This paper presents a machine learning-based framework for real-time short-term snowfall forecasting by integrating atmospheric and topographic data. The model uses real-time meteorological data such as temperature, humidity, and pressure, along with terrain data like elevation and land cover, to predict snowfall occurrence within a 12-hour forecast window. Random Forest (RF) and Support Vector Machine (SVM) models are employed to process these multi-source inputs, demonstrating a significant improvement in prediction accuracy over traditional methods. Experimental results show that (...) the RF model achieved a root mean square error (RMSE) of 3.2 cm, while traditional regression models achieved an RMSE of 5.1 cm. The proposed framework provides a more reliable and accurate tool for real-time snowfall prediction, especially in mountainous regions, and can be deployed for operational weather forecasting applications. (shrink)

This study employs machine learning to predict diabetes using a Kaggle dataset with 13 features. Our three-layer model achieves an accuracy of 98.73% and an average error of 0.01%. Feature analysis identifies Age, Gender, Polyuria, Polydipsia, Visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, Muscle stiffness, Alopecia, Genital thrush, Weakness, and Obesity as influential predictors. These findings have clinical significance for early diabetes risk assessment. While our research addresses gaps in the field, further work is needed (...) to enhance model generalizability. (shrink)

Prediction-based decisions, which are often made by utilizing the tools of machine learning, influence nearly all facets of modern life. Ethical concerns about this widespread practice have given rise to the field of fair machine learning and a number of fairness measures, mathematically precise definitions of fairness that purport to determine whether a given prediction-based decision system is fair. Following Reuben Binns (2017), we take ‘fairness’ in this context to be a placeholder for a variety of (...) normative egalitarian considerations. We explore a few fairness measures to suss out their egalitarian roots and evaluate them, both as formalizations of egalitarian ideas and as assertions of what fairness demands of predictive systems. We pay special attention to a recent and popular fairness measure, counterfactual fairness, which holds that a prediction about an individual is fair if it is the same in the actual world and any counterfactual world where the individual belongs to a different demographic group (cf. Kusner et al. (2018)). (shrink)

Data normalization is a crucial step in database management systems (DBMS), ensuring consistency, minimizing redundancy, and enhancing query performance. Traditional methods of normalization in supermarket sales databases often demand significant manual effort and domain expertise, making the process time-consuming and prone to errors. This paper introduces an innovative machine learning (ML)-based framework to automate data normalization in supermarket sales databases. The proposed approach utilizes both supervised and unsupervised ML techniques to identify functional dependencies, detect anomalies, and suggest optimal (...) schema transformations. Experiments on supermarket sales datasets show substantial improvements in accuracy, scalability, and processing time compared to traditional approaches. The results emphasize the potential of incorporating ML into database management practices to boost operational efficiency and support better decision-making. (shrink)

Alzheimer's disease (AD) poses a significant public health challenge. The hippocampus is one of the most affected brain regions and a readily accessible biomarker for diagnosis through MRI imaging in machine learning applications. However, utilizing entire MRI image slices in machine learning for AD classification has shown reduced accuracy. This study introduces the novel 'select slices' method, which involves identifying and focusing on specific landmarks within the hippocampus region in MRI images. This approach aims to improve (...) classification accuracy by eliminating irrelevant information from the analysis. Our research aims to identify which views of MRI images produce higher accuracy for AD classification. We used multiclass classification using the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, utilising Resnet50 and LeNet models to evaluate the value of three specific views (sagittal, coronal, and axial) and categories (normal, mild, and severe AD). The dataset comprised 4,500 MRI slices across these three views and categories. Our findings unequivocally demonstrate that the 'select slices' approach surpasses the use of entire slices in MRI images for AD classification. Specifically, our method elevates machine learning accuracy, with the coronal view showcasing exceptional performance. This methodology is a game-changer in improving the accuracy of machine learning models for AD classification, with results closely mirroring those of medical experts, the gold standard for AD diagnosis. Moreover, we observed that LeNet models hold great promise as effective tools for AD classification. (shrink)

Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.

Current methods of operationalizing concepts of misinformation in machine learning are often problematic given idiosyncrasies in their success conditions compared to other models employed in the natural and social sciences. The intrinsic value-ladenness of misinformation and the dynamic relationship between citizens' and social scientists' concepts of misinformation jointly suggest that both the construct legitimacy and the construct validity of these models needs to be assessed via more democratic criteria than has previously been recognized.

