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6th International Conference on Intelligent Systems and computer Vision, ISCV 2024

作者： Ait Skourt, Brahim Nikolov, Nikola S. Majda, Aicha Computer Science Department Faculty of Science and Technology University of Sidi Mohammed Ben Abdellah Fez Morocco Department of Computer Science and Information Systems University of Limerick Limerick Ireland

ISBN: (纸本)9798350350180

As an integral part of a convolutional neural network, the pooling layers are responsible for the down-sampling operation which aims at preventing overfitting. Besides the conventional pooling methods (max and average), various methods that involve mixing max pooling and average pooling have been proposed. In this work, we propose a new mixed pooling method, called fully mixed max-average pooling (FMMAP), and evaluate its performance within a comparative study of various conventional and state-of-the-art pooling methods. FMMAP consists of fully mixing max pooling and average pooling features instead of stochastically selecting them as done in other popular mixed-pooling methods. The experimental results suggest that FMMAP outperforms the conventional pooling methods in accuracy, and while being very close (within 0.05%) to the other mixed-pooling methods accuracy-wise, it significantly outperforms them in terms of running time, being at least 1.7 times and up to 2.7 times faster. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep learning applications in the Internet of Things: a review, tools, and future directions

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Evolutionary Intelligence 2024年第5-6期17卷 3621-3654页

作者： Raoufi, Parisa Hemmati, Atefeh Rahmani, Amir Masoud Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin64002 Taiwan

The emergence of the Internet of Things (IoT) has enabled the proliferation of interconnected devices and sensors, generating vast amounts of often complex and unstructured data. Deep learning (DL), a subfield of machine learning (ML), has shown great promise in addressing the challenges of processing and analyzing such data. Considering the increasing importance of DL and data analysis, we decided to review the articles of the last few years in this field to pave the way for researchers. In this article, we used the systematic literature review (SLR) method, and in line with that, we selected and analyzed 56 articles published from 2019 to April 2024. We first discuss the DL models used in the IoT field and clarify their specific use cases. Secondly, we outline an analysis of research areas in DL-based IoT. In addition, our research extends to the tools and simulators used to evaluate studies in the DL-based IoT domain. We also examine the DL-based IoT research datasets. Finally, our review identifies future directions and open issues in DL-based IoT. We aim to contribute to an accurate understanding of the current state, challenges, and potential breakthroughs at the intersection of DL and the IoT. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Internet of things

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A Detailed Analysis of Colorectal Polyp Segmentation with U-Network 1

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International Conference on Internet of Everything and Quantum Information Processing, IEQIP 2023

作者： Ramakrishna, Talluri Shiva Asritha, Gamini Rama Sai Lakshman, Nagendla Guru, D.S. Prakash, Kolla Bhanu Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India Department of Computer Science and Engineering Mysore University Mysore India

ISBN: (数字)9783031619298

ISBN: (纸本)9783031619281

Colonoscopy is the most important technique for detecting colorectal cancer and its precursors. High miss rates continue to be a problem, leaving many abnormalities undiscovered. Systems for computer-aided diagnosis (CAD) have been developed using machine learning techniques to increase the accuracy of colonoscopies. CAD systems can aid in the discovery and characterization of lesions by pointing out areas in the colon that may have gone unnoticed during endoscopic examinations. To analyze colonoscopy pictures, these systems employ several approaches, including feature extraction, image processing, and classification. CAD systems can aid in the discovery and characterization of lesions by pointing out areas in the colon that may have gone unnoticed during endoscopic examinations. To analyze colonoscopy pictures, these systems employ several approaches, including feature extraction, image processing, and classification. Additionally, real-time assistance from CAD systems during colonoscopy examinations can give doctors a second opinion and lower the possibility of missing lesions. Additionally, by tracking and keeping an eye on lesions over time, these systems can aid in the early identification of potential malignant growth. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Endoscopy

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Teaching Assistants as Assessors: An Experience Based Narrative 16

Teaching Assistants as Assessors: An Experience Based Narrat...

