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Machine Learning Algorithms for Stroke Risk Prediction Leveraging on Explainable Artificial Intelligence Techniques (XAI)
Stroke poses a significant global health challenge, contributing to widespread mortality and disability. Identifying predictors of stroke risk is crucial for enabling timely interventions, thereby reducing the increasing impact of strokes. This research addresses this imperative by employing Explainable Artificial Intelligence (XAI) techniques to pinpoint stroke risk predictors. To bridge existing gaps, six machine learning models were assessed using key performance metrics. Utilising the Synthetic Minority Over-sampling Technique (SMOTE) to minimize the impact of the imbalanced nature of the dataset used in this research, the Random Forest algorithm emerged as the most effective among the algorithms with an accuracy of 94.5%, AUC-ROC of 0.95, recall of 0.96, precision of 0.93, and an F1 score of 0.95. This study explored the interpretation of these algorithms and results using Local Interpretable Model-agnostic Explanations (LIME) and Explain Like I'm Five (ELI5). With the interpretations, healthcare providers can gain insight into patients' stroke risk predictions. Key stroke risk factors highlighted by the study include Age, Marital Status, Glucose Level, Body Mass Index, Work Type, Heart Disease, and Gender. This research significantly contributes to healthcare and healthcare informatics by providing insights that can enhance strategies for stroke prevention and management, ultimately leading to improved patient care. The identified predictors offer valuable information for healthcare professionals to develop targeted interventions, fostering a proactive approach to mitigating the impact of strokes on individuals and the healthcare system. 2024 IEEE. -
Machine Learning and Artificial Intelligence Techniques for Detecting Driver Drowsiness
The number of automobiles on the road grows in lockstep with the advancement of vehicle manufacturing. Road accidents appear to be on the rise, owing to this growing proliferation of vehicles. Accidents frequently occur in our daily lives, and are the top ten causes of mortality from injuries globally. It is now an important component of the worldwide public health burden. Every year, an estimated 1.2 million people are killed in car accidents. Driver drowsiness and weariness are major contributors to traffic accidents this study relies on computer software and photographs, as well as a Convolutional Neural Network (CNN), to assess whether a motorist is tired. The Driver Drowsiness System is built on the Multi-Layer Feed-Forward Network concept CNN was created using around 7,000 photos of eyes in both sleepiness and non-drowsiness phases with various face layouts. These photos were divided into two datasets: training (80% of the images) and testing (20% of the images). For training purposes, the pictures in the training dataset are fed into the network. To decrease information loss as much as feasible, backpropagation techniques and optimizers are applied. We developed an algorithm to calculate ROI as well as track and evaluate motor and visual impacts. 2022 Boppuru Rudra Prathap et al., published by Sciendo. -
Machine Learning and Artificial Intelligence Techniques for Detecting Driver Drowsiness
The number of automobiles on the road grows in lock-step with the advancement of vehicle manufacturing. Road accidents appear to be on the rise, owing to this growing proliferation of vehicles. Accidents frequently occur in our daily lives, and are the top ten causes of mortality from injuries globally. It is now an important component of the worldwide public health burden. Every year, an estimated 1.2 million people are killed in car ac-cidents. Driver drowsiness and weariness are major con-tributors to traffic accidents this study relies on computer software and photographs, as well as a Convolutional Neural Network (CNN), to assess whether a motorist is tired. The Driver Drowsiness System is built on the Multi-Layer Feed-Forward Network concept CNN was created using around 7,000 photos of eyes in both sleepiness and non-drowsiness phases with various face layouts. These photos were divided into two datasets: training (80% of the images) and testing (20% of the images). For training purposes, the pictures in the training dataset are fed into the network. To decrease information loss as much as feasible, backpropagation techniques and optimizers are applied. We developed an algorithm to calculate ROI as well as track and evaluate motor and visual impacts. 2022, Industrial Research Institute for Automation and Measurements. All rights reserved. -
Machine Learning and Deep Learning Analysis of Vehicle Carbon Footprint
