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7th International Conference on Materials for Advanced Technologies, ICMAT 2013

作者： Wong, Yat Choy Wang, James Teh, Geok Bee Faculty of Engineering and Industrial Sciences Swinburne University of Technology PO box 218 Hawthorn VIC 3122 Australia Faculty of Applied Science and Computing Tunku Abdul Rahman University College 53300 Setapak Kuala Lumpur Malaysia

Strontium hexaferrite (SrFe12O19) particles were prepared by sol gel method at two different calcination temperatures, namely, at 800°C and 1000°C. It was found that SrFe12O19prepared, at 800°C showed a mixture of Fe2O3 co-existed while at a higher temperature of 1000°C, no other oxide impurities was detected. Superconducting quantum interference device (SQUID) was performed on both the sol-gel prepared samples. The coercivity for the low temperature prepared SrFe12O19 showed a much higher value of 6250 Oe compared with the high temperature prepared SrFe12O19 which was only 4500 Oe. In addition to the calcination temperature, the presence of Fe2O3 also affected the coercivity of the hexaferrite particles. Thermogravimetric analysis (TGA) was performed at 950°C on both the sol-gel samples and it was noted that the low temperature prepared SrFe12O19 has a higher weight loss about 3.2% compared to the high temperature sample which has a much smaller weight loss of only 0.6%. The high weight loss was attributed by the breakdown of Fe2O3.

关键词： SQUIDs

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Nutritional Values and Effects of Selected Solvents on the Phytochemical Properties of Moringa Oleifera Leaves Obtained from Sokoto, Nigeria

Nutritional Values and Effects of Selected Solvents on the P...

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2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Jamiu, Aasia Muhammed Gumi, Ahmad Salma Alabi, Dorcas Toyin Ahmed-Umar, Fatimah Habib, Nabila Muhammed Adebayo-Gege, Grace I Victoria, Adegunloye Deke Obianuju, Ojikah Kolawole, Muftaudeen Taoheed Abimbola, Ogundolie Frank Baze University Faculty of Computing and Applied Sciences Abuja Nigeria Baze University College of Medicine and Health Sciences Abuja Nigeria Federal University of Technology Department of Microbiology Akure Nigeria

ISBN: (纸本)9798350358155

Moringa Oliefera (M. oleifera) contains high amounts of antioxidants and phytochemicals which have been explored for their therapeutic effects. Solvents used in extraction have been noted to influence the availability of specific phytochemicals. In this study, the nutritional values of M. oleifera and effects of extracting solvents namely;ethanol, n-hexane, ethyl-acetate, and aqueous solution on the phytochemical constituents of *** leaves from Sokoto were investigated. M. oleifera leaves were obtained from Sokoto, Nigeria and dried. The Proximate and trace metal analysis were carried out by use of Atomic absorption spectrophotometer (AAS). *** sample was extracted by soxhlet extractor using the different solvents, and then concentrated. Concentrated compounds were further analyzed by GC-MS. Proximate analysis of the leaves yielded crucial nutritional values which includes, crude protein (13.31%),crude lipid (14.01%), and dietary fibre (6.37%). Furthermore, sodium and magnesium concentrations were notably elevated, with magnesium reaching 15.1 mg/l. Ethanol and aqueous extracts yielded positive results for flavonoids, carbohydrates, phenols, tannins, cardiac glucosides, and alkaloids. Conversely, n-hexane exhibited negative effects on these compounds, while ethyl-acetate demonstrated mixed outcomes.. The GC-MS analysis of the extracts showed the presence of 2-Heptanol, 6-meth 1,2-Benzenedicarboxylic acid, Phthalic acid, cyclobutyl-heptyl ester, Diethyl-Phthalate, ethylisopropyl ester, Trimethyl(4-tert.-butyl-phenoxy)silane, and 2,4,6-Cycloheptatrien-1-one. The findings from this work suggested that *** leaves possess important nutritional values ,and using solvents such as ethanol and aqueous solution to extract the bioactive compounds presents a rich reservoir of such compounds (especially cardiac glycosides) which could be used as therapeutics in management of cardiovascular disorders. © 2024 IEEE.

