A Clinical Decision Support Model for Predicting Avoidable Re-hospitalization of Breast Cancer Patients in Kenyatta National Hospital |Chapter 7 | Mathematics and Computer Science: Contemporary Developments Vol. 2

 

This study develops a Clinical Decision Support Model (DSM) to aid in assessing and recommending the discharge of a breast cancer patient from the hospital ward since the discharge problem is often overwhelming for clinicians to process at the point of care or in urgent situations. The model incorporates breast cancer patient-specific data that is well-structured, having been obtained from pre-study administered questionnaires and current evidence-based guidelines. The obtained dataset of the pre-study questionnaires is processed using data mining techniques to generate an optimal clinical decision tree classifier model. This model assists physicians in enhancing their decision-making process when discharging a patient, based on basic cognitive processes in medical thinking. This resulted in new, better-formed, and superior discharge outcomes. The model improves the quality of patient discharge assessments through a predictive discharging model outcome designed from individual unique risk attributes at the point of discharge. This enables timely detection of possible deterioration in health quality upon said discharge, which is noted as a major contributor to ward congestion as a result of re-hospitalization from poor discharge assessment that currently wholly relies on overwhelmed clinicians at the point of care or in urgent situations. The outcome of the implemented model is that it bridges the gap caused by less informed clinical discharge, and reinforces discharge decisions that ensure better treatment outcomes, thus reducing unforeseeable deterioration in the quality of health for discharged patients and surges in the mortality rate blamed on mistrusted discharge decisions. This paper is organized to start with a discussion of the breast cancer scourge and clinical knowledge for discharging patients, data mining techniques, the classifying model accuracy, and the Python web-based decision support model that predicts avoidable re-hospitalization of a breast cancer patient through an informed clinical discharging support model.

Author(s) Details

Christopher Oyuech Otieno
Department of Computer Science, University of Nairobi, Nairobi, Kenya.

Oboko Robert Obwocha
Department of Computer Science, University of Nairobi, Nairobi, Kenya.

Andrew Mwaura Kahonge
Department of Computer Science, University of Nairobi, Nairobi, Kenya.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v2/1022

Machine Learning Approaches for Gender Identification Using Fingerprint Biometrics: Role of Ridge Flow, Minutiae, and Multi-resolution DWT Features | Chapter 2 | New Horizons of Science, Technology and Culture Vol. 3

Fingerprints serve as an extraordinary biological signature that encapsulates both identity and subtle gender-specific characteristics through their complex structural and spectral properties. This study introduces an advanced computational framework for gender classification by synergistically combining three distinct fingerprint feature domains: ridge geometry for macroscopic pattern analysis, minutiae distribution for microscopic feature examination, and frequency decomposition through sophisticated wavelet transformation. The methodology processes high-resolution fingerprint images through a multi-stage analytical pipeline that precisely quantifies ridge length variations (capturing minimum, maximum, and average measurements), systematically enumerates minutiae points (including ridge terminations and bifurcations), and performs multi-resolution spectral analysis using a six-level discrete wavelet transform to isolate discriminative frequency components. Validated on a carefully balanced dataset of 100 subjects (50 males and 50 females), the extracted features are intelligently organised into gender-specific clusters through an optimised stratification process, yielding an impressive overall classification accuracy of 88.28%. Notably, the right ring finger demonstrated exceptional diagnostic performance with 95.46% accuracy, a finding consistent with established embryological research on androgen-influenced ridge formation patterns. The technical sophistication of this approach lies in its ability to achieve high accuracy without relying on computationally intensive deep learning architectures, making it particularly suitable for real-world applications where efficiency is crucial. Beyond its immediate results, this research opens promising avenues for future investigation, including the incorporation of additional discriminative features such as sweat pore distribution and three-dimensional ridge curvature analysis, as well as expansion to larger, more diverse demographic datasets to enhance generalizability. By demonstrating that fingerprints contain a wealth of underutilised gender information, this work makes a significant contribution to the field of soft biometrics, with important implications for forensic science, security systems, and demographic research, while simultaneously establishing a foundation for future exploration of ancillary biometric markers embedded within fingerprint patterns. The balanced integration of robust methodology, empirical validation, and practical applicability positions this research as a valuable reference point for both academic investigation and applied biometric solutions, bridging the gap between theoretical innovation and real-world implementation in the evolving landscape of gender classification technologies.

 

Author(s) Details

Sayed Abulhasan Quadri
SECAB Institute of Engineering and Technology, Vijayapura, India.

