Search results for: project classification

Authors: Leandros A. Maglaras, Jianmin Jiang

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The protection of the national infrastructures from cyberattacks is one of the main issues for national and international security. The funded European Framework-7 (FP7) research project CockpitCI introduces intelligent intrusion detection, analysis and protection techniques for Critical Infrastructures (CI). The paradox is that CIs massively rely on the newest interconnected and vulnerable Information and Communication Technology (ICT), whilst the control equipment, legacy software/hardware, is typically old. Such a combination of factors may lead to very dangerous situations, exposing systems to a wide variety of attacks. To overcome such threats, the CockpitCI project combines machine learning techniques with ICT technologies to produce advanced intrusion detection, analysis and reaction tools to provide intelligence to field equipment. This will allow the field equipment to perform local decisions in order to self-identify and self-react to abnormal situations introduced by cyberattacks. In this paper, an intrusion detection module capable of detecting malicious network traffic in a Supervisory Control and Data Acquisition (SCADA) system is presented. Malicious data in a SCADA system disrupt its correct functioning and tamper with its normal operation. OCSVM is an intrusion detection mechanism that does not need any labeled data for training or any information about the kind of anomaly is expecting for the detection process. This feature makes it ideal for processing SCADA environment data and automates SCADA performance monitoring. The OCSVM module developed is trained by network traces off line and detects anomalies in the system real time. The module is part of an IDS (intrusion detection system) developed under CockpitCI project and communicates with the other parts of the system by the exchange of IDMEF messages that carry information about the source of the incident, the time and a classification of the alarm.

Keywords: cyber-security, SCADA systems, OCSVM, intrusion detection

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Authors: Ruixin Zhang, Joel Carboni, Songchenchen Gong

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Since the Sustainable Development Goals (SDGs) officially became the 17 development goals set by the United Nations in 2015, it has become an inevitable trend in project management development globally. Since Sustainability and glob-alization are the main focus and trends in the 21st century, project management contains system-based optimization, and or-ganizational humanities, environmental protection, and economic development. As a populous country globally, with the advanced development of economy and technology, China becomes one of the biggest markets in the wine industry. However, the develop-ment of society also brings specific environmental issues. Climate changes have already brought severe impacts on the Chinese wine market, including consumer behavior, wine production activities, and organizational humanities. Therefore, the implementation of sustainable project management in Chinese wine market is essential. Surveys based analysis is the primary method to interpret how the climate variation effect the Chinese wine market and the importance of sustainable project management implementation for green market growth in China. This paper proposes the CWW Conceptual model that can be used in the wine industry, the new 7 Drivers Model, and SPM Framework to interpret the main drivers that impact project management implementation in the wine industry and to offer the directions to wine companies in China which would help them to achieve the green growth.

Keywords: project management, sustainability, green growth, climate changes, Chinese wine market

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Authors: Atefeh Saljooghi Khoshkar, Jafar Hassanpour

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One of the main problems of the design stage relating to many tunneling projects is the lack of an appropriate standard for the provision of engineering geological data in a predefined format. In particular, this is more reflected in highway and railroad tunnel projects in which there is a number of tunnels and different professional teams involved. In this regard, comprehensive software needs to be designed using the accepted methods in order to help engineering geologists to prepare standard reports, which contain sufficient input data for the design stage. Regarding this necessity, applied software has been designed using macro capabilities and Visual Basic programming language (VBA) through Microsoft Excel. In this software, all of the engineering geological input data, which are required for designing different parts of tunnels, such as discontinuities properties, rock mass strength parameters, rock mass classification systems, boreability classification, the penetration rate, and so forth, can be calculated and reported in a standard format.

Keywords: engineering geology, rock mass classification, rock mechanic, tunnel

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Authors: Francis Kwesi Bondinuba, Seidu Abdullah, Mewomo Cecilia, Opoku Alex

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Purpose: The purpose of this research is to develop a framework for understanding how sustainable project management contributes to the construction industry's pursuit of sustainable development goals. Study design/methodology/approach: The study employed a theoretical methodology to review existing theories and models that support Sustainable Project Management (SPM) in the construction industry. Additionally, a comprehensive review of current literature on SPM is conducted to provide a thorough understanding of this study. Findings: Sustainable Project Management (SPM) practices, including stakeholder engagement and collaboration, resource efficiency, waste management, risk management, and resilience, play a crucial role in achieving the Sustainable Development Goals (SDGs) within the construction industry. Conclusion: Adopting Sustainable Project Management (SPM) practices in the Ghanaian construction industry enhances social inclusivity by engaging communities and creating job opportunities. The adoption of these practices faces significant challenges, including a lack of awareness and understanding, insufficient regulatory frameworks, financial constraints, and a shortage of skilled professionals. Recommendation: There should be a comprehensive approach to project planning and execution that includes stakeholders such as local communities, government bodies, and environmental organisations, the use of green building materials and technologies, and the implementation of effective waste management strategies, all of which will ensure the achievement of SDGs in Ghana's construction industry. Originality/value: This paper adds to the current literature by offering the various theories and models in Sustainable Project Management (SPM) and a detailed review of how Sustainable Project Management (SPM) contribute to the achievement of the Sustainable Development Goals (SDGs) in the Ghanaian Construction Industry.

