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Deep Learning-Based Cloud Security: Innovative Attack Detection and Privacy Focused Key Management

IEEE Transactions on computers 2025年第6期74卷 1978-1989页

作者： Ahmad, Shahnawaz Arif, Mohd Mehfuz, Shabana Ahmad, Javed Nazim, Mohd Bennett University School of Computer Science Engineering and Technology Greater Noida201310 India Galgotias University School of Computer Science & Engineering Greater Noida203201 India Jamia Millia Islamia Department of Electrical Engineering New Delhi110025 India Sharda University Department of Computer Science and Engineering Sharda School of Engineering and Technology Greater Noida201310 India Noida Institute of Engineering and Technology Department of Computer Science and Engineering School of Computer Science and IT Greater Noida201306 India

Cloud Computing (CC) is widely adopted in sectors like education, healthcare, and banking due to its scalability and cost-effectiveness. However, its internet-based nature exposes it to cyber threats, necessitating advanced security frameworks. Traditional models suffer from high false positives and limited adaptability. To address these challenges, VECGLSTM, an attack detection model integrating Variable Long Short-Term Memory (VLSTM), capsule networks, and the Enhanced Gannet Optimization Algorithm (EGOA), is introduced. This hybrid approach enhances accuracy, reduces false positives, and dynamically adapts to evolving threats. EGOA is employed for its superior optimization capability, ensuring faster convergence and resilience. Additionally, Chaotic Cryptographic Pelican Tunicate Swarm Optimization (CCPTSO) is proposed for privacy-preserving key management. This model combines chaotic cryptographic techniques with the Pelican Tunicate Swarm Optimization Algorithm (PTSOA), leveraging the pelican algorithm’s exploration strength and the tunicate swarm’s exploitation ability for optimal encryption security. Performance evaluation demonstrates 99.675% accuracy, 99.5175% recall, 99.7075% precision, and 99.615% F1-score, along with reduced training (1.79s), encryption (0.986s), and decryption (1.029s) times. This research significantly enhances CC security by providing a scalable, adaptive framework that effectively counters evolving cyber threats while ensuring efficient key management. © 1968-2012 IEEE.

关键词： Cloud security

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EARLY PREDICTION OF CYCLE LIFE OF SUPERCAPACITORS BASED ON GA-LSTM 14

EARLY PREDICTION OF CYCLE LIFE OF SUPERCAPACITORS BASED ON G...

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14th International Conference on Quality, Reliability, Risk, Maintenance, and Safety engineering, QR2MSE 2024

作者： Liu, Bo Yu, Haoren Zou, Jiawei Yang, Tonggou Institute of Sensor and Reliability Engineering Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781837242672

Supercapacitors are increasingly utilized in the new energy automotive industry, favored for their long cycle life, high power density, and environmental sustainability. Accurately predicting the Remaining Useful Life (RUL) of supercapacitors is crucial for timely replacement and avoiding losses associated with unexpected failures. This paper introduces a novel cycle life prediction method for supercapacitors, employing a Genetic Algorithm-Long Short-Term Memory (GA-LSTM) approach. Initially, a Moving Average Filter (MAF) is applied to the degradation data of 20 different supercapacitors for noise reduction. Subsequently, a multi-task learning strategy is implemented, training the LSTM model multiple times to enhance its predictive accuracy. The Genetic Algorithm is then utilized to optimize the LSTM's hyperparameters, significantly refining the prediction model. By inputting only 10% of the test data, the trained model successfully predicts the supercapacitor’s RUL. To validate the method's efficacy, performance tests on eight diverse datasets were conducted. The results indicate that the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of this method are maintained within 0.0041 and 0.0032, respectively, showcasing a more precise RUL prediction for supercapacitors compared to traditional LSTM approaches and those without noise reduction. © The Institution of engineering & technology 2024.