 

Chandrakant P. Divate
SECAB Institute of Engineering and Technology, Vijayapura, India.

Tabasum Guledgudd
SECAB Institute of Engineering and Technology, Vijayapura, India.

Sayed Abdulhayan
PACE College, Mangalore, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/nhstc/v3/5745

Machine Learning for Maximizing the Detection Rate of Diabetic Retinopathy Using Image Processing | Chapter 5 | Scientific Research, New Technologies and Applications Vol. 2

Diabetic retinopathy (DR) is a serious diabetes condition that harms the retina and can result in blindness if not treated early. DR is diagnosed by examining the retina pictures of the eye. However, manually grading photos to determine the severity of DR disease needs a significant amount of resources and time. Automated systems give accurate results along with saving time. Ophthalmologists may find it useful in reducing their workload. The aim of the study is to present an approach to maximize the DR detection rate. The proposed work presents the method to correctly identify the lesions and classify DR images efficiently. Blood leaking out of veins forms features such as exudates, microaneurysms, and hemorrhages, on the retina. Image processing techniques assist in DR detection. Median filtering is used on gray-scale converted images to reduce noise. The features of the pre-processed images are extracted by textural feature analysis. Optic disc (OD) segmentation methodology is implemented for the removal of OD. Blood vessels are extracted using haar wavelet filters. KNN classifier is applied for classifying retinal images into diseased or healthy. The proposed algorithm is executed in MATLAB software and analyzed results with regard to certain parameters such as accuracy, sensitivity, and specificity. The outcomes prove the superiority of the new method with a sensitivity of 92.6%, specificity of 87.56%, and accuracy of 95% on the Diaretdb1 database. The study concluded that the new method is capable of identifying true lesions and rejecting false ones. The suggested technique provides better results in comparison with the state-of-the-art technique.

 

Author (s) Details

Ujwala W. Wasekar
Department of Computer Science, Sri Guru Gobind Singh College, Chandigarh, India.

 

R. K. Bathla
Department of Computer Science, Desh Bhagat University, Mandi Gobindgarh, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/srnta/v2/1801

New Classification Model for Chemical Elements Based on the Characteristic Graph of the Atom | Chapter 8 | Chemical and Materials Sciences: Developments and Innovations Vol. 6

The classification of chemical elements has long been a major challenge and a rich source of knowledge in chemistry. After several attempts, the current classification has been accepted by the scientific world. The periodic table is one of the most profound and unifying concepts in modern science. However, beyond Mendeleev’s famous table, there have been several models for this classification: the spiral model, the tabulated model, Pierre Demers' pyramid model, etc. This chapter explores a new classification model based on the characteristic graph of the atom presented in previous publications. It elucidates the periodic and ordered classifications of the elements. Three types of classification emerge from the graph: periodic, ordered and hybrid. The hybrid classification resembles the periodic table, but incorporates elements from the ordered form. In this model, all the normal elements are arranged on the right and the transition elements on the left. In addition, new methods of illustration, such as condensed ‘order’ and ‘period’ tables, were introduced. These results reveal that the classification extends beyond periodicity, encompassing an ordered classification of chemical elements. This research sheds light on the various approaches to the classification of elements and opens the way to new explorations in the field of chemistry.

 

 

Author (s) Details

Ousmane Barry
Department of Chemistry, University Gamal Abdel Nasser of Conakry, Conakry, Guinea.

 

Mamadou Yaya Balde
Department of Chemistry, University Gamal Abdel Nasser of Conakry, Conakry, Guinea and Environmental Research Institute of Guinea (IREG), DGRS / MESRSI, Conakry, BP: 1615, Guinea.

 

Souleymane Balde
Department of Chemistry, University Gamal Abdel Nasser of Conakry, Conakry, Guinea.

 

Lamine Kaba
Department of Chemistry, University Gamal Abdel Nasser of Conakry, Conakry, Guinea.

 

Aboubacar Safie Sylla
Department of Chemistry, University Gamal Abdel Nasser of Conakry, Conakry, Guinea.