Keywords: sustainable development, sustainable development goals, construction industry, ghana, sustainable project management

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Authors: Marzieh Khaleghi Baygi, Maryam Khaleghy Baygy

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The main aim of this project was designing and making sustainable architectural wearable dress inspired by reconstruction project of Bam’s Bazar in Iran. To achieve the goals of this project, Bam Bazar became the architectural reference. A mixed research method (including applied, qualitative and case studies methods) was used. After research, data gathering and considering related intellectual, mental and cultural background, the garment was modeled by using 3ds Max's modeling tools and Marvelous. After making the pattern, the wearable architecture was built and an architectural and historical building converted to a clothing. The implementation of sustainable architectural clothing, took seventeen months. The result of this project was a cloth in a new form that had been worn on its architect body. The comparison between present project and previous research were focusing on the same subjects (architectural clothing) shows some dramatic differentiations, including, the architect, designer and executive of this project was the same person who was the main researcher. Also, in this research, special attention was paid to the sustainability, volume and forms. Most projects in this subject (especially pervious related Iranian research) relied on painting and not on the volumes and forms. The sustainable immovable architecture had worn on its architect, became a cloth on a human's body that was moving.

Keywords: wearable architecture, clothing, bam bazar, space, sustainability

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Authors: Mei-Hui Liu

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This paper reports 20 preservice EFL teachers’ learning-to-teach experience when they were engaged in an online project-based practicum implemented on a Cloud Platform. This 10-month study filled in the literature gap by documenting the impact of online project-based instruction on preservice EFL teachers’ professional development. Data analysis showed that the online practicum was regarded as a flexible mechanism offering chances of teaching practices without geographical barriers. Additionally, this project-based practice helped the participants integrate the theories they had learned and further foster them how to create a self-directed online learning environment. Furthermore, these preservice teachers with experiences of technology-enabled practicum showed their motivation to apply technology and online platforms into future instructional practices. Yet, this study uncovered several concerns encountered by these participants during this online field experience. The findings of this study rendered meaning and lessons for teacher educators intending to integrate online practicum into preservice training courses.

Keywords: online teaching practicum, project-based learning, teacher preparation, English language education

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Authors: Dongju Kim, Youngjoo Suh, Hyojin Kim, Gyeongyeong Kim

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Acoustic Emission Testing (AET) is widely used to test the structural integrity of an operational hydrogen storage container, and clustering algorithms are frequently used in pattern recognition methods to interpret AET results. However, the interpretation of AET results can vary from user to user as the tuning of the relevant parameters relies on the user's experience and knowledge of AET. Therefore, it is necessary to use a deep learning model to identify patterns in acoustic emission (AE) signal data that can be used to classify defects instead. In this paper, a deep learning-based model for classifying the types of defects in hydrogen storage tanks, using AE sensor waveforms, is proposed. As hydrogen storage tanks are commonly constructed using carbon fiber reinforced polymer composite (CFRP), a defect classification dataset is collected through a tensile test on a specimen of CFRP with an AE sensor attached. The performance of the classification model, using one-dimensional convolutional neural network (1-D CNN) and synthetic minority oversampling technique (SMOTE) data augmentation, achieved 91.09% accuracy for each defect. It is expected that the deep learning classification model in this paper, used with AET, will help in evaluating the operational safety of hydrogen storage containers.

Keywords: acoustic emission testing, carbon fiber reinforced polymer composite, one-dimensional convolutional neural network, smote data augmentation

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Authors: Julia Wicker, Alexander Lang