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16th International Conference on computer Supported Education, CSEDU 2024

作者： Ahmed, Faizan Bouali, Nacir Gerhold, Marcus Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

ISBN: (纸本)9789897586972

This study explores the role of teaching assistants (TAs) as assessors in a university’s computer science program. It examines the challenges and implications of TAs in grading, with a focus on their expertise and grading consistency. The paper analyzes grading experiences in various exam settings and investigates the impact on assessment quality. We adopt an empirical methodology and answer the research question by analyzing the data from two exams. The chosen exams have similar learning objectives but they differ in how TAs graded them, thus providing an opportunity to reflect on different grading styles. It concludes with recommendations for enhancing TA grading effectiveness, emphasizing the need for detailed rubrics, training, and monitoring to ensure fair and reliable assessment in higher education. Copyright © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Grading

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Diabetes Prediction Using Hybrid 1D CNN and PeeHole Long Short-Term Memory Algorithm 5

Diabetes Prediction Using Hybrid 1D CNN and PeeHole Long Sho...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Behera, Mandakini Mishra, Debahuti Sarangi, Archana Sarangi, Shubhendu Deemed to be University School of Computer Engineering Bhubaneswar India University Department of Computer Science & Engineering Bhubaneswar India University Department of Electronics and Telecommunications Bhubaneswar India University Faculty of Engineering Bhubaneswar India

ISBN: (纸本)9798331522100

According to the survey of International Diabetes Federation (IDF) the number of persons with diabetes is continuously increasing. There are currently 3820 million diabetics worldwide, and during the next 15 years, this figure might double. Diabetes can create several life-threatening diseases, including heart attack, renal failure, and stroke. To sustain a healthy life, early diabetes identification is crucial. machine learning (ML) is a approach which automatically learns from experience to improve performance and produce more accurate predictions, that can be used to forecast diabetes. Deep learning is a branch of machine learning, and the human brain inspires both its architecture and the way it *** manuscript presents a hybrid method that combines peephole LSTM and 1D CNN to improve the accuracy of diabetes prediction. Performance is evaluated based on F1 score, accuracy, specificity, sensitivity, and epoch-wise accuracy and *** Pima Indian diabetes dataset is utilized for experimental validation. Experimental results demonstrate high prediction accuracy of 96.74% on training and 75.32% on testing for diabetes prediction. © 2025 IEEE.

关键词： Deep learning

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Optimization of Transmission Distance in Symmetric Wireless Power Transfer Topology 47

Optimization of Transmission Distance in Symmetric Wireless ...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Vinko, Davor Srnović, Mario Bilandžija, Domagoj Filipović, Luka Faculty Of Electrical Engineering Computer Science And Information Technology Osijek Osijek Croatia

ISBN: (纸本)9798350382495

Inductive wireless power transfer (WPT) system uses alternating magnetic field to transmit power from the transmitter to the receiver. To confine the magnetic field, WPT coils are realized with high permeability substrate material that ensures shielding. Aside from shielding, high permeability substrate also increases the coil inductance. When transmitter and receiver coils are placed in close proximity, high permeability substrate of transmitter coil increases the inductance of the receiver coil, and vice versa. This additional inductance increase is a function of the substrate material type and the distance between the coils. To achieve high efficiency and low losses of the WPT system, a capacitor network is added to both transmitter and receiver coil. In a common serial-serial (S-S) WPT system topology, the capacitor networks are asymmetric, i.e. the capacitor network of the transmitter and the receiver are not identical. The efficiency of asymmetric S-S topology is significantly impaired due to variable coil inductance. To attain high efficiency in such operating conditions, this paper proposes symmetric S-S WPT topology with emphasis on transmission distance optimization. © 2024 IEEE.

关键词： Inductive power transmission

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Machine Learning-Based Calibration Approaches for Single-Beam and Multiple-Beam Distance Sensors

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IEEE Sensors Journal 2024年第1期24卷 975-983页

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Biernacki, Piotr Ziebinski, Adam National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice44-100 Poland Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice44-100 Poland

Sensors are the foundation to facilitate smart cities, smart grids, and smart transportation, and distance sensors are especially important for sensing the environment and gathering information. Researchers have developed various methods to calibrate distance sensors;however, they mainly focus on using external information for calibration, which is inefficient and computationally expensive. To alleviate the above problems, we propose machine learning (ML)-based calibration approaches for distance sensors. We develop two calibration mechanisms, where the naive mechanism directly predicts the true distance, while the residual mechanism predicts the residual for calibration. The conventional ML regression algorithms are then adopted to predict the true distance or calibration residual. Experimental results show that the designed residual calibration mechanism outperforms the naive mechanism by reducing the root mean square error (RMSE) by 92.2% and mean absolute error (MAE) by 93.3% in the fitting task. In prediction, the proposed 2-D-residual mechanism achieves an improvement of 78.6% and 65.7% compared to the state-of-the-art method. The experimental results show the effectiveness and generalization of the proposed ML-based calibration method, especially for the 2-D mechanism, which achieves the highest robustness in all fitting and prediction tasks. © 2001-2012 IEEE.

