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GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images

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Mathematical Biosciences and engineering 2024年第8期21卷 6847-6869页

作者： Aggarwal, Sonam Gupta, Isha Kumar, Ashok Kautish, Sandeep Almazyad, Abdulaziz S. Mohamed, Ali Wagdy Werner, Frank Shokouhifar, Mohammad Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Model Institute of Engineering and Technology J&K Jammu India Chandigarh University Punjab Mohali140413 India Department of Computer Engineering College of Computer and Information Sciences King Saud University P.O. Box 51178 Riyadh11543 Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza12613 Egypt Applied Science Research Center Applied Science Private University Amman11931 Jordan Faculty of Mathematics Otto-von-Guericke University Magdeburg39016 Germany Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam

Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpreting endoscopic images, due to their capacity to provide results equivalent to or exceeding those of medical specialists. This capability is particularly crucial in the realm of gastrointestinal disorders, where even experienced gastroenterologists find the automatic diagnosis of such conditions using endoscopic pictures to be a challenging endeavor. Currently, gastrointestinal findings in medical diagnosis are primarily determined by manual inspection by competent gastrointestinal endoscopists. This evaluation procedure is labor-intensive, time-consuming, and frequently results in high variability between laboratories. To address these challenges, we introduced a specialized CNN-based architecture called GastroFuse-Net, designed to recognize human gastrointestinal diseases from endoscopic images. GastroFuse-Net was developed by combining features extracted from two different CNN models with different numbers of layers, integrating shallow and deep representations to capture diverse aspects of the abnormalities. The Kvasir dataset was used to thoroughly test the proposed deep learning model. This dataset contained images that were classified according to structures (cecum, z-line, pylorus), diseases (ulcerative colitis, esophagitis, polyps), or surgical operations (dyed resection margins, dyed lifted polyps). The proposed model was evaluated using various measures, including specificity, recall, precision, F1-score, Mathew’s Correlation Coefficient (MCC), and accuracy. The proposed model GastroFuse-Net exhibited exceptional performance, achieving a precision of 0.985, recall of 0.985, specificity of 0.984, F1-score of 0.997, MCC of 0.982, and an accuracy of 98.5%. ©2024 the Author(s), licensee AIMS Press.

关键词： Endoscopy

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Real-Time Eyes Monitoring System Using OpenCV Autonomous filtering using RNN 3

Real-Time Eyes Monitoring System Using OpenCV Autonomous fil...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Prajapati, Dhruvansh Joshi, Jaynil Das, Nibedita Satapathy, Santosh Pandit Deendayal Energy University Information & Communication Technology Gujarat Gandhinagar India Institute of Advanced Research Department of Computer Science and Engineering Gujarat Gandhinagar India

ISBN: (纸本)9798350354171

This paper introduces a novel Real-Time Eye Gazing and Monitoring System aimed at objectively and continuously assessing individuals across various environmental conditions. Addressing the challenge of accurate performance in diverse lighting conditions, the system employs Recurrent Neural Network (RNN) trained filters to enhance eye detection accuracy. Traditional methods often falter in non-ideal lighting, prompting the need for adaptive solutions. Through extensive experimentation, optimal filter combinations were identified, ensuring robustness across a spectrum of scenarios. Implementation via an OpenCV facilitates efficient processing without compromising detection accuracy. This paper achieved the list of main filters and their combinations which can be helpful to detect eyes in different environmental conditions. © 2024 IEEE.

关键词： Recurrent neural networks

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Cluster-Factors of Mobile Sensor Network technology for Security Enhanced PEGASIS

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第18期17卷 143-155页

作者： Sivakumar, T.K. Dhyani, Saurabh Manikanthan, S.V. Kaliappan, Vishnu Kumar Baskaran, K. Kishore, Kakarla Hari Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chennai India Department of USCS Uttaranchal University Uttarakhand Dehradun India Melange Academic Research Associates Puducherry India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India Department of Biomedical Engineering Chennai Institute of Technology Tamil Nadu Chennai India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India