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The shortage of skilled workers is no longer unique to Europe; Australia now faces similar challenges, particularly in the field of project management. Project managers, essential to the success of a wide range of industries, frequently operate under intense stress and, as a result, may choose to leave their positions before the completion of their projects. This trend poses significant risks to project continuity, budget stability, and the long-term success of organizations. Consequently, it is crucial to explore strategies aimed at improving the retention of project managers, with a specific focus on fostering intrinsic motivation -an essential factor for achieving sustained success and commitment within project-based roles. The aim of this paper is to investigate retention strategies from other industries to identify effective practices that could be adapted to the unique challenges faced by project managers. In particular, the paper draws inspiration from the volunteer sector, an industry also heavily reliant on intrinsic motivation to drive commitment and performance. By examining how the volunteer sector sustains retention through a focus on intrinsic motivation, this paper seeks to highlight potential parallels and offer actionable insights for improving the retention of project managers. The paper includes an overview of the current landscape of retention challenges in project management, highlighting key factors that contribute to early departures and their impacts on organizations. This is followed by an analysis of interviews conducted with both active volunteers and those who have left their roles, leading to the development of a model that categorizes different types of volunteers and explores their behaviours. The model identifies specific reasons for volunteer terminating their assignments and proposes strategies to mitigate these issues. The paper then adapts these volunteer retention strategies to address the challenges faced by project managers, concluding with actionable recommendations for fostering an intrinsically motivated and resilient project management workforce. Ultimately, this research aims to contribute to broader efforts in mitigating skilled workforce shortages by offering sustainable retention strategies.

Keywords: skilled workforce shortages, retention challenges in project management, retention strategies in the volunteering sector, retention strategies for project managers

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Authors: Vendula Safarova

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The author describes the architectural and social research through the project, Interventions Ostrava City 2013 (the idea came from Vallo + Sadovský architects), in which she participated as an organizer and as an architect. The project invited the public to actively participate, logging their "hits" or proposals (58), and resulted in three exhibitions in Ostrava, a catalog of the exhibition called Urban interventions Ostrava 2013 (published in 2014) and the implementation of two interventions (2014), with a third intervention still in preparation. The article dealt with the public's views and reactions of local authorities. The project also engaged Ostrava City council, who began to talk about the future of the city of Ostrava, taking part in public debates (organized by Fiducia), invited new associations, civil society - city for people (workers from Cooltour), as well as more established clubs such as the Beautification Committee for beautiful Ostrava (newsletter published since 2008). Currently, the City Interventions project has taken place in more than 10 cities, including Slovakia, where it originated, and in Bratislava in 2009. The aim of this article is to inform the public about the so-called Activism in architecture, which manifests itself in the form of community projects that are organized by volunteers (sometimes financially supported by local authorities). It is a unique way to survey public relations and representatives of state and local government for a public urban area.

Keywords: architecture, community project, public urban space, society and planning

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Authors: Seyed Habib A. Rahmati, Parsa Fallah Sheikhlari, Morteza Musakhani

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This paper studies the structure of financial investment and efficiency on the subway would be created between Hashtgerd and Qazvin in Iran. Regarding ascending rate of transportation between Tehran and Qazvin which directly air pollution, it clearly implies to public transportation requirement between these two cities near Tehran. The railway transportation like subway can help each country to terminate traffic jam which has some advantages such as speed, security, non-pollution, low cost of public transport, etc. This type of transportation needs national infrastructures which require enormous investment. It couldn’t implement without leading and managing funds and investments properly. In order to response 'needs', clear norms or normative targets have to be agreed and obviously it is important to distinguish costs from investment requirements critically. Implementation phase affects investment requirements and financing needs. So recognizing barrier related to investment and the quality of investment (what technologies and services are invested in) is as important as the amounts of investment. Different investment methods have mentioned as follows loan, leasing, equity participation, Line of financing, finance, usance, bay back. Alternatives survey before initiation and analyzing of risk management is one of the most important parts in this project. Observation of similar project cities each country has the own specification to choose investment method.

Keywords: subway project, project investment, project contract, project management

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Authors: Onyedikachi Ulelu, Andrew P. Longstaff, Simon Fletcher, Simon Parkinson

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The widespread interest in 'Industry 4.0' or 'digital manufacturing' has led to significant research requiring the acquisition of data from sensors, instruments, and machine signals. In-depth research then identifies methods of analysis of the massive amounts of data generated before and during manufacture to solve a particular problem. The ultimate goal is for industrial Internet of Things (IIoT) data to be processed automatically to assist with either visualisation or autonomous system decision-making. However, the collection and processing of data in an industrial environment come with a cost. Little research has been undertaken on how to specify optimally what data to capture, transmit, process, and store at various levels of an edge-cloud network. The first step in this specification is to categorise IIoT data for efficient and effective use. This paper proposes the required attributes and classification to take manufacturing digital data from various sources to determine the most suitable location for data processing on the edge-cloud network. The proposed classification framework will minimise overhead in terms of network bandwidth/cost and processing time of machine tool data via efficient decision making on which dataset should be processed at the ‘edge’ and what to send to a remote server (cloud). A fast-and-frugal heuristic method is implemented for this decision-making. The framework is tested using case studies from industrial machine tools for machine productivity and maintenance.

