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IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with Deep Learning

Computers, Materials & Continua 2023年第2期74卷 3085-3100页

作者： Saeed Masoud Alshahrani Fatma S.Alrayes Hamed Alqahtani Jaber S.Alzahrani Mohammed Maray Sana Alazwari Mohamed A.Shamseldin Mesfer Al Duhayyim Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Nowadays,Internet of Things(IoT)has penetrated all facets of human life while on the other hand,IoT devices are heavily prone to *** has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security *** is one of the dreadfulmalicious entities that has affected many users for the past few *** is challenging to recognize Botnet since it has excellent carrying and hidden *** approaches have been employed to identify the source of Botnet at earlier *** Learning(ML)and Deep Learning(DL)techniques are developed based on heavy influence from Botnet detection *** spite of this,it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet *** current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizingRat SwarmOptimizer with Deep Learning(BDC-RSODL)*** presented BDC-RSODL model includes a series of processes like pre-processing,feature subset selection,classification,and parameter ***,the network data is pre-processed to make it compatible for further ***,RSO algorithm is exploited for effective selection of subset of ***,Long Short TermMemory(LSTM)algorithm is utilized for both identification and classification of ***,Sine Cosine Algorithm(SCA)is executed for fine-tuning the hyperparameters related to LSTM *** order to validate the promising 3086 CMC,2023,vol.74,no.2 performance of BDC-RSODL system,a comprehensive comparison analysis was *** obtained results confirmed the supremacy of BDCRSODL model over recent approaches.

关键词： Internet of things cloud computing long short termmemory deep learning sine cosine algorithm feature selection

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Sweat colorimetric detection system based on OpenMV 5

Sweat colorimetric detection system based on OpenMV

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5th International Conference on Machine Learning and Computer Application, ICMLCA 2024

作者： Meng, Yuanyuan Chen, Yanxia Li, Chennong Liu, Yandi Qin, Lei Beijing Information Science & Technology University Beijing Sensor Key Laboratory Beijing100101 China College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150006 China

ISBN: (纸本)9798331530334

The demand for real-time sweat biosensing is increasing in daily health monitoring. Colorimetric detection, as a simple and intuitive method, plays an important role in sweat biosensing. However, achieving an instant and effective colorimetric detection remains a challenge in real-time sweat biosensing for daily health monitoring. Herein, we design a sweat colorimetric detection system based on OpenMV. The colorimetric detection system is capable of sensitively capturing images and quickly recognizing color changes. The colorimetric detection system based on OpenMV provides a simple and effective technology to realize real-time sweat detection and shows great potential as a colorimetric detection system platform for color recognition and image processing. We believe that our colorimetric detection system will have great potential applications and offer effective means for the design of smart biosensors and have great potential in the development of portable and intelligent sweat analysis equipment. © 2024 IEEE.

关键词： Color image processing

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Discriminative feature encoding for intrinsic image decomposition

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Computational Visual Media 2023年第3期9卷 597-618页

作者： Zongji Wang Yunfei Liu Feng Lu Key Laboratory of Network Information System Technology(NIST) Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100190China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peng Cheng Laboratory Shenzhen 518000China

Intrinsic image decomposition is an important and long-standing computer vision *** an input image,recovering the physical scene properties is *** physically motivated priors have been used to restrict the solution space of the optimization problem for intrinsic image *** work takes advantage of deep learning,and shows that it can solve this challenging computer vision problem with high *** focus lies in the feature encoding phase to extract discriminative features for different intrinsic layers from an input *** achieve this goal,we explore the distinctive characteristics of different intrinsic components in the high-dimensional feature embedding *** define feature distribution divergence to efficiently separate the feature vectors of different intrinsic *** feature distributions are also constrained to fit the real ones through a feature distribution *** addition,a data refinement approach is provided to remove data inconsistency from the Sintel dataset,making it more suitable for intrinsic image *** method is also extended to intrinsic video decomposition based on pixel-wise correspondences between adjacent *** results indicate that our proposed network structure can outperform the existing state-of-the-art.