关键词： Ethanol

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Renewable energy EV charging station

Renewable energy EV charging station

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2021 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2021 and 2021 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2021

作者： Dorin, Petreus Toma, Patarau Radu, Etz Marcian, Cirstea Technical University of Cluj-Napoca Faculty of Electronics Telecommunications and Information Technology Department of Applied Electronics Cluj-Napoca Romania Anglia Ruskin University School of Computing and Information Science Faculty of Science and Engineering Cambridge United Kingdom

ISBN: (纸本)9781665402989

Due to the increased interest in electric vehicle (EV) technologies and the cost reduction of photovoltaic systems, industrial development of battery charging stations for electric vehicles based on solar energy has started. Although it is convenient to charge EVs at night, when there is less electricity consumption, there will always be consumers who prefer or need to charge their EV during daytime and even during peak hours. In this paper, an EV charging station integrating renewable energy in the form of solar energy is proposed and analyzed. Using a local battery pack, the charging station allows semi-fast and fast charging and can be installed in individual homes and apartment buildings, where the maximum power delivered by the grid is limited to 3.6kW. The charging station uses converters widely available on the market. A simulation model for this charging station is developed and various distinct operation modes are presented, validating the correct operation of the entire system. © 2021 IEEE.

关键词： Solar energy

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Automatic detection of ischemic-stroke-lesion with CNN segmentation: A study 8

Automatic detection of ischemic-stroke-lesion with CNN segme...

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8th International Virtual Conference on Biosignals, Images, and Instrumentation, ICBSII 2022

作者： Al Attar, Ferasnadhimhasoon Kadry, Seifedine Manic, K. Suresh Meqdad, Maytham N. Department of Electrical and Communication Engineering National University of Science and Technology Muscat Oman Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Al-Mustaqbal University College Babil Hillah Iraq

The vital organ in human physiology is the brain, and abnormality in the brain will reason for various behavioural problems. Ischemic-Stroke is a medical emergency, and early detection and action will help the patient recover quickly. This scheme aims to implement Convolutional-Neural-Network (CNN) segmentation method to extract and evaluate the infected portion from the MRI slice of the brain. In our study the pre-trained UNet scheme is adopted to extract the stroke region from the Flair modality MRI slice with axial-, coronal- and sagittal plane. In this work, the ISLES2015 database is used for the experimental investigation. The segmented portion is further evaluated to the ground-truth and the metrics such as Jaccard, Dice and Accuracy are computed. The experimental investigation is implemented using Python software. The experimental outcome of this research proves that the proposed CNN scheme aids to improve segmentation accuracy on axial-plane images compared with other images. The performance of the CNN segmentation scheme is then validated with other related results existing in the literature. The outcome of this study confirms that UNet supported technique helps extract the stroke lesion from the MRI slice with more accurate accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Image enhancement

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A novel deep learning-based spider wasp optimization approach for enhancing brain tumor detection and physical therapy prediction

Computer Methods and Programs in Biomedicine Update

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Computer Methods and Programs in Biomedicine Update 2025年 7卷

作者： Suleiman Daoud Ahmad Nasayreh Khalid M.O. Nahar Wlla k. Abedalaziz Salem M. Alayasreh Hasan Gharaibeh Ayah Bashkami Amer Jaradat Sultan Jarrar Hammam Al-Hawamdeh Absalom E. Ezugwu Raed Abu Zitar Aseel Smerat Vaclav Snasel Laith Abualigah Neurosurgery Department Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Artificial Intelligence and Data Innovation Office King Hussein Cancer Center Amman Jordan Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Department of Rehabilitation science Faculty of applied medical science Jordan University of Science and Technology King Abdullah University Hospital Irbid Jordan Department of Medical Laboratory Sciences Al Balqa Applied University Salt Jordan Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa College of Engineering and Computing Liwa University Abu Dhabi United Arab Emirates Faculty of Educational Sciences Al-Ahliyya Amman University Amman 19328 Jordan Computer Technologies Engineering Mazaya University College Nasiriyah Iraq Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava 70800 Poruba-Ostrava Czech Republic Computer Science Department Al al-Bayt University Mafraq 25113 Jordan

