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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Pardamean, Bens Mathematics Department School of Computer Science Bina Nusantara University 11480 Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

As the dengue infection still impacts hundreds of millions of people globally, unprecedented efforts in dengue drug development have been more progressive in recent decades. Computational methods provide a fast, sustainable, and efficient screening of active compounds and newly created drug molecules, including those specifically targeting nonstructural proteins (NS) of dengue viruses. In this work, protein modeling for the NS proteins of DENV-2/16681 strain was performed using a template-based homology modeling for the NS3 protein and an Artificial Intelligence (AI)-based prediction via AlphaFold for the NS4B protein. Moreover, the protein-protein interaction between the two structures was predicted using the HADDOCK server, which employs information about active and passive residues of the interaction interface to guide the docking process. After the modeling and its respective refinement process, the predicted structures of NS3 and NS4B improved their steric clashing scoring from MolProbity assessment. The refined models were then docked, and the resulting docking pose was analyzed to extract the interacting residues based on the polar contacts within the interface of the two proteins. Our result presents a preliminary study to create a dataset related to in silico molecular interactions of the NS3-NS4B interaction of different DENV types. It is helpful for building a computational pipeline for elucidating protein-ligand problems in dengue drug screenings. © 2024 The Authors.

关键词： Molecular docking

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Adaptive nonsingular integral-type dynamic terminal sliding mode synchronizer for disturbed nonlinear systems and its application to secure communication systems

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JVC/Journal of Vibration and Control 2023年第11-12期29卷 2566-2578页

作者： Vaseghi, Behrouz Mobayen, Saleh Din, Sami ud Hashemi, Seyedeh Somayeh Vu, Mai The Department of Electrical and Computer Engineering Abhar Branch Islamic Azad University Abhar Iran Department of Electrical Engineering University of Zanjan Zanjan Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Electrical Engineering Namal University Mianwali Mianwali Pakistan School of Intelligent Mechatronics Engineering Sejong University Seoul Korea Republic of

This study proposes an adaptive nonsingular integral dynamic terminal sliding mode tracker/synchronizer for disturbed nonlinear systems along with its usage in safe communication systems. The convergence of the closed-loop structure under unknown uncertainty and disturbances is guaranteed via Lyapunov analysis. Furthermore, a parameter-tuning method is planned to approximate the upper bound of uncertainty and disturbance terms, since this latter is typically unknown in practice. The proposed approach is used to design a digital secure transmission scheme according to the chaotic systems. The effectiveness of the suggested approach is validated using computer simulations on a benchmark example of chaotic system. The obtained outcomes clearly confirm the ability of the planned control approach enables to attain the desired tracking/synchronizing performance despite the disturbances. Additionally, when implemented to the data encryption of a communication system, the proposed control and secure communication techniques enabled the complete and secure retrieval of the original digital sequences. © The Author(s) 2022.

关键词： Chaotic systems

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Decoding the Biomimetic Mineralization of Metal-Organic Frameworks in Water

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ACS Nano 2024年第36期18卷 25170-25182页

作者： Lin, Shang-Wei Lam, Phuc Khanh Wu, Chin-Teng Su, Kuan-Hsuan Sung, Chi-Fang Huang, Sen-Ruo Chang, Je-Wei Shih, Orion Yeh, Yi-Qi Vo, Trung Hieu Tsao, Heng-Kwong Hsieh, Haw-Ting Jeng, U-Ser Shieh, Fa-Kuen Yang, Hsiao-Ching Department of Chemistry Fu Jen Catholic University New Taipei City24205 Taiwan Department of Chemistry National Central University Taoyuan32001 Taiwan National Synchrotron Radiation Research Center Hsinchu Science Park Hsinchu300092 Taiwan Department of Chemical and Materials Engineering National Central University Taoyuan32001 Taiwan Department of Electrical Engineering and Computer Sciences University of California at Berkeley CA94720 United States Department of Chemical Engineering College of Semiconductor Research National Tsing Hua University Hsinchu300044 Taiwan

