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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Jadhav, Balasaheb Wanjale, Pooja Gavli, Renuka Thopate, Kaushalya Maharnawar, Niranjan Karmankar, Nikhil Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India Vishwakarma Institute of Technology Department of Engineering Sciences and Humanities Pune India Ajeenkya D. Y. Patil School of Engineering Department-Computer Engineering India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Sagitech Solutions Pvt. Ltd. India

ISBN: (纸本)9798350308167

a radar system built on the Internet of Things (IoT) that uses ultrasonic sensors to find things within 150 degrees. The system's rotation is controlled by a servo motor, and a Graphical User Interface (GUI) built using Java Swing and applets displays the results of detection. In addition, the system has a feature that, upon the observation of an object, sends notifications to a user's mobile device. The proposed system can be used to detect objects in its vicinity efficiently and effectively in a variety of scenarios, such as security surveillance and environmental monitoring. The system's hardware and software components are covered in the article along with the testing and validation processes applied to gauge the system's performance. The suggested IoT-based radar system can, in general, improve object recognition abilities and give users real-time notifications, making it a desirable complement to the current range of IoT devices. © 2023 IEEE.

关键词： Arduino IoT object detection Radar tracking system Ultrasonic Sensor

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Involution-based efficient autoencoder for denoising histopathological images with enhanced hybrid feature extraction

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computers in Biology and Medicine 2025年 192卷 110174-110174页

作者： Islam, Md. Farhadul Reza, Md. Tanzim Manab, Meem Arafat Zabeen, Sarah Islam, Md. Fahim-Ul Shahriar, Md. Fahim Kaykobad, Mohammad Husna, Md. Golam Zel Asmaul Noor, Jannatun Research Group Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Department of Mathematics and Natural Sciences School of Data and Sciences BRAC University Dhaka Bangladesh School of Law and Government Dublin City University Dublin Ireland National Institute of Cancer Research and Hospital Dhaka Bangladesh

Noise in histopathology images from hardware limitations, preparation artifacts, and environmental factors complicates disease analysis and increases risks. With growing workloads and the complexity of histopathology images, developing efficient and precise histopathology image analysis methods is essential. However, many denoising models struggle to extract spatial features and are computationally expensive, primarily due to the limited capacity of convolutions to capture visual patterns across spatial locations, and tend to occupy the largest share of computational costs. In histopathology, many spatial features, such as anomalies or microorganisms, located sparsely across an image, are crucial for the final diagnosis, and many denoising processes often either blur them or introduce artifacts. In this study, we propose a lightweight autoencoder (43.11 kilobytes) for denoising histopathology images by fusing a single involution layer within a small convolution model, resulting in better denoising performance in a hybrid model, which has both channel-specific and location-specific feature extraction capabilities. Building upon the idea of a shallow autoencoder, our model results in much lower memory and compute overhead requirements, while also not avoiding the generation of artifacts. On Malaria Blood Smear and CRC datasets, SSIM Loss and Peak-Signal-to-Noise-Ratio were used for performance evaluation, with lower SSIM Loss (0.058 and 0.34) in denoising images with an added Gaussian noise of 0.3. Our proposed autoencoder, with low weight parameters of 11,037 and 81,630,000 floating point operations (FLOPs), is over 20 times less computationally expensive than Xception, the second-best performing model, establishing ours as the most efficient denoising autoencoder for histopathology images. © 2025 Elsevier Ltd

关键词： Risk assessment

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Understanding the Effects of Projectors in Knowledge Distillation

arXiv

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arXiv 2023年

作者： Chen, Yudong Wang, Sen Liu, Jiajun Xu, Xuwei de Hoog, Frank Kusy, Brano Huang, Zi School of Electrical Engineering and Computer Science The University of Queensland Australia CSIRO Data61 Australia

