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In this dissertation, a series of studies are designed to investigate the brain connectivity patterns (both functional connectivity and effective connectivity), comparing subjects with depression history vs. control and subjects with OCD vs. control. Depression and OCD are examples of trans-diagnostic disorders, which may be more likely due to alteration of connectivity patterns (ex., hyper-connectivity in default mode network or other networks) rather than localized brain abnormality.

Longitudinal EHRs contain a wealth of clinical information and can potentially support diagnostic tasks that target disease prevention. Although the exploding amount of data increases information, clinicians cannot assess this during consultation and, therefore, do not exploit the full potential. There is accumulating evidence that machine learning can assist in analyzing large-scale EHRs, but studies mainly focus on designing techniques and lack healthcare focus. Because machine learning is a black box for clinicians, it is significant to map the generated knowledge and benefits for preventive healthcare.

The delay at signalized intersections is a crucial parameter that determines the performance and level of service (LOS). The estimation models are commonly used to model delay; however, inaccurate predictions from these models can pose a significant limitation. Consequently, this study aimed to compare a wide array of machine learning algorithms, including Artificial Neural Networks (ANN), Random Forest (RF), decision tree, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), AdaBoost, Gradient Boost, XGBoost, and Partial Least Squares (PLS) regression. A comprehensive evaluation was undertaken across prediction accuracy, training-testing performance discrepancy, sensitivity to outliers, computational time cost, and model robustness. Additionally, the proposed methods were benchmarked against the Highway Capacity Manual (HCM), Webster, and Akçelik models. The results demonstrated that the RF model exhibited the most balanced performance across the specified criteria, with an average error below 4% and a rating of 35 out of 45 according to the proposed criteria. Moreover, the findings revealed that adopted analytical models should not be employed for vehicular delay estimation without calibration, as RMSE values were about 5 to 58 times higher than other models, varying by model.

As the development of machine learning and deep learning, more and more people or organizations use multiple algorithms to analyse large collections of data to produce meaningful results that help to predict behaviour. And this kind of technology is increasingly used in medical field to predict some severe illness in their early stage, for example, cervical cancer. Cervical Cancer is one of the main reasons of deaths in countries having a low capita income. It is the second most common cancer in India in women accounting for 22.86 of all cancer cases in women. It becomes quite complicated while examining a patient on the basis of result obtained from various doctor’s preferred test to determine if the patient is positive with the cancer. There were 96,922 new cases of cervical cancer diagnosed in India in 2018. Around the globe, around a quarter of million people die owing to cervical cancer. Screening and different deterministic tests confuse the available Computed Aided Diagnosis CAD to treat the patient correctly for the cancer. Machine learning and Deep learning algorithms are used in this project and determine if the patient has cancer based on the analyses of the risk factors available in the dataset. Predicting the presence of cervical cancer can help the diagnosis process to start at an early stage and comparing various models will help in finding out the best prediction model for predicting the presence of cervical cancer effectively. Kayalvizhi. K. R | N Kanimozhi "Prediction of Cervical Cancer using Machine Learning and Deep Learning Algorithms" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33378.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/33378/prediction-of-cervical-cancer-using-machine-learning-and-deep-learning-algorithms/kayalvizhi-k-r

We prove several results giving new and stronger connections between learning, circuit lower bounds and pseudorandomness. Among other results, we show a generic learning speedup lemma, equivalences between various learning models in the exponential time and subexponential time regimes, a dichotomy between learning and pseudorandomness, consequences of non-trivial learning for circuit lower bounds, Karp-Lipton theorems for probabilistic exponential time, and NC$^1$-hardness for the Minimum Circuit Size Problem.

There are several confounding factors that can reduce the accuracy of gait recognition systems. These factors can reduce the distinctiveness, or alter the features used to characterise gait, they include variations in clothing, lighting, pose and environment, such as the walking surface. Full invariance to all confounding factors is challenging in the absence of high-quality labelled training data. We introduce a simulation-based methodology and a subject-specific dataset which can be used for generating synthetic video frames and sequences for data augmentation. With this methodology, we generated a multi-modal dataset. In addition, we supply simulation files that provide the ability to simultaneously sample from several confounding variables. The basis of the data is real motion capture data of subjects walking and running on a treadmill at different speeds. Results from gait recognition experiments suggest that information about the identity of subjects is retained within synthetically generated examples. The dataset and methodology allow studies into fully-invariant identity recognition spanning a far greater number of observation conditions than would otherwise be possible.