A brain tumor, one of the deadliest disorders, is characterized by the abnormal growth of synapses in the brain. Early detection can improve brain tumor diagnosis, and accurate diagnosis is essential for effective treatment. Researchers have developed several deep-learning classification methods to diagnose brain tumors. Moreover, these types of tumorscan significantly impair physical activity, presenting a broad spectrum of symptoms. As a result, each patient requires an individualized physical therapy treatment plan tailored to their specific needs. However, some challenges remain, including the need for a competent expert in classifying brain tumors using deep learning models, as well as the challenge of creating the most accurate deep learning model for brain tumor classification. To address these challenges, we present a highly accurate and efficient methodology based on advanced metaheuristic algorithms and deep learning. To identify different types of pediatric brain tumors, we specifically develop an optimal residual learning architecture. We also present the Spider Wasp Optimization (SWO) algorithm, which aims to improve performance by feature selection. The algorithm enhances the effectiveness of optimization by balancing the speed of convergence and diversity of solutions. We first convert the algorithm from continuous to binary, combine it with the K-Nearest Neighbor (KNN) algorithm for classification, and evaluate it on a dataset of brain MRI images collected from King Abdullah Hospital. Our analysis revealed that in terms of metrics such as accuracy, sensitivity, specificity, and f1-score, it outperformed other conventional algorithms. We demonstrate the overall effectiveness of the proposed model by using it to select the optimal features extracted from the Resnet50V2 model for pediatric brain tumor detection. We compared the proposed SWO+KNN model with other deep learning architectures such as MobileNetV2, Resnet50V2, and machine learning algorithms

关键词： Brain tumor Pediatric Machine learning Rehabilitation Physical therapy Deep learning Metaheuristic optimization Feature selection

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An Integrated Image Classification Approach to Detect the Flood Prone Areas using Sentinel-1 Images 10

An Integrated Image Classification Approach to Detect the Fl...

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17th INDIACom;2023 10th International Conference on computing for Sustainable Global Development, INDIACom 2023

作者： Siddique, Mohammed Ahmed, Tasneem Husain, Mohd Shahid Integral University Advanced Computing Research Lab Department of Computer Application Lucknow India Majan University College Faculty of Information Technology Muscat Oman CAS-Ibri University of Technology and Applied Sciences Department of Information Technology Muscat Oman

ISBN: (纸本)9789380544472

The untimely monsoon floods and the rise in the level of rivers in the north Indian state of Uttar Pradesh (UP) is a major challenge for coordinating relief and rescue operations. To address this challenge, the experts that are deploying a variety of techniques to evaluate the damages might get useful information from synthetic aperture radar (SAR) images which offer images in close to real-time. The Sentinel-1A satellite images from the European Space Agency provide C-band SAR images that have helped researchers achieve enhanced processes of flood detection and flood mapping in flood-prone areas. In regular cases, the Random Forest (RF) and K-Nearest Neighbor (KNN) supervised classification techniques provide higher accuracies than the unsupervised K-Means classification techniques. The integrated approach with the resultant accuracy provides long-reliable elucidation towards flood damages and assessment control. Thenceforth, the RF and KNN supervised classifications based outcomes were integrated with the outcomes of K-Means iterative clustering unsupervised classification producing an enhanced overall accuracy. The overall accuracy from the integrated approach for the city of Basti and Ayodhya is 97% and 93.8% which is higher than the accuracies derived from the supervised and unsupervised classifications. Further, these processed results when refined by time-series analysis over a number of years along with implementing change detection techniques would help in monitoring the flood situation and undertaking precautionary measures. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Synthetic aperture radar

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Preface

SpringerBriefs in Applied Sciences and Technology

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SpringerBriefs in applied Sciences and technology 2025年 Part F127卷 v页

作者： Sambhav, Saurabh Singh, Deepak Kumar Pandey, Ashok Kumar Ismail, Azman Zulkipli, Fatin Nur Öchsner, Andreas Amity University Patna Bihar Patna India Madhya Pradesh Bhopal India Maritime Engineering Technology and Centre for Women Advancement and Leadership Universiti Kuala Lumpur Malaysian Institute of Marine Engineering Technology Perak Lumut Malaysia Information Science Studies College of Computing Informatics and Mathematics Universiti Teknologi MARA Machang Kelantan Malaysia Faculty of Mechanical Engineering Esslingen University of Applied Sciences Baden-Württemberg Esslingen Germany

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Classification of Retroviruses Based on Genomic Data Using RVGC

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Computers, Materials & Continua 2021年第12期69卷 3829-3844页

作者： Khalid Mahmood Aamir Muhammad Bilal Muhammad Ramzan Muhammad Attique Khan Yunyoung Nam Seifedine Kadry Department of CS&IT University of SargodhaSargodha40100Pakistan Department of CS&IT University of MianwaliMianwali42200Pakistan School of Systems and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway

Retroviruses are a large group of infectious agents with similar virion structures and replication ***,cancer,neurologic disorders,and other clinical conditions can all be fatal due to retrovirus *** of retroviruses by genome sequence is a biological problem that benefits from computational *** National Center for Biotechnology Information(NCBI)promotes science and health by making biomedical and genomic data available to the *** research aims to classify the different types of rotavirus genome sequences available at the ***,nucleotide pattern occurrences are counted in the given genome sequences at the preprocessing *** on some significant results,the number of features used for classification is reduced to *** classification shall be carried out in two *** first phase of classification shall select only two *** data in the first phase is transferred to the next phase,where the final decision is taken with the remaining three *** data sets of animals and human retroviruses are selected;the training data set is used to minimize the classifier’s number and training;the validation data set is used to validate the *** performance of the classifier is analyzed using the test data ***,we use decision tree,naive Bayes,knearest neighbors,and vector support machines to compare *** results show that the proposed approach performs better than the existing methods for the retrovirus’s imbalanced genome-sequence dataset.