Conventionally, during the knowledge distillation process (e.g. feature distillation), an additional projector is often required to perform feature transformation due to the dimension mismatch between the teacher and the student networks. Interestingly, we discovered that even if the student and the teacher have the same feature dimensions, adding a projector still helps to improve the distillation performance. In addition, projectors even improve logit distillation if we add them to the architecture too. Inspired by these surprising findings and the general lack of understanding of the projectors in the knowledge distillation process from existing literature, this paper investigates the implicit role that projectors play but so far have been overlooked. Our empirical study shows that the student with a projector (1) obtains a better trade-off between the training accuracy and the testing accuracy compared to the student without a projector when it has the same feature dimensions as the teacher, (2) better preserves its similarity to the teacher beyond shallow and numeric resemblance, from the view of Centered Kernel Alignment (CKA) [1], and (3) avoids being over-confident [2] as the teacher does at the testing phase. Motivated by the positive effects of projectors, we propose a projector ensemble-based feature distillation method to further improve distillation performance. Despite the simplicity of the proposed strategy, empirical results from the evaluation of classification tasks on benchmark datasets demonstrate the superior classification performance of our method on a broad range of teacher-student pairs and verify from the aspects of CKA and model calibration that the student's features are of improved quality with the projector ensemble design. Code is available at https://***/chenyd7/PEFD. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Accident detection using automotive smart black- box based

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Measurement: Sensors 2023年 27卷

作者： Josephinshermila, P. Sharon priya, S. Malarvizhi, K. hegde, Ramakrishna Gokul Pran, S. Veerasamy, B. Department of artificial intelligence and data science R.M.K.College of Engineering and Technology Tamil Nadu Chennai India Department of Computer Science Engineering B.S.Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering JCT College of Engineering and Technology Pichanur Tamil Nadu Coimbatore India Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysore India Department of Computer Science Engineering Sreevidyanikethan Engineering College Tirupathi India Department of Electronics and Communication Engineering Hindusthan College of Engineering and Technology Tamil Nadu Coimbatore641050 India

Autonomous vehicles want reliable and strong sensor suites and alert systems. This paper discusses the composition and performance of a sophisticated monitoring and alert system for automobile vehicle parameters. The number of automobiles has also grown quickly to meet the enormous population. Additionally, this resulted in an increase in accidents. The accident prevention strategies now in use are all static and dated. Additionally, there is no reliable accident detection system. Automobile vehicle parameters are continuously monitored by a micro-controller which stores the data logs containing vehicle parameter data into a sheltered digital memory card and in the cloud storage. The system doesn't solely record the vehicle parameters data of the automobile periodically, but also actively monitors for any sudden vehicle accident detection. The sensor may facilitate folks to analyze the accident quickly and lawfully when a collision happens to alert the emergency services to that location. The system will update the information whenever an abnormal system event happens. A black box in a vehicle gather driving information about the vehicle before, during and after a crash. The data gathered includes, speed, acceleration, braking, steering and air-bag deployment. The Automotive black box system can aid with vehicle safety, increase collision victim care, assist insurance corporations with vehicle crash investigations, and improve road conditions to reduce death rates. According to experimental findings, the proposed technique achieves 29.3103%, 22.70 %, 18.103% and 11.206 % higher accuracy compared to RFID, SVM,CNN and RNN Methods. © 2023 The Authors

关键词： Internet of things

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Improving Sentiment Classification on Restaurant Reviews Using Deep Learning Models

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Procedia computer science 2024年 235卷 3246-3256页

作者： Ratna Nitin Patil Yadvendra Pratap Singh Shitalkumar Adhar Rawandale Sofia Singh Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Pune 411048 India Computer Science and Engineering Department Manipal University Jaipur 303007 India Department of Mechanical Engineering Pimpri Chinchwad College of Engineering and Technology Pune 411044 India School of Engineering and Technology Amity University Noida 201301 India

Natural Language processing (NLP) includes the task of classifying texts. The proposed paper uses the ability and power of machine learning and deep learning techniques for sentiment analysis on restaurant reviews. With the increasing use of social media, classification of reviews has significant role in the development of public opinion. This work has extensive applications from client opinion study, market research and social media monitoring. The machine learning algorithms and NLP techniques are used classifying reviews and the results are rigorously analysed. It was noted that results obtained from logistic regression and multinomial naïve bayes algorithms are better than other machine learning models. Hence these two algorithms are selected to optimize further. After tuning the hyperparameters by grid search technique, the best accuracy obtained by logistic regression and multinomial naive bayes on validation dataset was 89.9% and 89.6% respectively. Deep learning architectures were also utilized for reviews classification. The accuracy obtained by Convolutional Neural Net (CNN) and Bi-Directional Long-short-term-memory (Bi-LSTM) was 89% and 90% respectively.

