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2024 International Conference on Horizons of information technology and engineering, HITE 2024

作者： Asif, Mugheez Manan, Abdul Ur Rehman, Abdul Moiz Asghar, Mamoona Naveed Umair, Muhammad University of Central Punjab Faculty of Information Technology and Computer Science Lahore Pakistan University of Galway School of Computer Science Galway Ireland

ISBN: (纸本)9798331516055

In today's contemporary digital landscape, chat-bots have become indispensable tools across various sectors, streamlining customer service, providing personal assistance, automating routine tasks, and offering health advice. However, their potential remains underexplored in the realm of network security, particularly for intrusion detection. To bridge this gap, we propose an architecture chatbot specifically designed to enhance security within edge networks specifically for intrusion detection. Leveraging advanced machine learning algorithms, this chatbot monitors network traffic to identify and mitigate potential intrusions. By securing the network environment using an edge network managed by a Raspberry Pi module and ensuring ethical user consent, promoting transparency and trust, this innovative solution aims to safeguard sensitive data and maintain a secure workplace, thereby addressing the growing need for robust network security measures in the digital age. © 2024 IEEE.

关键词： Network intrusion

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Selective feature-based ovarian cancer prediction using MobileNet and explainable AI to manage women healthcare

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Multimedia Tools and Applications 2024年 1-22页

作者： Almujally, Nouf Abdullah Alzahrani, Abdulrahman Hakeem, Abeer M. Attiah, Afraa Umer, Muhammad Alsubai, Shtwai Polsinelli, Matteo Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Hafr Al Batin39524 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Management & amp Innovation Systems University of Salerno Fisciano84084 Italy Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

Ovarian cancer is a global health concern due to the unavailability of an effective screening strategy and is often diagnosed at a late stage with approximately 70% of the case which reduces the survival chances of patients. Initial diagnosis is challenging due to inconspicuous symptoms in its initial stages which complicate its timely diagnosis. Regular screenings, such as pelvic exams, ultrasounds, and blood tests targeting specific biomarkers can be helpful for early diagnosis. In addition, the use of machine learning models can help automate this process thereby assisting medical experts for accurate diagnosis. This study aims at timely and accurate detection of ovarian cancer using a transfer learning approach that uses the MobileNet model. Moreover, the Chi-square technique is used to extract the most impactful features for better accuracy. For experiments, the Soochow University ovarian cancer dataset is employed. Extensive experiments are performed using all features from the dataset, as well as, using Chi-square-based selective features. The best accuracy of 90.88% is achieved with the Xception model when all features are used. Experimental results show a substantial increase when selective features are used indicating a 98.49% accuracy using the MobileNet model with 20 most important features. In addition, precision, recall, and F1 scores of 99.13%, 99.27%, and 99.20% are obtained showing the model’s robustness and generalization. This study also employs Shapley additive explanations to explain the importance of various features toward the model’s output thereby providing transparency for the model’s decision-making process. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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Application of Visible/Near-Infrared Spectroscopy and Hyperspectral Imaging with Machine Learning for High-Throughput Plant Heavy Metal Stress Phenotyping:A Review

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Plant Phenomics 2023年第1期5卷 657-672页

作者： Yuanning Zhai Lei Zhou Hengnian Qi Pan Gao Chu Zhang School of Information Engineering Huzhou UniversityHuzhou 313000China College of Mechanical and Electronic Engineering Nanjing Forestry UniversityNanjing 210037China College of Information Science and Technology Shihezi UniversityShihezi 832003China

Heavy metal pollution is becoming a prominent stress on *** contaminated with heavy metals undergo changes in external morphology and internal structure,and heavy metals can accumulate through the food chain,threatening human *** heavy metal stress on plants quickly,accurately,and nondestructively helps to achieve precise management of plant growth status and accelerate the breeding of heavy metal-resistant plant *** chemical reagent-based detection methods are laborious,destructive,time-consuming,and *** internal and external structures of plants can be altered by heavy metal contamination,which can lead to changes in plants'absorption and reflection of ***/near-infrared(V/NIR)spectroscopy can obtain plant spectral information,and hyperspectral imaging(HSI)can obtain spectral and spatial information in simple,speedy,and nondestructive *** 2 technologies have been the most widely used high-throughput phenotyping technologies of *** review summarizes the application of V/NIR spectroscopy and HSI in plant heavy metal stress phenotype analysis as well as introduces the method of combining spectroscopy with machine learning approaches for high-throughput phenotyping of plant heavy metal stress,including unstressed and stressed identification,stress types identification,stress degrees identification,and heavy metal content *** vegetation indexes,full-range spectra,and feature bands identified by different plant heavy metal stress phenotyping methods are *** advantages,limitations,challenges,and prospects of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping are *** studies are needed to promote the research and application of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping.

