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IEEE International Requirements Engineering Conference Workshops (REW)

作者： Ita Richardson Bilal Ahmad Shweta Premanandan Owen Doody Sarah Beecham Sofia Ouhbi Muneef Alsahmmari Åsa Cajander Dept of Computer Science and Information Systems Lero – the SFI Software Research Centre and Ageing Research Centre Health Research Institute University of Limerick Limerick Ireland Learnovate Centre Trinity College Dublin Dublin Ireland Dept of Informatics and Media Uppsala University Uppsala Sweden Dept of Nursing and Midwifery Health Implementation Science and Technology Cluster Health Research Institute University of Limerick Limerick Ireland Dept of Computer Science and Information Systems Lero – the SFI Software Research Centre University of Limerick Limerick Ireland Dept of Information Technology Uppsala University Uppsala Sweden Dept of Health Informatics College of Public Health and Health Informatics University of Hail Hail Saudi Arabia

As healthcare technology advances, it is important to ensure that everyone benefits from healthcare software, regardless of age or ability. That is why our research team has been working to identify and encapsulate the unique needs of two groups when developing software systems: older adults and people with mild intellectual and developmental disabilities. Through our research, we have developed design patterns that software engineers can use to create more usable healthcare software to support these groups. These patterns identify the generic requirements for usability including accessibility, crucial for creating software that meets the needs of marginalised populations. In this paper, we describe the steps we undertook to identify and develop these requirements. We discover many overlapping needs of these two cohorts. Our research aims to benefit older adults and people with mild intellectual and developmental disabilities and improve the lives of many others facing similar challenges. As we expand our research to other cohorts, we will be able to identify additional generic requirements that make healthcare software development more inclusive and effective.

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An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

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computers, Materials & Continua 2022年第4期71卷 199-213页

作者： Abdelhameed Ibrahim Hattan F.Abutarboush Ali Wagdy Mohamed Mohamad Fouad El-Sayed M.El-kenawy Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electrical Engineering Department College of EngineeringTaibah UniversityMedina42353Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyEgypt

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their *** size affects the quality factor and the radiation loss of the *** antennas can overcome the limitation of bandwidth for small *** learning(ML)model is recently applied to predict antenna *** can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated *** accuracy of the prediction depends mainly on the selected *** models combine two or more base models to produce a better-enhanced *** this paper,a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial *** base models are used namely:Multilayer Perceptron(MLP)and Support Vector Machines(SVM).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the *** Group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base *** proposed model is compared with three based models and the average ensemble *** results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

关键词： Metamaterial antenna machine learning ensemble model multilayer perceptron support vector machines

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Password-Sniffing Acoustic Keylogger Using Machine Learning

SSRN

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SSRN 2023年

作者： Akinbi, Alex Deniz, Erkan Ismael, Aras M. Rashid, Zryan N. Sengur, Abdulkadir School of Computer Science and Mathematics Liverpool John Moores University United Kingdom Firat University Technology Faculty Electrical-Electronics Engineering Department Elazig Turkey Sulaimani Polytechnic University College of Informatics Information Technology Department Sulaymaniyah Iraq

An acoustic keylogger is a form of side-channel attack where an attacker can extract and log what a victim typed on a keyboard by capturing the keystroke emanations and audio signals. Using a compromised smartphone microphone to capture keystroke audio recordings, this attack can be used to recognise confidential information such as passwords typed by a victim. In this paper, we demonstrate the training of a machine-learning model and develop a proof-of-concept password-sniffing acoustic keylogger. To this end, the input audio signals are initially converted to audio images by using the spectrogram representation of the Mel-Frequency Cepstral Coefficients (MFCC) method. MFCC applies the Fourier transform to yield the frequency content of the input signal. Then, a novel and less complex vision transformer-based approach namely ConvMixer is applied to the spectrogram images to detect the password according to its audio. In ConvMixer model group convolution and point convolution layers are located sequence for giving depth to the architecture. A novel key logger dataset is constructed by us and used in the experimental works. Various experiments are carried out and classification accuracy is applied for performance measurement tools. In the experimental works, an average 92.44% accuracy score is obtained with the proposed method. In addition, two pre-trained convolutional neural networks (CNN) models namely ResNet18 and VGG16 are used for comparison purposes. Comparisons show that the proposed approach has the potential for a compact acoustic keylogger system. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Watermarking and Encryption for Electronic Medical Records (EMR) Security Based on mCloud

Watermarking and Encryption for Electronic Medical Records (...