关键词： Natural Language Processing Sentiment Analysis CNN Bi-LSTM

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Optimization of Feature Selection to Improve Order Transaction Performance in the Manufacturing Industry

Optimization of Feature Selection to Improve Order Transacti...

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International Conference on Electrical engineering, computer science and Informatics (EECSI)

作者： Ery Hartati Deris Stiawan Wijang Widhiarso Faculty of Engineering Universitas MDP Faculty of Engineering Universitas Sriwijaya Palembang Palembang Indonesia Computer Engineering Department Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Computer Engineering Department Faculty of Computer Science Universitas Multi Data Palembang Palembang Indonesia

The development of business processes that occur in Industry 4.0 requires the optimization of processes in the system, especially in the process of order transactions. To optimize the transaction process, the assistance of the rank and weight system on dataset features, called the feature selection process, can be employed. Feature selection aims to improve the elimination of redundant and irrelevant features, which can reduce computation time and improve accuracy. In this research using multivariate datasets. Feature selection is used to eliminate some attributes that are irrelevant to the label class, resulting in improved performance of the classification algorithm. The author compares three algorithms: information gain, ratio gain, and chi-square. These are algorithms to find the rank and weight of data. In this research to measure feature selection performance using the Naïve Bayes classifier algorithm. The evaluation is performed by observing the level of classification accuracy generated without feature selection versus the classification accuracy generated after the implementation of feature selection. The experimental results showed that 14 features were included in the selection of features, and four relevant and best features were produced using the Chi-square binary classification algorithm. Classification using naïve Bayes algorithm reached 97.56%, while using ratio gain and information gain resulted in classification values of 75.57% and 92.69%, respectively.

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A Broadband Dual-Beam PIFA Antenna With Modified CPW Feed for Secure Sub-6 GHz 5G Applications

A Broadband Dual-Beam PIFA Antenna With Modified CPW Feed fo...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Saqer S. Alja'afreh Amjaad T. Altakhaineh Aser M. Matarneh Omar R. Daoud Electrical and Computer Engineering American International University Aljahra Kuwait Data Science and Machine Learning Department Pioneers International Academy Amman Jordan

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This work presents a broadband and low-profile printed Inverted-F antenna (PIFA). The design deploys a 1.5 mm thick FR-4 substrate with dimensions ( $\mathbf{4 0} \times \mathbf{2 5} \mathbf{ m m}^{2}$ ). A broadband operation is achieved through modifying a coplanar waveguide feed (CPW). Two $\lambda / 8$ stubs are created to enhance the entire operational frequency bandwidth. The simulation result of the reflection parameter achieves about 87 % fractional bandwidth ( $2.05-5.2 \text{GHz}$ ) based on the $\mathbf{- 1 0} \mathbf{d B}$ impedance bandwidth criterion. The design has total radiation efficiency above $\mathbf{7 2 \%}$ . Far-field radiation patterns results show a dualpattern (pattern diversity) among excited resonance modes frequencies. Such results make the proposed antenna an excellent candidate for current secure- and selective sub-6 GHz 5G communications systems.

关键词： 5G mobile communication Resonant frequency Bandwidth Directive antennas Resonance Broadband communication Coplanar waveguides Broadband antennas Impedance Antenna radiation patterns

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EG-SAN: Evolving Graph Self-attention Networks for Detecting Illicit Activities in Cryptocurrency

EG-SAN: Evolving Graph Self-attention Networks for Detectin...