The Ethiopian Commodity Exchange (ECX) faces significant challenges, including manual trading processes, market inefficiencies, and data fragmentation, which hinder its ability to operate effectively in a volatile and dynamic environment. This research develops an intelligent trading model leveraging Deep Reinforcement Learning (DRL) algorithms, specifically Deep Q-Networks (DQN), Double Deep Q-Networks (DDQN), and Advantage Actor-Critic (A2C), to address these issues. The proposed framework utilizes DRL to enable agents to learn optimal trading policies through interactions with simulated ECX market environments. The model employs historical market data, representing state features such as price trends, trading volumes, and external economic indicators. Actions are defined as buy, sell, or hold decisions, while reward structures are designed to incentivize profit and penalize excessive risk. The research integrates techniques such as experience replay and target networks in DQN, action evaluation in DDQN, and advantage functions in A2C to enhance model performance and stability. Experimental results demonstrate that the DRL models significantly improve trading efficiency and decision-making accuracy compared to manual processes. DDQN outperforms DQN in managing noisy and volatile market conditions, while A2C excels in handling continuous decision variables, such as dynamic trade volumes. The results highlight the scalability and adaptability of the proposed system in addressing ECX-specific challenges, including risk management and market transparency. The study concludes that the DRL-based trading model offers transformative potential for the ECX by automating decision-making, optimizing trade execution, and promoting equitable participation among stakeholders. This research provides a foundation for integrating advanced machine learning techniques into emerging commodity markets, ensuring their efficiency and competitiveness in a global context.

Malaria continues to be a major public health challenge in sub-Saharan Africa, where accurate and timely diagnosis is often hindered by limitations in traditional diagnostic methods. This review evaluated the role of machine learning (ML) algorithms in improving malaria diagnosis in primary healthcare settings. Specifically, it explores applications of ML in microscopic image analysis, rapid diagnostic test (RDT) optimization, and predictive modeling, with a focus on their potential to enhance diagnostic accuracy and decision-making in resource-limited environments. ML techniques, such as convolutional neural networks (CNNs) for image analysis and data-driven models for optimizing RDT interpretation, have shown promise in addressing inter-observer variability and improving test sensitivity and specificity. Furthermore, predictive modeling integrating clinical, demographic, and environmental data can help prioritize malaria cases and guide healthcare providers in making accurate diagnoses. Despite these advancements, challenges such as data limitations, infrastructure gaps, and ethical considerations remain significant barriers to widespread adoption. The methodology utilized in this review involved a comprehensive synthesis of current literature, examining empirical studies on ML applications in malaria diagnosis and assessing their feasibility in primary healthcare contexts. To overcome these challenges, the article suggested policy recommendations, including investments in data infrastructure, capacity building, and public-private partnerships. Ultimately, ML offers a promising solution to enhance malaria diagnostic capabilities, contributing to better health outcomes in endemic regions. 

Machine learning is recognized as a tool for improving the performance of many kinds of systems, yet most machine learning systems themselves are not well equipped to improve their own learning performance. By emphasizing the role of domain knowledge, learning systems can be crafted as knowledge directed systems, and with the addition of a knowledge store for organizing and maintaining knowledge to assist learning, a learning machine learning (L-ML) algorithm is possible. The necessary components of L-ML systems are presented along with several case descriptions of existing machine learning systems that possess limited L-ML capabilities. Keywords: Algorithms, Artificial intelligence. (SDW)

The concept of algorithm, as used in teaching and learning, is defined. Characteristics of algorithms are identified and described. The elements (operator, discriminator, syntactic structure) are described and illustrated. Methods of representing algorithms are portrayed. Differences between identification algorithms, transformation algorithms, and search algorithms are discussed. Use of algorithms in instruction and training are suggested. Several research and development tasks are proposed. (Author)

Ensembles of climate models are commonly used to improve climate predictions and assess the uncertainties associated with them. Weighting the models according to their performances holds the promise of further improving their predictions. Here, we use an ensemble of decadal climate predictions to demonstrate the ability of sequential learning algorithms (SLAs) to reduce the forecast errors and reduce the uncertainties. Three different SLAs are considered, and their performances are compared with those of an equally weighted ensemble, a linear regression and the climatology. Predictions of four different variables--the surface temperature, the zonal and meridional wind, and pressure--are considered. The spatial distributions of the performances are presented, and the statistical significance of the improvements achieved by the SLAs is tested. Based on the performances of the SLAs, we propose one to be highly suitable for the improvement of decadal climate predictions.

Reinforcement learning agents have traditionally been evaluated on small toy problems. With advances in computing power and the advent of the Arcade Learning Environment, it is now possible to evaluate algorithms on diverse and difficult problems within a consistent framework. We discuss some challenges posed by the arcade learning environment which do not manifest in simpler environments. We then provide a comparison of model-free, linear learning algorithms on this challenging problem set.

Reinforcement learning agents have traditionally been evaluated on small toy problems. With advances in computing power and the advent of the Arcade Learning Environment, it is now possible to evaluate algorithms on diverse and difficult problems within a consistent framework. We discuss some challenges posed by the arcade learning environment which do not manifest in simpler environments. We then provide a comparison of model-free, linear learning algorithms on this challenging problem set.

Reinforcement learning agents have traditionally been evaluated on small toy problems. With advances in computing power and the advent of the Arcade Learning Environment, it is now possible to evaluate algorithms on diverse and difficult problems within a consistent framework. We discuss some challenges posed by the arcade learning environment which do not manifest in simpler environments. We then provide a comparison of model-free, linear learning algorithms on this challenging problem set.

