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A review on co-pyrolysis of agriculture biomass and disposable medical face mask waste for green fuel production:recent advances and thermo-kinetic models

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Frontiers of Chemical Science and engineering 2023年第9期17卷 1141-1161页

作者： Melvin X.J.Wee Bridgid L.F.Chin Agus Saptoro Chung L.Yiin Jiuan J.Chew Jaka Sunarso Suzana Yusup Abhishek Sharma Department of Chemical and Energy Engineering Faculty of Engineering and ScienceCurtin University MalaysiaCDT 250Miri 98009Malaysia Energy and Environment Research Cluster Faculty of Engineering and ScienceCurtin University MalaysiaCDT 250Miri 98009Malaysia Department of Chemical Engineering and Energy Sustainability Faculty of EngineeringUniversiti Malaysia Sarawak(UNIMAS)Kota Samarahan 94300Malaysia Institute of Sustainable and Renewable Energy(ISuRE) Universiti Malaysia Sarawak(UNIMAS)Kota Samarahan 94300Malaysia Research Centre for Sustainable Technologies Faculty of EngineeringComputing and ScienceSwinburne University of TechnologyKuching 93350Malaysia Generation Unit(Fuel Technology&Combustion) Tenaga Nasional Berhad(TNB)Research Sdn BhdKajang 43000Malaysia Department of Chemical Engineering Manipal University JaipurJaipur 303007India Chemical&Environmental Engineering School of EngineeringRMIT UniversityMelbourneVictoria 3000Australia

The Association of Southeast Asian Nations is blessed with agricultural resources,and with the growing population,it will continue to prosper,which follows the abundance of agricultural *** biomass attracted researchers’interest in extracting bio-oil from these ***,the resulting bio-oil has low heating values and undesirable physical ***,co-pyrolysis with plastic or polymer wastes is adopted to improve the yield and quality of the ***,with the spread of the novel coronavirus,the surge of single-use plastic waste such as disposable medical face mask,can potentially set back the previous plastic waste reduction ***,studies of existing technologies and techniques are referred in exploring the potential of disposable medical face mask waste as a candidate for co-pyrolysis with *** parameters,utilisation of catalysts and technologies are key factors in improving and optimising the process to achieve commercial standard of liquid *** copyrolysis involves a series of complex mechanisms,which cannot be explained using simple iso-conversional ***,advanced conversional models are introduced,followed by the evolutionary models and predictive models,which can solve the non-linear catalytic copyrolysis reaction *** outlook and challenges for the topic are discussed in detail.

关键词： biomass COVID-19 wast catalyst pyrolysis kinetics

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Fuzzy Logic Based Perturb-and-Observe Algorithm for Minimizing Output Fluctuation of a PV System 5

Fuzzy Logic Based Perturb-and-Observe Algorithm for Minimizi...

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

作者： Tan, Min Keng Moloney, Braindane Owin Lim, Kit Guan Yang, Soo Siang Goh, Hui Hwang Kin Teo, Kenneth Tze Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia School of Electrical Engineering Guangxi University Nanning China

ISBN: (纸本)9798350304152

Photovoltaic systems generate direct current (DC) power by absorbing sunlight through photovoltaic cells. However, under non-uniform irradiance, multiple peaks in the Maximum Power Point Tracking (MPPT) pose challenges for precisely tracking the global maximum power point. Traditional methods often focus on local maximum power points instead of the global ones. This research aims to enhance the Perturb and Observe (P&O) approach and model the PV module to accurately track the Maximum Power Point (MPP). The study investigates the performance of PV arrays using models with non-uniform characteristics. While traditional MPPT techniques like perturb and observe (P&O) can track the MPP, they struggle with non-uniform irradiance, leading to output power fluctuations and losses. Simulation results demonstrate that the P&O-based MPPT algorithm, tailored for maximum power production, adjusts the operational point effectively. Comparing the conventional MPPT output voltage 17.8V under non-uniform irradiance of conventional P&O MPPT output voltage and a non-conventional Fuzzy Logic 33.8V under non-uniform irradiance indicates that the Fuzzy MPPT achieves 52.66% of the output voltage obtained by the non-conventional MPPT comparing to conventional MPPT. These algorithms offer improved precision and flexibility, although their implementation may require higher processing power and computational capabilities. Therefore, the next focus will be on developing a fuzzy logic based MPPT and enhancing the photovoltaic system. © 2023 IEEE.