Quantum machine learning (QML) provides a transformative approach to data analysis by integrating the principles of quantum computing with classical machine learning methods. With the exponential growth of data and the increasing complexity of computational tasks, quantum algorithms offer tremendous advantages in terms of processing speed, memory efficiency, and the ability to resolve issues intractable for classical systems. In this work, the use of QML techniques for both supervised and unsupervised learning problems is explored. Quantum-enhanced (...) models such Quantum Support Vector Machines (QSVMs) and Quantum Neural Networks (QNNs) show outstanding performance in classification and regression tasks by using quantum kernels and entanglement in supervised learning. Moreover, hybrid quantum-classical solutions offer useful implementations on noisy intermediate-scale quantum (NISQ) devices, hence bridging the gap between present quantum technology and practical uses. By means of comparative analysis, this paper emphasizes the possible benefits and drawbacks of QML, thereby providing understanding of its future importance in sectors including material science, finance, and healthcare. In the end, QML opens the path for a new era of intelligent data processing and solves until unthinkable difficult challenges. (shrink)

In the contemporary world, there is lot of research going on in creating novel nano materials that are essential for many industries including electronic chips and storage devices in cloud to mention few. At the same time, there is emergence of usage of machine learning (ML) for solving problems in different industries such as manufacturing, physics and chemical engineering. ML has potential to solve many real world problems with its ability to learn in either supervised or unsupervised means. (...) It is inferred from the state of the art that that it is essential to use ML methods for analysing imagery of nano materials so as to ascertain facts further towards characterization and analysis of microstructure and spectral data of materials. Towards this end, in this paper, we proposed a ML based methodology for STEM image analysis and spectral data analysis from STEM image of a nano material. We proposed an algorithm named Machine Learning for STEM Image Analysis (ML-SIA) for analysing STEM image of a nano material. We proposed another algorithm named Machine Learning for STEM Image Spectral Data Analysis (ML-SISDA) for analysing spectral data of STEM image of a nano material. We developed a prototype ML application to implement the algorithms and evaluate the proposed methodology. Experimental results revealed that the ML based approaches are useful for characterization of nano materials. Thus this research helps in taking this forward by triggering further work in the area of material analysis with artificial intelligence. (shrink)

Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in (...) which they are presumptuous. After elaborating this moral concern, I explore the possibility that carefully procuring the training data for image recognition systems could ensure that the systems avoid the problem. The lesson of this paper extends beyond just the particular case of image recognition systems and the challenge of responsibly identifying a person’s intentions. Reflection on this particular case demonstrates the importance (as well as the difficulty) of evaluating machine learning systems and their training data from the standpoint of moral considerations that are not encompassed by ordinary assessments of predictive accuracy. (shrink)

Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models (...) provide understanding misguided? In this paper, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding. (shrink)

Breast cancer is one of the leading causes of death among women worldwide. Early detection plays a crucial role in improving survival rates, and machine learning (ML) provides powerful tools for identifying cancerous tumors in medical imaging and diagnostic data. This paper explores various machine learning techniques used for breast cancer detection, with a particular focus on the Wisconsin Breast Cancer Dataset (WBCD). We evaluate several classification models, including Logistic Regression (LR), Support Vector Machine (SVM), (...) k-Nearest Neighbors (k-NN), and Random Forest (RF), for their ability to accurately classify benign and malignant tumors. The goal of this paper is to demonstrate the use of supervised learning techniques to enhance early breast cancer detection. (shrink)

Integrating medical imaging with computing technologies, such as Artificial Intelligence (AI) and its subsets: Machine learning (ML) and Deep Learning (DL) has advanced into an essential facet of present-day medicine, signaling a pivotal role in diagnostic decision-making and treatment plans (Huang et al., 2023). The significance of medical imaging is escalated by its sustained growth within the realm of modern healthcare (Varoquaux and Cheplygina, 2022). Nevertheless, the ever-increasing volume of medical images compared to the availability of imaging (...) experts. Biomedical experts and radiologists have resulted in a widening disparity, causing an excess and overwhelming workload on these healthcare professionals (Chen et al., 2021). Several studies indicate that the present-day biomedical radiologist is now saddled with the daunting task of interpreting an image almost every 10 seconds to keep pace with the burgeoning clinical demands (McDonald et al., 2015; Hosny et al., 2018; Lantsman et al., 2022). This cognitive drain has invariably led to inevitable consequences such as delays in diagnosis and an amplified risk of diagnostic errors – thus, the biomedical imaging aspect is in dire need of methods that would aid accurate diagnostics and analytics for improved decision making. In this review, the importance of AI-related technologies such as ML and/or DL methods are reviewed in relation to the processing of medical or biomedical images along with their potentials, challenges, and possible suggestions for future studies in the health landscape. The focus will be on machine learning methods associated with the medical field of image classification. (shrink)

Water is a fundamental resource for life, yet maintaining its quality remains a significant global challenge due to pollution from industrial, agricultural, and domestic sources. Traditional water quality monitoring methods are time-consuming and limited in scope. Machine Learning (ML) offers advanced capabilities for analyzing complex datasets, predicting pollution levels, and optimizing water resource management in real-time. This paper explores the integration of ML in water quality monitoring and management, reviewing current applications, methodologies, and future research directions. We present (...) a framework for real-time monitoring using ML models to enhance decision-making and resource planning. (shrink)

