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1st IEEE International Symposium on Consumer technology, ISCT 2024

作者： Syaifudin, Yan Watequlis Fatmawati, Triana Saputra, Pramana Yoga Funabiki, Nobuo Al-Maktary, Omar Abdul-Raoof T. G. Siradjuddin, Indrazno State Polytechnic of Malang Dept. of Information Technology Malang Indonesia Okayama University Dept. of Elec. and Comm. Engineering Japan State Polytechnic of Malang Dept. of Electrical Engineering Malang Indonesia

ISBN: (纸本)9798350365191

The significance of web applications has grown immensely due to the widespread availability of the internet and their extensive usage across various devices. Currently, *** emerged as the most extensively embraced web framework among software developers worldwide. As a result, there is a high demand for skilled web programmers capable of developing ***-based web applications. *** allows web application development using JavaScript on the server side, making it suitable for real-time applications like collaborative tools, online games, and chat applications. This paper presents a self-learning model for ***-based web programming that incorporates test-driven development (TDD), leveraging automated testing with Jest and Supertest for code verification. The Basic Web Interface topic within this model requires students to create web elements like headers and paragraphs for personal information display, accompanied by test codes for step-by-step source code verification. Evaluation results from 40 first-year university students indicate successful completion of assignments within varying timeframes and positive feedback on the learning model's effectiveness. © 2024 IEEE.

关键词： Students

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Image Caption Generation Through the Integration of CNN-Based Residual Network Architectures and LSTM 7

Image Caption Generation Through the Integration of CNN-Base...

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7th International Conference on Informatics and Computational Sciences, ICICoS 2024

作者： Santi, Dessy Ilham, Amil Ahmad Syafaruddin Nurtanio, Ingrid Universitas Hasanuddin Dept. of Electrical Engineering Gowa Indonesia Universitas Tadulako Gowa Dept. of Information Technology Palu Indonesia Universitas Hasanuddin Dept. of Informatics Gowa Indonesia

ISBN: (纸本)9798350375886

Image captioning improves understanding of visuals and words and impacts image retrieval and visual information. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), more especially long short-term memory (LSTM) systems, are being used in this field's recent advances to address the difficulty of using deep learning to produce concise narratives and highlight object-concept relationships. This study focuses on CNNs with residual network architectures (ResNet-50, ResNet-101, ResNet-152) tested on the Flickr 8k dataset. The aim is to explore and understand how the dept. of the network affects the understanding of visual structures and contexts in improving the quality of descriptions. Our methodology involves several stages, including image feature extraction, text preprocessing, and model optimization and evaluation using metrics like BLEU scores. Experimental results demonstrate the effectiveness of our approach, with the ResNet-101 model achieving the highest BLEU score among the tested models. This work contributes to the ongoing efforts to bridge the gap between visual data understanding and natural language generation, offering promising prospects for more natural and accurate image captioning systems. © 2024 IEEE.

关键词： Convolutional neural networks

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Automated Alzheimer's Disease Diagnosis Using Convolutional Neural Networks and Magnetic Resonance Imaging 11

Automated Alzheimer's Disease Diagnosis Using Convolutional ...

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11th IEEE International Conference on Systems and control, ICSC 2023

作者： Mohammed, Asmaa Nasr Albagul, Abdulgani Ahmad, Moamer Musbah College of Electronic Technology Dept. Control Engineering Bani Walid Libya Libyan Center for Engineering Research and Information Technology Bani Walid Libya University of Baniwalid Faculty of Engineering Bani Walid Libya

ISBN: (纸本)9798350304886

Alzheimer's disease is a debilitating neurological condition impacting millions globally. Therefore, an accurate diagnosis plays a major role in treating or managing it effectively. Convolutional neural networks (CNNs), popular deep learning algorithms applied to image processing tasks, offer such an opportunity in this study where a CNN model for classifying Alzheimer's patients was proposed. The research yielded impressive results: recall and precision scores as high as 0.9958 which indicate trustworthy identification of true positives while maintaining few false positives;test accuracy exceeding 99% confirming desirable generalization capabilities from the training dataset to live scenarios;ROC AUC score at an astronomical height of 0.9999 signifying great potential in distinguishing between afflicted individuals from their non-affected counterparts accurately. The proposed network achieved a good classification accuracy. Comparing the proposed model alongside two state of the art models VGG19 and ResNet50 resulted in superior performance for the proposed model demonstrating CNNs can increase diagnostic precision and effectiveness of Alzheimer's disease, leading to early detection, enhanced treatment plans, and enriching the quality of life for those affected. © 2023 IEEE.

关键词： Convolutional neural networks

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Enhancing Cabbage Plant Protection: CNN Algorithm for Insect Identification

Enhancing Cabbage Plant Protection: CNN Algorithm for Insect...

