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2023 6th international conference on Circuits, systems and Simulation, ICCSS 2023

作者： Yu, Tianqi Zhao, Zhiwei Information Display and Visualization International Cooperation Joint Laboratory Southeast University China

Electronic synapse based on two-dimensional material are equivalent to synapses because of their excellent properties, which is very pivotal for constructing neuromorphic computing to break through the traditional Von Neumann architecture. In the study, a vertical double-ended memristor is prepared by using the dispersion of molybdenum disulfide nanosheets. Memristors based on MoTe2 structure show stable bipolar nonvolatile resistive behavior. Based on this, we introduce carbon dots layer into the original device structure, and improve the device performance by using carbon atoms to form new conductive filaments driven by electric field. this research provides a reliable solution for the next generation of neuromorphic computing. © Published under licence by IOP Publishing Ltd.

关键词： Memristors

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Special issue on hybrid artificial intelligence systems from HAIS 2022 conference

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NEUROcomputing 2024年 584卷

作者： Quintian, Hector Corchado, Emilio Univ A Coruna Dept Ind Engn CTC CITIC La Coruna Spain Univ Salamanca Dept Comp Sci & Automat Salamanca Spain

the six papers included in this special issue represent a selection of extended contributions presented at the 17th international conference on Hybrid Artificial Intelligent systems, HAIS 2022 held in Salamanca, Spain, September 6th-8th, 2022, and organized by the BISITE group at the University of Salamanca. the international conference on Hybrid Artificial Intelligence systems (HAIS 2022) has become a unique, established, and broad interdisciplinary forum for researchers and practitioners who are involved in developing and applying symbolic and sub-symbolic techniques aimed at the construction of highly robust and reliable problem-solving techniques to present the most relevant achievements in this field. HAIS Series of conferences provides an interesting opportunity to present and discuss the latest theoretical advances and real-world applications in this multidisciplinary research field.

关键词： Hybrid Intelligent Artificial Intelligence Soft computing

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A Novel Data Flow Security Protection Algorithm for Digital Media in 6G Environment 8

A Novel Data Flow Security Protection Algorithm for Digital ...

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Zhang, Linqin Anhui Technical College of Industry and Economy School of Computer and Art Anhui Heifei230051 China

ISBN: (纸本)9798350396638

With the recent development and practice of the homomorphic encryption, privacy computing, blockchain, quantum computing and other technologies, the safe and stable transmission and analysis of information flow has become particularly important. Under this background, this paper proposes the novel data flow security protection algorithm for digital media in 6G environment. As a logical first step, the watermarking technology is studied. the traditional model is improved with the chaotic coding to meet with the complex scenarios. then, the statistical encryption algorithm is integrated to fit for the 6G scenario. Furthermore, the novel data flow security protection algorithm is achieve by the integration of the Hamming coding. In the experiment section, the 8, 12, 16, 32, 64, 128 bits scenarios are tested and 128 bits obtain the best performance. Our best model configuration empirically achieved the risk detection accuracy of 99.85%. © 2023 IEEE.

关键词： Digital storage

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Religious Sentiment Analysis and Detection on Social Media 6th

Religious Sentiment Analysis and Detection on Social Media

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6th international conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Riskhan, Basheer Hossain, Md Shakil Ashfaq, Farzeen Albukhary International University Kedah Malaysia School of Computer Science SCS Taylors University Subang Jaya Malaysia

ISBN: (纸本)9789819780891

this research addresses the rising concern of religious hate speech on Facebook, employing a comprehensive methodology to create a dataset by annotating statements from diverse religious backgrounds. After preprocessing steps like noise removal and tokenization, various linguistic features, including character N-grams, TF-IDF-weighted N-grams, and text quality indicators, were utilized. Multiple supervised learning algorithms, such as logistic regression, linear SVC, random forest, Naive Bayes, decision tree, and a GRU-based neural network model, were examined for optimal performance. Evaluation metrics like accuracy, precision, recall, and F1 score were employed. the study illuminates the prevalence and characteristics of religiously motivated hate speech on social media, emphasizing the importance of algorithm selection based on desired performance metrics. this research offers a valuable tool for academics and platform management to effectively identify and mitigate the impact of religious hate speech on digital platforms, supporting ongoing efforts to combat online discrimination. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Self-supervised learning

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Automated Hybrid Model for Detection and Classification of Brain Tumor 6

Automated Hybrid Model for Detection and Classification of B...

