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2023 International Conference on computer science, Information Technology and engineering, ICCoSITE 2023

作者： Sidharta, Sidharta Heryadi, Yaya Lukas, Lukas Suparta, Wayan Arifin, Yulyani Bina Nusantara University BINUS Graduated Program Computer Science Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Currently, the performance of the police in Indonesia is often in the spotlight of the public with cases that occur, both on a national and regional scale, including personal experiences who also feel disappointed with the performance of the police. The cases, such as the rape of three children by a biological father in East Luwu, and the barrage of other cases that have occurred, in their handling have left the public disappointed, giving rise to the hashtag percumlaporpolisi on social media, especially on twitter. This hashtag became trending and was widely responded to by the public. Using free hashtag keywords, tweet data is collected using Twitter's API for sentiment analysis. The datasets obtained from the crawling results is the latest data with a total of 100 datasets. This study applies the naïve bayes algorithm using textblob which aims to determine the value of polarity and subjectivity. From the results of the analysis carried out, the words that appear most often are useless police, police chief, and policeman. While the results of the sentiment analysis that has been carried out are 40 percent positive sentiment, 40 percent are neutral sentiment and 10 percent are negative sentiment from the hashtags used. From these results, according to the keywords used, the percentage of negative sentiment should be greater than positive and neutral sentiment. It is necessary to conduct a more in-depth study related to the model used in the next research. © 2023 IEEE.

关键词： Sentiment analysis

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Thermal Ablation Therapy Control with Tissue Necrosis-driven Temperature Feedback Enabled by Neural State Space Model with Extended Kalman Filter

Thermal Ablation Therapy Control with Tissue Necrosis-driven...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Ryo Murakami Satoshi Mori Haichong K. Zhang Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA The United States Department of Biomedical Engineering Robotics Engineering and Computer Science Engineering Worcester Polytechnic Institute Worcester MA The United States

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Thermal ablation therapy is a major minimally invasive treatment. One of the challenges is that the targeted region and therapeutic progression are often invisible to clinicians, requiring feedback provided in numerical information or imaging. Several emerging imaging modalities offer visualization of the ablation-induced necrosis formation; however, relying solely on necrosis monitoring can result in tissue overheating and endangering patients. Some of the necrosis monitoring modalities are known for their capabilities in temperature sensing, but the principles on which they are based have several limitations, such as sensitivity to the tissue motion and their environment. In this study, we propose a necrosis progression-based temperature estimation technique as an added safety feature for avoiding overheating. This model-based method does not require additional sensing hardware. It is designed to work as an independent estimator or a complimentary estimation component with other thermometers for improved robustness. For this objective, the Neural State Space model is used to approximate the ablation therapy, whose theoretical models involve nonlinear partial differential equations. Then, the Extended Kalman Filter is designed based on the model. The simulation study shows the estimation module robustly estimates the tissue temperature under several types of noise. The maximum estimation error observed before terminating ablation was around 1 °C, and the desired safety feature was successfully demonstrated. The estimator is expected to be used in a variety of necrosis monitoring modalities to guarantee more precise and safer treatment. More ambitiously, the architecture with the Neural State Space model and Extended Kalman Filter is generalizable to other medical/biological procedures involving nonlinear and patient/environment-specific physics and even to procedures having no reliable theoretical models.

关键词： Thermometers Estimation Medical treatment Mathematical models Robustness Sensors Safety Temperature control Kalman filters Monitoring

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Lightweight Authenticated Key Agreement Protocol for Smart Power Grid Systems Using PUF

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

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Synthetic Batik Pattern Generator Using Wasserstein Generative Adversarial Network with Gradient Penalty 6

Synthetic Batik Pattern Generator Using Wasserstein Generati...

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6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Andriadi, Kus Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Yogyakarta55281 Indonesia

ISBN: (纸本)9798350358346

Batik is an Indonesian world cultural heritage. Batik consists of many kinds of patterns depending on where the batik comes from, Batik-making techniques continue to develop along with technology development. Among the batik making techniques that are widely used are hand-written, stamping, and printing. Batik motifs have been widely used as research material, especially in the field of artificial intelligence. The diverse appearance of batik motifs has attracted many researchers to carry out research on making synthetic batik patterns, one of which uses a Generative Adversarial Network. This paper presents a synthetic batik pattern model based on the Wasserstein Generative Adversarial Network with Gradient Penalty. This model has been proven to create new synthetic batik patterns quite well and almost identical with images provided in the dataset, with the notes if the dataset provided is large. © 2023 IEEE.

