Due to climate change and rapidly increasing challenges of environmental disturbances, building coastal resilience is becoming necessary for coastal communities. Knowledge-based development and ventures are major subjects in the literature of building coastal resilience. However, current literature on the relationship between knowledge-based development and knowledge-based ventures in building coastal resilience is limited. In this paper, the author shows a design methodology of integrating both knowledge-based development and ventures in building coastal resilience. The methodology is grounded in the theories of input-process-output model of strategic entrepreneurship, docility and business incubation, distributed cognition and network effects, and process-centered value generation. The design is put into action to study the experiences of Louisiana in building coastal resilience during and after historical hurricane Katrina.
This study explores tacit knowledge transfer in healthcare, aiming to identify barriers and opportunities in sharing this nuanced expertise among practitioners and its impact on patient care standards. Employing a mixed-methods approach, it gathers data from healthcare professionals, analyzing it via reliability, statistical tests, and multinomial logistic regression. Results show moderate scale reliability, significant relationships between responses, and various factors influencing patient care quality, such as patient confidentiality, effectiveness of mentoring programs, and engagement in online forums. Challenges in tacit knowledge transfer were found insignificant, yet specific factors like mentoring and online participation are crucial for care quality. The study concludes that mentorship and organizational strategies play vital roles in enhancing care delivery by facilitating tacit knowledge sharing, underlining the importance of addressing confidentiality issues, and promoting innovation through active online engagement and effective knowledge use.
This book explores how blockchain technology is disrupting economies and transforming societies. It offers detailed insights into the synergistic and complementary effects of blockchain and other new and emerging technologies such as artificial intelligence, the internet of things, satellite imagery, and digital twins. The book highlights how the developments of ecosystems around blockchain are helping to realize the potential of this technology to act as a “trust machine” across more contexts and settings. Multidisciplinary in both scope and perspectives, the book looks at blockchain's potential for impacting challenges in supply chain management, security, privacy and compliancy issues, and transforming payment and settlement systems. It further provides insights into the opportunities, barriers, and enablers of blockchain adoption by organizations.
One of the emerging areas is knowledge co-creation, and more specifically, in an educational context, curriculum co-creation. The ability to solicit feedback, reactions, suggestions, and improvements with regards to courses and educational programs from students is an interesting and promising one, but a concept which requires that existing norms and practices be put aside to allow students to share in the evaluation, revision, and design process, together with instructors and administrators. This paper examines the foundations of curriculum co-creation, including its educational theory foundations, critical variables and considerations, and a review of recent programs and initiatives. A framework of the key curriculum co-creation variables is presented, followed by suggestions for future research.
Almost every country in the world is facing the issue of child trafficking. Besides abduction, children below 10 years sometimes get missed from their homes or other locations due to many reasons. following the criminal record, many of the abducted or missed children got received by police officials where majorly officials face difficulty to get correct information as the founded children are not in their normal state in general due to fear factor or less trust. It is also observed that such children also usually start stammering due to uncontrolled emotions. In this kind of situation, Police officials would not get the exact information of founded children and unable to contact their respective guardians. This paper proposes a possible solution “Rakshak” for this kind of difficult situation. The Rakshak is Machine Learning based software which inputs the recorded voice of such children by police officials and returns the voice as well as text output after being corrected by various techniques of the Rakshak.
Almost every country in the world is facing the issue of child trafficking. Besides abduction, children below 10 years sometimes get missed from their homes or other locations due to many reasons. following the criminal record, many of the abducted or missed children got received by police officials where majorly officials face difficulty to get correct information as the founded children are not in their normal state in general due to fear factor or less trust. It is also observed that such children also usually start stammering due to uncontrolled emotions. In this kind of situation, Police officials would not get the exact information of founded children and unable to contact their respective guardians. This paper proposes a possible solution “Rakshak” for this kind of difficult situation. The Rakshak is Machine Learning based software which inputs the recorded voice of such children by police officials and returns the voice as well as text output after being corrected by various techniques of the Rakshak.
In the last decade, Knowledge management (KM) has evolved to face new threats and challenges. It has been reorganized to facilitate information access. However, the excess of information may affect the understanding and induce the loss of information. Consequently, many scientific projects conflict are unfinished or poorly completed. For this reason, developing a collaborative KM based on transfer knowledge could be an excellent solution to face a specific problem in a scientific field. This article gives us many tools that could be used specifically in a research center and adapted in many scientific areas. The authors first define each main actor contributing to Knowledge sharing in a research center through a design science research approach. Then, they propose how a problem could be managed using the knowledge-sharing creation process. Finally, they summarize steps that could be used to implement collaborative knowledge management.
