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1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Prakash, Vijay Dudeja, Deepak Chhabra, Gunjan Garg, Lalit Department of Computer Information Systems University of Malta Msida Malta Department of Computer Science and Engineering MMEC MMDU Haryana Mullana India Department of Computer Science & Engineering Graphic Era Hill University Dehradun India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9781032874036

This paper analyzes whether Android apps may outsource computational activities to cloud servers. Due to the complexity of mobile apps, shifting computing operations to the cloud has become popular to improve performance and reduce energy consumption. This paper explains how to deploy an Android app on Cloudflare and tests it under various settings. The testing showed that cloud computing can boost Android app efficiency. The findings show that shifting computing workloads to the cloud reduces mobile device CPU utilization. This frees up device resources, allowing it to perform other tasks. However, cloud processing has limitations. Network latency between the mobile device and the cloud server may affect application performance. In addition, the cost of offloading computation to the cloud can be a concern for some users, especially in high data consumption scenarios. Our findings can be helpful for developers who want to optimize the performance of their Android applications and for researchers who wish to investigate this topic further. Future research could explore ways to minimize the impact of network latency on offloading computation and investigate the cost-effectiveness of this technique for different types of applications. © 2025 the Author(s).

关键词： Cloud security

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A STOCHASTIC-GRADIENT-BASED INTERIOR-POINT ALGORITHM FOR SOLVING SMOOTH BOUND-CONSTRAINED OPTIMIZATION PROBLEMS

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SIAM Journal on Optimization 2025年第2期35卷 1030-1059页

作者： Curtis, Frank E. Kungurtsev, Vyacheslav Robinson, Daniel P. Wang, And Q.I. Department of Industrial and Systems Engineering Lehigh University BethlehemPA18015 United States Department of Computer Science Czech Technical University PragueE-435 Czech Republic

A stochastic-gradient-based interior-point algorithm for minimizing a continuously differentiable objective function (that may be nonconvex) subject to bound constraints is presented, analyzed, and demonstrated through experimental results. The algorithm is unique from other interior-point methods for solving smooth nonconvex optimization problems since the search directions are computed using stochastic gradient estimates. It is also unique in its use of inner neighborhoods of the feasible region-defined by a positive and vanishing neighborhood-parameter sequence-in which the iterates are forced to remain. It is shown that with a careful balance between the barrier, step size, and neighborhood sequences, the proposed algorithm satisfies convergence guarantees in both deterministic and stochastic settings. The results of numerical experiments show that in both settings the algorithm can outperform projection-based methods. Copyright © by SIAM.

关键词： Numerical methods

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Cryptography machine: Encryption and decryption model for multimedia data 1st

Cryptography machine: Encryption and decryption model for mu...

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1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

ISBN: (纸本)9781032874036

The digital era has brought a surge in the amount of data generated, increasing the need for data security across individuals, organizations, and governments. Protecting sensitive information from unauthorized access is a top priority in today’s digital world. Cryptography is used to ensure secure communication over the internet, including email, instant messaging, and web browsing. Our paper’s primary goal is to develop a cryptography machine capable of encrypting and decrypting different data types, such as plain text, images, audio and videos using the cryptography of the symmetric key. The proposed model will be implemented using Python and Java. Developing the cryptography machine will significantly protect sensitive information and ensure its confidentiality and integrity. The machine’s ability to encrypt and decrypt different data types using symmetrickey cryptography will provide an added layer of security to individuals, organizations, and governments in the digital era. © 2025 the Author(s).