Keywords: data classification, decision making, edge computing, industrial IoT, industry 4.0

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Authors: M. S. Wickramarachchi, L. S. Nawarathna, C. M. B. Dematawewa

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Hatchery performance is critical for the profitability of poultry breeder operations. Some extrinsic parameters of eggs and breeders cause to increase or decrease the hatchability. This study aims to identify the affecting extrinsic parameters on the commercial hatchability of local chicken's eggs and determine the most efficient classification model with a hatchability rate greater than 90%. In this study, seven extrinsic parameters were considered: egg weight, moisture loss, breeders age, number of fertilised eggs, shell width, shell length, and shell thickness. Multiple linear regression was performed to determine the most influencing variable on hatchability. First, the correlation between each parameter and hatchability were checked. Then a multiple regression model was developed, and the accuracy of the fitted model was evaluated. Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), k-Nearest Neighbors (kNN), Support Vector Machines (SVM) with a linear kernel, and Random Forest (RF) algorithms were applied to classify the hatchability. This grouping process was conducted using binary classification techniques. Hatchability was negatively correlated with egg weight, breeders' age, shell width, shell length, and positive correlations were identified with moisture loss, number of fertilised eggs, and shell thickness. Multiple linear regression models were more accurate than single linear models regarding the highest coefficient of determination (R²) with 94% and minimum AIC and BIC values. According to the classification results, RF, CART, and kNN had performed the highest accuracy values 0.99, 0.975, and 0.972, respectively, for the commercial hatchery process. Therefore, the RF is the most appropriate machine learning algorithm for classifying the breeder outcomes, which are economically profitable or not, in a commercial hatchery.

Keywords: classification models, egg weight, fertilised eggs, multiple linear regression

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Authors: Annalakshmi G., Sakthivel Murugan S.

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This paper presents a local directional encoded derivative binary pattern (LDEDBP) feature extraction method that can be applied for the classification of submarine coral reef images. The classification of coral reef images using texture features is difficult due to the dissimilarities in class samples. In coral reef image classification, texture features are extracted using the proposed method called local directional encoded derivative binary pattern (LDEDBP). The proposed approach extracts the complete structural arrangement of the local region using local binary batten (LBP) and also extracts the edge information using local directional pattern (LDP) from the edge response available in a particular region, thereby achieving extra discriminative feature value. Typically the LDP extracts the edge details in all eight directions. The process of integrating edge responses along with the local binary pattern achieves a more robust texture descriptor than the other descriptors used in texture feature extraction methods. Finally, the proposed technique is applied to an extreme learning machine (ELM) method with a meta-heuristic algorithm known as weighted distance grey wolf optimizer (GWO) to optimize the input weight and biases of single-hidden-layer feed-forward neural networks (SLFN). In the empirical results, ELM-WDGWO demonstrated their better performance in terms of accuracy on all coral datasets, namely RSMAS, EILAT, EILAT2, and MLC, compared with other state-of-the-art algorithms. The proposed method achieves the highest overall classification accuracy of 94% compared to the other state of art methods.

Keywords: feature extraction, local directional pattern, ELM classifier, GWO optimization

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Authors: Jorine T. Ndoro, Marietjie Van Der Merwe

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Rural poverty remains a critical challenge in most developing countries and the participation of farmers in agricultural projects has taken a key role in development initiatives. Farmers’ participation in agricultural initiatives is crucial towards poverty alleviation and food security. Farmers’ involvement directly contributes towards sustainable agricultural development and livelihoods. This study focuses on investigating the perceptions of farmers’ participation in a community garden project. The study involved farmers belonging to community garden project in Imphendhle municipality in Mgungundlvu district of KwaZulu-Natal in South Africa. The study followed a qualitative research design using an interpretive research paradigm. The data was collected through conducting in-depth semi-structured interviews and a focus group was conducted with the eight farmers belonging to the community garden project. The findings show that the farmers are not involved in decision makings in the project. The farmers are passive participants. Participation of the farmers was mainly to carry out the activities from the extension officers. The study recommends that farmers be actively involved in projects and programmes introduced in their communities. Farmers’ active participation contributes to the sustainability of the projects through a sense of ownership.

