International Journal of Multidisciplinary Research and Explorer
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Empowering the Grid: Applications and Challenges of Machine Learning in Renewable Energy Resources
Get PDFIntegration of renewable energy systems, into the electrical grid has been investigated in the present research, with a special focus on the use of machine learning (ML) techniques in power system operations. In the framework of renewable energy, it critically investigates the applications of machine learning (ML) in forecasting, efficiency improvement, problem detection, and system optimization. The paper also discusses the primary challenges to implementing AI-driven solutions in contemporary power systems, including the need for quick decisions, cybersecurity risks, limitations on data availability and quality, and the difficulties of integrating with current grid infrastructure. This paper aims to provide an in-depth understanding of how intelligent algorithms are transforming the future of the electrical sector by highlighting both the revolutionary potential and the implementation challenges of AI technology in energy systems
Creative Accounting and Accountability Failures in the Philippine Health Sector: A Case Study of PhilHealth during COVID-19
Get PDFThis study investigates creative accounting practices in the Philippine Health Insurance Corporation (PhilHealth), focusing on financial manipulation and accountability breakdowns during the COVID-19 pandemic. The current paper uses a qualitative case study and document analysis of government audit reports, legislative inquiries, and media investigations (2018–2021) to reveal how procurement irregularities, unliquidated cash advances, and disguised operating losses distorted the agency’s financial position. These practices, facilitated by weak internal controls and governance failures, undermined transparency and jeopardized the implementation of universal health care. Anchored in public sector accountability and creative accounting theories, the study concludes with actionable recommendations, including the institutionalization of forensic audits, digital transparency reforms, and stronger whistleblower protections. The findings underscore systemic weaknesses that enabled financial misreporting and highlight the pressing necessity for structural reforms in public financial management
Synthesis and Characterization of Copper (II) Complex Bearing 1-Phenyliminomethyl-Naphthalen-2-ol
Get PDFA novel Schiff base ligand, 1-phenyliminomethyl-naphthalen-2-ol was synthesized by condensation of 2-hydroxyl-1-naphtaldehyde and aniline, and subsequently complexed with Cu(II) ions to become a stable metal-ligand complex. The synthesized Schiff base and Cu(II) complex have been characterized using UV–Visible spectroscopy and Fourier-transform infrared spectroscopy. The UV-Vis analysis revealed that metal-ligand charge transfer which depicted low band at 406, 350 nm (for the two molar ratio 1:1, 2:1) respectively. The FTIR absorption spectra confirmed the formation of the imine (C=N) bond and coordination of the phenolic oxygen and azomethine nitrogen. NH bonds revealed coordination at 3340 and 3366 cm-1 while CN bonds showed no coordination and 1161 cm-1, both at 1:1, 2:1 respectively. In conclusion, these findings supports the successful formation of a bidentate Schiff base to Cu(II) complex with a potential application in biological and catalysis studies
A Fuzzy Logic-Based Decision Support System for Early Detection of COVID-19: A Review and Comparative Analysis
Get PDFThe global COVID-19 pandemic, resulting from the infection by the SARS-CoV-2 virus, is emphasizing the urgent need for rapid and accurate diagnostic methods for the control of the infection spread. Lab-based testing methods can take time, leading to diagnostic delays and a high risk of transmission. Fuzzy Logic-Based Expert System in Early Detection of COVID-19 Symptoms and Risk Assessment in Real-Time. We provide a systematic review of published fuzzy logic models for the COVID-19 diagnosis along with details of their methodologies, accuracy, and clinical usability. Results support that fuzzy logic systems improve diagnostic efficiency, lessen healthcare pathways, and enable decision-making in a timely manner
Real-Time Path Planning for IoT-Enabled Autonomous Vehicle Robotics Using RRT and A * Algorithms
Get PDFOne of the main purposes of this work is to provide a path planning framework for IoT-enabled autonomous vehicles through the use of RRTs and A*. These were designed to maximize actual real-time navigation and decision-making in very dynamic and complex situations considering obstacles and uncertainties in the environment. In cases that have unknown or nonregular barriers, the RRT algorithm is employed to visualize the environment rapidly to derive an initial feasible path across the configuration space. Following the developments of RRT paths, the A algorithm* will address topics brought about by their construction in order for the route to be smooth, efficient, and have the shortest length. A synergism between the two techniques makes these systems adapt in real time to changes in the environment and in transportation conditions while preserving computational economy. From the performance evaluation, joining the strategy increases these very important parameters, such as the energy consumption, path length, and the time to reach the destination, by a huge percentage. The model consumes energy that is reduced by about 23% in comparison with conventional approaches, decreases path length by 12-15% and decreases time to objective up to 50%. These results indicate that the RRT + A* model works very well to enhance the effectiveness and efficiency of autonomous vehicle navigation in changing conditions. This framework can be used in applications like robotics and autonomous driving, and it represents a viable answer for real-time energy-efficient optimal path planning
SALES FORECASTING EFFECT ON PHARMACIES
Get PDFNowadays, as technology is advancing to previously unheard-of levels, every company and organization is finding it difficult to balance inventory and customer expectations. Every organization relies heavily on sales, and being able to predict the future helps in making strategic and intelligent sales decisions. The majority of businesses still struggle with revenue forecasting because it is usually the first step in developing the company\u27s annual budget. Over time, a company\u27s estimation could suffer if its sales projections are consistently inaccurate. Sales forecasting therefore affects the entire company to improve their overall growth strategy. An essential part of any business\u27s sales operations is sale forecasting.