关键词： algorithms Cryptography decryption encryption keys machine

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A Novel Framework for Gauging Information Extracted from Smartphones Using Neutrosophic Logic

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Neutrosophic Sets and systems 2025年 76卷 154-171页

作者： Abou Alzahab, R.M. Ismail, Amr Abd Elkhalik, S.H. Shams, M.Y. El-Bakry, H.M. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Department. of Communication and Computer Engineering Air Defense University Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Department of Information Systems. Faculty of Computer and Information Science Mansoura University Egypt

Smartphones contain a vast amount of information about their users, which can be used as evidence in criminal cases. However, the sheer volume of data can make it challenging for forensic investigators to identify and use the most relevant information. Neutrosophic logic is a generalization of fuzzy logic that allows for uncertainty and vagueness, making it a more potent tool for dealing with the ambiguity of real-world data. The proposed framework aims to identify and utilize the most relevant information for forensic investigators, making it easier to solve criminal cases using Neutrosophic logic. This novel approach provides a more powerful tool for dealing with the ambiguity of smartphone data, ultimately improving the accuracy and effectiveness of forensic investigations. Our research has utilized Neutrosophic logic to evaluate the degree of truth, falsity, or Indeterminacy of this information. Additionally, this study analyzes conversations between individuals using Excel’s fuzzy lookup add-in to determine the percentage of truth and false in each conversation. The results were compiled into a dataset and utilized a Neutrosophic Python code to evaluate the information. The results indicate the percentage of truth, false, and Indeterminacy in each conversation and which can be used to determine its admissibility as evidence and which not. © 2025, University of New Mexico. All rights reserved.

关键词： Smartphones

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Encryption and decryption framework using elliptic curve cryptography 1st

Encryption and decryption framework using elliptic curve cry...

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1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Prakash, Vijay Dudeja, Deepak Chhabra, Gunjan Garg, Lalit Department of Computer Information Systems University of Malta Msida Malta Department of Computer Science and Engineering MMEC MMDU Haryana Mullana India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9781032874036

Secure file encryption has become increasingly important in various disciplines, including finance, healthcare, and government, where protecting sensitive data is paramount. The proposed framework aims to meet this requirement by developing an encryption and decryption framework for files that employ Elliptic Curve Cryptography (ECC), a modern and practical cryptographic technique. The framework will employ ECC algorithms for key generation, encryption, and decryption, which offer several advantages over conventional methods such as RSA and AES. ECC provides equivalent security with shortened key lengths, resulting in faster encryption and decryption, decreased computational burden, and optimized resource utilization. A crucial aspect of the proposed framework is its user-friendly interface, making it easy for users to manage encrypted files. In addition, the framework will include key management and validation mechanisms to ensure the security and authenticity of encryption keys, thereby preventing unauthorized access to encrypted files. The proposed framework will be rigorously tested on a local machine and hosted on a server in the cloud to ensure its dependability and resiliency in real-world scenarios. The framework’s efficacy will be determined based on the encryption and decryption times for various file sizes. The outcomes will demonstrate the efficacy and efficiency of the proposed framework, demonstrating that it provides superior performance compared to conventional algorithms. © 2025 the Author(s).

关键词： decryption Elliptic Curve Cryptography Encryption

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Attention-Driven Emotion Recognition in EEG: A Transformer-Based Approach With Cross-Dataset Fine-Tuning

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IEEE Access 2025年 13卷 69369-69394页

作者： Ghous, Ghulam Najam, Shaheryar Alshehri, Mohammed Alshahrani, Abdulmonem AlQahtani, Yahya Jalal, Ahmad Liu, Hui Riphah International University Department of Electrical and Computer Engineering Islamabad44000 Pakistan Bahria University Department of Electrical Engineering Islamabad44000 Pakistan King Khalid University Department of Computer Science Abha61421 Saudi Arabia King Khalid University Department of Informatics and Computer Systems Abha61421 Saudi Arabia Air University Faculty of Computer Science and AI Islamabad44000 Pakistan Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of University of Bremen Cognitive Systems Laboratory Bremen28359 Germany Guodian Nanjing Automation Company Ltd. Nanjing210032 China