Please see the book here:- https://doi.org/10.9734/bpi/cmsdi/v6/2407

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

Sequential Mathematical Programming With \(\zeta\)-Analysis for HESVM | Chapter 9 | Mathematics and Computer Science: Contemporary Developments Vol. 7

Vapnik's quadratic programming (QP)-based support vector machine (SVM) is a state-of-the-art powerful classifier with high accuracy while being sparse. Moving one step further in the direction of sparsity, Vapnik proposed one more SVM that uses linear programming (LP) for the cost function. This machine, compared with the complex QP based one, is more sparse but offers similar accuracy, which is essential to work on any large dataset. However, further sparsity is optimum for computational savings as well as to work with very large and complicated datasets. Producing even more sparsity without reducing generalization capability of a detector is extremely challenging. In this dimension, we apply a distinct sequence of Mathematical Programming followed by slack variable analysis that leads to an exceptionally fast and accurate SVM based detector. Being immensely sparse and optimally complex, this Highly Effcient SVM (HESVM) can expertly work on very large and noise-effected complicated data. Experiments on Benchmark data shows that HESVM requires kernel execution as little as 6.8% of the classical QP based SVM while producing nearly the same classification accuracy on test data and demanding 42.7, 27.7 and 46.6% that of other three executed cutting-edge heavy-sparse machines while posing similar classification accuracy. It also claims the least Machine Accuracy Cost (MAC) value among all of these machines though producing very similar generalization performance, which is calculated statistically using the term Generalization Failure Rate (GFR). Being quite practical for contemporary technological development, it has become indispensable for optimum manipulation of the troublesome massive, and diffcult data.

 

Author (s) Details

 

Rezaul Karim
Uttara University, Bangladesh.

 

Amit Kumar Kundu
Uttara University, Bangladesh.

 

Ali Ahmed Ave
Uttara University, Bangladesh.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v7/2725

Data-Driven Approaches to Cancer Incidence Classification: Mining Health Data from Bhopal Gas Tragedy | Chapter 7 | Mathematics and Computer Science: Contemporary Developments Vol. 10

Cancer remains one of the most formidable health challenges globally, and India has seen a steady rise in cancer incidence rates over the years. The ability to effectively analyze large-scale cancer datasets is crucial for understanding disease patterns, improving diagnosis, and guiding public health interventions. In this research, advanced data mining techniques were leveraged, specifically classification and clustering, to examine cancer incidence patterns in the aftermath of the Bhopal Gas Tragedy, a catastrophic industrial disaster. Our study focuses on comparing the incidence rates of Tobacco-Related Cancer (TCR) and Non-Tobacco-Related Cancer (Non-TCR) in two distinct regions of Bhopal, which were partitioned after the tragedy.

Using over 40 years of data from the Population-Based Cancer Registry (PBCR) of Bhopal, data mining methodologies were applied to uncover hidden patterns and correlations within the cancer incidence data. The study seeks to explore the long-term impact of environmental exposure on cancer prevalence, particularly the difference in cancer types between the two regions. By employing the WEKA tool, a well-established platform for machine learning and data mining, cancer cases were systematically classified and significant insights were extracted from the data.

Our findings reveal notable differences in cancer incidence between the two regions, offering insights into how environmental factors, lifestyle choices, and socio-economic conditions may influence cancer development. The study highlights the value of data-driven approaches in health care, particularly as a decision support system for medical analysts. These insights not only contribute to the understanding of cancer epidemiology in Bhopal but also underscore the importance of continuous health monitoring in populations affected by industrial disasters. Furthermore, the methodology applied in this study serves as a foundation for future research aimed at improving cancer prevention, early detection, and personalized treatment strategies in similar contexts.

 

Author (s) Details

 

Sanjeev Gour
Department of Computer Science, Medicaps University, Indore, India.

 

Rajendra Randa
Department of Computer Science, Medicaps University, Indore, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v10/3147

Findings of Radar Cross Section Using AI and ML | Chapter 4 | Design and Simulation of Classical and Quantum Antennas in Gigahertz and Terahertz: Applications in Radar Using Deep Neural Techniques

This study aimed to discuss the monostatic and bistatic radar cross-section of an object with the help of simulation tools and to relate the findings with the radar cross-section definition using MATLAB. A Perfect Electrical Conductor (PEC) cylinder and sphere were used as an object for the simulation of the Radar Cross Section computed in CST Studio Suite software. The incident wave selected is normal and plane with TE polarization. The setup here discusses the different findings of the Radar Cross Section at different parameters like azimuth, elevation, and frequency and compares it with the model based on the theoretical formula. After simulation, results are compared with the theoretical formula using the modelling in MATLAB software.

 

Author (s) Details

 

R. Shandilya
Department of Electronics and Communication Engineering, Netaji Subhash University of Technology (East Campus), New Delhi, India.

