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Anti-Interference Low-Power Double-Edge Triggered Flip-Flop Based on C-Elements

Tsinghua science and technology 2022年第1期27卷 1-12页

作者： Zhengfeng Huang Xiao Yang Tai Song Haochen Qi Yiming Ouyang Tianming Ni Qi Xu School of Electronic Science and Applied Physics Hefei University of TechnologyHefei 230601China School of Computer and Information Hefei University of TechnologyHefei 230601China School of Electrical Engineering Anhui Polytechnic UniversityWuhu 241000China

When the input signal has been interfered and glitches occur,the power consumption of Double-Edge Triggered Flip-Flops(DETFFs)will significantly *** effectively reduce the power consumption,this paper presents an anti-interference low-power DETFF based on *** improved C-element is used in this DETFF,which effectively blocks the glitches in the input signal,prevents redundant transitions inside the DETFF,and reduces the charge and discharge frequencies of the *** C-element has also added pull-up and pull-down paths,reducing its *** with other existing DETFFs,the DETFF proposed in this paper only flips once on the clock edge,which greatly reduces the redundant transitions caused by glitches and effectively reduces power *** paper uses HSPICE to simulate the proposed DETFF and other 10 *** findings show that compared with the other 10 types of DETFFs,the proposed DETFF has achieved large performance indexes in the total power consumption,total power consumption with glitches,delays,and power delay product.A detailed analysis of variance indicates that the proposed DETFF features less sensitivity to process,voltage,temperature,and Negative Bias Temperature Instability(NBTI)-induced aging variations.

关键词： Double-Edge Triggered Flip-Flop(DETFF) glitch low-power C-element

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Intelligent Smart Grid Stability Predictive Model for Cyber-Physical Energy Systems

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computer Systems science & Engineering 2023年第2期44卷 1219-1231页

作者： Ashit Kumar Dutta Manal Al Faraj Yasser Albagory Mohammad zeid M Alzamil Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical *** smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical *** the same time,the machine learning(ML)modelsﬁnd useful for the smart grids integrated into the CPES for effective decision ***,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity *** this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in *** proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter *** OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing ***,the MBLSTM model is applied for the prediction of stability level of the smart grids in *** the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM *** ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several *** experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.

关键词： Stability prediction smart grid cyber physical energy systems deep learning data analytics moth swarm algorithm

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Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits

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Multimedia Tools and Applications 2024年第33期83卷 80201-80223页

作者： Dutta, Ashit Kumar Raparthi, Mohan Alsaadi, Mahmood Bhatt, Mohammed Wasim Dodda, Sarath Babu Prashant, G.C. Sandhu, Mukta Patni, Jagdish Chandra Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Alphabet Life Science DallasTX7506 United States Department of Computer Science Al-Maarif University College Al Anbar31001 Iraq Model Institute of Engineering and Technology J&K Jammu India Software Engineer Central Michigan University DallasTX75009 United States Department of Computer Science Texas Tech University Lubbock79409 United States Skill Faculty of Engineering & amp Technology Shri Vishwakarma Skill University Haryana Palwal India CSE School of advance Computing Alliance University Bengaluru India

The neurological condition epilepsy is demanding and even fatal. Electroencephalogram (EEG)-based epilepsy detection still faces various difficulties. EEG readings fluctuate, and different patients have various seizure activity patterns. EEG signal detection is time-consuming and labour-intensive, which puts a strain on medical staff and raises the possibility of erroneous detections. Usually, electrodes are inserted into the scalp or inside the brain for a brief period of time in order to obtain EEG data. It is essential to research efficient cross-patient automatic epilepsy detection techniques. The multi-head self-attention mechanism recognises long-distance dependencies with the same proficiency as it does temporal dynamic correlations between short-term temporal pattern characteristics and sequential relationships. The contextual representations are inputted into a bidirectional long short-term memory network (BiLSTM) so that information can be extracted in both directions. Classification and training are carried out utilising the log SoftMax algorithm. The experiments utilised scalp EEG data from the CHB-MIT database. Sensitivity, specificity, F1-score, and accuracy were computed to be 96.5 percent, 97.04 percent, 96.6 percent, and 96.2 percent, respectively. The results of the experiment show how well the technique works for detecting seizures in several patients utilising multi-channel EEG recordings. The results also demonstrate the method's improved generalisation capabilities and resilience and consistency in collecting seizure patterns. This is particularly critical for the tertiary diagnosis of epilepsy, and the findings indicate that the proposed method significantly improves the accuracy of detection. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electroencephalography

