International Journal of Emerging Trends in Science and Technology
IJETST is an international, peer-reviewed open access journal of Current Science and Advance Technology published in English. The journal's publisher is the IGM Publication. The Journal aims at publishing evidence-based, scientifically written articles from different disciplines of sciences and Technology. The Journal welcomes articles of general interest to audiences of Technical and Non-Technical researchers especially when they contain new information. IJETST is an online journal having full access to the research and review paper. The journal also seeks clearly written survey and review articles from experts in the field, to promote insightful understanding of the state-of-the-art and application trends.
A Study on Mechanical Properties of Fly Ash Based Concrete with the Addition of Glass Fibre
The present day world is going through the construction of very innovative and unsymmetrical engineering structures. Glass fibre is a material consisting of numerous extremely fine fibers of glass. Quite often, concrete possess very high flexural and shear strength properties. Researchers all over the world are attempting to develop high performance concretes by using various fibres and other admixtures in concrete up to certain proportions with low water-cement ratio. Glass-fibre reinforced concrete (GRC) is a material made of a cementitious matrix composed of cement, sand, water and superplasticizers, in which short length glass fibres are dispersed. It has been widely used in the construction industry till now for non-structural elements, like thin slab panels, piping and roof tiles ..etc. GRC offers many advantages, such as being lightweight, fire resistance, good appearance and strength. To increase the applications of Glass Fibre Reinforced High-Performance-Concrete (GFRHPC) in India, greater understanding of GFRHPC produced with locally available materials and indigenously produced mineral admixtures is essential. The present study includes testing of concrete cubes with different glass fibre proportions for its strength corresspondingly at 28 days. Keywords –glass fibres, admixtures , fly ash , concrete.
Copyright (c) 2026 International Journal of Emerging Trends in Science and Technology https://creativecommons.org/licenses/by-nc-sa/4.0
Blockchain for Secure AI Development in Cloud and Edge Environments
The increasing use of artificial intelligence (AI) applications in cloud and edge environments raises significant concerns regarding security, data integrity, and the trustworthiness of models. Traditional security measures often struggle to provide adequate protection against tampering, unauthorized access, and privacy breaches. However, blockchain technology—with its decentralized architecture, immutability, and transparency—offers a promising solution to these challenges. This research explores the integration of blockchain in the development of AI for cloud and edge environments, emphasizing its potential to secure data provenance, protect AI models, and enable privacy-preserving learning. By analyzing use cases such as federated learning, decentralized AI marketplaces, and edge device security, this study provides insights into the opportunities and challenges presented by this convergence. The proposed framework highlights scalable blockchain solutions that align with the performance requirements of modern AI systems, offering a pathway for secure and trustworthy AI development in distributed settings
Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
AI Inference Optimization: Bridging the Gap Between Cloud and Edge Processing
The rapid spread of AI across various fields like IoT, autonomous systems, and real-time analytics has brought to light the need for smarter ways to handle data processing both in the cloud and at the edge. While cloud computing is known for its powerful processing capabilities, it often depends on stable internet connections and it can deal with high latency, making it less suitable for applications that need in stant responses. On the other hand, edge devices excel in providing quick responses but typically face limits in processing power and energy. This research dives into techniques that can effectively connect the strengths of both cloud and edge computing. By looking at strategies like dynamic workload distribution, model compression, and flexible resource management, the aim is to find a balance between speed, cost, and energy use. Ultimately, the goal is to create a scalable and adaptable framework for AI that allows for the smooth operation of models across different systems.