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2024 International Conference on information technology Research and Innovation, ICITRI 2024

作者： Senjaya, Remi Musa, Purnawarman Sularsih, Puji Novrina, Novrina Aryunani, Witari Suhartini, Suhartini Gunadarma University dept. of Informatics Engineering Depok Indonesia Gunadarma University dept. of Electrical Engineering Depok Indonesia Gunadarma University dept. of Information Technology Depok Indonesia Gunadarma University dept. of Computer Science Depok Indonesia

ISBN: (纸本)9798350376210

People frequently find it straightforward to identify insects that feed on cabbage plants based on the insects' characteristics. However, challenges arise when employing a computer for precise bug categorization and identification. The implementation of Deep Learning (DL) utilizes the Convolutional Neural Network (CNN) technique, renowned for its effectiveness in categorizing and recognizing objects in digital photo data. The CNN algorithm delivers notable results in photo identification, simulating the human visual brain's image recognition through artificial neural networks in the Python programming language. To enrich the user experience, frameworks like Keras and TensorFlow are employed in neural network development. Our research will use the CNN algorithm to determine insect species in cabbage images. Achieving high accuracy involves coupling the CNN algorithm with the VGGNet architectural model. Notably, utilizing an epoch value of 150 resulted in a 97.75% accuracy rate during the training model technique;however, the validation accuracy was slightly lower at 94.25%. The corresponding loss and validation rates were 0.0613% and 0.2945%, respectively. Moreover, based on the model (150 epochs), the testing outcomes in the study, involving 48 images beyond the dataset and incorporating additional information, resulted in an accuracy rate of 91.66%. © 2024 IEEE.

关键词： Convolutional neural networks

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Circuit Design Method of Rate Increase in Operational Amplifiers Based on the MH2XA031 Array Chip and CBJT Technological Routes

Circuit Design Method of Rate Increase in Operational Amplif...

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2024 Conference of Young Researchers in Electrical and Electronic engineering, ElCon 2024

作者： Titov, Alexey E. Kleimenkin, Dmitriy V. Prokopenko, Nikolay N. Southern Federal University Dept. of Automatic Control Systems Rostov-on-Don Russia Don State Technical University Dept. of Scientific Research Dept. of Information Systems and Radio Engineering Rostov-on-Don Russia

ISBN: (纸本)9798350360646

It has been revealed that in the conditions of small-scale production of discrete-analog filters on switched capacitors and ADC-drivers, it is most advisable to design high-speed operational amplifiers (OAs) based on the CBJT technological route and the MH2XA031 array chip, which allows operation in conditions of low temperatures and exposure to radiation. It has been established that most commercially produced O A s provide an average value of the slew rate (SR, up to 200 div 300 ~V / μ s). This is largely due to the limitations of the technologies used, and most importantly, to the irrational design of circuits. We study the maximum performance parameters of a CBJT OA with one integrating capacitor, which ensures the stability of the O A, and a differentiating transient correction circuit, which is implemented by connecting additional small capacitors to the original circuit. In this case, the S R of the O A increases by more than 500 times (up to 4000 ~V / μ s). © 2024 IEEE.

关键词： array chip CBJT operational amplifier slew rate

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Detecting adversarial examples using constraint-based learning

Detecting adversarial examples using constraint-based learni...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Kumar, Vivek Kumar, Kamal Singh, Maheep National Institute of Technology Dept. of Computer Science -and Engineering Uttarakhand Pauri India IGDTUW Department of Information Technology New Delhi India Doon University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350394412

Deep learning models have been applied in various fields and continue producing exceptional results. However, these models are vulnerable to adversarial attacks, which are modified data samples maliciously crafted to cause misclassification by a target model. Fields like Network intrusion detection systems (NIDS) are security-critical;therefore, adversarial attacks pose a high risk in such applications. The attacker not only aims for evasion but also tries to launch an attack once the adversarial example has evaded detection by a NIDS. This paper presents a novel technique for detecting adversarial examples targeted towards a NIDS by learning the dependencies between features known as domain constraints. Our proposed method detects adversarial examples that violate these constraints learned during training. Compared with prevalent detection techniques, our method does not require generating adversarial examples for training, which reduces its computational requirement. We assume a grey-box threat model where the attacker can access the training data. We perform experiments on the CICIDS2017 dataset against five state-of-the-art adversarial attacks: FGSM, BIM, PGD, Deep fool and BIM. The results show that our method can detect up to 100% adversarial attacks, outperforming the contemporary schemes. © 2024 IEEE.

关键词： Adversarial machine learning

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Development of Sensorless Based Bimanual Tele-Rehabilitation Robot for Hemiparetic Post-Stroke Patients 10

Development of Sensorless Based Bimanual Tele-Rehabilitation...