关键词： Fuzzy logic

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Development of IoT-Healthcare Model for Gastric Cancer from Pathological Images 1

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9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Belgaum, Mohammad Riyaz Momina, Shaik Maryam Farhath, L. Nousheen Nikhitha, K. Naga Jyothi, K. Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9783031440847

ISBN: (纸本)9783031440830

The diagnosis of stomach cancer automatically in digital pathology images is a difficult problem. Gastric cancer (GC) detection and pathological study can be greatly aided by precise region-by-region segmentation. On a technical level, this issue is complicated by the fact that malignant zones might be any size or shape and have fuzzy boundaries. The research employs a deep learning-based approach and integrates many bespoke modules to cope with these issues. The channel refinement model is the attentional actor on the chin channel. While implementing the feature channel, the learnt channel weight can be used to eliminate unnecessary features. Calibration is essential to improve classification precision. The results of channel recalibration can be improved with the help of a re-calibration (MSCR) model. The top pooling layer of the network is where the multiscale attributes are sent. The outcomes of channel recalibration may be enhanced by using the channel weights found at various scales as input to the next channel recalibration perfect. Our unique gastric cancer segmentation dataset, carefully glossed down to the pixel level by medical authorities, is used for extensive experimental comparisons. The numerical comparisons with other approaches show that our strategy is superior. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diagnosis

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An Efficient Algorithm for Software Reliability Prediction via Harris Hawks Optimization 2nd

An Efficient Algorithm for Software Reliability Prediction v...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Kong, Li Sheng Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Smart Computing and Cyber Resilience School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Informatics Engineering University of Aleppo Aleppo Syria School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Software reliability has become an increasingly important characteristic as software systems continue to be integrated into all parts of our lives. While the occurrence of failure after a period of time is within the nature of all software systems, the ability to estimate and predict failure is greatly beneficial to software development and maintenance. Hence, Software Reliability Growth Models (SRGMs) were developed to fulfill this requirement. Conventional parameter estimation methods of SRGM functions were complex and left more to be desired. Consequently, metaheuristics such as Swarm Intelligence algorithms were introduced to estimate and optimize the parameters of SRGM functions. Likewise, Swarm Intelligence algorithms were found to be exceptionally effective in estimating and optimizing the parameters of SRGM functions. This paper presents a novel approach to parameter estimation on three different SRGM functions through Harris’ Hawks Optimization (HHO). The proposed HHO design is compared against numerous different variants of Swarm Intelligence algorithms and is shown to be significantly more efficient than the majority of the compared designs. Moreover, as the proposed design is a base variant of Swarm Intelligence algorithms, various avenues of future improvements have also been presented to improve its overall prediction accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Harris’ Hawks Optimization Parameter Estimation Software Reliability Growth Models Software Reliability Prediction Swarm Intelligence

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Hybrid Solver Techniques for Wideband Antennas 27

Hybrid Solver Techniques for Wideband Antennas

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Kandregula, Venkat Reddy Lazaridis, Pavlos I. Ahmed, Qasim Zeeshan Khan, Faheem A. Zaharis, Zaharias D. Poulkov, Vladimir University of Huddersfield School of Computing and Engineering Huddersfield United Kingdom Aristotle University of Thessaloniki School of Electrical and Computer Engineering Thessaloniki Greece Technical University of Sofia Faculty of Telecommunications Sofia Bulgaria

ISBN: (纸本)9798350392319

Evaluating the performance of an antenna using commercially available electromagnetic (EM) solvers such as Computer simulation technology (CST) studio suite has become crucial. In this context, a unidirectional advanced technique is used for simulating a Printed Log-Periodic Antenna (PLPDA) enclosed in a fixture with foam. This method employs a hybrid solver approach, in which the PLPDA with foam (source) is simulated using a transient solver, and the fixture (platform) is simulated using an integral equation solver. The proposed technique significantly reduces the simulation time and RAM usage while maintaining high accuracy. It is a viable alternative to traditional full-wave analysis, where the source and platform are simulated using a single solver or bidirectional simulation techniques. A comparative analysis with measured radiation patterns demonstrates the effectiveness of this method, providing a reliable and efficient solution for antenna design and performance evaluation. © 2024 IEEE.