AI-Driving Anomaly Detention in Distributed Data Engineering Frameworks
Due to the overwhelming number of solutions for Big Data processing during the last few years, distributed data engineering frameworks have become one of the most effective tools for real-time analyses and decision-making in numerous domains. However, these frameworks are becoming more expansive due to the sheer complexity that is driving the scale of these systems, and this is where the anomalies – or variations from the norm or anticipated patterns – are proving to be very hard to identify. Originally applied anomaly detection techniques are not effective when dealing with dynamic high dimensional and real time-based distributed environments. Thus, the subject of this paper is the improvement of detecting anomalies in distributed data engineering frameworks using Artificial Intelligence (AI) approaches. This study aims at developing a rich framework for real-time anomaly detection with the help of the advanced machine learning techniques including deep learning-based auto encoder, generative adversarial network –GAN and recurrent neural network –RNN. It proposed solves the problem of unavailable labeled data by using unsupervised as well as semi supervised learning and targets scalability by integrating it to operate in big data ecosystems such as Apache Spark, Hadoop and Flink. Overall performance analysis of the proposed framework is carried out by employing both synthetic and real-life datasets for accuracy, recall, F1-measure and time complexity analysis. The obtained results confirm the superiority of the proposed system over basic approaches and indicate its flexibility under changing loads and the possibility of detecting different forms of anomalous behavior such as point anomalies, contextual anomalies, collective anomalies, etc. In the same way, it clarifies how model interpretability, data privacy, and efficient resource use in a distributed environment are critical issues in this study. These results also highlight the improvement prospects of AI-based anomaly detection in increasing the dependability, extensibility, and security of distributed data engineering platforms. It will open up new possibilities for further development of smart anomaly detection solutions, and provides the basis for further extension, possible in the form of federated learning or other combinations of AI approaches.
Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
Adaptive Data Reliability Engineering For AI-Driven Cloud Ecosystems
But even in the age of AI-driven cloud ecosystems, we continue to grapple with how to maintain data reliability when workloads are constantly shifting, data comes from many places, and the real-time expectation is often acute. Data reliability methods that are used in traditional approaches do not fully address the issues that arise with data in these environments. This research outlines an adaptive data reliability engineering model to AI cloud platforms specially designed for such environments. That is why, by using anomaly detection in real time, errors detecting and redundancy control algorithms, the framework is very flexible in dealing with changing data and provides an extreme reliability. Declarative concepts of consistency, availability, and tolerance to faults are clearly described and instrumented based on a set of benchmark test applications and realistic workloads. The studies also show better results regarding data to noise ratios and guarantee these improvements compared to conventional practices with the framework pointing to the prospect of improving efficacy and robustness of AI applications hosted on the cloud. But besides it being relevant, this study also fills gaps in current knowledge and creates the groundwork for future innovations in adaptive reliability engineering for industries that depend on sound data systems.
Comparative Analysis of Traditional and AI-based Demand Forecasting Models.
Demand forecasting is a critical function in supply chain management, enabling businesses to predict customer needs and optimize inventory, production, and logistics processes. Traditional forecasting methods, such as ARIMA and exponential smoothing, have been widely used due to their simplicity and interpretability. However, the growing complexity of market dynamics and data patterns has revealed limitations in their accuracy and adaptability. Recent advancements in Artificial Intelligence (AI) have introduced machine learning and deep learning-based models, such as Random Forest and Long Short-Term Memory (LSTM) networks, which offer enhanced performance in handling non-linear and high-dimensional data. This study presents a comparative analysis of traditional and AI-based forecasting models, focusing on their accuracy, computational efficiency, scalability, and interpretability. Using diverse datasets from industries such as retail, manufacturing, and e-commerce, the research evaluates the strengths and weaknesses of each approach. The findings highlight the conditions under which AI-based models significantly outperform traditional methods and discuss the trade-offs involved in resource consumption and ease of deployment. Practical recommendations and future trends, including hybrid models and explainable AI, are proposed to guide businesses in selecting the most appropriate forecasting techniques.