This study unveils the "green" metal-organic framework (MOF) structuring mechanism by decoding proton transfer in water during ZIF-8 synthesis. Combining in situ small- to wide-angle X-ray scattering, multiscale simulations, and quantum calculations, we reveal that the ZIF-8 early-stage nucleation and crystallization process in aqueous solution unfolds in three distinct stages. In stage I, imidazole ligands replace water in zinc-water cages, triggering an "acidity flip" that promotes proton transfer. This leads to the assembly of structures from single zinc ions to 3D amorphous cluster nuclei. In stage II, amorphous nuclei undergo a critical transformation, evolving into crystalline nuclei and subsequently forming mesoscale-ordered structures and crystallites. The process proceeds until the amorphous precursors are completely consumed, with the transformation kinetics governed by an energy barrier that determines the rate-limiting step. In stage III, stable crystallite nanoparticles form in solution, characterized by a temperature-dependent thermal equilibrium of molecular interactions at the crystal-solution interface. Beyond these core advancements, we explore the influence of encapsulated pepsin and nonencapsulated lysozyme on ZIF-8 formation, finding that their amino acid proton transfer capacity and concentration influence the resulting biomolecule-MOF composite’s shape and encapsulation efficiency. The findings contribute to understanding the molecular mechanisms behind biomimetic mineralization and have potential implications for engineering proteins within amorphous MOF nuclei as protein embryo growth sites. © 2024 American Chemical Society.

关键词： X ray scattering

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An Enhanced Authentication Protocol Suitable for Constrained RFID Systems

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IEEE Access 2024年 12卷 61610-61628页

作者： Hosseinzadeh, Mehdi Servati, Mohammad Reza Rahmani, Amir Masoud Safkhani, Masoumeh Lansky, Jan Janoscova, Renata Ahmed, Omed Hassan Tanveer, Jawad Lee, Sang-Woong Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Duy Tan University School of Medicine and Pharmacy Da Nang550000 Viet Nam Shahid Rajaee Teacher Training University Department of Computer Engineering Tehran16788-15811 Iran National Yunlin University of Science and Technology Future Technology Research Center Douliou Yunlin64002 Taiwan School of Computer Science Tehran19395-5746 Iran University of Finance and Administration Faculty of Economic Studies Department of Computer Science and Mathematics Prague101 00 Czech Republic University of Human Development Department of Information Technology Sulaymaniyah0778-6 Iraq Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of Gachon University Pattern Recognition and Machine Learning Laboratory Department of Ai Software Seongnam-si13557 Korea Republic of

RFID technology offers an affordable and user-friendly solution for contactless identification of objects and individuals. However, the widespread adoption of RFID systems raises concerns regarding security and privacy. Vulnerabilities such as message tampering, interception, and eavesdropping pose significant risks to the integrity of the system. This study examines the effectiveness of two recently proposed ultra-lightweight RFID authentication protocols, URASP and KUAJB, in addressing these security challenges. Surprisingly, our findings reveal that both URASP and KUAJB protocols are susceptible to secret disclosure attacks, despite their claims of providing robust security. Consequently, the development of a truly ultra-lightweight protocol that ensures adequate security becomes a formidable task. As a potential solution, the enhancement of the URASP protocol through the integration of a simple cryptographic primitive is suggested to bolster its security measures. © 2013 IEEE.

关键词： Hash functions

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Sand Cat Swarm Optimization with Deep Transfer Learning for Skin Cancer Classification

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computer Systems science & engineering 2023年第11期47卷 2079-2095页

作者： C.S.S.Anupama Saud Yonbawi G.Jose Moses E.Laxmi Lydia Seifedine Kadry Jungeun Kim Department of Electronics and Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Skin cancer is one of the most dangerous *** of the high melanoma death rate,skin cancer is divided into non-melanoma and *** dermatologist finds it difficult to identify skin cancer from dermoscopy images of skin ***,pathology and biopsy examinations are required for cancer *** studies have formulated computer-based systems for detecting skin cancer from skin lesion *** recent advancements in hardware and software technologies,deep learning(DL)has developed as a potential technique for feature ***,this study develops a new sand cat swarm optimization with a deep transfer learning method for skin cancer detection and classification(SCSODTL-SCC)*** major intention of the SCSODTL-SCC model lies in the recognition and classification of different types of skin cancer on dermoscopic ***,Dull razor approach-related hair removal and median filtering-based noise elimination are ***,the U2Net segmentation approach is employed for detecting infected lesion regions in dermoscopic ***,the NASNetLarge-based feature extractor with a hybrid deep belief network(DBN)model is used for ***,the classification performance can be improved by the SCSO algorithm for the hyperparameter tuning process,showing the novelty of the *** simulation values of the SCSODTL-SCC model are scrutinized on the benchmark skin lesion *** comparative results assured that the SCSODTL-SCC model had shown maximum skin cancer classification performance in different measures.

