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Machine Learning Algorithms for Optimizing Blockchain-Based Decentralized Autonomous Organizations
This research investigates the integration of machine learning algorithms within blockchain-based Decentralized Autonomous Organizations (DAOs) to enhance operational efficiency, resource allocation, decision-making, and governance. While DAOs provide a transparent and trustless mechanism for digital collaboration, they face challenges related to scalability, bias, data privacy, and coordination. We propose a novel framework that leverages supervises learning models for predictive analytics, reinforcement learning for autonomous decision-making, and unsupervised learning for anomaly detection in DAO voting and resource usage patterns. The study also addresses security and privacy risks by incorporating federated learning and homomorphic encryption. Our proposed model demonstrates improved throughput, decision accuracy, and fairness, as evidenced by performance benchmarks against traditional DAO implementations. The findings suggest that machine learning can significantly optimize DAO architecture and contribute to a more scalable, democratic, and intelligent decentralized ecosystem. 2025 IEEE. -
Machine Learning Algorithms for Prediction of Mobile Phone Prices
The drastic growth of technology helps us to reduce the man work in our day-to-day life. Especially mobile technology has a vital role in all areas of our lives today. This work focused on a data-driven method to estimate the price of a new smartphone by utilizing historical data on smartphone pricing, and key feature sets to build a model. Our goal was to forecast the cost of the phone by using a dataset with 21 characteristics related to price prediction. Logistic regression (LR), decision tree (DT), support vector machine (SVM), Naive Bayes algorithm (NB), K-nearest neighbor (KNN) algorithm, XGBoost, and AdaBoost are only a few of the popular machine learning techniques used for the prediction. The support vector machine achieved the highest accuracy (97%) compared to the other four classifiers we tested. K-nearest neighbors 94% accuracy was close to that of the support vector machine. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Machine Learning Algorithms for Predictive Maintenance in Hybrid Renewable Energy Microgrid Systems
The rapid expansion of hybrid renewable energy microgrid systems presents new challenges in maintaining system reliability and performance. This paper explores the application of machine learning algorithms for predictive maintenance in such systems, focusing on the early detection of potential failures to optimize operational efficiency and reduce downtime. By integrating real-time data from solar, wind, and storage components, the proposed models predict the remaining useful life (RUL) of critical components. The results demonstrate significant improvements in predictive accuracy, offering a robust solution for enhancing the reliability and longevity of renewable energy microgrids. The Authors, published by EDP Sciences. -
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 Approaches for Guava Disease Detection
A larger proportion of crops face disease outbreaks, making agricultural output difficult. Detecting and predicting diseases at an early stage can enhance productivity. Guava, a tropical and subtropical fruit, is cultivated in various countries. In regions such as Bangladesh, Pakistan, India, and South America, guava cultivation faces significant challenges due to diseases like Canker, Dot, Mummification, Phytophthora, Scab, and Styler and Root. Traditional diagnosis methods based on visual observation are often unreliable and time-consuming. To address this, we developed an automated system leveraging deep learning techniques. Our study utilized a dataset comprising 4046 guava leaf images categorized into these seven disease classes. We compared the performance of traditional methods with deep learning approaches using vision transformers and transfer learning. The results demonstrate the superiority of deep learning methods over traditional approaches, where traditional machine learning model SVM gave accuracy near 78% and deep learning methods gave over 90%. The transfer learning method gave an accuracy of nearly 97% and on the other hand, the vision transformer gave accuracy of 98%. This offers a promising solution for early disease detection in guava crops. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025. -
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 Ensemble Models for Hazardous Asteroids Prediction
The prediction of hazardous asteroids near Earth is critical for planetary defense and avoiding any possible impacts. This study investigates the use of five ensemble models, XGBoost, Gradient Boost, CatBoost, Voting Classifier, and Random Forest, as well as four standalone machine learning models, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression, and Decision Tree, to improve the prediction accuracy of identifying potentially hazardous asteroids. With 92% accuracy and 91% precision, Random Forest performed better than other models. It was the preferred choice for predicting hazardous asteroids because of its capacity to handle the hugedatasetwith efficiency and its ability tomanage non-linear data patterns. Additionally, XGBoost and CatBoost providedhigh accuracy at lowcomputational costs, making them suitable for real-time monitoring. KNN, on the other hand, did not perform well, and SVM's high processing time made it less useful. In particular, Random Forest ensemble modelperformed better at predicting hazardous asteroids. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Machine learning and image processing based smart prediction of human emotions and character /
Patent Number: 202141035789, Applicant: Ingeniouz.