关键词： Generative adversarial networks

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Detection of Gender and Age Category from Speech

Detection of Gender and Age Category from Speech

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2023 World Symposium on Digital Intelligence for Systems and Machines, DISA 2023

作者： Haluška, Renát Popovič, Michal Pleva, Matus Frohman, Matthew Department of Electronics and Multimedia Communications Faculty of Electrical Engineering and Informatics Technical University Kosice Kosice Slovakia Perception and Robotics Group Department of computer science University of Maryland College ParkMD United States

ISBN: (纸本)9798350343533

The main goal of this paper was to find out how the gender and age group acoustical models behave on audio data that is in no way related to the data corpora used to train and evaluate the models. These models could be used for early predator detection in minors’ audio chats. The datasets used for training were mainly composed of English speakers. One part was made up of speakers whose native language is English and the rest use English as a secondary language. © 2023 IEEE.

关键词： Electronics industry

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Dermatological Diagnostics: A Unified Deep Learning Framework for Skin Lesion and Cancer Classification 1

Dermatological Diagnostics: A Unified Deep Learning Framewor...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kumar, Vinod Jain, Isha Dongla, Durga Sritha Rai, Shivangi Rastogi, Vani Kaushik, Eva Kaushik, Rohit George Washington University School of Engineering and Applied Science WashingtonDC United States Dr. Akhilesh Das Gupta Institute of Technology and Management Computer Science and Engineering Department New Delhi India Gandhi Institute of Technology and Management Computer Science and Engineering Department Andhra Pradesh Visakhapatnam India Data Scientist DXC Technology Noida India Data Analytics Program University of Illinois CS and Mathematics Department Springfield United States

ISBN: (纸本)9798350356816

Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) for the classification of dermatological anomalies, benchmarking CNN diagnostic fidelity against dermatological expert evaluations. The study underscores the efficacy of CNN classifiers in expediting the diagnostic workflow for various cutaneous disorders. Advocating for an automated diagnostic framework, the research introduces a CNN-based system aimed at reducing human diagnostic load, accelerating diagnostic timelines, and enhancing survival outcomes. Utilizing advanced image processing algorithms and deep learning architectures, the research presents an automated classification system for skin pathologies, addressing both benign and malignant presentations. The classification matrix includes nine dermatological conditions: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, and vascular lesions. The objective is to engineer a CNN model with robust diagnostic performance across a diverse lesion dataset, ensuring accurate identification and categorization of dermatological conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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RiskMap: A Unified Driving Context Representation for Autonomous Motion Planning in Urban Driving Environment

RiskMap: A Unified Driving Context Representation for Autono...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Xin, Ren Wang, Sheng Chen, Yingbing Cheng, Jie Liu, Ming Ma, Jun The Hong Kong University of Science and Technology Division of Emerging Interdisciplinary Areas Hong Kong Hong Kong Robotics and Autonomous Systems Thrust Guangzhou511453 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong

ISBN: (纸本)9781665481090

Motion planning is a complicated task that requires the combination of perception, map information integration and prediction, particularly when driving in heavy traffic. Developing an extensible and efficient representation that visualizes sensor noise and provides basis to real-time planning tasks is desirable. We aim to develop an interpretable map representation, which offers prior of driving cost in planning tasks. In this way, we can simplify the planning process for dealing with complex driving scenarios and visualize sensor noise. Specifically, we propose a unified context representation empowered by deep neural networks. The unified representation is a differentiable risk field, which is an analytical representation of statistical cognition regarding traffic participants for downstream planning tasks. This representation method is nominated as RiskMap. A sampling-based planner is adopted to train and compare RiskMap generation methods. In this paper, the RiskMap generation tools and model structures are explored, the results illustrate that our method can improve driving safety and smoothness, and the limitation of our method is also discussed. © 2024 IEEE.