关键词： intrinsic image decomposition deep learning feature distribution data refinement

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Targeted regeneration and upcycling of spent graphite by defect‐driven tin nucleation

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Carbon Energy 2024年第4期6卷 91-103页

作者： Zhiheng Cheng Zhiling Luo Hao Zhang Wuxing Zhang Wang Gao Yang Zhang Long Qie Yonggang Yao Yunhui Huang Kun Kelvin Fu State Key Laboratory of Materials Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhanChina Key Laboratory of Automobile Materials(Jilin University) Ministry of EducationDepartment of Materials Science and EngineeringJilin UniversityChangchunChina Electronic Materials Research Laboratory(Key Lab of Education Ministry) School of Electronic and Information Engineeringand Instrumental Analysis CenterXi'an Jiaotong UniversityXi'anChina Department of Mechanical Engineering University of DelawareNewarkUSA

The recycling of spent batteries has become increasingly important owing to their wide applications,abundant raw material supply,and sustainable *** with the degraded cathode,spent anode graphite often has a relatively intact structure with few defects after long ***,most spent graphite is simply burned or discarded due to its limited value and inferior performance on using conventional recycling methods that are complex,have low efficiency,and fail in performance ***,we propose a fast,efficient,and“intelligent”strategy to regenerate and upcycle spent graphite based on defect‐driven targeted *** Sn as a nanoscale healant,we used rapid heating(~50 ms)to enable dynamic Sn droplets to automatically nucleate around the surface defects on the graphite upon cooling owing to strong binding to the defects(~5.84 eV/atom),thus simultaneously achieving Sn dispersion and graphite *** a result,the regenerated graphite showed enhanced capacity and cycle stability(458.9 mAh g^(−1) at 0.2 A g^(−1) after 100 cycles),superior to those of commercial *** from the self‐adaption of Sn dispersion,spent graphite with different degrees of defects can be regenerated to similar structures and *** analysis indicates that targeted regeneration and upcycling have significantly lower energy consumption(~99%reduction)and near‐zero CO_(2) emission,and yield much higher profit than hydrometallurgy,which opens a new avenue for direct upcycling of spend graphite in an efficient,green,and profitable manner for sustainable battery manufacture.

关键词： battery recycling spent graphite targeted regeneration upcycling graphite

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Structural engineering of vertically aligned nanocomposite films fabricated via magnetron and pulsed laser co-deposition for microwave application

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Journal of Materials science & technology 2024年第30期197卷 94-101页

作者： Changliang Li Yong Sun Xiuhong Dai Yinglong Wang Taifu Feng Jianxin Guo Baoting Liu Xiaobing Yan Hebei Provincial Key Lab of Optoelectronic Information Materials College of Physics Science and TechnologyHebei UniversityBaoding 071002China Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province College of Physics Science and Technology Hebei University Baoding 071002China Department of Primary Education Baoding Preschool Teachers CollegeBaoding 072750China

Microwaves are often used in communication engineering,including in microwave relays as well as mul-tichannel,mobile,and satellite *** meet the demand for a“small,light,thin,and precise”modern electronic machine,developing highly tunable microwave materials to manufacture miniaturized microwave devices has become a top *** materials are excellent microwave materi-als,showing outstanding dielectric properties and *** low dielectric loss oxides in the construction of vertical self-assembly nanocomposites(VSNs)effectively improves their tunability and reduces dielectric ***,the optimal doping content of simple VSN films limits further improve-ment of ***,we propose a strategy-gradient VSN to achieve combined improvement in tunability and dielectric constant by designing a sputtering and pulsed laser *** resulting VSN composite film has a dielectric constant of 428.08,a dielectric loss of 0.0212,and a tunability of 78.69%.Our results contribute to the development of filters and have broad application prospects in the microwave industry.