Mobile wireless sensor networks (MWSNs) have been a hot topic of research, and numerous routing methods have been developed to increase energy efficiency and extend longevity. Nodes close to the sink often use more energy to transmit data from their neighbors to the sink, which causes them to run out of energy faster. These places are also referred to as rendezvous points, and choosing the best one is a hard task. The likelihood of choosing an ideal node as the rendezvous point will be extremely low because hierarchical algorithms only use their local information to select these places. The warm spot problem is addressed from four angles in this work using the Enhanced Power-Efficient Gathering in Sensor Information Systems (EPEGASIS) technique. In order to limit the amount of energy used during transmission, the ideal communication distance is first calculated. To balance the energy consumption among the nodes, mobile sink technology is employed once a threshold value is set to safeguard the dying nodes. The node can then modify its communication range based on how far away the sink node is from it. Thorough testing has been done to demonstrate that our suggested EPEGASIS works better in terms of longevity, drive usage, and web latency. © 2023 by the authors of this article. Published under CC-BY.

关键词： Energy efficiency

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Mutual Coupling Reduction in MIMO Antenna for 5G Application by Self-Decoupled Method 2

Mutual Coupling Reduction in MIMO Antenna for 5G Application...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Rashid, Aamir Shah, Syed Shahid Hussain, Irshad Jalal, Muhammad Ramalakshmi, R. Ramachandran, V. University of Engineering and Technology Faculty of Electrical and Computer Engineering Peshawar Pakistan College of Computer and Information Engineering King Saud University Department of Computer Engineering Riyadh Saudi Arabia Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Virudhunagar India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad500090 India

ISBN: (纸本)9798350347982

This paper shows a simple, compact and small 8×8 MIMO antenna. The ability of MIMO antennas is to broadcast signals in a various patterns and polarizations is extremely advantageous for contemporary communications systems. Excellent diversity gain (DG > 9.99dB), a low envelop correlation coefficient (ECC 0.002), and extremely low mutual coupling (S2l≥ 27) dB are all characteristics of the constructed MIMO antenna. S-boundaries, energy pattern characteristics, maximum gain, diversity gain, and envelop coefficient are used to evaluate the antenna performance. The mutual Coupling is the most basic fundamental parameter for the MIMO antenna, was likewise analyzed and it was observed that its value is under -27dB. self-decoupled MIMO antennas have a loop type ground antenna which reduces the requirement for extra decoupling structures or a postponed tuning method Antennas for ground radiation and loop antenna are magnetic-current and electric-current components, respectively. The suggested MIMO antennas feature a self-decoupled despite the antenna parts being near together, reduced profile and easy fabrication advantages the suggested, a revolutionary approach called MIMO antenna system is expected to offer excellent isolation and minimal mutual coupling, making it suitable for 5G applications. © 2023 IEEE.

关键词： MIMO systems

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Dependency-Type Weighted Graph Convolutional Network on End-to-End Aspect-Based Sentiment Analysis 13th

Dependency-Type Weighted Graph Convolutional Network on End...

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13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024

作者： Mu, Yusong Shi, Shumin School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (纸本)9783031579189

Previous studies consider little on using dependency-type messages in the E2E-ABSA task. Studies using dependency-type messages just contact the dependency-type message and word embedding vectors, which may not fully fuse the context feature and information from the dependency type. This paper proposes a new model called Dependency-Type Weighted Graph Convolution Network (DTW-GCN) to compose dependency-type messages and word embedding. We use a type-weighted matrix to combine the dependency-type message, and DTW-GCN could fuse the dependency-type message and word embedding vectors. Experiments conducted on three benchmark datasets verify the effectiveness of our model. © IFIP International Federation for Information Processing 2024.