Keywords: farmers, participation, agricultural extension, community garden

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Authors: C. V. Aravinda, H. N. Prakash

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In this paper, we tried to convey fusion and state of art pertaining to SIL character recognition systems. In the first step, the text is preprocessed and normalized to perform the text identification correctly. The second step involves extracting relevant and informative features. The third step implements the classification decision. The three stages which involved are Data acquisition and preprocessing, Feature extraction, and Classification. Here we concentrated on two techniques to obtain features, Feature Extraction & Feature Selection. Edge-hinge distribution is a feature that characterizes the changes in direction of a script stroke in handwritten text. The edge-hinge distribution is extracted by means of a windowpane that is slid over an edge-detected binary handwriting image. Whenever the mid pixel of the window is on, the two edge fragments (i.e. connected sequences of pixels) emerging from this mid pixel are measured. Their directions are measured and stored as pairs. A joint probability distribution is obtained from a large sample of such pairs. Despite continuous effort, handwriting identification remains a challenging issue, due to different approaches use different varieties of features, having different. Therefore, our study will focus on handwriting recognition based on feature selection to simplify features extracting task, optimize classification system complexity, reduce running time and improve the classification accuracy.

Keywords: word segmentation and recognition, character recognition, optical character recognition, hand written character recognition, South Indian languages

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Authors: Soyon Kim, Edward Kim

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In order to retrieve information from the massive stream of songs in the music industry, music search by title, lyrics, artist, mood, and genre has become more important. Despite the subjectivity and controversy over the definition of music genres across different nations and cultures, automatic genre classification systems that facilitate the process of music categorization have been developed. Manual genre selection by music producers is being provided as statistical data for designing automatic genre classification systems. In this paper, an automatic music genre classification system utilizing non-negative matrix factorization (NMF) is proposed. Short-term characteristics of the music signal can be captured based on the timbre features such as mel-frequency cepstral coefficient (MFCC), decorrelated filter bank (DFB), octave-based spectral contrast (OSC), and octave band sum (OBS). Long-term time-varying characteristics of the music signal can be summarized with (1) the statistical features such as mean, variance, minimum, and maximum of the timbre features and (2) the modulation spectrum features such as spectral flatness measure, spectral crest measure, spectral peak, spectral valley, and spectral contrast of the timbre features. Not only these conventional basic long-term feature vectors, but also NMF based feature vectors are proposed to be used together for genre classification. In the training stage, NMF basis vectors were extracted for each genre class. The NMF features were calculated in the log spectral magnitude domain (NMF-LSM) as well as in the basic feature vector domain (NMF-BFV). For NMF-LSM, an entire full band spectrum was used. However, for NMF-BFV, only low band spectrum was used since high frequency modulation spectrum of the basic feature vectors did not contain important information for genre classification. In the test stage, using the set of pre-trained NMF basis vectors, the genre classification system extracted the NMF weighting values of each genre as the NMF feature vectors. A support vector machine (SVM) was used as a classifier. The GTZAN multi-genre music database was used for training and testing. It is composed of 10 genres and 100 songs for each genre. To increase the reliability of the experiments, 10-fold cross validation was used. For a given input song, an extracted NMF-LSM feature vector was composed of 10 weighting values that corresponded to the classification probabilities for 10 genres. An NMF-BFV feature vector also had a dimensionality of 10. Combined with the basic long-term features such as statistical features and modulation spectrum features, the NMF features provided the increased accuracy with a slight increase in feature dimensionality. The conventional basic features by themselves yielded 84.0% accuracy, but the basic features with NMF-LSM and NMF-BFV provided 85.1% and 84.2% accuracy, respectively. The basic features required dimensionality of 460, but NMF-LSM and NMF-BFV required dimensionalities of 10 and 10, respectively. Combining the basic features, NMF-LSM and NMF-BFV together with the SVM with a radial basis function (RBF) kernel produced the significantly higher classification accuracy of 88.3% with a feature dimensionality of 480.

Keywords: mel-frequency cepstral coefficient (MFCC), music genre classification, non-negative matrix factorization (NMF), support vector machine (SVM)

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Authors: J. Congalton, K. Logan, B. Crump

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Software is a critical aspect of modern life. However it is costly to develop and industry initiatives have focused on reducing costs and improving the productivity. Increasing, software is being developed in teams, and with greater globalization and migration, the teams are becoming more ethnically diverse. This study investigated whether diversity in terms of ethnicity impacted on the productivity of software development. Project managers of software development teams were interviewed. The study found that while some issues did exist due to language problems, when project managers created an environment of trust and friendliness, diversity made a positive contribution to productivity.

