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Iraqi Journal for computer science and Mathematics 2024年第3期5卷 473-488页

作者： Al-Janabi, Anmar A. Al-Janabi, Sufyan Al-Khateeb, Belal Computer Science Department University of Technology – Iraq Baghdad10066 Iraq University of Anbar Ramadi Iraq College of Computer Science & Information Technology Anbar Ramadi31001 Iraq

Digitization of healthcare data has shown an urgent necessity to deal with privacy concerns within the field of deep learning for healthcare organizations. A promising approach is federated transfer learning, enabling medical institutions to train deep learning models collaboratively through sharing model parameters rather than raw data. The objective of this research is to improve the current privacy-preserving federated transfer learning systems that use medical data by implementing homomorphic encryption utilizing PYthon for Homomorphic Encryption Libraries (PYFHEL). The study leverages a federated transfer learning model to classify cardiac arrhythmia. The procedure begins by converting raw Electrocardiogram (ECG) scans into 2-D ECG images. Then, these images are split and fed into the local models for extracting features and complex patterns through a finetuned ResNet50V2 pre-trained model. Optimization techniques, including real-time augmentation and balancing, are also applied to maximize model performance. Deep learning models can be vulnerable to privacy attacks that aim to access sensitive data. By encrypting only model parameters, the Cheon-Kim-Kim-Song (CKKS) homomorphic scheme protects deep learning models from adversary attacks and prevents sensitive raw data sharing. The aggregator uses a secure federated averaging method that averages encrypted parameters to provide a global model protecting users’ privacy. The system achieved an accuracy rate of 84.49% when evaluated using the MIT-BIH arrhythmia dataset. Furthermore, other comprehensive performance metrics were computed to gain deeper insights, including a precision of 72.84%, recall of 51.88%, and an F1-score of 55.13%, reflecting a better understanding of the adopted framework. Our findings indicate that employing the CKKS encryption scheme in a federated environment with transfer cutting-edge technology achieves relatively high accuracy but at the cost of other performance metrics, which is lower

关键词： Federated learning

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A prediction model for improving virtual machine live migration performance in cloud computing using artificial intelligence techniques

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International Journal of computers and Applications 2024年第12期46卷 1069-1087页

作者： Alubaidan, Haya A. Aljameel, Sumayh S. Department of Computer Science College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

Over recent years, virtualization has worked as the powerhouse of the data centers. To positively influence datacenter utilization, power consumption, and management, live migration presents a technique which must be employed. Live migration refers to the method whereby an online virtual machine or an application is moved from one physical server to another without shutting down the client or the program. However, a migration’s qualities, which define service quality, have to be evaluated prior to a migration. In this paper, we propose a novel approach using predictive machine learning and deep learning models to forecast key metrics such as total migration time, downtime, and total data transferred for two migration types: pre-copy migration and post-copy migration. It builds on methods from regression machine learning and deep learning, and hyperparameter optimization, and feature engineering to push past reported solutions. The novelty of this work is the development of the hybrid model that includes the positive aspects from both of the machine learning and the deep learning with an emphasis on the increased accuracy of the model’s predictions. Concretely, the proposed deep learning framework based on GRU provided outstanding performance with 99.6% in total migration time where the transferred data GRU achieved 99.9% Moreover, in Down time target GRU reached 98.3%. This outperforms the other machine learning model and prior studies to become the best model for live migration prediction. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Adversarial machine learning

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Design of a ML-based trust prediction model using intelligent TrustBoxes in challenged networks

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International Journal of systems, Control and Communications 2024年第3期15卷 209-234页

作者： Barai, Smritikona Kundu, Anindita Bhaumik, Parama Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu India Department of Information Technology Jadavpur University Kolkata India

