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Variational Autoencoders-BasedSelf-Learning Model for Tumor Identification and Impact Analysis from 2-D MRI Images

Journal of Healthcare engineering 2023年第1期2023卷 1566123页

作者： Naga Srinivasu, Parvathaneni Krishna, T. Balamurali Ahmed, Shakeel Almusallam, Naif Khaled Alarfaj, Fawaz Allheeib, Nasser Department of Computer Science and Engineering Prasad v Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada520007 India Department of Computer Science and Engineering Dhanekula Institute of Engineering and Technology Andhra Pradesh Vijayawada521139 India Department of Computer Science College of Computer Sciences and Information Technology King Faisal University Al-Ahsa31982 Saudi Arabia Department of Management Information Systems College of Business Administration King Faisal University Al-Ahsa31982 Saudi Arabia Department of Information Systems College of Computer and Information Science King Saud University Riyadh Saudi Arabia

Over the past few years, a tremendous change has occurred in computer-Aided diagnosis (CAD) technology. The evolution of numerous medical imaging techniques has enhanced the accuracy of the preliminary analysis of several diseases. Magnetic resonance imaging (MRI) is a prevalent technology extensively used in evaluating the progress of the spread of malignant tissues or abnormalities in the human body. This article aims to automate a computationally efficient mechanism that can accurately identify the tumor from MRI images and can analyze the impact of the tumor. The proposed model is robust enough to classify the tumors with minimal training data. The generative variational autoencoder models are efficient in reconstructing the images identical to the original images, which are used in adequately training the model. The proposed self-learning algorithm can learn from the insights from the autogenerated images and the original images. Incorporating long short-Term memory (LSTM) is faster processing of the high dimensional imaging data, making the radiologist's task and the practitioners more comfortable assessing the tumor's progress. Self-learning models need comparatively less data for the training, and the models are more resource efficient than the various state-of-Art models. The efficiency of the proposed model has been assessed using various benchmark metrics, and the obtained results have exhibited an accuracy of 89.7%. The analysis of the progress of tumor growth is presented in the current study. The obtained accuracy is not pleasing in the healthcare domain, yet the model is reasonably fair in dealing with a smaller size dataset by making use of an image generation mechanism. The study would outline the role of an autoencoder in self-learning models. Future technologies may include sturdy feature engineering models and optimized activation functions that would yield a better result. © 2023 Parvathaneni Naga Srinivasu et al.

关键词： Magnetic resonance imaging

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Exploring a multi-objective optimization operation model of water projects for boosting synergies and water quality improvement in big river systems

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Journal of Environmental management 2023年第1期345卷 118673页

作者： Zhu, Di Zhou, Yanlai Guo, Shenglian Chang, Fi-John Lin, Kangling Deng, Zhimin State Key Laboratory of Water Resources Engineering and Management Wuhan University Wuhan430072 China Department of Bioenvironmental Systems Engineering National Taiwan University Taipei10617 Taiwan Changjiang Water Resources Protection Institute Wuhan430051 China Bureau of Hydrology Changjiang Water Resources Commission Wuhan430010 China

Due to excessive nutrient enrichment and rapidly increasing water demand, the occurrence of riverine environment deterioration events such as algal blooms in rivers of China has become more frequent and severe since the 1990s, which has imposed harmful consequences on riverine ecosystems. However, tackling river algal blooms as an important issue of restoring riverine environment is very challenging because the complex interaction mechanisms between the causes are impacted by multiple factors. The contributions of our study consist of: (1) optimizing joint operation of water projects for boosting synergies of water quality and quantity, and hydroelectricity;and (2) preventing algal bloom from perspectives of hydrological and water-quality conditions by regulating water releases of water projects. This study proposed a multi-objective optimization methodology grounded on the Non-dominated Sorting Genetic Algorithm to simultaneously minimize the excess values of algal bloom indicators (water quality, O1), minimize the used reservoir capacity for water supply (water quantity, O2), and maximize the hydropower generation (hydroelectricity, O3). The proposed methodology was applied to several catastrophic algal bloom events that took place between 2017 and 2021 and thirteen water projects in the Hanjiang River of China. The results indicated that the proposed methodology largely stimulated the synergistic benefits of the three objectives by reaching a 36.7% reduction in total nitrogen and phosphorus concentrations, a 33.1% improvement in the remaining reservoir capacity, and a 41.0% improvement in hydropower output, as compared with those of the standard operation policy (SOP). In addition, the optimal water release schemes of water projects would increase the minimum streamflow velocity of downstream algal bloom control stations by 8.6%–9.4%. This study provides a new perspective on water project operation in the environmental improvement in big river systems while boost