关键词： 1–3-type composite film Heteroepitaxial nanocomposite film Dielectric

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Endoscopic Bladder Tissue Classification Using Seventeen Layered Deep Convolutional Neural Network 2

Endoscopic Bladder Tissue Classification Using Seventeen Lay...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Devi, M. Shyamala Pandian, J. Arun Umanandhini, D. Viknesh, V. Vishal, G.P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Computer Science & Engineering Tamilnadu Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350370188

After a diagnosis of bladder cancer, endoscopic surgery is carried out as it is thought to be the most efficient and appropriate method of treating superficial tumors. It is essential to classify the bladder tissues accurately using MRI since early diagnosis of the problem can aid in treatment and perhaps save lives. Early-stage diagnosis is still challenging due to the delayed onset of indications and tiny alterations in urinary tract tissue that are imperceptible to the naked eye for diagnosis. The automated approach might be utilized to automatically determine the type of endoscopic bladder tissue by using the proper deep learning model with the necessary convolutional layers. This paper suggests the Seventeen Layered Deep Convolutional Neural Network (17-DCNN) to classify the bladder tissue classes effectively. The Endoscopic bladder tissue Dataset from Kaggle is used for the implementation of DOMN that contains 1785 images with four classes as HGC, LGC, NTL, and NST. The 17-DCNN model's initial phase is an analysis of distribution of bladder tissue data images. After data preparation, the existing CNN like DenseNet121, ResNet152, VGG19Net, InceptionV3Net and proposed 17-DCNN model is chosen by fitting the Endoscopic bladder tissue images. The accuracy of the proposed 17-DCNN was 99.32% when compared to other CNN models after implementation. © 2024 IEEE.

关键词： Convolutional neural networks

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Towards Privacy-Aware Causal Structure Learning in Federated Setting

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IEEE Transactions on Big Data 2023年第6期9卷 1525-1535页

作者： Huang, Jianli Guo, Xianjie Yu, Kui Cao, Fuyuan Liang, Jiye Intelligent Interconnected Systems Laboratory of Anhui Province School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Taiyuan030006 China

Causal structure learning has been extensively studied and widely used in machine learning and various applications. To achieve an ideal performance, existing causal structure learning algorithms often need to centralize a large amount of data from multiple data sources. However, in the privacy-preserving setting, it is impossible to centralize data from all sources and put them together as a single dataset. To preserve data privacy, federated learning as a new learning paradigm has attached much attention in machine learning in recent years. In this paper, we study a privacy-aware causal structure learning problem in the federated setting and propose a novel federated PC (FedPC) algorithm with two new strategies for preserving data privacy without centralizing data. Specifically, we first propose a novel layer-wise aggregation strategy for a seamless adaptation of the PC algorithm into the federated learning paradigm for federated skeleton learning, then we design an effective strategy for learning consistent separation sets for federated edge orientation. The extensive experiments validate that FedPC is effective for causal structure learning in federated learning setting. © 2023 IEEE.

关键词： Big data

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Indirect Vector Control of Linear Induction Motors Using Space Vector Pulse Width Modulation

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Computers, Materials & Continua 2023年第3期74卷 6263-6287页

作者： Arjmand Khaliq Syed Abdul Rahman Kashif Fahad Ahmad Muhammad Anwar Qaisar Shaheen Rizwan Akhtar Muhammad Arif Shah Abdelzahir Abdelmaboud Electrical Engineering Department University of Engineering and TechnologyLahore54700Pakistan Department of Basic Sciences Deanship of Common First YearJouf UniversitySakakaAljouf72341Saudi Arabia Department of Information Science Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan Sub CampusPakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia

Vector control schemes have recently been used to drive linear induction motors(LIM)in high-performance *** trend promotes the development of precise and efficient control schemes for individual *** research aims to present a novel framework for speed and thrust force control of LIM using space vector pulse width modulation(SVPWM)*** framework under consideration is developed in four *** begin,MATlab Simulink was used to develop a detailed mathematical and electromechanical *** research presents a modified SVPWM inverter control *** tuning the proportional-integral(PI)controller with a transfer function,optimized values for the PI controller are *** the subsystems mentioned above are integrated to create a robust simulation of the LIM’s precise speed and thrust force control *** reference speed values were chosen to evaluate the performance of the respective system,and the developed system’s response was verified using various data *** the low-speed range,a reference value of 10m/s is used,while a reference value of 100 m/s is used for the high-speed *** speed output response indicates that themotor reached reference speed in amatter of seconds,as the delay time is between 8 and 10 *** maximum amplitude of thrust achieved is less than 400N,demonstrating the controller’s capability to control a high-speed LIM with minimal thrust *** to the controlled speed range,the developed system is highly recommended for low-speed and high-speed and heavy-duty traction applications.