Clearly climate change is one of the most significant hazards to mankind nowadays. And daily the situation has become worse. No other way characterises climate change except through changes in the patterns of temperature and weather. Human activity generates the primary greenhouse gas emissions. Among these activities are burning coal, oil, natural gas, as well as other fuels; agricultural techniques, industrial operations, deforestation, burning coal, oil. Mostly resulting from human activities, the average temperature of the planet has significantly increased by almost 1.1 degrees Celsius since the late 1800s. One theory holds that internal combustion engines affect roughly thirteen percent. The objective of this work is to do an analysis of a complicated dataset involving fuel consumption in urban and highway environments as well as mixed combinations since the relevance of these variables in modelling attempts dictates. Reduced CO2 emissions emissions and environmental impact follow from reduced fuel use. The project used numerous machine learning and deep learning approaches to comprehend data analysis. Moreover, this work investigates the dataset to acquire knowledge and concurrently solves problems such overfitting and outliers. Control of complexity is achieved using several methods like VIF, PCA, and Cross-Validation. Models combining CNN and RNN performed really well with an accuracy of 0.99. The R-squared metrics are utilized in order to do the evaluation of the model. Apart from linear regression, support vector machines, Elastic Net with a rewardable accuracy, random forest was applied. It has rather good 0.98 accuracy. We can therefore state that our model analyzed the data properly and generated accurate output since the results we obtained during the assessment phase exactly the same ones we obtained during the training stage. Mass data cleansing is required as well as further study to increase machine learning model accuracy and performance. 2024 The authors. -
Machine learning and deep learning techniques for breast cancer detection using ultrasound imaging
One of the greatest causes leading to death in women is breast cancer. Its prompt and precise identification can reduce the mortality risk associated with the disease. With the help of computer-based detection, radiologists can identify irregularities. To identify and diagnose numerous illnesses and anomalies, medical photographs are sources of important information. Various techniques help radiographers to examine the internal system, and these techniques have generated a significant amount of attention across several fields of research. Each of these approaches holds a great deal of relevance in many healthcare sectors. Using artificial intelligence techniques, this article aims to present a study that highlights current developments in the detection and classification of breast cancer. The categorization of breast cancer using many medical imaging modalities is discussed in this article. It initially offers a summary of the various machine learning methodologies, followed by a summary of the various deep learning algorithms used in the detection and characterization of metastatic breast tumors. To give an insight into the field, we also give a quick summary of the various imaging techniques. The chapter concludes by summarizing the upcoming developments and difficulties in the diagnosis and classification of breast cancer. 2024 Elsevier Inc. All rights reserved. -
Machine Learning and IoT in Smart Agriculture
Smart agriculture is becoming more necessary as food demands quickly rise in response to a growing global population. Additionally, agriculture serves as the primary source of income for almost 60% of India's people. Yet most of our farming practices are still archaic and out-of-date. The fast-expanding population may not be able to be fed using these methods. Smart agriculture uses cutting-edge technology, including Internet of Things (IoT), global positioning systems (GPS), machine learning, robots, and the use of linked gadgets. Smart agriculture could support an artificial intelligence (AI)-integrated agricultural system that gathers data about the agricultural area and then analyses it to help the farmer make the best decisions for producing high-quality crops. The field of AI, with its superior learning capability, is a critical method for tackling several difficulties related to agriculture. AI provides appealing computing and analytical techniques for the better integration of various information-gathering forms from various sources. This paper elaborates the innovative ways AI can be used in the field of Indian agriculture. The study also goes into detail on the impact of smart farming on agricultural research. The analysis demonstrates the range and impact of cutting-edge technology in Indian agriculture, including sensors for rainfall rate prediction, GPS, moisture and temperature sensors, and aerial satellite photos. 2025 selection and editorial matter, Sirisha Potluri, Suneeta Satpathy, Santi Swarup Basa, and Antonio Zuorro; individual chapters, the contributors. All rights reserved. -
Machine Learning and Signal Processing Methodologies to Diagnose Human Knee Joint Disorders: A Computational Analysis
