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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Almogahed, Abdullah Mahdin, Hairulnizam Omar, Mazni Zakaria, Nur Haryani Alawadhi, Abdulwadood Barraood, Samera Obaid Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia School of Computing Universiti Utara Malaysia Sintok Malaysia University of Science and Technology Faculty of Engineering and Computing Yemen Hadhramout University Collage of Computers and Information Technology Department of Computer Science Hadhramout Yemen

ISBN: (纸本)9798350305333

Recent research has shown that various refactoring methods have very diverse, often opposing, and contradicting impacts on software quality characteristics. While using refactoring methods to enhance software quality, developers confront difficulties in choosing effective refactoring methods. To date, there is a lack of research that has studied factors that might explain conflicting or divergent findings about the influence of refactoring methods on software quality. As a result, two factors were identified, explored, and fully evaluated in this study: refactoring tools and software size. Four of the most regularly used refactoring methods (Add Parameter, Remove Parameter, Extract Class, and Extract Superclass) in practice were selected and independently implemented in five case studies of varied sizes (small, medium, and large). The results give solid evidence that these factors have a substantial role in determining the diverse impacts of refactoring methods on quality characteristics. These insights may help software engineers understand how to employ refactoring methods to enhance software quality while taking these factors into account. Moreover, the results may provide guidelines for software engineers to apply refactoring methods to enhance the quality of software systems, depending on the optimal situation for employing the refactoring methods. © 2023 IEEE.

关键词： Computer software maintenance

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Sustainable bamboo and coconut shell activated carbon for purifying river water on Borneo Island

Waste Management Bulletin

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Waste Management Bulletin 2024年第1期2卷 39-48页

作者： Kuok, King Kuok Chiu, Po Chan Rahman, Md. Rezaur Chin, Mei Yun Bin Bakri, Muhammad Khusairy Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Sarawak Kuching 93350 Malaysia Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Sarawak Kota Samarahan 94300 Malaysia Faculty of Engineering Universiti Malaysia Sarawak Sarawak Kota Samarahan 94300 Malaysia Composites Materials and Engineering Center Washington State University 2001 East Grimes Way Pullman 99164 Washington State United States

This research aims to transform agricultural waste abundantly available in Sarawak into activated carbon as a sustainable water treatment source. Seven river water samples from Sarawak River were collected and treated with 2 g, 4 g, 6 g, 8 g, and 10 g of bamboo activated carbon (BAC) and coconut shell activated carbon (CSAC). Parameters investigated include Total Suspended Solids (TSS), Dissolved Oxygen (DO), Ammoniacal nitrogen (NH₃-N), pH, Temperature. The removal of TSS and NH₃-N increases with the increment of the BAC and CSAC dosages from 2 g to 10 g. 10 g of BAC is able to remove 83.35 % of TSS and 82.61 % of NH₃-N in 750 ml of river water, while 10 g of CSAC is only able to remove 79.18 % of TSS and 83.73 % of NH₃-N for 750 ml of river water. Results revealed that BAC is marginally more effective than CSAC in treating river water. DO levels were lowered undesirably after the treatment process. The temperature slightly increases after treatment due to exothermic chemical reactions and adsorption processes. The treatment brings little impact on pH level. The developed BAC and CSAC have proven sustainable, simple, low cost, and environmentally friendly with their excellent porous structure and large specific surface area for river water treatment. © 2023 The Authors

关键词： Ammoniacal nitrogen (NH3-N) Bamboo activated carbon Coconut shell activated carbon Sustainable water treatment method Total Suspended Solids (TSS)

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Proposing a Foundational IoT Smart Planter Concept towards Reducing Carbon Footprint in Malaysia via Gamification

Proposing a Foundational IoT Smart Planter Concept towards R...

