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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Singh, Shailendra Pratap Srivastava, Prateek Bennett University School of Computer Science Engineering and Technology UP Greater Noida India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350382723

The paper proposes a new method that combines the advantages of Deep Learning (DL) with the Differential Evolution (DE) algorithm to create a hybrid framework that can solve optimization problems more effectively. The proposed hybrid algorithm enhances the search and optimization procedures by the learning capabilities of DL and the population-based nature of DE algorithm. First, an initial population of solutions is produced by the DE algorithm and put into a DL model for additional refinement. In order to train the DL model - typically a deep neural network - a mix of supervised and unsupervised learning techniques is used. This enables the model to recognize complex relationships and patterns within the challenge space. The combined DE algorithm with DL, as demonstrated by experimental findings for optimum convergence rate and search for addressing complex real-world optimization problems effectively. © 2024 IEEE.

关键词： Optimization

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Critical evaluation of the glucose oxidase-like activity of gold nanoparticles stabilized by different polymers

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Nano Research 2023年第4期16卷 4758-4766页

作者： Lili Xu Jinxing Chen Qian Ma Daiyong Chao Xinyang Zhu Ling Liu Jin Wang Youxing Fang Shaojun Dong State Key Laboratory of Electroanalytical Chemistry Changchun Institute of Applied ChemistryChinese Academy of SciencesChangchun 130022China University of Science and Technology of China Hefei 230026China College of Chemistry Jilin UniversityChangchun 130012China Department of Chemistry and Physics Stony Brook UniversityStony BrookNY11794USA

Polymer stabilizers are widely used to synthesize gold nanoparticles(Au NPs)to prevent their aggregation and improve their *** stabilizers are known to greatly influence both the structure and size of Au NPs,limited efforts explore their effects on the activity of Au NPs for ***,different polymers are used as stabilizers to synthesize Au *** the glucose oxidase-like activity,we find that without the spatial barrier from stabilizers,naked Au NPs show significantly high catalytic activity as well as the worst *** the polymers,polyacrylic acid-stabilized Au NPs exhibit the highest activity,whose Vmax(0.74μM·s^(−1))is higher than that of the natural glucose oxidase(0.37μM·s^(−1))due to the smallest particle size(<2 nm)and the weak spatial resistance of polyacrylic *** variable catalytic results derive from the comprehensive effects including size,steric hindrance,and electronic ***,further selectivity and activity tests have exposed *** possess universal activities for aldose oxidation,whereas cannot retain activities in typical physiological *** findings highlight the role of polymer stabilizers in imposing effects on the glucose oxidase-like activity of Au NPs and provide a basis for further Au NPs engineering and applications.

关键词： polymer stabilizers gold nanoparticles glucose oxidase-like activity gold nanozyme

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The rapid growth of the internet and its corresponding data

The rapid growth of the internet and its corresponding data

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2024 IEEE International Conference on Signal Processing, Information, Communication and Systems, SPICSCON 2024

作者： Jafor, Md. Shakiul Paul, Liton Chandra Alam, Md. Nur Islam, Sheikh Shafaet Mimi, Afsana Hossain, Md. Sadik Al Mamun, Md. Shajol Rahman, Md. Hasibur Department of Electrical and Electronic Engineering Rajshahi Bangladesh Department of Electrical Electronic and Communication Engineering Rajshahi Bangladesh Department of Computer Science & Engineering Rajshahi Bangladesh Rajshahi University of Engineering & Technology Rajshahi Bangladesh Pundra University of Science & Technology Bogura Bangladesh Pabna University of Science & Technology Pabna Bangladesh

ISBN: (纸本)9798331510213

A wideband, small-size inset-fed slotted rectangular THz antenna with a polyamide substrate has been designed and analyzed for 6G applications. The substrate measures 27.809×40×10 µm3, which is significantly smaller than known antennas currently in use. The antenna has an operating bandwidth of 1060 GHz, which spans 4.46 THz to 5.52 THz, and it resonates at 5.01 THz. The radiation efficiency, return loss, and maximum gain of the proposed THz antenna are 6.29 dB, 97.6%, and -61.97 dB, respectively. In terms of bandwidth, maximum gain, radiation efficiency, and return loss, the proposed THz antenna performs better. A high-frequency structure simulator (HFSS) platform is used to simulate the proposed model and estimate its performance for 6G communication. © 2024 IEEE.