Autonomous systems, powered by machine learning (ML), have the potential to revolutionize various industries, including transportation, healthcare, and robotics. However, the integration of machine learning in autonomous systems raises significant challenges related to safety, ethics, and regulatory compliance. Ensuring the reliability and trustworthiness of these systems is crucial, especially when they operate in environments with high risks, such as self-driving cars or medical robots. This paper explores the intersection of machine learning and autonomous systems, (...) focusing on the challenges of ensuring safety, mitigating ethical concerns, and navigating evolving regulatory frameworks. We discuss key strategies for improving the transparency, fairness, and accountability of autonomous systems, as well as the role of machine learning in enabling safe decision-making. Additionally, we propose a roadmap for the future development of autonomous systems that incorporates robust safety measures, ethical guidelines, and regulatory compliance. (shrink)

The use of AI algorithms in criminal trials has been the subject of very lively ethical and legal debates recently. While there are concerns over the lack of accuracy and the harmful biases that certain algorithms display, new algorithms seem more promising and might lead to more accurate legal decisions. Algorithms seem especially relevant for bail decisions, because such decisions involve statistical data to which human reasoners struggle to give adequate weight. While getting the right legal outcome is a strong (...) desideratum of criminal trials, advocates of the relational theory of procedural justice give us good reason to think that fairness and perceived fairness of legal procedures have a value that is independent from the outcome. According to this literature, one key aspect of fairness is trustworthiness. In this paper, I argue that using certain algorithms to assist bail decisions could increase three diferent aspects of judges’ trustworthiness: (1) actual trustworthiness, (2) rich trustworthiness, and (3) perceived trustworthiness. (shrink)

Autonomous systems, powered by machine learning (ML), have the potential to revolutionize various industries, including transportation, healthcare, and robotics. However, the integration of machine learning in autonomous systems raises significant challenges related to safety, ethics, and regulatory compliance. Ensuring the reliability and trustworthiness of these systems is crucial, especially when they operate in environments with high risks, such as self-driving cars or medical robots. This paper explores the intersection of machine learning and autonomous systems, (...) focusing on the challenges of ensuring safety, mitigating ethical concerns, and navigating evolving regulatory frameworks. We discuss key strategies for improving the transparency, fairness, and accountability of autonomous systems, as well as the role of machine learning in enabling safe decision-making. Additionally, we propose a roadmap for the future development of autonomous systems that incorporates robust safety measures, ethical guidelines, and regulatory compliance. (shrink)

Issues of reliability are claiming center-stage in the epistemology of machine learning. This paper unifies different branches in the literature and points to promising research directions, whilst also providing an accessible introduction to key concepts in statistics and machine learning – as far as they are concerned with reliability.

With the rapid evolution of information technology, malware has become an advanced cybersecurity threat, targeting computer systems, smart devices, and large-scale networks in real time. Traditional detection methods often fail to recognize emerging malware variants due to limitations in accuracy, adaptability, and response time. This paper presents a comprehensive review of machine learning algorithms for real-time malware detection, categorizing existing approaches based on their methodologies and effectiveness. The study examines recent advancements and evaluates the performance of various (...) class='Hi'>machine learning techniques in detecting malware with minimal false positives and improved scalability. Additionally, key challenges, such as adversarial attacks, computational overhead, and real-time processing constraints, are discussed, along with potential solutions to enhance detection capabilities. An empirical evaluation is conducted to assess the effectiveness of different machine learning models, providing insights for future research in real-time malware detection. Keywords: Real-t. (shrink)

Integrating machine learning (ML) into network management and orchestration has revolutionized edgebased networking paradigms, where real-time decision-making is critical. Traditional network management approaches often struggle with edge environments' dynamic and resource-constrained nature. By leveraging ML algorithms, networks at the edge can achieve enhanced efficiency, automation, and adaptability in areas such as traffic prediction, resource allocation, and anomaly detection (Wang et al., 2021). Supervised and unsupervised learning techniques facilitate proactive network optimization, reducing latency and improving quality of service (...) (QoS) (Li & Zhang, 2020). Furthermore, reinforcement learning (RL) models enable autonomous network orchestration, allowing edge devices to adapt intelligently to fluctuating workloads and environmental conditions (Patel et al., 2019). However, challenges such as data privacy, computational overhead, and model interpretability remain key concerns in deploying ML-driven network orchestration at the edge (Chen et al., 2022). This paper explores state-of-the-art advancements, key challenges, and future research directions in ML-based edge network management, highlighting its potential to drive the next generation of intelligent, self-optimizing networks. (shrink)

