Which is a Better Predictive Model in Weather Forecasting: ARIMA or Fourier Transformation? | chapter 5 | Science and Technology - Recent Updates and Future Prospects Vol. 8

 

Time series prediction is a critical area of research with applications in various domains such as finance, weather forecasting, and stock market analysis. In this paper, we present a comparative analysis of two popular methods for time series prediction [1]: Fourier Transformation and the Autoregressive Integrated Moving Average (ARIMA) model. We took a dataset representing climate data for the state of Madhya Pradesh for a period of 2000 days. We took three analytical metrics: pressure, humidity and temperature to evaluate the performance of each method. We independently processed each metric and plotted the graph for predicted data to do the analysis. Subsequently, we implement the ARIMA model on the same dataset. We used Root Mean Squared Error test to find the accuracy for both Fourier and ARIMA model and compared the performance of the two methods based on the results of RMSE test. Our results indicate that temperature and humidity is predicted better with ARIMA model while pressure is better predicted by Fourier transformation. This study contributes to the existing body of knowledge by providing insights into the effectiveness of these methods for time series prediction, and can assist researchers and practitioners in selecting the appropriate approach for their specific needs.

 

 

Author(s) Details

Prerna Verma

Department of Mathematics, Birla Institute of Technology, Mesra Ranchi-835215, Jharkhand, India.

Soubhik Chakraborty

Department of Mathematics, Birla Institute of Technology, Mesra Ranchi-835215, Jharkhand, India.

 

Please see the link:- https://doi.org/10.9734/bpi/strufp/v8/1175

Machine Learning for Stock Market Forecasting: A Decision Support Framework Using Infosys Historical Data |Chapter 2 | New Advances in Business, Management and Economics Vol. 7

The volatility of stock markets makes prediction a complex but essential task for investors seeking to optimise their decision-making. This study presents a data-driven decision support model for stock market prediction using historical stock price data of Infosys Ltd., a leading IT firm in India. By applying data mining techniques—specifically classification and rule-based prediction—the model aims to identify meaningful patterns from past trends and assist in forecasting future price movements.

The study uses monthly trading data from the National Stock Exchange for the period 2010 to 2015, including open, high, low, and close price values. These values were transformed into symbolic categories (rise/fall) for analytical clarity. Using the ESTARD Data Miner tool, decision trees and rule-based classifiers were generated to derive actionable prediction rules. The rules were further tested using a What-If analysis for real-time prediction scenarios.

The findings demonstrate the effectiveness of symbolic conversion and decision tree modelling in predicting the stock trend classes. This predictive framework holds potential for guiding investors in making more informed buy/sell decisions and enhancing the reliability of investment strategies based on historical data patterns.

 

Author (s) Details

Sanjeev Gour
Medicaps University, Indore, MP, India.

 

Sanjana Sharma
Acropolis Institute of Technology & Research, Indore, MP, India.

 

Prerita Kulkarni
Medicaps University, Indore, MP, India.

 

Shimna Mohan K.
Medicaps University, Indore, MP, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/nabme/v7/5193

Prediction and Classification Machine Learning Models in Diabetic Studies | Chapter 9 | Socio-Scientific Interaction in Diabetes and Cancer and Its Management

 Diabetes is one of the major health problems globally. Diabetes is a metabolic disorder that causes various complications such as stroke, chronic kidney disease, foot ulcer, retinopathy, diabetic ketoacidosis, and many more. Timely predictions and classifying of the diabetes problem will help to prevent diabetes. There is various machine-learning approach that can be used to predict and classify diabetes problem in the general population. The diabetes prediction and classification process can help to further improve the disease. This paper will discuss the machine learning applications and steps to understanding the concepts. The purpose of this paper framework is to early prediction and classification of diabetes to reduce stress and save human life, and financial burden.

Author(s) Details:

Rakesh Kumar Saroj,
School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India.

Kanchan Yadav,
Department of Community Medicine, SMIMS-Sikkim Manipal University, India.

