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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Bhagyalaxmi, K. Dwarakanath, B. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Ramapuram India Srm Institute of Science and Technology Department of Information Technology Tamilnadu Ramapuram India

ISBN: (纸本)9798331529635

Brain tumors, characterized by abnormal cell growth within the brain, present significant challenges for early detection and accurate classification due to their complex and heterogeneous nature. Manual evaluation of MRI scans, though critical, is often laborintensive and prone to errors, highlighting the need for advanced, automated diagnostic methods. Convolutional Neural Networks (CNNs) have demonstrated strong potential in this domain by effectively learning hierarchical features from raw medical images to detect and classify brain tumors. However, single CNN models often encounter difficulties in handling the variability in tumor appearance, such as differences in shape, size, and intensity. To address these challenges, this research proposes an ensemble model that integrates pretrained CNN architectures-ResNet50, Inception, and Xception. The ensemble technique strengthens the adaptability and accuracy of brain tumor identification and categorization by incorporating the advantages of these distinct models. The proposed model was trained and evaluated using a large dataset of 5,316 MRI images, representing a range of brain tumor types, including gliomas, pituitary, meningiomas, and cases no tumor. With a high classification accuracy of 99.39%, our ensemble model proved to be useful in enhancing the reliability of diagnoses and assisting surgeons in making decisions on the treatment of brain tumors. © 2024 IEEE.

关键词： Convolutional neural networks

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SentiSense: Pioneering Emotional Intelligence through Advanced Detection Technology

SentiSense: Pioneering Emotional Intelligence through Advanc...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Bhatt, Chandradeep Tasleem, Albeena Rawat, Nishant Singh, Sachin Bahuguna, Tanisha Singh, Teekam Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350361537

Human emotion detection from images poses a significant challenge in social communication research. This paper introduces an artificial intelligence (AI) system designed for emotion detection through facial expressions, utilizing deep learning (DL) techniques. The proposed method surpasses traditional image processing approaches, showcasing improved performance. The emotion detection process consists of three key steps: face detection, feature extraction, and emotion classification. The paper presents a convolutional neural networks (CNN) based architecture tailored for deep learning in emotion detection from *** have a observe delves in addition into the version's conduct with the aid of analyzing a Confusion Matrix, providing valuable insights into times of misclassifications across exceptional emotional states. The studies underscore the significance of emotion reputation in numerous programs, together with human-pc interaction, intellectual health assessment, and sentiment assessment. Leveraging current-day deep studying techniques, in particular CNNs, proves promising in automatically extracting complex functions from facial expressions for proper emotion categorization. The technique includes acquiring and preprocessing a whole dataset to facilitate strong education and assessment of the CNN *** effects exhibit the efficacy of the proposed method in adeptly figuring out and categorizing emotional states. Rigorous assessment metrics, inclusive of precision, bear in thoughts, F1 rating, and accuracy, display off the version's capability to generalize well to severa emotional expressions. In stop, this study contributes to advancing emotion detection via cutting-edge CNNs, highlighting their capability effect on various actual-worldwide packages. © 2024 IEEE.

关键词： Convolutional neural networks

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Adjustable Model Compression Using Multiple Genetic Algorithm

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IEEE Transactions on Multimedia 2023年 25卷 1125-1132页

作者： Ople, Jose Jaena Mari Huang, Tai-Ming Chiu, Ming-Chih Chen, Yi-Ling Hua, Kai-Lung National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan

Generative Adversarial Networks (GAN) is a popular machine learning method that possesses powerful image generation ability, which is useful for different multimedia applications (e.g., photographic filters, image editing). However, typical GAN models have a large memory footprint that limits their practical applications for resource-constrained devices (e.g., smartphones). To deploy GAN models on devices with various hardware constraints, we propose our method, AdjustableGAN, which can compress a pretrained GAN model to different compression ratios. Our method compresses GAN by performing filter-wise pruning that follows these objectives: (1) deactivate convolutional filters for minimal performance decrease, (2) reactivate convolutional filters for maximal performance increase. We implement multiple Genetic Algorithms (GA) to perform each of these objectives - Downsize GA for best filter deactivations, while Upsize GA searches for best filter reactivations. By selective utilization of Upsize/Downsize GA, we could explicitly control the compression ratio of the model. For finalization, we fine-tune the compressed output model using the training dataset of the original input model. Our experimental results show that our method can reliably compress generative networks with minimal accuracy drop compared to other state-of-the-art compression algorithms. © 1999-2012 IEEE.

