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A Low-Cost Smart Office Design Framework Using Arduino

Advances in Internet of Things 2023年第3期13卷 83-108页

作者： Ssemwogerere Rajab Nabagesera Veronica Kawalya Melese Alemsra Tsehayu Logose Masitula Wamwoyo Faruk Rahman Shiddiqur Ekwaro Dominic Mbalyowere Pasis Maritah Nambobi Mutwalibi Sayo Nakeleh Turay Mugerwa Derrick Kasagga Usama Seguya Asad Department of Computer Science & Information Technology Islamic University in Uganda Kampala Uganda College of Computing & Informatics Technology Makerere University Kampala Uganda School of Information and Software Engineering University of Electronic Science & Technology of China Chengdu China School of Computer Science Neijiang Normal University Neijiang China School of Computer Science and Engineering University of Electronic Science & Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science & Technology of China Chengdu China

The main motive of our research work is security enhancement and light energy conservation. This paper describes a study investigating the potential of a controlled office solution by integrating the Internet of Things (IoT) with wireless sensor networks (WSNs). A prototype of a smart office is developed using a global system of mobile Bluetooth and Radio Frequency Identification (RFID) technology. The user can turn on and off the fan remotely at any time. This prototype focuses on security and provides human-friendly assistance when in or out of the Office by integrating a mobile application platform. The innovative automated smart Office is designed with intelligent Security doors, lights, alarms, temperature humidifiers, and bright Liquid Crystal Display (LCD) screens for viewing. Our study has opened up virtual possibilities for producing cheap innovative frameworks in this Generation of IoT and the fifth Generation (5G) technology. Therefore, when implemented, this innovation will ease and improve human quality of life. So, this paper aims to provide a low-cost, effective Internet of the things-based automated smart Office.

关键词： Intelligent Office Automation System Wireless Sensor Networks Internet of Things Ultrasonic Radio Frequency Identification Arduino

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Continuous Prompt for Chemical Language Model Aided Anticancer Synergistic Drug Combination Prediction

Continuous Prompt for Chemical Language Model Aided Anticanc...

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IEEE International Conference on Big Data

作者： Guannan Geng Lingling Zhao Chunyu Wang Xiaoyan Liu Junjie Wang Department of Endocrinology The First Affiliated Hospital of Harbin Medical University Harbin China Faculty of Computing Harbin Institute of Technology Harbin China Department of Medical Informatics School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China

Identifying synergistic drug combinations is paramount significance in addressing complex diseases while reducing the risks of toxicities and other adverse effects. Although a plethora of computational methods have been proposed in this domain, most of them are underpinned only by physicochemical or biological features. Recently, Chemical Language Models (CLMs) are shown to be capable of learning better representations that hold utility across diverse tasks, from molecular property prediction, de novo drug design, drug-target interaction, and more. In this study, we proposed CLMSyn, a continuous prompt for CLM aided synergistic drug combinations prediction. Unlike existing works employ CLMs for downstream tasks, we adopt the prompt learning to fine-tune CLM, that is, only train small-scale prompt while keeping CLM fixed. Furthermore, we harness the the multi-head attention mechanism to fuse the learned vector from the CLM, chemical descriptors and gene expression of cell line. A comprehensive array of experiments conducted on a benchmark dataset, encompassing four distinct synergy types, substantiates the superior performance of CLMSyn when contrasted against existing state-of-the-art methods. These empirical findings provide compelling evidence attesting to the efficacy of CLMSyn as a potent instrumentality in expediting the identification of pioneering combination therapies.

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Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions

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Complex and Intelligent Systems 2023年第4期9卷 4705-4731页

作者： Alamoodi, A.H. Zaidan, B.B. Albahri, O.S. Garfan, Salem Ahmaro, Ibraheem Y. Y. Mohammed, R.T. Zaidan, A.A. Ismail, Amelia Ritahani Albahri, A.S. Momani, Fayiz Al-Samarraay, Mohammed S. Jasim, Ali Najm R.Q.Malik Faculty of Computing and Meta-Technology (FKMT) Universiti Pendidikan Sultan Idris (UPSI) Perak Malaysia Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliu 64002 Taiwan Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia Computer Science Department College of Information Technology Hebron University Hebron Palestine Department of Computing Science Komar University of Science and Technology (KUST) Sulaymaniyah Iraq SP Jain School of Global Management Sydney Australia Department of Computer Science Kulliyyah of Information and Communication Technology International Islamic University Malaysia Kuala Lumpur Malaysia Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq E-Business and Commerce Department Faculty of Administrative and Financial Sciences University of Petra Amman 961343 Jordan Foundation of Alshuhda Baghdad Iraq Medical Intrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq

When COVID-19 spread in China in December 2019, thousands of studies have focused on this pandemic. Each presents a unique perspective that reflects the pandemic’s main scientific disciplines. For example, social scientists are concerned with reducing the psychological impact on the human mental state especially during lockdown periods. Computer scientists focus on establishing fast and accurate computerized tools to assist in diagnosing, preventing, and recovering from the disease. Medical scientists and doctors, or the frontliners, are the main heroes who received, treated, and worked with the millions of cases at the expense of their own health. Some of them have continued to work even at the expense of their lives. All these studies enforce the multidisciplinary work where scientists from different academic disciplines (social, environmental, technological, etc.) join forces to produce research for beneficial outcomes during the crisis. One of the many branches is computer science along with its various technologies, including artificial intelligence, Internet of Things, big data, decision support systems (DSS), and many more. Among the most notable DSS utilization is those related to multicriterion decision making (MCDM), which is applied in various applications and across many contexts, including business, social, technological and medical. Owing to its importance in developing proper decision regimens and prevention strategies with precise judgment, it is deemed a noteworthy topic of extensive exploration, especially in the context of COVID-19-related medical applications. The present study is a comprehensive review of COVID-19-related medical case studies with MCDM using a systematic review protocol. PRISMA methodology is utilized to obtain a final set of (n = 35) articles from four major scientific databases (ScienceDirect, IEEE Xplore, Scopus, and Web of Science). The final set of articles is categorized into taxonomy comprising five groups: (1) diagnosis

关键词： COVID-19 Data privacy Federated learning Monoclonal antibodies Multi-criterion decision making Treatment

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Comfort Shoulder (Rhomboid Major) Muscles Chair Identifier System

Comfort Shoulder (Rhomboid Major) Muscles Chair Identifier S...

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International Conference on Communications, Information System and Computer Engineering (CISCE)

作者： Ata Jahangir Moshayedi Yuexin Yu Kimia Savoji Mehran Emadi Andani School of Information Engineering Jiangxi University of Science and Technology Ganzhou Jiangxi China Biomedical Data Science and Informatics School of computing Clemson University Clemson South Carolina USA Department of Neurosciences Biomedicine and Movement Sciences University of Verona Verona Italy

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

Nowadays, Sitting is one of the frequent activities of humans. particularly with the rise of office work, which is leading to an increase in the sitting periods for many individuals. Research highlights the importance of studying sitting posture to enhance seat quality, correct sitting methods, and prevent various health issues like neck and shoulder strains, etc. This article focuses on designing an IoT-based system that enables web-based data visualization without specialized software that can enable distance sampling. Through temperature sensors (DS18b20 and SHT11) and an FSR sensor, the system can identify distinct patterns in the main rhomboid muscles (the most important muscles of humans). The results show a significant relationship between individuals' body mass (BMI) and the observed muscle patterns, highlighting the importance of this correlation for researchers and industry designers alike.

关键词： Temperature sensors Pediatrics Shape Ergonomics Muscles Software Neck

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Cryptographic Based Secure Model on Dataset for Deep Learning Algorithms

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Computers, Materials & Continua 2021年第10期69卷 1183-1200页

作者： Muhammad Tayyab Mohsen Marjani N.Z.Jhanjhi Ibrahim Abaker Targio Hashim Abdulwahab Ali Almazroi Abdulaleem Ali Almazroi School of Computer Science and Engineering(SCE) Taylor’s University Lake-Side CampusSubang Jaya47500Malaysia Department of Computer Science College of Computing and InformaticsUniversity of SharjahSharjah27272UAE University of Jeddah College of Computing and Information Technology at KhulaisDepartment of Information TechnologyJeddahSaudi Arabia Department of Computer Science Rafha Community CollegeNorthern Border UniversityArar91431Saudi Arabia