Please see the link here: https://stm.bookpi.org/SSIDCIM/article/view/12727

Artificial Intelligence (AI) and Its Application in Pharmacy | Chapter 12 | Advances and Challenges in Science and Technology Vol. 9

 The AI is an active tool for data excavating based on the gigantic pharmacological data and machine learning process. Therefore, AI has been used in again drug design, activity scoring, in essence screening and in silico judgment in the properties (absorption, classification, metabolism, excretion and toxicity) of a drug particle. AI has made important contributions to the healthcare industry in any of areas, including the administration and storage of data and news about patient medical histories, cure stocks, sale records, and more; automated appliance; software and computer uses; and diagnostic finishes like CT and MRI diagnostics. All of these have been grown to support and streamline healthcare procedures. As expected, artificial intelligence (AI) has transformed healthcare expected more effective and adept, and the pharmaceutical industry is not exempt. All along the past few years, a substantial amount of increasing interest in the uses of AI science has been identified for resolving as well as interpreting few important fields of pharmacy like drug finding, dosage form crafty, poly pharmacology, and hospital pharmacy. AI-located solutions have been labeled which involve platforms that can make use of a difference of data types viz. manifestations reported for one patients, biometrics, imaging, biomarkers, etc. We engaged to produce a thorough report that would aid every undertaking pharmacist in understanding the major progresses made likely by the application of machine intelligence (AI), in light of the field's expanding meaning. In order to conclude the outbreaks of COVID-19, Zika, Ebola, and seasonal influenza, deep education and neural networks were utilized. Accompanying the advancement of AI technologies, the experimental community concede possibility witness rapid and cost-effective healthcare and drug research as well as provide upgraded service to the society.

Author(s) Details:

R. Radha,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

V. Neelima,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

M. P. VedaVarshan,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

S. Saqib Basha,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

U. Jeevitha,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

M. A. KapilKumar,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

G. Sateesh Kumar,
Department of Pharmaceutical Chemistry, Seven Hills College of Pharmacy (Autonomous), Tirupati, India.

Please see the link here: https://stm.bookpi.org/ACST-V9/article/view/12634

Efficient Use of Markov Chains in Time Chains Forecasting (Population Forecasting Study in the Syrian Arab Republic) | Chapter 1 | An Overview on Business, Management and Economics Research Vol. 4

 In this research, the Markov chain plan was used to predict the populace of Syria in terms of instructional status. As we noticed, population enumerations are conducted each ten years, so only the sample of dossier obtained from 1980 to 2010 was intentional. It was forecast for three years, 2008, 2009 and 2010, place the forecasting process endured the following steps. In the first stage, the model was created based on the measure of the mean certain relative error (MAPE) and the measure of the root mean square wrong (RMSE). In the second stage, the chart was formed, and in the after second stage, the transition forge from one old age to another was formed, utilizing the MATLAB program, and in the final stage, the principles were predicted and before the values acquired were compared accompanying the actual principles. The obtained principles confirm that Markov chains have extreme reliability and are a trustworthy method in this place study. The research aimed to study all facets of demographics in accordance with educational stage, build an persuasive Markov chart model, and use Markov chains in prediction and clarify their importance. However, this study presented a set of results and recommendations, ultimate important of that is that the Markov chain technique is thought-out one of the main techniques that maybe used to predict the future in business-related and social studies. There is a large size of credibility when utilizing Markov chains and their prediction in examining opportunity series of the Syrian culture by educational level. The study demonstrated the importance concerning this technique in business-related and social studies on account of its dependability and credibility.

Author(s) Details:

Bassel Anwar Asaad,
Faculty of Science, University of Tartous, Tartous, Syria.

Ghazal Shafik Safia,
Faculty of Management, Arab Academy for Maritime Transport, Lattakia, Syria.