For a business to supply the necessary quantity at the appropriate time, an accurate sales forecast is essential. Executives use the predictions to assess future performance and plan for organizational expansion. In this study, we use the machine learning techniques of naive forecasting and linear regression to try and predict a retail company\u27s sales. The difference between the linear regression and naïve forecasting approaches is demonstrated using a computational example, and we have found that the linear regression yields better results than the naïve forecasting approaches. Additionally, we used the ARIMA model for the linear regression approach to forecast the sales for the upcoming five days.
Comparative Analysis of Machine Learning Algorithms to Predict Type II Diabetes
Get PDFMachine Learning (ML) models are becoming robust and more accurate nowadays as the rapid increase in the amount and quality of training data. Researchers are proposing complex models for real-life problems to achieve higher accuracy, which requires high computing and other resources. In the context of the healthcare disease diagnosis, detection and prediction is still a challenge. Early diagnosis of a disease or ailment helps in timely recovery. Moreover, health been core to every individual, a lot of work is being done in this field to improve upon by using all available information.
Current paper experiments on Pima Indian Diabetes Dataset (PIDDS) in two stages A and B. The main objective of this study is to review the accuracy of the applied machine learning algorithms and analyze their efficiency in predictions. Another essential objective is to show the efficacy of simpler models. Fields like computer vision and NLP have given rise to deep learning with complex and high computational models setting the trend to apply them in almost all the fields While they help where we have an abundance of data and complex relationships, simpler models still can do wonders and on their day can challenge these behemoths. We have also applied preprocessing methods (imputation, feature selection, scaling and discretization) to improve the classification accuracy. The algorithms selected for this problem are Logistic regression (LR), Artificial Neural Networks (ANN), Support Vector Machine(SVM), Naïve Bayes (NB), and Decision Tree(DT). LR provided the best accuracy, and the rest of the models are very close to each other
Trends and Implications: Analysis of Collection of Direct Tax from Financial Year 2017-18 to 2022-23
Get PDFThis research paper presents a thorough examination of the collection of direct taxes spanning from the financial year 2017-18 to 2022-23. Direct taxes play a vital role in government revenue mobilization and economic governance, making it imperative to understand the trends, patterns, and implications associated with their collection. Through a comprehensive analysis of tax data, this study aims to shed light on the dynamics of direct tax collection over the specified period. By employing statistical techniques and empirical analysis, the paper identifies key trends, factors influencing tax collection, and their broader economic implications. The findings provide valuable insights for policymakers, tax authorities, and stakeholders, enabling them to formulate informed strategies for enhancing revenue mobilization, fostering economic growth, and ensuring fiscal sustainability
Sources of health funding in households in the Barrière health area, Kenge Health Zone, Kwango, Democratic Republic of Congo
Get PDFSeveral studies carried out in low- and middle-income countries show that common sources of distress financing can take the form of interest-bearing or interest-free loans from a financial institution, friends, or family members, selling assets such as crops and property, livestock, and mortgaged assets. The study aimed to determine sources of financing for healthcare in households in the Barrière health area. The sampling for this study was non-probability, chosen for convenience. The target population consisted of 83500 heads of households in the Barrière health area, from which 200 heads of households were selected. The survey method was used to collect the data, using a semi-structured interview technique. Data analysis was descriptive, based on the calculation of frequencies and proportions. The Chi-2 test was used to check the links between the characteristics of the participants and the sources of funding for care in the households. The results showed that 35% came from the sale of agricultural produce; 30% from the sale of household valuables; 15% received assistance from third parties; 12% obtained funding from other sources; and only 7.5% financed care from household reserves. In the Barrière health area, as elsewhere in the Democratic Republic of Congo, households are the main sources of health funding.
Keywords: sources of financing, health financing, health care financing, households, health expenditur
Creating A Python Algorithm for A Robot to Identify RGB Colours of A 2D Captured Image
Get PDFRobots face many problems in identifying RGB colours in captured images, which affects their performance in many applications. These challenges include different lighting, colour matching, constraints, and environment. The same object will appear in assorted colours in different light, making it difficult for robots to identify and distinguish colours. differences, especially in complex scenes. For example, under certain conditions blue and green hues will be remarkably similar to each other, resulting in misclassification. Cheap or poorly calibrated sensors do not detect colour accurately, resulting in inaccurate identification. Additionally, reflections and shadows can alter colour perception, adding another layer of difficulty to accurate colour identification. Calibration, normalization, and machine learning techniques. A simple Python algorithm for colour correction and analysis is here