Deep learning(DL)algorithms have been widely used in various security applications to enhance the performances of decision-based *** data added by an attacker can cause several security and privacy problems in the operation of DL *** two most common active attacks are poisoning and evasion attacks,which can cause various problems,including wrong prediction and misclassification of decision-based ***,to design an efficient DL model,it is crucial to mitigate these *** this regard,this study proposes a secure neural network(NN)model that provides data security during model training and testing *** main idea is to use cryptographic functions,such as hash function(SHA512)and homomorphic encryption(HE)scheme,to provide authenticity,integrity,and confidentiality of *** performance of the proposed model is evaluated by experiments based on accuracy,precision,attack detection rate(ADR),and computational *** results show that the proposed model has achieved an accuracy of 98%,a precision of 0.97,and an ADR of 98%,even for a large number of ***,the proposed model can be used to detect attacks and mitigate the attacker *** results also show that the computational cost of the proposed model does not increase with model complexity.

关键词： Deep learning(DL) poisoning attacks evasion attacks neural network hash functions SHA512 homomorphic encryption scheme

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Preserving indigenous knowledge through e-learning: A conceptual theoretical model 20

Preserving indigenous knowledge through e-learning: A concep...

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20th International Conferences on WWW/Internet 2021 and Applied computing 2021

作者： Amunkete, Katazo N. van Staden, Corne J. Schoeman, Marthie A. Namibia University of Science and Technology Faculty of Computing and Informatics 13 Jackson Kaujeua Street Windhoek Namibia University of South Africa School of Computing 28 Pioneer Ave Florida Park Roodepoort 1709 South Africa

ISBN: (纸本)9789898704344

Indigenous knowledge can advance sustainable development, and its preservation is of the utmost importance. Indigenous knowledge is mostly present within older generations, and without preservation, it will die with its custodians. African communities rely heavily on indigenous knowledge, which is dynamic and is constantly evolving. A digital tool that can preserve practices relating the knowledge can highlight its dynamic nature. This paper presents a conceptual theoretical model to guide the design of an e-learning system aimed at facilitating the preservation of indigenous knowledge. Data was collected utilizing a literature review. The conceptual theoretical model shows that preserving indigenous knowledge through e-learning would require, among other things, engagement with the relevant knowledge custodians and compliance with necessary legal requirements. © 20th International Conferences on WWW/Internet 2021 and Applied computing 2021.

关键词： Digital storage

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Low-cost Modular Smart Home Controlling System for Minimising Energy Consumption: A Conceptual Study 1

Low-cost Modular Smart Home Controlling System for Minimisin...

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1st International Visualization, informatics and technology Conference, IVIT 2022

作者： Putra Ahmad Baidowi, Zaid Mujaiyid Danial Hakim Nazrin Izuan Gerard, Muhammad Ramly, Ruwaida Mohd Isa, Mohd Rizal Yusof Darus, Mohamad Universiti Teknologi MARA Cawangan Selangor Kampus Dengkil Centre of Foundation Studies Selangor Dengkil43800 Malaysia Malaysian Institute of Information Technology Universiti Kuala Lumpur Malaysia National Defence University of Malaysia Computer Science Department Kuala Lumpur Malaysia College of Computing Informatics and Media Universiti Teknologi MARA School of Computing Sciences Malaysia

ISBN: (纸本)9781665486484

Internet of Things (IoT) has become one of the popular topics in Information and Communication technology (ICT) recently. IoT has been widely used in many fields, and one of the most topics that had gained significant interest of many researchers is minimising energy consumption. IoT which is implemented in a wireless, connects multiple devices via the Internet. It allows data to be gathered and transferred without human intervention. In this paper, a new solution is to propose an automatic control of several electrical appliances such as air-conditioners, lights, and fans. These three electrical appliances contribute to the highest energy consumption. This paper aims to contribute to the solution of minimising energy consumption by deploying a low-cost modular system to be installed into readily houses. Thus, it can adapt any old infrastructure to an efficient home. Minimising power consumption has been studied in a smart home but less work deployed Arduino Nano 33 IoT as compared to the existing available Arduino system. Finally, this work is hoped lead to related studies in minimising energy consumption. © 2022 IEEE.