关键词： Log periodic antennas

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Entropy Based Feature Fusion Using Deep Learning for Waste Object Detection and Classification Model

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Computer Systems Science & engineering 2023年第12期47卷 2953-2969页

作者： Ehab Bahaudien Ashary Sahar Jambi Rehab B.Ashari Mahmoud Ragab Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt

Object Detection is the task of localization and classification of objects in a video or *** recent times,because of its widespread applications,it has obtained more *** the modern world,waste pollution is one significant environmental *** prominence of recycling is known very well for both ecological and economic reasons,and the industry needs higher *** object detection utilizing deep learning(DL)involves training a machine-learning method to classify and detect various types of waste in videos or *** technology is utilized for several purposes recycling and sorting waste,enhancing waste management and reducing environmental *** studies of automatic waste detection are difficult to compare because of the need for benchmarks and broadly accepted standards concerning the employed data ***,this study designs an Entropy-based Feature Fusion using Deep Learning forWasteObject Detection and Classification(EFFDL-WODC)*** presented EFFDL-WODC system inherits the concepts of feature fusion and DL techniques for the effectual recognition and classification of various kinds of waste *** the presented EFFDL-WODC system,two major procedures can be contained,such as waste object detection and waste object *** object detection,the EFFDL-WODC technique uses a YOLOv7 object detector with a fusionbased backbone *** addition,entropy feature fusion-based models such as VGG-16,SqueezeNet,and NASNetmodels are ***,the EFFDL-WODC technique uses a graph convolutional network(GCN)model performed for the classification of detected waste *** performance validation of the EFFDL-WODC approach was validated on the benchmark *** comprehensive comparative results demonstrated the improved performance of the EFFDL-WODC technique over recent approaches.

关键词： Object detection object classification waste management deep learning feature fusion

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A New SVM-STEG Embedding Model in Steganography 8

A New SVM-STEG Embedding Model in Steganography

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8th IEEE International Conference on Software engineering and Computer Systems, ICSECS 2023

作者： Hussain, Hanizan Shaker Ghazali, Fazali Hafiza, A.S. Ali, Hanif Mohd Sangodiah, Anbuselvan Oumar, Kante Koli Quest International University Faculty of Computing and Engineering Malaysia Universiti Islam Antarabangsa Sultan Abdul Halim mu'Adzam Shah Kulliyyah of Muamalat & Management Sciences Malaysia Universiti Poly-Tech Malaysia Faculty of Computing and Multimedia Malaysia

ISBN: (纸本)9798350310931

One of the sub-fields in information security is called information hiding and can be applied to protect data and information nowadays. This is a method in where secret-messages are hidden stealthily in an image file. This method has been used in various fields especially in digital image steganography. Most of the techniques proposed to date have various problems i.e., non-random changes will obviously occur especially when the secret message is embedded in an inappropriate area and when the load capacity exceeds the number of bits allowed. This paper proposes a machine learning steganographic method called SVM-Steg model that uses embedding and extracting algorithms by exploiting SVM classification and SVM-Steg embedding to achieve good performance. In addition, the distance of the embedding location is also taken into account so that more pixels can be embedded at more distance locations. The results show a quality cover-image when high peak signal-to-noise ratio (PSNR) values are recorded greater for all types of cover images. In comparison to the other technique, all PSNRs for the proposed technique, SVM-Steg Method achieved 40 or higher. It is not only succeeding in providing a secure embedding position, but also increases the number of secret-bits embedded. © 2023 IEEE.

关键词： Support vector machines

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Machine Learning-based Soil Fertility Analysis to Maintain Environmental Sustainability 2

Machine Learning-based Soil Fertility Analysis to Maintain E...

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2nd International Conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Prakash, Y. Rama, P. Jagadeesh, S. Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar TN Kattankulathur603203 India