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Learning Discrete Latent Variable Structures with Tensor Rank Conditions 38

Learning Discrete Latent Variable Structures with Tensor Ran...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Zhengming Cai, Ruichu Xie, Feng Qiao, Jie Wu, Anpeng Li, Zijian Hao, Zhifeng Zhang, Kun School of Computer Science Guangdong University of Technology China Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Department of Applied Statistics Beijing Technology and Business University China Department of Computer Science and Technology Zhejiang University China College of Science Shantou University Guangdong Shantou China Department of Philosophy Carnegie Mellon University United States

Unobserved discrete data are ubiquitous in many scientific disciplines, and how to learn the causal structure of these latent variables is crucial for uncovering data patterns. Most studies focus on the linear latent variable model or impose strict constraints on latent structures, which fail to address cases in discrete data involving non-linear relationships or complex latent structures. To achieve this, we explore a tensor rank condition on contingency tables for an observed variable set Xp, showing that the rank is determined by the minimum support of a specific conditional set (not necessary in Xp) that d-separates all variables in Xp. By this, one can locate the latent variable through probing the rank on different observed variables set, and further identify the latent causal structure under some structure assumptions. We present the corresponding identification algorithm and conduct simulated experiments to verify the effectiveness of our method. Our results elegantly expand the application scope of causal discovery with latent variables. © 2024 Neural information processing systems foundation. All rights reserved.

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Warehouse Demand Variation Prediction and Optimization Based on Machine Learning Algorithms 24

Warehouse Demand Variation Prediction and Optimization Based...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Lu, Yuting Jomhari, Nazean Liao, Shengshi Goyal, Shyam Bihari Jiangxi College of Applied Technology Jiangxi Ganzhou China Faculty of Information Technology City University Malaysia Kuala Lumpur Malaysia Faculty of Computer Science & Information Technology Universiti Malaya Kuala Lumpur Malaysia Chitkara University Rajpura India

ISBN: (纸本)9798400710865

With the intensification of market competition and the diversification of customer demands, efficient inventory management has become a key factor for the success of enterprises. Warehouse demand forecasting, as a core aspect of inventory management, is of great significance for enterprises to reasonably arrange production, optimize inventory levels, and reduce costs. This paper delves into the prediction and optimization methods of warehouse demand changes based on machine learning algorithms, with a particular focus on three powerful algorithms: Random Forest, XGBoost, and *** LightGBM model performed the best. The lowest MSE can provide a lower prediction error, and the highest R2 value, which is close to 0.786, can better fit the *** research results fully demonstrate that machine learning algorithms have significant application value and potential in the field of predicting changes in warehouse demand. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Software Defect Prediction Using Support Vector Machine

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2022年第2期7卷 37-47页

作者： Alhija, Haneen Abu Azzeh, Mohammad Almasalha, Fadi Department of Computer Science Applied Science Private University Amman Jordan Department of Data Science Princess Sumaya University for Technology Amman Jordan

Software defect prediction is an essential task during the software development Lifecycle as it can help managers to identify the most defect-proneness modules. Thus, it can reduce the test cost and assign testing resources efficiently. Many classification methods can be used to determine if the software is defective or not. Support Vector Machine (SVM) has not been used extensively for such problems because of its instability when applied on different datasets and parameter settings. The main parameter that influences the accuracy is the choice of the kernel function. The use of kernel functions has not been studied thoroughly in previous papers. Therefore, this research examines the performance and accuracy of SVM with six different kernel functions. Various public datasets from the PROMISE project empirically validate our hypothesis. The results demonstrate that no kernel function can give stable performance across different experimental settings. In addition, the use of PCA as a feature reduction algorithm shows slight accuracy improvement over some datasets. © 2022. All Rights Reserved.

