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Design and Optimization of Hybrid Reactive-Extractive Distillation for Ternary Azeotropic Separation: A Case Considering the Effect of Side Reactions

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Industrial and Engineering Chemistry Research 2023年第27期62卷 10601-10610页

作者： Kong, Zong Yang Yang, Ao Tsai, Chen-Cheng Adi, Vincentius Surya Kurnia Saptoro, Agus Sunarso, Jaka Department of Engineering School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia College of Safety Engineering Chongqing University of Science & Technology Chongqing401331 China Department of Chemical Engineering National Chung Hsing University Taichung40227 Taiwan Department of Chemical and Energy Engineering Curtin University Malaysia CDT 250 Sarawak Miri98009 Malaysia Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching93350 Malaysia

Hybrid reactive-extractive distillation (RED) has been popular for separating azeotropic mixtures, as it combines the chemical reaction and physical azeotropic separation in a single unit operation. At least 18 studies have been published between 2020 and 2023, all of them relying on the hydration of ethylene oxide (EO) to form ethylene glycol (EG), without considering the potential side reactions that could impact the RED performance. This study shows one possible scenario where the formation of higher-level glycols during side reactions has led to significant increases in the total energy consumption and the total annual cost (TAC) by about 839 and 154%, respectively. These increases were due to the larger column size, low operating pressure, and more expensive cooling refrigerant. Hence, the performance of RED may not be as favorable as previously reported, and side reactions can significantly reduce its effectiveness. Future RED studies must consider the potential side reactions. © 2023 American Chemical Society.

关键词： Ethylene glycol

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Identifying Gamified Teaching Elements in Computer science Course 9

Identifying Gamified Teaching Elements in Computer Science C...

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9th International Conference on Platform technology and Service, PlatCon 2023

作者： Tanalol, Hasnah Hashim, Hazmi Turumogan, Punitha Noor, Noorsidi Aizuddin Mat Baharum, Aslina Deris, Farhana Diana Universiti Malaysia Sabah Preparatory Center for Science & Technology Kota Kinabalu Malaysia University Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia Tunku Abdul Rahman University of Management and Technology Department of Computing and Information Technology Penang Malaysia Universiti Teknologi Malaysia Mass Appraisal Housing and Planning Research Group Centre for Real Estate Studies Faculty of Built Environment and Surveying Real Estate Department Johor Johor Bahru81310 Malaysia School of Engineering and Technology Sunway University Computing and Information System Bandar Sunway Selangor Malaysia Universiti Teknologi Malaysia Universiti Malaysia Sabah Faculty of Social Sciences and Humanities Johor Johor Bahru81310 Malaysia

ISBN: (纸本)9798350305999

Traditional education methods, having said, lack motivation and self-regulation. The 5G and 6G technology have improved education;one such is gamification. Gamification applies gaming mechanisms to non-gaming platforms. Sadly, the use of gamification is still limited, especially in Malaysia. Gamification affects user experience, so it is intriguing to see if it can solve education problems. A study was conducted to identify elements of gamification. A systematic literature review (SLR) determines the gamification elements. Results show the list of identified 30 elements as guidelines for developing gamified teaching in Computer science Courses. Thus, it is hoped that the improved teaching and learning process can affect students' performance. © 2023 IEEE.

关键词： Curricula

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Blockchain-based federated learning with checksums to increase security in Internet of Things solutions

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Journal of Ambient Intelligence and Humanized computing 2023年第5期14卷 4685-4694页

作者： Prokop, Katarzyna Polap, Dawid Srivastava, Gautam Lin, Jerry Chun-Wei Faculty of Applied Mathematics Silesian University of Technology Kaszubska 23 Gliwice44-100 Poland Dept. of Math and Computer Science Brandon University BrandonMBR7A 6A9 Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Western Norway University of Applied Sciences Bergen Norway Dept. of Computer Science and Math Lebanese American University Beirut1102 Lebanon