Keywords: diversity, project management, software development, team work

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Authors: P. Bharathiraja

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Computer Aided decision making system is used to enhance diagnosis and prognosis of diseases and also to assist clinicians and junior doctors in clinical decision making. Medical Data used for decision making should be definite and consistent. Data Mining and soft computing techniques are used for cleaning the data and for incorporating human reasoning in decision making systems. Fuzzy rule based inference technique can be used for classification in order to incorporate human reasoning in the decision making process. In this work, missing values are imputed using the mean or mode of the attribute. The data are normalized using min-ma normalization to improve the design and efficiency of the fuzzy inference system. The fuzzy inference system is used to handle the uncertainties that exist in the medical data. Equal-width-partitioning is used to partition the attribute values into appropriate fuzzy intervals. Fuzzy rules are generated using Class Based Associative rule mining algorithm. The system is trained and tested using heart disease data set from the University of California at Irvine (UCI) Machine Learning Repository. The data was split using a hold out approach into training and testing data. From the experimental results it can be inferred that classification using fuzzy inference system performs better than trivial IF-THEN rule based classification approaches. Furthermore it is observed that the use of fuzzy logic and fuzzy inference mechanism handles uncertainty and also resembles human decision making. The system can be used in the absence of a clinical expert to assist junior doctors and clinicians in clinical decision making.

Keywords: decision making, data mining, normalization, fuzzy rule, classification

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Authors: Paolo Grazioli

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In this paper, we present the main results achieved during an eight-week international design project on Colorado Future Sustainable Mobilitycarried out at Metropolitan State University of Denver. The project was born with the intention to seize the opportunity created by the Colorado government’s plan to promote e-bikes mobility by creating a large network of dedicated tracks. The project was supported by local entrepreneurs who offered financial and professional support. The main goal of the project was to engage design students with the skills to design a user-centered, original vehicle that would satisfy the unarticulated practical and emotional needs of “Gen Z” users by creating a fun, useful, and reliablelife companion that would helps users carry out their everyday tasks in a practical and enjoyable way. The project was carried out with the intention of proving the importance of the combination of creative methods with practical design methodologies towards the creation of an innovative yet immediately manufacturable product for a more sustainable future. The final results demonstrate the students' capability to create innovative and yet manufacturable products and, especially, their ability to create a new design paradigm for future sustainable mobility products. The design solutions explored n the project include collaborative learning and human-interaction design for future mobility. The findings of the research led students to the fabrication of two working prototypes that will be tested in Colorado and developed for manufacturing in the year 2024. The project showed that collaborative design and project-based teaching improve the quality of the outcome and can lead to the creation of real life, innovative products directly from the classroom to the market.

Keywords: sustainable transportation design, interface design, collaborative design, user -centered design research, design prototyping

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Authors: Haonan Hu, Shuge Lei, Dasheng Sun, Huabin Zhang, Kehong Yuan, Jian Dai, Jijun Tang

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This paper focuses on the classification task of breast ultrasound images and conducts research on the reliability measurement of classification results. A dual-channel evaluation framework was developed based on the proposed inference reliability and predictive reliability scores. For the inference reliability evaluation, human-aligned and doctor-agreed inference rationals based on the improved feature attribution algorithm SP-RISA are gracefully applied. Uncertainty quantification is used to evaluate the predictive reliability via the test time enhancement. The effectiveness of this reliability evaluation framework has been verified on the breast ultrasound clinical dataset YBUS, and its robustness is verified on the public dataset BUSI. The expected calibration errors on both datasets are significantly lower than traditional evaluation methods, which proves the effectiveness of the proposed reliability measurement.

Keywords: medical imaging, ultrasound imaging, XAI, uncertainty measurement, trustworthy AI

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Authors: Jaiden Xuan Schraut, Leon Liu, Yiqiao Yin

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Computer vision in medical diagnosis has achieved a high level of success in diagnosing diseases with high accuracy. However, conventional classifiers that produce an image to-label result provides insufficient information for medical professionals to judge and raise concerns over the trust and reliability of a model with results that cannot be explained. In order to gain local insight into cancerous regions, separate tasks such as imaging segmentation need to be implemented to aid the doctors in treating patients, which doubles the training time and costs which renders the diagnosis system inefficient and difficult to be accepted by the public. To tackle this issue and drive AI-first medical solutions further, this paper proposes a multi-output network that follows a U-Net architecture for image segmentation output and features an additional convolutional neural networks (CNN) module for auxiliary classification output. Class activation maps are a method of providing insight into a convolutional neural network’s feature maps that leads to its classification but in the case of lung diseases, the region of interest is enhanced by U-net-assisted Class Activation Map (CAM) visualization. Therefore, our proposed model combines image segmentation models and classifiers to crop out only the lung region of a chest X-ray’s class activation map to provide a visualization that improves the explainability and is able to generate classification results simultaneously which builds trust for AI-led diagnosis systems. The proposed U-Net model achieves 97.61% accuracy and a dice coefficient of 0.97 on testing data from the COVID-QU-Ex Dataset which includes both diseased and healthy lungs.