 

Dr. Manisha Khulbe
Netaji Subhash University of Technology Delhi (East Campus), New Delhi, India.

 

A. Jain
Department of Electronics and Communication Engineering, Netaji Subhash University of Technology (East Campus), New Delhi, India.

 

S. Kaushik
Department of Electronics and Communication Engineering, Netaji Subhash University of Technology (East Campus), New Delhi, India.

 

R. Yadav
Department of Electronics and Communication Engineering, Netaji Subhash University of Technology (East Campus), New Delhi, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-49238-41-1/CH4

Chemical and Biological Profiling of Narmada River: A Random Forest Model-Based Water Quality Analysis | Chapter 2 | Recent Developments in Chemistry and Biochemistry Research Vol. 9

Water quality is a critical indicator of the health of aquatic ecosystems and the sustainability of water resources for human use. In this study, the water quality of the Narmada River was analyzed by examining key physicochemical and biological parameters that impact the river's ecosystem. Utilizing a secondary dataset spanning from 1990 to 2012, collected from the Hoshangabad district of Madhya Pradesh, seven water quality parameters were evaluated, including pH, dissolved oxygen, biochemical oxygen demand, and nitrate concentrations. The objective of this study is to assess the condition of the Narmada River water in relation to the Surface Water Quality Standards for Indian Rivers and to provide insights into the degradation trends over time.

To achieve this, a Random Forest algorithm, a robust machine learning technique, was implemented using the RapidMiner analytical tool to classify and predict water quality. This model effectively identifies patterns in the dataset, enabling a thorough understanding of the factors contributing to the declining water quality. Our findings indicate that the river’s water quality has steadily deteriorated, primarily due to increasing domestic sewage and industrial effluent discharge into the river, particularly in urban areas along its course. The results of this analysis present a critical alert to environmental policymakers and water resource managers, emphasizing the urgent need for improved water treatment facilities and regular monitoring protocols to mitigate further pollution.

This study highlights the efficacy of data-driven approaches like Random Forest in environmental monitoring and underscores the importance of integrating machine learning techniques with traditional water quality assessments to enable more informed decision-making for sustainable water management.

 

 

Author (s) Details

Mamta Gour
Department of Chemistry, Medicaps University, Indore, India.

 

Sanjeev Gour
Department of Computer Science, Medicaps University, Indore, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/rdcbr/v9/3146

A Comparative Study of Naive Bayes and Enhanced Random Forest Algorithms in Spam Detection | Chapter 4 | Science and Technology: Developments and Applications Vol. 3

Online Social Networks (OSNs) or simply social Media such as Facebook, X (Formerly known as Twitter), Instagram have recently emerged as one of the crucial platforms in human communication worldwide that allows individual users to send messages, build friendships, share perceptions and voice out their opinions, and get inspired. In this era of technology, online social media has become a rapidly growing phenomenon. The main social media platforms such as Instagram, Facebook, and X (formerly known as Twitter) connect and unite people globally, as quickly as any other communication medium. The growth of social media is expected to increase tremendously in the future. Online social media users generate and consume information independently. Many domains recognize the vital role of analyzing social media data, as this can improve operations and enable organizations to stay competitive. Nowadays, people spend a significant amount of time on social media platforms. However, the growing popularity of social media has also led to an increase in spamming and hacking activities. Cyber-criminals often spam and hack through external phishing sites or malware downloads, which pose significant security risks and poor user experience in social media networks. To combat the issue of spam, several methods have been proposed, but there is still no perfect, effective solution for detecting spam with high accuracy. In this chapter, we propose a spam detection approach using Naive Bayes (NB) and Enhanced Random Forest (ERF) classifiers. The Naive Bayes classifier applies Bayes' theorem with feature independence assumptions, while the Enhanced Random Forest improves on the traditional Random Forest with optimized feature handling. In order to assess the effectiveness of the proposed model, metrics such as accuracy, precision, recall and F1 scores are used to compare the model’s performance in recognizing spam messages. In conclusion, this study emphasizes that Enhanced Random Forest is more balanced in terms of efficiency and performance for social network spam detection and it can perform well with lower computational requirements.

 

Author (s) Details

 

M. Arunkrishna
PG & Research Department of Computer Science, Christhu Raj College (Affiliated to Bharathidhasan University), Tiruchirappalli - 620 012, Tamil Nadu, India.

 

B. Mukunthan
Department of Computer Science, Jairams Arts and Science College (Affiliated to Bharathidhasan University), Karur - 639003, Tamil Nadu, India.