Computer-aid diagnostic (CAD) has emerged as a highly innovative research topic in diverse fields which includes medical imaging systems, radiology diagnostics, and so on. These are the systems that majorly assist doctors by the way of interpretation of medical data or images. In the diagnosis of knee joint disorder technique, both time and frequency-based analysis can be done. These non-stationary and non-linear signals are processed into three important methods, namely VMD, TVF-EMD, and CEEMDAN. To analyze the vibroarthrographic (VAG) signal, the initial stage is to compute the mode strategies termed as intrinsic mode functions (IMFs) which can be attained only after performing the transformations. In our chapter, we analyzed Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for computing the mode signals. The CEEMDAN method utilized the time and frequency data for the available features. The feature extraction depends purely on pixel intensity and the statistical parameters. The classification of available data samples is done through the Least Square Support Vector Machine (LS-SVM) and SVM-Recursion of Feature Elimination (SVM-RFE) for the efficient analysis of healthy and unhealthy data samples. 2024 selection and editorial matter, Hemachandran K., Raul V. Rodriguez, Umashankar Subramaniam, and Valentina Emilia Balas; individual chapters, the contributors. -
Machine learning approach for automatic solar panel direction by using nae bayes algorithm
The upsurge in fuel prices are pointing out the fact that, the deficiency of conventional form of natural resources and building dams can never fulfill the demand of the growing population and it is exponentially increasing the electricity demand. Electricity is a day-to-day component, which is utilized for lighting, running appliances, machines. Moreover a large number of people are now switching to electric cars. Henceforth, it is equally important to achieve self-sustainability in energy needs and also it is necessary to have an infinite energy source. Sustainable power is the solitary solution to resolve this issue. On the other hand, the Indian government is promoting solar technology a lot in the year 2021 by providing subsidies to a maximum limit of 65% for the installation of home solar projects and this encourages people to switch to electric vehicles to reduce the pollution. This article presents a machine learning based dual-axis solar tracker to enhance the energy harnessing efficiency. Furthermore, the proposed method utilizes Nae Bayes algorithm to develop a better solution for producing higher energy from the solar panel. The Nae Bayes algorithm is a type of machine learning algorithm, which has been used to predict the reliable direction. This proposed method generates higher electricity, when compared with the traditional method. The experimental results aim to fix the north east direction of solar panel that produces 17.4 watts per hour, wherein the proposed method produces 24.8 watts. It is indicated that, more than 25% additional power generation is obtained by using Nae Bayes algorithm method. 2021 IEEE. -
Machine Learning Approach for Evaluating Industry-Based Employer Ranking and Financial Stability
Using the computational prowess of machine learning, this study presents a fresh method for assessing the relative standing and fiscal health of employers across different sectors. The research makes use of a wide variety of data, including financial reports, statistics on the labor market, employee evaluations, and indicators unique to the business, to arrive at in-depth judgements. The financial stability assessment applies a linear regression model, whereas employer ranking is predicted using a logistic regression model. Financial data, employment market dynamics, and sentiment research are used as foundational characteristics for these models. Company A is more financially stable than Company B, yet it is anticipated to be ranked lower as an employer. This highlights the difficulty of judging businesses. The implications of these results for job-seekers, investors, and businesses are varied. The study also highlights the significance of ethics, openness, and addressing biases in assessment. This study paves the way for future advancements in this crucial subject and provides a basis for data-driven, well-informed decision-making in the ever-changing landscapes of contemporary industrial evaluations. 2024 IEEE. -
Machine Learning Approach for the Prediction of Consumer Food Price Index
The price of food and food related items are dynamic. A measure change in the price affects the buying behaviour of the consumer and monetary policies by the Government. The Consumer Food Price Index (CFPI) reflects the variations in food prices during a certain period. In India, the CFPI is released monthly by the Central Statistical Organization. It also reflects the inflation and helps the Government to take corrective measures in time. In this paper we have applied the machine learning approach in forecasting the consumer food price index in India. In specific, this work has focused on the applicability of Artificial Neural Network (ANN) models with back propagation learning in predicting the future values of CFPI. The monthly data for rural, urban and combined from the period 2013 to 2021 have been used to train and validate the models. The Mean Absolute Percentage Error (MAPE) values have been used to validate the accuracy of the models. The experimental results show that a simple ANN model with back propagation algorithm is highly capable in forecasting the future values of CFPI. 2021 IEEE. -
Machine Learning Approaches for Suicidal Ideation Detection on Social Media