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6th international conference of Women in Data Science at Prince Sultan University, WiDS-PSU 2023

作者： Khan, Ayesha Akbar, Shahzad Alghanim, Amerah Hassan, Syed Ale Ayesha, Noor Riphah International University Department of Computing Faisalabad Campus Pakistan Prince Sultan University AIDA Lab CCIS Riyadh Saudi Arabia Zhengzhou University School of Clinical Medicine Henan Zhengzhou China

ISBN: (纸本)9781665477239

Detection of brain tumors remains a critical challenge in the medical field. However, early brain tumor detection improves treatment options and enhances the likelihood of survival. Nevertheless, due to an extensive set of features in magnetic resonance images (MRI), manually checking them is a time-consuming and difficult task. To address these issues, brain tumor segmentation and classification must be done automatically. this research presents a novel framework to detect brain tumors through MRI using U-Net architecture and SVM classifier. the proposed research is based on pre-processing MRI that enhance and filter the images to improve contrast and eliminate noise. Moreover, segmentation of the MRI to extract the area of interest is performed through the modified U-Net architecture. After segmentation, a support vector machine (SVM) algorithm is employed to classify the normal and tumor-affected images. the proposed method is trained, validated, and tested on publicly available benchmarked BRATS 2019 and BRATS 2020 datasets. According to experimental findings, the proposed hybrid method achieved an overall accuracy of 98.95%, specificity of 98.13%, and sensitivity of 97.46%. this approach can be used in the medical field to assist clinicians and doctors dealing with a brain tumor diagnosis and treatment. © 2023 IEEE.

关键词： Health risks

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Industry 4.0: Discovering Scientific Trends through a Bibliometric Analysis 1

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8th international conference on Information and Communication Technology for Intelligent systems, ICTIS 2024

作者： Ventayen, Randy Joy M. International Accreditation Office Pangasinan State University Pangasinan Philippines

ISBN: (数字)9789819758104

ISBN: (纸本)9789819758098

the emergence of revolutionary Industry 4.0 in manufacturing businesses has become a significant area of interest. this research paper examines the current state of knowledge and advancements in the field of Industry 4.0. To achieve this, the authors utilized the PRISMA method to analyze and identify published research studies from the Scopus database. the study found a total of 14,435 documents published on this topic between 2011 and 2021. Articles were found to be the most common type of publication, with Engineering being the predominant subject theme. the most influential source of publications in this area was the conference proceedings, specifically Procedia Computer Science by Elsevier. Additionally, Erwin Rauch, affiliated with the Free University of Bozen-Bolzano in Italy, emerged as the most influential researcher. the analysis also revealed that Germany, Italy, India, and the United States were the countries with the most prolific authors in this field. In conclusion, the research indicates that Industry 4.0 has undergone substantial scientific development and significantly impacted the global manufacturing industry by improving industrial processes. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bibliographies

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Data Mining Approach in Predicting House Price for Automated Property Appraiser systems 6th

Data Mining Approach in Predicting House Price for Automated...

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6th international conference on Innovative computing and Communication, ICICC 2023

作者： Yousir, Naeem th. Abdulameer, Shaymaa Mohammed Mostafa, Salama A. College of Information Engineering Al-Nahrain University Baghdad10072 Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Johor86400 Malaysia

ISBN: (纸本)9789819930098

the house price prediction model can be a logistic tool in assisting individuals and companies to determine the cost of a property or a house on sale and the best time to acquire a house. With the ever-increasing in house price rates, data mining approaches have been used as a property appraiser system to predict a house price based on house features before making a deal. Existing research in house prediction involves collecting a number of house price estimation attributes into a dataset of house price features. this study is aimed at building a model for predicting house price based on the house price features dataset. then dataset is run through machine learning (ML) algorithms to learn the pattern and predict new house price cases. the cross-industry standard process for data mining (CRISP-DM) method is used for this research. Decision tree regression (DTR), linear regression (LR), multiple linear regression (MLR), and random forest regression (RFR) are the ML algorithms applied in this research. the results have shown that RFR is the most suitable algorithm for this dataset, with an average coefficient of determination (R square) of 82.09%, followed by the DTR with an R square score of 73.32% when using different training and learning splitting ratios. An application of the RFR model can be used in real estate companies for estimating the prices of properties. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multiple linear regression

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Predictive Maintenance Using Fouling Detection on Heat Exchangers 34

Predictive Maintenance Using Fouling Detection on Heat Excha...