Please see the link here: https://stm.bookpi.org/AOBMER-V4/article/view/12234

A Fast and Accurate Prediction of BPG Compression Parameters in Optimal Operation Point Neighbourhood for Three-channel Noisy Images | Chapter 7 | Fundamental Research and Application of Physical Science Vol. 8

This stage considers the lossy compression of three-channel images debased by additive silvery Gaussian noise. The better portable drawings (BPG) encoder that has three different movement modes is studied. As for additional encoders, a noise draining effect is observed and it influences the encoder performance. For certain environments (image complicatedness and noise intensity), the supposed optimal movement point (OOP) may exist. The main feature of OOP is that the quality of a compacted image (calculated concerning the true countenance) is better than the quality of the corresponding strident image in accordance with a given metric (either common such as mean square mistake or some visual value metric). Knowledge having to do with the OOP existence and compression limits in it for a given strident image can be valuable. In the case of OOP existence, it wash to compress a given representation in OOP or its community. If OOP does not exist, a more “careful” lossy compression is expedient. The question is that in practice the valid image is not available, and, so, it is impossible to reckon full-reference versification for compressed and roar-free images. Meanwhile, we show in this paper that it is likely to predict condensation parameters for a given cacophonous image earlier. Moreover, this prediction is fast and accurate enough for a conclusion undertaking on which coder parameter to set to reach OOP. In addition, in this place paper, we demonstrate that it is likely to predict the compression limits not only in OOP but also in allure neighborhood. This allows for endeavor more reliable conclusions. Prediction opportunities are shown for two optic quality versification and three typical modes of the BPG coder movement: 4:4:4, 4:2:2, and 4:2:0. One novel way of prognosis accuracy improvement established the joint use of two input limits is shown.

Author(s) Details:

Vladimir Lukin,
National Aerospace University, Kharkov, Ukraine.

Bogdan Kovalenko,
National Aerospace University, Kharkov, Ukraine.

Benoit Vozel,
University of Rennes, Lannion, France.

Please see the link here: https://stm.bookpi.org/FRAPS-V8/article/view/11676

Internet Data Bandwidth Prediction and Optimization in Educational Institutions| Chapter 8 | Research and Applications Towards Mathematics and Computer Science Vol. 2

 Today, computer network connectivity and dependability is the heart of every trade operation. This division discusses a solution to the issue of Internet frequency range optimization and predicting in Nigerian higher education institutions. The report still suggests utilizing Langrage's interpolation method to authorize the predictability of cyberspace bandwidth in the instructional institutions.  The research presents a unique graphical answer of effective real bandwidth (Mbps) and the associated satisfactory number of internet Users ('000) in the organizations.  The forecast enables us to monitor the actual cyberspace bandwidth and the satisfactory number of internet users as the consumer base grows.

Author(s) Details:

Michael F. Adaramola,
Electrical and Electronics Engineering Department, Lagos State University of Science and Technology, Ikorodu, P.M.B. 21,606, Ikeja, Lagos State, Nigeria.

Olugbemiga O. Shoewu,
Electronics and Computer Engineering Department, Lagos State University, Epe, Lagos State, Nigeria.

Please see the link here: https://stm.bookpi.org/RATMCS-V2/article/view/11111

Enhanced Antimalarial Therapy Using Mefloquine and Artesunate in the Treatment of Uncomplicated Plasmodium falciparum| Chapter 5 | New Advances in Medicine and Medical Science Vol. 8

 The current study proposed to investigate hopeful alternative regimens of artesunate-mefloquine with revised efficacy to deal with multidrug resistant P. falciparum. Malaria-accompanying mortality has wonderfully reduced during the last two decades because the introduction of artemisinin-located combination remedy (ACT). Data from the previously written article on the clinical efficiency of the three-day artesunate-mefloquine blend in the Thai-Myanmar borders during 2008-2009 were secondhand for pharmacokinetic-pharmacodynamic (PK/PD) analysis. All subjects were treated with a three-era combination of artesunate-mefloquine. Population PK-PD models were grown. Data from clinical remarks were used to validate the grown models. Based on mefloquine sensitivity, patient adherence, and dependent biomass, simulations of the clinical productiveness of alternative mefloquine regimens were completed activity. The created PK/PD models are well described accompanying data from dispassionate observations. For mefloquine-opposing P. falciparum, a three-day standard menu of artesunate-mefloquine is suitable (>50% efficacy) only when the level of bootlicker sensitivity was < 1.5-fold of the halt level (IC50 < 36 nM). For mefloquine-sensitive deadbeats with IC50 < 23.19 nM (0.96-fold), all procedures provided satisfactory productiveness. In the isolates with IC50 of 24 nM, regime-I is recommended. Curative situation criteria for mefloquine and artesunate were C336h (>408 ng.mL-1) or Cmax/IC50 (>130.1 g.m/M) and Cmax/IC50 (>381.2 g.m/M), individually. A three-day artesunate-mefloquine is suitable for mefloquine-opposing P. falciparum with IC50 of < 36 nM. Efficacy of the shortened dose procedures is satisfactory only when IC50 was < 24 nM.