关键词： Automotive industry

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Parameter Design Method of the Magnetic Coupler for Wireless Charging System Using Metamodel of Optimal Prognosis 7

Parameter Design Method of the Magnetic Coupler for Wireless...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Ma, Jun He, Jiafang Li, Zhenjie Dong, Shuai Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology Harbin China Northeast Forestry University College of Computer and Control Engineering Harbin China

ISBN: (纸本)9798350359558

This paper addresses the parameter design problem of magnetic couplers and proposes a multi-objective optimization design method based on the Metamodel of Optimal Prognosis (MOP). The method involves mathematically fitting optimization objectives and design parameters using the optimal predictive metamodel, thereby establishing response surfaces for multi-objective optimization. By transforming finite element calculations of the magnetic coupler into numerical computations through fitting, the optimization process is accelerated. The parameters ultimately selected are verified through simulation, thus validating the effectiveness of the proposed optimization method. © 2024 IEEE.

关键词： Multiobjective optimization

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HViTML: Hybrid vision transformer with machine learning-based classification model for glaucomatous eye

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Multimedia Tools and Applications 2025年 1-24页

作者： Singh, Piyush Bhushan Singh, Pawan Dev, Harsh Batra, Devanshu Chaurasia, Brijesh Kumar Department of Information Technology Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Babu Banarasi Das University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India

Glaucoma is currently one of the most significant causes of permanent blindness. Fundus imaging is the most popular glaucoma screening method because of the compromises it has to make in terms of portability, size, and cost. In recent years, convolution neural networks (CNNs) have revolutionized computer vision. Convolution is a "local" CNN technique that is only applicable to a small region surrounding an image. Vision Transformers (ViT) use self-attention, which is a "global" activity since it collects information from the entire image. As a result, the ViT can successfully gather distant semantic relevance from an image. This study examined several optimizers, including Adamax, SGD, RMSprop, Adadelta, Adafactor, Nadam, and Adagrad. With 1750 Healthy and Glaucoma images in the IEEE fundus image dataset and 4800 healthy and glaucoma images in the LAG fundus image dataset, we trained and tested the ViT model on these datasets. Additionally, the datasets underwent image scaling, auto-rotation, and auto-contrast adjustment via adaptive equalization during preprocessing. The results demonstrated that preparing the provided dataset with various optimizers improved accuracy and other performance metrics. Additionally, according to the results, the Nadam Optimizer improved accuracy in the adaptive equalized preprocessing of the IEEE dataset by up to 97.8% and in the adaptive equalized preprocessing of the LAG dataset by up to 92%, both of which were followed by auto rotation and image resizing processes. In addition to integrating our vision transformer model with the shift tokenization model, we also combined ViT with a hybrid model that consisted of six different models, including SVM, Gaussian NB, Bernoulli NB, Decision Tree, KNN, and Random Forest, based on which optimizer was the most successful for each dataset. Empirical results show that the SVM Model worked well and improved accuracy by up to 93% with precision of up to 94% in the adaptive equalization preprocess

关键词： Decision trees

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Data driven discovery of an analytic formula for the life prediction of Lithium-ion batteries

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Progress in Natural science:Materials International 2022年第6期32卷 793-799页

作者： Jie Xiong Tong-Xing Lei Da-Meng Fu Jun-Wei Wu Tong-Yi Zhang School of Materials Science and Engineering Harbin Institute of Technology Hong Kong University of Science and Technology (Guangzhou)

Predicting the cycle life of Lithium-Ion Batteries（LIBs） remains a great challenge due to their complicated degradation *** present work employs an interpretative machine learning of symbolic regression（SR） to discover an analytic formula for LIB life prediction with newly defined *** novel features are based on the discharging energies under the constant-current（CC） and constant-voltage（CV） modes at every five cycles *** cycle life is affected by the CC-discharging energy at the 15th cycle(E15-CCD) and the difference between the CC-discharging energies at the 45th cycle and 95th cycle(Δ45-95).The cycle life highly correlates with a simple indicator(E15-CCD-3)/Δ45-95with a Pearson correlation coefficient of *** machine learning tools provide a rapid and accurate prediction of cycle life at the early stage.