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34th Annual international conference on Collaborative Advances in Software and computing, CASCON 2024

作者： Strzelczyk, Nicholas Gomez-Rosero, Santiago Capretz, Miriam A.M. Western University Department of Electrical and Computer Engineering LondonONN6A 5B9 Canada

ISBN: (纸本)9798331504830

Predictive maintenance (PdM) in heating, ventilation, and air conditioning (HVAC) systems improves energy efficiency and extends equipment lifespan. However, developing deep learning (DL) models for PdM faces challenges due to limited labeled datasets, particularly for image-based fouling detection in cooling tower heat exchangers. To address this, a synthetic image dataset is introduced, simulating dust and particulate accumulation on heat exchanger grids. the synthetic fouling data approach generates daily fouling growth and blends synthetic dust into real images. Using this dataset, convolutional neural networks (CNNs), including UNet, UNet++, and CGNet, are trained for semantic segmentation to detect fouling levels. the models are evaluated using F1 scores and precision-recall curves. Results demonstrate that the synthetic dataset effectively enables CNNs to detect fouling, with UNet achieving the best balance between accuracy and efficiency. Contributions include the development of the first synthetic dataset tailored for fouling simulation and a trained CNN model that can be integrated into PdM systems. this work addresses data scarcity and advances the use of computer vision in PdM. © 2024 IEEE.

关键词： Semantic Segmentation

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Design and Performance Analysis of an Asynchronous 16×2 DRAM Architecture Using 2T1C DRAM Bitcell and 70.15 μW GDI Based 2 to 4 Decoder 6

Design and Performance Analysis of an Asynchronous 16×2 DRA...

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6th international conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2024

作者： Biswas, Richard Victor Islam Mim, Md. Tanvir Rahman, Md. Shajidur American International University-Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

DRAM is a necessary component for memory-based electronics, and its use is growing constantly as engineers have been trying to meet customer requirements by making portable and low-power gadgets for the last couple of decades. One of the most significant issues is with the power dissipation of circuits which requires a lot of electricity. In this paper, to enhance the performance criteria of such systems, a novel (4×4) DRAM array has been designed using 45 nm technology in cadence tool where a different read or write path has been proposed to improve read and write abilities. Among different logic techniques, Gate-Diffusion Input (GDI) has been found to have the best performance matrices. In this regard, two 2 to 4 decoders have been constructed utilizing GDI technology which offers a propagation delay of 0.034ns, speed power product of 0.0024 pJ, and slew rate of 208.85 GV/s, to perform read or write operation in a particular location of the memory array. To evaluate, the schematic of proposed the 16×2 DRAM Architecture has been first simulated by setting up proper analog stimuli for 160 ns time frame, resulting in only 974.98 μW power dissipation. A clean physical layout of the model has also been built in accordance with the rules of Design Rule Check (DRC), Electrical Rule Check (ERC), and Layout vs. Schematic (LVS). the proposed model has also been verified by PVT corner analysis and Monte Carlo simulation to examine the uncertainty that can affect the operation and performance in temperature variation, ranging from -40°C and 125°C, in a full custom chip. though compared to conventional DRAM cells, the suggested DRAM cell shows improved write and read margins, the designed layout has some blank space inside the PR boundary. © 2024 IEEE.

关键词： Monte Carlo methods

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FLID: Intrusion Attack and Defense Mechanism for Federated Learning Empowered Connected Autonomous Vehicles (CAVs) Application 6

FLID: Intrusion Attack and Defense Mechanism for Federated L...

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6th IEEE conference on Dependable and Secure computing, DSC 2023

作者： Hossain, Md Zarif Imteaj, Ahmed Zaman, Saika Shahid, Abdur R. Talukder, Sajedul Amini, M. Hadi CarbondaleIL62901 United States Southern Illinois University School of Computing CarbondaleIL62901 United States Florida International University Knight Foundation School of Computing and Information Sciences MiamiFL33199 United States

ISBN: (纸本)9798350382112

Connected autonomous vehicles (CAVs) are transforming the transportation business by incorporating advanced technology such as sensors, communication systems, and artificial intelligence. However, the interconnectedness and complexity of CAVs pose security vulnerabilities, making them possible targets for assaults. Intrusion detection is critical in protecting CAVs from harmful actions. this research investigates the use of federated learning, a privacy-preserving machine learning approach, for intrusion detection in CAVs. Federated Learning (FL) can improve the detection capabilities and robustness of intrusion detection systems in the CAV ecosystem by using the collective capacity of various CAVs while protecting data privacy. this paper provides an in-depth analysis of tailoring FL for collaborative intrusion detection in CAVs, as well as prospective future research areas in this domain. the findings of this study contribute to the advancement of secure and dependable CAV systems, opening the path for the widespread use of connected autonomous vehicles in the transportation industry. All code, data, and experiments are accessible on our Github1 repository.1https://***/speedlab-git/FLID © 2023 IEEE.

关键词： Intrusion detection

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