关键词： Water quality

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A group decision-making method to measure national energy architecture performance: A case study of the International energy Agency

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Applied Energy 2023年第PartA期330卷

作者： Yu, Yinyun Li, Congdong Fu, Yelin Yang, Weiming School of Management Jinan University Guangzhou China College of Economics Shenzhen University Shenzhen China Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong Hong Kong Management Teaching Section Party School of the Guangdong Provincial Committee of CPC Guangzhou China

The World Economic Forum has developed the Energy Architecture Performance Index (EAPI) to help nations or regions gain insights into the status of energy systems, with the goal of Steering energy systems to be more affordable, sustainable, and secure. The EAPI is the arithmetic mean of energy triangle: economic growth and development (EGD), environmental sustainability (ES), and energy access and security (EAS). However, the preference diversity among EGD, ES and EAS of energy analysts or decision-makers has been largely unexplored. This study, based on the EAPI framework, proposes a group decision-making (GDM) method with preference analysis to measure national energy architecture performance. Specifically, an initial group decision matrix is firstly developed in terms of identifying the importance orders of energy triangle as individual decision makers;then the preference diversities of decision-makers are extensively investigated from the three aspects of psychological preferences, preferential differences, preferential priorities to construct a revised group decision-making matrix;the technique for order preference by similarity to an ideal solution (TOPSIS) is ultimately employed to calculate and compare the national energy architecture performance. A case study using the data of the International Energy Agency (IEA) is conducted to validate the proposed method, in which the satisfaction levels and Spearman's rank correlation coefficients analysis are carried out to demonstrate the merits of our method. © 2022 Elsevier Ltd

关键词： Decision making

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Voice-Based Virtual-Controlled Intelligent Personal Assistants

Voice-Based Virtual-Controlled Intelligent Personal Assistan...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Satya Prakash Yadav Amit Gupta Caio Dos Santos Nascimento Victor Hugo C. de Albuquerque Mahaveer Singh Naruka Sansar Singh Chauhan Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management (GLBITM) GreaterNoida U.P. India Graduate Program in Telecommunications Engineering. (PPGET) Federal Institute of Education Science and Technology of Ceará (IFCE) Fortaleza-CE Brazil Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Teleinformatics Engineering Federal University of Ceará Fortaleza Fortaleza/CE Brazil Computer Science & Engineering GL Bajaj Institute of Technology & Management Greater Noida India

Virtual assistants are improving and providing consumers with greater advantages. The comprehension and fulfilment of requests by virtual assistants will increase as voice recognition and natural language processing continue to grow. Virtual assistants are projected to be employed in more commercial activities as speech recognition technology advances. The main goal of developing personal assistant software (virtual assistant) is to use web-based semantic data sources, user-generated content, and knowledge from knowledge libraries. Basically, main objective of making this Voice-Based Virtual Assistant is to make life easier and having a personal assistant to everyone which can perform many tasks. As the end user interacts with a virtual assistant, the AI programming learns from the data provided and improves its ability to forecast the end user's needs. Virtual assistants are often used to do things like add tasks to a calendar, provide information that would normally be found in a website, and operate and monitor Smart Home devices like lighting and cameras and thermostats. Massive volumes of data are required to fuel virtual assistant technologies, which feed Artificial Intelligence (AI) platforms such as machine learning, natural language processing, and speech recognition. Speech recognition has a lengthy history and has seen several key advancements. On smartphones and wearable devices, speech recognition for dictation, search, and voice commands has become a standard feature. Design of a small, large vocabulary speech recognition system that can run quickly, accurately, and with minimum latency on mobile devices.