关键词： Generative adversarial networks

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High-Dynamic Range ADC for Finite-Rate-of-Innovation Signals 48

High-Dynamic Range ADC for Finite-Rate-of-Innovation Signals

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Mulleti, Satish Eldar, Yonina C. Bombay Department of Electrical Engineering India Weizmann Institute of Science Faculty of Math and Computer Science Israel

ISBN: (纸本)9781728163277

Modulo folding can be used to sample high-dynamic range signals without increasing the dynamic range of the sampler. Specifically, folding is used prior to sampling and then the folded signal is sampled. After sampling, unfolding algorithms are used to compute the true samples (up to a constant factor) from the folded ones. In this work, we consider a modulo sampling framework for finite rate of innovation (FRI) signals which are used to model signals in time of flight imaging. We suggest compactly supported sum-of-sincs filter as a sampling kernel prior to modulo folding and sampling. We derive conditions on the sampling rate and filter coefficients which lead to unfolding of the samples up to an unknown constant. We then propose a modified annihilating filter approach that can uniquely determine the FRI parameters from the unfolded samples with a constant unknown offset. We show that the proposed framework outperforms existing techniques in the presence of noise. © 2023 IEEE.

关键词： Signal sampling

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A combinatorial algorithm for finding straight skeleton of a digital object

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Multimedia Tools and Applications 2025年 1-21页

作者： Maity, Anukul Dutt, Mousumi Biswas, Arindam Department of Computer Science and Engineering Narula Institute of Technology 81 Nilgunj Road Agarpara West Bengal Kolkata700109 India Department of Computer Science and Engineering St. Thomas’ College of Engineering and Technology 4 Diamond Harbour Road Kidderpore West Bengal Kolkata700023 India Department of Information Technology Indian Institute of Engineering Science and Technology Botanical Garden Road Shibpur West Bengal Howrah711103 India

The paper presents a combinatorial algorithm to find the straight skeleton of the inner isothetic cover of a digital object imposed on a uniform background grid. The isothetic polygon (orthogonal polygon) tightly inscribes the given digital object. The algorithm stated here finds the straight skeleton of any isothetic polygon (simple or non-simple) in O(nglogng) time in a single traversal by applying combinatorial rules, where n is the number of pixels on the boundary of the digital object and g is the grid size on which the digital object is imposed. To find the straight skeleton of non-simple orthogonal polygon, the polygon is divided into sub-parts which are a set of simple orthogonal polygons. The straight skeleton is generated for each of the sub-parts and the corresponding results are merged. The orthogonal polygon containing holes is cut at each holes and the polygon becomes hole-free. The algorithm to obtain the straight skeleton is applied on it. The disconnected hole parts are rejoined and the result is merged. The straight skeleton is useful shape descriptor of digital object. This algorithm stated here has applications in shape analysis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Combinatorial optimization

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An EfficientNet to Classify Monkeypox-Comparable Skin Lesions Using Transfer Learning

An EfficientNet to Classify Monkeypox-Comparable Skin Lesion...