Challenged networks (CNs) contain resource-constrained nodes deployed in regions where human intervention is difficult. Opportunistic networks (OppNets) are CNs with no predefined source-to-destination paths. Due to their inherent properties, CNs and OppNets are highly susceptible to black-hole (BH) attacks, resulting in degraded packet-delivery ratio. In this work, an ML-based trust prediction model (MLTPM) is proposed to identify potential BH nodes in OppNets. MLTPM uses a novel function to calculate the total-trust-value (TTV) of each node. Intelligent TrustBoxes are introduced in the network to identify possible BH nodes, using TTV, along with five more node-behaviour features. TrustBoxes reduce the computational overhead of the resource-constrained nodes. Three simulated scenarios are compared - no detection, non-ML-based detection, and MLTPM, each using epidemic, prophet, and spray-and-wait routing protocols. MLTPM performs best with spray-and-wait, exhibiting about 25.21% and 80% mean improvement in delivery-ratio and dropped-message numbers respectively, compared to non-ML-based detection. An overall 12.62% improvement in delivery-ratio and 26.7% improvement in dropped messages is observed using MLTPM, compared to the above-mentioned scenarios. © 2024 Inderscience Enterprises Ltd.

关键词： HTTP

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SpikingMiniLM: energy-efficient spiking transformer for natural language understanding

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science China(Information sciences) 2024年第10期67卷 115-128页

作者： Jiayu ZHANG Jiangrong SHEN Zeke WANG Qinghai GUO Rui YAN Gang PAN Huajin TANG College of Computer Science and Technology Zhejiang University The State Key Lab of Brain-Machine Intelligence Zhejiang University Collaborative Innovation Center of Artificial Intelligence Zhejiang University Advanced Computing and Storage Laboratory Huawei Technologies Co. Ltd. College of Computer Science and Technology Zhejiang University of Technology MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University

In the era of large-scale pretrained models, artificial neural networks（ANNs） have excelled in natural language understanding（NLU） tasks. However, their success often necessitates substantial computational resources and energy consumption. To address this, we explore the potential of spiking neural networks（SNNs） in NLU——a promising avenue with demonstrated advantages, including reduced power consumption and improved efficiency due to their event-driven characteristics. We propose the SpikingMiniLM,a novel spiking Transformer model tailored for natural language understanding. We first introduce a multi-step encoding method to convert text embeddings into spike trains. Subsequently, we redesign the attention mechanism and residual connections to make our model operate on the pure spike-based paradigm without any normalization technique. To facilitate stable and fast convergence, we propose a general parameter initialization method grounded in the stable firing rate principle. Furthermore, we apply an ANN-to-SNN knowledge distillation to overcome the challenges of pretraining SNNs. Our approach achieves a macro-average score of 75.5 on the dev sets of the GLUE benchmark, retaining 98% of the performance exhibited by the teacher model MiniLMv2. Our smaller model also achieves similar performance to BERTMINIwith fewer parameters and much lower energy consumption, underscoring its competitiveness and resource efficiency in NLU tasks.

关键词： spiking neural networks natural language understanding spiking Transformer spike-based attention multi-step encoding ANN-to-SNN distillation

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Predicting Diabetes Disease Occurrence Using Logistic Regression: An Early Detection Approach

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2024年第1期5卷 160-167页

作者： Abdalrada, Ahmad Shaker Neamah, Ali Fahem Murad, Hayder Department of Software Faculty of Computer Science and Information Technology Wasit University Iraq Department of Computer Faculty of Computer Science and Information Technology Wasit University Iraq College of Medicine Wasit University Iraq

Diabetes disease is prevalent worldwide, and predicting its progression is crucial. Several model have been proposed to predict such disease. Those models only determine the disease label, leaving the likelihood of developing the disease unclear. Proposing a model for predicting the progression of disease becomes essential. Therefore, this article proposes a logistic regression model to anticipate the likelihood of Diabetes syndrome incidence. The model exploit capabilities of logistic regression by using sigmoid function. The model's performance was evaluated using the Pima Indians Diabetes dataset and demonstrated high accuracy, sensitivity, and specificity. The prediction accuracy rate was 77.6%, with a sensitivity of 72.4%, specificity of 79.6%, Type I Error of 27.6%, and Type II Error of 20.4%. Furthermore, the model indicates the feasibility of using laboratory tests, such as Pregnancies, Glucose, Blood Pressure, BMI, and DiabetesPedigreeFunction, to predict disease progress. The proposed model can aid patients and physicians in understanding the disease's progression and implementing timely interventions © 2024 college of Education, Al-Iraqia University. All rights reserved.