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Dealing with the latency problem to support 5g-urllc: A strategic view in the case of an indonesian operator 2

Dealing with the latency problem to support 5g-urllc: A stra...

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2nd International Conference on Broadband Communications, Wireless Sensors and Powering, BCWSP 2020

作者： Yogapratama, Ari Sadewa Suryanegara, Muhammad Graduate Program in Telecommunications Management Department of Electrical Engineering Universitas Indonesia Jakarta Indonesia

ISBN: (纸本)9781728174495

Before implementing 5G technology, each operator first needs to know their existing network capability. The operator's network must be able to meet the stringent 5G network requirements, including a very low latency, especially for the URLLC (Ultra Reliable Low Latency Communication) scenario. This scenario requires 1 ms latency for applications to run smoothly. In this paper, we use a case study from one of the operators in Indonesia to obtain measurements of the network, evaluate it, and then develop strategic steps that will allow the operator to determine whether the network can implement 5G URLLC. A correlation is observed between the latency and the distance from the site to the data center in the operator's network in Indonesia. The maximum site to data center distance to achieve 1 ms latency is 21.4 km using the existing 4G network infrastructure. We also discuss the optimum design for a new data center location and an efficient schema for building new 5G infrastructure. © 2020 IEEE.

关键词： 5G mobile communication systems

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Multimodal Brain Tumor Segmentation Using Transformer and UNET 20

Multimodal Brain Tumor Segmentation Using Transformer and UN...

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20th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Osei, Isaac Appiah-Kubi, Benjamin Frimpong, Bill Kissi Hayfron-Acquah, James Ben Owusu-Agyemang, Kwabena Ofori-Addo, Samuel Turkson, Regina Esi Mawuli, Cobbinah Bernard Kwame Nkrumah University of Science and Technology Department of Computer Science Kumasi Ghana Ho Technical University Department of Computer Science Ho Ghana University of Cape Coast PMB University Post Office Department of Computer Science and Information Technology Cape Coast Ghana School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China College of Management Information Systems University of Ghana Ghana

ISBN: (纸本)9798350318982

In medical image analysis, accurate 3D brain tissue segmentation is crucial. This study undertakes a meticulous comparative evaluation of two 3D segmentation models: the established 3DUnet and the innovative 3DUnet Transformer. The assessment is anchored on the segmentation accuracy of three critical brain tissue classes;Whole Tumor (WT) Enhancing Tumor (ET) and Tumor Core (TC), with the Dice similarity coefficient (DSC) serving as the principal metric of performance. 3DUnet model demonstrated a consistent advantage over the 3DUnet Transformer, yielding superior DSC scores for ET (0.771 vs. 0.753), WT (0.891 vs. 0.874), and TC (0.804 vs. 0.779). Complementing the DSC, the Hausdorff distance was employed as an additional metric, further evidencing the 3DUnet's enhanced precision in segmentation with lower distance values across the board. These findings highlight the 3DUnet's robust performance and its potential to significantly refine brain tissue segmentation processes within medical imaging applications. © 2023 IEEE.