Copyright (c) 2020 International Journal of Emerging Trends in Science and Technology https://creativecommons.org/licenses/by-nc-sa/4.0
Adaptive Power System Load Frequency Control based on Metaphorless Optimizers
This research proposes an alternative Load Flow Controller (LFC) to power systems stability studies based on the population-oriented mathematical optimizer called the PI-Rao. This approach is applied to stabilizing frequency deviations in single-area power systems and compared with a basic PI controller. The research employed a dynamic systems model for a singular area system to capture the dynamics of an LFC. The system is composed of two parts: Part 1 is the LFC optimization systems part which makes a global search for the optimal P & I factors based on some prespecified frequency fluctuation. In contrast, Part 2 is the METARPHOLESS-PI part which uses a population-oriented mathematical logic. The presented optimization in (Part 1) follows classical methods inspired by evolutionary approaches to search for the optimal set of fitting parameters. The minimization of area control error (ACE) is considered an objective function involving the minimization of an Integral Time multiplied Absolute Error (ITAE). The results of simulations have shown the superiority of the proposed solution considering the loading changes of 0.02p.u to 0.1p.u and at intervals of 0.02p.u. Thus, the PI-Rao should serve as another potential solution for power systems LFC applications. The current challenges and future research directions for the existing and prospective projects of the power system operators seek microprocessor-based fault analysis solutions.
Copyright (c) 2024 International Journal of Emerging Trends in Science and Technology https://creativecommons.org/licenses/by-nc-sa/4.0
Fraud Detection Combating Mobile Money Fraud in SMS Messages Using Machine Learning
This paper presents a machine learning-based fraud detection model to combat the SMS based mobile money fraud. The systems use an XGBoost classifier to process SMS message content real-time and boast a high level of accuracy with predicting malicious intent. Built using Flask framework, the system allows administrators to monitor in real-time all reported alarms for specific transactions regarding fraud and keep statistical records. Future research includes improving the interpretability of models and making them both more scalable as well as robust to adversarial attacks for better fraud detection in mobile money services.
According to the conjecture that P ¹ NP, we recall in this paper that class NP includes P, NP-intermediate and NP-complete problems (some of Co-NP and NP-hard problems also belong to NP). It is obvious that if a single problem belonging to NP is formally proved non-polynomial, then P ¹ NP no longer remains a conjecture but rather becomes a formal statement. In this purpose, we formally prove that the Graph-isomorphism problem (belonging to class NP) is non-polynomial time, which leads that P ¹ NP is a formal statement, not a conjecture.
A Pandemic Predictive Model with Convolutional Neural Networks and Deep Reinforcement Learning using Simulated Partial Differential Equations Data.
Detecting the spread of pandemics will greatly reduce human and economic loss. Existing Epidemiological models used for coronavirus disease 2019 (COVID-19) prediction models are too slow and fail to capture epidemic development thoroughly. This research presents a Physics-based Machine Learning Architecture (PMLA) to improve the processing speed and accuracy of epidemic forecasting governed by susceptible–exposed–infected–recovered–deceased (SEIRD) model equations. The dynamics of the epidemic were extracted using Convolutional Neural Networks (CNN) and Deep Reinforcement Learning (Deep RL) from data simulated with Partial Differential Equations (PDEs). The PMLA accuracy is measured using mean squared error. The PMLA prediction model enhances the ability of health authorities to predict the spread of COVID-19 in real time efficiently and effectively. Keywords: COVID-19, Convolutional Neural Networks, Deep Reinforcement Learning, Partial Differential Equations, Machine learning, Finite Element Method.
Forecasting Population demographics in Lilongwe city: Leveraging Prophet and Time series analysis Techniques
Population demographics provides grounds for forecasting due to complex nature of various editions of population figures. The accurate prediction of population demographics is pivotal for urban planning, resource allocation, and the development of effective policies. In this paper, we utilize historical demographic data collected over time to forecast future demographic trends, enabling informed decision-making by local authorities and urban planners. The study begins by gathering and preprocessing a comprehensive dataset that encompasses various demographic variables, such as age, gender, income, education level, and occupation, among others. We employ Prophet, a robust forecasting tool, with diverse time series analysis methods. It examines historical demographic data, refining models using Prophet’s flexibility and traditional time series techniques like ARIMA and exponential smoothing. By leveraging Prophet and time series analysis, this paper aims to offer accurate forecasts of population dynamics, age structures and migration trends, providing valuable insights for urban planning and policy formulation. Keywords: population demographics, ARIMA, prophet, time series analysis.