Author(s) Details:

Teerachat Saeheng,
Center of Excellence in Pharmacology and Molecular Biology of Malaria and Cholangiocarcinoma, Chulabhorn International College, 99 moo 18, Phaholyothin Road, Thammasat University (Rangsit Campus), Klongneung, Klongluang District, Pathumthani-12121, Thailand.

Kesara Na-Bangchang,
Center of Excellence in Pharmacology and Molecular Biology of Malaria and Cholangiocarcinoma, Chulabhorn International College, 99 moo 18, Phaholyothin Road, Thammasat University (Rangsit Campus), Klongneung, Klongluang District, Pathumthani-12121, Thailand and Drug Discovery and Development Center, Office of Advanced Science and Technology, 99 Moo 18, Phaholyothin Road, Thammasat University (Rangsit Campus), Klongneung, Klongluang District, Pathumthani-12121, Thailand.

Please see the link here: https://stm.bookpi.org/NAMMS-V8/article/view/11073

Greenhouse Humidity Prediction and Control Models by Using Fuzzy Inference Systems | Chapter 06 | Research Developments in Science and Technology Vol. 6

 Greenhouse horticulture is a popular method for growing high-value crops with a large profit margin. To enable excellent management of environmental elements, fuzzy inference systems have been successfully used in prediction and control models. The goal of this research is to discover the many relationships in fuzzy inference systems now used for greenhouse modelling, prediction, and humidity management, as well as their change through time, in order to design more robust and understandable models. The major goal is to apply optimization techniques to find distinct linkages inside fuzzy inference systems, their configurations, and models, which are currently used for greenhouse forecast, control, and humidity modelling. The procedure is based on the PRISMA working guide. Four academic databases were combed through for a total of 93 questionnaires. Its bibliometric features have been retrieved and analysed, which helps the survey achieve its goal. Finally, it was discovered that combining Mamdani's fuzzy inference systems with optimization and fuzzy clustering methodologies, as well as tactics like model-based predictive control, assures great accuracy and interpretability.

Author(S) Details

Sebastian-Camilo Vanegas-Ayala
Faculty of Engineering, Universidad Distrital Francisco José de Caldas, Bogotá D.C. 11021-110231588, Colombia.

Julio Barón-Velandia
Faculty of Engineering, Universidad Distrital Francisco José de Caldas, Bogotá D.C. 11021-110231588, Colombia.

Daniel-David Leal-Lara
Faculty of Engineering, Universidad Distrital Francisco José de Caldas, Bogotá D.C. 11021-110231588, Colombia.

View Book:- https://stm.bookpi.org/RDST-V6/article/view/7012

Computational Rainfall Forecasting Models | Chapter 03 | Recent Recent Advances in Mathematical Research and Computer Science Vol. 8

 Rainfall forecasting is critical for planning and understanding rainfall variability, which aids agricultural management in making decisions. Rainfall forecasting is a key scientific concern in meteorology. Many academics sought to develop linear models to predict rainfall, but the revelation of nonlinearity in the nature of weather data has changed the attention to nonlinear rainfall prediction. Through a simple procedure utilising historical data, neural networks can automatically construct a forecasting model. This type of training allows the neural system to capture complex and non-linear correlations that are difficult to examine using traditional approaches. Neural networks are a novel type of computational technology that can be used to investigate the dynamics of a wide range of hydrological applications. An Artificial Neural Network (ANN) is a massively parallel, distributed information processing system with a highly flexible architecture that captures nonlinearity well. In hydrologic modelling, Artificial Neural Networks with one hidden layer are often utilised. The standard data pre-processing approach of Moving Average (MA) was combined with Artificial Neural Network as MA – ANN model in this research study to improve rainfall forecast in Tamilnadu. The MA – ANN hybrid model is a better tool than the Moving Average and ANN models when used separately, according to the experimental results.