Emotion recognition from EEG (electroencephalogram) signals is crucial in mental health diagnostics and human-computer interaction but is often hindered by high dimensionality, noise, and complex temporal dependencies in the data. This paper presents a novel approach that integrates transformer models, attention mechanisms, and transfer learning to enhance emotion recognition accuracy from EEG signals. The proposed methodology consists of two phases: Attention Enhanced Base Model Development (AE-BMD) and Cross-Dataset Fine Tuning Adaptation (CD-FTA). In the AE-BMD phase, the base model is developed and trained on the SEED-IV dataset (15 participants, 62 EEG channels), achieving an accuracy of 84%, with an average precision of 84.75%, recall of 84% and F1-score of 84%. This phase employs optimized feature extraction from key EEG frequency bands (Delta, Theta, Alpha, Beta, Gamma) using techniques such as MFCC, GFCC, power spectral density, and Hjorth parameters. A transformer encoder with integrated spectral and temporal attention mechanisms captures intricate patterns and long-range dependencies within the EEG signals. In the CD-FTA phase, the model undergoes fine-tuning on the SEED-V dataset (20 participants, 62 channels) leading to an improved accuracy of 90%, with an average precision of 90.6%, recall of 90.6%, and F1-score of 90.6%. The model’s generalization is further validated on the MPED dataset (23 participants, 62 channels, seven emotion classes), achieving 79%, with an average precision of 79.3%, recall of 79.3% and F1-score of 79.1% across diverse emotional states. This cross-dataset adaptation leverages transfer learning to enhance the model’s generalization across different emotional states and EEG datasets. Experimental results show that the proposed approach outperforms traditional methods, achieving superior accuracy and robustness in emotion recognition tasks. This work advances emotion recognition systems by addressing challenges in EEG signal proc

关键词： Electroencephalography

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Network-Based Rail Running Band Anomaly Recognition via Recurrent Attention Graphs

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IEEE Transactions on Automation science and engineering 2025年 22卷 15381-15395页

作者： Ni, Xuefeng Fieguth, Paul W. Ma, Ziji Shi, Bo Liu, Hongli Hunan University College of Electrical and Information Engineering China University of Waterloo Department of Systems Design Engineering Canada The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

Anomaly detection for rail running bands, the pattern of wheel–rail contact area, is crucial to analyze composite rail irregularities. This paper presents an all-weather vision-based solution for running-band inspection. However, two major algorithmic challenges restrict inspection effectiveness: 1) the identification of high-quality features for diversified and imbalanced data subject to noise and outliers, and 2) inferring implicit anomaly co-occurrence patterns. We regard overall running-band anomaly detection as a multi-label classification problem and develop a novel deep multi-anomaly recognition network via recurrent attention graphs (RAGRN). The proposed RAGRN consists of two fundamental components, each directly addressing the two major challenges of this paper: 1) Class-specific features are extracted for fine-grained discrimination via split-channel and gradient-guided class-specific attention mechanisms;2) We develop a multi-anomaly classifier, which effectively captures long-distance correlation features via a recurrent attention graph with visual and statistical guidance for graph propagation, containing prior statistical and image-specific information. The experiments and statistical analyses demonstrate that RAGRN outperforms all related state-of-the-art frameworks and has the potential to be applied to practical inspection. © 2004-2012 IEEE.

关键词： attention Graph neural network multi-anomaly recognition multi-label classification rail running band

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Fuzzy Decision-Based Clustering for Efficient Data Aggregation in Mobile UWSNs

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computers, Materials & Continua 2025年第4期83卷 259-279页

作者： Aadil Mushtaq Pandith Manni Kumar Naveen Kumar Nitin Goyal Sachin Ahuja Yonis Gulzar Rashi Rastogi Rupesh Gupta Department of Computer Science and Engineering Lovely Professional UniversityJalandhar144402PunjabIndia Department of Computer Science and Engineering Chandigarh UniversityMohali140413PunjabIndia Salesforce Inc. DallasTX 75201USA Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendragarh123031HaryanaIndia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Application Sir Chhotu Ram Institute of Engineering and TechnologyCh.Charan Singh UniversityMeerut250001Uttar PradeshIndia Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpura140401PunjabIndia

Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the ***,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater ***,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole ***,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network *** address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile *** proposed method has four main phases:clustering,CH selection,data aggregation,and *** CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy *** the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving *** adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects *** results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.