Social media suicidal ideation has become a serious public health issue that requires creative solutions for early diagnosis and management. An extensive investigation of machine-learning techniques for the automated detection of suicidal thoughts in internet postings is presented in this research. We start off by talking about the concerning increase in information on social media about mental health issues and the pressing need to create efficient monitoring mechanisms. The research explores the several methods used to identify the subtleties of suicidal thought conveyed in text, photographs, and audio-visual information. These methods include sentiment analysis, natural language processing, and deep learning models. We look at the problems with unbalanced data, privacy issues, and the moral ramifications of keeping an eye on user-generated material. We also go over the research's practical ramifications, such as the creation of instruments for real-time monitoring and crisis response techniques. Through comprehensive experiments and benchmarking, we demonstrate the potential of machine learning in providing timely support for those in need, thereby reducing the impact of suicidal ideation on society. 2023 IEEE. -
Machine learning approaches towards medical images
Clinical imaging relies heavily on the current medical services' framework to perform painless demonstrative therapy. It entails creating usable and instructive models of the human body's internal organs and structural systems for use in clinical evaluation. Its various varieties include signal-based techniques such as conventional X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US) imaging, and mammography. Despite these clinical imaging techniques, clinical images are increasingly employed to identify various problems, particularly those that are upsetting the skin. Imaging and processing are the two distinct patterns of clinical imaging. To diagnose diseases, automatic segmentation using deep learning techniques in the field of clinical imaging is becoming vital for identifying evidence and measuring examples in clinical images. The fundamentals of deep learning techniques are discussed in this chapter along with an overview of successful implementations. 2023, IGI Global. All rights reserved. -
Machine Learning based Candidate Recommendation System using Bayesian Model
Online websites that recommend books, music, movies, and other media are becoming increasingly prevalent because of collaborative filtering. This online websites are using many algorithms to provide the better recommendation to attract the customer. Bayesian statistics, which is based on Bayes' theorem, is a technique for data analysis in which observable data are used to update the parameters of a statistical model. To discuss a strategy called item-based collaborative filtering, which bases predictions on the similarities between the said objects. This uses Machine Learning based Candidate Recommendation System which uses Bayesian Model database to assess the proposed method. The actual results show that for collaborative filtering which is based on correlation, the Bayesian techniques we have proposed outperform traditional algorithms. Also discuss a technique for improving prediction accuracy that combines the Simple Bayesian Classifier with user- and item-based collaborative filtering. The user-based recommendation is then applied to the matrix once the user-item rating matrix has been filled out with pseudo-scores produced by the item-based filter. This model is demonstrated that the combined approach outperforms the individual collaborative recommendation approach. The creation of UI based web application will help Students to manage achievement details. Job seekers and admin will be given a separately formatted version of the application where, students can upload and view their certificate, wherein admin can access student achievement details categorized by different parameters. This proposed model is developed under the service learning scheme to benefit both job seeker and recruiter. 2023 IEEE. -
Machine Learning Based Crime Identification System using Data Analytics
Poverty is known to be the mother of all crimes, and a vast percentage of people in India live below the poverty line. In India, the crime rate is rapidly rising. The police officers must spend a significant amount of time and personnel to identify suspects and criminals using current crime investigation. In this research, the method presented for designing and implementing crime identification and criminal recognition systems for Indian metropolitans is utilizing techniques of data mining. These occurrences are represented by 35 predefined crime attributes. Access to the crime database is protected by safeguards. The pending four subjects are important for crime unmasking, identification and estimation of criminals, and crime authentication, in that order. The detection of crime is investigated with the help of K-Means clustering, which iteratively builds two crime batches based on congruent criminal features. Google Maps is to enhance the k-means visualization. K-Nearest Neighbor classification is used to examine criminal identification and forecasting. This is used