Federated learning is becoming a practical solution for machine learning (ML) in industry. This is due to the possibility of implementing artificial intelligence (AI) systems and training its models on private data sets. However, this is not an ideal solution as it is possible to manipulate or even intercept the model during its transmission between the server and workers. In this paper, we propose a solution to ensure the security of the model transmitted between units in FL. The given model is encrypted with AES, DES, RSA algorithms, then a checksum is determined. This checksum with a private key is stored as a transaction on a blockchain. In the case of sending the model and its modification, the recipient can easily verify whether it is correct. The proposed solution has been described, tested, and compared to indicate its advantages and disadvantages. Conducted experiments were based on analyzing the communication time between participants, the accuracy of machine learning models, and attack detection. In terms of attack detection on the blockchain, we reached 81% thanks to the checksum mechanism. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Blockchain

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A Framework for Tele-rehabilitation Gaming System 6

A Framework for Tele-rehabilitation Gaming System

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6th International Conference on Applied Computational Intelligence in Information Systems, ACIIS 2023

作者： Elaklouk, Ahmed M. S. Zuarni Ramli, Ratna Edris, Ibrahim Padilla-Valdez, Nena Jumaily, Adel Al Universiti Teknologi Brunei School of Computing and Informatics Bandar Seri Begawan Brunei Universiti Teknologi Mara Negeri College of Computing Informatics and Mathematics Seremban Malaysia Universiti Teknologi Brunei Centre for Communication Bandar Seri Begawan Brunei Faculty of Engineering University of Technology Brunei Brunei Department of Computer Science and Software Engineering School of Computing University of Western Australia Australia Ensta Bretagne University France

ISBN: (纸本)9798350317428

Brain damage resulting from stroke and traumatic brain injury is the primary cause of complicated disability and one of the major causes of mortality in most countries, burdening healthcare systems owing to the growth in the number of brain injury victims. Rehabilitation is required to deal with the brain damage. With fewer therapists available, it is very challenging to manage patients and even motivate them to participate actively in their rehabilitation. Patients have frequently complained that many traditional rehabilitation systems are monotonous and uninteresting. The study's objective is to propose a telerehabilitation gaming system framework that can serve as guidance for developing an affordable rehabilitation gaming system that can motivate and engage patients and increase rehabilitation effectiveness. The research methodology is based on synthesizing related research and currently available technologies. The proposed framework includes the therapist and patient attached with vital constructs: tailoring tools, instructional content, game characteristics, tailored games, and performance. It is an internet-based communication structure that connects the Rehabilitation Gaming System to the hospital and other care provider networks. The proposed framework is evaluated by a panel of five experts. The results reveal that all experts believe that the proposed framework can serve as a useful guide for creating gaming interventions that can boost the patient's motivation and engagement and increase the effectiveness of rehabilitation. © 2023 IEEE.

关键词： Serious games

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A Self-adaptive Step-size Search Algorithm for the Cardinality Constrained Portfolio Optimisation Problem

A Self-adaptive Step-size Search Algorithm for the Cardinali...

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International Colloquium on Signal Processing & Its Applications, CSPA

作者： Zi Xuan Loke Say Leng Goh Jonathan Likoh Faculty of Computing and Informatics Universiti Malaysia Sabah Labuan International Campus Labuan Malaysia Preparatory Centre for Science and Technology Universiti Malaysia Sabah Sabah Malaysia

ISBN: (数字)9798350382310

ISBN: (纸本)9798350382327

This paper proposes a Self-adaptive Step-size Search (SASS) algorithm to address a Cardinality Constrained Portfolio Optimisation Problem (CCPOP). The proposed methodology is tested using five datasets from OR-Library. Experiments are conducted to test different settings of the particles in the SASS algorithm. The computational results are compared in terms of performance measures. The SASS algorithm achieves a lower value for most of the performance measures when the number of particles increases.

