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A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid

Building Simulation 2024年第3期17卷 441-457页

作者： Mohammad Kaosain Akbar Manar Amayri nizar Bouguila Concordia Institute for Information Systems Engineering Concordia UniversityMontrealCanada

Non-intrusive load monitoring(NILM)is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial *** plays a pivotal role in modernizing building energy management by disaggregating total energy consumption into individual appliance-level *** enables informed decision-making,energy optimization,and cost ***,NILM encounters substantial challenges like signal noise,data availability,and data privacy concerns,necessitating advanced algorithms and robust methodologies to ensure accurate and secure energy disaggregation in real-world *** learning techniques have recently shown some promising results in NILM research,but training these neural networks requires significant labeled *** initial sets of labeled data for the research by installing smart meters at the end of consumers’appliances is laborious and expensive and exposes users to severe privacy *** is also important to mention that most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states(On/Off)from their respective energy consumption *** paper proposes a novel semi-supervised multilabel deep learning technique based on temporal convolutional network(TCN)and long short-term memory(LSTM)for classifying appliance operation states from labeled and unlabeled *** two thresholding techniques,namely Middle-Point Thresholding and Variance-Sensitive Thresholding,which are needed to derive the threshold values for determining appliance operation states,are also compared *** superiority of the proposed model,along with finding the appliance states through the Middle-Point Thresholding method,is demonstrated through 15%improved overall improved F1micro score and almost 26%improved Hamming loss,F1 and Specificity score for the p

关键词： semi-supervised learning non-intrusive load monitoring middle-point thresholding deep learning TCN LSTM

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Study of New Planar Resonator’s Topologies Little Perceptible in Variations of Substratum’s Height

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Circuits and Systems 2012年第3期3卷 223-229页

作者： nizar zrigui Lahbib Zenkouar Seddik Bri Department of Electrical Engineering Graduate School of Technology Moulay Ismail university Meknes Morocco Laboratory of Electronics and Communication (LEC) Ecole Mohammadia d’Ingénieurs (EMI) Mohammed V university - Agdal Rabat Morocco

The mobile communication (radio, mobile, satellite telephony…) insert band filters which are required to comply with stringent pass-band characteristics concerning the selectivity, the group delay, the insertion losses and the mass and volume. The mass and volume of payload electronic equipment is a significant contributor to the overall cost of space systems. Within this framework, many improvements were made in the design of filters and multiplexers which represent a considerable portion of the overall satellite payload. The High Temperature Superconducting (HTS) planar technology permits to realize compact size filtering devices. Moreover, it could also provide a superior performance not attainable by any other technology. However, not controllable parameters in the manufacturing processes (indistinctness of the engraving, the not uniformity of substratum thickness) do not allow to obtain a precision on the echo’s frequency and to keep a high quality factor. So, it is necessary to use method to tune the center frequency of filters after the fabrication or to study new resonator topologies which present lower sensitivity to LAIO3 substrate thickness. This last point is the object of this article.

关键词： Resonator Planar Technology Topologies Echo’s Frequency Substratum’s Height Quality Factor

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Alert Device for Social Distancing

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Communications and Network 2024年第2期16卷 31-50页

作者： Amer nizar AbuAli College of Computer Science and Engineering Taibah University Medina Saudi Arabia

Social distancing among people is vital in minimizing spread of COVID-19 within community and can be effective in flattening the outbreak. This research work focuses on developing a close contact proximity detection system among smartphone users, particularly of COVID-19 patient, using Bluetooth signal to identify and analyze close contact proximity and social distancing from other anonymous smartphone users in their surroundings. Covering a physical space of six feet, a mandatory safety measure in shopping centers, schools, and other crowded areas, is a social solution advocated by World Health Organization (WHO) officials in this COVID-19. Everyone is concerned about their safety in the COVID-19 environment, so we came up with the concept of producing this new equipment. Most of the time, our attention is drawn to those in front of us and to our sides, but we are unable to keep track of those behind us. The major goal of this project is to keep individuals at a safe distance from one another. PIR sensor is used in this proposed work. Why did the World Health Organization (WHO) put 6 feet as a social distancing? When someone coughs or sneezes, small droplets are spread from the cough or sneeze. If you are in close proximity, you can breathe in those droplets, which may contain the COVID-19 virus, according to the World Health Organization. Vanderbilt University infectious disease expert Dr. William Schaffner said the “6 feet distance” rule comes from studies of respiratory physiology. Schaffner explains that even “without a cough or sneeze, the exhaled air mixes with the surrounding air within a distance of 3 to 6 feet, which is known as the breathing zone.” Schaffner continues: “If you are standing 3 to 6 feet away from me, you may inhale droplets that spread through coughing or sneezing. Of course, if I am infected with the virus, these droplets will contain the virus.”