Reinforcement learning agents have traditionally been evaluated on small toy problems. With advances in computing power and the advent of the Arcade Learning Environment, it is now possible to evaluate algorithms on diverse and difficult problems within a consistent framework. We discuss some challenges posed by the arcade learning environment which do not manifest in simpler environments. We then provide a comparison of model-free, linear learning algorithms on this challenging problem set.

Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to intelligent information retrieval and indexing. More recently, information science researchers have turned to other newer inductive learning techniques including symbolic learning, genetic algorithms, and simulated annealing. These newer techniques, which are grounded in diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information systems. In this article, we first provide an overview of these newer techniques and their use in information systems. In this article, we first provide an overview of these newer techniques and their use in information retrieval research. In order to familiarize readers with the techniques, we present three promising methods: The symbolic ID3 algorithm, evolution-based genetic algorithms, and simulated annealing. We discuss their knowledge representations and algorithms in the unique context of information retrieval. An experiment using a 8000-record COMPEN database was performed to examine the performances of these inductive query-by-example techniques in comparison with the performance of the conventional relevance feedback method. The machine learning techniques were shown to be able to help identify new documents which are similar to documents initially suggested by users, and documents which contain similar concepts to each other. Genetic algorithms, in particular, were found to out-perform relevance feedback in both document recall and precision.

We consider the neurologically-inspired hypothesis that higher level cognition is built on the same fundamental building blocks as low-level perception. That is, the same basic algorithm that is able to represent and perform inference on low-level sensor data can also be used to process relational structures. We present a system that represents relational structures as feature bags. Using this representation, our system leverages algorithms inspired by the sensory cortex to automatically create an ontology of relational structures and to efficiently retrieve analogs for new relational structures from long-term memory. We provide a demonstration of our approach that takes as input a set of unsegmented stories, constructs an ontology of analogical schemas (corresponding to plot devices), and uses this ontology to find analogs within new stories in time logarithmic in the total number of stories, yielding significant time-savings over linear analog retrieval with only a small sacrifice in accuracy. We also provide a proof of concept for how our framework allows for cortically-inspired algorithms to perform analogical inference. Finally, we discuss how insights from our system can be used so that a corticallyinspired model can serve as the core mechanism for a full cognitive architecture.

This thesis explores various theoretical aspects of machine learning with particular emphasis on techniques for designing and analyzing computationally efficient learning algorithms. Many of the results in this thesis are concerned with a model of concept learning proposed by Valiant. The thesis begins in Chapter 2 with a proof that any 'weak' learning algorithm in this model that performs slightly better than random guessing can be converted into one whose error can be made arbitrarily small. Several interesting consequences of this result are also described. Chapter 3 next explores in detail a simple but powerful technique for discovering the structure of an unknown read-once formula from random examples. An especially nice feature of this technique is its powerful resistance to noise. Chapter 4 considers a realistic extension of the PAC model to concepts that may exhibit uncertain or probabilistic behavior. A range of techniques are explored for designing efficient algorithms for learning such probabilistic concepts. In the last chapter, we present new algorithms for inferring an unknown finite-state automation from its input-output behavior. This problem is motivated by that faced by a robot in unfamiliar surroundings who must, through experimentation, discover the structure of its environment.

Automatic face recognition has ample significance in biometric research. Recent decades have witnessed enormous growth in this research area. Face-based identification is always considered more expedient as compared to other biometric authentications owing to its uniqueness and wide acceptance. The major contribution of this work is twofold; firstly, it comprises an extension of manual thresholding feature-based face recognition approach to an automatic feature-based supervised learning face recognition. Secondly, various new feature sets are proposed and tested on several classifiers for 2, 3, 4, and 5 persons. In addition, the use of slope features of facial components, such as the nose, right eye, left eye, and lips along with other conventional features for face recognition is also a unique contribution of this research. Multiple experiments were performed on the UMT face database. The results demonstrated a comparison of 5 different sets of feature-based approaches on 7 classifiers using the metrics of time efficiency and accuracy. They also depicted that the proposed approaches achieve a percentage accuracy of up to 95.5%.

Modern data analysis operations are continuously flooded with streams of noisy, incomplete, and sometimes intentionally misleading data. Traditional analysis methods cannot scale to handle these issues. We developed a battery of new, efficient, parallel, statistical machine learning algorithms to push the boundaries of machine learning capabilities under these circumstances. We have made much of our mature algorithms available as open source tools and published in peer-reviewed academic journals and conferences. The algorithms cover a wide range of learning applications, but all rest on strong statistical foundations and in that sense that they all speak the same language. We have provided theoretical guarantees and proofs were possible and demonstrated the value of our algorithms on many interesting problems.

Recent research suggests that algorithms that can learn will mitigate the effects of bad reactions to algorithm errors. We expect that telling participants that the algorithm can learn from mistakes will lessen the negative reactions to errors.