Author(S) Details

M. Nirmala
Sathyabama Institute of Science and Technology, Chennai, India.

View Book:- https://stm.bookpi.org/RAMRCS-V8/article/view/5743

A Compendium of Precision Oncology: An Approach towards Unveiling the Unique and Individualized Reality of Cancer | Chapter 4 | Current Aspects in Pharmaceutical Research and Development Vol. 6

 The employment of a "one size fits all" strategy, that is, a comparable line of therapy or the use of the same drug/medicine, that too in a conventional method, to cure a certain condition, has been witnessed for several decades. Rather than specific human characteristics, this strategy is linked to unique genetic constitutions and processes. Precision oncology has the potential to improve patient prediction, treatment, and follow-up care dramatically. As a result of recent biotechnological developments, identifying the various and diverse biological components involved to carcinogenesis has only lately become possible. The main goal of this chapter is to bridge the gap between Precision Oncology research and clinical practise by raising awareness among scientists, clinicians, and the general public, as well as providing a detailed representation of all advances made in the field, taking into account almost all aspects and possibilities. It also gives a thorough picture of the genes linked to many cancer types, including overlapping genes that are involved in numerous cancers. Knowing a patient's genetic information and the genes that are susceptible to mutations can aid in accurate diagnosis and the delivery of the best customised medicines, reducing the risk of treatment failure, which is common in cancer therapy. This chapter covers all aspects of Precision Oncology and gives an overview of overlapping genes as well as genes with cancer-causing mutations. This gathered data could aid future clinical investigations and academic research aimed at predicting and preventing cancer.

Author(S) Details

Sohini Kulavi
Department of Biotechnology, Maulana Abul Kalam Azad University of Technology, West Bengal, India.

Sirshendu Chatterjee
Department of Biotechnology, Techno India University, West Bengal, India.

Chandreyi Ghosh
Department of Biotechnology, Techno India University, West Bengal, India.

Moumita Saha
Department of Biotechnology, Techno India University, West Bengal, India.

View Book:- https://stm.bookpi.org/CAPRD-V6/article/view/5156

Prediction of Thermal Strains in Fibre Reinforced Plastic Matrix by Discretisation of the Temperature Exposure History: An Advanced Study | Chapter 4 | Novel Perspectives of Engineering Research Vol. 3

 The significant unpredictability of the natural service environment makes predicting environmental effects on fibre reinforced polymers problematic. By discretizing the exposure history, this research proposes a method for predicting thermal strain distribution across the material lifespan. The predicted and experimentally measured strain distributions are well correlated, according to laboratory findings. It is demonstrated that by discretizing the exposure history, thermal stresses due to varying environmental temperature may be predicted.

Author(S) Details

E. K. Ngoy
Department of Chemical Engineering, Vaal University of Technology, South Africa.

View Book:- https://stm.bookpi.org/NPER-V3/article/view/4755

Study on Basic Principles of Mathematical Modelling of "Genotype-Environment” Interaction| Chapter 3 | Recent Progress in Plant and Soil Research Vol. 2

 V. A. Dragavtsev [1] established and examined 20 important issues for future quantitative technologies of eco-genetic growth of plant production and yield. The authors present mathematical modelling principles for each of these actions.

Author (s) Details

I. M. Mikhailenko
Laboratory of Information and Measuring Means, Institute of Agrophysics, St. Petersburg, 195220, Grazdansky Prospect, 14, Russia.


V. A. Dragavtsev
Plants Ecologic Physiology Laboratory, Institute of Agrophysics, St.-Petersburg, 195220, Grazhdansky Prospect 14, Russia.