关键词： Motion planning

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A Novel Hybrid Framework for Energy-Efficient Clustering and Routing in Heterogeneous IoT-Driven Wireless Sensor Networks 7

A Novel Hybrid Framework for Energy-Efficient Clustering and...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Gupta, Gopal Kumar Jain, Arpit Sharma, Khemraj Ashok Kumar, P.M. Gupta, Shubhi Agarwal, Sheenu Nagpur Campus Pune440008 India Koneru Lakshmaiah Education Foundation Department of Computer Science Engineering AP Vaddeswaram522502 India Nanhua University Program in Management Sciences Department of Business Administration Dalin Chiayi62248 Taiwan Amity University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Uttarakhand Technical University Department of Electronics and Communication Engineering U.K. Dehradun India

ISBN: (纸本)9798350350067

Smart computing has been particularly notable in the development of wireless sensor networks (WSNs), which have many applications. Battery-powered, self-configuring sensor nodes are the basis of these networks. Energy and resources are limited when it comes to sensors. Unbalanced nodes in the network consume more power, which adversely affects the network's lifespan. An improved energy-efficient cluster-based routing protocol for heterogeneous wireless sensor networks (WSNs) based on the Internet of Things is proposed in this study. Several performance metrics were used to evaluate the proposed model's effectiveness, including energy efficiency, alive nodes, dead nodes, network lifetime, and residual energy. The results of the comparison were compared with existing methodologies, such as LEACH, PSO, and hybrid PSO. The proposed approach outperforms the existing model by a substantial margin in networks with 50 and 100 nodes, according to the simulation results. © 2024 IEEE.

关键词： Sensor nodes

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3D Image Generation from X-ray Projections Using Generative Adversarial Networks 23

3D Image Generation from X-ray Projections Using Generative ...

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23rd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2023

作者： AlShurbaji, Mohammad Dhou, Salam Biomedical Engineering Graduate Program American University of Sharjah Sharjah United Arab Emirates Department of Computer Science and Engineering American University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350393118

The human brain can effortlessly imagine a 3D image from only 2D images with a little expertise and imagination, but for machines, this is not a trivial task. Because of this, reconstructing 3D images from 2D ones is a hot topic and has many applications. In this paper, we propose a Generative Adversarial Network (GAN)-based approach that generates CT-like images using pairs of orthogonal X-ray projections taken from different angles. In this work, a variety of orthogonal pairs from different angles, ranging from 0 &90 to 60 &150, were considered as input to the 3D image generation model. The effectiveness of the proposed method was assessed by measuring the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), which resulted in values of 0.641 and 29.21, respectively. Furthermore, the model's ability to capture the respiratory motion in the input projections and reflect it in the generated images was also assessed. This work demonstrated the feasibility of generating CT-like images from X-ray projections captured from different orthogonal angles taking into consideration the respiratory motion exhibited in these projections. © 2023 IEEE.

关键词： Generative adversarial networks

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Regulation of regeneration in Arabidopsis thaliana

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aBIOTECH 2023年第4期4卷 332-351页

作者： Md Khairul Islam Sai Teja Mummadi Sanzhen Liu Hairong Wei Computational Science and Engineering Program Michigan Technological UniversityHoughtonMI 49931USA Computer Science Michigan Technological UniversityHoughtonMI 49931USA Department of Plant Pathology Kansas State UniversityManhattanKS 66506USA College of Forest Resources and Environmental Science Michigan Technological UniversityHoughtonMI 49931USA

We employed several algorithms with high efficacy to analyze the public transcriptomic data,aiming to identify key transcription factors(TFs)that regulate regeneration in Arabidopsis ***,we utilized CollaborativeNet,also known as TF-Cluster,to construct a collaborative network of all TFs,which was subsequently decomposed into many subnetworks using the Triple-Link and Compound Spring Embedder(CoSE)*** analysis of these subnetworks led to the identification of nine subnetworks closely associated with *** further applied principal component analysis and gene ontology(GO)enrichment analysis to reduce the subnetworks from nine to three,namely subnetworks 1,12,and *** for TF-binding sites in the promoters of the co-expressed and co-regulated(CCGs)genes of all TFs in these three subnetworks and Triple-Gene Mutual Interaction analysis of TFs in these three subnetworks with the CCGs involved in regeneration enabled us to rank the TFs in each ***,six potential candidate TFs-WOx9A,LEC2,PGA37,WIP5,PEI1,and AIL1 from subnetwork 1-were identified,and their roles in somatic embryogenesis(GO:0010262)and regeneration(GO:0031099)were discussed,so were the TFs in Subnetwork 12 and 17 associated with *** TFs identified were also assessed using the CIS-BP database and Expression *** analyses suggest some novel TFs that may have regulatory roles in regeneration and embryogenesis and provide valuable data and insights into the regulatory mechanisms related to *** tools and the procedures used here are instrumental for analyzing high-throughput transcriptomic data and advancing our understanding of the regulation of various biological processes of interest.

关键词： Regeneration Pluripotency Transcription factor Collaborative network

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