problem and consistently enhances the quality and quantity of them. It utilizes computer vision technology to establish a secure environment for the crop. This system is called Quality of Crop Device (qCrop), and it works with a You Only Look Once (Yolov8m) model to detect birds with high accuracy to protect farms. © 2024 IEEE.

关键词： Agri-food

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Design and Fabrication of an IoT based Air Purifier using HEPA Filter 11

Design and Fabrication of an IoT based Air Purifier using HE...

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11th International Conference on Internet of Everything, Microwave engineering, Communication and Networks, IEMECON 2023

作者： Choudhary, Arpita Saini, Lakhan Ahmad, Afaq Banerjee, Hriday Gazi, Firoj University of Engineering and Management Jaipur Dept. of Computer Science Jaipur India UEM Jaipur Dept. of Computer Science Jaipur India University of Alberta Dept. of Computer Science Edmonton Canada

ISBN: (纸本)9781665475129

Air pollution has become one of the major problems for human health across the globe. Indoor air pollution poses more risk than outdoor air pollution, since the human body is more exposed to the inside. It refers to the contamination of indoor air that causes harmful health problems. To purify the polluted air, the air purifier is a main necessity. In this Internet of Things (IoT) project, we have created an air purifier that purifies the indoor air by passing it through the High Efficiency Particulate Air (HEPA) filter and Ultraviolet light. The microcontroller used is the NodeMCU based on the ESP8266 WiFi enabled chip. Two MQ135 air quality sensors used to monitor the change and calculate the efficiency of the purified air. IoT customization eases the manual work of changing the purifier manually every time by providing a platform that operates on the mobile phone via Wi-Fi where the user can operate it accordingly. It provides an equivalent grade of services at a very moderate cost that is affordable even for the average person and can provide real time analytics. The proposed system is capable for providing hands-on practical operations, which enables more exciting possibilities in IoT based automation systems. © 2023 IEEE.

关键词： Internet of things

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EfficientNet Convolutional Neural Network for Automated Cervical Spine Fracture Identification 6

EfficientNet Convolutional Neural Network for Automated Cerv...

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6th International Conference on Computational Intelligence and Networks, CINE 2024

作者： Sidpara, Pragati Agarwal, Anshul Swain, Debabrata Gupta, Rajeev Kumar Bhilare, Amol Upkare, Makarand Pandit Deendayal Energy University Dept. of Computer Science and Engineering Gandhinagar India Vishwakarma Institute of Technology Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798331516796

The aim of this study is to develop an automatic system that uses deep learning techniques to detect cervical spine fractures from medical images. Using a DICOM (Digital imaging and communications in medicine) images of cervical spine, the system can identify a fracture and specify which vertebrae has a fracture. This process involves first processing provided DICOM images, then applying data augmentation techniques including metadata information, finally designing neural network architecture for feature extraction. We use a method called transfer learning, utilizing a pre-trained model like EfficientNet, which is used for feature extraction, while optimizing network parameters using AdanW optimizer and CosineAnnealingWarmup learning rate scheduler. The results demonstrate the successful training of neural network models to identify fracture and that fractured vertebrae, with commendable performance and accuracy level noted during evaluation on validation data. This study has important implications for medical imaging and healthcare industries, since automated fracture detection can increase diagnostic efficiency and accuracy. Moreover, such a system would provide good support to healthcare professionals, specifically radiologists, to identify cervical spine fracture easily. © 2024 IEEE.

关键词： Digital Imaging and Communications in Medicine (DICOM)

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Towards Early Diagnosis and Intervention: An Ensemble Voting Model for Precise Vital Sign Prediction in Respiratory Disease

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IEEE Journal of Biomedical and Health Informatics 2023年 PP卷 1-13页

作者： Ahmed, Usman Lin, Jerry Chun-Wei Srivastava, Gautam Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Dept. of Math and Computer Science Brandon University Canada

Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, cholesterol, as well as blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aims to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies caregivers and medical professionals when necessary. Utilizing real-world data, a linear regression model inspired by the Facebook Prophet model was developed to predict vital signs for the upcoming 180 seconds. With 180 seconds of lead time, caregivers can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, a Naïve Bayes classification model, a Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outdoes previous attempts at vital sign prediction. Compared with alternative methods, the Facebook Prophet model has the best mean square in predicting vital signs. A hyperparameter-tuning is utilized to refine the model, yielding improved short- and long-term outcomes for each and every vital sign. Furthermore, the F-measure for the proposed classification model is 0.98 with an increase of 0.21. The incorporation of additional elements, such as momentum indicators, could increase the model's flexibility with calibration. The findings of this study demonstrate that the proposed model is more accurate in predicting vital signs and trends. IEEE

关键词： Decision trees

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Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification 38

Unified Mechanism-Specific Amplification by Subsampling and ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Schuchardt, Jan Stoian, Mihail Kosmala, Arthur Günnemann, Stephan Dept. of Computer Science Munich Data Science Institute Technical University of Munich Germany Dept. of Engineering University of Technology Nuremberg Germany

Amplification by subsampling is one of the main primitives in machine learning with differential privacy (DP): Training a model on random batches instead of complete datasets results in stronger privacy. This is traditionally formalized via mechanism-agnostic subsampling guarantees that express the privacy parameters of a subsampled mechanism as a function of the original mechanism's privacy parameters. We propose the first general framework for deriving mechanism-specific guarantees, which leverage additional information beyond these parameters to more tightly characterize the subsampled mechanism's privacy. Such guarantees are of particular importance for privacy accounting, i.e., tracking privacy over multiple iterations. Overall, our framework based on conditional optimal transport lets us derive existing and novel guarantees for approximate DP, accounting with Rényi DP, and accounting with dominating pairs in a unified, principled manner. As an application, we analyze how subsampling affects the privacy of groups of multiple users. Our tight mechanism-specific bounds outperform tight mechanism-agnostic bounds and classic group privacy results. © 2024 Neural information processing systems foundation. All rights reserved.

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