As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments (...) with the goal of predicting their efficiency. In the first step, we assessed the project efficiency using Data Envelopment Analysis of 67 experiments conducted in the HEP laboratory Fermilab. In the second step, we employed predictive algorithms to detect which team structures maximize the epistemic performance of an expert group. For this purpose, we used the efficiency scores obtained by DEA and applied predictive algorithms – lasso and ridge linear regression, neural network, and gradient boosted trees – on them. The results of the predictive analyses show moderately high accuracy, indicating that they can be beneficial as one of the steps in grant review. Still, their applicability in practice should be approached with caution. Some of the limitations of the algorithmic approach are the unreliability of citation patterns, unobservable variables that influence scientific success, and the potential predictability of the model. (shrink)

The increasing volume of online transactions has heightened the risk of fraud, making real-time fraud detection crucial for safeguarding financial systems. This paper explores the development and application of machine learning (ML) models for detecting fraudulent activities in real-time online transactions. The study investigates various ML algorithms, including supervised and unsupervised learning techniques, to identify patterns indicative of fraud. We evaluate the performance of different models based on accuracy, precision, recall, and F1-score. The results show that ensemble (...) methods and deep learning techniques outperform traditional approaches in terms of accuracy and detection speed. The study also emphasizes the importance of data preprocessing, feature selection, and real-time model deployment for achieving robust fraud detection systems. This research contributes to the ongoing efforts to enhance online transaction security and provides insights into implementing effective fraud detection mechanisms using machine learning. (shrink)

Typically, fair machine learning research focuses on a single decision maker and assumes that the underlying population is stationary. However, many of the critical domains motivating this work are characterized by competitive marketplaces with many decision makers. Realistically, we might expect only a subset of them to adopt any non-compulsory fairness-conscious policy, a situation that political philosophers call partial compliance. This possibility raises important questions: how does partial compliance and the consequent strategic behavior of decision subjects affect the (...) allocation outcomes? If k% of employers were to voluntarily adopt a fairness-promoting intervention, should we expect k% progress (in aggregate) towards the benefits of universal adoption, or will the dynamics of partial compliance wash out the hoped-for benefits? How might adopting a global (versus local) perspective impact the conclusions of an auditor? In this paper, we propose a simple model of an employment market, leveraging simulation as a tool to explore the impact of both interaction effects and incentive effects on outcomes and auditing metrics. Our key findings are that at equilibrium: (1) partial compliance by k% of employers can result in far less than proportional (k%) progress towards the full compliance outcomes; (2) the gap is more severe when fair employers match global (vs local) statistics; (3) choices of local vs global statistics can paint dramatically different pictures of the performance vis-a-vis fairness desiderata of compliant versus non-compliant employers; (4) partial compliance based on local parity measures can induce extreme segregation. Finally, we discuss implications for auditors and insights concerning the design of regulatory frameworks. (shrink)

The project titled "Efficient Machine Learning Algorithm for Future Gold Price Prediction" focuses on the development of a machine learning model that can accurately predict future gold prices using historical data and various economic indicators. Gold has long been regarded as a safe-haven asset, and its price is influenced by multiple factors, including global economic conditions, inflation rates, interest rates, and geopolitical events. This research aims to design and implement a robust machine learning model (...) that can analyze complex patterns and trends in gold prices to generate reliable predictions. The project employs a range of machine learning techniques such as Linear Regression, Random Forest, and Support Vector Machines (SVM) to build the prediction model. It utilizes key features like historical price data, currency exchange rates, commodity prices, and stock market trends as input variables. The performance of the model is evaluated using standard metrics such as accuracy, precision, recall, and RMSE (Root Mean Squared Error). The final model is optimized for accuracy and efficiency, making it a useful tool for investors and financial analysts who seek to forecast gold prices and make informed investment decisions. (shrink)

Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD.

This article assesses how autonomy and machine learning impact the existential risk of nuclear war. It situates the problem of cyber security, which proceeds by stealth, within the larger context of nuclear deterrence, which is effective when it functions with transparency and credibility. Cyber vulnerabilities poses new weaknesses to the strategic stability provided by nuclear deterrence. This article offers best practices for the use of computer and information technologies integrated into nuclear weapons systems. Focusing on nuclear command and (...) control, avoiding autonomy and machine learning is recommended as one means to reduce the existential risk of unintended nuclear conflict. (shrink)