关键词： Deep learning skin cancer dermoscopic images sand cat swarm optimization machine learning

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Transforming Hand Drawn Wireframes into Front-End Code with Deep Learning

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computers, Materials & Continua 2022年第9期72卷 4303-4321页

作者： Saman Riaz Ali Arshad Shahab S.Band Amir Mosavi Department of Computer Science National University of TechnologyIslamabad44000Pakistan Department of Computer Science Institute of Space TechnologyIslamabad44000Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliu64002YunlinTaiwan Faculty of Civil Engineering Technische Universitat DresdenDresden 01069Germany

The way towards generating a website front end involves a designersettling on an idea for what kind of layout they want the website to have, thenproceeding to plan and implement each aspect one by one until they haveconverted what they initially laid out into its Html front end form, this processcan take a considerable time, especially considering the first draft of the designis traditionally never the final one. This process can take up a large amountof resource real estate, and as we have laid out in this paper, by using a Modelconsisting of various Neural Networks trained on a custom dataset. It can beautomated into assisting designers, allowing them to focus on the other morecomplicated parts of the system they are designing by quickly generating whatwould rather be straightforward busywork. Over the past 20 years, the boomin how much the internet is used and the sheer volume of pages on it demands ahigh level of work and time to create them. For the efficiency of the process, weproposed a multi-model-based architecture on image captioning, consisting ofConvolutional neural network (CNN) and Long short-term memory (LSTM)models. Our proposed approach trained on our custom-made database can beautomated into assisting designers, allowing them to focus on the other morecomplicated part of the system. We trained our model in several batches overa custom-made dataset consisting of over 6300 files and were finally able toachieve a Bilingual Evaluation Understudy (BLEU) score for a batch of 50hand-drawn images at 87.86%.

关键词： Deep learning wireframes front-end low fidelity high fidelity design process html computer vision dsl

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Sockpuppets Detection Using Markov-Driven Graph Convolutional Networks with Ensemble Learning 7

Sockpuppets Detection Using Markov-Driven Graph Convolutiona...

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7th IEEE Conference on Dependable and Secure Computing, DSC 2024

作者： Fan, Chun-I Tsai, Ming-Feng Hu, Chun-Pin Wang, Ming-Hung National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan Information Security Research Center National Sun Yat-sen University Kaohsiung804 Taiwan Intelligent Electronic Commerce Research Center National Sun Yat-sen University Kaohsiung804 Taiwan National Chung Cheng University Departrment of Computer Science and Information Engineering Chiayi621 Taiwan

ISBN: (纸本)9798331540289

This paper introduces a novel framework for detecting sockpuppets in online social networks, utilizing an advanced approach that integrates Markov-driven Graph Convolutional Networks (MDGCN) with ensemble learning techniques. Our method capitalizes on the data inherent in social networks by applying GCNs to capture and learn from intricate user interactions. To address the class imbalance problem often encountered in sockpuppet detection, we incorporate Focal Loss into our training regimen, enhancing the model's capability to identify less frequent, harder-to-detect instances of false information spreaders. Additionally, we implement ensemble learning strategies to refine and bolster the predictive power of our model. Through extensive testing on multiple social network platform datasets, our proposed method demonstrates superior performance in identifying sockpuppets compared to existing techniques. This underscores its potential as a robust solution for safeguarding against the rapid spread of false information online. © 2024 IEEE.

关键词： Markov processes

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Reconciling constraints from the supernova remnant HESS J1731-347 with the parity doublet model

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Physical Review C 2024年第6期109卷 065807-065807页

作者： Bikai Gao Yan Yan Masayasu Harada Department of Physics School of Microelectronics and Control Engineering Kobayashi-Maskawa Institute for the Origin of Particles and the Universe Advanced Science Research Center

The recent discovery of a central compact object (CCO) within the supernova remnant HESS J1731-347, characterized by a mass of approximately 0.77−0.17+0.20 M⊙ and a radius of about 10.4−0.78+0.86 km, has opened up a new window for the study of compact objects. This CCO is particularly intriguing because it is the lightest and smallest compact object ever observed, raising questions and challenging the existing theories. To account for this light compact star, a mean-field model within the framework of the parity doublet structure is applied to describe the hadron matter. Inside the model, part of the nucleon mass is associated with the chiral symmetry breaking while the other part is from the chiral invariant mass m0 which is insensitive to the temperature and density. The value of m0 affects the nuclear equation of state for uniform nuclear matter at low density and exhibits strong correlations with the radii of neutron stars. We point out that HESS J1731-347 can be explained as the lightest neutron star for m0≃850 MeV.