关键词： Tumors

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Health risk assessment of soil contamination with heavy metals in a child care center co-located in vicinity to small scale industrial area: Case study of Thailand

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Case Studies in Chemical and Environmental engineering 2024年 9卷

作者： Simatupang, Cathleen Ariella Santhaweesuk, Kanitthika Strezov, Vladimir Pongkiatkul, Prapat Boontanon, Narin Jindal, Ranjna Boontanon, Suwanna Kitpati Graduate Program in Environmental and Water Resources Engineering Department of Civil and Environmental Engineering Faculty of Engineering Mahidol University Nakhon Pathom 73170 Thailand School of Natural Sciences Faculty of Science and Engineering Macquarie University 2109 NSW Australia Department of Environmental Engineering King Mongkut's University of Technology Thonburi Bangkok 10800 Thailand Faculty of Environment and Resource Studies Mahidol University Nakhon Pathom 73170 Thailand Environmental Engineering and Management Program Department of Energy Environment and Climate Change School of Environment and Resources Development Asian Institute of Technology Pathumthani 12120 Thailand Graduate School of Global Environmental Studies Kyoto University Yoshida Sakyo-Ku Kyoto 606-8501 Japan

Environmental impacts of small scale industries are still not well investigated, especially for estates where multiple small scale industries are clustered causing cumulative effects potentially impacting most vulnerable groups. This study aimed to investigate the concentrations of heavy metals (Mn, Fe, Ni, Cu, Pb, Al, Zn, and Cr) and the metalloid (As) in surface soils near the intensely active small-scale industrial area in Thailand, assess the contamination levels in the co-located child care center, and estimate potential human health risks from exposure to the heavy metals. Surface soil heavy metal(loid) elements were evaluated across a background area, area near main roads, a small-scale industrial area, and a child care centre. The concentrations of Al were found be the highest at all investigation sites, with the most elevated concentrations observed in the small-scale industrial area. The concentration of Pb in the industrial area reached 1176 mg/kg, exceeding the national standard and acceptable limit. Moreover, the contamination level for Cu, Pb, and Zn in the industrial area imply a considerable risk with enrichment factor EF > 40, and geo-accumulation index Igeo ≥5. Heavy metal levels (Ni, Cu, Pb) in the soil near the road and the child care center were similar, suggesting shared contamination sources. Conversely, Al, Zn, As, and Cr in the child care center had slightly higher concentrations than near the road but lower than the small-scale industrial area, indicating potential industrial influence on metal levels within the child care center. The principal component analysis showed Mn and Fe distributed in the background, while As, Pb, Ni, Cu, Al, Zn, and Cr accumulated in the small-scale industrial area. The findings indicate that small-scale industrial activities contribute to elevated levels of heavy metals in the soil, particularly within the child care center, posing significant ingestion risks, especially for children (HI > 1). Implementing conta

关键词： Children Contamination sources Environmental impact PCA Surface soil

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Real engineering Problems in an Undergraduate Course: The learning methodologies and assessment tools

Real Engineering Problems in an Undergraduate Course: The le...

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World engineering Education Forum - Global engineering Deans Council (WEEF-GEDC)

作者： Ricardo A. Diogo André L.A.C. Venâncio Maria Angela M. Roveredo Santos Eduardo F.R. Loures Neri dos Santos Knowledge Engineering&Management Graduate Program Federal University of Santa Catarina Florianopolis Brazil Industrial&Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Brazil Control and Automation Engineering/Mechatronics Engineering Pontifical Catholic University of Parana Curitiba Brazil

ISBN: (纸本)9781665424899

This paper presents the context of the Ubiquitous Computing course carried out in an Industrial engineering undergraduate program throughout 2020 and the first semester of 2021. This course took advantage of the Institutional Modernization program led by the Control and Automation engineering Undergraduate program, which consists in the modernization of undergraduate engineering programs at the Pontifical Catholic University of Paraná. The course aimed to help develop competencies, i.e., a set of knowledge, skills, and attributes aligned with Student Outcomes suggested by ABET. Also, the course proposal brought the productive sector very close to the academic environment, proposing real engineering problems as challenges. However, what are the methods and assessment tools for practical learning in a course with modern elements? This paper proposed the flipped learning and the Challenge-Based Learning Framework with the support of the CDIO Framework as learning methodologies to answer such question. Additionally, quizzes, tests, presentations, rubrics, and peer evaluation were applied as assessment tools to measure the student's progress. Finally, students were listened to about their perceptions during the course. The results suggested that the learning methodologies and assessment tools are suitable for the course context, although some improvements are expected for the following course offers. Moreover, students approved the initiative to bring in real challenges proposed by companies, the mentoring hours, and the feedback about the projects.