关键词： Social Distancing COVID-19 Pandemic

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Using IoT to Smart Security Door System with Face Tracking Camera

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Intelligent Information Management 2024年第4期16卷 157-175页

作者： Amer nizar AbuAli College of Computer Science and Engineering Information Systems Department Taibah University Medina KSA

Security is a serious concern, whether it may be the security of assets, data and human life. Providing humans with security and safety for their belongings and items is an important need. A smart lock door project/ with different types of methods for entry, like fingerprint and authentication PIN code is suggested with an unnoticeable face tracking camera capturing a photo in case of error data entry. It is to be controlled via the user’s smartphone using Blynk with the implementation of IoT. This technology is made with two microcontrollers. ESP32 is used to control the solenoid lock, fingerprint sensor and keypad. ESP32-CAM is used to capture a photo and send it to the owner’s smartphone to be viewed on Telegram application. Many conclusions are extracted from system results, as well as suggested ideas for future work.

关键词： Security Block Diagram Microcontroller Bluetooth Dual Mode

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Utilization of Logistical Regression to the Modified Sine-Gordon Model in the MST Experiment

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Open Journal of Modelling and Simulation 2024年第2期12卷 43-58页

作者： nizar J. Alkhateeb Hameed K. Ebraheem Eman M. Al-Otaibi Department of Electronic Engineering Technology College of Technological Studies (CTS) The Public Authority of Applied Education and Training (PAAET) Kuwait City Kuwait

In this paper, a logistical regression statistical analysis (LR) is presented for a set of variables used in experimental measurements in reversed field pinch (RFP) machines, commonly known as “slinky mode” (SM), observed to travel around the torus in Madison Symmetric Torus (MST). The LR analysis is used to utilize the modified Sine-Gordon dynamic equation model to predict with high confidence whether the slinky mode will lock or not lock when compared to the experimentally measured motion of the slinky mode. It is observed that under certain conditions, the slinky mode “locks” at or near the intersection of poloidal and/or toroidal gaps in MST. However, locked mode cease to travel around the torus;while unlocked mode keeps traveling without a change in the energy, making it hard to determine an exact set of conditions to predict locking/unlocking behaviour. The significant key model parameters determined by LR analysis are shown to improve the Sine-Gordon model’s ability to determine the locking/unlocking of magnetohydrodyamic (MHD) modes. The LR analysis of measured variables provides high confidence in anticipating locking versus unlocking of slinky mode proven by relational comparisons between simulations and the experimentally measured motion of the slinky mode in MST.

关键词： Madison Symmetric Torus (MST) Magnetohydrodyamic (MHD) Sine-Gordon Toroidal Dynamic Modelling Reversed Field Pinch (RFP) Logistical Regression

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Dunbar Syndrome and Hypoxic Hepatitis: An Unusual Presentation

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Open Journal of Gastroenterology 2024年第6期14卷 226-232页

作者： Hajar Cherkaoui Ismail Chaouech Yousra El Abidi Haroun Robleh Asmae Lamine Maria Lahlali Nada Lahmidani Amine Mekkaoui Mounia Elyousfi Dafr Allah Benajah Adil Ibrahimi Mohammed El Abkari nizar Bouardi Mohammed Maaroufi Hakima Abid Department of Gastroenterology University Hospital Hassan II Fez Morocco Faculty of Medicine and Pharmacy Fez Morocco Department of Radiology University Hospital Hassan II Fez Morocco