This paper presents new and effective algorithms for learning kernels. In particular, as shown by our empirical results, these algorithms consistently outperform the so-called uniform combination solution that has proven to be difficult to improve upon in the past, as well as other algorithms for learning kernels based on convex combinations of base kernels in both classification and regression. Our algorithms are based on the notion of centered alignment which is used as a similarity measure between kernels or kernel matrices. We present a number of novel algorithmic, theoretical, and empirical results for learning kernels based on our notion of centered alignment. In particular, we describe efficient algorithms for learning a maximum alignment kernel by showing that the problem can be reduced to a simple QP and discuss a one-stage algorithm for learning both a kernel and a hypothesis based on that kernel using an alignment-based regularization. Our theoretical results include a novel concentration bound for centered alignment between kernel matrices, the proof of the existence of effective predictors for kernels with high alignment, both for classification and for regression, and the proof of stability-based generalization bounds for a broad family of algorithms for learning kernels based on centered alignment. We also report the results of experiments with our centered alignment-based algorithms in both classification and regression.

We consider the problem of learning the structure of Ising models (pairwise binary Markov random fields) from i.i.d. samples. While several methods have been proposed to accomplish this task, their relative merits and limitations remain somewhat obscure. By analyzing a number of concrete examples, we show that low-complexity algorithms often fail when the Markov random field develops long-range correlations. More precisely, this phenomenon appears to be related to the Ising model phase transition (although it does not coincide with it).

Coronary artery disease (CAD) is the most ubiquitous and protuberant cause of fatal death. The hit in mortality rate is because of certain lifestyle variables including unhealthy diet, usage of tobaccos and drugs, physical inactivity, and environmental pollution. Traditional screening tests including computed tomography, angiography, electrocardiography, and magnetic resonance imaging are employed for diagnosis and would necessitate more manpower. Machine learning (ML) has been utilized in healthcare to create early predictions from massive volumes of data. The Scopus, Web of Science databases were exhaustively searched utilizing a search strategy that comprised CAD prediction, cardiac illness detection, and heart disease categorization. After applying the inclusion and exclusion criteria to the 99 articles obtained, the population of the study was composed of 30 articles. This review study offers an organized look at the articles published in ML based CAD detection and classification models that include clinical variables. The use of ML could produce amazing results in CAD detection, as evidenced by the classifiers random forest, decision tree, and k-nearest-neighbour with accuracy being >90%. The use of ML in CAD diagnosis lowers false-positive, and false-negative errors, and presents a special opportunity by providing patients quick, and affordable diagnostic services.

It is well known in the literature that the problem of learning the structure of Bayesian networks is very hard to tackle: its computational complexity is super-exponential in the number of nodes in the worst case and polynomial in most real-world scenarios. Efficient implementations of score-based structure learning benefit from past and current research in optimisation theory, which can be adapted to the task by using the network score as the objective function to maximise. This is not true for approaches based on conditional independence tests, called constraint-based learning algorithms. The only optimisation in widespread use, backtracking, leverages the symmetries implied by the definitions of neighbourhood and Markov blanket. In this paper we illustrate how backtracking is implemented in recent versions of the bnlearn R package, and how it degrades the stability of Bayesian network structure learning for little gain in terms of speed. As an alternative, we describe a software architecture and framework that can be used to parallelise constraint-based structure learning algorithms (also implemented in bnlearn) and we demonstrate its performance using four reference networks and two real-world data sets from genetics and systems biology. We show that on modern multi-core or multiprocessor hardware parallel implementations are preferable over backtracking, which was developed when single-processor machines were the norm.

It is well known in the literature that the problem of learning the structure of Bayesian networks is very hard to tackle: its computational complexity is super-exponential in the number of nodes in the worst case and polynomial in most real-world scenarios. Efficient implementations of score-based structure learning benefit from past and current research in optimisation theory, which can be adapted to the task by using the network score as the objective function to maximise. This is not true for approaches based on conditional independence tests, called constraint-based learning algorithms. The only optimisation in widespread use, backtracking, leverages the symmetries implied by the definitions of neighbourhood and Markov blanket. In this paper we illustrate how backtracking is implemented in recent versions of the bnlearn R package, and how it degrades the stability of Bayesian network structure learning for little gain in terms of speed. As an alternative, we describe a software architecture and framework that can be used to parallelise constraint-based structure learning algorithms (also implemented in bnlearn) and we demonstrate its performance using four reference networks and two real-world data sets from genetics and systems biology. We show that on modern multi-core or multiprocessor hardware parallel implementations are preferable over backtracking, which was developed when single-processor machines were the norm.

The present study aims to perform a systematic literature mapping to identify studies that address the use of machine learning algorithms for predicting various outcomes in children and adolescents diagnosed with COVID-19.

