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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Babu, Padma Priya S. Brindha, T. Noorul Islam Centre for Higher Education Department of Computer Science and Engineering Tamil Nadu India Noorul Islam Centre for Higher Education Department of Information Technology Tamil Nadu India

ISBN: (纸本)9798350372816

Several research projects have been centred on computer-aided diagnosis techniques. They operate on the presumption that photographs of various human body components may be processed and assessed to provide an immediate and precise diagnosis. Brain haemorrhage is a kind of stroke. An artery in the brain rupture, causing nearby tissues to bleed selectively. Brain tissue is destroyed by this hemorrhage. Brain hemorrhage requires an immediate diagnosis and rapid treatment to improve patient outcomes. Clinicians must be able to recognize and categorise brain haemorrhage in order to successfully treat it. CT (Computed Tomography) scans are used to detect internal bleeding and fractures. Due to their better accessibility, reduced price, and sensitivity to early hemorrhages, CT scans have several advantages over Magnetic Resonance Imaging (MRI) images. Deep learning is being employed in this situation. In this study, a machine learning method is proposed by using medical imaging to identify brain hemorrhages. © 2024 IEEE.

关键词： computerized tomography

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Leveraging Deep Learning for Accurate Detection and Precise Localization of Vertebral Fractures in Medical Imaging 4

Leveraging Deep Learning for Accurate Detection and Precise ...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Kumar, M. Lakshmana Sampath, Patchigolla Srinivas, P.V.V.S. Dommeti, Dhiren Nallapati, Siva Ramakrishna Koneru Lakshmaiah Education Foundation Department of Electronics and Communication Engineering India Koneru Lakshmaiah Education Foundation Department of Computer Science and Information Technology India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering India

ISBN: (纸本)9798350300888

Cervical spine fractures present a serious risk to the patient's health and necessitate an early and precise diagnosis for the right course of action. Deep learning techniques have recently demonstrated promising results in the analysis of medical imaging, offering a potential remedy for automated fracture detection. An innovative method for identifying cervical spine fractures using deep learning algorithms is presented in this abstract. Our suggested approach makes use of a Convolutional Neural Network (CNN) architecture that was trained on a sizable dataset of CT scans of the cervical spine (neck) at both the level of a single vertebrae and the entire patient. To ensure that the model can distinguish between fracture patterns and healthy anatomy, the dataset includes both normal and fractured cases. To increase the model's robustness and generalizability, preprocessing methods like image normalization and augmentation are used. To maximize its performance in fracture detection, the CNN is trained using a combination of supervised learning and transfer learning strategies. The experiments are performed on a separate test set made up of a wide variety of cervical spine fracture cases in order to assess the model's performance. The outcomes show how effective our deep learning methodology is at detecting fractures, with a high accuracy rate and a low false-negative rate. Additionally, the model performs better than conventional manual interpretation by radiologists, demonstrating its potential to support clinical judgement. Rapid and automated analysis, reduced human error, and potentially improved patient outcomes are just a few benefits of the proposed deep learning-based cervical spine fracture detection system. Its incorporation into current radiology workflows may increase productivity and permit prompt interventions, which would improve patient care. © 2023 IEEE.

关键词： Medical imaging

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A Survey on Wheat Disease Identification and Classification Using Deep Learning 1

A Survey on Wheat Disease Identification and Classification ...

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1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Naveen Raja, S.M. Shajin Nargunam, A. Noorul Islam Centre for Higher Education Kumaracoil Department of Information Technology Thuckalay India Noorul Islam Centre for Higher Education Kumaracoil Department of Computer Science and Engineering Thuckalay India

ISBN: (纸本)9798350367003

Wheat is one of the crucial cereal crops globally, and its productivity is affected by various diseases. Therefore, the timely and precise detection and classification of these diseases are important. The advancements in deep learning (DL), especially convolutional neural networks, have observed significant evolution in wheat disease identification. This article provides a comprehensive overview of research works, which effectively utilized DL for wheat disease prediction and classification. The paper begins by introducing the importance of wheat disease management and the challenges associated with traditional disease identification methods. The survey further explores the general architecture of the DL-based wheat disease prediction framework the study highlights the significance of DL models in achieving accurate and effective disease classification. In addition, it demonstrates the challenges associated with different DL models relative to wheat disease identification. Furthermore, it examines the research works related to wheat disease detection using DL models and highlights the limitations of those methods. Finally, the survey provides the future research scope for an improved disease identification model. © 2024 IEEE.