for the authentication of the results. The technique benefits society by helping investigative authorities in crime solving and criminal recognition, resulting in lower crime rates. This research study describes a way for creating and deploying crime solving and criminal recognition systems for Indian metro's using data mining tools in this study. The method consists of data evulsion, data pre- processing, clustering, Google map delegation and classification. The first module, data evulsion, retrieves unformed or unrecorded crime datasets from several criminal sources online from 2000 to 2012. In the second module, Data pre-processing cleans, assimilates, and reduces the obtained criminal data into organized 5,038 crime occurrences. Several predefined criminal traits represent these instances. Safeguards are in place to prevent unauthorized access to the crime index. The remaining components are critical for detecting crimes, criminal identity and prediction, and crime verification, in that sequence. The investigation of crimes is investigated using k-means clustering, which gives results repeatedly. 2023 IEEE. -
Machine Learning based Food Sales Prediction using Random Forest Regression
Sales forecasting is crucial in the food industry, which experiences high levels of food sales and demand. The industry has concentrated on a well-known and established statistical model. Due to modern technologies, it has gained tremendous appeal in improving market operations and productivity. The main objective is to find the most accurate algorithms to predict food sales and which algorithm is most suitable for sales forecasting. This research work has mentioned and discussed about several research articles that revolve around the techniques usedfor sales prediction as well as finding out the advantages and disadvantages of the said techniques. Various techniques were discussed as to predicting the sales but mainly Incline Increasing Regression and Accidental Forestry Lapse is used for attention. The manufacturing has concentrated on a well-known and established statistical model. Although algorithms like Modest Direct Regression, Incline Increasing Lapse, Provision Course Lapse, Accidental Forest Lapse, Gradient Boosting Regression, and Random Forest Regression are well familiar for outdoing others, it has remained decisively established that Random Forest Regression is the most appropriate technique when associated to the others. After doing the whole examination, the Random Forest Regression technique fared well when compared to other algorithms. The feature importance is generated for the selected dataset using Python and Random Forest Regression and the nose position chart is also explainedin detail. The proposed model is compared three major parameters that are accuracy score, mean absolute error and max error. The proposed random forest regression accuracy score is improved nearly 1.83% and absolute error rate is reduced 4.66%. 2022 IEEE. -
Machine Learning based Loan Eligibility Prediction using Random Forest Model
When one or more people, organizations, or other entities lend money to other people, organizations, or entities, it is known as a loan. The recipient (that is the borrower) takes on a debt for which he or she is normally accountable for interest payments until the loan is repaid. The major goal of this proposed model is to ensure that an individual, institution, or organization seeking for a loan is properly verified before granting them the loan they require. Before authorizing a loan for any individual or business several factors must be considered. That including gender, education, and the number of dependents. The goal of proposed model is to automate the method, which will save time and energy while improving the efficiency of the process. This particular process input is having two different kind of data set. First one is train data set and second set is test data set. The first date set that is train data set is generally used to train and assess the machine learning model accuracy. The loan eligibility predictions are generated using the test data set. To forecast loan eligibility and train this random forest, machine learning method called Random Forest. The proposed random forest model is providing higher accuracy level. This model is providing 28 % higher accuracy level compare to regular prediction. 2022 IEEE. -
Machine Learning Based Optimal Feature Selection for Pediatric Ultrasound Kidney Images Using Binary Coati Optimization
Chronic kidney disease (CKD) one of the most dangerous illnesses. Early detection is vital for improving survival rates and underscoring the need for an intelligent classifier to differentiate between normal and abnormal kidney ultrasound images. Features extracted from an image have a significant impact on classification accuracy. In this study, we present a Binary Coati optimization algorithm (BCOA) for feature selection in CKD, which focuses on reducing the high dimensionality features extracted from ultrasound images, including GLCM, GLRLM, GLSZM, GLDM, NGTDM, and first order, by employing BCOA-S shaped and BCOA-V shaped transfer functions that convert BCOA from a continuous search space to a binary form, which helps in the selection of optimal features to improve the classification performance while reducing the feature dimensionality. The reduced feature was evaluated using six machine-learning classifiers: Random Forest, Support Vector Machine, Decision tree, K-nearest Neighbor, XG-boost, and Nae Bayes. The efficiency of the proposed framework was assessed based on accuracy, precision, recall, specificity, f1 score and AUC curve. BCOA-V outperformed in terms of accuracy, precision, recall, specificity, F1 score and AUC curve by 99%,100%,97%,100%, 98%, and 98%, respectively. This makes it a superior choice for CKD diagnosis and is a valuable tool for feature selection in medical diagnosis. (2024), (Intelligent Network and Systems Society). All rights reserved. -