B. Senthilkumaran
PG & Research Department of Computer Science, Christhu Raj College (Affiliated to Bharathidhasan University), Tiruchirappalli - 620 012, Tamil Nadu, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/stda/v3/3442

Leveraging Artificial Intelligence for Brain Tumor Classification | Chapter 3 | Science and Technology: Developments and Applications Vol. 5

Brain cancer, characterized by the uncontrolled growth of abnormal cells in the brain, is a severe neurological disorder that can be either primary or metastatic. Early detection and accurate classification of brain tumors are crucial for effective management and improved patient outcomes. Brain tumors are classified based on various factors such as their nature, cell origin, grade, and progression stage. Traditional methods of detection, segmentation, and classification are time-consuming, require extensive expertise, and are prone to errors. Artificial Intelligence (AI), including its subtypes Machine Learning (ML) and Deep Learning (DL), holds promise for improving accuracy and expediting detection. AI-based technologies can be categorized into binary classification (e.g., determining whether a tumor is malignant or benign) and multimodal classification (e.g., categorizing tumors into various types). Most AI applications in brain tumor classification focus on radiological images, particularly Magnetic Resonance Imaging (MRI).

AI-based technologies must achieve high accuracy to be effectively integrated into real-life clinical practice. This chapter summarizes the current advances in AI techniques for brain tumor classification, highlighting their potential and ongoing challenges.

 

Author (s) Details

 

Adham Al-Rahbi
Sultan Qaboos University, College of Medicine and Health Sciences, Muscat, P.O. Box-35, Postal Code 123, Sultanate of Oman.

Tariq Al-Saadi
Department of Neurosurgery, Khoula Hospital, Muscat, P.O. Box-35, Postal Code 123, Sultanate of Oman and Department of Neurosurgery, Cedars-Sinai Medical Centre, 8700 Beverly Blvd, Los Angeles, CA 90048, USA.

 

Please see the book here:- https://doi.org/10.9734/bpi/stda/v5/1964

Management of Hemorrhagic Shock According to the Revised “Physiological Classification” - Update 2024 | Chapter 11 | New Visions in Medicine and Medical Science Vol. 4

 Hemorrhagic shock management is based on a timely, rapid, definitive source control of bleeding/s and on blood loss replacement. Stopping the hemorrhage progressing from any named and visible vessel is the main stem fundamental praxis of therapeutic efficacy, ultimately an essential, obligatory, life-saving step. Blood loss replacement serves mainly the purpose of preventing ischemia/reperfusion toxemia and optimizing tissue oxygenation and microcirculation dynamics. The “revised physiological classification” is the only classification that suits timely intervention, tactics like titrated hypotensive resuscitation and iatrogenic vasoconstriction, and strategies like titrated-to-response anesthesia and damage control surgery. Timing and approach to management should follow the classification, from onset of the hemorrhage to cardiac arrest by exsanguination. In hypotensive shock, the body’s response to a fluid load test is the diriment cut-off information to have for distinguishing between compensation and progression, between the time for adopting conservative treatment and preparing for surgery or rushing to the theater for rapid bleeding source control. Up to 20% of the total blood volume is to be given stat to refill the unstressed venous return volume. Progressing hypotensive shock is a danger scenario warranting rapid source control. In critical level of shock with signs indicating critical physiology of imminent/impending cardiac arrest the balance between the life-saving reflexes stretched to the maximum and the insufficient distal perfusion (blood, oxygen, and substrates) stays in a liable and delicate equilibrium, susceptible to any minimal change or interfering variable; minimal interference with this actual physiological equilibrium and a rapid safe general anesthesia and surgery remain crucial for survival. Source control of any named and visible vessel must be fast and effective.

This is accomplishable rapidly and efficaciously only by a direct ingress for source control, which is a direct limb ingress, a crush laparotomy if the bleeding is coming from an abdominal +/- proximal lower limb, a rapid sternotomy if coming from mediastinum and an anterolateral thoracotomy if the bleeding is coming from chest +/- proximal upper limbs. Neck and limb bleedings require direct source access but bleeding from injury to the groin folds and thoracic outlet/neck base necessitates often of double compartment incision for proximal and distal control. In cardiac arrest by exsanguination, source control and heart refilling must be effected synchronously to a direct heart access and control via sternotomy. The priority and the core of the physiological rescue remains the rapid restoration of a sufficient venous return and left diastolic filling volume to make enough pressure to allow the heart pumping it back into systemic circulation either by open massage via sternotomy or anterolateral thoracotomy, or spontaneously after aortic clamping in the chest or in the abdomen. Without first stopping the bleeding and refilling the heart, any resuscitation of advanced progressive HS or cardiac arrest by exsanguination is an exercise doomed to failure. Extracorporeal life support and induced hypothermia under sternotomy and direct vision is the last ditch.