关键词： Infrared consuming precise

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Adversarial Domain Adaptation for Non-Intrusive LOAD Monitoring 20

Adversarial Domain Adaptation for Non-Intrusive LOAD Monitor...

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20th International computer Conference on Wavelet Active Media technology and information Processing, ICCWAMTIP 2023

作者： Bongyi, Gao Bei, Hui School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9798350318982

Non-Intrusive Load Monitoring (NILM), known as energy disaggregation, is a method for determining the usage of separated devices by analyzing the overall energy consumption of an entire household. Understanding individual appliance energy consumption is crucial for customers to effectively manage usage and plan demand response programs. Recent advancements in data-driven methods have demonstrated promising performance in NILM. However, these methods rely on massive labeled data and would be dramatically degraded in novel environments. This paper presents ADAED, an energy disaggregation algorithm based on adversarial domain adaptation to monitor the loads of an unlabeled target domain by learning shared representations from a related source domain. Through the application of a sequence-to-point architecture and shared representations, ADAED can achieve more accurate predictions. Extensive experiments on three open benchmarks verify that our method can achieve better results than existing methods. © 2023 IEEE.

关键词： Energy utilization

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Impairment-Aware Symbolic Modulation for Automatic Signal Processing

Impairment-Aware Symbolic Modulation for Automatic Signal Pr...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Rengarajan, A. Agarwal, Ankita Bishnoi, Sunita Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Chemistry Jaipur India

ISBN: (纸本)9798350383348

Impairment-aware Symbolic Modulation for computerized sign Processing is a singular technique utilized in sign processing. This technique makes use of symbolic modulation to simulate sign degradations caused by natural or conventional impairments. The proposed method allows the signals to be modulated in a manner that's privy to impairments. The method uses a combination of neural networks and fuzzy common sense to perceive the most suitable modulated sign representation. This technique enables in improvement of sign intelligence with the aid of expanding the sign representation space. It similarly enables one to become aware of signal components accountable for the presence or absence of impairment and to conform to sign adjustments correctly. This method gives a green way of dealing with the diffusion of signal degradations and may be used to discover and classify alerts dependably. © 2024 IEEE.

关键词： Dependably Impairment Modulation Networks Processing Symbolic

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Dempster-shafer deep capsule attention model (DDCAM)

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Multimedia Tools and Applications 2025年 1-29页

作者： Mehravaran, Zahra Ghanizadeh, Ahmad Navid Hamidzadeh, Javad Harati, Ahad Mashhad Iran Department of Computer Science Saarland University Saarbrücken Germany Faculty of Computer Engineering and Information Technology Sadjad University Mashhad Iran

A capsule neural network faces significant challenges in achieving high accuracy on complex datasets due to its high computational complexity and limited ability to represent features. To overcome these limitations, this paper proposes a novel framework, Dempster-Shafer Deep Capsule Attention Model (DDCAM), that combines 3D convolution with attention mechanisms to improve feature representation. This model utilizes the Dempster-Shafer Theory to manage uncertainty and optimize feature selection, resulting in more reliable and accurate learning. In this architecture, dense capsule blocks are used to improve gradient flow and mitigate the effects of vanishing gradients. The DDCAM consistently outperforms state-of-the-art capsule networks on a wide range of datasets, especially complex datasets such as MNIST, CIFAR10, SVHN, and Fashion MNIST. DDCAM improves accuracy by 1.29% on CIFAR10 and 0.76% on SVHN compared to existing models, while achieving precision improvements of 1.22% and 1.08% on CIFAR10 and SVHN, respectively. Additionally, experiments conducted on the AffNIST dataset demonstrate the robustness of DDCAM in handling affine transformations, where it achieved significantly higher accuracy compared to existing models. These results illustrate the model’s enhanced ability to capture complex data relationships and improve both accuracy and precision, advancing capsule neural network performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Affine transforms

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An Innovative Structural V AR Analysis for Network Congestion Monitoring

An Innovative Structural V AR Analysis for Network Congestio...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kumar, Deepak Menaka, C. Das, Neeraj Vivekananda Global University Department of Mathematics Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

it is a revolutionary Structural Vector automobile regression (SV AR) approach for the cause of monitoring and predicting congested networks. The proposed technique utilizes community congestion-conscious prior data enforcement to improve the parameter estimation and forecasting overall performance of the SV AR. The community congestion-conscious regularization is generated from a distributed linear programming (DLP) set of rules. The proposed approach's performance is classed by using historic congestion facts for two congested networks in an urban place. The results show that the proposed technique considerably outperforms classic SVAR fashions in parameter estimation and forecasting accuracy. © 2024 IEEE.