关键词： Forecasting

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An Industrial Intrusion Detection Method Based on Hybrid Convolutional Neural Networks with Improved TCN

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computers, Materials & Continua 2024年第1期78卷 411-433页

作者： Zhihua Liu Shengquan Liu Jian Zhang College of Computer Science and Technology Xinjiang UniversityXinjiang Uygur Autonomous RegoinUrumqi830000China

Network intrusion detection systems(NIDS)based on deep learning have continued to make significant ***,the following challenges remain:on the one hand,simply applying only Temporal Convolutional Networks(TCNs)can lead to models that ignore the impact of network traffic features at different scales on the detection *** the other hand,some intrusion detection methods considermulti-scale information of traffic data,but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal *** address both of these issues,we propose a hybrid Convolutional Neural Network that supports a multi-output strategy(BONUS)for industrial internet intrusion ***,we create a multiscale Temporal Convolutional Network by stacking TCN of different scales to capture the multiscale information of network ***,we propose a bi-directional structure and dynamically set the weights to fuse the forward and backward contextual information of network traffic at each scale to enhance the model’s performance in capturing the multi-scale temporal features of network *** addition,we introduce a gated network for each of the two branches in the proposed method to assist the model in learning the feature representation of each *** experiments reveal the effectiveness of the proposed approach on two publicly available traffic intrusion detection datasets named UNSW-NB15 and NSL-KDD with F1 score of 85.03% and 99.31%,respectively,which also validates the effectiveness of enhancing the model’s ability to capture multi-scale temporal features of traffic data on detection performance.

关键词： Intrusion detection industrial internet channel spatial attention multiscale features dynamic fusion multi-output learning strategy

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Optimizing Coverage in Wireless Sensor Networks Using the Tumbleweed Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 250-261页

作者： Zhang, Mengfei Chu, Shu-Chuan Liu, Nengxian Huang, Kuan-Chun Pan, Jeng-Shyang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Industrial and Systems Engineering Chung Yuan Christian University Taoyuan320314 Taiwan School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

Wireless sensor networks (WSNs) have found extensive applications across various fields, significantly enhancing the convenience in our daily lives. Hence, an in-creasing number of researchers are directing their attention toward the performance of sensor nodes, with a particular emphasis on addressing the coverage challenges in WSNs. Node coverage plays a crucial role in determining the detection performance of WSNs, making it imperative to attain maximum coverage with a given number of sensor nodes. This paper applies the Tumbleweed Algorithm (TA) to the coverage problem in WSNs and validates its effectiveness through simulation experiments. The results demonstrate its unique advantages in global search and avoiding local optima, providing a new solution for the WSN coverage problem and offering valuable references for future research and practical applications. The TA algorithm simulates the lifecycle of tumbleweed from seedling to mature plant and includes the concept of growth cycles. This paper compares the TA algorithm with PSO, GWO, DE, BOA, SCA, and AOA, setting different numbers of wireless sensor nodes and varying sensing radii for the experiments. The experimental results show that the TA algorithm significantly improves coverage rates under various configurations and consistently outperforms other commonly used algorithms. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Sensor nodes

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An Optimal Pricing and Ordering Policy with Trapezoidal-Type Demand Under Partial Backlogged Shortages

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Tsinghua science and technology 2024年第6期29卷 1709-1727页

作者： Chunming Xu Mingfei Bai Chenchen Wu Qiyue Wang Yiwei Wang Institute of Operations Research and Systems Engineering College of ScienceTianjin University of TechnologyTianjin 300384China School of Electrical and Electronic Engineering TianjinUniversity of TechnologyTianjin 300384China

Based on the retail inventory operation of Heilan Home,this study incorporates the price factor into inventory environment involving trapezoidal time-varying products.A joint pricing and ordering issue with deteriorating items under partial backlogged shortages is firstly explored in a fixed selling *** corresponding optimization model aiming at maximizing profit performance of inventory system is developed,the theoretical analysis of solving the model is further provided,and the modelling frame generalizes some inventory models in the existing ***,a solving algorithm for the model is designed to determine the optimal price,initial ordering quantity,shortage time point,and the maximum inventory ***,numerical examples are presented to illustrate the model,and the results show the robustness of the proposed model.