引用

Cyber Resilience (ICCR), International Conference on

作者： Haider Rasheed Abdulshaheed Yaseen Saadi Abbas Jamal F. Tawfeq Karamath Ateeq Saif. E. A. Alnawayseh Taher M. Ghazal Computer Engineering Techniques Department Baghdad College of Economic Sciences University Baghdad Iraq Electronic and Communications Engineering Department College of Engineering Al-Nahrain University Baghdad Iraq Department of Medical Instrumentation Technical Engineering Medical Technical College Al-Farahidi University Baghdad Iraq School of Computing Skyline University College University City Sharjah Sharjah UAE Department of Electrical Engineering College of Engineering Mutah University Al-Karak Jordan Computer Science Department Centre for Cyber Physical Systems Khalifa University Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Providing quality healthcare services and promoting collaborative clinical research are easier and more efficient with electronic medical record (EMR) systems. All aspects of care are included in electronic health records. Data characterized by volume, variety, and velocity is what IBM defines as big data. Therefore, we evidently have a significant problem managing “big EMR data” from a volume, accuracy, and velocity perspective. Data misuse and patient safety are significant risks associated with poorly implemented EMR-based systems. Storage, access, and retrieval of big data are all aspects of security to consider. Location is another important aspect of secure data use, in addition to strong access control mechanisms. Researchers are investigating secure mobile data usage based on location after recent reports of illegal trade and theft of patient data over mobile devices. To improve work flow dynamics and efficiency, healthcare facilities are increasingly using mobile devices. In spite of this, mobile devices are becoming more and more common. A new technique for preventing EMR infiltration and manipulation by hackers using the cloud is presented in this article.

关键词： Cloud computing Hospitals Watermarking Big Data Mobile handsets Robustness Safety

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xIDS-EnsembleGuard: An Explainable Ensemble Learning-based Intrusion Detection System

arXiv

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arXiv 2025年

作者： Adil, Muhammad Jan, Mian Ahmad Hakim, Safayat Bin Song, Houbing Herbert Jin, Zhanpeng Deptartment of Computer Science University at Buffalo Buffalo United States College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates School of Information Systems University of Maryland Baltimore County United States School of Future Technology South China University of Technology Guangdong China University at Buffalo Buffalo United States

In this paper, we focus on addressing the challenges of detecting malicious attacks in networks by designing an advanced Explainable Intrusion Detection System (xIDS). The existing machine learning and deep learning approaches have invisible limitations, such as potential biases in predictions, a lack of interpretability, and the risk of overfitting to training data. These issues can create doubt about their usefulness, and transparency, and decrease the trust of involved stakeholders. To overcome these challenges, we propose an ensemble learning technique called the"EnsembleGuard". This approach uses the predicted outputs of multiple models, including tree-based (LightGBM, GBM, Bagging, XGBoost, CatBoost) and deep learning models such as neural network (LSTM (long short-term memory networks) and GRU (gated recurrent unit), to maintain a balance and achieve trustworthy results. Our work is unique because it combines both tree-based and deep learning models to design an interpretable and explainable meta-model through model distillation. By considering the predictions of all individual models, our neta-model effectively addresses key challenges, and ensures both explainable and reliable results. We evaluate our model using well-known datasets, including UNSWNB15, NSL-KDD, and CIC-IDS-2017, to assess its reliability against various types of attacks. During analysis, we found that our model outperforms both tree-based models and other comparative approaches when it comes to different kinds of attack scenarios. © 2025, CC BY-NC-ND.

关键词： Network intrusion

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WIP: Technical Interview Preparation Initiative: Promoting Faculty Awareness and Intervention with Computing Majors

WIP: Technical Interview Preparation Initiative: Promoting F...