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10th International Multidisciplinary engineering Research Conference on Moratuwa engineering Research, MERCon 2024

作者： Perera, K.L.M. Hettiarachchi, T.I. Kumara, J.A.S.S. Senadeera S D, Arunya P. Ruwanthika, R.M.M. University of Moratuwa Dept. of Electrical Engineering Moratuwa Sri Lanka Asian Institute of Technology Klong Luang Dept. of Information & Communications Technologies Thailand

ISBN: (纸本)9798331529048

Stroke patients experience difficulties doing bimanual activities due to loss of inter-limb coordination. This is commonly in arms, and most patients continue to suffer from paralysis in one arm after recovery from the stroke. Our focus is to implement the master-follower robotic system to regain the functionality of the paretic arm using the healthy arm to enhance the rehabilitation process. This robot includes two basic and important exercises: flexion & extension of elbow joint and internal & external rotation of shoulder joint. The system consists of two single Degrees of Freedom (DOF) robotic arms capable of giving resistance according to the torque profiles of a healthy arm. Both arms are moved simultaneously using bi-manual motor control, simulating the assistance from the therapist by virtual spring action. The study applied sensorless sensing techniques: the Reaction Torque Observer (RTOB) and the Disturbance Observer (DOB) for motor torque estimation and robust control respectively. Hence, with this robotic approach, the paper provides a comfortable and robust rehabilitation solution. © 2024 IEEE.

关键词： Robust control

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Kalman Filter: A Crucial Step Towards the Development of NavIC 10

Kalman Filter: A Crucial Step Towards the Development of Nav...

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10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Sharma, Avinash Dutta, Abhishek Deb, Sujay National Institute of Technology Durgapur Dept. Of Electronics and Communication Engineering West Bengal Durgapur India Indraprastha Institute of Information Technology Delhi Dept. Of Electronics and Communication Engineering Delhi New Delhi India

ISBN: (纸本)9798331533229

Navigation with Indian Constellation (NavIC) is an autonomous satellite system consisting of seven constellations, with three in geostationary orbit and four in geosynchronous orbit. Achieving precise positioning and velocity estimations under noisy conditions is crucial for applications such as vehicle guidance, disaster management, and transportation improvement. We propose a novel implementation of a Kalman Filter for processing NavIC satellite measurements, optimizing position, velocity, and timing estimations. The innovation in this filter design lies in its efficient noise reduction and high accuracy in estimations. Our design achieves a power consumption of 0.103 Wand an area of 84303.850 μ m2. To further enhance the design, we aim to incorporate clock gating and power gating techniques, reducing overall power consumption and making it highly suitable for low-power, real-time navigation applications. © 2024 IEEE.

关键词： Kalman filters

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Characterization of ScAlN/GaN Toward Electronic Device Application

Characterization of ScAlN/GaN Toward Electronic Device Appli...

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2024 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and technology Symposium, BCICTS 2024

作者： Maeda, Takuya Wakamoto, Yusuke Kobayashi, Atsushi The University of Tokyo Dept. of Electrical Engineering and Information Systems Tokyo Japan Tokyo University of Science Dept. of Material Science and Technology Tokyo Japan

ISBN: (纸本)9798331541248

Scandium Aluminum Nitride (ScAIN) is an attractive material as a barrier layer of GaN HEMTs owing to its ultrawide bandgap, strong polarization and ferroelectric. In the paper, the recent progress in the characterization of ScAlN/GaN heterostructure prepared by low-temperature sputtering method is presented. Structural and optical properties of epitaxial pseudomorphic ScAIN with Sc compositions of 6,10 and 14% on GaN are investigated by AFM, XRD and spectroscopic ellipsometry. Consistent thicknesses of ∼20nm were obtained by XRD and spectroscopic ellipsometry. Dielectric function and optical bandgap are determined. © 2024 IEEE.

关键词： Gallium nitride

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A Fault-prone Module Prediction Combining Past and Ongoing Test Results

Computer Software

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Computer Software 2024年第4期41卷 67-73页

作者： Hirose, Ryoichi Monden, Akito Nishiura, Kinari Tsunoda, Masateru Graduate School of Environmental Life Natural Science and Technology Okayama University Japan Faculty of Information and Human Sciences Kyoto Institute of Technology Japan Dept. of Informatics Faculty of Engineering and Science Kinadai University Japan

The aim of this study is to improve the efficiency of software testing and/or review by predicting modules that are likely to contain a bug. In cross-version bug prediction, which uses bug data from a previous version to predict bugs in software currently under development, it is difficult to achieve high prediction accuracy due to differences in bug factors between versions. In this research, three methods (test method, mix method, select method) are proposed to address this challenge by incorporating ongoing test results, in addition to bug data from past versions. An evaluation experiment targeted 13 open-source software projects, comparing conventional cross-version prediction with the three proposed methods using seven evaluation metrics. The result indicates that each proposed method achieves higher accuracy in bug prediction than the conventional cross-version prediction when utilizing test results for approximately 10% of the modules in the developing software. Particularly, the select method demonstrated superior performance in comparison. © 2024 Japan Society for Software Science and technology. All rights reserved.

关键词： Software testing

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