Hypoxic hepatitis, also known as ischemic hepatitis, is characterized by acute hepatocellular injury due to inadequate oxygen delivery to the liver. Celiac trunk stenosis can lead to hepatic ischemia and subsequent liver damage. We present the case of an 81-year-old patient with a history of hypertension, ischemic heart disease, hypothyroidism, and biliary lithiasis, who developed hypoxic hepatitis secondary to Dunbar syndrome and a stenosis of the superior mesenteric artery. The patient improved symptoms and liver function tests with conservative management, including intravenous fluids and supportive care. Long-term management involved continued antiplatelet therapy and statins, with consideration of further interventions for celiac trunk stenosis. This case underscores the importance of recognizing Dunbar syndrome as well as superior mesentery trunk stenosis as a potential cause of hypoxic hepatitis. It highlights the need for multidisciplinary management in such cases.

关键词： Dunbar Syndrome Hypoxic Hepatitis Stenosis of Mesentery Artery

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Community Knowledge and Attitude about Burns First Aid (BFA) in Jazan, Saudi Arabia

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Journal of Biosciences and Medicines 2024年第4期12卷 135-148页

作者： Md. Mazharul Hoque Mohamed E. Moukhyer nizar A. Khamjan Bahja Al Siddig Elsie Maguen Shane Alam Abdulrahman Ibrahim Ali Hakami Siraj Yahya Mohammed Mobarki Bandar Hassan Sulaiman Ghazwani Ayman Abdulrhman Alhathiq Lamyaa El Hassan Aziza Mukhayer Emergency Medical Services Department Faculty of Applied Medical Sciences Jazan University Jazan Saudi Arabia Public Health Programmes School of Medicine University of Limerick Limerick Ireland School of Medicine Ahfad University for Women Omdurman Sudan Department of Medical Laboratory Technology Faculty of Applied Medical Sciences Jazan University Jazan Saudi Arabia Department of Basic Medical Sciences Faculty of Medicine Jazan University Jazan Saudi Arabia Department of Health Promotion University of Maastricht Maastricht Netherland

Introduction and Significance: Burn injury (BI) is a considerable health issue which is responsible for around 300,000 deaths and affecting about 11 million people every year worldwide. In Saudi Arabia, the prevalence of BIs array from 112 to 518 per 100,000 per year. The appropriate awareness of performing first aid could facilitate to improve the outcomes of burns. Purpose and Objectives: To appraise the community that acknowledges burns, first aid, and associated factors among the community population in Jazan City, Saudi Arabia. The paper aims to identify limitations to encourage additional research and persuade legislators to develop improved burn-injury care recommendations and training programs. Materials and Methods: An observational-based sample survey was conducted among the people who live in Jazan City aging 13 years or more, during April 5 to May 5, 2023. Data collection was done by a validated online self-administrated questionnaire sent randomly to community members in different parts of Jazan City via social media platforms. Collected data were coded and cleaned by an excel program, and finally exported on SPSS 26.0 software. The variables were analyzed using descriptive statistics like frequencies and percentages. Also, the Chi-square test was used to investigate the relation between different variables, with a significance value of P Results: This study included 243 participants (about 62%) among them were mostly male participants (151) having a university degree. The majority of participants 75% did not take any form of BFA training in the past. This study shows that 69.9% of the participants have inadequate awareness, despite 72% having a constructive attitude towards burn first aid. Previous burn-related first aid training was significantly associated with participants’ knowledge of BFA at a p-value less than 0.05. Conclusion: This study indicates a high frequency of Jazan population having inadequate knowledge of burn first aid despite the high

关键词： Burn Injury (BI) Burns First Aid Knowledge First Aid Training Jazan City

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Effective Frameworks Based on Infinite Mixture Model for Real-World Applications

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Computers, Materials & Continua 2022年第7期72卷 1139-1156页

作者： Norah Saleh Alghamdi Sami Bourouis nizar Bouguila Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia College of Computers and Information Technology Taif UniversityTaif21944Saudi Arabia The Concordia Institute for Information Systems Engineering(CIISE) Concordia UniversityMontrealQC H3G 1T7Canada