关键词： Clustering data aggregation data collection fuzzy model monitoring UWSN

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A Data-Driven Adaptive Control Approach for Enhancing the Dynamic Response of VSGs in Varying Grid Conditions

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IEEE Transactions on Power Delivery 2025年第3期40卷 1421-1433页

作者： Fahad, Shah She, Buxin Yin, Junjie Li, Fangxing Cui, Hantao Bo, Rui Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Senior Power Systems Engineer ETAP Irvine CA USA Optimization & Control Group Parcific Northwest National Laboratory Richland USA Department of Electrical Engineering and Computer Science University of Tennessee at Knoxville Knoxville TN USA Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA

Conventionally, a virtual synchronous generator (VSG) is designed for islanded mode (IM) operation to meet specific operational requirements such as the rate of change of frequency (RoCoF). However, the operation of VSG designed for IM may not meet the operational and control criteria in grid connected mode (GCM) when the grid conditions vary. In addition, conventional VSG control technology does not consider the influence of the presynchronization scheme when connected to a weak grid, which degrades the RoCoF in IM. To overcome the aforementioned challenges, the proposed study presents a twin-delayed deep deterministic policy gradient (TD3) algorithm to improve the seamless transition performance of VSG from IM to GCM and vice versa. In the first step, the VSG-based power system model is used as a foundation for problem formulation of the proposed TD3 algorithm. Secondly, a reward function is designed according to the performance requirements, i.e., frequency and RoCoF requirements, of the VSG in order to guide the training of the agent in varying load, power reference, and grid conditions. Finally, the superiority of the proposed algorithm over existing methods is validated in MATLAB/SIMULINK and RTDS based real-time simulation environment. © 1986-2012 IEEE.

关键词： Deep reinforcement learning

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Enhanced Neural Differential Distinguisher for Speck32/64 Using Attention Mechanisms and Multi Ciphertext Inputs

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第19期49卷 197-210页

作者： Jiang, Xue Li, Min Kaiyrbek, Makulov Lakhno, Valerii Andrii, Sahun School of Computer and Information Engineering Bengbu University Bengbu233030 China Department of Computer Systems Networks and Cybersecurity National University of Life and Environmental Sciences of Ukraine Kyiv03041 Ukraine Department of Computer Science Caspian University of Technology and Engineering Almaty050151 Kazakhstan

In recent years, neural network-based differential distinguishers have demonstrated significant advantages in accuracy and effi-ciency over traditional differential distinguishers in symmetric cipher differential analysis. However, when dealing with ciphers involving a higher number of encryption rounds, neural network differential distinguishers still struggle to accurately identify ci-phertext pairs. To address this issue, this study proposes a neural network differential distinguisher model based on attention mechanisms and optimized ciphertext input structures. Specifically, the model first innovates the residual structure within the attention mechanism to maximize the weight of highly discriminative features, enhancing the feature extraction capability of the improved model. Secondly, a multi-scale convolution method is employed, integrating the network structure ideas of RegNet, with the addition of convolutional branches and optimization of activation functions, which further enhances the model's feature ex-traction capability. Finally, a multi-ciphertext input pattern is introduced to improve the input data information, and random key encryption is applied to the input ciphertext structure to construct multi-feature information representations of the ciphertext and encryption functions. The results from 5-8 rounds of experiments on Speck 32/64 indicate that the proposed new neural distinguisher can significantly improve discrimination accuracy to a maximum of 1.65%. On this basis, we carried out an optimization study on the construction method of the multi-ciphertext-pair dataset. The new dataset can increase the accuracy of the distinguisher by 49.16% compared to that of the single-ciphertext-pair case, and can extend the number of attack rounds from 7 to 8. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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