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2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Hossen, Md. Rifat Alfaz, Nazia Sami, Adnan Tanim, Sharia Arfin Bin Sarwar, Talha Islam, Md. Kamrul Department of Computer Science and Engineering Dhaka Bangladesh Department of Computer Science Dhaka Bangladesh College of Computing and Applied Science Universiti Malaysia Pahang Faculty of Computing Pekan Malaysia

ISBN: (纸本)9798350346473

Monkeypox is an infectious illness caused by the DNA-based monkeypox virus, which has raised public health concerns due to its rapid transmission to over 50 countries. Direct physical interaction with infected humans or infected animals is the main reason behind the spread of this virus. The appearance of skin problems such as smallpox and rashes are the most frequently reported symptoms of this virus. Since cases of monkeypox are increasing rapidly around the world, stopping the spread of this zoonosis by providing early diagnosis and treatment is crucial before the emergence of a pandemic similar to COVID-19. In this study, we aim to propose a transfer learning-based approach using the EfficientNet-B0 architecture to identify monkeypox subjects by using skin lesion image samples. However, distinguishing monkeypox from other comparable infectious skin illnesses like chickenpox and measles is challenging. Therefore, additionally, this study identifies other diseases that also cause blisters and rashes on the skin, like chickenpox, and measles. During the data distribution phase, 5-fold cross-validation is used to validate the work's reliability by assuring that every sample is utilized for training and validation. For the evaluation of the model's classification performance, accuracy and loss are recorded for each training epoch. Moreover, precision, recall, F1-score, and confusion matrix are generated upon completion of the model training. This proposed approach is experimented on a public dataset and has shown remarkable performance by providing an overall 96.53% classification accuracy, 96.57% precision, 96.53% recall, and 96.52% F1-score. © 2023 IEEE.

关键词： Viruses

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CBAR-UNet: A novel methodology for segmentation of cardiac magnetic resonance images using block attention-based deep residual neural network

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Multimedia Tools and Applications 2024年第37期83卷 85047-85063页

作者： Kumar, Rakesh Gupta, Meenu Agarwal, Aman Nayyar, Anand Department of Computer Science and Engineering Chandigarh University Punjab140413 India Graduate School Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam

computer-aided Medical Image Segmentation (MIS) plays a leading role in diagnosing diseases automatically. MIS is used extensively in diagnosing medical ailments to obtain clinically relevant information of the shapes and volumes of the target organs and tissues. In order to facilitate accurate segmentation, a Block Attention Based Deep Residual Neural Network, i.e., CBAR-UNet model, is proposed to perform cardiac image segmentation on short axis Magnetic Resonance Images (MRI) stacks from top to bottom slice. The Automated Cardiac Diagnosis Challenge (ACDC) dataset is used in the proposed work, which comprise of a 3D MRI of 100 patients (i.e., 200 MRI stacks). Further, 3D MRIs are converted into 2D by slicing each image to train the model, and Contrast Limited Adaptive Histogram Equalization (CLAHE) is performed on the 2D dataset. Residual connections are introduced that aid in training deeper models. Convolutional Block Attention Module (CBAM) is also added in the proposed network, which consists of Spatial and Channel attention that enhances the spatial and channel-wise features in the image by providing information like ‘what’ and ‘where’ to pay attention respectively. To test and validate the proposed methodology, extensive experimentations were conducted and it was observed that proposed image segmentation model delivers better results by 2.47% when compared to both 2D and 3D State-Of-The-Art (SOTA) methods with a Dice Score of 0.9428. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image segmentation

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Reliability Evaluation of 4:2 Compressors Based on Hammock Networks 13

Reliability Evaluation of 4:2 Compressors Based on Hammock N...

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Safaei, Farshad Emadi Kouchak, Mohammad Mahdi Talebpour, Sara Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran Islamic Azad University Department of Computer Engineering Science and Research Branch Tehran Iran