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2022 International Conference on Computer and Drone Applications, IConDA 2022

作者： Philimon, Sheena Punai Tsun Tee, Mark Kit Yan Lo, Chyzien Chang Hung Ho, Dexter Chu Yan Chieng, Jake Zen Bee Lian Soon, Rachel May Swinburne University of Technology Sarawak Campus Faculty of Engineering Computing and Science Kuching Malaysia

ISBN: (纸本)9781665492355

The rise in the concentration of greenhouse gases, predominantly carbon dioxide (CO2), has contributed to the rising rate of climate change and global warming. In 2021, global energy-related carbon dioxide emissions increased by 6% to 36.3 billion tonnes. Vertical planters and green roof farming are capable of contributing significantly to the transition of green, circular economy in urban areas through carbon sequestration. Previous literature has found that carbon sequestration varies across different geophysical categories and plant species. While green farming is a well-known efficient solution for mitigating climate change, achieving eventual carbon neutrality as a global community is even more challenging as it requires cooperative action not only by the government but also from citizens. In this viewpoint, the integration of internet of things (IoT) and gamification into urban farming is proposed to generate more dynamic user engagement outcomes by raising awareness on carbon footprint to stimulate behavioural changes. The plant's species, state of growth, surrounding environment parameters, and location can all be identified using IoT infrastructure and cloud computing. This system's initial prototype was built as a foundation for further development of the Gamified IoT Planter concept. This article details the study team's initial investigation into consumer behaviours that can help society achieve carbon neutrality by 2050 and the state of the art in carbon footprint awareness. © 2022 IEEE.

关键词： Carbon footprint

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Exploring the Potential of IoT-based Injury Detection and Response by Combining Human Activity Tracking and Indoor Localization

Exploring the Potential of IoT-based Injury Detection and Re...

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2022 International Conference on Computer and Drone Applications, IConDA 2022

作者： Bong, Paul Cornelius Tsun Tee, Mark Kit Jo, Hudyjaya Siswoyo Swinburne University of Technology Sarawak Campus Faculty of Engineering Computing and Science Kuching Malaysia

ISBN: (纸本)9781665492355

Despite an increasing number of technological advancements in workplace safety, current state of the art solutions have proven partially successful at best as there are still a staggering number of workplace injuries with more than 424, 360 cases logged by the USA Bureau of Labor Statistics in the United States in 2020 alone. This article will span across a wide coverage of current state of the art IoT solutions in detecting, preventing, and reacting to workplace injuries to identify the current challenges faced by them. The key requirements that every IoT workplace safety solution should have - injury detection, injury prevention and injury response. This study aims to explore the viability of a multi-modal fusion solution with localization data for improved injury detection and response in indoor industrial environments while logging the causing factors or behavioral patterns for injury prevention strategies. © 2022 IEEE.

关键词： Internet of things

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Optimizing Energy Efficiency in 5G Networks: Integrating Multi-Packet Reception with Reinforcement Learning and Genetic Algorithms 8

Optimizing Energy Efficiency in 5G Networks: Integrating Mul...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Arun, V. Gowtham, A. Arul, U. Angeljulie, J. School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Kattankulathur India Madanapalle Andhra Pradesh Annamayya India Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Karpagam Academy of Higher Education Department of Cse Tamilnadu Coimbatore India

ISBN: (纸本)9798331505462

The rapidly expanding data demand and the explosion of connected devices require energy-efficient answers in 5G networks. This study proposes a three-pronged solution that integrates Multi-Packet Reception (MPR), Reinforcement Learning (RL), and Genetic Algorithms (GA) to improve energy efficiency in 5G networks. MPR allows base stations to process incoming data more efficiently by receiving multiple packets at once. This not only helps the base station 'talk' more efficiently with the devices that are supposed to be connected to it at any moment (which obviously is more inefficient under 5G, the way that MPR can help be more efficient, makes it the prime candidate for meeting the challenge at hand) but also allows the incoming data to be processed at the same rate it could under the old conditions of processing one packet at a time. We use Genetic Algorithms to directly tackle what we consider the core problems of the 5G system: how to efficiently (jointly) serve users and how to efficiently allocate power among the serving base stations to minimize power consumption without sacrificing service quality (QoS). We developed an integrated method that demonstrates how to simultaneously achieve three goals: (1) significant energy savings;(2) improved network performance;and (3) enhanced sustainability under 5G conditions.... We assume a base station [BS] model close to real-world. Based on this model, we simulate our method in several scenarios. In these scenarios, we validate the network performance gains, energy savings, and enhanced sustainability that we design our method to achieve. © 2024 IEEE.