关键词： Symbolic regression Machine learning Cycle life prediction Lithium-ion batteries

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RPF-ELD: Regional Prior Fusion using Early and Late Distillation for Breast Cancer Recognition in Ultrasound Images

RPF-ELD: Regional Prior Fusion using Early and Late Distilla...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Haosen Zhang, Gengyuan Zhao, Yingnan Lai, Fang Cui, Wenwei Xue, Jiexiao Wang, Qihang Zhang, Hao Lin, Yi Beijing Jiaotong University School of Computer Science and Information Technology Beijing China Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China Harbin Engineering University School of Computer Science and Technology Harbin China University of Denver Department of Computer Science Denver United States University of York Wentworth College York United Kingdom Institute of Heilongjiang The Second Surveying and Mapping Harbin China Southeast University School of Information Science and Engineering Nanjing China Harbin Institute of Technology Faculty of Computing Harbin China Harbin Medical University School of Interdisciplinary Medicine and Engineering Harbin China

ISBN: (纸本)9798350386226

Breast cancer is one of the main factors responsible for the deaths of women worldwide. Ultrasound imaging is a key method for early detection of breast cancer, which can help patients gain valuable treatment time and improve their chances of survival. The computer-aided system of breast cancer recognition has started to receive attention due to the lack of experienced sonographers. Presently, most breast cancer recognition methods typically suffer from uncertain locations and proportions of tumor regions in ultrasound images. In this paper, we propose a novel Regional Prior Fusion framework using Early and Late Distillation (RPF-ELD), inspired by the knowledge distillation of the teacher-student framework, for breast cancer recognition in ultrasound images. Firstly, to enhance the concentration of the tumor regions, a high-performing prior-fused model is trained as the teacher model using ultrasound images with the corresponding regional prior information. Next, a diagnostic model is trained as the student model under the prior-fused model distillation using early and late features to implicitly obtain the regional prior knowledge. Finally, the diagnostic model recognizes the categories of breast cancer from only ultrasound images using the experience from distilled prior knowledge. Two publicly released datasets are used to evaluate the proposed RPF-ELD framework. Experimental results demonstrate that the proposed RPF-ELD surpasses current state-of-the-art methods. © 2024 IEEE.

关键词： Ultrasonic imaging

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Improving Machine Translation Formality with Large Language Models

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computers, Materials & Continua 2025年第2期82卷 2061-2075页

作者： Murun Yang Fuxue Li School of Computer Science and Engineering Northeastern UniversityShenyang110819China College of Electrical Engineering Yingkou Institute of TechnologyYingkou115014China

Preserving formal style in neural machine translation (NMT) is essential, yet often overlooked as an optimization objective of the training processes. This oversight can lead to translations that, though accurate, lack formality. In this paper, we propose how to improve NMT formality with large language models (LLMs), which combines the style transfer and evaluation capabilities of an LLM and the high-quality translation generation ability of NMT models to improve NMT formality. The proposed method (namely INMTF) encompasses two approaches. The first involves a revision approach using an LLM to revise the NMT-generated translation, ensuring a formal translation style. The second approach employs an LLM as a reward model for scoring translation formality, and then uses reinforcement learning algorithms to fine-tune the NMT model to maximize the reward score, thereby enhancing the formality of the generated translations. Considering the substantial parameter size of LLMs, we also explore methods to reduce the computational cost of INMTF. Experimental results demonstrate that INMTF significantly outperforms baselines in terms of translation formality and translation quality, with an improvement of +9.19 style accuracy points in the German-to-English task and +2.16 COMET score in the Russian-to-English task. Furthermore, our work demonstrates the potential of integrating LLMs within NMT frameworks to bridge the gap between NMT outputs and the formality required in various real-world translation scenarios.

关键词： Neural machine translation formality large language model text style transfer style evaluation reinforcement learning

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IoT-Based Women Safety Gadgets (WSG): Vision, Architecture, and Design Trends

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computers, Materials & Continua 2023年第7期76卷 1027-1045页

作者： Sharad Saxena Shailendra Mishra Mohammed Baljon Shamiksha Mishra Sunil Kumar Sharma Prakhar Goel Shubham Gupta Vinay Kishore Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatialaPunjab147001India Department of Computer Engineering College of Computer&Information ScienceMajmaah UniversityMajmaah11952Saudi Arabia Department of Computer Science and Engineering National Institute of TechnologyJamshedpur831001India