关键词： Space vector pulse width modulation linear induction motor proportional-integral controller indirect vector control electromechanical dynamic modeling

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Deep Q-Network Based Reinforcement Learning for Bitcoin Future Price Prediction 2

Deep Q-Network Based Reinforcement Learning for Bitcoin Futu...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Devi, M. Shyamala Pandian, J. Arun Umanandhini, D. Surekha, B. Sree, K. Bhavya Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Computer Science & Engineering Tamilnadu Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350370188

Bitcoin is the very first decentralized digital money in the entire world. Bitcoin is exceptionally reliable since it uses a block chain instead of a service provider like a banking institution. As a type of digital precious metal like gold, the price of a single bitcoin seems to have been significantly increasing since the year 2010. As a result, bitcoin is extremely risky for speculators as its value varies often. Since prices may now be predicted in real time, traditional techniques to price forecasting have failed to provide sufficient information and responses. This paper recommends Deep Q-Network based Reinforcement Learning technique (DQNRL) to predict the bitcoin price more effectively. The Binance Bitcoin Futures Price Dataset from Kaggle that contains 3082284 samples and is used towards the bitcoin future cost prediction for DQNRL. The DQNRL model starts with the data exploration that portray the bitcoin price with the standard deviation and mean, log transformation of data. After data preparation, the Binance Bitcoin Futures Price dataset is fitted with the existing regression technique and proposed DQNRL model and the performance is analyzed with MAE, MSE and RMSE. The proposed DQNRL exhibits low MAE with 0.2471, low MSE with 0.061 and low RMSE of 0.2469 when compared to other existing regression models after implementation. © 2024 IEEE.

关键词： Metadata

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Anti-aging performance improvement and enhanced combustion efficiency of boron via the coating of PDA

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Defence technology（防务技术） 2024年第3期33卷 399-410页

作者： Shuai Ma Qinghai Shu Mengyang Zhang Hongyu Huang Yansong Shi Xijuan Lv Shuai Zhao School of Materials Science and Engineering Beijing Institute of TechnologyBeijing100081China Key Lab of Advanced Optoelectronic Quantum Architecture and Measurement(MOE) Beijing Key Lab of Nanophotonics&Ultrafine Optoelectronic SystemsSchool of PhysicsBeijing Institute of TechnologyBeijing100081China Tangshan Research Institute of Beijing Institute of Technology Tangshan063000HebeiChina

Boron is an ambitious fuel in energetic materials since its high heat release values,but its application is prohibited by low combustion efficiency and oxidization during *** polydopamine(PDA)was introduced into boron particles,investigating the impact of PDA content on the energetic behavior of *** results indicated that the PDA coating formed a fishing net structure on the surface of boron *** heat release results showed that the combustion calorific value of B@PDA was higher than that of the raw ***,the actual combustion heat of boron powder in B@10%PDA increased by 38.08%.Meanwhile,the DSC peak temperature decreased by 100.65℃under similar oxidation rate compared to raw ***,the B@PDA@AP and B@AP composites were prepared,and their combustion properties were *** was demonstrated that B@10%PDA@AP exhibited superior performance in terms of peak pressure and burning time,*** peak pressure is 12.43 kPa more than B@AP and burning time is 2.22 times higher than B@***,the coating of PDA effectively inhibits the oxidization of boron during storage and enhances the energetic behavior of boron and corresponding composites.

关键词： Boron particles Polydopamine Anti-aging performance improvement Heat release

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