International Journal of Communication Networks and Information Security (IJCNIS)
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EVALUATION AND APPLICATION OF MACHINE LEARNING PRINCIPLES TO ZEOLITE LTA SYNTHESIS
No full textNot only was the association between zeolite synthesis descriptors identified, but the possibility of machine learning predicting the quantitative output of synthesis pathways was also assessed using a progressive machine learning approach. Applying concepts from statistics and machine learning was thought to allow for pre-evaluation, which might lead to improvements in zeolite performance and yield. A number of machine learning methods were used to analyze the zeolite LTA synthesis data. These algorithms included XGBoost, regression trees, random forests, ridge regression, regression analysis, and linear regression. Among the most important results was the realization that input synthesis factors and product yield might be used to train models. More specifically, a "hybrid XRD" strategy combining quantitative and qualitative X-ray diffraction (XRD) data was necessary for precise product composition determination. The problem's intrinsic complexity was shown by models, such as linear regression, ridge regression, and regression trees, which all gave R2 values less than 0.5. Testing accuracies of R2=0.700 and R2=0.620 were achieved using embedded tree-based models, XGBoost and random forest, respectively. With an R2 value of 0.84, an ANN model outperformed all other machine learning techniques in terms of accuracy. Particularly, this model outperformed the others because it took use of the complex and non-linear interactions present in a multidimensional and intercorrelated dataset, such the one produced via zeolitev synthesis. The ANN model accuracy kept going up as the network size went up, even after attaining an accuracy of over 80%; this suggests that advanced deep learning models should be investigated for future study
Application of Fuzzy Delphi Method to Identify the Construct for Designing and Developing the Multimodal Learning Framework for Writing Skills in ESL Context
No full textThe increasing complexity of educational demands in the 21st century has emphasized the need for innovative pedagogical frameworks, particularly in enhancing writing skills among ESL students. This study explores the application of the Fuzzy Delphi Method (FDM) to identify and validate the critical constructs necessary for designing and developing a Multimodal Learning Framework aimed at improving writing skills in academic settings. The design of this study is a quantitative study using the Fuzzy Delphi technique. A questionnaire instrument was used to collect research data. Eleven experts in TESL, educational technology, multimodal learning and curriculum participated in the study. Data analysis results showed that the experts accepted all these elements through the expert consensus value above 75%, the threshold value (d) ? 0.2, and the fuzzy score (A) ? ? - cut value = 0.5. Therefore, it shows that these elements have gained expert consensus and are needed to design and develop the Multimodal Learning Framework for Writing Skills. The findings not only contribute to the theoretical understanding of multimodal learning in writing instruction but also offer practical guidelines for educators aiming to implement this framework in foundation-level classrooms. This research underscores the value of FDM in educational design, particularly in areas where expert judgment is critical for addressing complex instructional challenges
Machine Learning as a Tool to Forecast the Power Quality of wind Energy Power Plants: A Systemic Literature Review
No full textThe growing inclusion of renewable energy utilities into the national energy system grid presents an ever-increasing need for a high level of quality in output power injected into the grid. The need for grid expansion to include wind energy renewables requires accurate forecasts of the power quality. The country’s grid operator needs real-time accuracy to stabilize and monitor the grid capacity and load demands continuously. The industry currently uses tools to predict the wind energy production power curve. computational intelligence techniques are needed to evaluate the availability of this energy beforehand, due to the variable and unpredictable nature of the wind behaviour. The purpose of this work is to highlight the current research conducted and associated gaps in the field of renewable wind power production forecasting techniques using Machine Learning as a tool to predict the power quality on the national grid. The Preferred Reporting Items for Systemic Review and Meta-Analysis (PRISMA 2020) protocol guides the study. The study drew from several literature sources, most of which focused on forecasting wind power output. The keywords below and Boolean operators were used in our search criteria in Google Scholar, Web of Science, and IEEE Explore. Section II details the exclusion and inclusion criteria employed in this work. There is more research and study that needs to be conducted around machine learning and deep learning algorithms in the wind industry-particularly around forecasting techniques that aim to predict the quality of power generated by wind power plants. The results also show that there are available public datasets. The restriction of this study is the English language papers, forecasting, variable power generation, wind energy, and renewables power quality applications
Implementing Scalable Big-Data Tech Stacks in Pre- Seed Start-ups: Challenges and Strategies for Realizing Strategic Vision