Feelings are a major piece of human correspondence. Detecting and recognizing human emotion is a big challenge in computer vision and artificial intelligence. Though there are methods to identify expressions using machine learning and Artificial Intelligence techniques, here we use deep learning and image classification method to recognize expressions and classify the expressions according to the images. With the remarkable success of Deep Learning the different types of architecture techniques are exploited to achieve a better performance. -
Machine learning and IOT based smart human activity discovering system for health care applications /
Patent Number: 202111051973, Applicant: Dr. G S Pradeep Ghantasala.
Rural areas are home to more than two-thirds of the world's population, while metropolitan areas are home to less than one-third. According to the census, the world's rural population was 55% and its urban population was 45% in 1995. The rural population (47 percent) will grow significantly faster than the urban population (59 percent) by 2025. (41 percent ). According to the latest research, most people are moving from rural to urban areas, and they've grown accustomed to smart technology with little regard for their health. -
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 and Word Representation Techniques in Medical Transcription Data Analysis
Dermatology is the branch of medicine that deals with the diagnosis, treatment, and prevention of skin diseases. Dermatological diseases can be difficult to diagnose, treat, and manage because there are several skin conditions, each with its unique set of symptoms and causes. Underlying medical conditions, environmental causes, or hereditary characteristics can cause complex skin problems. Furthermore, because skin problems can present in a variety of ways, obtaining an appropriate diagnosis and efficient treatment may be difficult. Treating dermatological disorders is a difficult endeavor. This article proposes an integrated model to assist people in understanding and discussing the nature of dermatology. This model's capabilities include text pre-processing, audio-to-text translation, named entity recognition (NER) for extracting crucial information, and text clustering and classification based on content. The necessity for precise and efficient analysis of large amounts of text data, notably the identification and standardisation of abbreviations and the extraction of relevant information, has been identified as a problem in dermatology and medical transcription. By grouping similar cases, clustering can make it easier to spot patterns and trends in dermatological disorders. However, classification can help automatically group text data into pre-established categories, such as various kinds of skin conditions or treatments. These methods simplify data analysis, increase accuracy, and assist healthcare professionals in reaching accurate conclusions regarding patient care. This article explores the partitioning algorithm for clustering, while logistic regression is used in classification. The model analysed in this article helps dermatologists and patients understand and manage skin problems. 2025 IEEE. -
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 Detection of Cyberbullying in Virtual Space
Cyberbullying, hostile behavior of a group or an individual to defame or harass the victim mentally with the help of social media and other e-communication platforms, has the potential to create a lifelong negative impact on mental health with the power of inducing suicidal thoughts. It is on the rise among the early adolescents of the age group from 8 to 16. Hence it is vital to detect Cyberbullying at an early stage to safeguard the victims at the high risk of developing depression, anxiety, and suicidal ideas. It also helps to mitigate psychological, academic, and social consequences. Existing cyberbullying detection approaches primarily depend on static monolingual questionnaires and are not personalised. With the developments in Artificial Intelligence, many neural network-based approaches are explored to detect cyberbullying. This study discusses and provides comparative analysis of various machine learning approaches for detecting cyberbullying victimization among school students highlighting their effectiveness and limitations. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Machine Learning Approaches for Predicting Player Position in Football
This paper presents a comparative study of different machine learning algorithms, including K- Nearest Neighbor (KNN), Random Forest, Gradient Boosting, XG Boost, Support Vector Machine, Voting Classifier and Logistic regression to develop a Player Position Prediction System in football. Initially, the study utilized a modified dataset containing 18434 records, focusing on simplicity for ease of analysis. Through experimentation, it was found that Logistic regression provided a strong balance between efficiency and scalability, making them ideal for rapid decision-making in environments with limited features. In contrast, Support Vector Machine, XGboost and voting classifier excelled in offering more detailed, feature-rich analyses, which are particularly beneficial when handling complex data. Building on these findings, the plan is to apply the same algorithms to improve the system's overall accuracy and efficiency. By leveraging the strengths of each approach, the aim is to create a scalable, effective recommendation system tailored for real-world applications in the car industry. This study highlights the importance of choosing the right algorithm based on the tradeoffs between computational efficiency and the depth of analysis required in recommendation systems. 2025 IEEE.