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Frontiers in Education (FIE) Conference

作者： Edward Dillon Kinnis Gosha Krystal L. Williams John J. Porter Portia Smith Department of Information Systems University of Maryland Baltimore County Baltimore USA Department of Computer Science Morehouse College Atlanta USA Department of Educational Leadership and Policy Analysis University of Wisconsin-Madison Madison USA Executive Recruiter & Director of Programs and Partnerships KARAT Overland Park USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This innovative practice WIP paper describes an initiative that seeks to promote faculty awareness and interventions towards student-based technical interview preparations. In the field of Computing, there is a trend for having a high demand of jobs in industry and a low supply of candidates to fill them. Literature indicates notable challenges that could pose as influential factors for this job/candidate ratio discrepancy in the field: 1) student retention challenges in computing areas at the collegiate level; 2) fostering stronger K-12 student interests and engagement towards computing fields as a career choice; and 3) representation barriers that exist within marginalized groups in computing. Yet, computing majors' inability to showcase their developed computational skills as prospective candidates to tech employers and hiring managers can persist as another barrier. Candidates are typically expected to demonstrate their computational skills and abilities practically. A candidate's inability to demonstrate such skills and abilities effectively could lead to missed job opportunities and even lucrative career pursuits in tech. One notable approach used by tech industry companies to examine the skillset compositions of their candidates is through technical interviews. This interview-based examination allows a hiring manager(s) to observe a candidate's ability to solve problems computationally in a think-aloud format, which also enables these professionals to gauge the candidate's verbal and communication skills during this interaction. When cross referencing the practice of technical interview examination in industry with the styles of learning and application fostered in computing classrooms in the academy, there exists a potential contrast between this expectation imposed by tech industry companies and professional preparation protocols fostered at academic institutions. There are computing-focused departments/schools and institutions at large that have identified

关键词： Industries Surveys Protocols Engineering profession Conferences Refining Stars Companies Market research Interviews

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Partial Discharge Measurements in Substation with Synchronised Power Quality Measurements

Partial Discharge Measurements in Substation with Synchronis...

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2022 9th International Conference on Condition Monitoring and Diagnosis (CMD)

作者： Shekhar Mahmud Faisal. P. Mohamed Md Saiful Islam Saleh Hassan Elkelani Babaa George Chen Systems Engineering Department Military Technological College Sultanate of Oman Civil Engineering Department Military Technological College Sultanate of Oman Electronics and Computer Science University of Southampton UK

ISBN: (数字)9784886864314

ISBN: (纸本)9781665470155

This paper presents requirement of a system to synchronously monitor partial discharge activity with synchronized power quality (PQ) measurements from a substation. Increased use of non-linear loads in power systems leads to higher harmonic content which has an impact on partial discharge activity in high voltage equipment. Hence there is a requirement to synchronously monitor PD activity with PQ measurements, which can inform asset manager to take management decisions affecting the maintenance regimes that will result improved power quality PQ or minimising the cost of insulation repair. Such a system to synchronously monitor PD with PQ is not readily available. This paper presents a system which can monitor PD activity with synchronized PQ measurements using Field Programmable Gate Arrays (FPGA) and Global Positioning System (GPS). The system design, laboratory tests and on-site measurements are discussed.

关键词： Partial discharge measurement Partial discharges Power measurement Substations Power quality Maintenance engineering Synchronization

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State Estimation for Integrated Energy systems: Motivations, Advances, and Future Work

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IEEE Transactions on Power systems 2024年第4期40卷 3057-3073页

作者： Zhao, Junbo Srivastava, Ankur Guo, Ye Cetenovic, Dragan Lin, Yuzhang Levi, Victor Yin, Guanxiong Huang, Manyun Zhang, Tingting Li, Zhigang Chen, Yanbo Exposito, Antonio G. Zhang, Wen Zheng, Weiye Chakrabarti, Saikat Li, Yang Sharma, Ankush Kamwa, Innocent Guo, Qinglai Terzija, Vladimir Mili, Lamine University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States Tsinghua University Tsinghua-Berkeley Shenzhen Institute Shenzhen518055 China The University of Manchester Department of Eee ManchesterM13 9PL United Kingdom University of Kragujevac Faculty of Technical Sciences Čačak Čačak32000 Serbia New York University Department of Electrical and Computer Engineering NY11201 United States Tsinghua University Department of Electrical Engineering Beijing100084 China Hohai University College of Energy and Electrical Engineering Nanjing210098 China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan250061 China The Chinese University of Hong Kong-Shenzhen School of Science and Engineering Shenzhen518172 China North China Electric Power University School of Electrical and Electronic Engineering Beijing102206 China University of Sevilla Spain Sevilla41092 Spain South China University of Technology School of Electrical Power Engineering Guangzhou510641 China Indian Institute of Technology Kanpur Department of Electrical Engineering Kanpur208016 India Northeast Electric Power University School of Electrical Engineering Jilin132012 China Laval University Department of Electrical and Computer Engineering QuebecQCG1V 0A6 Canada Tsinghua University State Key Laboratory of Power Systems Department of Electrical Engineering Beijing100084 China Newcastle University School of Engineering Newcastle upon TyneNE1 7RU United Kingdom Virginia Polytechnic Institute and State University Bradley Department of Electrical and Computer Engineering Falls ChurchVA22043 United States