关键词： Asymmetric nuclear matter Equations of state of nuclear matter Nuclear matter in neutron stars Nuclear physics of explosive environments Chiral symmetry

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Reviewing metal supported solid oxide fuel cells for efficient electricity generation with biofuels for mobility

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Journal of Energy Chemistry 2025年第4期103卷 106-153页

作者： Fábio C.Antunes João P.J.de Oliveira Ricardo S.de Abreu Thiago Dias Bruno B.N.S.Brandão Josué M.Gonçalves Josimar Ribeiro Julian Hunt Hudson Zanin Gustavo Doubek School of Electrical and Computer Engineering University of CampinasCampinas 13083-852Brazil School of Food Engineering University of CampinasCampinas 13083-852Brazil School of Chemical Engineering University of CampinasCampinas 13083-852Brazil Mackenzie Institute for Research in Graphene and Nanotechnologies(MackGraphe) Mackenzie Presbyterian InstituteSão Paulo 01302-907Brazil Electrochemistry Research and Development Laboratory(LPDE) Department of ChemistryCenter of Exact SciencesFederal University of Espírito SantoCampus GoiabeirasVitória 29075-910Brazil Biological and Environmental Science and Engineering Division King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia

Metal-Supported Solid Oxide Fuel Cells(MS-SOFCs)hold significant potential for driving the energy *** electrochemical devices represent the most advanced generation of Solid Oxide Fuel Cell(SOFCs)and can pave the way for mass production and wider adoption than Proton Exchange Membrane Fuel Cells(PEMFCs)due to their fuel flexibility,higher power density and the absence of noble metals in the fabrication *** review examines the state-of-the-art of SOFCs and MS-SOFCs,presenting perspectives and research directions for these key technological devices,highlighting novel materials,techniques,architectures,devices,and degradation mechanisms to address current challenges and future *** such as infiltration/impregnation,ex-solution catalyst synthesis,and the use of a pre-catalytic reformer layer are discussed as their impact on efficiency and prolonged *** concepts are also described and connected with well-dispersed nano particles,hindrance of coarsening,and an increased number of Triple Phase Boundaries(TPBs).This review also describes the synergistic use of reformers with MS-SOFCs to compose solutions in energy generation from readily available ***,the End-of-Life(EoL),recycling,and life-cycle assessments(LCAs)of the Fuel Cell Hybrid Electric Vehicles(FCHEVs)were *** comparing Fuel Cell Electric Vehicles(FCEVs)equipped with(PEMFCs)and FCHEVs equipped with MS-SOFCs,both powered with hydrogen(H_(2))generated by different routes were *** review aims to provide valuable insights into these key technological devices,emphasizing the importance of robust research and development to enhance performance and lifespan while reducing costs and environmental impact.

关键词： Reform Metal-supported solid oxide fuel cell Powertrain systems Fuel cell Hybrid electric vehicle Biofuel

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Synergizing Deep Belief Networks and Multi-Layer Perceptrons for Advanced Facial Expression Identification: A Leap in Deep Learning Applications 9

Synergizing Deep Belief Networks and Multi-Layer Perceptrons...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Sharada, K. Anandhan, K. Jeevanandam, Prabahar Mohanavel, V. Naik, B. Ravi Kannan, Sathish Department of Computer Science And Engineering Visakhapatnam India Gitam University Department of Computer Science And Engineering Bengaluru India Park College of Engineering And Technology Department of Mechanical Engineering Coimbatore Kaniyur641659 India Bharath Institute of Higher Education And Research Centre For Materials Engineering And Regenerative Medicine Tamil Nadu Chennai600073 India Chandigarh University Department of Mechanical Engineering Punjab Mohali140413 India St. Martin's Engineering College Department of Mechanical Engineering Telangana Secunderabad India Saveetha Institute of Medical And Techinical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamilnadu Chennai602105 India

ISBN: (纸本)9798350394474

Deep learning, a relatively recent breakthrough in machine learning, has ignited immense interest and applications across diverse fields, including but not limited to signal processing, computer vision and pattern recognition. Within this realm, deep belief networks (DBNs) stand out for their remarkable capacity for unsupervised feature learning. In our research, we introduce an innovative deep learning-based approach for the task of facial expression identification. This approach involves the integration of DBNs with a multi-layer perceptron (MLP), thereby leveraging the complementary strengths of these two neural network architectures. DBNs excel in autonomously extracting meaningful features from raw data, making them invaluable for tasks where feature engineering can be a bottleneck. On the other hand, MLPs are renowned for their ability to perform complex classification tasks effectively. The unsupervised feature learning capabilities of DBNs and the classification ability of MLPs are combined in one single framework. The experimental findings we obtained show that the suggested strategy outperforms other contemporary classification strategies. MLP, sparse representation, nearest subspace, Closest neighbor, and support vector machine were some of the other categorization methods we compared ours to. Our combined method achieved superior results in both scenarios, demonstrating its promise as a reliable and efficient approach to face emotion recognition. In especially for human emotion analysis and computer vision tasks, this study offers a major step forward in the realm of deep learning applications. © 2024 IEEE.

关键词： Multilayer neural networks

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