In recent years,the growth of female employees in the commercial market and industries has *** a result,some people think travelling to distant and isolated locations during odd hours generates new threats to women’s *** exponential increase in assaults and attacks on women,on the other hand,is posing a threat to women’s growth,development,and *** the time of the attack,it appears the women were immobilized and needed immediate *** self-defense isn’t sufficient against abuse;a new technological solution is desired and can be used as quickly as hitting a switch or *** proposed Women Safety Gadget(WSG)aims to design a wearable safety device model based on Internet-of-Things(IoT)and Cloud *** is designed in three layers,namely layer-1,having an android app;layer-2,with messaging and location tracking system;and layer-3,which updates information in the cloud *** can detect an unsafe condition by the pressure sensor of the finger on the artificial nail,consequently diffuses a pepper spray,and automatically notifies the saved closest contacts and police station through messaging and location *** has a response time of 1000 ms once the nail is pressed;the average time for pulse rate measure is 0.475 s,and diffusing the pepper spray is 0.2–0.5 *** average activation time is 2.079 s.

关键词： Perpetrator safety IoT women security GPS tracking GSM

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Flame features and oscillation characteristics in near-blowout swirl-stabilized flames using high-speed OH-PLIF and mode decomposition methods

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Chinese Journal of Aeronautics 2023年第1期36卷 191-200页

作者： Zhen CAO Xin YU Jiangbo PENG Bin HU Zhonghao WANG Yang YU Long GAO Minghong HAN Xun YUAN Guohua WU National Key Laboratory of Science and Technology on Tunable Laser Harbin Institute of TechnologyHarbin 150001China Institute of Opt-Electronics Harbin Institute of TechnologyHarbin 150001China Institute of Engineering Thermophysics Chinese Academy of SciencesBeijing 100190China School of Aeronautics and Astronautics University of Chinese Academy of SciencesBeijing 100190China

Flame features and dynamics are important to the explanation and prediction of a lean blowout(LBO)*** this paper,recognition of near-LBO flame features and oscillation characterization methods were proposed based on flame spectroscopic ***-speed planar laser-induced fluorescence measurements of OH were used to capture unique dynamic features such as the local extinction and reignition feature and entrained reactant *** Zernike moment demonstrated a good performance in recognition of stability and near-LBO conditions,though the geometric moment had more advantages to characterize frequency ***-frequency oscillations,especially at the obvious self-excited oscillation frequency around 200 Hz,were found when approaching an LBO condition,which can be expected to be used as a novel prediction characteristic parameter of the flameout *** orthogonal decomposition(POD)and dynamic mode decomposition(DMD)were used to conduct dynamic analysis of near-LBO *** modes spectra showed the unique frequency characteristics of stable and near-LBO flames,which were basically in line with those at the heat-release *** primary POD modes demonstrated that the radial vibration mode dominated in a stable flame,while the rotation mode was found to exist in a near-LBO *** of modal decomposition showed that flame shedding and agminated entrained reactant pockets were responsible for generating self-excited flame oscillations.

关键词： Dynamic mode decomposi-tion(DMD) High-speed OH-PLIF Moment descriptors Near-blowout Proper orthogonal decom-position(POD)

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Detuned Wireless Charging System with Constant Power Charging Ability and High-Misalignment Tolerance 7

Detuned Wireless Charging System with Constant Power Chargin...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： He, Jiafang Ma, Jun Dong, Shuai Li, Zhenjie Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology Harbin China Northeast Forestry University College of Computer and Control Engineering Harbin China

ISBN: (纸本)9798350359558

In practical applications, wireless charging systems (WCS) should solve unavoidable misalignment problems and realize stable output over a wide load range. Therefore, a detuned WCS with solid anti-misalignment capacity and constant power (CP) output over a wide load range using the overlapping coil magnetic coupler (OCMC) and Buck converter is proposed in this paper. First, the system structure is designed, and then the relationship between the mutual inductance, load resistance, and output power is investigated. Second, the detailed coupling characteristic of the optimized OCMC is analyzed. Finally, the closed-loop workflow of the WCS is described, and simulation results are given. © 2024 IEEE.

关键词： Inductance

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