关键词： Sentiment analysis

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Optimizing Quality of Service Parameters for Optical Networks 15

Optimizing Quality of Service Parameters for Optical Network...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Khurana, Jatin Jayasree, S. Gupta, Shivangi Gowrishankar, J. Birare, Komal M. Balaji, N.V. Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research for Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Quantum University Research Center Quantum University India Jain [Deemed to be University School of Engineering and Technology Computer Science Engineering Karnataka Bangalore India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India

ISBN: (纸本)9798350370249

Optical networks are generally designed to offer best of provider (QoS) guarantees to users, which will deliver provider of the appropriate satisfactory. However, because of availability, capacity, and infrastructure constraints, those QoS parameters might not be without problems optimized. This paper explores exclusive strategies for optimizing the QoS parameters in optical networks. It examines numerous techniques inclusive of community slicing and clever manipulate for improving carrier availability, quit-to-end latency, and quit-to-end throughput. It also investigates a couple of routing strategies, together with redundant packet routing, multipath routing, and dynamic routing, to improve hyperlink and node reliabilities, and common hop-matter. Sooner or later, it discusses the potential of the use of disbursed manipulate algorithms for adjusting various QoS parameters in real-time. The paper draws on a spread of simulation situations to demonstrate the effectiveness of these optimization strategies and algorithms. Common, this paper gives an in-depth examination of the various optimization strategies to be had for boosting satisfactory of provider in optical networks. © 2024 IEEE.

关键词： Examination Manipulate Parameters Provider Reliabilities Throughput

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A Chic Giza reformed deep attention model (GizRAM) for opinion mining on Twitter with text and emojis

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Iran Journal of computer science 2025年 1-17页

作者： Kothandan, Prema Pushpavalli, Rajaram Thirumalaikumari, T. Selvanathan, Pournima Sunitha, Pamarthi Nimmagadda, Padmaja Subburayalu, Gopalakrishnan Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Tamil Nadu Chennai 624622 India PG & Research Department of Computer Science Sri Sarada College for Women (A) Salem Fairlands Tamil Nadu Salem 636016 India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to Be University Tamil Nadu Chennai India Department of Computer Science and Engineering Sona College of Technology (Autonomous) Salem 636 005 India Department of Electronics and Communication Engineering Aditya University Andhra Pradesh Surampalem 533437 India Department of Electronics and Communication Engineering Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Andhra Pradesh Tirupati 517102 India Department of Information Technology Hindustan Institute of Technology and Science Tamil Nadu Chennai 603103 India

Today more than ever, social media like Twitter is significant. However, it has transformed into a public bulletin board where regular people, businesses, and even well-known figures like celebrities regularly post their opinions and take part in discussions. Also, Twitter persists as a useful tool for friends to stay in touch. Despite the widespread adoption of Twitter globally, it is more important than ever to be able to do trustworthy analyses of opinions to assess public opinion and perceptions on a variety of topics. Emojis are often used to convey emotions or thoughts that are challenging to put into words concisely. This paper establishes a new way of analyzing opinions by merging deep learning-based sentiment analysis approaches and integrating them with emojis. In this line of action, we propose the Giza reformed deep attention model for both textual as well as emoji-based feature extraction to classify user sentiments. To construct an enhanced opinion mining, a feature extraction and data preparation process based on multi-sets is carried out, followed by the application of the Giza pyramid construction optimizer to feature selection. It significantly reduces the dimensions to lead toward improved accuracy in predictions. This paper proposes the reformed temporal attention-based gated convoluted network (RefTAC) for effective sentiment classification based on the selected features. We validate the effectiveness of our proposed approach on popular Twitter datasets, and the results show impressive performance: 99.1% accuracy on the Airlines dataset, 99% accuracy on the Crowd Flower dataset, and 99% accuracy on the Apple dataset. Our method is better in terms of accuracy and efficiency than the state-of-the-art methods, which shows that adding emojis to sentiment analysis works well. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： And Giza reformed deep attention model (GizRAM) Deep learning Emojis text Opinion mining Sentiment analysis Social data Twitter

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Thermometer: Towards Universal Calibration for Large Language Models 41

Thermometer: Towards Universal Calibration for Large Languag...