关键词： Base stations

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Quantum characteristics near event horizons

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Physical Review D 2024年第6期110卷 064001页

作者： Asad Ali Saif Al-Kuwari Mehrdad Ghominejad M. T. Rahim Dong Wang Saeed Haddadi Qatar Centre for Quantum Computing College of Science and Engineering Faculty of Physics School of Physics and Optoelectronics Engineering

We investigate quantum characteristics around Schwarzschild black hole, exploring various quantum resources and their interplay in curved space-time. Our analysis reveals intriguing behaviors of quantum coherence, global and genuine multipartite entanglement, first-order coherence, and mutual information in different scenarios. Initially, we consider three particles shared among Alice, Bob, and Charlie in a Minkowski space far from the event horizon, where these particles are correlated via Greenberger-Horne-Zeilinger-type correlation. While Alice’s particle remains in Minkowski space, Bob and Charlie accelerate towards the event horizon, experiencing black hole evaporation and generating antiparticles correlated via the Hawking effect. We employ the Kruskal basis formulation to derive a pentapartite pure state shared among particles inside and outside the event horizon. Investigating different scenarios among particles both inside and outside the event horizon, we observe how quantum resources evolve and distribute among consideration of different particles with Hawking temperature and mode frequency. The trade-off relationship between first-order coherence and concurrence fill persists, indicating the intricate interplay between coherence and entanglement. Notably, the mutual information between external observers and particles inside the black hole becomes nonzero, deepening our understanding of quantum effects in curved space-time and shedding light on the quantum nature of the black hole. We believe that these findings will pave the way for future investigations into the fundamental quantum mechanical aspects of gravity under extreme environments.

关键词： Quantum information processing Relativistic quantum information Quantum aspects of black holes

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Optimization Techniques in University Timetabling Problem:Constraints,Methodologies,Benchmarks,and Open Issues

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Computers, Materials & Continua 2023年第3期74卷 6461-6484页

作者： Abeer Bashab Ashraf Osman Ibrahim Ibrahim Abakar Tarigo Hashem Karan Aggarwal Fadhil Mukhlif Fuad A.Ghaleb Abdelzahir Abdelmaboud Faculty of Computer Science&Information Technology Alzaiem Alazhari University13311KhartoumSudan Faculty Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400SabahMalaysia Department of Computer Science University of SharjahSharjah27272United Arab Emirates Electronic and Communication Engineering Department Maharishi Markandeshwar Deemed to be UniversityMullanaAmbala133207HaryanaIndia Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi Malaysia81310JohorMalaysia Department of Information Systems King Khaled UniversityMuhayel61913AseerSaudi Arabia

University timetabling problems are a yearly challenging task and are faced repeatedly each *** problems are considered nonpolynomial time(NP)and combinatorial optimization problems(COP),which means that they can be solved through optimization algorithms to produce the aspired optimal *** techniques have been used to solve university timetabling problems,and most of them use optimization *** paper provides a comprehensive review of the most recent studies dealing with concepts,methodologies,optimization,benchmarks,and open issues of university timetabling *** comprehensive review starts by presenting the essence of university timetabling as NP-COP,defining and clarifying the two formed classes of university timetabling:University Course Timetabling and University Examination Timetabling,illustrating the adopted algorithms for solving such a problem,elaborating the university timetabling constraints to be considered achieving the optimal timetable,and explaining how to analyze and measure the performance of the optimization algorithms by demonstrating the commonly used benchmark datasets for the *** is noted that meta-heuristic methodologies are widely used in the ***,recently,multi-objective optimization has been increasingly used in solving such a problem that can identify robust university timetabling ***,trends and future directions in university timetabling problems are *** paper provides good information for students,researchers,and specialists interested in this area of *** challenges and possibilities for future research prospects are also explored.