关键词： Convolutional neural networks

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Growing Greener: Exploring the Creative Potential of Deep Learning in Agricultural Disease Control

Growing Greener: Exploring the Creative Potential of Deep Le...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Kiran, Ajmeera Rahman, Syed Ziaur Sreelatha, P. Dhanasekaran, S. Ayyasamy, Ramesh Kumar Swetha, Devulapally MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Majan University College Faculty of Information Technology Oman A.P Tirupati India Kalasalingam Academy of Research and Education Deemed to be University Department of Information Technology Tamilnadu India Faculty of Information and Communication Technology Kampar Malaysia Vaagdevi Engineering College Department of Computer Science and Engineering Telangana Bolli Kunta India

ISBN: (纸本)9798331540685

According to estimates, by mid-2030, there will be 8.6 billion people on the planet. The human population has been growing exponentially. There will be a rise in food production and consumption due to the massive population growth. Crop disease is the primary danger to food security, which damages agricultural goods while growing. Due to the limited availability of infrastructure, it remains challenging to recognize crop-damaging diseases in various parts of the world. Deep learning methods and computer vision technologies are used in this work to help identify agricultural illnesses. The train set is used to train the model, and the data split is used for validation. The farmer can use the app (Identifying and Resolving Crop Diseases using Deep Learning) to take pictures of the crops and examine whether or not there are any infections, proving the solution's viability. © 2024 IEEE.

关键词： Plant diseases

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Exploring Machine Learning Methods to Identify Patterns in Students' Solutions to Programming Assignments 53

Exploring Machine Learning Methods to Identify Patterns in S...

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53rd IEEE ASEE Frontiers in education International Conference, FIE 2023

作者： Liu, Xiaojin Castellanos, Hugo Wiese, Lucas Magana, Alejandra J. Purdue University Department of Computer Science Department of Computer and Information Technology West LafayetteIN United States Purdue University Department of Computer and Information Technology West LafayetteIN United States School of Engineering Education Purdue University Department of Computer and Information Technology West LafayetteIN United States

ISBN: (纸本)9798350336429

technology and automation have become increasingly critical for organizations today, and programming has become an essential skill for all STEM majors to meet this demand. Graduates are expected to possess programming skills to meet the needs of the modern workforce. Acquiring programming skills is a challenging task, and institutions often struggle to provide adequate resources to meet the industry's demand for proficient computer programmers. To take steps toward better understanding programming challenges among undergraduate students in science disciplines, this study aims to characterize patterns in students' solutions to programming assignments over the course of a semester. With this, the goal is to characterize students' most common challenges and take steps toward providing automated feedback. Specifically, this study applies machine learning (ML) classification algorithms to analyze student artifacts from a college-level introductory Python programming course for science majors, including source code from labs, homework assignments, projects, and two live-coding exams. Data was collected as part of a semester-long course and pre-processed and de-identified. The researchers labeled the data, and relevant features were selected to prepare the data for training the ML algorithms. Various classification algorithms were trained, and the resulting ML models were evaluated for their accuracy. Then, the study deployed a quantitative research method to evaluate both the effectiveness of various ML models and the quality of the feedback the model could provide, such as efficiency and accuracy. The research results are expected to inform the development of machine-learning algorithms to provide higher-quality feedback mechanisms for students in introductory programming courses. In that manner, this study contributes to improving the quality of programming education. © 2023 IEEE.

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Face micro-expression recognition algorithm based on ResNet depth model 8

Face micro-expression recognition algorithm based on ResNet ...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Wang, Liquan Zhan, Shu Hefei University of Technology Key Laboratory of Big Data Knowledge Engineering Ministry of Education School of Computer and Information Engineering Hefei China

ISBN: (纸本)9798350302455

Micro Expression (ME) is the subtle facial expressions that people show when they express their inner feelings. To address the problem that micro-expression recognition is difficult and less accurate due to the small number of samples and uneven distribution of different categories, we propose a model framework to improve the accuracy of micro-expression recognition. The peak frames containing more key expression information in the micro-expression video sequences are extracted;SE-ResNeXt-50, an improved residual network with SE module, is used to extract features from the peak frames of micro-expressions, where the SE module can better learn the key information in the features, and ResNeXt simplifies the structure by replacing the dense structure with the sparse structure through group convolution, which improves the recognition efficiency. The recognition efficiency is improved by replacing the dense structure with the sparse structure by group convolution. At the same time, the Focal Loss loss function can better solve the model performance problem caused by the imbalance of micro-expression data. Simulation experiments are conducted on the micro-expression dataset CASME, and it is found that the improved residual network and peak frame improve the accuracy and F1 value of micro-expression recognition. The improved residual network and peak frame can reduce the effect of small data set, make the model have good fitting effect, and improve the performance of different categories, improve the recognition accuracy of micro-expressions, and have better recognition performance for micro-expression recognition. © 2023 IEEE.

关键词： Convolution

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SFA-Net: A Selective Features Absorption Network for Object Detection in Rainy Weather Conditions

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IEEE Transactions on Neural Networks and Learning Systems 2023年第8期34卷 5122-5132页

作者： Huang, Shih-Chia Hoang, Quoc-Viet Le, Trung-Hieu National Taipei University of Technology Department of Electronic Engineering Taipei10608 Taiwan National Taipei University of Technology International Graduate Program in Electrical Engineering and Computer Science Taipei10608 Taiwan Hung Yen University of Technology and Education Faculty of Information Technology Hungyen 17000 Viet Nam