Keywords: multi-output network model, U-net, class activation map, image classification, medical imaging analysis

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Authors: Seyed Hossein Iranmanesh, Hassan Nouri, Seyed Reza Iranmanesh

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In the dynamic and complex landscape of today’s business, organizations often face challenges in impactful decision-making across multi-project settings. To efficiently allocate resources, coordinate tasks, and optimize project outcomes, establishing robust decision-making processes is essential. Furthermore, the increasing importance of information systems and their integration within organizational workflows introduces an additional layer of complexity. This research proposes the use of blockchain technology as a suitable solution to enhance decision-making in multi-project environments, particularly within the realm of information systems. The conceptual framework in this study comprises four independent variables and one dependent variable. The identified independent variables for the targeted research include: Blockchain Layer in Integrated Systems, Quality of Generated Information ,User Satisfaction with Integrated Systems and Utilization of Integrated Systems. The project’s performance, considered as the dependent variable and moderated by organizational policies and procedures, reflects the impact of blockchain technology adoption on organizational effectiveness1. The results highlight the significant influence of blockchain implementation on organizational performance.

Keywords: multi-project environments, decision support systems, information systems, blockchain technology, decentralized systems.

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Authors: Wilailuk Meepracha

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The results of the study on the project evaluation to develop the competencies, capabilities, and skills in repairing computers of people in Jompluak Local Municipality, Bang Khonthi District, Samut Songkram Province showed that the overall result was good (4.33). When considering on each aspect, it was found that the highest one was on process evaluation (4.60) followed by product evaluation (4.50) and the least one was on feeding factor (3.97). When considering in details, it was found that: 1) the context aspect was high (4.23) with the highest item on the arrangement of the training situation (4.67) followed by the appropriateness of the target (4.30) and the least aspect was on the project cooperation (3.73). 2) The evaluation of average overall primary factor or feeding factor showed high value (4.23) while the highest aspect was on the capability of the trainers (4.47) followed by the suitable venue (4.33) while the least aspect was on the insufficient budget (3.47). 3) The average result of process evaluation was very high (4.60). The highest aspect was on the follow-op supervision (4.70) followed by responsibility of each project staffs (4.50) while the least aspect was on the present situation and the problems of the community (4.40). 4) The overall result of the product evaluation was very high (4.50). The highest aspect was on the diversity of the activities and the community integration (4.67) followed by project target achievement (4.63) while the least aspect was on continuation and regularity of the activities (4.33). The trainees reported high satisfaction on the project management at very high level (43.33%) while 40% reported high level and 16.67% reported moderate level. Suggestions for the project were on the additional number of the computer sets (37.78%) followed by longer training period especially on computer skills (43.48%).

Keywords: project evaluation, competency development, the capability on computer repairing and computer skills

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Authors: Li Yan Hei, Au Wing Tung

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Knowledge sharing is a process in which individuals mutually exchange existing knowledge and co-create new knowledge. Previous research has confirmed that trust is positively associated with knowledge sharing. However, only few studies systematically examined the antecedents of trust and these antecedents’ impacts on knowledge sharing. In order to explore and understand the relationships between trust and knowledge sharing in depth, this study proposed a relationship maintenance-based model to examine the antecedents of trust in knowledge sharing in project teams. Three critical elements within a project team were measured, including the environment, project team partner and interaction. It was hypothesized that the trust would lead to knowledge sharing and in turn result in perceived good team performance. With a sample of 200 Hong Kong employees, the proposed model was evaluated with structural equation modeling. Expected findings are trust will contribute to knowledge sharing, resulting in better team performance. The results will also offer insights into antecedents of trust that play a heavy role in the focal relationship. The present study contributes to a more holistic understanding of relationship between trust and knowledge sharing by linking the antecedents and outcomes. The findings will raise the awareness of project managers on ways to promote knowledge sharing.

Keywords: knowledge sharing, project management, team, trust

Procedia PDF Downloads 617

Authors: M. Kiarna, S. Junjira, S. Casey, M. Nolwazi, M. S. Marcos, A. T. Tatiana, L. Cassandra

Abstract:

This paper details a rebrand design project developed for a non-profitable organization called Te Roopu Waiora (TRW), which is currently located in Auckland, Aotearoa New Zealand. This social enterprise is dedicated to supporting the Māori community living with sensorial, physical and intellectual disabilities (whānau hauā). As part of a year three bachelor design brief, the rebrand project enabled students to reflect on Kaupapa Māori principles and appropriately address the values of the organisation. As such, the methodology used a pragmatic paradigm approach and mixed methods design practices involving a human-centred design to problem solving. As result, the student project culminated in the development in a range of cohesive design artefacts, aiming to improve the rentability and perception of the brand with the audience and stakeholders.