Context-aware recommender systems (CARSs) are attracting more and more attention from both the academic community and from industry. Users' contextual situations (e.g., location, time, companion, etc.) which can influence their ratings on items, are taken into consideration. Therefore, more accurate and personalized recommendations can be generated. The integration of contextual information in recommender systems to better model users' preferences under different contextual situations is a key research topic. In this paper, the authors propose a new method for representing contextual situations in recommender systems based on the influence of contextual conditions on ratings using Pearson Correlation Coefficient. The authors show the effectiveness of the proposed method compared to state-of-art methods by experiments on three different datasets widely used in CARSs research community.
This article raises a vital question as to how executives who act as transformational leaders can unleash the power of knowledge in companies. The author posits that transformational leadership's direct impact on firms' internal resources can also promote knowledge management processes. In particular, transformational leaders strongly manifest themselves as change agents who have a significant impact on the culture, structure, and strategy of a firm. This article suggests that managers should develop a supportive workplace to promote knowledge management processes. In this way, culture, structure, and strategy constitute the foundation of this supportive workplace. In fact, it can be seen that if firms' culture, structure, and strategy are not completely in favor of supporting knowledge management processes, organizations cannot effectively implement knowledge management projects to promote knowledge management. Accordingly, this article suggests that knowledge management efforts depend on a supportive culture, structure, and strategy in organizations.
A group of wireless nodes forms a configuration that is linked to a remote medium forming dynamic topology. A routing protocol enables packets to travel from the sender to the destination via intermediate nodes. When a device or intermediate node delivers information to other nodes in an Ad hoc network, the node consumes some energy, and data transmission may be interrupted as all of the power is consumed. MANET is often power-driven devices, the difficult component in MANET is to lower the power consumption of energy by the intermediate devices in the network so that the network remains active at the time of data transmission. The characteristics, uses, and problems of MANET are described in this research. In addition, we look at the MANET routing protocols. The performance of alternative routing protocols, such as DSDV, AODV, AOMDV, and DSR, is then compared using performance measures. The NS2.35 simulator is used to conduct the performance assessment.
Ensuring the quality of higher education is the major concern of a developing country like Bangladesh. Despite having many universities as options, people are still fascinated to study in the University of Dhaka. This paper attempts to determine the quality of higher education in Bangladesh by measuring the service quality of the University of Dhaka based on the responses of the students. To evaluate its overall service quality 5 variables have been considered, such as tangibility, responsiveness, assurance, reliability, and empathy. The study conducted a survey of 200 respondents including current and past university students. The result shows there is a mediocre level of satisfaction among the respondents on quality issues. Why the students are not overall satisfied and specifically on which criteria they are not satisfied have been clearly demonstrated in this paper. The study has also recommended along with the findings what new adjustments the university authority make in this context.
The purpose of this study is to analyze the results of the impact of digitalization to Greek accountants. How technology has greatly improved the work of the accountant since technology nowadays is evolving rapidly in relation to the past. Finally, it highlights disruption to traditional accounting practice and research. In order to ascertain the views of professional accountants on the impact of digitalization, a research conducted in October and November 2019, in the areas of Alexandroupolis, Drama, Kavala, Komotini and Xanthi. A 40-item self-administrative questionnaire was chosen to collect the data for the research. One hundred accountants of the aforementioned areas replied to the questionnaire. The findings present their current technology infrastructure and their use of digital services. Finally, it draws useful insights regarding the effect of digital disruption on the profession of accountant.
This paper proposes an approach to design a visual chatbot to enhance the virtual knowledge sharing process. Existing chatbots are either textual or vocal whose performance has not exceeded 60%. However, in various fields a textual description is no longer sufficient, and it is so essential for users to exchange images to better express their preferences. This prevents them from individually describing the image content and transmitting it in writing, which is not always obvious. This work developed a preliminary version of a visual chatbot called SIRSBot (Smart Information Retrieval System roBot). The objective of this paper is to make experiments to identify the main challenges which may face visual information identification. The role of the visual chatbot is (1) to understand the user request, (2) to extract the characteristics of each object in the image that ultimately represent the user's preferences and finally (3) to find a response that meets the user's needs.
The intention of the paper is to improve a neural network methodology to accomplish enhanced predictions of the sales market. The data downloaded by Kaggle, data is surveyed for more than six months and the data was collected through prevalent markets for online and offline analysis with results of data visualization and prediction to illustrate sales forecasting. The traditional model like arima, RNN, and long short-term memory are not effective to provide sales forecasting with consideration of numerous constraints of the market and predict the sales incorrectly, because the RNN model suffers from vanishing gradient problems and LSTM are prone to overfitting. Therefore, these models are intensely prone to erroneous forecasts. The author suggests the “Long Term Short Memory (LSTM)” with three layers which are dropout layers, early stop layers, and simplifying layers to reduce overfitting. The result shows that the adapted “LSTM '' with the inclusion of three layers is an improved version as compared with traditional ''LSTM ``. The accuracy of the proposed model is 82%
Scrum approach is now an important component of project management courses in universities. Experiential learning approaches as serious games seem to be more effective for Scrum learning since they have the advantage of providing students with an opportunity of encountering complex, realistic project situations. However, these educational games often lack clear specifications as to how they are to be incorporated within an experiential learning approach following a systematic instructional design. In this paper, we propose a conceptual framework that shapes serious games design, providing guidelines that allow integrating mechanisms of transfer aligned with the nature of knowledge. These transfer mechanisms rely on a “boundary perspective” with the use of boundary objects and boundary actors as a means to fostering social exchange during the game. Our framework is tested through the design of a LEGO serious game, MindScrum, to strengthen Scrum learning. We present the design principles of the game and discuss its effectiveness in the learning process.