关键词： Auto regression enforcement forecasting performance Structural

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Unraveling environmental influences on the spatial and temporal dynamics of cyanobacterial blooms in Lake Erhai during its early stage of eutrophication

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地球空间信息科学学报（英文版） 2024年第6期27卷中插14,1989-2007页

作者： Shangbo Yang Jianzhong Lu Xiaoling Chen Xuejiao Hou Zushuai Wei Jinru Wu State Key Laboratory of Information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhanChina School of Environmental Science and Engineering Southern University of Science and TechnologyShenzhenChina State Key Laboratory of Information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhanChina School of Geospatial Engineering and Science Sun Yat-Sen UniversityGuangzhouChina South China Institute of Environmental Sciences Ministry of Ecology and EnvironmentGuangzhouChina

Lake Erhai,a lake in the early stage of eutrophication,has been threatened by algal blooms(particularly the overproliferation of blue-green algae),which can have an impact on drinking water safety and the lake's *** the governing factors of cyanobacterial blooms is critical for taking timely and effective action during this key eutrophication-transition ***,long-term records of cyanobacterial bloom and its key dominating factors remain *** is,therefore,essential to understand the bloom dynamics and the driving forces before any control strategies can be *** investigated the cyanobacterial phycocyanin concentration variability based on satellite observations from 2003 to 2019,by using the empirical orthogonal function *** observed a decrease in the coverage of the dominant mode of variability in phycocyanin magnitudes compared to the period 2003 to 2011,with variations primarily occurring in the northern *** largest variability was identified to be predominant in July,and an apparent timing shift in variability was observed in December 2016 and *** 95％quantile regression model indicated a distinct upper boundary response in cyanobacteria proliferation to the joint Total Nitrogen(TN)and Total Phosphorus(TP)concentrations,which occurred in summer from 2003 to *** apparent response of cyanobacterial bloom to TP was observed during the winters from 2016 to ***,water level and TN:TP ratio played a central role in summer from 2003 to 2011,while from 2016 to 2019,TN:TP ratio was found to dominate in the summer *** winter,air temperature turned out to be a significant modulating factor compared to water *** results suggest that implementing a phosphorus reduction strategy,while controlling TN:TP ratio and suitable water level manipulation,should be considered to ensure the sustainability of Lake Erhai,especially under a consistent global warming scenario.

关键词： Cyanobacterial phycocyanin concentration spatio-temporal variability nutrient reduction remote sensing

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A low-cost buoyant force measurement device for monitoring sugar concentration in water solution

A low-cost buoyant force measurement device for monitoring s...

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2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Kaučić, Luka Kovačević, Ivana Aleksi, Ivan Keser, Tomislav Zidar, Josip Rimac-Drlje, Snježana Computer Science and Information Technology Osijek Faculty of Electrical Engineering Department of Computer Engineering and Automation Osijek Croatia Computer Science and Information Technology Osijek Faculty of Electrical Engineering Department of Communications Osijek Croatia

ISBN: (纸本)9798350322972

Monitoring sugar concentration during fermentation is crucial for producing high-quality alcoholic beverages. Traditional methods for measuring sugar concentration can be costly and time-consuming, especially for small-scale producers. In this study, we developed a low-cost buoyant force measurement device for monitoring sugar concentration in water solutions. The device consists of a buoyant object fully submerged in the solution and connected to a load-cell sensor. As the sugar concentration in the liquid increases, the buoyant force on the object increases, and the load-cell measures this force. Proposed device is calibrated using solutions of known sugar concentrations and its high accuracy and precision is presented. The device is tested in controlled environment to ensure accurate tracking changes in sugar concentration over time. Proposed device can be a valuable tool for small-scale producers looking to optimize their fermentation processes while minimizing costs. © 2023 IEEE.

关键词： Fermentation

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A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

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computer Modeling in engineering & sciences 2024年第7期140卷 907-927页

作者： Yong Ma Han Zhao Kunyin Guo Yunni Xia Xu Wang Xianhua Niu Dongge Zhu Yumin Dong School of Computer Information Engineering Jiangxi Normal UniversityNanchang330000China School of Digital Industry Jiangxi Normal UniversityShangrao334000China The College of Computer Science Chongqing UniversityChongqing400044China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing400030China School of Computer and Software Engineering Xihua UniversityChengdu610039China Electric Power Research Institute of State Grid Ningxia Electric Power Company Ltd. Yinchuan750002China College of Computer and Information Science Chongqing Normal UniversityChongqing401331China

Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to *** the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user ***,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource *** the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant *** response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance *** approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached ***,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and *** results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.

关键词： Mobile edge networks mobility fault tolerance cooperative caching multi-agent deep reinforcement learning content prediction

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