Author(s) Details:

Fabrizio G. Bonanno,
Department of Surgery, Polokwane Provincial Hospital, Cnr Hospital & Dorp Street, Polokwane-0700, South Africa.


Please see the link here: https://stm.bookpi.org/NVMMS-V4/article/view/14009

An Advanced Study on the Recognition of Coronavirus Disease (COVID-19) Using Deep Learning Network | Chapter 12 | Recent Updates in Disease and Health Research Vol. 1

A new virus disease spread last December in Wuhan city in China for uncertain reasons and it was named by the World Health Organization (WHO) as COVID-19.  The Coronavirus disease (COVID-19) has had an incredible influence in the last few years. It causes thousands of deaths around the world. This makes a rapid research movement to deal with this new virus. As a computer science, much technical research has been done to tackle it by using image processing algorithms. This study was conducted by experimenting on the recent dataset, the Kaggle dataset of COVID-19 X-ray images, and used the ResNet50 deep learning network with 5 and 10-fold cross-validation. This study introduces a method based on deep learning networks to classify COVID-19 based on X-ray images. This result is encouraging to rely on to classify the infected people from the normal. The experiment results show that 5 folds give more effective results than 10 folds with an accuracy rate of 97.28%. Henceforth, deep learning can offer significant results in recognizing the virus in its earliest stages. Future studies can be conducted on different architectures of deep learning using different datasets which helps to recognize the infected people in earlier stages and save their lives.

Author(s) Details:

Ashwan A. Abdulmunem,
College of Computer Science and Information Technology, University of Kerbala, Iraq.

Zinah Abulridha Abutiheen,
College of Computer Science and Information Technology, University of Kerbala, Iraq.

Hiba J. Aleqabie,
College of information Technology (Engineering), Al-Zahraa University for Women, Kerbala, Iraq.

Please see the link here: https://stm.bookpi.org/RUDHR-V1/article/view/13233

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

Development of Protected Underwater Landscapes Network's in the Black and Azov Seas | Chapter 8 | Emerging Issues in Environment, Geography and Earth Science Vol. 5

 This division concerned with the development of a inclusive methodology to demonstrate a MPAs (marine protected districts) network in the Black Sea and Azov Expanse region. The Russian terrestrial science school has made important progress in the development of methods of complex terrestrial studies of underwater countrysides and their classifications. The MPAs network is a sophisticated system of countryside zoning and plan, defining their sustainability, variability, and geo-environmental state, and taking into consideration an evaluation of the undersea countryside's degradation over the past centennial. These claims are supported by scientific evidence. In accordance with our classification, terrestrial zoning of the Azov-Black Lake basin—the MBNCs zoning map was founded with scale of 1:1,250,000. Established the developed classification and design of the Black and Azov Seas MBNCs with various hierarchical levels of the physiographic edging, following methodology could be submitted for the development of an ecological network for MPAs. In accordance with the ecosystem approch projected by MPAs, a map was created. In the Azov-Dark Sea physiographic country example of disgraced MBNCs requiring necessary conservation measures are Zernov Phyllophora Field, oyster beds; MBNCs complicated mussels biocenoses (Mytilus galloprovincialis), MBNCs, employed biocenoses eelgrass (Zostera noutii); MBNCs Mediterranean eel residences (Conger conger), MBNCs of species listed operating at a loss Book, and estuary zone Kuban waterway Kuban to restore communities of commercial fish variety, the MBNC, commonly used types of sturgeon for augmenting, wintering, training, and transit. This study showed that the experimental basis for the justification of locating a network of MPA is a complex project including countryside zoning and mapping, habitual by the analysis of the main elements of the MBNCs indicators that decide their variability, stability, and geo-environmental condition.

Author(s) Details:

Natalia Mitina,
Institute of Water Problems of the Russian Academy of Sciences, Moscow 119333, Russia.

Katerina Chuprina,
Institute of Water Problems of the Russian Academy of Sciences, Moscow 119333, Russia.