In recent years, object detection approaches using deep convolutional neural networks (CNNs) have derived major advances in normal images. However, such success is hardly achieved with rainy images due to lack of visibility. Aiming to bridge this gap, in this article, we present a novel selective features absorption network (SFA-Net) to improve the performance of object detection not only in rainy weather conditions but also in favorable weather conditions. SFA-Net accomplishes this objective by utilizing three subnetworks, where the feature selection subnetwork is concatenated with the object detection subnetwork through the feature absorption subnetwork to form a unified model. To promote further advancement in object detection impaired by rain, we propose a large-scale rainy image dataset, named srRain, which contains both synthetic rainy images and real-world rainy images for training and testing purposes. srRain is comprised of 25 900 rainy images depicting diverse driving scenarios in the presence of rain with a total of 181 164 instances interpreting five common object categories. Experimental results display that our SFA-Net reaches the highest mean average precision (mAP) of 77.53% on a normal image set, 62.52% on a synthetic rainy image set, 37.34% on a collected natural rainy image set, and 32.86% on a published real rainy image set, surpassing current state-of-the-art object detectors and the combination of image deraining and object detection models while retaining a high speed. © 2012 IEEE.

关键词： Feature extraction

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Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1566-1578页

作者： Yuhan Zhang Zidong Wang Lei Zou Yun Chen Guoping Lu the College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China IEEE the Department of Computer Science Brunel UniversityUB83PHUnited Kingdom the College of Information Science and Technology Donghua UniversityShanghai 201620 the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China the School of Automation Hangzhou Dianzi UniversityHangzhou 310018China the School of Electrical Engineering Nantong UniversityNantong 226019China

This paper concerns ultimately bounded output-feedback control problems for networked systems with unknown nonlinear dynamics. Sensor-to-observer signal transmission is facilitated over networks that has communication *** transmissions are carried out over an unreliable communication channel. In order to enhance the utilization rate of measurement data, a buffer-aided strategy is novelly employed to store historical measurements when communication networks are inaccessible. Using the neural network technique, a novel observer-based controller is introduced to address effects of signal transmission behaviors and unknown nonlinear *** the application of stochastic analysis and Lyapunov stability, a joint framework is constructed for analyzing resultant system performance under the introduced controller. Subsequently, existence conditions for the desired output-feedback controller are delineated. The required parameters for the observerbased controller are then determined by resolving some specific matrix inequalities. Finally, a simulation example is showcased to confirm method efficacy.

关键词： Buffer-aided strategy neural networks nonlinear control output-feedback control unreliable communication channel

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Privacy Protection in Learning Management Systems’ Mobile technology-Based Learning Analytics

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第6期19卷 197-208页

作者： Rawat, Manoj Kumar Jha, Suresh Kumar Sree Lakshmi, A. Venkata Ramana, J. Gonge, Sudhanshu S. De, Arunava Department of Computer Science and Engineering Medi-Caps University Madhya Pradesh Indore India Manipal University Rajasthan Jaipur India Geetanjali College of Engineering and Technology Telangana Hyderabad India Department of MBA KL Business School Koneru Lakshmaiah Education Foundation Vaddeswaram Campus Guntur District Andhra Pradesh India Department of Computer Science & Information Technology Symbiosis Institute of Technology Lavale A Constituent of Symbiosis International University Maharashtra Pune India Faculty of Information Technology Gopal Narayan Singh University Bihar Jamuhar India

Mobile technology use in education is changing, much like in the business and health sectors. Research is now conducted on designing user-centric platforms that allow people to engage in teaching and learning activities. Although the learning management systems (LMS) field supports students and academic endeavors, the application’s high requirements prevent it from having the desired effect. More significantly, although mobile applications are conve-nient, there is a chance that user data could be maliciously exploited and eavesdropped. Due to the communication overhead of sending raw data to the central learning processor, wire-less application structures cannot adapt to the original structure of mobile learning, which calls for data and processing heads to have a centralized entity. The goal is to offer fresh viewpoints and approaches that bring educational assessment up to date. This project aims to give educators scientifically based assessment tools and tactics by thoroughly examining the use of mobile technology in academic assessment. This will allow for more accurate teaching management and individualized instruction. © 2025 by the authors of this article. Published under CC-BY.

关键词： Mobile computing

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Classification of Rice Variant using Deep Learning

Classification of Rice Variant using Deep Learning

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Kumar, K Susheel Verma, Prem Kumari Chaurasia, Subham Singh, Nagendra Pratap Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Karnataka Manipal India National Institute of Technology Department of Computer Science and Engineering Punjab Jalandhar India Axis Institute of Technology and Management Department of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798331533311

Rice is a popular crop in the world. This paper has constructed a model using deep Learning for classification. Classification is a critical process in identifying the class of an image. Here, the Rice dataset has been taken to classify the class of rice Breed. Image Classification is a complex process that is affected by various factors. The emphasis is placed on summarizing essential superior class procedures and the strategies used for improving classification accuracy. This paper uses the deep learning approach for classification, such as CNN, and some activation functions, such as ReLu Softmax with Dense layers. The classification process used various domains, such as Machine Learning and deep learning. In this model, five classes of rice breed have been taken and classified with an accuracy of 9 6%. © 2024 IEEE.

关键词： Adversarial machine learning

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