Fruit detection using deep learning is yielding very good performance, the goal of this work is to detect small fruits in images under these occlusion and overlapping conditions. The overlap among fruits and their occlusion can lead to false and missing detection, which decreases the accuracy and generalization ability of the model. Therefore, a small orange fruit recognition method based on improved Feature Pyramid Network was developed. To begin with, multi-scale feature fusion was used to fuse the detailed bottom features and high-level semantic features to detect small-sized orange to improve recognition rate. And then repulsion loss was used to take place of the original smooth L1 loss function. Besides, Soft non-maximum suppression was adopted to replace non-maximum suppression to screen the bounding boxes of orange to construct a recognition model of orange fruits. Finally, the network was trained and verified on the collected image data set. The results showed that compared with the traditional detection models, the mean average precision was improved from 79.7 to 82.8%.
Identifying and choosing the most pertinent Key Performance Indicators (KPIs) has a crucial impact on decision making: if the wrong KPIs are measured, or if they are measured in the wrong way, the information may be misleading and the quality of decisions can be significantly affected. Choosing appropriate KPIs requires a deep understanding of the organization. Moreover, a successful selection of appropriate KPIs relies on a number of well-defined criteria. We propose in this paper a new approach to facilitate the structuring, and/or synthesis of a set of specific, measurable, attainable, realistic and timebased KPIs (SMART KPIs) based on the Analytic Hierarchy Process (AHP) method. The approach steps are illustrated and validated with a real case study. This case study is related to the candidates’ selection process in a Tunisian higher education institute.
In this research, DNA cryptography-based Morse code encoding is proposed to bulwark the delicate knowledge within the demanding environment. The plaintext is changed over to DNA sequences utilizing the Morse code encoding table to create original DNA references. The encoded information is transcribed and translated by the Ribonucleic Acid (RNA) arrangements of Morse code. Translated RNA Morse code is hidden by the standard genetic code utilizing organic compounds, and the hidden DNA Morse code is again ciphered by Morse code encoding table to create bogus Morse DNA references. However, the Morse code encoding pattern is used to compress the generated ciphertext with a reducible size rate of 25% to 30% of the plaintext value. The designed bio explores analysis, and results show that the transmission's security is high compared to the existing techniques in terms of cryptography properties, attacks, CIA triad, and it preserves the biological process without repudiating the nature of the organic forms of DNA.
The intrusion interjects network devices and holds a switch of the network with the command which regulates the programmer and programmer govern the nasty code inoculated in the device for attaining intelligence about the devices. In this paper, the researchers organized the IDS framework by using machine learning algorithms like Linear SVM, RBF SVM, Sigmoid SVM, and Polynomial SVM to detect intrusions and estimate the performance of numerous algorithms for attaining the optimized algorithm. The researchers utilized the KDDCUP99 for equating the accuracy, precision, and recall of the algorithms, and for classifications, the researchers utilized the binary encoder tools. The performance analysis calculates that RBF SVM is the finest classifier amongst the other SVMs, and the prediction report predicts that Linear SVM results with 99.2% accuracy, Sigmoid SVM results with 99.7% accuracy, Polynomial SVM results with 99.5% accuracy, and RBF SVMs results with 99.99% accuracy.
The world economy has been stable by emerging into online business and activity with increased online users. There is likelihood to escalate the fraud activity and misuse the corporation's network. Hence, strengthening of network security is necessary to prevent such unwanted activities. In this work, Anomaly Detection System (ADS) is proposed to detect the anomalous activities in the network. Firstly, network packets with the tagging are trained with the k-nearest neighbor algorithm (KNN) and Kohonen’s Self-Organizing Maps (KSOM) algorithm clusters the network packets. Initially, the Tagging Application (TA) dataset is created that contains network packets with the labelling of applications by extracting captured live packets using high computing server that is configured in data center which are used for the proposed Fix Weight Kohonen's Self-Organizing Maps (FW-KSOM) to cluster different activities in the network. Implementation of the proposed ADS model for labelling and clustering is carried out in real time networking scenario to identify the applications for anomaly detection.