关键词： Kernel functions Software Defect Prediction Support Vector Machine

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Exploring household food security through institutional factors: A statistical and mathematical analysis

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9179-9195页

作者： Khan, Younas Ashraf, Shahzaib Farman, Muhammad Abdallah, Suhad Ali Osman Department of Sociology Kohat University of Science and Technology Kohat Pakistan Institute of Mathematics Khawaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Mathematics Faculty of Arts and Science Near East University Turkey Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Applied College Khamis Mushait King Khalid University Abha Saudi Arabia

Achieving household food security is the tumbling issue of the century. This article explores the factors affecting household food security and solutions by utilizing a synergy of statistical and mathematical models. The methodology section is divided into two portions namely sociological and mathematical methods. Sociologically, 379 household heads were interviewed through structured questions and further analyzed in terms of descriptive and binary logistic regression. The study found that 4 independent variables (poverty, poor governance, militancy, and social stratification) showed a significant association (P = 0.000) to explain variations in the dependent variable (household FS). The Omnibus test value (χ2= 102.386;P = 0.000) demonstrated that the test for the entire model against constant was statistically significant. Therefore, the set of predictor variables could better distinguish the variation in household FS. The Nagelkerke's R Square (R2 = .333) helps to interpret that the prediction variable and the group variables had a strong relationship. Moreover, 23% to 33% variation in FS was explained by the grouping variables (Cox and Snell R2 = 0.237 and Nagelkerke's R2 = 0.333). The significant value of Wald test results for each variable confirmed that the grouping variables (poor governance P = 0.004, militancy P = 0.000, social stratification P = 0.021 and poverty P = 0.000) significantly predicted FS at the household level. Mathematically, all the statistics were validated further through the application of spherical fuzzy mathematics (TOPIS and MADM) to explore what factors are affecting household FS. Thus, the study found that F3 (poverty) > F2 (militancy) > F4 (social stratification) > F1 (poor governance) respectively. Thus, it could be concluded from these findings that the prevalence of poverty dysfunctional all the channels of household FS at the macro and micro levels. Therefore, a sound and workable model to eradicate poverty in the study area by

关键词： Food supply

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A Review on Audio Encryption Algorithms Using Chaos Maps-Based Techniques

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Journal of Cyber Security and Mobility 2022年第1期11卷 53-82页

作者： Albahrani, Ekhlas Abbas Alshekly, Tayseer Karam Lafta, Sadeq H. Department of Computer Science Mustansiriyah University Baghdad Iraq Department of Banking and Finance science Al-Imam Al-A’tham University College Baghdad Iraq Department of Applied Science University of Technology Iraq

Due to the quick improvement in digital communications and multimedia applications during recent periods up to the current time, data protection of digital data such as image, audio and video becomes a significant challenge. The security of audio data that transfer through different networks was rated as a preferred research field in the preceding years. This review covers the recent contribution for audio encryption and gives the most evaluations for audio encryption algorithm involving security analysis, computational complexity and quality analysis and their requirements. This paper fundamentally concentrates on displaying the different types of audio encryption and decryption techniques based on chaotic maps. Digital and analog audio algorithms were displayed, discussed and compared with the illustration of the important features and drawbacks. Various digital and audio proposed projects for audio encryption using chaotic maps have been covered, which they showed extreme sensitivity to initial conditions, unpredictability and conducting in a quasi-random manner. A comparison among the proposedJournal of Cyber Security and Mobility, Vol. 11 1, 53–82. doi: 10.13052/jcsm2245-1439.1113 © 2021 River Publishers algorithms in the key space, chaotic maps sensitivity and statistical analysis were provided. © 2022 River Publishers. All rights reserved.

关键词： Chaotic systems

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A Comprehensive study on the Detection of Pneumonia using Machine Learning and Deep Learning Approaches

A Comprehensive study on the Detection of Pneumonia using Ma...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Saparna, P. Mary, A.Viji Amutha Saintgits College of Applied Sciences Department of Computer Applications Kerala Kottayam India Satyabama Institute of Science and Technology Department of CSE Chennai India

ISBN: (纸本)9798350396485

Pneumonia is an inflammation of the lungs caused by a bacterial or viral infection. The air bags of the lungs fill with pus when infected with bacteria or viruses. It can affect both lungs or a single. It can also be caused by fungi or parasites. This is an illness that threatens the lives of millions of people worldwide.. At present, the main challenge is to detect the disease in itsearliest stages. It is typically diagnosed by examining a chest X-ray taken by a trained physician or radiologist. In this review paper, a database of X-ray, CT-Scan images from patients was used to automatically detect *** patient's breathing becomes progressively unpleasant and difficult as a result of pneumonia. Machine learning-based diagnosis techniques can aid in the early and efficient detection of disease. Medical imaging research is utilizing computer vision-related automatic detection algorithm. © 2023 IEEE.

关键词： computerized tomography

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