ISBN: (纸本)9798350330151

Reliability is defined as the system's ability to remain in its operational state after failure in some of its components. Moore and Shannon introduced a probabilistic model in which network nodes are assumed to be completely reliable, but communication links can fail with a specified and assumed probability like p. The main problem is to determine the probability that the said network will remain connected under these conditions. They proposed their reliability analysis on specific networks known as hammock networks. These networks are fitted to circuits with array structures such as FinFET, VSFET, MOSFET, NEMS, and carbon nanotubes. In this article, we leverage combinations of hammock networks to design and implement MOS-based transistors, specifically nMOS and pMOS. Our focus is on evaluating the reliability of 4:2 compressors designed using these networks. The resulting designs excel in terms of time efficiency, occupied area, and power consumption. Within this context, the compressor takes four inputs, intended for addition, and generates two outputs, referred to as sum and carry. Diverse circuit models can be configured for the compressor's internal circuit, each exhibiting differences in speed, power efficiency, and physical footprint. By employing simulation results, we extract several efficiency and reliability metrics for all networks and circuits, meticulously comparing and analyzing their reliability. © 2023 IEEE.

关键词： Compressors

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Detection of Corn Leaf Infection Using CNN with Various Optimizers 2

Detection of Corn Leaf Infection Using CNN with Various Opti...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Sreekanth, G.R. Latha, R.S. Roja, R. Sankarnanth, S. Gayathri, A. Kongu Engineering College Department of Computer Science and Engineering Erode India Kongu Engineering College Department of Computer Technology Erode India

ISBN: (纸本)9798350349221

Corn, a grain categorized within the grass family, stands as a fundamental staple crop globally. It plays a crucial role in supplying sustenance for both humans and livestock, in addition to serving as a raw material for various industries. In 2017, Africa produced 7.4% of the 1135 million tons produced worldwide in 40 million hectares. Throughout its growing process, corn plant is impacted by numerous illnesses. Lack of early detection of its diseases may result in a loss in productivity and even crop failure. It is proposed to analyze the leaves of the corn plant for identifying the diseases by exploiting deep learning methods. A wide range of deep learning algorithms are being utilized to detect the corn leaf with infection with good range of accuracy. To categorize the leaf, image processing models are mostly used for these kind of disease detections. Since it has in-built convolutional layer, CNN can reduce the image with high dimensionality without losing any kind of information. In this proposed work CNN with Adam optimizer has been implemented for better results and achieved 91.68 % of accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Facial Emotion Analysis for Evaluating the Suitability of Online Learning Modules: A Case Study on a MERN-Based Platform 10

Facial Emotion Analysis for Evaluating the Suitability of On...

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10th International Conference on Education and Technology, ICET 2024

作者： Wardaya, Utara Setya Bachtiar, Fitra Abdurrachman Pradana, Fajar Marutschke, Daniel Moritz Brawijaya University Faculty of Computer Science Information System Department Malang Indonesia Brawijaya University Faculty of Computer Science Informatics Department Malang Indonesia Kyoto University of Advanced Science Faculty of Economics and Business Administration Kyoto Japan

ISBN: (纸本)9798331522483

Learning is a process that involves the progressive transfer of knowledge, encompassing both the physical and psychological components of the learner. Emotions play a crucial role in the success of learning, yet they are often overlooked in the context of online education. Facial emotion detection technology and emotion analysis offer a solution for effectively recognizing and accommodating students' emotions in digital learning environments. The use of a Learning Management System (LMS) based on the MERN architecture (MongoDB, ***, ***, ***) is also critical in supporting the flexibility and responsiveness of online learning platforms. This research employed the Agile model, which was systematically planned and organized. The Agile model is an approach that allows for continuous adjustments during development. The aim of this study is to integrate artificial intelligence into an e-learning system to detect and evaluate students' emotions, thereby enabling the development of more effective online learning methods. The design process following the Agile Method involved needs analysis, modeling with UML (Use Case, Activity Diagram, Sequence Diagram, and Class Diagram), as well as database and code implementation. The backend development focused on database management, server configuration, and API integration, while the frontend was designed for a responsive user interface. Testing was conducted using Black Box and White Box methods, which demonstrated 100% validity for system functionality, while user feedback through UAT yielded a 94% validity score. The results indicate that emotion detection functions effectively in e-learning, and emotion analysis can enhance the quality of learning, with positive emotional states leading to better outcomes. © 2024 IEEE.

关键词： Black-box testing

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