Empirical Study of the Relationship between Capital Formation and Saving in Rwanda: An Econometric Approach
The objective of this study was to investigate the empirical relationship between capital formation and saving in the case of Rwanda. The empirical literature review reveals that saving has a positive effect on capital formation. Results of the research obtained using the Ordinary Least Square method show that in Rwanda there is positive correlation between capital formation and saving. Saving is used to finance investments useful for the development of the country. In the equation formulated, endogenous variable was capital formation while exogenous variables were saving, foreign direct investments and money supply. It has been demonstrated that coefficient of saving is positive. In Rwanda, an increase 1% of saving ends at an increase of gross capital formation of 1.271241%. Analyses done end at results that estimated coefficients are statistically significant at 5% margin of error i.e. 95% level of confidence. The relative probability for LSAV is less than 0.5. R-squared is 0.980003. This goes to mean that exogenous variables selected explain Gross Capital Formation at 98% and the model if fitted. Therefore, the government of Rwanda should make supplementary effort to encourage saving in the country so to boost the economy through more investment in order to meet its major objective, i.e the economic and social development.
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
A Review of gentamicin sulphate oral preparation containing natural oil (pumpkin seed oil) for systemic indication
Delivery of gentamicin sulphate through the oral route has always been difficult because the drug is not permeable in the intestinal mucosa. There is hardly any oral preparation of this valuable drug effective for many gram-negative and gram-positive organisms. This work reviewed research works to aid achieve this systemic circulation of orally-administered gentamicin sulphate prepared with a natural oil. Natural oils have not only proven a great deal of source of bioactive phytochemicals but also can be a proven vehicle for oral preparations as they can confer some desirable antimicrobial synergism with conventional therapies. Various bioavailability studies of gentamicin sulphate were reviewed which revealed a great importance of a specialized formulation need that either enhances entrapment of the drug in a mix, sustained release formulation, or as solid-lipid microparticles e.t.c. These are all pharmaceutically possible and can be achieved using a natural oil as a vehicle plus permeation enhancers for such oral drug preparation.
Leveraging on Green Energy Harvesting via Dye Sensitized Solar Cells; Their Components, Operations and Graphene Based Electrodes
One possible technological development in solar cells is thought to be dye-sensitized solar cells (DSSCs). The energy-producing method employed by DSSCs is similar to that of plant cells and is based on the photo-electrochemical moments of electrons that are brought about by the interaction of photon energy and chemical reaction. Synthetic dyes that are employed as sensitizers in DSSCs have higher efficiency and longer endurance, but they also have drawbacks such the use of rare metallic components that are vulnerable to environmental degradation. As a result, researchers are experimenting with natural alternative sensitizers that are biocompatible. Plant pigments like carotenoids, flavonoids, anthocyanins, and chlorophyll are in charge of the chemical reactions in these natural sensitizers. Therefore, it is relatively easy to extract dyes containing this pigment from natural sources such as flowers, seeds, and leaves, among others. The structure and functioning of DSSCs are the main topics of this review. This review focuses on the structure and operations of DSSCs. Components of DSSCs photosensitizers, photo-anodes and counter electrodes and their functions have been outlined. Detailed summary of synthetic dye based DSSCs carried out by different groups, have also been reviewed. Details of numerous plant pigments found in plant-based products that are employed as sensitizers in DSSCs have been collated and explained. Open circuit voltage (Voc), short circuit current (Isc), fill factor (FF), and efficiency (η) based on both synthetic and natural dye have also been reported as DSSC performance metrics. Leveraging on the photosensitizers and maximizing on the working electrodes and counter electrodes, DSSCs would serve as the best alternative to meet the world energy demand.