关键词： Automation Companies Industrial engineering Ubiquitous computing Proposals Mentoring engineering education

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AI-Powered Precision: Revolutionizing Skin Cancer Detection with Advanced Machine Learning

AI-Powered Precision: Revolutionizing Skin Cancer Detection ...

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Novel Intelligent and Leading Emerging Sciences Conference (NILES)

作者： Mariam M. Abd-elaziz Basma M. Elshafie Hagar Abdullah Karen Y. Gerges Ingy A. Ghanem Khaled M. Fouad Amir El-Ghamry Ibrahim F. Moawad Sara El-Metwally Faculty of computer science and engineering New Mansoura university Dakahlia Egypt Faculty of Engineering Computer and Communication program Faculty of Computer Science and Engineering Mansoura University New Mansoura University Information Systems Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer and Information Sciences Faculty of Computer Science and Engineering Mansoura University New Mansoura University Egypt School of Engineering and Computer Science University of Hertfordshire GAF Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Department of Artificial Intelligence College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

Skin cancer's increasing incidence rates necessitate advanced diagnostic tools. This research uses MobileNet architecture to develop an enhanced system for skin cancer detection. MobileNet's efficient CNN architecture, combined with Keras for model lifecycle management and TensorFlow for real-time inference, forms the foundation of this study. Using 10,015 dermatoscopic pictures from seven different classifications of skin cancer in the HAM10000 dataset, the methodology includes several key stages: image preprocessing to correct illumination and adjust resolution, data augmentation to balance the dataset, and model training using Transfer Learning. The MobileNet model was trained over 50 epochs with a comprehensive architecture incorporating multiple specialized layers. By combining large-scale data analysis and adaptive learning, this methodology demonstrates The revolutionary possibilities of AI and ML in improving skin cancer diagnostics and public health outcomes. Keywords Skin Cancer Detection, Dermatoscopic Images, Machine Learning, Deep Learning

关键词： Training Deep learning Adaptation models Image resolution Transfer learning Lighting Real-time systems Image preprocessing Public healthcare Skin cancer

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Factors for design of performance measurement systems for not-for-profit organizations

Factors for design of performance measurement systems for no...

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2016 Industrial and systems engineering Research Conference, ISERC 2016

作者： Moura, Louisi Francis de Lima, Edson Pinheiro Deschamps, Fernando van Aken, Eileen Gouvea da Costa, Sergio E. Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Brazil Federal University of Technology - Parana Curitiba Brazil Department of Mechanical Engineering Federal University of Parana Curitiba Brazil Grado Department of Industrial and Systems Engineering Virginia Tech Blacksburg United States

The scope of this research is delimited by the study of not-for-profit organizations performance through operations management lens. According to a systematic literature review, the performance measurement frameworks applied to not-for-profit organizations are adaptations of models developed for for-profit companies. However, the specific characteristics related to participatory governance, responsibility to declare their tangible and intangible results, and accountability of different revenue sources that combine donations, governmental aids, volunteering or public financing are usually not properly considered. In this context, this paper intends to identify factors that intervene in not-for-profit organizations' performance measurement systems, and to organize them in a conceptual framework that could assist not-for-profit organizations to better design their performance measurement systems. The paper also analyzes how enterprise engineering design principles can be applied in this context, relating the intervening factors to enterprise engineering guidelines. © 2016 Proceedings of the 2016 Industrial and systems engineering Research Conference, ISERC 2016. All rights reserved.

关键词： Industrial research

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