In this paper, we present a web application designed for learning and visualizing Support Vector Machine (SVM) algorithms, which are key components in the fields of machine learning and data processing. The application was developed as an interactive tool that allows students and researchers to experiment with SVM models, providing insight into their structure and functionality. By using modern web technologies, the application offers a user environment that is accessible, intuitive, and adaptable for learning and research. In addition to implementing a web tool for learning the SVM algorithm, this study proposes a method for its application in teaching and analyzes the impact of applying the new interactive method on final learning outcomes. To assess the effectiveness of this tool, an experiment was conducted consisting of three phases: pre-testing, training, and post-testing. To evaluate students’ experiences with the applied alternative learning method using the auxiliary tool and their perception of the software system’s effectiveness, the standardized System Usability Scale (SUS) was used. Enable Ginger Cannot connect to Ginger Check your internet connection or reload the browserDisable GingerRephraseRephrase with Ginger (Ctrl+Alt+E) Edit in Ginger Ginger is checking your text for mistakes... Disable Ginger in this text fieldDisable Ginger on this website×

Includes bibliographical references (p. 11)

This work aims at improving the energy consumption forecast of electric vehicles by enhancing the prediction with a notion of uncertainty. The algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts.

This article is from Frontiers in Public Health , volume 2 . Abstract Background: Active travel is an important area in physical activity research, but objective measurement of active travel is still difficult. Automated methods to measure travel behaviors will improve research in this area. In this paper, we present a supervised machine learning method for transportation mode prediction from global positioning system (GPS) and accelerometer data.Methods: We collected a dataset of about 150 h of GPS and accelerometer data from two research assistants following a protocol of prescribed trips consisting of five activities: bicycling, riding in a vehicle, walking, sitting, and standing. We extracted 49 features from 1-min windows of this data. We compared the performance of several machine learning algorithms and chose a random forest algorithm to classify the transportation mode. We used a moving average output filter to smooth the output predictions over time.Results: The random forest algorithm achieved 89.8% cross-validated accuracy on this dataset. Adding the moving average filter to smooth output predictions increased the cross-validated accuracy to 91.9%.Conclusion: Machine learning methods are a viable approach for automating measurement of active travel, particularly for measuring travel activities that traditional accelerometer data processing methods misclassify, such as bicycling and vehicle travel.

Talk by Marc Wanner - Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms @QTMLConference

ABSTRACT: In the field of computer science known as "machine learning," a computer makes predictions about the tasks it will perform next by examining the data that has been given to it. The computer can access data via interacting with the environment or by using digitalized training sets. In contrast to static programming algorithms, which require explicit human guidance, machine learning algorithms may learn from data and generate predictions on their own. Various supervised and unsupervised strategies, including rule-based techniques, logic-based techniques, instance-based techniques, and stochastic techniques, have been presented in order to solve problems. Our paper's main goal is to present a comprehensive comparison of various cutting-edge supervised machine learning techniques. 

ABSTRACT: In the field of computer science known as "machine learning," a computer makes predictions about the tasks it will perform next by examining the data that has been given to it. The computer can access data via interacting with the environment or by using digitalized training sets. In contrast to static programming algorithms, which require explicit human guidance, machine learning algorithms may learn from data and generate predictions on their own. Various supervised and unsupervised strategies, including rule-based techniques, logic-based techniques, instance-based techniques, and stochastic techniques, have been presented in order to solve problems. Our paper's main goal is to present a comprehensive comparison of various cutting-edge supervised machine learning techniques. 

Preeclampsia and eclampsia are the most common obstetric disorders associated with poor maternal and neonatal outcome. The study’s primary objective is to assess the accuracy of novel high-risk factors core using machine learning algorithms in predicting preeclampsia. The study included 400 pregnant women and used 27 novel high-risk factors to predict preeclampsia. The target variables for predicting preeclampsia are systolic and diastolic blood pressures. Various algorithms, including decision tree (DT), random forest (RF), gradient boosting, support vector machine (SVM), K-neighbors, light gradient boosting machine (LGBM), multi-layer perceptron (MLP), Adaboost classifier, and extra trees classifier are used in the analysis. The accuracy and precision of the LGBM classifier (0.85 and 0.9583 with F1 0.7188), support vector classifier (0.8417 and 0.92 with F1 0.7077), DT (0.825 and 0.913 with F1 0.6667), and extra trees (0.8167 and 0.9091 with F1 0.6452) are found to be better algorithms for prediction of preeclampsia. According to the novel high-risk factors score, 17.5% of pregnant women were identified as being at high risk for preeclampsia during the first trimester, which increased to 18.7% in 3rd trimester; in addition, 16% of pregnant women had a blood pressure of 140/90 mmHg and the above. Novel, high-risk scores and machine learning algorithms can effectively predict preeclampsia at an early period.

Email phishing is a manipulative technique aimed at compromising information security and user privacy. To overcome the limitations of traditional detection methods, such as blacklists, this research proposes a phishing detection model that leverages natural language processing (NLP) and deep learning technologies to analyze Indonesian email headers. The primary objective is to more efficiently detect zero-day phishing attacks by focusing on the unique linguistic and cultural context of the Indonesian language. This enables the development of models capable of recognizing phishing attack patterns that differ from those in other language contexts. Four models are tested, combining Indonesian bidirectional encoder representation of transformers (IndoBERT) and FastText feature extraction techniques with convolutional neural network (CNN) and long short-term memory (LSTM) deep learning algorithms. The results indicate that the combination of FastText and CNN achieved the highest performance in accuracy, precision, and F1-score metrics, each at 98.4375%. Meanwhile, the FastText model with LSTM showed the best performance in recall, with a score of 98.9583%. The research suggests exploring deeper into email content or integrating analysis between headers and email content in future studies to further improve accuracy and effectiveness in phishing email detection.