This paper summarizes the technical activities of a three-year-long IEEE Task Force (TF) on State Estimation (SE) for Integrated Energy systems (IES). It presents the formal definition and characteristics of IES, along with the comprehensive discussion on Electric Power systems (EPS) model, and static and dynamic models associated with heating and natural gas systems. The paper also identifies the barriers of SE for IES, such as estimation modeling, observability analysis, and measurement requirements, together with addressing multi-scale dynamics. An extensive comparative analysis between Integrated Energy systems-State Estimation (IES-SE) and more established Electric Power System-State Estimation (EPS-SE) is presented. The paper also provides future research needs and directions related to IES-SE. © 1969-2012 IEEE.

关键词： Power system analysis

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The Application of Effective Machine Learning Tools in Digital Marketing for Enhancing Brand Presence and Image Among the Fast-Moving Consumer Goods in the Developing Countries 2

The Application of Effective Machine Learning Tools in Digit...

引用

2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Nirmal, Ekanath Sitaram Narang, Pravin Rajeswari, T.S. Usman, Mohammed Subha, B. Jadhav, Vandana Sandeep Arts Commerce and Satral College Savitribai Phule Pune University Department of Political Science Pune India Dr v N Bedekar Institute of Management Studies Department of Management India Koneru Lakshmaiah Education Foundation Department of English AP Vaddeswaram India College of Economics Management and Information Systems University of Nizwau Department of Management Oman Kristu Jayanti College Department of Management Bengaluru India School of Computer Sciences and Engineering Sandip University Nashik India

ISBN: (纸本)9781665437899

By using updated information techniques to digital marketing activities, increasing technological progress generates competitive edge over its rivals. By extracting insights from massive amounts of data obtained, machine learning (ML) can forecast future trends and assist decision-making. This feature has a significant impact on and accelerates an organisational future decision-making process. Machine learning regarding digital clients helps understand him properly by categorising the vast cache of data generated every day into many sectors and analysing it to develop patterns. There is no apparent boundary between digital and non-digital technology. Digital technology has an impact on human behaviour. Image processing and machine learning are widely employed to change a company's operations. Both input and output have an impact on the machine's learning abilities, which are constantly improving. The research given here relies on three diverse groups' decision and implementation of machine learning-driven analytical tools: 'marketing agencies', 'media businesses', and 'advertisers'. This research paper has considered method such as secondary qualitative method to collect both statistical and evidence-based information. © 2022 IEEE.

关键词： Decision making

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Prediction of groundwater table for Chennai Region using soft computing techniques

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Arabian Journal of Geosciences 2022年第9期15卷 1-19页

作者： Ramasamy, Viswanathan Alotaibi, Youseef Khalaf, Osamah Ibrahim Samui, Pijush Jayabalan, Jagan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Science College of Computer and Information Systems Umm Al-Qura University Makkah Saudi Arabia Al-Nahrain University Al-Nahrain Nano-renewable Energy Research Center Baghdad Iraq Department of Civil Engineering National Institute of Technology Patna Patna India Department of Civil Engineering Galgotias University Greater Noida India

Groundwater is one of the fundamental sources for the people in Chennai Region; also, determination of groundwater depth will be utilized for prior planning on excavation and construction purposes. This research article determines the depth of groundwater table by adopting various soft computing techniques like support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), and backpropagation (BP). The dataset was collected from the Tamil Nadu Water Supply and Drainage Board (TWAD), Chennai. It consists of input latitude (Lx), longitude (Ly), and the depth of groundwater (dw). The data were processed, and it was incorporated in the above-stated predicting models. The color map was the yield from the developed models. The performance of the developed model was assessed by various statistical parameters, and it was found that all the adopted models yield better performance for predicting the groundwater table. The comparison between the models depicted that support vector machine gave the promising result in determining the groundwater table with an equation for the futuristic purposes. Multifarious statistical analysis was computed in order to justify the attainment of the adopted models. The Taylor diagram and William’s plot were also included in order to substantiate the capability of the developed soft computing models to foretell the groundwater depth for the Chennai City.

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