关键词： Bandwidth

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Synchronization and implementation of real-time traffic signal optimization regulator

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Multimedia Tools and Applications 2025年第12期84卷 10725-10745页

作者： Varshney, Abhilasha Dakshayini, M. Gururaj, H.L. Hu, Yu-Chen Department of Information Science and Engineering B.M.S. College of Engineering Karnataka Bangalore India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung407224 Taiwan

In most cosmopolitan cities like Bengaluru, the major problems are the woes of congestion, such as increased vehicular density, travel time, stress, and vehicular carbon emissions are the major problems. These issues have led to quantifiable and non-quantifiable losses, such as high vehicle operation and maintenance costs, reduced human productivity and mobility, increased health hazards, etc. Sustainable transportation coupled with Technological interventions could alleviate these issues by efficiently managing the traffic flow. The major aims of this work are to reduce the overall commute time, traffic congestion, and air pollution, making the commutation of public stress-free and happy. So, we propose a comprehensive Real-time Traffic Signal Optimizer and Regulator (RTSOR) that identifies the right High-Density Corridors (HDC) and achieves these aims by successfully attaining the coordination among the Adaptive Traffic Signal Control System (ATSCS) fixed on the successive junctions of HDC considered in this work as a pilot initiative by upgrading the existing traffic signal in Bengaluru City to the latest ATSCS technology. Implementing this proposed RTSOR has effectively lessened the waiting time in traffic, travel time, and fuel consumption and improved air quality by reducing pollution and lowering stress levels, leading to improved quality of health and life of the public. In Bengaluru city, the current innovation would be implemented for the first time, and no prior model exists that could help optimize the travel time. The method that the study envisaged through RTSOR focuses on collecting real-time data on an HDC to elucidate our claim of originality in reducing the delay time on roads by signal synchronization. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Travel time

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Deep Learning Tools for Covid-19 Pneumonia Classification

Deep Learning Tools for Covid-19 Pneumonia Classification

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International Conference on Electrical and Electronics Engineering, ICEEE 2023

作者： Kanyangarara, Ngonidzashe Mathew Soumya, D.R. Sahana, Subrata Das, Sanjoy Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Computer Science Indira Gandhi National Tribal University RCM Manipur India

ISBN: (纸本)9789819986606

The outbreak of Covid-19 has triggered a worldwide problem, especially in Asia and America. The World Health Organization (WHO) declared the sickness a pandemic on March 20, 2020. It arrived in waves, and most countries worldwide have now experienced two waves and are on the approach of experiencing the third. The goal of this study is to build up and certify a computer-Aided Diagnosis (CADx) system for distinguishing between COVID-19-positive patients and non-COVID Patients people. Chest X-ray (CXR) images will be used to accomplish this. From public datasets which we got on GitHub 2295 CXR images were obtained which included 712 COVID-19 positive and 1583 normal. The proposed CADx system utilized a Conventional Neural Network (CNN) model for data argumentation and CNN was built, compiled, and trained with the help of TensorFlow and Keras. For the sake of appraisal, our datasets were separated into three categories: Train/Test and Validation. The three sets’ accuracy was evaluated and the results for Training, Validation, and Test were 97.77%, 97.81%, and 97.72% respectively. In the end, this study was able to create a precise computer-Aided Diagnosis system for the two categories of classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer aided diagnosis

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Low-temperature metal–oxide thin-film transistor technologies for implementing flexible electronic circuits and systems

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Journal of Semiconductors 2023年第9期44卷 3-10页