Please see the link here: https://stm.bookpi.org/EIEGES-V5/article/view/12704

Determination of Performance Parameters of Insulated Diesel Engine with Plastic Oil with Supercharging| Chapter 8 | Technological Innovation in Engineering Research Vol. 8

 This paper focuses on elective fuel innovation for diesel motor and ecological insurance. Squander plastics are not biodegradable. They cause ecological debacles. A great many cows pass on each year in the wake of consuming these plastics. They produce poisonous exhaust, when they are scorched. Be that as it may, when these plastics are changed over into plastic oil by the course of pyrolysis, plastic oil can be utilized in diesel motors, as the properties of plastic oil are tantamount with diesel fuel. With regards to quick consumption of non-renewable energy sources, increment of monetary weight on agricultural nations because of increment of cost of import of unrefined oil and increment of contamination levels with petroleum products, the quest for elective powers has become appropriate. Vegetable oils and alcohols are significant substitutes for diesel fuel, as they are sustainable in nature. However vegetable oils have practically identical properties with diesel fuel, be that as it may, they have high consistency and low unpredictability causing burning issues in diesel motors. Alcohols have high unpredictability however low Cetane number (a proportion of ignition quality in diesel motor). Plastic oil got from squander plastic gathered from flotsam and jetsam by the course of pyrolysis has equitant calorific worth with diesel fuel. Notwithstanding, its thickness is higher than diesel fuel calls for low intensity dismissal (LHR) diesel motor. The idea of LHR diesel motor is to limit the intensity stream to the coolant there by increment of warm proficiency. This LHR motor is helpful for consuming high thick and low calorific worth energizes. LHR motor comprised of fired covered chamber head motor. Theperformance boundaries of brake warm productivity (BTE), fumes gas temperature (EGT), volumetric proficiency and coolant load were assessed at different upsides of brake mean successful strain (BMEP) of the motor. Brake explicit energy utilization not set in stone at full burden activity of the motor with differed infusion timing. To further develop execution of the motor, supercharging was applied at a tension of 0.8 bar. Information was contrasted and perfect diesel procedure on customary motor (CE). Infusion timing was changed with an electronic sensor. The exhibition of the two forms of the motor improved with supercharging of the motor.

Author(s) Details:

Mohammad Attalique Rabbani,
Department of Mechanical Engineering, Osmania University, Hyderabad, India.

M. V. S. Murali Krishna,
Department of Mechanical Engineering, Chaitanya Bharathi Institute of Technology, Hyderabd, India.

P. Usha Sree,
Department of Mechanical Engineering, University College of Engineering, Osmania University, India.

Please see the link here: https://stm.bookpi.org/TIER-V8/article/view/8255

A Novel Approach to Analyze Pranayama through Machine Learning Techniques | Chapter 07 | New Approaches in Engineering Research Vol. 12

 Pranayama (breathing exercises) is an integral part of yoga practise. When practising pranayama, it's important to keep track of how many cycles you've done. The most important thing is to keep track of how long someone is inhaling and exhaling. Maintaining a proper ratio throughout the inhalation: exhalation cycle is critical. The counting procedure is so taxing for a beginning that it is difficult to retain awareness of the breathing process, and the standard of pranayama practise suffers as a result. The goal of the proposed system is to develop a new method for analysing the quality of Pranayama using machine learning techniques. The main goal of the proposed project is to develop an application that can count inhalations and exhalations. It guarantees that users receive feedback based on their breathing and exhalation patterns. It analyses each breath and exhalation pattern and uses Clustering algorithms to classify inhalation and exhalation. This paper's proposed structure aids in improving the uniformity of pranayama. As a result, respiratory performance improves, which reduces melancholy and anxiety. To test the validity of breathing patterns, the KNN, SVM, Random Forest, and Decision Tree algorithms are used.

Author(S) Details

A. Parkavi
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.

V. Sangeetha
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.

G. R. Amith
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.

B. K. Harini
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.

M. Supriya
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.

K. N. Tejasvini
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.

View Book:- https://stm.bookpi.org/NAER-V12/article/view/3841

The Maximum Euclidean Distance in a Class Defines the Boundary of Neighborhood and Leads to a New Machine Learning Algorithm | Chapter 06 | Recent Advances in Mathematical Research and Computer Science Vol. 7