This work explores the potential of big data analytics, natural language processing (NLP), and machine learning (ML) techniques in predicting cyberbullying on social media. By analyzing large-scale datasets consisting of user comments, posts, and interactions, the study aims to detect harmful content patterns, abusive language, and behavioral trends that indicate cyberbullyingThe rapid proliferation of social media has transformed communication and interaction, but it has also led to an alarming rise in cyberbullying incidents. Cyberbullying, characterized by repeated and intentional (...) harassment through digital platforms, has significant psychological and social consequences for victims, often leading to anxiety, depression, and even self-harm. Traditional methods of identifying and mitigating cyberbullying are often reactive and inefficient due to the vast volume of data generated across multiple platforms. Therefore, predictive models leveraging big data and artificial intelligence (AI) offer a proactive approach to detecting and preventing online harassment. Sentiment analysis, text classification, and deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are employed to enhance the accuracy of cyberbullying predictions. Additionally, graph-based techniques are utilized to examine social network structures and identify potential perpetrators and victims based on interaction patterns. Despite the promising potential of big datadriven approaches, challenges such as data privacy, ethical concerns, and algorithmic bias must be addressed to ensure fair and responsible implementation. The study also highlights the importance of real-time monitoring systems that can alert platform administrators or authorities when cyberbullying behavior is detected. The findings demonstrate that integrating AI with big data analytics significantly improves the accuracy and efficiency of cyberbullying detection, enabling early intervention and fostering a safer digital environment. Future research will focus on refining detection models to handle multilingual data, cultural nuances, and evolving forms of cyberbullying across diverse social media platforms. (shrink)

Customer churn prediction is a critical challenge for businesses in retaining their customer base and optimizing their marketing strategies. Machine learning (ML) techniques offer a powerful approach to predict customer churn by analyzing historical customer behavior, demographic information, and usage patterns. This paper provides an overview of machine learning-based models used for predicting customer churn, including classification algorithms such as logistic regression, decision trees, random forests, support vector machines (SVM), and neural networks. We explore how businesses (...) can leverage these models to identify customers who are likely to churn and take proactive measures to retain them. The paper also highlights the challenges in churn prediction, such as imbalanced data, overfitting, and the interpretability of models, and provides insights into the future of churn prediction models. Case studies in telecommunications, retail, and banking sectors are discussed to demonstrate practical applications. (shrink)

Earthquakes are among the most destructive natural disasters, yet accurately predicting them remains one of science’s greatest challenges. Traditional seismological approaches struggle to interpret complex patterns from vast seismic datasets. Recently, machine learning (ML) has shown promise in seismology by identifying hidden patterns, detecting microseismic activities, and forecasting earthquake probabilities. This paper explores the integration of ML into earthquake prediction, reviewing current models, methodologies, and challenges. It also proposes a data- driven framework for improving seismic event forecasting using (...) supervised and unsupervised ML algorithms. (shrink)

The Agricultural Portal is an innovative platform designed to improve crop production by providing farmers with easy access to agricultural information, resources, and tools. The portal offers a wide range of features including weather forecasts, crop shopping, crop prediction, yield prediction, crop stock and purchase History. This technical paper outlines the development and implementation of the Agricultural Portal, highlighting its features and functionalities. The paper also explores the benefits of the portal for farmers, including increased productivity, improved decision-making, and enhanced (...) profitability. Advanced machine learning models are integrated into the portal to improve its predictive capabilities. The portal is built on a robust technology platform that is scalable and adaptable to the needs of farmers of different sizes and geographies. The Agricultural Portal represents a significant step forward in the use of technology in agriculture. By providing farmers with easy access to information and resources, it has the potential to transform the way they farm and improve crop production across the globe by using different Machine Learning Algorithms. In the existing system There is only one feature but we are trying to add extra features and making into a single portal in the proposed system. (shrink)

Wine quality prediction is a significant task in the wine industry, as it helps producers and consumers determine the quality of a wine based on its chemical properties. Traditional methods of evaluating wine quality are subjective and time-consuming, relying on human tasters. However, with the advancement of machine learning (ML), it is now possible to predict wine quality in a more objective, scalable, and efficient manner. This paper explores various machine learning algorithms for predicting wine quality, (...) evaluates their performance, and demonstrates how these models can be applied to improve wine classification systems. (shrink)

The Movie Recommendation System using Machine Learning Techniques is a data-driven approach designed to provide personalized movie suggestions based on user preferences and historical data. This system leverages advanced machine learning algorithms, including collaborative filtering, content-based filtering, and hybrid methods, to predict the most relevant movies for individual users. The system's primary goal is to enhance user experience by recommending movies that align with their tastes, thereby promoting user engagement and satisfaction. The recommendation process starts by (...) collecting user data, such as viewing history, ratings, and genre preferences, which are then processed to generate personalized suggestions. Collaborative filtering analyzes the behavior of similar users to recommend movies based on common interests, while content-based filtering focuses on movie features such as genre, director, actors, and plot summary. By combining both methods, the hybrid approach further refines the recommendation, ensuring diverse and accurate results. The model's effectiveness is evaluated using standard metrics like precision, recall, and F1-score, demonstrating its ability to accurately predict user preferences. The system can be integrated into various platforms, offering real-time recommendations and improving user satisfaction. This project can be expanded by incorporating more sophisticated algorithms, such as deep learning, to further enhance recommendation accuracy and scalability. Overall, this movie recommendation system aims to optimize content discovery and cater to diverse audience needs. (shrink)