Machine Learning based Plant Disease Identification by using Hybrid Nae Bayes with Decision Tree Algorithm
Artificial intelligence or machine learning as a domain started as a distinct domain marketplace for enthusiasts. Over an extended period of time, this has evolved into an industry with boundless potential. This is the focal point of a plethora of technologies like real-time analytics, deep learning in computer science. It's inherent to various customer needs such as fault detection, home automation, health monitoring devices as well as appliances, and multiple RPM devices Artificial intelligence which has been tested and trained to recognize and determine a plethora of flaws and inaccuracies. This could be intriguing procedures in day-to-day applications. An unimaginable number of prediction models, packages, libraries as well as sensors are utilized to sieve through flaws with the aid of mobile app development and other multispectral sensors. These trendy devices have become ever present and a part of our extensive routine. The demand for dependable and efficient algorithms is satisfied while implementing these devices. The objective primarily dictates emphasis on the prediction of plant diseases in the agricultural arena in reality by providing aid in the field of agriculture, and industry. In this case, the device incorporates a database which stores and keeps track of previously detected flaws or defects. In addition, the history of detected plant infections is maintained in an online repository. This can help with the forecast of the defects within the gadgets that are to be enhanced. Furthermore, the suggested approach of this text inculcates the invigilation of every leaf in the plant via machine learning model. Hence, this approach of implementation limits interaction of humans with the interface and it detects disease ridden plants efficiently with accuracy. The plant disease identification problem is to solve the proposed hybrid Nae Bayes with Decision Tree algorithm. The proposed model provides higher accuracy level compare to the regular model. 2023 IEEE. -
Machine Learning Based Recession Prediction Analysis Using Gross Domestic Product (GDP)
This research article aims to explore the prediction and analysis of recessions, with a particular focus on Gross Domestic Product (GDP). The study examines the impact of recessions on different countries, namely India, USA, Germany, China, and Bangladesh, while also considering the influence of the COVID-19 pandemic on these nations in relation to the recessionary effects. Furthermore, the study lists many machine learning techniques that could be used to anticipate recessions. This research mainly focuses on predicting recession using different machine learning models. The research not only provides an in-depth analysis of the recessionary impacts on different economies but also serves as a foundation for future implementation of these algorithms for accurate recession prediction and proactive economic decision-making. This research study mainly focuses on machine learning algorithms like Random Forest, Support Vector Machines and Regression Model. The GDP prediction comparison is taking last twenty years data. This is mainly compared before and after COVID-19 situation. 2023 IEEE. -
Machine Learning Based recommendation system Using User-Item Interaction
Electronic commerce, or e-commerce, is the activity of trading services and commodities through the internet. Identifying the item that the consumer may buy from the enormous number of possibilities accessible to solve this difficulty is now one of the key difficulties encountered by most E-commerce businesses. Recommender systems have been implemented. Recommender systems (RS) are systems that collect information from users about their preferences and allow them to make decisions from the available options. Today, various recommender systems are growing with the advent of web-based information. As you can see from various articles, such recommender systems are used in a variety of industries, from simple objects to more sophisticated objects.RS has gained popularity in the previous decade, particularly in the realm of E-Commerce and related sectors. This report aims to identify recent developments as well as their potential for improvement. It is intended to elaborate on a number of points. And also work more on user-item based recommendation. These types of user-item based recommendation will be more effective in fashion area. 2022 IEEE.