No full textIn order to prepare for choosing of pertinent data needed to prototype models, train them, and use the model, the massive volume of data generated by the Internet of Things must be verified and curated. However, open data and block chains are also significant data resources that must be included into the suggested integrative frameworks. The Impact Tech Start-up (ITS) is a brand-new, quickly evolving category of businesses. ITSs, which are typically supported by private investment, use creative approaches to address a range of environmental and social problems within a for-profit framework. These approaches are based on an entrepreneurial attitude and technology underpinnings. Currently, there is no discussion of this new organisational class in the academic literature. Machine Learning (ML) is an emerging development in technology. It is a subset of Artificial Intelligence (AI) that employs computer algorithms based on data that is accessible and may make decisions or improve upon them automatically based on experience and without requiring programmatic inputs beforehand. The first section of the article offers a theoretical framework for researching this organisational category, which combines elements of start-up companies and social enterprises. After that, it suggests a method based on Machine Learning (ML) to find ITSs in start-up datasets. ITSs are characterised using the Sustainable Development Goals (SDGs) of the UN, with indicators pertaining to the 17 objectives that meet the requirements for a start-up to be included in the impact category. The paper's conclusion discusses potential avenues for future research using the ML technique to examine ITSs as a unique organisational category
IMPACT ON ENHANCING CLOUD DATA STORAGE SECURITY THROUGH BLOCKCHAIN INTEGRITY DEVELOMENT
No full textCloud computing is becoming more and more popular, butworries about data security and privacy stem from theregularity of hostile assaults on wireless and mobile networks.One of the goals of the created IAS protocol is to address theseissues. Access control, secure authentication, and identificationwill all be integrated into this protocol. The proposed IASprotocol, which was created to guarantee the secrecy andintegrity of data transmissions in cloud computingenvironments, is based on blockchain technology. Theimplementation of decentralized identity verification and keyrecovery/revocation management is made possible as a resultof this. The effectiveness of this strategy can be evaluated by theutilization of a cloud-based simulation of the proposed idea thatmakes use of Identity management, access control, and securesharing based on block chains (BC-IAS). The simulation is usedto evaluate key performance characteristics such as the pace atwhich data is accessed, the ratio of messages delivered, thelatency from beginning to end, and the amount of energyconsumed. When it comes to enhancing the privacy and securityof On top of blockchain technology, cloud computing and theBC-IAS protocol that is being proposed appear to be promisingdevelopments. To further improve cloud computing's securityand integrity, the BC-IAS protocol, which is constructed usingblockchain technology, is an appealing alternative. By virtue ofBecause of the decentralized nature of blockchain technology,data is stored in an accessible and immutable manner.Furthermore, smart contracts allow for the automaticenforcement of access control restrictions using blockchaintechnology. By adopting identity verification and secureauthentication, which restrict access to sensitive information toonly authorized personnel, it is possible to reduce the likelihoodof data breaches and cyberattacks occurring
GENETIC COUNSELLING
No full textGenetic counselling is a service that provides information and advice about genetic condition. These are condition caused by changes known as mutation in certain genes and are usually passed down through a family. Genetic counselling is the process through which knowledge about the genetic aspects of illnesses is shared by trained professionals with those who are at an increased risk or either having a heritable disorder or of passing it on to their unborn offspring. A genetic counsellor provides information on the inheritance of illnesses and their recurrence risks; addresses the concerns of patients, their families, and their health care providers; and supports patients and their families dealing with these illnesses. The Heredity Clinic was the first genetic counselling service centre established in 1940 at the University of Michigan, USA. Since then, the many such centers have been opened around the world
Use of information technologies in administrative management: A bibliometric analysis
No full textThe use of Information and Communication Technologies has significantly transformed administrative management in both public and private organizations. This article presents a bibliometric analysis of the scientific production related to the use of information technologies in administrative management, aiming to identify the main research trends, sources, authors, and influential institutions. To conduct this analysis, 1,772 articles were examined from the Scopus database between 2010 and 2023, using bibliometric analysis tools such as Biblioshiny. The indicators considered were annual scientific production, the most relevant publication sources, institutional affiliations, most frequent keywords, and the collaboration network between countries. The results reveal a notable growth in scientific production since 2018, with "Government Information Quarterly" being the most prolific source and the University of Brasilia leading in publications. The most recurrent concepts include "e-government," "digital transformation," and "artificial intelligence," while Spain, the United States, and China stand out as the countries with the highest international collaboration. In conclusion, research on ICT and administrative management continues to expand, reflecting the impact of digitalization on administrative processes
TRANSFORMATION BUMN SUGAR : A STUDY OF PARADIGM AND ECOSYSTEM CHANGES TOWARDS SELF-SUFFICIENCY Review Sociology Institutional Karl Polanyi's