关键词： Atmospheric measurements Search problems Particle measurements Optimization Portfolios

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Deep Reinforcement Learning for Addressing Disruptions in Traffic Light Control

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Computers, Materials & Continua 2022年第5期71卷 2225-2247页

作者： Faizan Rasheed Kok-Lim Alvin Yau Rafidah Md Noor Yung-Wey Chong School of Engineering and Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computing and Information Systems Sunway UniversitySubang Jaya47500Malaysia Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur50603Malaysia National Advanced IPv6 Centre Universiti Sains MalaysiaUSMPenang11800Malaysia

This paper investigates the use of multi-agent deep Q-network(MADQN)to address the curse of dimensionality issue occurred in the traditional multi-agent reinforcement learning(MARL)*** proposed MADQN is applied to traffic light controllers at multiple intersections with busy traffic and traffic disruptions,particularly *** is based on deep Q-network(DQN),which is an integration of the traditional reinforcement learning(RL)and the newly emerging deep learning(DL)*** enables traffic light controllers to learn,exchange knowledge with neighboring agents,and select optimal joint actions in a collaborative manner.A case study based on a real traffic network is conducted as part of a sustainable urban city project in the Sunway City of Kuala Lumpur in *** is also performed using a grid traffic network(GTN)to understand that the proposed scheme is effective in a traditional traffic *** proposed scheme is evaluated using two simulation tools,namely Matlab and Simulation of Urban Mobility(SUMO).Our proposed scheme has shown that the cumulative delay of vehicles can be reduced by up to 30%in the simulations.

关键词： Artificial intelligence traffic light control traffic disruptions multi-agent deep Q-network deep reinforcement learning

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Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System

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Computers, Materials & Continua 2023年第2期74卷 2889-2903页

作者： Mahmoud Ragab Mohammed W.Al-Rabia Sami Saeed Binyamin Ahmed A.Aldarmahi Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Centre for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah 21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City 11884CairoEgypt Department of Medical Microbiology and Parasitology Faculty of MedicineKing Abdulaziz UniversityJeddah 21589Saudi Arabia Health Promotion Center King Abdulaziz UniversityJeddah 21589Saudi Arabia Computer and Information Technology Department The Applied CollegeKing Abdulaziz UniversityJeddah 21589Saudi Arabia Basic Science Department College of Science and Health ProfessionsKing Saud Bin Abdulaziz University for Health SciencesJeddah 21423Saudi Arabia King Abdullah International Medical Research Center Ministry of National Guard-Health AffairsJeddah 21423Saudi Arabia

With the increasing and rapid growth rate of COVID-19 cases,the healthcare scheme of several developed countries have reached the point of *** important and critical steps in fighting against COVID-19 is powerful screening of diseased patients,in such a way that positive patient can be treated and isolated.A chest radiology image-based diagnosis scheme might have several benefits over traditional *** accomplishment of artificial intelligence(AI)based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition *** study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19Monitoring System(IFFA-DTLMS).The proposed IFFADTLMSmodelmajorly aims at identifying and categorizing the occurrence of COVID19 on chest *** attain this,the presented IFFA-DTLMS model primarily applies densely connected networks(DenseNet121)model to generate a collection of feature *** addition,the firefly algorithm(FFA)is applied for the hyper parameter optimization of DenseNet121 ***,autoencoder-long short term memory(AE-LSTM)model is exploited for the classification and identification of *** ensuring the enhanced performance of the IFFA-DTLMS model,a wide-ranging experiments were performed and the results are reviewed under distinctive *** experimental value reports the betterment of IFFA-DTLMS model over recent approaches.

关键词： COVID-19 artificial intelligence intelligent systems deep learning decision making

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Evaluating the Effectiveness of E-Learning Website Using Electroencephalogram 8th

Evaluating the Effectiveness of E-Learning Website Using Ele...