 When we are given a data set, we assign a class to each data point based on the values and/or characteristics of attributes. In machine learning, the k-Nearest Neighbor (kNN) method is a relatively basic and powerful tool for performing this. It is built on the concept of data points belonging to the same class being neighbours. In kNN, one evaluates the Euclidean distances of the test data or unknown data from all the data points of all the classes in the training data to discover the class to which it belongs. The class to which test data or unknown data is closest the most number of times, out of the k nearest distances, where k is any number higher or equal to 1, is the class assigned to the test data or unidentified data. In this chapter, I offer an alternative to kNN, which I refer to as the ANN technique (Alternative Nearest Neighbor). The defining aspect of ANN that distinguishes it from kNN is the concept of neighbour. The unknown data is neighboured to the class whose maximum Euclidean distance from its data points is smaller than or equal to the maximum Euclidean distance between all of the class's training data points in ANN. As a result, each unknown data will always receive a unique solution from ANN. The solution in kNN, on the other hand, may vary depending on the number of nearest neighbours k. As a result, the performance of kNN may vary as k is changed. This is not the case with ANN, whose performance is tailored to a given training dataset.

The fundamental goal for developing the ANN machine learning method was to improve on the traditional kNN method by making it independent of the parameter k and eliminating the necessity for the user to select k neighbours based on experience or other criteria.

The ANN produces a 100 percent accurate result for the training data [1] used in this article.


Author(S) Details

Pushpam Kumar Sinha
Department of Mechanical Engineering, Netaji Subhas Institute of Technology, Amhara, Bihta, Patna, India.

View Book:- https://stm.bookpi.org/RAMRCS-V7/article/view/5491

A Novel Approach to Analyze Pranayama through Machine Learning Techniques | Chapter 7 | New Approaches in Engineering Research Vol. 12

Pranayama (breathing exercises) is an integral part of yoga practise. When practising pranayama, it's important to keep track of how many cycles you've done. The most important thing is to keep track of how long someone is inhaling and exhaling. Maintaining a proper ratio throughout the inhalation: exhalation cycle is critical. The counting procedure is so taxing for a beginning that it is difficult to retain awareness of the breathing process, and the standard of pranayama practise suffers as a result. The goal of the proposed system is to develop a new method for analysing the quality of Pranayama using machine learning techniques. The main goal of the proposed project is to develop an application that can count inhalations and exhalations. It guarantees that users receive feedback based on their breathing and exhalation patterns. It analyses each breath and exhalation pattern and uses Clustering algorithms to classify inhalation and exhalation. This paper's proposed structure aids in improving the uniformity of pranayama. As a result, respiratory performance improves, which reduces melancholy and anxiety. To test the validity of breathing patterns, the KNN, SVM, Random Forest, and Decision Tree algorithms are used.

Author (S) Details

A. Parkavi
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.

V. Sangeetha
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.

G. R. Amith
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.

B. K. Harini
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.

M. Supriya
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.

K. N. Tejasvini
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.

View Book :- https://stm.bookpi.org/NAER-V12/article/view/3841

Decision Tree-based Machine Learning Algorithms to Classify Rice Plant Diseases: A Recent Study | Chapter 5 | Advanced Aspects of Engineering Research Vol. 16

 One of the most vital nutrients for humans on the planet is rice. India and China are two of the most rice-dependent nations on the planet. A variety of factors influence the production of this crop, including soil, water supply, pesticides employed, time period, and disease infection. One of the most important factors impacting rice yield and quality is rice plant disease (RPD). Farmers are always faced with the issue of recognising the type of rice plant disease and adopting appropriate corrective treatment. The most prevalent diseases that affect rice plants are Bacterial Leaf Blight (BLB), Brown Spot (BS), and Leaf Smut (LS). It is particularly difficult to diagnose this disease since the contaminated leaf must be processed by the human eye. In this chapter, we used machine learning techniques to describe and classify the RPD. To collect data on infected rice plants, we used the UCI Machine Learning repository. There are 120 photos of contaminated rice plants in the data set, containing 40 BLB images, 40 BS images, and 40 LS images. In the studies, decision tree-based machine learning algorithms RandomForest, REPTree, and J48 were used. To extract numerical characteristics from the infected photos, we used ColorLayoutFilter, which is provided by WEKA. 65 percent of the data is used for training and 35 percent is used for testing in the experimental analysis. According to the trials, the Random Forest algorithm works remarkably well in predicting RPD.

Author (S) Details

R. Sahith
CSE, CVR College of Engineering, Hyderabad, India.

P. Vijaya Pal Reddy
CSE, Matrusri Engineering College, Hyderabad, India.

Satyanarayana Nimmala
CSE, CVR College of Engineering, Hyderabad, India.

View Book :- https://stm.bookpi.org/AAER-V16/article/view/1990