Handwritten digit recognition is a technique or technology for automatically recognizing and detecting handwritten digital data through different Machine Learning models. In this paper we use various Machine Learning algorithms to enhance the productiveness of technique and reduce the complexity using various models. Machine Learning is an application of Artificial Intelligence that learns from previous experience and improves automatically through experience. We illustrate various Machine learning algorithms such as Support Vector Machine, Convolutional Neural Network, Quantum Computing, K-Nearest Neighbor Algorithm, Deep Learning used in Recognition technique.

Cervical cancer remains a significant global health issue, particularly in underdeveloped nations, where it contributes to high mortality rates. Early detection is critical for improving treatment outcomes and survival rates. This study employs machine learning (ML) algorithms to predict cervical cancer risk using a dataset from the University of California at Irvine (UCI), which includes demographic and clinical attributes such as age, sexual history, smoking habits, and medical history. After applying data preprocessing techniques, several classification algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree, adaptive boosting (AdaBoost), and artificial neural networks (ANN), were trained and evaluated. The models were assessed using classification metrics such as precision, recall, and F1 score. Among the models, the ANN demonstrated the highest accuracy, achieving a score of 0.95. In addition, correlation analysis revealed significant relationships between various risk factors, providing insights into cervical cancer mechanisms and potential preventive measures. The study highlights the potential of ML in improving cervical cancer detection and patient outcomes, suggesting that advanced ML techniques can be valuable tools in healthcare research and clinical applications.

In response to the growing threat posed by COVID-19, several initiatives have been launched to develop methods of halting the progression of the disease. In order to diagnose the COVID-19 infection, testing kits were utilized; however, the use of these kits is time-consuming and suffers from a lack of quality control measures. Computed tomography is an essential part of the diagnostic process in the treatment of COVID-19 (CT). The process of disease detection and diagnosis could be sped up with the help of automation, which would cut down on the number of exams that need to be carried out. A number of recently developed deep learning tools make it possible to automate the Covid-19 scanning process in CT scans and provide additional assistance. This paper investigates how to quickly identify COVID-19 using computational tomography (CT) scans, and it does so by using a deep learning technique that is derived from improving ResNet architecture. In order to test the proposed model, COVID-19 CT scans that include a patient-based split are utilized. The accuracy of the model’s core components is 98.1%, with specificity at 97% and sensitivity at 98.6%.

Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a Systematic-narrative hybrid literature review