Keywords: design in Aotearoa New Zealand, Kaupapa Māori, branding, design education, human-centered design

Procedia PDF Downloads 135

Authors: Medya Fathi

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Today, success in the construction industry is not merely about project completion in a timely manner within an established budget and meeting required quality considerations. Management practices and partnerships need to be emphasized as well. In this regard, critical success factors (CSFs) cover necessary considerations for a successful project beyond the traditional success definition, which vary depending on project outcomes, delivery methods, project types, and partnering processes. Despite the extensive research on CSFs, there is a paucity of studies that examine CSFs for public-private partnership (PPP); the delivery method, which has gained increasing attention from researchers and practitioners over the last decade with a slow but growing adoption in the transportation infrastructure, particularly, highway industry. To fill this knowledge gap, data are collected through questionnaire surveys among private and public parties involved in PPP transportation projects in the United States. Then, the collected data are analyzed to explore the causality relationships between CSFs and PPP project success using structural equation model and provide a list of factors with the greatest influence. This study advocates adopting a critical success factor approach to enhance PPP success in the U.S. transportation industry and identify elements essential for public and private organizations to achieve this success.

Keywords: project success, critical success factors, public-private partnership, transportation

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Authors: Yousef S. Al-Othman

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The growth and expansion of the industrial facilities comes proportional to the market increasing demand of products and services. Furthermore, raw material producers such as oil companies usually undergo massive revamping projects to maintain a synchronized supply. These revamping projects are usually delivered through challenging construction projects held and associated with daily site risks related to the construction process. Henceforth, a case study related to these risks and corresponding on-spot corrective measurements has been made on a certain number of construction project contractors at Kuwait Oil Company (KOC) to derive the benefits and overall effectiveness of the on-spot corrective measurements during the construction phase of a project, and how would the same help in avoiding major incidents, ensuring a smooth, cost effective and on time delivery of the project. Findings of this case study shall have an added value to the overall risk management process by minimizing the daily site risks that may affect the project lead time, resulting in an undisturbed on-site construction process.

Keywords: oil and gas, risk management, construction projects, project lead time

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Authors: Maria Ledinskaya

Abstract:

This paper is part case study and part literature review. It seeks to introduce Agile, Lean, and Hybrid project management concepts from business, software development, and manufacturing fields to museum conservation by looking at their practical application on a recent conservation project at the Sainsbury Centre for Visual Arts. The author outlines the advantages of leaner and more agile conservation practices in today’s faster, less certain, and more budget-conscious museum climate where traditional project structures are no longer as relevant or effective. The Morris Collection Conservation Project was carried out in 2019-2021 in Norwich, UK, and concerned the remedial conservation of around 150 Abstract Constructivist artworks bequeathed to the Sainsbury Centre by private collectors Michael and Joyce Morris. It was a medium-sized conservation project of moderate complexity, planned and delivered in an environment with multiple known unknowns – unresearched collection, unknown conditions and materials, unconfirmed budget. The project was later impacted by the COVID-19 pandemic, introducing indeterminate lockdowns, budget cuts, staff changes, and the need to accommodate social distancing and remote communications. The author, then a staff conservator at the Sainsbury Centre who acted as project manager on the Morris Project, presents an incremental, iterative, and value-based approach to managing a conservation project in an uncertain environment. The paper examines the project from the point of view of Traditional, Agile, Lean, and Hybrid project management. The author argues that most academic writing on project management in conservation has focussed on a Traditional plan-driven approach – also known as Waterfall project management – which has significant drawbacks in today’s museum environment due to its over-reliance on prediction-based planning and its low tolerance to change. In the last 20 years, alternative Agile, Lean and Hybrid approaches to project management have been widely adopted in software development, manufacturing, and other industries, although their recognition in the museum sector has been slow. Using examples from the Morris Project, the author introduces key principles and tools of Agile, Lean, and Hybrid project management and presents a series of arguments on the effectiveness of these alternative methodologies in museum conservation, including the ethical and practical challenges to their implementation. These project management approaches are discussed in the context of consequentialist, relativist, and utilitarian developments in contemporary conservation ethics. Although not intentionally planned as such, the Morris Project had a number of Agile and Lean features which were instrumental to its successful delivery. These key features are identified as distributed decision-making, a co-located cross-disciplinary team, servant leadership, focus on value-added work, flexible planning done in shorter sprint cycles, light documentation, and emphasis on reducing procedural, financial, and logistical waste. Overall, the author’s findings point in favour of a hybrid model, which combines traditional and alternative project processes and tools to suit the specific needs of the project.

Keywords: agile project management, conservation, hybrid project management, lean project management, waterfall project management

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Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 167

Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 159