Copyright (c) 2023 International Journal of Emerging Trends in Science and Technology https://creativecommons.org/licenses/by-nc-sa/4.0
The Role of Educational Technology: Teaching and Learning Process
Educational technology has played a critical role in enhancing the teaching and learning process, particularly in the digital age. This paper provides an overview of the role of educational technology in teaching and learning, including its benefits, challenges, and future trends. It examines how educational technology can support effective teaching practices, engage learners, and facilitate personalized learning experiences. The paper also explores the challenges associated with educational technology implementation, such as the digital divide, and discusses strategies for overcoming these challenges. Finally, the paper highlights emerging trends in educational technology, such as artificial intelligence and virtual and augmented reality, and their potential impact on teaching and learning. Overall, this paper demonstrates the importance of educational technology as a tool for promoting student engagement, improving learning outcomes, and preparing learners for success in a rapidly changing world. Keywords: EdTech, Education, Modern technology, Science, Teaching, students, Digital operation, Online, Learning, Internet.
Impact of the Internet on healthcare marketing
For the last 20 years, the Internet has become one of the most used ways to get information, including healthcare. Healthcare marketing can be a potential driving force for the digital population to use the Internet to surf regarding healthcare information. This paper talks about factors influencing the Internet on Healthcare marketing perceived advantages and disadvantages of its use it.
Copyright (c) 2022 International Journal of Emerging Trends in Science and Technology https://creativecommons.org/licenses/by-nc-sa/4.0
Relationship and Differences of Teacher Attitude towards Their Skills in the Utilization of Ict in the Learning Process in West Sumatera
The purpose of this study was to explore the differences and relationships of teachers' ICT knowledge by school location, work experience, and to explore the relationship between teachers' attitudes and skills in using ICT.This study report states that there is a significant difference between the knowledge of science teachers who teach in cities and science teachers in villages. In addition, there is a significant difference in the knowledge of science teachers about ICT based on work experience. There is also a strong relationship between the attitudes and skills of science teachers in utilizing ICT during the learning process. There is a strong relationship between the attitude of science teachers with ICT skills and attitudes of science teachers on the use of ICT. The attitude of science teachers to use ICT has a weak and positive correlation with the skills of teachers to use ICT in the science learning process. The implication of this research is that computer hardware, internet access, and training facilities can increase teachers' knowledge of ICT, especially experienced teachers.
Object Detection in Unmanned Aerial Vehicle Imagery
Recently drones have emerged as a great new tool that many companies and even personal entities have decided to exploit. Looking at the potential these new drones have it would be in everyone’s best interest to use these drones or Unmanned Air Vehicles to their fullest potential. One way to help achieve that is by mounting cameras onto these drones and using the data captured by the video feed received from these cameras. This project is using machine learning and computer vision to help make use of the data captured by these drones. A RetinaNet model is used to train various models on images captured from the Stanford campus. The model is detecting various classes of urban artifacts such as pedestrians and cyclists.
Skills and Ict Science Learning Toward Teachers’ South Coast District
This research focuses on studying about the differences of science’ teachers proficiency on ICT’s utilizing for learning process based on schools location and teachers’ education. Finding out the contribution of knowledge, attitude, and ICT’s skills toward ICT utilizing on science learning process. The study shows, there are significant differences between high school science’ teachers in urban and rural areas (t=-5,094, p=0.00). Furthermore, there are notable dissimilar finding between under graduated science’ teachers and postgraduated science’ teachers (t=-3,617, p=0.00). 4 (four) independent variables have major roles to the skills of science teachers’ on ICT utilizing for learning process, which is 43.0 percents. Teachers’ attitude toward ICT contributes the highest roles (35, 3 percents), these are followed by teachers’ attitude on ICT utilizing (3, 4 percents), teachers’ knowledge on ICT utilizing (2,3 percents), and ICT facilities (2,0 percents).