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15th EAI International Conference on Digital Forensics and Cyber Crime, ICDF2C 2024

作者： Cui, Bo Zhang, Jia Engineering Research Center of Ecological Big Data Ministry of Education Beijing China College of Computer Science Inner Mongolia University Hohhot China

ISBN: (纸本)9783031893599

In recent years, illicit activities in cryptocurrency transactions have posed a severe threat to financial systems and society. Current detection methods mainly rely on machine learning and graph neural networks but often overlook the dynamic evolution of transaction graphs over time. To address this, we propose a novel Evolutionary Graph Self-Attention Network (EG-SAN) that can simultaneously capture the spatial relationships among nodes as well as their temporal evolution features through a joint self-attention mechanism across structural and temporal dimensions. Specifically, the graph attention network aggregates features from node neighborhoods in the graph data, while gated recurrent units dynamically update its layer weights to capture the evolution of the graph over the temporal dimension. Subsequently, a self-attention mechanism further optimizes these weights, enabling the model to more accurately reflect the salient features of the data at different time steps. We evaluate EG-SAN on a Bitcoin transaction dataset, and the results demonstrate its superiority over existing state-of-the-art methods. Further application to graph datasets from other domains also confirms the effectiveness and robustness of EG-SAN, showcasing its broad application potential. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Graph neural networks

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Clear Text Visibility: A CNN-LSTM Approach for Frame of Interest Identification in Book Flipping Videos

Clear Text Visibility: A CNN-LSTM Approach for Frame of Inte...

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Modeling, Simulation & Intelligent Computing (MoSICom), International Conference on

作者： Gaurav Buddhawar Dharin Dave Krupa Jariwala Chiranjoy Chattopadhyay Dept. of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Surat India Dept. of Computer Science and Engineering (AI & ML) Pimpri Chinchwad College of Engineering Pune India School of Computing and Data Sciences FLAME University Pune India

ISBN: (数字)9798331533311

ISBN: (纸本)9798331533328

Book flipping videos present a distinctive challenge for information extraction, requiring the identification of frames with clear text visibility during dynamic page turns. This paper introduces a novel approach to frame classification in book flipping videos, leveraging the combined effect of Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs). In particular, a CNN-LSTM model is employed, where a pre-trained CNN captures spatial features, and LSTMs extend the model’s capability to distinguish temporal dependencies critical for page turn detection. Unlike conventional methods, this approach processes entire video sequences, enabling the model to learn intricate Frame of Interest (FoI) detection features indicative of page turns and text visibility. The proposed model offers improved interpretability, effectively classifying frames without compromising transparency. The research opens avenues for automated book digitization, digital library creation, and educational technology applications, enhancing information extraction and reliability from book flipping videos.

关键词： Digital transformation Video sequences Educational technology Information retrieval Feature extraction Libraries Convolutional neural networks Reliability Long short term memory Videos

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Analysis of Knowledge Distillation on Image Captioning Models

Analysis of Knowledge Distillation on Image Captioning Model...

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2022 Workshop on Advances in Computational Intelligence, its Concepts and Applications, ACI 2022

作者： Srivatsan, S. Shridevi, S. School of Computer Science and Engineering Vellore Institute of Technology India Centre for Advanced Data Science Vellore Institute of Technology India

Image Captioning involves generating a textual description of an image, in the most accurate way possible. It requires a combination of computer Vision and Natural Language Processing techniques, which can both be enhanced individually to improve the overall performance of the model. Knowledge Distillation is a model compression technique, where a smaller network learns from the predictions of a larger network to find a more optimal convergence space. It effectively improves the performance of the smaller network, without any problems like over fitting. Typically, the performance of image captioning is measured in terms of the BLEU score and CIDER score. In this work, we have tested and recorded the performance of three different Image Captioning model architectures, in terms of a large network, small network and knowledge distilled small network to track and analyse the effects of Knowledge Distillation. The results are promising when compared with the state of art models. © 2020 Copyright for this paper by its authors.

关键词： Distillation

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