关键词： Energy utilization

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Reliability Analysis of Fault Tolerant Memory Systems

arXiv

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arXiv 2023年

作者： Yigit, Yagmur Maglaras, Leandros Ferrag, Mohamed Amine Moradpoor, Naghmeh Lambropoulos, Georgios School of Computing Engineering and The Build Environment Edinburgh Napier University United Kingdom Technology Innovation Institute United Arab Emirates University of Piraeus Department of Informatics Piraeus Greece

This paper delves into a comprehensive analysis of fault-tolerant memory systems, focusing on recovery techniques modelled using Markov chains to address transient errors. The study revolves around the application of scrubbing methods in conjunction with Single Error Correction and Double Error Detection (SEC-DED) codes. It explores three primary models: 1) Exponentially distributed scrubbing, involving periodic checks of memory words within exponentially distributed time intervals;2) Deterministic scrubbing, featuring regular, periodic word checks;and 3) Mixed scrubbing, which combines both probabilistic and deterministic scrubbing approaches. The research encompasses the estimation of reliability and Mean Time to Failure (MTTF) values for each model. Notably, the findings highlight the superior performance of mixed scrubbing over simpler scrubbing methods in terms of reliability and MTTF. © 2023, CC BY.

关键词： Error correction

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Ensemble-Based Approach for Efficient Intrusion Detection in Network Traffic

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Intelligent Automation & Soft computing 2023年第8期37卷 2499-2517页

作者： Ammar Almomani Iman Akour Ahmed M.Manasrah Omar Almomani Mohammad Alauthman Esra’a Abdullah Amaal Al Shwait Razan Al Sharaa School of Computing Skyline University CollegeUniversity City of SharjahP.O.Box 1797SharjahUnited Arab Emirates IT-Department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan Information Systems Department College of Computing&InformaticsUniversity of SharjahUnited Arab Emirates Comp.Info Sciences(CIS)Division Higher Colleges of TechnologySharjahUnited Arab Emirates Computer Sciences Department Yarmouk UniversityIrbidJordan Computer Network and Information Systems Department The World Islamic Sciences and Education UniversityAmman11947Jordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan

The exponential growth of Internet and network usage has neces-sitated heightened security measures to protect against data and network ***,executed through network packets,pose a significant challenge for firewalls to detect and prevent due to the similarity between legit-imate and intrusion *** vast network traffic volume also complicates most network monitoring systems and *** intrusion detection methods have been proposed,with machine learning techniques regarded as promising for dealing with these *** study presents an Intrusion Detection System Based on Stacking Ensemble Learning base(Random For-est,Decision Tree,and k-Nearest-Neighbors).The proposed system employs pre-processing techniques to enhance classification efficiency and integrates seven machine learning *** stacking ensemble technique increases performance by incorporating three base models(Random Forest,Decision Tree,and k-Nearest-Neighbors)and a meta-model represented by the Logistic Regression *** using the UNSW-NB15 dataset,the pro-posed IDS gained an accuracy of 96.16%in the training phase and 97.95%in the testing phase,with precision of 97.78%,and 98.40%for taring and testing,*** obtained results demonstrate improvements in other measurement criteria.

关键词： Intrusion detection system(IDS) machine learning techniques stacking ensemble random forest decision tree k-nearest-neighbor

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Reliability Analysis of Fault Tolerant Memory Systems

Reliability Analysis of Fault Tolerant Memory Systems

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South Eastern European Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM)

作者： Yagmur Yigit Leandros Maglaras Mohamed Amine Ferrag Naghmeh Moradpoor Georgios Lambropoulos School of Computing Engineering and The Build Environment Edinburgh Napier University UK Technology Innovation Institute United Arab Emirates UAE Department of Informatics University of Piraeus Piraeus Greece

This paper delves into a comprehensive analysis of fault-tolerant memory systems, focusing on recovery techniques modelled using Markov chains to address transient errors. The study revolves around the application of scrubbing methods in conjunction with Single Error Correction and Double Error Detection (SEC-DED) codes. It explores three primary models: 1) Exponentially distributed scrubbing, involving periodic checks of memory words within exponentially distributed time intervals; 2) Deterministic scrubbing, featuring regular, periodic word checks; and 3) Mixed scrubbing, which combines both probabilistic and deterministic scrubbing approaches. The research encompasses the estimation of reliability and Mean Time to Failure (MTTF) values for each model. Notably, the findings highlight the superior performance of mixed scrubbing over simpler scrubbing methods in terms of reliability and MTTF.

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