The DESIGN AND DEVELOPMENT OF ARTIFICIAL INTELLIGENCE BASED ‘DENV SHIELD’ APP FOR PREDICTING DENGUE
Dengue fever, dengue hemorrhagic fever and dengue stroke syndrome are caused by dengue virus (DENV) belonging to the family Flaviviridae and spread through Aedes mosquitoes. Incessant mutation resulting from the proofreading inefficiency of their RNA polymerases under increasing air temperature and CO2 concentration make it thorny to diagnose or strengthening immune system against DENV. The vector competence of Aedes is influenced by diurnal temperature range (DTR). Under these circumstances predicting the genotype of the imminent DENV and vector competence in advance is momentous for vaccine development and vector surveillance. In order to make it efficient, this research aims in designing mobile app ‘DENV SHIELD’ which uses a novel In Vitro Replication Model (IVRM) to predict the nucleotide sequence of imminent genotype of DENV in prior. This app also makes real time prediction of the Vector Competence (VC) based on prediction of DTR corresponding to the region of the user using an Adaptive Network-Based Fuzzy Inference System (ANFIS) model. A self aware artificial intelligence model (SA-AIM) is used for forecasting air temperature and CO2 in advance. DTR is predicted using random forest algorithm which uses ARIMA time series prediction model for getting forecast of wind velocity and solar radiation. Training data are collected from National Center for Biotechnology Information, Centre for Diseases Control and Prevention and National Climate Data Center. The outcome of this research is an early warning system app which will help diagnostic centers and health workers to know genotype of forthcoming variant of virus and vector competence.
Dr. T. Ruban Deva Prakash Dr. T. Ruban Deva Prakash
Combining AI with Data Engineering Pipelines: Improving Real-Time Decision-Making Systems
AI with data engineering pipelines as an innovative way has become relevant in the context of optimizing the real-time decision-making systems. The objective of this research is to explore the potential of AI models in making decision support more efficient when integrated into today’s data science platforms. The overarching goal is to provide an ability to fill the gap between data collection, analysis and decision-making through use of intelligent systems, which adapt to environments and continually learn. To this end, the study uses a mix of architectural assessment, a prototypical implementation, and case studies. State-of-art technologies like machine learning and deep learning are combined with data engineering technologies like Apache Kafka, Spark, Kubernetes for real time data processing and management. It involves emulating high velocity data streams and applying AI’s analytics to it what would be accomplished in real time. The results show increased adaptability, speed and effectiveness in decision-making systems. By directly integrating AI into an organization’s data streams, it is possible to decrease the time taken to process the data by half, and get a more accurate analysis by 30%. Real-world applications of the technique involve fraud detection in financial services and improved supply chain management; performance of the method in larger organizational contexts is also demonstrated. The implication from this research is that integration of AI with data engineering pipelines is not only possible but imperative especially to organizations that are willing to step up their game and competitiveness in the current era characterised by an influx in data generation. Subsequent work will investigate how new technologies like federated learning and edge computing can be applied progressively to boost the effectiveness of real-time decision making frameworks.
AI-Powered Data Engineering for Accelerating Digital Transformation in Healthcare
Where AI Data Engineering is now shaping and advancing healthcare delivery involves some of the solutions to the problems associated with complex, large and multistructured data systems. This transformation can allow organisations to manage real-time data streams and flows, operate and integrate different systems at once and facilitate the use of high level analytics for better decision making processes. AI acts through the automation of data pipelines to improve the efficiency and efficacy of healthcare service delivery in aspects like; diagnosis, resource allocation & patient care delivery. Therefore, this paper focuses on examining the approaches, uses, and impacts of using artificial intelligence-driven data engineering in healthcare. They argue that potential benefits of telemedicine in cost saving, in terms of scale, and efficiency and improvements in patient care particularly in chronic disease are apparent in the highlighted use cases. The threats like data privacy, the compliance issue, and bias are analyzed, and new opportunities like the federated learning and quantum computing are explored. It enlightens the world with the potential of AI in revolutionizing digital change in healthcare delivery while calling for public policy standards that would safeguard against rampant incorporation of AI in the sector due to its inherent challenges.