In this paper, we investigated multiple machine learning classifiers which are, Multinomial Naive Bayes, Support Vector Machine, Decision Tree, K Nearest Neighbors, and Random Forest in a text classification problem. The data we used contains real and fake job posts. We cleaned and pre-processed our data, then we applied TF-IDF for feature extraction. After we implemented the classifiers, we trained and evaluated them. Evaluation metrics used are precision, recall, f-measure, and accuracy. For each classifier, results were summarized (...) and compared with others. (shrink)

As our epistemic ambitions grow, the common and scientific endeavours are becoming increasingly dependent on Machine Learning (ML). The field rests on a single experimental paradigm, which consists of splitting the available data into a training and testing set and using the latter to measure how well the trained ML model generalises to unseen samples. If the model reaches acceptable accuracy, an a posteriori contract comes into effect between humans and the model, supposedly allowing its deployment to target (...) environments. Yet the latter part of the contract depends on human inductive predictions or generalisations, which infer a uniformity between the trained ML model and the targets. The paper asks how we justify the contract between human and machine learning. It is argued that the justification becomes a pressing issue when we use ML to reach ‘elsewheres’ in space and time or deploy ML models in non-benign environments. The paper argues that the only viable version of the contract can be based on optimality (instead of on reliability which cannot be justified without circularity) and aligns this position with Schurz’s optimality justification. It is shown that when dealing with inaccessible/unstable ground-truths (‘elsewheres’ and non-benign targets), the optimality justification undergoes a slight change, which should reflect critically on our epistemic ambitions. Therefore, the study of ML robustness should involve not only heuristics that lead to acceptable accuracies on testing sets. The justification of human inductive predictions or generalisations about the uniformity between ML models and targets should be included as well. Without it, the assumptions about inductive risk minimisation in ML are not addressed in full. (shrink)

Abstract: Ensuring the proactive detection of transaction risks is paramount for financial institutions, particularly in the context of managing credit scores. In this study, we compare different machine learning algorithms to effectively and efficiently. The algorithms used in this study were: MLogisticRegressionCV, ExtraTreeClassifier,LGBMClassifier,AdaBoostClassifier, GradientBoostingClassifier,Perceptron,RandomForestClassifier,KNeighborsClassifier,BaggingClassifier, DecisionTreeClassifier, CalibratedClassifierCV, LabelPropagation, Deep Learning. The dataset was collected from Kaggle depository. It consists of 164 rows and 8 columns. The best classifier with unbalanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, precession 100.0%,Recall100.0% (...) and the F1-score 100.0%. However, the best classifier with balanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, precession 100.0%, Recall 100.0% and the F1-score 100.0%. (shrink)

Electrocardiogram (ECG) signal analysis is a critical task in healthcare for diagnosing cardiovascular conditions such as arrhythmias, heart attacks, and other heart-related diseases. With the growth of Internet of Things (IoT) networks, real-time ECG monitoring has become possible through wearable devices and sensors, providing continuous patient health monitoring. However, real-time ECG signal analysis in IoT environments poses several challenges, including data latency, limited computational power of IoT devices, and energy constraints. This paper proposes a framework for Optimized Machine (...) class='Hi'>Learning Algorithms designed to analyze ECG signals in real time within IoT networks. The proposed system leverages lightweight machine learning models, including support vector machines (SVM) and convolutional neural networks (CNNs), optimized to run efficiently on low-power IoT devices while maintaining high accuracy. The system addresses the computational limitations of IoT devices by employing edge computing techniques that distribute the processing load between IoT devices and edge servers. Additionally, data compression and feature extraction techniques are applied to reduce the size of the data transmitted over the network, thereby minimizing latency and bandwidth usage. This paper reviews the current advancements in real-time ECG analysis, explores the challenges posed by IoT environments, and presents the optimized machine learning algorithms that enhance real-time monitoring of heart health. The system is evaluated for its performance in terms of accuracy, energy efficiency, and data transmission speed, showing promising results in improving real-time ECG signal analysis in resource-constrained IoT networks. (shrink)