关键词： University timetabling timetabling approaches meta-heuristics combinatorial optimization

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A Deep Drive Analysis Framework on Emotions of Users in Attention Driven Systems 8

A Deep Drive Analysis Framework on Emotions of Users in Atte...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Dharshini, S. Akshita, Ravula Panigrahi, Suman Varma, Nadimpalli Sai Kiran Kumar Thangavel, Senthil Somasudaram, K. Abel Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Tamil Nadu Coimbatore India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Mathematics Tamil Nadu India Cape Peninsula University of Technology Faculty of Informatic and Design Department of Information Technology South Africa

ISBN: (纸本)9798350376425

One of the ways humans communicate is by using facial expressions. In psychology, the detection of emotions or facial expressions requires analysis and assessment of decisions in predicting a person's emotions or group of people in communicating. Emotion is powerful in many ways because it can influence a decision and urge people to act. This emotion like anger, happiness, sadness, disgust, fear, surprise, and neutral detection is used for human behaviour and understanding, action of mental disorders, and synthetic human expressions. This can be used in healthcare, marketing, and manufacturing. Here the paper is based on marketing where we can detect the emotions of the people watching the ads. This emotion detection for the ads can be used for predictions like whether customers will like the product or not, whether the sales will increase or not, etc. The methodology used to predict human emotion through the face is CNN and MobileNetV2 with LSTM which is a pretrained model. The architecture involves pre-processing of the data, feature detection, face detection, and detection of emotion. There are various datasets used for detecting emotions like FER-2013, JAFFE, CK+, etc. Here the FER-2013 is used. The performance of the model is measured by using confusion matrix, recall, precision, f1-score, and accuracy. © 2024 IEEE.

关键词： Face recognition

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UNet-Based Polyp Segmentation: A Survey

UNet-Based Polyp Segmentation: A Survey

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2023 IEEE International Conference on computing, ICOCO 2023

作者： Si, Chen Shafry Mohd Rahim, Mohd Mianzhou, You Li, Nie Hongyu, Cheng Universiti Teknologi Malaysia Faculty of Computing Johor Malaysia Guangzhou Institute of Science and Technology Faculty of Computer Science and Engineering Guangzhou China Krirk University Faculty of Business Administration Bangkok Thailand Guangdong Provincial Expressway Co. Ltd Engineering Department Guangzhou China

ISBN: (纸本)9798350302684

Colorectal polyps are common intestinal diseases, in which image segmentation of colorectal polyps is crucial for diagnosis and treatment. With the development of deep learning, semantic segmentation technology has been widely used in colorectal polyp image analysis. Among them, the U-Net model is a widely used deep learning model, which is simple and effective and shows good performance in medical image segmentation tasks. This article mainly introduces the semantic segmentation technology of colorectal polyp images based on U-Net, including the basic structure, characteristics and advantages of the U-Net model, and its application in semantic segmentation of colorectal polyp images. At the same time, this article also reviews colorectal polyp image segmentation technology based on deep learning in recent years, focuses on the related research on the U-Net model and its improvements, and compares and analyzes various methods. In the methods and experiments section, this article introduces the structure, data set and experimental settings of the U-Net model in detail, as well as analyzes and compares the experimental results of U-Net-based colorectal polyp image semantic segmentation technology. Finally, this article summarizes the main contributions of the research and discusses prospects and challenges for future applications. © 2023 IEEE.

关键词： Semantic Segmentation

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Advancing Weed Detection in Agricultural Landscapes Using Computer Vision 5

Advancing Weed Detection in Agricultural Landscapes Using Co...

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5th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2023

作者： Tan, Bei Ren Moung, Ervin Afnan Uda, Muhd. Nur Kin Teo, Kenneth Tze Angeline, Lorita School of Computer Science Taylor's University Subang Jaya Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (纸本)9798350304152

This paper explores the need for weed detection in agricultural landscapes, where weed infestations pose significant crop productivity challenges. To address this matter, computer vision techniques are utilised to create a weed detection algorithm that accurately detects weed patches inside agricultural field images. To ensure reliable model evaluation, the dataset is split into training and testing subsets before being fed into the YOLOv3 model. The YOLOv3 model performs well in terms of precision, with an average precision of 66.4% for crop regions and a notable precision of 92.5% for detecting weeds. These results highlight the potential of artificial intelligence-driven weed detection techniques to optimize crop management practices and foster sustainable agriculture in the modern era. This research advances precision agriculture by effectively addressing weed infestations, paving the way for more efficient and targeted weed control strategies, which ultimately lead to increased crop yield and improved agricultural sustainability. © 2023 IEEE.

关键词： Computer vision

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