Shortly following arrest, judicial officers must decide whether to detain the arrested person in jail or to release him or her back into the community while awaiting trial. This is an extremely important decision in a criminal case (Bechtel, Holsinger, Lowenkamp, Warren, 2017). This decision relates to later case decisions (e.g., Ulmer, 2012; Kutateladze, Andiloro, Johnson, & Spohn, 2014), case outcomes (Oleson, Lowenkamp, Wooldredge, VanNostrand, & Cadigan, 2015), as well as outcomes even after a case is disposed (Cadigan & Lowenkamp, 2011; Lowenkamp, VanNostrand, & Holsinger, 2013). For example, those detained pre-trial are much more likely than those released pre-trial to plead guilty (Patterson & Lynch, 1991; Sutton, 2013), to be convicted of a felony (Schlesinger, 2007), and to receive a longer final sentence (Sacks & Ackerman, 2012). According to McCoy (2007), the decision to detain or release someone pre-trial is so critical that it determines mostly everything in a criminal case. Both the American Bar Association (2002) and the National Association of Pre-trial Services Agencies (2004) strongly recommend the use of an objective and research-based pre-trial risk assessment instrument to assist judicial officers’ in making this decision. One goal of these instruments is to identify people who are likely to recidivate. Researchers and practitioners have developed various pre-trial risk assessment instruments within the last two decades. Some prominent examples include the Virginia Pre-trial Risk Assessment Instrument (VPRAI) developed by the Virginia Department of Criminal Justice Services (VanNostrand, 2003), and the Public Safety Assessment (PSA) developed by the Laura and John Arnold Foundation (Lowenkamp, VanNostrand, & Holsinger, 2013; VanNostrand & Lowenkamp, 2013). Desmarais, Zottola, Clarke, and Lowder (2020) reviewed several risk assessment instruments including the VPRAI and PSA and found that they predicted recidivism with good to excellent accuracy. For example, the VPRAI discriminated those who had new arrests during the pre-trial period from those who did not 64 – 69% of the time. Moreover, these instruments were similarly predictive across racial and ethnic groups and were similarly predictive for both men and women. Still, Desmarais et al. emphasized the need for continued investigation of the predictive accuracy of pre-trial risk assessment instruments. Once judicial officers decide to release someone back into the community, they either release that person under supervision or without any supervision. Pre-trial supervision comes with certain conditions and restrictions that can include periodic check-ins with a case manager, maintaining employment, ankle monitoring, alcohol testing and treatment, and cognitive behavioral therapy (Clarke, 1988; Mamalian, 2011; VanNostrand & Keebler, 2009; VanNostrand, Rose, & Weibrecht, 2011). Those released pre-trial may also have access to social services that include opportunities to take part in education programs or employment training as well as transitional housing. The goal of pre-trial supervision is to provide an alternative to detention while minimizing recidivism and failures to appear in court. It is not clear whether pre-trial supervision reduces recidivism more so than simply releasing people pre-trial. The available research on the effectiveness of pre-trial supervision is limited. Bechtel et al. (2017) conducted a meta-analysis of 16 studies that investigated the impact of various pre-trial supervision conditions (e.g., ankle monitoring) on recidivism and found that none of the conditions reduced recidivism. Bechtel et al. tempered these findings by emphasizing that the quality of the research included in the meta analysis was poor and that the field of pre-trial research lacks methodological rigor. For example, they state, “the quality of the research that could be included in the current analysis was not very good” (p. 460). Elsewhere, they note that, “it is striking that although more than 800 potential studies on pre-trial were identified, less than 20% contained data, and the percentage of studies with the information necessary to synthesize the findings into a meta-analytic review was even lower than 20%” (p. 459). In fact, of the 16 studies that were included in the meta-analysis only four studies were peer-reviewed. They also added that there is a “great need for new and more rigorous pre-trial research in all related areas” (p. 459). They conclude by calling for researchers to “conduct methodologically rigorous studies that are submitted to peer-reviewed journals” (p. 463). Here, we answer the calls from Desmarais et al. (2020) and Bechtel et al. (2017). First, we compare the predictive accuracy of the VPRAI and the PSA. Second, we use two modern machine-learning-based matching algorithms to determine the causal impact of pre-trial supervision on recidivism. These algorithms, called Fast Large-scale Almost Matching Exactly -- FLAME (Wang et al. (2021) and Dynamic Almost Matching Exactly -- DAME (Liu, Dieng, Roy, Rudin, and Volfovsky (2019), stem from a new causal inference framework called Almost Matching Exactly, or Learning-to-Match. We discuss these machine-learning algorithms in detail below, but in general, they match people who receive pre-trial supervision to similar individuals who were released pre-trial. The virtue of this machine-learning matching process is that it establishes causality and can therefore test whether pre-trial supervision has a causal effect on recidivism. Next, we detail our confirmatory and exploratory hypotheses.

In recent years discriminative learning techniques have seen a surge of interest in the NLP community due their ability to tractably incorporate millions of dependent and linguistically rich features. In many fields, most notably information extraction, discriminative models have become the standard. In this talk I will describe a generalization of the multi-class online large-margin algorithms of Crammer and Singer (2003) to structured outputs. I apply this learning framework to the problem of extracting dependency tree representations of sentences in conjunction with a spanning tree (maximum branching) parsing framework that leads to efficient algorithms for projective and non-projective structures. I show that parsers trained under this framework can achieve state-of-the-art accuracies when combined with a rich feature set. Further more I will describe experiments displaying that these parsers are naturally extendable and can be adapted to new domains through additional features defined from information from in and out-of-domain classifiers. ©2005 Microsoft Corporation. All rights reserved.

Diabetes mellitus (DM) is a serious worldwide health issue, and its prevalence is rapidly growing. It is a spectrum of metabolic illnesses defined by perpetually increased blood glucose levels. Undiagnosed diabetes can lead to a variety of problems, including retinopathy, nephropathy, neuropathy, and other vascular abnormalities. In this context, machine learning (ML) technologies may be particularly useful for early disease identification, diagnosis, and therapy monitoring. The core idea of this study is to identify the strong ML algorithm to predict it. For this several ML algorithms were chosen i.e., support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT), according to studied work. Two, Pima Indian diabetic (PID) and Germany diabetes datasets were used and the experiment was performed using Waikato environment for knowledge analysis (WEKA) 3.8.6 tool. This article discussed about performance matrices and error rates of classifiers for both datasets. The results showed that for PID database (PIDD), SVM works better with an accuracy of 74% whereas for Germany KNN and RF work better with 98.7% accuracy. This study can aid healthcare facilities and researchers in comprehending the value and application of ML algorithms in predicting diabetes at an early stage.

In paediatrics, bone age is a crucial indicator of how a child's skeleton is developing. They have had great success ever since the creation of deep learning (DL)-based bone age prediction tools. Deep features learning, however, has a significant computing overhead problem. Deep convolution layers are used in this technique to learn representative features in the small yet useful regions that are extracted for feature learning. This work suggests using an extreme learning machine algorithm as the fundamental architecture in the final bone age assessment study to realise the rapid computation speed and feature interaction. The viability and efficacy of the suggested strategy have been verified by experiments using data that is openly accessible. The suggested model is explicitly trained using a cutting-edge end-to-end learning architecture employing bone scans to extract the most discriminative patches from the original high-resolution image. The bone picture is the foundation of the procedure. Our main objective is to categorize individuals by age using convolution neural network (CNN) classification models, such as the Xception and Mobile Net models. As a result, we have achieved results that are 90% and 94% accurate in classifying people by age using CNN models.