No full textThe rapid decline in domestic sugarcane productivity has for decades thrown Indonesia into relatively absolute dependence on sugar imports as it continuously weakens productive capacity of both the sugarcane farmers on-farm and sugar mills off farm. The issuance of Presidential Decree 40/2023 on national sugar self-sufficiency then provides policy framework and governance guideline upon which Ministry of Badan Usaha Milik Negara-BUMN (State Owned Enterprise-SOE) is authorised to execute the policy. Central to this initiative is the transformation of both BUMN ministry and sugar SOEs into PTPN III Holding and its subsidiary PT SGN. Againts the backdrop, this study addresses three key questions: (1) How is the transformation of sugar SOEs advancing toward the national sugar self-sufficiency targets set for 2028-2030? (2) What are the normative prerequisites and institutional actions that affect the success of this transformation in driving national sugarcane-sugar production?, and (3) in what ways has such transformation deeply impacted the socio-economic relationships between PT Sinergi Sugar Nusantara’s (PT SGN) sugar mills and their partner farmer groups as the primary suppliers of raw sugarcane? By deploying Karl Polanyi’s Institutional Sociology approach (1957, 1944, 1968) this study dissects the transformation in PTPN III Holding and its subsidiary PT SGN within an intricate context of nationwide sugar industry led by oligarchic force and institutional restructuring on the part of the SOE. Key concepts are embedding, dis-embedding, and re-embedding used to analyze how far and deeply ingrained the three pillars of economic reintegration (redistribution, reciprocity, and householding) have been applied in the whole process of institutional, managerial and operational transformation. Specific attention given to the governance of sugarcane production upstream in two practical breakthroughs namely regionalization of sugar mills and the implementation of a profit-sharing system (SBH). Drawn upon qualitative case study at Gempolkrep Sugar Factory in Mojokerto, Pesantren Baru Sugar Factory in Kediri, and Meritjan Sugar Factory in Kediri, this eight months inquiry (August 2023-April 2024) results in three core findings relating to paradigm, ecosystem and partnership. First, there has been norms-compatibility between productivity paradigm applied in PTPN III Holding since 2021 and eternity paradigm as working norms for institutional transformation in the BUMN ministry since 2019. Second, PTPN III Holding through regionalization of the sugar mills undertaken by PT SGN has succeeded in boosting up productivity of the consolidated sugar mills within collaborative management. Third, productive partnership between the sugar mills and sugarcane farmers, on farm and off farm, has gradually been reinvigorated with the sole mechanism of SBH which proves effective in disentangling them from rent-seeking ecosystem called Pok-Pokan benefiting only the local big capitalists and private sugar mills. This study offers both theoritical and practical policy significances. It sheds more light on the strenghts of Polanyi’s institutional approach in addressing root causes of social- economic disintegration between BUMN and the farmer community in current economic neoliberalization. While in terms of policy significance, this study endorses the collaborative role of BUMN realigning with the farmer community for national sugar self-sufficiency 2028-2030. 
Electric Cars Meet AI: Machine Learning Revolutionizing the Future of Transportation
No full textThe integration of artificial intelligence (AI) and electric vehicles (EVs) is set to revolutionize the transportation industry. This article explores how machine learning algorithms are enhancing the performance, efficiency, and user experience of electric cars. By examining advancements in autonomous driving, battery management systems, predictive maintenance, and personalized user interfaces, we highlight the transformative impact of AI on electric mobility. The findings suggest that the synergy between AI and EVs will accelerate the adoption of sustainable transportation solutions, addressing environmental concerns and reshaping urban mobility
An Extensive Review of Developments and Methods in Super-Resolution Image Reconstruction
No full textSuper-resolution (SR) image reconstruction plays a vital role in enhancing the resolution of low-resolution (LR) images, benefiting various fields such as remote sensing, medical imaging, and surveillance. The SR problem entails reconstructing high-resolution (HR) images from LR inputs, typically due to the loss of high-frequency information and the problem's ill-posed nature. This review study presents a comprehensive overview of recent developments and approaches in SR image reconstruction. This study primarily presents three approaches: methods based on learning, methods based on reconstruction, and methods based on interpolation. Despite the fact that interpolation-based approaches, such as bicubic interpolation, are straightforward and quick, they frequently result in blurring and loss of high-frequency features. Reconstruction-based methods leverage prior knowledge of image characteristics to recover HR images, often through optimization techniques. However, these methods may suffer from slow convergence and high computational cost. Because of their capacity to learn complicated mappings between LR and HR picture spaces, learning-based methods—and deep learning approaches in particular—have been the center of a lot of attention lately. These methods leverage large datasets to train convolutional neural networks (CNNs) for image super-resolution, achieving remarkable performance in terms of visual quality and computational efficiency. Furthermore, we discuss the challenges and future directions in SR research, including the development of more robust and efficient algorithms, handling noisy real-world data, and exploring novel architectures and loss functions to further improve SR performance. The purpose of this review paper is to provide a comprehensive overview of strategies for SR image reconstruction. It focuses on the progression from conventional interpolation methods to cutting-edge deep learning approaches. They hope that this publication will serve as a valuable resource for scholars and practitioners in the field of computer vision and image processing