Copyright (c) 2021 International Journal of Emerging Trends in Science and Technology https://creativecommons.org/licenses/by-nc-sa/4.0
Internet of Things (IOT): Research Challenges and Future Applications
With the Internet of Effects (IoT) gradationally evolving as the posterior phase of the elaboration of the Internet, it becomes pivotal to fete the colorful implicit disciplines for operation of IoT, and the exploration challenges that are associated with these operations. Ranging from smart metropolises, to health care, smart husbandry, logistics and retail, to indeed hurt living and smart surroundings IoT is anticipated to insinuate into nearly all aspects of diurnal life. Indeed though the current IoT enabling technologies have greatly bettered in the recent times, there are still multitudinous problems that bear attention. Since the IoT conception ensues from miscellaneous technologies, numerous exploration challenges are bound to arise. The fact that IoT is so extensive and affects virtually all areas of our lives, makes it a significant exploration content for studies in colorful affiliated fields similar as information technology and computer wisdom. Therefore, IoT is paving the way for new confines of exploration to be carried out. This paper presents the recent development of IoT technologies and discusses unborn operations and exploration challenges. Keywords — Internet of Effects; IoT operations; IoT challenges; unborn technologies; smart metropolises; smart terrain; smart husbandry; smart living
AI-Powered Automation of Data Pipelines: Bridging Data Engineering and Intelligent Systems
The presence of AI and advanced data engineering has transformed ITS and physical infrastructure and resolved main issues to urban mobility. This article is aimed at revealing the role of AI solutions in improving traffic flow, safety levels and emissions. Smart traffic signals, big data technology, and predictive algorithms have gone a long way to negate traffic jams, as well as emissions and enhance travel time and security. Real examples illustrate the applicability of AI when it comes to traffic control in cities and in self-driving cars. The modern nature of the business requires openness and connectivity, making it difficult to manage ever-increasing volumes of data in enterprises. Application of Artificial Intelligence in automation of data pipelines is a game changer in data engineering that seeks to close data to intelligence chasm. This gives AI the ability to co-ordinate machine learning, natural language processing and advanced orchestration tools to create complex and sustainable pipelines which allows for real-time data ingestion, transformation and delivery. This article aims to demonstrate the change that AI brings into organizations’ daily vocabulary by automating mostly clerklike tasks, making use of various methodologies like, Anomaly detection, Predictive analytics and Dynamic resource allocation. Through case studies, large scale implementation of AI DP in areas of financial fraud detection, IoT based smart manufacturing, and smart retail experiences are shown. The foremost advantage is that, using MEAN, it becomes possible to decrease latency, improve scalability, and increase the overall operational efficiency as a result of proper integration and better inbuilt functions of corresponding supplying tools and technologies Nevertheless, there are still crucial problems such as data security, ethical approaches to the usage of AI, and integration of MEAN with legacy systems. The article also expands to future possibilities such as linking of quantum computing and generative AI to create optimizing pipelines. In doing so, this research also draws focus on the future of data engineering powered by AI automation to promote intelligent decision making and innovation in the industries.
Development of Integrated Inventory Management Models for Manufacturing Companies
Efficient inventory management is pivotal for manufacturing companies striving to maintain operational excellence, minimize costs, and meet dynamic market demands. Traditional inventory management systems, while functional, often fall short in addressing complex supply chain challenges such as real-time visibility, demand variability, and integration with modern technologies. This study explores the development of an integrated inventory management model designed to optimize inventory control, enhance decision-making, and reduce operational inefficiencies. By leveraging advanced technologies, including IoT, AI, and ERP systems, the proposed model integrates demand forecasting, inventory optimization, and supplier collaboration into a cohesive framework. Data-driven insights and predictive analytics form the cornerstone of this approach, enabling manufacturing companies to adapt to shifting market conditions seamlessly. The research evaluates the model's performance through simulations and case studies, highlighting significant improvements in inventory turnover, cost reduction, and operational agility. This study provides a pathway for manufacturing companies to transition to scalable, efficient, and technologically advanced inventory management systems.