Interest in automated data classification and identification systems has increased over the past years in conjunction with the high demand for artificial intelligence and security *** particular,recognizing human activities with accurate results have become a topic of high *** the current tools have reached remarkable successes,it is still a challenging problem due to various uncontrolled environments and *** this paper two statistical frameworks based on nonparametric hierarchical Bayesian models and Gamma distribution are proposed to solve some realworld *** particular,two nonparametric hierarchical Bayesian models based on Dirichlet process and Pitman-Yor process are *** models are then applied to address the problem of modelling grouped data where observations are organized into groups and these groups are statistically linked by sharing mixture *** choice of the Gamma mixtures is motivated by its flexibility for modelling heavy-tailed *** addition,deploying the Dirichlet process prior is justified by its advantage of automatically finding the right number of components and providing nice ***,a learning step via variational Bayesian setting is presented in a flexible *** priors over the parameters are selected appropriately and the posteriors are approximated effectively in a closed *** results based on a real-life applications that concerns texture classification and human actions recognition show the capabilities and effectiveness of the proposed framework.

关键词： Infinite Gamma mixture model variational Bayes hierarchical Dirichlet process Pitman-Yor process texture classification human action recognition

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Bridging finite element and deep learning: High-resolution stress distribution prediction in structural components

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Frontiers of Structural and Civil Engineering 2022年第11期16卷 1365-1377页

作者： Hamed BOLANDI Xuyang LI Talal SALEM Vishnu Naresh BODDETI nizar LAJNEF Department of Civil and Environmental Engineering Michigan State UniversityEast LansingMI 48824USA Department of Computer Science and Engineering Michigan State UniversityEast LansingMI 48824USA

Finite-element analysis(FEA)for structures has been broadly used to conduct stress analysis of various civil and mechanical engineering *** methods,such as FEA,provide high fidelity results but require the solution of large linear systems that can be computationally ***,Deep Learning(DL)techniques can generate results significantly faster than conventional run-time *** can prove extremely valuable in real-time structural assessment *** proposed method uses deep neural networks in the form of convolutional neural networks(CNN)to bypass the FEA and predict high-resolution stress distributions on loaded steel plates with variable loading and boundary *** CNN was designed and trained to use the geometry,boundary conditions,and load as input to predict the stress *** proposed technique’s performance was compared to finite-element simulations using a partial differential equation(PDE)*** trained DL model can predict the stress distributions with a mean absolute error of 0.9%and an absolute peak error of 0.46%for the von Mises stress *** study shows the feasibility and potential of using DL techniques to bypass FEA for stress analysis applications.

关键词： Deep Learning finite element analysis stress contours structural components

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MNN-XSS:Modular Neural Network Based Approach for XSS Attack Detection

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Computers, Materials & Continua 2022年第2期70卷 4075-4085页

作者： Ahmed Abdullah Alqarni nizar Alsharif Nayeem Ahmad Khan Lilia Georgieva Eric Pardade Mohammed Y.Alzahrani Department of Computer Sciences and Information Technology AlBaha UniversityAlBahaSaudi Arabia Department of Computer Science Heriot-Watt UniversityEdinburghUK Department of Computer Science and Information Technology La Trobe UniversityMelbourneVIC 3086Australia

The rapid growth and uptake of network-based communication technologies have made cybersecurity a significant challenge as the number of cyber-attacks is also increasing.A number of detection systems are used in an attempt to detect known attacks using signatures in network *** recent years,researchers have used different machine learning methods to detect network attacks without relying on those *** methods generally have a high false-positive rate which is not adequate for an industry-ready intrusion detection *** this study,we propose and implement a new method that relies on a modular deep neural network for reducing the false positive rate in the XSS attack detection *** were performed using a dataset consists of 1000 malicious and 10000 benign *** model uses 50 features selected by using Pearson correlation method and will be used in the detection and preventions of XSS *** results obtained from the experiments depict improvement in the detection accuracy as high as 99.96%compared to other approaches.

关键词： Cybersecurity XSS deep learning modular neural network

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