In the era of exponential data growth, the efficient migration of data in automotive manufacturing systems is a critical challenge for enterprises. Traditional approaches are often time-intensive and error-prone. This paper proposes an intelligent data transition framework leveraging machine learning algorithms to automate, optimize, and ensure the reliability of data migration processes in automotive manufacturing databases. By integrating supervised learning and reinforcement learning techniques, the framework identifies optimal migration paths, predicts potential bottlenecks, and ensures minimal downtime. (...) Experimental results demonstrate significant improvements in data transfer efficiency and accuracy compared to traditional methods. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and (...) classify cyberbullying behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. To optimize the performance of the detection model, techniques such as Grid Search, Genetic Algorithms, and Particle Swarm Optimization are used to fine-tune hyperparameters, resulting in improved accuracy and reduced false positives. The system's effectiveness is validated through experiments conducted on various social media platforms, demonstrating its potential to detect cyberbullying with high precision. Future work will focus on enhancing the model's adaptability to emerging slang and evolving language patterns in social media. (shrink)

In today's data-driven business environment, organizations increasingly rely on advanced analytics and decision support systems to gain a competitive edge. Azure AI and Machine Learning (ML) provide powerful tools for predictive analytics, enabling businesses to forecast trends, optimize processes, and make more informed decisions. By leveraging the capabilities of Microsoft Azure, businesses can integrate AI and ML into their decision-making processes, enhancing productivity and improving strategic outcomes. This paper explores how Azure's AI and ML tools can be applied (...) to predictive analytics and decision support systems, with a focus on real-time decision-making, risk mitigation, and operational efficiency. We will discuss the key Azure services like Azure Machine Learning, Azure Cognitive Services, and Azure Databricks, as well as real-world use cases that highlight their impact on business performance. The paper will also highlight the challenges associated with implementing these technologies and suggest best practices for organizations aiming to integrate AI and ML into their systems. (shrink)

As machine learning (ML) models become increasingly integrated into mission-critical applications and production systems, the need for robust and scalable MLOps (Machine Learning Operations) practices has grown significantly. This paper explores key strategies and best practices for building scalable MLOps pipelines to optimize the deployment and operation of machine learning models at an enterprise scale. It delves into the importance of automating the end-to-end lifecycle of ML models, from data ingestion and model training to (...) testing, deployment, and monitoring. Approaches for implementing continuous integration and continuous deployment (CI/CD) pipelines tailored for ML workflows are discussed, enabling efficient and repeatable model updates and deployments. The paper emphasizes the criticality of implementing comprehensive monitoring and observability mechanisms to track model performance, detect drift, and ensure the reliability and trustworthiness of deployed models. The paper also addresses the challenges of managing model versioning and governance at scale, including techniques for maintaining a centralized model registry, enforcing access controls, and ensuring compliance with regulatory requirements. The paper aims to provide a comprehensive guide for organizations seeking to establish scalable and robust MLOps practices, enabling them to unlock the full potential of machine learning while mitigating risks and ensuring responsible AI deployment. (shrink)

Agriculture plays a crucial role in the economic stability of many nations, and optimizing crop selection is essential for enhancing agricultural productivity and sustainability. The "Crop Recommender System Using Machine Learning Approach" aims to leverage machine learning techniques to provide precise crop recommendations based on various environmental and soil conditions. By incorporating factors such as soil composition, pH level, temperature, humidity, rainfall, and geographic location, this system suggests the most suitable crops for a given area. The (...) system utilizes machine learning models, particularly Random Forest and Decision Trees, to analyze historical agricultural data, predict optimal crops, and improve the decision-making process for farmers. By training the model on large datasets, it ensures accurate predictions that align with real-world agricultural practices. The application of this system can lead to higher crop yields, sustainable farming practices, and reduced risks associated with poor crop choices. Through rigorous evaluation using standard classification metrics, the model's performance demonstrates its potential to revolutionize farming practices by aiding farmers in making informed decisions. The system has the potential to be an invaluable tool for agricultural consultants, farmers, and policymakers, ensuring long-term sustainability and improved productivity. (shrink)

SMS spam has become a widespread issue, leading to significant inconvenience and security risks for users. Detecting and filtering out such spam messages is crucial for enhancing the user experience and ensuring privacy. TThe dataset used for training and testing the model consists of labeled SMS messages, which are processed using feature extraction techniques such as TF-IDF and word tokenization. Several machine learning algorithms, including Naive Bayes, Support Vector Machine (SVM), and Random Forest, are evaluated to determine (...) the best-performing model for spam detection. The system is trained and tested using a variety of performance metrics, including accuracy, precision, recall, and F1-score. The results show that machine learning models, particularly Naive Bayes, exhibit high accuracy in distinguishing spam from legitimate messages. This system can be implemented in real-time applications such as mobile phones and email services to improve spam detection and reduce unwanted content. By automating the spam filtering process, the system enhances the efficiency and reliability of communication systems. (shrink)