作者： Runxiao Shi Tengteng Lei Zhihe Xia Man Wong State Key Laboratory of Advanced Displays and Optoelectronics and Technologies Department of Electronic and Computer EngineeringThe Hong Kong University of Science and TechnologyHong KongChina

Here we review two 300℃metal–oxide(MO)thin-film transistor(TFT)technologies for the implementation of flexible electronic circuits and ***-enhanced TFTs for suppressing the variation and shift of turn-on voltage(VON),and dual-gate TFTs for acquiring sensor signals and modulating VON have been deployed to improve the robustness and performance of the systems in which they are *** circuit building blocks based on fluorinated TFTs have been designed,fabricated,and characterized,which demonstrate the utility of the proposed low-temperature TFT technologies for implementing flexible electronic *** construction and characterization of an analog front-end system for the acquisition of bio-potential signals and an active-matrix sensor array for the acquisition of tactile images have been reported recently.

关键词： flexible electronics metal-oxide semiconductor thin-film transistor dual gate fluorination analog front-end system sensors

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Enhancing Cloud Computing Security through Deep Learning and Attention Mechanism Intrusion Detection Systems 4

Enhancing Cloud Computing Security through Deep Learning and...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Shukla, Sunil Singh, Jagendra Ramya, T. Rahul, Satyakam Mallick, Arnab Kumar Pandey, Pawan Graphic Era Hill University School of Computing Dehradun India Bennett University School of Computer Science Engineering & Technology Greater Noida India Cvr College of Engineering Department of CSE-DS Hyderabad India Mit Adt University School of Computer Science Engineering Pune India Indian Institute of Technology Madras Department of Computer Science Engineering Chennai India Raj Kumar Goel Institute of Technology Department of Computer Science Ghaziabad India

ISBN: (纸本)9798350349900

This research investigates the enhancement of cloud computing security through adaptive neural network intrusion detection systems, employing Deep Belief Networks (DBN) and Attention Mechanism. The study aims to address the growing concerns surrounding security threats in cloud environments by leveraging advanced machine learning techniques. Experimental evaluations were conducted to assess the performance of the proposed approach, with specific emphasis on detection accuracy, false positive rate, and computational efficiency. Results indicate that the DBN-based intrusion detection system achieved an average detection accuracy of 94.4%, with a false positive rate of 2.2% and a computational efficiency of 1 5 8. 4 milliseconds. In comparison, the IDS utilizing Attention Mechanism demonstrated superior performance, with an average detection accuracy of 96.2%, a false positive rate of 1.1%, and a computational efficiency of 153.2 milliseconds. These findings underscore the effectiveness of DBN and Attention Mechanism in bolstering the security posture of cloud computing environments. By integrating adaptive neural network-based intrusion detection systems, organizations can enhance threat detection capabilities, mitigate risks, and safeguard sensitive data and critical assets in the cloud. © 2024 IEEE.

关键词： Cloud security

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OFDM with Generalized Cascade Index Modulation for Secure Communications

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China Communications 2024年第10期21卷 210-225页

作者： Hu Zeng Jin Xiaopin Yao Yudong Chen Fangjiong Wan Dehuan Liu Yun College of Information Science and Technology Zhongkai University of Agriculture and EngineeringGuangzhou 510225China College of Information Engineering China Jiliang UniversityHangzhou 310018China Department Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ 07030USA School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510641China Center for Data Science and Artificial Intelligence Guangdong University of FinanceGuangzhou 510521China