View Book :-https://stm.bookpi.org/RPPSR-V2/article/view/2664

Position, Velocity and Acceleration Tracking Using Kalman Filter | Chapter 3 | Theory and Practice of Mathematics and Computer Science Vol. 9

 Time invariant models can be used to model constant acceleration movement in one, two, and three dimensions. The time invariant Kalman filter and the steady state Kalman filter are used to monitor position, velocity, and acceleration. The estimate and prediction that have been derived are accurate.

Author (s) Details

G. Tziallas
General Department, University of Thessaly, Lamia, Greece.

M. Adam
Department of Computer and Biomedical Informatics, University of Thessaly, Lamia, Greece.

N. Assimakis
General Department, National and Kapodistrian University of Athens, Greece.

A. Polyzos
Cross Software Solutions IKE, Piraeus, Greece.

View Book :- https://stm.bookpi.org/TPMCS-V9/article/view/850

Surface Roughness Investigation and Stress Modeling by Finite Element on Orthogonal Cutting of Copper| Chapter 6 | Recent Developments in Engineering Research Vol. 8

 To study the surface roughness of commercially pure copper, a modern non-contacting optical technique was used in this work. The finite element (FE) technique was applied by simulating the machining process to predict the stress during orthogonal cutting. The experimental work focused primarily on the impact of the cutting speed (N) and the feed rate (f) on copper surface roughness. To evaluate surface variations under different machining conditions, the scanning electron microscope (SEM) was used. The mathematical model of Johnson-Cook was adopted and employed to determine the material parameters. In addition, as a function of machining, the maximum Von-Mises stress was projected The circumstances. For the analysis and response surface methodology (RSM) to visualise the outcomes, a software package of code (ABAQUS/CAE) was used. The findings showed an important impact of the interaction of feed rate/cutting speed on surface roughness and copper stress from Von-Mises. By increasing the cutting speed, a 14 percent improvement in surface roughness was observed. A good agreement between the experimental and analytical results was observed. Circumstances. For the study and response surface methodology (RSM) to visualise the findings, a software package of code (ABAQUS/CAE) was used. The results showed a substantial impact on surface roughness and Von-Mises copper stress from the feed rate/cutting speed interaction. By increasing the cutting speed, a 14% increase in surface roughness was perceived. A strong agreement between experimental and analytical findings has been observed. The terms. For the analysis and response surface methodology (RSM) to visualise the effects, a software package of code (ABAQUS/CAE) was used. The results showed a major effect of the interaction between feed rate/cutting speed on surface roughness and copper stress from Von-Mises. By increasing the cutting speed, an increase of 14 percent in surface roughness was perceived. A strong agreement between experimental and analytical findings has been noted.

Author (s) Details

Hussein Zein
Mechanical Engineering Department, College of Engineering, Qassim University, Saudi Arabia and Mechanical Design and Production Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt.

Osama M. Irfan
Mechanical Engineering Department, College of Engineering, Qassim University, Saudi Arabia and Mechanical Engineering Department, Beni Suef University, Beni Suef 62764, Egypt.


View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/333

Descriptive Study One-Health Application for Women Using Decision Tree-Based Classifier | Chapter 1 | Recent Developments in Engineering Research Vol.4

 

This study focuses on the development of an eHealth application system using open-access datasets
from UCI Machine Learning Repository. This attempts to predict the onset of diabetes and chronic
kidney diseases grounding from the generated predictive models. Decision models are created using
C4.5, ID3 and CART algorithms with RapidMiner data science platform. Models incurred the highest
assessment are the bases of the developed system following Agile Software Development Life Cycle
Model. Easy access to healthcare workers through teleconsultation, diabetes and chronic kidney
disease (CKD) online diagnosis, and maternal care videos are possible with this study. Based on the
respondents’ response, the strongest point of the system was its portability, which earned the highest
average mean among categories of system evaluation. Thus, the system addresses the shortcomings
of healthcare in terms of distance and timeliness of treatment fostering an equal access to healthcare.

Author (s) Details

DR. Chona B. Sabinay
Biliran Province State University, Biliran, Philippines.

Maria Visitacion N. Gumabay
St. Paul University Philippines, Cagayan 3500, Philippines.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/263