The use of machine learning to medical image classification has seen significant development and implementation in the last several years. Computers can learn to identify patterns, make predictions, and use data to inform their judgements; this capability is known as machine learning, a branch of Artificial intelligence (AI). Classifying images according to their contents allows us to do things like identify the type of sickness, organ, or tissue depicted. Medical picture classification and interpretation using machine (...) learning algorithms has greatly improved the speed and accuracy of diagnosis and treatment. Machine learning algorithms can aid radiologists and doctors in diagnosing patients using medical pictures from X-rays, positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), and other scans. (shrink)

The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. This approach not only aims to improve diagnostic accuracy but also provides a data-driven solution to assist healthcare professionals in making informed decisions. The outcome of this project can (...) contribute to better management of CKD, ultimately helping to reduce the burden on healthcare systems and improving patient care. (shrink)

Ensuring agricultural profitability is a vital issue in developing countries like India, where over a third of the population earns their income directly or indirectly through agriculture. Estimating and evaluating crop yields is done globally to achieve high yields and appropriate pricing. However, there is no accurate procedure in place to provide farmers with insights on which crops should be grown. This project aims to predict crop prices by analysing historical data, such as precipitation, temperature, market prices, land area, and (...) crop yield, using supervised machine learning models. We have focused on crops from the Rabi and Kharif seasons and applied models like Decision Trees, Random Forest, and Support Vector Regression to develop a robust system for predicting crop prices, providing farmers with data-driven insights for decision-making. (shrink)

The proliferation of the Internet of Things (IoT) has transformed various industries by enabling smart environments and improving operational efficiencies. However, this expansion has introduced numerous security vulnerabilities, making IoT systems prime targets for cyberattacks. This paper proposes a machine learning-based intrusion detection framework tailored to the unique characteristics of IoT environments. The framework leverages feature engineering, advanced machine learning algorithms, and real-time anomaly detection to identify and mitigate security threats effectively. Experimental results demonstrate the efficacy (...) of the proposed approach in detecting diverse IoT-specific attacks, including denial-of-service (DoS), man-in-the-middle (MITM), and malware-based attacks. The Internet of Things (IoT) has revolutionized modern living by interconnecting devices and enabling seamless communication. However, the increasing reliance on IoT systems has exposed significant vulnerabilities, making them a prime target for security attacks. This paper proposes a machine learning-based intrusion detection framework to detect and mitigate security attacks in IoT environments. The framework integrates diverse machine learning algorithms to identify abnormal behavior and potential threats. Through comprehensive experiments and evaluations, this research demonstrates the efficacy of the proposed framework in terms of accuracy, scalability, and robustness. (shrink)

This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques (...) such as normalization and missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. (shrink)

The rapid advancement of artificial intelligence (AI) and machine learning (ML) has led to significant innovations in the healthcare sector. One such development is AI-powered healthcare chatbots, which assist patients and medical professionals by providing medical guidance, symptom assessment, and appointment scheduling. This paper presents the design and implementation of an AI healthcare chatbot using machine learning techniques. The chatbot leverages natural language processing (NLP) and deep learning models to understand and respond to user queries (...) effectively. Experimental results demonstrate the chatbot's efficiency in delivering accurate and context-aware medical information. (shrink)

Heart disease remains one of the leading causes of mortality worldwide. Early prediction and diagnosis are critical in preventing severe outcomes and improving the quality of life for patients. This project focuses on developing a robust heart disease prediction system using machine learning techniques. By analyzing a comprehensive dataset consisting of various patient attributes such as age, sex, blood pressure, cholesterol levels, and other medical parameters, the system aims to predict the likelihood of a patient having heart disease. (...) The project employs various machine learning algorithms such as Logistic Regression, Decision Trees, Support Vector Machines (SVM), and Random Forests to classify the data and provide an accurate prediction. The system's performance is evaluated using metrics like accuracy, precision, recall, and F1-score, ensuring that it can offer reliable results in real-world applications. Furthermore, feature selection techniques are applied to identify the most significant factors contributing to heart disease, thus improving the model's interpretability. The proposed solution is intended to aid healthcare professionals by providing early alerts and recommendations, ultimately facilitating timely interventions. (shrink)

Whether it would take one decade or several centuries, many agree that it is possible to create a *superintelligence*---an artificial intelligence with a godlike ability to achieve its goals. And many who have reflected carefully on this fact agree that our best hope for a "friendly" superintelligence is to design it to *learn* values like ours, since our values are too complex to program or hardwire explicitly. But the value learning approach to AI safety faces three particularly philosophical puzzles: (...) first, it is unclear how any intelligent system could learn its final values, since to judge one supposedly "final" value against another seems to require a further background standard for judging. Second, it is unclear how to determine the content of a system's values based on its physical or computational structure. Finally, there is the distinctly ethical question of which values we should best aim for the system to learn. I outline a potential answer to these interrelated puzzles, centering on a "miktotelic" proposal for blending a complex, learnable final value out of many simpler ones. (shrink)