Cascade index modulation(CIM) is a recently proposed improvement of orthogonal frequency division multiplexing with index modulation(OFDM-IM) and achieves better error *** CIM, at least two different IM operations construct a super IM operation or achieve new functionality. First, we propose a OFDM with generalized CIM(OFDM-GCIM) scheme to achieve a joint IM of subcarrier selection and multiple-mode(MM)permutations by using a multilevel digital ***, two schemes, called double CIM(D-CIM) and multiple-layer CIM(M-CIM), are proposed for secure communication, which combine new IM operation for disrupting the original order of bits and symbols with conventional OFDM-IM, to protect the legitimate users from eavesdropping in the wireless communications. A subcarrier-wise maximum likelihood(ML) detector and a low complexity log-likelihood ratio(LLR) detector are proposed for the legitimate users. A tight upper bound on the bit error rate(BER) of the proposed OFDM-GCIM, D-CIM and MCIM at the legitimate users are derived in closed form by employing the ML criteria detection. computer simulations and numerical results show that the proposed OFDM-GCIM achieves superior error performance than OFDM-IM, and the error performance at the eavesdroppers demonstrates the security of D-CIM and M-CIM.

关键词： cascade index modulation low-complexity detector maximum likelihood OFDM physical layer security

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XcepCovidNet: deep neural networks-based COVID-19 diagnosis

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Multimedia Tools and Applications 2024年第37期83卷 85195-85225页

作者： Juneja, Akshay Kumar, Vijay Kaur, Manjit Singh, Dilbag Lee, Heung-No Department of Electrical & amp Instrumentation Thapar University Punjab 147001 India Department of Information Technology Dr. B. R. Ambedkar National Institute of Technology Punjab 147001 India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Research and Development Cell Lovely Professional University Punjab Phagwara144411 India Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of

Coronavirus (also known as COVID-19) is an extremely contagious disease spreading around the globe. An efficient and fast diagnostic method must be designed to identify COVID-19 patients. There are several methods for identification and monitoring of the disease, namely radiological imaging of the patient's chest and polymerase chain reaction (RT-PCR) test. Recent investigations have shown that radiological images are used to observe the effect of COVID on the lungs. Deep Learning is proven effective for image detection and classification in many applications. The majority of existing COVID-19 architectures detect irrelevant features for decision-making. In this paper, a novel network called XcepCovidNet is proposed for feature detection of chest X-rays. It employs transfer learning using hyperparameter-tuning to account for the inadequacies of the training dataset. The proposed model is found superior to pre-trained models such as VGG-19, ResNet-50, DenseNet-201, Xception, and DarkNet-19, in terms of different performance metrics. It is an automated, fast, reliable, and precise COVID-19 detection system for initial screening and diagnosing infected individuals. The obtained results indicate that XcepCovidNet yielded an accuracy of 98.67% and 93.66% for binary and four-class classification, respectively. A two-step verification is performed to validate the proposed model using different models of explainable artificial intelligence, i.e., LIME and occlusion sensitivity. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： COVID-19

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Abnormal Vibration Detection by 2D Digital Image Correlation Analysis

Abnormal Vibration Detection by 2D Digital Image Correlation...

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2024 IEEE International Symposium on Product Compliance Engineering-Asia, ISPCE-AS 2024

作者： Sun, Yuan-Heng Yue, Hao-Chun Chen, Yan-Ann Lin, Cheng-Kuan Tseng, Yu-Chee National Yang Ming Chiao Tung University Department of Computer Science Taiwan Industrial Technology Research Institute Taiwan Yuan Ze University Department of Computer Science and Engineering Taiwan

ISBN: (纸本)9798331523008

In industrial IoT, sensors are embedded in machinery to monitor critical factors such as bearing wear, motor degradation, and environmental conditions. Traditional sensor-based IoT setups are costly and invasive. With the advancements in computer vision, image-based defect detection has made significant progress across various domains. This study proposes to integrate deep learning, via a high-speed camera, with DIC (digital image correlation) techniques for non-intrusive, full-field anomaly detection of a vibrating object, such as industrial motors. Through image processing, the surface of the vibrating object is partitioned into sub-regions, each undergone FFT analysis. By combining the results from all sub-regions, the normal/abnormal state of the vibrating object is determined. Experimental results demonstrate the feasibility of this approach. We also contribute a dataset with detailed annotations collected from our experimental environment for future work. © 2024 IEEE.

关键词： Image correlation

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