关键词： Calibration

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A Novel Optimizer in Deep Neural Network for Diabetic Retinopathy Classification

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computer Systems science & engineering 2022年第12期43卷 1099-1110页

作者： Pranamita Nanda N.Duraipandian Department of Computer Science and Engineering Velammal Institute of TechnologyChennai601204TamilnaduIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennai602105TamilnaduIndia

In severe cases, diabetic retinopathy can lead to blindness. For decades,automatic classification of diabetic retinopathy images has been a challenge. Medical image processing has benefited from advances in deep learning systems. Toenhance the accuracy of image classification driven by Convolutional Neural Network (CNN), balanced dataset is generated by data augmentation method followed by an optimized algorithm. Deep neural networks (DNN) are frequentlyoptimized using gradient (GD) based techniques. Vanishing gradient is the maindrawback of GD algorithms. In this paper, we suggest an innovative algorithm, tosolve the above problem, Hypergradient Descent learning rate based Quasi hyperbolic (HDQH) gradient descent to optimize the weights and biases. The algorithms only use first order gradients, which reduces computation time andstorage space requirements. The algorithms do not require more tuning of thelearning rates as the learning rate tunes itself by means of gradients. We presentempirical evaluation of our algorithm on two public retinal image datasets such asMessidor and DDR by using Resnet18 and Inception V3 architectures. The findings of the experiment show that the efficiency and accuracy of our algorithm outperforms the other cutting-edge algorithms. HDQHAdam shows the highestaccuracy of 97.5 on Resnet18 and 95.7 on Inception V3 models respectively.

关键词： CNN diabetic retinopathy data augmentation gradient descent deep learning optimization

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Design of an Efficient and Provable Secure Key Exchange Protocol for HTTP Cookies

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computers, Materials & Continua 2024年第7期80卷 263-280页

作者： Waseem Akram Khalid Mahmood Hafiz Burhan ul Haq Muhammad Asif Shehzad Ashraf Chaudhry Taeshik Shon Graduate School of Engineering Science and Technology National Yunlin University of Science and TechnologyYunlin64002TaiwanChina Future Technology Research Center National Yunlin University of Science and TechnologyYunlin64002TaiwanChina Department of Computer Science Lahore Garrison UniversityLahore54920Pakistan Department of Computer Science and Information Technology College of EngineeringAbu Dhabi UniversityAbu Dhabi69911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbul34398Turkey Department of Cybersecurity Ajou UniversitySuwon16499Republic of Korea

Cookies are considered a fundamental means of web application services for authenticating various Hypertext Transfer Protocol(HTTP)requests andmaintains the states of clients’information over the *** cookies are exploited to carry client patterns observed by a *** client patterns facilitate the particular client’s future visit to the corresponding ***,security and privacy are the primary concerns owing to the value of information over public channels and the storage of client information on the *** protocols have been introduced that maintain HTTP cookies,but many of those fail to achieve the required security,or require a lot of resource *** this article,we have introduced a lightweight Elliptic Curve Cryptographic(ECC)based protocol for authenticating client and server transactions to maintain the privacy and security of HTTP *** proposed protocol uses a secret key embedded within a *** proposed protocol ismore efficient and lightweight than related protocols because of its reduced computation,storage,and communication ***,the analysis presented in this paper confirms that proposed protocol resists various known attacks.

关键词： Cookies authentication protocol impersonation attack ECC

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Current Trends, Challenges and Techniques in XAI Field;A Tertiary Study of XAI Research 47

Current Trends, Challenges and Techniques in XAI Field;A Ter...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Brdnik, Saša Šumak, Boštjan University Of Maribor Institute Of Informatics Faculty Of Electrical Engineering And Computer Science Slovenia

ISBN: (纸本)9798350382495

This tertiary systematic literature review examines 29 systematic literature reviews and surveys in Explainable Artificial Intelligence (XAI) to uncover trends, limitations, and future directions. The study explores current explanation techniques, providing insights for researchers, practitioners, and policymakers interested in enhancing AI transparency. Notably, the increasing number of systematic literature reviews (SLRs) in XAI publications indicates a growing interest in the field. The review offers an annotated catalogue for human-computer interaction-focused XAI stakeholders, emphasising practitioner guidelines. Automated searches across ACM, IEEE, and science Direct databases identified SLRs published between 2019 and May 2023, covering diverse application domains and topics. While adhering to methodological guidelines, the SLRs often lack primary study quality assessments. The review highlights ongoing challenges and future opportunities related to XAI evaluation standardisation, its impact on users, and interdisciplinary research on ethics and GDPR aspects. The 29 SLRs, analysing over two thousand papers, include five directly relevant to practitioners. Additionally, references from the SLRs were analysed to compile a list of frequently cited papers, serving as recommended foundational readings for newcomers to the field. © 2024 IEEE.

关键词： Artificial intelligence

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