Today, one of the top concerns for governments is road safety. There are many safety features built into cars, yet traffic accidents still happen frequently and are unavoidable. To lessen the harm caused by traffic accidents, predicting their causes has become the primary goal. In this situation, it will be beneficial to examine the frequency of accidents so that we can use this information to further aid us in developing strategies to lessen them. From this, we can deduce the connections between traffic accidents, road conditions, and the impact of environmental factors on accident occurrence. In order to construct an accident prediction model, I used machine learning techniques, including the Decision Tree, Random Forest, and Logistic Regression. The development of safety measures and accident prediction will both benefit from these classification systems. Several elements, including weather, vehicle condition, road surface condition, and light condition, can be used to predict road accidents. Three dataset files—accidents, casualties, and vehicles are loaded into this dataset. This allows us to forecast the severity of accidents.

Artificial intelligence (AI) is one of the most common and essential technologies in this modern era, especially in the education and research sectors. It mimics machine-processed human intellect. In modern times, ChatGPT is one of the most effective and beneficial tools developed by OpenAI. Provides prompt answers and feedback to help academics and researchers. Using ChatGPT has various advantages, including improving methods of instruction, preparing interactive lessons, assessment, and advanced problem-solving. Threats against ChatGPT, however, include diminishing creativity, and analytical thinking. Additionally, students would adopt unfair procedures when submitting any tests or assignments online, which would increase their dependency on AI systems rather than thinking analytically. In this study, we have demonstrated arguments on both sides of AI technology. We believe that our study would provide a depth of knowledge and more informed discussion. Data is collected via an offline platform and then machine learning algorithms such as K-nearest neighbour (K-NN), support vector machine (SVM), naive bayes (NB), decision tree (DT), and random forest (RF) are used to analyze the data which helps to improve teaching and learning techniques where SVM shows best performance. The results of the study would offer several significant learning and research directions as well as ensure safe and responsible adoption.

Artificial intelligence (AI) is one of the most common and essential technologies in this modern era, especially in the education and research sectors. It mimics machine-processed human intellect. In modern times, ChatGPT is one of the most effective and beneficial tools developed by OpenAI. Provides prompt answers and feedback to help academics and researchers. Using ChatGPT has various advantages, including improving methods of instruction, preparing interactive lessons, assessment, and advanced problem-solving. Threats against ChatGPT, however, include diminishing creativity, and analytical thinking. Additionally, students would adopt unfair procedures when submitting any tests or assignments online, which would increase their dependency on AI systems rather than thinking analytically. In this study, we have demonstrated arguments on both sides of AI technology. We believe that our study would provide a depth of knowledge and more informed discussion. Data is collected via an offline platform and then machine learning algorithms such as K-nearest neighbour (K-NN), support vector machine (SVM), naive bayes (NB), decision tree (DT), and random forest (RF) are used to analyze the data which helps to improve teaching and learning techniques where SVM shows best performance. The results of the study would offer several significant learning and research directions as well as ensure safe and responsible adoption.

This thesis derives, tests and applies two linear projection algorithms for machine learning under non-stationarity. The first finds a direction in a linear space upon which a data set is maximally non-stationary. The second aims to robustify two-way classification against non-stationarity. The algorithm is tested on a key application scenario, namely Brain Computer Interfacing.

We present a general framework for applying machine-learning algorithms to the verification of Markov decision processes (MDPs). The primary goal of these techniques is to improve performance by avoiding an exhaustive exploration of the state space. Our framework focuses on probabilistic reachability, which is a core property for verification, and is illustrated through two distinct instantiations. The first assumes that full knowledge of the MDP is available, and performs a heuristic-driven partial exploration of the model, yielding precise lower and upper bounds on the required probability. The second tackles the case where we may only sample the MDP, and yields probabilistic guarantees, again in terms of both the lower and upper bounds, which provides efficient stopping criteria for the approximation. The latter is the first extension of statistical model-checking for unbounded properties in MDPs. In contrast with other related approaches, we do not restrict our attention to time-bounded (finite-horizon) or discounted properties, nor assume any particular properties of the MDP. We also show how our techniques extend to LTL objectives. We present experimental results showing the performance of our framework on several examples.

Approximate Bayesian Computation has been successfully used in population genetics to bypass the calculation of the likelihood. These methods provide accurate estimates of the posterior distribution by comparing the observed dataset to a sample of datasets simulated from the model. Although parallelization is easily achieved, computation times for ensuring a suitable approximation quality of the posterior distribution are still high. To alleviate the computational burden, we propose an adaptive, sequential algorithm that runs faster than other ABC algorithms but maintains accuracy of the approximation. This proposal relies on the sequential Monte Carlo sampler of Del Moral et al. (2012) but is calibrated to reduce the number of simulations from the model. The paper concludes with numerical experiments on a toy example and on a population genetic study of Apis mellifera, where our algorithm was shown to be faster than traditional ABC schemes.