关键词： Retroviruses machine learning bioinformatics classification

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Blockchain and IIoT Enabled Solution for Social Distancing and Isolation Management to Prevent Pandemics

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Computers, Materials & Continua 2023年第7期76卷 687-709页

作者： Muhammad Saad Maaz Bin Ahmad Muhammad Asif Muhammad Khalid Khan Toqeer Mahmood Elsayed Tag Eldin Hala Abdel Hameed College of Computing and Information Sciences Karachi Institute of Economics&TechnologyKarachiPakistan Department of Computer Science Lahore Garrison UniversityLahorePakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Faculty of Engineering and Technology Future University in Egypt New Cairo11835Egypt Faculty of Computer and Information Systems Fayoum UniversityFaiyumEgypt Khaybar Applied College Taibah UniversityMedinaSaudi Arabia

Pandemics have always been a nightmare for humanity,especially in developing *** lockdowns are considered one of the effective ways to deal with spreading such ***,developing countries cannot afford such solutions because these may severely damage the country’s ***,this study presents the proactive technological mechanisms for business organizations to run their standard business processes during pandemic-like situations *** novelty of this study is to provide a state-of-the-art solution to prevent pandemics using industrial internet of things(IIoT)and blockchain-enabled *** to existing studies,the immutable and tamper-proof contact tracing and quarantine management solution is *** use of advanced technologies and information security is a critical area for practitioners in the internet of things(IoT)and corresponding ***,this study also emphasizes information security,end-to-end solution,and experimental ***,a wearable wristband is proposed,incorporating 4G-enabled ultra-wideband(UWB)technology for smart contact tracing mechanisms in industries to comply with standard operating procedures outlined by the world health organization(WHO).Secondly,distributed ledger technology(DLT)omits the centralized dependency for transmitting contact tracing ***,a privacy-preserving tracing mechanism is discussed using a public/private key cryptography-based authentication ***,based on geofencing techniques,blockchain-enabled machine-to-machine(M2M)technology is proposed for quarantine *** step-by-step methodology and test results are proposed to ensure contact tracing and quarantine *** existing research studies,the security aspect is also considered in the realm of *** practical implementation of the proposed solution also obtains the *** results indicate the successful implementation of blockchain-enabled contac

关键词： Blockchain contact tracing distributed ledger technology geo-fencing internet of things industrial internet of things isolation management social distancing ultra-wideband

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Enhancing Battery Voltage Prediction with Deep Learning: A Comparative Analysis of LSTM and Traditional Models 6

Enhancing Battery Voltage Prediction with Deep Learning: A C...

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6th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Niakan, Masoumeh Rostam Hajihosseini, Mojtaba Madani, Seyed Saeed Ziebert, Carlos Nri Department of Electricity and Energy Economics Tehran Iran University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Renewable Energy Systems Zagreb Croatia Institute of Applied Materials-Applied Materials Physics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798350305623

The growing demand for efficient energy storage solutions has sparked increased interest in precise battery voltage prediction. In this study the Long Short-Term Memory networks, are applied in the time series forecasting, to improve battery voltage prediction compared to conventional models. The developed forecasting system includes three main parts: Pre-processing, modeling, and evaluation. In the pre-processing, the voltage and current time series are normalized, and divided to the test and train subsets. Then the windows of consecutive samples are generated for both train and test subsets. The forecasting LSTM models are learnt from training data set, in the modeling phase. Finally, the Mean Absolute Error (MAE) is used as the evaluation criterion. This model is compared to two other neural network models. The study concludes that LSTM outperforms the other models, highlighted by a significantly lower MAE. With a comprehensive methodology and successful experimental framework, this paper demonstrates the efficacy of LSTM in predicting battery voltage, indicating its superiority over simple neural networks for time series forecasting. © 2023 IEEE.

关键词： Battery voltage prediction Deep learning Long Short-Term Memory neural networks

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