关键词： inventory trapezoidal-type demand pricing deteriorating items partial backlogged shortages

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Distribution,enrichment mechanism and risk assessment for fluoride in groundwater:a case study of Mihe-Weihe River Basin,China

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Frontiers of Environmental science & Engineering 2023年第6期17卷 63-83页

作者： Xingyue Qu Peihe Zhai Longqing Shi Xingwei Qu Ahmer Bilal Jin Han Xiaoge Yu College of Earth Science and Engineering Shandong University of Science and TechnologyQingdao 266590China College of Safety and Environmental Engineering Shandong University of Science and TechnologyQingdao 266590China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China Department of Resource and Civil Engineering Shandong University of Science and TechnologyTai'an 271019China

Due to the unclear distribution characteristics and causes of fluoride in groundwater of Mihe-Weihe River Basin(China),there is a higher risk for the future development and utilization of ***,based on the systematic sampling and analysis,the distribution features and enrichment mechanism for fluoride in groundwater were studied by the graphic method,hydrogeochemical modeling,the proportionality factor between conventional ions and factor *** results show that the fluorine content in groundwater is generally on the high side,with a large area of medium-fluorine water(0.5–1.0 mg/L),and high-fluorine water is chiefly in the interfluvial lowlands and alluvial-marine plain,which mainly contains HCO_(3)·Cl-Na-and HCO_(3)^(-)Na-type *** vertical zonation characteristics of the fluorine content decrease with increasing depth to the water *** high flouride groundwater during the wet season is chiefly controlled by the weathering and dissolution of fluorine-containing minerals,as well as the influence of rock weathering,evaporation and *** weak alkaline environment that is rich in sodium and poor in calcium during the dry season is the main reason for the enrichment of ***,an integrated assessment model is established using rough set theory and an improved matter element extension model,and the level of groundwater pollution caused by fluoride in the Mihe-Weihe River Basin during the wet and dry seasons in the Shandong Peninsula is defined to show the necessity for local management measures to reduce the potential risks caused by groundwater quality.

关键词： Groundwater in the Mihe-Weihe River Basin Distribution characteristics of fluorine Factors influencing fluoride Enrichment mechanism of fluorine Hydrogeochemical modeling Pollution and risk assessment

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CCGS-based discriminatory recognition of skeleton-based actions

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Multimedia Tools and Applications 2025年第6期84卷 3161-3174页

作者： Sameem, M Shujah Islam Department of Computer Science College of Computer Sciences & Information Technology King Faisal University Al Ahsa Saudi Arabia

As life forms feature joints, dynamic skeletons have been studied extensively as a potential tool for action detection. As a result of their focus on modelling skeletons, older methods were unable to adequately convey the whole range of motion at more erratic skeletal joints. Despite introducing spatial methods for unstructured data, detecting many simultaneous motions remains challenging. As part of this study, now article proposes a CCGS for skeleton-based action recognition by depicting skeletons organically on joints and therefore attempting to capture the spatiotemporal variation in the data. Let here propose a motion regression to learn the shape statistically from numerous data points since the representation of the joints is crucial to motion convolution. To achieve this sparsity in the underlying CCGS for efficient representation, now give spatial modelling of skeletons and formulate an optimisation problem with a slip-critical joint. Each joint has a strong or weak connection to the other joints in the same frame and to key joints in the action frames that have been processed. The coordinates of the skeleton sequence and the positions of the joints are then sent to the motion-caused graph, which has been tuned, so that features based on Chebyshev, spatial, and angular approximations of the motion may be learned. Also, the article examines the motion features approximation's representation of variation. The results of the experiments show that the recommended joints regression works and the proposed CCGS achieves state-of-the-art performance on the most popular datasets. The present research suggests a CCGS-based method for skeleton-based action recognition that captures spatiotemporal variations by intuitively depicting skeletons on joints. The experimental results suggest that the proposed method obtains state-of-the-art performance, with accuracy enhancements of up to 2% over existing methods on prominent datasets. © The Author(s), under exclusive licenc

关键词： Chebyshev approximation

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