ISBN: (纸本)9798350340235

India, the world's second-most populous nation, is deeply rooted in agriculture, with 60% of its population engaged in farming. However, traditional farming practices have led to significant challenges, including reduced productivity and soil degradation. To address these longstanding issues, we are embarking on a transformative mission to harness the potential of machine learning to revolutionize Indian farming. Machine learning, often perceived as complex, excels at processing specific data, uncovering insights for informed decisions. In the agricultural context, it's a potent tool for reshaping traditional practices. Our primary focus is on modernizing soil analysis, which has traditionally been labor-intensive and time-consuming, leading to delayed decision-making—a luxury that farmers can ill afford. By leveraging machine learning, we aim to provide swift and precise solutions, offering real-time insights into soil fertility for farmers. This project seamlessly aligns with the broader objective of bolstering food security and promoting sustainable agricultural practices. The impact of this technological transformation transcends the farm, nurturing both agriculture and the environment in a mutually beneficial relationship where both thrive. As we delve into the "Soil Analyzer" project, our vision is clear: to empower India's farming community with knowledge and arm them with the tools for informed decisions. We aim to guide agriculture toward a prosperous, sustainable, and environmentally responsible future. With technology as the catalyst, Indian farming is poised for a revolution that will benefit not only the farmers but also the entire nation, ensuring a brighter future for India's agricultural landscape. This mission is driven by the urgent need to modernize agriculture in India and to provide solutions that empower farmers and contribute to the nation's food security and environmental sustainability. © 2023 IEEE.

关键词： Agricultural Decision Support Fertilizer Optimization Machine Learning Predictive Modeling Random Forest SFI (Soil Fertility Index) Soil Parameters User-Friendly Interface

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Advancing Embedded System Design: FPGA-Based Digital Frequency Meter with Adaptive Range Commutation

Advancing Embedded System Design: FPGA-Based Digital Frequen...

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2024 International Conference on Computer and Applications, ICCA 2024

作者： Raouf, Youssef Benmamoun, Zoubida Hachimi, Hanaa Jebbor, Ikhlef National School of Applied Sciences Ensa Ibn Tofail University Laboratory of Advanced Systems Engineering Kenitra Morocco Liwa College Faculty of Engineering and Computing Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350367560

Frequency measurement is crucial in electronic measurement, and digital frequency meters are essential for engineering. Their use in smart appliances increases demand for wider ranges and complex designs. This paper presents the design and implementation of a digital frequency meter using FPGA technology, in particular the Xilinx Spartan 7. The system incorporates automatic range commutation, providing frequency measurements from 0.01 Hertz to 9.99 Megahertz. The frequency is displayed on a three-digit display with two decimal positions, while the range is shown on eight LEDs. Measurement is based on counting signal cycles within a time frame defined by a basic time generator. When the frequency exceeds the counting limits, the system automatically changes scale. The development part of this paper is divided into two steps: first, a digital architecture is designed using VHDL software for the combinational and sequential logic functions, preceded by validation by simulation. In the second step, analogue conditioning circuits are developed, including filters for noise reduction and a comparator for signal shaping. The system is tested with a signal generator to validate the functionality of the FPGA-based frequency meter. Findings show that the FPGA implementation provides accurate frequency measurements with effective automatic range commutation. © 2024 IEEE.

关键词： Electric frequency measurement

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Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble

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Big Data Mining and Analytics 2024年第2期7卷 247-270页

作者： Mona Ahamd Alghamdi Abdullah S.A.L-Malaise AL-Ghamdi Mahmoud Ragab Information Systems Department Faculty of Computing and Information Technology(FCIT)King Abdulaziz University(KAU)Jeddah 21589Kingdom of Saudi Arabia Information Systems Department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah 22246Kingdom of Saudi Arabia Information Technology Department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with Mathematics DepartmentFaculty of ScienceAl-Azhar UniversityNaser City 11884Egypt

The ability to make accurate energy predictions while considering all related energy factors allows production plants,regulatory bodies,and governments to meet energy demand and assess the effects of energy-saving *** energy consumption falls within normal parameters,it will be possible to use the developed model to predict energy consumption and develop improvements and mitigating measures for energy *** objective of this model is to accurately predict energy consumption without data limitations and provide results that are easily *** proposed model is an implementation of the stacked Long Short-Term Memory(LSTM)snapshot ensemble combined with the Fast Fourier Transform(FFT)and *** and Berard’s Individual Household Electric-Power Consumption(IHEPC)dataset incorporated with weather data are used to analyse the model’s accuracy with predicting energy *** model is trained,and the results measured using Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),and coefficient of determination(R^(2))metrics are 0.020,0.013,0.017,and 0.999,*** stacked LSTM snapshot ensemble performs better than the compared models based on prediction accuracy and minimized *** results of this study show that prediction accuracy is high,and the model’s stability is high as *** model shows that high levels of accuracy prove accurate predictive ability,and together with high levels of stability,the model has good interpretability,which is not typically accounted for in ***,this study shows that it can be inferred.

关键词： energy consumption prediction Artificial Intelligence(AI) Deep Learning(DL) snapshot ensemble

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