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41st International Conference on Machine Learning, ICML 2024

作者： Shen, Maohao Das, Subhro Greenewald, Kristjan Sattigeri, Prasanna Wornell, Gregory Ghosh, Soumya Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge United States MIT-IBM Watson AI Lab IBM Research United States

We consider the issue of calibration in large language models (LLM). Recent studies have found that common interventions such as instruction tuning often result in poorly calibrated LLMs. Although calibration is well-explored in traditional applications, calibrating LLMs is uniquely challenging. These challenges stem as much from the severe computational requirements of LLMs as from their versatility, which allows them to be applied to diverse tasks. Addressing these challenges, we propose THERMOMETER, a calibration approach tailored to LLMs. THERMOMETER learns an auxiliary model, given data from multiple tasks, for calibrating a LLM. It is computationally efficient, preserves the accuracy of the LLM, and produces better-calibrated responses for new tasks. Extensive empirical evaluations across various benchmarks demonstrate the effectiveness of the proposed method. Copyright 2024 by the author(s)

关键词： Calibration

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Control Flow Divergence Optimization by Exploiting Tensor Cores 24

Control Flow Divergence Optimization by Exploiting Tensor Co...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Pang, Weiguang Jiang, Xu Liu, Songran Qiao, Lei Fu, Kexue Gao, Longxiang Yi, Wang University of Electronic Science and Technology of China China Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Computer Science and Engineering Northeastern University China Beijing Institute of Control Engineering Beijing China Uppsala University Sweden

ISBN: (纸本)9798400706011

Kernels are scheduled on Graphics Processing Units (GPUs) in the granularity of GPU warp, which is a bunch of threads that must be scheduled together. When executing kernels with conditional branches, the threads within a warp may execute different branches sequentially, resulting in a considerable utilization loss and unpredictable execution time. This problem is known as the control flow divergence. In this work, we propose a novel method to predict threads' execution path before the launch of the kernel by deploying a branch prediction network on the GPU's tensor cores, which can efficiently parallel run with the kernels on CUDA cores, so that the divergence problem can be eased in a large extent with the lowest overhead. Combined with a well-designed thread data reorganization algorithm, this solution can better mitigate GPUs' control flow divergence problem. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Tensors

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Deep Learning-based Classification of Lung CT Scan for Accurate Cancer Diagnosis 7

Deep Learning-based Classification of Lung CT Scan for Accur...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sujatha, D.Christy Lakshmi, T.R. Vijaya Surya, G. Surendar, U. Rajendiran, M. Maranan, Ramya Periyar Maniammai Institute of Science And Technology Department of Software Engineering Tamail Nadu Thanjavur India Mahatma Gandhi Institute of Technology Department of Electronics And Communication Engineering Telangana Hyderabad India K. Ramakrishnan College of Engineering Department of Computer Science And Engineering Tamil Nadu Tiruchirappalli India Srm Institute of Science And Technology Department of Electronics And Communication Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science And Engineering Chennai Tamil Nadu Poonamallee India Saveetha School of Engineering SIMATS Department of Research And Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350359299

This paper investigates the suitability of advanced deep learning models for precise diagnosis of lung cancer from MRI images. Recurrent neural networks (RNN), K-Nearest Neighbors (KNN), ResNet50, and convolutional neural networks (CNN) were all carefully evaluated to determine their unique contributions. The CNN showed off its good performance and capacity to recognize intricate patterns in lung images, achieving an accuracy of 92.3%. KNN demonstrated competitive results, demonstrating the adaptability of non-parametric methods for medical image classification. Remarkably, ResNet50 fared extremely well, exhibiting a remarkable accuracy of 94.8% and verifying the value of deep residual networks in differentiating between intricate features. RNNs gave the analysis a temporal dimension and contributed to its 89.5% accuracy. Information from confusion matrices containing comprehensive classification results was useful in refining the model. Spatial representations of expected cancer cell locations showed the effectiveness of the models by giving doctors visual cues for targeted interventions. Comparisons with the literature show that the results are in line with recent developments in deep learning for medical image analysis. Because of its comprehensive assessment of different deep learning architectures, which provides fresh perspectives that advance the field of lung cancer detection technologies, this work is an invaluable resource for future research. © 2024 IEEE.

关键词： computerized tomography

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