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8th International Visual Informatics Conference, IVIC 2023

作者： Aning, Alberto Baharum, Aslina Mohd Naim, Nur Faraha Mohd Yusop, Nurhafizah Moziyana Darius, Dian Darina Indah Mat Noor, Noorsidi Aizuddin Deris, Farhana Diana Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Computing and Information System School of Engineering and Technology Sunway University 5 Jalan Universiti Selangor Sunway City Malaysia Faculty of Defence Science and Technology National Defence University of Malaysia Kuala Lumpur Malaysia Faculty of Engineering Universiti Pertahanan Nasional Malaysia Kem Sungai Besi Kuala Lumpur57000 Malaysia Mass Appraisal Housing and Planning Research Group Centre for Real Estate Studies Real Estate Department Faculty of Built Environment and Surveying Universiti Teknologi Malaysia Johor Johor Bahru Malaysia Faculty of Social Sciences and Humanities Universiti Teknologi Malaysia Johor Johor Bahru Malaysia

ISBN: (纸本)9789819973385

Although e-learning technology provides numerous benefits for educators, enticing students to use e-learning services is a challenge, particularly for the e-learning websites of higher education institutions in Malaysia. E-learning websites of Malaysian higher education institutions have few guidelines for user interface design that foster emotional engagement. Due to this problem, there is a significant percentage of student disengagement on e-learning platforms. A visualization pattern as a guideline has been proposed for designing an e-learning GUI website for HEIs in Malaysia. The proposed guideline has been implemented on the prototype website’s graphical user interface (GUI). The effectiveness of the GUI was evaluated using an electroencephalogram (EEG) device and resulted in 65% of the participants being in the ‘Most Effective’ category, with the highest average of most effectiveness standing at 32.6%. The research has demonstrated that the developed guideline increased student engagement with the GUI of the e-learning website prototype. The guideline is intended to assist higher education institutions (HEIs) and website developers and designers in creating e-learning websites that can sustain students’ interest in e-learning over time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Graphical user interfaces

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A Novel DAAM-DCNNs Hybrid Approach to Facial Expression Recognition to Enhance Learning Experience 23rd

A Novel DAAM-DCNNs Hybrid Approach to Facial Expression Rec...

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23rd International Conference on Computational science, ICCS 2023

作者： Alfred, Rayner Pailus, Rayner Henry Obit, Joe Henry Lim, Yuto Sukirno, Haviluddin Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia School of Information Science Japan Advanced Institute of Science and Technology Access 1-1 Asahidai Ishikawa Nomi923-1292 Japan Department of Informatics Universitas Mulawarman Kalimantan Timur Samarinda Indonesia

ISBN: (纸本)9783031360268

Many machine learning models are applied on facial expression classification and there are three main issues affecting the performance of any algorithms in classifying emotions based on facial expressions, and these issues include image illumination, image quality and partial features recognition. Many approaches have been proposed to handle these issues. Unfortunately, one of the main challenges in detecting and classifying facial expression process is minimal differences of features between different types of emotions that can be used to differentiate these different types of emotions. Thus, there is a need to enrich each type of emotion with more relevant extracted features by having a more effective approach to extract features that can be used to represent each type of emotions more effectively and efficiently. This work addresses the issue of improving the emotion recognition accuracy by introducing a novel hybrid approach that combines the Depth Active Appearance Model (DAAM) and Deep Convolutional Neural Networks (DCNNs). The proposed DAAM and DCNNs model can be used to assist one in identifying emotions and classify learner involvement and interest in the topic which are plotted as feedback to the instructor to improve learner experience. The proposed method is evaluated on two publicly available datasets namely, JAFFE and CK+ and the results are compared to the state-of-the-art results. The empirical study showed that the proposed DAMM-CNNs hybrid method managed to perform the face expression recognition with 97.4% for the JAFFE dataset and 96.9% for the CK+ dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Contrastive Learning

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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Computer Modeling in Engineering & sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize Computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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