We utilize web 2.0 in today's atmosphere to access and update information on the internet. In today's society, we use websites and web applications to do jobs more quickly. Web development has introduced new opportunities as well as new obstacles to web-based education and learning. The first version of the web, Web 1.0, was a static (read-only) medium, whereas Web 2.0 is a Read/Write medium. Web 3.0, the most current and still-evolving version of the web, is now a technologically advanced medium that allows us (users) to read, write, and execute, as well as robots to conduct some of the intellectual thinking once reserved for humans.
What Determines mixed-income housing development in Developing economies. A Confirmatory Factor Analysis Model
The aim of this present resaerch work is to develope and identify what are the relevant key indicators which guide the implementation of mixed-income housing development and to find a way to compare two indicators: intergovernmental support and collaboration and Town Planning regulation policy. Sample size of 200 were selected randomly out of a total population of 300 residents interviewed in a mixed-income residential neighbourhood at Cosmo City, Johannesburg. The data was Analysed using a statistical methods of confirmatory factor analysis and structural equation model. The research questions is on Whether Town Planning Policies and Regulation impact positively or negatively on the development of Mixed-income housing projects and if there is any correlation between Town Planning Approval and Policies and Inter-governmental collaboration in the development of a mixed-income housing framework? Results of the findings are as follows:(1) The construct fitted the empirical data as mixed-income housing development framework is guided by six dimensions or indicators. (2) The study also show the relevance of townplanning approval and policy in a given mixed-income housing development as it helps to maintain and conserve the neighbourhood. The conclusion and recommendation drawn from this study is that both intergovernmental support and collaboration as well as Town Planning regualtions both play very important role in the location of mixed-income housing development in South Africa and other developing economies. Keywords: confirmatory factor analysis, structural equation model, mixed-income housing, Town Planning regulations and intergovenmental collaborations.
Artificial Intelligence and Coronavirus Crisis Management
The research paper seeks to distinguish how applications of artificial intelligence were utilized during the outbreak of the Coronavirus. Where the facts proved to the conditions of acceleration and facilitation to which these applications contributed, especially in terms of data and information analysis, and to draw accurate results. A tremendous amount of information directly related to the Coronavirus, in addition to the largest amount of information related to diseases close to it, as it provides an effective reading based on comparison and inventory. It has been called the applications of artificial intelligence and machine learning, in reducing the element of time, whether in terms of discovering the genetic strain of the virus or at the level of vaccine manufacture. While these applications have proven their utmost benefit in monitoring the movement of the epidemic and limiting the speed of its spread.
Copyright (c) 2022 International Journal of Emerging Trends in Science and Technology
Measuring Aspects of Sustainable Manufacturing Practices in Steelmaking Plant Industry In Iran
Since Bruntland Comission report established the concept of sustainable development , the manufacturing and mine industry have been in effort to obtain and extend their planning in implementing sustainable manufacturing for them to meet the sustainable management criterion laid down by ISO 14000 series standards. These criterion have been explained and established to determine and evaluate the key performance indicators for manufacturing flow. The aim of this paper is to examine a case study in order to understand manufacturing procedure stages of steel industry in the context of its sustainable practices. Energy analysis and natural minerals such as Iron ore have been shown as main key performances for this paper.
Semantik Assosiatif SEMANTIK ASSOSIATIF DALAM NOVEL BUKAN CINTA CINDERELLA KARYA USSY SULISTIAWATI DAN ANDIKA PRATAMA.
Research with the title Analysis of Associative Meaning in the novel not Cinta Cinderella by Ussy Sulistiawati and Andika Pratama offers to describe the types of associative meanings. This type of research used in this research is descriptive qualitative. The data in this study consisted of words, phrases and sentences that contained associative meaning in the Novel not Cinta Cinderella. The data source is in the form of character dialogue including the introduction to the text. This study uses three studies, namely research on the provision of data, data analysis, and analysis of results.